Working Group on Fisheries Acoustics, Science and Technology (WGFAST)

Type Article
Date 2020
Language English
Author(s) ICES
Contributor(s) Blanluet Arthur, Berger LaurentORCID, Doray MathieuORCID, Le Bouffant Naig, Poncelet CyrilleORCID
Source ICES Scientific Reports/Rapports scientifiques du CIEM (2618-1371) (ICES), 2020 , Vol. 2 , N. 70 , P. 18pp.
DOI 10.17895/ices.pub.7444
Abstract

The Working Group on Fisheries Acoustics, Science and Technology (WGFAST) focuses on the development and application of science and technology to observe the marine environment. In this report, WGFAST describe their appreciation for David MacLennan, who was a founding member of WGFAST, and who’s pioneering work on several theoretical and practical elements of fisheries acoustics were compiled in the seminal book on Fisheries Acoustics that he co-au-thored. The report also addresses ‘big data’ as the next frontier in fisheries acoustics; the WGFAST strategy to respond to a request from the Working Group on International Pelagic Sur-veys (WGIPS) on the acoustic detection of herring; updates from the Topic Group on Collecting Quality Underwater Acoustic Data (TGQUAD) that is writing an ICES Co-operative Research Report in this topic; updates from the Topic Group on Acoustic Metadata (TGMETA) on the development of acoustic metadata guidelines (AcMeta); and progress by the newly formed Working Group on Acoustic Trawl Data Portal Governance (WGAcousticGov) to establish a gov-ernance framework for the ICES Acoustic Trawl Data portal. WGFAST identified Dr Toby Jarvis as a new representative on the International Organization for Standardization (ISO) Liaison Committee linked to ongoing ISO work on underwater acoustics.

This report also summarizes WGFAST considerations of acoustic scattering properties of organ-isms and open-source acoustic backscatter models; the Sonar-netCDF4 open source data format for acoustic data; open-source software to read, process, and analyse acoustic data; and applica-tions of artificial intelligence (AI) and machine learning (ML) methods to acoustic data.

In relation to “big data” as the next frontier in fisheries acoustics, WGFAST note that many fish-eries institutions and agencies now have terabytes of data recorded over decades, and are col-lecting data at astonishing rates. Efficient discovery, access, processing, and analysis of these data will require open and available data repositories with transparent and efficient ways to discover and access data, data recorded and archived in open formats, and open-source software so that these data can be available to the scientific community beyond fisheries acousticians. Ap-plication of advanced methods such as AI and ML will expand the utility of fisheries acoustic data beyond stock assessment to inform conservation and management of ecosystems.

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