Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types

Abstract The mesopelagic zone (200–1000 m depth) contains high fish species diversity but biomass and abundances are uncertain yet essential to understand ecosystem functioning. Hull-mounted acoustic systems (usually 38 kHz) often make assumptions on average target strength (TS) of mesopelagic fish...

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Published in:ICES Journal of Marine Science
Main Authors: Agersted, Mette Dalgaard, Khodabandeloo, Babak, Liu, Yi, Melle, Webjørn, Klevjer, Thor A
Other Authors: Proud, Roland, Research Council of Norway, MEESO, EU H2020
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2021
Subjects:
Online Access:http://dx.doi.org/10.1093/icesjms/fsab167
https://academic.oup.com/icesjms/article-pdf/78/8/2907/41764791/fsab167.pdf
id croxfordunivpr:10.1093/icesjms/fsab167
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spelling croxfordunivpr:10.1093/icesjms/fsab167 2024-05-12T08:08:44+00:00 Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types Agersted, Mette Dalgaard Khodabandeloo, Babak Liu, Yi Melle, Webjørn Klevjer, Thor A Proud, Roland Research Council of Norway MEESO EU H2020 2021 http://dx.doi.org/10.1093/icesjms/fsab167 https://academic.oup.com/icesjms/article-pdf/78/8/2907/41764791/fsab167.pdf en eng Oxford University Press (OUP) https://creativecommons.org/licenses/by/4.0/ ICES Journal of Marine Science volume 78, issue 8, page 2907-2921 ISSN 1054-3139 1095-9289 Ecology Aquatic Science Ecology, Evolution, Behavior and Systematics Oceanography journal-article 2021 croxfordunivpr https://doi.org/10.1093/icesjms/fsab167 2024-04-18T08:18:06Z Abstract The mesopelagic zone (200–1000 m depth) contains high fish species diversity but biomass and abundances are uncertain yet essential to understand ecosystem functioning. Hull-mounted acoustic systems (usually 38 kHz) often make assumptions on average target strength (TS) of mesopelagic fish assemblages when estimating biomass/abundance. Here, an unsupervised clustering algorithm was applied on broadband acoustic data (54–78 kHz), collected by a towed instrumented platform in the central Northeast Atlantic, to identify different mesopelagic target types based on similarity of individual TS spectra. Numerical density estimates from echo-counting showed spatial differences in vertical distribution patterns of the different target types and TS spectra data suggested that >30% of the gas-bearing targets had high resonance frequencies (>60 kHz) with low scattering strength at 38 kHz. This conceptual study highlights the importance of separating targets into different target groups to obtain correct backscatter information and to account for all relevant scatterers when estimating average TS at 38 kHz, in order to achieve more accurate biomass/abundance estimates. It furthermore demonstrates the use of a towed broadband acoustic platform for fine-scale numerical density estimates as a complementary method to hull-mounted acoustic data to increase knowledge on mesopelagic ecosystem structure. Article in Journal/Newspaper Northeast Atlantic Oxford University Press ICES Journal of Marine Science
institution Open Polar
collection Oxford University Press
op_collection_id croxfordunivpr
language English
topic Ecology
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Oceanography
spellingShingle Ecology
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Oceanography
Agersted, Mette Dalgaard
Khodabandeloo, Babak
Liu, Yi
Melle, Webjørn
Klevjer, Thor A
Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
topic_facet Ecology
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Oceanography
description Abstract The mesopelagic zone (200–1000 m depth) contains high fish species diversity but biomass and abundances are uncertain yet essential to understand ecosystem functioning. Hull-mounted acoustic systems (usually 38 kHz) often make assumptions on average target strength (TS) of mesopelagic fish assemblages when estimating biomass/abundance. Here, an unsupervised clustering algorithm was applied on broadband acoustic data (54–78 kHz), collected by a towed instrumented platform in the central Northeast Atlantic, to identify different mesopelagic target types based on similarity of individual TS spectra. Numerical density estimates from echo-counting showed spatial differences in vertical distribution patterns of the different target types and TS spectra data suggested that >30% of the gas-bearing targets had high resonance frequencies (>60 kHz) with low scattering strength at 38 kHz. This conceptual study highlights the importance of separating targets into different target groups to obtain correct backscatter information and to account for all relevant scatterers when estimating average TS at 38 kHz, in order to achieve more accurate biomass/abundance estimates. It furthermore demonstrates the use of a towed broadband acoustic platform for fine-scale numerical density estimates as a complementary method to hull-mounted acoustic data to increase knowledge on mesopelagic ecosystem structure.
author2 Proud, Roland
Research Council of Norway
MEESO
EU H2020
format Article in Journal/Newspaper
author Agersted, Mette Dalgaard
Khodabandeloo, Babak
Liu, Yi
Melle, Webjørn
Klevjer, Thor A
author_facet Agersted, Mette Dalgaard
Khodabandeloo, Babak
Liu, Yi
Melle, Webjørn
Klevjer, Thor A
author_sort Agersted, Mette Dalgaard
title Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
title_short Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
title_full Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
title_fullStr Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
title_full_unstemmed Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
title_sort application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types
publisher Oxford University Press (OUP)
publishDate 2021
url http://dx.doi.org/10.1093/icesjms/fsab167
https://academic.oup.com/icesjms/article-pdf/78/8/2907/41764791/fsab167.pdf
genre Northeast Atlantic
genre_facet Northeast Atlantic
op_source ICES Journal of Marine Science
volume 78, issue 8, page 2907-2921
ISSN 1054-3139 1095-9289
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.1093/icesjms/fsab167
container_title ICES Journal of Marine Science
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