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...
Published in: | ICES Journal of Marine Science |
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2021
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Online Access: | http://dx.doi.org/10.1093/icesjms/fsab167 https://academic.oup.com/icesjms/article-pdf/78/8/2907/41764791/fsab167.pdf |
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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 |
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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 |
_version_ |
1798851848647475200 |