Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm
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 va...
Published in: | ICES Journal of Marine Science |
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Oxford University Press
2023
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Online Access: | http://nora.nerc.ac.uk/id/eprint/536454/ https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsad192/7460294 |
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ftnerc:oai:nora.nerc.ac.uk:536454 2024-01-14T10:04:22+01:00 Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm Dunn, Muriel McGowan-Yallop, Chelsey Pedersen, Geir Falk-Petersen, Stig Daase, Malin Last, Kim Langbehn, Tom J Fielding, Sophie Brierley, Andrew S Cottier, Finlo Basedow, Sünnje L Camus, Lionel Geoffroy, Maxime Wieczorek, Alina 2023-12-07 http://nora.nerc.ac.uk/id/eprint/536454/ https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsad192/7460294 unknown Oxford University Press Dunn, Muriel; McGowan-Yallop, Chelsey; Pedersen, Geir; Falk-Petersen, Stig; Daase, Malin; Last, Kim; Langbehn, Tom J; Fielding, Sophie orcid:0000-0002-3152-4742 Brierley, Andrew S; Cottier, Finlo; Basedow, Sünnje L; Camus, Lionel; Geoffroy, Maxime; Wieczorek, Alina. 2023 Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm. ICES Journal of Marine Science, fsad192. https://doi.org/10.1093/icesjms/fsad192 <https://doi.org/10.1093/icesjms/fsad192> Publication - Article PeerReviewed 2023 ftnerc https://doi.org/10.1093/icesjms/fsad192 2023-12-15T00:03:29Z 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. Article in Journal/Newspaper Arctic Ny Ålesund Ny-Ålesund polar night Svalbard Zooplankton Copepods Natural Environment Research Council: NERC Open Research Archive Arctic Svalbard Ny-Ålesund ICES Journal of Marine Science |
institution |
Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
unknown |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Dunn, Muriel McGowan-Yallop, Chelsey Pedersen, Geir Falk-Petersen, Stig Daase, Malin Last, Kim Langbehn, Tom J Fielding, Sophie Brierley, Andrew S Cottier, Finlo Basedow, Sünnje L Camus, Lionel Geoffroy, Maxime Wieczorek, Alina |
spellingShingle |
Dunn, Muriel McGowan-Yallop, Chelsey Pedersen, Geir Falk-Petersen, Stig Daase, Malin Last, Kim Langbehn, Tom J Fielding, Sophie Brierley, Andrew S Cottier, Finlo Basedow, Sünnje L Camus, Lionel Geoffroy, Maxime Wieczorek, Alina Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
author_facet |
Dunn, Muriel McGowan-Yallop, Chelsey Pedersen, Geir Falk-Petersen, Stig Daase, Malin Last, Kim Langbehn, Tom J Fielding, Sophie Brierley, Andrew S Cottier, Finlo Basedow, Sünnje L Camus, Lionel Geoffroy, Maxime Wieczorek, Alina |
author_sort |
Dunn, Muriel |
title |
Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
title_short |
Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
title_full |
Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
title_fullStr |
Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
title_full_unstemmed |
Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
title_sort |
model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm |
publisher |
Oxford University Press |
publishDate |
2023 |
url |
http://nora.nerc.ac.uk/id/eprint/536454/ https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsad192/7460294 |
geographic |
Arctic Svalbard Ny-Ålesund |
geographic_facet |
Arctic Svalbard Ny-Ålesund |
genre |
Arctic Ny Ålesund Ny-Ålesund polar night Svalbard Zooplankton Copepods |
genre_facet |
Arctic Ny Ålesund Ny-Ålesund polar night Svalbard Zooplankton Copepods |
op_relation |
Dunn, Muriel; McGowan-Yallop, Chelsey; Pedersen, Geir; Falk-Petersen, Stig; Daase, Malin; Last, Kim; Langbehn, Tom J; Fielding, Sophie orcid:0000-0002-3152-4742 Brierley, Andrew S; Cottier, Finlo; Basedow, Sünnje L; Camus, Lionel; Geoffroy, Maxime; Wieczorek, Alina. 2023 Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm. ICES Journal of Marine Science, fsad192. https://doi.org/10.1093/icesjms/fsad192 <https://doi.org/10.1093/icesjms/fsad192> |
op_doi |
https://doi.org/10.1093/icesjms/fsad192 |
container_title |
ICES Journal of Marine Science |
_version_ |
1788058915211051008 |