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dc.contributor.authorDunn, Muriel
dc.contributor.authorMcGowan-Yallop, Chelsey
dc.contributor.authorPedersen, Geir
dc.contributor.authorFalk-Petersen, Stig
dc.contributor.authorDaase, Malin
dc.contributor.authorLast, Kim
dc.contributor.authorLangbehn, Tom J
dc.contributor.authorFielding, Sophie
dc.contributor.authorBrierley, Andrew S
dc.contributor.authorCottier, Finlo
dc.contributor.authorBasedow, Sünnje L
dc.contributor.authorCamus, Lionel
dc.contributor.authorGeoffroy, Maxime
dc.date.accessioned2023-12-19T15:30:02Z
dc.date.available2023-12-19T15:30:02Z
dc.date.issued2023-12-07
dc.identifier297515398
dc.identifierec8f271d-9571-4fc7-83bc-074f9450a5ad
dc.identifier.citationDunn , M , McGowan-Yallop , C , Pedersen , G , Falk-Petersen , S , Daase , M , Last , K , Langbehn , T J , Fielding , S , Brierley , A S , Cottier , F , Basedow , S L , Camus , L & Geoffroy , M 2023 , ' Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm ' , ICES Journal of Marine Science . https://doi.org/10.1093/icesjms/fsad192en
dc.identifier.issn1054-3139
dc.identifier.otherJisc: 1592940
dc.identifier.otherORCID: /0000-0002-6438-6892/work/149333117
dc.identifier.urihttps://hdl.handle.net/10023/28902
dc.descriptionFunding: The fieldwork was registered in the Research in Svalbard database (RiS ID 11578). Fieldwork and research were financed by Arctic Field Grant Project AZKABAN-light (Norwegian Research Council project no. 322 332), Deep Impact (Norwegian Research Council project no. 300 333), Deeper Impact (Norwegian Research Council project no. 329 305), Marine Alliance for Science and Technology in Scotland (MASTS), the Ocean Frontier Institute (SCORE grant no. HR09011), and Glider Phase II financed by ConocoPhillips Skandinavia AS. Geir Pedersen’s participation was co-funded by CRIMAC (Norwegian Research Council project no. 309 512). Maxime Geoffroy was financially supported by the Ocean Frontier Institute of the Canada First Research Excellence Fund, the Natural Sciences and Engineering Research Council Discovery Grant Programme, the ArcticNet Network of Centres of Excellence Canada, the Research Council of Norway Grant Deep Impact, and the Fisheries and Oceans Canada through the Atlantic Fisheries Fund.en
dc.description.abstractClassification 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.
dc.format.extent14
dc.format.extent1639255
dc.language.isoeng
dc.relation.ispartofICES Journal of Marine Scienceen
dc.subjectMachine learningen
dc.subjectZooplanktonen
dc.subjectClassificationen
dc.subjectBroadband acousticsen
dc.subjectCage experimenten
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subject.lccQH301en
dc.titleModel-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosmen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Pelagic Ecology Research Groupen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.identifier.doi10.1093/icesjms/fsad192
dc.description.statusPeer revieweden


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