Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape
The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative sta...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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Canadian Science Publishing
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crcansciencepubl:10.1139/cjfas-2019-0251 2024-09-30T14:32:10+00:00 Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape Smoliński, Szymon Schade, Franziska Maria Berg, Florian 2020 http://dx.doi.org/10.1139/cjfas-2019-0251 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2019-0251 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2019-0251 en eng Canadian Science Publishing http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining Canadian Journal of Fisheries and Aquatic Sciences volume 77, issue 4, page 674-683 ISSN 0706-652X 1205-7533 journal-article 2020 crcansciencepubl https://doi.org/10.1139/cjfas-2019-0251 2024-09-19T04:09:48Z The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic Sea and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak, and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers. For both species, support vector machines (SVM) resulted in the highest classification accuracy. These findings suggest that modern machine learning algorithms, like SVM, can help to improve the accuracy of fish stock discrimination systems based on the otolith shape. Article in Journal/Newspaper atlantic cod Gadus morhua Norwegian Sea Canadian Science Publishing Norwegian Sea Canadian Journal of Fisheries and Aquatic Sciences 77 4 674 683 |
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Canadian Science Publishing |
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crcansciencepubl |
language |
English |
description |
The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic Sea and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak, and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes. We observed significant differences in the accuracy obtained by the tested classifiers. For both species, support vector machines (SVM) resulted in the highest classification accuracy. These findings suggest that modern machine learning algorithms, like SVM, can help to improve the accuracy of fish stock discrimination systems based on the otolith shape. |
format |
Article in Journal/Newspaper |
author |
Smoliński, Szymon Schade, Franziska Maria Berg, Florian |
spellingShingle |
Smoliński, Szymon Schade, Franziska Maria Berg, Florian Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
author_facet |
Smoliński, Szymon Schade, Franziska Maria Berg, Florian |
author_sort |
Smoliński, Szymon |
title |
Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
title_short |
Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
title_full |
Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
title_fullStr |
Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
title_full_unstemmed |
Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
title_sort |
assessing the performance of statistical classifiers to discriminate fish stocks using fourier analysis of otolith shape |
publisher |
Canadian Science Publishing |
publishDate |
2020 |
url |
http://dx.doi.org/10.1139/cjfas-2019-0251 http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2019-0251 http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2019-0251 |
geographic |
Norwegian Sea |
geographic_facet |
Norwegian Sea |
genre |
atlantic cod Gadus morhua Norwegian Sea |
genre_facet |
atlantic cod Gadus morhua Norwegian Sea |
op_source |
Canadian Journal of Fisheries and Aquatic Sciences volume 77, issue 4, page 674-683 ISSN 0706-652X 1205-7533 |
op_rights |
http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining |
op_doi |
https://doi.org/10.1139/cjfas-2019-0251 |
container_title |
Canadian Journal of Fisheries and Aquatic Sciences |
container_volume |
77 |
container_issue |
4 |
container_start_page |
674 |
op_container_end_page |
683 |
_version_ |
1811636405892284416 |