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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Language: | English |
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NRC Research Press
2020
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Online Access: | http://hdl.handle.net/1956/22564 https://doi.org/10.1139/cjfas-2019-0251 |
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ftunivbergen:oai:bora.uib.no:1956/22564 2023-05-15T15:27:32+02:00 Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape Smoliński, Szymon Schade, Franziska M. Berg, Florian 2020-01-10T12:43:15Z application/pdf http://hdl.handle.net/1956/22564 https://doi.org/10.1139/cjfas-2019-0251 eng eng NRC Research Press urn:issn:0706-652X urn:issn:1205-7533 http://hdl.handle.net/1956/22564 https://doi.org/10.1139/cjfas-2019-0251 cristin:1739420 Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Authors Canadian Journal of Fisheries and Aquatic Sciences Peer reviewed Journal article 2020 ftunivbergen https://doi.org/10.1139/cjfas-2019-0251 2023-03-14T17:43:28Z 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 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. publishedVersion Article in Journal/Newspaper atlantic cod Gadus morhua Norwegian Sea University of Bergen: Bergen Open Research Archive (BORA-UiB) Norwegian Sea Canadian Journal of Fisheries and Aquatic Sciences 77 4 674 683 |
institution |
Open Polar |
collection |
University of Bergen: Bergen Open Research Archive (BORA-UiB) |
op_collection_id |
ftunivbergen |
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 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. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Smoliński, Szymon Schade, Franziska M. Berg, Florian |
spellingShingle |
Smoliński, Szymon Schade, Franziska M. Berg, Florian Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape |
author_facet |
Smoliński, Szymon Schade, Franziska M. 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 |
NRC Research Press |
publishDate |
2020 |
url |
http://hdl.handle.net/1956/22564 https://doi.org/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 |
op_relation |
urn:issn:0706-652X urn:issn:1205-7533 http://hdl.handle.net/1956/22564 https://doi.org/10.1139/cjfas-2019-0251 cristin:1739420 |
op_rights |
Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Authors |
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_ |
1766357954841280512 |