Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape

• Published in: Canadian Journal of Fisheries and Aquatic Sciences
• Main Authors: Smolinski, Szymon, Schade, Franziska Maria, Berg, Florian
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2020
• Subjects: Text; ddc:570; Norwegian Sea; atlantic cod; Gadus morhua; Mer de Norvège
• Online Access: https://doi.org/10.1139/cjfas-2019-0251; https://www.openagrar.de/receive/openagrar_mods_00057945; https://www.openagrar.de/servlets/MCRFileNodeServlet/openagrar_derivate_00028464/dn062063.pdf

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 otolithshape 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. L’affectation d’un poisson donné à son stock d’origine est importante pour la fiabilité des évaluations de stocks et de la gestion des pêches. La forme des otolites est communément utilisée comme marqueur de stocks distincts dans des études de discrimination. Notre examen de la documentation a montré que l’application et la comparaison de différents critères de classification statistiques pour discriminer des stocks de poissons sur la base de la forme des otolites constituent une approched’usage limité. Nous avons donc comparé la performance de deux critères de classification traditionnels et quatre critères d’apprentissage machine reposant sur l’analyse de Fourier de la forme des otolites pour des stocks choisis de morue (Gadus morhua) dans la mer Baltique méridionale et de hareng (Clupea harengus) dans la mer de Norvège occidentale, le Skagerrak et la mer Baltique ...