Assessing the performance of statistical classifiers to discriminate fish stocks using Fourier analysis of otolith shape - Canadian Journal of Fisheries and Aquatic Sciences

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.