Exploration of statolith shape variation in jumbo flying squid, Dosidicus gigas, based on wavelet analysis and machine learning methods for stock classification

The statolith in cephalopods has a stable morphology and contains important ecological information. Influenced by genetic structures and environmental variability, statolith shapes often vary among different stocks and are ideal indices for stock discrimination. In the present study, wavelet analysis was used to explore the statolith shape variations in Dosidicus gigas (D'Orbigny, 1835 in 1834-1847) among four geographic stocks obtained by Chinese jigging fleets in the eastern tropical Pacific Ocean (ETP). In addition, machine learning methods were compared with traditional classification methods to improve the stock classification results of D. gigas. According to our analyses, statolith shapes of D. gigas differed significantly among the four stocks. Wavelet coefficients extracted from the statolith images by computer software were used to reconstruct the mean statolith shape for every stock. The rostrum and wing of the statolith are two main components determining the variances among stocks. Canonical analysis of principal coordinates dearly separated Costa Rican from other stocks. Machine learning methods performed better than the traditional method of statolith shape dassification. The results of our study supported the geographical separation of D. gigas stocks (Costa Rican and equatorial stock in the northern hemisphere, and Peruvian and Chilean stock in the southern hemisphere) reported in previous studies. Wavelet analysis is an appropriate method for stock classification and machine learning methods can effectively improve the classification accuracy and is a promising method for determining the stock structure.