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Content loaded within last 14 days Exploration of statolith shape variation in jumbo flying squid, Dosidicus gigas, based on wavelet analysis and machine learning methods for stock classification

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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 clearly separated Costa Rican from other stocks. Machine learning methods performed better than the traditional method of statolith shape classification. 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.
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Document Type: Research Article

Affiliations: 1: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China, National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China, The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China 2: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China, National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China, The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;, Email: [email protected] 3: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China 4: School of Marine Sciences, University of Maine, Orono, Maine 04469

Publication date: 01 October 2018

This article was made available online on 27 March 2018 as a Fast Track article with title: "Exploration of statolith shape variation in jumbo flying squid, Dosidicus gigas, based on wavelet analysis and machine learning methods for stock classification".

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  • The Bulletin of Marine Science is dedicated to the dissemination of high quality research from the world's oceans. All aspects of marine science are treated by the Bulletin of Marine Science, including papers in marine biology, biological oceanography, fisheries, marine affairs, applied marine physics, marine geology and geophysics, marine and atmospheric chemistry, and meteorology and physical oceanography.
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