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SONG Junjie, ZHAO Bo, LIU Jinhu, CAO Liang, DOU Shuozeng. Comparison of otolith shape descriptors and morphometrics for stock discrimination of yellow croaker along the Chinese coast[J]. Journal of Oceanology and Limnology, 2018, 36(5): 1870-1879

Comparison of otolith shape descriptors and morphometrics for stock discrimination of yellow croaker along the Chinese coast

SONG Junjie1,3, ZHAO Bo1,3, LIU Jinhu1, CAO Liang1, DOU Shuozeng1,2,3
1 CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;
2 Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China;
3 University of Chinese Academy of Sciences, Beijing 100049, China
This study compared and evaluated the efficiency of two otolith shape descriptors (i.e., the elliptic Fourier transform (EFT) and discrete wavelet transform (DWT)) and morphometrics for stock discrimination. To accomplish this, sample fish from three stocks of yellow croaker Larimichthys polyactis along the Chinese coast (LDB stock from the Liaodong Bay of the Bohai Sea, JZB stock from the Jiaozhou Bay of the Yellow Sea and CJE stock from the Changjiang River estuary of the East China Sea) were used for otolith morphology analyses. The results showed that morphometrics produced an overall classification success rate of 70.8% in contrast with success rates of 80.0% or 82.0% obtained using EFT or DWT, respectively. This suggests that the two shape descriptors comparably discriminated among the stocks and performed more efficiently than morphometrics. During data adjustment and acquisition, some size variables were excluded from the subsequent discriminant analysis for stock discrimination because they were statistically "ineffective," which could reduce the efficiency of morphometrics and lead to relatively low overall classification success. Both EFT and DWT retain the contour coefficients and thus provide a detailed description of otolith shape, which could improve discriminatory efficiency compared with morphometrics.
Key words:    otolith|stock discrimination|discrete wavelet transform|elliptic Fourier transform|morphometrics|Larimichthys polyactis   
Received: 2017-07-31   Revised:
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