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

• Published in: Journal of Oceanology and Limnology
• Main Authors: Song Junjie, Zhao Bo, Liu Jinhu, Cao Liang, Dou Shuozeng
• Format: Report
• Language: English
• Published: SCIENCE PRESS 2018
• Subjects: otolith; stock discrimination; discrete wavelet transfonn; elliptic Fourier transform; morphometrics; Laritnichthys polyactis; Marine & Freshwater Biology; Oceanography; Limnology; HERRING CLUPEA-HARENGUS; NORTHEAST ATLANTIC; PERSIAN-GULF; IDENTIFICATION; FOURIER; SEA; MICROCHEMISTRY; REPRESENTATION; ENVIRONMENT; TELEOST; Northeast Atlantic
• Online Access: http://ir.qdio.ac.cn/handle/337002/156357; https://doi.org/10.1007/s00343-018-7228-0

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 Larintichthys 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.