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Saudi J Biol Sci. 2018 Sep;25(6):1163-1169. doi: 10.1016/j.sjbs.2017.09.006. Epub 2017 Sep 22.

Stock discrimination and connectivity assessment of yellowfin seabream (Acanthopagrus latus) in northern South China Sea using otolith elemental fingerprints.

Author information

1
College of Fisheries Science, Guangdong Ocean University, Zhanjiang 524088, China.
2
Gulf of Maine Research Institute, Portland, ME 04101, USA.

Abstract

Connectivity between fish stocks is fundamental to the understanding of population dynamics and the implementation of sustainable fisheries management. Otolith microchemistry is a promising tool as it can provide information on the continuous growth of otoliths and the environmental effects on otolith composition. Such elemental fingerprints can help distinguish different stocks or life history stages, identify the origins or nursery areas of fish, and assess population structure. In this study, we examined the stock discrimination and spatial connectivity of cage-cultured and wild stocks of yellowfin seabream (Acanthopagrus latus) from the coastal waters of Shantou, Yangjiang, and Zhanjiang in China southern province Guangdong during 2012-2014, based on otolith trace-elemental signatures using multivariate statistical analysis and machine learning approaches. The concentrations of 13 elements (7Li, 23Na, 24Mg, 40Ca, 55Mn, 56Fe, 59Co, 59Ni, 64Cu, 65Zn, 88Sr, 122Sb, and 137Ba) in the natal spot of fish otoliths, representing the embryonic and paralarval stages of fish, were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Stepwise discriminant analysis and random forests were used to distinguish the cultured and wild stocks of yellowfin seabream, and non-metric multidimensional scaling (NMDS) and cluster analysis were used to determine the spatial variation and connectivity of yellowfin seabream stocks. Overall, the cultured and wild stocks of yellowfin seabream could be identified with classification accuracy of 80.7% and 99.2% by using stepwise discriminant analysis and random forests respectively. When we compared site difference between cultured and wild stocks (site × stock interactions), the classification success was 60.4% for stepwise discriminant analysis and 85.7% for random forests. The misclassification of cultured and wild stocks within the three sites suggested the spatial connectivity between stocks and among sampling locations. Our findings suggested that the three wild stocks of yellowfin seabream from Guangdong coastal waters could be considered as one unit for management, and the difference between cultured and wild stocks was significant for yellowfin seabream from Shantou and Yangjiang, but less significant for yellowfin seabream from Zhanjiang. This study demonstrated that otolith elemental fingerprints can help improve our knowledge on the spatial connectivity, population structure, and life history of fish stocks, and random forests can be a useful tool for identifying cultured and wild stocks compared to the traditional stepwise discriminant analysis.

KEYWORDS:

Cluster analysis; Non-metric multidimensional scaling (NMDS); Random forests; Stepwise discriminant analysis; Stock discrimination; Sustainable fisheries management

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