Discrimination of natural and cultured Oplegnathus fasciatus populations in Zhoushan sea area based on otolith morphology

Jia-Hao Wang ¹, Kai Zhu ¹, Kai-da Xu ¹

⁰Zhejiang Marine Fisheries Research Institute/Ministry of Agriculture and Rural Affairs Scientific Observation and Experimental Station of Fishery Resources of Key Fishing Grounds/Zhejiang Province Key Laboratory of Sustainable Utilization of Technology Research for Fisheries Resources, Zhoushan 316021, Zhejiang, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology +

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February 10, 2025

View abstract on PubMed

Summary

Neural network analysis effectively distinguishes wild and farmed populations of Oplegnathus fasciatus using otolith morphometrics. This advanced technique offers superior accuracy over traditional statistical methods for fish population identification.

Area Of Science

Ichthyology
Aquaculture Science
Biometrics

Background

Distinguishing wild and farmed fish populations is crucial for fisheries management and aquaculture.
Otoliths (ear stones) are valuable for fish identification due to their growth patterns.
Morphometric analysis of otoliths can reveal population-specific characteristics.

Purpose Of The Study

To compare the effectiveness of traditional statistical analysis and neural network methods in differentiating wild and cultured Oplegnathus fasciatus populations.
To identify key otolith shape and truss indices that best discriminate between the two populations.

Main Methods

Collected otoliths from 174 Oplegnathus fasciatus specimens (100 wild, 74 cultured).
Measured six otolith shape indices and twenty-one truss indices.
Applied traditional statistical analysis and neural network techniques for discriminant analysis.

Main Results

Ellipticity, roundness, and aspect ratio showed significant differences between populations.
Twelve of twenty-one truss indices were significantly different.
Neural network methods achieved higher correct discrimination rates (81.4% for shape indices, 85.4% for truss indices) compared to traditional methods (57.5% for shape indices, 69.5% for truss indices).

Conclusions

Neural network analysis is a more effective tool than traditional statistical methods for distinguishing wild and cultured Oplegnathus fasciatus populations based on otolith morphometrics.
Otolith shape and truss indices provide valuable data for population discrimination.

Keywords:

otolith neural network population discrimination stepwise discriminant analysis