한국 서해 참조기(Larimichthys polyactis)의 육안판별법과 GSI판별법에 의한 성숙체장 추정
Estimating Length at Sexual Maturity of the Small Yellow Croaker Larimichthys polyactis in the Yellow Sea of Korea Using Visual and GSI Methods
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Determination of the precise size at sexual maturity is very important for science-based stock assessment and fisheries resource management. In this study, two different models, (1) a visual method and (2) a gonadosomatic index (GSI) method, were employed to estimate length at sexual maturity of the small yellow croaker Larimichthys polyactis in the Yellow Sea of Korea. The visual method is a common qualitative method using visual gonadal identification. Conversely, the GSI method is a quantitative method using the GSI, which can be easily and precisely collected. We compared results from these methods to determine the best approach, and to examine the practicality of the GSI method. Logistic regression of the maturity ogive was conducted using a general linear model (GLM) with the R statistics program. Also, the bootstrapped 95% confidence intervals of all estimates were calculated. The best-fit model was the visual method (RMc 2 =0.805, AUC=0.989, L50=15.1). Among models using the GSI method, the model computing GSIref=0.94 was the best-fit model (RMc 2 =0.792, AUC=0.989, L50=15.2). There was no significant difference between the two models, evidencing the effectiveness and accuracy of the GSI method.
Abstract
Determination of the precise size at sexual maturity is very important for science-based stock assessment and fisheries resource management. In this study, two different models, (1) a visual method and (2) a gonadosomatic index (GSI) method, were employed to estimate length at sexual maturity of the small yellow croaker Larimichthys polyactis in the Yellow Sea of Korea. The visual method is a common qualitative method using visual gonadal identification. Conversely, the GSI method is a quantitative method using the GSI, which can be easily and precisely collected. We compared results from these methods to determine the best approach, and to examine the practicality of the GSI method. Logistic regression of the maturity ogive was conducted using a general linear model (GLM) with the R statistics program. Also, the bootstrapped 95% confidence intervals of all estimates were calculated. The best-fit model was the visual method (RMc 2 =0.805, AUC=0.989, L50=15.1). Among models using the GSI method, the model computing GSIref=0.94 was the best-fit model (RMc 2 =0.792, AUC=0.989, L50=15.2). There was no significant difference between the two models, evidencing the effectiveness and accuracy of the GSI method.
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