Abstract
In order to develop a method that apply sampling survey data randomly obtained at fishing ports to fish stock assessments, based on fish landing surveys conducted at fishing ports in the northern South China Sea (SCS), 19 Threadfin porgy (Evynnis cardinalis) catch per unit effort (CPUE) datasets were collected for fishing vessels with different gear types and engine powers and incorporated into surplus production models. Considering only the fitting performance, the Schaefer model had the best overall goodness of fit, followed by the Fox, CYP, W–H, and Schnute models. Among fishing vessels with different gears and engine powers, the data were best fitted for single-trawl vessels powered by 301–400-kW engines and for gillnet vessels powered by > 200-kW engines. Eight model expressions were superior and selected for subsequent analyses based on their goodness of fit and relative residuals. The Kobe plot analysis results showed an optimistic fish stock status when using the four model expressions, required more caution when using three model expressions and output pessimistic estimations using one model expression. Considering the incomplete information acquired, a compromising decision-making method was used to derive a 2017 northern SCS E. cardinalis total allowed catch (TAC) of 44,691.21 t. The different conclusions drawn from estimations using CPUEs reflect variable exploitation and utilization fish stock statuses among fishing vessels with different gears and engine powers. Hence, the fishing operations were grouped according to their CPUE relationship, and recommendations regarding optimum fishing efforts were assigned to the groups following a fundamental principle: to improve fishery TAC management, fishing efforts should be reduced if the fish stock assessment is pessimistic and maintained if the assessment is optimistic. This study providing a feasible technical method for the TAC management of China's offshore fisheries.
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