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Fish community structure (FCS) of the Yellow Sea (YS) is affected by multiple pressures. Quantifying the responses of indicators of FCS (IFCSs) to pressures is a key aspect of ecosystem-based fisheries management. Quantitative methodology has hitherto been rarely applied to evaluate the performance of ecological indicators in response to physical and anthropogenic pressures and management actions. In this study, we adopted a quantitative and flexible framework to quantify the performance of IFCSs in the YS as well as to identify a suite of operational IFCSs to evaluate the status of the FCS via two state-space approaches. A total of 22 IFCSs were tested for their responses to three types of pressures including anthropogenic activities (fishing), large-scale climate change, and regional environmental variables. Our results indicate that the majority of IFCSs have good performance in terms of sensitivity in their responses to pressures, but weak performance in terms of robustness. The IFCSs tend to respond stronger to fishing than to large-scale climatic indices and regional environmental indices both in terms of sensitivity and robustness. A final indicator suite of five best-performing IFCSs was identified. The five IFCSs include total catch (ToC), mean trophic level (MTL), the ratio of catch of large predatory groups to total catch (LPC/ToC), mean temperature of catch (MTC) [or alternatively catch of small pelagic groups (SPC)], and functional evenness based on thermal groups (T-J′FD), all of which show regime shift patterns consistent with climate change. Compared to a reference period (1960–1964), the status of the current FCS has been obviously changed, and the long-term trajectories of the final indicator suite is consistent with that of fishing pressure. This study demonstrates the applicability of the indicator-testing framework in appraising the status of FCS, and facilitates moving towards ecosystem-based fisheries management in the YS.

Appraising the Status of Fish Community Structure in the Yellow Sea Based on an Indicator-Testing Framework