Title: Using fisheries‐independent survey data to reinforce China’s data‐limited fisheries management: Management strategy evaluation of survey‐based management procedures
Abstract: Abstract Fisheries‐independent survey data are vital to stock assessment and management because they provide reliable information on stock status. Although survey data have been increasingly recognised for their contributions to fisheries management, they have often not been adequately used to provide quantitative management recommendations for China's fisheries that are subject to limited data. In the present study, a framework was proposed to integrate two types of survey‐based management procedures (MPs) into China's fisheries management: the slope‐based MP and the target‐based MP. Specifically, the former generates fishing effort limits based on the trend in recent years’ abundance index, while the latter sets effort limits based on a static abundance index target. To test the performance of these MPs, management strategy evaluation was performed using a simulated fishery based on the small yellow croaker, Larimichthys polyactis (Bleeker) in Haizhou Bay, China. The sensitivity of MPs was investigated under different algorithm parameterisations and historical exploitation levels. Additionally, their robustness was evaluated in the face of survey uncertainty and changes in recruitment success. Both MPs could effectively improve the stock status compared with the status quo management when appropriately parameterised regardless of exploitation history. The target‐based MP was more robust to survey uncertainty than the slope‐based MP. Non‐stationary changes in recruitment success strongly impaired the management effects, while using recruitment‐specific instead of the age‐aggregated abundance index could slightly enhance their performance. This work indicates that survey‐based MPs can address the current challenges in China's fisheries management and reinforce the status quo management practice with limited data and highlights the potential risks.
Publication Year: 2020
Publication Date: 2020-09-18
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 2
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