Scientific Reports (Apr 2025)

Enhancing catch-based stock assessment in data-limited fisheries with proxy CPUE indicators in the Yellow Sea

  • Kun Wang,
  • Qi Li,
  • Chongliang Zhang,
  • Binduo Xu,
  • Yiping Ren

DOI
https://doi.org/10.1038/s41598-025-95092-w
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 15

Abstract

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Abstract Catch-based methods are widely used in marine fisheries management, particularly for assessing fish stock status in data-limited fisheries. However, their reliability remains controversial, especially when only catch data are available. In fisheries with inadequate monitoring, Catch Per Unit Effort (CPUE) data are often unavailable, despite the potential availability of total fishing effort records for entire areas. Here, we evaluate the potential of a proposed proxy-CPUE indicator, defined as the ratio of total catch to total fishing effort metrics, as a substitute for CPUE to enhance catch-based methods. Using chub mackerel (Scomber japonicus) in the Yellow Sea as a case study, we developed proxy-CPUE indicators using three types of large-scale effort metrics: Gross Vessel Count (GVC), Gross Vessel Power (GVP), and Target Vessel Count (TVC). These indicators were incorporated into a Bayesian state-space Schaefer surplus production model (BSM) and their performance was compared to catch-only methods (CMSY) across key evaluation criteria, including robustness of estimation, reliability in retrospective analyses, and performance when encountering catch observation errors. Additionally, we conducted simulations to assess the impact of dynamic catchability, demonstrating that proxy-CPUE methods remain robust even when catchability varies over time. Results indicate that proxy-CPUE substantially improves the robustness of stock status estimates, especially by mitigating the impact of high catch observation errors—reducing estimate variations by 50% compared to catch-only methods. Both GVC-based and GVP-based proxy-CPUE demonstrated reliable performance in retrospective analyses. This study provides a practical and scalable solution for the management of fisheries facing similar data constraints.

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