The decline in common squid (Todarodes pacificus) stocks, combined with increasing environmental variability, underscores the need for science-based approaches to support adaptive fisheries management in Korea. This study aimed to standardize the catch per unit effort (CPUE) of common squid in Korean waters, specifically in the offshore jigging fishery, by incorporating spatiotemporal and sea surface temperature (SST) factors, and to assess future shifts in thermally suitable fishing grounds under the shared socioeconomic pathway 3-7.0 (SSP3-7.0) climate change scenario. A generalized additive model (GAM) revealed a significant nonlinear relationship between CPUE and SST, with squid abundance concentrated within the 13–23 °C range. The peak response was observed around 21 °C, which was identified as the species’ optimal temperature. We applied this preferred thermal range to projected SST data for 2050 and 2100 under the SSP3-7.0 scenario to map the spatial extent of thermally suitable habitats. Results indicated a contraction of suitable fishing areas during the main fishing season (September–October), while suitable temperatures reemerged in November, suggesting a potential delay in fishing ground formation. These findings highlight the importance of flexible fishery scheduling and management criteria in response to climate-driven changes in habitat suitability. This study provides foundational insights for spatially explicit resource management and the development of climate-adaptive fishery strategies.

Small yellow croaker (Larimichthys polyactis) is a critical economic fish species in South Korea, where effective management is essential due to concerns over declining populations. This study aims to enhance fishery management strategies by applying machine learning techniques to classify the maturity stages and estimate the length at first maturity (L50 and L95), comparing these results with those obtained using traditional macroscopic methods. Five machine learning models, including Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), EXtreme Gradient Boosting (XGB) and Support Vector Machine (SVM), were developed and evaluated for their effectiveness in predicting maturity stages. The XGB model demonstrated superior performance with the highest evaluation final score and low computation time. Using generalized linear models (GLM), this study estimated L50 and L95 for both machine learning predictions and macroscopic observations. The results showed that machine learning models, particularly XGB, provided more precise estimates with narrower confidence intervals and better model fit than the traditional macroscopic methods. These findings can support more sustainable fisheries management practices by offering reliable tools for setting appropriate regulatory measures, such as minimum landing sizes, which contribute to the conservation of marine resources.

Accurate catch data are essential for effective fisheries management. This study reconstructs the historical recreational catch of hairtail (Trichiurus lepturus) in Korean waters by incorporating unreported catches to improve stock assessment accuracy. Using a Bayesian state-space surplus production model, we conducted a multi-fishery stock assessment by integrating abundance indices from eight major fisheries. The multigear mean standardization (MGMS) method was applied to derive standardized CPUE indices for each fishery, providing a more comprehensive evaluation of stock trends. The results indicate that excluding recreational catches and multiple CPUE indices may lead to biased stock assessments of hairtail in Korean waters. Models using an integrated CPUE index (SMSC) yielded higher MSY and biomass estimates, suggesting a more optimistic stock condition, whereas fishery-specific CPUE models (MSC) provided more precautionary estimates. The Kobe plot analysis indicates recent stock recovery, but continued monitoring and adaptive management are required to ensure long-term sustainability. This study highlights the importance of integrating recreational catch data and multi-fishery approaches in stock assessments, particularly under data-limited conditions, to enhance resource management and policy decision-making.