Chub mackerel (Scomber japonicus), Pacific saury (Cololabis saira), and Pacific sardine (Sardinops sagax) are key economic and ecological species in the Northwest Pacific Ocean (NPO). In recent years, there have been some interannual changes in their catches due to the increasing number of fishing vessels and climate change. With the establishment of the North Pacific Fisheries Commission (NPFC) to better manage these three species, it is particularly important to develop an accurate understanding of the stock status of those fisheries resources. According to the production statistics of Chub mackerel, Pacific saury, and Pacific sardine in the NPO, the length-based Bayesian evaluation (LBB) method was adopted to conduct a stock assessment on the three fisheries in this study. Research results show that the asymptotic length of Chub mackerel in the NPOL_infis 37.54 cm, with the parameter ratios ofL_c/L_{c_opt}= 1.10,F/M= 0.57,B/B₀= 0.65, andB/B_MSY= 1.10. The asymptotic length of Pacific saury in the NPOL_infis 33.24 cm, with the ratios ofL_c/L_{c_opt}= 1.10,F/M= 0.14,B/B₀= 0.82, andB/B_MSY= 2.10. While the asymptotic length of Pacific sardineL_infis 39.33 cm, with the ratios ofL_c/L_{c_opt}= 1.20,F/M= 0.20,B/B₀= 0.77, andB/B_MSY= 2.20. At present, the three species in the NPO are in a healthy state and have not yet been overfished. Body length bin may affect the estimation of some parameters without compromising the estimation of stock status. Our study indicates that the LBB model serves as an efficient method to evaluate the fisheries resources in the NPO, especially when length frequencies are the only available data. Hopefully, the results in this study can provide technical support for the conservation and management of Chub mackerel, Pacific saury, and Pacific sardine in the NPO.

The Northwest Indian Ocean is a key fishing ground for China’s pelagic fisheries, with the purpleback flying squid being a significant target. This study uses commercial fishing logs of the Indian Ocean between 2015 and 2021, alongside pelagic seawater temperature and its vertical temperature difference within the 0–200 m depth range, to construct generalized additive models (GAMs) and gradient boosting tree models (GBTs). These two models are evaluated using cross-validation to assess their ability to predict the distribution of purpleback flying squid. The findings show that factors like year, latitude, longitude, and month significantly influence the distribution of purpleback flying squid, while surface water temperature, 200 m water temperature, and the 150–200 m water layer temperature difference also play a role in the GBT model. Similar factors also take effects in the GAM. Comparing the two models, both GAM and GBT align with reality in predicting purpleback flying squid resource distribution, but the precision indices of GBT model outperform those of the GAM. The predicted distribution for 2021 by GBT also has a higher overlap with the actual fishing ground than that by GAM, indicating GBT’s superior forecasting ability for the purpleback flying squid fishing ground in the Northwest Indian Ocean.

Fishery resources of tuna serve as a vital source of global protein. This study investigates the key environmental drivers influencing the spatial distribution of yellowfin tuna (Thunnus albacares) in the western tropical Pacific Ocean. A comprehensive dataset was constructed by linking the catch per unit effort (CPUE) from 43 Chinese longline fishing vessels (2008–2019) with 24 multi-source environmental variables. To accurately model this complex relationship, a total of 16 machine learning regression models, including advanced ensemble methods like Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting Regressor (CatBoost), were evaluated and compared using multiple performance metrics (e.g., Coefficient of Determination [R²], Root Mean Squared Error [RMSE]). The results indicated that the Light Gradient Boosting Machine (LightGBM) model achieved superior performance, demonstrating excellent nonlinear fitting capabilities and generalization ability. For robust feature interpretation, the study employed both the model’s internal feature importance metrics and the SHapley Additive exPlanations (SHAP) method. Both approaches yielded highly consistent results, identifying temporal (month), spatial (longitude, latitude), and key seawater temperature indicators at intermediate depths (T450, T300, T150) as the most critical predictors. This highlights significant spatiotemporal heterogeneity in the distribution of Thunnus albacares. The analysis suggests that mid-layer ocean temperatures directly influence catch rates by governing the species’ vertical and horizontal movements. In contrast, large-scale climate indices such as the Oceanic Niño Index (ONI) exert indirect effects by modulating ocean thermal structures. This research confirms the dominance of spatiotemporal and thermal variables in predicting yellowfin tuna distribution and provides a reliable, data-driven framework for supporting sustainable fishery management, resource assessment, and operational forecasting.

Catch-per-unit-effort (CPUE) standardization in fisheries is a critical foundation for conducting stock assessment and fishery conservation. The Pacific sardine (Sardinops sagax) is one of the economically important fish species in the Northwest Pacific Ocean (NPO). Hence, the importance of choosing an appropriate CPUE standardization model cannot be overstated when it comes to achieving a precise relative abundance index for the efficient management of Pacific sardine fishery. This study’s main aim was to assess and compare the efficacy of three models, specifically the General Linear Model (GLM), the Generalized Linear Mixed Model (GLMM), and the spatio-temporal GLMM (VAST), in the CPUE standardization for Pacific sardine fishery in the NPO, with the ultimate goal of identifying the most appropriate model. An influence analysis was applied to analyze the impact of individual variables on the disparity among standardized and nominal CPUE, and the main explanatory variables influencing standardized CPUE were identified. A coefficient–distribution–influence (CDI) plot was generated to analyze the impact of the different models on the annual standardized CPUE. Additionally, a simulation testing framework was developed to evaluate the estimated accuracy of the three models. The results indicated that the standardized CPUE and the nominal CPUE exhibited similar trends between 2014 and 2021 for the three models. Compared to the GLM and the GLMM, the VAST demonstrates larger conditional R² and smaller conditional AIC, indicating a better performance in standardizing the CPUE for Pacific sardines due to its consideration of spatial and temporal variations. The interaction terms within the three models exert significant influences on the annual standardized CPUE, necessitating their inclusion in the model construction. CDI plots indicate that the spatio-temporal influence of the VAST model exhibits a smaller variation trend, suggesting that the VAST is more robust when standardizing the CPUE for Pacific sardines. Simulation testing additionally demonstrated that the VAST model displays smaller model root mean squared error (RMSE) and bias, establishing it as the superior performer for standardizing CPUE. Our results provide a theoretical basis for the scientific management of Pacific sardines in the NPO and can be extended to CPUE standardization for other small pelagic fish species worldwide.

Chub mackerel (Scomber japonicus) is a typical small pelagic fish in the high seas of the Northwest Pacific Ocean (NPO), with great ecological and economic values. It is crucial to understand the spatio-temporal distribution and the population dynamics of chub mackerel for commercial fishing and fishery management. In this study, we analyzed the population dynamics and stock status of chub mackerel based on commercial fishing data. Results showed that from 2016 to 2021, high catches of chub mackerel were concentrated along the outer edge of Japan or Russia’s exclusive economic zone line, and the gravity center of the fishing grounds moved to the northwest gradually, with a decrease in the average value of catch per unit effort (CPUE) year by year. The fishing pressure may not be the major reason for the decrease in chub mackerel as the resource biomass was at a high level. The body length structure was reasonable, and large individuals were still present. The study highlights the fact that the population status of chub mackerel was healthy in 2016–2021 according to our results. However, it has to be emphasized that resource exploitation and fishery management also need to take into account the influence of climatic conditions.

The Japanese sardine (Sardinops sagax), a key economic species in the Northwest Pacific Ocean (NWPO), has shown significant increases in both population abundance and catch volume over the past decade. To understand its spatiotemporal dynamics under climate change, this study analyzed light purse seine fishery data (2014–2021) from the NWPO. The results showed that the primary fishing season spans March to December, with peak catches concentrated in 40–43° N, 149–155° E. Annual catches grew steadily, accelerating notably in 2021. The fishing grounds’ center shifted northeastward annually and seasonally (southwest-to-northeast trajectory), driven by directional aggregation. Spatial clustering with global positive autocorrelation was observed, weakening as distance thresholds increased. Resource hotspots migrated northeast, narrowing from 40–42° N (2016) to 42–44° N (2017–2021), while coldspots showed complementary fluctuations. Generalized additive model (GAM) analysis revealed that the catch per unit effort (CPUE) of Japanese sardine in the high seas of the NWPO was governed by temporal–spatial drivers and multivariate environmental determinants. Analytical findings substantiate the significant climate-driven impacts on the spatiotemporal distribution and population dynamics of Japanese sardine. The non-stationary interannual and seasonal patterns of fishing grounds highlight the need for adaptive management strategies.