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.

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations.

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.

To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1° × 0.1° and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42° S in the north and between 45° S and 47° S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees—AdaBoost and PSO-RF—aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species’ resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species’ resources.

The marine fishery is a high-risk industry, and when a fishing vessel is in distress at sea, it is essential to protect the lives of the fishermen by implementing a fast and effective rescue for the vessel in distress. In this process, quickly retrieving the closest neighboring vessel in distress based on an effective rescue time is critical to the vessel’s rescue. In order to improve the time efficiency of retrieving the nearest available rescue vessels in the current sea area, this paper proposes a method based on the Google S2 grid coding for ships in distress at sea. The method is divided into three parts: (1) encoding the ship’s position based on the Google S2 algorithm, (2) retrieving the set of available ships in the current sea area based on the effective rescue distance and its corresponding coding level, and (3) sorting the set of available ships in the current sea area according to the proximity to the ship in distress by using the method of “Alternating Grid Sorting Based on Neighborhoods and Different Coding Levels”. The effective rescue distance is set by the rescue time, the type of rescue vessel, and the speed. This paper sets the simulation experiment area as the East China Sea area. Different magnitudes of ship position datasets (1 × 10², 1 × 10³, 1 × 10⁴, 1 × 10⁵, 1 × 10⁶) are generated by simulating the scenarios where a ship’s location is reported or collected by AIS or VMS. The temporal retrieval efficiencies of querying based on the two methods, the Euclidean distance and the Google S2 grid encoding, are compared and analyzed. The experimental results show that the total time consumed by the query method based on the Google S2 grid encoding and the query method based on Euclidean distance is reduced by 37.06%, 29.83%, 72.75%, 94.43%, and 94.53%, respectively, in the process of generating the set of rescuable ships retrieved, based on the effective rescue distance. Therefore, the time retrieval efficiency of the maritime vessel search and rescue method based on the Google S2 coding is high, which can effectively improve the query efficiency of rescue vessels in the neighborhood of distressed vessels.

The abundant Argentine shortfin squid resource plays a key role in the Patagonian Large Marine Ecosystem, the Polar Frontal Zone Ecosystem, and the South Atlantic Subtropical Gyre Ecosystem. In this article, we analyzed the annual and monthly changes in catch per unit effort (CPUE) of Argentine shortfin squid with a spatial resolution of 0.25° × 0.25° and constructed three ensemble learning and two deep learning fishing grounds forecasting models using catch information and spatial–temporal and marine environmental data. The results of the study were as follows: 1. From 2016 to 2021, Argentine shortfin squid in the Southwest Atlantic experienced notable interannual fluctuations, with the resource showing an increase and then remaining stable from 2016 to 2018, a decline in 2019, and a substantial increase from 2020 to 2021. Seasonally, CPUE was low from November to January, rose significantly from February to May, and declined in June; 2. The XGBoost model had the best overall performance among the three tree models, achieving an average of 68.86% accuracy, 70.19% F1-score; 3. In the 2021 actual production data validation, the Fusion ResNet18 model achieved an average production data accuracy of 74.47%, F1-score of 73.85%; the Fusion 3DResNet18 model achieved an average production data accuracy of 81.27%, F1-score of 82.43%. This indicates that convolutional neural networks, particularly 3D versions, are more suitable than decision tree-based ensemble models for predicting Argentine shortfin squid fishing grounds. Highly accurate fishing grounds forecasts help enterprises save production costs while providing some reference for the sustainable development of fishery resources.