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.

To evaluate and compare the effectiveness of prediction models for Argentine squid Illex argentinus trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop Illex argentinus trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., Illex argentinus). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas.

Understanding the alignment between fisher behavior and habitat dynamics is essential for data-driven fisheries management. This study analyzed high-resolution Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data, integrated with logbooks from 10 stick-held dipnet vessels targeting Pacific saury (Cololabis saira) in the North Pacific high seas. We developed an optimized CNN-LSTM-SE model to classify vessel trajectories into eight operational states, achieving 91% accuracy. This model generated a high-confidence presence dataset, addressing spatiotemporal data limitations in pelagic species research. A dynamic Ensemble Species Distribution Model (ESDM) mapped habitat suitability index (HSI) for the primary fishing seasons (June–September) of 2023–2024, revealing seasonal northward migrations and an interannual eastward shift in core habitats, primarily driven by sea surface temperature (SST: 6.4–19.1 °C), chlorophyll-a (CHL: 0.2–2.0 mg/m³), mixed layer depth (MLD: 14–30 m), and dissolved oxygen (DO: 220–290 mmol/m³). Receiver operating characteristic (ROC) sensitivity analysis identified an HSI threshold of ≥0.4 for suitable habitats, where 98.4% of fishing effort was concentrated. Kaplan–Meier survival analysis demonstrated that vessels in high-quality habitats (HSI ≥ 0.8) exhibited significantly longer fishing bout durations and lower cessation probabilities (log-rank test, χ² = 20.9, p < 0.001), providing empirical evidence for the Marginal Value Theorem and Optimal Foraging Theory. Although HSI showed a weak direct correlation with catch rates (R² = 0.007), it effectively delineated high-potential fishing grounds (>90% of high-catch days > 30 tonnes in HSI ≥ 0.6). By demonstrating that fishers’ spatial decisions appear to reflect environmental gradients, suggesting that fishing effort may indirectly act as an ecological indicator, this integrated framework bridges fisher behavior with ecological theory, supporting dynamic ocean management in climate-variable fisheries.

This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch.

Global climate change poses a significant management challenge for vital transboundary resources like the Pacific saury (Cololabis saira). To address this, we developed an innovative framework that uses high-resolution Automatic Identification System (AIS) data and deep learning to define species distribution, which then informs a robust Ensemble Species Distribution Model (ESDM). The model (TSS > 0.89, AUC > 0.97) identifies sea surface temperature (SST) and chlorophyll-a (CHL) as key habitat drivers. Projections under future climate scenarios reveal two critical threats: (1) a continuous northeastward migration of the habitat’s centroid, exceeding 400 km by 2100 under a high-emission SSP5-8.5 scenario, and (2) a drastic contraction of highly suitable habitat (suitability > 0.8), shrinking by up to 94% under the high-emission SSP3-7.0 scenario. By directly linking key oceanographic features to these climate-driven risks, this study delivers an essential scientific decision-support tool for management bodies like the North Pacific Fisheries Commission (NPFC) to develop climate-adaptive strategies.

Chub mackerel (Scomber japonicus) is a key pelagic commercial fish species in the North Pacific Ocean and is one of the priority species managed by the North Pacific Fisheries Commission (NPFC). This study assessed the stock status and maximum sustainable yield (MSY) of S. japonicus using the Length-based Bayesian Biomass (LBB) estimator and the Catch-MSY model. The assessment was based on length-frequency data collected from the light purse seine fishery on the high seas of the North Pacific (6061 individuals sampled during April–December, 2019–2024) combined with historical catch data spanning 1995 to 2024. Management implications were further analyzed using the Kobe plot and the Kobe II Strategy Matrix (K2SM). The results showed that: (1) The LBB model revealed significant interannual variability in stock status of S. japonicus. Although the stock was within sustainable limits (B/B_MSY ≥ 1.0) in 2019, 2021, and 2022, it suffered from overfishing or was heavily overfished in 2020, 2023, and 2024. Notably, integrated data analysis indicated that during 2023–2024, the B/B_MSY ratio dropped to 0.51, reflecting a marked deterioration in the stock condition. (2) The Catch-MSY model estimated the maximum sustainable yield (MSY) at 278,000 t, with a 95% confidence interval (CI) ranging from 162,000 t to 475,000 t. (3) Based on catch data, the Kobe plot indicated that in the final assessment year, the probability of the stock being in a sustainable state was only 23.2% at the 95% confidence level. The probabilities of being recruitment-overfished, fishing-overfished, and severely overfished were 37.6%, 14.9%, and 24.3%, respectively. With relative biomass B/B_MSY < 1 and relative fishing mortality F/F_MSY < 1, the stock status in the final assessment year was diagnosed as recruitment overfishing. (4) Integrating the outputs of the two models, the Kobe plot, and the probabilistic projections of future stock status from the Kobe II Strategy Matrix (K2SM), it is recommended that the total allowable catch (TAC) be set within 60–80% of the baseline TAC during the medium-term management phase (5–10 years), corresponding to a range of 211,300 t to 281,700 t. In the long term, if the stock exhibits positive recovery trends, the TAC could be gradually increased to the MSY level to achieve the maximum sustainable utilization of this fishery resource.