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.

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.

The accurate assessment and management of Blue Mackerel (Scomber australasicus) and Chub Mackerel (Scomber japonicus) resources in the high seas of the Northwest Pacific are constrained by the persistent issue of data misreporting in catch records, which arises from their high morphological similarity. This study integrates fishery logbooks and field sampling data from Chinese purse seine fleets (2014–2023), along with key oceanographic factors—six of which were finally selected after correlation analysis. We introduce, for the first time, a Zero-One Inflated Beta Model (ZOIBM) to analyze the spatiotemporal distribution of the relative abundance of these two mackerel species. Furthermore, a Generalized Additive Model (GAM) was employed to reveal the environmental mechanisms driving their niche differentiation. The results show that the ZOIBM demonstrates excellent performance (R² = 0.63, RMSE = 0.305), effectively quantifying the proportional composition of the two species in mixed catches. Spatially, high-abundance areas of Blue Mackerel were concentrated within 35–44° N, 145–160° E, with its proportion decreasing at higher latitudes. In contrast, Chub Mackerel exhibited an opposite latitudinal pattern, with its high-abundance areas covering a broader latitudinal range (35–47.5° N). The analysis of environmental drivers indicated that SST was the most critical factor for differentiation, while Chla and VO further amplified the divergence in resource utilization strategies between the species. From 2014 to 2023, the distribution centroids of both mackerel species showed significant northward and eastward shifts, and their spatial overlap has been continuously increasing. This research provides a methodological reference for the fine-scale assessment of co-occurring fish resources and offers a scientific basis for the sustainable management of the North Pacific mackerel fishery.