High-resolution spatiotemporal prediction information on fishing vessel activities is essential for formulating and effectively implementing fisheries policies that ensure the sustainability of marine resources and fishing practices. This study focused on the tuna longline fishery in the Western and Central Pacific Ocean (130° E–150° W, 20° S–20° N) and constructed a CLA U-Net deep learning model to predict fishing effort (FE) distribution based on 2017–2023 FE records and environmental variables. Two modeling schemes were designed: Scheme 1 incorporated both historical FE and environmental data, while Scheme 2 used only environmental variables. The model predicts not only the binary outcome (presence or absence of fishing effort) but also the magnitude of FE. Results show that in short-term predictions, Scheme 1 achieved F1 scores of 0.654 at the 0.5°-1-day scale and 0.763 at the 1°-1-day scale, indicating substantial improvement from including historical FE data. In medium-term predictions, Scheme 1 and Scheme 2 reached maximum F1 scores of 0.77 and 0.72, respectively, at the optimal spatiotemporal scale of 1°-30 days. The analysis also quantified the relative importance of environmental variables, with sea surface temperature (SST) and chlorophyll-a (Chl-a) identified as the most influential. These findings provide methodological insights for spatiotemporal prediction of fishing effort and support the refinement of fisheries management and sustainability strategies.

The dynamic monitoring of fishing activities is fundamental to fishery management. Leveraging multi-year (2020–2023) AIS data from squid jigging vessels, this study employed a multi-level data mining and spatial statistical approach to decode the spatiotemporal patterns of fishing effort in the Southeast Pacific Ocean. Our analysis reveals a highly concentrated and cyclical operation model: temporally, 20% of days contributed 46% of the total effort; spatially, 30% of the fishing grounds accounted for 80% of the effort, forming four persistent hotspots. Vessels exhibited a distinct bimodal speed distribution, enabling clear behavioral differentiation. While no fishing was detected inside the seasonal no-take zone, activities were observed near its boundaries and Exclusive Economic Zones, highlighting compliance and potential risks. The significant spatial aggregation, strongest in June, underscores the tight linkage between fleet operations and resource distribution. These findings provide a scientific basis for spatially explicit management strategies to ensure the sustainable harvesting of squid resources.