The Western and Central Pacific Oceans are the primary operational areas of tuna purse seiners worldwide. Describing and analysing the fishing behaviour of vessels is highly significant for the protection of sustainable tuna resources. This study uses Automatic Identification System (AIS) data of 130 tuna purse seiners from July 2017 to May 2018 and uses data mining methods to identify the operating status of tuna purse seiners; describes the spatial characteristics of fishing intensity and the distribution of hot spots; and analyses vessel spatial characteristics to describe their fishing behaviour. The results show that the tuna purse seiner speed has a marked bimodal distribution, which corresponds to high-speed transiting and low-speed seine operation. Additionally, from July to September 2017, the amount of fishing effort invested by tuna purse seiners was lower than that in other months. The tuna purse seiner activity range includes 120° E–60° W, 30° S–30° N, and the activities for fish and seine operations are primarily concentrated at 140° E–150° W, 15° S–15° N. There are differences between the space for fishing search operations and space where fishing events took place in each month. Spatial analysis shows that the high-speed transiting fishing effort map covers a large area, while seine fishing covers a small area. The global spatial autocorrelation analysis shows that the fishing effort devoted to searching for fish stocks has a spatial distribution pattern of aggregation and close aggregation. The results of a hot-spot analysis show that the hot spots on a heat map for finding fish, which are closely spatially clustered, correspond to vessels searching for fish concentration areas and seine operation areas. Correlation testing shows that under a 5° × 5° grid, there is a high positive correlation between the fishing effort invested in finding fish stocks and the yield data, nets (r > 0.8), and a moderate correlation with catch per unit of effort(CPUE) (r > 0.3). Based on vessel behaviour, the location of the fish school can be directly determined, and the distribution of fish clusters and fishing grounds can be predicted. This study can aid in managing tuna purse seiners in the Western and Central Pacific Oceans and analysing changes in fishery resources.

Pacific sardine (Sardinops sagax) is a commercially important species and supports important fisheries in the Northwest Pacific Ocean (NPO). Understanding the habitat distribution patterns of Pacific sardine is of great significance for fishing ground prediction and stock management. In this study, both single-algorithm and ensemble distribution models were established through the Biomod2 package for Pacific sardine by combining the species occurrence data, sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS) and chlorophyll-a concentration (Chla) in the NPO during the main fishing season (June–November) from 2015 to 2020. The results indicated that the key environmental variables affecting the habitat distribution of Pacific sardine were the SSH and SST. The suitable habitat area for Pacific sardine showed significant monthly changes: the suitable habitat range in June was larger than that in July and August, while the suitable habitat range gradually increased from September to November. Furthermore, the monthly geometric centers of habitat suitability index (HSI) for Pacific sardine presented a counterclockwise pattern, gradually moving to the northeast from June, and then turning back to the southwest from August. Compared with single-algorithm models, the ensemble model had higher evaluation metric values and better spatial correspondence between habitat prediction and occurrence records data, which indicated that the ensemble model can provide more accurate prediction and is a promising tool for potential habitat forecasting and resource management.

Understanding the spatial pattern of human fishing activity is very important for fisheries resource monitoring and spatial management. The environmental preferences of tropical tuna purse seine fleet in the Western and Central Pacific Ocean (WCPO) were constructed and compared at different spatial scales based on the fishing effort (FE) data from the available automatic identification system (AIS) and commercial fishery data compiled from the Western and Central Pacific Fisheries Commission (WCPFC), using maximum entropy (MaxEnt) methods. The MaxEnt models were fitted with FE and commercial fishery data and remote sensing environmental data. Our results showed that the area under the curve (AUC) value each month based on the commercial fishery data (1°) and FE at 0.25° and 0.5° spatial scales was greater than 0.8. The AUC values each month based on the FE data at a 1° scale ranged from 0.775 to 0.829. The AUC values based on commercial fishing data at the 1° scale were comparable to the model results based on FE data at the 0.5° scale and inferior to the model results based on FE data at the 0.25° scales. Overall, the sea surface temperature (SST), temperature at 100 metres (T100), oxygen concentration at 100 metres (O100) and total primary production (PP) had the greatest influence on the distribution of the purse seine tuna fleet. The oxygen concentration at 200 metres (O200), distance to shore (DSH), dissolved oxygen (Dox), EKE, mixed layer depth (Mld), sea surface salinity (SSS), salinity at 100 metres (S100) and salinity at 200 metres (S200) had moderate influences, and other environmental variables had little influence. The suitable habitat areas varied in response to environmental conditions. The purse seine tuna fleet was mostly present at locations where the SST, T100, O100, O200 and PP were 28–30 °C, 27–29 °C, 150–200 mmol/m³ and 5–10 mg/m⁻³, respectively. The MaxEnt models enable the integration of AIS data and high-resolution environmental data from satellite remote sensing to describe the spatiotemporal distribution of the tuna purse seine fishery and the influence of environmental variables on the distribution, and can provide forecasts for fishing ground distributions based on future remote sensing environmental data.

Information and understanding of fishing activities at sea is important to fisheries science, public authorities and policy-makers. To understand the spatial–temporal distribution characteristics of squid-jigging vessels and how the marine environment affects the distribution of squid-jigging vessels in the North Pacific Ocean, operation behavior of the squid-jigging vessels was analysed by using spatial–temporal factors and marine environmental factors. The fishing effort (FE) information was calculated based on automatic identification system (AIS) data of squid-jigging fishing vessels in the North Pacific Ocean from July to November in 2017 to 2020, and the overlay maps of the spatial distribution of environmental factors and fishing effort were plotted together with four environmental variables (sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and concentration of chlorophyll-a (Chla)). A generalized additive model (GAM) was used to investigate the nonlinear influence of the marine environment on squid-jigging fishing vessel activity in the North Pacific Ocean. The results show that fishing effort increased from July to August and then decreased from September to November. The fishing effort was highest in August. The intensity of the fishing effort suggests squid-jigging vessel operations had significant seasonal variations. The overlay maps suggest that the fishing effort of squid-jigging vessels was mainly located in areas where SST was between $10{}^{\circ }\mathrm{C}$ and $20{}^{\circ }\mathrm{C}$, SSH was between $-0.3\mathrm{m}$ and $0.2\mathrm{m}$, SSS was between $32\times {10}^{-3}$ and $34\times {10}^{-3}$, and Chla was between $0.1\mathrm{mg}·{\mathrm{m}}^{-3}$ and $0.4\mathrm{mg}·{\mathrm{m}}^{-3}$. The generalized additive model indicated evidence of nonlinear relationships between fishing effort and the three environmental factors. The favorable ranges of SST, SSH, SSS and Chla for fishing effort were $15{}^{\circ }\mathrm{C}\sim 18{}^{\circ }\mathrm{C}$, $0\sim 0.2\mathrm{m}$, $33\times {10}^{-3}\sim 34\times {10}^{-3}$ and $0.2\mathrm{mg}·{\mathrm{m}}^{-3}\sim 0.4\mathrm{mg}·{\mathrm{m}}^{-3}$, respectively. Moreover, the area beneficial to fishing effort was in ${41}^{\circ }\mathrm{N}\sim {44}^{\circ }\mathrm{N}$, ${152}^{\circ }\mathrm{E}\sim {165}^{\circ }\mathrm{E}$.

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.