## 2 Materials and Methods
### Vessel Position Data  
The AIS satellite constellation data source of the Canadian company exactView was ordered through an AIS commercial agent. The data include static information such as the vessel name, vessel length, gross tonnage, and Maritime Mobile Service Identity (MMSI), as well as dynamic information such as date, time, latitude and longitude, course, and speed. The fishing log data includes the name of the fishing vessel, operation time, operation location, towing speed, net mouth depth, catch amount, etc. A total of 206,202 pieces of AIS data (excluding transshipment status data) of all Norwegian krill pump-suction trawlers (Antarctic Endurance, Saga Sea, Antarctic Sea) during the fishing seasons from 2021 to 2023 were collected. Since it is difficult to obtain the fishing logs of the Norwegian pump-suction trawlers and it is impossible to identify the location of their fishing grounds through the logs, the available AIS vessel position data and fishing logs of the same type of Antarctic krill pump-suction vessels were used to establish a vessel position identification model to determine their fishing status. Combinedwith environmental factors, an integrated species distribution model was used to predict and identify the habitats of Antarctic krill. A total of 9255 pieces of AIS vessel position data of the same type of Chinese Antarctic krill pump-suction vessels during 2023-2024, and 3614 pieces of fishing log data were collected to train the vessel position status identification model (CNN-attention model). When the krill pump-suction trawler is operating, it usually sails at a high speed to the central fishing ground sea area (the vessel speed is greater than about 5 Kn and the course change is small) and then prepares for fishing (the vessel speed is greater than about 3 Kn and the course change is large). The fishing net is put into the water, the pump-suction equipment is turned on, and the net mouth is aimed at the shrimp school. At this time, the pump-suction vessel conducts fishing at a low speed (the speed is greater than about 0.5 Kn and less than about 3 Kn, and the course change is large). The krill pump-suction vessel does not need to haul and set the trawl like a traditional trawler and can continuously fish for several days to thirty days. Excluding the time for detecting the shrimp school, it is in the fishing state for the rest of the time. In this paper, the vessel position status of the vessel position points was identified based on the constructed CNN-attention model [19]. The CNN (Convolutional Neural Network), a deep learning model capable of automatically extracting spatial features from data, was integrated with an attention mechanism in this study's CNN-attention model. This enabled the model to focus on critical features in vessel position data [19], such as speed and heading changes, thereby accurately determining fishing or non-fishing states and enhancing recognition accuracy. All AIS vessel position data points in the fishing time period of the fishing logs of the same type of Chinese Antarctic krill pump-suction vessels were marked as the fishing state, and the remaining data were marked as the non-fishing state. The five-dimensional data of binary classification (time, longitude, latitude, speed, course, status) were input into the model for training, and then the AIS vessel position data of the three Norwegian Antarctic krill pump-suction vessels were input into the model to determine the fishing and non-fishing states. The duration of each state was calculated by taking the trajectory from the first vessel position point identified as each state every day to the next vessel position point identified as another state as the basis [20]. For example, the Antarctic krill pump-suction trawler usually sails at a high speed to the target fishing ground first, prepares for fishing, starts  
Figure 1: Pearson correlation test. low-speed pump-suction fishing after aiming the pump-suction net mouth at the krill, and then sails at a high speed to the target fishing ground again.