## 4 Discussion  
### Accuracy of the Integrated Species Distribution Model Based on Vessel Position Data  
Antarctic krill is an important economic species. A small number of scholars have conducted many studies on the habitats of Antarctic krill. These studies mainly use data such as collected fishing logs and on-site scientific surveys combined with environmental factors to describe the habitats of Antarctic krill [2; 5; 31; 32; 33]. There is no analysis of the habitats of Antarctic krill based on vessel position data. Therefore, compared with previous studies, this study is based on vessel position data, extracts the fishing ground positions when the vessels are in the fishing state, and sets them as the predictive variables of the integrated species distribution model. The reason for adopting this approach is that, compared with traditional fishery data, the fishing state of the vessels extracted from the  
Figure 8: Linear fitting relationships between the area of each suitable habitat zone and the catch (**a–d**).  
Figure 9: Linear fitting relationships between the fishing duration, non-fishing duration, and the catch (**a,b**).  
vessel position data can more truly and effectively reflect the behavioral characteristics of the vessels during the actual fishing process. It should be clear that the core purpose of Antarctic krill fishing vessels entering the fishing area for operation is to obtain catches. Every period of time that a vessel stays at sea will inevitably increase the corresponding costs and may even incur additional costs and expenditures [34]. Therefore, analyzing and mastering the spatio-temporal distribution characteristics of the optimal habitats of Antarctic krill can provide guidance for fishing activities to a certain extent. In this study, the vessel positions of Norwegian pumping vessels were selected as the research objects because Norway started early in the field of krill fishing and has mastered advanced fishing technologies, and its krill fishery level is at the world's leading position [35; 36]. Therefore, in-depth exploration of the fishing habitats of Norwegian krill vessels is of great guiding significance for global krill fishing.  
Due to the difficulty of obtaining foreign fishing logs, and the lengths, tonnages, and fishing methods of pumping vessels at home and abroad are relatively similar, this study uses Chinese fishing logs as a basis to classify the status of the vessel position data of Chinese pumping vessels of the same type, and it is highly credible to estimate the fishing behavior of Norwegian pumping vessels. Subsequently, we input the vessel position data after status classification into the constructed deep learning model for training. Through this process, it is possible to achieve the status recognition of the vessel position data of fishing vessels of the same type.  
In this study, the vessel position data of Antarctic krill vessels and the integrated species distribution model were used to construct their habitats. The AUC and TSS indices of the model for each month exceed 0.9, with an average AUC of 0.997 and an average TSS of 0.973. It can accurately define the distribution of habitats, monitor changes, and identify fishing hotspots. Although vessel position data have significant advantages in terms of real-time performance and spatial coverage [37], vessel position data have certain limitations. Vessel position data can only reflect the operation points of fishing vessels and cannot provide information on the catch of each haul, which may lead to false positive or false negative results in habitat prediction. However, such errors are extremely rare for Antarctic krill fishing because the resource density of Antarctic krill is relatively high, and in actual fishing, it is very rare to have an empty net or a low catch (less than 5 tons per net).