## 2 Materials and Methods
### Habitat Simulation Based on the Integrated Species Distribution Model  
The integrated species distribution model (ISDM), as a cutting-edge predictive technical method, constructs a comprehensive overall prediction framework by integrating multiple single species distribution models (SDMs). Among them, the single species distribution models involved cover various types such as ANN (Artificial Neural Network), CTA (Classification and Regression Tree Analysis), FDA (Flexible Discriminant Analysis), GAM (Generalized Additive Model), GBM (Gradient Boosting Machine), GLM (Generalized Linear Model), MARS (Multivariate Adaptive Regression Splines), and MAXNET (Maximum Entropy Neural Network). This method was proposed by [PERSON] et al. (2018), and its core principle focuses on fusing the prediction results generated by multiple independent species distribution models. Compared with a single species distribution model, the integrated species distribution model can effectively reduce the internal bias and variance of the model, and thus significantly enhance the generalization performance of the entire model ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). Based on these remarkable advantages, the integrated species distribution model has demonstrated excellent application effectiveness in the prediction of habitats of many marine species ([PERSON] et al., 2018).  
The relevant parameters of the algorithm are adjusted using RStudio 4.2.3. The fishing points identified based on the ship position data and the marine environmental data (SST, SSH, CHL, SIC, SSS, GLD) are input to predict the habitat of krill. The data are divided into a training set accounting for 80% and a test set accounting for 20%. Cross-validation is adopted during the model training and evaluation stage. The cross-validation strategy is set as randomly dividing the training set and the test set to ensure the randomness of each division, and the cross-validation process is repeated to more stably estimate the model performance. An integrated algorithm is used to construct the model. On a monthly basis, TSS is used as the screening index with a threshold set at 0.85. Several species distribution models with TSS values higher than 0.85 are screened out for combination, and a weighted average is used to construct the integrated distribution model for each month in the integrated algorithm ([PERSON] et al., 2018).  
The \"bm_PlotResponseCurve\" function in the biomod2 software package is used to generate environmental response curves for the integrated species distribution model ([PERSON] et al., 2018). The environmental response curves allow us to visualize and analyze the relationship between the six environmental variables and the probability of the presence of Antarctickrill, and to understand how each environmental variable affects the habitat suitability of the species and determine the optimal habitat conditions under these variables.  
AUC and TSS are important evaluation indicators in the integrated species distribution model ([PERSON] et al., 2018). The AUC value represents the area under the ROC curve. The larger the area, the better the classification performance. The range of the AUC value is from 0 to 1, and the higher the value, the better the model performance ([PERSON] et al., 2018). TSS is a comprehensive evaluation indicator that comprehensively considers the difference between the model's ability to correctly identify positive cases (true positive rate) and the ability to mistakenly identify negative cases as positive cases (true negative rate). The range of the TSS value is from \(-1\) to 1, and the higher the value, the higher the accuracy ([PERSON] et al., 2018).