The epidemics of cholera are impacted by many climatic and environmental factors such as precipitation, temperature, elevation and so on. The paper analyzed the suitable degree of V. cholerae in China using MaxEnt based on some geographic and climatic factors, and predicted the cholera risk in each district of China according to the suitable degree. The result shows that the areas in coastal southeast, central China and western Sichuan Basin are relatively suitable for V. cholerae and the suitable degree is higher in the Xinjiang Basin than in surrounding areas. The variables of precipitation, temperature and DEM are three main environmental risky factors that affecting the distribution of cholera in China. The variables of relative humidity, the distance to the sea and air pressure also have impacts on cholera, but sunshine duration and drainage density have little impact. The AUC value of MaxEnt based model is above 0.9 which indicates a high accuracy.
The Qinghai-Tibet Plateau is the word＇s highest and largest plateau. Due to increasing demands for environment exploration and tourism, a large transitional area is required for altitude adaptation. Hehuang valley, which locates in the transition zone between the Loess Plateau and the Qinghai-Tibet Plateau, has convenient transportation and relatively low elevation. Our question is whether the geographic conditions here are appropriate for adapted stay before going into the Qinghai-Tibet Plateau. Therefore, in this study, we examined the potential use of ecological niche modeling （ENM） for mapping current and potential distribution patterns of human settlements. We chose the Maximum Entropy Method （Maxent）, an ENM which integrates climate, remote sensing and geographical data, to model distributions and assess land suitability for transition areas. After preprocessing and selection, the correlation between variables and spatial auto- correlation input data were removed and 106 occurrence points and 9 environmental layers were determined as the model inputs. The threshold- independent model performance was reasonable according to lO times model running, with the area under the curve （AUC） values being 0.917± 0.01, and 0.923±0.002 for test data. Cohen＇s kappa coefficient of model performance was 0.848. Results showed that 82.22% of the study extent was not suitable for human settlement. Of the remaining areas, highly suitable areas aceounted for 1.19%, moderately for 5.3% and marginally for 11.28%. These suitable areas totaled 418.79 km2, and 86.25% of the sample data was identified in the different gradient of suitable area.The decisive environmental factors were slope and two climate variables： mean diurnal temperature range and temperature seasonality. Our model showed a good performance in mapping and assessing human settlements. This study provides the first predicted potential habitat distribution map for human settlement in Ledu County, which could also help in land use management.
