Prediction of Potential Suitable Distribution Areas of Quasipaa spinosa in China Based on MaxEnt Optimization Model
Jinliang Hou1, Jianguo Xiang1, Deliang Li1
1College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China.
Abstract:
Quasipaa spinosa is a large cold-water frog unique to China, with great ecological and economic value. In recent years, due to the impact of human activities on the climate, its habitat has been destroyed, resulting in a sharp decline in natural population resources. Based on the existing distribution records of Q. spinosa, this study uses the optimized MaxEnt model and ArcGis 10.2 software to screen out 10 factors such as climate and altitude to predict its future potential distribution area because of climate change. The results show that when the parameters are FC = LQHP and RM = 3, the MaxEnt model is optimal and AUC values are greater than 0.95. The precipitation of the driest month (bio14), temperature seasonality (bio4), elevation (ele), isothermality (bio3), and the minimum temperature of coldest month (bio6) were the main environmental factors affecting the potential range of the Q. spinosa. At present, high-suitability areas are mainly in the Hunan, Fujian, Jiangxi, Chongqing, Guizhou, Anhui, and Sichuan provinces of China. In the future, the potential distribution area of Q. spinosa may gradually extend to the northwest and north. The low-concentration emissions scenario in the future can increase the area of suitable habitat for Q. spinosa and slow down the reduction in the amount of high-suitability areas to a certain extent. In conclusion, the habitat of Q. spinosa is mainly distributed in southern China. Because of global climate change, the high-altitude mountainous areas in southern China with abundant water resources may be the main potential habitat area of Q. spinosa. Predicting the changes in the distribution patterns of Q. spinosa can better help us understand the biogeography of Q. spinosa and develop conservation strategies to minimize the impacts of climate change.
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