Simulation of human activity intensity and its influence on mammal diversity in sanjiangyuan National Park, China

Abstract

The rapid pace of development in western China has brought about inevitable concerns for environmental conditions and their management. The Sanjiangyuan National Park strives to address concerns for sustainable water resources management and biodiversity management, especially for the protection of endangered flora and fauna. In this study, a machine learning model (MaxEnt) was used to predict the human activity intensity and its effects on species in Sanjiangyuan protected by the Convention on International Trade in Endangered Species ofWild Fauna and Flora (CITES). The model used human settlements as input and datasets such as terrain factors, climate, and artificial structures as environmental factors. The results showed that human activity intensity was significantly different between the East and theWest. The area with the highest human activity intensity was Yushu County in the south area, and Xinghai-Zeku County in the east. By comparing the mammal richness with human activity intensity, we found human-wildlife coexistence in Sanjiangyuan. A detailed analysis on the CITES protected species showed that many important species, such as snow leopards, red pandas, and small Indian civets, occupied areas with high human activity intensity. The national park protects 3/4 CITES species with 1/3 in the area of the Sanjiangyuan region, owing to the relatively low human activity intensity.