Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China

Abstract Climate change influences ecosystem by altering the habitat of species in it. We report the quantitative predictions of climate change on riparian species. Homonoia riparia (H. riparia) Lour, a species native to Yunnan Province, China, is a medicinal plant with high ecological and economic value. Its population has declined significantly, and the species has become locally endangered in recent decades. Understanding the habitat requirements of this species, evaluating habitat quality, and predicting its potential habitat are significant for protecting H. riparia Lour. One positional variable, three topographic variables and eight bioclimatic variables were used to model its distribution and potential habitat. The eight main bioclimatic variables influencing species distribution were selected from 19 bioclimatic variables based on correlation analysis and principal component analysis. An MAXENT model, because of the advantages of using presence-only data and performing well with incomplete data, small sample sizes and gaps, was employed to simulate the habitat suitability distribution. The results show that seven variables, namely, annual mean temperature, altitude, precipitation seasonality, precipitation of coldest quarter, the distance to the nearest river, temperature seasonality, and precipitation during the driest month, are significant factors determining H. riparia Lour’s suitable habitat. Habitat suitability for three historical periods and two future climate warming scenarios were calculated. The habitat suitability of H. riparia Lour in Yunnan Province is predicted to improve with global warming.