Estimation of reserves of wild Dipsacus chinensis based on MaxEnt and ArcGIS model / 中草药

ABSTRACT

Objective To study the estimating reserves of wild Dipsacus chinensis and provide a new method for the Chinese materia medica (CMM) resource quantitative evaluation. Methods The major contributing factors for ecological distribution of developing potentiality medicinal plants of D. chinensis were screened on the GIS platform by using the MaxEnt model, and raster data of species density and individual biomass of D. chinensis were generated by MaxEnt model and patial interpolation technology based on the field survey data. Spatial quantitative analysis and assessment of the distribution of resources of D. chinensis was performed by using ArcGIS. On this basis, a reserves assessment model was formatted for sustainable utilization of CMM resources evaluation. Results The analysis showed that the distribution area of D. chinensis was about 3.34 × 105 km2 which was mainly distributed in Hengduan Mountains and the Qinghai Plateau southeast area, and resources reserves was about 3.08 × 107 kg. The results showed that more than 69.48% resources of D. chinensis was located in Sichuan Province, and the western Sichuan such as Aba, Ganzi, and Liangshan prefectures was the highly populated areas. According to that regional center into the surrounding showed a trend of decline, the overall characteristic of individual biomass geographic distribution showed an increased trend from the vertical plane of the Hengduan Mountains as interface to both sides. The results also indicated that seasonal temperature variation coefficient, annual average temperature range, and average rainfall in April were the dominant factors contributing to the underground biomass accumulation. While mean temperature in December, monthly average rainfall in July and August were negative correlated with the underground biomass accumulation. Conclusion This study provides strong data support for the development and utilization of new resources of wild D. chinensis, and helps to establish a new way to the visual quantitative evaluation of CMM resources.