基于MaxEnt和ArcGIS的大头续断资源蕴藏量的估算研究

目的对大头续断Dipsacus chinensis蕴藏量进行研究,为中药资源量化提供新方法。方法以全国大头续断的主要分布区的实地调研数据为依据,分别利用MaxEnt模型和空间插值法建立大头续断物种密度栅格数据和单株生物量栅格数据,借助ArcGIS空间分析功能对大头续断资源分布进行空间量化分析和评估。结果全国大头续断野生资源适生区面积3.34×105 km~2左右,资源药材蕴藏量约3.08×10~7 kg,主要分布在横断山脉及青海高原东南部地区,其中四川省大头续断资源蕴藏量约为全国的69.48%,尤以四川省的阿坝州境内资源蕴藏量高;大头续断分布密度较高区段主要分布在四川省的阿坝州、甘孜州和凉山州境内,此区域为大头续断的核心分布区,并以此区域为中心向周围呈递减趋势;单株生物量地理分布整体特征呈现以横断山脉的垂直面为分界面,向两侧递增的趋势;生物量与温度季节性变异系数、年均温变化范围和4月份降水量呈显著正相关,与12月份均温、月均(7、8月)降水量、季节降水量变异系数呈显著负相关。结论该研究为大头续断新资源的开发及利用提供有力的数据支撑,为中药资源可视量化评估提供了新方法。

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

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.