Simulation of potential suitable distribution of original species of Fritillariae Cirrhosae Bulbus in China under climate change scenarios

生态毒理学 气候变化 中国 地理 分布(数学) 环境科学 自然地理学 生态学 统计 生物 数学 数学分析 考古
作者
Lei Liu,Yuanyuan Zhang,Yi Huang,Jindong Zhang,Qiuyu Mou,Jian-Yue Qiu,Rulin Wang,Yujie Li,Dequan Zhang
出处
期刊:Environmental Science and Pollution Research [Springer Nature]
卷期号:29 (15): 22237-22250 被引量:33
标识
DOI:10.1007/s11356-021-17338-0
摘要

Fritillariae Cirrhosae Bulbus (FCB) is a famous traditional Chinese medicine, mainly used for relieving cough and resolving phlegm. According to Chinese Pharmacopoeia (2020), the medicine comes from dried bulbs of five species and one variety in Fritillaria. Due to climate change and human disturbance, the wild resources have become critically endangered in recent years. Following three climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) under 2050s and 2070s, geographic information technology (GIS) and maximum entropy model (MaxEnt) were used to simulate the ecological suitability of FCB, a third-grade rare and endangered medicinal plant species. The results showed that the key environmental variables affecting the distribution of FCB were altitude, human activity intensity, and mean temperature of coldest quarter. Under current climate situation, the highly suitable areas were mainly located in the east of Qinghai Tibet Plateau, including Western Sichuan, southeastern Tibet, southern Gansu, Northwestern Yunnan, and Eastern Qinghai, with a total area of 31.47×104 km2, the area within the nature reserve was 7.13×104 km2, indicating that there was a large protection gap. Under the future climate change scenarios, the areas of the highly and poorly suitable areas of FCB showed a decreasing trend, while the areas of the moderately and total suitable areas showed an increasing trend. The geometric center of the total suitable area of the medicine will move to the northwest. The results could provide a strategic guidance for protection,development, and utilization of FCB though its prediction of potential distribution based on the key variables of climate change.
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