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Predicting suitable cultivation regions of medicinal plants with Maxent modeling and fuzzy logics: a case study of Scutellaria baicalensis in China

机译:利用Maxent建模和模糊逻辑预测药用植物适宜栽培区-以中国黄S为例。

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摘要

Scientific prediction of suitable cultivation regions is an effective way for the assessment of habitat suitability and resource conservation to protect endangered medicinal plants. In recent years, the natural habitat of Scutellaria baicalensis Georgi has been degenerating and disappearing in China owing to excessive market demand of medicinal plant resource. This paper reports a new approach to predict potential suitable cultivation regions and to explore the key environmental factors affecting the content of active ingredients in S. baicalensis using integrated Maxent (maximum entropy) modeling and fuzzy logics. The modeling procedure used 275 occurrence records and baicalin contents of S. baicalensis collected through 2000-2014, and 16 Worldclim environmental factors as well as HWSD soil data. The result showed that six environmental factors (alt, prec7, prec1, bio4, bio1 and t_ph) were determined as key influential factors that mostly affect both the habitat distribution and baicalin content of S. baicalensis. The highly suitable cultivation regions of S. baicalensis mainly distribute (with probability >= 0.50) in the northeast, the north-central and the northwest of China (total 419,857 km(2)). The statistically significant AUC (area under the curve) value (0.952) of ROC (receiver operating characteristic) curve indicated that integrated Maxent modeling and fuzzy logics could be used to predict the potential suitable cultivation regions of medicinal plants. These results could pave the road for the habitat conservation and resource utilization of endangered medicinal plants.
机译:科学预测合适的种植区域是评估生境适宜性和资源保护以保护濒危药用植物的有效途径。近年来,由于市场对药用植物资源的需求过大,黄cut的自然栖息地已经在退化和消失。本文报告了一种新的方法,该方法使用集成的Maxent(最大熵)模型和模糊逻辑来预测潜在的合适种植区域并探索影响黄ical中有效成分含量的关键环境因素。该建模程序使用了2000年至2014年期间收集的275条黄ba的发生记录和黄ical苷含量以及16种Worldclim环境因子以及HWSD土壤数据。结果表明,六个环境因素(alt,prec7,prec1,bio4,bio1和t_ph)被确定为主要影响黄ical生境分布和黄ical苷含量的关键影响因素。高度适合的黄cultivation栽培区主要分布在中国的东北,中北部和西北(总概率为419,857 km(2))(概率> = 0.50)。 ROC(接收器工作特性)曲线的统计上显着的AUC(曲线下面积)值(0.952)表明,集成的Maxent建模和模糊逻辑可用于预测药用植物的潜在适宜栽培区域。这些结果可为濒危药用植物的生境保护和资源利用铺平道路。

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