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Predicting the global areas for potential distribution of Gastrodia elata based on ecological niche models
Abstract:Aims Previous studies on the globally suitable areas for growing the medicinal plant Gastrodia elata is lacking. This study aims to predict the global areas for potential distribution of this plant based on multiple ecological niche models. Methods A total of 220 global distribution points of G. elata and 19 ecological variables were compiled and eight environmental variables were selected for the model training. Three ecological niche models, including BIOCLIM, DOMAIN, and MAXENT, were used to predict the global areas for potential distribution of G. elata. The resulting data of different models were analyzed and compared with two statistical criteria: the area under the receiver operating characteristic curve (AUC) and Kappa value. Important findings The predictions of the three models are basically identical, showing that the global areas for potential distribution of G. elata are predominantly in the range of 20° N to 50° N in Asia, mainly in China, South Korea and Japan. A small proportion of the suitable areas occur in India, Nepal and the European countries near Mediterranean. The most suitable areas distribute in provinces close to the Sichuan Basin and the central East China, the mid-eastern parts of South Korea such as Chungcheongbuk-do, Gyeongsangbuk-do and Gyeongsangnam- do, and the Kyushu region and the Shikoku region on Japan’s main island. Therefore, these three countries can be used as the main production areas of G. elata for its commercial development. The AUC average values of the three models are all above 0.9 and the Kappa average values all above 0.65, justifying their applications for predicting the potential areas of G. elata. Among them, the MAXENT model appears to perform the best, followed by DOMAIN and BIOCLIM.
Keywords:Gastrodia elata" target="_blank">Gastrodia elata')">Gastrodia elata    ecological niche models    potential suitable distribution area    model evaluation
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