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Chinese Journal of Applied Ecology ›› 2016, Vol. 27 ›› Issue (11): 3607-3615.doi: 10.13287/j.1001-9332.201611.040

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Potential distribution of Panax ginseng and its predicted responses to climate change.

ZHAO Ze-fang1,2, WEI Hai-yan1*, GUO Yan-long3, GU Wei2,4   

  1. 1College of Tourism and Environment, Shaanxi Normal University, Xi’an 710119, China;
    2National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest China, Shaanxi Normal University, Xi’an 710119, China;
    3Cold and Arid Regions Environments and Engineering Research Institute, Chinese Academy of Scienses, Lanzhou 730000, China;
    4College of Life Sciences, Shaanxi Normal University, Xi’an 710119, China
  • Received:2016-04-05 Online:2016-11-18 Published:2016-11-18
  • Contact: E-mail: weihy@snnu.edu.cn
  • Supported by:
    This paper was supported by the National Natural Science Foundation of China (31070293).

Abstract: This study utilized Panax ginseng as the research object. Based on BioMod2 platform, with species presence data and 22 climatic variables, the potential geographic distribution of P. ginseng under the current conditions in northeast China was simulated with ten species distribution model. And then with the receiver-operating characteristic curve (ROC) as weights, we build an ensemble model, which integrated the results of 10 models, using the ensemble model, the future distributions of P. ginseng were also projected for the periods 2050s and 2070s under the climate change scenarios of RCP 8.5, RCP 6, RCP 4.5 and RCP 2.6 emission scenarios described in the Special Report on Emissions Scenarios (SRES) of IPCC (Intergovernmental Panel on Climate Change). The results showed that for the entire region of study area, under the present climatic conditions, 10.4% of the areas were identified as suitable habitats, which were mainly located in northeast Changbai Mountains area and the southeastern region of the Xiaoxing’an Mountains. The model simulations indicated that the suitable habitats would have a relatively significant change under the different climate change scenarios, and generally the range of suitable habitats would be a certain degree of decrease. Meanwhile, the goodness-of-fit, predicted ranges, and weights of explanatory variables was various for each model. And according to the goodness-of-fit, Maxent had the highest model performance, and GAM, RF and ANN were followed, while SRE had the lowest prediction accuracy. In this study we established an ensemble model, which could improve the accuracy of the existing species distribution models, and optimization of species distribution prediction results.

Key words: BioMod2, climate change, ensemble model, Panax ginseng