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Prediction and analysis of the potential risk of sudden oak death in China

  • Bo Xie
  • Chunxiang Cao
  • Wei Chen
  • Bing Yu
Original Paper
  • 49 Downloads

Abstract

Sudden oak death (SOD) is one of the most rapid and destructive forest pathogens, which has caused the death of many host plants in Europe and America. There are currently no cases in China where there are more host plants and a more suitable climate for this pathogen to survive. Therefore, it is vital to discern the potential suitable habitat, quantify the risk levels, and monitor the potential high-risk areas. In this study, we modelled the potential invasion range and risk level of this pathogen at present and in future scenarios in China, using the least correlated components of all the environmental factors based on the Genetic Algorithm for Ruleset Production niche model and GIS analysis. The results indicate that most areas in China are free from a potential SOD risk, and the majority of potential occurrence areas are concentrated in Southern China (Yunnan, Sichuan, Guizhou, Chongqing, Hunan, Fujian). The area of high and extremely high risk in 2050 (RCP26, RCP45, RCP60, and RCP85) is larger than that at present. The most susceptible area is Yunnan province with 80% of the area prone to SOD at extremely high risk in present and future scenarios. The results will be important for monitoring potential high-risk areas in the currently uninfected parts of China.

Keywords

Phytophthora ramorum GARP Ecological niche models Suitable habitat Risk level 

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Copyright information

© Northeast Forestry University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.School of Civil Engineering and ArchitectureSouthwest Petroleum UniversityChengduPeople’s Republic of China

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