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Journal of Tea Science ›› 2020, Vol. 40 ›› Issue (6): 817-829.doi: 10.13305/j.cnki.jts.2020.06.009

• Research Paper • Previous Articles     Next Articles

Potential Climate-suitable Distribution of Ectropis Grisescens in China Based on the CLIMEX and ArcGIS Prediction

CHEN Lilin1,2, ZHOU Hao1,2, ZHAO Jie3   

  1. 1. State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou 350002, China;
    2. Anxi College of Tea Science, Fujian Agriculture and Forestry University, Anxi 362406, China;
    3. College of Natural Resources and Environment, Northwest Agriculture and Forestry University, Yangling 712100, China
  • Received:2019-09-03 Revised:2020-09-20 Online:2020-12-15 Published:2020-12-10

Abstract: Based on the data of 820 meteorological stations in China, known geographic distribution data and biological data, the current and potential geographic distribution of a major pest Ectropis grisescens Warren in tea plantations were predicted using CLIMEX models and ArcGIS software. The potential effects of climate change on the future distribution of E. grisescens were also evaluated. The results suggest that the potential geographic distribution area was between 3°51′N and 40°6′N, which accounts for 34.27% of the total area of the country. The climatic conditions of most provinces and regions in China were suitable for the survival of E. grisescens. Due to climate change, the increasing rate of potential suitable area for E. grisescens grew slowly, but its composition changed greatly. By 2050, the predicted proportion of highly suitable area reached a maximum of 22.23%. Compared with A1B, the A2 scenario would accelerate E. grisescens extension in Shaanxi-Gansu-Ningxia region. As E. grisescens are widely distributed in China, we suggested that monitoring measures should be improved and pest control should be taken as early as possible to ensure the safe production and quality of tea.

Key words: tea geometrid, climate change, suitable areas, potential distribution, prediction

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