Predicting the Potential Distribution of Pine Wilt Disease in China under Climate Change

被引:7
|
作者
Ouyang, Xianheng [1 ]
Chen, Anliang [1 ]
Li, Yan [2 ]
Han, Xiaoxiao [3 ]
Lin, Haiping [1 ]
机构
[1] Zhejiang A&F Univ, Sch Forestry & Biotechnol, Hangzhou 311300, Peoples R China
[2] Nanjing Forestry Univ, Collaborat Innovat Ctr Sustainable Forestry, Nanjing 210037, Peoples R China
[3] Northwest A&F Univ, Coll Plant Protect, Xianyang 712100, Peoples R China
关键词
pine wilt disease; climate change; MaxEnt model; climate factors; the potential distribution; BURSAPHELENCHUS-XYLOPHILUS; TRUNK-INJECTION; EMAMECTIN BENZOATE; MAXENT; MODEL; PLANT; TEMPERATURE; NEMATODE; AGENT; L;
D O I
10.3390/insects13121147
中图分类号
Q96 [昆虫学];
学科分类号
摘要
Simple Summary Pine forests have been hugely damaged by pine wilt disease (PWD). Climate change may affect the geographic distribution of PWD. Based on 646 PWD infestation sites and seven climate variables, the current and potential geographic distribution of PWD was predicted by using the maximum entropy (MaxEnt) model, which can provide a scientific basis for the prevention and control of PWD. This study shows that the fundamental climate variables influencing PWD distribution were rainfall and temperature. Under different climate scenarios in the future, the areas of potential geographic distribution habitats of PWD will increase to varying degrees compared with the area of modern potential geographic distribution habitats, and the centroid of suitable areas of PWD will move to the northeast. The primary culprits of pine wilt disease (PWD), an epidemic forest disease that significantly endangers the human environment and the world's forest resources, are pinewood nematodes (PWN, Bursaphelenchus xylophilus). The MaxEnt model has been used to predict and analyze the potential geographic spread of PWD in China under the effects of climate change and can serve as a foundation for high-efficiency monitoring, supervision, and prompt prevention and management. In this work, the MaxEnt model's criteria settings were optimized using data from 646 PWD infestation sites and seven climate variables from the ENMeval data package. It simulated and forecasted how PWD may be distributed under present and future (the 2050s and 2070s) climatic circumstances, and the key climate factors influencing the disease were examined. The area under AUC (area under receiver operating characteristic (ROC) curve) is 0.940 under the parameters, demonstrating the accuracy of the simulation. Under the current climate conditions, the moderately and highly suitable habitats of PWD are distributed in Anhui, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Sichuan, and other provinces. The outcomes demonstrated that the fundamental climate variables influencing the PWD distribution were rainfall and temperature, specifically including maximum temperature of warmest month, mean temperature of driest quarter, coefficient of variation of precipitation seasonality, and precipitation of wettest quarter. The evaluation outcomes of the MaxEnt model revealed that the total and highly suitable areas of PWD will expand substantially by both 2050 and 2070, and the potential distribution of PWD will have a tendency to spread towards high altitudes and latitudes.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Potential distribution of pine wilt disease under future climate change scenarios
    Hirata, Akiko
    Nakamura, Katsunori
    Nakao, Katsuhiro
    Kominami, Yuji
    Tanaka, Nobuyuki
    Ohashi, Haruka
    Takano, Kohei Takenaka
    Takeuchi, Wataru
    Matsui, Tetsuya
    [J]. PLOS ONE, 2017, 12 (08):
  • [2] Occurrence and potential diffusion of pine wilt disease mediated by insect vectors in China under climate change
    Gao, Ruihe
    Liu, Lei
    Fan, Shiming
    Zheng, Wenfang
    Liu, Ruyuan
    Zhang, Zhiwei
    Huang, Ruifen
    Zhao, Lijuan
    Shi, Juan
    [J]. PEST MANAGEMENT SCIENCE, 2024,
  • [3] Habitat Suitability of Pine Wilt Disease in Northeast China under Climate Change Scenario
    Wang, Jue
    Deng, Jifeng
    Yan, Wenfeng
    Zheng, Yanan
    [J]. FORESTS, 2023, 14 (08):
  • [4] Predicting the potential distribution of Campsis grandiflora in China under climate change
    Ouyang, Xianheng
    Pan, Jiangling
    Wu, Zhitao
    Chen, Anliang
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (42) : 63629 - 63639
  • [5] Predicting the potential distribution of Perina nuda under climate change in China
    Mao, Xinjie
    Zheng, Huisen
    Liao, Songkai
    Wei, Hongjian
    Lin, Haoyu
    Wang, Qi
    Chen, Hui
    [J]. ENTOMOLOGIA EXPERIMENTALIS ET APPLICATA, 2024, 172 (08) : 738 - 750
  • [6] Predicting the potential distribution of Campsis grandiflora in China under climate change
    Xianheng Ouyang
    Jiangling Pan
    Zhitao Wu
    Anliang Chen
    [J]. Environmental Science and Pollution Research, 2022, 29 : 63629 - 63639
  • [7] PREDICTING POTENTIAL DISTRIBUTION OF STELLERA CHAMAEJASME UNDER GLOBAL CLIMATE CHANGE IN CHINA
    Li, L.
    Zhang, B.
    Wen, A. M.
    Xao, X. J.
    [J]. APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2022, 20 (05): : 3977 - 3993
  • [8] Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios
    Chen, Shuhan
    You, Chengming
    Zhang, Zheng
    Xu, Zhenfeng
    [J]. FORESTS, 2024, 15 (06):
  • [9] DISTRIBUTION OF PINE SPECIES WITH RESPECT TO PINE WILT DISEASE AND CLIMATE
    RUTHERFORD, TA
    WEBSTER, JM
    [J]. JOURNAL OF NEMATOLOGY, 1987, 19 (04) : 555 - 555
  • [10] Predicting the Potential Distribution of Rare and Endangered Emmenopterys henryi in China Under Climate Change
    Cai, Hanwei
    Zhang, Guangfu
    [J]. ECOLOGY AND EVOLUTION, 2024, 14 (10):