Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China
Pine wilt disease is a devastating forest disease caused by the pinewood nematode Bursaphelenchus xylophilus, which has been listed as the object of quarantine in China. Climate change influences species and may exacerbate the risk of forest diseases, such as the pine wilt disease. The maximum entro...
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doaj-664a4d0a17bc4883997e4e8bdfe42a822021-04-23T05:06:23ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2021-04-011210.3389/fpls.2021.652500652500Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in ChinaXinggang Tang0Yingdan Yuan1Xiangming Li2Jinchi Zhang3Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key Laboratory of Soil and Water Conservation and Ecological Restoration, Nanjing Forestry University, Nanjing, ChinaCo-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key Laboratory of Soil and Water Conservation and Ecological Restoration, Nanjing Forestry University, Nanjing, ChinaCollege of Materials Sciences and Technology, Guangdong University of Petrochemical Technology, Maoming, ChinaCo-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key Laboratory of Soil and Water Conservation and Ecological Restoration, Nanjing Forestry University, Nanjing, ChinaPine wilt disease is a devastating forest disease caused by the pinewood nematode Bursaphelenchus xylophilus, which has been listed as the object of quarantine in China. Climate change influences species and may exacerbate the risk of forest diseases, such as the pine wilt disease. The maximum entropy (MaxEnt) model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China. Further, the potential distribution was modeled using the current (1970–2000) and the projected (2050 and 2070) climate data based on two representative concentration pathways (RCP 2.6 and RCP 8.5), and fairly robust prediction results were obtained. Our model identified that the area south of the Yangtze River in China was the most severely affected place by pine wilt disease, and the eastern foothills of the Tibetan Plateau acted as a geographical barrier to pest distribution. Bioclimatic variables related to temperature influenced pine trees’ distribution, while those related to precipitation affected B. xylophilus’s distribution. In the future, the suitable area of B. xylophilus will continue to increase; the shifts in the center of gravity of the suitable habitats of the three pine species and B. xylophilus will be different under climate change. The area ideal for pine trees will migrate slightly northward under RCP 8.5. The pine species will continue to face B. xylophilus threat in 2050 and 2070 under the two distinct climate change scenarios. Therefore, we should plan appropriate measures to prevent its expansion. Predicting the distribution of pine species and the impact of climate change on forest diseases is critical for controlling the pests according to local conditions. Thus, the MaxEnt model proposed in this study can be potentially used to forecast the species distribution and disease risks and provide guidance for the timely prevention and management of B. xylophilus.https://www.frontiersin.org/articles/10.3389/fpls.2021.652500/fullclimate changepine wilt diseasespecies distribution modelrisk predictionpine species |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xinggang Tang Yingdan Yuan Xiangming Li Jinchi Zhang |
spellingShingle |
Xinggang Tang Yingdan Yuan Xiangming Li Jinchi Zhang Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China Frontiers in Plant Science climate change pine wilt disease species distribution model risk prediction pine species |
author_facet |
Xinggang Tang Yingdan Yuan Xiangming Li Jinchi Zhang |
author_sort |
Xinggang Tang |
title |
Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China |
title_short |
Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China |
title_full |
Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China |
title_fullStr |
Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China |
title_full_unstemmed |
Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China |
title_sort |
maximum entropy modeling to predict the impact of climate change on pine wilt disease in china |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2021-04-01 |
description |
Pine wilt disease is a devastating forest disease caused by the pinewood nematode Bursaphelenchus xylophilus, which has been listed as the object of quarantine in China. Climate change influences species and may exacerbate the risk of forest diseases, such as the pine wilt disease. The maximum entropy (MaxEnt) model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China. Further, the potential distribution was modeled using the current (1970–2000) and the projected (2050 and 2070) climate data based on two representative concentration pathways (RCP 2.6 and RCP 8.5), and fairly robust prediction results were obtained. Our model identified that the area south of the Yangtze River in China was the most severely affected place by pine wilt disease, and the eastern foothills of the Tibetan Plateau acted as a geographical barrier to pest distribution. Bioclimatic variables related to temperature influenced pine trees’ distribution, while those related to precipitation affected B. xylophilus’s distribution. In the future, the suitable area of B. xylophilus will continue to increase; the shifts in the center of gravity of the suitable habitats of the three pine species and B. xylophilus will be different under climate change. The area ideal for pine trees will migrate slightly northward under RCP 8.5. The pine species will continue to face B. xylophilus threat in 2050 and 2070 under the two distinct climate change scenarios. Therefore, we should plan appropriate measures to prevent its expansion. Predicting the distribution of pine species and the impact of climate change on forest diseases is critical for controlling the pests according to local conditions. Thus, the MaxEnt model proposed in this study can be potentially used to forecast the species distribution and disease risks and provide guidance for the timely prevention and management of B. xylophilus. |
topic |
climate change pine wilt disease species distribution model risk prediction pine species |
url |
https://www.frontiersin.org/articles/10.3389/fpls.2021.652500/full |
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