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Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China
Many forests have suffered serious economic losses and ecological consequences of pine wilt disease (PWD) outbreaks. Climate change and human activities could accelerate the distribution of PWD, causing the exponential expansion of damaged forest areas in China. However, few studies have analyzed the spatiotemporal dynamics and the factors driving the distribution of PWD-damaged forests using continuous records of long-term damage, focusing on short-term environmental factors that influence multiple PWD outbreaks. We used a maximum entropy (MaxEnt) model that incorporated annual meteorological and human activity factors, as well as temporal dependence (the PWD distribution in the previous year), to determine the contributions of environmental factors to the annual distribution of PWD-damaged forests in the period 1982–2020. Overall, the MaxEnt showed good performance in modeling the PWD-damaged forest distributions between 1982 and 2020. Our results indicate that (i) the temporal lag dependence term for the presence/absence of PWD was the best predictor of the distribution of PWD-damaged forests; and (ii) Bio14 (precipitation in the driest month) was the most important meteorological factor for affecting the PWD-damaged forests. These results are essential to understanding the factors governing the distribution of PWD-damaged forests, which is important for forest management and pest control worldwide.
Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China
Many forests have suffered serious economic losses and ecological consequences of pine wilt disease (PWD) outbreaks. Climate change and human activities could accelerate the distribution of PWD, causing the exponential expansion of damaged forest areas in China. However, few studies have analyzed the spatiotemporal dynamics and the factors driving the distribution of PWD-damaged forests using continuous records of long-term damage, focusing on short-term environmental factors that influence multiple PWD outbreaks. We used a maximum entropy (MaxEnt) model that incorporated annual meteorological and human activity factors, as well as temporal dependence (the PWD distribution in the previous year), to determine the contributions of environmental factors to the annual distribution of PWD-damaged forests in the period 1982–2020. Overall, the MaxEnt showed good performance in modeling the PWD-damaged forest distributions between 1982 and 2020. Our results indicate that (i) the temporal lag dependence term for the presence/absence of PWD was the best predictor of the distribution of PWD-damaged forests; and (ii) Bio14 (precipitation in the driest month) was the most important meteorological factor for affecting the PWD-damaged forests. These results are essential to understanding the factors governing the distribution of PWD-damaged forests, which is important for forest management and pest control worldwide.
Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China
Wei Wang (author) / Wanting Peng (author) / Xiuyu Liu (author) / Geng He (author) / Yongli Cai (author)
2022
Article (Journal)
Electronic Resource
Unknown
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