Predicting the potential distribution of pine wilt disease in China under climate change
Xianheng Ouyang, Anliang Chen, Haiping Lin*
School of Forestry and biotechnology, Zhjiang A&F University, Hanzhou 311300, China
*Correspondence: Haiping Lin, E-mail: 13396557805@163.com
Acknowledgements
The work was supported by the Key Research and Development Program of Zhejiang Province [grant numbers 2019C02024].
Abstract. Pine wilt disease (PWD) cause by pine wood nematodes (PWN, Bursaphelenchus xylophilus ) is an epidemic forest disease that seriously threatens the world’s forest resources and human ecological environment. Predicting the potential geographical distribution of PWD in China under climate change and studying the impact of climate change on the distribution of PWD using the MaxEnt model can provide a basis for high - efficiency quarantine, supervision, and timely prevention and control. In our study, the ENMeval data package was used to optimize the parameter setting of the MaxEnt model based on 647 geographical distribution locations of PWD and seven climate factors, the potential distribution areas of PWD under current and future climate conditions (2050s, 2070s) were simulated and predicted, and the dominant environmental factors affecting the geographical distribution of PWD were analyzed. The results showed that the value of AICc of the Akaike information criterion was 0, and the prediction accuracy was good when the feature combination (FC) was LQHPT and the regularization multiplier (RM) was 0.5. The results showed that the main climate factors affecting the distribution of PWD were temperature (max temperature of warmest month (bio5), mean temperature of driest quarter (bio9), rainfall (coefficient of variation of precipitation seasonality (bio14) and precipitation of wettest quarter (bio16). The prediction results of the MaxEnt model showed that the area of the total suitable habitat and highly suitable habitat will expand significantly in 2050 and 2070, and the potential distribution of PWD will tend to spread to high latitude and altitude.
Key words : Pine wilt disease, potential geographical distribution, climate change, MaxEnt model, climate factors
INTRODUCTION
Climate is one of the decisive factors affecting species distribution (Willis & Bhagwat, 2009). In recent years, the process of urbanization has accelerated, and the natural ecological environment is facing rapid fragmentation (Qi et al. , 2014). In the fifth assessment report (AR5), the United Nations Intergovernmental Panel on climate change stated that the global climate will continue to warm, and the average temperature of the earth will increase by 0.3 to 4.5℃ by the end of the 21st century (Li et al. , 2019). The seasonal variation of potential evapotranspiration and other climate variables will also change with climate warming (Stocker et al. , 2013). The change in ecological environments will directly affect the geographical distribution pattern of species and the structure, function, and stability of the ecosystem (Dieleman et al. , 2015).
Predicting the distribution of suitable habitats under climate change has become a major research endeavor (Fitzpatrick et al. , 2008; Li et al. , 2013). There are many models for species’ potential distribution prediction, such as climate change experiment(CLIMEX), genetic algorithm for rule - set production(GARP), ecological niche factor analysis (ENFA) and maximum entropy species prediction model (MaxEnt) (Sutherst & Maywald, 1985). MaxEnt has the highest accuracy (Padalia et al. , 2014). MaxEnt is a niche species distribution prediction method that simulates the distribution probability of species based on actual distribution points of species combined with ecological variables in the target distribution area (Phillips & Dudík, 2008). Its advantage is that the accuracy of its results is high even if the species distribution data are incomplete (Elith et al. , 2011). It has been widely used in potential planting area prediction, invasive plant distribution area prediction, quarantine pest prediction, and so on (Kroschel et al. , 2013; Qin et al. , 2015; Sanchez et al. , 2010). Recent studies have found that when the MaxEnt model is used to simulate the potential distribution area of a species, it offers high complexity and is not conducive to model transfer. After the parameters of the MaxEnt model are adjusted by ENMeval packet, it can better predict potential suitable areas of species (Yan et al. , 2021).
Pine wood nematode (PWN, Bursaphelenchus xylophilu ) causes pine wilt disease (PWD), a worldwide plant disease (Mamiya, 1983). So far, PWD has occurred in at least eight countries: Canada, Mexico, and the United States in North America; China, Japan, and South Korea in Asia; and Portugal and Spain in Europe. In Asia, PWD is causing great damage to the ecological environment and may cause major ecological disasters in the future (Abelleira et al. , 2011). Since PWD was first reported in Nanjing in 1982 (Liu et al. , 2021), more provinces have reported PWD, such as Jiangsu, Anhui, Guangdong, and Zhejiang (Zhao et al. , 2009). If a tree is infected with PWD, it can die within a few months (Mamiya, 1983). PWD has caused billions of dollars in losses annually, severely threatening pine resources (Tan et al. , 2013). In the future climate, the geographical distribution pattern of pine PWD may change. Its potential distribution area and important environmental factors affecting its distribution should be urgently understood for the prevention and control of PWD.
Herein, the optimized MaxEnt model and ArcGIS V10.5 software were used to simulate and predict potential PWD distribution in China. In our study, we aimed to (1) determine the potential habitats of PWD, (2) identify the dominant climate factors affecting the geographical distribution of PWD, and (3) predict PWD’s distribution shift in future climate scenarios to provide a basis for formulating quarantine measures and monitoring management and timely control of PWD.