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.