Spatiotemporal trends in pine wilt disease in China and the impact of climatic factors

doi:10.21203/rs.3.rs-1535647/v1

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Spatiotemporal trends in pine wilt disease in China and the impact of climatic factors

https://doi.org/10.21203/rs.3.rs-1535647/v1

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Bursaphelenchus xylophilus is number one invasive alien species in China's forestry. Since it invaded China in 1982, it has spread to 18 provinces (cities) in 2020. In order to clarify its expansion law and the impact of climate factors, we collected the the census data of pine wilt disease (PWD) from 2010 to 2020, as well as the meteorological data. The "centroid model" was used to analyze the movement of the centroid of the epidemic area in China, and the results showed that the center of PWD occurrence was basically located in Jiangxi Province. Compared with the centroid position of North America (The native production), the centroid position where PWD occurs in China is southward, and the difference between the two is 10.5195°. By comparing the distribution of south boundary, north boundary and centroid of PWD in the native area of North America, it shows that Bursaphelenchus xylophilus can flourish in all regions of China, and the PWD epidemic in China will spread to the North in the future. In addition, the relationship between PWD epidemic and temperature, precipitation, wind speed, and relative humidity in Jiangxi Province was quantitatively analyzed using redundancy analysis (RDA). It shows that the relationship between PWD and temperature is the most closely, which is positive correlation, followed by precipitation, which is negative correlation. Meanwhile, the effect of wind speed on PWD is second only to precipitation and temperature. These analysis results have certain significance for the prevention and control of PWD.

centroid

climatic factors

invasion

Pine wilt disease

redundancy analysis

spatiotemporal spread

Pine wood nematode disease, also known as pine wilt disease (PWD), is a devastating forest disease caused by the pine wood nematode (PWN), Bursaphelenchus xylophilus, PWN is not only a major invasive species in China, but also a high-risk quarantine pest worldwide that is difficult to eradicate and control (Dropkin et al., 1981; Yoshimura et al., 1999). PWN has been prevalent in China, including areas in 18 provinces or cities, since its introduction in 1982 (Sun, 1982; Yu et al.,2011). Specifically, between 2017 and 2020, the number of county-level PWD occurrence has dramatically increased from 244 to 728. PWD is spreading rapidly toward north in a leap-forward pattern to Liaoning Province located in northeastern China, and has caused tremendous economic losses and ecological disruptions.

The transmission of PWN includes natural transmission and human transmissions. In natural transmission, PWN is carried by vector insects and is transmitted to healthy tress when they feed on tree branches. Human transmission refers to the transportation of pinewoods, semi-finished or finished pinewood products infested with PWN to another area. In China, M. alternatus has been considered as the vector of PWN. However, after PWN invaded northeastern China, new susceptible host trees (Yu and Wu, 2018; Peng, 2018), such as Pinus koraiensis, P. tabuliformis, and P. armandii were identified, as well as new insect vector, namely M. saltuarius, was discovered. M. saltuarius is widely distributed in northeastern and northwestern China, and is prone to attack tree species such as P. koraiensis and P. tabuliformis. It has previously been recognized as a transmission vector of PWN in neighboring countries such as Japan and Korea (Yu et al. 2018).

Climatic factors affect the occurrence and spread of PWD (Jikumaru and Togashi, 2008; Osada et al., 2018). Warm temperatures and moisture deficits promote the occurrence of PWD (Xi and Niu, 2008). Among climatic factors, temperature and humidity shape the activity of the vector insect M. alternatus, as well as the development and reproduction of PWN (Nie et al., 2000; Gao et al., 2007). On the other hand, precipitation, light, and relative humidity greatly affect the growth and mortality of host trees (Xi and Niu, 2008).

Temperature is the most important climatic factor affecting the occurrence and incidence of PWD (Jikumaru and Togashi, 2000). Warm temperatures significantly accelerate the metabolic rates of endocrine hormones and enzymes in both vector insects and PWN, and subsequently accelerate the growth, development, and reproduction of PWN. Mamiya (1983) have reported that a mean annual temperature of 10°C is the minimum required for PWN development. PWN would not gain enough annual accumulated temperature to satisfy its growth and development if the mean annual temperature is below 10°C. The growth and reproduction of PWN is restricted if the temperature is higher than 28°C. PWN is not able to reproduce if the temperature exceeds 33°C. In recent years, PWN has invaded northeastern China in a leap-forward pattern, overturning the previous theory that the favorable temperature of PWN was above 10°C (Li and Zhang, 2018). The incidence of PWD is mainly determined by mean annual temperature of the area (Xi and Niu, 2008). Temperatures also affect the growth and development of M. alternatus. In Zhejiang Province, the lower limit of temperature for M. alternatus egg development is 11.2–13.0°C, and the optimal temperature for larvae incubation is 19–28°C, with an effective accumulated temperature of 65–89°C (Jiang et al., 2001). Warmer temperatures accelerate the development of M. alternatus and increase the frequency of its activity, while lower temperatures keep M. alternatus in a latent state (Wang and Xu, 2002; Kong et al., 2006). Global warming has led to a rapid spread of PWD in China. At the end of this century, the areas where habitat suitability for PWN in China is likely to double the current area, and the spread rate of PWD will also increase (Cheng et al., 2015).

The humidity of air and soil are determined by rainfall. The water content of the PWN host trees and the development of M. alternatus are also associated with rainfall (Huang, 2004). June is the prevailing period for the emergence of M. alternatus, excessive rainfall causes high mortality on M. alternatus (Xi and Niu, 2008). Furthermore, the activity frequency and transmission capability of M. alternatus are suppressed in rainy days (Zhang et al., 2007; Jikumaru and Togashi, 2008). The decrease in precipitation of a certain area within a specific period will increase the population size and density of PWN (Gao et al., 2019). From this point of view, there is a negative correlation between rainfall and the occurrence of PWN. In addition, air moisture is closely related to relative humidity. The relative humidity is an important climatic factor that influences insect occurrence in the field (Leong and Ho, 1990; Wang et al., 2015). The decreases in precipitation and relative humidity directly increase the air moisture, leading to an increase in PWD incidence (Jikumaru and Togashi, 2008).

Lai Y. (1998) pointed out that M. alternatus is extremely "lazy", although it has a strong flying ability, it often shows the characteristics of reluctance to fly. When the spawning conditions are suitable, the range of natural spread is generally not more than 60 meters (Zhang et al., 2007). The large-scale spread of M. alternatus in the natural state is mainly caused by the increase of the average wind speed (Jiang et al., 2001; Yang, 2004), and the increase of the average wind speed will increase the occurrence area of PWN (Xi and Niu, 2008). In addition, the pupae of M. alternatus usually emerge into adults from May to August when the summer monsoon prevails in China. Therefore, it is very likely that M. alternatus spread widely by the wind, thereby expanding the occurrence area of PWD (Zhang et al., 2007).

In this study, the total area of PWD-affected regions, the number of PWD-killed trees, and the number of PWD-affected regions (county and provincial levels) between 2010 and 2020 were analyzed to reveal the dynamic pattern of PWD epidemic in China. Then, the boundaries and centroid of PWD-affected regions (i.e., the center of geometric shape formed by all affected regions) were examined to analyze the trend in PWD development over the years. Meanwhile, by comparing the northern and southern boundaries, and centroids of the distribution areas of PWN between China and North America, the areas where habitat suitability for PWN in China and the future vulnerable regions of PWD were predicted. Finally, Jiangxi Province was taken as an example to explore the association between climatic conditions and the incidence of PWD in the years when serious PWD outbreaks occurred. Our findings may provide theoretical basis for future prevention and control of PWD in China.

Data sources

The data sources of this study were listed as follows: 1) Bulletins of PWD distribution in China between 2011 and 2021 were obtained from the National Forestry and Grassland Administration (http://www.forestry.gov.cn), because the bulletin will lag one year, that is, the bulletins of PWD distribution announced in 2011 is the PWD distribution in 2010. 2) Global distributions of PWN were downloaded from Center for Agriculture and Bioscience International (CABI) and European and Mediterranean Plant Protection Organization (EPPO). 3) Damage data, including the number of PWD-killed trees and the total area of PWD-affected regions in China between 2010 and 2020, were retrieved from the Forest Pest Control Station of National Forestry and Grassland Administration. 4) The occurrence data of PWD in Jiangxi Province between 2010 and 2020 were obtained from the Jiangxi Provincial Forestry Bureau (Personal Communication). These data include the following indices: the numbers of county- and township-level PWD occurrence, the number of PWD-killed trees, and the total area of PWD-affected regions. 5) The meteorological data, including mean monthly and annually values of temperature, precipitation, relative humidity of the 18 basic stations (Appendix 1) in Jiangxi Province, were obtained from China Meteorological Data Sharing Platform (http://www.cma.gov.cn/2011qxfw/2011qsjgx/). The mean was used if a meteorological data was missing.

Climate data processing

The average temperature, precipitation and relative humidity from 1990 to 2020 in Jiangxi Province were selected to analyze their effects on PWD. The condition of each climate factor in a specific period of time is reflected by its arithmetic average value. Climate change in different time periods is described by anomaly value. The anomaly value has obvious advantages in describing the difference between the current value of climate elements and the average value of previous years (Yang, 1956). It is the difference between a value in the group and the average value of the group of data, which is divided into positive anomaly and negative anomaly. In climatology and prediction, it can intuitively describe whether the current value of climate factors is higher (positive anomaly) or lower (negative anomaly) than the historical value. The following is the formula for calculating the anomaly value:

$${D}_{t}={M}_{t}-\sum _{t=1990}^{t}{M}_{t}/(t-1990+1)$$

Where: ${D}_{t}$ represents the anomaly value of climate factors (annual precipitation, annual average temperature and annual average relative humidity) in year t, ${M}_{t}$ refers to the climate factor in year t. Since the time period selected in this study is 1990–2020, the initial value of t is 1990. Formula (1) can also be used to describe the anomaly value of each month, and the statistical time interval is required to be consistent. For example, when calculating the anomaly value of precipitation in January of a year, use the precipitation in January of that year minus the average precipitation from the starting time to January of that year.

Occurrence data processing

Since the Bulletins of PWD distribution is a county-level epidemic area and the data is in an irregular text format, it is necessary to sort out the epidemic area data into provincial, city and county format and save it as a CSV file. Import the sorted CSV file into ArcGIS 10.2, match it with the vector data of China's administrative regions, export the matched county-level regions. Then this completes the conversion of irregular text format epidemic area data into vector data (Appendix 2) with geographic coordinates for analysis of the expansion law of China's PWD distribution.

According to the purpose of this study, it is necessary to preprocess the geographical distribution points of PWN in the world: a. remove distribution points other than those located in China and North America; b. If a country has the distribution point information of specific provinces, the representative points of the country will be deleted; c. If a country has only one national representative point, verify the point first. If there is no record or the record is unclear, delete it; d. If reports have confirmed that PWN does not exist, but there are distribution points in the database, it will be deleted; e. The distribution points far away from the centralized distribution points shall be verified. If they do not exist or the records are unclear, they shall be deleted; f. Re-adding points that have been controlled or eradicated.

After processing these data, the distribution difference of PWN in China and native (North America) was compared using the northern boundary, southern boundary and centroid position as indicators, and the latitude difference was calculated to predict the future expansion of PWN in China trends and suitable areas.

Centroid model

The centroid can well reflect the change trend of elements in space (Wood et al., 1990; Klingenberg, 2016). By calculating the centroid of their distribution in space, the change law of elements in space can be mined to reflect the future expansion trend (and Gunz, 2009; Zhang et al., 2012). In this paper, by calculating the centroid of China's PWD distribution, we can see the concentrated position of the whole China's PWD distribution, and the change of the centroid position can represent the spatial distribution change. By comparing the changes in the position of the centroid of the PWD distribution in different periods, the spatial variation law of the China's PWD distribution can be obtained, and the distance of the centroid movement can reflect the spatial proximity of the increased or decreased China's PWD distribution to the original distribution. The direction of the centroid movement reflects the direction of the spread of PWD.

Firstly, calculating the longitude and latitude of the centroid and area of each epidemic region; Secondly, taking the epidemic area as the weight, the sum of the product of longitude, latitude and epidemic area was calculated respectively; Finally, the centroid coordinates of the whole epidemic area are obtained by dividing the results calculated in the previous step by the total area of the epidemic area. The calculation formula of the model is:

$${X}_{t}=\sum _{i=1}^{n}\left({C}_{i}\times {X}_{i}\right)/\sum _{i=1}^{n}{C}_{i}$$

$${Y}_{t}=\sum _{i=1}^{n}\left({C}_{i}\times {Y}_{i}\right)/\sum _{i=1}^{n}{C}_{i}$$

Where: ${X}_{t}$ and ${Y}_{t}$ respectively represent the longitude and latitude coordinates of the centroid of the PWD-affected regions in China in the t year; ${C}_{i}$ represents the area of the i PWD-affected region; ${X}_{i}$ and ${Y}_{i}$ represent the longitude and latitude coordinates of the i PWD-affected region respectively; n indicates the number of PWD-affected regions.

Epidemic index

The severity of PWD can be measured by the area of PWD-affected regions, the number of PWD-killed trees, average density of damaged trees, the number of PWD-affected regions and the ratio of damaged area. The smallest unit of PWD-affected regions is the Sub-Compartment. If there is PWD in a Sub-Compartment, the area of the Sub-Compartment will be counted. The PWD-killed trees include dead pine trees and dying pine trees in the Sub-Compartment where the PWD occurs. The number of PWD-affected regions is counted by different administrative levels of provinces, counties and townships. The numbers of provincial- and county-level PWD occurrence were used as indices to reveal the trend in PWD development in China, while county- and township-level were for Jiangxi Province. The ratio of damaged area is the area of PWD-affected regions to the total area of pine forest.

The association between PWD severity and climatic factors

(1) The impact of interannual climate change on the epidemic of PWD

Based on the anomaly values of annual precipitation, annual average temperature and annual average relative humidity in Jiangxi Province from 2010 to 2020, combined with the epidemic indicators, to draw a change trend map, this study qualitatively analyzed the relationship between the harm degree of PWN and climate factors in Jiangxi Province.

(2) Redundancy analysis between epidemic index and climate factor

The data of RDA analysis consists of epidemic data and climate factor data. For the PWD epidemic in Jiangxi Province in 2020, the epidemic index of each meteorological station was calculated. The climate factor took the precipitation, average temperature, and relative humidity in 2020 and 2019 into account. At the same time, the average wind speed in 2019 was added as a climatic factor, because the wind promoted the spread of M. alternatus, which indirectly affected the incidence of PWN in the second year.

A detrended correspondence analysis (DCA) was conducted to determine whether linear or unimodal numerical methods should be used (Feng et al., 2017). In this study, the largest DCA gradient length values were all shorter than 3.0; thus, a Constrained Sorting Analysis — redundancy analysis (RDA) was applied (Hu et al.,2013). A Monte Carlo permutation test based on 999 random permutations was conducted to test the relative importance and significance of each variable in explaining the PWD variation (Tsui et al.,2004). The RDA was mainly performed using the vegan package of R software (Oksanen et al., 2010; R Development Core Team, 2010; Lai and Mi, 2010).

Changes in the boundaries of PWD-affected regions in China

Table 1 presented the geographical details of the boundaries of PWD-affected regions in China between 2010 and 2020, which has been gradually expanding toward north (Fig. 1). The eastern boundary changes as the northern boundary moves, and all of which are located in coastal provinces. During the investigation period, the western and southern boundaries hardly moved, and they were located in Sichuan Province and Guangdong Province, respectively. Before 2015, the PWD epidemic in China was relatively stable, and the northern boundary of the distribution was limited to Changdao County (N0-N5), Yantai City, Shandong Province. In 2016, the B. xylophilus invaded Dalian City (N6), Liaoning Province, pushing the northern boundary 109.26 kilometers north. In 2017, serious PWD outbreaks occurred in Liaoning Province, with 13 new county-level epidemic areas. The northern boundary continued to move toward north by 368.69 km and reached Tieling County (N7), Tieling City, Liaoning Province. As B. xylophilus has adapted to the low-temperature climate of Liaoning in the process of gradually spreading northward (Zheng et al., 2021), the northern boundary remains in Kaiyuan City (N8-N10), Tieling City from 2018 to 2020, 35.88 km to the north of that in 2017. Between 2010 and 2020, the northern boundary moved northward by 4.6249° in a distance of 513.83 km from Shandong Province to Liaoning Province. The leap-forward spread of PWD in China has further increased the risk of PWN colonization and establishment in Hebei, Jilin, Heilongjiang Provinces, Inner Mongolia Autonomous Region, and Beijing City in China.

Table 1

Geographical boundaries of PWD-affected regions in China from 2010 to 2020
North/East		South		West
Year	location	Year	location	Year	location
2010–2015	Changdao County, Yantai City, Shandong Province (120.7366,37.9214)	2010–2012	Longgang District, Shenzhen City, Guangdong Province (114.2469,22.7209)	2010–2013	Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province (102.2346,29.9142)
2016	Shahekou District, Dalian City, Liaoning Province (121.5942,38.9048)	2013–2017	Huiyang District, Huizhou City, Guangdong Province (114.4567,22.7887)	2010–2013
2017	Tieling County, Tieling City, Liaoning Province (123.7290,42.2234)	2018	Yangchun County, Yangjiang City, Guangdong Province (111.7817,22.1656)	2014–2020	Xichang City, Liangshan Yi Autonomous Prefecture, Sichuan Province (102.2644,27.8945)
2018–2020	Kaiyuan City, Tieling City, Liaoning Province (124.0383,42.5463)	2019–2020	Gaozhou City, Maoming City, Guangdong Province (110.8533,21.9179)	2014–2020

The moving pattern of the centroid

The centroid model was used to calculate the centroid of the PWD-affected regions from 2010 to 2020 to illustrate the overall change of the PWD-affected regions in China (Fig. 2). During this period, the centroid of the PWD occurrence in China moved westward by 2.4223° in the longitude direction, and the moving distance was about 236.22 km; There is little change in the latitude direction, but the overall trend shows a northward movement. From 2010 to 2016, the centroid of PWD occurrence in China moved to the southwest, and the distribution was relatively concentrated, all of which were located in Jiangxi Province. During this period, the PWD spread more seriously in eastern Sichuan, northern Guizhou, Hunan, Guangdong, Jiangxi, Fujian and other provinces, but the spatial change of the centroid was relatively stable, and the centroid only moved southward by 0.1428° in latitude (C0- C6), about 15.85Km. There is no long-distance transmission outside the province based on the existing epidemic area. In 2017, the centroid of the PWD occurrence in China moved significantly to the northwest which moved northward by 0.6167° (C7-C6) in latitude, about 68.45Km, and moved to Hubei Province for the first time. In this year, the PWD spread widely in Sichuan, Chongqing, Hubei, Shaanxi, Liaoning and other provinces, with a large latitude span, which may be the main reason for the large movement of the centroid. From 2018 to 2020, the centroids of the PWD-affected region in China moved to the southwest. Although the PWD first spread to Tianjin in the north, PWD spread more seriously in Southern Guizhou, Guangxi, Guangdong and other regions (Appendix 2). In addition, PWD occurred again in Yunnan Province in 2019, which is another reason for this change.

Comparisons on PWN distribution between China and North America

PWN is native to North America. The southern boundary of PWN distribution in North America was in Nuevo León, Mexico, and the northern boundary was in Nunavut, Canada. The southern boundary of PWN distribution in China (including Hong Kong, Macao and Taiwan) was in Hong Kong Special Administrative Region, and the northern boundary was in Kaiyuan City, Tieling City, Liaoning Province. A relatively small difference was observed between southern boundaries, while a significant difference was found between northern boundaries (Fig. 3). The northern boundary in North America (Nunavut, Canada) is to the north of that in China (Kaiyuan City, Tieling City, Liaoning Province), with a difference of 11.4878° in latitude and 1276.2994 km in distance, which has exceeded the northern border of China.

It has been well established that invasive organism would occupy regions with similar climatic and environmental conditions as those of regions where they originated (Shi, et al. 2010). Therefore, a wider range of habitats is suitable for PWN in China, in which Hebei, Jilin, Heilongjiang Provinces, Inner Mongolia Autonomous Region, and Beijing City are at high risk of PWN invasion. We have demonstrated that the centroid of PWN distribution in North America was to the north of that in China. Considering the PWN has strong capability to adapt to low temperatures and hosts (Li and Zhang, 2018), the result implies that PWD may continue to develop toward north in China.

Dynamics in PWD occurrence in China and Jiangxi Province over the years

From 2010 to 2020, the provincial-level epidemic areas of China's pine wood nematode increased by 20%, while the county-level epidemic areas increased by 291.40%, which shows that the spread of China's pine wood nematode disease is particularly serious in the provinces where it has occurred. In particular, the growth rate of the number of PWD-affected areas at the provincial and county levels in 2018 reached the maximum compared with the previous year, by 12.5% and 86.08%, respectively (Fig. 4a). Correspondingly, the damage degree of PWD in China has gradually increased, and PWD-affected region and the number of PWD-killed trees increased by 31.79 times and 32.96 times respectively (Fig. 4b). Compared with 2017, the PWD-affected region and the number of PWD-killed trees in 2018 increased explosively, with growth rates reaching 663.60% and 767.44% respectively, but before that, they only increased by 59.6% and 107.06%. Between 2019 to 2020, the area affected by PWD and the number of damaged trees remained high, each reaching 1.81 million and 19.47 million.

During this period, the number of county- and township- level PWD-affected regions of in Jiangxi Province also increased year by year. Before 2018, except for a slight decrease in 2013, the number of PWD-affected regions in other years showed a slow increasing trend (Fig. 4c). However, the number of PWD occurrences increased sharply in 2018, and more than 80% of counties were infected with PWD. Compared with 2010, the number of county- and township-levels occurrences increased by 3.73, 8.37 folds respectively. Similarly, with the sudden increase in the number of epidemic areas, the PWD-affected area and the number of PWD-killed trees increased by 4250.83% and 13374.36% respectively (Fig. 4d). In the following two years, the number of PWD occurrences continued to grow, in particular, the number of township-level occurrences increased by 105.34% comparing with 2018, and the area of pine forests affected by PWD reached 0.3 million hectares, the number of PWD-killed trees even reached 4.71 million.

Associations between the epidemic and the interannual variation of climatic factors

We have showed previously that the centroid of PWD-affected regions in China was in Jiangxi Province. Through the analysis of the epidemic index in China and Jiangxi Province, the results show that the epidemic index in Jiangxi Province is basically consistent with the dynamic change of the epidemic index in China except for the average density of damaged trees (Fig. 5a). Compared with the average density of China, the average density of damaged trees in Jiangxi province fluctuates greatly. The average density of Jiangxi reached the first peak in 2012, 13 plants/ha, higher than that of the whole country in the same year. It is speculated that the main reason is the extreme drought in 2011, which is 412.61mm less than the historical average(Fig. 5b), and the relative humidity is 4.15% lower than the historical average(Fig. 5c). Drought provides a suitable living environment for M. alternatus (and Jenkins, 2018) which is the vector of B. xylophilus in Jiangxi Province. When M. alternatus carrying B. xylophilus emerges the following year, B. xylophilus enters the body of healthy pine trees causing a large number of healthy pine trees to suffer (Fu et al., 2010). As a result, the average density of the PWD-affected trees in 2012 is higher than in previous years.

From 2013 to 2017, the average density of Jiangxi was kept at a low level, with an average of no more than 7 trees per hectare. During this period, there was abundant precipitation, especially in 2015 and 2016, which was 350mm to 450mm higher than the historical average. Likewise, the relative humidity was 2.7% above normal. This humid and rainy climate has curbed the spread of M. alternatus. However, the average density of Jiangxi reached the peak again in 2018, and the average density of China also set a record in this year. In terms of climate, the temperature anomalies continued to rise (Fig. 5d), the precipitation was lower than the historical average, and the drought was very obvious. Studies have shown that temperature plays a crucial role in insect development (Merrill and Peairs, 2017), the increase in annual average temperature will promote the development of insects, and the high temperature and dry climate will accelerate the mortality of weakened wood (Gao et al., 2019; Matsuhashi et al., 2020), which may be the main reason for the sudden increase in the area of PWD in China in 2018. On the one hand, climate warming has led to the serious spread of PWD. On the other hand, the state has implemented stricter management policies, requiring that all forest areas affected by PWD must be checked, exposing many undiscovered PWD-affected regions. After that, although the average density decreased slightly from 2019 to 2020, the area affected by PWD increased rapidly. This situation may be caused by the gradual approach of PWD outbreak from general forest areas to scenic spots and key forest areas due to continuous temperature rise (Xiao et al., 2019).

Redundancy analysis of epidemic and climate factors

The RDA analysis showed that the most influential drivers of PWD epidemic were temperature, precipitation, which together accounted for 91.08% of the total variation explained by the model (Table 2). TEM2020 was significantly positively correlated with RDA1, with a correlation coefficient of 0.9672 (Table 3, P = 0.002 < 0.01), and PRE2020 was negatively correlated with the first two ranking axes. From the spatial distribution of the samples (Fig. 6), it can be seen that the temperature changes continuously, and the sample exhibits a certain degree of aggregation (Fig. 7). Where the annual average temperature is higher than the average, the PWD epidemic is more serious. In southern parts of Jiangxi Province such as Ganxian (GAN), Xunwu (XW), Suichuan (SC), Nancheng (NC), and Guixi (GX), the temperature is higher than that in the north, and the precipitation will be less than that in the north, but the epidemic situation is more serious, which shows that the warm climate promotes the development of the PWD epidemic, while abundant precipitation hinders the PWD epidemic from getting worse (Jikumaru and Togashi, 2008). However, it should be noted that although the relative humidity is not significant (P = 0.722 > 0.05), the correlation coefficient between RHU2020 and RDA1 reaches − 0.9392, which is opposite to the temperature. Coincidentally, the northern regions of Jiangxi Province, such as Jingdezhen, Jinggangshan (JGS), Xiaping (XP), Boyang (BY), Yichun (YC), Xiushui (XS), and Yushan (YS), have a cool climate and high relative humidity, and the PWD epidemic is relatively mild compared to the southern regions. The reason the test result was not significant is that it is closely related to temperature, which explains the variation in PWD outbreaks. In addition, the impact of Wind2019 on the epidemic index ranked third, second only to TEM2020 and PRE2020, suggesting that wind also has an impact on the PWD epidemic to a certain extent (Gao et al., 2019).

Table 2

Redundancy analysis results of PWD epidemic index and climate factors in Jiangxi Province in 2020
RDA axes	Eigenvalues	The contribution of eigenvalues to the variance		Accumulated constrained eigenvalues
RDA axes	Eigenvalues	Proportion Explained	Cumulative Proportion	Proportion Explained	Cumulative Proportion
RDA1	1.6636	0.4542	0.4159	0.6702	0.6702
RDA2	0.6578	0.1630	0.6172	0.2406	0.9108
RDA3	0.2251	0.0562	0.6734	0.0830	0.9938
RDA4	0.0168	0.0042	0.6776	0.0062	1

Table 3

The correlation coefficients between climate factors and RDA1 and RDA2 and significance test results of the model
Climate factors	RDA1	RDA2	R²	P
TEM2020	0.9672	0.2541	0.6653	0.002**
PRE2020	-0.4620	-0.8869	0.3437	0.072.
Wind2019	0.5315	-0.8471	0.2171	0.186
PRE2019	0.0099	0.9998	0.1773	0.245
TEM2019	0.7425	0.6699	0.0863	0.537
RHU2020	-0.9392	-0.3434	0.0424	0.722
RHU2019	-0.8555	0.5179	0.0355	0.765
Model	-	-	-	0.015*
Signif. codes: 0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

However, Lushan (LS) is an exception. Although its average temperature is lower than that of the whole province and its precipitation is relatively abundant, its damage rate ranks third and the density of damaged wood is higher. It is speculated that there are two reasons for the serious epidemic of pine wood nematode disease in Lushan Mountain: firstly, Lushan Mountain is a famous tourist attraction with a large flow of people; secondly, Lushan Mountain is close to Nanchang, the capital city of Jiangxi Province with developed transportation. This also had a serious impact on the PWD outbreak in Jiujiang (JJ) where is close to Lushan and Nanchang. Therefore, human interference (Choi and Park, 2012) is a nonnegligible factor in the management of PWD outbreaks.

It has gradually spread from the subtropical monsoon climate area to the temperate monsoon climate area since PWN invaded Nanjing, Jiangsu Province, China. Many scholars have studied the suitable area of PWN (Feng et al., 2009; He et al., 2012; Han et al., 2015), but there is few relevant research on the expansion law of PWN based on time series. This study describes the expansion law of PWN in China from a macroscopic perspective based on the movement of the centroid. The centroid model can well reflect the change trend of organisms in space. By calculating its spatial distribution in different years, and then mining the change law in space to reflect the future expansion trend. It has been applied on population dynamics of invasive plant Spartina alterniflora and landscape pattern change of Mangrove (Wang, 2020; Dai et al., 2020). In this study, it is feasible to apply the centroid model to the variation of the spread of PWD, but the difference in the severity of the epidemic situation in each infected area was not considered. In the future study, a modified method based on Zhang (2020) and Tang (2008) would be considered. That is, evaluate and grade epidemic indices for each PWD-affected regions before any analysis.

In this study, the total area of PWD-affected regions, the number of PWD-killed trees, and the number of PWD occurrence at provincial and county levels between 2010 and 2020 were used as indices to investigate the patterns of PWD occurrence in China. The data was collected from the internal documents of the Forest Pest Control Station of National Forestry and Grassland Administration. Nevertheless, variations in records may exist among different provinces. In addition, prior to the formulation and implementation of Measures for the Supervision and Responsibility of the Ecological Disaster of Pine Wilt Disease in 2019, the underreporting or failure of reporting of PWD occurrence may result in gaps between the data and the actual situation. This could affect the result of the analyses. Therefore, it is possible to include the stock of infested woods and the amount of economic loss in the future study to achieve a more comprehensive investigation on damages and losses caused by PWD.

By comparing the north-south boundaries and centroids of PWN distributions between China and North America, it is speculated that all regions of China are vulnerable to PWN intrusion, which is consistent with the report of Li and Zhang (2018). The latest report shows that PWN has invaded Wangqing County, Yanbian Korean Autonomous Prefecture, Jilin Province, China (Fig. 1, N11) where the annual average temperature is 4.6 ℃ and the annual precipitation is 574.9mm, which further confirms that PWN will continue to invade northward in the future. Our results would provide a theoretical basis for monitoring, prevention, and control of PWD in China. However, it is not possible to grade areas where habitat suitability for PWN in China in this study, because different topography, elevation, and land-sea distribution and other factors would lead to small-scale differences. In future research, these factors can be taken into account and discussed in detail. In addition, future research can refer to the improved evapo-transpiration model of Gruffudd et al. (2016), using more refined daily climate data, to predict the risk of PWD and the probability of PWD across a wide range of locations.

In the RDA analysis, only the association between PWD epidemic and climate factors in 2020 was analyzed, because more than 80% of the counties in Jiangxi Province were affected by the PWD, and the epidemic spread almost throughout the province, while the distribution of PWD epidemic areas was relatively small and scattered before 2020. In terms of data processing, the meteorological station was used as a sample, and the epidemic data of several surrounding counties were aggregated as the original variable of the sample, rather than divided by municipal administrative districts. If data are available, multi-scale modelling (Martins et al., 2016) can be done in future studies to make the results more precise. Secondly, since urbanization has a greater impact on climate, in order to reduce errors, the Nanchang meteorological station located in the center of the city is excluded as an outlier. This paper combines qualitative and quantitative analysis to explore the influence of climatic factors on the PWD epidemic. However, the infection of PWD is affected by diverse and complex factors, and the magnitude of the influencing factors varies in different regions (Xi and Niu, 2008), and there are also certain differences in the prevention and control measure, supervision and management among different regions. Therefore, the conclusions obtained from this study are mainly suitable for Jiangxi Province, and serves as a reference for regions with similar natural environment and the same control strategies as Jiangxi Province.

Climate factors affect the occurrence and spread of PWD (Xi and Niu, 2008; Gao et al., 2019). According to climatic conditions, prevention strategies and measures should be formulated as early as possible. The strategies vary from region to region due to the large difference in climate between the north and the south of Jiangxi Province and the different generations of M. alternatus (He et al., 2011; Wu et al., 2013; Xiao et al., 2019). It is recommended that areas with high temperatures and more generations of M. alternatus increase census intensity to detect and treat diseased trees early. In addition, human factors are the main reason for the long-distance transmission of PWN. It is necessary to improve the awareness of understanding, prevention, and control of PWD, and strengthen the source management and quarantine law enforcement of infested woods to prevent their losses.

PWN is native to North America, and the northern boundary of PWN distribution areas in North America (Nunavut, Canada) is to the north of that in China (Kaiyuan City, Tieling City, Liaoning Province), which has exceeded the northern border of China in latitude. Moreover, we have showed that the centroid of the PWN distribution in North America was 10.5195° to the north of that in China, which implies that PWD may continue to spread toward northern China. Therefore, areas such as Hebei, Jilin, Heilongjiang Provinces, Inner Mongolia Autonomous Region, and Beijing City are all at high risk of PWN invasion. In terms of the driving forces of PWD outbreak, the analysis results of PWD in Jiangxi Province showed that high temperature and arid climate will aggravate the PWD; abundant precipitation and high humidity can alleviate the PWD to a certain extent. In addition, RDA analysis showed that temperature had the most significant effect on the epidemic situation of PWD (P = 0.002 < 0.05), followed by precipitation (P = 0.072 < 0.1). Our findings may provide theoretical basis for future prevention and control of PWD in China.

Acknowledgments

We thank editors for research insights and editorial assistance, and we also thank the anonymous reviewers for helpful suggestions. We appreciate the support and cooperation from staff of the State Forestry and Grassland Administration Forest and Grassland Pest Control Station. We are especially thankful to the R software development team for helping with statistical analysis. We would like to thank the Forestry Science and Technology Innovation Project of Jiangxi Provincial Forestry Department and the National Key R&D Program for funding our research.

Funding

This research was funded by the Forestry Science and Technology Innovation Special of Jiangxi Forestry Department (No.201912) and National Key R&D Program of China (Grant No. 2021YFC2600400).

Data availability

Due to non-disclosure agreement terms, the PWD epidemic and meteorological datasets for redundant analyses during the current study are not public, but are available from the respective authors upon reasonable request. While other data generated or analyzed are included in this published article and its Supplementary Information file.

Conﬂict of interest The authors have no conﬂicts of interest to declare that are relevant to the content of this article.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Du Jingjing, Wu Zongren, Zhang Chao, Yu Ailin and Li Ping. The first draft of the manuscript was written by Du Jingjing and Wu Zongren, all authors commented on previous versions of the manuscript, Luo Youqing and Shi Juan guide data processing, model application, supervision and reviewing. All authors read and approved the final manuscript.

Cheng G, Lu Q, Feng YM (2015) Temporal and Spatial Dynamic Pattern of Pine Wilt Disease Distribution in China Predicted under Climate Change Scenario. Scientia Silvae Sinicae 51(6):119–126. http://www.linyekexue.net/CN/Y2015/V51/I6/119 (in Chinese)
Choi WI, Park Y-S (2012) Dispersal patterns of exotic forest pests in South Korea. Insect Sci 19:535–548. https://doi.org/10.1111/j.1744-7917.2011.01480.x
Dropkin VH, Foudin A, Kondo E, Linit M, Smith M, Robbins K (1981) Pinewood nematode: a threat to US forests? Plant Dis 65(12):1022–1027. https://doi.org/10.1094/PD-65-1022
Dai LJ, Liu HY, Wang G, Wang C, Guo ZR, Zhou Y, Li YF (2020) Modelling the effects of Spartina alterniflora invasion on the landscape succession of Yancheng coastal natural wetlands China. PeerJ 8:e10400. https://doi.org/10.7717/peerj.10400
Feng Q, Zhao WW, Fu BJ, Ding JY, Wang S (2017) Ecosystem service trade-offs and their influencing factors: A case study in the Loess Plateau of China. Sci Total Environ 31607–608. 1250–1263. https://doi.org/10.1016/j.scitotenv.2017.07.079
Feng YM, Zhang HJ, Lv Q, Liang J, Zhang XY (2009) Quantification of Suitability Distribution Region of Bursaphelenchus xylophilus in China. Scientia Silvae Sinicae 45(2):65–71. https://doi.org/10.11707/j.1001-7488.20090211(in Chinese)
Fu DY, Hu SJ, Ye H, Robert AH, Ping YZ (2010) Pine wilt disease in Yunnan China: Evidence of non-local pine sawyer Monochamus alternatus (Coleoptera: Cerambycidae) populations revealed by mitochondrial DNA. Insect Sci 17:439–447. https://doi.org/10.1111/j.1744-7917.2010.01329.x
Gao RH, Wang Z, Wang H, Hao Y, Shi J (2019) Relationship between Pine Wilt Disease Outbreaks and Climatic Variables in the Three Gorges Reservoir Region. Forests 10(9):816. https://doi.org/10.3390/f10090816
Gruffudd HR, Jenkins TAR, Evans HF (2016) Using an evapo-transpiration model (ETpN) to predict the risk and expression of symptoms of pine wilt disease (PWD) across Europe. Biol Invasions 18:2823–2840. https://doi.org/10.1007/s10530-016-1173-7
Han YY, Wang Y, Xiang Y, Ye JR (2015) Prediction of potential distribution of Bursaphelenchus xylophilus in China based on Maxent ecological niche model. J Nanjing Forestry Univ (Natural Sci Edition) 39(01):6–10. https://doi.org/10.3969/j.issn.1000-2006.2015.01.002(in Chinese)
He SY, Wen JB, Luo YQ, Zong SX, Zhao YX, Han J (2012) The predicted geographical distribution of Bursaphelenchus xylophilus in China under climate warming. Chin J Appl Entomol 49(01):236–243 (in Chinese)
He XL, Liu JS, Wen XS (2011) Study on the Life History of Monochamus alternatus in Anfu County Jiangxi Province. Biol Disaster Sci 34(2):64–66. https://doi.org/10.3969/j.issn.2095-3704.2011.02.006(in Chinese)
Hu HB, Liu HY, Hao JJ, An J (2013) Spatiotemporal variation in the value of ecosystem services and its response to land use intensity in an urbanized watershed. Acta Ecol Sin 33(08):2565–2576. https://doi.org/10.5846/stxb201201100048(in Chinese)
Ikegami M, Jenkins TAR (2018) Estimate global risks of a forest disease under current and future climates using species distribution model and simple thermal model – Pine Wilt disease as a model case. For Ecol Manage 409:343–352. https://doi.org/10.1016/j.foreco.2017.11.005
Jiang P, Zhao JN, Chai XM, Wu ZL, He ZH (2001) Integrated Control Techniques of Bursaphelenchus xylophilus. J Zhejiang Forestry Sci Technol 21(4):1–6. https://doi.org/10.3969/j.issn.1001-3776.2001.04.001(in Chinese)
Jikumaru S, Togashi K (2000) Temperature Effects on the Transmission of Bursaphelenchus xylophilus (Nemata: Aphelenchoididae) by Monochamus alternatus (Coleoptera: Cerambycidae). J Nematology 32(1):110–116. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2620426/
Jikumaru S, Togashi K (2008) Resistance of an indigenous biological system against expansion of the invasive nematode Bursaphelenchus xylophilus in cool areas of Japan. Nematology 10(5):679–687
Klingenberg CP (2016) Size shape and form: concepts of allometry in geometric morphometrics. Dev Genes Evol 226(3):113–137. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2620426/
Kong WN, Wang H, Li J (2006) The Effects of Temperature and Humidity on the Longevity of Monochamus alternatus Hope (Coleoptera:Cerambycidae). J Shanxi Agricultural Univ (Natural Sci Edition) 03:294–295. https://doi.org/10.3969/j.issn.1671-8151.2006.03.027(in Chinese)
Lai JS, Mi XC (2010) Ordination Analysis of Ecological Data Using Vegan Package in R. Proceedings of the 9th National Symposium on Conservation and Sustainable Utilization of Biodiversity, 332–343. https://d.wanfangdata.com.cn/conference/ChZDb25mZXJlbmNlTmV3UzIwMjIwMzE3Egc4NDkyMjcxGggxdnI0NWNqcQ%3D%3D (in Chinese)
Lai YX (1998) Flight behavor of Monochamus alternatus and strategic thoughts to control pine wilt disease. J Zhejiang F Univ 15(3):320–323 (in Chinese)
Leong ECW, Ho SH (1990) Techniques in the culturing and handling of Liposcelis entomophilus (Enderlein) (Psocoptera: Liposcelidae). J Stored Prod Res 26(2):67–70. https://doi.org/10.1016/0022-474X(90)90002-A
Li YX, Zhang XY (2018) Analysis on the trend of invasion and expansion of Bursaphelenchus xylophilus. For Pest Disease 37(5):1–4. https://doi.org/10.3969/j.issn.1671-0886.2018.05.001(in Chinese)
Mamiya Y (1983) Pathology of the Pine Wilt Disease Caused by Bursaphelenchus xylophilus. Annu Rev Phytopathol 21(1):201–220. https://doi.org/10.1146/annurev.py.21.090183.001221
Martins J, Richardson DM, Henriques R, Marchante E, Marchante H, Alves P, Gaertner M, Honrado JP, Vicente JR (2016) A multi-scale modelling framework to guide management of plant invasions in a transboundary context. For Ecosyst 3:17. https://doi.org/10.1186/s40663-016-0073-8
Matsuhashi S, Hirata A, Akiba M, Nakamura K, Oguro M, Takano K, Nakao K, Hijioka Y, Matsui T (2020) Developing a point process model for ecological risk assessment of pine wilt disease at multiple scales. For Ecol Manag 463. https://doi.org/10.1016/j.foreco.2020.118010
Merrill SC, Peairs FB (2016) Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology. Pest Manag Sci 73:380–388. https://doi.org/10.1002/ps.4320
Mitteroecker P, Gunz P (2009) Advances in Geometric Morphometrics. Evol Biol 36:235–247. https://doi.org/10.1007/s11692-009-9055-x
Nie SF, Lin ZG, Peng ZB, Kuang JJ (2000) The occurrence law of Monochamus alternatus Hope in scenic area of Nanyue Mountain. J Cent South Univ Forestry Technol 4:96–98. https://doi.org/10.3969/j.issn.1673-923X.2000.04.010(in Chinese)
Oksanen J, Blancher F, Kindt R, Legendre P, Minchin P, O'Hara RB, Simpson G, Solymos P, Stevens MHH, Wagner H (2010) Vegan: Community Ecology Package R package version 117–3 http://veganr-forger-projectorg/
Osada Y, Yamakita T, Shoda-Kagaya E, Liebhold AM, Yamanaka T (2018) Disentangling the drivers of invasion spread in a vector-borne tree disease. J Anim Ecol 87:1512–1524. https://doi.org/10.1111/1365-2656.12884
Peng CH (2018) Analysis on the harmfulness and control measures of pine wood nematode disease. Mod Agric Res 07:69–70. https://doi.org/10.3969/j.issn.1674-0653.2018.07.034(in Chinese)
R Development Core Team (2010) R: A language and environment for statistical computing R Foundation for Statistical Computing Vienna Austria. http://wwwR-projectorg
Shi J, Luo YQ, Zhou F, He P (2010) The relationship between invasive alien species and main climatic zones. Biodivers Conserv 19(9):2485–2500. https://doi.org/10.1007/s10531-010-9855-4
Sun YC (1982) Pine wood nematodes found in Nanjing Zhongshan Mausoleum. J Jiangsu Forestry Sci Technol 04:47–27 (in Chinese)
Tang WD, Shi J, Luo YQ (2008) Evaluation of the potential risk of Dendroctonus valens with @risk software. For Pest Disease 27(4):7–9. https://doi.org/10.3969/j.issn.1671-0886.2008.04.003(in Chinese)
Tsui CC, Chen ZS, Hsieh CF (2004) Relationships between soil properties and slope position in a lowland rain forest of southern Taiwan. Geoderma 123(1–2):131–142. https://doi.org/10.1016/j.geoderma.2004.01.031
Wang BQ, Xu MF (2002) Study on the Biological Characteristics and Occurrence Law of Monochamus alternatus Hope in the Southwest Area of Hubei. J HuBei Minzu Univ (Natural Sci Edition) 20(2):21–24. https://doi.org/10.3969/j.issn.1008-8423.2002.02.006(in Chinese)
Wang H, Ren GB, Wu PQ, Liu AC, Pan LH, Ma YM, Ma Y, Wang JJ (2020) Analysis on the Remote Sensing Monitoring and Landscape Pattern Change of Mangrove in China from 1990 to 2019. J Ocean Technol 39(05):1–12. https://doi.org/10.3969/j.issn.1003-2029.2020.05.001(in Chinese)
Wang Q, Bi M, Ma SJ, Shi L (2015) Research on Spatial Pattern of Monochamus alternatus's Occurrence Rate Based on Meteorological Factors. For Res 28(1):61–66. https://doi.org/10.13275/j.cnki.lykxyj.2015.01.010(in Chinese)
Wood GB, Wiant HV, Loy RJ, Miles JA (1990) Centroid sampling: a variant of importance sampling for estimating the volume of sample trees of radiate pine. For Ecol Manage 36:233–243. https://doi.org/10.1016/0378-1127(90)90027-9
Wu JF, Zhang WL, Zhang RX, Chen WJ (2013) Research on the Life History of Monochamus alternatus Hope in South Jiangxi. Hubei Agricultural Sciences 52(2):321–323. https://doi.org/10.3969/j.issn.0439-8114.2013.02.021(in Chinese)
Xi Y, Niu SK (2008) The Effects of Climatic Factors on Pine Wilt Disease. For Resour Manage 04:70–76. https://doi.org/10.13466/j.cnki.lyzygl.2008.04.013
Xiao XY, Liu P, Gong XS, Song Q, Jin MX, Ying Q (2019) The distribution and of research situation of Monochamus alternates in Jiangxi Province. South China Forestry Science 47(02):50–53. https://doi.org/10.16259/j.cnki.36-1342/s.2019.02.011(in Chinese)
Yang AM (2004) Studys on the Change Law of Population Dynamics of Monochamus alternatus Hope Adults in Forests. J Fujian Forestry Sci Technol 31(2):62–64. https://doi.org/10.3969/j.issn.1002-7351.2004.02.017(in Chinese)
Yang JC, ON THE OCCURRENCE OF LARGE SCALE MEAN MONTHLY TEMPERATURE DEPARTURE IN CHINA (1956) Acta Meteorologica Sinica 0(2):135–152. https://doi.org/10.11676/qxxb1956.012(in Chinese)
Yoshimura A, Kawasaki K, Takasu F, Togashi K, Futai K, Shigesada N (1999) Modeling the spread of pine wilt disease caused by nematodes with pine sawyers as vector. Ecology 80:1691–1702. https://doi.org/10.1890/0012-9658(1999)080[1691:MTSOPW]2.0.CO;2
Yu HY, Wu H (2018) New host plants and new vector insects of pine wood nematode found in Liaoning. For Pest Disease 37(05):61. https://doi.org/10.3969/j.issn.1671-0886.2018.05.015(in Chinese)
Yu MJ, Xu XH, Ding P (2011) Economic loss versus ecological gain: the outbreaks of invaded pinewood nematode in China. Biol Invasions 13:1283–1290. https://doi.org/10.1007/s10530-010-9889-2
Zhang JJ, Zhang RZ, Chen JY (2007) Species and their dispersal ability of Monochamus as vectors to transmit Bursaphelenchus xylophilus. Journal of Zhejiang A & F University, 24(3):350–356. https://doi.org/10.3969/j.issn.2095-0756.2007.03.018 (in Chinese)
Zhang X, Zhao JJ, Yan J, Fang GF, Huang JX (2020) Economic loss assessment of pine wilt disease in mainland China in 2017. J Beijing Forestry Univ 42(10):96–106. https://doi.org/10.12171/j.1000-1522.20190210(in Chinese)
Zheng YN, Liu PX, Shi Y, Wu H, Yu HY, Jiang SW (2021) Difference analysis on pine wilt disease between Liaoning Province of northeastern China and other epidemic areas in China. J Beijing Forestry Univ 43(05):155–160. https://doi.org/10.12171/j.1000-1522.20200108(in Chinese)

Appendix.rar
Appendix 1 The basic stations in Jiangxi ProvinceAppendix 2 Geographical distribution of PWD-affected regions in China from 2010 to 2020

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Spatiotemporal trends in pine wilt disease in China and the impact of climatic factors

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Abstract

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Introduction

Materials And Methods

Data sources

Climate data processing

Occurrence data processing

Centroid model

Epidemic index

The association between PWD severity and climatic factors

Results

Changes in the boundaries of PWD-affected regions in China

The moving pattern of the centroid

Comparisons on PWN distribution between China and North America

Dynamics in PWD occurrence in China and Jiangxi Province over the years

Associations between the epidemic and the interannual variation of climatic factors

Redundancy analysis of epidemic and climate factors

Discussions

Conclusions

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References

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