Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Guangdong, Guangxi, and Jiangsu Provinces in China
Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(6), 934; https://doi.org/10.3390/f15060934
Submission received: 26 March 2024
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Revised: 24 May 2024
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Accepted: 26 May 2024
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Published: 28 May 2024
(This article belongs to the Section Genetics and Molecular Biology)
Abstract
:This study aimed to investigate the genetic structures of pine wood nematodes (PWNs, Bursaphelenchus xylophilus (Steiner & Buhrer) Nickle)), in Guangdong (GD), Guangxi (GX), and Jiangsu (JS) Provinces (the major PWN dispersal centers). Furthermore, we also explored potential migration routes among the different provinces in order to provide insights into the epidemic source of PWNs in the three provinces in China. We re-sequenced a total of 241 PWNs collected from the above provinces using next-generation sequencing to obtain raw genomic data. Bioinformatics analysis was used to identify the SNPs, genetic structures, and selective sweeps of the PWNs. The results indicate that the PWNs from these three provinces can be classified into five groups (A, B, C, D, and E), among which the genetic variations are significant. All PWN strains from JS were exclusively found in Group A. The PWNs in Groups B and C were composed of strains from GD and GX, while Groups D and E comprised only GD strains. Introgression analysis identified two possible pathways: (1) from Group A to Group B-GX and (2) from Group E to Group D. Selective sweep analysis showed that in Groups B and C, the candidate genes of Group B were mainly related to pectin lyase activity.
1. Introduction
Pine wood nematode (PWN) (Bursaphelenchus xylophilus (Steiner & Buhrer) Nickle)) is the pathogen of pine wilt disease (PWD). Due to its complex pathogenic mechanism [1,2] and drug resistance [3], as well as the poor resistance of the host pine to pine wood nematode [4,5], the disease has become a major threat to pine forests worldwide, remaining a concern for many countries. The population genetics, geographical origin, and spread of PWN have also received much attention [6,7]. It is now generally accepted that PWN originated in North America [8,9] and was then introduced to Japan in the early 20th century [9], gradually spreading to China and Korea [10] and, eventually, to Europe around the 21st century [11,12,13]. Currently, China is suffering from the highest incidence of PWD [14,15]. According to the No. 7 announcement of the National Forestry and Grassland Administration in 2023, 701 counties in 19 provinces of China were listed as epidemic areas. Therefore, PWD is a great threat to China’s vast forest resources.
PWN is an invasive species with a complex life cycle and the ability to spread rapidly, causing high mortality rates in host trees [16]. Under natural conditions, at least 17 pine species are naturally susceptible in China [16]. Japanese scholars once proposed that the suitable temperature for PWNs is above 10 °C, but, in recent years, they have been found in northeastern China (where the average annual temperature is lower than 10 °C) [17,18]. Previous studies have indicated that PWD may occur in all low-altitude pine forests in China, especially pine forests in southern China [19]. Guangdong (GD) Province and the Guangxi Zhuang Autonomous Region (GX) are located in the south of China. They have rich pine forest resources (GX has a pine forest area of 2.332 million hm2 and GD has a pine forest area of 2.45 million hm2) and a high average annual temperature (>10 °C). Thus, these areas provide excellent host and environmental conditions for the survival of PWNs. PWD first occurred in Shenzhen in Guangdong Province in 1988 and in Guilin in Guangxi Province in 2001. The long-distance spread of PWNs in China is mainly caused by human activities. Guangdong Province has a developed economy and a large volume of logistics and trade, so the risk of PWNs being introduced to and flowing out from this region is naturally very high [20,21]. PWD saw its earliest occurrence in Jiangsu Province in China. It was first found on Zijin Mountain in Nanjing in 1982 and then spread to the surrounding areas.
There are some studies [22,23] that suggest that an important measure in controlling biological invasion is detecting and analyzing the pathogen or its related products to trace the transmission source. Using molecular genetic markers to study the genetic diversity of PWN populations and analyze the relationships between strains in different regions is helpful in understanding the transmission and diffusion of PWD. There are many genetic molecular markers applied to PWNs, such as RFLP [24], RAPD [25], SSR [26,27], SCAR [28], and AFLP [29,30]. Due to the increasingly complex distribution characteristics of PWN populations, the original molecular markers have been unable to adapt to the complex genetic structural changes in pine wood nematode populations in different geographical regions. Therefore, it is very important to find new genetic molecular markers with high sensitivity and resolution to study the genetic structure and geographical region of the PWN population [31]. With the development of molecular marker technology, SNPs (single-nucleotide polymorphisms) are considered the most promising molecular markers. They are widely used in many applications for population tracking and molecular genetics [32]. Ding Xiaolei [31] explored the population genetic structure of PWNs in China and found that they could be divided into four major groups. Among them, the PWNs from GD and the USA are in one group. Yang Aixia [32] used SNP molecular markers to explore the genetic structure and potential genetic pathways of PWNs in central China. However, they did not report the genetic structure and geographical region of the PWN population in GD, GX, and JS Provinces in detail.
In order to identify the genetic diversity of PWNs in GD, GX, and JS Provinces, we attempted to elucidate the genetic variation, gene flow, and potential selective sweeping of PWNs from the aforementioned areas. Our study will provide abundant genomic information regarding PWNs and provide a theoretical basis for the control and monitoring of this invasive species in the three provinces.
2. Materials and Methods
2.1. Geographical Origin and Preservation of Nematode Samples
The Baermann funnel method [33] was employed to collect pine wood nematodes (PWNs) from various epidemic areas in Guangdong, Guangxi, and Jiangsu Provinces. Under a microscope, the nematodes obtained were initially verified based on their morphological characteristics [16,28]. Following this, they underwent molecular confirmation using the SCAR (sequence-characterized amplified region) marker [28].
Once the samples were confirmed to be PWNs, each strain consisted of 50 handpicked individuals that were then separately cultured on Botrytis cinerea Pers fungus [34]. After 4 to 6 days, when the fungus was consumed by the PWNs, the nematodes were collected using the Baermann funnel method. The purified PWN strains from different endemic areas were then rinsed with 0.05% streptomycin sulfate, 0.01% kanamycin sulfate, and sterile water in order to wash away the microorganisms and reduce contamination before resequencing [31]. These PWN strains were subsequently maintained in the Forest Pathology Laboratory of Nanjing Forestry University for further analysis.
2.2. DNA Extraction and High-Throughput Genome Re-Sequencing
In this study, the DNA of PWNs from different areas was extracted using the CTAB (cetyltrimethylammonium bromide) method [28]. The concentration and quality of the extracted DNA were assessed using a Nanodrop 2000/2000 c (Thermo Fisher, Waltham, MA, USA). The DNA samples from different PWN strains were stored in the PWN DNA resource bank at −80 °C, located in the Forest Pathology Laboratory of Nanjing Forestry University.
High-quality DNA samples were sent to Beijing Norhe Zhiyuan Technology Company for high-throughput genome sequencing using the Illumina (San Diego, CA, USA) HiSeq 4000 (150 bp paired-end reads) platform with 40× coverage. A total of approximately 8G raw data were generated for each PWN strain.
2.3. Sequencing Data Processing
The quality of the raw sequencing data was first assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 23 February 2023). Filtered reads were aligned to the PWN reference genome [31] announced in 2021 by BWA (http://bio-BWA.SourceForge.net/BWA.shtml, accessed on 24 March 2023). The parameter was set to mem -t 4 -k 32 -m. SAMtools (http://samtools.sourceforge.net/samtools.shtml, accessed on 26 March 2023) and Picard (http://broadinstitute.github.io/picard/, accessed on 27 March 2023) were used to remove duplicates. Putative SNPs were established using FreeBayes (https://github.com/ekg/freebayes, accessed on 29 March 2023) with a minimum coverage of >10, and VCFtools (https://github.com/vcftools, accessed on 31 March 2023) was used for SNP site statistical analysis.
2.4. Analysis of Population Genetic Differentiation
The SNPs with a low allele frequency and high linkage disequilibrium and missing rate were filtered using the SNPRelate package (https://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html, accessed on 3 April 2023) and RStudio software (v2023.6.2.561) (https://www.rstudio.com/, accessed on 8 April 2023). A principal component analysis (PCA) diagram was drawn using the same package mentioned above. PLINK (v1.9) (https://www.cog-genomics.org/plink/, accessed on 12 April 2023) was used to extract the filtered site information to generate a new VCF file for phylogenetic tree analysis using the VCF-kit (https://vcf-kit.readthedocs.io/en/latest/, accessed on 23 July 2023). MEGA (v11.0.11) (https://www.megasoftware.net/, accessed on 25 April 2023) and iTOL (v6) (https://itol.embl.de/, accessed on 28 April 2023) were used to construct a phylogenetic tree.
The Admixture software (v1.3) (https://dalexander.github.io/admixture/, accessed on 2 May 2023) and the pophelper package (https://www.royfrancis.com/pophelper/articles/index.html, accessed on 5 May 2023) were used to generate the nematode population genetic structures and calculate the best K-value. The Treemix software (v1.13) (https://bitbucket.org/nygcresearch/treemix, accessed on 15 May 2023) was used to construct a population-splitting tree.
2.5. Selective Sweep and GO Enrichment Analysis
We used vcftools v0.1.15 [35] software to calculate the Pi and FST indices with the window size set to 50 k and a sliding window of 10 k. To identify genome-wide selective sweeps associated with PWN adaptation, we calculated the genome-wide distribution of the fixation index (FST) values and θπ ratios for the defined group pairs. The FST values were Z-transformed as follows: Z(FST) = (FST − µFST)/σFST, in which µFST is the mean FST, and σFST is the standard deviation of FST. The θπ ratios were log2-transformed. Subsequently, we estimated and ranked the empirical percentiles of Z(FST) and log2(θπ ratio) in each window. We considered the windows with the top 5% Z(FST) and log2(θπ ratio) values simultaneously as candidate outliers under strong selective sweeps. All the outlier windows were assigned to the corresponding SNPs and genes. (The candidate window selection method should be modified according to the actual situation).
The functional annotation of the protein-coding genes of PWN was achieved using BLASTP (E-value < 10−5) [36] against the protein sequence database SwissProt. One of the main uses of GO (Gene Ontology) is to perform enrichment analysis on gene sets. The GO enrichment analysis of differentially expressed genes of PWN was implemented using the GOseq R package [37], in which gene length bias was corrected. GO terms with corrected p-values of less than 0.05 were considered significantly enriched by differential expressed genes.
3. Results
3.1. Sampling of PWN Strains
A total of 241 PWN samples were collected and purified from GD, GX, and JS (see Figure 1 and Appendix A–Table A1 for detailed geographical sources). Among all the PWN strains, 117 PWN strains were sampled from GD, 64 from GX, and 60 from JS. All collected PWN samples covered 99 infected areas in these three provinces. Generally, GD samples were collected from 56 local areas, and GX and JS samples were collected from 24 and 19 local areas, respectively (Figure 2).
3.2. Statistics of SNP Genotypes and SNP Loci
The SNP locus information of the 241 strains from GD, GX, and JS in China showed that there were 9,508,393 SNP sites in total, and the number of SNP sites significantly varied between the different strains. The PWN strains from GD had significantly more SNPs and homozygotes than other strains, the PWN strains from Guangxi had the next largest amounts, and the PWN strains from Jiangsu had the least (Figure 3). However, there were also considerable differences between the SNPs in the sampled individuals. GD08 had the highest SNP count (1,180,311), while GD03 showed the lowest count (98,881). The highest number of homozygotes found in GD98 was 908,259, and the lowest was 7314 in GX68 (Figure 3a). Also, the genotypes found in GD and GX were obviously higher than those in JS strains. Among the 12 gene types, A>G, C>T, G>A, and T>C occurred more frequently than the other 8 gene types (Figure 3b).
3.3. Analysis of Genetic Structure and Genetic Diversity
The principal component analysis (PCA) of the 185 PWN strains from GD and GX revealed five distinct groups: A, B, C, D, and E. GX strains were found only in Groups A, B, and C, while GD strains were present in all five groups. This suggests that GD strains exhibit greater genetic diversity than GX strains and have a more extended genetic distance (Figure 4b).
To further explore the relationship between the PWN strains from GD, GX, and JS, a total of 241 samples from the three regions were analyzed using PCA and phylogenetic tree analysis. The results indicate that all samples could be classified into five groups: A, B, C, D, and E. This classification is consistent with the self-clustering observed in GD and GX (Figure 4a–c). Group A comprised 92 strains from all three provinces, whereas JS strains were exclusively found in Group A. Both Groups B and C had GD and GX strains (GD: 70 samples and GX: 30 samples). Groups D and E consisted solely of 49 GD strains. Clustering analysis identified the strongest support for K = 9 (Figure 4d) populations. This underlined how A, B, C, D, and E represented the classification results of the phylogenetic tree and PCA.
Introgression analysis was conducted to explore potential PWN transmission routes in GD, GX, and JS Provinces. The five PCA clusters were further categorized based on geographical origin, resulting in a total of nine groups: A-JS, A-GD, A-GX, B-GD, B-GX, C-GD, C-GX, D-GD, and E-GD. Two potential transmission pathways were identified: Groups E-GD to D-GD and Group A to Group B-GX. This suggests that genetic exchange occurs between PWN strains in the GD-GX area and JS, as well as within the GD-GX area itself (Figure 5 and Figure 6).
3.4. Selective Sweep Analysis
The selective sweep analysis based on previous clustering results indicated that Group A exhibited the lowest nucleotide diversity, while Groups B, C, D, and E displayed high nucleotide diversity. Furthermore, Group A, which contained all strains from JS Province and some strains from GD and GX, exhibited low strain differentiation, high genetic similarity, close genetic distance, and minimal population differentiation. Conversely, Groups B, C, D, and E displayed high Pi values, reflecting the substantial genetic differentiation between groups and considerable genetic distance (Figure 7).
Groups B and C comprised PWN strains from both GD and GX. To explore the genetic differences between the PWN strains in these two regions and to identify genes under natural selection, selective sweep analysis based on the FST and θπ ratio analyses was performed. The results show that 208 genes (green) in Group B and 55 genes (blue) in Group C were located in the candidate regions (Figure 8).
Among the candidate genes in Group B, GO (Gene Ontology) enrichment analysis was conducted to identify putative functions. The primary functions of these genes were found to be associated with the entry of pine wood nematodes into host plants, their activity within these plants, and pectin lyase activity (Figure 9). These findings provide insights into the potential roles of these genes in PWN biology and host–pathogen interactions.
4. Discussion
Previous studies have often been limited by small sample sizes [24,25,26,27,28,29,30] and geographically distant sources of PWNs. For instance, a study in Portugal [38] analyzed the genetics of 15 PWN strains from Portugal, China, Japan, and the USA. The researchers concluded that the Portuguese strain was most closely related to the Chinese and Japanese strains. In contrast, the present study collected a total of 241 PWN strains from GD, GX, and JS Provinces in China. A sample with a larger size and more diverse geographical regions would be beneficial to conduct a comprehensive and detailed genetic analysis of PWN populations in the above three provinces.
Ding Xiaolei et al. [31] analyzed the genetic structure of 181 PWN strains from China, the USA, and Japan, identifying four major groups of PWNs in China, with two major transmission centers located in Jiangsu and Guangdong Provinces. Previously, researchers have suggested that the GD and GX regions, characterized by abundant pine forest resources and high annual average temperatures, provide ideal environments for PWN survival [39]. Additionally, the regions’ thriving economic activities increase the likelihood of human-mediated PWN transmission [6]. This rapid human-induced transmission has led to a significant increase in PWN populations in the Guangdong–Guangxi area. The complex population genetics of PWNs in this region can be attributed to the rapid invasion and establishment of those from different geographic origins within a relatively short time. Based on the population structure analysis of PWNs in China [31], this study revealed the finer population structures in GD, GX, and JS. The results show that the PWN strains from Guangdong had higher genetic diversity, which is consistent with previous studies.
The first report of PWNs in Guangdong Province dates back to 1988, which is also known as the second report of this forest pest in China following its initial appearance in Jiangsu Province. Previous research by Ding Xiaolei et al. [31] suggested that the Guangdong strains had high genetic diversity and were genetically close to American strains, whereas the genetic diversity of strains in other areas tended to decrease. Yang [32] found that the PWNs from Guangdong Province had a long genetic distance from those in other regions, including Jiangsu Province. This is consistent with the results of this study, in which we identified five distinct groups of PWN strains from Guangdong Province, revealing a more complex genetic diversity than previously assumed. The substantial genetic differences observed between the PWN strains from Guangdong and Jiangsu Provinces, except for Group A, suggest that their sources in Guangdong Province are more diverse than those in Guangxi and Jiangsu Provinces. Introgression analysis did not detect gene flow between Group A strains (mostly JS) and other groups in GD Province, indicating the presence of additional invasion sources in Guangdong Province beyond these strains. Group E was geographically spread from other groups, which were concentrated in the southern part of Guangdong Province near the Pearl River Delta. The developed economy and extensive trade activities in this region, including the importing of wooden shipping containers from foreign countries, suggest a high likelihood of alien introduction.
PWNs were first detected in the Guangxi Zhuang Autonomous Region in 2001, later than in Jiangsu and Guangdong Provinces. Previous studies [31] suggested that there was less gene exchange between PWNs in the Guangdong and Guangxi regions. In this study, however, PWN strains from both regions appeared in the same groups. Most PWN strains from Guangxi were genetically similar to those from Guangdong (Figure 4 and Figure 5). In Group B, the earliest epidemic area was Shantou, Guangdong Province (2005), suggesting that Group B strains may have first spread to Guangdong from other areas and then spread to Guangxi. In Group C, the strains from Guilin, Guangxi, emerged in the earliest affected area (2001), indicating that Group C strains may have spread from other places to Guangxi and transmitted to Guangdong. The maximum likelihood tree analysis revealed the parallel branching of A-GX, A-GD, and A-JS strains within Group A, suggesting independent transmission to their respective regions from Jiangsu strains. Therefore, while there is evidence of a transmission relationship between the two regions and Jiangsu Province, most of the strains were likely not transmitted from Jiangsu, but rather from abroad.
Previous studies on the adaptation and evolution of PWN after its invasion into China have primarily focused on differences between northern and southern regions, particularly regarding genes related to low-temperature tolerance in newly infected northern areas [40]. These studies have mostly involved genome-wide association analysis [41], as well as differences in virulence expression [42] and fecundity [41]. However, there have been no reports conducting a selective sweep analysis of adaptive evolution following the invasion of PWNs in China. To explore the genetic differences between PWN populations in Guangdong and Guangxi Provinces, this study identified 208 genes selected by Group B after selective sweep analysis. Among the candidate genes of Group B, GO enrichment analysis revealed that these genes were mainly involved in biological processes related to PWN infection. The molecular function of these genes was mainly related to pectin lyase activity. Pectin lyase is one of the primary enzymes involved in pectin decomposition [43,44]; it plays a crucial role in the pathogenic process of PWN [45]. Kikuchi et al. [46] cloned pectin lyase-related genes from pine wood nematodes, which are only expressed in their esophageal gland cells, indicating that they may be secreted into plant tissues, thus helping PWNs to forage and invade host plants.
The results provide a foundation for future experiments for verifying genes identified through the screening process and understanding their potential roles in PWN’s virulence and adaptation. Further research involving protein experiments, pathogenicity tests, and gene function analysis could help elucidate the roles of these candidate genes in PWN–host interactions. This additional information would contribute to the development of targeted management strategies for controlling PWNs and provide a theoretical basis for protecting pine forests from pine wilt disease.
5. Conclusions
Through a genetic diversity analysis of PWNs in the Guangdong and Guangxi area, it was found that most PWNs in this region were genetically distinct from those in JS Province, although some gene exchange was observed between strains within the region. Selective sweep analysis revealed that candidate genes of Group B strains in both regions were enriched in genes related to pectin lyase activity. These findings contribute to a better understanding of the geographical distributions, genetic structures, and potential transmission dynamics of PWNs in the Guangdong–Guangxi area and Jiangsu Province. This study provides valuable information regarding the population structures and spread routes of PWNs in the above regions.
Author Contributions
Y.F. completed the data analysis and the first draft of this manuscript. Y.F. completed the experiments. Y.F. and W.J. contributed to the sample acquisition. J.Y. and X.D. directed the experimental design, data analysis, and manuscript writing and revision. All authors have read and agreed to the published version of the manuscript.
Funding
This project was supported by the National Key Research and Development Project 2021YFD1400903 (J.Y.).
Data Availability Statement
The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.
Acknowledgments
We thank all the Chinese forestry bureaus that kindly provided nematode samples.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A
Table A1.
Geographic origins of 241 PWNs.
| Strain No. | Origin | Host | Sampling Date |
|---|---|---|---|
| GD01 | Fengkai County, Zhaoqing City, Guangdong Province | Pinus. massoniana | January 2015 |
| GD02 | Qingcheng District, Qingyuan City, Guangdong Province | P. massoniana | January 2015 |
| GD03 | Zengcheng City, Guangzhou City, Guangdong Province | P. massoniana | January 2015 |
| GD04 | Huiyang District, Huizhou City, Guangdong Province | P. massoniana | January 2015 |
| GD06 | Huidong County, Huizhou City, Guangdong Province | P. massoniana | January 2015 |
| GD08 | Boluo County, Huizhou City, Guangdong Province | P. massoniana | January 2015 |
| GD09 | Wujiang District, Shaoguan City, Guangdong Province | P. massoniana | January 2015 |
| GD11 | Zijin County, Heyuan City, Guangdong Province | P. massoniana | January 2015 |
| GD13 | Huicheng District, Huizhou City, Guangdong Province | P. massoniana | January 2015 |
| GD14 | Zhangmutou Town, Dongguan City, Guangdong Province | P. massoniana | January 2015 |
| GD15 | Zhangmutou Town, Dongguan City, Guangdong Province | P. massoniana | January 2015 |
| GD16 | Zhangmutou Town, Dongguan City, Guangdong Province | P. massoniana | January 2015 |
| GD17 | Tianhe District, Guangzhou City, Guangdong Province | P. massoniana | January 2015 |
| GD19 | Tianhe District, Guangzhou City, Guangdong Province | Pinus. yunnanensis | January 2015 |
| GD20 | Meixian District, Meizhou City, Guangdong Province | P. massoniana | January 2015 |
| GD22 | Qujiang District, Shaoguan City, Guangdong Province | P. massoniana | January 2015 |
| GD23 | Meijiang District, Meizhou City, Guangdong Province | P. massoniana | January 2015 |
| GD24 | Guangning County, Zhaoqing City, Guangdong Province | P. massoniana | August 2017 |
| GD25 | Guangning County, Zhaoqing City, Guangdong Province | P. massoniana | August 2017 |
| GD26 | Fengshun County, Meizhou City, Guangdong Province | P. massoniana | August 2017 |
| GD27 | Jiaoling County, Meizhou city, Guangdong Province | P. massoniana | August 2017 |
| GD28 | Jiaoling County, Meizhou city, Guangdong Province | P. massoniana | August 2017 |
| GD30 | Haifeng County, Shanwei City, Guangdong Province | P. massoniana | August 2017 |
| GD31 | Dongyuan County, Heyuan City, Guangdong Province | P. massoniana | August 2017 |
| GD32 | Dongyuan County, Heyuan City, Guangdong Province | P. massoniana | August 2017 |
| GD33 | Dongguan City, Guangdong Province | Unknown | Unknown |
| GD34 | Meizhou City, Guangdong province | Unknown | August 2022 |
| GD35 | Lianping County, Heyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD36 | Huaiji County, Zhaoqing City, Guangdong Province | P. massoniana | September 2022 |
| GD37 | Renhua County, Shaoguan City, Guangdong Province | Pinus. elliottii | September 2022 |
| GD39 | Guangzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD41 | Longping Town, Lianzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD42 | Wengyuan County, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD43 | Lianping County, Heyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD44 | Xinhui District, Jiangmen City, Guangdong Province | Unknown | September 2022 |
| GD45 | Nanlang Town, Zhongshan City, Guangdong Province | P. massoniana | September 2022 |
| GD46 | Yuancheng District, Meizhou City, Guangdong Province | Unknown | September 2022 |
| GD47 | Conghua District, Guangzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD48 | Lianzhou city, Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD49 | Luhe County, Shanwei City, Guangdong Province | P. massoniana | September 2022 |
| GD51 | Jiedong District, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD52 | Luoding city, Yunfu city, Guangdong Province | P. massoniana | September 2022 |
| GD53 | Chenghai District, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD54 | Deqing County, Zhaoqing City, Guangdong Province | P. massoniana | September 2022 |
| GD57 | Wengyuan County, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD58 | Yuancheng District, Heyuan City, Guangdong Province | Unknown | September 2022 |
| GD59 | Huadu District, Guangzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD60 | Qingyuan City, Guangdong province | P. massoniana | September 2022 |
| GD61 | Qujiang District, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD62 | Yuancheng District, Heyuan City, Guangdong Province | Unknown | September 2022 |
| GD63 | Lianping County, Heyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD64 | Lechang City, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD65 | Qujiang District, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD66 | Yingde City, Qingyuan City, Guangdong Province | Unknown | September 2022 |
| GD67 | Xiangqiao District, Chaozhou City, Guangdong Province | Unknown | September 2022 |
| GD68 | Xingning City, Meizhou City, Guangdong Province | P. massoniana | September 2022 |
| GD69 | Chenghai District, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD70 | Nanxiong City, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD71 | Chenghai District, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD72 | Chenghai District, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD73 | Lechang City, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD74 | Lianping County, Heyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD75 | Jieyang County, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD76 | Meizhou City, Guangdong Province | Unknown | September 2022 |
| GD77 | Longping Town, Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD78 | Jiangxiong Village, Heyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD79 | Jieyang County, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD81 | Lechang city, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD82 | Meizhou City, Guangdong Province | Unknown | September 2022 |
| GD84 | Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD85 | Nanao County, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD86 | Qiufeng Town, Zhaoqing City, Guangdong Province | P. massoniana | September 2022 |
| GD87 | Fengwei Town, Zhaoqing City, Guangdong Province | Unknown | September 2022 |
| GD89 | Nanxiong City, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD90 | Deqing County, Zhaoqing City, Guangdong Province | P. massoniana | September 2022 |
| GD91 | Deqing County, Zhaoqing City, Guangdong Province | P. massoniana | September 2022 |
| GD92 | Shixing County, Shaoguan City, Guangdong Province | Unknown | September 2022 |
| GD93 | Xinbu, Meizhou City, Guangdong Province | Unknown | September 2022 |
| GD94 | Baiyun District, Guangzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD95 | Shuikou Town, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD96 | Jiedong District, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD97 | Yangshan County, Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD98 | Sanshui District, Foshan, Guangdong Province | P. massoniana | September 2022 |
| GD99 | Jiedong District, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD100 | Yuancheng District, Heyuan City, Guangdong Province | Unknown | September 2022 |
| GD101 | Fengwei Town, Zhaoqing City, Guangdong Province | P. massoniana | September 2022 |
| GD102 | Meizhou City, Guangdong Province | Unknown | September 2022 |
| GD103 | Yangchun City, Yangjiang City, Guangdong Province | P. massoniana | September 2022 |
| GD104 | Lianzhou, Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD105 | Chaoan District, Chaozhou City, Guangdong Province | P. massoniana | September 2022 |
| GD106 | Puning City, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD107 | Huadu District, Guangzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD108 | Chaonan District, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD109 | Yangchun City, Yangjiang City, Guangdong Province | P. massoniana | September 2022 |
| GD110 | Xiangqiao District, Chaozhou City, Guangdong Province | Unknown | September 2022 |
| GD111 | Longchuan County, Heyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD112 | Yangchun City, Yangjiang City, Guangdong Province | P. massoniana | September 2022 |
| GD113 | Chaoan District, Chaozhou City, Guangdong Province | Unknown | September 2022 |
| GD114 | Yangchun City, Yangjiang City, Guangdong Province | P. massoniana | September 2022 |
| GD115 | Chaonan District, Shantou City, Guangdong Province | P. massoniana | September 2022 |
| GD116 | Jiedong District, Jieyang City, Guangdong Province | P. massoniana | September 2022 |
| GD117 | Shixing County, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD119 | Qingxin District, Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD120 | Qingxin District, Qingyuan City, Guangdong Province | P. massoniana | September 2022 |
| GD121 | Huadu District, Guangzhou City, Guangdong Province | P. massoniana | September 2022 |
| GD123 | Luhe county, Shanwei City, Guangdong Province | P. massoniana | September 2022 |
| GD124 | Heping County, Heyuan City, Guangdong Province | Unknown | September 2022 |
| GD125 | Haojiang District, Shantou City, Guangdong Province | Unknown | September 2022 |
| GD126 | Xiangqiao District, Chaozhou City, Guangdong Province | Unknown | September 2022 |
| GD127 | Chaoan District, Chaozhou City, Guangdong Province | P. massoniana | September 2022 |
| GD128 | Meizhou City, Guangdong Province | Unknown | September 2022 |
| GD129 | Park of Meizhou city, Guangdong Province | Unknown | September 2022 |
| GD130 | Nanlang Town, Zhongshan City, Guangdong Province | P. massoniana | September 2022 |
| GD131 | Xingning City, Meizhou City, Guangdong Province | P. massoniana | September 2022 |
| GD132 | Lechang city, Shaoguan City, Guangdong Province | P. massoniana | September 2022 |
| GD133 | Xinfeng County, Shaoguan City, Guangdong Province | P. massoniana | October 2022 |
| GD135 | Xinfeng County, Shaoguan City, Guangdong Province | P. massoniana | October 2022 |
| GX01 | Yulin City, Guangxi Zhuang Autonomous Region | P. massoniana | January 2015 |
| GX03 | Yulin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2016 |
| GX04 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | April 2019 |
| GX05 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | April 2019 |
| GX07 | Chongzuo City, Guangxi Zhuang Autonomous Region | P. massoniana | April 2019 |
| GX08 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | April 2019 |
| GX10 | Guigang City, Guangxi Zhuang Autonomous Region | Unknown | April 2019 |
| GX11 | Wuzhou City, Guangxi Zhuang Autonomous Region | Unknown | April 2019 |
| GX12 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX13 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX14 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX15 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX16 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX17 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX18 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX19 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX20 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX21 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX22 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX23 | Hezhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX24 | Yulin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX26 | Yulin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX27 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX28 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX29 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX30 | Laibin city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX31 | Hezhou, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX32 | Laibin city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX33 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX34 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX35 | Hezhou City, Guangxi Zhuang Autonomous Region | P. massoniana | October2021 |
| GX36 | Liuzhou city, Guangxi Zhuang Autonomous Region | P. massoniana | October2021 |
| GX37 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | October2021 |
| GX38 | Hezhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX39 | Hezhou city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX41 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX42 | Guilin city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX43 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX44 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX45 | Liuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX46 | Liuzhou city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX47 | Qinzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX48 | Qinzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX50 | Qinzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX51 | Hezhou City, Guangxi Zhuang Autonomous Region | P. massoniana | September 2021 |
| GX52 | Nanning city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX53 | Nanning city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX54 | Rongxian, Yulin, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX55 | Rongxian, Yulin, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX56 | Yulin city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX59 | Hezhou City, Guangxi Zhuang Autonomous Region | P. massoniana | September 2021 |
| GX60 | Hezhou city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX61 | Hezhou city, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX62 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX63 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX64 | Guigang City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX65 | Wuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX66 | Wuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX67 | Wuzhou City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX68 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX69 | Guilin City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX71 | Nanning City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX72 | Nanning City, Guangxi Zhuang Autonomous Region | P. massoniana | August 2021 |
| GX74 | Hezhou City, Guangxi Zhuang Autonomous Region | P. massoniana | October 2021 |
| JS01 | Lishui District, Nanjing City, Jiangsu Province | P. massoniana | December 2014 |
| JS02 | Runzhou District, Zhenjiang City, Jiangsu Province | P. massoniana | December 2014 |
| JS03 | Dantu District, Zhenjiang City, Jiangsu Province | P. massoniana | December 2014 |
| JS04 | Jurong city, Zhenjiang City, Jiangsu Province | P. massoniana | December 2014 |
| JS05 | Mausoleum of Sun Yat-sen in Nanjing, Jiangsu Province | P. massoniana | December 2014 |
| JS06 | Binhu District, Wuxi City, Jiangsu Province | P. massoniana | December 2014 |
| JS07 | Huishan District, Wuxi City, Jiangsu Province | P. massoniana | December 2014 |
| JS08 | Yixing city, Wuxi City, Jiangsu Province | P. massoniana | December 2014 |
| JS09 | Guiwu Town, Xuyi County, Huai’an City, Jiangsu Province | P. massoniana | January 2015 |
| JS10 | Jintan City, Changzhou City, Jiangsu Province | P. massoniana | January 2015 |
| JS11 | Haizhou District, Lianyungang City, Jiangsu Province | Pinus. densiflora | January 2015 |
| JS12 | Yizheng City, Yangzhou City, Jiangsu Province | P. massoniana | January 2015 |
| JS13 | Lianyun District, Lianyungang City, Jiangsu Province | P. massoniana | January 2015 |
| JS14 | Pukou District, Nanjing City, Jiangsu Province | P. massoniana | February 2015 |
| JS15 | Changshu City, Suzhou City, Jiangsu Province | P. massoniana | February 2015 |
| JS16 | Baima Town, Lishui County, Nanjing city, Jiangsu Province | P. massoniana | February 2015 |
| JS17 | Gaochun District, Nanjing City, Jiangsu Province | P. massoniana | February 2015 |
| JS18 | Tianmuhu Town, Changzhou City, Jiangsu Province | P. massoniana | February 2015 |
| JS19 | Changshu City, Suzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS20 | Changshu City, Suzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS21 | Changshu City, Suzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS22 | Pukou District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS23 | Pukou District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS24 | Pukou District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS25 | Dingshu Town, Yixing City, Wuxi City, Jiangsu Province | P. massoniana | October 2017 |
| JS26 | Dingshu Town, Yixing City, Wuxi City, Jiangsu Province | P. massoniana | October 2017 |
| JS27 | Hufu Town, Yixing City, Wuxi City, Jiangsu Province | P. massoniana | October 2017 |
| JS29 | Yizheng City, Yangzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS30 | Yizheng City, Yangzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS31 | Lishui District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS32 | Lishui District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS33 | Lishui District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS34 | Liyang City, Changzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS35 | Liyang City, Changzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS36 | Liyang City, Changzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS38 | Lianyun District, Lianyungang City, Jiangsu Province | Pinus. thunbergii | November 2017 |
| JS39 | Lianyun District, Lianyungang City, Jiangsu Province | P. thunbergii | November 2017 |
| JS41 | Runzhou District, Zhenjiang City, Jiangsu Province | P. massoniana | November 2017 |
| JS42 | Runzhou District, Zhenjiang City, Jiangsu Province | P. massoniana | November 2017 |
| JS44 | Jurong city, Zhenjiang City, Jiangsu Province | P. massoniana | October 2017 |
| JS45 | Jurong city, Zhenjiang City, Jiangsu Province | P. massoniana | October 2017 |
| JS47 | Jiangning District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS48 | Tea Hill, Jiangning District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS49 | Jintan District, Changzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS50 | Jintan District, Changzhou City, Jiangsu Province | P. massoniana | October 2017 |
| JS56 | Binhu District, Wuxi City, Jiangsu Province | P. massoniana | October 2017 |
| JS58 | Xuanwu District, Nanjing city, Jiangsu Province | P. massoniana | November 2017 |
| JS63 | Huai’an City, Jiangsu Province | P. massoniana | November 2017 |
| JS64 | Qixia District, Nanjing City, Jiangsu Province | P. massoniana | November 2017 |
| JS65 | Qixia District, Nanjing City, Jiangsu Province | P. massoniana | November 2017 |
| JS67 | Jianshan, Yuhuatai District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS70 | Liuhe District, Nanjing City, Jiangsu Province | P. massoniana | October 2017 |
| JS77 | Jurong Forest Farm, Zhenjiang City, Jiangsu Province | P. massoniana | September 2021 |
| JS78 | Jurong Forest Farm, Zhenjiang City, Jiangsu Province | P. massoniana | September 2021 |
| JS79 | Jurong Forest Farm, Zhenjiang City, Jiangsu Province | P. massoniana | September 2021 |
| JS80 | Jurong Forest Farm, Zhenjiang City, Jiangsu Province | P. massoniana | September 2021 |
| JS82 | Jurong Forest Farm, Zhenjiang City, Jiangsu Province | P. massoniana | September 2021 |
| JS84 | Gaochun District, Nanjing City, Jiangsu Province | P. massoniana | October 2022 |
| JS85 | Gaochun District, Nanjing City, Jiangsu Province | P. massoniana | October 2022 |
| JS86 | Gaochun District, Nanjing City, Jiangsu Province | P. massoniana | October 2022 |
References
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Figure 1.
Geographic origins of 241 PWNs (indicated by green dots).
Figure 2.
The bar chart indicates the sample sizes and the infected areas in the three provinces.
Figure 3.
The 12 gene types and SNP types in PWNs. (a) Box plots of homozygotes and SNP counts in 241 PWN strains. (b) Box plots of SNP types in 241 PWN strains.
Figure 3.
The 12 gene types and SNP types in PWNs. (a) Box plots of homozygotes and SNP counts in 241 PWN strains. (b) Box plots of SNP types in 241 PWN strains.
Figure 4.
Population genetics of pine wood nematodes in GD, GX, and JS Provinces. (a) Maximum likelihood phylogenetic tree of 241 pine wood nematode strains based on 996 SNP molecular markers. The line colors correspond to the five groups obtained by PCA clustering. The color of the outer ring represents the province. (b) PCA results of 181 PWNs (GD and GX) based on 998 SNP molecular markers. (c) PCA results of 241 PWNs (GD, GX, and JS) based on 996 SNP molecular markers. (d) Genetic structures of pine wood nematode populations. The ancestral component ratio fits best when K = 9. The length of each colored segment represents the fraction of individual genomes in the K = 9 ancestral population. Sample IDs are below, and cat stands for cluster taxa.
Figure 4.
Population genetics of pine wood nematodes in GD, GX, and JS Provinces. (a) Maximum likelihood phylogenetic tree of 241 pine wood nematode strains based on 996 SNP molecular markers. The line colors correspond to the five groups obtained by PCA clustering. The color of the outer ring represents the province. (b) PCA results of 181 PWNs (GD and GX) based on 998 SNP molecular markers. (c) PCA results of 241 PWNs (GD, GX, and JS) based on 996 SNP molecular markers. (d) Genetic structures of pine wood nematode populations. The ancestral component ratio fits best when K = 9. The length of each colored segment represents the fraction of individual genomes in the K = 9 ancestral population. Sample IDs are below, and cat stands for cluster taxa.
Figure 5.
Introgression analysis revealed possible B. xylophilus migration routes in three provinces. (a) The potential migration pathways between two gene transfer events, Group A to Group B-GX and Group E-GD to Group D-GD. (b) Treemix residual heat maps.
Figure 5.
Introgression analysis revealed possible B. xylophilus migration routes in three provinces. (a) The potential migration pathways between two gene transfer events, Group A to Group B-GX and Group E-GD to Group D-GD. (b) Treemix residual heat maps.
Figure 6.
Geographic origins and taxonomic relationships of 241 PWN strains. The arrows represent two possible migration events (different colors and shapes represent different clustering groups. (1) Group A to Group B-GX. (2) Group E-GD to Group D-GD).
Figure 6.
Geographic origins and taxonomic relationships of 241 PWN strains. The arrows represent two possible migration events (different colors and shapes represent different clustering groups. (1) Group A to Group B-GX. (2) Group E-GD to Group D-GD).
Figure 7.
Population polymorphism Pi analysis of the five groups.
Figure 8.
θπ and Fst ratios for Groups B and C.
Figure 9.
GO enrichment map of candidate genes in selective elimination analysis of Group B (adjusted p-value ≤ 0.05).
Figure 9.
GO enrichment map of candidate genes in selective elimination analysis of Group B (adjusted p-value ≤ 0.05).
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MDPI and ACS Style
Feng, Y.; Jian, W.; Ding, X.; Ye, J. Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Guangdong, Guangxi, and Jiangsu Provinces in China. Forests 2024, 15, 934. https://doi.org/10.3390/f15060934
AMA Style
Feng Y, Jian W, Ding X, Ye J. Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Guangdong, Guangxi, and Jiangsu Provinces in China. Forests. 2024; 15(6):934. https://doi.org/10.3390/f15060934
Chicago/Turabian StyleFeng, Yuan, Wenjing Jian, Xiaolei Ding, and Jianren Ye. 2024. "Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Guangdong, Guangxi, and Jiangsu Provinces in China" Forests 15, no. 6: 934. https://doi.org/10.3390/f15060934
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