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Article

Genetic Structure and Historical Dynamics of Pinus densiflora Siebold & Zucc. Populations

by
Zhiyuan Jia
1,
Tao Yu
2,
Bin Jiang
3,4,
Xin Song
3,4 and
Junqing Li
1,*
1
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
2
Eco-Product Development Research Center, China Energy Conservation and Environmental Protection Group, Beijing 100084, China
3
Kunyu Mountain National Nature Reserve Administration, Yantai 264113, China
4
Yantai Kunyu Mountains Forest Farm, Yantai 264113, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(12), 2078; https://doi.org/10.3390/f13122078
Submission received: 15 November 2022 / Revised: 2 December 2022 / Accepted: 3 December 2022 / Published: 6 December 2022
(This article belongs to the Section Genetics and Molecular Biology)
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Abstract

:
The study of population genetic structure and historical dynamics of species with disjunct distribution can reveal the mechanisms through with they were formed. Pinus densiflora is an essential tree species with ecological and economic value, and its natural distribution shows a disjunct pattern. Using transcriptome-level SNP data from 220 samples representing 32 naturally-distributed populations in East Asia, we investigated Pinus densiflora genetic diversity and structure, divergence time, and ancestral distribution. We identified five subpopulations which diverged approximately 2.02–1.49 million years ago, and found relatively low genetic differentiation among the three large subpopulations (SL, JH, and JK). Northeast China is the most likely origin, and its current distribution is the result of dispersal and vicariance events. It migrated southwest through the Liaodong Peninsula to the Shandong Peninsula and southeast through the Korean Peninsula to Japan. These results provide a basis for the conservation and management of P. densiflora in the future and the evolutionary study of species with similar life histories.

1. Introduction

During the Quaternary Pleistocene period, repeated changes in sea level due to climatic fluctuations resulted in intermittent connection and separation between eastern China, the Korean Peninsula, and the Japanese Archipelago in East Asia [1,2,3]. These processes have implications for interpopulation connectivity, species dispersal, geography, reproductive systems, and historical migration patterns that drive evolution and population genetic structure [4,5,6,7]. Although there have been many studies investigating species with discontinuous distribution in East Asia [8,9], Pinaceae species in this region have been of little interest. It remains unclear how this model species’ interpopulation migration and dispersal occurs. Did land bridges play an essential role in its migration and dispersal in this region?
With the development of next-generation sequencing technology, molecular data have become faster and easier to obtain, and many studies have combined molecular with geographic and environmental data to create phylogeographic histories. Phylogeography enables exploration of a population’s genetic structure and evolutionary history [10]. In East Asia, the genesis and historical and evolutionary dynamics of many species with discontinuous distributions have been studied using phylogenetic methods [8,11]. The barriers between the East China Sea and the Korea Strait have played a vital role in genetic differentiation between populations [12,13,14,15], and the Donghai and Korea Strait land bridges have provided corridors during dispersal and migration of both plants and animals [8,9,16,17,18]. RAPD (random amplified polymorphic DNA), AFLP (amplified fragment length polymorphism), and SSR (simple sequence repeats or microsatellites) were primarily used as molecular markers in these earlier studies. However, after years of development, SNPs (single nucleotide polymorphisms) quickly became the standard markers for assessing genetic diversity and elucidating phylogenetic relationships between populations [19]. SNP markers have been used to study the evolutionary history of species including ginkgo [20], chrysanthemum [21], and spruce [22]. However, obtaining SNPs from large-genome species using whole-genome sequencing is difficult, and transcriptome sequencing is the preferred method to isolate SNPs in species with large genomes. This approach has been used widely in phylogenetic and evolutionary studies [23,24].
Pinaceae plants play a vital role in the composition of biological communities in the northern hemisphere [23], and P. densiflora is an essential species with ecological and economic value in Pinaceae [25,26]. The natural distribution of P. densiflora is primarily within the Shandong Peninsula and Northeast China, the Korean Peninsula, Japan, and the Russian Far East [26,27,28], and its distribution is discontinuous in the Shandong and Liaoning Peninsulas, Japan, Northeast China, and the Korean Peninsula. Earlier studies of P. densiflora have focused on its physical and chemical properties [29,30,31,32], while studies of its genetic diversity and population structure have relied on a single nature reserve or country as a study unit. Genetic structure has been observed among populations in China [33], South Korea [26], and Japan [25], but the genetic structure and differentiation among disjunct populations in East Asia, and their migration, have not yet been investigated.
Due to the large genome of P. densiflora, many samples’ sequencing increased the huge cost and computational volume, so we used transcriptome sequencing to obtain molecular data. In this study, we used transcriptome sequencing data from 220 P. densiflora samples collected in East Asia to reveal the evolutionary processes impacting its distribution and answer the following questions: (1) What is the genetic structure of P. densiflora populations in East Asia? (2) What is the degree of genetic differentiation among these populations? (3) When did P. densiflora subpopulations diverge? and (4) How did P. densiflora migrate?

2. Materials and Methods

2.1. Sampling and Transcriptome Sequencing

We collected 220 individuals from 32 P. densiflora populations (16 in China, 7 in Japan, and 9 in Korea) (Figure 1). Selected trees were spaced >50 m apart, well-grown, and free of diseases and insects. Total RNAs were extracted from fresh shoots of the current year branches. RNA libraries and paired-end raw reads (2 × 150 bp) were generated using an Illumina HiSeq X-ten platform by BioMarker Co. (Beijing, China). Clean reads were obtained by removing adapter reads, ploy-N, and low-quality reads from raw data using Perl, resulting in approximately 6 Gb reads per individual.

2.2. Mapping, SNP Calling, and Filtering

STAR v2.7.0 [34] was used to map clean reads to a P. taeda reference genome v2.01 (https://treegenesdb.org/FTP/Genomes/Pita/v2.01/genome/) (accessed on 31 October 2020) and sort them in BAM format. Read group information was added, PCR duplicates were removed by Picard-tools (http://broadinstitute.github.io/picard/ (accessed on 21 December 2020)), and files were indexed using Samtools [35]. The span introns were reformatted by GATK v.4.1.4 with the command “SplitNCigarReads” [36]. A genomic variant call format (gVCF) file was generated for variants in each sample with “HaplotypeCaller”. All individual files were combined into a single VCF file using Bcftools (http://samtools.github.io/bcftools/howtos/publications.html (accessed on 21 December 2020)). Using VCFtools v.0.1.15 [37] and Plink v1.9 [38], we set minimum read depth to 10, minimum mapping quality to 30, maximum missing to 20%, and minor allele frequency to 5%, and removed indels, keeping only biallelic SNPs. Further, to minimize linkage disequilibrium (LD), we filtered out SNPs with correlations above 0.2 in a 50 kb window every 10-SNPs sliding step. In total, 8376 SNPs were retained for analyses.

2.3. Summary Statistics and Population Structure

We used the 8376 SNP dataset to calculate nucleotide diversity (θπ), observed heterozygosity (HO), expected heterozygosity (HE), and inbreeding coefficient (FIS) for each population using VCFtools v0.1.15 [37]. Pairwise population differentiation (FST) were calculated using Arlequin v3.5.2 [39]. Population structure and admixture were evaluated using Structure v2.3.1 [40] with clustering numbers (K) ranging from one to ten, a burn-in period of 10,000 and 100,000 Markov Chain Monte Carlo (MCMC) iterations based on ten runs per clustering number (K). The optimal K was determined using Structure Harvester [41] delta K statistics. The function bar plots visualized structure output using R package Pophelper 2.3.1 [42].
Principal component analysis (PCA) was used to study the relatedness of populations and individuals [43]. It was performed using Plink 1.9 [38]. The top 10 feature vectors were extracted, and their relative contributions were calculated. The complex multivariate information contained in the SNPs was downscaled into three comprehensive variables (PC1, PC2, PC3), and scatter plots were drawn to describe the relationships among populations of P. densiflora.
Neighbor-joining (NJ) trees were constructed based on 8376 SNP at the individual level using MEGAX software [44] and Figtree v.1.4.4 software (http://tree.bio.ed.ac.uk/software/Figtree/ (accessed on 16 May 2021)) was used for visualization.

2.4. Population Divergence Time Estimation

The P. sylvestris L. var. mongolica Litv. from Zhangwu, Inner Mongolia, was selected as the outgroup, and its VCF file was obtained by identifying and screening SNPs, and processing them using the method described above. The phylogenetic tree was constructed using the NJ method.
The divergence time of P. densiflora populations was estimated using the MCMCTree and BASEML programs in paml4.9j [45] with the time unit set to 100 MYA. We used the age of the common ancestor of P. densiflora and the outgroup to calibrate stem age (1.5, 12.9 MYA) [46,47,48,49,50,51] and a fossil node within the P. densiflora population to calibrate crown age (0.86, 1.75 MYA) [49]. The clock variable was one, and the substitution model used GTR + G (model = 7) to execute the procedure. The process sampled 500,000 times, with sampling frequency set to 100 after a burn-in of 10,000,000 iterations. Tracer1.7.1 software [52] was used to check for convergence of the run results and ensure that each parameter’s effective sample size (ESS) was >200. Figtree1.4 was used to visualize tree files.

2.5. Reconstruction of the Ancestral Distribution

To reconstruct the possible ancestral ranges of P. densiflora, we performed biogeographic inference using RASP (Reconstruct Ancestral State in Phylogenies) [53]. The input file was used as the phylogenetic tree file with time calibration generated by Paml4.9j. Six geographic regions were selected based on locations of P. densiflora. A represented Liaoning Peninsula, B represented Shandong Peninsula, C represented Jilin and Heilongjiang, D represented South Korea, E represented the Japanese Archipelago, and F represented the outgroup. We tested six models in RASP, including the likelihood version of Dispersal–Vicariance Analysis (“DIVALIKE”), LAGRANGE’s Dispersal–Extinction–Cladogenesis (DEC) model, and BAYAREA, as well as “+J” versions of these models. The maximum number of regions was set to six. The best model was selected as the posterior for event simulation based on AICc values.

3. Results

3.1. SNPs Statistics

We obtained 1.32 Tb clean data after initial quality control, with sequencing quality scores of 85% for each sample. A total of 18,500,983 loci were identified using SNP calling. These loci were filtered through quality control, yielding 209,690 SNPs. Finally, 8376 SNPs were identified for downstream analysis by removing the minimum allele frequency within 0.05 and linkage disequilibrium loci.

3.2. Population Genetic Diversity and Structure

We used three approaches to reveal genetic structure and relationships among P. densiflora populations. The genetic structure analysis of 32 P. densiflora populations showed that the best K value was five (Figure 2). When K = 5, the P. densiflora were divided into three subpopulations (SL, JH, and CH) in China, and Japan and Korea were divided into two subpopulations (JP and JK). In China, Shandong and Liaoning were combined into one cluster (SL), Jilin and Heilongjiang were combined into one cluster (JH), and Antu in Jilin was one cluster (CH). In Japan, the KNO population was one cluster (JP), and the remaining populations in Japan were combined into one cluster with all of the populations in Korea (JK). The PCA revealed five clusters in the P. densiflora populations, where individuals in subpopulation JH were admixed with individuals in JK, individuals in subpopulations CH and JP were more dispersed. Some individuals were admixed in subpopulations JK and JH (Figure 3). Additionally, the NJ method (Figure 4) showed three major branches, consistent with the structure and PCA analysis results. However, the two subpopulations (CH and JP) analyzed using the first two methods were not well represented in the phylogenetic tree, neither of them clustered into separate clusters in the phylogenetic tree, and even some individuals clustered with JH and some individuals clustered with JK, respectively.
Based on the 8376 SNPs screened, the θπ of individual populations of P. densiflora ranged from 0.0031 (TFZ, HL, and HHO) to 0.0067 (GTJ) with a mean value of 0.0039. The mean HO and HE were 0.3778 and 0.2594, respectively. HO fluctuated from 0.3188 (GBC) to 0.5046 (AT), and HE fluctuated from 0.2583 (GTJ) to 0.2609 (AT). All FIS values were negative (mean is −0.4560), and varied between −0.9320 (AT) and −0.2319 (GBC) (Table 1). FST values ranged from 0 to 0.118 (Table S1), with the highest and significant divergence between HNH and LTS populations.
We calculated the genetic diversity of the five subpopulations based on their genetic structure (Table 2). θπ varied between 0.0035 (JH) and 0.0040 (JK), with an HO maximum of 0.2953 (CH) and minimum of 0.1842 (JK), and HE between 0.1496 (SL and JH) and 0.1522 (JK). FIS was negative for all five subpopulations, with two separate subpopulations having values very close to −1. FST varied from 0.006 to 0.087 between subpopulations and reached moderate divergence between JH and JP, JH and CH, JK and CH, and JP and JK. Moreover, they were all significantly differentiated (p < 0.05) (Table 3).

3.3. Time of Differentiation

The stem age of P. densiflora was 2.12 (1.84, 2.42) MYA (Figure 5). At 2.02 (1.76, 2.32) MYA, P. densiflora split into two branches, one becoming the Liaoning and Shandong Peninsula populations and the other becoming the northeastern China, Korean Peninsula, and Japan populations. P. densiflora of the Shandong Peninsula separated from P. densiflora of the Liaoning Peninsula at 2 (1.73, 2.29) MYA, and the Jilin and Heilongjiang populations in China separated from the Japan and Korean Peninsula populations at 1.58 (1.38, 1.82) MYA. The Korean Peninsula population separated from the Japan population at 1.49 (1.29, 1.70) MYA.

3.4. Reconstruction of Ancestral Distribution

The DIVALIKE + J model was the best model (LnL = −194.9, AICc = 395.9) (Table S2). The ancestral distribution area simulation showed that P. densiflora experienced 42 dispersal events and 41 vicariance events (Figure 6). Three dispersal and two vicariance events at the base node influenced the distribution pattern, and these events were traced to the possible regions of origin. One dispersal and one vicariance event occurred at node 439. This node showed two possible ancestral distribution ranges, AC (Liaoning Peninsula and Jilin and Heilongjiang regions, i.e., Northeast China) and C (Jilin and Heilongjiang). The probability of occurrence in each range was 50%. Dispersal events showed that P. densiflora spread from C to the A (Liaoning Peninsula) and then differentiated due to geographic separation. Node 437 showed three possible ancestral distribution areas, AC, A, and C, and the probability of occurrence in each range was 33%. A dispersal event occurred at node 437, indicating that P. densiflora spread from A to C. A vicariance event occurred at node 428. The ancestral distribution of this node was AC, and the probability of occurrence in this range was 100%. At this time, P. densiflora had geographically separated between A and C.
We traced the migration history of P. densiflora across each geographic plate. At node 413, A was the ancestral distribution of P. densiflora, and the probability of occurrence in this range was 100%. Two dispersal events occurred at node 413, in which firstly spread from A to B (Shandong Peninsula) and then spread to each other. Two vicariance events occurred at nodes 411 and 412 which led to the divergence of A and B populations. At node 368, the ancestral distribution was C, and the probability of occurrence in this range was 100%. Two dispersal events occurred at node 368, from C to A and from C to D (Korean Peninsula). The ancestral distribution range of node 361 was CD (Jilin and Heilongjiang regions and Korean Peninsula), and the probability of occurrence in this range was 100%. One vicariance event occurred at node 361, and P. densiflora in A differed from D. The ancestral distribution range of node 327 was D, and the probability of occurrence in this range was 100%. A dispersal event occurred from D to E (Japan Archipelago). The ancestral distribution range of node 323 was DE (the Korean Peninsula and the Japan Archipelago), and the probability of occurrence in this range was 100%. At this time, a vicariance event occurred, leading to differentiation of P. densiflora in D and E. In addition to the events described, complex dispersal and vicariance events also occurred after the formation of P. densiflora on various plates.

4. Discussion

4.1. Population Genetic Structure

Population genetic structure can reflect genetic variation [54]. Three methods of analysis of the genetic structure of 32 populations showed five subpopulations of P. densiflora (SL, CH, JH, JP, and JK) (Figure 2, Figure 3 and Figure 4). The genetic differentiation among the three largest subpopulations (SL, JH, and JK) was not high. However, the genetic differentiation between the two separate subpopulations (CH and JP) was relatively high for JH and JK, respectively. The genetic differentiation among the 32 populations was not high, but four populations (QD, LTS, AT, and KNO) and most Japanese populations were moderately differentiated. A population study by Iwaizumi et al. [25] in 62 natural distribution areas of P. densiflora in Japan showed three main clusters corresponding to western, central, and northeastern populations. A study of 60 P. densiflora populations in South Korea also found genetic structure, and the genetic differentiation between populations was relatively low [26]. The weak population genetic structure of P. densiflora may be related to its evolutionary history and biological characteristics. Historically, its long-term evolution was affected by both environmental and human factors, and oscillating ecological changes, human activities, and agricultural land-use changes affected the distribution of P. densiflora [25]. As P. densiflora is a wind-pollinated and outcrossing plant, long-distance gene flow via pollen can weaken genetic differentiation among populations, but gene flow may be reduced due to longer distances between populations [55]. Subpopulations CH and JP were composed of single populations and comparatively differed from subpopulations JH and JK, which may be related to their geographical location and population size. We noted that subpopulation JP is in the marginal zone and CH is in an area with low population size. Populations with relatively connected distributions received more frequent gene flow than those in the marginal zone, and that differences in population size could also cause asymmetry in gene flow. Therefore, further studies on the relationship between genetic differentiation and geography, environmental isolation, and gene flow are needed to better understand P. densiflora evolution.

4.2. Population Genetic Diversity

Population genetic diversity is the product of long-term evolution [56]. Genetic diversity of other species in the Pinaceae family, including P. yunnanensis (0.0018) [57], Platycladus orientalis (0.0029) [58], and Picea var. rubescens (0.0026) [22] was lower than that of P. densiflora. Similarly, the HE (0.2594) was higher than that of P. sylvestris (0.167) [59] and P. tabulaeformis (0.206) [60] but lower than that of P. koraiensis (0.352) [55]. Therefore, the genetic diversity (θπ = 0.0039) of P. densiflora was relatively high among conifer species. All FIS were negative, indicating that the breeding mode of the P. densiflora was primarily hybridization and individuals received foreign pollen, which was consistent with its biology (wind pollination and outcrossing) [61,62]. This was also the cause of the P. densiflora HO higher than HE.

4.3. Divergence Time and Ancestral Range

Land bridges play a crucial role in connecting species distributed between islands. The divergence of P. densiflora and P. sylvestris L. var. mongolica Litv. occurred approximately 2.12 MYA, more recently than that of P. densiflora and P. sylvestris [24]. At approximately 1.49–2.02 MYA, P. densiflora populations among the five plates (Shandong Peninsula, Liaoning Peninsula, Jilin and Heilongjiang, Korean Peninsula, and Japanese Islands) diverged. The plates between China’s continental and Japan separated millions of years ago [63,64], much earlier than P. densiflora speciated. P. densiflora is now present in China, the Korean Peninsula, and Japan, so the current distribution is likely the result of migration. RASP results indicated that the distribution in northeast China was the ancestral distribution, and this population migrated through the Liaoning Peninsula to the Shandong Peninsula and southeast to the Korean Peninsula and Japan. Periodic climatic fluctuations in the Quaternary included many glacial periods, and roads and bridges appeared many times during the Pleistocene period [65,66] connecting some areas more typically blocked by the sea [11]. In particular, the emergence of the Bohai and the Korea Strait land bridges provided essential opportunities for migration to the Shandong Peninsula and Japan. A study of the Picea jezoensis, similar in life history and distribution to P. densiflora, found that some individuals spread from the Korean Peninsula and Russia via land bridges [16]. Northeast China, the Korean Peninsula, and Japan were connected by land bridges, and they played an essential role in their colonization and evolution of Japanese frogs (Pelophylax nigromaculatus) [67]. However, because the SNPs obtained by transcriptome sequencing are not necessarily neutral, and some populations have few numbers, these may affect the population history inference results of P. densiflora. Therefore, we will remove selected SNP loci and increase the sample size to further study the population history of Pinus sylvestris in the future.

5. Conclusions

This study was the first discussion of P. densiflora population genetic structure and dynamic history based on the transcriptome sequencing data. P. densiflora had higher genetic diversity than other conifer species, and the phenomenon of population genetic structure existed in them. However, the degree of genetic differentiation among subpopulations was not high. The divergence time between each plate was between 1.49 and 2.02 MYA. Northeast China was likely the origin region, and land bridges played a crucial role in establishing the current distribution of the species. P. densiflora in the Liaoning Peninsula spread to the Shandong Peninsula through the land bridges of Bohai, and in Japan, spread from the Korean Peninsula. In the future, it is necessary to strengthen historical research on P. densiflora. For example, samples from North Korea and Russia should be added to analyses of the origin of P. densiflora, and multiple methods should be used to analyze historical dynamics and reveal evolutionary history.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f13122078/s1, Table S1: FST and p-values of 32 Pinus densiflora populations; Table S2: RASP models tested.

Author Contributions

Conceptualization, Z.J.; Methodology, Z.J. and T.Y.; Formal analysis, Z.J.; Investigation, Z.J., B.J., and X.S.; Writing—Original Draft Preparation, Z.J.; Supervision, J.L.; Funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Kunyu Mountain National Nature Reserve Administration.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We would like to acknowledge all the people who helped us with the sampling.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of Pinus densiflora sample collection. Each red point represents a sampled population, and the italicized black words are the names of each population.
Figure 1. Locations of Pinus densiflora sample collection. Each red point represents a sampled population, and the italicized black words are the names of each population.
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Figure 2. Results of Bayesian clustering analysis using Structure. (a) The x-axis shows populations, and the y-axis quantifies the proportion of inferred ancestral lineages. SL, JH, CH, JP, and JK are the subpopulation names at K = 5, respectively. The colors represent ancestral components. (b) The best K = 5 value for all 32 populations obtained by the Evanno method. The x-axis represents the K value, and the y-axis represents the delta K value.
Figure 2. Results of Bayesian clustering analysis using Structure. (a) The x-axis shows populations, and the y-axis quantifies the proportion of inferred ancestral lineages. SL, JH, CH, JP, and JK are the subpopulation names at K = 5, respectively. The colors represent ancestral components. (b) The best K = 5 value for all 32 populations obtained by the Evanno method. The x-axis represents the K value, and the y-axis represents the delta K value.
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Figure 3. Principal component analysis of Pinus densiflora. The proportion of variance explained by PC1, PC2, and PC3 were 40.07%, 10.81%, and 8.74%, respectively. (a) for PC1 and PC2, (b) for PC1 and PC3. Symbol shapes represent samples from different countries, circles are individuals in China, triangles are individuals in Japan, and squares are individuals in Korea. Five colors represent different subpopulations: blue for CH, gray for JH, red for JK, green for JP, and purple for SL.
Figure 3. Principal component analysis of Pinus densiflora. The proportion of variance explained by PC1, PC2, and PC3 were 40.07%, 10.81%, and 8.74%, respectively. (a) for PC1 and PC2, (b) for PC1 and PC3. Symbol shapes represent samples from different countries, circles are individuals in China, triangles are individuals in Japan, and squares are individuals in Korea. Five colors represent different subpopulations: blue for CH, gray for JH, red for JK, green for JP, and purple for SL.
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Figure 4. A neighbor-joining phylogenetic tree of Pinus densiflora. Five colors represent different subpopulations: red for CH, blue for JH, green for JK, purple for JP, and orange for SL.
Figure 4. A neighbor-joining phylogenetic tree of Pinus densiflora. Five colors represent different subpopulations: red for CH, blue for JH, green for JK, purple for JP, and orange for SL.
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Figure 5. Divergence times of Pinus densiflora based on MCMCTree. Horizontal bars at nodes indicate 95% credible intervals of the divergence time estimates. Each color represents a geographic plate. The numbers on the phylogenetic tree represent age and the unit is one million years. Different colors represent Pinus densiflora samples from different geographic plates, consistent with the RASP analysis. Blue represents outgroups, orange represents samples from Heilongjiang and Jilin, purple represents samples from Shandong, red-purple represents samples from Liaoning Province, red represents samples from Korea, and gray-green represents samples from Japan.
Figure 5. Divergence times of Pinus densiflora based on MCMCTree. Horizontal bars at nodes indicate 95% credible intervals of the divergence time estimates. Each color represents a geographic plate. The numbers on the phylogenetic tree represent age and the unit is one million years. Different colors represent Pinus densiflora samples from different geographic plates, consistent with the RASP analysis. Blue represents outgroups, orange represents samples from Heilongjiang and Jilin, purple represents samples from Shandong, red-purple represents samples from Liaoning Province, red represents samples from Korea, and gray-green represents samples from Japan.
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Figure 6. Ancestral areas of Pinus densiflora estimated under the DIVALIKE + J model using RASP. The numbers on the phylogenetic tree represent nodes, pie charts on the nodes indicate the relative probabilities of all possible geographical ranges, and the different node colors represent the corresponding geographical plates. The geographic plates represented by the letters in parentheses at the end of the phylogenetic tree are consistent with the meaning of the pie chart colors. Red-purple for A, Liaoning Peninsula; purple for B, Shandong Peninsula; light red for C, Jilin and Heilongjiang regions; red for D, Korean Peninsula; gray-green for E, Korean Peninsula, and blue for F, Zhangwu, Inner Mongolia. The five subgroups are marked in the vertical bar on the right.
Figure 6. Ancestral areas of Pinus densiflora estimated under the DIVALIKE + J model using RASP. The numbers on the phylogenetic tree represent nodes, pie charts on the nodes indicate the relative probabilities of all possible geographical ranges, and the different node colors represent the corresponding geographical plates. The geographic plates represented by the letters in parentheses at the end of the phylogenetic tree are consistent with the meaning of the pie chart colors. Red-purple for A, Liaoning Peninsula; purple for B, Shandong Peninsula; light red for C, Jilin and Heilongjiang regions; red for D, Korean Peninsula; gray-green for E, Korean Peninsula, and blue for F, Zhangwu, Inner Mongolia. The five subgroups are marked in the vertical bar on the right.
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Table
Table 1. Genetic diversity of 32 Pinus densiflora populations.
Table 1. Genetic diversity of 32 Pinus densiflora populations.
PopulationHOHEθπFIS
KYS0.4327 0.2587 0.0032 −0.6725
YS0.4356 0.2596 0.0039 −0.6781
WH0.4273 0.2595 0.0040 −0.6467
QD0.4311 0.2596 0.0041 −0.6603
RZ0.4513 0.2596 0.0041 −0.7382
DD0.4388 0.2597 0.0037 −0.6895
FS0.4225 0.2600 0.0037 −0.6249
XRD0.4251 0.2598 0.0036 −0.6361
DN0.3673 0.2596 0.0034 −0.4149
ML0.3844 0.2596 0.0044 −0.4804
TFZ0.3411 0.2595 0.0031 −0.3144
DGZ0.3459 0.2598 0.0037 −0.3317
HL0.3459 0.2594 0.0031 −0.3334
TH0.3645 0.2596 0.0035 −0.4043
AT0.5046 0.2609 0.0039 −0.9320
LTS0.4970 0.2599 0.0040 −0.9106
KNO0.4716 0.2590 0.0039 −0.8209
HWT0.3484 0.2592 0.0032 −0.3441
HIH0.3332 0.2590 0.0033 −0.2865
HHO0.3444 0.2593 0.0031 −0.3282
HOT0.3536 0.2594 0.0033 −0.3629
HNM0.3312 0.2591 0.0034 −0.2781
HNH0.3430 0.2591 0.0032 −0.3242
GHB0.3349 0.2592 0.0042 −0.2919
GHM0.3232 0.2592 0.0042 −0.2470
CYS0.3237 0.2594 0.0044 −0.2476
GTJ0.3240 0.2583 0.0067 −0.2542
Mean0.3778 0.2594 0.0039 −0.4560
Note: θπ, nucleotide diversity; HO, observed heterozygosity; HE, expected heterozygosity; FIS, fixation index.
Table
Table 2. Genetic diversity of five Pinus densiflora subpopulations.
Table 2. Genetic diversity of five Pinus densiflora subpopulations.
GroupHOHEθπFIS
SL0.2441 0.1496 0.0038 −0.6327
JH0.1952 0.1496 0.0035 −0.3048
CH0.2953 0.1489 0.0039 −0.9881
JP0.2936 0.1508 0.0039 −0.9528
JK0.1842 0.1522 0.0040 −0.2099
Table
Table 3. FST and p-values for the five Pinus densiflora subpopulations.
Table 3. FST and p-values for the five Pinus densiflora subpopulations.
GroupSLJHCHJPJK
SL 0.0000.1000.0050.000
JH0.018 0.0000.0020.004
CH0.0160.062 0.3030.000
JP0.0330.0540.01 0.000
JK0.020.0060.0870.067
Note: FST values are below the diagonal and p-values are above the diagonal.
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Jia, Z.; Yu, T.; Jiang, B.; Song, X.; Li, J. Genetic Structure and Historical Dynamics of Pinus densiflora Siebold & Zucc. Populations. Forests 2022, 13, 2078. https://doi.org/10.3390/f13122078

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Jia Z, Yu T, Jiang B, Song X, Li J. Genetic Structure and Historical Dynamics of Pinus densiflora Siebold & Zucc. Populations. Forests. 2022; 13(12):2078. https://doi.org/10.3390/f13122078

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Jia, Zhiyuan, Tao Yu, Bin Jiang, Xin Song, and Junqing Li. 2022. "Genetic Structure and Historical Dynamics of Pinus densiflora Siebold & Zucc. Populations" Forests 13, no. 12: 2078. https://doi.org/10.3390/f13122078

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