Research Progress on Iron-Heart *Cunninghamia lanceolate*

*Ninghua Zhu, Xiaowei Yang, Zhiqiang Han and Xiao Can*

### **Abstract**

*Cunninghamia lanceolate* (Lambert.) Hooker is one of the main fast-growing timber forest species in southern China which has a long history of cultivation and spreads across 28 provinces, cities, and regions. Recently, a variant of fir was discovered in the Xiaoxi National Nature Reserve in Hunan Province. The heartwood is hard as iron and its ratio is more than 80%, with the especial character of anti-corruption. It is a natural germplasm resource, called Iron-heart *Cunninghamia lanceolate*. Study on it is still in the stage of data accumulation. In this paper, we studied it from three points as follows: (1) Plus tree selection and construction of germplasm resources nursery. (2) Study on cone and seed quality. (3) Genetic structure analysis of natural population. The research of Iron-heart *Cunninghamia lanceolate* lays a theoretical foundation for the protection, development, and utilization of the black-heart wood germplasm resources of Iron-heart *Cunninghamia lanceolate* in the future.

**Keywords:** germplasm collection, plus tree selection, seed and cone quality, genetic diversity

### **1. Introduction**

The Chinese fir, *Cunninghamia lanceolate* (Lambert) Hooker, belongs to the *Cupressaceae* family, which is the family with the largest number of genera among Gymnospermae and includes a number of other significant species in particular, Taiwania Hayata, Cryptomeria D. Don, Glyptostrobus Endl, etc. [1, 2]. As an evergreen coniferous tree species, *C. lanceolate* is native to northern Vietnam and southern China. Because of its desirable wood properties, fast growth, and high disease resistance, *C. lanceolate* has been widely grown in China for 3000 years [3–5]. Recently, a unique natural wild variety of Chinese fir with a high ratio of heartwood and high wood quality was inadvertently found in provenance. Importantly, this Chinese fir has a high corrosion prevention property compared to other species, its wood is dark, and native people use it to make furniture, buildings, and even coffins [6, 7].

The study of cone and seed morphological characteristics of Iron-heart *Cunninghamia lanceolate* is helpful to master phenotypic diversity and formulate population protection strategies [8]. Selecting the best family for seed collection and seedling breeding has a key impact on improving the quality of Iron-heart China fir seedlings [9, 10]. Wild plants are important gene resources for breeding excellent varieties, so it is more important to study the genetic diversity and variation of wild populations [11].

For the germplasm resources of *Cunninghamia lanceolate*, the most common way is to preserve them by ex-situ conservation [12], and the establishment of germplasm collection area of *Cunninghamia lanceolate* is usually realized by grafting. Combining the conservation and application of germplasm resources in the nursery, on the one hand, the improved varieties were screened and preserved by selecting the best in the experimental area, on the other hand, the germplasm resources bank was enriched and high-quality breeding materials were provided. At present, in the field of science, the conservation and application of germplasm resources have been adopted by seed banks and gene banks in most countries, which can be summarized as "two less and one rich", with less use area, less funds, and rich germplasm resources [13]. In addition, the rapid development of modern biotechnology makes it possible to use tissue culture in vitro preservation of *Cunninghamia lanceolate*. In a word, we can take a variety of forms to achieve the preservation of Chinese fir germplasm resources, but we should consider different places, depending on the situation, choose the best way to collect and preserve high-quality resources.

Determining genetic diversity and population structure, which are important for characterizing germplasm under investigation, constitute important steps in plant breeding [14, 15]. However, due to the impact of agricultural climate change, morphological characteristics provide limited genetic information [16]. Therefore, molecular markers unaffected by environmental changes are necessary to estimate genetic diversity and population structure [17, 18]. Based on molecular markers, genetic diversity analysis, germplasm characterization and evolution studies have been possible in the last 30 years [19, 20]. Molecular markers, such as restriction fragment length polymorphism (RFLP), random-amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), inter-simple sequence repeat (ISSR) and simple sequence repeat, microsatellite (SSR), have previously been used to study the genetic diversity and population structure of cultivated and natural breeding populations of many conifers [21]. SSR markers, which are relatively abundant, inexpensive, and provide more informative than bi-allelic markers, have been used to detect the genetic diversity, population structure, and even genetic relationships among landraces and cultivars of *Cunninghamia lanceolate* [22–24]. Single-nucleotide polymorphisms (SNPs), as a type of third-generation molecular marker with high stability and diversity, are expensive to analyze compared with SSR and AFLP markers [25–27].

#### **2. Research contents**

#### **2.1 Area sampled**

Xiaoxi National Nature Reserve is located in Yongshun County, Western Hunan Province, at the western end of Wuling Mountain. It is located at 110°6<sup>0</sup> <sup>50</sup>″–110° 21<sup>0</sup> <sup>35</sup>″ E and 28°42<sup>0</sup> <sup>15</sup>″–28°53<sup>0</sup> <sup>55</sup>″ N. The annual average temperature is between 11 and 12°C, the frost-free period is 250 days, the annual precipitation is between 1300 and 1400 mm, the parent material of soil is sand shale, the soil fertility is high, the total forest storage is 2,223,500 cubic meters, the area has high mountains, dense forests, crisscross valleys, a wide variety of rare plants, rare birds and wild animals, with more than 1000 species of plants in 94 families. There are nearly 200 species of wild animals in the original secondary forest, including 68 species of national key protected animals such as leopard, clouded leopard, and white-necked long-tailed pheasant [28], which are rare in the world and unique in China. The only surviving evergreen broad-leaved primary secondary forest in the 13 provinces of central and southern China is protected from Quaternary glaciers.

## **2.2 Sampling design**

The Iron-heart *Cunninghamia lanceolate* was listed and numbered. According to the principle of uniform dispersion and a random selection, 33 cones of mother trees (52 years old) were collected in the mother forest in mid-October 2019 and were brought back to Central South University of Forestry and Technology to dry naturally for later use in 10, 2019.

About 35 plus trees of Iron-heart *Cunninghamia lanceolate* were selected. Fresh cuttings are collected and used as materials for the establishment of a germplasm resource nursery. See Appendix **Table S1** for the basic information. The germplasm resource nursery is set up in the Chinese fir test demonstration forest base in Xichong Village, Majiang Town, Chaling County, Zhuzhou City, Hunan Province. It has red soil and good site conditions. The demonstration forest has Guangxi provenance seedlings (Guangxi-2.5) and Fujian provenance vegetative Line cutting seedlings-020 (Fujian-020) and Fujian clone Zhongyuan cutting seedlings-061 (Fujian-061) pure forest of young Chinese fir, and grow well.

In total, 548 Iron-heart *Cunninghamia lanceolate* from nine plots (CTY, JZW-1, JZW-2, JZW-3, LYP-1, LYP-2, LYP-3, LYP-4, and XNC) were collected, covering the entire range of Iron-heart *Cunninghamia lanceolate* from (Appendix **Table S2**) (According to the natural distribution of the natural population of iron-heart Cunn*inghamia lanceolate*, we found that it is concentrated in 9 mountains. Therefore, we divided it into 9 plots for population genetic structure analysis). Growth indexes and morphological parameters were considered as selection criteria for the sampled trees, which were chosen by a dominant comparative and comprehensive evaluation method in typical natural forests. The longitude, latitude, and altitude of each sample were determined using a handheld GPS (WGS-84) (Garmin eTrex Handheld GPS; Garmin). Fresh leaves of each voucher sample were collected in a 10 ml freezing tube, transported back to the laboratory in a liquid nitrogen tank, and deposited at 80°C.

## **2.3 Data sampled**

## *2.3.1 Quality determination of cones and seeds*


count the germination situation once every 5 days, and count the real number of germinated species after 15 days to calculate the germination rate (%). Germination rate = real number of germinated seeds ÷ real number of initial seeds � 100%.

#### *2.3.2 DNA extraction, amplification, and microsatellite genotyping*

A Plant Genomic DNA Kit (TIANGEN Biotech, Beijing, China) was used to extract total genomic DNA. Genomic-SSR polymerase chain reaction (PCR) was performed in a 20 μl reaction volume containing 4.0 μl double-distilled water, 4.0 μl genomic DNA, 10.0 μl 2� Taq Plus PCR MasterMix (TIANGEN, Beijing, China), 1.0 μl forward primer and 1.0 μl reverse primer. The PCR conditions included denaturation for 5 min at 95°C, 30 cycles of 30 s at 94°C, 90 s at the annealing temperature for each SSR marker in the reaction, 1 min at 72°C, and 10 min at 60°C for a final extension. In total, 15 primer pairs with highly polymorphic loci (**Table 1**), for which the clarity and reproducibility of the DNA fragments were amplified, were selected from published papers [29–31].

The forward primer had a universal M13 primer tail and a universal M13 primer fluorescently labeled with 6-carboxy-x-rhodamine, tetramethyl-6 carboxyrhodamine, 6-carboxy-fluorescein, or 5-hexachlorofluorescein. The final PCR products were separated based on capillary electrophoresis fluorescence using an ABI3730xl DNA Analyzer (Genewiz Inc., Beijing, China). The results were analyzed using GeneMarker 1.75 software (SoftGenetics LLC, State College, PA, USA).

#### **2.4 Statistical analyses**

Excel 2019 and R4.0.3, Rstudio software were used for summary processing and nested analysis of variance, Pearson correlation analysis, and principal component analysis. Among them, R4.0.3 calculates the mean value, standard deviation, and coefficient of variation of the seed and cone traits; based on the nest's linear model variance analysis between and within groups differences, and Tukey HSD test; using R package Hmisc 4.4.2 [32] to calculate Pearson Correlation coefficient and p-value, use corrplot 0.84 [33] to draw the correlation graph.

The polymorphism information content (PIC) was used to estimate the allelic variation of SSR by applying the formula PIC = 1-P<sup>n</sup> <sup>i</sup>¼<sup>0</sup>Pi2 , where Pi is the frequency of the ith allele and n is the number of alleles detected for given SSR markers. GenALEx 6.5 [34] was used to estimate the genetic diversity indices of each locus and population.

The genetic diversity and population structure of the accessions were further investigated by analysis of molecular variance (AMOVA) using GenAlEx 6.5. The program STRUCTURE v2.3.4 [35] was used to analyze the genetic structure by employing Bayesian clustering analysis with the admixture model of independent allele frequencies. STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structureHarvester/) was used to evaluate the most likely number (K) of genetic clusters. The data derived from the STRUCTURE analysis were visualized as bar charts and pie charts using ArcMap v10.0 and DISTRUCT v1.1 software [36, 37]. Interpolation of ArcGIS was used to forecast the expected heterozygosity (He) and the private allele frequency (Fp) of all Chinese firs included. The ArcGIS (Esri) program was used to map the distribution of the He of populations and Fp by employing a kriging spherical interpolation method.


> **Table 1.**

*Primary simple sequence repeat primers used in the study.*

## **3. Results**

## **3.1 Cone and seed quality**

In this study, we use 12 traits (germination rate, seed quality, seed length-width ratio, seed length, cone seed extracting percentage, seed width, total cone quality, goodness, cone length, Seed quality (1000), cone length-to-width ratio, cone width) to assess the of quality of cones and of seeds.

## *3.1.1 Differences in seed quality of different families*

The results in **Table 2** (code is the number of different mother trees) show that the variation range of the cone length of Iron-heart *Cunninghamia lanceolate* is 3.15–6.13 cm, the average is 4.66 cm, and the coefficient of variation is 18.97%; the variation range of cone width is 3.56–2.15 cm, the coefficient of variation is 12.18%, and the average is 2.95 cm; the variation range of the total quality of the cone is 1.15–2.40 kg, with an average value of 1.66 kg. The variation range of seed quality is large, between 0.09–0.33 kg, the coefficient of variation is 36.50%, and the average value is 0.20 kg; the cone seed extracting percentage is 5.59–19.02%, and the coefficient of variation is 24.42%, but the overall cone seed extracting percentage is low. Seed quality of Iron-heart *Cunninghamia lanceolate* from different families is quite different. The largest seed length, seed width, and seed length-to-width ratio are 9.77 mm, 5.75 mm, and 3.03 respectively; the smallest ones are 4.36 mm, 2.05 mm, and 1.16 mm, respectively; the average value are 6.31 mm, 2.35 mm, and 2.69 mm; the coefficients of variation are 26.02%, 22.15%, and 26.91%, respectively. The average seed quality of 1000 seeds is 7.06 g, the maximum is 10.54 g; the average of goodness is 67.65%, and the coefficient of variation is 20.53%. The variation range of seed germination rate is 5.33% 63.00%, the coefficient of variation is 52.34%; the seed germination rate of TXS-256 and TXS-234 families is the highest, TXS-29 and TXS -30 is the next; TXS-205, TXS-265, TXS-16 germination rates are all lower than 10%. The quality of cones and seeds of families is different in different traits, so it is impossible to evaluate the quality of cones and seeds from a single character.

The coefficient of variation is the comprehensive performance of the discrete characteristics of phenotypic traits. The greater the coefficient of variation, the greater the degree of dispersion of traits. The coefficient of variation of seed traits of 33 families is between 12.18% and 51.34%, and the coefficient of variation of each trait has a certain difference. From large to small, it is germination rate > seed quality >seed length-width ratio > seed length > cone seed extracting percentage > seed width > total cone quality> goodness> cone length > seed quality(1000) > cone length-to-width ratio > cone width (**Tables 2** and **3**).

The P value associated with total cone quality, seed quality, seed germination rate, seed goodness, seed quality (1000), seed width, and cone length-width ratio was less than 0.001 (see the **Table 4**, variance analysis of 33 iron-heart *Cunninghamia lanceolate*), indicating that these traits varied greatly among families; the P value associated with of the cone-length factor is less than 0.01, and the P values associated with other factors of other characteristics were less than 0.1. There are minor differences, and differences mainly exist between individuals. The F value of 12 seed characteristics varies from 0.757 to 965.1 between families, and the order of size is cone seed extracting percentage (0.757) < cone width (1.591) < seed length to width ratio (1.704) < seed length (1.91) < germination rate (2.87) < cone length (2.885) < seed


*Research Progress on Iron-Heart* Cunninghamia lanceolate *DOI: http://dx.doi.org/10.5772/intechopen.101286*

**Table 2.**

*Cone characteristics (average standard deviation value) of different iron-heart* Cunninghamia lanceolate*.*


#### **Table 3.**

*Seed characteristics (average standard deviation value) of different iron-heart* Cunninghamia lanceolate*.*


#### **Table 4.**

*Variance analysis of 33 iron-heart* Cunninghamia lanceolate*\*.*

quality (3.221) < cone length-to-width ratio (3.845) < total cone quality (5.454) < seed width (14.8) < goodness (22.39) < seed quality (1000, 965.1).

## *3.1.2 Correlation analysis of seed traits of iron-heart* Cunninghamia lanceolate

It can be seen from the **Figure 1** that the seed germination rate of iron-heart *Cunninghamia lanceolate* is positively correlated with the other 8 characteristics except for the total cone quality, seed quality, and cone seed extracting percentage. Among them, the germination rate is positively correlated with the cone length, seed quality, seed length-to-width ratio, and seed length are extremely significantly positively correlated at the level of P < 0.001, and are more correlated with seed width, seed length-to-width ratio, seed quality (1000), and goodness at P < 0.01; There was a very significant negative correlation (r = 0.56, P < 0.001) between cone seed extracting percentage and total cone quality, and a very significant

#### **Figure 1.**

*Correlation analysis of seed and cone characters of iron-heart* Cunninghamia lanceolate*. Note: A–L are: cone length, cone width, cone length-width ratio, total cone quality, seed quality, cone seed extracting percentage, seed length, seed width, seed length-width ratio, seed quality (1000), goodness, and germination rate.*

positive correlation (r = 0.84, P < 0.001) with seed quality, and the correlation between these three traits and the other eight traits was not significant.

### *3.1.3 Comprehensive evaluation of seed quality of iron-heart* Cunninghamia lanceolate

Analyzing the various characteristics that affect the quality of the cones and seeds of the Iron-heart *Cunninghamia lanceolate*, it can be seen from the figure that principal components 1 and 2 can explain 65.8% of the variation (**Figure 2**). Among them, traits A, B, C, G, L has a greater contribution rate to principal component 1, and most of them are cone traits; traits H, J, K, I has a large contribution rate to principal component 2, and most of them are seed traits. The principal component dimensionality reduction method is used to comprehensively evaluate the 12 cones and seed traits of iron-heart *Cunninghamia lanceolate*. It can be seen from the **Table 5** that the cumulative variance contribution rate of the first three main factors can reach 82.30%, which can satisfy the traits of each half-sibling progeny. Therefore, the first three main factors are selected to make a comprehensive evaluation score for iron-heart *Cunninghamia lanceolate*. Take the characteristic value of the main factor as the weight of each index, and multiply each index to obtain the calculation formula of the main factor comprehensive evaluation score:

$$F\_1 = (0.408\mathbf{X}\_1 + 0.308\mathbf{X}\_2 + 0.308\mathbf{X}\_3 + 0.145\mathbf{X}\_5 + 0.106\mathbf{X}\_6 + 0.389\mathbf{X}\_7$$

$$+ 0.286\mathbf{X}\_8 + 0.135\mathbf{X}\_9 + 0.315\mathbf{X}\_{10} + 0.310\mathbf{X}\_{11} + 0.403\mathbf{X}\_{12}) \times \sqrt{2.334} \tag{1}$$

$$F\_2 = (0.156\mathbf{X}\_1 + 0.142\mathbf{X}\_2 + 0.104\mathbf{X}\_3 - 0.124\mathbf{X}\_4 + 0.130\mathbf{X}\_6 + 0.191\mathbf{X}\_7)$$

$$- 0.453\mathbf{X}\_8 + 0.573\mathbf{X}\_9 - 0.417\mathbf{X}\_{10} - 0.369\mathbf{X}\_{11} + 0.160\mathbf{X}\_{12}) \times \sqrt{1.564}$$

$$F\_3 = (0.444\mathbf{1}X\_4 - 0.54\mathbf{1}X\_5 - 0.668\mathbf{X}\_6 + 0.152\mathbf{X}\_7 + 0.161\mathbf{X}\_9) \times \sqrt{1.408} \tag{3}$$

The variance contribution rates of the first three main factors are different. In the comprehensive evaluation of growth traits, the focus of each factor needs to be coordinated. The contribution rates of the three factors are 45.4%, 20.4%, and 16.5% as weights, combined with 3 common factors. The contribution rate and

#### **Figure 2.**

*PCA analysis. Note: A–L are: cone length, cone width, cone length-width ratio, total cone quality, seed quality, cone seed extracting percentage, seed length, seed width, seed length-width ratio, seed quality (1000), goodness and germination rate.*

factor score Fi, refer to the calculation formula of the comprehensive score, the mathematical model of the comprehensive score of seed traits of iron-heart *Cunninghamia lanceolate* can be established:

$$D\_n = F\_1 \times 45.4\% + F\_2 \times 20.4\% + F\_3 \times 16.5\% \tag{4}$$

Using the comprehensive ranking as an indicator, a total of 14 excellent Ironheart Cunninghamia lanceolate were selected with a 40% selection rate (**Table 6**).

#### **3.2 Seed garden construction**

## *3.2.1 Grafting and management of the germplasm resource nursery of Iron-heart* Cunninghamia lanceolate

## *3.2.1.1 Seedling grafting*

Before grafting, we selected high-quality rootstocks to mark and hang tags. The height of the rootstocks was uniformly about 15.6 cm. The rows are 2 m � 2 m, and at least 10 plants should be planted for each clone. After grafting, apply an appropriate amount of organic fertilizer according to the standard of 30–60 t per hectare to promote the growth and development of Iron-heart *Cunninghamia lanceolate* and improve the survival rate, stress resistance, cold resistance, and adaptability of grafted seedlings. The trails are set up in Iron-heart *Cunninghamia lanceolate* germplasm resource nursery, which is mainly used for convenient work such as planting, cultivation, observation, management, and protection. At present, 35 genotypes of superior trees selected from nature reserves are still preserved in the germplasm resource nursery (**Table 7**). In May of the same year, the research team conducted statistics and surveys on the survival rate of grafting.


#### **Table 5.**

*PCA analysis of iron-heart* Cunninghamia lanceolate*.*


#### **Table 6.**

*Comprehensive score and ranking of principal components of 33 black-heart wood Chinese fir.*

#### *3.2.1.2 Statistics of graft survival rate*

The construction of iron-heart *Cunninghamia lanceolate* germplasm resource nursery was uniformly carried out by splitting, and from the results (**Table 8**), the average survival rate of grafting was 83%, among which the minimum survival rate of grafting with number TXS-35 was 50%, and the number TXS-30, the highest


#### **Table 7.**

*The information of the 35 Iron-heart* Cunninghamia lanceolate*.*

survival rate of grafting is 96%. It shows that TXS-30 has a high degree of adherence to the test forest fir, and it is suitable as a material for remote preservation of ironheart *Cunninghamia lanceolate* germplasm. Experiments have proved that it is feasible and suitable to use the test forest of Chinese fir in Majiang Town as a place


#### **Table 8.**

*Statistics of grafting survival rate of iron-heart* Cunninghamia lanceolate*.*

where the iron-heart *Cunninghamia lanceolate* is preserved in a different place, and the method of splitting can realize the clonal reproduction of iron-heart *Cunninghamia lanceolate* and has a higher survival rate.

## **3.3 Analysis of sub-populations genetic structure**

## *3.3.1 Genetic diversity*

The evolutionary potential and adaptation of a species are reflected by its genetic diversity, the more genetic variation a species has, the more adaptive it is. The study of the genetic diversity of iron-heart *Cunninghamia lanceolate* is necessary to understand its biological characteristics. In total, 133 alleles were observed among all samples for 15 polymorphic loci, which is higher than the amount previously reported. This difference may have been caused by the sample size, reproductive properties, and molecular marker characteristics of the species. The microsatellites used in the study yielded moderately to highly variable allele numbers per locus, in which 15 SSR primer pairs generated a total of 133 alleles, with a mean of 8.87 alleles at each locus, ranging from 5 for the contig5410\_1886A locus to 18 for the contig406\_1209 locus, except the two loci CLSSR6 and CLSSR8. Both the CLSSR6 locus and CLSSR8 locus had only 2 alleles, producing the lowest Ne (0.641, 0.691). The expected and observed heterozygosity of all the loci ranged from 0.442 to 0.870 and from 0.270 to 0.700, with averages of 0.654 and 0.474, respectively (**Table 9**). As an important index for measuring the genetic diversity of a population, the He of the SSRs was 0.654, which indicated that a higher genetic diversity existed in the population, suggesting that these accessions varied with high genetic diversity. The high genetic diversity may be due to being a predominantly outcrossing species. Meanwhile, the Ne was significantly smaller than the Na for each loci, which may be because the natural ecological conditions became severe suddenly during the process of alternation generation because of the high altitude of the site, and collapse of the large population occurred, leading to the loss of rare alleles in the population and the bottleneck effect. The results also revealed a range of PIC values from 0.348 (CLEER6) to 0.858 (contig406\_1209C), and among these, the values of three loci (contig476\_526D, 0.421; CLSSR6, 0.348; and CLSSR8, 0.374) were less than 0.5, indicating that the other 12 primers were accessible for identifying the genetic diversity of Chinese fir in Xioxi, Hunan Province. The average Shannon's Information Index (I) value was 1.350, with a minimum of 0.285 (contig 406\_1209C) and a maximum of 0.641 (CLSSR8). However, the effective number of alleles (Ne) ranged from 1.792 to 7.677 per locus for all accessions, and the mean value was 3.325. Overall, the mean values of Ne, He, Ho, PIC, Fst, and Gst were 1.933, 0.654, 0.474, 0.566, 0.090, and 0.076, respectively.

There were high levels of differentiation and genetic diversity at these loci. The 15 polymorphic loci showed that the G'stN value was between 0.259 (contig6319\_250C) and 0.001 (CLSSR6), with an average value of 0.083. This finding shows that the genetic difference among populations was 8.3%, and 91.7% of the genetic difference existed among individuals in the population. The average Nm of 15 SSR loci in nine populations was 9.163, indicating that gene exchange was frequent.

Na: Number of alleles; Ne: effective number of alleles; I: shannon's Information Index; Ho: Observed heterozygosity; He: Expected heterozygosity with populations; G'stN: Nei's standardized Gst; PIC: The polymorphism information content; Nm = [(1/Fst)-1]/4; Fis (Inbreeding coefficient within individuals) = (Hs-Ho)/Hs; Fst (Inbreeding coefficient within subpopulations) = (Ht-Hs)/Ht; Gis (Analog of Fst, adjusted for bias) = (cHs-Ho)/cHs; Gst (Analog of Fst, adjusted for bias) = (cHt-cHs)/cHt.\*\*\*

The highest number of alleles was observed in population JZW-3 (Na = 8), and three populations (LYP-2, LYP-3, and LYP-4) had the lowest number of alleles, which was only 4. The observed heterozygosity within a population ranged from


*Conifers - Recent Advances*

**Table 9.**

*Characterization of 15 simple sequence repeat loci in iron-heart* Cunninghamia lanceolate *based on 548 accessions representing 9 sampling sites.*

**76**

0.416 to 0.506, varying little. The mean of the expected heterozygosity within populations was significantly higher than the observed heterozygosity (Ho) within populations, while the highest value was found for population LYP-1 (He = 0.637), and the lowest value of 0.524 was found in LYP-4 (**Table S2**). LYP-4 was the least diverse population (I = 0.997 and He = 0.524) of all the sites sampled. The highest genetic diversity was recorded for sites located in JWZ-2, JWZ-3, and LYP-1 (I = 1.244, 1.294, and 1.241 and He = 0.622, 0.636, and 0.637, respectively). In **Figure 3**, the geographic distribution of the population diversity based on Fp and He is presented, which indicated that JZW- (1,2,3) was likely the center of genetic diversity of this Chinese fir variety.

Molecular variance analysis was used to assess the population differentiation among 9 subgroups, which demonstrated that approximately 11% of the total variance was explained among the groups and 89% of the total variance was explained within accessions (**Table 10**). The population differentiation study that included red-heartwood Chinese fir and clones from six different provinces produced similar results to our study and identified a slightly higher genetic variance in subgroups. However, a moderate degree of variability was present among some populations. Previous studies [38] have shown that severe genetic drift, which might be intensified by long-term habitat isolation, is widespread in small populations. This effect will result in a low level of genetic diversity within a population and genetic differentiation among populations. Meanwhile, the results were almost consistent with G'stN = 0.083, indicating that variation mainly existed between individuals, so it was unreasonable to divide the groups according to geographical locations and administrative boundaries.

#### *3.3.2 Genetic structure and divergence*

The study of population structure is important for the formulation of strategies utilizing special germplasms for breeding objectives and conserving species effectively. Meanwhile, the genetic structure largely determines the evolutionary potential of a species or population. To verify the results of the neighbor-joining cluster analysis and PCA principal component analysis, the results of 15 pairs of SSR primer polymorphisms of 548 wild germplasm resources in Xiaoxi, Hunan Province, were further analyzed by STRUCTURE v2.3.4. The results showed that L(K) increased with the increase of K. A clear peak appeared at the value of ΔK at K = 2 (**Figure 4A** and **B**). When k = 2, ΔK reached the peak value, which indicated that the 548 accessions were clearly differentiated into two clusters according to STRUCTURE analysis (**Figure 4**). All the accessions from JZW-2, JZW-3, and LYP-1 were present

#### **Figure 3.**

*Distribution of population diversity based on the expected heterozygosity and private allele frequency. (A) The private allele frequency (Fp) in all populations. (B) The expected heterozygosity (He) in all populations.*


**Table 10.**

*Analysis of molecular variance (AMOVA) among populations of iron-heart* Cunninghamia lanceolate*.*

in two clusters, with approximately one-half of each population in each cluster, which can be considered admixed. Materials from different sources were distributed in the populations, there was no obvious regional differentiation, and the results of the population structure analysis were consistent with the results of SSR genetic diversity clustering. According to previous research, in the genetic structure analysis of a structured population, when the genetic component (Q value) of material is ≥0.6, the genetic background of the material is relatively simple, and when the Q value is <0.6, the genetic background of the material is relatively complex. With the increase of the K value (k = 3, k = 4), a new gene classification appeared in the wild Chinese fir population, but the high variance was inconsistent (**Figure 4D**). The clustering of CTY, JZW-1, JZW-2, JZW-3, and XNC showed some evidence that these populations can be broken down into further clusters, while LYP-1, LYP-2, LYP-3, and LYP-4 were relatively stable for higher K values. Excluding the CTY and JZW-1 populations, a new gene classification appeared in the other seven populations, which showed that there were significant differences among other populations. This finding suggested that the heterozygosity and genetic background of the wild Chinese fir are higher. When K = 4, the population was divided into four groups. The accessions that originated from the same population, including JZW-1, JZW-3, and XNC, were divided into different clusters. This result indicated that the four clusters are not geographically independent. Several populations (i.e., the LYP-3 and LYP-4 populations) that consisted of a single genetic component might have experienced founder effects or significant bottlenecking. The results also show low levels of mixing, which account for the hybridization or outcrossing of individuals between populations. Classifying accessions according to administrative boundaries and geographical distributions is very subjective, and it is very difficult to grade traits accurately in the provenance of this specific Chinese fir. In some cases, the population structure may not be predicted via administrative boundaries and geographical distributions. Therefore, the relationship between the population structure and phylogenetic clustering is not obvious, which is consistent with previous research results [22] for the Chinese fir. Wind pollination and a high natural outcrossing frequency among the species may lead to inconsistencies in population classifications and geographical locations. As a result, the geographical origin and genetic structure of a population should be simultaneously considered for the screening of this special germplasm breeding material. That is, geographical features are not obvious among distribution regions. From the principal component analysis results, we were able to identify two main populations with some sub-populations in each group. Obviously, the distributions of accessions from the same location in the two groups were not concentrated and scattered in each group. Additionally, one group contained all the individuals from JZW-1 and approximately 60% of the accessions from the other three locations (ZJW-2, JZW-3, and LYP-1), which occupied approximately 40% in the other group.

#### **Figure 4.**

*Population classification based on the consensus of STRUCTURE analysis across 10 replications for per K clusters. (A) Circles with standard deviations represent the average log-likelihoods across per K runs independently. (B) Solid circles indicate the values of Evanno's ΔK based on the rate of change of the loglikelihood. (C) Bar plots express the population structure. The number of clusters is shown from K = 2 to K = 4. Vertical bars represent each genotype, and the length of each colored bar represents the proportion of membership for each cluster. (D) the distribution of 2 to 4 clusters of 9 populations is visualized as a pie chart, with each population divided into colored segments based on the proportion of its members in a given cluster.*

The lowest Gst and Fst values between populations JWZ-2 and LYP1 were 0.004 and 0.010, respectively (**Table S3**). The highest values, which were 0.104 for Fst and 0.093 for Gst, were observed between populations CTY and LYP-2. Most of the values for both parameters were within the limits of moderate genetic differentiation between populations (**Table S3**).

#### **4. Discussion**

Seed yield and quality are the basis for the collection and preservation of improved seeds and the construction of seed orchards, which has a great impact on the efficiency of plantation and industrial development in the later stage [39, 40]. It is found that the variation range of seed and cone traits is 12.18–51.34%, among which the variation of cone length, cone width, cone length-width ratio, seed length, seed width, and seed length-width ratio is relatively small, indicating that these seed traits of iron-heart *Cunninghamia lanceolate* are relatively stable [41, 42]. The order of coefficient of variation from large to small is: seed germination rate > seed quality > seed length to width ratio > seed length > cone seed yield > seed width > total cone mass > seed goodness > cone length > seed quality (1000) > cone length to width ratio > cone width. The results of the analysis of variance showed that among families, the differences of total cone quality, seed quality, seed germination rate, seed goodness, seed quality (1000), seed width, and cone length-width ratio were very significant (P < 0.001), the differences of cone length were significant (P < 0.01), and the differences of the other four traits were not significant. The results showed that the phenotypic characters of different Ironheart *Cunninghamia lanceolate* families had high diversity and rich variation.

Genetic diversity of a species reflects its evolutionary potential and allows for evolution and adaptation. The more abundant the genetic variation of a species is, the more adaptable it is. Thus, it is necessary to study the genetic diversity of a species to understand its biological properties [43]. All previous studies on this species revealed a relatively high level of genetic diversity [22]. In the current study, 15 SSR markers were used to evaluate the population genetics of a large number of specific Chinese fir individuals across its distribution range in Xiaoxi Hunan. Amplification results of the 548 germplasms only existed Hunan Xiaoxi gave a total of 133 alleles with a mean of 8.87 at each locus, a value higher than those in previous reports [1, 22]. The difference may relate to the reproductive attributes of this species, the sample size, and/or the characteristics of the molecular markers. Understanding population structure is useful for developing strategies for the conservation of new species and effectively utilizing genotypes for breeding purposes. Genetic distance is commonly used to describe the genetic structure of a population and the differences among populations [44]. The evolutionary potential of a species or population depends to a large extent on the genetic structure of the population [45]. The results of the STRUCTURE analysis performed for this study indicate that the most likely genetic structure of the 548 studied accessions is two clusters.

#### **5. Conclusions**

Through this study, we constructed a germplasm resource nursery of Iron-heart China fir, and the grafting survival rate was as high as 83%. 27 families of iron-heart *Cunninghamia lanceolate* seeds were collected, and the highest germination rate was 68%; 15 highly polymorphic and stable SSR markers were selected to analyze the genetic structure of the natural population of iron-heart *Cunninghamia lanceolate*. In total, the study got 133 alleles, and the GestN's = 0.083. AMOVA analysis showed that the variation among populations was only 11%, and 89% of the variation came from individuals. In addition, STRUCTURE analysis showed that the whole samples could be divided into two groups, and there was no correlation between population division and geographical location. This study will lay a foundation for the protection of the new species of Iron-heart *Cunninghamia lanceolate*. In this study, only the genetic structure of its natural population was analyzed, but the heartwood variation was not deeply discussed. In addition, we only used the single method of STRUCTURE to analyze its genetic structure and did not use PCA, neighbor-joining (NJ) cluster analysis, and other methods to analyze its genetic structure. This will be what we will study in the next step.

## **Acknowledgements**

I would like to thank all the scholars for their monographs and their research achievements for their inspiration and help. Secondly, I would like to express my heartfelt thanks to the colleagues who provided help in this work was funded by Hunan Provincial Forestry Science and Technology Innovation Project (Project No. XLK201921).

## **A. Summary materials**

Gst values above the diagonal; Fst values below the diagonal.


## *Conifers - Recent Advances*


**Table S1.**

*The information of the 35 Iron-heart* Cunninghamia lanceolate*.*








## **Code Plot Position longitude latitude Altitude(m)** 3 JZW-2 110.259326 28.814976 535 3 JZW-2 110.259218 28.814882 525 3 JZW-2 110.259218 28.814699 523 3 JZW-2 110.259218 28.814599 526 3 JZW-2 110.259326 28.814552 516 3 JZW-2 110.259433 28.814458 510 3 JZW-2 110.259863 28.813611 464 2 JZW-1 110.263011 28.80788 498 2 JZW-1 110.263 28.808087 503 2 JZW-1 110.263183 28.808167 506 2 JZW-1 110.263571 28.808077 510 3 JZW-2 110.265989 28.811247 545 3 JZW-2 110.266004 28.811278 576 3 JZW-2 110.266103 28.811241 523 3 JZW-2 110.266153 28.811309 535 3 JZW-2 110.266199 28.811258 537 3 JZW-2 110.266199 28.811259 537 3 JZW-2 110.266079 28.811436 520 3 JZW-2 110.266025 28.811335 520 3 JZW-2 110.265996 28.811373 532 3 JZW-2 110.265996 28.811372 520 3 JZW-2 110.265858 28.811433 528 3 JZW-2 110.265956 28.81142 531 3 JZW-2 110.265863 28.811323 529 3 JZW-2 110.265504 28.811433 525 3 JZW-2 110.265889 28.81137 529 3 JZW-2 110.265938 28.811365 530 3 JZW-2 110.265968 28.81137 531 3 JZW-2 110.265889 28.811301 529 3 JZW-2 110.265745 28.81161 526 3 JZW-2 110.265873 28.811646 525 3 JZW-2 110.265485 28.811865 536 3 JZW-2 110.265524 28.811862 537 3 JZW-2 110.265556 28.811825 537 3 JZW-2 110.265618 28.811889 526 3 JZW-2 110.265554 28.811823 530 3 JZW-2 110.265658 28.811802 530 3 JZW-2 110.265503 28.811767 535 3 JZW-2 110.265296 28.811861 557 3 JZW-2 110.265234 28.811896 539




## **Code Plot Position longitude latitude Altitude(m)** 4 JZW-3 110.251885 28.805843 637 4 JZW-3 110.251858 28.805867 642 4 JZW-3 110.251724 28.805867 649 4 JZW-3 110.25167 28.805867 652 4 JZW-3 110.251563 28.805941 659 4 JZW-3 110.251536 28.805914 654 4 JZW-3 110.251429 28.805891 651 4 JZW-3 110.251429 28.805914 650 4 JZW-3 110.251402 28.805914 648 4 JZW-3 110.251348 28.805961 644 4 JZW-3 110.251187 28.806008 648 4 JZW-3 110.25116 28.806126 652 4 JZW-3 110.25116 28.806126 648 4 JZW-3 110.25116 28.806102 644 4 JZW-3 110.251026 28.807115 641 4 JZW-3 110.250918 28.807044 660 4 JZW-3 110.250864 28.807185 662 4 JZW-3 110.250891 28.807303 658 4 JZW-3 110.250891 28.807303 657 4 JZW-3 110.250918 28.807303 656 4 JZW-3 110.250891 28.80735 654 4 JZW-3 110.250811 28.80742 658 4 JZW-3 110.250838 28.807444 659 2 JZW-1 110.250945 28.81041 671 2 JZW-1 110.251052 28.810457 663 2 JZW-1 110.251106 28.81048 662 2 JZW-1 110.25116 28.810504 663 2 JZW-1 110.251267 28.810551 659 2 JZW-1 110.251321 28.810551 661 2 JZW-1 110.252557 28.810716 629 2 JZW-1 110.252637 28.810833 624 2 JZW-1 110.252745 28.810904 624 2 JZW-1 110.253013 28.810951 607 2 JZW-1 110.252986 28.81097 601 2 JZW-1 110.253121 28.810998 613 2 JZW-1 110.253121 28.81099 612 2 JZW-1 110.253175 28.811092 609 2 JZW-1 110.253443 28.811116 613 2 JZW-1 110.253604 28.811116 605 2 JZW-1 110.253631 28.811163 603



#### **Table S2.**

*Location and number of trees sampled for 9 populations in provenance.*


*Na: number of different alleles; Ne: number of effective alleles; I: Shannon's Information Index; Ho: observed heterozygosity; He: expected heterozygosity with populations; uHe: unbiased expected heterozygosity with populations; F: fixation Index; Fp: no. private alleles (no. of alleles unique to a single population).*

#### **Table S3.**

*Genetic diversity parameters of 9 populations of Chinese fir. All values were multilocus estimates based on 15 microsatellite loci.*


#### **Table S4.**

*Pair-wise estimates of genetic differentiation between Chinese fir populations using Fst and Gst coefficients based on 15 SSR markers.*


#### **Table S5.**

*Genetic distance between the different population.*

## **Author details**

Ninghua Zhu1 , Xiaowei Yang<sup>1</sup> \*, Zhiqiang Han<sup>1</sup> and Xiao Can<sup>2</sup>

1 Central South University of Forestry Science and Technology, Changsha, China

2 Jiangxi Environmental Engineering Vocational College, Ganzhou, China

\*Address all correspondence to: 1498105349@qq.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## **Chapter 4**
