3.2. Characterisation of Correlations between Phenotypic Traits
The results of the correlation analysis indicate varying degrees of associations among phenotypic traits (
Figure 1). Apart from length of node at breast diameter (LN), branch moisture content, and leaf traits, diameter at breast height (DBH) exhibits significant or highly significant correlations with other traits. The correlation coefficients between DBH and diameter at ground, weight of branches, total weight, and cavity diameter exceed 0.9, while negative correlations are observed with wall-to-cavity ratio and Bmc, with coefficients of −0.50 and −0.11, respectively. Taper grade is highly negatively correlated with LN, leaf thickness, leaf area, leaf width, and leaf length–width ratio. LN shows highly significant positive correlations with leaf thickness, leaf area, leaf length, and leaf width and highly significant negative correlations with branch moisture content, leaf moisture content, leaf length–width ratio, and specific leaf area. Leaf thickness, leaf area, leaf length, and leaf width are highly negatively correlated with culm moisture content and leaf moisture content but are unrelated to branch moisture content. Specific leaf area is significantly positively correlated with branch moisture content, branch moisture content, and leaf moisture content, while exhibiting a highly significant negative correlation with under-branch height; plant crown is highly positively correlated with leaf length–width ratio.
3.3. Screening for Key Phenotypic Characteristics
Since there are varying degrees of correlation between phenotypic traits, direct evaluation of the germplasm based on this information will affect its authenticity. The use of a principal component analysis for comprehensive evaluation of the participating germplasm can explain the variation in moso bamboo phenotypic traits with fewer traits. Using an eigenvalue greater than 1.0 as the basis for principal component screening, the first eight principal components were extracted with a cumulative contribution rate of 81.64%, which can better summarize most of the information of the 28 phenotypic traits of the participating germplasm (
Table 2). The eigenvalues of the principal components were 9.241, 3.985, 2.335, 1.967, 1.83, 1.29, 1.185, and 1.026, respectively, among which the contribution rate of the first principal component was 33.002%, and the eigenvectors of breast diameter, total weight of moso bamboo, ground diameter, and cavity diameter were larger; the contribution rate of the second principal component was 14.231%, and the eigenvectors of leaf area, leaf width, leaf length, node length at breast diameter, and specific leaf area were larger leaf area; the contribution rate of the third principal component was 8.338%, the eigenvectors of leaf weight, branch and leaf weight, height under branch, leaf length, and leaf aspect ratio were larger. The first and third principal components reflected the biomass of moso bamboo. The contribution rate of the fourth principal component was 7.026%, and the eigenvectors of branch and leaf ratio and sharpness were larger, which reflected the plant type of moso bamboo; the contribution of the fifth principal component was 6.536%, the eigenvectors of leaf water content and specific leaf area were larger, and the second and fifth principal components reflected the phenotypic characteristics of moso bamboo leaf blades; the contribution rate of the sixth principal component was 4.608%, the eigenvectors of wall to cavity ratio, wall thickness at breast diameter and culm water content were larger, reflecting the situation of the morphological characteristics of the wall of the culm of moso bamboo. The largest eigenvectors of the seventh and eighth principal components were branch-to-leaf ratio and node length at breast diameter, respectively. Seven phenotypic traits, namely breast diameter, leaf area, leaf weight, branch-to-leaf ratio, leaf water content, wall-to-cavity ratio, and node length, were extracted from the 28 traits, which were the main factors leading to the differences in phenotypic traits of moso bamboo and were used as important indices for evaluating the germplasm resources of moso bamboo.
3.4. Comprehensive Evaluation of Phenotypic Characteristics
By standardizing the 28 trait values of the moso bamboo germplasm and substituting them into the above eight principal components, the eight principal component scores of each germplasm were obtained, the eight principal component scores were normalized using the fuzzy affiliation function method, and the weight coefficients of the eight principal components were calculated (0.404, 0.174, 0.102, 0.086, 0.08, 0.056, 0.052, and 0.045), and then, the composite scores (D-value) of each type of germplasm were calculated (
Table 3), and all the germplasm were comprehensively evaluated by D-value. The results showed that the average composite score (D-value) of phenotypic characteristics of moso bamboo germplasm was 0.563, with the highest D-value of Wuyi 1 moso bamboo in Fujian Province (0.803) and the lowest D-value of Pingle 2 moso bamboo in the Guangxi Zhuang Autonomous Region (0.317), indicating that Wuyi 1 moso bamboo had the best comprehensive characteristics and Pingle 2 moso bamboo had the worst comprehensive characteristics.
Correlation analyses were conducted based on 28 phenotypic traits and D-values (
Table 4). The results showed that the D-value was positively correlated with 13 trait indices, including diameter at breast height, diameter at ground level, plant height, and number of whole culm nodes, and the correlation reached a highly significant level (
p < 0.001), was negatively correlated with the wall-to-cavity ratio and reached a highly significant level (
p < 0.001), whereas diameter at node length, branch-to-leaf ratio, culm water content, leaf water content, leaf thickness, leaf area, leaf length, leaf width, and the composite value of D were not correlated with the composite value of D.
3.5. Comprehensive Evaluation of Phenotypic Traits
Based on the above 28 characters, when the Euclidean distance was 62, all the moso bamboo germplasm could be divided into four clusters using the full maximum distance method (
Figure 2a), and there were differences in moso bamboo phenotypic traits among the clusters (
Table 5). The clustering of germplasm from different provinces was not strictly based on geographical location (
Figure 2b).
Group I includes 54 germplasm with a large diameter at breast height, large diameter at ground level, large total biomass, high plant height, high under branching, thick culm wall, high number of nodes in the whole culm, large leaf blade area, small specific leaf area, and low water content in the culm and branches. Overall, the germplasm is excellent, containing all the germplasm from Yunnan, more than 70% of the germplasm from Anhui and Fujian, and more than 38% of the germplasm from Hubei, Sichuan, Chongqing, Zhejiang, Jiangxi, and Hunan.
Group II includes 26 germplasm with a small diameter at breast height, small diameter at ground level, low sharpness, small biomass, low plant height, low height under branches, small branch-to-leaf ratio, and thin culm walls, but long nodes at breast height. The germplasm was poor overall, containing all the germplasm from Henan and 75% of the germplasm from Jiangsu.
Group III includes 25 germplasm with a larger diameter at breast height, large diameter at ground level, large biomass, large sharpness, short node length at breast height, high culm water content, thin leaf thickness, and large leaf aspect ratio and contains all the germplasm from Guizhou and 56% of the germplasm from Jiangxi.
Group IV includes eight germplasm with a large branch-to-leaf ratio, high branch water content, high leaf water content, small leaf area, and larger than leaf area, and contains 50% of the germplasm from Guangxi.
3.6. Identification of Different Taxa of Moso Bamboo Germplasm
A random forest discriminant model was constructed to classify and predict the four taxa using seven phenotypic characters, namely, breast diameter, leaf area, leaf weight, branch-to-leaf ratio, leaf water content, wall-to-cavity ratio, and node length. From 113 germplasm, 70% of the samples were selected as the training set, and 30% were made as the independent test set. The random forest algorithm was used to train the training set to construct the prediction model, and the number in the random forest was 500, and when the number of model node variables was 6, the mean of the model misclassification rate was the lowest at 26.58%, and the prediction accuracy of its cross-validation was 70.59% (
Table 6). The importance of the phenotypic traits of moso bamboo based on the random forest classification output is shown in
Figure 3. The significance of the average reduction in accuracy is in the following order: diameter of breast > leaf water content > leaf area > diameter of breast node length > leaf weight > branch-to-leaf ratio > wall cavity ratio. The established random forest discriminant model can effectively discriminate different types of germplasm and verifies that these seven phenotypic traits can be used as indicators to evaluate the germplasm resources of moso bamboo.