Analysis method for plant phenotypic character diversity of rubus idaeus group

Abstract

The invention discloses a method for analyzing the phenotypic character diversity of ongcao group plants, and belongs to the technical field of genetic diversity analysis. The analysis method provided by the invention comprises the following steps: (1) measuring phenotypic characters of plants in a rudfruit tea group to be measured; and (2) performing nested variance analysis, variation analysis, genetic diversity analysis, correlation analysis, clustering analysis and principal component analysis on the phenotypic characters measured in the step (1) so as to evaluate the diversity of the phenotypic characters of the tumescent tea group plants. The invention provides a systematic method for measuring, counting and analyzing the phenotypic characters of the plants in the rubus idaeus group, and a research method for the diversity of the phenotypic characters of the plants in the rubus idaeus group is standardized. The method provided by the invention can provide a standard and a basis for interspecific identification, geographical distribution identification and classification system construction of the plant phenotypic characters of the rubus idaeus group, and also provides an important reference for genetic mechanism and germplasm resource protection, development and utilization of the group.

Description

Technical Field

[0001] The invention relates to the technical field of genetic diversity analysis, and in particular to a method for analyzing the diversity of phenotypic traits of tumor-fruit tea group plants. Background Art

[0002] Plant phenotypic traits are measurable physical, physiological and biochemical characteristics exhibited by plants during their growth and development. These traits directly reflect the composition and structure of plants and their growth and development process. Phenotypic traits are an important bridge connecting genotype and environment. After long-term natural selection and environmental stress, plants usually show phenotypic variation, which is also a survival strategy for plants to adapt to different habitats. Through the observation and analysis of phenotypic traits, we can reveal the impact of the interaction between genotype and environment on plant growth and development, and provide an important basis for genetic breeding. For example, in crop breeding, new varieties with excellent traits can be cultivated through phenotypic selection. At the same time, phenotypic traits are also a direct reflection of plant adaptation to the environment. Plants adapt to different environmental conditions, such as drought, salinity, pests and diseases, by adjusting their phenotypic traits such as morphological structure and physiological mechanism. This adaptability is the key to plant survival and reproduction. Morphological classification is the most important means of conducting phenotypic research. Based on the differences or similarities in the external morphology of plants, plant species can be identified in a simple, intuitive and vivid way. Therefore, based on the research results of phenotypic traits, a reasonable plant classification system can be constructed to provide a framework and guidance for botanical research. It is also the initial way to breed excellent plant varieties, which is of great significance for the protection and utilization of rare plants.

[0003] The Camellia group belongs to the genus Camellia, which is endemic to China and has high ornamental, economic, medicinal and health value. At present, the research on the Camellia group plants mainly focuses on species classification, leaf anatomy, chloroplast genome, seed germination, species distribution pattern, etc. There are few reports on their phenotypic traits, and there is a lack of research on the correlation between the flowers, fruits and leaves of the Camellia group plants and environmental factors, which greatly hinders the development and utilization of its resources. The phenotypic styles of this group are complex and diverse, and the phenotypic variation of key traits is still unclear. Exploring the phenotypic traits of this group is conducive to providing a scientific basis for the classification and identification of this group and its development and utilization. However, the morphological characteristics of some species in this group are relatively similar, and they are often easily confused. In the research process, without standardized measurement and analysis methods, it is impossible to accurately, comprehensively and systematically evaluate the diversity of this group. Therefore, providing a systematic phenotypic measurement, statistical and analysis method is important for studying the phenotypic genetic diversity of the Camellia group plants and accurately distinguishing their species categories and distribution ranges. Summary of the invention

[0004] The purpose of the present invention is to provide a method for analyzing the diversity of phenotypic traits of the tumor-fruit tea group of plants, so as to solve the problems existing in the above-mentioned prior art. The present invention provides a systematic method for measuring, counting and analyzing the phenotypic traits of the tumor-fruit tea group of plants, and standardizes the research method of the diversity of phenotypic traits of the tumor-fruit tea group of plants. The method of the present invention can provide standards and basis for the interspecific identification, geographical distribution identification and classification system construction of the phenotypic traits of the tumor-fruit tea group of plants, and also provide important references for the genetic mechanism, germplasm resource protection and development and utilization of this group.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a method for analyzing the diversity of phenotypic traits of tumor-fruit tea group plants, comprising the following steps:

[0007] (1) measuring the phenotypic traits of the plants in the tumor-fruit tea group to be tested; the phenotypic traits include qualitative traits and quantitative traits;

[0008] The quality traits include flower color, sepal tip, fruit cracking type, leaf type, leaf shape, leaf base, leaf color, leaf margin, and leaf tip;

[0009] The quantitative traits include calyx number, calyx length, calyx width, petal number, petal length, petal width, style length, style number, fruit diameter, fruit stalk length, fruit length, shell thickness, seed length, ovary number, seed number, leaf length, leaf width, petiole length, leaf area, leaf shape index and vein logarithm;

[0010] (2) performing nested variance analysis, variation analysis, genetic diversity analysis, correlation analysis, cluster analysis and principal component analysis on the phenotypic traits measured in step (1) to evaluate the diversity of the phenotypic traits of the tumor-fruit tea group plants;

[0011] The correlation analysis includes correlation analysis between the phenotypic traits and correlation analysis between the phenotypic traits and geographical and climatic factors; the geographical and climatic factors include longitude, latitude, altitude, annual average temperature, annual average rainfall, and annual average relative humidity.

[0012] Preferably, the nested ANOVA includes calculating the variance components and phenotypic differentiation coefficients of the phenotypic traits between and within populations;

[0013] The variation analysis includes calculating the mean, standard deviation and coefficient of variation of the phenotypic traits;

[0014] The genetic diversity analysis includes calculating the Shannon-Wiener diversity index of the phenotypic traits;

[0015] The cluster analysis includes R-type cluster analysis and Q-type cluster analysis; the R-type cluster analysis is performed using the Pearson correlation coefficient as a metric; the Q-type cluster analysis is performed using the square Euclidean distance as a genetic distance.

[0016] Preferably, the varieties of the plants to be tested include Camellia anlungensis Chang, Camellia obovatifolia Chang, Camellia ilicifoliaY.K.Li, Camellia hupehensisChang, Camellia acutiperulata Chang&Ye, Camellia acutisepala Chang, Camelliarubimuricata Chang&Z.R.Xu, Camellia tuberculata Chien, Camellia lipingensisChang, Camellia leyeensis Chang&Y.C.Zhong, Camellia rubituberculata Chang&Yu, Camellia pyxidiacea Xu FPchen&C.Y.Deng, Camellia parvimuricata Chang, Camellia neriifolia Hung T.Chang, Camellia rhytidocarpa Chang&Liang, Camelliaatuberculata Chang, Camellia zengii Chang, Camellia rhytidophylla Y. K. Li & M. Z. Yang.

[0017] The present invention also provides an application of the above analysis method in distinguishing Camellia rubituberculata Chang & Yu and Camellia parvimuricata Chang, wherein the above analysis method is used for analysis, and when PC1 of the principal component analysis is greater than PC2, the plant variety of the tumor-fruit tea group to be tested is Camellia rubituberculata Chang & Yu;

[0018] When PC1 of the principal component analysis is smaller than PC2, the plant variety of the tumor-fruit tea group to be tested is Camellia parvimuricata Chang.

[0019] The present invention also provides an application of the above analysis method in determining the planting area of ​​the tumor-fruit tea group plants, and the geographical location of the planting area of ​​the tumor-fruit tea group plants is determined based on the correlation analysis results of the phenotypic traits and geographical climatic factors in the above analysis method.

[0020] Preferably, the judgment method is that the leaf shape index is positively correlated with longitude; the number of petals, calyx width, calyx length, fruit diameter, leaf base, leaf area, leaf type, and leaf width are negatively correlated with longitude; the logarithm of leaf veins is positively correlated with latitude, and the number of styles, style length, calyx width, leaf shape, petiole length, and leaf length are negatively correlated with latitude; petal width, calyx width, flower color, fruit length, and fruit diameter are positively correlated with altitude, and the number of petals and leaf type are negatively correlated with altitude; the number of petals and leaf base are positively correlated with the annual average temperature, and the petal width, calyx width, calyx length, and sepal tip are negatively correlated with the annual average temperature; seed length is positively correlated with the annual average rainfall, and the petal length, calyx length, and sepal tip are negatively correlated with the annual average rainfall; leaf tip and leaf shape index are positively correlated with the annual average relative humidity, and the style length, leaf type, and leaf width are negatively correlated with the annual average relative humidity.

[0021] The present invention also provides the application of the above analysis method in distinguishing populations of the tea group plants. According to the cluster analysis results in the above analysis method, the tea group plants are classified into populations with different kinship relationships.

[0022] Preferably, the classification criteria are: red flower color, average leaf length of more than 10 cm, average shell thickness of more than 1 cm, and average fruit diameter of more than 5.5 cm, which is classified as Class I;

[0023] The flowers are white, the tips of the sepals are rounded, and the leaves are elliptical or oblong, belonging to the same category;

[0024] Sepals have pointed tips, leaves are elliptical or oblong, and the leaf margins are serrated, belonging to the same category;

[0025] Sepals are rounded at the tip, styles are 3 or 4, leaves are lanceolate, margins are serrated, flowers are white, and they belong to the same category;

[0026] The sepals have rounded tips, the leaves are oblong or lanceolate, and the average petiole length is more than 0.9 cm, which is a type;

[0027] The sepals have rounded tips, the leaves are ovate-lanceolate, and the average length of the petals is more than 2.5 cm, which is a type;

[0028] The sepals have rounded tips, ovate leaves, small leaves, petiole length less than 0.5 cm on average, white flowers, and leaves with micro-wave-like serrations on the edges, which is a type;

[0029] The sepals have rounded tips, the leaves are elliptical or obovate, the flowers are white, and the number of petals is more than 10, which is a type;

[0030] Sepals have pointed tips, leaves are lanceolate, flowers are white, and they are of the same type;

[0031] The sepals have rounded tips, the leaf margins are serrated, the average petiole length is more than 0.9 cm, the flowers are white, and they belong to the first category;

[0032] The sepals are pointed at the tip, the leaves are oval, and the flowers are white, which are a type.

[0033] The present invention also provides the application of the above-mentioned analysis method in screening the main traits for distinguishing and evaluating the tumor-fruit tea group plants, and determines the main traits for distinguishing and evaluating the tumor-fruit tea group plants according to the correlation analysis results and principal component analysis results in the above-mentioned analysis method.

[0034] Preferably, the main traits include leaf length, leaf width, leaf color, leaf tip, fruit diameter, shell thickness, flower color, sepal tip, sepal width, petal length, petal width, and number of styles.

[0035] The present invention discloses the following technical effects:

[0036] The present invention measures and analyzes the specific phenotypic traits of the tumor-fruit tea group plants, and adopts methods such as nested variance analysis, variation analysis, genetic diversity analysis, correlation analysis, cluster analysis and principal component analysis to systematically and comprehensively evaluate the diversity of the phenotypic traits of the tumor-fruit tea group plants. The present invention also proposes to analyze the correlation between the phenotypic traits and geographical and climatic factors, and provides an empirical method and theoretical basis for in-depth research on the variation and evolution of the phenotypic traits of the tumor-fruit tea group plants. The present invention can intuitively distinguish Camellia rubituberculata Chang & Yu and Camellia parvimuricata Chang from the tumor-fruit tea group plants through principal component analysis; can preliminarily determine the planting area of ​​the tumor-fruit tea group plants through correlation analysis; and can classify populations with closer kinship through cluster analysis, which is convenient for further research on the genetic evolution of species. The analysis method of the present invention can provide standards and basis for interspecific identification, geographical distribution identification and classification system construction of the phenotypic traits of the tumor-fruit tea group plants, and also provide important references for the genetic mechanism, germplasm resource protection and development and utilization of the group. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1These are the phenotypic traits of some plants in the tumor-fruit tea group; A: flower, A1, A2, A3 are the color and morphological characteristics of the flower; A4 is the measurement of the petal; A5 is the measurement of the calyx; A6 is the measurement of the style; B: fruit, B1 is the morphological characteristics of the fruit; B2 is the morphological measurement of the fruit; B3 is the type of cracked fruit; B4 is the measurement of the shell; C: leaf; C1 is the morphological characteristics of the leaf; C2 is the size of the leaf and its measurement; C3 is the serrations on the edge of the leaf; C4 is the type of the leaf tip; C5 is the type of the leaf base; D: the color standard of flowers and leaves;

[0039] Figure 2 This is the correlation analysis of phenotypic traits of the tumor tea group plants; A: Correlation heat map between 30 phenotypic traits; B: Correlation heat map between 30 phenotypic traits and altitude and environmental factors;

[0040] Figure 3 R-type clustering and principal component analysis of phenotypic traits of the tuber tea group plants; A: R-type clustering based on 30 phenotypic traits; B: Three-dimensional projection of phenotypic traits on the first three principal components; C: Scatter distribution diagram of 212 samples constructed with PC1 and PC2; D: PCA eigenvalues, contribution rates and cumulative contribution rates based on 27 phenotypic traits;

[0041] Figure 4 Q-type cluster analysis of 212 samples of the tuber-fruit tea group; A: cluster tree when the Euclidean distance is 7.5; B: distance tree when the Euclidean distance is 10. DETAILED DESCRIPTION

[0042] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0043] It should be understood that the terms described in the present invention are only for describing a particular embodiment and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. The intermediate value in any stated value or stated range, and each smaller range between any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0044] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0045] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present invention description and examples are exemplary only.

[0046] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0047] Example 1

[0048] 1. Experimental Materials

[0049] Based on a large amount of literature reading, specimen verification and field investigation in the early stage, the distribution range of the plants in the Tumor Tea Group was determined. According to its distribution range, 212 healthy adult plants of 18 species of the Tumor Tea Group were selected for research. Most of this group grows in mountains with moderate altitudes, along river valleys or evergreen forests, and prefers moist and well-drained soil environments. Therefore, the population size of some species is small and sampling is difficult. In the sampling, 2-17 healthy adult plants were selected for each population, and the distance between samples was greater than 20 meters to reduce the kinship between samples. The geographical location information of each sample was recorded using GPS positioning, and the meteorological data from 1991 to 2020 was downloaded from the China Meteorological Administration (https: / / weather.cma.cn, accessed on September 7, 2024). The geographical, climatic and sample information of the 18 populations of the Tumor Tea Group are shown in Table 1.

[0050] Table 1 Geographical, climatic and sample information of 18 populations of the Tuber Tea Group

[0051]

[0052]

[0053]

[0054] 2. Measurement methods of apparent traits

[0055] Flowers, fruits and leaves of the plants in the tumor-fruit tea group were selected for measurement. For each plant, 10 groups of leaves were selected from the upper, middle and lower parts, 10 fruits were selected, and 10 typical and consistent flowers were selected from the middle and above parts in full bloom. Finally, the average value was taken for statistical analysis. Finally, 30 phenotypic trait data of 212 tumor-fruit tea plants were obtained, including 9 quality traits (Table 2): flower color, sepal tip, fruit cracking type, leaf type, leaf shape, leaf base, leaf color, leaf edge, leaf tip, and 21 quantitative traits (Table 3): calyx number, calyx length, calyx width, petal number, petal length, petal width, style length, style number, fruit diameter, fruit stalk length, fruit length, shell thickness, seed length, ovary number, seed number, leaf length, leaf width, petiole length, leaf area, leaf shape index, and vein logarithm. Referring to the description specifications and data standards of tea germplasm resources, the plant quality trait data of the tuber tea group were graded and standardized (Table 2). The specific measurement methods of quantitative traits are as follows ( Figure 1 ):

[0056] Flowers: Use a vernier caliper to measure the petal length (the length from the base to the top of the petal), petal width (the length of the widest part of the petal), calyx length (the length from the base to the top of the sepal), calyx width (the length of the widest part of the sepal), and style length (the length from the base of the style to the stigma). Count the number of sepals, petals, and styles. Repeat each trait 6 times and take the average value.

[0057] Fruit: Use a vernier caliper to measure the fruit length (the length of the longest axis of the fruit), fruit diameter (the length of the widest part of the fruit), fruit stalk length (the length from the base of the fruit to the end of the stalk), shell thickness (the average value of the thickest, thinnest and middle parts of the shell), and seed length, and calculate the number of ovaries and seeds. Each trait was repeated 6 times and the results were averaged.

[0058] Leaves: Use a vernier caliper to measure leaf length (the length from the base of the leaf to the tip), leaf width (the length of the widest part of the leaf), leaf area (the projected area from the base of the leaf to the top) and petiole length (from the base of the leaf to the junction of the petiole and the stem), and calculate the number of vein pairs (the number of pairs between the main vein and the lateral veins of the leaf) and leaf shape index (the ratio of leaf length to leaf width). Calculate the number of ovaries and seeds. Each trait was repeated 6 times and the results were averaged.

[0059] Table 2 Quality characteristics, grading and standardization of the tumor fruit tea group

[0060]

[0061] Table 3 Quantitative traits and abbreviations of the tumor fruit tea group

[0062]

[0063]

[0064] 3. Data Analysis

[0065] Microsoft Excel 2021 software was used to calculate the mean, standard deviation (SD), coefficient of variation, and Shannon-Weaver genetic diversity index (H′) of the measured phenotypic trait data. ArcGIS10.6 software was used to produce the population geographical distribution map. SPSS19.0 software was used to standardize the phenotypic trait data, perform multiple comparisons, nested analysis of variance, and correlation analysis. The inter-group link was used with the Pearson correlation coefficient as the metric for R-type cluster analysis, and the Ward method was used with the square Euclidean distance as the genetic distance for Q-type cluster analysis. The principal component analysis graphics were generated using Origin 2021 software. The coefficient of variation (CV), phenotypic differentiation coefficient (Vst), and Shannon-Wiener diversity index (H′) were used to directly measure genetic diversity and trait variation. The calculation formula is as follows:

[0066] Among them, CV represents the degree of dispersion of the trait, and the calculation formula is CV / %=s / xˉ×100. Among them, xˉ is the average value of the trait, and s is the standard deviation; Vst represents the percentage of inter-population variation in the total variation of the trait, and the calculation formula is Vst / %=[δ 2 t / s / (δ 2 t / s+δ 2 s)]×100. Among them, δ 2 t / s is the variance component between groups, δ 2 s is the variance component within the group;

[0067] The calculation formula of Shannon-Weaver genetic diversity index is H = -∑Pi × lnPi, where Pi is the percentage of the number of materials in the i-th level of a certain trait to the total number of materials.

[0068] In the quantitative trait data, all materials were divided into 10 levels, from the 1st level [Xi<(X-2s)] to the 10th level [Xi>(X+2s)], with each 0.5s as a level, Xi being the data in the i-th level, and the relative frequency (Pi) of each level being used to calculate the diversity index Shannon-Weiner index (H).

[0069] 4. Results

[0070] 4.1 Differentiation of phenotypic traits among and within populations of the tea group

[0071] The nested variance method was used to calculate the variance components and phenotypic differentiation coefficients of 30 phenotypic traits of the tumor-fruit tea group plants between and within populations, as shown in Table 4. The results showed that the phenotypic traits of flowers, fruits and leaves of the tumor-fruit tea group plants were quite different between populations. The variance components of the 30 phenotypic traits between and within populations were 41.152%-50.661% and 3.483%-50.266%, respectively, and the means of the variance components were 47.363% and 32.705%, respectively, indicating that the differentiation of the phenotypic traits of the tumor-fruit tea group plants existed both within and between populations, mainly from between populations. There were 19 phenotypic traits with significant differences between populations, and 11 phenotypic traits with significant differences within populations, indicating that most traits were quite different between populations, but not significantly different within populations. The average phenotypic differentiation coefficient of 30 phenotypic traits was 61.405%, the minimum FC was 45.026%, and the maximum LC was 93.214%. Overall, the average phenotypic differentiation coefficient of the tumor fruit tea group was flower (53.476%) < fruit (64.209%) < leaf (66.804%), and quality traits (70.264%) > quantitative traits (57.608%).

[0072] Table 4 Variance components and phenotypic differentiation coefficients of phenotypic traits in different populations of the tuber tea group

[0073]

[0074]

[0075] 4.2 Analysis of variation in phenotypic traits

[0076] By calculating the mean, standard deviation and coefficient of variation of 21 quantitative traits in 18 populations of the tumor tea group, the variation analysis was performed. The average coefficient of variation was 14.88%, indicating that the phenotypic traits of the tumor tea group were rich in variation. The average coefficient of variation between different populations was 10.05%-19.33%, and 9 populations were greater than 15%. SJL was the highest, ZGC was the smallest, and the SJL population had the richest variation in phenotypic traits. The average coefficient of variation within the population between different traits was 6.66%-27.98%, FSL was the largest, and the coefficients of variation of LA (26.96%), PEL (23.56%), FST (22.59%) and SDL (20.91%) were also relatively large, indicating that it had rich variation. The coefficient of variation of NP (6.66%) was small and relatively stable. The mean value of the coefficient of variation was fruit (18.57%)> leaf (17.85%)> flower (9.41%).

[0077] 4.3 Evaluation of diversity of phenotypic traits

[0078] The 30 phenotypic traits of the tumor-fruit tea group plants were analyzed, and the diversity index was 0.48-2.07, with a mean of 1.47. The diversity index of 21 traits was greater than 1, which indicated that the phenotypic traits of the tumor-fruit tea group plants had high uniformity and richness. The 21 quantitative traits of the tumor-fruit tea group plants were analyzed, and the diversity index of the quantitative traits was 0.76-2.07, with a mean of 1.72. Among them, the diversity index of 18 traits was greater than 1, the diversity index of NO was the smallest (0.76), and the diversity index of LL and PEL was the largest (2.07). The 9 quality traits of the tumor-fruit tea group plants were analyzed, and the diversity index of the quality traits was 0.48-1.27, with a mean of 0.88. Among them, the diversity index of 3 quality traits was greater than 1, TL (1.27) was the largest, and LM (0.48) was the smallest. The 9 quality traits showed a total of 29 types of variation, with high diversity.

[0079] Further characteristic analysis of each quality trait is shown in Table 5. The results show that the middle leaf type accounts for 43.87% in total; the lanceolate leaf type accounts for 50.94% of the total; the leaf base is mainly wedge-shaped (63.21%); the leaf color types are relatively rich, mainly green (35.85%) and dark green (29.25%); most of the leaf margins are wavy (85.85%); the leaf tip type is acuminate, accounting for 48.58% of the total; the fruit cracking type is longitudinal cracking, accounting for 81.60% of the total; the white flower color accounts for 90.57% of the total; the proportion of the sepal tip type at the tip pointed (51.89%) and the tip rounded (48.11%) is not much different.

[0080] In general, the phenotypic diversity index of quantitative traits was much greater than that of qualitative traits, indicating the relative abundance of quantitative traits, and the mean diversity index was flower (1.53) > leaf (1.46) > fruit (1.40).

[0081] Table 5 Frequency and diversity index of each classification of quality traits

[0082]

[0083] 4.4 Correlations between phenotypic traits

[0084] Through the correlation analysis of 30 phenotypic traits of 18 populations of the tumor-fruit tea group, we found Figure 2 The results showed that there were correlations of varying degrees among the 30 phenotypic traits, of which 211 pairs were extremely significantly or significantly correlated, 169 pairs were positively correlated, and 42 pairs were negatively correlated.

[0085] There are 149 pairs in flower, 12 pairs in FC, which are negatively correlated with NC, LV, NP, TL, and CL, and positively correlated with LS, NS, SDL, FST, FD, FL, and PW; there are 11 pairs in CA, which are negatively correlated with NC, LV, and LB, and positively correlated with NO, NS, FST, FD, STL, CL, PW, and CW; there are 15 pairs in NC, which are negatively correlated with LSI, NOS, LL, LB, PEL, SDL, FC, FST, FD, FL, STL, CL, PL, CA, and CW; there are 17 pairs in CL, which are negatively correlated with NC, SDL, FC, and FL, and positively correlated with NOS, NO, NS, LL, TL, LW, LA, PEL, STL, PL, PW, CA, and CW; there are 18 pairs in CW, which are negatively correlated with NC and LSI, and positively correlated with NOS, LS, NO, NS, LL, TL, LW, LA, FST, FD, FL, STL, CL, PL, PW, and CA; there are 14 pairs in NP There were 12 pairs of PL, which were negatively correlated with NC and positively correlated with LSI, NOS, TF, NO, LL, TL, FST, STL, CL, PW and CW; there were 13 pairs of PW, which were negatively correlated with NP and PEL and positively correlated with FST, NO, NS, FC, FST, FD, FL, CL, PL, CA and CW; there were 21 pairs of STL, which were negatively correlated with LEA and NC and positively correlated with NP, NOS, FSL, NO, NS, LL, TL, LW, LA, LB, PEL, SDL, FST, FD, FL, CL, PL, CA and CW; there were 16 pairs of NOS, which were negatively correlated with NC and positively correlated with NP, LS, TF, LL, TL, LW, LA, LB, PEL, SDL, FD, STL, CL, PL and CW.

[0086] There were 122 pairs of fruits, 7 pairs of TF, positively correlated with NOS, LS, LL, TL, LW, LA, and PL; 18 pairs of FD, negatively correlated with NC and NP, and positively correlated with NOS, FSL, NS, LL, TL, LW, LA, LB, SDL, FC, FST, FL, STL, PW, CA, and CW; 10 pairs of FSL, positively correlated with NO, NS, LL, TL, LW, LA, PEL, FD, STL, and PW; 18 pairs of FL, negatively correlated with CL, LEA, NC, and NP, and positively correlated with NO, NS, LL, TL, LW, LA, LB, SDL, FC, FST, FD, STL, PW, and CW; 17 pairs of FST, negatively correlated with LEA and NC, and positively correlated with NP, NOS, NO, NS, LL, TL, LW, LA, SDL, FC, FD, FL, STL, PL, PW, CA, and CW were positively correlated; SDL had 18 pairs, negatively correlated with LEA, NC, and CL, and positively correlated with LSI, NP, NOS, NO, NS, LL, LW, LA, LB, PEL, FC, FST, FD, FL, and STL; NO had 17 pairs, positively correlated with FSL, NS, LL, TL, LW, LA, LB, PEL, SDL, FST, FL, STL, CL, PL, PW, CA, and CW; NS had 17 pairs, negatively correlated with LEA, and positively correlated with FSL, NO, TL, LW, LA, LB, SDL, FC, FST, FD, FL, STL, CL, PW, CA, and CW.

[0087] There were 149 pairs of leaves; 8 pairs of LEA were significantly or extremely significantly negatively correlated with NS, LL, TL, LW, SDL, FST, FL, and STL; 15 pairs of LB were negatively correlated with NC and CA, and positively correlated with NP, NOS, NO, NS, LL, TL, LW, LA, PEL, SDL, FD, FL, and STL; 6 pairs of LV were positively correlated with LL, TL, LA, and PEL, and negatively correlated with FC and CA; 9 pairs of LSI were negatively correlated with NP, LS, TL, LW, LA, and CW, and significantly positively correlated with LL, SDL, and PL; 21 pairs of LA were negatively correlated with LSI, and positively correlated with LV, NP, NOS, LS, TF, FSL, NO, NS, LL, TL, LW, LB, PEL, SDL, FST, FD, FL, STL, CL, and CW; 9 pairs of LS were negatively correlated with LSI and PEL, and positively correlated with NOS, TF, TL, LW, LA, FC, and CW; There were 23 pairs, negatively correlated with LSI, LEA, and FC, and positively correlated with LV, NP, NOS, LS, TF, FST, NO, NS, LL, LW, LA, LB, PEL, FST, FD, FL, STL, SL, PL, and CW; there were 16 pairs of PEL, negatively correlated with NC, LS, and PW, and positively correlated with LV, NP, NOS, FSL, NO, LL, TL, LW, LA, LB, SDL, STL, and CL; there were 21 pairs of LW, negatively correlated with LEA and LSI, and positively correlated with NP, NOS, LS, TF, FSL, NO, NS, LL, TL, LA, LB, PEL, SDL, FST, FD, FL, STL, CL, and CW; there were 21 pairs of LL, negatively correlated with LEA and NC, and positively correlated with LSI, LV, NOS, TF, FSL, NO, TL, LW, LA, LB, PEL, SDL, FST, FD, FL, STL, CL, PL, and CW.

[0088] 4.5 Correlation analysis between phenotypic traits and geographical and climatic factors

[0089] The correlation analysis of 30 phenotypic traits with longitude, latitude, altitude, annual average temperature, annual average rainfall, and annual average relative humidity was performed. Figure 2B. The results showed that some traits of the plants in the group of tuber tea were significantly correlated with geographical and climatic factors. LSI was significantly positively correlated with longitude (E), while NP, CW, CL, FD, LB, LA, TL, and LW were extremely significantly negatively correlated with longitude; LV was extremely significantly positively correlated with latitude (N), while NOS, STL, CW, LS, PEL, and LL were extremely significantly negatively correlated with latitude; PW, CW, FC, FL, and FD were extremely significantly positively correlated with altitude, while NP and TL were extremely significantly negatively correlated with altitude; NP and LB were extremely significantly positively correlated with annual mean temperature (AMT), while PW, CW, CL, and CA were extremely significantly negatively correlated with annual mean temperature; SDL was extremely significantly positively correlated with annual mean precipitation (AP), while PL, CL, and CA were extremely significantly negatively correlated with annual mean precipitation; LEA and LSI were extremely significantly positively correlated with annual mean relative humidity, while STL, TL, and LW were extremely significantly negatively correlated with annual mean relative humidity. In summary, the phenotypic traits of flowers, fruits and leaves of the tumor-fruit tea group plants are affected by geographical and climatic factors to a certain extent, among which the annual average relative humidity has less influence on the phenotypic traits of flowers of the tumor-fruit tea group plants than other factors, longitude, latitude and annual average rainfall have greater influence on the phenotypic traits of fruits of the tumor-fruit tea group plants, and longitude, latitude and annual average relative humidity have greater influence on the phenotypic traits of leaves of the tumor-fruit tea group plants.

[0090] 4.6 Principal component analysis of phenotypic shape

[0091] R-type cluster analysis was performed on 30 phenotypic traits of 18 populations of the tuber tea group. Figure 3 The results showed that LW, LA, and TL were highly correlated, and the differences between them were not significant. The same was true for FD and FL, so one trait, LW and FD, was retained in each group. The other traits were not highly correlated with each other and could play different roles in plant classification. Finally, 27 phenotypic traits were selected for subsequent principal component analysis.

[0092] The principal component analysis was performed on the 27 phenotypic traits selected. Figure 3B and D. The results showed that there were 10 principal components with eigenvalues ​​greater than 1, with a contribution rate of 3.76%-18.84% and a cumulative contribution rate of 73.49%, which could reflect most of the information of phenotypic traits. In principal component 1, the absolute values ​​of the eigenvalue vectors of CW (0.29), STL (0.28), CL (0.21), and NOS (0.20) were large, mainly reflecting the width of the calyx and the length of the style; the absolute values ​​of the eigenvalue vectors of FD (0.32), FST (0.32), SDL (0.23), NO (0.23), and NS (0.21) were large, mainly reflecting the diameter of the fruit and the thickness of the shell; the absolute values ​​of the eigenvalue vectors of LL (0.30) and LW (0.28) were large, mainly reflecting the width of the leaves. In principal component 2, the absolute values ​​of the eigenvalue vectors of FC (-0.43), PW (-0.36), NP (0.35), and CA (-0.23) are relatively large, which mainly reflects the color of the flower and the number and width of the petals; the absolute value of the eigenvalue vector of FST (-0.22) is relatively large, which mainly reflects the thickness of the fruit shell; the absolute values ​​of the eigenvalue vectors of PEL (0.32), LL (0.29), LW (0.26), LV (0.22), and LB (0.21) are relatively large, which mainly reflects the length of the petiole. In principal component 3, the absolute values ​​of the eigenvalue vectors of CL (0.37), NOS (0.27), FC (-0.23), and CA (0.22) were large, mainly reflecting the length of the calyx and the number of styles; the absolute values ​​of the eigenvalue vectors of SDL (-0.32), FD (-0.22), and TF (0.21) were large, mainly reflecting the length of the seeds; the absolute values ​​of the eigenvalue vectors of LSI (-0.32), LS (0.25), and LEA (0.22) were large, mainly reflecting the leaf shape index. In principal component 4, the absolute values ​​of the eigenvalue vectors of PL (0.45), CL (0.35), and NP (-0.20) were large, mainly reflecting the length of the calyx and petals; the absolute value of the eigenvalue vector of FD (-0.22) was large, mainly reflecting the diameter of the fruit; the absolute values ​​of the eigenvalue vectors of LSI (0.48), LS (-0.33), and LW (-0.23) were large, mainly reflecting the shape characteristics of the leaves. In principal component 5, the absolute values ​​of the eigenvalue vectors of NC (0.34), NP (-0.28), and NOS (-0.21) were large, mainly reflecting the changes in the number of sepals; the absolute values ​​of the eigenvalue vectors of FSL (0.36), NS (0.33), NO (0.28), and SDL (-0.23) were large, mainly reflecting the length of the fruit stalk and the number of seeds; the absolute values ​​of the eigenvalue vectors of LV (0.38) and LB (-0.27) were large, mainly reflecting the logarithm of leaf veins and the type of leaf base.In principal component 6, the absolute values ​​of the eigenvalue vectors of CA (-0.42) and NP (-0.32) were large, mainly reflecting the number of sepal tips and petals; the absolute values ​​of the eigenvalue vectors of TF (0.32), NS (-0.27), and NO (-0.21) were large, mainly reflecting the type of fruit cracking; the absolute values ​​of the eigenvalue vectors of LC (0.34), LS (0.33), LM (-0.23), and LL (0.22) were large, mainly reflecting the color of the leaves. In principal component 7, the absolute values ​​of the eigenvalue vectors of NC (0.25) and STL (-0.20) were large, mainly reflecting the number of sepals; the absolute values ​​of the eigenvalue vectors of NS (-0.33), NO (-0.31), and FST (0.23) were large, mainly reflecting the number of seeds and ovaries; the absolute values ​​of the eigenvalue vectors of LM (0.30), LEA (0.30), PEL (0.29), LB (-0.25), and LC (-0.22) were large, mainly reflecting the leaf margin and leaf tip. In principal component 8, the absolute value of the eigenvalue vector of NC (-0.25) is large, mainly reflecting the number of calyxes; the absolute value of the eigenvalue vector of TF (-0.44) is large, mainly reflecting the type of fruit cracking; the absolute values ​​of the eigenvalue vectors of LM (0.59), LC (0.38), LS (0.23), and LB (0.22) are large, mainly reflecting the color and leaf margin of the leaves. In principal component 9, the absolute values ​​of the eigenvalue vectors of STL (-0.33) and PW (-0.20) are large, mainly reflecting the length of the style; the absolute values ​​of the eigenvalue vectors of FSL (-0.46), NO (0.36), and NS (0.29) are large, mainly reflecting the length of the fruit stalk; the absolute values ​​of the eigenvalue vectors of LEA (0.43) and LM (-0.30) are large, mainly reflecting the leaf tip and leaf margin. In principal component 10, the absolute values ​​of the eigenvalue vectors of NC (0.37), PL (0.29), and CA (-0.28) were large, mainly reflecting the number of calyxes; the absolute values ​​of the eigenvalue vectors of SDL (0.23) and FSL (0.22) were large, mainly reflecting the length of fruits and seeds; the absolute values ​​of the eigenvalue vectors of LEA (0.44), LB (0.25), LM (0.24), LC (-0.22), and LV (-0.20) were large, mainly reflecting the characteristics of leaf tips.

[0093] The scatter plot of the relationship between samples is constructed with PC1 and PC2 as the horizontal and vertical coordinates, see Figure 3The results showed that there were differences in the phenotypic traits among the 18 tumor-fruit tea plant populations. The width of the calyx and the length of the style, the diameter of the fruit and the thickness of the shell, and the width of the leaves had a greater impact on the phenotypic traits of the tumor-fruit tea plants than the color of the flower and the number and width of the petals, the thickness of the shell, and the length of the petiole. The PC1 vector of HKH was large and the PC2 vector was small, indicating that the population as a whole showed the morphological characteristics of red flowers, large fruits, and thick shells; the PC2 of XLG was greater than PC1, indicating that the population as a whole showed morphological characteristics such as small leaves and small petioles. The samples of HKH, XLG, LPL, HBL, and XYL were relatively concentrated, the distribution points of the samples of ZGC, ALL, DLL, ZSL, JBL, and DQY were relatively overlapping, and the distribution of the samples of the remaining populations was relatively scattered.

[0094] The above results indicate that 12 traits, including leaf length, leaf width, leaf color, leaf tip, fruit diameter, shell thickness, flower color, sepal tip, sepal width, petal length, petal width, and number of styles, are the main factors causing the phenotypic differences of the tumor-fruit tea group of plants.

[0095] 4.7 Cluster analysis of the tumor fruit tea group

[0096] In order to further study the relationship between the groups of the tuber-fruit tea group, based on the different performance of 27 phenotypic traits among the 18 groups of tuber-fruit tea plants, the ward method was used for systematic cluster analysis according to the Euclidean distance. The dendrogram can intuitively reflect the closeness of the relationship between the taxa. When the Euclidean distance is 7.5, the 212 tuber-fruit tea plants can be divided into 18 types ( Figure 4 A), indicating that there are obvious differentiation characteristics among different groups. When the Euclidean distance is 10, the 212 tea plants can be divided into 11 types ( Figure 4B): Group I includes 1 subclass, with a total of 10 samples, from Zima Township, Qinglong County, Guizhou Province. Its main characteristics are red flowers, long leaves (average leaf length is 10.83cm), thick shells (average shell thickness is 1.04cm), and large fruit diameter (average diameter is 5.57cm); Group II includes 3 subclasses, with a total of 12 samples, most of which are from Sanjiangkou Town, Xingyi City, Guizhou Province, and Wulong Mountain, Qiutuan Village, Liping County, Guizhou Province. Its main characteristics are white flowers, rounded sepal tips, and elliptical or oblong leaves; Group III includes 1 subclass, with a total of 9 samples, from Dayuji, Yuanhou, Chishui. Its main characteristics are pointed sepal tips, elliptical leaves, or oblong, with serrated leaf margins; Group IV includes 2 subclasses, with a total of 25 samples, from Lao Mao, Tianping Mountain, Longsheng, Guangxi Zhuang Autonomous Region, and Baixianglin, Shuangshan Village, Fengsan Town, Kaiyang County, Guizhou Province. Its main characteristics are rounded sepal tips, 3 or 4 styles, lanceolate leaves, serrated edges, and white flowers; Group V includes 2 subclasses, with a total of 21 samples, from Moganzhai, Libo, Guizhou Province, and Hutoushan, Yuanhou, Chishui, Guizhou Province. Its main characteristics are rounded sepal tips, oblong or lanceolate leaves, and relatively long petioles (average length of 0.95 cm); Group VI includes 5 subclasses, with a total of 25 samples, most of which are mainly from Banpo, Maoba Town, Lichuan City, Hubei Province. Its main characteristics are The sepals have rounded tips, ovate-lanceolate leaves, and long petals (average petal length is 2.78 cm); Group VII includes 2 subclasses, with a total of 17 samples, most of which are mainly from Yuntai Mountain, Shibing County, Guizhou Province. Its main characteristics are rounded sepal tips, ovate leaves, small leaves, relatively short petioles (average length is 0.49 cm), white flowers, and micro-wave-like serrations on the edges of the leaves; Group VIII includes 3 subclasses, with a total of 26 samples, most of which are mainly from Liaokang Village, Zhexiang Town, Wangmo County, Guizhou Province, and Xiangjiaping, Yachang Town, Leye County, Guangxi Zhuang Autonomous Region. Its main characteristics are rounded sepal tips, elliptical or obovate leaves, white flowers, and a large number of petals (10-1 4); Group IX includes 3 subclasses, a total of 22 samples, most of which are mainly from the wasteland beside Liping County, Guizhou Province. Its main characteristics are pointed sepal tips, lanceolate leaves, and white flowers; Group X includes 3 subclasses, a total of 11 samples, most of which are mainly from Jinshagou, Chishui City, Guizhou Province. Its main characteristics are rounded sepal tips, serrated leaf margins, long petioles (average length of 0.98cm), and white flowers; Group XI includes 7 subclasses, a total of 34 samples, most of which are mainly from Jinzhongshan Township, Longlin County, Guangxi Zhuang Autonomous Region, Yehou Valley, Sanjiangkou Town, Xingyi City, Guizhou Province, and Jinyunshan Temple in Chongqing City. Its main characteristics are pointed sepal tips, elliptical leaves, and white flowers. At the same time, according to the cluster analysis results of the tumor tea group plants, the geographical distribution of 18 populations was projected. The results showed that the 212 plant samples were not completely clustered according to the geographical distance of the plants, and some populations overlapped.

[0097] 5. Discussion

[0098] Phenotypic diversity analysis is an important means to study the variation of morphological, physiological and biochemical characteristics of organisms under different environmental conditions. For woody plants, various factors such as the environment in which the species is located, the ecological niche and the interaction with other species will affect the growth, development and physiological processes of the plants, thereby causing variation in the morphology, structure and function of the plants. The plants of the group of tuber tea spread to neighboring provinces with Guizhou as the distribution center, and the environmental factors in their distribution areas are very different. In addition to directly affecting phenotypic variation, these environmental factors can also indirectly affect the phenotypic diversity of woody plants by affecting genetic differentiation. When plants are distributed in different environments, due to the effect of natural selection, some genotypes that can adapt to specific environments may be retained and gradually dominate in the population. Over time, these adaptive genotypic differences may accumulate between populations, leading to genetic differentiation, further exacerbating the degree of phenotypic variation of plants under different environments.

[0099] Phenotypic trait variation plays an important role in plant adaptation to different habitats and evolution. The phenotypic coefficient of variation can reflect the potential of plants to adapt to the environment. The larger the coefficient of variation, the stronger the plant's ability to adapt to the environment. In the present invention, the diversity index of 30 phenotypic traits of the tumor-fruit tea group plants is 0.48-2.07, with a mean of 1.47, and 21 trait diversity indexes are greater than 1, which indicates that the phenotypic traits of the tumor-fruit tea group plants have higher uniformity and richness. At the same time, the coefficients of variation of the 21 quantitative traits of the tumor-fruit tea group plants are different, and the variation range of each trait is significantly different, indicating that there are differences in the adaptation of different traits to environmental changes; the mean value of the coefficient of variation (CV) is 14.88%, and the maximum is 27.98%, indicating that its species diversity is high and has a strong ability to adapt to the environment. Further research found that the traits of different groups of the tumor tea group plants vary greatly, while most of the traits within the group do not vary much; the average phenotypic differentiation coefficient of 30 phenotypic traits of the tumor tea group plants is 61.405, and the variance component mean within the population (32.705%) is less than the variance component mean between populations (47.363%), indicating that the differentiation of phenotypic traits of the tumor tea group plants exists both within and between populations, and mainly comes from between populations. We speculate that the main reason for this phenomenon is the difference in climate and environmental conditions, and the discontinuous distribution of most populations in the region has greatly led to the growth and development of species and the gene exchange between populations.

[0100] In the present invention, the mean phenotypic diversity index of 30 phenotypic traits of the tumor fruit tea group is 1.3, which is significantly higher than that of the oil tea group (1.06) and the tea group (0.26-1.38, 0.91), indicating that the tumor fruit tea group plants have rich phenotypic diversity. In addition, there is no significant correlation between the fragrance diversity index and the coefficient of variation of the 30 phenotypic traits of the tumor fruit tea group plants.

[0101] Each plant trait is not isolated, but adapts to changes in the external environment by forming a replication relationship between mutual synergy or trade-offs (Xian et al., 2024). In the present invention, first, the number of petals (NP) is negatively correlated with the petal size (length (PL) and width (PW)), indicating that there is a "one-up and one-down" trade-off relationship in the allocation of reproductive resources in the tumor-fruit tea group plants. This is consistent with the trade-off hypothesis between the number and size of flowers in the plant reproductive allocation theory. Secondly, the leaf area is extremely significantly positively correlated with the number of petals and fruit size, and negatively correlated with the flower size (petal length and width), indicating that the larger the leaf area of ​​the tumor-fruit tea group plants, the more petals, the larger the fruit, and the smaller the flower shape. This may be because larger leaves can retain more water, which is conducive to promoting the formation of flower and fruit organs, increasing the number of petals and the size of the fruit; however, it is difficult for plants to evenly distribute resources, so a trade-off strategy is adopted between the number of petals and the size of the flower. At the same time, there is a significant correlation between leaf phenotypic traits and some flower phenotypic traits, indicating that there is a mutual dependence between the reproductive organs and nutritional organs of the plants in the Tumor Fruit Tea Group. The phenotypic traits of flowers, fruits and leaves of the Tumor Fruit Tea Group are significantly correlated with each other as a whole, indicating that there is a complex and close relationship between the phenotypic traits, which is similar to Camellia plants such as Camellia oleifera and Camellia oleifera. However, the adaptive mechanisms between these traits may need to be further explored from the perspective of ecological mechanisms and molecular biology.

[0102] Studies have shown that the phenotypic traits of various woody plants are significantly affected by geographical and climatic factors, and different plants have different degrees of adaptability and sensitivity to their environments, resulting in different geographical variation patterns. For example, the seed traits of Vietnamese oil tea are strongly correlated with factors such as longitude, latitude, and altitude. The results of the present invention show that the phenotypic traits of the tumor-fruit tea group plants are significantly affected by longitude, latitude, and altitude. With the increase of longitude, the number of petals, calyx size, and fruit size decrease; with the increase of latitude, the number of veins increases, and the number of styles, the length of styles, the width of calyx, the shape of leaves, the length of petioles, and the size of leaves gradually decrease; with the increase of altitude, the size of petals and fruits increases, and the number of petals and the type of leaves decrease. This is consistent with the characteristics of the Camellia genus, which is mainly distributed in the middle and lower reaches of the Yangtze River and its southern regions, preferring acidic soils and particularly warm and humid environments. At the same time, with the increase of annual average relative humidity and annual average rainfall, the length of seeds increases, and the length of petals and calyx decreases. This shows that different plants have different adaptability to different environments, resulting in different variation patterns. Therefore, in the process of promoting excellent species of the tumor-fruit tea group plants, the adaptability of the improved variety characteristics to environmental factors should be fully considered.

[0103] R-type clustering is a classification of traits, which can reflect the correlation between traits and the rationality of their selection. James first used R-type clustering in quantitative classification, and then it was widely used in woody plants. The present invention selected 30 phenotypic traits to distinguish the plants of the tumor-fruit tea group. According to the research results of R-type clustering and its correlation analysis, LA, TL, and FL of the tumor-fruit tea group plants were deleted, and more representative traits LW and FD were selected. Therefore, 27 relatively ideal phenotypic traits were finally selected, which can be used as classification indicators for the tumor-fruit tea group plants. Principal component analysis can select core indicators from a large number of trait indicators and represent the original data to the greatest extent. The present invention extracted 10 principal components with eigenvalues ​​greater than 1 for 27 phenotypic traits, and the cumulative contribution rate reached 73.49%, which can reflect most of the information of phenotypic traits. The principal component vector scatter plot distinguishes some groups (HKH, XLG) of the tumor-fruit tea group, but most of the populations are relatively concentrated and partially overlapped, which also explains the problems of trait overlap and difficulty in classification and identification of the group. At the same time, by combining principal component analysis with correlation analysis, 12 traits were screened out, including leaf length, leaf width, leaf color, leaf tip, fruit diameter, shell thickness, flower color, sepal tip, calyx width, petal length, petal width, and number of styles. These will serve as the main traits to distinguish and evaluate the plants in the tumor-fruit tea group.

[0104] In traditional classification, there is a great deal of classification controversy in this group. Zhang and Min divided it into 18 and 6 species based on different classification viewpoints. The results of the Q-type cluster analysis based on phenotypic traits in the present invention show that when the genetic distance is equal to 7.5, 212 tumor-fruit tea group plants are divided into 18 groups, indicating that the present invention supports the classification of tumor-fruit tea group plants into 18 species; when the genetic distance is equal to 10, 212 tumor-fruit tea group plants are divided into 11 major categories. This clustering result has both similarities and differences compared with the traditional classification. For example, the clustering results of group V and group VIII show that there are similar phenotypic traits between LBL and XYL, ALL and LYL, which increases the difficulty of distinguishing the two; at the same time, this further supports the research results that XYL and DQY are independent species (Ran et al., 2024). The clustering results of 18 populations of the tumor-fruit tea group are closely related to flower color, petal size, number of petals, sepal tip, style length, shell thickness, fruit diameter, leaf size, leaf margin, leaf shape, and petiole length. The clustering results can provide basic data for hybrid breeding of tumor-fruit tea plants and the study of phenotypic diversity of hybrid offspring. Overall, the phenotypic characteristics of the 18 populations of the tumor-fruit tea group are not completely clustered according to their geographical distribution areas, indicating that geographical isolation is not a key factor in the phenotypic differentiation of the tumor-fruit tea group plant populations. This may be due to the unique geographical environment and climatic factors in the karst area. The populations are in different microclimates, which has led to phenotypic changes in response to climate change. In the field survey, it was found that there are three populations of XYL, DQY, and ZML in Yuanhou Town, Chishui City. The main distribution areas are shrubs, slopes, and valleys. The unique microclimate formed in this area makes the phenotypic diversity of its population extremely complex, and some species different from the three populations have been found. We speculate that this may be a transitional population. Future research can carry out resource evaluation, breeding of improved varieties and classification and identification of the Tumor-fruit Tea Group plants based on these phenotypic traits. At the same time, molecular techniques such as DNA molecular markers can be used to more deeply analyze the diversity of Tumor-fruit Tea Group plant variety resources, providing a solid foundation for the protection of Tumor-fruit Tea Group plant germplasm resources and the efficient creation of new germplasm.

[0105] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for analyzing the diversity of phenotypic traits of plants in the tumor-fruit tea group, characterized in that: The steps include: (1) measuring the phenotypic traits of the plants in the tumor-fruit tea group to be tested; the phenotypic traits include qualitative traits and quantitative traits; The quality traits include flower color, sepal tip, fruit cracking type, leaf type, leaf shape, leaf base, leaf color, leaf margin, and leaf tip; The quantitative traits include calyx number, calyx length, calyx width, petal number, petal length, petal width, style length, style number, fruit diameter, fruit stalk length, fruit length, shell thickness, seed length, ovary number, seed number, leaf length, leaf width, petiole length, leaf area, leaf shape index and vein logarithm; (2) performing nested variance analysis, variation analysis, genetic diversity analysis, correlation analysis, cluster analysis and principal component analysis on the phenotypic traits measured in step (1) to evaluate the diversity of the phenotypic traits of the tumor-fruit tea group plants; The correlation analysis includes correlation analysis between the phenotypic traits and correlation analysis between the phenotypic traits and geographical and climatic factors; the geographical and climatic factors include longitude, latitude, altitude, annual average temperature, annual average rainfall, and annual average relative humidity.

2. The analysis method according to claim 1, characterized in that The nested ANOVA includes calculating the variance components and phenotypic differentiation coefficients of the phenotypic traits between and within populations; The variation analysis includes calculating the mean, standard deviation and coefficient of variation of the phenotypic traits; The genetic diversity analysis includes calculating the Shannon-Wiener diversity index of the phenotypic traits; The cluster analysis includes R-type cluster analysis and Q-type cluster analysis; the R-type cluster analysis is performed using the Pearson correlation coefficient as a metric; the Q-type cluster analysis is performed using the square Euclidean distance as a genetic distance.

3. The analysis method according to claim 1, characterized in that The varieties of the Camellia tea group plants to be tested include Camellia anlungensis Chang, Camellia obovatifolia Chang, Camellia ilicifoliaY.K.Li, Camellia hupehensis Chang, Camellia acutiperulata Chang&Ye, Camelliaacutisepala Chang, Camellia rubimuricata Chang&Z.R.Xu, Camellia tuberculataChien, Camellia lipingensis Chang, Camellia leyeensis Chang&Y.C.Zhong, Camelliarubituberculata Chang&Yu, Camellia pyxidiacea Xu FPChen&C.Y.Deng, Camelliaparvimuricata Chang, Camellia neriifolia Hung T.Chang, Camelliarhytidocarpa Chang&Liang, Camellia atuberculata Chang, Camellia zengii Chang, Camellia rhytidophylla Y. K. Li & M. Z. Yang.

4. Use of the analytical method of claim 1 in distinguishing Camellia rubituberculata Chang & Yu and Camellia parvimuricata Chang, characterized in that: When the analysis method of claim 1 is used for analysis, when PC1 of the principal component analysis is greater than PC2, the plant species of the tumor-fruit tea group to be tested is Camellia rubituberculata Chang&Yu; When PC1 of the principal component analysis is smaller than PC2, the plant variety of the tumor-fruit tea group to be tested is Camellia parvimuricata Chang.

5. Application of the analytical method of claim 1 in determining the planting area of ​​the tuber-fruit tea group plants, characterized in that: According to the correlation analysis results of the phenotypic traits and geographical climatic factors in the analysis method of claim 1, the geographical location of the planting area of ​​the tumor-fruit tea group plants is determined.

6. The use according to claim 5, characterized in that: The judgment method is as follows: the leaf shape index is positively correlated with longitude; the number of petals, calyx width, calyx length, fruit diameter, leaf base, leaf area, leaf type, and leaf width are negatively correlated with longitude; the logarithm of leaf veins is positively correlated with latitude, and the number of styles, style length, calyx width, leaf shape, petiole length, and leaf length are negatively correlated with latitude; the petal width, calyx width, flower color, fruit length, and fruit diameter are positively correlated with altitude, and the number of petals and leaf type are negatively correlated with altitude; the number of petals and leaf base are positively correlated with the annual average temperature, and the petal width, calyx width, calyx length, and sepal tip are negatively correlated with the annual average temperature; the seed length is positively correlated with the annual average rainfall, and the petal length, calyx length, and sepal tip are negatively correlated with the annual average rainfall; the leaf tip and leaf shape index are positively correlated with the annual average relative humidity, and the style length, leaf type, and leaf width are negatively correlated with the annual average relative humidity.

7. Application of the analytical method of claim 1 in distinguishing plant populations of the tea group, characterized in that: According to the cluster analysis results in the analysis method of claim 1, the plants in the tumor-fruit tea group are classified into populations with different genetic relationships.

8. The use according to claim 7, characterized in that: The classification criteria are: red flower color, average leaf length of more than 10 cm, average shell thickness of more than 1 cm, and average fruit diameter of more than 5.5 cm, which is classified as Class I; The flowers are white, the tips of the sepals are rounded, and the leaves are elliptical or oblong, belonging to the same category; Sepals have pointed tips, leaves are elliptical or oblong, and the leaf margins are serrated, belonging to the same category; Sepals are rounded at the tip, styles are 3 or 4, leaves are lanceolate, margins are serrated, flowers are white, and they belong to the same category; The sepals have rounded tips, the leaves are oblong or lanceolate, and the average petiole length is more than 0.9 cm, which is a type; The sepals have rounded tips, the leaves are ovate-lanceolate, and the average length of the petals is more than 2.5 cm, which is a type; The sepals have rounded tips, ovate leaves, small leaves, petiole length less than 0.5 cm on average, white flowers, and leaves with micro-wave-like serrations on the edges, which is a type; The sepals have rounded tips, the leaves are elliptical or obovate, the flowers are white, and the number of petals is more than 10, which is a type; Sepals have pointed tips, leaves are lanceolate, flowers are white, and they are of the same type; The sepals have rounded tips, the leaf margins are serrated, the average petiole length is more than 0.9 cm, the flowers are white, and they belong to the first category; The sepals are pointed at the tip, the leaves are oval, and the flowers are white, which are a type.

9. Use of the analytical method of claim 1 in screening for distinguishing and evaluating the main traits of plants in the group of tuber tea, characterized in that: According to the correlation analysis results and principal component analysis results in the analysis method of claim 1, the main traits used to distinguish and evaluate the plants in the tumor-fruit tea group are determined.

10. The use according to claim 9, characterized in that: The main traits include leaf length, leaf width, leaf color, leaf tip, fruit diameter, fruit shell thickness, flower color, sepal tip, sepal width, petal length, petal width, and style number.

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