Construction of a Core Collection for Morchella Based on Phenotypic Traits from China

Abstract

To rationally utilize Morchella germplasm resources, this study investigated 13 phenotypic traits in 231 Chinese Morchella germplasm accessions. Accessions were stratified by cap color and subjected to comparative analyses using four sampling methods, five sampling intensities, two genetic distance metrics, and four hierarchical clustering algorithms to determine the optimal strategy for core collection construction. The optimal sampling strategy for core collection construction was identified using six evaluation. Phenotypic traits of the core collection were evaluated using genetic diversity eigenvalues, t-tests, F-tests, and systematic clustering, with confirmation via principal component analysis. The results indicate that the logarithmic ratio method yielded the smallest differences in group proportions, making it the optimal sampling method. A 15% sample intensity proved optimal, with Euclidean distance outperforming Mahalanobis distance. The longest-distance method was determined to be the optimal clustering approach. Within the optimal sampling strategy combination, the CR value reached its maximum (97.77%). Ultimately, 34 Morchella germplasm resources were extracted, accounting for 14.72% of the total germplasm (original germplasm). The mean values, standard deviations, and genetic diversity of phenotypic traits were similar between the original germplasm and the core collection. However, the coefficient of variation for quantitative traits showed significant differences. In the t-test, only the maturity period showed a significant difference. In the F-test, only the cap length/width and maturity period showed significant differences. Cluster analysis grouped the germplasm resources of the core collection and the original germplasm into relatively consistent clusters. In principal component analysis, the eigenvalues and cumulative contribution rates of the first four principal components were higher for the core collection than for the original germplasm. This indicates that the core collection eliminated most genetic redundancy while preserving the genetic diversity of the original germplasm. The core collection selection is representative and can be effectively utilized as breeding material. This study provides a reference for the effective utilization and germplasm innovation of Morchella germplasm resources.

1. Introduction

Morchella spp. is one of the most recognized and few cultivable ascomycetes among edible and medicinal fungi [1]. Due to its unique flavor, it is highly favored in the market. With exceptional nutritional value, it is a healthy food rich in protein, low in fat, and abundant in vitamins and minerals. It also possesses medicinal benefits such as boosting immunity, anti-tumor properties, antioxidant effects, and anti-fatigue properties, making it a rare edible fungus that combines medicinal and dietary uses [2,3,4]. Since the successful commercial cultivation of Morchella in 2012, Morchella farming has demonstrated characteristics of “short cycles, low investment, and rapid returns” compared to traditional crops. With significant yield per acre and rapid industry growth, Morchella cultivation and new variety breeding have become key research focuses [5,6]. The most representative varieties currently include Morchella importuna, Morchella sextelata, and Morchella eximia. Morchella importuna stands out for its high vitamin and mineral content and early cultivation period. Morchella sextelata is the primary cultivated variety, known for its strong adaptability, high yield, and balanced amino acid profile. Morchella eximia features a thick texture, rich umami flavor, and high polysaccharide content. These three varieties exhibit significant differences in cultivation methods, nutritional profiles, and applications. However, with the continuous breeding and introduction of new Morchella varieties, the population and quantity of cultivable germplasm resources have gradually expanded. The asexual reproduction characteristic of Morchella has led to unclear germplasm traits and genetic background homogenization in new varieties, hindering the exploration and utilization of Morchella germplasm resources. Therefore, to enhance the utilization efficiency of germplasm resources and enable efficient and precise evaluation of germplasm resources, it is necessary to establish a core collection for Morchella.

Since the concept of core collection was proposed [7,8], scholars worldwide have established core collections for crops [9,10], cash crops [11,12], vegetables [13,14], fruit trees [15,16], and ornamental plants [17,18]. By constructing a core collection, genetic redundancy is eliminated and high-quality germplasm resources are preserved, thereby achieving more efficient germplasm resource management and utilization [8]. The population structure, genetic foundation, genetic characteristics, and morphological traits of crops all influence the construction of a core collection [19]. Molecular markers have become dominant tools in recent years, with commonly used markers including SSRs (simple sequence repeats) and SNPs (single nucleotide polymorphisms). For instance, a small sorghum core collection preserving 100% allelic variation was constructed using 30 SSR marker pairs, while 61,214 SNP markers enabled precise classification of maize germplasm into core, related, and heterogeneous groups. A typical workflow encompasses genotyping, genetic diversity analysis (using Nei’s gene diversity index and Shannon’s index), and structural feature characterization (UPGMA clustering, PCOA analysis). Subsequently, stratified sampling preserves key genetic clusters while minimizing redundancy [20,21]. A primary core collection for common edible fungi species has been established using molecular markers [22,23,24]. Using SSR molecular marker technology in enoki mushrooms, 44 core collection lines were screened from 105 germplasm samples, accounting for 41.90% of all tested germplasm and retaining 100% of alleles. Phylogenetic analysis was conducted on 360 button mushroom germplasm samples based on SNP genotyping, and a core collection comprising 67 germplasm lines was established. However, phenotypic traits serve as fundamental data for core collection construction and provide an intuitive representation of germplasm genetic diversity. Methods based on phenotypic traits for core collection construction are widely applied. As an ascomycete, Morchella exhibit a life cycle vastly different from common edible basidiomycetes. Phenotype-based core collection construction enables efficient conservation of existing Morchella germplasm resources, reduces redundancy in resource preservation, and enhances utilization efficiency. This approach provides a foundational research framework and reference for subsequent Morchella germplasm resource development and breeding efforts.

This study systematically compared four sampling methods and five sampling intensity based on the precise identification of 231 Morchella germplasm accessions and 13 phenotypic traits. It determined the optimal intragroup sampling method and constructed a core collection for Morchella by evaluating different sampling strategies. This aims to provide theoretical foundations and parental materials for the utilization of Morchella germplasm resources and variety breeding.

2. Materials and Methods

2.1. Experimental Materials

A total of 231 germplasm accessions were provided for testing, including 62 specimens of Morchella importuna, 114 specimens of Morchella sextelata, and 55 specimens of Morchella eximia, supplied by the Sichuan Institute of Edible Fungi. Detailed information is presented in

Table 1. Test strains.

No.	Germplasm	Species	Source
1	M1–M48, M48–M60, M82, M215–M217	M. importuna	Chengdu City, Sichuan Province
2	M62–M72, M86–M106, M110–M111, M115–M119, M125, M129–M148, M153–M160, M218–M219, M221–M222, M227, M230, M236–M237	M. sextelata	Chengdu City, Sichuan Province
3	M73–M74, M220		Shiyan City, Hubei Province
4	M75, M112, M223–M224		Mianyang City, Sichuan Province
5	M76		Zunyi City, Guizhou Province
6	M77		Dazhou City, Sichuan Province
7	M78, M232–M233		Hanzhong City, Shaanxi Province
8	M79		Taiyuan City, Shanxi Province
9	M80–M81		Lanzhou City, Gansu Province
10	M82–M83, M114		Nanyang City, Henan Province
11	M84–M85		Neijiang City, Sichuan Province
12	M107–M109		Jinan City, Shandong Province
13	M113		Guangzhou City, Guangdong Province
14	M120		Changsha City, Hunan Province
15	M121–M123		Suzhou City, Anhui Province
16	M124, M229, M231, M240–M241		Kunming City, Yunnan Province
17	M126, M234–M235		Bazhong City, Sichuan Province
18	M127		Shenyang City, Liaoning Province
19	M225–M226		Hohhot City, Inner Mongolia Autonomous Region
20	M228		Shijiazhuang City, Hebei Province
21	M161, M166–M175, M177–M178, M180, M182–M185, M187–M197, M199–M202, M205, M207–M214, M238–M239	M. eximia	Chengdu City, Sichuan Province
22	M162, M165		Hanzhong City, Shanxi Province
23	M163		Yibin City, Sichuan Province
24	M164		Kangding City, Sichuan Province
25	M176		Urumqi City, Xinjiang Uygur Autonomous Region
26	M179		Mianyang City, Sichuan Province
27	M181		Shenyang City, Liaoning Province
28	M186, M242		Kunming City, Yunnan Province
29	M198		Bazhong City, Sichuan Province

.

2.2. Field Trial

December 2023–March 2025 at the Modern Agricultural Science and Technology Innovation Demonstration Park of Sichuan Academy of Agricultural Sciences (104.12′42.48″ E, 30.46′37.82″ N, elevation 472 m). Managed using the Sichuan Morchella high-efficiency cultivation model [25].

2.3. Data Organization

The phenotypic trait survey referenced NY/T 4221-2022 “Guidelines for the conduct of tests for distinctness, uniformity and stability Morchella” [26]. Trait descriptions and scoring methods are detailed in Figure 1 and

Table 2. Phenotypic Trait Description and Scoring.

No.	Characteristics	Characteristics Type	Method of Observation
C1	Cap: Shape (Triangular = 1; Ovate = 2; Rectangular = 3)	QL	VG
C2	Cap: Lateral Ridges Density (Sparse = 1; Moderate = 2; Dense = 3)	QL	VG
C3	Cap: Color (Light Brown = 1; Medium Brown = 2; Dark Brown = 3; Reddish Brown = 4)	QL	VG
C4	Cap: Base Shape (Concave = 1; Convex = 2)	QL	VG
C5	Cap: Length	QN	MS
C6	Cap: Width	QN	MS
C7	Cap: Length/Width Ratio	QN	MS
C8	Cap: Thickness	QN	MS
C9	Stem: Length	QN	MS
C10	Stem: Diameter	QN	MS
C11	Stem: Longitudinal Section Shape (Rectangular = 1; Trapezoidal = 2)	QL	VG
C12	Stem: Color (White = 1; Yellowish-white = 2)	QL	VG
C13	Maturity Period (Early = 93; Mid = 103; Late = 113)	QL	MG

Characteristics type: QL: qualitative characteristic; QN: quantitative characteristic. Method of observation: MG: single measurement of a group of fruiting body or parts of fruiting body; MS: measurement of a number of individual fruiting body or parts of fruiting body; VG: visual assessment by a single observation of a group of fruiting body or parts of fruiting body [27].

.

Raw data were organized using Excel 2019. Descriptive statistical analysis of phenotypic traits included minimum, maximum, mean (X), standard deviation (S), and coefficient of variation (CV). For frequency distribution calculations, quantitative traits were categorized into 10 levels, with adjacent levels differing by 0.5S. Level 1 < X − 2S, Level 10 ≥ X − 2S. The Shannon–Weaver genetic diversity index (H′) was calculated. All phenotypic data were standardized (z-score normalization) prior to multivariate analyses (PCA, cluster analysis), cluster analysis and principal component analysis were performed using Chiplot.

The calculation methods for key indicators are as follows:

CV = S/X × 100%

H′ = −∑P_ilnP_i (i = 1, 2, …, n)

Pi: The percentage of germplasm units at the i-th level of a certain trait relative to the total number of units; ln: Denotes the natural logarithm.

2.4. Screening of Sampling Strategies

Based on cap color, 231 Morchella germplasm accessions were grouped into four categories: light brown (I), medium brown (II), dark brown (III), and reddish-brown (IV). Five sampling intensity were set at 10%, 15%, 20%, 25%, and 30%. Within these groups, four sampling methods—simple proportion (P), logarithmic proportion (L), square root proportion (S), and diversity proportion (G) [13]—were compared to identify the method with the strongest corrective capacity for intra-group sampling. The sampling method with the strongest corrective ability for intra-group sampling was selected. Under the optimal sampling method, two genetic distances (Euclidean and Mahalanobis) and four systematic hierarchical clustering algorithms (class-means, minimum distance, maximum distance, and sum of squared deviations) were applied for stepwise clustering analysis at different sampling intensity [28]. This identified the optimal sampling strategy and established the Morchella Core collection The calculation formula is as follows:

$$\begin{gathered} \mathit{P}={\displaystyle \frac{{\mathit{X}}_{\mathit{i}}}{{\sum }_{\mathit{i}}{\mathit{X}}_{\mathit{i}}}} \\ \mathit{L}={\displaystyle \frac{\log {\mathit{X}}_{\mathit{i}}}{{\sum }_{\mathit{i}}\log {\mathit{X}}_{\mathit{i}}}} \\ \mathit{S}={\displaystyle \frac{sqrt{\mathit{X}}_{\mathit{i}}}{{\sum }_{\mathit{i}}sqrt{\mathit{X}}_{\mathit{i}}}} \\ {\mathit{H}}_{\mathit{i}}=-\left(\sum _{\mathit{i}=1}^{\mathit{k}}\sum _{\mathit{j}=1}^{\mathit{l}}{\mathit{P}}_{ij}ln{\mathit{P}}_{ij}\right)/\mathit{n} \\ \mathit{G}={\displaystyle \frac{{\mathit{H}}_{\mathit{i}}}{{\sum }_{\mathit{i}}{\mathit{H}}_{\mathit{i}}}} \end{gathered}$$

X_i denotes the number of samples in group i, and H_i denotes the diversity index of group i.

2.5. Evaluation of Core Collection

Maximize the retention of genetic variation from the original population when constructing the core collection. A comprehensive evaluation of the representativeness of core collection was conducted using six assessment parameters [13,17,29], namely Mean Difference Percentage (MD), Variance Difference Percentage (VD), Range Compliance Rate (CR), Change Rate of Coefficient of Variation (VR), Phenotypic Frequency Variance (VPF), and Phenotypic Retention Ratio (RPR). To evaluate and validate the representativeness of the screened core collection for the original germplasm, Excel 2019 was used to calculate the six indicators. The minimum, maximum, mean, standard deviation, coefficient of variation (CV), and Shannon–Weaver genetic diversity index (H′) of phenotypic traits for both the original germplasm and core collection were statistically analyzed, followed by t-tests and F-tests.

The evaluation parameters are as follows:

$$\mathrm{M}\mathrm{D}={\displaystyle \frac{S_t}{\mathrm{n}}}\times 100\%$$

Among these, S_t denotes the number of traits exhibiting significant mean differences (p = 0.05) between the core collection and the original germplasm as determined by the t-test, while n represents the total number of traits.

$$\mathrm{V}\mathrm{D}={\displaystyle \frac{S_F}{\mathrm{n}}}\times 100\%$$

where S_F denotes the number of traits exhibiting significant mean differences (p = 0.05) between the core collection and the original germplasm F test, and n represents the total number of traits.

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{1}{n}}\sum _{i=1}^n{\displaystyle \frac{R_{C\left(i\right)}}{R_{l\left(i\right)}}}\times 100\%$$

R_C(i): Range of the i-th trait in the core collection; R_l(i): Range of the i-th trait in the parental germplasm; n: Total number of traits.

$$\mathrm{V}\mathrm{R}=\sum _{i=1}^n{\displaystyle \frac{{CV}_{C\left(i\right)}}{{CV}_{I\left(i\right)}}}\times 100\%$$

CV_C(i): Coefficient of variation for trait i in the core collection; CV_I(i): Coefficient of variation for trait i in the original germplasm.

$$\mathrm{V}\mathrm{P}\mathrm{F}={\displaystyle \frac{\sum \frac{{(P_{ij} - {\overline{P}}_{ij})}^2}{M_i - 1}}{n}}$$

P_ij: Frequency of the j th phenotype for the i th trait; P_ij: Average of the frequencies of each phenotype for the i th trait.

$$\mathrm{R}\mathrm{P}\mathrm{R}=\sum _i{M}_i/\sum _i{M}_{io}$$

M_io: Number of phenotypes for trait i in the original germplasm; M_i: Number of phenotypes for trait i in the core collection.

2.6. Core Collection Validation

Principal component analysis and scatter plots were used to compare the dispersion levels of various traits among the original germplasm and the constructed core collection, thereby confirming the effectiveness of the core collection construction [29].

3. Results

3.1. Establishment of Core Collection

3.1.1. Analysis of Genetic Diversity in Morchella Germplasm Resources

As shown in Appendix A 

Table A1. Frequency distribution and diversity of 13 phenotypic characteristics.

No.	Frequency Distribution (%)										Genetic Diversity (H’)
	1	2	3	4	5	6	7	8	9	10
C1	3.90	91.34	4.76								0.35
C2	4.33	77.92	17.75								0.64
C3	14.29	34.63	15.15	35.93							1.30
C4	41.56	58.44									0.68
C5	2.60	3.03	10.39	13.42	19.48	19.05	20.35	4.76	4.76	2.16	2.04
C6	1.30	3.46	10.39	16.45	18.18	24.68	10.39	8.66	3.03	3.46	2.03
C7	1.73	3.03	7.79	18.18	23.38	19.48	12.12	6.06	5.19	3.03	2.03
C8	4.33	10.39	15.15	22.51	22.51	12.55	5.19	3.90	3.46		1.99
C9	0.87	3.03	8.23	19.05	24.68	16.88	12.99	7.36	3.46	3.46	2.00
C10	0.43	2.60	15.58	16.45	16.02	17.32	15.15	7.36	5.63	3.46	2.06
C11	9.52	90.48									0.31
C12	6.49	93.51									0.24
C13	6.93	69.26	23.81								0.78

, the genetic diversity indices for seven qualitative traits ranged from 0.24 to 1.30. Among these, the cap color index exhibited the highest value (1.30), dominated by medium brown (34.63%) and reddish brown (35.93%). The genetic diversity index for stem color was the lowest (0.24), dominated by yellowish-white, accounting for 93.51%. The varying degrees of genetic diversity among qualitative traits indicate that the 231 Morchella germplasm accessions exhibit good phenotypic diversity, making them suitable for constructing a core collection resource.

The magnitude of the coefficient of variation reflects the degree of variation in each trait among Morchella germplasm resources, indicating the potential for variation in these traits.

Table 3. Diversity analysis of numerical characteristics of 231 germplasm accessions.

Characteristics	Min	Max	Average Value	Standard Deviation	Coefficient of Variation
C5	30.85	62.3	47.74	5.79	12.12
C6	16.95	43.47	28.67	3.79	13.22
C7	0.94	2.79	1.69	0.26	15.64
C8	5.05	14.03	7.37	1.34	18.23
C9	13.88	49.58	26.56	5.10	19.22
C10	7.22	25.35	15.33	3.91	25.49

shows that the coefficients of variation for quantitative traits, from highest to lowest, were: stem diameter (25.49%) >stem length (19.22%) > cap thickness (18.23%) > cap length-to-width ratio (15.64%) > cap width (13.22%) > cap length (12.12%). with variation ranges from 12.12% to 25.49% and an average coefficient of variation of 17.32%. Among these, stem diameter exhibited the highest coefficient of variation (7.22–25.35 mm), while cap length showed the lowest (30.85–62.30 mm). The substantial variation among traits indicates significant potential for genetic improvement in these germplasm resources. The average genetic diversity index for the six quantitative traits was 2.03. The cap length-to-width ratio exhibited the lowest genetic diversity index (1.99), while all other traits exceeded 2.00. This confirms the high genetic diversity and rich germplasm resource types, facilitating the establishment of a core collection.

3.1.2. Determination of Grouping and Sampling Methods

Based on cap color, the 231 Morchella germplasm accessions were classified into four groups: Group I (light brown), Group II (medium brown), Group III (dark brown), and Group IV (reddish brown). These groups accounted for 14.29%, 34.63%, 15.15%, and 35.93% of the original germplasm, respectively. Four sampling methods and five sample strategies were used to calculate the number of specimens in each group (

Table 4. Sampling Method.

Sampling Method	Data Grouping	Proportion of Each Group	Total Sample Intensity					Original Germplasm
			10%	15%	20%	25%	30%	Number with in the Group	Intra-Group Proportion
Simple Proportion	I	13.04	3	5	7	8	10	33	14.29
	II	34.78	8	12	16	20	24	80	34.63
	III	17.39	4	5	7	9	11	35	15.15
	IV	34.78	8	12	17	21	25	83	35.93
Logarithmic Proportion	I	22.06	5	7	10	13	15	33	14.29
	II	27.64	6	9	13	16	19	80	34.63
	III	22.43	5	8	11	13	16	35	15.15
	IV	27.87	7	10	13	16	20	83	35.93
Square Root Proportion	I	19.33	4	7	9	11	13	33	14.29
	II	30.10	7	10	14	17	21	80	34.63
	III	19.91	5	7	9	12	14	35	15.15
	IV	30.66	7	10	15	18	22	83	35.93
Diversity Proportion	I	21.34	5	7	10	12	15	33	14.29
	II	28.19	6	9	13	16	19	80	34.63
	III	22.24	5	8	10	13	16	35	15.15
	IV	28.22	7	10	14	17	20	83	35.93

). The light brown group contained the fewest specimens. The proportions within each group, arranged from smallest to largest, were: simple proportion (13.04%), square root proportion (19.33%), diversity proportion (21.34%), and logarithmic proportion (22.06%). Red-brown germplasm had the highest number, with proportions increasing in the following order: logarithmic proportion, diversity proportion, square root proportion, and simple proportion, corresponding to 27.87%, 28.22%, 30.66%, and 34.78%. At different sampling intensity, the differences in proportions among groups were relatively small for the logarithmic proportion, but significant for the simple proportion, square root proportion, and diversity proportion. Therefore, using the logarithmic proportion as the sampling method to construct the core collection is more reasonable.

3.1.3. Sampling Strategy Determination

Using a logarithmic proportional sampling method, we constructed 40 sets of candidate core collection using two genetic distance measures (Euclidean distance and Mahalanobis distance), four hierarchical clustering algorithms (group-mean method, minimum distance method, maximum distance method, and sum of squared deviations method), and five sampling intensity (10%, 15%, 20%, 25%, and 30%) (see

Table A2. Comparison of 40 Sets of Candidate Core Collection Evaluation Parameters.

Clustering Distance	Clustering Method	Overall Sampling Proportion	Core Collection	Percentage of Mean Difference (MD/%)	Percentage of Variance Difference (VD/%)	Coincidence Rate of Range (CR/%)	Change Rate of Coefficient of Variation (VR/%)	Variance of Phenotypic Frequency (VPF)	Phenotypic Retention Rate (RPR/%)
Euclidean Distance (D1)	Average Linkage (C1)	10%	D1C1-10	0.00	33.34	87.30	135.78	0.07	52.00
	Single Linkage (C2)		D1C2-10	0.00	20.92	93.64	141.71	0.06	56.00
	Complete Linkage (C3)		D1C3-10	0.00	32.96	91.84	135.03	0.10	56.00
	Ward’s Method (C4)		D1C4-10	0.00	37.92	89.30	123.29	0.10	52.00
	Average Linkage (C5)	15%	D1C5-15	0.00	36.96	94.01	125.85	0.08	78.00
	Single Linkage (C6)		D1C6-15	0.00	25.05	95.13	136.94	0.06	81.00
	Complete Linkage (C7)		D1C7-15	0.00	44.55	97.77	139.34	0.09	75.00
	Ward’s Method (C8)		D1C8-15	0.00	50.25	94.74	129.07	0.10	72.00
	Average Linkage (C9)	20%	D1C9-20	0.00	35.19	95.63	127.18	0.08	101.00
	Single Linkage (C10)		D1C10-20	0.00	34.37	95.18	132.71	0.08	105.00
	Complete Linkage (C11)		D1C11-20	0.00	43.97	88.42	119.36	0.08	122.00
	Ward’s Method (C12)		D1C12-20	0.00	44.04	89.14	111.60	0.10	98.00
	Average Linkage (C13)	25%	D1C13-25	0.00	40.12	90.76	117.92	0.08	122.00
	Single Linkage (C14)		D1C14-25	0.00	29.09	95.18	130.64	0.07	125.00
	Complete Linkage (C15)		D1C15-25	0.00	45.88	97.08	122.73	0.09	115.00
	Ward’s Method (C16)		D1C16-25	0.00	43.83	96.29	119.88	0.09	117.00
	Average Linkage (C17)	30%	D1C17-30	0.00	47.73	95.63	122.60	0.08	141.00
	Single Linkage (C18)		D1C18-30	0.00	30.66	95.18	125.24	0.08	144.00
	Complete Linkage (C19)		D1C19-30	0.00	49.62	97.08	119.68	0.09	133.00
	Ward’s Method (C20)		D1C20-30	0.00	41.28	96.29	117.89	0.09	137.00
Mahalanobis Distance (D2)	Average Linkage (C21)	10%	D2C21-10	0.00	31.39	91.29	132.25	0.09	55.00
	Single Linkage (C22)		D2C22-10	0.00	19.95	92.72	137.35	0.07	54.00
	Complete Linkage (C23)		D2C23-10	0.00	14.57	91.05	138.83	0.06	58.00
	Ward’s Method (C24)		D2C24-10	0.00	35.48	92.05	129.32	0.07	57.00
	Average Linkage (C25)	15%	D2C25-15	0.00	33.77	95.31	129.62	0.08	78.00
	Single Linkage (C26)		D2C26-15	0.00	35.37	95.47	136.25	0.07	80.00
	Complete Linkage (C27)		D2C27-15	0.00	21.98	93.51	131.23	0.07	75.00
	Ward’s Method (C28)		D2C28-15	0.00	34.65	94.17	131.02	0.07	78.00
	Average Linkage (C29)	20%	D2C29-20	0.00	32.08	95.49	128.52	0.07	101.00
	Single Linkage (C30)		D2C30-20	0.00	32.15	95.52	131.94	0.07	104.00
	Complete Linkage (C31)		D2C31-20	0.00	22.41	93.51	126.56	0.07	98.00
	Ward’s Method (C32)		D2C32-20	0.00	37.35	94.53	125.37	0.08	99.00
	Average Linkage (C33)	25%	D2C33-25	0.00	32.79	95.52	126.40	0.08	121.00
	Single Linkage (C34)		D2C34-25	0.00	29.08	95.52	128.05	0.07	121.00
	Complete Linkage (C35)		D2C35-25	0.00	30.71	95.13	124.68	0.08	120.00
	Ward’s Method (C36)		D2C36-25	0.00	39.90	95.52	126.68	0.08	121.00
	Average Linkage (C37)	30%	D2C37-30	0.00	34.57	95.52	121.82	0.08	137.00
	Single Linkage (C38)		D2C38-30	0.00	33.91	95.52	125.14	0.07	143.00
	Complete Linkage (C39)		D2C39-30	0.00	35.24	95.18	121.77	0.08	140.00
	Ward’s Method (C40)		D2C40-30	0.00	46.35	95.52	121.38	0.08	137.00

). Core collection uniformity is considered good and representative when MD is below 20%, CR exceeds 80%, and VR surpasses 100%.

Comparing the two genetic distances reveals that the MD value is 0. For Euclidean distance, three datasets have VD values below 30%, while five datasets have VD values below 30% for Mahalanobis distance. The minimum VPF value is 0.06. Among Euclidean distances, two datasets have VPF values of 0.06, while one dataset has a VPF value of 0.06 for Mahalanobis distance. The maximum values for CR and VR were observed in Euclidean distance. RPR values were generally higher in Euclidean distance than in Mahalanobis distance, indicating that Euclidean distance is more suitable for constructing the core collection of Morchella.

Comparing four hierarchical clustering algorithms, the Longest Distance method yielded the smallest VD value (14.57%) and VPF value (0.06). The CR value (97.77%) was highest in the maximum distance method. The VR values were relatively large in both the minimum distance and maximum distance methods using Euclidean distance. The RPR value (144.00%) was highest in the minimum distance method using Euclidean distance. The minimum distance and maximum distance methods using Mahalanobis distance yielded similar results. After comparison, the maximum distance method was selected as the system clustering method.

At different sampling levels, the VD value shows an increasing trend with sampling size, slowing after 15% sampling; the MD value (0.00%) remains consistent; the VPF value exhibits minor variations. The CR value first increases then decreases, reaching its maximum (97.77%) at a sampling intensity of 15%. The VR value shows a decreasing trend, with the rate of decline accelerating when the sampling intensity exceeds 15%. The RPR value increases with the sampling intensity. After comparing the variation patterns of these six core collection evaluation parameters, a sampling intensity of 15% is suitable for core collection construction.

In summary, under the log-proportional sampling method, the Euclidean distance was selected as the genetic distance, the maximum distance clustering method was chosen, and a sampling intensity of 15% (D1C7-15) was adopted as the sampling strategy for core collection. For detailed strain information, please refer to Appendix A 

Table A3. Core Collection information.

Germplasm	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13
M7	1	2	1	1	42.09	30.30	1.39	10.23	49.58	17.32	2	2	117
M242	3	2	1	1	62.30	16.95	0.94	5.09	13.88	8.19	2	2	117
M172	1	2	1	1	61.04	18.97	3.22	5.11	16.53	9.14	2	2	98
M162	2	2	1	2	61.04	22.32	1.12	5.28	18.38	9.48	1	2	117
M184	2	2	1	1	60.17	22.65	1.26	5.51	18.65	9.65	2	2	107
M190	2	2	1	1	59.10	22.98	1.26	5.68	19.49	9.68	2	2	98
M176	2	2	1	1	57.89	23.51	1.30	5.70	20.56	10.00	1	2	107
M167	2	2	2	1	46.96	30.05	1.56	6.40	23.01	10.61	2	2	117
M215	1	2	2	1	45.50	42.20	1.08	7.67	20.59	19.23	1	2	107
M4	2	2	2	1	55.66	33.06	1.68	10.55	34.50	18.11	1	2	117
M82	2	2	2	2	46.27	24.09	1.92	5.11	36.85	20.96	1	2	117
M51	2	2	2	1	60.17	30.26	1.99	8.65	42.18	17.94	2	2	96
M202	2	2	2	1	62.30	22.32	2.79	7.55	22.77	10.55	2	2	96
M208	2	2	2	1	61.04	29.23	2.09	6.05	32.92	10.86	2	2	103
M23	2	2	2	1	39.21	26.88	1.46	5.09	24.37	17.99	2	2	98
M58	2	1	2	2	57.89	32.94	1.76	8.58	21.26	15.47	1	2	103
M219	2	2	3	2	34.63	16.95	2.04	7.42	23.39	9.65	2	2	103
M225	2	2	3	2	45.23	35.87	1.26	9.39	24.09	23.90	2	2	105
M209	2	2	3	2	47.63	30.89	1.54	7.81	26.46	9.14	2	2	105
M241	2	2	3	2	30.85	24.58	1.26	5.85	13.88	8.19	2	2	117
M238	1	2	3	2	44.09	28.06	1.57	6.25	19.49	16.32	2	2	98
M90	2	2	3	2	48.88	28.87	1.69	6.77	34.30	20.47	2	2	103
M117	2	2	3	2	50.93	36.79	1.38	8.09	37.20	18.05	2	2	103
M220	2	1	3	2	52.52	41.14	1.28	9.98	28.54	22.49	2	1	107
M84	2	2	4	2	37.84	29.20	1.30	7.22	39.80	22.15	2	2	117
M222	2	2	4	2	34.57	36.94	0.94	7.42	18.38	22.35	2	1	107
M230	2	2	4	2	48.85	43.47	1.12	14.03	22.30	21.93	2	2	103
M218	2	2	4	2	35.41	35.51	1.00	8.67	30.73	25.35	2	1	107
M221	3	3	4	2	32.21	22.98	1.40	5.75	21.08	14.38	2	2	98
M121	2	2	4	2	37.78	18.97	1.99	5.51	33.05	10.87	2	2	103
M102	2	2	4	2	46.15	30.29	1.52	8.35	23.32	21.34	2	2	103
M140	2	2	4	2	50.46	30.93	1.63	5.70	30.39	12.53	2	2	103
M100	2	3	4	2	41.41	23.81	1.74	6.18	28.09	10.85	2	2	103
M232	2	2	4	2	46.39	23.51	1.97	5.28	16.53	11.27	2	2	103

.

3.2. Core Collection Evaluation

3.2.1. Comparison of Eigenvalues Between Original Germplasm and Core Collection

As shown in

Table 5. Comparison of Characteristic Values between Original Germplasm and Core Collection.

Characteristics	Coefficient of Variation		Genetic Diversity		Average Value			Standard Deviation
	Original Germplasm	Core Collection	Original Germplasm	Core Collection	Original Germplasm	Core Collection	p Value (t-Test)	Original Germplasm	Core Collection	p Value (F-Test)
C1	14.44	21.43	0.35	0.58	2.01	1.94	0.24	0.29	0.42	0.24
C2	21.10	17.15	0.64	0.44	2.13	2.00	0.10	0.45	0.34	0.10
C3	40.21	42.47	1.30	1.38	2.73	2.62	0.59	1.10	1.11	0.59
C4	31.10	30.04	0.68	0.67	1.58	1.62	0.71	0.49	0.49	0.71
C5	12.12	19.71	2.04	1.84	47.74	48.37	0.59	5.79	9.53	0.59
C6	13.22	24.18	2.03	1.90	28.67	28.46	0.79	3.79	6.88	0.79
C7	15.64	30.75	2.03	1.78	1.69	1.57	0.19	0.26	0.48	0.04 *
C8	18.23	27.69	1.99	1.85	7.37	7.17	0.59	1.34	1.99	0.47
C9	19.22	32.42	2.00	1.89	26.56	26.07	0.64	5.10	8.45	0.64
C10	25.49	35.26	2.06	1.83	15.33	15.19	0.85	3.91	5.35	0.85
C11	15.41	20.91	0.31	0.47	1.90	1.82	0.15	0.29	0.38	0.15
C12	12.73	14.84	0.24	0.30	1.94	1.91	0.62	0.25	0.28	0.62
C13	5.13	6.46	0.78	0.99	101.81	105.97	0.00 *	5.23	6.85	0.00 *

* represented significant correlation (p < 0.05).

and Figure 2, the mean values, standard deviations, and genetic diversity of phenotypic traits in the original germplasm and core collection are similar, but the coefficient of variation for quantitative traits exhibits significant differences. The t-test indicates that maturity period differs among phenotypic traits, while differences in other traits are not significant. In the F-test, only cap length/width and maturity period showed significant differences. The standard deviation of phenotypic traits in the core collection was generally higher than that in the original germplasm. This may be because the core collection, after multi-level sampling, excluded more similar germplasm resources, reducing genetic redundancy between populations and increasing mutation rates. While reducing the number of germplasm resources, it still maintains strong representativeness.

3.2.2. Cluster Analysis of Original Germplasm and Core Collection

Cluster analysis of both the original germplasm and core collection based on 13 phenotypic traits (Figure 3) yielded four major clusters. Minimal differences existed between the two germplasm samples, as repeated clustering significantly reduced genetic redundancy among germplasm. The core collection fully expressed the traits of the original germplasm, enabling the differentiation of relationships between different varieties even at greater genetic distances. However, the core collection retained the structural characteristics of all samples, with no significant alteration to the fundamental structure of the population. This indicates that the strategy for constructing the core collection was reasonably sound.

3.3. Identification of Core Collection

Principal component analysis was employed to identify core collection (

Table 6. Comparison Table of Principal Components between Origina Germplasm and Core Collection.

Principal Component	Eigen Value		Contribution Rate (%)		Total Contribution Rate (%)
	Original Germplasm	Core Collection	Original Germplasm	Core Collection	Original Germplasm	Core Collection
1	3.09	3.65	23.78	28.04	23.78	28.04
2	2.01	2.43	15.46	18.71	39.25	46.75
3	1.62	1.63	12.43	12.57	51.68	59.31
4	1.17	1.21	9.00	9.33	60.67	68.64

and

Table 7. Table of Principal Components for Primary Origina Germplasm and Core Collection.

Characteristics	Original Germplasm				Core Collection
	1	2	3	4	1	2	3	4
C1	−0.08	−0.17	−0.01	0.62	−0.23	0.33	−0.22	−0.49
C2	−0.15	−0.40	0.22	−0.60	−0.62	0.48	−0.00	0.22
C3	0.59	−0.62	0.32	0.08	0.23	0.88	0.21	−0.00
C4	0.60	−0.52	0.18	0.25	0.22	0.73	−0.01	−0.15
C5	−0.13	0.59	0.72	0.05	−0.03	−0.84	0.12	−0.11
C6	0.68	0.47	−0.05	−0.31	0.88	0.10	−0.09	0.17
C7	−0.66	0.09	0.61	0.31	−0.35	−0.29	0.66	0.28
C8	0.54	0.50	0.01	0.21	0.74	0.05	−0.01	0.32
C9	0.44	0.30	0.35	−0.02	0.20	0.11	−0.09	0.81
C10	0.79	0.07	0.32	−0.12	0.77	0.33	−0.11	0.29
C11	0.15	−0.42	0.22	−0.01	−0.07	0.45	0.56	−0.06
C12	−0.43	0.12	−0.05	−0.18	−0.64	−0.24	0.00	0.36
C13	0.39	0.22	−0.52	0.26	−0.00	−0.04	−0.88	0.08

). Among the first four principal components, the eigenvalues, contribution rates, and cumulative contribution rates of the original germplasm were all lower than those of the core collection. The eigenvalues of the original germplasm and core collection were 1.17 and 1.21, respectively, with cumulative contribution rates of 60.67% and 68.64%, respectively. This indicates that the core collection reduced genetic redundancy among germplasm to a certain extent, effectively increased the cumulative contribution rate of the original germplasm, and maximized the retention of genetic diversity in the original germplasm.

The principal component distribution of the original germplasm is relatively concentrated (Figure 4), with significant overlap among germplasm lines and high genetic similarity, indicating a certain degree of redundancy among them. In contrast, the principal component distribution of the core collection more dispersed, suggesting that the core collection eliminates most genetic redundancy while retaining the genetic diversity and population structure of the original germplasm. This demonstrates its excellent representativeness, confirming the rationality of the constructed core collection.

4. Discussion

With the advancement of Morchella domestication and cultivation, along with the continuous breeding of new varieties, the quantity and diversity of cultivable Morchella germplasm resources have steadily increased. The effective utilization of superior Morchella germplasm resources has become a key research issue, and the construction of a core collection represents an effective solution [30]. This study systematically investigated the construction of a core collection for Morchella based on phenotypic traits. Our research team has completed high-quality genome assembly. Moving forward, we will integrate the core collection resources identified through phenotypic screening with whole-genome resequencing data. Utilizing molecular marker technology, we will establish a more comprehensive integrated evaluation system for germplasm resource collection, preservation, and utilization.

Germplasm grouping and sampling strategies are pivotal to constructing core collections. Germplasm grouping reflects representativeness and genetic diversity under varying conditions, while also minimizing environmental influences. Research has found that saffron’s phenotypic traits exhibit varying degrees of environmental sensitivity under different cultivation conditions, with quantitative traits such as bud germination and leaf number being particularly susceptible to environmental influences [31]. Therefore, different crops can be grouped according to specific selection methods [17]. For instance, Luffa, peanut, and upland cotton were grouped by geographic origin [14,32,33], while Cymbidium and chili pepper were grouped by petal and fruit morphology [34,35]. When traditional chrysanthemums were grouped by geographic origin or petal type, the number of accessions per group varied too widely. However, grouping by flower color resulted in a tendency toward normal distribution among groups, increasing the sampling of rare colors and ensuring sampling uniformity [17]. In this study, grouping germplasm by cap color resulted in a normally distributed and relatively uniform distribution with high genetic diversity indices. This approach increases the sample intensity for rare colors while ensuring sampling uniformity.

When constructing sampling strategies, it is necessary to determine the sampling method, genetic distance, hierarchical clustering algorithms, and sample intensity. Comparative analysis of sampling methods for peppers and sweet potatoes revealed that the log-ratio method yielded the most representative core collections [35,36]. This study employed the log-ratio method to achieve uniform proportions across traits, effectively correcting for rare cap colors within germplasm groups and balancing the distribution of different colors across groups. In genetic distance clustering for crops [9,10], cash crops [11,12], vegetables [13,14], fruit trees [15,16], and ornamentals [17,18], Euclidean distance is widely used. In this study, Euclidean distance outperformed Mahalanobis distance. Sampling intensity primarily depends on germplasm quantity, typically ranging from 5% to 40% [37]. Given the short development history of the Morchella industry, its cultivation area is far smaller than that of crops, cash crops, or horticultural crops, and the original germplasm pool is limited. Miao Liming et al. suggest that the preserved germplasm scale is significantly smaller than that of crop species, making a sampling intensity of 10–30% more reasonable [38]. This study found that the CR value was higher at a 15% sampling intensity than at other scales, indicating that a 15% sampling intensity is appropriate.

5. Conclusions

Based on cap color, 231 Morchella germplasm accessions were grouped into four categories. Comparing sampling strategies revealed the logarithmic proportion method as optimal. A sampling strategy was constructed using Euclidean distance, maximum distance method, and a 15% sample intensity. The genetic diversity characteristics of the core collections showed no significant difference from the original germplasm. However, the principal component analysis (PCA) eigenvalues and contribution rates of the core collections were higher than those of the original germplasm, indicating that the selected core collections is representative. Ultimately, 34 Morchella core collection accessions were obtained, accounting for 14.72% of the original germplasm. This study established a core collection system based on phenotypic traits to efficiently and accurately evaluate Morchella germplasm resources. The 34 selected core collection lines can also serve as breeding materials, providing a scientific basis for genetic improvement and variety selection in Morchella.