Phenotypic Diversity and Seed Germination of Elaeagnus angustifolia L. in Relation to the Geographical Environment in Gansu Province, China

Abstract

Elaeagnus angustifolia L. is a highly adaptable urban ornamental plant, playing a key role in dry land and saline-alkali protective forests. The diverse geographical and climatic conditions in Gansu Province have resulted in variations in its distribution and growth. This study assesses the phenotypic diversity of fruits and seeds, and the seed germination characteristics of 82 E. angustifolia plants from nine populations in Gansu Province, exploring their relationship with geographical and climatic factors. We measured 12 phenotypic traits and five germination indices. This study included germination tests under standard conditions, statistical analysis of phenotypic differences, and Pearson and Spearman correlation analyses to examine relationships between traits and geo-climatic factors. Principal component and cluster analyses were also performed to identify key traits and classify populations. The findings were as follows: (1) Significant differences were observed in phenotypic traits and germination characteristics among populations. Single fruit weight showed the highest variation (27.56%), while seed transverse diameter had the lowest (8.76%). The Lanzhou population exhibited the greatest variability (14.27%), while Linze had the lowest (6.29%). (2) A gradient change pattern in traits was observed, primarily influenced by longitude and a combination of geographical and climatic factors. Seed germination was positively correlated with altitude, annual precipitation, and relative humidity, but negatively affected by latitude and traits such as fruit weight. (3) Principal component analysis identified germination rate, germination index, seed shape index, and fruit shape index as primary factors, contributing 27.4%, 20.6%, and 19.9% to the variation, respectively. Cluster analysis grouped the 82 plants into four clusters, not strictly based on geographical distance, suggesting influence from factors such as genotype or environmental conditions. In conclusion, this study lays a foundation for understanding the genetic mechanisms behind the phenotypic diversity and germination characteristics of E. angustifolia. It offers insights into how geo-climatic factors influence these traits, providing valuable information for the species’ conservation, cultivation, and management.

1. Introduction

Fruits and seeds are critical components in the plant life cycle, consisting of the peel, pulp, and seeds, with their morphology and structure varying among different plants. Characteristics such as mass, shape, dormancy, and dispersal ability are crucial for a plant’s environmental adaptability and evolutionary success [1]. Theoretically, the phenotypes of fruits and seeds are closely linked to environmental factors. From an evolutionary perspective, plants have developed various strategies to cope with diverse environments, directly influencing traits such as quantity, size, dispersal methods, dormancy, viability, and germination. Seed germination is a pivotal step in the reproductive process, lifecycle, and population expansion of plants [2]. Given that fruit and seed traits are typically heritable and significantly impact survival and reproduction [3,4,5], they have become a focus in studies on natural selection processes and outcomes.

The interaction between the phenotypic traits of fruits and seeds and the environment is a central focus in evolutionary biology. These traits reflect how plants adapt to survive in different environments, leading to a variety of fruit and seed characteristics within species and even within individual plants [6,7,8,9,10,11,12]. Environmental factors such as altitude and latitude significantly affect seed size and shape, with smaller seeds being particularly sensitive to these changes [6]. Research on Ericaceae species from Tibet has shown that seed traits are negatively correlated with altitude, suggesting that seeds at lower altitudes are larger and have longer wings compared to those found at higher altitudes [13]. In alpine herbaceous plants in the Tatra Mountains, altitude has been found to correlate with seed size in some species and seed quantity in others, demonstrating how different environmental factors can influence various aspects of seed development [14]. In another study focusing on Magnolia changhungtana Noot. (Magnoliaceae, synonym Manglietia pachyphylla H.T.Chang) across 16 populations, significant variability in fruit traits was observed, which was attributed to differences among the populations, indicating the role of geographical variation in shaping these traits [15]. These studies collectively highlight the complex relationship between fruit and seed traits and geographical environmental factors, which is crucial for understanding species’ adaptability in the context of environmental changes and conservation efforts.

Seed dormancy and germination characteristics are adaptations that allow plants to respond to their environments. Seeds of the same species in different areas often exhibit varying dormancy and germination characteristics, while different species under the same environmental conditions may share similarities in these traits [1,16]. Similarly, the resource investment in fruits and seeds by plants is part of the overall resource allocation of the plant. The resource allocation to seeds is reflected in the number, size, weight, and structure of the seeds, and different resource allocation strategies are also adaptations to the environment. Therefore, seed germination characteristics and the phenotypic traits of fruits and seeds are also closely related [17]. Hu et al.’s [18] study revealed that the germination indices of major seeds from Plantago asiatica L. (Plantaginaceae) are significantly influenced by environmental factors, such as precipitation, temperature, altitude, and longitude. Seed weight and dormancy rate are positively correlated with temperature and precipitation, while the rot rate is negatively correlated with altitude and positively correlated with longitude, temperature, and precipitation. Wang et al.’s [19] study indicated that there was a significant correlation between seed germination characteristics and phenotypic traits in the alpine meadow area on the eastern edge of the Qinghai-Tibet Plateau. Seed germination rate and speed are positively correlated with the seed’s three-dimensional variance and relative surface area and negatively correlated with seed volume. Smaller seeds generally have higher germination rates and speeds, which may be related to their larger relative surface area. Seeds with larger and rounder volumes require longer germination times. These correlations illustrate the relationship between seed germination characteristics, the environment, and phenotypic traits.

Gansu Province, located at the intersection of the Loess Plateau, Mongolian Plateau, and Qinghai-Tibet Plateau, is characterized by a range of climates from subtropical monsoon to alpine. This results in significant variability in precipitation, ranging from over 600 mm in the east to less than 100 mm in the northwest, and contributes to distinct biodiversity patterns [20]. Previous studies have shown differences in seed size among populations living under different environmental conditions [21]. Luo et al. [22] explored walnut phenotypic diversity across 22 populations in Gansu, revealing significant differences attributed to environmental variability. The walnut resource displayed rich diversity, with major variations within populations driving phenotypic diversity in Gansu varieties. In addition, researchers found significant phenotypic variation in the fruits and seeds of Calligonum mongolicum Turcz. (Polygonaceae) across 12 populations, primarily influenced by latitude and with a systematic gradient. A negative correlation between seed size and both latitude and altitude was observed in C. mongolicum populations [23], indicating the impact of diverse geographical environments on plant phenotype differentiation in Gansu Province. Research has also demonstrated the relationship between plant seed germination characteristics and environmental factors in Gansu Province. Xu et al. [24] studied the alpine meadows on the eastern Tibetan Plateau with 31 common species, finding that germination behavior is influenced by altitude and temperature. At 15 °C/5 °C, altitude had no significant effect on germination time to 50% (GT₅₀), but germination potential (GP) increased with altitude. In contrast, at 25 °C/10 °C, GT₅₀ was prolonged with altitude, while GP decreased. Bao et al. [25] focused on Pedicularis kansuensis Maxim. (Orobanchaceae), revealing differences in base temperature and water requirements across habitats. Seeds from cooler habitats had higher base temperatures, and responses to water potential varied among populations. These findings reflect the close link between seed germination and environmental conditions in Gansu Province.

Elaeagnus angustifolia L., a deciduous tree or shrub from the Elaeagnaceae family, is native to southern Europe and Asia [26]. It thrives primarily in the arid and semi-arid regions of northwestern China and western Inner Mongolia. Known for its resilience to wind, sand, salt, and drought, as well as its ability to thrive in poor soils, E. angustifolia serves an essential role in ecological, ornamental, and economic contexts [27,28]. Previous research on this species has focused on aspects such as pharmacological effects [29], nutritional value [30], active ingredient extraction [31], and stress resistance [32]. Recent studies have employed multivariate analysis to investigate the phenotypic diversity related to the leaf and fruit quality of E. angustifolia in various cities across Iran, providing insights into the variability within this species [33]. In the northern Gilgit-Baltistan region of Pakistan, research on E. angustifolia fruits (including seeds) and leaves has shown that altitude has a positive influence on the size of fruits and seeds, particularly in terms of their length and weight [34]. Shi et al. [35] conducted a phenotypic diversity analysis of 35 traits (plant shape, branches, leaves, and flower-related traits) of 90 E. angustifolia trees from 10 populations in Gansu Province, China. They concluded that the phenotypic variation of E. angustifolia mainly comes from inter-population sources and is influenced by factors such as altitude, latitude, and precipitation. In recent years, research on the germination of E. angustifolia seeds has primarily focused on factors such as temperature, humidity, and exogenous hormones [36,37]. However, despite some progress, our understanding of the evolutionary growth patterns of E. angustifolia and its adaptability in different environments remains limited. Based on the preliminary research results of our research group, we have found that the E. angustifolia populations in Gansu Province exhibit rich phenotypic diversity influenced by geographical distribution, climatic conditions, and human activities [35]. E. angustifolia plays a crucial role in soil and water conservation and ecological construction in arid and semi-arid regions. The degree of its adaptation between the phenotypic traits of fruits and seeds and the environment is of great importance and significance for the maintenance of its population in this area, yet there is a lack of related research.

Since the study of the phenotypic traits and germination characteristics of E. angustifolia fruits and seeds is still ongoing, this research specifically focused on nine E. angustifolia populations in Gansu Province. We measured and analyzed 12 phenotypic traits related to fruits and seeds, as well as five parameters related to seed germination. This research explores the relationship between these variations and geographical climatic factors, as well as the seed germination status among different populations. By examining the interrelationships among these aspects, this study aims to predict future trends for E. angustifolia populations in the context of global climate change. The findings are significant for establishing a system for the identification and evaluation of E. angustifolia germplasm resources and provide a data foundation and assessment methods for introducing and cultivating high-quality varieties.

2. Materials and Methods

2.1. Plant Material

Based on the distribution records of E. angustifolia in the digital herbarium of China (http://www.cvh.ac.cn/ accessed on 20 August 2022) and field surveys, we selected nine counties in Gansu Province as the study populations for E. angustifolia. These areas differ by a maximum of 9.37° in longitude and 4.53° in latitude. The geographic and ecological environments of the nine population sites vary significantly, as do the potential influences of human activities, such as road traffic, agricultural practices near rural farmland, and urban park management. During field surveys, we used the BeiDou satellite navigation system to obtain the altitude and latitude/longitude information of each population. Climate data were based on the averages from 1981 to 2010 of meteorological stations in the surrounding areas of the sampling points of the nine counties/districts as published on the China Meteorological Administration’s data network (http://data.cma.cn/ accessed on 6 September 2022). For detailed information, please refer to

Table 1. The sample sizes and geographical and climatic information of the nine Elaeagnus angustifolia populations in Gansu Province. Sample sizes indicate the number of individuals sampled from each population. The data include altitude (AL) in meters above sea level, longitude (E) in degrees east, latitude (N) in degrees north, annual mean temperature (AMT) in degrees Celsius, annual precipitation (AP) in millimeters, and annual mean relative humidity (AMRH) in percentages. The populations listed are Dunhuang (DH), Jiuquan (JQ), Linze (LZ), Yongchang (YC), Gulang (GL), Minqin (MQ), Linxia (LX), Lanzhou (LA), and Jingtai (JT).

Population	Sample Sizes	Altitude (m, AL)	Longitude (°E, E)	Latitude (°N, N)	Annual Mean Temperature (°C, AMT)	Annual Precipitation (mm, AP)	Annual Mean Relative Humidity (%, AMRH)
Dunhuang (DH)	10	1168.20	94.66	40.14	9.90	42.20	40
Jiuquan (JQ)	10	1492.00	98.51	39.75	7.80	88.40	48
Linze (LZ)	10	1453.10	100.26	39.09	8.30	113.40	49
Yongchang (YC)	5	1728.70	102.06	38.37	5.40	211.80	52
Gulang (GL)	10	1792.40	102.97	37.60	5.70	352.30	51
Minqin (MQ)	10	1481.70	103.15	38.59	8.80	113.20	44
Linxia (LX)	10	2025.10	103.19	35.61	7.30	501.30	67
Lanzhou (LA)	7	1544.50	103.86	36.07	10.50	360.00	60
Jingtai (JT)	10	1620.50	104.03	37.47	9.10	179.70	48

.

During the E. angustifolia fruiting period from 25 August 2022 to 5 September 2022, we randomly sampled mature fruit trees that were naturally grown at each sampling point. At each population site, 5 to 10 healthy adult trees without obvious defects, diseases, or pests, and with individuals entering the reproductive phase, were randomly selected as research samples. The sampling principle was to ensure a minimum of 10 trees per population site. However, due to the scarcity or difficulty of sampling in certain areas, the sample size in some locations was less than 10 trees. To ensure the accuracy of this study, selected tree samples were typically separated by a minimum distance of 50 m to reduce the likelihood of sampling closely related individuals. On each sampled plant, we uniformly selected 10 complete fruits. Subsequently, these collected samples were brought back to the laboratory to measure the relevant parameters.

2.2. Morphometric Analysis

The phenotypic traits of E. angustifolia fruits and seeds can be categorized into quantitative traits and qualitative traits. Quantitative traits encompass nine trait indicators, including fruit length diameter (FLD), fruit transverse diameter (FTD), fruit shape index (FSI), single fruit weight (FW), fruit stalk length (FRS), seed longitudinal diameter (SLD), seed transverse diameter (STD), seed shape index (SSI), and seed thousand-grain weight (TGW) (

Table 2. Quantitative traits and abbreviations of E. angustifolia fruits and seeds. The traits include measurements such as fruit and seed dimensions, weight, and shape indices. Abbreviations and their corresponding traits are as follows: FLD means fruit longitudinal diameter, mm, FTD means fruit transverse diameter, mm, FSI means fruit shape index, coded as the ratio of longitudinal to transverse diameter, FW means fruit weight (g), FRS means fruit stalk length (mm), SLD means seed longitudinal diameter (mm), STD means seed transverse diameter (mm), SSI means seed shape index, coded as the ratio of longitudinal to transverse diameter, and TGW means thousand-grain weight (g).

No.	Abbreviation	Character	Unit of Measurement
1	FLD	Fruit longitudinal diameter	Mm
2	FTD	Fruit transverse diameter	Mm
3	FSI	Fruit shape index	Code
4	FW	Fruit weight	G
5	FRS	Fruit stalk length	Mm
6	SLD	Seed longitudinal diameter	Mm
7	STD	Seed transverse diameter	Mm
8	SSI	Seed shape index	Code
9	TGW	Thousand-grain weight	G

), which will be measured using quantitative assessment methods. On the other hand, qualitative traits comprise fruit shape (FS), fruit color (FRC), and speckles on fruit (SF) (

Table 3. Qualitative trait assignment and description of E. angustifolia fruits and seeds. The traits and their abbreviations are: FS means fruit shape, with categories assigned as circular (1) and elliptical (2); FRC means fruit color, graded from yellow (1) to red (4) based on color intensity, including intermediate stages of orange-yellow (2) and orange-red (3); SF means speckles on fruit, graded by the density of speckles on the fruit surface as low (1), medium (2), or high (3).

No.	Abbreviation	Character	Grading Assignment
			1	2	3	4
1	FS	Fruit shape	Circular	Elliptical
2	FRC	Fruit color	Yellow	Orange-yellow	Orange-red	Red
3	SF	Speckles on fruit	Low	Medium	High

), which will be evaluated using qualitative assessment methods. FLD, FTD, FRS, SLD, and STD were measured with a vernier caliper (accuracy of 0.01 cm). FW and TGW were weighed using an electronic balance (accuracy of 0.01 g). FRC was compared using a color chart (Pantone, Inc., Carlstadt, NJ, USA) and recorded. The FSI was defined as the FLD divided by the FTD, while the SSI was defined as the SLD divided by the STD. When measuring the qualitative traits of E. angustifolia, due to the lack of standardized descriptions and data standards for germplasm resources, we used a graded assignment method to record the FS and the SF. We referred to the phenotype analysis methods of Ali et al. [38] to determine the fruit shape and speckles on fruit indicators, and the methods of Zeng et al. [39] to determine the fruit color indicator to comprehensively reflect the phenotypic characteristics of E. angustifolia fruits and seeds grown at each sampling point (Figure 1). Additionally, during the observation process, we found that the fruit color and the extent of scale coverage on the surface of the fruit displayed instability on the same plant, often showing multiple colors or degrees simultaneously. Therefore, we selected the most frequently occurring color within the same plant as the basis for determination.

2.3. Seed Germination Test

The seeds of the nine populations were removed from the seed storage box set at 4 °C. Next, seeds were randomly selected from each population for disinfection treatment, using 10% sodium hypochlorite for sterilization for 30 min, followed by rinsing with distilled water five times. The seeds were then soaked in distilled water at room temperature (20–25 °C) in clean containers for 24 h to ensure consistent pre-germination conditions. The treated, plump, and equally sized seeds were evenly distributed on culture dishes with a diameter of 90 mm and, covered with two layers of moist filter paper, with each culture dish containing 20 seeds. Prior to the germination experiment, the culture dishes were sealed with sealing film to reduce moisture evaporation, with each population repeated three times. The experiment was conducted under lighting conditions of 12 h light/12 h darkness, with a light intensity of 1500 lx and a relative humidity of 80 ± 5% [40]. Germination progress was checked and recorded daily, and at the end of the experiment, seeds that had not germinated were counted. The sterilized water was changed every three days, and new sterilized culture dishes and filter paper were replaced. It was essential to maintain the sterilization of the culture dishes and filter paper throughout the experiment.

2.4. Statistical Analysis

2.4.1. Analysis of Variance

Using nested analysis of variance to determine statistically significant differences within and between populations, the linear model is as follows:

$$Y_{ijk}=\mu +{\tau }_i+{\delta }_{j\left(i\right)}+{\epsilon }_{ijk},$$

(1)

where $Y_{ijk}$ represents the kth observation in the jth family of the ith population, $\mu$ represents the overall mean, τ_i represents the between-population effect, ${\delta }_{j\left(i\right)}$ represents the within-population individual (family) random effect, and ${\epsilon }_{ijk}$ represents random error [41].

2.4.2. Phenotypic Differentiation Coefficient

Using the coefficient of variation of phenotypic differentiation (V_ST) to investigate the phenotypic differentiation within and between populations, the calculation formula is as follows:

$${\mathrm{V}}_{\mathrm{S}\mathrm{T}}={\displaystyle \frac{{\sigma }_{t/s}^2}{{\sigma }_{t/s}^2+{\sigma }_s^2}}$$

(2)

where ${\sigma }_{t/s}^2$ represents the between-population variance component and ${\sigma }_s^2$ represents the within-population (family) variance component [42].

2.4.3. Phenotypic Variation Analysis of Fruit and Seed Traits

In plant phenotype research, the coefficient of variation (CV) is commonly used to describe the degree of dispersion of traits. A larger CV value indicates a higher degree of trait dispersion and greater phenotypic variation. Conversely, a smaller CV value indicates lower trait dispersion and lesser phenotypic variation. By calculating the coefficients of variation of fruit and seed traits for nine different populations of E. angustifolia, the phenotypic variation of E. angustifolia fruits and seeds was analyzed. Descriptive statistics of morphological measurements for each population were conducted using SPSS 26.0 software. Based on the mean and standard deviation (SD) of each trait, the coefficient of variation (CV) was calculated, including the arithmetic mean (X), standard deviation (SD), Duncan’s multiple comparisons, and coefficient of variation (CV) to determine the range of variation [43].

CV = SD/X

(3)

To further analyze the phenotypic variation and test the statistical significance of the differences among populations, a Chi-square (χ²) test was performed. The Chi-square test is used to determine whether there is a significant association between categorical variables, in this case, the phenotypic traits across different populations. The formula for the Chi-square test is:

$${\mathrm{\chi }}^2=\Sigma {\displaystyle \frac{(O_i-E_i)²}{E_i}}$$

(4)

where χ² represents the Chi-square statistic, $O_i$ represents the observed frequency for category $i$, and $E_i$ is the expected frequency for category $i$.

2.4.4. Diversity Index

Using the Shannon–Wiener index (H’) to describe the diversity and variability of trait phenotypes, the diversity index for each trait was calculated using EXCEL 2010 software, with the formula:

$$H\prime =-\Sigma \left(P_i\times \mathit{ln}{P}_i\right)$$

(5)

where $H^{\prime }$ represents the diversity index and $P_i$ represents the effective percentage of distribution frequency within the ith level of a particular trait material [44].

2.4.5. Estimation of Germination Parameters

To monitor the seed germination process, germination status was recorded every 24 h until there was no additional germination for three consecutive days. Seed germination was determined by observing whether the embryo root length exceeds 1 mm.

The seed germination indices included total germination rate (TG) [45], germination potential (GP), germination index (GI) [45], vigor index (VI) [46], and germination time to 50% (GT₅₀) [18]. The calculation formulas for these parameters were as follows:

$$\mathrm{T}\mathrm{G}(\%)={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{m}\mathrm{u}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{y} \mathrm{g}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{s}}{N}}\times 100$$

(6)

$$\mathrm{G}\mathrm{P}(\%)={\displaystyle \frac{Nm}{N}}\times 100$$

(7)

where Nm represented the number of germinated seeds on the day when the counts of germinated seeds reached the maximum, and N was the total number of seeds.

$$\mathrm{GI}={\displaystyle \sum _{i=1}^{\mathrm{n}}\frac{Gi}{Ni}}$$

(8)

Gi is the number of germinated seeds on day i. Ni is the number of days after the beginning of the experiment.

VI = GI × radicle length

(9)

$${\mathrm{GT}}_{50}=\mathrm{T}1+(\mathrm{T}2-\mathrm{T}1)\times (50\%-\mathrm{P}1)/(\mathrm{P}2-\mathrm{P}1)$$

(10)

where T1 is the time corresponding to P1, T2 is the time corresponding to P2, P1 is the threshold where the cumulative germination percentage is less than 50%, and P2 is the threshold where the cumulative germination percentage is greater than 50%.

2.4.6. Multivariate Analysis

Correlation Analysis

Pearson correlation analysis and Spearman correlation analysis were conducted using SPSS 26.0 software to study the relationships among different traits of E. angustifolia fruits and seeds, as well as the correlations between traits and geographical climate factors. The correlation between phenotypic traits, seed germination indices, and geographical climatic factors was analyzed. To ensure that the data met the assumptions of normality and homoscedasticity, the original data were first standardized.

Principal Component Analysis

The principal component analysis method was used to explore the contribution of the phenotypic characteristics of different fruits and seeds to population variation. Initially, the indicators were reduced to identify key indicators influencing the phenotype of E. angustifolia fruits and seeds. Subsequently, loading plot and biplot analyses were performed based on the obtained PC1, PC2, and PC3.

Cluster Analysis

Ward cluster analysis was applied to group the research samples and a circular dendrogram based on the squared Euclidean distance was plotted. Prior to conducting the cluster analysis, the original data were standardized using Z-score normalization (STD) to eliminate the influence of different measurement scales on the analysis results [47].

All statistical analyses and graph plotting were conducted using SPSS 26.0 and Origin 2021 software.

3. Results

3.1. Phenotypic Characteristics of Fruits and Seeds of Elaeagnus angustifolia from Different Populations

Through the analysis of the mean, standard deviation, and multiple comparisons of the phenotypic traits of fruits and seeds from various populations of E. angustifolia (

Table 4. The average values ± standard deviations and multiple comparisons of phenotypic traits for E. angustifolia fruits and seeds across nine populations in Gansu Province. The populations include Dunhuang (DH), Jiuquan (JQ), Linze (LZ), Yongchang (YC), Minqin (MQ), Gulang (GL), Jingtai (JT), Lanzhou (LA), and Linxia (LX). The quantitative traits measured include fruit longitudinal diameter (FLD), fruit transverse diameter (FTD), fruit shape index (FSI), fruit weight (FW), fruit stalk length (FRS), seed longitudinal diameter (SLD), seed transverse diameter (STD), seed shape index (SSI), and thousand-grain weight (TGW). Qualitative traits assessed include fruit shape (FS), fruit color (FRC), and speckles on fruit (SF). Different letters within the same trait column indicate significant differences between populations (p < 0.05).

Population	FLD	FTD	FSI	FW	FRS	SLD
DH	13.45 ± 1.47 d	9.69 ± 0.94 d	1.39 ± 0.08 ab	0.79 ± 0.19 a	4.73 ± 1.18 bc	12.87 ± 1.38 bcd
JQ	15.32 ± 1.11 abc	10.96 ± 0.53 bc	1.40 ± 0.12 ab	0.62 ± 0.12 b	4.23 ± 0.83 bcd	13.48 ± 1.27 abcd
LZ	15.42 ± 1.07 abc	10.47 ± 0.84 cd	1.48 ± 0.04 a	0.57 ± 0.07 bc	5.96 ± 0.50 a	14.79 ± 0.67 a
YC	16.70 ± 1.90 a	11.90 ± 0.65 a	1.40 ± 0.09 ab	0.62 ± 0.11 b	4.66 ± 1.03 bc	14.35 ± 1.42 a
MQ	14.61 ± 1.89 bcd	11.65 ± 0.97 ab	1.26 ± 0.11 c	0.62 ± 0.11 b	4.26 ± 0.98 bcd	13.92 ± 1.45 ab
GL	13.23 ± 1.29 d	10.67 ± 0.55 c	1.24 ± 0.09 c	0.46 ± 0.09 cd	4.92 ± 0.78 b	12.43 ± 0.76 cd
JT	14.00 ± 1.73 cd	10.68 ± 0.94 c	1.32 ± 0.12 bc	0.61 ± 0.09 b	3.73 ± 0.49 de	12.29 ± 1.44 d
LA	15.83 ± 1.81 ab	12.41 ± 1.36 a	1.28 ± 0.11 c	0.69 ± 0.20 ab	3.36 ± 0.33 e	14.59 ± 1.80 a
LX	15.34 ± 0.96 abc	10.91 ± 0.84 bc	1.41 ± 0.04 ab	0.39 ± 0.06 d	3.93 ± 0.41 cde	13.63 ± 0.51 abc
Mean	14.73 ± 1.73	10.94 ± 1.11	1.35 ± 0.12	0.59 ± 0.16	4.44 ± 1.03	13.51 ± 1.45 d
Population	STD	SSI	TGW	FS	FRC	SF
DH	4.93 ± 0.25 c	2.62 ± 0.27 cd	171.64 ± 8.49 cd	2.00 ± 0.00 a	2.30 ± 0.82 d	2.50 ± 0.53 a
JQ	4.34 ± 0.43 d	3.15 ± 0.41 a	150.14 ± 40.93 d	1.80 ± 0.42 a	2.80 ± 0.42 bcd	2.50 ± 0.71 a
LZ	4.55 ± 0.10 d	3.25 ± 0.12 a	166.98 ± 11.74 cd	2.00 ± 0.00 a	2.70 ± 0.68 cd	2.90 ± 0.32 a
YC	5.04 ± 0.13 bc	2.85 ± 0.26 bc	210.41 ± 25.13 ab	2.00 ± 0.00 a	3.80 ± 0.45 a	1.60 ± 0.55 bc
MQ	5.35 ± 0.32 a	2.62 ± 0.35 cd	217.73 ± 30.72 a	1.70 ± 0.48 a	3.30 ± 0.68 abc	1.40 ± 0.52 bc
GL	5.07 ± 0.16 abc	2.45 ± 0.17 d	178.67 ± 7.50 cd	1.70 ± 0.48 a	3.10 ± 0.88 abc	1.50 ± 0.71 bc
JT	5.01 ± 0.31 bc	2.46 ± 0.31 d	183.08 ± 5.64 bc	1.70 ± 0.48 a	2.60 ± 1.17 cd	1.60 ± 0.70 bc
LA	5.25 ± 0.38 ab	2.79 ± 0.33 bc	208.24 ± 55.83 ab	1.86 ± 0.38 a	3.57 ± 0.54 ab	1.86 ± 0.90 b
LX	4.53 ± 0.21 d	3.02 ± 0.07 ab	154.74 ± 34.63 cd	2.00 ± 0.00 a	2.80 ± 0.42 bcd	1.00 ± 0.00 c
Mean	4.87 ± 0.43	2.8 ± 0.39	179.75 ± 35.23	1.85 ± 0.36	2.93 ± 0.81	1.89 ± 0.83

), significant differences were found among the 12 traits in different populations. The FTD (9.691 mm) of the DH sample was the smallest among all populations, while the FW (0.79 g) was the largest. The STD (4.338 mm) and TGW (150.14 g) of the JQ sample were the smallest among all populations. The FSI (1.482), FRS (5.961 mm), SLD (14.793 mm), and SSI (3.254) of the LZ sample had the largest mean values among all populations. The FLD (16.7 mm) of the YC sample was the largest among all populations. The SLD (5.35 mm) and TGW (217.727 g) of the MQ sample were the largest among all populations. The FLD (13.232 mm), FSI (1.24), and SSI (2.454) of the GL sample were the smallest among all populations. The SLD (12.288 mm) of the JT sample had the smallest mean value. The FTD (12.41 mm) of the LA sample plant was the largest among all seed populations, while the FRS (3.3557 mm) was the smallest. The FW (0.394 g) of the LX sample plant was the smallest among all populations.

In terms of qualitative traits, the YC sample had the highest mean value for FRC (3.8), while the DH sample had the lowest FRC (2.3). The LZ sample had the highest mean value for SF (2.9), while the LX sample plant had the lowest value for this trait.

3.2. Phenotypic Differences between and within Populations of Elaeagnus angustifolia Species

Variance analysis at two levels, between populations and within populations (among plants), of the phenotypic traits of E. angustifolia fruits and seeds in Gansu Province (

Table 5. The variance analysis for phenotypic traits of E. angustifolia fruits and seeds among and within different populations. The traits analyzed include fruit longitudinal diameter (FLD), fruit transverse diameter (FTD), fruit shape index (FSI), fruit weight (FW), fruit stalk length (FRS), seed longitudinal diameter (SLD), seed transverse diameter (STD), seed shape index (SSI), thousand-grain weight (TGW), fruit shape (FS), fruit color (FRC), and speckles on fruit (SF). The F-values highlight significant differences (** p < 0.01) among populations, indicating how these traits vary across different groups. Random errors reflect the variation that is not accounted for by population differences.

Traits	Mean Square		F-Value
	Among Populations	Within Population	Random Errors	Among Populations	Within Population
FLD	10.054	3.212	2.011	5.001 **	1.598
FTD	5.022	1.082	0.708	7.091 **	1.528
FSI	0.066	0.007	0.009	7.459 **	0.812
FW	0.133	0.026	0.013	9.906 **	1.946
FRS	5.378	0.286	0.640	8.399 **	0.446
SLD	7.272	1.616	1.469	4.952 **	1.100
STD	1.121	0.096	0.075	15.039 **	1.285
SSI	0.845	0.072	0.075	11.232 **	0.956
TGW	5021.391	1347.434	721.009	6.964 **	1.869
FS	0.201	0.105	0.122	1.646	0.864
FRC	1.710	0.987	0.475	3.597 **	2.077
SF	3.803	0.319	0.350	10.870 **	0.911

) showed that there were 11 traits with extremely significant differences between populations, while there were no traits with extremely significant differences within populations.

3.3. Differences in Phenotypic Traits of Elaeagnus angustifolia from Different Populations and Phenotypic Differentiation within Populations

Based on the analysis of phenotypic differentiation coefficients, the variance components and phenotypic differentiation coefficients of 12 phenotypic traits of E. angustifolia fruits and seeds between populations and within populations were calculated (

Table 6. The variance components and phenotypic differentiation coefficients of the phenotypic traits of E. angustifolia fruits and seeds in Gansu Province. The traits analyzed include fruit longitudinal diameter (FLD), fruit transverse diameter (FTD), fruit shape index (FSI), fruit weight (FW), fruit stalk length (FRS), seed longitudinal diameter (SLD), seed transverse diameter (STD), seed shape index (SSI), thousand-grain weight (TGW), fruit shape (FS), fruit color (FRC), and speckles on fruit (SF). The Table presents the variance components for each trait and the phenotypic differentiation (V_ST) among populations.

Traits	Variance Component			Proportion of Variance Component/%			Differentiation Coefficients of Phenotypic Traits (VST) (%)
	Among Populations	Within Populations	Random Error	Among Populations	Within Populations	Random Error
FLD	80.431	28.910	128.674	33.79	12.15	54.06	73.56
FTD	40.173	9.740	45.325	42.18	10.23	47.59	80.49
FSI	0.526	0.064	0.564	45.58	5.55	48.87	89.15
FW	1.060	0.234	0.856	49.30	10.88	39.81	81.92
FRS	43.023	2.572	40.977	49.70	2.97	47.33	94.36
SLD	58.177	14.545	93.987	34.90	8.72	56.38	80.00
STD	8.967	0.862	4.770	61.42	5.90	32.67	91.23
SSI	6.756	0.647	4.812	55.31	5.30	39.39	91.26
TGW	40,171.129	12,126.910	46,144.584	40.81	12.32	46.87	76.81
FS	1.607	0.949	7.808	15.51	9.16	75.34	62.87
FRC	13.683	8.886	30.428	25.82	16.77	57.41	60.63
SF	30.422	2.867	22.390	54.64	5.15	40.21	91.39
Average				42.41	8.75	47.16	81.13

). The average variance component of phenotypic traits of E. angustifolia fruits and seeds among populations accounted for 42.41% of the total variability, while within populations, it accounted for 8.75%. The phenotypic differentiation coefficients among populations for each trait ranged from 60.63% to 94.36%, with an average of 81.13%. The traits with the highest and lowest phenotypic differentiation coefficients were FRS and FRC, respectively. Additionally, the phenotypic differentiation coefficients of three other traits—speckles on fruit (SF), seed transverse diameter (STD), and seed shape index (SSI)—also exceeded 90%.

3.4. Variation Degree of Phenotypic Traits in Elaeagnus angustifolia Species

The range and degree of variation in nine quantitative traits (Table 2) of E. angustifolia fruits and seeds from nine populations were reflected by the mean, standard deviation, and coefficient of variation (CV) (

Table 7. The coefficient of variation for quantitative phenotypic traits in E. angustifolia fruits and seeds. This Table presents the coefficient of variation (CV) and chi-square (χ²) test results for phenotypic quantitative traits of E. angustifolia fruits and seeds across nine populations. Traits include fruit longitudinal diameter (FLD), fruit transverse diameter (FTD), fruit shape index (FSI), fruit weight (FW), fruit stalk length (FRS), seed longitudinal diameter (SLD), seed transverse diameter (STD), seed shape index (SSI), and thousand-grain weight (TGW). The CV values reflect the relative variability of each trait within and across populations. The chi-square values assess the significance of variability among populations, with * indicating significant differences at p < 0.05 and ** indicating highly significant differences at p < 0.01.

Traits	Coefficient of Variation (CV)/%										Chi-Square
	DH	JQ	LZ	YC	MQ	GL	JT	LA	LX	Total	χ2
FLD	10.91	7.27	6.97	11.36	12.94	9.77	12.38	11.46	6.26	11.73	6.74 *
FTD	9.71	4.84	8.05	5.47	8.30	5.19	8.78	10.92	7.70	10.11	8.84 *
FSI	5.63	8.63	2.82	6.26	8.38	7.02	9.34	8.91	2.64	8.80	10.67
FW	24.13	19.46	13.01	17.50	18.07	18.91	14.97	29.39	15.75	27.56	31.08 **
FRS	24.90	19.70	8.42	22.02	22.97	15.80	13.14	9.70	10.35	23.30	32.22 **
SLD	10.75	9.46	4.56	9.92	10.42	6.14	11.70	12.30	3.77	10.70	10.50 *
STD	5.01	9.85	2.21	2.61	6.01	3.09	6.20	7.24	4.64	8.76	18.44
SSI	10.29	13.07	3.58	9.06	13.43	6.83	12.41	11.70	2.48	13.87	23.71 **
TGW	4.94	27.26	7.03	11.94	14.11	4.20	3.08	26.81	22.38	19.60	55.62 **
Average	11.80	13.28	6.29	10.68	12.73	8.55	10.22	14.27	8.44	14.93	21.98

). Among all the populations, the maximum coefficient of variation for the phenotypic traits of fruits and seeds was in LA (CV = 14.27%), while the minimum coefficient of variation was in LZ (CV = 6.29%). There were significant differences in the levels of variation among different traits, with coefficients of variation ranging from 8.76% to 27.56%, with a mean of 14.93%. The trait with the highest coefficient of variation was FW (CV = 27.56%), and FRS (CV = 23.30%) and TGW (CV = 19.60%) also exhibited high variations, indicating rich polymorphism and variability. The two traits with the lowest coefficients of variation were STD (CV = 8.76%) and FSI (CV = 8.80%). Overall, phenotypic traits related to fruits showed higher coefficients of variation, while those related to seeds exhibited lower coefficients of variation. Additionally, there were substantial differences in the coefficients of variation for different traits within the same population. For example, in the LX population, the coefficient of variation for TGW was the highest (22.38%) and was 9.02 times that of the lowest-variation coefficient trait, SSI (2.48%). Similarly, the coefficients of variation for the same trait in different populations also showed significant differences. For instance, TGW had the highest coefficient of variation in JQ (27.26%), which was 8.85 times that of the same trait in JT (3.08%).

In addition to calculating the CV, a chi-square test was performed to assess the significance of variability among populations for each trait. The chi-square test results showed that most traits exhibited significant differences among populations (p < 0.05). Notably, traits related to fruit characteristics, such as FW, FRS, SSI, and TGW, showed highly significant differences (** p < 0.01). In contrast, some seed-related traits such as STD and FSI did not exhibit significant differences.

3.5. Diversity Index of Phenotypic Characteristics of Elaeagnus angustifolia

Overall, the nine quantitative traits exhibit high phenotypic diversity, which is significantly higher than their qualitative trait diversity index (

Table 8. The maximum and minimum values of quantitative phenotypic traits of E. angustifolia fruits and seeds, as well as the diversity index. Nine populations: Dunhuang (DH), Jiuquan (JQ), Linze (LZ), Yongchang (YC), Minqin (MQ), Gulang (GL), Jingtai (JT), Lanzhou (LA), and Linxia (LX). Traits include fruit longitudinal diameter (FLD), fruit transverse diameter (FTD), fruit shape index (FSI), fruit weight (FW), fruit stalk length (FRS), seed longitudinal diameter (SLD), seed transverse diameter (STD), seed shape index (SSI), and thousand-grain weight (TGW). The diversity index (H’) reflects the phenotypic diversity within each population.

Trait	Extremes	DH	JQ	LZ	YC	MQ	GL	JT	LA	LX	H′
FLD	Min	11.87	13.27	13.55	14.19	11.99	11.11	11.05	12.90	13.70	2.056
	Max	16.44	16.23	16.44	18.61	18.17	15.45	16.71	18.24	16.54
FTD	Min	8.63	10.33	9.17	10.91	10.63	9.93	9.06	10.36	9.54	2.033
	Max	11.32	11.79	11.49	12.53	13.38	11.70	12.64	14.28	12.10
FSI	Min	1.29	1.19	1.43	1.30	1.08	1.11	1.14	1.04	1.37	2.029
	Max	1.58	1.52	1.54	1.50	1.40	1.38	1.58	1.38	1.47
FW	Min	0.53	0.51	0.46	0.44	0.43	0.32	0.44	0.37	0.30	1.992
	Max	1.10	0.84	0.68	0.70	0.81	0.62	0.75	1.04	0.49
FRS	Min	3.42	3.27	5.22	3.44	3.28	3.78	3.23	3.08	3.34	1.926
	Max	7.24	5.56	6.74	6.17	6.35	6.02	4.60	3.89	4.64
SLD	Min	11.26	11.21	13.59	12.52	12.17	11.42	9.53	12.41	12.79	2.022
	Max	16.29	14.89	15.56	16.40	16.37	13.59	14.54	17.71	14.41
STD	Min	4.58	3.93	4.41	4.89	4.96	4.85	4.70	4.78	4.17	2.016
	Max	5.35	5.04	4.73	5.17	6.13	5.36	5.68	5.73	4.82
SSI	Min	2.11	2.47	3.05	2.55	2.16	2.25	1.82	2.26	2.88	2.023
	Max	3.09	3.50	3.41	3.18	3.15	2.80	2.76	3.10	3.11
TGW	Min	159.60	122.60	150.40	172.40	158.28	163.33	174.40	138.67	88.92	1.951
	Max	185.20	234.60	181.80	237.27	273.33	188.50	193.00	313.33	182.34
mean value											2.005

and

Table 9. The frequency distribution Shannon index of qualitative traits of E. angustifolia fruits and seeds in Gansu Province. Traits include fruit shape (FS), fruit color (FRC), and speckles on fruit (SF). The diversity index (H’) reflects the variability of each trait across grades.

Traits	Distribution Frequency of Each Grade				Diversity
	1	2	3	4
FS	14.6	85.4			0.416
FRC	4.9	22.0	48.8	24.4	1.175
SF	40.2	30.5	29.3		1.088
Average					0.893

). The phenotypic diversity index of the nine quantitative traits ranges from 1.926 to 2.056 (Table 8), with a mean of 2.005. Among them, the diversity of the FLD was the highest, and the diversity of the FRS was the lowest; the diversity indices of six traits, including the FLD, FTD, FSI, SLD, STD, and SSI, all exceed 2. From the Shannon–Wiener diversity indices of the three qualitative traits, it was evident that there were different diversity levels among the traits. The H′ values of the three qualitative traits ranged from 0.416 to 1.175, with a mean of 0.893. Among them, fruit color (FRC) showed higher diversity, while fruit shape exhibited the lowest diversity.

As seen in Table 9, there were varying degrees of differences in the distribution frequencies of three qualitative traits of E. angustifolia fruits and seeds. Specifically, fruit shape (FS) was predominantly elliptical (85.4%); fruit color (FRC) was mainly orange-red (48.8%); and speckles on fruit (SF) varied slightly across multiple levels.

3.6. The Germination Indicators of Elaeagnus angustifolia Seeds from Nine Populations

Based on the data presented in

Table 10. The germination indicators of E. angustifolia seeds from nine populations. Indicators include total germination percentage (TG%), germination percentage (GP%), germination index (GI), vigor index (VI), and germination time to 50% (GT₅₀ in days). The populations are represented by DH (Dunhuang), JQ (Jiuquan), LZ (Linze), YC (Yongchang), GL (Gulang), MQMQ (Minqin), LX (Linxia), LA (Lanzhou), and JT (Jingtai).

Population	TG (%)	GP (%)	GI	VI	GT50 (d)
DH	65.00	15.00	1.47	15.33	8.50
JQ	70.00	10.00	2.19	11.14	6.33
LZ	75.00	5.00	1.80	21.47	6.00
YC	85.00	5.00	2.28	22.85	4.57
GL	90.00	25.00	3.07	27.14	4.80
MQ	65.00	5.00	1.67	24.12	6.50
LX	95.00	25.00	2.61	30.48	4.33
LA	75.00	10.00	2.31	34.18	7.57
JT	85.00	10.00	2.44	33.57	7.17

, there are significant differences in the germination indicators of E. angustifolia seeds from nine different populations. Among these populations, LX exhibited the highest total germination (TG), reaching 95%, while DH and MQ recorded the lowest TG at 65%. Additionally, the germination percentage (GP) of both the LX and GL populations was the highest, standing at 25%. In contrast, the GP for LZ, YC, and MQ was the lowest, at just 5%. When it comes to the germination index (GI), the GL population again ranked highest, with a value of 3.07, while DH had the lowest GI at only 1.47. The vigor index (VI) of the LA population was the most impressive among the nine, achieving a score of 34.18, whereas the TG of JQ was the lowest, at merely 11.14. Finally, the germination time (GT₅₀) for the DH population was the highest, recorded at 8.50 days, while LX had the shortest GT₅₀ at only 4.33 days.

Based on the variance analysis results, the variance components of five seed germination indicators of E. angustifolia among different populations and within populations were calculated (

Table 11. The variance analysis of seed germination indicators among the E. angustifolia populations. The indicators analyzed include total germination (TG), germination percentage (GP), germination index (GI), vigor index (VI), and germination time to 50% (GT₅₀). F-values indicate significant differences among populations (* p < 0.05, ** p < 0.01). The variance components are categorized into among populations, within populations, and random error, reflecting the proportion of variation attributed to each source.

Traits	F-Value		Proportion of Variance Component/%
	Among Populations	Within Populations	Among Populations	Within Populations	Random Error
TG	3.725 **	1.076	28.35	22.87	42.86
GP	2.135 **	0.703	25.48	21.76	53.84
GI	1.721 *	0.543	20.73	17.02	67.31
VI	4.512 **	1.643	51.53	20.16	30.13
GT50	4.124 **	1.302	48.12	24.29	27.60
Mean			34.84	21.22	55.435

). The average variance components of the seed germination indicators accounted for 34.84% and 21.22% of the total variation between and within populations, respectively. This indicates that there is a certain degree of variation in the seed germination indicators of E. angustifolia both among and within populations, with greater variation found among populations than within populations.

3.7. Correlation Analysis

3.7.1. Correlation Analysis of Fruits and Seeds

Using Pearson correlation coefficients to show the relationships between traits (Figure 2a), different degrees of correlation existed among the 12 phenotypic traits of the tested E. angustifolia fruits and seeds. It was found that there were 33 pairs of traits that showed highly significant correlation, and eight pairs of traits showed significant correlation. Specifically, the FSI, SLD, SSI, and FRS, as well as FW, FS, and SF, were positively correlated with each other. Additionally, SLD is positively correlated with FTD, FSI, FW, SSI, TGW, FS, and FRC, along with STD and FW, SLD, TGW, and FRC; FSI and SLD, SSI, FS, and SF; FW and SLD, STD, and TGW; FRS and SF; SLD and SSI, TGW, FS, and FRC; STD and TGW and FRC; SSI and SF and FS; and TGW and FRC also exhibit highly significant positive correlations. Meanwhile, significant negative correlations were observed between FTD and FSI, STD and SF, and FS; FTD and SF, FSI and STD, and STD and SSI showed highly significant negative correlations.

3.7.2. Correlation Analysis between Phenotypic Traits and Geo-Climatic Factors

The correlation between the phenotypic traits of E. angustifolia fruits and seeds in different populations and geo-climatic factors of the populations (Figure 2b) showed a strong relationship between the phenotypic traits of E. angustifolia fruits and seeds and geo-climatic factors. The traits FTD, STD, and FRC showed highly significant positive correlations with E, and FTD exhibited a highly significant negative correlation with N. FW and SF showed highly significant positive correlations with N, and significant negative correlations with AL, E, AP, and AMRH. Conversely, FRS showed a negative correlation with E, AMT, AP, and AMRH, and a highly significant positive correlation with N. In addition, FTD and SSI showed significant or highly significant negative correlations with N. In addition, FTD and SSI showed significant or highly significant negative correlations with relative humidity, TGW showed a negative correlation with longitude, and SF showed a negative correlation with average temperature. Furthermore, FSI displayed a significant negative correlation with longitude.

3.7.3. Correlation Analysis of Seed Germination Indicators with Geo-Climatic Factors

After conducting a correlation analysis between geo-climatic factors and seed germination indicators, including total germination rate (TG), germination index (GI), vigor index (VI), and others from nine populations (Figure 3a), we observed significant relationships. Specifically, TG was significantly positively correlated with AL and positively correlated with AP and AMRH, while showing a negative correlation with N. Similarly, GI exhibited a strong positive correlation with AL and AP, as well as a positive correlation with AMRH, but showed a negative correlation with N. VI was significantly positively correlated with E and positively correlated with AP, yet it demonstrated a significant negative correlation with N. Additionally, GT₅₀ showed a highly positive correlation with AMT, while being negatively correlated with AL. These findings highlight the substantial impact of geo-climatic factors on seed germination performance.

3.7.4. Correlation Analysis of Seed Germination Indicators with the Phenotypic Traits of Fruits and Seeds

The relationship between the germination indicators of E. angustifolia seeds from different populations and the phenotypic traits of fruits and seeds is depicted in Figure 3b. This study highlights that fruit weight (FW) has the most significant impact on the seed germination index. Specifically, both the germination rate (TG) and the germination index (GI) were found to be negatively correlated with FW, while the time to reach 50% germination (GT₅₀) showed a positive correlation with FW. Additionally, there was a strong correlation between the vigor index (VI) in seed germination indicators and the phenotypic traits of fruits and seeds. The VI displayed significant positive correlations with fruit-to-seed ratio (FRC) and thousand-grain weight (TGW).

3.8. Principal Component Analysis of Phenotypic Traits and Germination Indicators

Using principal component analysis (PCA) on 12 phenotypic traits and five germination indicators of E. angustifolia fruits and seeds, the top three principal components with the largest contribution were extracted. All of these components had eigenvalues greater than 1, and the cumulative contribution rate reached 67.90%, indicating that these three principal components can objectively reflect the basic information of the phenotypic traits of E. angustifolia fruits and seeds (

Table 12. The principal component analysis (PCA) results of the phenotypic traits of E. angustifolia fruits and seeds in Gansu Province. Traits include the following: FLD (Fruit longitudinal diameter), FTD (Fruit transverse diameter), FSI (Fruit shape index), FW (Fruit weight), FRS (Fruit stalk length), SLD (Seed longitudinal diameter), STD (Seed transverse diameter), SSI (Seed shape index), TGW (Thousand-grain weight), FS (Fruit shape), FRC (Fruit color), SF (Speckles on fruit), TG (Total germination percentage), GP (Germination percentage), GI (Germination index), VI (Vigor index), and GT₅₀ (Germination time to 50%). The first three components explain a cumulative 67.9% of the total variance, with the eigenvalues and variance percentages for each component listed.

Trait	Component
	1	2	3
FLD	3.49	0.94	4.05
FTD	0.58	4.16	3.04
FSI	4.06	−3.40	1.82
FW	4.10	3.44	−0.55
FRS	2.16	−1.72	−0.20
SLD	4.20	0.73	3.33
STD	−1.11	5.39	−0.47
SSI	3.94	−3.01	2.76
TGW	1.10	5.17	1.29
FS	2.99	−2.17	1.88
FRC	0.11	2.77	2.43
SF	4.09	−2.00	−1.59
TG	−4.58	−1.78	2.92
GP	−4.19	−2.23	0.93
GI	−4.90	−0.97	2.46
VI	−3.75	1.95	2.02
GT50	2.95	2.10	−3.34
Eigenvalue	4.67	3.50	3.38
% of Variance	27.40	20.60	19.90
Cumulative %	27.40	48.00	67.90

, Figure 4a).

The contribution rate of the first principal component was 27.4%, with high loadings for TG, GI, and SLD, mainly reflecting characteristics related to total seed germination rate, germination index, and germination potential. The contribution rate of the second principal component was 20.6%, with high loadings for STD, TGW, and FTD, mainly reflecting characteristics related to seed transverse diameter, thousand-grain weight, and fruit transverse diameter. The contribution rate of the third principal component was 19.9%, with high loadings for FLD, GT₅₀, and SLD, mainly reflecting characteristics related to fruit longitudinal diameter, germination time to 50%, and seed longitudinal diameter.

A scatter plot was built based on PC1 and PC2 to show the relationship among E. angustifolia samples. As shown in Figure 4b, the geographical distribution of different populations of E. angustifolia significantly influences the phenotypic traits of E. angustifolia fruits and seeds. The phenotypic traits of DH and LA populations were relatively less affected by geographical factors, while those of other populations were more affected. The individuals of the LZ and LX populations were more concentrated in the scatter plot, while the individuals of the YC and MQ populations had the most consistent scatter plot distribution. The scatter plot of other populations was more dispersed.

3.9. Cluster Analysis of Elaeagnus angustifolia Fruit and Seed Samples

Using Ward’s method based on the squared Euclidean distance for cluster analysis, 82 E. angustifolia samples were categorized into four groups based on their morphological characteristics and germination indicators (Figure 5). Group A consists of five subgroups with a total of 27 samples of E. angustifolia, mainly from JT and GL. Samples in Group A had larger TG, GI, VI, and STD values, but smaller FW and SLD values. Group B contains two subgroups with 20 samples, primarily from DH and JQ. A significant characteristic of this group was that the samples had smaller TGW, FTD, STD, TG, GI, and VI values, but larger FW, FRS, and GT₅₀ values compared to other groups. Group C includes three subgroups with a total of 25 samples, mainly from MQ and LZ. Samples in this group had smaller GP values and larger SLD and TGW values. Group D includes 10 samples from two subcategories, primarily from LX. The samples in this group had larger SSI, TG, GI, and GP values, while the FW, FRS, TGW, SF, and GT₅₀ values were relatively smaller.

4. Discussion

Plant seeds are vital for storing and transmitting genetic material, and their physical and chemical characteristics are crucial for plant adaptation to environmental changes. In the context of global warming and phenomena such as El Niño and La Niña, genetic differentiation among plants in different geographical environments is becoming more likely. Therefore, it is increasingly important to assess the relationship between the population differentiation of plant fruits and seeds and geographical environmental factors. In nature, differences in phenotypic traits among populations of the same plant species are common and influenced by both genetic material and environmental factors. This is particularly true for woody plants, where complex environmental conditions, long-term geographical isolation, natural selection, and genetic material contribute to inevitable phenotypic variation within species [47]. Our study found significant differentiation in the phenotypic traits of E. angustifolia fruits and seeds across different populations in Gansu Province, likely influenced by factors such as altitude, temperature, and annual precipitation. Seeds can act as detectors of environmental changes early in their formation, responding to their living environment and acquiring adaptive characteristics based on past fluctuations. This adaptability influences both the current dormancy and germination characteristics of seeds and their future development [48,49]. In this study, E. angustifolia seeds from different populations exhibited varying dormancy and germination characteristics, suggesting a strong relationship between these traits and the diverse environmental conditions across Gansu Province. The results align with previous studies indicating that seeds of the same species in different distribution areas often exhibit distinct dormancy and germination characteristics. This differentiation among E. angustifolia populations may be attributed to the variation in geographical factors, such as altitude and precipitation, which impact seed development and adaptive traits. For instance, higher altitudes may lead to larger seeds with longer dormancy periods as an adaptation to colder environments, whereas lower altitudes might favor smaller seeds with quicker germination. Given the wide distribution range of E. angustifolia in Gansu, these findings highlight the species’ adaptability to different environmental conditions. The genetic differentiation observed in certain fruit and seed characteristics across populations suggests that E. angustifolia has developed strategies to cope with environmental variability. This adaptability has important implications for conservation and breeding programs, as understanding the relationship between seed traits and environmental factors can guide the selection of populations for restoration projects and improve the management of E. angustifolia germplasm resources.

4.1. Different Populations of Elaeagnus angustifolia Fruits and Seeds Show Significant Differentiation in Phenotypic Traits

The coefficient of phenotypic differentiation (V_ST) is an indicator of the proportion of variation among populations in the total variation. It reflects the degree of differences in species’ phenotypic traits and can be used to evaluate the extent of phenotypic differentiation among populations. A higher phenotypic differentiation coefficient (V_ST) indicates a greater likelihood of population differentiation. In our research, we found that the fruits and seeds of E. angustifolia from nine populations in Gansu Province exhibit considerable phenotypic variability. The calculated V_ST values showed that the average variance component among populations accounts for 42.41% of the total variation, while within populations, it accounts for only 8.75%. This indicated that phenotypic variation in E. angustifolia fruits and seeds primarily arises from differences among populations, with significant trait variations observed between different populations, while most trait variations within populations were not significant.

Furthermore, the average phenotypic differentiation coefficient among E. angustifolia populations was 81.13%, suggesting a high level of differentiation in phenotypic traits of fruits and seeds among different populations. This phenomenon may be attributed to genetic differences among populations and the influence of varying geographical environmental conditions, resulting in different levels of plasticity and stability in phenotypes [50]. In this study, we observed that the geographic and ecological environments of the nine population sites vary significantly, as do the potential influences of human activities, such as road traffic, agricultural practices near rural farmland, and urban park management. These human disturbances likely led to changes in the natural habitat of E. angustifolia, along with habitat fragmentation and isolated populations, restricting gene flow and contributing to population differentiation and phenotypic variation [51].

Additionally, we found that the average variance component among populations was significantly greater than within populations, further indicating notable phenotypic variability in E. angustifolia fruits and seeds in Gansu Province. The high level of phenotypic variation observed may result from both geographical environmental differences and human activities (such as agriculture, road traffic, and urban greening management), which confer great potential for adaptability to E. angustifolia trees. This variability lays a crucial foundation for future genetic improvement and cultivar selection efforts.

4.2. Variability and Differential Analysis of Phenotypic Characteristics among Different Populations of Elaeagnus angustifolia

The coefficient of variation (CV) of phenotypic traits reflects the degree of dispersion of those traits. A lower CV value indicates less dispersion of the traits, and vice versa. The CV value is generally considered a primary indicator of variability [52]. For instance, if the CV of a particular trait is greater than 10%, it indicates significant differences in that trait among different varieties [53,54]. In our study of nine E. angustifolia populations, there were significant differences in the level of variation in fruit and seed traits, with the population in LA showing the highest coefficient of variation, while the population in LZ had the lowest coefficient of variation. This suggests that the stability of the Lanzhou population is lower, while that of the Linze population is higher. The high variation in the Lanzhou population may be due to the presence of seven samples distributed in parks, which are greatly influenced by human activities. Additionally, differences in seedling age and location may also contribute to the variation. For example, growth conditions within the forest may not be as favorable as at the forest edge, where there is ample sunlight and less competition, leading to significant phenotypic differences.

Moreover, there were significant differences in the coefficients of variation among different phenotypic traits, with the variation coefficients of the nine quantitative traits ranging from 8.76% to 27.56%, with an average of 14.93%. Most features have CV values less than 20%, indicating minimal environmental influence and less variation, demonstrating strong genetic stability. This is consistent with the findings reported by Ligarreto et al. [55] in Vaccinium meridionale Sw. (Ericaceae) and Yang et al. [56] in Yulania sprengeri (Pamp.) D.L.Fu (Magnoliaceae). These differences may be attributed to variations in ecological and geographical conditions at the species collection sites and differences in genetic structures among each population.

To further validate the observed variability among populations, chi-square tests were performed on the CVs of phenotypic traits. The chi-square results indicated that most traits exhibited significant differences among populations (p < 0.05), reinforcing that the phenotypic variation is not random but is influenced by specific factors, possibly including genetic divergence and environmental conditions. Traits such as FW, FRS, SSI, and TGW showed highly significant differences (** p < 0.01), suggesting that these traits are particularly variable and may be strongly influenced by local conditions or management practices. This additional analysis underscores the complex interaction between genetics and environment in shaping the phenotypic traits of E. angustifolia, highlighting the importance of considering both genetic and ecological factors in understanding the variability within and among populations.

4.3. Elaeagnus angustifolia Fruits and Seeds from Nine Different Populations Exhibit Rich Phenotypic Diversity

The Shannon–Wiener diversity index is an important indicator for evaluating genetic resources and analyzing the richness of plant traits in terms of phenotypic diversity, and it is widely used [57]. According to research on plant diversity along different altitudinal gradients of the Shikui River in Yunnan Province, a Shannon–Wiener diversity index of 1 signifies a high level of diversity [58]. In this study, the mean Shannon–Wiener diversity index of the quantitative and qualitative traits of fruit quality in the nine populations of E. angustifolia was greater than 1, except for fruit shape (FS). The diversity index of quantitative traits was significantly higher than that of qualitative traits. Similar findings have been observed in Euscaphis japonica (Thunb.) Kanitz (synonym Euscaphis japonica (Thunb.) Kanitz) [59] and Cucumis melo L. (Cucurbitaceae) [60] germplasm resources. One possible reason for this pattern is that quantitative traits are more influenced by factors such as germplasm type, environmental conditions, and genotypes, leading to greater variability, while qualitative traits tend to be more stable and exhibit smaller variations. Furthermore, the overall analysis of quantitative traits showed that the change pattern of the Shannon–Wiener diversity index and the coefficient of variation (CV) was not consistent and even exhibited opposite trends. For example, traits such as fruit longitudinal diameter (FLD), seed longitudinal diameter (SLD), and fruit transverse diameter (FTD) had Shannon–Wiener diversity index values greater than 2, but their CV values were much lower than 20%. While a higher coefficient of variation indicates a greater degree of trait variation, a higher diversity index reflects richer trait diversity. However, there does not appear to be a direct correlation between the two metrics. This observation aligns with findings in Dendrobium nobile Lindl. (Orchidaceae) and Salvia splendens Sellow ex Nees (Lamiaceae) [61,62].

4.4. Significant Correlations Exist between Phenotypic Traits, Germination Indicators, and Geo-Climatic Factors of Elaeagnus angustifolia Fruits and Seeds from Different Populations

Phenotypic trait correlation is defined as the ability of a specific genotype to express different phenotypes under varying environmental conditions, and it is considered a primary evolutionary mechanism for plants to adapt to new environments [63,64]. Observing the expression of one trait can help indirectly infer the correlation with another trait, improving the efficiency of selecting superior plant varieties and advancing breeding efforts [65]. In our study of nine populations of E. angustifolia in Gansu Province, we identified 41 pairs of traits that had significant or highly significant correlations, indicating a mutually supportive and co-varying relationship among these morphological features. This finding aligns with previous research on the correlation of other traits in E. angustifolia [34]. By understanding these correlations, plant selection can be more efficient, providing guidance for breeding and agricultural applications of E. angustifolia [66]. However, while these correlations are evident, the underlying mechanisms of interaction among these traits remain unclear.

Evaluating the germplasm resources of E. angustifolia from different geographical locations is crucial for understanding their genetic variation and improving breeding goals. While the variation in fruit and seed traits is primarily influenced by the genetic resources of the germplasm, geo-climatic factors also play important but distinct roles [67]. Species exhibit different geographical patterns of variation due to differences in adaptability and sensitivity to specific environmental factors such as climate, soil conditions, and altitude [68]. In this study, we found that traits such as fruit transverse diameter (FTD), seed transverse diameter (STD), and fruit color (FRC) were positively influenced by longitude, while fruit transverse diameter was negatively influenced by latitude. Additionally, single fruit weight (FW) and speckles on fruit (SF) increased with higher latitudes but decreased with higher elevations, longitudes, precipitation, and relative humidity. These results suggest that geo-climatic factors, particularly longitude and relative humidity, have a significant impact on the phenotypic traits of E. angustifolia fruits and seeds.

Within suitable ranges, conditions of low altitude, high longitude, low latitude, and high relative humidity result in larger and more vibrantly colored fruits and seeds. Conversely, hot, dry, high-altitude, and high-latitude areas tend to produce smaller fruits and seeds. This may be attributed to selective pressures mediated by pollinating insects [69,70]. Additionally, plants in more challenging environments might allocate fewer resources to fruit production to sustain normal growth and development. Our findings are consistent with recent studies suggesting that smaller seed sizes are associated with higher altitudes and latitudes, where plants produce smaller fruits compared to those at lower altitudes and latitudes [6,9]. This further validates the influence of geographical and environmental factors on plant phenotypic traits.

Seeds are vital organs for sexual reproduction in plants, playing significant roles in seed dispersal, dormancy, and germination [1]. In our study, we found significant correlations between the phenotypic traits of E. angustifolia fruits and seeds and geo-climatic factors. Notably, populations with higher annual precipitation, relative humidity, and altitude exhibited higher total seed germination rates (TG). The germination index (GI) and total germination rate (TG) showed similar correlations with geo-climatic factors, likely due to their high interrelationship.

Interestingly, GT₅₀ showed a significant negative correlation with altitude, indicating that seeds in higher altitude regions achieve a 50% germination rate faster, which is closely related to seed germination capability. The seed vigor index (VI) displayed significant positive correlations with longitude and annual precipitation while being negatively correlated with latitude. Although we are currently unable to fully explain this correlation, it could be influenced by the limited data available from sampling sites.

Further, our analysis of the relationship between seed germination and phenotypic characteristics revealed that fruit weight (FW) is a significant phenotypic trait affecting the total seed germination rate (TG) and the germination index (GI), showing a negative correlation. Larger and heavier seeds may have lower germination rates, possibly due to factors such as seed moisture content and environmental conditions affecting seed vigor [71]. This was supported by our findings in the LX region, where lower fruit weight and size corresponded to higher germination rates and indices.

The seed vigor index (VI) correlated with various phenotypic traits, showing positive relationships with fruit transverse diameter (FTD) and thousand-grain weight (TGW). Larger and heavier seeds can provide more nutrients for seedling growth, which is why these traits are positively correlated with seed vigor [72]. Additionally, fruit color showed a significant positive correlation with seed vigor as it can reflect the fruit’s maturity [73].

Given the current trends of global warming and shifts in climate patterns, such as the eastward shift of the Asian monsoon rain belt [74], changes in the distribution and abundance of E. angustifolia populations in Gansu Province are likely. These findings uncover complex and intriguing associations between seed germination and the phenotypic traits of E. angustifolia, providing a foundation for further research on the ecological adaptability and conservation of this species.

4.5. Principal Component Analysis

Principal component analysis (PCA) was used to determine the contributions of each variable to population divergence [75]. PCA can reduce the dimensionality of a dataset, highlight the most influential features contributing to the variance in the dataset, and provide essential information on the integrated quantitative traits. In this study, PCA was employed to extract the first three principal components from the phenotypic traits of E. angustifolia fruits and seeds, with a cumulative contribution rate of 67.9%. The primary and key traits influencing the phenotypic diversity of E. angustifolia fruits and seeds and germination indices were traits related to seed longitudinal diameter (SLD) and TGW, as well as traits related to fruit transverse diameter (FTD) and germination indices related to TG, GI, and GT₅₀. The PCA results indicated that the phenotypic characteristics of E. angustifolia fruits and seeds and germination indices were significantly influenced by the different geographical distributions of populations. Most samples from the DH and LA populations exhibited relatively stable phenotypic traits, while the phenotypes of the YC and MQ populations were more similar. Samples from other populations showed greater diversity and variability in phenotypic traits, possibly influenced by environmental factors.

4.6. Cluster Analysis of 82 Elaeagnus angustifolia Trees from Nine Different Seed Populations

Cluster analysis is used to classify samples based on their characteristics, ensuring high similarity within clusters and significant differences between clusters [76]. In our analysis of E. angustifolia, we identified four distinct clusters from the nine populations sampled. Notably, the 82 E. angustifolia trees did not cluster strictly according to geographical distance. While there was some overlap in the geographical distribution within each cluster, the discontinuity in phenotypic trait variations suggests that factors such as complex and diverse geographical environments, climate differences within Gansu Province, and human activities at sampling sites may play a role.

Different populations exhibited specific phenotypic characteristics, ecological ranges, and distribution patterns. Variations in traits between individuals were likely influenced by specific genotypes or environmental factors.

Group A included trees from nine populations, such as LA, Gulang, JT, and MQ. The wide distribution of this group suggests it may represent a transitional group without distinct characteristics.

Group B mainly comprised the DH and JQ populations, which were characterized by larger fruit weights (FW). Correlation analysis indicated that FW was positively correlated with relative humidity, with DH and JT ranking second and third in relative humidity among the populations. This group also exhibited lower total germination rate (TG), germination index (GI), and vigor index (VI) values, with FW being significantly negatively correlated with these germination indicators.

Group C predominantly included sources from MQ and LZ, which showed larger thousand-grain weight (TGW) and seed longitudinal diameter (SLD) values. This may be influenced by longitude, as previous correlation analyses showed that TGW is positively correlated with longitude.

Group D primarily consisted of samples from LX, located at the southernmost end of the survey range. This group had the highest elevation among the populations and the lowest average single fruit weight (FW). Conversely, DH had the highest FW, likely due to it having the lowest altitude among the populations, reaffirming the influence of altitude on fruit size [6,9].

Although the clustering was not strictly based on geographical distance, the results align well with previous studies on phenotypic traits and geographical factors, further emphasizing that geographical factors are the primary influence on the phenotypic differences of E. angustifolia fruits and seeds.

5. Conclusions

This study conducted a phenotypic diversity analysis of the fruits and seeds of 82 individuals from nine geographically diverse E. angustifolia populations in Gansu Province. It was found that there was rich phenotypic variation in E. angustifolia fruits and seeds among different populations, with variation among populations being greater than within populations. The quantitative traits of E. angustifolia fruits and seeds were relatively diverse, while the qualitative traits were more stable, with a high level of diversity in traits related to fruit. Analysis of the correlation between phenotypic traits revealed varying degrees of correlation between the phenotypic traits of E. angustifolia fruits and seeds, with most traits being closely associated. This indicated that there was a relationship of mutual promotion and co-variation among these traits.

Furthermore, the study of the correlation between phenotypic traits and geographical environmental factors revealed that some phenotypic traits of E. angustifolia fruits and seeds were strongly correlated with geographic and climatic factors. Longitude and relative humidity were identified as the main environmental factors influencing the phenotypic traits of E. angustifolia fruits and seeds. Within a certain range, an increase in longitude and rainfall was associated with larger fruits and larger and longer seeds in E. angustifolia. Through analyzing the correlation between E. angustifolia seed germination indices and geographical environmental factors, we found that the seed germination rate (TG), germination index (GI), and GT₅₀ showed more positive trends with increasing altitude, annual precipitation, and relative humidity. Additionally, our study revealed a correlation between seed germination indices and phenotypic traits. Specifically, in cases where the fruit weight is larger and the seeds are bigger, the performance of the seed germination rate (TG), germination index (GI), and GT₅₀ tends to be poorer. However, with larger and heavier seeds and brighter fruit colors, the vigor index (VI) performs better. These findings provide foundational support for enhancing population selection efficiency, conserving genetic evolutionary diversity, and screening for friendly varieties. This is of significant importance in accelerating the selection and breeding work on E. angustifolia.

In the future, the selection of different E. angustifolia varieties for various purposes should be based on factors such as geographical distribution and utilization direction. Through research, it has been found that regions with higher longitudes and abundant precipitation tend to have larger and plumper E. angustifolia fruits. Additionally, through the correlation analysis between geographical environmental factors, the phenotypic traits of fruits and seeds, and seed germination indices, it can be observed that E. angustifolia populations at higher altitudes are more likely to germinate, which will be more beneficial for the future development of populations.

Overall, efforts should be made to select trees with enlarged and plump fruits and dense fruiting that are unaffected by pests and diseases as parent trees for seed collection. In particular, targeted breeding should be conducted for varieties with bright fruit colors and plump fruits, gradually phasing out degenerated varieties. Cultivating a group of excellent varieties that integrate ornamental and ecological functions and economic benefits into sustainable development will help promote the sustainable utilization of E. angustifolia resources.