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Article

Study on the Natural Regeneration Characteristics and Influencing Factors of Typical Quercus Forests in Northern China

1
Beijing Key Laboratory of Greening Plants Breeding, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China
2
College of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China
3
Henan Dabieshan National Field Observation & Research Station of Forest Ecosystem, Zhengzhou 450046, China
4
Xinyang Academy of Ecological Research, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(2), 250; https://doi.org/10.3390/f16020250
Submission received: 16 December 2024 / Revised: 14 January 2025 / Accepted: 26 January 2025 / Published: 28 January 2025
(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)
Figure 1
<p>Location of survey plots for five <span class="html-italic">Quercus</span> species in Beijing: <span class="html-italic">Q. aliena</span>, <span class="html-italic">Q. acutissima</span>, <span class="html-italic">Q. dentata</span>, <span class="html-italic">Q. variabilis</span>, and <span class="html-italic">Q. mongolica</span>.</p> ">
Figure 2
<p>Pairwise relationship plots between natural regeneration density (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and three site factors. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; AL: altitude; SA: slope aspect; SP: slope position.</p> ">
Figure 3
<p>Heatmap of correlation coefficients between natural regeneration densities (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and stand factors. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage.</p> ">
Figure 4
<p>Heatmap of correlation coefficients between natural regeneration densities (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and soil factors. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.</p> ">
Figure 5
<p>Relative importance ranking of environmental factors affecting regeneration grade of seedlings ((<b>a</b>): Seedling 1, (<b>b</b>): Seedling 2, (<b>c</b>): Seedling 3, (<b>d</b>): all seedlings) based on the Gini index reduction method. AL: altitude; SA: slope aspect; SP: slope position; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.</p> ">
Figure 6
<p>Relative importance ranking of environmental factors affecting regeneration density of seedlings ((<b>a</b>): Seedling 1, (<b>b</b>): Seedling 2, (<b>c</b>): Seedling 3, (<b>d</b>): all seedlings) based on the node purity improvement method. AL: altitude; SA: slope aspect; SP: slope position; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.</p> ">
Versions Notes

Abstract

:
This study aims to analyze the natural regeneration characteristics and the key factors of Quercus forests, providing a theoretical foundation for maintaining the ecological stability of Quercus forests in northern China. In June and July 2023, 17 square plots of five Quercus species in Beijing were surveyed, and seedling regeneration and environmental factors (site, stand and soil factors) were measured. Pearson correlation and random forest algorithms were used to identify the relevant and key environmental factors affecting seedling regeneration density (Seedling 1, Seedling 2, Seedling 3). The natural regeneration capabilities of the five Quercus species in the Beijing area vary, with Quercus aliena and Quercus variabilis being stronger, while Quercus mongolica, Quercus acutissima and Quercus dentata are relatively weaker. Correlation analysis showed that Seedling 1 has no significant correlation with environmental factors; Seedling 2 is significantly negatively correlated with Pielou’s evenness (J) and exchangeable calcium (ECa) (p < 0.05); Seedling 3 is significantly positively correlated with species richness (S), Shannon–Wiener index (H), stand volume (M), and litter layer thickness (LT) (p < 0.05), and significantly negatively correlated with Pielou’s evenness (J) (p < 0.01). The random forest algorithm indicated that the regeneration of Seedling 1 is mainly affected by stand factors, while the regeneration of Seedling 2 and Seedling 3 is more influenced by soil and site factors. The Quercus forests in the Beijing region exhibit a rich species composition and demonstrate a certain capacity for natural regeneration. However, seedling growth is more constrained by soil and site factors in the later stages. Therefore, in the management of Quercus forests, environmental factors can be regulated during the seedling growth stage to create more suitable conditions for regeneration.

1. Introduction

The natural regeneration of forests is crucial for maintaining ecological balance, stabilizing community structures, and conserving biodiversity [1,2,3]. It plays a pivotal role in the natural restoration of forest ecosystems and in preserving the genetic diversity of species in the regeneration layer [4,5]. The regeneration of forests is influenced by human and habitat factors, the latter of which include site factors [6,7,8], stand factors [9,10,11], and soil factors [12,13,14]. However, the dominant factors affecting natural regeneration vary among different forest types. In fact, studies have shown that site factors (e.g., altitude, slope, and aspect [15,16,17]), stand factors (e.g., tree diversity, competition index, and understory vegetation [18,19,20]), and soil factors (e.g., soil nutrients, soil organic matter and total phosphorus [21,22,23,24]) are major factors influencing the density of regenerating seedlings, respectively. Furthermore, the forest regeneration stage (sapling stage, seedling stage, young tree stage) is the most sensitive period to environmental factors during tree growth and a crucial period determining the success of natural forest regeneration [25,26]. The factors influencing regeneration vary across different forest types and species, and even for the same forest type, differences at regeneration stages can result in varying factors influencing regeneration. Therefore, a comprehensive understanding of the regeneration characteristics of juvenile plants and the heterogeneity of environmental conditions is of practical significance for forest ecosystem protection and management.
The area of Quercus forests accounts for 11.95% of China’s total forest area, and the volume of Quercus forests accounts for 9.98% of China’s forests, making them a major component of China’s natural forests [27]. Quercus species are also an important species in the natural zonal forests of northern China, providing vital ecological services such as water conservation, soil erosion control, and forest fire prevention, thereby contributing to the country’s dual carbon goals. In Beijing, Quercus forests cover 122,700 hectares, accounting for 19.74% of the city’s arboreal forests area. Natural and secondary Quercus forests are mainly distributed in the mountainous regions of Beijing, with Q. aliena, Q. mongolica, Q. acutissima, Q. variabilis, and Q. dentata being the predominant species. However, the connections between natural regeneration characteristics and environmental factors across different Quercus forest regeneration stages remain unexplored. Hence, exploring the natural regeneration characteristics and its influencing factors of the typical Quercus forests could improve the quality of forest resources and their management, directly affecting the overall quality of Beijing’s forest resources.
Therefore, this study focuses on Quercus forests in the Beijing region. We addressed the following research contents. (1) The species composition and regeneration characteristics of different Quercus stands are analyzed to understand the current regeneration status of Quercus forests. (2) Through correlation analysis, the study explores the impact of environmental factors on seedling regeneration and (3) applies random forest algorithms to rank the importance of these factors in influencing regeneration density. The findings aim to provide a theoretical basis for the efficient utilization and management of Quercus forest resources in Beijing, contributing to the maintenance of biodiversity and ecological balance.

2. Materials and Methods

2.1. Study Site

Beijing (39°24′~41°36′ N, 115°42′~117°24′ E) is located in the northern part of the North China Plain. It covers an area of approximately 16,410.54 km2, with a forested area of 1.1062 million hectares, a forest coverage rate of 44.4%, a total forest resource area of 848,300 hectares, and a forest volume of 25.2 million cubic meters [28]. The elevation of the Beijing Plain ranges from 20 to 60 m, while the mountains generally have elevations between 1000 and 1500 m, with an average elevation of 43.5 m. Beijing has a warm temperate semi-humid continental monsoon climate, with an annual average temperature of 10–13 °C and a frost-free period of 180–200 days, with most rainfall occurring in summer. The main soil types include mountain brown soil, cinnamon soil, and mountain meadow soil [29]. The predominant vegetation in the study area is composed of tree species such as Quercus acutissima, Quercus variabilis, Quercus mongolica, Quercus aliena, Quercus dentata, Pinus tabuliformis, Larix principis-rupprechtii, Acer truncatum, Ailanthus altissima, Ginkgo biloba, Populus tomentosa, and Fraxinus chinensis; shrub species include Leptopus chinensis, Spiraea cantoniensis, Syringa oblata, Lonicera maackii, and Jasminum nudiflorum; herbaceous species include Allium ochotense, Polygonatum odoratum, Sedum spectabile, Hemerocallis fulva, Chrysanthemum indicum, and Potentilla chinensis [30].

2.2. Experiment Design

Field surveys were conducted in June and July of 2023 in typical Quercus-dominated communities in Beijing. Plots were established in areas where Quercus species were relatively concentrated, covering five types of Quercus stands: Q. aliena (Bailongtan Forest Farm), Q. variabilis (Xishan Experimental Forest Farm), Q. mongolica (Beijing Western For-est Farm), Q. acutissima (Shangfangshan National Forest Park), and Q. dentata (Xishan Ex-perimental Forest Farm). The plot locations are shown in Figure 1.
A total of 17 square plots were set up. Trees with a diameter at breast height (DBH) greater than 1 cm were measured for tree height, DBH, origin and dry biomass, and tree locations were also recorded with a real-time kinematic (RTK). For each plot, 5 m × 5 m subplots were set up at the four corners and the center for seedling and shrub surveys, and 1 m × 1 m subplot was set up for herbaceous vegetation surveys. These subplots recorded species names, quantities, height, and coverage. GPS was used to measure altitude, slope aspect, slope position, and other information. Basic plot information is summarized in Table 1.

2.3. Index Measurement

Soil samples from the 0–30 cm soil layer were collected at the four corners and center of each fixed plot using aluminum boxes and soil augers for analysis of soil physical and chemical properties. Five additional 1 m × 1 m subplots near each sample point were established to measure the thickness of the litter layer and humus layer. Soil pH was measured using a pH meter, organic matter was determined by the potassium dichromate method, total nitrogen was measured by the Kjeldahl method, total phosphorus and available phosphorus were determined by the molybdenum-antimony colorimetric method, available potassium was measured using a flame photometer, available manganese, iron, copper, and zinc were measured by the DTPA extraction-atomic absorption spectrophotometry method, exchangeable calcium and magnesium were determined by ammonium acetate extraction-atomic absorption spectrophotometry, alkali-hydrolyzed nitrogen was measured by alkali diffusion, and electrical conductivity (EC) was measured using the conductivity method. The cation exchange capacity (CEC) was determined by the cobalt chloride extraction-spectrophotometric method [31,32,33].
In total, 36 environmental variables were selected for analysis, including 4 site factors (altitude, slope aspect, slope position, slope gradient), 14 stand factors (total tree count, species richness, Shannon–Wiener index, Pielou evenness index, crown density, stand volume, stand density, average DBH, shrub species richness, shrub density, shrub height, herbaceous species richness, herbaceous coverage, and herbaceous height), and 18 soil factors (soil thickness, litter layer thickness, humus layer thickness, organic matter, total nitrogen, total phosphorus, alkali-hydrolyzed nitrogen, available phosphorus, available potassium, pH value, EC value, cation exchange capacity, exchangeable calcium, exchangeable magnesium, available iron, available manganese, available copper, and available zinc).

2.4. Index Calculation and Selection

2.4.1. Seedling Density and Importance Value

Seedlings were divided into three categories: saplings (height < 30 cm), small seed-lings (31 cm < height ≤ 51 cm), and young trees (height > 51 cm). The densities of each category were recorded as Seedling1 (S1), Seedling2 (S2), and Seedling3 (S3). The evaluation of seedling regeneration was carried out according to the technical regulations of the National Forestry and Grassland Administration (GB/T 38590-2020) [34]. The importance value (IV) for each species was calculated as follows [35]:
IV = R A + R F + R D / 3
where RA represents relative abundance (proportion of species in the total number of individuals); RF represents relative frequency (proportion of species in the total number of plots); and RD represents relative dominance (proportion of species’ average height in the total average height of all species).

2.4.2. Plant Species Diversity Indices

Species diversity in Quercus stands was measured using species richness (S), Shan-non–Wiener index (H), and the Pielou evenness index (J). The formulas for these indices are as follows [36]:
H = i = 1 S P i ln P i ,
J = H / ln S
where S is the total number of species in the tree, and Pi is the proportion of all individuals belonging to the species in the sample to the total number of individuals.

2.4.3. Index Selection

To better understand the factors influencing Quercus stand regeneration, 16 environmental factors were selected after analyzing the correlation and collinearity between the variables. These include 3 site factors, 6 stand factors, and 7 soil factors. The distribution of the regeneration density and influencing factors in the Quercus stands is shown in Table 2.

2.5. Statistical Analysis

Correlation analysis was used to examine the relationships between the seedling re-generation density (S1, S2, and S3), site factors (altitude, slope aspect, slope position), stand factors (species richness, Shannon–Wiener index, Pielou evenness index, stand volume, shrub density, and herbaceous coverage), and soil factors (litter layer thickness, total nitrogen, available phosphorus, available potassium, pH value, exchangeable calcium, and available manganese). The seedling densities (S1, S2, and S3) were treated as continuous variables, the seedling regeneration stages (stage 1, stage 2, and stage 3) were treated as categorical variables, and a random forest model (random forest function) was used for processing. For continuous variables, the random forest regression algorithm was employed to rank the relative importance of variables based on node purity improvement. For categorical variables, the random forest classification algorithm was used to rank the relative importance based on the decrease in Gini index. Correlation analysis and random forest analysis were performed using R v4.4.1 software (R Development Core Team, 2024) [37], with the corrplot [38] and GGally [39] packages for correlation analysis and the randomForest package [40] for random forest analysis. Graphs were generated using the ggplot2 package [41].

3. Results

3.1. Characteristics of Quercus Stand Regeneration

3.1.1. Regeneration Quantity and Grade Evaluation in Quercus Stands

The regeneration quantity and grade evaluation for the five types of Quercus stands are shown in Table 3. From the regeneration quantity perspective, the numbers of regenerated individuals in Q. aliena stands and Q. variabilis stands were higher than that of Q. dentata stands, Q. mongolica stands, and Q. acutissima stands. In terms of growth status, at the <30 cm regeneration level, the average height of Q. aliena, Q. acutissima, Q. mongolica, and Q. variabilis stands was higher than that of Q. dentata stands. At the 31–50 cm regeneration level, the average height differences across the five Quercus stand types were minimal. However, at the >51 cm regeneration level, the average height of Q. aliena stands was lower than that of the other Quercus stands. Regarding regeneration density, at the <30 cm and >51 cm regeneration levels, the regeneration density of Q. aliena stands was significantly higher than that of the other Quercus stands. At the 31–50 cm regeneration level, the regeneration density of Q. aliena and Q. dentata stands was greater than that of the other three Quercus stands. From a regeneration grade perspective, at the <30 cm level, natural regeneration was poor in all five Quercus stand types. At the 31–50 cm level, Q. aliena, Q. dentata, and Q. variabilis had moderate regeneration, while Q. acutissima and Q. mongolica had poor regeneration. At the >51 cm level, Q. aliena and Q. variabilis showed good regeneration, while the other three Quercus stand types exhibited moderate regeneration. In summary, Q. aliena and Q. variabilis exhibited stronger regeneration potential, while Q. dentata, Q. acutissima, and Q. mongolica had relatively weaker regeneration abilities.

3.1.2. Seedling Regeneration Characteristics in Quercus Stands

Based on the seedling survey data from 17 sample plots, the species composition and importance values of the regenerated seedlings in the five Quercus stand types in Beijing are shown in Table 4.
As shown in Table 4, there were differences in species richness and importance values of regenerated tree species among the five Quercus stands in Beijing. In Q. aliena stands, a total of 19 species were recorded, belonging to 9 families and 14 genera. The dominant species was Celtis koraiensis, with an importance value of 14.9%, and the main accompanying species were Celtis sinensi and Prunus triloba, both with importance values greater than 9%. In Q. dentata stands, a total of 11 species were recorded, belonging to 8 families and 9 genera. The dominant species were Q. dentata and Fraxinus chinensis, with importance values of 14.9% and 14.8%, respectively, and the main accompanying species were Morus mongolica, Morus alba, and Diospyros lotus, with importance values all greater than 10%. In Q. acutissima stands, a total of 12 species were recorded, belonging to 8 families and 9 genera. The dominant species were Q. acutissima and Q. dentata, with importance values of 15.6% and 14.5%, respectively, and the main accompanying species were Ziziphus jujuba and Robinia pseudoacacia, both with importance values greater than 9%. In Q. mongolica stands, a total of four species were recorded, belonging to four families and four genera. The dominant species was Fraxinus chinensis, with an importance value of 48.1%, and the main accompanying species were Cotinus coggygria and Q. mongolica, with importance values greater than 18%. In Q. variabilis stands, a total of 14 species were recorded, belonging to 11 families and 13 genera. The dominant species were Koelreuteria paniculata and Q. variabilis, with importance values of 16% and 15.5%, respectively, and the main accompanying species were Quercus. aliena and Cotinus coggygria, with importance values greater than 9%.

3.2. Correlation Analysis of Factors Affecting Natural Regeneration in Quercus Stands

3.2.1. Correlation Between Natural Regeneration and Site Factors in Quercus Stands

The pairwise relationship between the three site factors (altitude, slope aspect, and slope position) and three natural regeneration densities (S1, S2, and S3) in Quercus stands is shown in Figure 2 (continuous variables as scatter plots, discrete variables as box plots). The results show that altitude was positively correlated with S1 density, while negatively correlated with S2 and S3 densities, though these relationships were not statistically significant. Slope aspect and slope position, being categorical variables, did not show any obvious correlation with S1, S2, and S3 densities in the box plots.

3.2.2. Correlation Between Natural Regeneration and Stand Factors in Quercus Stands

The correlation coefficients between six stand factors (S, H, J, M, SD, HC) and three natural regeneration densities (S1, S2, and S3) are shown in Figure 3. From the results, it is evident that S1 is negatively correlated with J, while the other five factors are positively correlated, but none of these correlations are significant. S2 is negatively correlated with J (r = −0.5, p < 0.05), while the other five factors were positively correlated but not significant. Seedling 3 showed a significant negative correlation with J (r = −0.62, p < 0.01), and a significant positive correlation with S (r = 0.76, p < 0.001), H (r = 0.74, p < 0.001), and M (r = 0.5, p < 0.05).

3.2.3. Spatial Distribution Pattern of Forest Stand

The correlation coefficients between seven soil factors (LT, TN, AP, AK, pH, ECa, and AM) and three natural regeneration densities (S1, S2, and S3) are shown in Figure 4. The results show that S1 density was negatively correlated with AK and AM, while positively correlated with the other variables, although none of these relationships were statistically significant. S2 density showed a positive correlation with litter LT but was not significant, while it was significantly negatively correlated with ECa (r = −0.48, p < 0.05), with other variables showing no significant correlation. S3 density showed positive correlations with LT, pH, and AM, with LT showing a significant positive correlation (r = 0.49, p < 0.05), while the other four variables showed negative correlations, none of which were significant.
Moreover, Figure 2, Figure 3 and Figure 4 show that the relationships between the three natural regeneration densities (S1, S2, and S3) were different. S1 density is significantly positively correlated with S2 density (r = 0.65, p < 0.01), and S2 density is significantly positively correlated with S3 density (r = 0.55, p < 0.05), while the correlation between S1 and S3 density is positive but not significant.

3.3. Random Forest Algorithm for Ranking Important Factors

3.3.1. Ranking of Important Factors Affecting Regeneration Grade in Quercus Stands

Using the random forest algorithm, the relative importance of the environmental factors affecting the natural regeneration grade (a categorical variable with three levels) in Quercus stands is ranked based on the Gini index reduction method, as shown in Figure 5. The top five important factors affecting S1 regeneration grade were H, SD, S, J, and AM, including four stand factors and one soil factor (Figure 5a). The top five important factors affecting S2 regeneration grade were AP, H, J, AL and S, including one site factor, three stand factors, and one soil factor (Figure 5b). The top five important factors affecting S3 regeneration grade were AK, H, TN, AP, and AL, including one site factor, one stand factor, and three soil factors (Figure 5c). The top five important factors affecting the regeneration grade of all seedlings (S1, S2, and S3) were AP, J, S, TN, and H, including three stand factors and two soil factors (Figure 5d). In summary, as the seedling growth stage progresses, the influence of site and soil factors on the regeneration grade increases.

3.3.2. Ranking of Important Factors Affecting Regeneration Density in Quercus Stands

Using the random forest algorithm, the relative importance of the environmental factors affecting the natural regeneration density (a continuous variable) in Quercus stands is ranked based on the node purity improvement method, as shown in Figure 6. The top five important factors affecting S1 density were H, ECa, HC, J, and S, including four stand factors and one soil factor (Figure 6a). The top five important factors affecting S2 density were ECa, AP, M, AK, and J, including two stand factors and three soil factors (Figure 6b). The top five important factors affecting S3 density were H, S, J, AK, and AM, including three stand factors and two soil factors (Figure 6c). The top five important factors affecting the regeneration density of all seedlings (S1, S2, and S3) were J, H, AK, AP, and S, including three stand factors and two soil factors (Figure 6d). In summary, as the seedling growth stage progresses, the importance ranking of stand factors (reflecting species diversity H, S, and J) initially decreases and then increases, while the importance ranking of soil factors gradually increases.
Additionally, the results of the random forest algorithm indicate that the factor importance rankings for continuous variables (S1, S2, and S3) and categorical variables (grade 1, grade 2, and grade 3) differ. Figure 5 and Figure 6 show that the major factors affecting Seedling 1 (grade 1) regeneration density are mainly stand factors, while soil and site factors rank higher for Seedling 2 (grade 2) and Seedling 3 (grade 3) regeneration density.

4. Discussion

4.1. Analysis of Different Regeneration Characteristics in Quercus Stands

The survey of five types of Quercus stands in Beijing showed that Quercus stands with either excessively dense or sparse canopy layers had poor natural regeneration, while stands with moderate density showed good regeneration. This finding is consistent with the results of [42]. The possible reason for this could be the differences in understory vegetation competition intensity and soil physical–chemical properties under different stand density conditions. Furthermore, the species richness and composition of regenerated seedlings in the five Quercus stands in Beijing differed significantly, which is similar to [43]. Due to the different spatial distributions of the five Quercus stand types, there were variations in site factors, and differences in the dominant tree species and stand structure also led to significant differentiation in natural regeneration species. The species composition of the five Quercus stands was relatively complex, with a high species richness, and no single dominant species of regenerated seedlings was evident. Especially in Q. mongolica stands, the dominant regenerated seedling species was Fraxinus chinensis, with an importance value of 48.1%, and the number of regenerating species was limited, leading to a clear competitive relationship.

4.2. Effects of Environmental Factors on Natural Regeneration in Quercus Stands

The study indicated that site factors such as altitude, slope aspect, and slope position had no significant effect on the natural regeneration density in Quercus stands. This contradicts the findings of [44], which found slope aspect to significantly affect the regeneration density of Q. aliena. The lack of significant results in this study may be due to the relatively small number of plots sampled and the limited variation in site factors across those plots. As the seedlings’ growth levels improve, the importance ranking of site factors (altitude, slope aspect, and slope position) on seedling regeneration density gradually increases. In the early stages of seedling growth, resources such as light, heat, and moisture are relatively abundant, allowing seedlings to access sufficient resources under varying site conditions. However, as seedling growth progresses, competition for resources intensifies, and site conditions influence the available resources within the stand, thereby affecting regeneration outcomes [45,46].
Plant species diversity index is one of the factors influencing natural regeneration in Quercus stands in Beijing. The correlation analysis revealed that seedling regeneration density was significantly positively correlated with Species richness and Shannon–Wiener index, but significantly negatively correlated with the Pielou evenness index. Higher species richness and Shannon–Wiener index suggest a greater number of ecological niches and more complex ecological relationships, providing more resources for seedling growth, which helps maintain ecosystem stability and enhance regeneration ability [47]. A higher evenness index indicates that, in areas with higher seedling density, the evenness of species distribution decreases, leading to dominance by a few species and suppression of regeneration of other species. This study also showed that stand volume directly and indirectly affects seedling regeneration density. Stands with larger volume may provide more nutrients, which benefit seedling growth and regeneration [48].
Soil factors are closely related to the regeneration density of Quercus stands. Some studies suggest that thicker litter layers may inhibit seed germination and seedling growth [20,49,50]. However, this study found that litter layer thickness was significantly positively correlated with the regeneration density of Seedling 2 and Seedling 3. Generally, a thicker litter layer provides a protective cover for seedlings, reducing soil surface temperature fluctuations and moisture evaporation, thus creating favorable conditions for seedling growth. Additionally, the study found that exchangeable calcium was significantly negatively correlated with Seedling 2 regeneration density and positively correlated with soil pH. In acidic soils, high concentrations of exchangeable calcium may neutralize soil acidity, which could affect seedling growth. Seedlings are better adapted to slightly acidic or neutral soils [51], and excessively high calcium levels may alter soil pH, negatively affecting seedling development.
Correlation analysis revealed that the primary environmental factors influencing seedling regeneration density were stand and soil factors. However, the random forest algorithm’s ranking of important factors indicated that, as seedlings’ growth levels increased, the importance of altitude as a site factor also increased. This may be due to the relatively few study plots and the small variation in site factors among the stands. Therefore, future research should expand the sample size and comprehensively consider both biotic and abiotic factors to deepen our understanding of the key environmental factors affecting natural regeneration in Quercus stands in northern China.

5. Conclusions

In Beijing, Q. aliena and Q. variabilis stands exhibited stronger regeneration abilities, while Q. dentata, Q. acutissima, and Q. mongolica stands had relatively weaker regeneration capacities. The main environmental factors influencing natural regeneration in Quercus stands in Beijing include altitude, species richness, Shannon–Wiener index, Pielou evenness index, stand volume, shrub density, litter layer thickness, total nitrogen, available phosphorus, exchangeable calcium, and available manganese. Among these, altitude was the most significant site factor affecting regeneration, species richness, Shannon–Wiener index, Pielou evenness index, stand volume, and shrub density were the most influential stand factors, and litter layer thickness, total nitrogen, available phosphorus, exchangeable calcium, and available manganese were the most significant soil factors. Therefore, we recommend augmenting the cultivated area of Quercus species in low-altitude regions by strategically supplementing Quercus seedlings within the forest understory. Additionally, enhancing the natural regeneration capacity of Q. mongolica forests can be prioritized by managing their stand density effectively. Overall, Quercus stand management should consider the impacts of site factors, stand factors, and soil factors comprehensively, creating conditions that are more favorable for natural regeneration in Quercus stands.

Author Contributions

Conceptualization, Y.J. and X.H.; methodology, G.D. and X.H.; software, G.D.; validation, Y.C.; formal analysis, G.D. and Y.C.; investigation, F.L. (Fang Li) and X.H.; resources, F.L. (Fang Liang); data curation, Y.W. and H.C.; writing—original draft preparation, X.H.; writing—review and editing, Y.J.; visualization, Z.L.; supervision, X.H.; project administration, X.H.; funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Study on Natural Regeneration Characteristics and Influencing Factors of Oak Forests in Beijing, Phase II (YKYQN202502); Research on the multifunctionality and driving factors of oak forest ecosystems in Beijing (YZQN202405); the Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2023SZ23); the Xinyang Academy of Ecological Research Open Foundation (2023XYMS10); and the key scientific research projects in universities of Henan (23A220003).

Data Availability Statement

The data are not publicly available due to proprietary rights.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of survey plots for five Quercus species in Beijing: Q. aliena, Q. acutissima, Q. dentata, Q. variabilis, and Q. mongolica.
Figure 1. Location of survey plots for five Quercus species in Beijing: Q. aliena, Q. acutissima, Q. dentata, Q. variabilis, and Q. mongolica.
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Figure 2. Pairwise relationship plots between natural regeneration density (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and three site factors. *: p < 0.05; **: p < 0.01; AL: altitude; SA: slope aspect; SP: slope position.
Figure 2. Pairwise relationship plots between natural regeneration density (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and three site factors. *: p < 0.05; **: p < 0.01; AL: altitude; SA: slope aspect; SP: slope position.
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Figure 3. Heatmap of correlation coefficients between natural regeneration densities (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and stand factors. *: p < 0.05; **: p < 0.01; ***: p < 0.001; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage.
Figure 3. Heatmap of correlation coefficients between natural regeneration densities (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and stand factors. *: p < 0.05; **: p < 0.01; ***: p < 0.001; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage.
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Figure 4. Heatmap of correlation coefficients between natural regeneration densities (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and soil factors. *: p < 0.05; **: p < 0.01; ***: p < 0.001; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.
Figure 4. Heatmap of correlation coefficients between natural regeneration densities (Seedling 1 (S1), Seedling 2 (S2), and Seedling 3 (S3)) and soil factors. *: p < 0.05; **: p < 0.01; ***: p < 0.001; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.
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Figure 5. Relative importance ranking of environmental factors affecting regeneration grade of seedlings ((a): Seedling 1, (b): Seedling 2, (c): Seedling 3, (d): all seedlings) based on the Gini index reduction method. AL: altitude; SA: slope aspect; SP: slope position; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.
Figure 5. Relative importance ranking of environmental factors affecting regeneration grade of seedlings ((a): Seedling 1, (b): Seedling 2, (c): Seedling 3, (d): all seedlings) based on the Gini index reduction method. AL: altitude; SA: slope aspect; SP: slope position; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.
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Figure 6. Relative importance ranking of environmental factors affecting regeneration density of seedlings ((a): Seedling 1, (b): Seedling 2, (c): Seedling 3, (d): all seedlings) based on the node purity improvement method. AL: altitude; SA: slope aspect; SP: slope position; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.
Figure 6. Relative importance ranking of environmental factors affecting regeneration density of seedlings ((a): Seedling 1, (b): Seedling 2, (c): Seedling 3, (d): all seedlings) based on the node purity improvement method. AL: altitude; SA: slope aspect; SP: slope position; S: species richness; H: Shannon–Wiener index; J: Pielou evenness index; M: stand volume; SD: shrub density; HC: herbaceous coverage; LT: litter layer thickness; TN: total nitrogen; AP: available phosphorus; AK: available potassium; pH: pH value; ECa: exchangeable calcium; AM: available manganese.
Forests 16 00250 g006
Table 1. Basic information of the study plots.
Table 1. Basic information of the study plots.
Stand TypePlot NumberPlot Area (m2)Altitude (m)Slope AspectSlope PositionStand Density (n/ha)
Q. aliena1900483NU1621
2400470NU899
3400465NM999
Q. mongolica1600950NM2581
2600930NM2914
3600925NM1832
Q. acutissima1600284SM566
2600284SM683
3600284SL549
4400284NM350
Q. variabilis1900192SM699
2900195SM533
3900186SM766
42500186NM1139
Q. dentata1900755NM1365
2900780NU1365
3900776NU1543
Note: N is the north-facing slope, S is the south-facing slope; U is upper, M is middle, L is lower.
Table 2. Environmental characteristics of the study plots.
Table 2. Environmental characteristics of the study plots.
Factor TypeVariableAbbreviationMeanRangeStandard Error
Regeneration indicatorsSeeding 1 (n/ha)S11584320–3836955
Seeding 2 (n/ha)S2917160–3037769
Seeding 3 (n/ha)S3217716–64741710
Site factorsAltitude (m)AL495.82186.00–950.00291.70
Slope aspectSA---
Slope positionSP---
Stand factorsSpecies richness (n)S84–204
Shannon–Wiener indexH1.481.09–2.340.33
Pielou evenness indexJ0.210.12–0.290.05
Stand volume (m3/ha)M94.6242.71–162.5432.35
Shrub density (10,000/ha)SD1.140.58–2.282.27
Herbaceous coverage (%)HC13.170.6–36.69.72
Soil factorsLitter layer thickness (cm)LT5.652.00–10.002.52
Total nitrogen (g/kg)TN2.010.70–2.990.59
Available phosphorus (mg/kg)AP3.491.22–6.951.65
Available potassium (mg/kg)AK101.0738.15–161.9133.75
pH valuepH6.465.41–7.050.48
Exchangeable calcium (mg/kg)ECa2774.541197.11–4191.62707.26
Available manganese (mg/kg)AM25.999.43–71.8016.39
Table 3. Regeneration quantity, average tree height, regeneration density, and grade evaluation at different levels for various Quercus stands.
Table 3. Regeneration quantity, average tree height, regeneration density, and grade evaluation at different levels for various Quercus stands.
LevelStand TypesAverage Height (cm)Regeneration Density (n/ha)Grade
<30 cmQ. aliena211971Poor
Q. dentata151678Poor
Q. acutissima201379Poor
Q. mongolica221545Poor
Q. variabilis201459Poor
31–50 cmQ. aliena411332Moderate
Q. dentata401332Moderate
Q. acutissima41519Poor
Q. mongolica42453Poor
Q. variabilis411039Moderate
>51 cmQ. aliena1004236Good
Q. dentata1171945Moderate
Q. acutissima1251039Moderate
Q. mongolica1121225Moderate
Q. variabilis1132657Good
Table 4. Species composition and importance values of regenerated tree species in Quercus stands.
Table 4. Species composition and importance values of regenerated tree species in Quercus stands.
Stand TypesTree SpeciesRA/%RF/%RD/%IV/%
Q. alienaCeltis koraiensis23.315.16.314.9
Celtis sinensi13.415.14.911.1
Prunus triloba9.89.68.09.1
Fraxinus chinensis9.46.84.36.8
Fraxinus pennsylvanica6.76.87.06.8
Ulmus laevis8.36.83.26.1
Morus mongolica3.96.85.75.5
Sophora tomentosa3.56.85.15.1
Prunus padus0.81.411.14.4
Ailanthus altissima0.81.410.94.4
Pistacia chinensis1.22.77.93.9
Cotinus coggygria8.61.41.63.9
Morus alba0.82.77.73.7
Acer truncatum1.64.15.53.7
Quercus aliena2.04.12.22.8
Koelreuteria paniculata2.02.73.12.6
Celtis tetrandra1.62.71.62.0
Broussonetia papyrifera0.71.52.61.7
Quercus dentata1.61.51.31.5
Q. dentataQuercus dentata19.421.73.514.9
Fraxinus chinensis15.618.310.614.8
Morus mongolica5.915.016.212.4
Morus alba8.413.311.611.1
Diospyros lotus24.03.34.110.5
Koelreuteria paniculata14.78.34.59.2
Acer pictum1.31.721.78.2
Acer truncatum3.25.013.87.3
Celtis bungeana3.85.05.04.6
Cotinus coggygria2.55.05.94.4
Quercus variabilis1.23.43.12.6
Q. acutissimaQuercus acutissima16.726.83.315.6
Quercus dentata20.219.63.714.5
Ziziphus jujuba21.03.63.89.5
Robinia pseudoacacia7.912.57.69.3
Prunus sibirica7.98.97.48.1
Celtis bungeana3.51.819.18.1
Ulmus pumila7.07.16.76.9
Morus alba1.81.816.66.7
Koelreuteria paniculata6.17.15.06.1
Acer truncatum3.53.610.15.7
Morus mongolica1.81.811.65.1
Quercus variabilis2.65.45.14.4
Q. mongolicaFraxinus chinensis78.756.59.048.1
Cotinus coggygria2.44.367.524.7
Quercus mongolica14.234.87.218.7
Crataegus pinnatifida4.74.416.38.5
Q. variabilisKoelreuteria paniculata23.017.97.016.0
Quercus variabilis24.217.94.315.5
Quercus aliena11.713.44.79.9
Cotinus coggygria11.414.93.29.8
Morus mongolica6.47.510.78.2
Broussonetia papyrifera7.09.06.27.4
Styphnolobium japonicum1.83.017.37.4
Diospyros lotus2.96.09.86.2
Morus alba2.31.513.45.7
Fraxinus chinensis2.33.07.64.3
Acer truncatum1.21.59.34.0
Ailanthus altissima3.51.53.72.9
Platycladus orientalis1.21.52.21.6
Pistacia chinensis1.11.50.61.1
Note: RA represents relative abundance; RF represents relative frequency; RD represents relative dominance; IV represents importance value.
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Hu, X.; Duan, G.; Jin, Y.; Cheng, Y.; Liang, F.; Lian, Z.; Li, F.; Wang, Y.; Chen, H. Study on the Natural Regeneration Characteristics and Influencing Factors of Typical Quercus Forests in Northern China. Forests 2025, 16, 250. https://doi.org/10.3390/f16020250

AMA Style

Hu X, Duan G, Jin Y, Cheng Y, Liang F, Lian Z, Li F, Wang Y, Chen H. Study on the Natural Regeneration Characteristics and Influencing Factors of Typical Quercus Forests in Northern China. Forests. 2025; 16(2):250. https://doi.org/10.3390/f16020250

Chicago/Turabian Style

Hu, Xuefan, Guangshuang Duan, Yingshan Jin, Yuxin Cheng, Fang Liang, Zhenghua Lian, Fang Li, Yuerong Wang, and Hongfei Chen. 2025. "Study on the Natural Regeneration Characteristics and Influencing Factors of Typical Quercus Forests in Northern China" Forests 16, no. 2: 250. https://doi.org/10.3390/f16020250

APA Style

Hu, X., Duan, G., Jin, Y., Cheng, Y., Liang, F., Lian, Z., Li, F., Wang, Y., & Chen, H. (2025). Study on the Natural Regeneration Characteristics and Influencing Factors of Typical Quercus Forests in Northern China. Forests, 16(2), 250. https://doi.org/10.3390/f16020250

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