Distinguishing the Mechanisms Driving Community Structure Across Different Growth Stages in Quercus Forests

Abstract

Understanding the mechanisms governing forest community assembly across different growth stages is essential for revealing succession dynamics and guiding forest restoration. While much attention has been given to overstory trees, the understory regeneration layer, critical for forest succession, remains less explored, particularly regarding its stage-specific survival strategies and assembly processes. This study investigates the natural regeneration of Quercus variabilis forests in northern China, focusing on the transition from early to later growth stages. Our objectives were to (1) identify the phylogenetic and functional structures of regeneration communities at early and later stages, (2) explore their responses to environmental gradients, and (3) assess the roles of deterministic and stochastic processes in shaping community assembly. We integrated phylogenetic structure, functional traits, and environmental gradients to examine natural regeneration communities. The results revealed clear stage-dependent patterns: communities exhibited random phylogenetic and functional structures in the early growth stage, suggesting a dominant role of stochastic processes during early recruitment. In contrast, communities showed phylogenetic clustering and functional overdispersion in later growth stages, indicating the increasing influence of environmental filtering and interspecific competition as individuals developed. Generalized Dissimilarity Modeling (GDM) further revealed that dispersal limitation and pH were key predictors of phylogenetic β-diversity in the later growth stage, while total phosphorus drove functional β-diversity in the later growth stage. No significant predictors were found for β-diversity in the early stage. These findings highlight the shift from stochastic to deterministic processes during forest regeneration, emphasizing the stage-dependent nature of assembly mechanisms. Our study elucidates the stage-specific assembly rules of Q. variabilis forests and offers theoretical guidance for stage-targeted interventions in forest management to promote positive succession.

1. Introduction

What ecological processes regulate the community structure remains a central topic in ecology [1,2]. In general, niche theory and neutral theory constitute two dominant frameworks for explaining community assembly [3]. Niche theory posits that species coexistence, driven by resource partitioning and environmental adaptation, arises from the occupation of distinct ecological niches. Key processes underpinning these mechanisms and biodiversity patterns include habitat filtering and interspecific interaction [4]. Habitat filtering, which describes environmental factors as a metaphorical “filter”, selects species with similar traits, leading to species clustering [5]; whereas interspecific interaction promotes overdispersed ecological strategies among co-occurring species [6]. However, neutral theory attributes community diversity patterns primarily to stochastic processes, proposing that species distributions are governed by random immigration and dispersal limitation rather than functional differentiation [7,8]. For instance, dispersal limitation directly manifests as a decline in species establishment probability with increasing distance from parent plants, an observable phenomenon resulting from stochastic seed dispersal. This pattern highlights the spatial stochasticity inherent in community assembly.

Simultaneous investigation of phylogenetic and functional structures is regarded as a powerful way to infer these community assembly processes. Based on the phylogenetic conservatism hypothesis, closely related species are more functionally similar [9,10,11]. This hypothesis implies that phylogenetic and functional structures are complementary in elucidating species coexistence and adaptation mechanisms [12]. Thus, when habitat filtering acts as the dominant process in community assembly, if there is conservation of functional traits within the evolutionary lineages of the most closely related species, phylogenetic clustering will be observed. When interspecific interaction serves as the dominant process in community assembly, species that are phylogenetically close and have conserved specific functional traits will compete with one another for resources, ultimately resulting in phylogenetic overdispersion. Although functional and phylogenetic structures often exhibit a complementary relationship, they do not always change in synchrony [13]. Numerous studies have revealed that functional and phylogenetic structures are frequently decoupled, each reflecting distinct assembly processes [12,14]. For instance, communities often show functional overdispersion with phylogenetic clustering, or the opposite scenario, a phenomenon attributed to the dominant influence of different ecological processes.

The functional and phylogenetic structures of forest communities not only reflect the associations and divergences among species in terms of ecological functions and evolutionary history but also reveal that distinct habitats form differentiated community structure patterns due to the unique biogeographic evolutionary histories of species pools [15,16]. Phylogenetic and functional β-diversity, which refer to the evolutionary and functional differences between communities, show changes along environmental gradients [17]. Such changes can provide new perspectives for revealing the assembly mechanisms of forest communities. In extreme or resource-limited environments (such as high-elevation regions), phylogenetic β-diversity typically increases, manifesting as greater differences in species lineages between communities [18]. This reflects the enhanced role of environmental filtering, where only specific lineages can adapt to extreme conditions. Because functional β-diversity is dominated by nested patterns in some cases, partial functions may exhibit loss along environmental gradients, and its change patterns with environmental gradients may not be completely consistent with those of phylogenetic β-diversity [19].

Additionally, the relative importance of ecological processes demonstrates dynamic changes during community succession, where the ecological mechanisms dominating the early stages may gradually weaken as succession progresses [20,21]. In heterogeneous forests, plants in different strata, such as the overstorey and understorey are also regulated by distinct ecological processes [22]. It is noteworthy that most existing studies focus on the community assembly mechanisms of overstory trees, while the understory regeneration layer, which serves as the important foundation for forest succession, still lacks systematic exploration on whether their specific survival strategies and resource requirements lead to differentiated community assembly processes.

In this study, we focus on the natural regeneration community of Quercus variabilis forests in northern China, a dominant broad-leaved tree species in China that plays a critical role in ecological restoration. We expect that deterministic and stochastic processes play important roles in the community assembly of the study sites. For different vegetation growth stages, the abiotic environmental factors controlling species composition may shift from early growth stages to later growth stages. Furthermore, phylogenetic structure and functional structure may exhibit contrasting structural patterns. We hypothesize that trees in the early growth stage of natural regeneration experience birth and death that are more stochastic than those in the later growth stage, so the community at the early growth stage is more likely to show a stochastic structure. As a key developmental stage transitioning to adult trees, trees in the later growth stage also experience environmental filtering and interspecific competition; thus, the phylogenetic and functional structures of the community may exhibit opposite clustering or overdispersion patterns. The objectives of this study are as follows: (1) to identify the phylogenetic and functional structures of communities during the early and later growth stages of natural regeneration; (2) to explore how the phylogenetic and functional structures within communities, as well as the phylogenetic and functional β-diversity between communities, respond to environmental gradients; and (3) to assess how different ecological processes, including deterministic and stochastic processes, affect community assembly at different growth stages. Distinguishing such stage dependent assembly mechanisms is not only crucial for understanding the succession rules of warm temperate forest communities but also provides a key theoretical basis for artificially promoting the positive succession of Q. variabilis forests.

2. Materials and Methods

2.1. Study Area

Beijing is located in the northwestern margin of the North China Plain, spanning 39°24′–41°36′ N and 115°42′–117°24′ E. The terrain slopes from high elevations in the northwest to lowlands in the southeast, with mountainous regions dominated by the Taihang and Yanshan mountain systems reaching 1000 to 1500 m and a southeastern plain averaging 20 to 60 m in elevation. The climate is warm temperate semi-humid continental monsoon, characterized by distinct seasons with summer rainfall and winter dryness and an annual mean temperature of 10 to 13 degrees Celsius [23]. Major soil types include mountain brown soil, cinnamon soil, and mountain meadow soil. Zonal vegetation consists primarily of warm temperate deciduous broad-leaved forests and temperate coniferous forests. Deciduous broad-leaved forests are dominated by Quercus, Populus, and Betula species forming typical zonal forest communities, while evergreen coniferous ecosystems are constructed by Pinus tabuliformis and Platycladus orientalis [24].

2.2. Experiment Design and Surveys

Three Q. variabilis forests with minimal anthropogenic disturbance were selected as study sites: YJS, WZL, and XS (Figure 1). In July–August 2024, four 20 m × 20 m plots were randomly set up in each site, totaling 12 plots for field surveys. Each 20 m × 20 m plot was further subdivided into 16 adjacent 5 m × 5 m subplots. A total of 2811 naturally regenerating tree individuals (DBH < 5 cm), representing 22 species across 17 families, were recorded within subplots for species identity, basal diameter, height, and coordinates. Based on the height distribution of all naturally regenerating tree individuals (n = 2811), those with heights ≤ 15 cm are classified as early growth stage (n = 421), while those taller than 15 cm are classified as later growth stage (n = 2390,

Table 1. Abiotic and biotic conditions of the study plots.

Sits	Plot	Altitude (m)	Slope (°)	Aspects	OM (g/kg)	TN (g/kg)	TP (g/kg)	pH	CEC (cmol/kg)	NRD (n/ha)	ES (n/ha)	LS (n/ha)
YJS	1	173.65	46.59	S	81.40	4.27	0.44	6.89	24.97	2100	100	2000
	2	154.01	24.45	S	55.24	2.65	0.34	6.34	17.81	13,975	2550	11,425
	3	150.33	34.29	S	77.24	3.87	0.43	6.42	24.85	7300	1425	5875
	4	154.85	33.36	S	58.88	2.93	0.47	6.14	16.63	3900	625	3275
WZL	5	186.99	30.53	SW	33.89	1.66	0.25	6.11	9.85	1575	125	1450
	6	190.3	30.61	NE	37.49	2.16	0.32	5.55	14.45	975	200	775
	7	195.25	29.24	S	36.1	2.0	0.37	6.14	15.42	800	250	550
	8	175.25	35.74	SE	21.20	1.14	0.30	5.94	9.18	2025	100	1925
XS	9	190.18	33.26	SE	23.555	1.318	0.30	6.55	14.03	14,100	1650	12,450
	10	193.93	21.24	SE	74.828	2.787	0.41	6.29	15.48	10,950	1675	9275
	11	185.17	32.23	SE	38.982	1.622	0.32	6.44	18.21	7700	1250	6450
	12	186.29	10.26	N	55.150	2.249	0.33	6.42	20.57	4875	575	4300

Aspect is defined as 0° at true north and converted to -cos(θ) in the clockwise direction.

) [25]. In each subplot, at least three individuals per species were sampled, with a total of 1108 individuals collected overall, and at least five healthy and intact leaves collected per individual. If species abundance or leaf availability was insufficient, the actual number was used. The natural regeneration density (NRD) of each plot, along with the density of early growth stage (ES) and later growth stage (LS), is presented in Table 1.

Leaf samples were scanned for area quantification using Image-Pro Plus 6.0 software. After oven-drying at 75 °C for 48 h, dry weights were measured to calculate specific leaf area (SLA). RTK is used to record the coordinates of each regenerated individual, as well as information such as the elevation, slope, and aspect of the sample plot.

Soil samples from the 0–30 cm soil layer were collected at the four corners and the center of each plot using a soil auger. Soil pH was measured with a pH meter. Organic matter (OM) content was determined by potassium dichromate titration, total nitrogen (TN) by Kjeldahl method, total phosphorus (TP) by molybdenum-antimony colorimetry, and cation exchange capacity (CEC) by cobalt chloride extraction-spectrophotometry [26]. Plot information is summarized in Table 1.

2.3. Statistical Analysis

According to the phylogenetic conservatism hypothesis, closely related species typically exhibit similar functional traits [27,28]. To test this hypothesis, we quantified the phylogenetic signal strength of plant functional traits using Blomberg’s K statistic [29]. A K value greater than 1 indicates significant clustering of traits on the phylogenetic tree, reflecting strong phylogenetic signals, while a K value less than 1 suggests random trait distribution with weak phylogenetic signals. We constructed null models by randomly permuting species labels at the phylogenetic tree tips 999 times to assess signal significance. Observed values exceeding 95% of simulated values (p < 0.05) were considered statistically significant. This approach effectively distinguishes evolutionary conservatism from stochastic trait distributions, providing quantitative evidence for community assembly mechanisms.

A species-level phylogenetic tree was reconstructed (Figure A1) with the R(4.5.0) package “S.PhyloMaker” [30]. Phylogenetic distance matrices were generated using the cophenetic function in the ape package. The Net Relatedness Index (NRI) was calculated by the picante package to quantify phylogenetic structure.

We computed the observed mean phylogenetic distance (MPD) among all species pairs within subplots. Null models assumed random phylogenetic relationships by reshuffling species identities 999 times. Observed values were standardized against null distributions to derive NRI as follows:

$$NRI=-{\displaystyle \frac{(MPD\_obs-mean(MPD\_null\left)\right)}{sd(MPD\_null)}}$$

(1)

here, $MPD\_obs$ represent observed values, while $mean(MPD\_null)$ and $sd(MPD\_null)$ denote the mean and standard deviation of 999 null permutations. Positive NRI values indicate phylogenetic clustering, negative values suggest overdispersion, and values near zero imply random structure [31].

For functional structure, analogous indices (SES.FD_MPD) were computed using Euclidean distance matrices derived from standardized trait data:

$$SES.FD\_MPD=-{\displaystyle \frac{(FD\_MPD\_obs-mean(FD\_MPD\_null\left)\right)}{sd(FD\_MPD\_null)}}$$

(2)

Positive SES values denote functional clustering, negative values indicate overdispersion, and zero implies random structure [32,33].

To evaluate how environmental factors influenced NRI or SES.FD_MPD, we fitted linear mixed-effects models (LMMs) with plot included as a random intercept to account for potential plot effects. The full model included all candidate environmental variables. Prior to modeling, we assessed multicollinearity using the vif function from the car package. After excluding TN, all remaining variables had VIF < 10, indicating no strong multicollinearity (Figure A2). Model selection was performed using maximum likelihood estimation and the dredge function from the MuMIn package, which ranked all fixed-effect subsets based on AIC while keeping the random-effect structure fixed. The final model was selected based on the lowest AIC. Model explanatory power was evaluated using marginal R² (fixed effects only) and conditional R² (fixed + random effects), calculated via the r.squaredGLMM function.

We used the Sørensen dissimilarity index, calculated via the betapart::phylo.beta.pair function in R, to quantify phylogenetic and functional compositional dissimilarity (β-diversity patterns). Leveraging these metrics, we applied Generalized Dissimilarity Modeling to disentangle how environmental gradients and geographical distances shape community assembly [34]. GDM, implemented using the gdm package in R, extends matrix regression by using flexible I-spline basis functions to model nonlinear responses of compositional dissimilarity to predictors [35]. This allowed us to assess the relative contributions of dispersal limitation (stochastic processes) and abiotic filtering (deterministic processes). Spatial distance was derived from geographic coordinates, while environmental distance was computed as the Euclidean distance between environmental variables of the plots. Model significance and predictor importance were evaluated via permutation tests using the gdm::gdm.varImp function, enabling robust inference on drivers of phylogenetic and functional β-diversity.

3. Results

3.1. Results of Phylogenetic Signals for Functional Traits

We assessed phylogenetic signals of functional traits using Blomberg’s K statistics (

Table 2. Phylogenetic signal of the traits, using Blomberg’s K statistics.

Functional Traits	Mean ± SD	K Value	p Value
Basal Diameter (mm)	12.63 ± 12.65	0.087	0.213
Height (cm)	108.80 ± 90.00	0.126	0.116
Leaf Area (cm2)	106.49 ± 116.39	0.034	0.453
Specific Leaf Area (cm2/g)	1207.65 ± 5358.35	0.390	0.003

p < 0.05 indicates significant phylogenetic signal.

). Among the analyzed traits, specific leaf area (SLA) exhibited a significant phylogenetic signal (K = 0.390, p = 0.003). This indicates that SLA shows strong phylogenetic conservatism, meaning species with closer evolutionary relationships tend to have more similar SLA values. In contrast, basal diameter (in mm), height (in cm), and leaf area (in cm²) showed no significant phylogenetic signals. For basal diameter, the K value was 0.087 (p = 0.213); for height, K = 0.126 (p = 0.116); and for leaf area, K = 0.034 (p= 0.453). These results suggest that the evolution of these traits is weakly constrained by phylogenetic relationships, and non-phylogenetic factors such as environmental selection may have relatively stronger influences on their variation.

3.2. Community Phylogenetic and Functional Structure Across Growth Stages

Phylogenetic and functional structure revealed distinct assembly patterns between the early growth stage and the later growth stage. For the later growth stage, a one-tailed t-test revealed that the NRI values were significantly greater than 0 (Figure 2), indicating phylogenetic clustering. In contrast, NRI for the early growth stage was not significantly different from 0 (Figure 2), indicating a phylogenetically random structure.

For functional structure, SES.FD_MPD values exhibited significantly less than 0 for the later stage (Figure 3), indicating significant functional divergence, while the early stage showed no such deviation, consistent with a random functional structure.

3.3. Variation in Phylogenetic and Functional Structure Along Environmental Gradients

Linear mixed-effects models (LMMs) identified stage-specific environmental controls on phylogenetic and functional structure (

Table 3. Results of linear mixed-effects models of environmental factors on phylogenetic and functional structure at different growth stages.

	NRI (ES)	NRI (LS)	SES.FD_MPD (ES)	SES.FD_MPD (LS)
Intercept	−16.227 (8.318)	0.319 (0.088) **	21.512 (7.689) **	4.928 (1.536) **
Elevation	−0.078 (0.016) ***	—	−0.015(0.007)	−0.029 (0.006) **
Slope	−0.096 (0.027) **	—	0.068 (0.027) *	0.041 (0.011) **
Aspect	—	—	−2.286 (0.863) *	—
OM	—	—	—	—
TP	—	—	—	—
pH	6.028 (1.643) ***	—	−2.961 (1.188) *	—
CEC	−0.314 (0.067) ***	—	—	−0.060 (0.023) *
R2c	0.428	0.192	0.215	0.530
R2m	0.428	0	0.215	0.451

The values in parentheses represent the standard errors of the estimates; * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001; “—” indicates that this variable was removed after being filtered by the model.

). Model explanatory power varied between stages and response metrics, with the highest conditional R² observed for SES.FD_MPD in the later stage (R²c = 0.530), followed by NRI in early stage (R²c = 0.428).For early stage, NRI was significantly shaped by multiple abiotic factors. CEC (β = −0.314, p < 0.001) had strong negative effects on phylogenetic clustering, whereas pH (β = 6.028, p < 0.001) had a strong positive effect. Slope (β = −0.096, p < 0.01) and elevation (β = −0.078, p < 0.001) also negatively affected NRI. The final model explained 42.8% of the variance (R²c = 0.428), all of which was attributed to fixed effects (R²m = 0.428), indicating no added explanatory power from plot-level variance.

In contrast, NRI in the later stage exhibited no explanatory power from fixed effects (R²m = 0), while the total variance explained by the model was 19.2% (R²c = 0.192), indicating that the retained variation was solely attributed to random effects associated with plot identity. This suggests that the measured environmental variables had negligible influence on NRI at the later stage, but there was moderate unexplained variation among plots.

For SES.FD_MPD in the early stage, three environmental variables were retained: slope (β = 0.068, p < 0.05) had a positive effect, while aspect (β = −2.286, p < 0.05) and pH (β = −2.961, p < 0.05) both had negative effects. The model explained 21.5% of the variance (R²c = 0.215), again entirely attributed to fixed effects (R²m = 0.215).

For later stage, SES.FD_MPD was negatively influenced significantly by elevation (β = −0.029, p < 0.001) and CEC (β = −0.060, p < 0.05), while slope (β = 0.041, p < 0.01) showed a positive effect. The final model explained 53.0% of the total variance (R²c = 0.530), with 45.1% attributable to fixed effects (R²m = 0.451), indicating a modest contribution from plot-level variation.

3.4. Drivers of Functional and Phylogenetic β-Diversity by Geographic Distance and Environmental Variables

Generalized Dissimilarity Models (GDMs) results revealed distinct disparities in the explained deviance of functional and phylogenetic β-diversity between early and later growth stages (Figure 4, Figure 5, Figure 6 and Figure 7), as well as across diversity metrics (

Table 4. Percent deviance in functional and phylogenetic β-diversity explained by geographic distance and environmental variables based on generalized dissimilarity models (GDMs), along with the importance and significance of each predictor.

	ESβpd	LSβpd	ESβfd	LSβfd
Percent deviance explained (%)	7.36	20.86	1.73	7.62
Predictor	Importance
Geographic	0.485	36.462 ***	74.599	0.625
Elevation	2.621	0.262	174.150	9.246
Slope	6.641	1.431	—	—
Aspect	0.340		—	—
OM	—	2.062	—	3.697
TP	10.409	0.181	—	48.720 *
pH	2.145	6.894 *	—	0.778
CEC	5.704	2.425	—	—

Asterisks indicate significant effects; *: p < 0.05; ***: p < 0.001; “—” indicates that this variable was removed after being filtered by the model.

).

The explained deviance for phylogenetic β-diversity was higher in the later growth stage (20.86%) than in the early stage (7.36%). Geographic distance emerged as the primary driver of phylogenetic β-diversity (importance = 36.462, p < 0.001) in the later growth stage, with its contribution increasing nonlinearly with distance, as shown by the I-spline curve in Figure 5. Among environmental variables, pH exerted a significant positive effect (6.894, p < 0.05), whereas elevation, slope, and other factors showed weak and non-significant effects. None of the variables significantly influenced phylogenetic β-diversity in early growth stage.

Functional β-diversity in the early growth stage exhibited the lowest explained deviance (1.73%), with neither geographic distance nor elevation having significant effects. For the later stage, functional β-diversity (explained deviance = 7.62%) had total phosphorus (TP) as the only significant predictor (48.72, p < 0.05), where f(TP) remained basically at a low value before a certain threshold (around 0.35), and after exceeding the threshold, f(TP) increased rapidly with the rise in TP, with its contribution increasing nonlinearly under high phosphorus gradients (Figure 7).

4. Discussion

Forest community assembly involves multi-temporal interactions between environmental factors and species, shaping structural dynamics through different ecological processes [36,37,38,39]. Our analysis of natural regeneration communities in Q. variabilis stands revealed significant differentiation in the phylogenetic and functional structures between early and later growth stages, demonstrating stage-dependent community structures and their underlying ecological processes.

The community structure of the early growth stage tends to be random in both phylogenetic and functional dimensions. In contrast, the later growth stage exhibited opposing trends, with phylogenetic clustering and functional overdispersion. These results highlight the dynamic shift in ecological mechanisms during forest regeneration: the early stage is dominated by stochastic processes (e.g., such as seed dispersal and random establishment), while the later stage is increasingly driven by deterministic processes (environmental filtering, interspecific interaction). This stage-specific disparity is closely linked to plant life-history strategies. Plant establishment in the early stage heavily relies on seed dispersal capacity, and the stochastic nature of wind- or animal-mediated seed dispersal in Q. variabilis forests results in a phylogenetic structure unconstrained by evolutionary relationships [40]. Additionally, trees in early growth stages exhibit low resource requirements, leading to random functional trait distributions. In contrast, as trees in later stages compete more intensely for light, water, and nutrients, environmental filtering strengthens, promoting the clustering of closely related species with similar adaptive strategies (phylogenetic clustering). Concurrently, interspecific interaction drives functional trait divergence (functional overdispersion) to reduce resource overlap [5]. Similar patterns of phylogenetic clustering and functional overdispersion have also been observed in European grassland ecosystems [12].

Habitat heterogeneity is a key driver of variation in community species composition [41,42,43]. For understory species in particular, their composition is considered to be associated with local environmental factors such as topography and soil. In the early growth stage, the marginal R² and conditional R² for phylogenetic structure (NRI) were equal, both at 0.428. Similarly, the marginal and conditional R² for functional structure (SES.FD_MPD) were also equal, both at 0.215. This indicates that the variation in both NRI and SES.FD_MPD was almost entirely explained by fixed environmental factors, suggesting that environmental filtering played a dominant role during this stage. Specifically, in our study, pH and CEC exhibited strong and opposite effects on the phylogenetic clustering of early communities. Phylogenetically diverse ones in the early growth stage. However, a low-CEC environment promotes divergent phylogenetic structure in plant species. This indicates that most trees in the early stage are sensitive to soil conditions in the study area. For example, Xu et al. [44] found that a single low-phosphorus environment promotes divergent phylogenetic structure in plant species.

In contrast, at the later growth stage, the explanatory power for phylogenetic structure was almost entirely attributed to random effects among plots, with fixed environmental variables contributing nothing to the explanation of NRI (R²m = 0). This suggests that the phylogenetic pattern at this stage is no longer significantly influenced by major environmental factors. It may reflect that, as individuals grow and resource acquisition intensifies, interspecific interactions—such as competition or neutral processes—begin to play an increasingly important role in shaping community structure [5]. Functional structure (SES.FD_MPD), however, still showed a strong response to environmental variables (R²m = 0.451), with elevation, CEC, and slope exerting significant effects. This indicates that niche-related traits begin to reflect differentiation in resource use and habitat adaptation at the later stage [45]. Nonetheless, random effects accounted for an additional 8% of the variance (R²c − R²m = 0.079), suggesting the presence of spatial heterogeneity or historical contingency among communities.

Overall, the results on the variation in phylogenetic and functional structure along environmental gradients support the stage-dependent response of community structure to environmental gradients. Specifically, environmental filtering acts on the early stages of natural regeneration communities, but in terms of the performance of community structure, stochastic processes play a more important role at this stage. In subsequent growth or successional stages, the influence of interspecific interactions also gradually becomes more prominent [46,47].

The GDM results in this study clearly illuminate how different ecological processes govern community assembly at distinct developmental stages. Our findings demonstrate that during the natural regeneration of Q. variabilis forests, species β-diversity is significantly influenced by both geographic distance (a proxy for dispersal limitation) and specific soil environmental filters, particularly during the transition from early to later growth stages. At the later growth stage, phylogenetic β-diversity was notably more responsive to spatial and environmental gradients than at the early stage. The dominant role of geographic distance in explaining phylogenetic β-diversity (importance = 36.462, p < 0.001) highlights the strong influence of dispersal limitation at local scales. This spatial constraint is a common pattern in forest ecosystems, where proximity often dictates the similarity of co-occurring lineages, likely due to both limited seed dispersal and local adaptation [48,49]. In contrast, the early growth stage exhibited low and insignificant model deviance explained, suggesting that early community structure is more randomly assembled, likely governed by stochastic demographic processes. This supports the hypothesis that chance events such as seed rain, microdisturbances, and transient microclimatic conditions dominate in the earliest phase of recruitment [50,51]. Among environmental predictors, soil pH was the only significant abiotic factor associated with phylogenetic β-diversity (importance = 6.894, p < 0.05) in the later stage, suggesting that pH-driven environmental filtering begins to shape lineage turnover as individuals mature [52,53]. The nonlinear response function further suggests that only certain phylogenetic clades are able to tolerate or prefer particular pH conditions, reflecting lineage-specific physiological tolerances. For functional β-diversity, although the overall explanatory power was lower, a different pattern emerged. Total phosphorus (TP) was the only significant environmental driver for functional β-diversity (importance = 48.720, p < 0.05) in the later stage. This result indicates that TP may act as a strong selective force by influencing resource acquisition traits such as leaf morphology and photosynthetic efficiency [54]. The rapid increase in dissimilarity under high-TP conditions suggests niche differentiation among functional strategies in nutrient-enriched soils [55,56,57]. Again, functional β-diversity in early stage showed minimal explained variation (1.73%), reinforcing the idea that trait-based filtering is not yet prominent at the earliest growth stage. Together, these findings underscore a stage-dependent transition in assembly mechanisms during early forest regeneration: while communities in the early stage are shaped primarily by stochastic processes, communities in the later stage are increasingly influenced by deterministic processes, including environmental filtering (e.g., pH, phosphorus) and dispersal limitation [58]. This ontogenetic shift has important implications for forest management and restoration.

These findings suggest that targeted interventions can promote the establishment of naturally regenerated tree species at the later growth stage and enhance both functional and phylogenetic diversity. Such measures include improving soil nutrient availability, for example, through phosphorus enrichment, and adjusting microsite pH conditions. In addition, strategies aimed at enhancing seed dispersal may help alleviate the observed spatial constraints and promote either community convergence or diversification depending on specific conservation goals. Examples of these strategies include increasing connectivity between forest patches and implementing mixed-species plantations. Practically, our results only detected the existence of interspecific interactions but did not assess their relative contributions to community assembly. Future research should focus on simultaneously quantifying and evaluating the relative importance of these ecological processes in order to provide a theoretical basis for promoting the positive succession of naturally regenerating communities towards stable, mature forests.

5. Conclusions

This study reveals that community assembly in naturally regenerating Quercus variabilis forests is strongly stage-dependent. In the early stage, communities are predominantly structured by stochastic processes, whereas communities are shaped by increasing environmental filtering and interspecific interactions in the later stage. Dispersal limitation and soil pH emerged as key drivers of phylogenetic β-diversity in the later stage, while total phosphorus was the primary determinant of functional β-diversity in the later stage. No significant environmental predictors were identified for the early growth stage, underscoring the dominant role of neutral processes in early regeneration. Based on these findings, it can be concluded that community assembly mechanisms vary across developmental stages, emphasizing the importance of implementing stage-specific management strategies to facilitate successful forest succession and biodiversity conservation.