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Article

Tree Diversity and Identity Effects on Aboveground Biomass Are Stronger than Those of Abiotic Drivers in Coniferous and Broadleaved Forest Restoration Sites of South Korea

by
Ji-Soo Kwak
1,2,
Joonhyung Park
3,
Yong-Ju Lee
1,2,
Min-Ki Lee
2,4,
Chae-Young Lim
3 and
Chang-Bae Lee
1,2,4,*
1
Department of Climate Technology Convergence (Biodiversity and Ecosystem Functioning Major), Kookmin University, 77 Jeongneungro, Seongbukgu, Seoul 02707, Republic of Korea
2
Forest Carbon Graduate School, Kookmin University, 77 Jeongneungro, Seongbukgu, Seoul 02707, Republic of Korea
3
Forest Ecology and Restoration Division, Korea Forest Conservation Association, Daejeon 35262, Republic of Korea
4
Department of Forest Resources, Kookmin University, 77 Jeongneungro, Seongbukgu, Seoul 02707, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2025, 16(6), 979; https://doi.org/10.3390/f16060979
Submission received: 9 April 2025 / Revised: 27 May 2025 / Accepted: 6 June 2025 / Published: 10 June 2025
(This article belongs to the Special Issue Forest Ecosystem Services and Sustainable Management)
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Abstract

Forest restoration sites have a critical role in the maintenance and improvement of forest ecosystem health and resilience, as well as increasing carbon storage capacity. However, previous studies on forest restoration sites have primarily focused on monitoring vegetation changes and investigating changes in carbon storage (e.g., aboveground biomass). Research on identifying the controlling drivers of aboveground biomass (AGB) between/among forest types according to stand age within restoration sites remains limited. Our study analyzed data from a total of 149 plots in forest restoration sites in South Korea, comprising 57 coniferous forest plots (38.3%) and 92 broadleaved forest plots (61.7%). This study employed a piecewise structural equation model to determine the main biotic (i.e., stand structural diversity, species diversity, functional diversity, and tree identity) and abiotic drivers (i.e., topographic, climate factors driver, stand age, and soil properties) influencing AGB in each forest type. The results revealed that stand structural diversity was the most critical driver of AGB across all forest types, highlighting the importance of structural complexity in early stage restoration. Specifically, in coniferous forests, stand structural diversity (DBH STD) and tree identity (CWM WD) were more influential, whereas in broadleaved forests, SR and climatic conditions played a greater role. Therefore, our findings provide empirical evidence for understanding AGB dynamics in early stage forest restoration sites and may help inform the development of management strategies for each forest type and early restoration planning in similar ecosystems.

1. Introduction

Forests have an important role in the sequestration of carbon, accounting for an estimated 25% of the world’s aboveground carbon storage. Their ability to mitigate carbon dioxide emissions has garnered increasing global attention amid rising concerns about climate change [1,2]. Globally, forests are estimated to sequester 1.1 ± 0.8 Pg of carbon annually, and forests hold a greater quantity of carbon in their biomass and soils compared to what exists in the atmosphere [3]. However, both natural and anthropogenic forest degradation reduce not only carbon sequestration and storage capacities but also other essential ecosystem functions (e.g., water retention, air purification, etc.) [4]. Forest degradation refers to a marked reduction in forest structure or function caused over time by human activities or natural disasters [5]. Key drivers of forest degradation include fire, logging, fuelwood collection, and cultivation [6,7]. Biodiversity loss due to forest degradation leads to declining ecosystem functions, such as organic matter decomposition, soil nutrient cycling, water retention, and pollination [8,9].
Forest restoration refers to re-establishing forests that have disappeared or restoring forest conditions to their pre-degradation condition [10]. In this context, forest restoration can contribute to mitigating climate change by increasing carbon storage, while also enhancing biodiversity and strengthening ecosystem functions through the reintroduction of native species and improvement of habitat quality [1,11]. Therefore, forest restoration has the potential as the most effective long-term solution to help mitigate climate change and enhance biodiversity [12,13].
Most of the terrestrial carbon stored in forests is in the form of aboveground biomass (AGB), stored in trees through photosynthesis, which enhances the functions and services of forests, making it an important driver in early stages of forest restoration [14]. Measuring AGB is essential for assessing the carbon storage capacity of forests, as it is directly linked to ecosystem services. In particular, effective forest restoration can lead to an increase in AGB, thereby enhancing carbon storage capacity. Therefore, analyzing AGB changes in restoration sites provides crucial baseline data for developing restoration strategies that maximize carbon storage functions. Moreover, biodiversity plays a key role at all levels of forest ecosystem functions and services [15]. Recent studies have shown that native mixed-species plantings in forest restoration have the potential to provide biodiversity benefits in addition to enhancing carbon storage (i.e., AGB) [16,17,18]. However, AGB and biodiversity that are closely related to these forest ecosystem functions and services are increasingly threatened by forest degradation through direct and indirect drivers including fire, logging, and fuelwood extraction [19]. Therefore, conducting research related to forest restoration is crucial to maintaining and enhancing the functions and services of ecosystems [20].
Two main effects have been proposed to explain how biotic drivers influence AGB: the hypotheses of mass ratio and niche complementarity [21,22]. The first of these hypotheses proposes that dominant species’ functional traits are crucial determinants of AGB and productivity, as evaluated by community-weighted mean (CWM) values of functional traits based on species abundance [23]. The second of these hypotheses explains that higher tree diversity (e.g., species diversity and functional diversity) increases AGB by promoting niche partitioning, interspecific facilitation, and the efficient use of complementary resources [24,25].
Furthermore, topographic factors (e.g., elevation) indirectly influence AGB through their role in shaping the local environment, microclimate, and soil properties (e.g., soil pH), which in turn have both direct and indirect effects on plant growth and productivity [26,27]. Tree diversity and AGB are also influenced by climatic factors, including mean annual temperature (MAT) and mean annual precipitation (MAP), which are shaped by elevation and reflect water availability and temperature conditions [28,29]. Forest succession refers to the continuous changes in the structure, function, and species composition of a forest over time following a disturbance (i.e., forest restoration) [30,31]. During this process, stand structural diversity and tree diversity interact, exerting complementary effects that lead to increased AGB as the forest stand ages [32]. Stand structural diversity represents the structural complexity of a forest and serves as a key indicator for assessing forest health and habitat complexity [33]. Species diversity, which is included in tree diversity, refers to the diversity of tree species that make up a forest and includes the number of tree species present (i.e., species richness) [34]. The interaction between these two factors has been previously researched and is known to play a crucial role in enhancing AGB during the forest succession process [18,35]. For example, in the early successional stage, when species richness is low, a few dominant species with tall growth are likely to drive the stand structural diversity of the forest [36]. These changes suggest that even if a few dominant species shape the stand structural characteristics of the forest in the early succession stage, AGB can potentially be effectively accumulated if they form diverse canopy structures and vertical stratification [9,37]. Based on these various interactions, abiotic drivers such as topographic and climate factors can influence tree diversity (e.g., species richness) and stand structural diversity, which in turn may enhance resource-use efficiency and thereby lead to an increase in AGB.
AGB can be influenced by forest types (e.g., coniferous and broadleaved forests) [21]. Within coniferous forests, the rapid early growth of dominant species, such as pine trees, plays a crucial role in biomass accumulation, potentially leading to distinct AGB formation patterns in forest restoration sites [24,38]. In contrast, broadleaved forests are characterized by their ability to develop diverse vertical stand structures, which can serve as a key factor contributing to AGB accumulation [39]. Coniferous and broadleaved forests exhibit distinct ecological characteristics in terms of successional processes, species composition, and structural traits [18]. Therefore, the mechanisms through which biotic and abiotic drivers influence AGB are also likely to differ by forest type [40]. Accordingly, conducting forest type-specific analyses may help derive more effective and ecologically sound restoration strategies. Most of the previous research exploring the association between drivers and AGB in forest restoration sites has been conducted across geographic regions (i.e., countries and local scale), rather than focusing on individual forest types such as coniferous and broadleaved forests [41]. In addition, much of the previous research has focused on tracking changes in vegetation and/or carbon storage (i.e., AGB) [42], with research on identifying the drivers of AGB largely concentrated in tropical forests [43]. Although numerous studies have investigated biodiversity–ecosystem functioning (BEF) relationships in temperate forests, the underlying mechanisms remain debated and not fully understood. Particularly, how biotic and abiotic drivers jointly influence AGB across forest types during the early stages of restoration in temperate ecosystems has received limited empirical attention [24,44]. Therefore, researching how biotic and abiotic drivers affect AGB by each forest type in a forest restoration site will help effectively enhance the functions and services of forests in each type of restoration site.
In this study, the focus was on analyzing the drivers that control AGB between two forest types. To do so, we conducted field surveys in 149 forest restoration plots across South Korea to examine the relationships between AGB and potential biotic and abiotic drivers. Our analysis focused on biotic drivers, including stand structural diversity, tree diversity (i.e., species diversity and functional diversity), and tree identity (i.e., community-weighted mean values of traits), as well as abiotic drivers (i.e., topographic factors, climatic factors, stand age, and soil properties) in each forest type. As noted above, given the complex interactions between biotic and abiotic drivers and AGB, it remains unclear how these drivers jointly influence AGB across different forest types during the early stages of restoration. To address this question, we focus on two hypotheses that reflect key ecological mechanisms, as illustrated in our conceptual model (Figure 1): (1) As stand age increases, the process of stand development alters functional traits and species diversity, which enhances resource use efficiency and, in turn, raises AGB across all forest types; (2) Due to the ecological characteristics of coniferous and broadleaved forests such as their successional processes, species composition, and structural complexity, the relative influence of biotic and abiotic drivers on AGB is expected to differ between forest types. Overall, we aim to identify the drivers and mechanisms of AGB development by different forest types in forest restoration sites of South Korea and propose appropriate sustainable forest management strategies for each forest type accordingly.

2. Materials and Methods

2.1. Study Sites, Data Collection, and Calculation of AGB

The present study analyzed the drivers controlling AGB across different forest types (i.e., coniferous, broadleaved, and total) using data from 149 plots (a plot size of 100 m2) collected between 2020 and 2022 as part of the Korea Forest Service’s ‘Forest Restoration Sites Monitoring Survey’ (Figure 2) [45]. Among these, 57 plots (38.3%) represented coniferous forest and 92 plots (61.7%) represented broadleaved forest. For the coniferous forest, Pinus thunbergii Parl. and P. densiflora Siebold & Zucc. showed the highest abundance among tree species. In the broadleaved forest, Robinia pseudoacacia L. and Fraxinus rhynchophylla Hance. showed the highest abundance among tree species.
This survey targeted forest restoration projects conducted throughout South Korea. Plots were randomly selected from restored sites encompassing stand ages ranging from 1 to 15 years after restoration to capture the temporal gradient of restoration effects. In this study, we employed a 100 m2 plot size, which we considered appropriate given the structural characteristics of the study sites. The average tree height within the plots was approximately 4 m, and most individuals were shorter than 5 m. Given that these stands were largely composed of small-sized trees typical of early successional stages, this plot size is considered sufficient to reasonably capture their contributions to ecological processes, including AGB [46,47]. Following the national restoration monitoring guidelines by Korea Forest Service [47], all woody stems with DBH ≥ 0.2 cm were identified to species level and measured for DBH and height. To ensure accuracy, particularly for small individuals, DBH measurements were made using digital calipers. Although the threshold allowed for the inclusion of trees as small as 0.2 cm DBH, the actual minimum values observed were 0.92 cm in coniferous and 0.43 cm in broadleaved plots. The average DBHs were 6.24 cm and 5.19 cm in coniferous and broadleaved forests, respectively (Table S1), indicating that most individuals were sufficiently large to contribute to AGB accumulation [48,49]. The AGB of tree species was then calculated using a biomass estimation equation based on DBH and biomass expansion factor derived from the National Institute of Forest Science [45] (see Table S2).

2.2. Quantification of Biotic Drivers

We considered stand structural diversity, tree diversity (i.e., species diversity and functional diversity), and tree identity values to explain the influence of biotic drivers on AGB [50]. Stand structural diversity metrics were calculated based on the complete set of DBH measurements collected from all trees within each plot. These metrics included mean DBH (DBH MEAN), standard deviation of DBH (DBH STD), maximum DBH (DBH MAX), skewness of DBH (DBH Skew), and DBH diversity (DBH DIV). Among these indices, DBH diversity was calculated based on the Shannon–Wiener index using 2 cm intervals of DBH class. Tree diversity included species diversity, which was assessed using species richness (SR), Pielou’s species evenness (SE), and the Shannon H index (SD), while functional diversity was assessed using functional dispersion (FDis). We use the term tree identity to refer to the dominant functional characteristics of the tree community, as represented by CWM trait values [29,51]. These values were evaluated based on seven key functional traits: specific leaf area (SLA), wood density (WD), seed mass (SM), leaf carbon content (C), leaf nitrogen content (N), leaf phosphorus content (P), and leaf dry matter content (LDMC). The CWMs of the seven functional traits were calculated within each plot by applying the relative basal area of each species at breast height as a weighting factor. The following equation was used:
C W M x = ∑ i = 1 n p i t i
where C W M x denotes the CWM of trait x, n is the number of species present in the plot, and p i and t i correspond to the relative basal area and trait value of species i, respectively. All CWM calculations were conducted using the ‘FD’ package in R version 4.3.3 [52,53]. Previous research has shown that these variables are fundamental to plant growth and survival and are pivotal for forest ecosystem functions and AGB stock [21,54,55]. Using standardized protocols, functional trait data were obtained from open databases, published references (Table S2), or directly measured (i.e., LDMC, SLA, WD, C, N, and P) [21,55,56]. Averages at the genus-level were employed in situations where species level data were missing [21,56]. Summary statistics for variables related to biotic drivers are shown in Table S1 and Figures S1 and S2.

2.3. Quantification of Abiotic Drivers

For the evaluation of abiotic drivers influencing AGB, we used topographic factors, climate factors and soil properties. For the soil properties, we focused only on soil pH among several soil properties because it is a critical property that affects AGB through its influence on nutrient availability and organic matter decomposition. Soil pH also impacts physical properties, which in turn affect root growth and overall plant health, making it a key indicator of nutrient cycling and AGB productivity [57,58]. Soil samples, following the methodology outlined in the monitoring survey, were collected from 0 to 30 cm depths at two or three points per plot. The collected samples from each plot were mixed together to facilitate the measurement of their soil pH. Using a digital climatic map produced by the National Center for Agricultural and Meteorological Sciences, MAT and MAP were calculated as key climatic factors [59]. Elevation was used as a topographic factor because it regulates environmental factors essential for plant growth, such as temperature and precipitation [60,61]. These factors influence processes such as photosynthesis, growth rate, and biomass accumulation in trees, which are directly related to AGB [62]. Principal component analysis (PCA) was performed using elevation, which is included in topographic factors, and MAT and MAP, which are included in climate factors, to reduce the number of variables and covariance. A new variable, PC1Elev-climate, was introduced into the analysis on the first PCA axis, explaining 66.2% of the total variance (Table S3). Using the 3D Analyst extension tool in ArcGIS version 10.5, the extraction of all abiotic variables was performed using a digital elevation model [63]. The stand age represents the number of years since restoration, recorded as an integer from 1 to 15 (e.g., 3 years, 10 years). Summary statistics for variables related to biotic drivers are shown in Table S1 and Figure S3.

2.4. Statistical Analysis

The data were prepared for statistical analyses by applying log or square root transformations to biotic and abiotic drivers and AGB, aiming to improve linearity and normality, followed by standardizing the variables to ensure consistency [50]. To examine spatial autocorrelation, we employed generalized least squares (GLS) to compare two models: one that included spatial variables (i.e., the geographic coordinates of each plot) and another that did not. Model performance was assessed using Akaike’s Information Criterion (AIC), and the comparison indicated no substantial influence of spatial autocorrelation on model fit (Table S4). Pearson correlation analysis was performed to find highly correlated variables (|r| ≥ 0.7) (Figure S4). Multicollinearity was evaluated through the variance inflation factor (VIF), and all models showed VIF values below 5, suggesting that multicollinearity had no significant impact on the regression results [50,64].
Multi-model inference tests, conducted separately for biotic and abiotic drivers, were used to identify the most impactful variables on AGB for each forest type [56]. Accordingly, we selected the most impactful variables, determined by their highest standardized regression coefficients (β), as the main factors driving AGB for each category of biotic and abiotic drivers (Figure S5).
In this study, with the foundational conceptual model serving as a basis (Figure 1), we performed piecewise structural equation modelling (pSEM) to analyze the various pathways connecting biotic and abiotic drivers of AGB [21,50,56,64,65]. Three pSEMs were developed to represent different forest types such as coniferous, broadleaved, and total forests. We initially built models that included all potential pathways for each forest type, and then gradually eliminated non-significant pathways one by one [66]. AIC, Fisher’s C statistics, and p-value were calculated to assess how well each model fit the data. After analyzing and comparing the results, the model with the lowest AIC value was selected, alongside the Fisher’s C statistic and p-value, which supported its data fit. For coniferous and broadleaved forest types, the final model with all non-significant pathways removed had the lowest AIC value. However, for the total forest types, although the final model with all non-significant pathways removed had the lowest AIC value, the Fisher’s C and p-value were unavailable (NA), leading to its exclusion from the evaluation. Consequently, the significant model with the second-lowest AIC value, which best explained the data fit, was selected.

3. Results

In the coniferous forest model (Figure 3a), DBH STD (β = 0.602, p < 0.001) and SR (β = 0.310, p < 0.05) positively influenced AGB through direct effects, but FDis (β = −0.394, p < 0.01) and CWM WD (β = −0.267, p < 0.001) negatively influenced AGB through direct effects. Indirectly, stand age contributed positively to AGB through DBH STD and SR, but negatively through CWM WD. In addition, soil pH (β = −0.424, p < 0.001) indirectly lowered AGB through DBH STD. Along with the results of multi-model inference tests (Figure 4a), AGB was mainly driven by DBH STD and CWM WD, and each of the important values was 1 and 0.99, respectively.
In the broadleaved forest model (Figure 3b), PC1Elev-climate (β = 0.124, p < 0.01), stand age, DBH MEAN (β = 0.773, p < 0.001), and SR (β = 0.554, p < 0.001) positively influenced AGB through direct effects, but FDis (β = −0.159, p < 0.01) negatively influenced AGB through direct effects. Indirectly, stand age contributed positively to AGB through FDis and SR. Along with the results of multi-model inference tests (Figure 4b), the AGB were mainly driven by DBH MEAN and SR, and each of the important values was 1.
In the total forest model (Figure 3c), PC1Elev-climate, stand age (β = 0.129, p < 0.01), DBH MEAN (β = 0.776, p < 0.001), and SR (β = 0.608, p < 0.001) positively influenced AGB through direct effects, while FDis (β = −0.182, p < 0.001) negatively influenced AGB through direct effects. Stand age positively influenced AGB and SR through direct effects. Indirectly, PC1Elev-climate contributed positively to AGB through SR. Along with the results of multi-model inference tests (Figure 4b), AGB were mainly driven by DBH MEAN and SR, and each of the important values was 1. The bivariate relationships conducted within each forest type were supported by the pSEM results (Figure 5).

4. Discussion

This study aimed to identify the key biotic and abiotic factors determining AGB across different forest types (i.e., coniferous, broadleaved, and total) in restoration sites of South Korea. The analysis revealed that stand structural diversity was the most important driver of AGB increase across all forest types (Figure 3). This suggests that the presence of individuals with large DBH is a critical factor in AGB accumulation during the early stages of restoration, and that stand structural diversity can effectively facilitate AGB increase [67,68]. Although different stand structure diversity indicators were significant for coniferous and broadleaved forest types, both DBH STD and DBH MEAN showed a strong correlation with DBH MAX (Figure S4). Although restoration sites in this study were relatively young, we observed that some plots included individuals with relatively large DBH, with maximum values reaching 44.8 cm in coniferous forests and 41.7 cm in broadleaved forests, as shown in Table S1. These individuals may have contributed to structural diversity and played a role in AGB accumulation, supporting the potential relevance of the “big tree effect” even in early successional stages [64,69].
There were also distinct differences in the drivers influencing AGB between coniferous and broadleaved forest types (Figure 3a,b). First, in coniferous forest types, stand age had a positive effect on both DBH STD and SR, thereby indirectly increasing AGB, while FDis negatively influenced AGB through direct effects. This can be interpreted as an indication that, in early stage coniferous forest restoration sites, AGB increased over time as certain trees grew rapidly, leading to growth differentiation among individuals and the development of ecological conditions that allow for the establishment and coexistence of multiple species with similar functional traits [21,70]. Accordingly, our results are consistent with previous studies suggesting that, during the initial stages of ecosystem development, species characterized by rapid growth and efficient resource use tend to dominate, thereby contributing positively to the accumulation of AGB [66,71]. Additionally, soil pH had a negative effect on DBH STD, thereby indirectly contributing to an increase in AGB. The decomposition of fallen leaves tends to produce organic acids, leading to a decrease in soil pH [72]. As soil acidification intensifies in forests, nutrient availability in the soil (e.g., calcium, magnesium, and phosphate) may decrease [73,74]. This may lead to some trees absorbing sufficient nutrients and growing rapidly, while others experience growth limitations, potentially leading to size variation among individuals [58,75].
In contrast, in broadleaved forest types, stand age primarily influenced SR rather than stand structural diversity (i.e., DBH MEAN), and AGB increased indirectly through SR as a mediating driver. Broadleaved forests generally provide environments that can accommodate diverse canopy structures and ecological niches [39,76]. These structural characteristics facilitate complementary resource use and facilitative interactions among various species, enabling them to utilize resources efficiently and contribute to ecosystem functioning. This aligns with the niche complementarity theory [24,25]. However, considering our finding that FDis negatively affected AGB, it is also plausible that functionally similar yet diverse species enhance overall resource-use efficiency by differentially utilizing light, water, and nutrients, thereby contributing to increased AGB [16,22]. Furthermore, as such structurally and functionally supportive environments develop over time, they may enhance ecosystem stability and resilience against external disturbances and environmental changes, positively influencing the maintenance and accumulation of AGB [10,77,78]. Considering both our results and previous studies, it can be inferred that, in broadleaved forest types of restoration sites, species that rapidly occupy space and gain a competitive advantage (e.g., Betula spp., Ulmus spp., and Salix spp.) coexist with species that grow more slowly but perform functions steadily (e.g., Quercus spp., Carpinus spp., and Zelkova serrata), leading over time to a balanced community in terms of structure and function, which positively influences AGB [79,80,81].
CWM WD showed a significant negative correlation with AGB in coniferous forests, whereas no significant trend was observed in broadleaved forests. This difference can be attributed to the variation in growth characteristics of dominant species between forest types [24,82]. In coniferous forests, it appears that certain low wood-density species (e.g., Larix spp.) became dominant during the early stages of restoration, resulting in higher resource-use efficiency and contributing positively to rapid growth and AGB accumulation in the short term [83,84]. In contrast, broadleaved forest types tend to exhibit a relatively even distribution of diverse species that grow together while efficiently utilizing resources, which may have diluted the influence of individual species’ functional traits on overall AGB [85,86]. This suggests that, in broadleaved forests, species composition and structural interactions may play a more significant role in determining AGB than functional traits alone [32,87].
Among the abiotic drivers, the influence of climatic and topographic variables was particularly significant in broadleaved forests. The PC1Elev-climate variable was associated with higher elevation, greater precipitation, and lower temperatures, indicating that these conditions contributed to increased AGB in broadleaved forest types (Table S3). This indicates that broadleaved forests are better suited for growth in cool and moist environments, and supports the need to consider regional climatic conditions when selecting and managing future restoration sites for broadleaved forests [88,89].
The patterns observed in this study suggest that different forest types (i.e., coniferous, broadleaved, and total) may promote short-term AGB accumulation in early stage restoration sites. However, considering that the maximum tree age in the dataset was limited to 10 years, the long-term resilience and adaptive capacity of forest ecosystems to environmental changes may have been only partially assessed [90] (Figure S3). Therefore, further studies need to clarify the mechanisms of how changes in environmental conditions, biodiversity, and species compositions due to long-term vegetation changes after ecosystem restoration affect AGB. Moreover, the contrasting results between forest types support the two key hypotheses proposed in the Introduction concerning the influence of biotic attributes on AGB: the mass ratio and niche complementarity hypotheses. In coniferous forests, the influence of stand structural diversity and CWM WD on AGB supports the mass ratio hypothesis, which posits that dominant species’ functional traits drive biomass accumulation. In contrast, in broadleaved forests, SR acted as a mediating driver for AGB increase, thereby supporting the niche complementarity hypothesis, which states that higher species diversity enhances resource partitioning and efficient resource use [25]. Overall, this study highlights stand structural diversity as a key factor in AGB accumulation in early stage forest restoration sites. Such stand structural diversity is not only associated with increased AGB but is also closely linked to enhanced carbon sequestration and storage capacities, as well as broader improvements in ecosystem services [14,15]. Particularly in the early stages of restoration, the influence of stand structural diversity and species composition on AGB accumulation suggests potential contributions to ecosystem productivity, stability, and resilience. Moreover, our findings confirm that biotic and abiotic factors influencing AGB vary across forest types, highlighting the need for type-specific restoration strategies. By considering the ecological characteristics and community structures of coniferous and broadleaved forests, the selection and maintenance of functionally effective and structurally important species during early restoration could not only promote effective AGB recovery but also contribute to maximizing long-term carbon storage and advancing sustainable forest management [40,91].

5. Conclusions

This study investigated the key biotic and abiotic drivers influencing AGB across forest types (i.e., coniferous, broadleaved, and total) in early stage restoration sites throughout South Korea. Our results highlighted that stand structural diversity was the most consistent and influential driver of AGB across all forest types, supporting the “big tree effect” reported in previous studies. The key mechanisms identified for each forest type are as follows: (1) In coniferous forests, stand structural diversity and tree identity (i.e., CWM WD) were more influential. (2) However, in broadleaved forests, species richness and climatic conditions played a greater role. These distinctions emphasize the need for forest type-specific restoration strategies. Furthermore, our results indicate that functional diversity may not always have a positive effect on AGB, especially in the early stages of restoration when communities are still assembling. Instead, the dominance of certain functionally effective species—those capable of rapid growth and efficient resource use—may play a more critical role in biomass accumulation, particularly in resource-limited or early successional environments. Given these findings, we recommend that restoration efforts prioritize species selection and structural management strategies that align with the ecological characteristics of each forest type. By fostering stand structural complexity and incorporating species with desirable functional traits, it is possible to accelerate AGB recovery, enhance carbon sequestration, and promote long-term ecosystem resilience.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16060979/s1, Figure S1: Box plots for the comparison of stand structure diversity and species diversity indices including (a) DBH MEAN (cm), (b) DBH STD (cm), (c) DBH MAX (cm), (d) DBH Skew (cm), (e) DBH DIV (cm), (f) SR, (g) SD, and (h) SE among three forest types (i.e., coniferous, broadleaved and total forests) in forest restoration sites in South Korea. All the abbreviations for variables are described in Table S1; Figure S2: Box plots for the comparison of stand community weighted-mean and functional diversity indices including (a) FDis, (b) CWM SLA (mm2 g−1), (c) CWM WD (g cm−3), (d) CWM SM (mg), (e) CWM LDMC, (f) CWM C (mg g−1), (g) CWM N (mg g−1), and (h) CWM P (mg g−1) pH among three forest types (i.e., coniferous, broadleaved and total forests) in forest restoration sites in South Korea. All the abbreviations for variables are described in Table S1; Figure S3: Box plots for the comparison of abiotic drivers including (a) Elevation (m), (b) MAP (mm), (c) MAT (°C), (d) stand age, and (e) soil pH among three forest types (i.e., coniferous, broadleaved and total forests) in forest restoration sites in South Korea. All the abbreviations for variables are described in Table S1; Figure S4: Pearson’s correlation coefficient among abiotic and biotic drivers, and AGB in (a) coniferous, (b) broadleaved, and (c) total forests. All the abbreviations for variables are described in Tables S1 and S2; Figure S5: Parameter estimates with 95% confidence intervals calculated using a model averaging approach represent the effect size (circle) with standard error (bar) of stand structural diversity, community-weighted mean, and species diversity value for AGB among (a) coniferous, (b) broadleaved, and (c) total forests. Abbreviations for the variables are shown in Tables S1 and S2. Significance levels are * p < 0.05, ** p < 0.01, and *** p < 0.001; Table S1: Summary of indices for species diversity, stand structural diversity, functional diversity, community-weighted mean and aboveground biomass as biotic drivers, and abiotic drivers across forest types (coniferous: n = 57, broadleaved: n = 92, total: n = 149) in forest restoration sites in South Korea; Table S2: Wood density and biomass expansion factor for biomass estimation equation and the data source of functional traits by each woody species recorded in this study. Scientific names follow The WFO Plant List (www.wfoplantlist.org accessed on 10 April 2025). Functional traits were obtained from the listed literature; Table S3: Result of principal component analysis (PCA) of elevation, MAT, and MAP such as mean annual temperature (MAT) and mean annual precipitation (MAP); Table S4: Summary of the generalized least squares (GLS) models to test the influence of spatial autocorrelation of the environmental effects on abiotic, biotic driver and AGB.

Author Contributions

J.-S.K.: methodology, formal analysis, data curation, visualization, and writing—original draft preparation. J.P.: methodology, formal analysis, data curation, and visualization; Y.-J.L.: methodology, visualization, and formal analysis. M.-K.L.: methodology, and visualization. C.-Y.L.: methodology, supervision, and data curation; C.-B.L.: conceptualization, funding acquisition, methodology, supervision, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was conducted with support from the R&D Program for Forest Science Technology (No. RS-2024-00404816) by Korea Forest Service (Korea Forestry Promotion Institute), development of forest restoration index using ecomorphological traits of tree species (2021346A00-2123CD01) of the research and development on the problem solving of disaster in the forests, and was also financially supported by Korea Forest Service as ‘Graduate School specialized in Carbon Sink’. We would like to thank the members of the Biodiversity–Ecosystem Functioning Laboratory for their invaluable assistance in this study. We also express our gratitude to the anonymous editor and reviewers for their helpful comments and careful revision of this manuscript.

Data Availability Statement

The original contributions presented in the study are available in Supplementary Material S1. Further inquiries can be directed to the corresponding author, Chang-Bae Lee.

Acknowledgments

We sincerely thank the members of the Biodiversity–Ecosystem Functioning Laboratory at Kookmin University for their invaluable assistance with this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 16 00979 g001
Figure 1. Conceptual model indicating how multiple biotic and abiotic drivers affect AGB by forest types (i.e., coniferous, broadleaved, and total) in forest restoration sites of South Korea. Abbreviations: PCA, principal component analysis; MAT, mean annual temperature; MAP, mean annual precipitation; DBH SKEW, the skewness of diameter at breast height; DBH DIV, the diversity of diameter at breast height; DBH STD, the standard deviation of diameter at breast height; CWM SLA, the specific leaf area of community-weighted mean; CWM WD, the wood density of community-weighted mean; CWM SM, the seed mass of community-weighted mean; CWM C, the leaf carbon content of community weighted-mean; CWM N, the leaf nitrogen content of community-weighted mean; CWM P, the leaf phosphorus content of community-weighted mean; CWM LDMC, the leaf dry matter content of community-weighted mean.
Figure 1. Conceptual model indicating how multiple biotic and abiotic drivers affect AGB by forest types (i.e., coniferous, broadleaved, and total) in forest restoration sites of South Korea. Abbreviations: PCA, principal component analysis; MAT, mean annual temperature; MAP, mean annual precipitation; DBH SKEW, the skewness of diameter at breast height; DBH DIV, the diversity of diameter at breast height; DBH STD, the standard deviation of diameter at breast height; CWM SLA, the specific leaf area of community-weighted mean; CWM WD, the wood density of community-weighted mean; CWM SM, the seed mass of community-weighted mean; CWM C, the leaf carbon content of community weighted-mean; CWM N, the leaf nitrogen content of community-weighted mean; CWM P, the leaf phosphorus content of community-weighted mean; CWM LDMC, the leaf dry matter content of community-weighted mean.
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Forests 16 00979 g002
Figure 2. Locations of 149, 10 × 10 m2 study plots including 57 plots (38.2%) of coniferous forests and 92 plots (61.7%) of broadleaved forests in restoration sites, South Korea.
Figure 2. Locations of 149, 10 × 10 m2 study plots including 57 plots (38.2%) of coniferous forests and 92 plots (61.7%) of broadleaved forests in restoration sites, South Korea.
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Figure 3. pSEM model accounting for the effects of biotic and abiotic drivers on AGB for (a) coniferous, (b) broadleaved, and (c) total forest types in restoration sites. Green and red arrows indicate positive and negative paths, respectively. Solid and dashed arrows indicate significant (p < 0.05) and non-significant (p > 0.05) pathways, respectively. Standardized coefficients are shown for each arrow and covariance. Significance levels: * p < 0.05, ** p < 0.01, and *** p < 0.001. Abbreviations: PC1Elev_climate; the first axis of principal component analysis for elevation and climate variables, SR, species richness; FDis, functional dispersion; AGB, aboveground biomass; the abbreviations for the remaining variables are presented in Figure 1.
Figure 3. pSEM model accounting for the effects of biotic and abiotic drivers on AGB for (a) coniferous, (b) broadleaved, and (c) total forest types in restoration sites. Green and red arrows indicate positive and negative paths, respectively. Solid and dashed arrows indicate significant (p < 0.05) and non-significant (p > 0.05) pathways, respectively. Standardized coefficients are shown for each arrow and covariance. Significance levels: * p < 0.05, ** p < 0.01, and *** p < 0.001. Abbreviations: PC1Elev_climate; the first axis of principal component analysis for elevation and climate variables, SR, species richness; FDis, functional dispersion; AGB, aboveground biomass; the abbreviations for the remaining variables are presented in Figure 1.
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Figure 4. Parameter estimates with 95% confidence intervals and relative importance were calculated using a model averaging approach of multi-model inference test for (a) coniferous, (b) broadleaved, and (c) total forests in restoration sites of South Korea. The bold letters indicate statistically significant variables. Significance levels: * p < 0.05, ** p < 0.01, and *** p < 0.001. Abbreviations for variables are presented in Figure 1.
Figure 4. Parameter estimates with 95% confidence intervals and relative importance were calculated using a model averaging approach of multi-model inference test for (a) coniferous, (b) broadleaved, and (c) total forests in restoration sites of South Korea. The bold letters indicate statistically significant variables. Significance levels: * p < 0.05, ** p < 0.01, and *** p < 0.001. Abbreviations for variables are presented in Figure 1.
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Figure 5. Bivariate relationships between AGB and explanatory variables. Significant regressions are shown with fitted lines (p < 0.05). The dots are colored differently to distinguish each forest type. Blue, green, and orange dots represent coniferous, broadleaved, and total forests, respectively. Abbreviations for variables are presented in Figure 1.
Figure 5. Bivariate relationships between AGB and explanatory variables. Significant regressions are shown with fitted lines (p < 0.05). The dots are colored differently to distinguish each forest type. Blue, green, and orange dots represent coniferous, broadleaved, and total forests, respectively. Abbreviations for variables are presented in Figure 1.
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MDPI and ACS Style

Kwak, J.-S.; Park, J.; Lee, Y.-J.; Lee, M.-K.; Lim, C.-Y.; Lee, C.-B. Tree Diversity and Identity Effects on Aboveground Biomass Are Stronger than Those of Abiotic Drivers in Coniferous and Broadleaved Forest Restoration Sites of South Korea. Forests 2025, 16, 979. https://doi.org/10.3390/f16060979

AMA Style

Kwak J-S, Park J, Lee Y-J, Lee M-K, Lim C-Y, Lee C-B. Tree Diversity and Identity Effects on Aboveground Biomass Are Stronger than Those of Abiotic Drivers in Coniferous and Broadleaved Forest Restoration Sites of South Korea. Forests. 2025; 16(6):979. https://doi.org/10.3390/f16060979

Chicago/Turabian Style

Kwak, Ji-Soo, Joonhyung Park, Yong-Ju Lee, Min-Ki Lee, Chae-Young Lim, and Chang-Bae Lee. 2025. "Tree Diversity and Identity Effects on Aboveground Biomass Are Stronger than Those of Abiotic Drivers in Coniferous and Broadleaved Forest Restoration Sites of South Korea" Forests 16, no. 6: 979. https://doi.org/10.3390/f16060979

APA Style

Kwak, J.-S., Park, J., Lee, Y.-J., Lee, M.-K., Lim, C.-Y., & Lee, C.-B. (2025). Tree Diversity and Identity Effects on Aboveground Biomass Are Stronger than Those of Abiotic Drivers in Coniferous and Broadleaved Forest Restoration Sites of South Korea. Forests, 16(6), 979. https://doi.org/10.3390/f16060979

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