Biodiversity primarily drives the spatial stability of ecosystem functions and multifunctionality, along with biotic and abiotic factors across South Korean forests

Introduction: Maintaining the stability of forest ecosystem functions and mitigating climate-driven declines in ecosystem multifunctionality (EMF) are central objectives of contemporary forest management.

Methods: We evaluated the spatial stability of 11 forest ecosystem functions and overall multifunctionality using data from 2,859 natural forest plots in South Korea’s 7th National Forest Inventory. Specifically, we investigated how biotic factors (species, functional, and structural diversity), abiotic factors (elevation and aridity), and stand age influence the spatial stability of EMF and individual functions. Variance partitioning and regression analyses were conducted to determine the relative contributions of these factors.

Results: Biodiversity-related biotic factors—particularly species richness and structural diversity—were the main determinants of spatial stability for most individual functions and multifunctionality, generally showing positive effects. However, these relationships varied among different functions. Among abiotic variables, higher elevations and lower water stress (i.e., a higher aridity index) were associated with greater stability. In addition, community-weighted means of functional traits influenced EMF, with maximum tree height showing a particularly strong link to multifunctionality and its stability.

Discussion: Overall, our findings underscore the importance of developing targeted management strategies to enhance EMF and individual ecosystem functions. They further suggest that biodiversity alone does not guarantee stability across all ecosystem functions, highlighting the need to consider both biotic and abiotic contexts in forest management planning.

1 Introduction

For a long time, humans have derived advantages from the interplay of organisms and energy circulation in ecosystems (Costanza et al., 1997). Forest ecosystems, in particular, perform various functions and provide essential services such as wood production, carbon storage, climate regulation, and recreation (van der Plas et al., 2018). Still, massive deforestation and the loss of natural habitats have contributed to declines in forest biodiversity and ecosystem services (Newbold et al., 2015; Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2019; Pärtel et al., 2025). Managing forests to maintain and enhance their multiple functions is increasingly critical in the face of rapid environmental change (Byrnes et al., 2014; Manning et al., 2018). There is growing recognition that focusing on a single function is insufficient to ensure the stability and sustainability of ecosystems (Yuan et al., 2020; Garland et al., 2021; Dietrich et al., 2024). To address this, the concept of ecosystem multifunctionality (EMF) has been introduced, defined as the capacity of ecosystems to deliver multiple functions and services simultaneously (Hector and Bagchi, 2007; Gamfeldt et al., 2008; Manning et al., 2018). EMF emphasizes the interactions and balance among various functions and serves as a key indicator for sustainable ecosystem management and assessing functional stability (Manning et al., 2018).

Extensive research on forest EMF has investigated the effects of biotic drivers (e.g., biodiversity, stand age, structural diversity) and abiotic drivers (e.g., topography, climate) on ecosystem functions including aboveground biomass, soil organic carbon, and microbial diversity (Jing et al., 2015; Lee et al., 2023; Lee et al., 2024). These studies generally find that biodiversity promotes EMF (Felipe-Lucia et al., 2018; Yuan et al., 2020), while abiotic factors also play critical roles (Jucker et al., 2018). More recently, attention has turned to synergies and trade-offs among functions and to projecting EMF responses under climate change (Felipe-Lucia et al., 2018; Du et al., 2024). These efforts aim to support the sustained delivery of ecosystem services. However, most of this research has addressed the stability of individual functions, particularly productivity, rather than the overall stability of EMF or the mechanisms that underpin it (Jing et al., 2022; Dietrich et al., 2024).

The ability to preserve homeostasis under both natural and anthropogenic disturbances, known as ecosystem stability, represents a central trait of forest ecosystems (Naeem and Li, 1997; Tilman et al., 2006). In general, higher levels of biodiversity, particularly species richness and structural diversity, correspond to increased stability (e.g., Naeem and Li, 1997; Wang S. et al., 2019; Gao et al., 2021). By increasing species asynchrony in fluctuating environments, biodiversity helps maintain the long-term stability of functions like aboveground productivity (Isbell et al., 2009; Craven et al., 2018) and also contributes to greater spatial stability of multifunctionality across landscapes, supporting ecosystem services under changing conditions (Mori et al., 2013). Moreover, older plant communities are more likely to sustain stable ecosystems, thereby enhancing the temporal stability of EMF (Dietrich et al., 2024). Moreover, ecosystem stability varies under different environmental conditions, with increased water stress emerging as a key factor that significantly reduces stability (Wang et al., 2023; Klimešová et al., 2023; Lee et al., 2025). Most studies examining the relationship between biodiversity and ecosystem functioning (BEF) have focused on temporal stability, particularly that of productivity. However, ecosystem functioning cannot be fully represented by biomass alone, and stability should be assessed across a broader set of functions. Moreover, spatial stability, the consistency of ecosystem functions across space at a given time, has received relatively little attention despite being equally important for understanding and managing large-scale ecosystems (Weigelt et al., 2008; Gonzalez et al., 2020; Gao et al., 2021). Studies on the spatial stability of forest ecosystems remain limited due to the challenges of obtaining spatially extensive and functionally diverse data. Although research on the spatial stability of forest ecosystem functions has increased in recent years (e.g., Gao et al., 2021; Qiao et al., 2023; Lin et al., 2023), it is still less common than comparable studies in grassland systems. Consequently, most biodiversity–ecosystem functioning (BEF) stability research has focused on grasslands and biomass, leaving substantial knowledge gaps regarding the spatial stability of ecosystem multifunctionality (EMF) in forests (Lin et al., 2023; Lee et al., 2025). To address this gap, our study examines how biotic and abiotic factors influence the spatial stability of multiple ecosystem functions and EMF across natural forests in South Korea, offering new insights into an underexplored dimension of BEF research.

In this context, this study aims to quantify the spatial stability of EMF and 11 individual ecosystem functions constituting it, using data from the 7th National Forest Inventory (NFI) in South Korea, and to identify the relative importance of biotic and abiotic forest drivers, and stand age. Based on these objectives, the following hypotheses were established: (1) As biodiversity increases, the likelihood that multiple functions will be performed simultaneously within ecosystems also rises, thereby exerting a positive effect on the spatial stability of EMF (Yuan et al., 2020). (2) Increased water stress, resulting from reduced resource availability and declines in soil nutrients and functions, is expected to decrease the spatial stability of EMF (Zhang et al., 2024). (3) Due to the unique characteristics of individual ecosystem functions, biodiversity will not necessarily enhance the spatial stability of all ecosystem functions.

2 Materials and methods

2.1 Study area and data acquisition

The National Forest Inventory (NFI) of South Korea has been conducted over seven cycles since its initiation in 1972. While the 1st to 4th inventories were carried out at irregular intervals of 2 to 7 years, the 5th inventory (2005–2010) marked the beginning of regular surveys conducted every 5 years (Korea Forest Service, 2017). In this study, we utilized data from the 7th NFI (2016–2020) to analyze the factors controlling EMF. The NFI employs a systematic sampling design, in which sample points are distributed across all forested areas nationwide at regular 4 km × 4 km intervals (2 km or 1 km for areas with small forest areas), using an arbitrary origin point (TM coordinates: X = 200,000, Y = 500,000) as a reference (Korea Forest Service, 2017). Among these grid points, those located within forested areas are designated as fixed sample plots and are subjected to field surveys for data collection. Each fixed sample plot is designed as a cluster plot composed of four circular subplots. One central plot is established at the origin point, and three additional plots are placed at 0° (true north direction), 120°, and 240° directions, each located 50 meters from the center. Nationwide, approximately 4,500 cluster plots have been installed, yielding a total of around 16,000 subplots. Each subplot is a circular plot with a radius of 11.3 meters, equivalent to an area of approximately 0.04 ha. In this study, we selected 2,859 subplots from the central plot of each cluster in the 7th NFI, excluding those with missing biotic, abiotic, or stand age data or where ecosystem functions could not be assessed. The excluded subplots were primarily located in coastal or island regions where spatial data could not be reliably collected or where elevation values were below sea level. In each subplot, the species name, DBH, and height of all woody individuals (i.e., trees and shrubs) with a DBH of 6 cm or more but less than 30 cm were measured. A sapling survey plot (DBH less than 6 cm) with a radius of 3.1 m (approximately 0.003 ha) was also established around the circular plot sampling point. For the understory vegetation survey, three 2 m × 2 m (totaling 12 m²) square quadrats were established at 10 m points at 0°, 120°, and 240° from the center of the circular plot. Accordingly, the number of all herbaceous and sapling individuals was measured (Figure 1).

Figure 1

Figure 1. Location and distribution of a total of 2,859 study plots in the temperate forests of South Korea. The dimension survey circle (DBH <6 cm) is measured at a radius of 3.1 m. The understory vegetation was surveyed in 2 × 2 m² plots at the 0, 120, and 240-degree corners of each square survey plot.

The final data set used in this study and summary statistics are presented in Supplementary Tables S1, S2, respectively.

2.2 Quantification of ecosystem multifunctionality and the stability

Based on the classification criteria and case examples of ecosystem functions and services presented by the Millennium Ecosystem Assessment (2005) and Garland et al. (2021), we selected forest ecosystem functions for calculating the EMF index. A total of 11 ecosystem functions were selected, each representing a service from one of four ecosystem service categories (Table 1). In the provisioning service category, we quantified water and wood provision functions using water resources content and wood production indices. We also used the population of plants registered as edible and medicinal to represent the provisioning of food and medicinal production (Millennium Ecosystem Assessment, 2005). For the regulating service category, we selected variables representing atmospheric carbon capture functions (e.g., aboveground biomass, soil organic carbon stock; Table 1), ultimately utilizing factors representing climate regulation functions (Lee et al., 2024). Additionally, we utilized the forest disaster prevention index, which represents landslide and erosion prevention functions, and the soil bulk density, which represents soil structure (Temme, 2021). In the cultural services category, the forest recreation index, which represents potential forest recreation functions, and the population of ornamental plants were used (Garland et al., 2021; Roh et al., 2024). Finally, in the supporting services category, we utilized soil nitrogen content, a function related to plant growth and photosynthetic efficiency (Singh et al., 2022). Detailed descriptions of data sources and summary statistics are provided in Supplementary material 2. For the EMF index, we adopted the averaging approach, recognized as the most intuitive and widely used method for combining multiple ecosystem functions (Byrnes et al., 2014; Wang et al., 2022; Wang et al., 2025). Its calculation is shown below (Maestre et al., 2012; Byrnes et al., 2014) (Equation 1).

$\begin{array}{ll}\mathrm{EMF}=\frac{1}{F}\sum _{i=1}^{F}g\left(f_i\right),g\left(f\right)=\frac{f_i-min\left(f_i\right)}{max\left(f_i\right)-min\left(f_i\right)}& (1)\end{array}$

where F is the number of measured ecosystem function variables; f_i is the value of function i; and g is a min-max standardization to rescale ecosystem function scores on the same value range between 0 and 1 to assess their variability magnitude instead of their actual value (Byrnes et al., 2014). We used the multifunc package in R version 4.5.0 for these calculations.

Table 1

Table 1. Ecosystem functions, the variables to quantify ecosystem functions and data sources used to calculate ecosystem multifunctionality in this study.

We quantified the spatial stability of EMF and individual ecosystem functions using the coefficient of variation (CV = σ/μ), calculated as the ratio of the standard deviation to the mean within plots of equal species richness. CV has been widely applied as a robust metric of ecosystem stability variability (e.g., McArdle and Gaston, 1995; Lehman and Tilman, 2000; Isbell et al., 2009; Valencia et al., 2020). To provide a more intuitive measure, we used its reciprocal (1/CV), since higher CV values indicate lower stability.

2.3 Quantification of biotic factors

We used key biotic factors—namely, taxonomic and functional diversity, stand structural diversity of trees, and community-weighted mean (CWM) values of functional traits—to assess their influence on EMF. Functional and structural diversity were derived from tree-level biological traits and stand structural attributes and thus were treated as biotic factors. Taxonomic diversity was measured using tree species richness (SR), defined as the number of species within each plot. Functional diversity was quantified using functional divergence (FDiv), an index that captures the degree of variation in tree functional traits within a community. To evaluate both FDiv we selected and measured five functional traits of trees. These traits included specific leaf area (SLA, mm² g⁻¹), leaf phosphorus content (P, mg g⁻¹), leaf nitrogen content (N, mg g⁻¹), maximum tree height (MH, m), and wood density (WD, g cm⁻³). These traits are closely associated with plant growth, nutrient acquisition and use, and competitive ability (Wright et al., 2004; Anderegg et al., 2018). Trait data for the observed species were obtained through direct leaf and wood sampling and analysis conducted by Dr. Lee’s laboratory, following standardized procedures and publicly available trait databases (Pérez-Harguindeguy et al., 2016). CWM values for five traits were calculated as the mean trait value of each plot, weighted by the relative basal area of each species. The calculation formula is presented as follows (Equation 2).

$\begin{array}{ll}{\mathrm{CWM}}_x=\sum _{i=1}^n{p}_i{t}_i& (2)\end{array}$

where, CWMx is the CWM for trait x, n is the total number of species in the study area, and p_i and t_i represent the relative breast height area and trait values of species i in the study area, respectively. FDiv and CWM of each study area were calculated using the FD package in R version 4.5.0 (Laliberté et al., 2014). To evaluate structural diversity within each plot, DBH diversity (DBH.Div) was measured using the Shannon–Wiener index (Lee et al., 2024). DBH values were divided into 5-cm intervals (e.g., 0–5 cm, 5–10 cm, etc.), and the frequency of individuals in each class was used to calculate DBH diversity.

2.4 Quantification of abiotic factors and stand age

In order to evaluate the degree of control and relative importance of abiotic and forest stand successional stages on the spatial stability of EMF and each ecosystem function, topographic factors, climate factors, and stand age were extracted and calculated.

The topographic factor used elevation, which is a factor that predominantly affects the determination of plant habitats. At this time, the elevation database was calculated based on the digital elevation model, which is spatial data provided by the Korea Geographic Information Institute. The climate factor selected the aridity index. To assess vegetation water stress, we calculated the aridity index, a humidity-related index based on evapotranspiration and mean annual precipitation. To assess vegetation water stress, we calculated the aridity index, a humidity-related index based on evapotranspiration and mean annual precipitation. Evapotranspiration was modeled using global data from Trabucco and Zomer (2018). All topographic and climatic variables were processed using ArcGIS 10.5.

Stand age, a proxy for forest successional stage, was calculated as the average tree age of the five dominant individuals per plot, which were directly measured by increment coring at breast height as part of the NFI survey (Lee et al., 2024).

2.5 Statistical analysis

Before statistical analyses, all independent and dependent variables were log- or square-root-transformed to improve linearity and normality and subsequently standardized.

We applied multimodel inference (MuMIn) to identify the key CWM traits governing EMF and individual ecosystem functions. To avoid multicollinearity, highly correlated variables were excluded (|r| > 0.65; Supplementary Table S3). For each function, we then selected the CWM variable with the highest standardized regression coefficient (β). Consequently, CWM.WD was identified as the main driver of soil organic carbon and nitrogen content, while CWM.MH was emerged as the dominant controlling factor for EMF and the remaining functions (Supplementary Figure S1).

We performed variance partitioning analysis (VPA) to evaluate the relative contributions of biotic factors, abiotic factors, and stand age to the spatial stability of EMF and individual functions. In addition, simple regression analyses were conducted to quantify the effect of individual controlling factors on stability, based on standardized regression coefficients (β). All analyses were conducted in R version 4.5.0 using the packages MuMIn, variancePartition, and nlme.

3 Results

The VPA analysis results on the spatial stability of EMF showed that biotic, abiotic, and successional stage-related factors explained 78.44, 38.16, and 24.79% of the variation in the spatial stability of EMF, respectively (Figure 2a). Among the abiotic factors, elevation and aridity together accounted for 15.66% of the variation. Within the biotic factors, biodiversity (SR, FDiv), CWM, and structural diversity explained 79.43, 42.07, and 84.85% of the variation, respectively. Furthermore, the spatial stability of EMF tended to increase with higher species richness, functional divergence (FDiv), and structural diversity (Figure 2b; Supplementary Figure S2). In contrast, increases in CWM.MH (i.e., the mean height of maximum trees) were associated with decreases in the spatial stability of EMF.

Figure 2

Figure 2. A Venn diagram (a) showing the relative importance of each control group on spatial stability of ecosystem multifunctionality calculated using variance partitioning analysis and standardized parameter estimates represent (b) the effects size (circle) with standard error (bar) of biotic, abiotic, and forest successional stage-related factors. The open and closed circles indicate significant (p < 0.05) and non-significant relationships, respectively. Aridity, aridity index; SR, species richness; FDiv, functional divergence; Stand structure and DBH_Div, diameter at breast height diversity index; CWM, community weighted mean; MH, maximum tree height.

The results of VPA on the spatial stability of individual ecosystem functions showed that, except for a few functions, most were primarily explained by biotic factors. Edible plant population (biotic 95.03%; abiotic 41.92%; stand age 20.35%), medicinal plant population (biotic 94.88%; abiotic 53.99%; stand age 20.04%), wood provision (biotic 81.16%; abiotic 73.08%; stand age 15.66%), water provision (biotic 6.28%; abiotic 6.10%; stand age 2.59%), ornamental plant population (biotic 92.70%; abiotic 66.97%; stand age 19.11%), forest recreation (biotic 70.34%; abiotic 32.21%; stand age 25.30%), aboveground biomass (biotic 76.09%; abiotic 65.91%; stand age 26.62%), soil organic carbon stock (biotic 81.23%; abiotic 51.74%; stand age 57.20%), total soil nitrogen content (biotic 68.74%; abiotic 43.70%; stand age 12.89%) were predominantly influenced by biotic factors. In contrast, the spatial stability of the Forest Disaster Prevention Index (biotic factors: 35.67%; abiotic factors: 35.72%; stand age: 20.24%) and soil bulk density (biotic factors: 32.49%; abiotic factors: 32.79%; stand age: 26.62%) were predominantly influenced by abiotic factors (Figure 3).

Figure 3

Figure 3. A Venn diagram and a bar graph show the relative importance of each control factor (bar graph) and group (Venn diagram) on the spatial stability of individual ecosystem functions derived using the variance partitioning analysis. E, elevation; A, aridity index; D, diversity factor; CW, community weighted mean factors; DB, diameter at breast height diversity index; R, residual.

Among the abiotic factors, elevation and aridity jointly acted as major drivers of the spatial stability of most ecosystem functions, with the exception of the Wood Supply Index and soil nitrogen content. Within the biotic factors, the spatial stability of all ecosystem functions was most strongly explained by diversity indices, except for the Forest Recreation Index, the abundance of edible plants (structural diversity), and the water supply function (CWM.MH).

Figure 4

Figure 4. The effects size (circle) with standard error (bar) calculated using the linear regression model between each control factor and the spatial stability of ecosystem functions. The open and closed circles indicate significant (p < 0.05) and non-significant relationships, respectively. WD, wood density, abbreviations of other variables are defined in Figure 2.

The relationships between the spatial stability of individual ecosystem functions and their controlling factors exhibited patterns similar to those observed for the spatial stability of EMF (Figure 4; Supplementary Figures S3–S13). In particular, increases in elevation and aridity (i.e., reduced water stress) commonly acted as major drivers enhancing the spatial stability of most functions. By contrast, no significant controlling factor was detected for soil nitrogen content. Unlike other functions, the spatial stability of soil organic carbon stock and soil bulk density decreased with increasing elevation, aridity, and stand age. Specifically, soil bulk density stability declined in response to greater species richness and CWM.MH.

4 Discussion

We quantified the spatial stability of EMF and its 11 component ecosystem functions across forests in South Korea and analyzed how they were influenced by biotic and abiotic factors and stand age. The findings can be discussed from three main perspectives. (1) The spatial stability of most ecosystem functions and multifunctionality was best explained by diversity-related biotic factors (biodiversity and structural diversity), showing generally positive relationships. However, biodiversity did not consistently enhance the spatial stability of all functions, as soil bulk density exhibited the opposite pattern. (2) Among abiotic factors, increases in elevation and reductions in water stress (i.e., increases in aridity index) tended to enhance the spatial stability of most ecosystem functions overall. (3) Community-weighted means (CWMs) of functional traits also influenced the spatial stability of EMF and certain individual functions. In particular, except for soil bulk density, increases in the mean height of maximum trees were associated with decreases in the spatial stability of both EMF and most ecosystem functions.

4.1 Biotic factors controlling the spatial stability of ecosystem multifunctionality

In this study, biotic factors were found to be the strongest predictors of the spatial stability of EMF, with species richness, functional divergence (FDiv), and structural diversity identified as positive drivers. To interpret the influence of biotic factors on the stability of EMF or individual functions, it is essential to understand the dynamics of synchrony and asynchrony at the community scale. Specifically, when responses across sites are synchronous, external disturbances or environmental changes can propagate simultaneously across multiple areas, negatively affecting community structure and ecosystem functions, thereby reducing overall stability (Tilman, 1999; Walter et al., 2017; Wilcox et al., 2017). Conversely, when responses are asynchronous, declines in function in some sites may be offset by function maintenance in others, ultimately enhancing system-wide stability (Wang Y. et al., 2019; Wilcox et al., 2017; Walter et al., 2021).

Within this context, the observed positive effect of biodiversity on the spatial stability of EMF can be explained by two major mechanisms. First, functional complementarity, based on niche complementarity, enables diverse species to perform different ecosystem functions, such that declines in particular functions can be substituted or buffered by others (Blüthgen and Klein, 2011). Second, response diversity, grounded in the insurance hypothesis, reflects the variability in sensitivity and response traits among species performing the same function; this ensures that even if functions decline temporarily in one site, they are maintained elsewhere, thereby securing the stability of the overall system (Elmqvist et al., 2003). As biodiversity increases, spatial asynchrony in ecosystem functions also increases, providing a basis for functional compensation across sites and thus enhancing the spatial stability of EMF (Sosa-Panzera et al., 2025). Similarly, higher structural diversity in forest stands can maximize the benefits of niche complementarity by enabling more efficient light capture and resource use (Ali et al., 2016), which prevents simultaneous functional losses and, together with biodiversity, reduces overall ecosystem vulnerability.

Interestingly, however, among the 11 ecosystem functions analyzed, soil bulk density (a function associated with soil erosion prevention) exhibited the opposite trend, with spatial stability decreasing as species richness increased. This suggests that biodiversity does not consistently enhance the stability of all ecosystem functions, and that for certain functions, higher species diversity may lead to more similar functional responses across sites, thereby inducing synchrony (Sosa-Panzera et al., 2025). For example, in the case of soil bulk density, increasing species richness may lead to greater variation in root structures, densities, and distribution patterns, which, in turn, heterogenize their effects on soil physical properties (Fischer et al., 2015). Such heterogeneity in soil structure may increase variation in soil bulk density across sites, ultimately undermining the spatial stability of this function.

These findings indicate that while biodiversity can enhance the spatial stability of ecosystem functions, its effects are not universally beneficial; instead, they vary depending on the specific function. Therefore, management strategies should aim to regulate biodiversity in a “function-specific” or “tailored” manner, depending on the target ecosystem service. In addition, this study assessed the spatial stability of multiple forest ecosystem functions and multifunctionality (EMF) at a national scale. By integrating biotic and structural factors, our analysis provides a more comprehensive understanding of how biodiversity regulates spatial stability.

4.2 Abiotic factors controlling the spatial stability of ecosystem multifunctionality

In this study, elevation and aridity were identified as key abiotic factors simultaneously explaining the spatial stability of most individual ecosystem functions. Except for soil organic carbon stock and bulk density, both factors generally enhanced the spatial stability of ecosystem functions. These findings are closely related to the increase in extreme spatiotemporal environmental heterogeneity with elevation (Yang et al., 2015). As elevation increases, environmental conditions such as temperature, humidity, soil properties, and solar radiation change sharply even across short distances, creating a wide range of microenvironments (Billings and Mooney, 1968). Such environmental heterogeneity promotes differentiation in species composition and functional strategies across sites, ultimately enhancing response diversity. This suggests that high-elevation forest ecosystems function as critical spaces for maintaining and promoting the spatial stability of most forest ecosystem functions. Therefore, the role of elevation in shaping functional stability should be understood not merely as a geographical factor but as a complex ecological driver underpinning biodiversity and structural diversity.

The results also demonstrated that increased water stress (at lower elevations and lower aridity) negatively affects the spatial stability of most ecosystem functions. In forest ecosystems, higher water availability enhances the range of resources accessible to different plant species, thereby promoting ecological niche complementarity. As a result, plant productivity increases and ecosystem functions are improved (Guo et al., 2023). Furthermore, higher soil moisture mitigates water stress, stimulating plant growth and microbial activity in soils, which in turn bolsters nutrient-associated functions such as carbon and nitrogen storage and habitat provisioning (Zhang et al., 2024). By contrast, increases in water stress threaten plant survival and reproduction, disrupt interspecific interactions, reduce functional performance, and weaken both resistance to external disturbances and functional stability (Hu et al., 2021). Ultimately, such stress impairs fundamental ecosystem functions—carbon sequestration, soil nutrient cycling, water retention, and wood provision—thus eroding the ecological basis that supports the spatial stability of EMF (Garland et al., 2021). Our study highlights how abiotic gradients—particularly elevation and aridity—govern the spatial stability of multiple forest ecosystem functions at a national scale. This integrated perspective reveals that topographic and climatic heterogeneity play a fundamental role in sustaining multifunctionality in complex forest systems, thereby extending the biodiversity–stability framework to spatial environmental gradients.

4.3 Changes in ecosystem multifunctionality and spatial stability with increasing maximum height

In this study, CWM.MH was identified as a significant factor influencing the spatial stability of EMF and most individual ecosystem functions. However, spatial stability was found to decrease as CWM.MH increased; that is, as maximum tree height increased, there was an increase in CWM.MH reflects higher stand maturity and greater biomass accumulation, which strengthens ecosystem functions such as long-term carbon storage (Stephenson et al., 2014). It also promotes the development of vertical stratification, enhancing interspecific resource partitioning and functional complementarity. In particular, the formation of multilayered canopies through crown plasticity provides diverse microhabitats and light conditions, thereby increasing understory richness and EMF (Jucker et al., 2015). Nevertheless, such structural development simultaneously intensifies heterogeneity in light availability and soil resource distribution, thereby amplifying spatial variability among functions and ultimately reducing spatial stability. This paradox suggests that while tall forests with high maximum tree height can provide a wide array of ecosystem functions, these functions are not evenly maintained across space, leading to greater local disparities. These findings provide important implications for forest management. While maximizing EMF through increased maximum height can be beneficial, strategies such as promoting mixed stands and ensuring species diversity are necessary to reduce functional imbalances. Moreover, management practices that mitigate excessive structural heterogeneity—such as thinning and conserving understory vegetation to enhance structural diversity—should be implemented in parallel. Unlike previous studies that primarily emphasized the direct positive effects of stand height or biomass on ecosystem functioning, our study revealed a paradoxical pattern in which greater maximum tree height reduced the spatial stability of EMF. This finding provides a novel perspective by linking forest structural maturity with spatial variability in ecosystem functions, thereby extending the understanding of how structural development influences multifunctionality beyond simple productivity relationships.

5 Conclusion

We analyzed the relative contributions of biotic and abiotic factors, along with stand age, to the spatial stability of EMF and 11 ecosystem functions across forests in South Korea. The results revealed that the spatial stability of EMF and most individual functions increased with greater diversity, particularly biodiversity and structural diversity. However, for certain ecosystem functions, these factors were found to reduce spatial stability instead. Elevation was shown to enhance the spatial stability of most ecosystem functions, suggesting that South Korea’s subalpine ecosystems function as critical spaces for maintaining stability. In contrast, water stress (i.e., at lower elevations and lower aridity) generally reduced the spatial stability of most functions. Maximum tree height exacerbated habitat heterogeneity, increasing the spatial variability of EMF and ecosystem function. These findings underscore the need for function-specific and tailored management strategies to enhance the EMF and spatial stability of individual functions in Korean forests. For example, promoting maximum tree height can positively strengthen key functions such as biomass accumulation and long-term carbon storage, but it may also undermine spatial stability, necessitating balanced management approaches. To achieve this, fostering mixed stands and ensuring species diversity are crucial for enhancing functional complementarity and response diversity, while preventing excessive spatial concentration of particular functions. In addition, management practices such as thinning to adjust canopy stratification and conserving understory vegetation can mitigate excessive heterogeneity in light and soil resources that arise during stand development. These measures would allow forests to retain the benefits of multilayered structures while minimizing excessive local variability in function. Finally, it is important to recognize that biodiversity does not universally enhance stability across all functions. For instance, in the case of soil bulk density, higher species diversity may increase heterogeneity in root structures and their effects on soil physical properties, ultimately reducing stability. Thus, such functions require management approaches tailored more toward soil management and vegetation–soil interactions rather than species composition alone.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

M-KL: Methodology, Formal analysis, Visualization, Writing – original draft, Data curation. C-BL: Methodology, Supervision, Writing – review & editing, Funding acquisition, Conceptualization.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was conducted with support from the R&D Program for Forest Science Technology provided by Korea Forest Service (Korea Forestry Promotion Institute) (Project No. RS-2024-00404816) and the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (Project No. RS-2024-00358413). This study was also financially supported by Korea Forest Service Government (KFSG) as (Graduate School specialized in Carbon Sink).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc.2025.1719682/full#supplementary-material

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