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Article

Regulatory Effects of Soil Microbes and Soil Properties on Ecosystem Multifunctionality Differ Among Grassland Types in the Qinghai-Tibetan Plateau

by
Zeying Yao
1,2,
Xiaoting Wei
3,
Chunyang Liu
1,
Lina Shi
2,
Meng’ai Hu
2,
Guihe Liu
1,* and
Xinqing Shao
1,2,*
1
College of Animal Science and Technology, Hebei North University, Zhangjiakou 075000, China
2
College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China
3
Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
Agriculture 2025, 15(13), 1410; https://doi.org/10.3390/agriculture15131410
Submission received: 24 May 2025 / Revised: 26 June 2025 / Accepted: 27 June 2025 / Published: 30 June 2025
(This article belongs to the Special Issue Soil Health and Microbiomes: A One Health Approach to Sustainable Crop Productivity and Ecosystem Resilience)
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Abstract

Alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM) are the principal grassland types on the Tibetan Plateau, which not only contribute to the maintenance of local ecosystem functions but also play a crucial role in global ecological processes. Soil microbial communities act as indispensable linchpins in modulating ecosystem functions. However, there is still a lack of general understanding about the regulatory mechanisms of soil fungi and bacteria with their multidimensional attributes on ecosystem multifunctionality (EMF) in different grassland types. Here, we comprehensively investigated the relative impacts of microbial diversity, community composition, network complexity, as well as the soil environmental factors on EMF in the three grassland types. Our results indicated that EMF was positively regulated by soil bacterial community composition, particularly the phyla Proteobacteriota and Verrucomicrobiota in AS. Additionally, both fungal diversity and network complexity exhibited significant positive correlations with EMF, with fungal network complexity identified as the primary driver of EMF in AM. Notably, the EMF in ASM was predominantly affected by soil moisture, rather than soil microbial community attributes. This study provides comprehensive evidence on the regulatory mechanisms of soil microbial and environmental factors in the EMF of different grassland types. These findings have significant implications for maintaining the ecosystem multifunctionality of specific grassland types.

1. Introduction

Soil microbes are key drivers of biogeochemical cycles and exert direct feedback effects on ecosystem functions [1,2]. In recent years, research on the relationship between soil microbial community characteristics and ecosystem multifunctionality (the capacity of ecosystems to concurrently supply a variety of services and functions, EMF) has gradually developed from a focus on species diversity to a more comprehensive perspective, including community composition and network complexity [3,4,5,6]. These studies have confirmed that soil microbial community attributes are important driving factors of EMF, including primary production, nutrient cycle, and climate regulation in terrestrial ecosystems [7,8,9,10]. Notably, different hydrothermal conditions shape different grassland types and influence their functional processes [11]. In addition, the relationship between soil microbial community characteristics and EMF differs among grassland types [12]. Therefore, it is crucial to comprehensively investigate the effects of soil microbial multidimensional attributes and environmental factors on ecosystem multifunctionality across different grassland types to understand the regulatory mechanisms on the multifunctionality of specific grassland types.
Soil microbial diversity is essential for supporting the multifunctionality of grassland ecosystems [13,14,15]. Increasing soil microbial diversity can lead to a greater variety of microorganisms that support different ecosystem functions [16]. However, a global meta-analysis suggested that the response of multifunctionality to global change is driven by changes in the microbial community structure rather than by α-diversity [17]. Additionally, some ecosystem functions are particularly sensitive to specific microbial taxa [18]. For instance, some Proteobacteriota bacteria are involved in nitrogen fixation and nitrification [19], and members of Verrucomicrobiota are primarily involved in carbohydrate metabolism [20]. In addition, certain Chloroflexota play significant roles in the carbon cycle [21]. A recent study verified that across the temperate and alpine steppe areas of China, the diversity of soil fungi as well as the dominant bacterial phyla (Actinobacteriota and Proteobacteriota) has positive regulatory effects on EMF [9]. Consequently, microbial diversity and community composition uniquely regulate ecosystem functional processes through distinct mechanisms. It should be emphasized that the microbial communities are highly structured, and the interactions between taxa form a complex co-occurrence network [22,23]. The relationship between microbial network complexity and ecosystem functions is not solely determined by the number of taxa in the community but also by the associations among these taxa [24,25]. Recently, research has confirmed that potential interactions within soil microbial communities could enhance the network stability, thereby promoting ecosystem multifunctionality [4,6,22,26]. Therefore, a comprehensive investigation into the regulatory roles of microbial multidimensional attributes on EMF from the perspectives of diversity, abundance, and complex interconnections within microbial communities is essential for understanding the primary regulatory mechanisms of ecosystem multifunctionality.
In addition, the differences in microbial functional assemblages (i.e., fungi and bacteria) in terms of morphology and physiological characteristics are particularly significant. For instance, soil fungi exhibit a greater ability to tolerate water stress compared to soil bacteria [27]. Furthermore, the growth and turnover rates of soil fungi (k-strategists) are lower than those of soil bacteria (r-strategists) [16], which may result in their distinct roles in maintaining ecosystem multifunctionality. Compared with soil bacteria, soil fungi play a key role in supporting multifunctionality in the dryland ecosystems of northern China, due to their higher drought tolerance and efficiency in decomposing complex and refractory organic polymers into simpler substances [13]. Conversely, due to the relatively fast growth and turnover rate of bacteria, during the grassland restoration process, the recovery rate of soil bacterial communities is faster than that of fungal communities [28]. Therefore, compared to soil fungal diversity, soil bacterial diversity plays a more significant role in promoting soil multifunctionality during the aerial seeding restoration process in Mu Us Sandy Land, China [8]. Thus, the community structures of soil fungi and bacteria might influence ecosystem multifunctionality through various pathways.
The Tibetan Plateau holds a crucial position in global ecological security due to its unique geographical location and distinct climatic conditions, which foster diverse vegetation patterns [29]. Additionally, soil properties and ecological functions differ among different alpine grassland types [30,31]. Moreover, soil properties play a crucial role in regulating ecosystem multifunctionality, both directly and indirectly, by influencing microbial communities [15,32]. While a series of studies have investigated the mechanisms by which soil microbial community traits and soil environmental factors regulate EMF in this region [10,22], there is still an insufficiency of comprehensive understanding regarding the relative contributions of the multidimensional attributes of soil microbial communities to EMF, and the relative importance of these factors on the EMF of different grassland types.
In this study, three typical grassland types (alpine steppe, alpine meadow, and alpine swamp meadow) of the Qinghai–Tibetan Plateau were selected to investigate the effects of the soil microbial multidimensional attributes (microbial diversity, community composition, and network complexity) of different microbial functional groups (soil fungi and bacteria) and soil environmental factors on ecosystem multifunctionality. We proposed the following hypotheses: (1) Given the different adaptability of fungi and bacteria to environmental conditions and the fact that different microbial community attributes regulate ecosystem functional processes in distinct ways, the multidimensional attributes of fungal and bacterial communities would differentially regulate ecosystem multifunctionality based on the grassland types. (2) The relatively stringent environmental conditions of alpine swamp meadow place higher survival requirements on species, making soil properties more significant in regulating EMF. Conversely, in alpine steppe and alpine meadow, EMF may be predominantly influenced by soil microbial communities.

2. Materials and Methods

2.1. Study Areas and Sampling

This study was conducted in the Qilian County, Qinghai Province, China, which is located in the northeast of the Tibetan Plateau (Figure S1). This region is characterized by a plateau continental climate. The mean annual temperature (MAT) and mean annual precipitation (MAP) in the study area varied from −5 °C to 4 °C and 200 mm to 700 mm, respectively. Three typical grassland types, including alpine steppe (AS) (99.71° E, 38.36° N; 3100 m a.s.l.), alpine meadow (AM) (99.51° E, 38.49° N; 3330 m a.s.l.), and alpine swamp meadow (ASM) (99.89° E, 38.84° N; 3780 m a.s.l.) were selected. Each grassland type features different species’ distribution patterns and soil environmental factors (Tables S1 and S2). The common dominant species in AS include Leymus secalinus, Stipa purpurea, and Carex alatauensis, and the associate species include Medicago archiducis-nicolai and Leontopodium nanum. Carex parvula, a dwarf and slow-growing plant, is the predominant species in AM, while the corresponding associate species are Potentilla bifurca and Leontopodium nanum. Additionally, the plant community of alpine swamp meadow is characterized by cold- and moisture-tolerant plant species, such as Carex tibetikobresia and Carex moorcroftii. The plant community coverage in the alpine steppe, alpine meadow, and alpine swamp meadow is 80%, 74%, and 81%, respectively [33]. In addition, the soil types of the three grassland types (alpine steppe, alpine meadow, and alpine swamp meadow) are Calcic Cryosol, Turbic Cryosol, and Histic Gleysol, respectively.
In late July 2023, 5 experimental blocks were set up in every grassland type, with a distance of approximately 2 km between each block, and three 1 × 1 m2 quadrats (30–50 m apart) were randomly placed within each block. Plant species found within every quadrat were registered according to the species level. The aboveground components and roots of the plants were harvested and oven-dried to a constant weight. Then, the aboveground biomass (AGB) and belowground biomass (BGB) were weighed [34]. Soil samples of 0–10 cm were collected using a soil drill (5 cm diameter). Four soil cores were sampled within each quadrat and homogenized into one sample in the field. Subsequently, the samples were transported on ice to the laboratory, where they were sieved with a 2 mm mesh and split into three parts. One part was rapidly cryopreserved at −80 °C, another was kept under refrigeration at 4 °C, and the last was air-dried.

2.2. Analysis of Soil Physicochemical Variables

In this study, soil environmental factors included soil texture (clay, silt, and sand content), pH, soil moisture (SM), and soil bulk density (BD). Additionally, we also measured several ecosystem function variables, such as soil organic carbon (SOC), soil ammonium nitrogen (NH4+-N), soil nitrate nitrogen (NO3−-N), soil total nitrogen (STN), soil total phosphorus (STP), soil available phosphorus (AvP), microbial biomass carbon (MBC), soil N-acetyl-β-D-glucosaminidase (NAG), soil cellobiohydrolase (CBH), soil leucine arylamidase (LAP), and soil alkaline phosphatase (ALP). The measurement methods of the above indicators are presented in Table S3.

2.3. Quantification of Ecosystem Multifunctionality

Ecosystem multifunctionality (EMF) is a comprehensive metric employed to quantify the capacity of ecosystems to concurrently supply a variety of services and functions [26,35]. In this study, the 13 functional variables (AGB, BGB, SOC, MBC, CBH, STN, NO3−-N, NH4+-N, LAP, NAG, STP, AvP, and ALP) were used to assess EMF (Table S4). These functional variables are crucial for regulating soil biogeochemical processes and have been extensively utilized in previous research [5,9,10,36,37]. The average approach and the multi-threshold approach were employed for assessing EMF [8,9,15]. For the average approach, first, the 13 functional indicators mentioned above were standardized using the minimum–maximum method:
X ′ = X − X m i n X m a x − X m i n
In the formula, X′ represents the standardized value, while X, Xmin, and Xmax denote the original value, the minimum value, and the maximum value, respectively, in the data series of indicators characterizing ecosystem functions.
Then, the standardized values (X′) of the 13 functional indicators were averaged to calculate the EMF [38]. To examine the contributions of aboveground ecosystem functions and belowground ecosystem functions to EMF, we calculated aboveground EMF (based on plant productivity, AEMF) and belowground EMF (based on nutrient cycle indicators, BEMF) using the average approach [39] (Table S4). In addition, we computed the weighted EMF (Wet.EMF) through the average approach. To ensure that each ecological function had an equal influence on the overall EMF, we assigned equal weights (1/4 for each) to plant productivity, the C cycle, the N cycle, and the P cycle (Table S4). Since Wet.EMF exhibited a significant positive correlation with EMF calculated by the average approach, the latter was primarily employed in this study (Figure S6). The multi-threshold approach considers the potential trade-off effects among the number of ecosystem functions, allowing for a systematic examination of the effects of diversity on EMF across the full range of possible thresholds, from 1% to 100% at one-percent intervals [7].

2.4. Soil DNA Extraction and High-Throughput Sequencing

Total soil DNA was extracted from fresh soil (0.2–0.5 g) using the Omaga DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The bacterial 16S rRNA gene was amplified using the primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACNVGGGTWTCTAAT-3′). The fungal ITS gene was amplified using primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′). All amplified products were sequenced on the Illumina MiSeq paired-end PE300 platform (Illumina Corporation, San Diego, CA, USA). The raw sequence data were demultiplexed using the demux plugin, followed by primer trimming using the cutadapt plugin in the QIIME2 (v2022.11) software platform [40]. Subsequently, the DADA2 plugin was used to perform a series of quality control operations, including sequence quality filtering, denoising, merging, and chimera removal [41]. Each unique sequence generated through quality control using DADA2 was defined as an amplicon sequence variant (ASV). Sequences of bacteria and fungi were classified and annotated according to the SILVA and UNITE databases, respectively. The diversity indices (Shannon–Wiener index, Simpson index, and Chao1 index) were calculated using the software QIIME2.

2.5. Data Analysis

The normality of the data was assessed using the Shapiro–Wilk test, with log10 transformation applied if necessary [42]. The linear mixed-effects model (LMM) was employed to evaluate the impacts of the grassland type on soil microbial diversity, ecosystem multifunctionality, and soil properties using the ‘predictmeans’ package in R (4.4.1), with blocks as random factors [43]. SPSS Statistics 23 was employed to conduct one-way ANOVA and Duncan’s multiple comparisons for the purpose of assessing the significance of differences (Figure 1; Tables S1 and S5). The ‘multifunc’ package in R was utilized to conduct an analysis of multiple-threshold multifunctionality (Figure 2) [39]. A principal coordinate analysis (PCoA) and a permutational multivariate analysis of variance (PERMANOVA) were employed to investigate differences in microbial community composition among grassland types [44] (Figure S2). The microbial co-occurrence networks were constructed based on the Sparse Correlations for Compositional Data (SparCC) method (Figure 3a,b) [8]. To enhance statistical confidence, the data filtering was conducted before network construction. ASVs constituting < 0.01% of the total reads were excluded. Subsequently, microbial species exhibiting significant relevance (|r| > 0.6 and p < 0.05) were included in the network construction [45]. The topology features (including the number of nodes, number of edges, diameters, density, degree centralization, average nearest neighbor degree, and betweenness centrality) of microbial co-occurrence networks (Table S10) were extracted using the ‘igraph’ package [23]. These metrics have been utilized in assessments of network complexity [7,46]. The Gephi 0.10.1 software was used to visualize the network. Finally, the topological features of these subnetworks were normalized according to the maximum–minimum method, and their average values were calculated as the network complexity index [46].
The correlations between EMF and soil microbial diversity, community composition, and network complexity were analyzed using LMM (Figure 3c,d, Figure 4, Figure 5, Figures S5 and S8). The ‘corrplot’ package in R was employed to carry out Pearson’s correlation analysis for evaluating the correlations between soil microbial traits and ecosystem functions (Figures S7 and S8). Additionally, the ‘rfPermute’ package was utilized to conduct a random forest classification analysis for the purpose of identifying the major influencing factors of EMF. The structural equation model (SEM) was developed using the ‘piecewiseSEM’ package to investigate the direct and indirect impacts of soil microbial characteristics (microbial diversity, microbial community composition, and network complexity) and soil properties (pH, moisture, bulk density, and texture) on EMF (Figure 6). Given that certain explanatory variables encompass multiple explanatory elements, a composite index was created for soil microbial diversity (including the Shannon–Wiener index, Simpson index, and Chao1 index), soil microbial community composition, and soil texture (including sand, silt, and clay) using the principal component analysis (PCA) [47]. The first principal component score was extracted through the application of SPSS Statistics 23 (Tables S7–S9). The model with the lowest Akaike Information Criterion (AIC) and Fisher’s C value (p > 0.05) was selected [48].

3. Results

3.1. Soil Microbial Community Multidimensional Attributes and Ecosystem Multifunctionality

The results demonstrated significant variations in soil fungal and bacterial diversity across grassland types (Figure 1, p < 0.001). In general, fungal diversity was higher in AS than in AM and ASM (Figure 1a–c, p < 0.05). Additionally, the Chao1 index of fungi was higher, whereas the Simpson index of fungi was lower in AM than in ASM (Figure 1a,c, p < 0.05). The bacterial diversity was significantly higher in AS and AM than in ASM (Figure 1d–f, p < 0.05). PCoA showed that the first two principal coordinates explained 37.49% of the total variation in fungal communities and 62.01% in bacterial communities. The soil microbial community composition differed significantly among grassland types (Figure S2, p = 0.001).
Soil bacteria and fungi at the phylum level with a relative abundance greater than 5% were classified as the dominant phyla. Among the three grassland types, Ascomycota and Basidiomycota emerged as the predominant soil fungal phyla. Specifically, the relative abundance of Ascomycota and Basidiomycota constituted 74.65% and 17.17% of the total sequences in AS, 87.25% and 8.63% in AM, and 78.58% and 7.19% in ASM, respectively (Figure S3). The dominant bacterial phyla exhibited slight variations among grassland types. The relative abundances of Proteobacteriota, Actinobacteriota, and Acidobacteriota accounted for 25.68%, 22.57%, and 19.45% of the total sequences in AS; 17.54%, 26.73%, and 18.96% in AM; and 38.54%, 10.20%, and 17.99% in ASM, respectively. Additionally, the relative abundances of Chloroflexota and Gemmatimonadota in the AS were 7.22% and 6.16%, respectively. In AM, the relative abundances of Chloroflexota, Gemmatimonadota, and Planctomycetota were 9.44%, 6.00%, and 5.36%, respectively, while the relative abundance of Methylomirabilota in ASM was 5.29% (Figure S4).
For soil fungi co-occurrence networks, the number of nodes was equal in both AS and AM (n = 100), and the numbers of edges were 1439 and 1388 for AS and AM, respectively. In contrast, the numbers of nodes and edges in ASM were 77 and 824, respectively (Figure 3a). For soil bacteria co-occurrence networks, the number of nodes showed less variation among grassland types, while the number of edges followed the following order: ASM > AM > AS (Figure 3b). In addition, most of the single ecosystem functions utilized to quantify ecosystem multifunctionality were significantly different among grassland types (Table S5, p < 0.05), but there was a lack of significant disparities in ecosystem multifunctionality among grassland types (Figure 1g–i).

3.2. Relationship Between Soil Microbial Multidimensional Attributes and Ecosystem Multifunctionality

The LMM indicated that only the Chao1 index of fungi exhibited a significant positive correlation with EMF in AM (Figure 4a, p < 0.05). The multiple-threshold approach also showed that the correlation between fungal diversity and EMF peaked at a threshold of 63% (Tmde) with a slope of 1.19 (Rmde), indicating that increasing fungal diversity might improve soil functions in AM (Figure 2B(c); Table S6). In AS, the minimum threshold (Tmin) and maximum threshold (Tmax) of fungal diversity on EMF were 62% and 68%, respectively (Figure 2B(a); Table S6). The Tmax of EMF in ASM was 72%, and the threshold of the maximum bacterial diversity effect (Tmde) was 70%, with a slope of 1.44 (Rmde) (Figure 2B(f); Table S6).
For fungal communities, we failed to find any significant relationship between community composition and EMF in each grassland type (Figure S5). For the bacterial communities, we found that Proteobacteriota, Verrucomicrobiota, and Bacteroidota were positively related to EMF, whereas Gemmatimonadota, Actinobacteriota, and Chloroflexota were negatively related to EMF in AS (Figure 5a,b,d,e,g,i, p < 0.05). In AM, Proteobacteriota were positively related to EMF, while Chloroflexota exhibited the opposite trend (Figure 5a,d, p = 0.001). Additionally, Proteobacteriota were negatively related to EMF in ASM (Figure 5a, p < 0.05). Furthermore, we also found that the network complexity of fungi and bacteria was negatively related to EMF in AS, whereas the network complexity of fungi exhibited a positive correlation with EMF in AM (Figure 3c,d, p < 0.05).

3.3. The Drivers of Ecosystem Multifunctionality

We investigated the driving factors of EMF among grassland types in conjunction with soil environmental factors. In AS, the SEM revealed that bacterial community composition significantly positively impacted EMF (Figure 6a, p < 0.001; Figure S10a). Soil moisture, pH, and soil texture had direct and indirect effects on EMF through fungal diversity and bacterial community composition (Figure 6a, p < 0.05). A random forest analysis also confirmed that bacterial community composition (Verrucomicrobiota and Proteobacteriota) and pH were important predictors of EMF in AS (Figure S9, p < 0.05). In AM, fungal network complexity had a direct positive effect on EMF (Figure 6b, p < 0.05; Figure S9b). Soil moisture was the main abiotic factor regulating EMF (Figure 6b and Figure S9b, p < 0.05; Figure S10b). Additionally, SEM indicated that EMF in ASM was mainly regulated by soil properties (Figure 6c, p < 0.05), especially for soil moisture and bulk density (Figure S9c, p < 0.05; Figure S10c).

4. Discussion

No significant differences were found in ecosystem multifunctionality among the three grassland types (Figure 1g–i), which might be attributed to the mutual cancellation between single ecosystem functions (Table S5). However, distinct differences exist in the soil microbial and abiotic factors’ regulatory mechanisms of multifunctionality among different grassland types. Therefore, exploring the regulatory mechanisms of multifunctionality in each grassland type is of great significance for the healthy and sustainable development of grasslands.

4.1. Bacterial Community Composition and Soil pH Regulate EMF in Alpine Steppe

We investigated the role of microbial community attributes and soil environmental factors in regulating EMF. The findings are consistent with the second hypothesis that EMF in alpine steppe is primarily influenced by soil microbial communities. Bacterial community composition plays an important role in maintaining EMF in alpine steppe. This interaction among species within a community can uphold multiple ecosystem functions via functional complementarity [49]. The results indicate that specific bacterial phyla (Proteobacteriota, Verrucomicrobiota, and Bacteroidota) are significantly and positively correlated with EMF in alpine steppe (Figure 5a,g,i). Generally, some active members of the Bacteroidota phylum are the primary metabolizers of labile carbon inputs in soil [3]. Previous studies have underscored the importance of Bacteroidota in the breakdown of cellulose in terrestrial ecosystems [50]. The positive correlation between Bacteroidota and soil organic carbon (SOC), as well as soil cellobiohydrolase (CBH), further highlights the importance of this taxon in regulating soil organic matter decomposition and the ecosystem carbon cycle in alpine steppe (Figure S7a).
The random forest model further confirmed that Verrucomicrobiota and Proteobacteriota are important predictors of EMF in alpine steppe (Figure S9a). The increasingly prominent role of Verrucomicrobiota in ecosystems has garnered heightened attention from researchers [51,52]. Members of Verrucomicrobiota are involved in carbohydrate metabolism [20]. The genome of Verrucomicrobiota contains a diverse array of glycoside hydrolases and carbohydrate lyases, which could efficiently hydrolyze polysaccharides [53]. Therefore, they are essential for promoting plant growth, maintaining soil fertility, and enhancing ecosystem health. Proteobacteriota are significantly positively correlated with most ecosystem functions in the present study (Figure S9a). Proteobacteriota can secrete a complex array of hydrolases and oxidases that drive key processes in soil nutrient cycles [54,55]. For example, certain Proteobacteriota facilitate the construction and decomposition of unstable and complex soil organic matter (SOM) [56] and participate in nitrogen fixation and nitrification processes [19]. These key roles suggest that Proteobacteriota are critical in maintaining the ecosystem multifunctionality of grassland ecosystems [9,52,57].
Additionally, Actinobacteriota and Chloroflexota are significantly negatively correlated with EMF (Figure 5b,d). Different from a previous study [9], this study reveals that Actinobacteriota have a significant negative correlation with soil total phosphorus (STP), microbial biomass carbon (MBC), and ammonium nitrogen (NH4+-N) (Figure S7a). This may be because Actinobacteriota are a globally distributed bacteria with strong environmental adaptability and resource competitiveness [58]. Therefore, an increase in its relative abundance may have a negative impact on the maintenance of ecosystem functions. Members of Chloroflexota have important roles in the subsurface carbon cycling process [21]. The significant negative correlations between Chloroflexota and soil organic carbon (SOC), cellobiohydrolase (CBH), ammonium nitrogen (NH4+-N), as well as soil-available phosphorus (AvP) (Figure S7a) further suggest that their decomposition of organic carbon and competition for nutrients may lead to a decrease in the availability of certain nutrients (such as nitrogen and phosphorus) in the soil. Consequently, this is manifested as a significant negative correlation with ecosystem multifunctionality.
In addition, soil properties also play an important role in this process. Soil moisture, texture, and pH affect EMF through both direct and indirect pathways (Figure 6a). Notably, soil pH has a more significant impact on EMF in alpine steppe (Figures S9a and S10a). Changes in soil pH exert a cascading effect on ecosystem functions [59]. Prior research has validated the role of soil pH as a key environmental filter in regulating multifunctionality directly and indirectly in grassland ecosystems [9,60,61,62,63]. We found a direct negative effect of soil pH on EMF (Figure 5a), which aligns with previous research suggesting that soil acidification inhibits the accumulation of soil organic carbon [64,65]. Additionally, soil pH might adversely affect ecosystem functions by inhibiting enzyme activity (NAG) and reducing nitrogen availability (NH4+-N) (Figure S6a).

4.2. Fungal Network Complexity and Soil Moisture Regulate EMF in Alpine Meadow

The Proteobacteriota have been interpreted to be a key driver of soil multi-nutrient cycling in alpine meadow ecosystems [52]. We also found a positive correlation between Proteobacteriota and EMF in alpine meadow (Figure 5a). In line with our second hypothesis, EMF in alpine meadow is primarily shaped by soil microbial communities. Notably, fungal network complexity has emerged as the dominant factor in regulating EMF (Figure 6b and Figure S10b). Interactions among soil microbes play a crucial role in predicting ecosystem functioning [6], as soil fungal interactions not only effectively promote nutrient cycling but also enhance soil nutrient availability [66,67]. Therefore, the interactions and network complexity in fungal communities are indispensable to EMF [68]. In this study, Ascomycota dominate both the fungal co-occurrence network and community composition (Figure 3a and Figure S3). The abundant taxon could occupy a broader niche and adapt to a wider range of environmental gradients, thereby maximizing their functional contributions [69]. Consequently, in a habit where water and other resources are relatively scarce (Table S1), members of Ascomycota exhibit a strong adaptability to a harsh environment [70,71], and the synergy among them can substantially maintain the EMF. Additionally, we found that the betweenness centrality and degree centralization of fungal co-occurrence network are positively related to EMF in alpine meadow (Figure S8d,e). These results further suggest that complex networks of fungal communities can effectively coordinate interspecific interactions effectively, thereby enhancing resource use efficiency and promoting the health and stability of alpine meadow ecosystems.
Additionally, both the average approach and the multiple-threshold approach indicate a positive correlation between fungal diversity and EMF (Figure 2c and Figure 4a), which is supported by other studies conducted in the alpine meadow [15] and northern China [13]. The underlying mechanism might be attributed to the great adaptability of fungi to varying environmental conditions [13]. Kobresia meadows in the Tibetan Plateau are limited by nutrient and water availability [72]. Therefore, higher microbial diversity, particularly fungal diversity, is crucial for accelerating the decomposition of complex organic matter [73,74], which contributes to maintaining the EMF capacity of the alpine meadow. Furthermore, soil moisture serves as the key environmental factor affecting EMF in alpine meadow (Figure 6b and Figure S10b), which could be supported by extensive evidence from numerous studies [31,75,76]. This might be ascribed to the close association between soil moisture and multiple ecosystem functions, including plant productivity, matter breakdown, and soil nutrient build-up [13,77]. Ensuring adequate soil moisture is essential for maintaining the multifunctionality of grassland ecosystems, particularly in arid and semi-arid areas [13,78].

4.3. Regulatory Effects of Soil Moisture and Bulk Density on EMF in Alpine Swamp Meadow

The multi-threshold multifunctionality indicates that bacterial diversity is positively related to EMF in alpine swamp meadow to a certain extent (Figure 2B(f); Table S6). Different from the lower growth and turnover rate of soil fungi, soil bacteria demonstrate the higher growth and turnover rates [16]. Furthermore, a meta-analysis confirmed the positive relationship between bacterial diversity and EMF in alpine grasslands, suggesting that bacteria might be better adapted to wet environments than fungi [12]. Therefore, to some extent, highly diverse bacterial communities increase the EMF in the alpine swamp meadow.
From the perspective of soil microbial and abiotic factors, soil properties are the primary determinants in the regulation of EMF in alpine swamp meadow, especially for soil moisture and soil bulk density, which supports our second hypothesis (Figure 6c, Figures S9c and S10c). Specifically, soil hydrological characteristics are critical factors for the healthy development of alpine swamp meadow [79]. Relevant research has indicated that the drop in groundwater levels induced by climate change is likely to impact species’ spatial distribution models, plant productivity, and soil nutrient turnover process, among other aspects, thereby exerting a detrimental impact on the ecological functions of alpine swamp meadows [80,81]. Additionally, the reduction in soil moisture is the major cause of EMF decline during the drought-induced succession of the alpine swamp meadow [31]. Furthermore, we found a positive correlation between soil moisture and most of the ecosystem function indicators (Figure S6c), which highlights the importance of soil moisture in maintaining the EMF in the alpine swamp meadow.
Soil bulk density is an important indicator of soil structural properties that regulate ecological functions, such as soil water movement and erosion potential [82,83]. Previous studies reported that soil bulk density negatively affected EMF [84,85]. Meanwhile, we found a direct positive effect of soil bulk density on EMF (Figure 6c). A study based on the meta-analysis revealed that increased soil surface water evaporation and soil bulk density weakened the physical support capacity of multifunctionality due to the reduced vegetation cover and root density caused by grassland degradation [86]. In this study, the alpine swamp meadow exhibits a higher water-holding capacity (Table S1), and higher bulk density implies lower soil porosity. The positive correlation between soil moisture and bulk density further suggests that, to some extent, increased bulk density could effectively reduce water loss (Figure S6c). Additionally, we found that soil bulk density is positively correlated with several single ecosystem functions (Figure S6c). Therefore, for the alpine swamp meadow, soil bulk density might support and enhance the ecosystem multifunctionality by improving soil structural stability and water- and nutrient-retention capacity. This study underscores the importance of soil physical properties and soil moisture in facilitating multiple ecological processes and maintaining EMF in the alpine swamp meadow.

5. Conclusions

This study revealed the regulatory effects of soil fungal and bacterial communities with their multidimensional attributes (microbial diversity, community composition, and network complexity) and soil environmental factors on the ecosystem multifunctionality of different types of grasslands on the Qinghai–Tibetan Plateau. We confirmed that the factors affecting EMF vary according to the grassland type. Our study demonstrated that bacterial community composition (Proteobacteriota and Verrucomicrobiota) and fungal network complexity were the main regulators of EMF on alpine steppe and alpine meadow, respectively. Nevertheless, the ecosystem multifunctionality (EMF) in alpine swamp meadow was predominantly affected by soil environmental factors (soil moisture followed by bulk density), rather than soil microbial community attributes. These findings deepened our understanding of the maintenance of ecosystem multifunctionality of different grassland types on the Qinghai–Tibetan Plateau and provided a theoretical basis for the protection and management of the alpine grassland ecosystem.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15131410/s1, Figure S1. Field survey area in Qilian county, Qinghai Province. Alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Figure S2. Principal coordinates analyses (PCoA) of soil fungi (a) and bacteria (b) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Figure S3. Soil fungal community composition in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Figure S4. Soil bacterial community composition in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Figure S5. Relationships between soil fungal community composition and ecosystem multifunctionality (EMF) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Figure S6. Relationships between ecosystem functions and soil properties and ecosystem multifunctionality in alpine steppe (a), alpine meadow (b), and alpine swamp meadow (c). Figure S7. Relationships between soil bacterial community composition and ecosystem functions and soil properties in alpine steppe (a), alpine meadow (b), and alpine swamp meadow (c). Figure S8. Relationships between network topological features and ecosystem multifunctionality in alpine steppe (a), alpine meadow (b), and alpine swamp meadow (c). Figure S9. Random forest analysis indicated the main predictors on EMF of alpine steppe (a), alpine meadow (b), and alpine swamp meadow. F and B denote soil fungi and bacteria, respectively. Figure S10 The standardized effects (direct and indirect effects) of soil microbial community attributes and soil properties on EMF derived from the SEM in alpine steppe (a), alpine meadow (b), and alpine swamp meadow (c). Table S1. The soil environmental factors of different grassland types. Table S2. Importance values of plant functional groups of different grassland types (%). Table S3. Methods for the determination of soil physicochemical variables. Table S4. The indicators and importance of ecosystem multifunctionality include plant productivity, carbon cycle, nitrogen cycle, and phosphorus cycle. Table S5. Ecosystem function indicators of different grassland types. Table S6. Values for indices generated by the multiple-threshold approach to multifunctionality from analyses of the microbial diversity among different grassland types. Table S7. Proxy of soil microbial diversity was calculated through principal component analyses (PCA). Table S8. Proxy of soil microbial community composition was calculated through principal component analyses (PCA). Table S9. Proxy of soil texture was calculated through principal component analyses (PCA). Table S10. Description the topological features of soil microbial networks in different grassland types.

Author Contributions

Z.Y.: Writing—review and editing, writing—original draft, methodology, investigation, formal analysis, data curation, conceptualization. G.L.: Methodology and conceptualization. C.L. and M.H.: Methodology and investigation. L.S. and X.W.: Formal analysis and data curation. X.S.: Funding acquisition, conceptualization. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the earmarked fund for CARS (CARS-34).

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Soil fungal diversity (a–c), bacterial diversity (d–f), and ecosystem multifunctionality (g–i) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Different lowercase letters are indicative of significant differences (p < 0.05).
Figure 1. Soil fungal diversity (a–c), bacterial diversity (d–f), and ecosystem multifunctionality (g–i) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Different lowercase letters are indicative of significant differences (p < 0.05).
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Figure 2. (A) Associations between soil fungal and bacterial diversity and the count of ecosystem functions meeting or exceeding a threshold (maximum observed functional level) across the 0–100% range in alpine steppe (a,b), alpine meadow (c,d), and alpine swamp meadow (e,f). (B) Rate of change (slope) in threshold-based multifunctionality (functions ≥ maximum observed value) per unit increase in fungal and bacterial diversity, calculated for alpine steppe (a,b), alpine meadow (c,d), and alpine swamp meadow (e,f). Shadowed areas indicate the 95% confidence intervals (CIs).
Figure 2. (A) Associations between soil fungal and bacterial diversity and the count of ecosystem functions meeting or exceeding a threshold (maximum observed functional level) across the 0–100% range in alpine steppe (a,b), alpine meadow (c,d), and alpine swamp meadow (e,f). (B) Rate of change (slope) in threshold-based multifunctionality (functions ≥ maximum observed value) per unit increase in fungal and bacterial diversity, calculated for alpine steppe (a,b), alpine meadow (c,d), and alpine swamp meadow (e,f). Shadowed areas indicate the 95% confidence intervals (CIs).
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Figure 3. Soil fungal (a) and bacterial (b) co-occurrence network of alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). (c,d) These indicate the relationships between fungal and bacterial network complexity and ecosystem multifunctionality (EMF), respectively. Solid lines represent significant fitting lines, and gray areas represent confidence intervals for fitting results.
Figure 3. Soil fungal (a) and bacterial (b) co-occurrence network of alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). (c,d) These indicate the relationships between fungal and bacterial network complexity and ecosystem multifunctionality (EMF), respectively. Solid lines represent significant fitting lines, and gray areas represent confidence intervals for fitting results.
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Figure 4. Relationships between soil fungal diversity (a–c), bacterial diversity (d–f) and ecosystem multifunctionality (EMF) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Solid lines represent significant fitting lines, and gray areas represent confidence intervals for fitting results.
Figure 4. Relationships between soil fungal diversity (a–c), bacterial diversity (d–f) and ecosystem multifunctionality (EMF) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Solid lines represent significant fitting lines, and gray areas represent confidence intervals for fitting results.
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Figure 5. Relationships between soil bacterial community composition (a–j) and ecosystem multifunctionality (EMF) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Solid lines represent significant fitting lines, and gray areas represent confidence intervals for fitting results.
Figure 5. Relationships between soil bacterial community composition (a–j) and ecosystem multifunctionality (EMF) in alpine steppe (AS), alpine meadow (AM), and alpine swamp meadow (ASM). Solid lines represent significant fitting lines, and gray areas represent confidence intervals for fitting results.
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Figure 6. Structural equation models (SEM) of soil microbial communities and soil environmental indicators as predictors of ecosystem multifunctionality (EMF) in alpine steppe (a), alpine meadow (b), and alpine swamp meadow (c). The red and black solid arrows denote significant positive and negative pathways, respectively (*, p < 0.05; **, p < 0.01; ***, p < 0.001). F and B represent soil fungi and bacteria, respectively.
Figure 6. Structural equation models (SEM) of soil microbial communities and soil environmental indicators as predictors of ecosystem multifunctionality (EMF) in alpine steppe (a), alpine meadow (b), and alpine swamp meadow (c). The red and black solid arrows denote significant positive and negative pathways, respectively (*, p < 0.05; **, p < 0.01; ***, p < 0.001). F and B represent soil fungi and bacteria, respectively.
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MDPI and ACS Style

Yao, Z.; Wei, X.; Liu, C.; Shi, L.; Hu, M.; Liu, G.; Shao, X. Regulatory Effects of Soil Microbes and Soil Properties on Ecosystem Multifunctionality Differ Among Grassland Types in the Qinghai-Tibetan Plateau. Agriculture 2025, 15, 1410. https://doi.org/10.3390/agriculture15131410

AMA Style

Yao Z, Wei X, Liu C, Shi L, Hu M, Liu G, Shao X. Regulatory Effects of Soil Microbes and Soil Properties on Ecosystem Multifunctionality Differ Among Grassland Types in the Qinghai-Tibetan Plateau. Agriculture. 2025; 15(13):1410. https://doi.org/10.3390/agriculture15131410

Chicago/Turabian Style

Yao, Zeying, Xiaoting Wei, Chunyang Liu, Lina Shi, Meng’ai Hu, Guihe Liu, and Xinqing Shao. 2025. "Regulatory Effects of Soil Microbes and Soil Properties on Ecosystem Multifunctionality Differ Among Grassland Types in the Qinghai-Tibetan Plateau" Agriculture 15, no. 13: 1410. https://doi.org/10.3390/agriculture15131410

APA Style

Yao, Z., Wei, X., Liu, C., Shi, L., Hu, M., Liu, G., & Shao, X. (2025). Regulatory Effects of Soil Microbes and Soil Properties on Ecosystem Multifunctionality Differ Among Grassland Types in the Qinghai-Tibetan Plateau. Agriculture, 15(13), 1410. https://doi.org/10.3390/agriculture15131410

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