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Soil organic matter composition affects ecosystem multifunctionality by mediating the composition of microbial communities in long-term restored meadows

Environmental Microbiome volume 20, Article number: 22 (2025) Cite this article

Abstract

Background

Soil organic matter composition and microbial communities are key factors affecting ecosystem multifunctionality (EMF) during ecosystem restoration. However, there is little information on their interacting mechanisms in degraded and restored meadows. To fill this knowledge gap, plant, root and soil samples from alpine swamp meadows, alpine Kobresia meadows, severely degraded alpine meadows, short-term restored meadows (< 5 years) and long-term restored meadows (6–14 years) were collected. We leveraged high-throughput sequencing, liquid chromatography and mass spectrometry to characterize soil microbial communities and soil organic matter composition, measured microbial carbon metabolism and determined EMF.

Results

It emerged that the similarity of soil microorganisms in meadows decreased with increasing heterogeneity of soil properties. Dispersal limitation and ecological drift led to the homogenization of the bacterial community. Based on co-occurrence network analysis, an increase in microbial network complexity promoted EMF. Root total phosphorus and soil organic matter components were the key predictors of EMF, while organic acids and phenolic acids increased the stability of the microbial network in long-term restored meadows. Carbon metabolism did not increase in restored meadows, but the niche breadth of soil microorganisms and the utilization efficiency of small molecular carbon sources such as amino acids did increase.

Conclusions

These findings emphasize the importance of soil organic matter composition in ecological restoration and that the composition should be considered in management strategies aimed at enhancing EMF.

Introduction

The meadow ecosystem is facing intense anthropogenic activities [1, 2], overgrazing, and climate change, causing a loss of biodiversity and ecosystem services [3,4,5] and a reduction in forage biomass and soil nutrients [6]. Biodiversity contributes to the functioning of ecosystems by mediating the rate and variability of ecosystem processes during meadow degradation and restoration [7,8,9]. As a result, severe degradation of alpine meadows is expanding rapidly on the Qinghai-Tibetan Plateau. Natural restoration can take decades, and restoration efforts are often unsuccessful due to abiotic limitations and adverse biological conditions [10, 11]. Currently, sowing local plant species is considered to be the most effective method to restore degraded meadows and stabilize nutrient cycling [12,13,14].

Metabolic coupling between soil organic matter and microorganisms is an important driving mechanism in terrestrial ecosystem restoration [15]. Microorganisms are highly involved in the transformation of soil organic matter composition and in nitrogen fixation, and, thus, in plant adaptations to the soil environment [16]. Soil organic matter components are precursors or intermediary substances and products of soil nutrients, and their interactions with soil microorganisms play a vital role in maintaining the stable functions of terrestrial ecosystems [17,18,19]. This occurs because the diversity and complexity of soil organic matter drive the structure and functional formation of microbial communities, which is the key to soil stability [19]. Despite its importance, information on the relationship between microorganisms and soil organic matter composition is still lacking [20].

Fungi and bacteria play different and important roles in ecosystem multifunctionality (EMF). Fungi drive the carbon cycle [21, 22], while bacteria drive N cycling, as a positive correlation was reported between denitrification activity and bacterial diversity [23, 24]. In addition, the bacterial community diversity has been reported to be correlated positively with the development of spatial heterogeneity and niche diversity in alpine grassland [25]. Therefore, interactions and feedback among soil microorganisms, plants and soil properties are critical to soil and habitat formation, maintenance and restoration. In fact, although current research focuses primarily on how microbial communities respond to environmental changes [10], studies have demonstrated that changes in soil fertility influenced microbial communities more so than climate [26, 27]. The identification of factors that influence the structure of soil microbial communities and that regulate soil system stability in restoring meadows [28,29,30], and the understanding of the underground ecological mechanisms of alpine meadow degradation and restoration are crucial in achieving stable and multi-functional restoration goals [12, 31].

Microbial community structure and diversity are key indicators of soil health [32]. The composition of soil organic matter plays a crucial role in shaping the interactions between soil microorganisms and vegetation, particularly in relation to processes involving carbon, nitrogen, and phosphorus [7, 19, 33]. Organic compounds not only influence the structure of soil microbial communities, but also impact plant development [3, 34, 35]. Beyond serving as substrates for plants, compounds such as sugars, amino acids, terpenes, and phenols act as signaling molecules for microorganisms [36]. Furthermore, microbial metabolism is instrumental in driving the composition of soil organic matter, thereby mediating changes and maintaining the balance of soil biological systems [34]. However, it remains unclear which factors influence shifts in soil microbial community structure and diversity during the restoration of alpine grasslands.

Soil organic matter composition and network complexes affect microbial stability and EMF [20, 34, 35], and their measurements would enable us to understand the complexity of microbial composition and changes in the soil with ecosystem degradation and restoration. In the current study, we selected 36 degraded and restored meadows (2–14 years) to examine how soil organic matter composition, microbial communities, microbial carbon metabolism and other factors influence EMF. The following questions, linked to degradation and restoration of alpine meadow, guided our research: (1) Which factors influence the composition of microbial community structure during the degradation and restoration of alpine grasslands? (2) How do soil microbial communities and EMF change, and what is their relationship? (3) Does the composition of soil organic matter affect EMF by altering the composition of the microbial community structure? Answers to these questions could provide insight into management strategy options for improving EMF.

Material and methods

Study sites

The study sites were in three counties on the Qinghai-Tibetan plateau (96° 56’-101°45’ E, 32°31′–35°45′ N, 3717–4129 m a. s. l.), namely, Maqu in Gansu province (100°45′–102°29′ E, 33°06′–34°30′ N, 3300–4806 m a. s. l.), and Maqin and Gande in Qinghai province (Figure S1; Table S1). The sites, located in the upstream area of the Yellow River, have a plateau continental climate with an average annual temperature of − 4 °C, an average annual rainfall of 400–700 mm, and a harsh climate with no frost-free days. The vegetation is mainly alpine meadow and alpine Kobresia meadow, and the soil is primarily meadow soil.

Typical non-degraded, degraded and restored (2–14 years) meadows were selected to represent the range of meadows. The alpine swamp meadow (ASM) is a transitional stage from wetland to alpine meadow. Drainage leads to the wetland formed ASM, and further drainage and grazing forms the alpine Kobresia meadow (AKM). Toxic plants propagate in the degraded meadow to form severely degraded alpine meadow (SDM). In recent years, mixtures of native plants’ seeds were sown to restore severely degraded alpine meadow (SDM) in the Qinghai-Tibetan plateau. In total 36 plots, including ASM (n = 4), AKM (n = 9), SDM (n = 8), short-term restored meadows (ASR, ≤ 5 years; n = 9) and long-term restored meadows (ALR, 6–14 years; n = 6) were selected. The dominant plants in ASM were Halerpestes ruthenica, Polygonum amphibium, and Triglochin maritimum; in the degraded meadows were Carex moorcroftii, Kobresia humilis, Ligularia virgaurea, Kobresia pygmaea, and Ajania tenuifolia; and in the restored meadows were Elymus nutans, Pedicularis kansuensis, Poa annua, Morina kokonorica, Aconitum pendulum, and Ajuga lupulina. Detailed information on the sites is presented in Table S1.

Collection of samples

Three 0.5 × 0.5 m subplots in each of the 36 plots were selected randomly to determine vegetation height and coverage. All above-ground biomass in each subplot was harvested to ground level, oven-dried at 105 °C to constant weight to measure water content, and ground for later analyses. Three soil cores (7 cm diameter × 10 cm depth) were collected in each subplot and combined into one sample. The roots were collected, rinsed with water to remove soil, dried at 65 °C to a constant weight and then ground and homogenized. In addition, a soil sample (3.8 cm diameter) to a depth of 10 cm was collected in each plot. After removing the debris, the samples were transported to the laboratory on ice, and, except for alpine swamp meadow, were passed through a 2 mm sieve. The cutting ring method was used to measure bulk density (100 g/cm3) of the soil [37]. The samples were divided into two parts: one part was stored at − 80 °C to determine the diversity of the soil microbial communities by high-throughput sequencing, and the other part was air-dried to determine the physical and chemical properties of the soil.

Soil properties were measured using standard methods as described by Page et al. [38]. In brief, soil organic carbon was measured using the potassium dichromate titration method, and soil pH (deionized water: soil ratio, w/w = 5:1) was determined using a pH meter (Sartorius PB-10, Göttingen, Germany). The above-ground plants and underground roots were dried at 65 °C and sieved through a 0.15 mm screen for determining total nitrogen and total phosphorous; total nitrogen by the Kjeldahl method, and total phosphorous by a flow injection analyzer after digestion with H2SO4-HClO4. Soil moisture content was calculated by drying the soil at 105 °C to constant weight.

Microbial community sequencing

Total soil DNA was extracted using kits (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s instructions, and after extraction, the quality of the DNA was tested. The PCR was performed in a total reaction volume of 10 μL: DNA template 5–50 ng, forward primer (10 μM) 0.3 μL, reverse primer (10 μM) 0.3 μL, KOD FX Neo Buffer 5 μL, dNTP (2 mM each) 2 μL, KOD FX Neo 0.2 μL, and ddH2O up to 10 μL. The initial denaturation at 95 °C for 5 min was followed by 25 cycles of denaturation at 95 °C for 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 40 s, and a final step at 72 °C for 7 min. The total PCR amplicons were purified with Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN, USA). Illumina novaseq 6000 (Illumina, Santiago, CA, USA) was used for sequencing [39, 40] to construct the library, Illumina Hiseq high-throughput sequencing platform amplified the V3-V4 regions of bacterial 16s rRNA using 314F-805R primer (314F: 5’-CCTACGGGNGGCWGCAG-3’, 805R: 5’-GACTACHVGGGTATCTAATCC-3’), fungal internal transcription spacer gene (ITS1) using universal primers (ITS1F: 5’-CTTGGTCATTTAGAGGAAGTAA-3’, ITS2: 5’-GCTGCGTTCTTCATCGATGC-3’), and Extaq enzyme ensured amplification efficiency and accuracy during the expansion process.

For each sample, the data were filtered and spliced using the PEAR [41] method. The spliced sequence was analyzed with QIIME [42], and reads with sequence similarity greater than 97% were classified as the same operational taxonomic unit (OTU). Random subsamplings of 30,725 sequences per bacteria sample and 42,495 sequences per fungi sample were used to generate OTU tables. The rarefaction curves plateaued, indicating that the sequencings were saturated and that all bacteria and fungi were identified. The phylogenetic tree for fungi was generated using ITS1 sequences based on fungal taxonomic ranks, as described in Tedersoo et al. [43].

Determination of soil organic matter composition

One mL of extraction solution (volume ratio methanol:acetonitrile:water = 2:2:1) was added to 0.1 g soil. Then 20 μL of L-2-chlorophenylalanine were added as an internal standard, mixed for 30 s, ground at 45 Hz for 4 min, sonicated for 5 min at 4 °C, left to stand at − 20 °C for 1 h, and centrifuged at 16,000 × g for 15 min at 4 °C [44, 45]. Then, 200 μL of supernatant were measured in positive and negative ion modes using a chromatographic column (ACQUITY UPLC HSS T3, 1.8 μm*2.1 mm*100 mm, Waters, Milford, MA, USA) of ultra-high performance liquid chromatography (UHPLC 1290, Agilent, Santa Clara, CA, USA). In the positive ion mode, mobile phase A: 0.1% formic acid aqueous solution, mobile phase B: acetonitrile; and in the negative ion mode, mobile phase A: 5 mM ammonium acetate aqueous solution, mobile phase B: acetonitrile.

Determination of soil microbial carbon metabolic activity

Ninety mL of sterile saline (0.85% NaCl) were added to 10 g of soil in a 250 mL conical flask, shaken at 200 rpm for 20 min and left to stand for 10 min at 4 °C. The supernatant was diluted to 103 with sterile normal saline. Biolog Eco-Microplates (Biolog, Hayward, CA, USA) were preheated to 25 °C, and 150 μL of diluted supernatant were added to 96 plates and incubated at 25 °C for 7 days [46]. Absorbance was read at 595 nm by a microplate reader every 24 h [47], and the distribution of 31 carbon sources were identified.

The relative absorbance of each carbon source pore indicates the ability of the microbial community to utilize the carbon source. The average well color development (AWCD) of the pores reflects the average metabolic capacity of the microbial community with 31 carbon sources, indicating the overall metabolic activity of the microorganisms. The calculation of AWCD was as follows:

$${\text{AWCD }} = \sum {\left( {A_{i} - R_{0} } \right)} /31$$

where Ai is the absorbance at 590 nm minus 750 nm of each carbon source pore, and R0 is the absorbance of the control pore.

Assessment of ecosystem multifunctionality

The soil variables measured included pH, bulk density, soil water content (SWC), soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), microbial carbon metabolism activity, and soil organic matter composition. The vegetation variables included above-ground plant nitrogen (ATN), above-ground plant phosphorus (ATP), root total nitrogen (RTN), root total phosphorus (RTP), vegetation height, vegetation coverage and above-ground vegetation biomass (AGB). To assess EMF, 15 soil and vegetation variables related to the storage of matter and energy [48] were measured, of which 13 were related to nutrient pools. SOC, soil organic matter composition and AWCD were used to calculate ecosystem carbon function; TN, ATN and RTN to calculate ecosystem N function; and TP, ATP and RTP to calculate ecosystem P function. Data were tested for normal distribution by the Shapiro–Wilk test. Non-normal distributed data were log or reciprocally transformed and variables with negative values were converted to positive values by subtracting the minimum value from the whole dataset. All variables were calculated by min–max normalization, and the EMF was the average value for all functions measured for each sample [49, 50].

Network construction and characterization

Co-occurrence networks of bacterial and fungal communities were constructed using the molecular ecological network analysis pipeline (MENAP, http://129.15.40.240/mena/) based on the Random Matrix Theory (RMT) approach [51, 52]. The network nodes were represented by operational taxonomic units (OTUs), while the correlations between OTUs were displayed as edges. For bacterial communities, the top 300 OTUs in relative abundance were selected; while for fungal communities, OTUs present in more than half of the samples were screened for analysis [53]. To increase the reliability of the predicted association relationships, only OTUs in at least two-thirds of the OTUs in the sample were used for network construction [54]. Abundance data of OTUs were centered-log-ratio transformed to mitigate the effects of compositional bias [55], and Pearson correlations generated the correlation matrix. This was followed by a random matrix theory (RMT) based approach [51, 52], which identified the similarity thresholds as 0.48 and 0.76 for bacterial and fungal communities, respectively. Network diagrams were drawn using gephi (0.9.2), network related parameters, including nodes, edges, degree, and average path length.

Statistical analyses

Data were analyzed with R software (V4.3.1), and graphs were generated using R package “ggplot2”. One-way analysis of variance (ANOVA) tested for differences of variables among meadows. The α diversity of the microbial community structure was determined using the “diversity” function program (vegan package), the beta diversity of the microbial community, based on the Bray–Curtis distance, was determined using the “vegdist” function, and the environmental variable distance was determined using Euclidean distance. “FactoMineR” and “factoextra” were used for principal component analysis (PCA) and plotting of soil organic matter composition. The package “ggpubr” generated splice graphs and the ggpubr function was used for statistical tests. The “niche.width” function in the “spaa” package in R was used to calculate niche breadth [56], while the “randomForest” function in “spaa” package in R was used to illustrate the important predictors of EMF. Each factor was determined by assessing the rise in mean square error when the predictor was rearranged randomly [57]. The accuracy of the importance results was measured for each tree and then averaged across the forest with 500 trees [58].

After comparison with the database, 183 compounds were screened initially. After calculating the variable importance in projection (VIP) of each compound, a total of 53 compounds were screened (VIP > 1.0) using SIMCA 14.1. Zero and neutral models were used to construct microbial community structure, and “igraph” package was used to process microbial co-occurrence networks. Details are presented in the Supporting information.

The structural equation model (SEM), using AMOS 22.0 software, determined the direct and indirect effects of plant and soil properties, microbial community composition, microbial carbon metabolism, and soil organic matter composition on EMF in degraded and restored meadows. Prior to modeling, bivariate correlations between variables confirmed the appropriateness of the linear model. A complex initial model was developed to illustrate all key variables through the reasonable interaction pathway. Non-significant variables were removed to identify the model with the lowest AIC [59]. The degree of fit of the SEM model was tested by the χ2 test (0 ≤ χ2 ≤ 2, p > 0.05) and root mean square error of approximation (RMSEA) (0 ≤ RMSEA ≤ 2, p < 0.05).

Results

Soil physiochemical properties, microbial carbon metabolic profiles and ecosystem multifunctionality

Soil WC, SOC, soil TP and soil TN contents of alpine swamp meadows were greater than those of alpine Kobresia meadows, severely degraded alpine meadows and restored meadows. However, TN and TP of above-ground plants and roots in the alpine swamp and alpine Kobresia meadows were lesser than in severely degraded alpine meadow, and short-term and long-term restored meadows (Table S2). The EMF and the utilization of amino acids and amines increased in restored meadows (p < 0.05, Table S4). Microbial metabolic activities, reflected by the AWCD, were greater in alpine swamp meadows than degraded meadows (Tables S3 and S4).

Soil properties and soil organic matter composition had a greater impact on the bacterial than on the fungal community. The contents of 53 compounds in short-term restored meadows and severely degraded alpine meadows were greater than in alpine swamp and alpine Kobresia meadows, while 25 compounds in long-term restored meadows were greater than in alpine swamp and alpine Kobresia meadows (Figure S2). The soil properties, TN of above-ground plants and TP of roots affected the similarity of bacterial and fungal communities (p < 0.05). TP in plants and soils affected the distance-decay rate of similarity in soil bacteria and fungi (p < 0.05) (Fig. 1a). The decay rate of bacteria (r = − 0.536, p < 0.001; Fig. 1b) and fungi (r = − 0.307, p = 0.005; Fig. 1c) decreased with an increase in the heterogeneity of soil properties (environmental distance), and the variability in bacteria was greater than in fungi. Therefore, although the soil nutrient content decreased, the plant nutrient content increased in long-term restored meadows. Soil microorganisms increased the utilization efficiency of some carbon sources, which had a positive impact on EMF.

Fig. 1
figure 1

Correlations between environmental factors and microbial community composition (a), bacterial communities and environmental distance (b) and fungal communities and environmental distance (c) in meadows. The bacterial and fungal community compositions, based on Bray–Curtis distance, were related to environmental factors by partial Mantel tests. The thickness of the line indicates the partial Mantel’s r statistic, and line colors represent the statistical significance based on 999 permutations. The pairwise comparisons of environmental factors are presented, and the color gradient denotes Pearson’s correlation coefficient. Asterisks represent the statistical significance (***p < 0.001; **p < 0.01; *p < 0.05). SOC, soil organic carbon. TN, soil total nitrogen. TP, soil total phosphorus. C/N, the ratio of soil carbon to nitrogen. N/P, the ratio of soil nitrogen to phosphorus. SWC, soil water content. Bulk, soil bulk density. Height, vegetation height. Cover, vegetation coverage. ATN, above-ground plant total nitrogen. ATP, above-ground plant total phosphorus. AN/P, the ratio of above-ground plant nitrogen to phosphorus. UTN, total nitrogen in roots. UTP, total phosphorus in roots. UN/P, the ratio of nitrogen to phosphorus in roots. Compounds, the PCA first axis of soil organic matter composition

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Microbial diversity and composition of the microbial communities

Degradation of alpine swamp meadows resulted in a decrease in bacterial diversity. Compared with alpine Kobresia meadows, the bacterial and fungal Shannon diversity indices and richness increased in restored meadows. There was no difference in bacterial diversity between severely degraded alpine meadows and restored meadows (Fig. 2a–c). The EMF increased with increasing soil microbial abundance and diversity (Fig. 2d–f), while ecosystem N function increased with abundance and diversity of bacterial communities (p < 0.05). The diversity of the fungal community was affected strongly by soil properties, and a decrease in soil nutrient content led to an increase in fungal community diversity (Figure S3).

Fig. 2
figure 2

Microbial diversity in meadows (a–c) and its impact on ecosystem multifunctionality (d–f). ASM, alpine swamp meadow. AKM, alpine Kobresia meadow. SDM, severely degraded alpine meadow. ASR, short-term restored meadow (≤ 5 years). ALR, long-term restored meadow (6–14 years). Error bars are standard errors of the mean. Means with different lowercase letters within bacteria and fungi differ from each other (p < 0.05). ACE, abundance-based coverage estimate

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In degraded alpine swamp meadows, the dissimilarity of bacterial and fungal community compositions increased. The β diversity decreased in the bacterial community but increased in the fungal community in restored grassland (Table S5). Based on the Anosim test and non-metric multidimensional scaling (NMDS) analysis, the compositions of the microbial communities were altered (p < 0.05), indicating that different clusters in bacterial (Figure S4a) and fungal (Figure S4b) communities were formed with meadow degradation. The dominant bacteria phyla (> 0.1%) in soils were similar, with the relative abundance of Ascomycota being greatest in alpine swamp meadow, reaching 61.5%, whereas, of Zygomycota being greatest in severely degraded alpine meadows (6.1%), and least in alpine Kobresia meadows (0.9%) (Figure S5). These results suggest that meadow degradation and restoration have substantial effects on soil microbial composition. The bacteria phyla Bacteroidetes, Planctomycetes, Acidobacteria, and Cyanobacteria, and the fungi phyla Zygomycota and Chytridiomycota were correlated positively with the soil organic matter composition in severely degraded alpine meadows and restored meadows (Figures S6 and S7).

Co-occurrence patterns of microbial communities and ecosystem multifuncationality

Based on network analysis, Proteobacteria (35%), Acidobacteria (30%), and Bacteroidetes (9%) were included in the bacterial network, and Ascomycota (37%), Basidiomycota (4%), and Zygomycota (3%) were included in the fungal network. Soil bacterial and fungal community networks displayed different symbiotic patterns (Fig. 4a, b). The number of nodes, degrees, and edges of bacterial and fungal community networks were greater but the average path lengths were lesser in severely degraded alpine meadows and short-term and long-term restored meadows than in alpine swamp and alpine Kobresia meadows (Fig. 4c–f). According to topological parameters, the stability of bacterial and fungal networks in severely degraded alpine meadows and restored meadows were greater than in alpine swamp and alpine Kobresia meadows.

Subsequently, we calculated the topological features of the extracted sub-networks by preserving the individual soil samples to estimate the potential complexity. The number of nodes, edges and average degrees were all correlated positively with EMF, while average path length, denoting network sparsity, was correlated negatively with EMF (p < 0.05) (Fig. 3g–j). These results suggest that soil microbial complexity could promote EMF.

Fig. 3
figure 3

Co-occurrence patterns in soil bacterial (a) and fungal (b) communities in meadows. The size of the nodes (OTUs) are proportional to the number of connections. Only nodes (OTUs) that are correlated to each other (Spearman’s > 0.7, p < 0.05) are connected (edges). The number of nodes (c), degrees (d), edges (e) and the average path lengths (f) of bacteria and fungi co-occurrence patterns among meadows. The relationship of ecosystem multifunctionality to number of nodes (g), degrees (h), edges (i) and the average path lengths (j) of microbial co-occurrence patterns. Error bars are standard errors of the mean. Means with different lowercase letters within bacteria and fungi differ from each other (p < 0.05). ASM, alpine swamp meadow. AKM, alpine Kobresia meadow. SDM, severely degraded alpine meadow. ASR, short-term restored meadow (≤ 5 years). ALR, long-term restored meadow (6–14 years)

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Assembly processes of the microbial communities and potential important predictors of ecosystem multifunctionality

The neutral community model had a low degree of fit (r2 = 0.25) for bacteria in alpine swamp meadows, but a high degree of fit (r2 > 0.50) for the other meadows (Table S6). The shape of the alpine swamp meadow community was affected by deterministic processes, while the other meadows were affected by stochastic processes. According to the zero-model analysis, the bacterial community was dominated by deterministic processes. The assembly process in degraded and restored meadows was mainly dispersal limitation, while the fungal community transferred from stochastic to deterministic processes after swamp meadow degradation (Fig. 4a). Five ecological processes were identified in severely degraded alpine meadows, while three ecological processes were identified in alpine swamp meadows (Fig. 4c), including homogenizing selection (50.0%), heterogeneous selection (33.3%), and ecological drift (16.7%). There was no dispersal limitation in the composition of the fungal community in any of the meadows (Fig. 4c). With the restoration of a meadow, the niche breadth and migration rate (m) of bacterial and fungal communities increased (Fig. 4b and Table S6), indicating that the microbial community in a restored meadow had high metabolic activity.

Fig. 4
figure 4

Microbial community assembly processes across degraded and restored meadows. a The βNTI of microbial communities; b the assembly process of bacterial communities; c the niche breadth of microbial communities; and d mean predictor importance (percent increased mean square error, MSE) of ecosystem multifunctionality. ASM, alpine swamp meadow. AKM, alpine Kobresia meadow. SDM, severely degraded alpine meadow. ASR, short term restored meadow (≤ 5 years). ALR, long-term restored meadow (6–14 years). UTP, Compounds, soil organic matter composition. UTN, total nitrogen in roots. ATP, above-ground plant total phosphorus. ATN, above-ground plant total nitrogen. AWCD, the average well color development. SOC, soil organic carbon. STN, soil total nitrogen. Fungi, fungal community composition. Bacteria, bacterial community composition. βNTI, β nearest taxa index. STP, soil total phosphorus. **p < 0.01; *p < 0.05

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Random forest models separated and assessed important predictors of EMF in degraded and restored alpine meadows. According to the models, the TP content in above-ground vegetation (7.9%) and roots (16.8%), soil organic compound composition (8.5%), TN content in above-ground vegetation (6.5%) and roots (8.2%), AWCD (5.1%), and soil organic carbon (5.0%) were important predictors of EMF (Fig. 4d). In degraded and restored meadows, microbial α diversity had a direct positive effect on the stability of microbial networks. Soil organic compound composition had direct effects on microbial α diversity, microbial community structure, microbial network stability and EMF in restored meadows (p < 0.05; Fig. 5). Flavonoids were not included in the restored meadow model due to their strong collinearity with EMF.

Fig. 5
figure 5

Structural equation models (SEMs) indicating the direct and indirect effects of plant properties, soil properties, soil organic matter composition, and soil microbial community composition on ecosystem multifunctionality in degraded meadows (a) and restored meadows (b). The single headed arrows indicate the hypothesized direction of causation. The red and blue labels and numbers represent positive and negative effects, respectively. Soil properties, the PCo1 score of pH, soil water content, soil bulk, soil organic carbon, soil total nitrogen, and soil total phosphorus. Composition, the first axis of NMDS of soil bacterial and fungal communities. Diversity, the z-value of the Shannon’s diversity of bacterial and fungal communities. βNTI, the weighed β nearest taxon index. AWCD, the average well color development. Plant property, the first axis of PCA of total nitrogen and total phosphorous in above-ground plant and root. Network complex, the z-value of microbial network nodes. EMF, ecosystem multifunctionality. *0.01 < p < 0.05; **0.001 < p < 0.01; ***p < 0.001

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Soil content of sugars, terpenoids, phenolic acids, flavonoids and amino acids were greater (p < 0.05) and of organic acids were lesser (p < 0.05) in severely degraded alpine meadows and restored meadows than in alpine swamp meadows (Figure S8). In addition, organic compounds, including formononetin, biochanin A, betulinic acid, abscisic acid, and γ-terpinene, but not geranylgeraniol, were lesser in alpine swamp meadows than in the other meadows (Figure S9). Results based on the content of individual compounds validated the positive effects of soil organic matter composition on EMF.

Discussion

Environmental factors influenced soil microbial composition

The decay distance of the microbial community in space or environmental distance was related to the decrease in stability of the microbial community [60], which may be due to the rapid disappearance and replacement of species during the shaping of the microbial community [61]. The α diversities of the bacterial and fungal communities and the β diversity of the fungal community were correlated positively, but the β diversity of bacterial community was correlated negatively with EMF. The β diversity of microorganisms is particularly important for EMF [62, 63]. Because the microbial α diversity increased during grassland restoration, the similarity of the bacterial community decreased and of the fungal community increased (Table S5), while the grassland restoration improved EMF.

Fungal communities generally display low mobility, but are able to survive extreme conditions, such as severe cold and extended drought, and are more resistant to biotic and abiotic stresses than bacteria [64]. These characteristics could explain why bacteria are more sensitive to changes than fungi. However, the high water content in alpine swamp meadows resulted in relatively uniform soil properties [65] and promoted the movement of fungi. Therefore, the assemblage of fungal communities in this meadow was a result of ecological drift (stochastic processes). The soil properties were altered in degraded meadows, while short-term and long-term restored meadows had similar soil properties, as was reported earlier [66]. Many microbial taxa had the same or overlapping ecological niches, but the competition was balanced so that one could not eliminate another [67]. Therefore, deterministic processes had a stronger impact than stochastic processes on the bacterial community composition.

Ecosystem multifunctionality improved in restored meadows

The input of organic matter into soil was higher in alpine swamp than alpine Kobresia meadows due to the greater root biomass in alpine swamp. Compared with alpine Kobresia meadows, soil quality in short-term and long-term restored meadows did not improve (Table S2). Soil organic C mineralization rate decreased with meadow degradation and increased after long-term restoration in previous studies [12, 68]. In the present study, soil microbial C metabolism decreased gradually with meadow degradation in the present study, but the content of small molecular C and the utilization efficiency of amino acids increased, thus leading to increased C functions in severely degraded alpine meadows and short-term and long-term restored meadows (Table S4). In addition, there was a positive correlation between C function and P function in the present study, A higher higher soil P availability promotes its uptake by plants, thus increasing tissue P concentration, which is conducive to enhancing the net photosynthetic rate, plant growth rate and vegetation C fixation [69, 70].

An improved EMF led to positive feedback on plant productivity. This increase in above-ground biomass may be further enhanced by the uptake of N and P. The total P content in plant roots of short-term restored meadows was greatest of all meadows. The P in roots was presumably from soil because the TP in soil decreased with meadow succession. TP was greater in soil but lesser in roots in alpine swamp and alpine Kobresia meadows than in the other meadows. This may be due to the better ability of roots to absorb P in severely degraded alpine meadows and short-term and long-term restored meadows than in alpine swamp and alpine Kobresia meadows. Soil organic phosphorus occurs primarily in stable compounds, which are difficult for plants to absorb. Plant roots secrete phosphatases, enzymes that hydrolyze organic phosphorus compounds, producing inorganic phosphorus that plants can absorb [71]. Soil pH increased with meadow restoration (Table S2), which promoted phosphatase activity, thereby enhancing the mineralization rate of soil organic phosphorus and releasing more available phosphorus for plant uptake [72].

Soil secondary metabolites increase in abundance with the degradation of alpine swampy meadows. Such is the case with flavonoids and terpenoids, typical defense and signaling substances that possess antibacterial properties [73]. In the present study, positive correlations emerged between organic compounds and microorganisms such as Rhizobium, Roseateles, Bacillus, Leptolyngbya, Thermomonas and Novosphingobium that could promote plant growth [74, 75]. Roots of plants release terpenoids that attract mycorrhiza [76], organic acids and amino acids that reduce microbial diversity [36]. There was a negative correlation between amino acids and microbial diversity in the current study, suggesting that amino acids served as a nitrogen source that affected microbial composition. However, an overabundance of amino acids could stimulate fast-growing r-strategists [77], which would lead to reduced bacterial diversity.

A possible reason for the high content of geranylgeraniol in alpine swamp meadows may be due to the high soil moisture that reduces oxygen availability to soil microorganisms, thereby reducing the decomposition of secondary metabolites by microorganisms [73]. Another possible reason is that geranylgeraniol is an intermediate product of the mevalonate pathway. Plants in degraded meadows produce large quantities of monoterpene through the methylerythritol phosphate pathway, thus reducing the content of intermediate products in the mevalonate pathway.

Microbial network stability determined ecosystem multifunctionality

Degradation of alpine meadows increased the connectivity of soil microbes in the present study, thereby altering the stability of the soil microbial community network. Network stability could be influenced by the following reasons. Firstly, the complexity of the microbial network in alpine meadow degradation could be due to microbial composition, as richness supports greater microbial complexity [34]. Degradation of alpine meadow to alpine Kobresia meadow leads to a reduction in the diversity and abundance of bacterial communities and in the stability of the soil microbial community network. Secondly, a decline in the availability of soil nutrients, such as organic carbon and TN, can increase network complexity. Such a decline decreases microbial carbon metabolism (Table S3), but increases the utilization efficiency of amino acids and amines (Table S4), thus, strengthening microbial community cooperation [78, 79]. Vegetation changes lead to an increase in secondary metabolites during meadow degradation and the soil legacy effect [34, 80]. Soil chemical compounds in severely degraded alpine meadows and short-term and long-term restored meadows differed from alpine swamp and alpine Kobresia meadows, which may explain the similarity nodes of the microbial co-occurrence networks among severely degraded alpine meadows, short-term restored meadows and long-term restored meadows. Thirdly, degraded alpine meadows increase soil heterogeneity, and the responses of soil microorganisms to soil heterogeneity are diverse [81]. Soil heterogeneity enhances the co-occurrence of soil microbiota, which is conducive to an increase in network complexity [79, 82]. Soil properties did not differ between severely degraded alpine meadows and restored meadows; however, connectivity of microbial taxa increased in long-term restored meadows compared with short-term restored and severely degraded alpine meadows.

A more connected network can utilize C more efficiently [79]. The degradation of alpine meadows promoted the instability of the bacterial network, including an increase in connectivity and complexity. The increases in node connectivity and complexity were related to a decrease in network stability, as was reported earlier [83]. Connectivity and complexity were reduced in long-term restored meadows when compared with severely degraded alpine meadows, indicating high stability of the microbial network in long-term restored meadows.

Conclusions

The present study emphasizes the importance of soil organic matter composition in supporting and maintaining EMF in restored meadows. Furthermore, soil microbial diversity and richness and the utilization efficiency of amino acids and amines increased in restored meadows when compared with degraded meadows. Soil sugars and amino acids had a strong direct effect on microbial diversity and microbial community structure, and flavonoids and organic acids displayed stronger EMF relationships in restored than degraded meadows. These results indicate that soil organic matter composition could reshape the composition of microbial communities in restored meadows and maintain EMF. Soil organic matter composition and microbial network complexity promote EMF and enhance the ability to predict the effects of microbial diversity on EMF and stability in restored meadows. It is recommended that the role of soil microbial network complexity and soil organic matter composition be considered in policy making and soil management to enhance EMF and sustainability when restoring degraded alpine meadows.

Data availability

Additional data are available under the Supplementary information sections. Main analysis scripts are available on GitHub (https://github.com/wangwy2015/data-for-EM.git).

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Acknowledgements

We would like to thank the Research Core Facilities, College of Ecology, Lanzhou University, for providing instruments and equipment.

Funding

This work was supported by the Natural Science Foundation of China (U21A20183; 31961143012; 32301397), the Chief Scientist Program of Qinghai Province (2024-SF-101), the Science-based Advisory Program of The Alliance of National and International Science Organizations for the Belt and Road Regions (ANSO-SBA-2023-02), and the ‘111’ Programme 2.0 (BP0719040), the China Post-doctoral Science Foundation (2024M751262), the Gansu Province Science and Technology Program (24JRRA526).

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  1. Wenyin Wang and Sisi Bi have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China

    Wenyin Wang, Sisi Bi, Fei Li, Mei Huang, Binyu Luo, Tao Zhang, Shuai Qi, Tianyun Qi, Yanfu Bai, Peipei Liu & Zhanhuan Shang

  2. Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 8410500, Beer Sheva, Israel

    A. Allan Degen

  3. Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810008, China

    Shanshan Li

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Contributions

Wenyin Wang, data curation, formal analysis, funding acquisition, methodology, writing- original draft; Sisi Bi, Fei Li, field sampling, laboratory assays, data curation; A.Allan Degen, writing—review and editing; Shanshan Li, Mei Huang, Binyu Luo, Data curation; Tao Zhang, Shuai Qi, Yanfu Bai, field sampling; Tianyun Qi, Peipei Liu, review and editing; Zhanhuan Shang, funding acquisition, conceptualization, methodology, formal analysis, writing—review and editing. All authors read and approved the final manuscript.

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Wang, W., Bi, S., Li, F. et al. Soil organic matter composition affects ecosystem multifunctionality by mediating the composition of microbial communities in long-term restored meadows. Environmental Microbiome 20, 22 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-025-00678-6

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-025-00678-6

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Environmental Microbiome

ISSN: 2524-6372