Analysis of soil microbial diversity of Miscanthus lutarioriparius communities in different areas of Dongting Lake

Miscanthus lutarioriparius communities are a crucial component of the Dongting Lake wetland ecosystem. To understand how habitat heterogeneity shapes their associated soil microbiome, this study investigated the spatial patterns of bacterial and fungal diversity across seven regions using high-throughput sequencing of 16S rRNA and ITS genes alongside soil physicochemical analyses. Our results revealed distinct assembly mechanisms for bacteria and fungi. Bacterial community composition and alpha diversity exhibited significant spatial heterogeneity, primarily correlated with altitude, pH, and ammonium nitrogen. In contrast, fungal communities were more homogeneous in composition, with their alpha diversity strongly linked to soil total phosphorus. Distance-based redundancy analysis confirmed that bacterial communities were mainly structured by abiotic factors (altitude, pH), whereas fungal communities were predominantly shaped by nutrient availability (total phosphorus, total nitrogen, soil organic matter, and total potassium). Co-occurrence network analysis indicated a modular structure with stronger intra-than inter-domain connections, dominated by saprotrophic Ascomycota hubs. Functional prediction further supported these trends, revealing a prevalence of biofilm-forming bacteria and site-specific saprotrophic fungal guilds. This study demonstrates that bacterial and fungal communities in M. lutarioriparius wetlands are filtered by fundamentally different environmental factors—altitude and related abiotic conditions versus soil nutrient availability. These findings provide a clear ecological framework for understanding microbial biogeography in dynamic wetlands and underscore the need to consider domain-specific responses in conservation and restoration strategies.

1 Introduction

Soil microorganisms are fundamental drivers of ecosystem processes, regulating nutrient cycling and energy flow in wetland ecosystems (Brockett et al., 2012). In wetlands, which are characterized by the land-water interface, hydrological conditions are considered a primary factor influencing soil microbial communities (Ren et al., 2022; Wang et al., 2022). Topographic variation, often proxied by altitude, shapes hydrological regimes such as flooding frequency and duration, thereby creating habitat heterogeneity that can significantly alter the structure and diversity of soil microbiomes (Wang et al., 2024b). Furthermore, the physicochemical properties of soil, including pH, nutrient availability (e.g., total nitrogen, TN; total phosphorus, TP), and organic matter (SOM) content, are also key determinants of microbial community composition (Chi et al., 2021). Understanding the interplay between these environmental filters is crucial for predicting ecosystem functioning.

Dongting Lake, the second largest freshwater lake in China, is a vital wetland system in the Yangtze River basin. Its complex hydrological connectivity and topography result in significant spatiotemporal heterogeneity in soil characteristics (Liu et al., 2023). The Miscanthus lutarioriparius-dominated community, a dominant emergent plant formation in these wetlands, plays a critical ecological role due to its strong reproductive capacity and adaptability (Xu et al., 2023; Xu et al., 2021). While previous microbial studies in Dongting Lake have focused on impacts from upstream hydrology, the Three Gorges Dam, or ecological restoration of poplar forests (Wu et al., 2022; Wu et al., 2015; Wu et al., 2013), research specifically on the soil microbial communities associated with the Miscanthus lutarioriparius community remains scarce.

Moreover, existing studies on wetland microbial biogeography in China, such as those in Poyang Lake, the Sanjiang Plain, and the Yellow River Delta, have established clear links between hydrology, nutrients, and microbes (Xu et al., 2023; Xu et al., 2021). However, the novelty of the present study lies in its focused examination of a plant community defined by a single, ecologically dominant species (M. lutarioriparius) across a heterogeneous landscape within one lake system. This focus on a specific, monodominant community type allows for a clearer dissection of how environmental gradients shape the associated microbiota, largely controlling for the potentially confounding influence of variations in plant species composition.

To address this knowledge gap, we systematically sampled soils from M. lutarioriparius communities across seven regions of Dongting Lake. We hypothesized that: (1) the composition and diversity of soil bacterial and fungal communities would exhibit significant spatial heterogeneity across different regions; (2) bacterial communities would be more strongly influenced by hydrological gradients (proxied by altitude), while fungal communities would be more responsive to soil nutrient status (e.g., TP, TN, SOM); and (3) inter- and intra-domain microbial interactions would reflect these differential responses to environmental filters.

The objectives of this study were to: (1) characterize the composition and diversity patterns of soil bacterial and fungal communities associated with M. lutarioriparius across Dongting Lake; (2) identify the key environmental drivers underlying the observed microbial community patterns; and (3) reveal the co-occurrence patterns among bacterial and fungal taxa. By testing these hypotheses, this research aims to provide a scientific basis for the protection and restoration of the Dongting Lake wetlands by elucidating the plant-microbe-environment interactions.

2 Materials and methods

2.1 Study area and sampling

2.1.1 Study area

Based on the research conducted by the Hunan Engineering Laboratory for Ecological Applications of Miscanthus Resource on the spatiotemporal distribution of the M. lutarioriparius community, this study sampled seven areas in the Dongting Lake region where the community has a concentrated and stable growth over the years (Figure 1). The sampling points included Yueyang (A), Liumenzha (B), Xinzhou (C), Luhu (D), Yuanjiang (E), Anxiang (F), and Lixian (G), covering the eastern, southern, and western parts of Dongting Lake.

Figure 1

Figure 1. Soil microbial sampling point. Note: A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian.

2.1.2 Sampling methods

Sampling was conducted in October 2023. In each of the seven sampling points, three independent replicate plots (5 m × 5 m each, located at least 10 m apart) were established, resulting in a total of 21 soil samples. Within each plot, after removing the surface litter, a five-point sampling method was employed using a stainless steel soil auger to collect soil cores from the 0–20 cm depth layer. The five sub-samples from the same plot were then thoroughly mixed to form one composite sample. This strategy was adopted to obtain a representative soil profile for each plot while minimizing the effect of micro-scale heterogeneity, which is standard practice in microbial ecology studies. The composite samples were immediately placed in a cooling box with ice packs for transportation to the laboratory. After removing visible plant debris and stones, each sample was passed through a 2 mm sieve, divided into aliquots for DNA extraction and soil physicochemical analysis, and stored at −80 °C and 4 °C, respectively.

2.2 DNA extraction and high-throughput sequencing

Total genomic DNA was extracted from approximately 0.5 g of soil using the E. Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, United States) according to the manufacturer’s instructions. The concentration and purity of the extracted DNA were checked by 1% agarose gel electrophoresis. The DNA samples were stored at −80 °C until further use. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified using the primers 343F (5′-TACGGRAGGCAGCAG-3′) and 798R (5′-AGG​GTA​TCT​AAT​CCT-3′). The fungal internal transcribed spacer (ITS1) region was amplified using the primers ITS1F (5′-CTT​GGT​CAT​TTA​GAG​GAA​GTA​A-3′) and ITS2 (5′-GCT​GCG​TTC​TTC​ATC​GAT​GC-3′). The PCR reaction system (20 μL) contained 5 × TransStart FastPfu buffer, 2.5 mM dNTPs, 5 μM of each primer, 0.4 μL TransStart FastPfu DNA Polymerase, and 10 ng of template DNA. The amplification conditions were as follows: initial denaturation at 95 °C for 3 min; followed by 27 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 45 s; with a final extension at 72 °C for 10 min. The PCR products were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, United States) and quantified using a Quantus™ Fluorometer (Promega, United States). The purified amplicons were pooled in equimolar amounts and paired-end sequenced (2 × 250 bp for 16S rRNA; 2 × 300 bp for ITS) on an Illumina MiSeq platform (Illumina, San Diego, CA, United States) at Shanghai Majorbio Bio-pharm Technology Co., Ltd.

2.3 Bioinformatic processing and OTU clustering

The raw sequencing reads were processed as follows: Primers and barcodes were trimmed, and quality filtering was performed using fastp (version 0.20.0) to remove low-quality sequences (e.g., Q-value <20 or length <200 bp). Paired-end reads were merged using FLASH (version 1.2.7) with a minimum overlap of 10 bp and a maximum mismatch ratio of 0.2. Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using the UPARSE algorithm in USEARCH (version 10.0). Chimeric sequences were identified and removed using UCHIME. The representative sequence of each OTU was taxonomically classified using the RDP Classifier (version 2.2) against the SILVA 138 database for bacteria and the UNITE (version 8.0) database for fungi, with a confidence threshold of 0.7. Sequences identified as chloroplasts, mitochondria, or non-fungal origins were removed from the dataset. To control for differences in sequencing effort, all samples were rarefied to 62,000 (bacterial 16S) or 63,000 (fungal ITS) sequences per sample prior to alpha- and beta-diversity analyses, which retained all 21 samples in the dataset.

2.4 Determination of soil physical and chemical properties

Soil pH was determined using a pH meter (water to soil ratio 2.5:1). Soil electrical conductivity (EC) was determined using a conductivity meter (water to soil ratio 5:1). Soil organic matter (SOM) was determined using the potassium dichromate external heating method. Soil available phosphorus (AP) was determined by the sodium bicarbonate leach-molybdenum-antimony colorimetric method. Soil total phosphorus (TP) was determined by the perchloric acid-sulfuric acid method. Soil Alkaline Hydrolyzed Nitrogen (AHN) was determined by the alkaline diffusion method. Soil total nitrogen (TN) was determined by the Kjeldahl method. Soil quick potassium (AK) was determined by ammonium acetate leaching flame photometric method. Soil total potassium (TK) was determined by sulfuric acid-perchloric acid, flame photometric method, all the above determination methods are referred to (Bao, 2000).

2.5 Statistical analysis of data

Alpha diversity indices, including the Chao1 and ACE (richness estimators), Shannon and Simpson (diversity indices), were calculated. Differences in these indices and soil properties among sampling sites were assessed using one-way ANOVA followed by Tukey’s HSD test (for normally distributed data) or the Kruskal–Wallis test (for non-normal data) in R. Significance was set at P < 0.05. Differences in microbial community structure (beta diversity) among regions were assessed using permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distance matrices, with 999 permutations to obtain statistical significance. This analysis was performed using the adonis2 function in the “vegan” R package, and the results are reported with R² and P-values. The correlation between overall microbial community structure and individual environmental factors was further examined using distance-based redundancy analysis (db-RDA). The significance of each environmental factor in explaining community variation was tested using permutation tests (999 permutations). Spearman’s rank correlation was employed to examine two sets of relationships: first, between environmental factors and microbial alpha diversity indices; second, between environmental factors and the relative abundance of the most abundant bacterial and fungal OTUs (specifically, the top 10 OTUs ranked by total relative abundance across all samples). Co-occurrence network analysis was constructed to infer potential associations between the most abundant bacterial and fungal taxa. Specifically, the top 100 most abundant bacterial OTUs and the top 100 most abundant fungal OTUs (each ranked by mean relative abundance across all samples) were selected for analysis. Pairwise Spearman correlations among these 200 OTUs were calculated. Only correlations satisfying |r| ≥ 0.8 with an FDR-adjusted P ≤ 0.05 were retained for network construction. The network was visualized in Gephi software.

3 Results and analysis

3.1 Differences in soil microbial structure of Miscanthus lutarioriparius community in different areas

3.1.1 Analysis of bacterial structural composition

The relative abundance of soil bacterial phyla (mean relative abundance >1%) across the seven study regions is shown in Figure 2. The ten most abundant bacterial phyla were Acidobacteriota, Proteobacteria, Actinobacteriota, Chloroflexi, Methylomirabilota, Myxococcota, Gemmatimonadota, Bacteroidota, Firmicutes, and Latescibacterota, which collectively accounted for 89.21% of the total bacterial sequences. Acidobacteriota and Proteobacteria were the dominant phyla across all regions. Specifically, Acidobacteriota was the predominant phylum in all regions except for Yuanjiang (site E), where Proteobacteria exhibited the highest relative abundance.

Figure 2

Figure 2. Bacterial community composition in different areas at the phylum level. Note: A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian. Each bar represents the mean relative abundance of microbial phyla based on three biological replicates per site. Individual phyla are displayed only if their relative abundance exceeded 1% in at least one sample; all remaining taxa are collectively represented as “others”.

Kruskal–Wallis rank-sum tests were performed to assess the differences in the relative abundance of these phyla among regions. The analysis revealed statistically significant differences in bacterial community composition at the phylum level (P < 0.05). Post-hoc analysis following false discovery rate (FDR) correction indicated that only the relative abundance of the phylum Latescibacterota varied significantly across the regions (FDR-adjusted P < 0.05), as visualized in Figure 3. No other major phyla showed significant spatial variation after FDR correction.

Figure 3

Figure 3. Difference test of soil bacterial species at phylum level in different regions. Note: Kruskal–Wallis H test results showing mean proportions of bacterial phyla across seven sampling regions. A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian. P-values on the right indicate statistical significance levels for intergroup differences.

3.1.2 Fungal community composition analysis

The composition of soil fungal phyla (mean relative abundance >1%) across the different regions is presented in Figure 4. The major phyla included Basidiomycota, Ascomycota, unclassified k__Fungi, Mortierellomycota, Glomeromycota, Rozellomycota, and others. Basidiomycota was the dominant phylum in most study areas. In contrast, Ascomycota exhibited the highest relative abundance in the Yuanjiang study area (site E).

Figure 4

Figure 4. Fungal community composition in different areas at the phylum level. Note: A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian. Each bar represents the mean relative abundance of microbial phyla based on three biological replicates per site. Individual phyla are displayed only if their relative abundance exceeded 1% in at least one sample; all remaining taxa are collectively represented as “others”.

Kruskal–Wallis tests revealed no statistically significant differences in the relative abundance of the major fungal phyla among the regions after false discovery rate (FDR) correction for multiple comparisons (FDR-adjusted P > 0.05). This indicates that the composition of the fungal community at the phylum level was relatively homogeneous across the spatial gradient sampled in this study, as further detailed by the post hoc test results shown in Figure 5.

Figure 5

Figure 5. The difference test of fungal species in different regions at the phylum level. Note: Kruskal–Wallis H test results showing mean proportions of bacterial phyla across seven sampling regions. A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian. P-values on the right indicate statistical significance levels for intergroup differences.

3.2 Analysis of soil microbial diversity in different regions of Miscanthus lutarioriparius communities

3.2.1 Alpha diversity analysis of bacteria and fungi

The analysis of bacterial diversity indices is illustrated in Figure 6. The Kruskal–Wallis test indicated no statistically significant overall differences in bacterial alpha diversity across the seven regions for three of the four indices (ACE, Chao1, Shannon; P > 0.05). To explore potential site-specific variations, exploratory pairwise comparisons with false discovery rate (FDR) correction were conducted. For the ACE index, significant differences were found between the Liumenzha area and the Luhu and Anxiang areas (FDR-adjusted P < 0.05), as well as extremely significant differences with the Lixian area (FDR-adjusted P < 0.01). A similar pattern was observed for the Chao1 index. For the Shannon index, significant differences were observed between the Liumenzha and Yueyang areas (FDR-adjusted P < 0.05), and extremely significant differences with the Luhu area (FDR-adjusted P < 0.01). Regarding the Simpson index, while the overall group difference was significant (P < 0.05), post hoc tests revealed specific pairwise differences between the Yuanjiang area and the Yueyang, Xinzhou, Luhu, and Anxiang areas (FDR-adjusted P < 0.05), as well as between the Luhu and Anxiang areas (FDR-adjusted P < 0.05).

Figure 6

Figure 6. Alpha diversity analysis of soil bacterial communities. Note: A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian.

The variance analysis of fungal diversity indices is illustrated in Figure 7. Consistent with the bacterial results, the Kruskal–Wallis test indicated no significant overall differences in fungal alpha diversity among regions for any of the four indices (P > 0.05). Exploratory pairwise comparisons with FDR correction identified specific differences: for the Shannon index, a significant difference was found between the Liumenzha and Lixian areas (FDR-adjusted P < 0.05). For the Simpson index, significant differences were observed between the Liumenzha area and both the Xinzhou and Lixian areas (FDR-adjusted P < 0.05).

Figure 7

Figure 7. Alpha diversity analysis of soil fungal communities. Note: A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian.

3.2.2 Bacterial and fungal beta diversity analysis

The beta-diversity of soil microbial communities associated with M. lutarioriparius populations was assessed using principal coordinates analysis (PCoA) based on Bray-Curtis distances to visualize the similarities and differences across the seven regions (Figure 8).

Figure 8

Figure 8. Soil microbial PCoA analysis. Note: (a) bacteria, (b) fungi, A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian. Ellipses indicate 95% confidence intervals.

For bacterial communities (Figure 8a), the first two principal coordinates (PC1 and PC2) explained 32.67% and 14.97% of the total variance, respectively, with a cumulative explanatory power of 47.64%. The spatial arrangement of samples revealed distinct clustering patterns. Samples from Yueyang (A), Liumenzha (B), Xinzhou (C), Luhu (D), and Anxiang (F) showed considerable overlap and were primarily distributed in the first and fourth quadrants, indicating similar bacterial community compositions among these sites. In contrast, samples from Lixian (G) clustered separately in the third quadrant, while those from Yuanjiang (E) were independently distributed between the second and third quadrants, suggesting distinct bacterial assemblages at these two locations. Permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distances confirmed that bacterial community structures differed significantly among regions (R² = 0.474, P = 0.001).

For fungal communities (Figure 8b), PC1 and PC2 explained 25.42% and 13.31% of the variance, respectively, cumulatively accounting for 38.73%. Samples from Yueyang (A), Liumenzha (B), Luhu (D), and Anxiang (F) clustered together in the second and third quadrants, indicating compositional similarity. Samples from Yuanjiang (E) and Lixian (G) formed a separate cluster in the first quadrant, while those from Xinzhou (C) were isolated in the fourth quadrant, demonstrating spatial heterogeneity in fungal community structure. PERMANOVA confirmed significant differences in fungal community composition across regions (R² = 0.481, P = 0.001).

3.3 Changes in environmental factors in different regions

Analysis of soil physicochemical properties revealed significant spatial heterogeneity across the seven sampling regions in the Dongting Lake wetland (Table 1), establishing a clear environmental template for testing our hypotheses. Among the ten parameters measured, pH and AP showed no significant differences among regions (P > 0.05). In contrast, EC varied significantly (F = 6.972, P < 0.01).

Table 1

Table 1. Environmental factors in different regions.

Crucially, most soil nutrient factors exhibited highly significant spatial variation (P < 0.01), including TP, AHN, TN, SOM, AK, TK, and TC. The Xinzhou region (site C) was particularly distinct, displaying the highest concentrations for multiple parameters: TP, AHN, TN, SOM, and AK. Furthermore, sampling sites spanned a distinct altitudinal gradient from 25 to 37 m. Notably, variations in several key soil properties (e.g., SOM, TK) appeared to correspond with this elevational gradient, providing a quantifiable proxy for the hypothesized hydrological variation across the wetland landscape.

In summary, the results confirm the existence of strong, co-varying gradients in both soil nutrient availability and altitude (a proxy for hydrology) across our study sites. This sets the stage for directly testing whether bacterial and fungal communities respond differentially to these two predominant environmental filters, as hypothesized.

3.4 Correlation of soil microbial diversity with environmental factors

Comprehensive correlation analysis revealed significant relationships between soil microbial diversity and environmental factors in the M. lutarioriparius community of Dongting Lake, elucidating the specific environmental drivers shaping microbial communities across different regions. The analysis demonstrated distinct response patterns between bacterial and fungal communities to environmental gradients.

For bacterial communities, ALT emerged as a key factor, with both the Ace index (P < 0.05) and Chao index (P < 0.05) showing significant positive correlations, indicating enhanced species richness at higher elevations. However, the Shannon index exhibited a significant negative correlation with ALT (P < 0.05), suggesting reduced diversity uniformity with increasing elevation. Most notably, the Simpson index demonstrated an extremely significant negative correlation with TK content (P < 0.01), reflecting the strong influence of potassium availability on dominant bacterial species distribution.

In contrast, fungal communities showed predominant dependence on phosphorus availability, with both the Ace index (P < 0.01) and Chao index (P < 0.01) exhibiting extremely significant positive correlations with TP, indicating phosphorus as a key determinant of fungal species richness. These findings collectively identify TP, TK, and ALT as the most influential environmental factors, highlighting the differential responses of bacterial and fungal communities to environmental gradients and their distinct ecological strategies in this wetland ecosystem. The correlation patterns provide important insights into microbial distribution mechanisms and contribute to developing targeted conservation strategies for the Dongting Lake wetland.

3.5 Correlation analysis between dominant OTUs and environmental factors

To elucidate the response patterns of dominant microbial taxa to environmental gradients, Spearman correlation analyses were performed between the relative abundance of dominant OTUs and key soil physicochemical properties. Statistical significance was stringently evaluated using the False Discovery Rate (FDR) correction, with FDR-adjusted P < 0.05 considered significant.

After FDR correction, none of the dominant bacterial OTUs showed a significant correlation with any of the measured environmental factors. In contrast, several fungal OTUs exhibited significant correlations. Specifically, the relative abundance of OTU886 was significantly positively correlated with TN, SOM, and TK (FDR-adjusted P < 0.05). The relative abundance of OTU2332 was significantly negatively correlated with TP (FDR-adjusted P < 0.05). Additionally, the relative abundance of OTU2288 showed a significant negative correlation with TK (FDR-adjusted P < 0.05).

In summary, at the OTU level and under stringent statistical control, the abundance of dominant bacterial OTUs was not linearly associated with the measured environmental variables. In contrast, the abundance of specific dominant fungal OTUs was significantly linked to the availability of major soil nutrients, including TN, SOM, TP, and TK. This reinforces the finding that fungal communities are more directly responsive to soil nutrient gradients in this ecosystem.

3.6 RDA of soil microbial community composition and environmental factors

Distance-based redundancy analysis (db-RDA) was employed to assess the relationships between soil microbial communities and environmental factors while addressing potential collinearity concerns. The analysis treated environmental variables as explanatory factors and microbial community composition as response variables, revealing distinct patterns for bacterial and fungal communities. For bacterial communities at the phylum level, environmental factors explained 27.71% of the total variance, with altitude (ALT, R² = 0.4461, P = 0.012), pH (R² = 0.3955, P = 0.010), and ammonium nitrogen (AHN, R² = 0.3396, P = 0.022) demonstrating significant correlations. Fungal communities showed a stronger response to environmental factors, with 37.98% of the total variance explained by the measured variables. Total potassium (TK, R² = 0.4540, P = 0.004) emerged as the most influential factor, followed by total phosphorus (TP, R² = 0.3955, P = 0.009), total nitrogen (TN, R² = 0.3715, P = 0.018), and soil organic matter (SOM, R² = 0.3389, P = 0.019). The differential explanatory power between bacterial and fungal communities highlights their distinct ecological responses to environmental gradients, with bacterial communities more influenced by abiotic factors like altitude and pH, while fungal communities showed stronger dependencies on nutrient availability. These findings provide valuable insights into the environmental drivers shaping microbial distribution patterns in the Dongting Lake wetland ecosystem.

3.7 Bacterial-fungal symbiotic network analysis

To visualize potential associations between dominant microbial taxa, a co-occurrence network was constructed based on Spearman correlation analysis. To ensure robustness given the sample size (n = 21), a stringent threshold of |r| ≥ 0.8 with FDR-corrected P < 0.05 was applied.

The resulting network comprised 156 nodes and 487 edges (Figure 12). Within the network, positive correlations (354 edges, 72.7%) significantly outnumbered negative correlations (133 edges, 27.3%). Key topological properties are summarized in Table 2. The network exhibited a modular structure (modularity = 0.515) and a moderate clustering coefficient (0.429). Notably, within the fungal subset, the top ten hub nodes (ranked by degree centrality) all belonged to Ascomycota, whereas the highest-degree nodes in the bacterial subset were dominated by Acidobacteriota and Proteobacteria. This indicates that Ascomycota governs the fungal sub-network, while Acidobacteriota and Proteobacteria govern the bacterial sub-network.

Table 2

Table 2. Topological properties of the microbial co-occurrence network.

3.8 Functional prediction of bacterial phenotypes and fungal guilds

To gain deeper insights into the potential ecological functions and adaptive strategies of the microbial communities, this study performed phenotypic profiling of the bacterial community using BugBase and annotated the functional guilds of the fungal community using the FUNGuild database (excluding sequences classified as ‘Unknown’ to focus on ecologically interpretable traits).

The predicted phenotypic profiles of bacterial communities are presented in Figure 13a. Biofilm Formation and Gram-negative cell walls were identified as the two most dominant phenotypes across all sampling sites. This suggests a prevalent adaptive strategy among bacterial communities to the fluctuating wetland environment through aggregated growth and specific cell wall structures. A clear spatial pattern was observed in phenotypes related to oxygen requirement. The Yuanjiang site, located at the highest altitude, exhibited the highest relative abundance of Aerobic phenotypes and the concurrently lowest abundance of Anaerobic phenotypes, providing evidence for redox gradient filtering. Notably, the abundance of phenotypes associated with Mobile Genetic Elements was markedly elevated at the Yuanjiang site compared to others, implying potential unique environmental pressures influencing genetic adaptability. Furthermore, the phenotypic richness (calculated as the number of distinct predicted phenotypes per sample) varied among sites, indicating differences in the functional genomic complexity of bacterial communities.

The annotation of fungal functional guilds (excluding ‘Unknown’ assignments) revealed pronounced spatial heterogeneity (Figure 13b). Across all sites, guilds corresponding to Undefined Saprotrophs and Endophyte-Litter-Saprotrophs were consistently core components, highlighting the foundational role of fungi in decomposition. However, distinct sites were characterized by different dominant guilds. The Xinzhou site showed a high proportion of Endophyte-Pathogen-Unspecified fungi, whereas the Anxiang site was predominantly associated with Dung-Plant Saprotrophs. In contrast, the fungal communities at the Yuanjiang and Lixian sites were overwhelmingly dominated by Endophyte-Litter-Saprotrophs. The functional profile at Yuanjiang was particularly distinctive, being virtually devoid of Plant Pathogens while highly enriched in saprotrophic guilds. This pattern aligns with the taxonomic dominance of Ascomycota at this site (Figure 4) and supports the inference that local plant litter chemistry is a key driver of fungal functional assembly. This pattern hypothetically aligns with the previously reported higher recalcitrant carbon content in M. lutarioriparius litter from this region (Xu et al., 2023), suggesting that local plant litter chemistry may be a key driver of fungal functional assembly.

In summary, the functional prediction analysis reinforces and adds a functional dimension to the earlier findings. The phenotypic structure of the bacterial community appears primarily shaped by physicochemical filters linked to the altitudinal (hydrological-redox) gradient. Conversely, the assembly of fungal functional guilds exhibits greater spatial heterogeneity and a strong coupling with local resource quality, particularly plant litter chemistry, underscoring a potentially resource-driven, bottom-up assembly mechanism.

4 Discussion

4.1 Changes in microbial community composition and diversity

The spatial heterogeneity of soil microbial communities associated with M. lutarioriparius across Dongting Lake was evident, but bacterial and fungal communities exhibited distinctly different response patterns to this heterogeneity (Figures 2–5, Figure 8). This aligns with the established paradigm that habitat heterogeneity, particularly driven by hydrology, is a primary force structuring wetland microbiomes (Wang et al., 2022; Wang, et al., 2016; Xu et al., 2017). In Dongting Lake, the topographic gradient (25–37 m a.s.l., Table 1) likely serves as a key proxy for variation in hydrological regimes, such as flooding frequency and duration, which are known to vary across the lake’s landscape (Wang et al., 2021). Our results support the hypothesis that this hydrologically-mediated habitat filtering is a stronger driver for bacterial communities, while fungal communities are more stable across the spatial gradient.

Bacterial alpha diversity (Chao1 and ACE indices) showed a significant positive correlation with altitude (ALT) (Figure 9), corroborating our first hypothesis regarding spatial heterogeneity. Lower-altitude sites like Liumenzha (B), likely experiencing longer inundation periods, exhibited significantly lower bacterial richness compared to higher-altitude sites (Figure 6). This pattern is consistent with findings across wetland ecosystems, where prolonged waterlogging creates anaerobic conditions that suppress the diversity of aerobic and facultative anaerobic bacteria, leading to a community dominated by fewer, specialized anaerobic taxa (Gu et al., 2024; Wang et al., 2022). The positive correlation with altitude suggests that better-drained, higher-elevation microniches within the wetland support a wider array of bacterial functional groups. This physiological filtering is corroborated by the predicted phenotypic profiles, which showed a markedly higher relative abundance of aerobic bacteria at the relatively higher-altitude sites (e.g., Yuanjiang, Site E) compared to sites at lower elevations (Figure 13a).

Figure 9

Figure 9. Correlation analysis between microbial diversity and environmental factors. Note: EC, Electrical Conductivity; AP, Available Phosphorus; TP, Total Phosphorus; AHN, Alkaline Hydrolyzed Nitrogen; TN, Total Nitrogen; SOM, Soil Organic Matter; AK, Available Potassium; TK, Total Potassium; TC, Total Carbon; ALT, Altitude.

Beyond richness, community composition also varied spatially. The distinct clustering of Yuanjiang (E) samples in PCoA (Figure 8a) and its shift in dominant phylum from Acidobacteriota to Proteobacteria (Figure 2) highlight its unique bacterial assemblage. This deviation from the altitudinal trend suggests that local factors override the broad hydrological filter at this site. The significant negative correlation between the Simpson index (reflecting dominance) and TK (Figure 9) hints at a potential link. Elevated TK, possibly from external inputs, might favor the proliferation of specific, competitive bacterial taxa (e.g., some Proteobacteria), reducing evenness—a pattern observed in soils under anthropogenic influence (Sui et al., 2019). This underscores that while altitude sets the broad template, local soil chemistry fine-tunes the bacterial community structure.

In contrast to bacteria, fungal alpha diversity did not show a significant response to the altitudinal gradient (Figures 7, 9), partially supporting our second hypothesis. The relative stability of fungal richness across sites may be attributed to the resilience of fungal hyphal networks and spores to fluctuating moisture conditions, granting them broader habitat tolerance compared to bacteria (de Vries et al., 2018). Instead, fungal community richness was predominantly governed by soil nutrient availability, particularly TP, with which both ACE and Chao indices showed a strong positive correlation (Figure 9). This underscores a fundamental niche differentiation: while bacteria are filtered by physical-hydrological conditions, fungal richness is more strongly constrained by the availability of key resources like phosphorus, which is essential for nucleic acid and membrane synthesis in fungi (Zhang et al., 2021).

Beta-diversity patterns reinforced the differential drivers of bacterial and fungal assemblies. The greater dispersion of bacterial communities in PCoA space (higher explanatory power of axes, Figure 8a) and the significant PERMANOVA result reflect their higher sensitivity to spatial and environmental gradients, primarily altitude. The unique position of Yuanjiang (E), characterized by a Proteobacteria-dominated community (Figure 2), may indicate a localized shift in ecosystem function. Proteobacteria include many copiotrophic and metabolically versatile taxa capable of denitrification and methane metabolism (Li et al., 2020). Their dominance could be a response to distinct nutrient stoichiometry or redox conditions at this site, potentially linking to the significantly higher lignin and hemicellulose content reported in M. lutarioriparius litter from this region (Xu et al., 2023), which may alter decomposition pathways and carbon substrates.

Fungal communities exhibited less spatial turnover than bacteria (Figure 8b), yet a notable compositional shift occurred in Yuanjiang, where Ascomycota replaced Basidiomycota as the dominant phylum (Figure 4). The prevalence of Basidiomycota elsewhere aligns with their recognized role as primary decomposers in wetlands (Meng et al., 2024). The shift to Ascomycota dominance in Yuanjiang provides a compelling link between plant traits and microbial assembly. Ascomycota harbor many specialists in degrading recalcitrant polymers (Mattila et al., 2020). This shift could hypothetically be linked to regional differences in litter quality. Notably, M. lutarioriparius litter in the Yuanjiang region has been reported to contain higher levels of lignin and hemicellulose (Xu et al., 2023), which might select for a fungal consortium more equipped to degrade complex carbon structures. This suggests a potential bottom-up control where plant litter quality influences fungal guild assembly, highlighting a possible mechanistic link between dominant vegetation traits and soil fungal communities. The functional guild prediction provides direct support for this mechanism, showing that the fungal community at Yuanjiang was overwhelmingly dominated by saprotrophic guilds (e.g., Endophyte-Litter-Saprotrophs) within the Ascomycota (Figure 13b), consistent with a community specialized in decomposing complex plant litter.

Collectively, our findings confirm significant spatial heterogeneity in the soil microbiome of M. lutarioriparius communities across Dongting Lake. This heterogeneity manifests differently for bacteria and fungi: bacterial communities are more spatially structured and appear filtered by physical gradients linked to altitude and hydrology, while fungal communities show greater compositional stability but are sensitive to resource availability, particularly phosphorus, and local plant litter quality. This sets the stage for a more detailed dissection of the specific environmental factors modulating these distinct patterns in the following section.

4.2 Influence of environmental factors on microorganisms

Our results strongly support the hypothesis that soil bacterial and fungal communities are governed by distinct environmental filters. Distance-based redundancy analysis (db-RDA) revealed that bacterial and fungal community structures are associated with different sets of environmental variables (Figure 11). Notably, bacterial community structure was primarily associated with abiotic factors linked to hydrology and soil chemistry: ALT, pH, and AHN were significant explanatory variables, collectively explaining 27.71% of the variance (Figure 11a). In this context, altitude served as our proxy for hydrological variation, supported by its known control over inundation patterns in lake wetlands (Dodds et al., 2019; Stagg et al., 2019). This suggests that hydrological gradients, inferred from altitude, likely play a fundamental role in filtering bacterial taxa adapted to specific moisture and redox niches (King and Henry, 2019). In contrast, fungal community structure was predominantly associated with soil nutrient availability: TK, TP, TN, and SOM were key drivers, explaining 37.98% of the variance (Figure 11b). This clear dichotomy underscores fundamental differences in the ecological niches of these microbial domains.

For bacteria, ALT emerged as a paramount factor (Figure 11a), integrating a suite of hydrologically-mediated conditions. The altitudinal gradient (25–37 m) in Dongting Lake is expected to create a mosaic of redox potentials, from frequently anoxic conditions in low-lying areas to more oxic conditions at higher elevations. This redox gradient can act as a stringent physiological filter, selecting for taxa with corresponding metabolic adaptations (de Freitas et al., 2024), which helps explain the observed spatial turnover in bacterial composition (Figure 8a). Superimposed on this hydrological template, soil pH exerted a significant influence. As a “master soil variable,” pH governs enzyme activities and nutrient bioavailability, thereby shaping the realized niche space for bacterial taxa (Zhou et al., 2024). Furthermore, the significant role of AHN highlights the importance of nitrogen availability, likely promoting the growth of fast-growing, copiotrophic bacterial groups (Meng-Yuan et al., 2024). The influence of these environmental filters is corroborated by multiple lines of evidence. The db-RDA results show clear associations between bacterial community structure and altitude, pH, and AHN (Figure 11a). Interestingly, at the OTU level and after stringent FDR correction, none of the dominant bacterial OTUs showed significant linear correlations with the measured environmental variables (Figure 10). This suggests that bacterial responses to these environmental gradients may be more complex, non-linear, or mediated by micro-niche adaptations within the broader hydrological and chemical template. The widespread prediction of ‘Biofilm Formation’ and ‘Gram-negative’ phenotypes across all sites (Figure 13a) further illustrates a common bacterial adaptive strategy to the fluctuating wetland environment, potentially mitigating stresses imposed by the hydrological and chemical gradients.

Figure 10

Figure 10. Spearman correlations between the ten most abundant OTUs (ranked by total relative abundance) and environmental factors. Only significance after FDR correction is indicated: *p < 0.05, **p < 0.01. B- = bacterial OTU, F- = fungal OTU. Note: EC, Electrical Conductivity; AP, Available Phosphorus; TP, Total Phosphorus; AHN, Alkaline Hydrolyzed Nitrogen; TN, Total Nitrogen; SOM, Soil Organic Matter; AK, Available Potassium; TK, Total Potassium; TC, Total Carbon; ALT, Altitude.

Conversely, fungal community structure showed a stronger dependence on soil nutrient factors (Figure 11b). The significant associations with TP and TN underscore the critical role of macronutrients in fungal physiology. Phosphorus is a key component of nucleic acids and membranes, while nitrogen is essential for protein synthesis (Tapia-Torres et al., 2016). The positive link with SOM is intuitive, as it represents the primary carbon and energy substrate for heterotrophic fungi. At the OTU level, several dominant fungal OTUs exhibited significant correlations with key nutrients after FDR correction: OTU886 was positively correlated with TN, SOM, and TK, while OTU2332 and OTU2288 showed negative correlations with TP and TK, respectively (Figure 10). These specific OTU-level responses highlight how individual fungal taxa may be directly influenced by nutrient availability. TK, while less commonly highlighted, may influence fungal community composition by affecting osmotic regulation and enzyme activity. The correlations observed between specific fungal OTUs and key nutrients, combined with the db-RDA results, suggest that nutrient gradients structure fungal communities through niche partitioning, where high nutrient availability may favor certain taxa while suppressing others. The composition of dominant fungal guilds varied markedly across sites. For instance, Endophyte-Litter-Saprotrophs prevailed at sites like Yuanjiang and Lixian, whereas Anxiang was characterized by guilds such as Dung-Plant Saprotrophs (Figure 13b). This spatial variation in functional guilds provides functional evidence that local resource matrices—namely soil organic matter (SOM) and nutrient pools—act as key filters during community assembly.

Figure 11

Figure 11. Redundancy analysis of soil microorganisms and soil physicochemical properties in different regions. (a,b) db-RDA on Phylum level. Note: EC, Electrical Conductivity; AP, Available Phosphorus; TP, Total Phosphorus; AHN, Alkaline Hydrolyzed Nitrogen; TN, Total Nitrogen; SOM, Soil Organic Matter; AK, Available Potassium; TK, Total Potassium; TC, Total Carbon; ALT, Altitude.

4.3 Analysis of bacterial-fungal symbiotic networks

Co-occurrence network analysis was employed to infer potential ecological associations among dominant microbial taxa, complementing insights from taxonomy and environmental correlations (Lee, et al., 2022) (Figure 12; Table 2). It should be noted that correlations in abundance may arise from shared environmental preferences (habitat filtering) as well as from direct biological interactions; thus, the following interpretations of potential interactions are putative. A key finding was the predominance of intra-domain (bacteria-bacteria, fungus-fungus) connections over inter-domain links. This topology likely reflects the distinct environmental filters identified earlier. Bacterial communities, filtered by hydrological and chemical gradients (ALT, pH), may have co-assembled into groups occupying similar niches, potentially fostering correlations indicative of cooperation (e.g., cross-feeding) or niche complementarity (Wang et al., 2024a). Similarly, fungal communities, structured by nutrient gradients, may consist of taxa with correlated occurrences, possibly reflecting complementary functional roles in decomposing complex litter, as suggested by the high proportion of positive correlations (72.7%) and significant modularity (0.515) within the fungal sub-network. This pattern is consistent with the concept that degrading recalcitrant plant polymers often involves complex, potentially synergistic microbial consortia (Zhao, et al., 2022; Zhu et al., 2022).

Figure 12

Figure 12. Co-occurrence network of dominant bacterial and fungal OTUs. The network was constructed based on Spearman correlations among the top 100 abundant OTUs (threshold: |r| ≥ 0.8, FDR-adjusted P < 0.05). Nodes represent OTUs, colored by domain (blue: bacteria; orange: fungi). Edges represent significant correlations (blue: positive; red: negative). The network comprises 156 nodes and 487 edges, exhibiting a modular structure (modularity = 0.515) with a predominance of positive correlations (72.7%).

In contrast, inter-domain connections showed a slight predominance of negative correlations. This suggests that competitive dynamics or antagonism might be more prevalent than mutualism between bacteria and fungi in this habitat. Potential mechanisms could include competition for labile carbon and inorganic nutrients, where faster-growing bacteria may outcompete fungi for readily available resources, while fungi might inhibit bacterial competitors through antibiotics or hyphal interference. This aligns with broad ecological strategies wherein bacteria often exhibit r-selected traits, whereas many fungi are more K-selected (Wang and Kuzyakov, 2024). Thus, the network structure mirrors the functional divergence at the ecosystem level: bacteria and fungi appear to occupy partially overlapping but distinct trophic niches, leading to patterns consistent with both within-domain co-occurrence and cross-domain competition.

Notably, when restricting the ranking to the fungal domain, the top ten hub nodes (by degree centrality) all belonged to Ascomycota (Figure 12; Table 2); in contrast, the highest-degree nodes within the bacterial domain were dominated by Acidobacteriota and Proteobacteria, indicating that each domain possesses its own set of keystone taxa. Within the fungal sub-network, these Ascomycota hubs likely act as keystone saprotrophs (Figure 13b), centrally positioned in the decomposition pathway and potentially governing carbon turnover (Likar, et al., 2022). For bacteria, the dominant hubs (mainly Acidobacteriota and Proteobacteria) were functionally characterized by a high prevalence of biofilm-forming and Gram-negative phenotypes (Figure 13a). These traits are widely recognized as advantageous for persistence and rapid response in environments with fluctuating redox and nutrient conditions (Dang and Lovell, 2016; Stewart and Franklin, 2008). The co-occurrence of these phenotypically distinct taxa as network hubs suggests that clusters along the altitudinal gradient may be anchored by taxa adept at either stress tolerance (via biofilm) or metabolic versatility, reflecting different adaptive strategies to the hydrological and chemical filters identified earlier.

Figure 13

Figure 13. Predictive analysis of bacterial phenotypes and fungal functional guilds in soils from different regions. Note: (a) Bacterial phenotypic prediction based on BugBase; (b) Fungal functional guild annotation based on FUNGuild. Abbreviations: Undefined Sap. = Undefined Saprotroph; Endo-Litter-Soil = Endophyte-Litter-Saprotroph; Dung-Plant Sap. = Dung-Plant Saprotroph; Plant Path. = Plant Pathogen; Endo-Path.-Uns. = Endophyte-Pathogen-Unspecified; Endophyte = Endophyte; Animal Path. = Animal Pathogen; Fungal Parasite = Fungal Parasite; Animal Path.-Multi = Animal Pathogen-Multihost; Arbuscular Myc. = Arbuscular Mycorrhizal Fungi. A, Yueyang; B, Liumenzha; C, Xinzhou; D, Luhu; E, Yuanjiang; F, Anxiang; G, Lixian.

5 Conclusion

This study demonstrates that the soil microbial communities associated with the dominant Miscanthus lutarioriparius in the Dongting Lake wetland exhibit pronounced spatial heterogeneity. Crucially, bacterial and fungal communities respond to distinct environmental filters. Bacterial diversity and composition are primarily structured by abiotic factors linked to hydrology and soil chemistry—most notably the topographic gradient (altitude), which likely serves as a proxy for hydrological regimes, as well as soil pH and ammonium nitrogen. In contrast, fungal communities are predominantly shaped by the availability of soil nutrients, including total phosphorus, total nitrogen, soil organic matter, and total potassium. Co-occurrence network analysis further revealed that microbial associations were primarily structured within, rather than between, bacterial and fungal domains. The overall network exhibited significant modularity, and within the fungal subset, keystone hubs were predominantly occupied by saprotrophic Ascomycota, a pattern consistent with putative synergistic roles in decomposition processes.

These findings provide a clear ecological framework: bacterial assembly is governed by top-down physical-chemical filters, while fungal assembly is driven by bottom-up resource availability. This functional dichotomy implies that bacterial and fungal components of the wetland microbiome may respond differently to environmental changes, such as altered hydrological patterns or nutrient inputs. Therefore, effective conservation and restoration strategies for the Dongting Lake wetland ecosystem should consider these differential responses. Our work underscores the importance of integrating both microbial domains and their specific environmental drivers to predict and manage ecosystem functioning in freshwater wetlands under future environmental change.

Data availability statement

The raw data supporting the conclusions of this article are available in the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA1394632, with no access restrictions upon publication. Additional data will be made available by the authors upon reasonable request without undue reservation.

Author contributions

ZY: Conceptualization, Data curation, Investigation, Resources, Software, Validation, Writing – original draft. PZ: Formal Analysis, Investigation, Methodology, Resources, Validation, Writing – original draft. BL: Investigation, Writing – original draft. H-GQ: Data curation, Resources, Writing – review and editing. QH: Investigation, Writing – original draft. SY: Conceptualization, Data curation, Formal Analysis, Project administration, Supervision, Visualization, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was financially supported by the Hunan Provincial Major Special Project “Key Technology Research and Demonstration of Full-scale Multi-level Utilization of Reed in Dongting Lake Area” (Grant No. 2021NK1010) and the Hunan Postgraduate Scientific Research Innovation Project (Grant No. CX20220682).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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