Threshold Effects of Moderate Wildfire Drive Depth-Dependent Responses in Subtropical Forest Soil Microbial Communities

Threshold Effects of Moderate Wildfire Drive Depth-Dependent Responses in Subtropical Forest Soil Microbial Communities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Threshold Effects of Moderate Wildfire Drive Depth-Dependent Responses in Subtropical Forest Soil Microbial Communities Shaqian Liu, Rui Yang, Hui Zhou, Xiao Zou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7550755/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims Against the backdrop of global warming, the frequency and intensity of wildfires have significantly increased, exerting profound impacts on the structure and function of soil microbial communities. However, the mechanisms underlying microbial responses to varying levels of wildfire severity in subtropical montane forests remain poorly understood. Methods This study investigated subtropical forests in Huaxi District, Guizhou Province, employing a wildfire severity gradient design (unburned, light, moderate, severe) combined with depth-stratified soil sampling (topsoil: 0–20 cm, subsoil: 20–40 cm). Building on metagenomic sequencing and co-occurrence network analyses, we elucidate the coupled relationships among community diversity, interaction structure, and assembly processes. Results (1) The bacterial richness (ACE) increased continuously with wildfire severity, peaking at severe wildfire; evenness (Pielou_e) increased significantly only at moderate wildfire, exhibiting an intermediate-disturbance optimum. For fungi, richness in the topsoil layer increased with wildfire severity, whereas in the subsoil layer it peaked at moderate wildfire. (2) Co-occurrence networks showed a non-linear response: in bacteria, the proportion of positive edges rose sharply at moderate wildfire (> 90%); in fungi, modularity strengthened in the subsoil layer at moderate wildfire but decreased in the topsoil layer at severe wildfire, indicating “depth-differentiated” structural reorganization. (3) Neutral community model fitting indicated that bacterial assembly was dominated by stochastic processes ( R² >0.78), whereas fungi deviated from the neutral model and were more strongly shaped by deterministic processes (environmental filtering/niche selection), with these effects being more pronounced in the subsoil layer. Conclusion Overall, moderate wildfire constitutes an ecological threshold that optimizes microbial community structure and functional potential, while soil depth reshapes post-fire successional trajectories by altering assembly processes and network topology. This study provides a theoretical basis for targeted post-fire microbial restoration in subtropical forests. Wildfire Soil microbiota Co-occurrence network Community assembly Subtropical montane forest Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Against the backdrop of global warming, the frequency and intensity of forest fires exhibit a significant upward trend (Mansoor et al., 2022; Senande-Rivera et al., 2022). As one of the most significant ecological disturbance factors within forest ecosystems, wildfire directly modifies soil physicochemical properties through exposure to high temperatures (Agbeshie et al., 2022) and has a substantial impact on the structure and function of soil microbial communities (Nelson et al., 2022). Microorganisms play a pivotal role in key ecological processes such as soil organic matter decomposition (Strickland et al., 2009), nutrient cycling (Van Der Heijden et al., 2008), and vegetation recovery (Ibáñez et al., 2022). Consequently, their response patterns have a critical influence on ecosystem resilience. Existing research demonstrates that wildfire disturbance can reconfigure microbial diversity and co-occurrence networks (Singh et al., 2021), a phenomenon corroborated in both Himalayan and boreal forest ecosystems (Köster et al., 2021). However, in species-rich subtropical montane forests with warm and humid climates, the gradient responses of microbial communities to varying levels of wildfire severity remain poorly understood. Notably, there is a significant lack of systematic studies on microbial interactions and assembly processes across soil vertical profiles, particularly in deeper layers (20-40 cm). Subtropical forest ecosystems exhibit unique biogeochemical cycling patterns(Shi Xiuzhen et al., 2022). The sensitivity of their soil microbial communities to disturbance may differ significantly from that observed in high-latitude regions. Higher temperature and humidity may accelerate the proliferation of r-strategist microorganisms(Smith et al., 2022). Additionally, complex vegetation types (e.g., Pinus armandi , Pinus massoniana ) may regulate fungal symbiotic networks through the chemical properties of their litter, including tannin concentrations(Scalbert, 1991; Wu Donghao, 2021). In recent years, subtropical montane forests in Southwest China have experienced frequent wildfires(Du Jun et al., 2023). However, current research predominantly focuses on post-fire vegetation recovery at the surface level, while the functional reorganization mechanisms of soil microbiomes during post-fire succession remain insufficiently understood. Therefore, established four fire‐severity classes in Huaxi District, Guizhou Province—unburned (CK), light (L), moderate (M), and severe (S)—and collected stratified soil samples from 0–20 cm and 20–40 cm. By integrating metagenomics with co-occurrence network analyses, we systematically evaluated the coupled effects of fire severity and soil depth on microbial diversity, network architecture, and community assembly. Our goal was to test the “moderate-fire threshold effect” and the “depth-dependent response,” and to provide a theoretical basis for targeted, post-fire microbial restoration. Materials and methods Study area The study area is located in Maling Township, Huaxi District, Guizhou Province, China (26°13'28.87" N to 26°19'16.69" N, 106°28'49.47" E to 106°37'51.32" E). The region has a mean annual temperature of 14.5 °C, with mean temperatures of 4.5 °C in the coldest month (January) and 23.5 °C in the warmest month (July), a frost-free period of ~210 days, and a mean annual precipitation of 1,150 mm. Elevation ranges from 999 to 1,573 m. The climate is classified as a subtropical plateau monsoon climate, and the dominant soils are yellow soils. Table 1. Details of wildfire plots Wildfire severity Latitude and longitude Altitude/m Slope direction Slope/° Vegetation types Soil types Fire type Burned wood Flame height/m Severity Burning level Unburned control (CK) E 106°30′30.93″ N 26°16′01.60″ 1419.46 N 3.5 Pinus armandii Loam >/ / / / / Light wildfire (L) E 106°30′42.17″ N 26°16′42.69″ 1429.06 SE 10.5 Pinus armandii Loam Surface fire ≤30% <1.5 The trunk is scorched, and the tree is still covered with green leaves The soil organic matter layer is intact, and the carbonization depth is only a few millimeters Moderate wildfire (M) E 106°31′03.69″ N 26°16′59.64″ 1358.90 E 10.6 Pinus massoniana Loam Surface fire 30%-70% 1.5-3.0 Severe wildfire (S) E 106°30′39.32″ N 26°16′46.51″ 1317.60 NW 21.9 Cryptomeria fortunei Loam Surface fire Crown fire ≥70% >3.0 The crown was burned, and no green leaves covered it. Ash deposits and charred organic matter to a few centimeters thick Note: Reference basis for classification of wildfire level(LIU Falin et al., 2019; SHU Yang et al., 2024). Soil sample collection Soil sampling was conducted in June 2024. Four quadrats were established within each sample plot. Soil samples were collected from two depths: the topsoil layer (A layer: 0-20 cm) and the subsoil layer (B layer: 20-40 cm). Before soil sample collection, the surface litter layer was carefully removed using a shovel to avoid sample contamination. Soil sample collection adhered to a bottom-up principle, whereby deeper layers were sampled first to minimize potential cross-contamination between layers. During collection, visible gravel, coarse fragments, and plant residues (e.g., roots) were manually removed as thoroughly as possible. A total of 32 soil samples were obtained. All samples were immediately stored in insulated cooling containers with reusable ice packs and were promptly transported to the laboratory for further analysis. Experimental method DNA extraction, library construction, and metagenomic sequencing Total genomic DNA was extracted from soil samples using the Mag-Bind® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer’s instructions. The concentration and purity of extracted DNA were determined with TBS-380 and NanoDrop2000, respectively. The quality of the DNA extracts was evaluated via electrophoresis on a 1% agarose gel. DNA extraction was performed, and the resulting DNA fragments were size-selected to an average length of approximately 350 bp using the Covaris M220 (Gene Company Limited, China) for paired-end library construction. Subsequently, the paired-end library was constructed using NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing complete sequencing primer hybridization regions were ligated to the blunt-ended DNA fragments. Paired-end sequencing was performed on Illumina NovaSeq (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles) according to the manufacturer’s instructions (www.illumina.com). Sequence data associated with this project have been deposited in the NCBI Short Read Archive database (Accession Number: SRP593774). Sequence quality control and genome assembly The data were analyzed using the free online platform of Majorbio Cloud Platform (www.majorbio.com). Briefly, paired-end Illumina reads were processed to remove adapter sequences, and low-quality reads (length < 50 bp or with a quality score < 20) were filtered out using fastp(Chen et al., 2018) (https://github.com/OpenGene/fastp, version 0.23.0). Metagenomics data were assembled using MEGAHIT (Dinghua et al., 2015) (https://github.com/voutcn/megahit, version 1.1.2), which utilizes succinct de Bruijn graphs. Contigs with a length ≥ 300 bp were selected as the final assembling result and subsequently used for gene prediction and functional annotation. Gene prediction, taxonomy, and functional annotation Gene prediction: Open reading frames (ORFs) were predicted for each assembled contig using Prodigal 2.6.3(Hyatt et al., 2010) (http://metagene.cb.k.u-tokyo.ac.jp/). The predicted ORFs with a length ≥ 100 bp were retrieved and translated into amino acid sequences using Emboss 6.6.0 (Rice et al., 2000) (http://emboss.open-bio.org/, V6.6.0) and the NCBI translation table. (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1). Taxonomy: A non-redundant gene catalog was constructed using CD-HIT (Fu et al. , 2012) (http://www.bioinformatics.org/cd-hit/, version 4.6.1) with 90% sequence identity and 90% coverage. High-quality reads were aligned to the non-redundant gene catalogs to determine gene abundance, with an alignment identity threshold set at 95% (Li et al., 2008) (http://soap.genomics.org.cn/, version 2.21). Functional annotation: Representative sequences of the non-redundant gene catalog were aligned to the NR database with an e-value cutoff of 1e -5 using Diamond (Benjamin et al. , 2015) (https://github.com/bbuchfink/diamond, version 0.8.35) for taxonomic annotations. Statistical analysis Alpha diversity (ACE, Simpson, Pielou_e) was analyzed using linear mixed‑effects models (LMMs) with soil depth as a fixed effect and site/plot as nested random effects. We report F, df, P, partial η², and marginal/conditional R². P‑values from multiple indices were adjusted using the Benjamini–Hochberg procedure (q). Beta diversity was assessed using Bray–Curtis-based PCoA and PERMANOVA (adonis2; 9,999 permutations; strata = Site). Within‑group dispersion was evaluated with betadisper (distance to group centroid) followed by Tukey’s HSD for pairwise comparisons. See Tables S1–S7 for details. All analyses were conducted in R 4.4.3. Results Response of microbial alpha diversity to wildfire severity The ACE index of the bacterial community increased with rising wildfire severity in both soil layers (0-20 cm, 20-40 cm), reaching its highest value in the severe wildfire treatment ( P <0.05). In contrast, the Simpson and Pielou_e indices—which reflect species diversity and evenness, respectively—exhibited distinct patterns across soil depths. In topsoils (0-20 cm), all burned plots demonstrated higher Simpson and Pielou_e indices relative to CK, with the highest values observed under the light wildfire plot ( P <0.05). Notably, in subsoils (20-40 cm), only moderate wildfire plots induced significant increases in both Simpson and Pielou_e indices ( P <0.05), with the highest values observed under this specific condition. Fungal alpha diversity exhibited more complex patterns of variation. In topsoil, fungal richness (ACE index) increased significantly with wildfire severity, reaching its peak in the severe wildfire plot and exceeding that of the CK ( P <0.05). Conversely, the highest fungal richness in subsoil occurred under the moderate wildfire plot, significantly surpassing all other treatments ( P <0.05). In contrast to bacterial communities, the fungal Simpson and Pielou_e indices showed a decrease and then an increase with the wildfire severity. In subsoil, fungal Simpson and Pielou_e indices reached their lowest values under the light wildfire plot, then rose progressively with increasing burn intensity, ultimately peaking at the severe wildfire plot. Linear mixed-effects models (LMMs) showed that bacterial Simpson and Pielou_e were significantly influenced by depth (B layer A layer). For fungi, Simpson was significantly affected by depth, Pielou_e was marginally significant, and ACE was not significant (see Tables S1 and S2). Compared with the topsoil layer, the subsoil layer showed significantly lower evenness and dominance (consistent for both bacteria and fungi), suggesting that deep communities are more susceptible to environmental filtering or dispersal limitation. In contrast, bacterial richness was slightly higher in the subsoil layer, possibly reflecting post-fire dispersal/replenishment effects. Response of microbial Beta diversity to wildfire severity Principal Coordinates Analysis (PCoA) revealed distinct separations in soil bacterial and fungal communities between wildfire plots and CK (Figure 4). For bacterial communities, PC1 (76.3% of variance explained) and PC2 (15.8%) collectively demonstrated significant interactions between wildfire severity and soil depth (Figure 5a, b). Under moderate wildfire conditions, bacterial communities exhibited high similarity across both soil layers (0-20 cm and 20-40 cm), forming closely clustered assemblages. In contrast, CK, light wildfire, and severe wildfire exhibited greater dispersion between soil depths, suggesting increased heterogeneity in community composition with depth stratification. Notably, in the subsoil layer, light and severe wildfire plots demonstrated substantial community similarity. However, these treatments demonstrated significantly higher dispersion in the topsoil, indicating a heightened sensitivity of surface microbial communities to fire disturbance. Fungal communities exhibited distinct response patterns. The co-variation along PC1 (64.2% of variance explained) and PC2 (27.8%) indicated clear successional dynamics following wildfires (Figure 5c,d). Similar to bacterial communities, fungal assemblages under moderate wildfire conditions demonstrated high similarity across soil strata at both depths. In contrast, CK, light wildfire, and severe wildfire plots displayed significant vertical differentiation among soil layers and exhibited distinct community separation across wildfire severity gradients. These patterns collectively highlight the substantial impact of wildfire severity on fungal community architecture. PCoA indicated that fire severity and soil layer jointly structured community separation; PERMANOVA showed high explanatory power ( R² = 0.981, F = 239.14, P Severe > CK (control) > Moderate; Depth: B layer > A layer (Tables S3–S7). Nonlinear responses of microbial co-occurrence networks to wildfire severity Co-occurrence networks constructed at the species level revealed divergent network architectures and topological parameters between soil layers for both bacterial and fungal communities (Tables 2 and 3). In bacterial communities, wildfires generally resulted in an increase in node numbers compared to CK, indicating enhanced species richness. However, network complexity metrics (edges, average degree), such as edges and average degree, exhibited pronounced nonlinear responses along the wildfire severity gradient. The number of bacterial community nodes initially increased and subsequently decreased with the wildfire severity. In the topsoil, the maximum number of nodes was observed under light wildfire conditions, whereas in the subsoil, it peaked under moderate wildfire conditions. Concurrent reductions in edge numbers and average degree (e.g., topsoil: CK=42.087→Moderate=20.390) suggest that environmental stress leads to a simplification of species interaction networks, potentially reflecting niche compression processes. Notably, positive edge proportions significantly increased in wildfire plots versus CK (topsoil: peak at 90.70% under moderate wildfire; subsoil: 82.02% under light wildfire), with a decreasing trend observed as wildfire severity increased (see Table 2). This nonlinear response aligns with the Stress Gradient Hypothesis, where moderate disturbance promotes microbial facilitation. From the perspective of network modularity, the modularity index of bacterial co-occurrence networks in wildfire plots was higher than that in CK, suggesting that fire disturbance enhances modularity. This indicates a stronger formation of functionally differentiated modules, potentially contributing to improved overall system stability, particularly in the case of moderate wildfire. Comparing different layers of the same treatment, it was observed that the number of nodes in the bacterial co-occurrence network of CK, light, and severe wildfire in topsoil was higher than that in subsoil, while the number of nodes in moderate wildfire was higher in subsoil than in topsoil. Across all plots, the number of edges, average degree, and modularity index were higher in topsoil than in subsoil. Additionally, the proportion of negatively correlated edges in subsoil was higher than that in topsoil, with the negative edges in CK accounting for 47.11%, indicating that resource competition was more pronounced in deeper soil layers, particularly under the CK condition. Fungal co-occurrence networks demonstrated a consistent increase in node abundance with escalating wildfire severity (Table 3). In topsoil, node counts elevated sharply to 921 under the Severe wildfire plot, representing a 36.7% increase relative to CK. Conversely, subsoil exhibited even greater proportional enhancement under the Severe wildfire plot, reaching 781 nodes, representing a 39.5% increase compared to CK. In contrast to bacterial networks, fungal communities exhibited positive species enrichment under wildfire disturbance, with topsoil potentially acting as a trigger for the activation of dormant taxa. The proportion of positive edges exhibited distinct layer-specific maxima: peaking in the topsoil under severe wildfire (92.31%), while reaching its highest value in the subsoil under moderate wildfire (82.42%), followed by a slight decline under severe wildfire. Co-occurrence network stability metrics diverged significantly from those of bacterial communities. In topsoil, the modularity index initially increased and then decreased with escalating wildfire intensity, reaching a maximum value (0.948) under moderate wildfire and dropping to a minimum value (0.454) under severe wildfire, indicating that intense disturbance disrupted the modular structure. Conversely, fungal modularity in subsoil increased progressively across the wildfire severity gradient, peaking at 0.924 under severe wildfire. These patterns demonstrate enhanced compartmentalized organization in subsoil communities under wildfire disturbance, potentially augmenting ecosystem stability. In comparison across different soil layers, the moderate wildfire fungal co-occurrence network exhibited higher values in the subsoil than in the topsoil concerning the number of nodes, edges, proportion of positively correlated edges, and average degree. Conversely, the modularity index was higher in the topsoil than in the subsoil (except for severe wildfire). Table 2. Co-occurrence network topology attributes (Bacteria) Topological features A (0-20 cm) B (20-40 cm) CK Light Moderate Severe CK Light Moderate Severe Node number 757 911 830 831 679 763 845 811 Edge number 15930 13685 8462 9394 8648 9567 6752 7868 Positive edge/% 66.94 84.68 90.70 75.90 52.89 82.02 75.09 64.54 Negative edge/% 33.06 15.32 9.30 24.10 47.11 17.98 24.91 35.46 Average degree 42.087 30.044 20.390 22.609 25.473 25.077 15.981 19.403 Diameter 1 1 1 1 1 1 1 1 Density 0.056 0.033 0.025 0.027 0.038 0.033 0.019 0.024 Clustering coefficient 1 1 1 1 1 1 1 1 Average path length 1 1 1 1 1 1 1 1 Modularity 0.858 0.882 0.935 0.917 0.858 0.889 0.968 0.947 Table 3. Co-occurrence network topology attributes (Fungi) Topological features A (0-20 cm) B (20-40 cm) CK Light Moderate Severe CK Light Moderate Severe Node number 674 564 642 921 560 453 661 781 Edge number 6833 4830 3874 87036 5744 5202 8738 7438 Positive edge/% 69.95 78.24 57.43 92.31 75.77 65.80 82.42 74.25 Negative edge/% 30.05 21.76 42.57 7.69 24.23 34.20 17.58 25.75 Average degree 20.276 17.128 12.069 189.003 20.514 22.967 26.439 19.047 Diameter 1 1 1 16.567 1 1 1 1 Density 0.030 0.030 0.019 0.205 0.037 0.051 0.040 0.024 Clustering coefficient 1 1 1 0.777 1 1 1 1 Average path length 1 1 1 3.714 1 1 1 1 Modularity 0.908 0.915 0.948 0.454 0.844 0.850 0.852 0.924 The bacterial and fungal communities in different soil layers were classified into distinct functional modules, with variations in module composition observed across soil depths (Figure 5, Table 4). In the topsoil, the bacterial community was primarily dominated by Module II (core module), while the fungal community exhibited higher abundances of Module II and Module IV. In contrast, the subsoil bacterial community was primarily characterized by Module III, accompanied by a relative decrease in the abundance of Module I and an increase in Module IV compared to the topsoil. Similarly, the fungal community was dominated by Module III, with a distribution pattern consistent with that of bacterial Module III. Network analyses demonstrated significant differences in microbial interaction intensity between soil depths (Table 4). For bacterial networks, although the subsoil exhibited a lower number of nodes than the topsoil, its number of edges increased significantly by 80.8%. This increase was accompanied by a rise in the average degree from 163.70 to 350.72 and a 154% surge in network density, indicating that the bacterial community in the subsoil formed a highly integrated and densely interconnected network. However, this enhanced connectivity was associated with a weakened modular structure, as reflected by a sharp decline in the modularity index from 0.384 in the topsoil to 0.108 in the subsoil. Furthermore, the proportion of positive correlations declined, while negative correlations increased significantly, suggesting an intensification of resource competition within the subsoil microbial community. The fungal network also exhibited enhanced interaction intensity with increasing soil depth, although this pattern deviated from that observed in bacterial networks. While the number of nodes in the subsoil showed a slight reduction, the number of edges and average degree remained at high levels, accompanied by an increase in network density. Similar to the bacterial community, the modularity index in the subsoil displayed a moderate decrease in fungi; however, this reduction was relatively less significant, suggesting that the modular structure of fungi demonstrates greater resilience to changes associated with soil depth. Notably, there was a pronounced shift toward competitive interactions: the proportion of positive correlations significantly decreased, while the proportion of negative correlations nearly doubled. This trend indicates that competitive interactions became predominant in shaping the fungal network within the subsoil. Table 4. Topological attributes of co-occurrence networks based on modular coloring Topological features Bacteria Fungi A (0-20 cm) B (20-40 cm) A (0-20 cm) B (20-40 cm) Node number 935 789 937 807 Edge number 76528 138359 49577 65751 Positive edge/% 73.19 63.19 77.10 61.68 Negative edge/% 26.81 36.81 22.90 38.32 Average degree 163.70 350.72 105.82 162.95 Diameter 5 5 5 5 Density 0.175 0.445 0.113 0.202 Clustering coefficient 0.621 0.799 0.494 0.620 Average path length 2.12 1.64 2.25 1.99 Modularity 0.384 0.108 0.441 0.371 Responses of Microbial Community Assembly Processes to Fire and Soil Depth Analysis using the Neutral Community Model (NCM) revealed that distinct fundamental mechanisms regulate the assembly of bacterial and fungal communities across soil depths (Figure 6). Bacterial communities in both the 0-20 cm and 20-40 cm soil layers exhibited a close fit to the neutral model ( R² = 0.813 and 0.787, respectively). Estimated migration rates (Nm) were exceedingly high (Nm =1.16×10⁷ and 1.36×10⁷, respectively). Furthermore, approximately 76% of bacterial species’ abundances fell within the model's 95% confidence interval. These findings suggest that stochastic processes, primarily dispersal limitation, predominantly influenced bacterial community assembly in these soil layers. In contrast, the assembly of fungal communities deviated substantially from the neutral model. The NCM goodness-of-fit for fungi ( R² = 0.497 for the topsoil and R² = 0.636 for the subsoil) was significantly lower than that observed for bacteria communities. Corresponding Nm values (5.28×10⁴ for the topsoil and 6.98×10⁴ for the subsoil) were substantially lower than those observed in the bacterial community. Notably, approximately 30% of fungal taxa exhibited significant deviations from the neutral model predictions. These results demonstrate that deterministic processes, driven by environmental filtering and niche selection, exerted a stronger influence on the assembly of fungal communities, with this deterministic effect being more pronounced in the topsoil compared to the subsoil. Discussion Soil microorganisms play a pivotal role in key ecosystem processes, such as the decomposition and stabilization of soil organic matter(Strickland et al., 2009 ), nutrient cycling dynamics(Van Der Heijden et al., 2008 ), and rhizosphere functioning (Mendes et al., 2011 ). Bacteria and fungi constitute the two dominant microbial groups within forest soil ecosystems(Tao Yuzhu and Di Xueying, 2013). Upon the occurrence of a forest fire, soil microorganisms are immediately affected by the loss of heat-sensitive species, followed by prolonged alterations due to changes in soil chemistry and vegetation succession(Hart et al., 2005 ). Wildfire events consistently reduce soil microbial biomass and community diversity across numerous ecosystems(Pulido-Chavez et al., 2021 ), with the extent of impact closely related to wildfire severity(Köster et al., 2021 ). Crucially, these microbial shifts may hinder post-fire plant recovery processes(Ibáñez et al., 2022 ). In this study, bacterial abundance increased with escalating wildfire severity, reaching its maximum in the severely burned plots. This observation is consistent with findings reported by Liu (Liu Jing, 2024 ), indicating that wildfire disturbance can promote the development of soil bacterial community diversity(She R et al., 2021 ). In contrast, fungal abundance exhibited an upward trend only in the topsoil layer. Within the subsoil, the highest fungal species richness was observed under the moderate wildfire plot. Although fungi are generally less heat-tolerant(Zhang Min and Hu Haiqing, 2002), they displayed the greatest species richness following severe fires. This counterintuitive result is explained by the primary deposition of wildfire ash in surface soils; severe wildfires typically generate greater ash quantities and induce more pronounced increases in nutrient content (Du Jun et al., 2023 ), thereby creating a transient "eutrophic" environment. Conversely, the maximum fungal species richness observed at 20–40 cm depth under moderate wildfire represents a classic manifestation of the Intermediate Disturbance Hypothesis. This result arises from a combination of moderate nutrient leaching inputs, sublethal thermal effects (which disrupt existing equilibria without causing widespread mortality), and relatively stable environmental conditions accompanied by reduced baseline competition levels typical of subsoil environments. These factors created favorable conditions for the broadest range of fungal taxa to develop, resulting in peak species richness. Collectively, this study revealed non-linear response patterns of soil microbial communities to varying wildfire severity in a subtropical montane forest system, with moderate wildfire inducing a distinct ecological threshold effect. In contrast to the meta-analysis conducted by Dooley and Treseder(Dooley and Treseder, 2012 ), which concluded that wildfire disturbance generally reduces microbial diversity, our findings demonstrate that moderate wildfire significantly enhanced bacterial community species abundance and diversity, as well as promoted community similarity across different soil depths. This divergence may be attributed to specific characteristics of the subtropical ecosystem: the warm and humid climate facilitates the rapid dispersal of r-strategist taxa (e.g., Actinomycetota), while moderate wildfire releases niche space by eliminating K-strategist competitors (e.g., Gram-negative bacteria), thereby facilitating the proliferation of functionally complementary taxa. Notably, the subsoil demonstrated distinct responses to varying wildfire intensities. While bacterial richness, as indicated by the ACE index, reached its maximum under severe wildfire conditions, community evenness increased significantly only under moderate wildfire conditions. This pattern aligns with the "delayed microbial response in subsoil" proposed by Meillilo et al.(Melillo et al., 2017 ), indicating that subsoil communities possess a more sensitive ecological threshold to disturbance intensity. The increase in species richness may be attributed to two potential factors: (1) the high initial microbial diversity (Shannon index > 6.5) in subtropical forest soils, providing stronger functional redundancy(Delgado-Baquerizo et al., 2020 ); (2) the activity of pioneer microbial groups (e.g., Actinomycetota) specifically utilizing charred substrates post-fire(Li BY et al., 2023 ), thereby reactivating previously dormant species pools. Microbial co-occurrence network analyses further elucidated the mechanisms by which moderate wildfire optimizes stability. The stability of microbial communities depends on both diversity and the complexity of interactions among members, including antagonistic, competitive, or mutualistic relationships. Greater network complexity generally correlates with increased community resilience(Wagg et al., 2019 ). In this study, the bacterial co-occurrence network under light wildfire exhibited a 20.3% increase in node count within the topsoil layer, while the fungal network showed a substantial 36.6% increase in node count under severe wildfire. Under moderate wildfire, positive correlations in the bacterial co-occurrence network accounted for 90.7%, significantly exceeding that of CK (66.94%), indicating that wildfire disturbance promotes enhanced cooperative relationships among microbes. The induction of a 90.7% positive interaction edge ratio under moderate wildfire conditions was accompanied by elevated modularity indices, leading to the formation of an integrated network architecture (Faust and Raes, 2012 ). These regional differences suggest a strong dependency on ecosystem structure in the successional pathways of post-fire microbial network restructuring. The divergent community strategies (r/K selection) and differing capacities for network topology reconstruction likely form the ecological basis for such differences(Faust and Raes, 2012 ). Notably, in subsoil layers, the modularity index increased under severe wildfire conditions (0.852→0.924). This finding contrasts with the results reported by Yu Jingjing et al.(Yu JJ et al., 2023 ), who observed a decrease in fungal network modularity in forests subjected to slash-and-burn disturbance. This difference can be explained by two potential mechanisms: (1) Topsoil fungal communities under severe wildfire suffered module collapse (0.454) due to hyper-dense connections (average degree 189), aligning with the "density-modularity" negative correlation theory of Ortiz-álvarez et al. (Ortiz-Álvarez et al., 2021 ); (2) In resource-limited subsoil environments (network density: 0.202), excessive connectivity was constrained, enabling severe wildfire to preserve modular stability. From an assembly perspective, the collapse of topsoil fungal networks is closely associated with weakened deterministic processes. Although ash inputs increased nutrient heterogeneity, high migration rates (Nm = 5.28×10⁴) diminished the efficacy of environmental filtering ( R² =0.497), leading to stochastic community reorganization and disruption of existing module boundaries. Conversely, in subsoil, deterministic assembly ( R² =0.636) in the subsoil interacted synergistically with resource limitations, allowing moderate wildfire to shape stable modular structures through selective pressures. This provides insights into resolving the "disturbance-stability paradox", demonstrating that moderate disturbance can enhance system robustness when the intensity of environmental filtering exceeds a critical threshold. Notably, the high proportion of positive correlation edges (92.31%) observed alongside a significant decline in modularity under severe wildfire conditions in topsoil indicates functional convergence. This phenomenon may reflect the decoupling of tannin-fungal interaction systems specific to conifers post-fire, potentially driving saprotrophic fungi toward cooperative resource utilization strategies. Such resilient restructuring of functional networks provides a microbial-level regulatory basis for ecological restoration in the post-fire area. Bacterial community assembly in both soil layers closely conformed to the neutral model ( R² >0.78), indicating the dominance of stochastic dispersal processes. This pattern supports the rapid recovery capacity of bacterial communities post-fire. Specifically, moderate wildfire did not alter the underlying assembly mechanism but increased bacterial migration rates, thereby promoting the uniform dispersal of r-strategists (e.g., Actinomycetota) across soil horizons. Consequently, this process elevated the similarity of bacterial communities between the two soil layers. Conversely, fungal assembly deviated significantly from the neutral model (topsoil R² =0.497; subsoil R² =0.636), with deterministic processes accounting for 32.3% of the assembly in the topsoil, consistent with the findings that "stressful environments enhance niche selection"(Zhou et al., 2017). The observed vertical differentiation arises from the stratified distribution of fire residues: Increased ash inputs in surface soil enhance nutrient heterogeneity(Du Jun et al., 2023 )while the downward percolation of black carbon (BC) particles into deeper soil layers creates micro-scale chemical potential barriers, intensifying environmental filtering. This heterogeneity presents a dual effect within the niche dimension: while it promotes the differentiation of functional modules, it concurrently intensifies resource competition among these modules. Notably, the dominance of deterministic assembly in subsoil fungal communities ( R² =0.636) is closely associated with increased resource competition, as evidenced by 38.32% of observed interactions being negative (Table 4 ). Wildfire residues that percolate through the soil generate fragmented microhabitats, thereby enhancing niche differentiation. This fragmentation compels microorganisms to engage in intensified competition for limited resources via antagonistic interactions, ultimately giving rise to a "high-connectivity, high-competition" network structure (average degree: 162.95; negative edge proportion increased by 67.3%). The self-reinforcing feedback loop between community assembly and competitive interactions serves as the primary driving mechanism by which moderate wildfires enhance the modularity index in subsoil microbial networks. Soil depth serves as a critical regulatory factor that reshapes microbial responses to wildfire. In the subsoil layer (20–40 cm), bacterial networks exhibited a "high-connectivity, high-competition" topology: The average degree increased dramatically to 350.72 (representing a 114% increase compared to topsoil), and the proportion of negative interactions reached 36.81%. These findings challenge the "resource partitioning by depth" theory introduced by Delgado-Baquerizo et al. (Delgado-Baquerizo et al., 2020 ). Our study demonstrates that the deep environment not only promotes functional differentiation but also enhances competitive exclusion through nutrient limitation. This competitive dynamic, in combination with deterministic assembly processes, indicates that moderate wildfire severity is the only level that effectively enhances microbial functionality in subsoil. Future research should integrate transcriptomic techniques to dissect the expression dynamics of key functional genes under moderate wildfire, thereby establishing definitive causal relationships within the "disturbance-modularity-function" framework. Conclusions Wildfires, as critical ecological disturbance factors under global climate change, exhibit intensity-dependent regulatory mechanisms on soil microbial communities that remain poorly understood. This knowledge gap is particularly evident in subtropical montane ecosystems, where systematic studies remain limited. Our study demonstrates that moderate wildfire constitutes an ecological threshold for restructuring microbial communities in these forests. Specifically, moderate wildfire significantly enhanced bacterial community evenness and fostered highly similar structural characteristics across soil depths. Simultaneously, fungal species richness peaked within the subsoil (20–40 cm), supporting the widespread applicability of the "intermediate disturbance optimization" effect. Non-linear responses within microbial co-occurrence networks further elucidated the underlying mechanisms of stability. Moderate wildfire enhanced systemic functional resilience by promoting positive bacterial interactions and strengthening the modular structure of fungal communities in the subsoil. Depth-differentiation in community assembly processes highlighted key governing factors: stochastic dispersal dominates bacterial communities, facilitating rapid recovery, whereas fungal communities are primarily shaped by deterministic processes. In subsoil, resource heterogeneity induced by percolating fire residues intensified environmental filtering and competitive exclusion, shaping a "high-connectivity, high-competition" interaction framework. Soil depth emerged as the pivotal dimension governing response patterns. The delayed threshold responses and intensified competitive strategies of subsoil microbes underscore their crucial role in post-fire ecosystem recovery and steady-state reconstruction. The primary contribution of this study is the establishment of an integrated "wildfire severity-soil depth-microbial function" response framework. This framework not only elucidates the adaptive strategies of soil microorganisms in response to wildfire gradient disturbances in subtropical forests, but also deciphers the mechanisms underlying ecological optimization by moderate wildfire through assembly processes and network stability. This discovery establishes a scientific foundation for microbiome-mediated ecological restoration in post-fire forest ecosystems. Future research should prioritize the investigation of spatiotemporal dynamics within biotic co-occurrence networks in deep soil layers and their contributions to sustaining ecosystem resilience. Declarations Ethics approval and consent to participate ： Not applicable. Consent for publication ： Not applicable. Funding ： This research was funded by the Study on the Carbon Sequestration Capacity of Forests and the Construction of Carbon Sequestration Monitoring System in Guizhou Province (GZTHJC-2023-04); the 2023 National Nature Reserve Grant Project of Dashahe National Nature Reserve in Guizhou Province (2023009390455714091-001); the Research on the Ecological Adaptability of the Endangered and Rare Plant Cathaya Argyrophylla (Qian Lin Ke He [2024]06); the Forestry Research Project (Subject) of Guizhou Province: Analysis of the Canopy Structure of Wild Cathaya Argyrophylla in Guizhou Dashanhe National Nature Reserve (Qian Lin Ke He J [2024]13) and the 2024 Guizhou Science and Technology Innovation Talent Team Construction Project: Wildlife Innovation Team of the Forestry college of Guizhou University (Qiankeherencai CXTD[2025]053). Data availability statement ： The Sequence data supporting the figures and tables in the manuscript is publicly available in the NCBI Short Read Archive database (Accession Number: SRP593774). Acknowledgements ： Technical support was provided by Shanghai Majorbio Bio-pharm Technology Co., Ltd. Editorial assistance was contributed by Leader Bio-Tech (Qingdao) Co., Ltd. Conflict of interest ： The authors declare that they have no conflict of interest. Authors’ contributions ： Shaqian Liu and Hui Zhou participated in the experiment. Rui Yang and Xiao Zou revised the article. Shaqian Liu wrote the article. All authors read and approved the final manuscript. Declaration of interests ： The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Agbeshie, A.A., Abugre, S., Atta-Darkwa, T. and Awuah, R., 2022. A review of the effects of forest fire on soil properties. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7550755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517682914,"identity":"16e2122f-3984-45aa-8876-000280ab24d9","order_by":0,"name":"Shaqian Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shaqian","middleName":"","lastName":"Liu","suffix":""},{"id":517682915,"identity":"4b5c567c-bd93-4f06-8cac-d551a6b8da18","order_by":1,"name":"Rui 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13:56:21","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165962,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/8731e1a68412f7d017b0fcda.html"},{"id":92599929,"identity":"22a0b3f9-ce02-4aeb-8577-8db411516143","added_by":"auto","created_at":"2025-10-01 13:56:21","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189870,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/9cdbeb41e204cd735c8c4a57.jpeg"},{"id":92599924,"identity":"7069516c-00e0-4b0c-902c-39b53a6982ab","added_by":"auto","created_at":"2025-10-01 13:56:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77328,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in soil bacterial community alpha-diversity indices across forest sites with differential fire severity levels\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/12acee2704daa4ca873fb879.png"},{"id":92600090,"identity":"08a525ff-75ce-42fa-8d4a-98d1860f6aac","added_by":"auto","created_at":"2025-10-01 14:04:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77093,"visible":true,"origin":"","legend":"\u003cp\u003eFungi alpha diversity index of forest soil with different fire levels\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/2e44513f23d9fb9300a681f5.png"},{"id":92600082,"identity":"2cf57f4b-c92f-4642-be0d-005bc07c64d5","added_by":"auto","created_at":"2025-10-01 14:04:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113094,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of PCoA of soil microorganisms in forest land with different fire levels\u003c/p\u003e\n\u003cp\u003eNote: PCoA was based on Bray–Curtis distances (relative abundance matrix). PERMANOVA (adonis2, 9,999 permutations, strata = Site) showed an overall model fit of R² = 0.981 (F = 239.14, P \u0026lt; 0.0001; Table S7). The within-group dispersion test (betadisper) was also significant: Fire (F(3,36) = 132.58, P \u0026lt; 0.0001) and Depth (F(1,38) = 86.21, P \u0026lt; 0.0001). For Fire groups, the mean “distance to centroid” followed Light \u0026gt; Severe \u0026gt; CK (control) \u0026gt; Moderate (means ± SE in Table S3; pairwise comparisons in Table S4). For Depth, B layer \u0026gt; A layer (means ± SE in Table S5; pairwise comparisons in Table S6). Therefore, group separation in the PCoA reflects not only differences in centroid locations but also significant differences in dispersion; interpretations should consider Tables S3–S7 together.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/74a4776598b1ad5b7200c775.png"},{"id":92599940,"identity":"bf8e23c5-905d-437f-b80a-4a1562415351","added_by":"auto","created_at":"2025-10-01 13:56:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2702914,"visible":true,"origin":"","legend":"\u003cp\u003eThe network diagram illustrating module coloring\u003c/p\u003e\n\u003cp\u003eNote: (a) Bacteria, Soil depth: 0-20 cm; (b) Bacteria, Soil depth: 20-40 cm; (c) Fungi, Soil depth: 0-20 cm; (d) Fungi, Soil depth: 20-40 cm.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/dfe6e123b7cdae3d43932ab0.png"},{"id":92599931,"identity":"7f9c49a8-874c-4356-a301-f985e9834b43","added_by":"auto","created_at":"2025-10-01 13:56:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131456,"visible":true,"origin":"","legend":"\u003cp\u003eNeutral community modeling (NCM) of soil bacteria and fungi in forested areas with varying levels of fire\u003c/p\u003e\n\u003cp\u003eNote: (a) Bacteria, Soil depth: 0-20 cm; (b) Bacteria, Soil depth: 20-40 cm; (c) Fungi, Soil depth: 0-20 cm; (d) Fungi, Soil depth: 20-40 cm. Points denote each species’ occurrence frequency versus the metacommunity mean relative abundance; the black line is the neutral prediction curve, and the gray band is the 95% prediction interval (Wilson). Parameters were estimated by nonlinear least squares with the constraint m \u0026gt; 0.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/48f0ee4920693268eef161f9.png"},{"id":97251052,"identity":"abcd056d-47e8-4ecc-9d0d-d3c7b91dc398","added_by":"auto","created_at":"2025-12-02 13:15:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3816799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/e8f6e975-bc44-4833-82b7-8341e4e9e992.pdf"},{"id":92599925,"identity":"46d18061-eedb-4843-a1d8-0843f87500cc","added_by":"auto","created_at":"2025-10-01 13:56:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2131851,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7550755/v1/3facb628ad8db8d4c8fc6e92.docx"}],"financialInterests":"","formattedTitle":"Threshold Effects of Moderate Wildfire Drive Depth-Dependent Responses in Subtropical Forest Soil Microbial Communities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAgainst the backdrop of global warming, the frequency and intensity of forest fires exhibit a significant upward trend\u0026nbsp;(Mansoor et al., 2022; Senande-Rivera et al., 2022). As one of the most significant ecological disturbance factors within forest ecosystems, wildfire directly modifies soil physicochemical properties through exposure to high temperatures\u0026nbsp;(Agbeshie et al., 2022)\u0026nbsp;and has a substantial impact on the structure and function of soil microbial communities\u0026nbsp;(Nelson et al., 2022). Microorganisms play a pivotal role in key ecological processes such as soil organic matter decomposition\u0026nbsp;(Strickland et al., 2009), nutrient cycling\u0026nbsp;(Van Der Heijden et al., 2008), and vegetation recovery\u0026nbsp;(Ib\u0026aacute;\u0026ntilde;ez et al., 2022). Consequently, their response patterns have a critical influence on ecosystem resilience. Existing research demonstrates that wildfire disturbance can reconfigure microbial diversity and co-occurrence networks\u0026nbsp;(Singh et al., 2021), a phenomenon corroborated in both Himalayan and boreal forest ecosystems\u0026nbsp;(K\u0026ouml;ster et al., 2021). However, in species-rich subtropical montane forests with warm and humid climates, the gradient responses of microbial communities to varying levels of wildfire severity remain poorly understood. Notably, there is a significant lack of systematic studies on microbial interactions and assembly processes across soil vertical profiles, particularly in deeper layers (20-40 cm).\u003c/p\u003e\n\u003cp\u003eSubtropical forest ecosystems exhibit unique biogeochemical cycling patterns(Shi Xiuzhen et al., 2022). The sensitivity of their soil microbial communities to disturbance may differ significantly from that observed in high-latitude regions. Higher temperature and humidity may accelerate the proliferation of r-strategist microorganisms(Smith et al., 2022). Additionally, complex vegetation types (e.g., \u003cem\u003ePinus armandi\u003c/em\u003e, \u003cem\u003ePinus massoniana\u003c/em\u003e) may regulate fungal symbiotic networks through the chemical properties of their litter, including tannin concentrations(Scalbert, 1991; Wu Donghao, 2021). In recent years, subtropical montane forests in Southwest China have experienced frequent wildfires(Du Jun et al., 2023). However, current research predominantly focuses on post-fire vegetation recovery at the surface level, while the functional reorganization mechanisms of soil microbiomes during post-fire succession remain insufficiently understood.\u003c/p\u003e\n\u003cp\u003eTherefore, established four fire‐severity classes in Huaxi District, Guizhou Province\u0026mdash;unburned (CK), light (L), moderate (M), and severe (S)\u0026mdash;and collected stratified soil samples from 0\u0026ndash;20 cm and 20\u0026ndash;40 cm. By integrating metagenomics with co-occurrence network analyses, we systematically evaluated the coupled effects of fire severity and soil depth on microbial diversity, network architecture, and community assembly. Our goal was to test the \u0026ldquo;moderate-fire threshold effect\u0026rdquo; and the \u0026ldquo;depth-dependent response,\u0026rdquo; and to provide a theoretical basis for targeted, post-fire microbial restoration.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003eStudy area\u003c/h2\u003e\n\u003cp\u003eThe study area is located in Maling Township, Huaxi District, Guizhou Province, China (26\u0026deg;13\u0026apos;28.87\u0026quot; N to 26\u0026deg;19\u0026apos;16.69\u0026quot; N, 106\u0026deg;28\u0026apos;49.47\u0026quot; E to 106\u0026deg;37\u0026apos;51.32\u0026quot; E). The region has a mean annual temperature of 14.5 \u0026deg;C, with mean temperatures of 4.5 \u0026deg;C in the coldest month (January) and 23.5 \u0026deg;C in the warmest month (July), a frost-free period of ~210 days, and a mean annual precipitation of 1,150 mm. Elevation ranges from 999 to 1,573 m. The climate is classified as a subtropical plateau monsoon climate, and the dominant soils are yellow soils.\u003c/p\u003e\n\u003cp\u003eTable 1. Details of wildfire plots\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"983\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWildfire severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLatitude and longitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAltitude/m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSlope direction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSlope/\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVegetation types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSoil types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFire type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBurned wood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFlame height/m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSeverity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBurning level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnburned control\u003c/p\u003e\n \u003cp\u003e(CK)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eE 106\u0026deg;30\u0026prime;30.93\u0026Prime;\u003c/p\u003e\n \u003cp\u003eN 26\u0026deg;16\u0026prime;01.60\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1419.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePinus armandii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLoam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLight wildfire\u003c/p\u003e\n \u003cp\u003e(L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eE 106\u0026deg;30\u0026prime;42.17\u0026Prime;\u003c/p\u003e\n \u003cp\u003eN 26\u0026deg;16\u0026prime;42.69\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1429.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePinus armandii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLoam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSurface fire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eThe trunk is scorched, and the tree is still covered with green leaves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eThe soil organic matter layer is intact, and the carbonization depth is only a few millimeters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate wildfire\u003c/p\u003e\n \u003cp\u003e(M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eE 106\u0026deg;31\u0026prime;03.69\u0026Prime;\u003c/p\u003e\n \u003cp\u003eN 26\u0026deg;16\u0026prime;59.64\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1358.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePinus massoniana\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLoam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSurface fire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30%-70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere wildfire\u003c/p\u003e\n \u003cp\u003e(S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eE 106\u0026deg;30\u0026prime;39.32\u0026Prime;\u003c/p\u003e\n \u003cp\u003eN 26\u0026deg;16\u0026prime;46.51\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1317.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eCryptomeria fortunei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLoam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSurface fire\u003c/p\u003e\n \u003cp\u003eCrown fire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe crown was burned, and no green leaves covered it.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAsh deposits and charred organic matter to a few centimeters thick\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Reference basis for classification of wildfire level(LIU Falin et al., 2019; SHU Yang et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSoil sample collection\u003c/h2\u003e\n\u003cp\u003eSoil sampling was conducted in June 2024. Four quadrats were established within each sample plot. Soil samples were collected from two depths: the topsoil layer (A layer: 0-20 cm) and the subsoil layer (B layer: 20-40 cm). Before soil sample collection, the surface litter layer was carefully removed using a shovel to avoid sample contamination. Soil sample collection adhered to a bottom-up principle, whereby deeper layers were sampled first to minimize potential cross-contamination between layers. During collection, visible gravel, coarse fragments, and plant residues (e.g., roots) were manually removed as thoroughly as possible. A total of 32 soil samples were obtained. All samples were immediately stored in insulated cooling containers with reusable ice packs and were promptly transported to the laboratory for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eExperimental method\u003c/h2\u003e\n\u003ch3\u003eDNA extraction, library construction, and metagenomic sequencing\u003c/h3\u003e\n\u003cp\u003eTotal genomic DNA was extracted from soil samples using the Mag-Bind\u0026reg; Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer\u0026rsquo;s instructions. The concentration and purity of extracted DNA were determined with TBS-380 and NanoDrop2000, respectively. The quality of the DNA extracts was evaluated via electrophoresis on a 1% agarose gel.\u003c/p\u003e\n\u003cp\u003eDNA extraction was performed, and the resulting DNA fragments were size-selected to an average length of approximately 350 bp using the Covaris M220 (Gene Company Limited, China) for paired-end library construction. Subsequently, the paired-end library was constructed using NEXTFLEX\u003cimg width=\"9\" height=\"9\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1759326181.gif\" alt=\"IMG_256\"\u003e\u0026nbsp;Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing complete sequencing primer hybridization regions were ligated to the blunt-ended DNA fragments. Paired-end sequencing was performed on Illumina NovaSeq (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles) according to the manufacturer\u0026rsquo;s instructions (www.illumina.com). Sequence data associated with this project have been deposited in the NCBI Short Read Archive database (Accession Number: SRP593774).\u003c/p\u003e\n\u003ch3\u003eSequence quality control and genome assembly\u003c/h3\u003e\n\u003cp\u003eThe data were analyzed using the free online platform of Majorbio Cloud Platform (www.majorbio.com). Briefly, paired-end Illumina reads were processed to remove adapter sequences, and low-quality reads (length \u0026lt; 50 bp or with a quality score \u0026lt; 20) were filtered out using fastp(Chen et al., 2018)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(https://github.com/OpenGene/fastp, version 0.23.0). Metagenomics data were assembled using MEGAHIT\u0026nbsp;(Dinghua et al., 2015)\u0026nbsp;(https://github.com/voutcn/megahit, version 1.1.2), which utilizes succinct de Bruijn graphs. Contigs with a length\u0026nbsp;\u0026ge;\u0026nbsp;300 bp were selected as the final assembling result and subsequently used for gene prediction and functional annotation.\u003c/p\u003e\n\u003ch3\u003eGene prediction, taxonomy, and functional annotation\u003c/h3\u003e\n\u003cp\u003eGene prediction: Open reading frames (ORFs) were predicted for each assembled contig using Prodigal 2.6.3(Hyatt et al., 2010) (http://metagene.cb.k.u-tokyo.ac.jp/). The predicted ORFs with a length \u0026ge; 100 bp were retrieved and translated into amino acid sequences using Emboss 6.6.0 (Rice et al., 2000) (http://emboss.open-bio.org/, V6.6.0) and the NCBI translation table. (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaxonomy: A non-redundant gene catalog was constructed using CD-HIT\u0026nbsp;(Fu\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2012)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(http://www.bioinformatics.org/cd-hit/, version 4.6.1) with 90% sequence identity and 90% coverage. High-quality reads were aligned to the non-redundant gene catalogs to determine gene abundance, with an alignment identity threshold set at 95%\u0026nbsp;(Li et al., 2008)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(http://soap.genomics.org.cn/, version 2.21).\u003c/p\u003e\n\u003cp\u003eFunctional annotation: Representative sequences of the non-redundant gene catalog were aligned to the NR database with an e-value cutoff of 1e\u003csup\u003e-5\u003c/sup\u003e using Diamond (Benjamin\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, 2015)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(https://github.com/bbuchfink/diamond, version 0.8.35) for taxonomic annotations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAlpha diversity (ACE, Simpson, Pielou_e) was analyzed using linear mixed‑effects models (LMMs) with soil depth as a fixed effect and site/plot as nested random effects. We report F, df, P, partial \u0026eta;\u0026sup2;, and marginal/conditional R\u0026sup2;. P‑values from multiple indices were adjusted using the Benjamini\u0026ndash;Hochberg procedure (q). Beta diversity was assessed using Bray\u0026ndash;Curtis-based PCoA and PERMANOVA (adonis2; 9,999 permutations; strata = Site). Within‑group dispersion was evaluated with betadisper (distance to group centroid) followed by Tukey\u0026rsquo;s HSD for pairwise comparisons. See Tables S1\u0026ndash;S7 for details. All analyses were conducted in R 4.4.3.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eResponse of microbial alpha diversity to wildfire severity\u003c/h2\u003e\n\u003cp\u003eThe ACE index of the bacterial community increased with rising wildfire severity in both soil layers (0-20 cm, 20-40 cm), reaching its highest value in the severe wildfire treatment (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). In contrast, the Simpson and Pielou_e indices\u0026mdash;which reflect species diversity and evenness, respectively\u0026mdash;exhibited distinct patterns across soil depths. In topsoils (0-20 cm), all burned plots demonstrated higher Simpson and Pielou_e indices relative to CK, with the highest values observed under the light wildfire plot (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Notably, in subsoils (20-40 cm), only moderate wildfire plots induced significant increases in both Simpson and Pielou_e indices (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), with the highest values observed under this specific condition.\u003c/p\u003e\n\u003cp\u003eFungal alpha diversity exhibited more complex patterns of variation. In topsoil, fungal richness (ACE index) increased significantly with wildfire severity, reaching its peak in the severe wildfire plot and exceeding that of the CK (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Conversely, the highest fungal richness in subsoil occurred under the moderate wildfire plot, significantly surpassing all other treatments (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). In contrast to bacterial communities, the fungal Simpson and Pielou_e indices showed a decrease and then an increase with the wildfire severity. In subsoil, fungal Simpson and Pielou_e indices reached their lowest values under the light wildfire plot, then rose progressively with increasing burn intensity, ultimately peaking at the severe wildfire plot.\u003c/p\u003e\n\u003cp\u003eLinear mixed-effects models (LMMs) showed that bacterial Simpson and Pielou_e were significantly influenced by depth (B layer \u0026lt; A layer), while ACE exhibited a weaker yet significant effect (B layer \u0026gt; A layer). For fungi, Simpson was significantly affected by depth, Pielou_e was marginally significant, and ACE was not significant (see Tables S1 and S2). Compared with the topsoil layer, the subsoil layer showed significantly lower evenness and dominance (consistent for both bacteria and fungi), suggesting that deep communities are more susceptible to environmental filtering or dispersal limitation. In contrast, bacterial richness was slightly higher in the subsoil layer, possibly reflecting post-fire dispersal/replenishment effects.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eResponse of microbial Beta diversity to wildfire severity\u003c/h2\u003e\n\u003cp\u003ePrincipal Coordinates Analysis (PCoA) revealed distinct separations in soil bacterial and fungal communities between wildfire plots and CK (Figure 4). For bacterial communities, PC1 (76.3% of variance explained) and PC2 (15.8%) collectively demonstrated significant interactions between wildfire severity and soil depth (Figure 5a, b). Under moderate wildfire conditions, bacterial communities exhibited high similarity across both soil layers (0-20 cm and 20-40 cm), forming closely clustered assemblages. In contrast, CK, light wildfire, and severe wildfire exhibited greater dispersion between soil depths, suggesting increased heterogeneity in community composition with depth stratification. Notably, in the subsoil layer, light and severe wildfire plots demonstrated substantial community similarity. However, these treatments demonstrated significantly higher dispersion in the topsoil, indicating a heightened sensitivity of surface microbial communities to fire disturbance.\u003c/p\u003e\n\u003cp\u003eFungal communities exhibited distinct response patterns. The co-variation along PC1 (64.2% of variance explained) and PC2 (27.8%) indicated clear successional dynamics following wildfires (Figure 5c,d). Similar to bacterial communities, fungal assemblages under moderate wildfire conditions demonstrated high similarity across soil strata at both depths. In contrast, CK, light wildfire, and severe wildfire plots displayed significant vertical differentiation among soil layers and exhibited distinct community separation across wildfire severity gradients. These patterns collectively highlight the substantial impact of wildfire severity on fungal community architecture.\u003c/p\u003e\n\u003cp\u003ePCoA indicated that fire severity and soil layer jointly structured community separation; PERMANOVA showed high explanatory power (\u003cem\u003eR\u0026sup2;\u0026nbsp;\u003c/em\u003e= 0.981, F = 239.14, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). betadisper indicated significantly unequal within-group dispersions for both Fire and Depth\u0026mdash;Fire: Light \u0026gt; Severe \u0026gt; CK (control) \u0026gt; Moderate; Depth: B layer \u0026gt; A layer (Tables S3\u0026ndash;S7).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eNonlinear responses of microbial co-occurrence networks to wildfire severity\u003c/h2\u003e\n\u003cp\u003eCo-occurrence networks constructed at the species level revealed divergent network architectures and topological parameters between soil layers for both bacterial and fungal communities (Tables 2 and 3). In bacterial communities, wildfires generally resulted in an increase in node numbers compared to CK, indicating enhanced species richness. However, network complexity metrics (edges, average degree), such as edges and average degree, exhibited pronounced nonlinear responses along the wildfire severity gradient. The number of bacterial community nodes initially increased and subsequently decreased with the wildfire severity. In the topsoil, the maximum number of nodes was observed under light wildfire conditions, whereas in the subsoil, it peaked under moderate wildfire conditions. Concurrent reductions in edge numbers and average degree (e.g., topsoil: CK=42.087\u0026rarr;Moderate=20.390) suggest that environmental stress leads to a simplification of species interaction networks, potentially reflecting niche compression processes. Notably, positive edge proportions significantly increased in wildfire plots versus CK (topsoil: peak at 90.70% under moderate wildfire; subsoil: 82.02% under light wildfire), with a decreasing trend observed as wildfire severity increased (see Table 2). This nonlinear response aligns with the Stress Gradient Hypothesis, where moderate disturbance promotes microbial facilitation. From the perspective of network modularity, the modularity index of bacterial co-occurrence networks in wildfire plots was higher than that in CK, suggesting that fire disturbance enhances modularity. This indicates a stronger formation of functionally differentiated modules, potentially contributing to improved overall system stability, particularly in the case of moderate wildfire. Comparing different layers of the same treatment, it was observed that the number of nodes in the bacterial co-occurrence network of CK, light, and severe wildfire in topsoil was higher than that in subsoil, while the number of nodes in moderate wildfire was higher in subsoil than in topsoil. Across all plots, the number of edges, average degree, and modularity index were higher in topsoil than in subsoil. Additionally, the proportion of negatively correlated edges in subsoil was higher than that in topsoil, with the negative edges in CK accounting for 47.11%, indicating that resource competition was more pronounced in deeper soil layers, particularly under the CK condition.\u003c/p\u003e\n\u003cp\u003eFungal co-occurrence networks demonstrated a consistent increase in node abundance with escalating wildfire severity (Table 3). In topsoil, node counts elevated sharply to 921 under the Severe wildfire plot, representing a 36.7% increase relative to CK. Conversely, subsoil exhibited even greater proportional enhancement under the Severe wildfire plot, reaching 781 nodes, representing a 39.5% increase compared to CK. In contrast to bacterial networks, fungal communities exhibited positive species enrichment under wildfire disturbance, with topsoil potentially acting as a trigger for the activation of dormant taxa. The proportion of positive edges exhibited distinct layer-specific maxima: peaking in the topsoil under severe wildfire (92.31%), while reaching its highest value in the subsoil under moderate wildfire (82.42%), followed by a slight decline under severe wildfire. Co-occurrence network stability metrics diverged significantly from those of bacterial communities. In topsoil, the modularity index initially increased and then decreased with escalating wildfire intensity, reaching a maximum value (0.948) under moderate wildfire and dropping to a minimum value (0.454) under severe wildfire, indicating that intense disturbance disrupted the modular structure. Conversely, fungal modularity in subsoil increased progressively across the wildfire severity gradient, peaking at 0.924 under severe wildfire. These patterns demonstrate enhanced compartmentalized organization in subsoil communities under wildfire disturbance, potentially augmenting ecosystem stability. In comparison across different soil layers, the moderate wildfire fungal co-occurrence network exhibited higher values in the subsoil than in the topsoil concerning the number of nodes, edges, proportion of positively correlated edges, and average degree. Conversely, the modularity index was higher in the topsoil than in the subsoil (except for severe wildfire).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Co-occurrence network topology attributes (Bacteria)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTopological features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 242px;\"\u003e\n \u003cp\u003eA (0-20 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 242px;\"\u003e\n \u003cp\u003eB (20-40 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNode number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eEdge number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e15930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e13685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e8462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e8648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePositive edge/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e66.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e84.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e90.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e75.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e52.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e82.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e75.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e64.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNegative edge/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e33.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e47.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e17.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e35.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAverage degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e42.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e20.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e22.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e25.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e25.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e15.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e19.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eClustering coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAverage path length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Co-occurrence network topology attributes (Fungi)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTopological features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 242px;\"\u003e\n \u003cp\u003eA (0-20 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 242px;\"\u003e\n \u003cp\u003eB (20-40 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eLight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNode number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eEdge number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e6833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e4830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e87036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e5202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e8738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePositive edge/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e69.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e78.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e57.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e92.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e75.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e65.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e82.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e74.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNegative edge/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e21.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e42.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e24.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e34.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e17.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e25.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAverage degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e20.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e17.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e12.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e189.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e20.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e22.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e26.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e19.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e16.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eClustering coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAverage path length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe bacterial and fungal communities in different soil layers were classified into distinct functional modules, with variations in module composition observed across soil depths (Figure 5, Table 4). In the topsoil, the bacterial community was primarily dominated by Module II (core module), while the fungal community exhibited higher abundances of Module II and Module IV. In contrast, the subsoil bacterial community was primarily characterized by Module III, accompanied by a relative decrease in the abundance of Module I and an increase in Module IV compared to the topsoil. Similarly, the fungal community was dominated by Module III, with a distribution pattern consistent with that of bacterial Module III.\u003c/p\u003e\n\u003cp\u003eNetwork analyses demonstrated significant differences in microbial interaction intensity between soil depths (Table 4). For bacterial networks, although the subsoil exhibited a lower number of nodes than the topsoil, its number of edges increased significantly by 80.8%. This increase was accompanied by a rise in the average degree from 163.70 to 350.72 and a 154% surge in network density, indicating that the bacterial community in the subsoil formed a highly integrated and densely interconnected network. However, this enhanced connectivity was associated with a weakened modular structure, as reflected by a sharp decline in the modularity index from 0.384 in the topsoil to 0.108 in the subsoil. Furthermore, the proportion of positive correlations declined, while negative correlations increased significantly, suggesting an intensification of resource competition within the subsoil microbial community.\u003c/p\u003e\n\u003cp\u003eThe fungal network also exhibited enhanced interaction intensity with increasing soil depth, although this pattern deviated from that observed in bacterial networks. While the number of nodes in the subsoil showed a slight reduction, the number of edges and average degree remained at high levels, accompanied by an increase in network density. Similar to the bacterial community, the modularity index in the subsoil displayed a moderate decrease in fungi; however, this reduction was relatively less significant, suggesting that the modular structure of fungi demonstrates greater resilience to changes associated with soil depth. Notably, there was a pronounced shift toward competitive interactions: the proportion of positive correlations significantly decreased, while the proportion of negative correlations nearly doubled. This trend indicates that competitive interactions became predominant in shaping the fungal network within the subsoil.\u003c/p\u003e\n\u003cp\u003eTable 4. Topological attributes of co-occurrence networks based on modular coloring\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003eTopological features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 32px;\"\u003e\n \u003cp\u003eBacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 32px;\"\u003e\n \u003cp\u003eFungi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eA (0-20 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eB (20-40 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eA (0-20 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eB (20-40 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eNode number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eEdge number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e76528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e138359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e49577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e65751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003ePositive edge/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e73.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e63.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e77.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e61.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eNegative edge/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e26.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e36.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e22.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e38.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eAverage degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e163.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e350.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e105.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e162.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eClustering coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eAverage path length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eModularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eResponses of Microbial Community Assembly Processes to Fire and Soil Depth\u003c/h2\u003e\n\u003cp\u003eAnalysis using the Neutral Community Model (NCM) revealed that distinct fundamental mechanisms regulate the assembly of bacterial and fungal communities across soil depths (Figure 6). Bacterial communities in both the 0-20 cm and 20-40 cm soil layers exhibited a close fit to the neutral model (\u003cem\u003eR\u0026sup2;\u0026nbsp;\u003c/em\u003e= 0.813 and 0.787, respectively). Estimated migration rates (Nm) were exceedingly high (Nm =1.16\u0026times;10⁷ and 1.36\u0026times;10⁷, respectively). Furthermore, approximately 76% of bacterial species\u0026rsquo; abundances fell within the model\u0026apos;s 95% confidence interval. These findings suggest that stochastic processes, primarily dispersal limitation, predominantly influenced bacterial community assembly in these soil layers.\u003c/p\u003e\n\u003cp\u003eIn contrast, the assembly of fungal communities deviated substantially from the neutral model. The NCM goodness-of-fit for fungi (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.497 for the topsoil and \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.636 for the subsoil) was significantly lower than that observed for bacteria communities. Corresponding Nm values (5.28\u0026times;10⁴ for the topsoil and 6.98\u0026times;10⁴ for the subsoil) were substantially lower than those observed in the bacterial community. Notably, approximately 30% of fungal taxa exhibited significant deviations from the neutral model predictions. These results demonstrate that deterministic processes, driven by environmental filtering and niche selection, exerted a stronger influence on the assembly of fungal communities, with this deterministic effect being more pronounced in the topsoil compared to the subsoil.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSoil microorganisms play a pivotal role in key ecosystem processes, such as the decomposition and stabilization of soil organic matter(Strickland et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), nutrient cycling dynamics(Van Der Heijden et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and rhizosphere functioning (Mendes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Bacteria and fungi constitute the two dominant microbial groups within forest soil ecosystems(Tao Yuzhu and Di Xueying, 2013). Upon the occurrence of a forest fire, soil microorganisms are immediately affected by the loss of heat-sensitive species, followed by prolonged alterations due to changes in soil chemistry and vegetation succession(Hart et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Wildfire events consistently reduce soil microbial biomass and community diversity across numerous ecosystems(Pulido-Chavez et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with the extent of impact closely related to wildfire severity(K\u0026ouml;ster et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Crucially, these microbial shifts may hinder post-fire plant recovery processes(Ib\u0026aacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, bacterial abundance increased with escalating wildfire severity, reaching its maximum in the severely burned plots. This observation is consistent with findings reported by Liu (Liu Jing, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), indicating that wildfire disturbance can promote the development of soil bacterial community diversity(She R et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, fungal abundance exhibited an upward trend only in the topsoil layer. Within the subsoil, the highest fungal species richness was observed under the moderate wildfire plot. Although fungi are generally less heat-tolerant(Zhang Min and Hu Haiqing, 2002), they displayed the greatest species richness following severe fires. This counterintuitive result is explained by the primary deposition of wildfire ash in surface soils; severe wildfires typically generate greater ash quantities and induce more pronounced increases in nutrient content (Du Jun et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thereby creating a transient \"eutrophic\" environment. Conversely, the maximum fungal species richness observed at 20\u0026ndash;40 cm depth under moderate wildfire represents a classic manifestation of the Intermediate Disturbance Hypothesis. This result arises from a combination of moderate nutrient leaching inputs, sublethal thermal effects (which disrupt existing equilibria without causing widespread mortality), and relatively stable environmental conditions accompanied by reduced baseline competition levels typical of subsoil environments. These factors created favorable conditions for the broadest range of fungal taxa to develop, resulting in peak species richness.\u003c/p\u003e\u003cp\u003eCollectively, this study revealed non-linear response patterns of soil microbial communities to varying wildfire severity in a subtropical montane forest system, with moderate wildfire inducing a distinct ecological threshold effect. In contrast to the meta-analysis conducted by Dooley and Treseder(Dooley and Treseder, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which concluded that wildfire disturbance generally reduces microbial diversity, our findings demonstrate that moderate wildfire significantly enhanced bacterial community species abundance and diversity, as well as promoted community similarity across different soil depths. This divergence may be attributed to specific characteristics of the subtropical ecosystem: the warm and humid climate facilitates the rapid dispersal of r-strategist taxa (e.g., Actinomycetota), while moderate wildfire releases niche space by eliminating K-strategist competitors (e.g., Gram-negative bacteria), thereby facilitating the proliferation of functionally complementary taxa. Notably, the subsoil demonstrated distinct responses to varying wildfire intensities. While bacterial richness, as indicated by the ACE index, reached its maximum under severe wildfire conditions, community evenness increased significantly only under moderate wildfire conditions. This pattern aligns with the \"delayed microbial response in subsoil\" proposed by Meillilo et al.(Melillo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), indicating that subsoil communities possess a more sensitive ecological threshold to disturbance intensity. The increase in species richness may be attributed to two potential factors: (1) the high initial microbial diversity (Shannon index\u0026thinsp;\u0026gt;\u0026thinsp;6.5) in subtropical forest soils, providing stronger functional redundancy(Delgado-Baquerizo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); (2) the activity of pioneer microbial groups (e.g., Actinomycetota) specifically utilizing charred substrates post-fire(Li BY et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thereby reactivating previously dormant species pools.\u003c/p\u003e\u003cp\u003eMicrobial co-occurrence network analyses further elucidated the mechanisms by which moderate wildfire optimizes stability. The stability of microbial communities depends on both diversity and the complexity of interactions among members, including antagonistic, competitive, or mutualistic relationships. Greater network complexity generally correlates with increased community resilience(Wagg et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this study, the bacterial co-occurrence network under light wildfire exhibited a 20.3% increase in node count within the topsoil layer, while the fungal network showed a substantial 36.6% increase in node count under severe wildfire. Under moderate wildfire, positive correlations in the bacterial co-occurrence network accounted for 90.7%, significantly exceeding that of CK (66.94%), indicating that wildfire disturbance promotes enhanced cooperative relationships among microbes. The induction of a 90.7% positive interaction edge ratio under moderate wildfire conditions was accompanied by elevated modularity indices, leading to the formation of an integrated network architecture (Faust and Raes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These regional differences suggest a strong dependency on ecosystem structure in the successional pathways of post-fire microbial network restructuring. The divergent community strategies (r/K selection) and differing capacities for network topology reconstruction likely form the ecological basis for such differences(Faust and Raes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Notably, in subsoil layers, the modularity index increased under severe wildfire conditions (0.852\u0026rarr;0.924). This finding contrasts with the results reported by Yu Jingjing et al.(Yu JJ et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who observed a decrease in fungal network modularity in forests subjected to slash-and-burn disturbance. This difference can be explained by two potential mechanisms: (1) Topsoil fungal communities under severe wildfire suffered module collapse (0.454) due to hyper-dense connections (average degree 189), aligning with the \"density-modularity\" negative correlation theory of Ortiz-\u0026aacute;lvarez et al. (Ortiz-\u0026Aacute;lvarez et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (2) In resource-limited subsoil environments (network density: 0.202), excessive connectivity was constrained, enabling severe wildfire to preserve modular stability. From an assembly perspective, the collapse of topsoil fungal networks is closely associated with weakened deterministic processes. Although ash inputs increased nutrient heterogeneity, high migration rates (Nm\u0026thinsp;=\u0026thinsp;5.28\u0026times;10⁴) diminished the efficacy of environmental filtering (\u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.497), leading to stochastic community reorganization and disruption of existing module boundaries. Conversely, in subsoil, deterministic assembly (\u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.636) in the subsoil interacted synergistically with resource limitations, allowing moderate wildfire to shape stable modular structures through selective pressures. This provides insights into resolving the \"disturbance-stability paradox\", demonstrating that moderate disturbance can enhance system robustness when the intensity of environmental filtering exceeds a critical threshold. Notably, the high proportion of positive correlation edges (92.31%) observed alongside a significant decline in modularity under severe wildfire conditions in topsoil indicates functional convergence. This phenomenon may reflect the decoupling of tannin-fungal interaction systems specific to conifers post-fire, potentially driving saprotrophic fungi toward cooperative resource utilization strategies. Such resilient restructuring of functional networks provides a microbial-level regulatory basis for ecological restoration in the post-fire area.\u003c/p\u003e\u003cp\u003eBacterial community assembly in both soil layers closely conformed to the neutral model (\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u0026gt;0.78), indicating the dominance of stochastic dispersal processes. This pattern supports the rapid recovery capacity of bacterial communities post-fire. Specifically, moderate wildfire did not alter the underlying assembly mechanism but increased bacterial migration rates, thereby promoting the uniform dispersal of r-strategists (e.g., Actinomycetota) across soil horizons. Consequently, this process elevated the similarity of bacterial communities between the two soil layers. Conversely, fungal assembly deviated significantly from the neutral model (topsoil \u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.497; subsoil \u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.636), with deterministic processes accounting for 32.3% of the assembly in the topsoil, consistent with the findings that \"stressful environments enhance niche selection\"(Zhou et al., 2017). The observed vertical differentiation arises from the stratified distribution of fire residues: Increased ash inputs in surface soil enhance nutrient heterogeneity(Du Jun et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)while the downward percolation of black carbon (BC) particles into deeper soil layers creates micro-scale chemical potential barriers, intensifying environmental filtering. This heterogeneity presents a dual effect within the niche dimension: while it promotes the differentiation of functional modules, it concurrently intensifies resource competition among these modules. Notably, the dominance of deterministic assembly in subsoil fungal communities (\u003cem\u003eR\u0026sup2;\u003c/em\u003e=0.636) is closely associated with increased resource competition, as evidenced by 38.32% of observed interactions being negative (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Wildfire residues that percolate through the soil generate fragmented microhabitats, thereby enhancing niche differentiation. This fragmentation compels microorganisms to engage in intensified competition for limited resources via antagonistic interactions, ultimately giving rise to a \"high-connectivity, high-competition\" network structure (average degree: 162.95; negative edge proportion increased by 67.3%). The self-reinforcing feedback loop between community assembly and competitive interactions serves as the primary driving mechanism by which moderate wildfires enhance the modularity index in subsoil microbial networks.\u003c/p\u003e\u003cp\u003eSoil depth serves as a critical regulatory factor that reshapes microbial responses to wildfire. In the subsoil layer (20\u0026ndash;40 cm), bacterial networks exhibited a \"high-connectivity, high-competition\" topology: The average degree increased dramatically to 350.72 (representing a 114% increase compared to topsoil), and the proportion of negative interactions reached 36.81%. These findings challenge the \"resource partitioning by depth\" theory introduced by Delgado-Baquerizo et al. (Delgado-Baquerizo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our study demonstrates that the deep environment not only promotes functional differentiation but also enhances competitive exclusion through nutrient limitation. This competitive dynamic, in combination with deterministic assembly processes, indicates that moderate wildfire severity is the only level that effectively enhances microbial functionality in subsoil. Future research should integrate transcriptomic techniques to dissect the expression dynamics of key functional genes under moderate wildfire, thereby establishing definitive causal relationships within the \"disturbance-modularity-function\" framework.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWildfires, as critical ecological disturbance factors under global climate change, exhibit intensity-dependent regulatory mechanisms on soil microbial communities that remain poorly understood. This knowledge gap is particularly evident in subtropical montane ecosystems, where systematic studies remain limited. Our study demonstrates that moderate wildfire constitutes an ecological threshold for restructuring microbial communities in these forests. Specifically, moderate wildfire significantly enhanced bacterial community evenness and fostered highly similar structural characteristics across soil depths. Simultaneously, fungal species richness peaked within the subsoil (20\u0026ndash;40 cm), supporting the widespread applicability of the \"intermediate disturbance optimization\" effect. Non-linear responses within microbial co-occurrence networks further elucidated the underlying mechanisms of stability. Moderate wildfire enhanced systemic functional resilience by promoting positive bacterial interactions and strengthening the modular structure of fungal communities in the subsoil. Depth-differentiation in community assembly processes highlighted key governing factors: stochastic dispersal dominates bacterial communities, facilitating rapid recovery, whereas fungal communities are primarily shaped by deterministic processes. In subsoil, resource heterogeneity induced by percolating fire residues intensified environmental filtering and competitive exclusion, shaping a \"high-connectivity, high-competition\" interaction framework. Soil depth emerged as the pivotal dimension governing response patterns. The delayed threshold responses and intensified competitive strategies of subsoil microbes underscore their crucial role in post-fire ecosystem recovery and steady-state reconstruction.\u003c/p\u003e\u003cp\u003eThe primary contribution of this study is the establishment of an integrated \"wildfire severity-soil depth-microbial function\" response framework. This framework not only elucidates the adaptive strategies of soil microorganisms in response to wildfire gradient disturbances in subtropical forests, but also deciphers the mechanisms underlying ecological optimization by moderate wildfire through assembly processes and network stability. This discovery establishes a scientific foundation for microbiome-mediated ecological restoration in post-fire forest ecosystems. Future research should prioritize the investigation of spatiotemporal dynamics within biotic co-occurrence networks in deep soil layers and their contributions to sustaining ecosystem resilience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis research was funded by the Study on the Carbon Sequestration Capacity of Forests and the Construction of Carbon Sequestration Monitoring System in Guizhou Province (GZTHJC-2023-04); the 2023 National Nature Reserve Grant Project of Dashahe National Nature Reserve in Guizhou Province (2023009390455714091-001); the Research on the Ecological Adaptability of the Endangered and Rare Plant Cathaya Argyrophylla (Qian Lin Ke He [2024]06); the Forestry Research Project (Subject) of Guizhou Province: Analysis of the Canopy Structure of Wild Cathaya Argyrophylla in Guizhou Dashanhe National Nature Reserve (Qian Lin Ke He J [2024]13) and the 2024 Guizhou Science and Technology Innovation Talent Team Construction Project: Wildlife Innovation Team of the Forestry college of Guizhou University (Qiankeherencai CXTD[2025]053).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability statement\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eThe Sequence data supporting the figures and tables in the manuscript is publicly available in the NCBI Short Read Archive database (Accession Number: SRP593774).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e：\u003c/em\u003e\u003c/strong\u003eTechnical support was provided by Shanghai Majorbio Bio-pharm Technology Co., Ltd. Editorial assistance was contributed by Leader Bio-Tech (Qingdao) Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eShaqian Liu and Hui Zhou participated in the experiment. Rui Yang and Xiao Zou revised the article. Shaqian Liu wrote the article. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of interests\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgbeshie, A.A., Abugre, S., Atta-Darkwa, T. and Awuah, R., 2022. A review of the effects of forest fire on soil properties. JOURNAL OF FORESTRY RESEARCH, 33(5): 1419-1441. https://doi.org/10.1007/s11676-022-01475-4\u003c/li\u003e\n \u003cli\u003eChen, S., Zhou, Y., Chen, Y. and Gu, J., 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17): i884-i890. https://doi.org/10.1093/bioinformatics/bty560\u003c/li\u003e\n \u003cli\u003eDelgado-Baquerizo, M. et al., 2020. 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Natural restoration characteristics and assembly mechanisms of soil microbial com⁃ munity in tropical rainforest under different disturbance types. Chinese Journal of Ecology, 42(03): 534-543. https://doi.org/10.13292/j.1000-4890.202303.007\u003c/li\u003e\n \u003cli\u003eZhang Min and Hu Haiqing, 2002. The Effect of Forest Fire on Microorganism in Soil. JOURNAL OF NORTHEAST FORESTRY UNIVERSITY, (04): 44-46. https://doi.org/10.13759/j.cnki.dlxb.2002.04.012\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wildfire, Soil microbiota, Co-occurrence network, Community assembly, Subtropical montane forest","lastPublishedDoi":"10.21203/rs.3.rs-7550755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7550755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e\u003cp\u003eAgainst the backdrop of global warming, the frequency and intensity of wildfires have significantly increased, exerting profound impacts on the structure and function of soil microbial communities. However, the mechanisms underlying microbial responses to varying levels of wildfire severity in subtropical montane forests remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study investigated subtropical forests in Huaxi District, Guizhou Province, employing a wildfire severity gradient design (unburned, light, moderate, severe) combined with depth-stratified soil sampling (topsoil: 0\u0026ndash;20 cm, subsoil: 20\u0026ndash;40 cm). Building on metagenomic sequencing and co-occurrence network analyses, we elucidate the coupled relationships among community diversity, interaction structure, and assembly processes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e(1) The bacterial richness (ACE) increased continuously with wildfire severity, peaking at severe wildfire; evenness (Pielou_e) increased significantly only at moderate wildfire, exhibiting an intermediate-disturbance optimum. For fungi, richness in the topsoil layer increased with wildfire severity, whereas in the subsoil layer it peaked at moderate wildfire. (2) Co-occurrence networks showed a non-linear response: in bacteria, the proportion of positive edges rose sharply at moderate wildfire (\u0026gt;\u0026thinsp;90%); in fungi, modularity strengthened in the subsoil layer at moderate wildfire but decreased in the topsoil layer at severe wildfire, indicating \u0026ldquo;depth-differentiated\u0026rdquo; structural reorganization. (3) Neutral community model fitting indicated that bacterial assembly was dominated by stochastic processes (\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u0026gt;0.78), whereas fungi deviated from the neutral model and were more strongly shaped by deterministic processes (environmental filtering/niche selection), with these effects being more pronounced in the subsoil layer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOverall, moderate wildfire constitutes an ecological threshold that optimizes microbial community structure and functional potential, while soil depth reshapes post-fire successional trajectories by altering assembly processes and network topology. This study provides a theoretical basis for targeted post-fire microbial restoration in subtropical forests.\u003c/p\u003e","manuscriptTitle":"Threshold Effects of Moderate Wildfire Drive Depth-Dependent Responses in Subtropical Forest Soil Microbial Communities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 13:56:16","doi":"10.21203/rs.3.rs-7550755/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3614a1f5-a13b-47d8-ab65-98c033adb578","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T20:56:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 13:56:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7550755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7550755","identity":"rs-7550755","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}