Community Assembly Mechanisms Facilitate Understanding of Microbial Responses to Habitat Succession along a Spatial Environmental Continuum in Alpine Plateaus

Community Assembly Mechanisms Facilitate Understanding of Microbial Responses to Habitat Succession along a Spatial Environmental Continuum in Alpine Plateaus | 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 Community Assembly Mechanisms Facilitate Understanding of Microbial Responses to Habitat Succession along a Spatial Environmental Continuum in Alpine Plateaus Wenrui Wang, Changjiang Huang, Congrui Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9428328/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract While soil microorganisms mediate important ecosystem functions, their community assembly mechanisms along a spatially connected environmental continuum during alpine habitat succession remain elusive. Here, we profiled the taxonomic and functional characteristics of soil microbiomes across three independent wetland-grassland-bareland continua on the Pamir Plateau. Along this successional gradient, microbial diversity, network complexity, and network stability decreased significantly towards barelands. Null model analyses revealed a pronounced shift in community assembly within barelands, characterized by increased heterogeneous selection and dispersal limitation. This mechanistic shift, driven by severe water scarcity and salt stress, underpins the diminished diversity and network robustness. Notably, soil microbial functional potential peaked in grasslands, likely reflecting enhanced plant-soil feedbacks. By linking community properties to functionality, we demonstrated that microbial diversity, rather than network complexity, serves as the primary biotic driver mediating shifts in soil microbial functional potential. Ultimately, these findings provide a mechanistic understanding of microbial community assembly and functional succession in sensitive alpine ecosystems. Habitat Succession Soil Microbiome Assembly Mechanisms Network Complexity Microbial Function Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Global ecosystems are currently facing dual pressures from anthropogenic disturbances and climate change, posing significant threats to ecosystem services [ 1 – 3 ]. In particular, high-altitude and mountainous ecosystems are extremely sensitive to perturbations [ 4 ], often undergoing profound biogeomorphic successions that manifest as spatial continua across wetlands, grasslands, and barelands. This successional transition is characterized not only by hydrological recession but also by marked changes in vegetation cover and diversity, which potentially reshape the soil environment through plant-soil feedbacks (e.g., litter decomposition and root exudation) [ 5 ]. As vital regulators of terrestrial ecosystems, soil microorganisms drive organic matter decomposition, nutrient cycling, and soil structure improvement, thereby sustaining overall ecological balance [ 6 – 8 ]. However, severe changes in environmental conditions, such as decreased soil water content and shifts in soil properties (e.g., salinity and nutrients), significantly threaten the diversity and structure of soil microbiomes [ 9 , 10 ]. Although the structural characteristics of microbial communities in isolated habitats have been well documented, their responses within spatially connected environmental continua remain poorly understood. To advance our understanding of belowground ecological responses, it is imperative to elucidate the underlying community assembly processes, given their profound implications for biodiversity and ecosystem functioning [ 11 – 13 ]. Generally, microbial community assembly is jointly governed by the interplay of stochastic and deterministic processes [ 14 – 16 ]. Deterministic processes underscore the pivotal role of selection, encompassing both environmental filtering by habitat conditions and biotic interactions such as competition and antagonism. In contrast, stochastic processes highlight the significance of probabilistic dispersal, ecological drift, and unpredictable disturbances [ 17 , 18 ]. These underlying mechanisms fundamentally shape the diversity and co-occurrence networks of soil microbiomes, thereby explaining the observed differences in taxonomic composition and species association patterns across distinct communities [ 19 – 21 ]. For instance, deterministic processes typically dominate in low-diversity communities [ 22 ], whereas stochastic processes drive the formation of more complex microbial co-occurrence networks that can influence microbial interactions [ 23 ]. However, it remains poorly understood how the balance between these assembly processes shifts along the alpine wetland-grassland-bareland continuum, specifically whether severe abiotic stress in barelands enhances deterministic selection. Microorganisms rarely function in isolation; rather, they form complex ecological networks through various interspecies interactions [ 24 , 25 ]. Key topological features of these networks serve as robust indicators of community complexity and resilience against external disturbances [ 26 ]. For example, as habitats transition from moist to arid environments, severe water scarcity significantly reduces the node number and average degree of microbial networks, resulting in simplified microbial co-occurrence networks and diminished community stability [ 27 ]. Furthermore, recent studies have not only revealed that biogeomorphic succession restructures soil microbial communities but also highlighted that both microbial diversity and network complexity are crucial predictors of soil functional potential [ 28 , 29 ]. For instance, microbial communities regulate the carbon (C), nitrogen (N), phosphorus (P), and sulfur (S) cycling potentials of soil ecosystems through the dual mechanisms of community diversity and network interactions [ 30 ]. However, a critical knowledge gap remains regarding whether microbial taxonomic diversity or network complexity acts as the primary biotic driver regulating soil functional potentials during successional transitions in extreme alpine environments. In this study, we focus on the Pamir Plateau, a region characterized by spatially connected environmental continua comprising wetlands, grasslands, and barelands, making it an ideal natural laboratory for investigating the rules of habitat succession in sensitive alpine regions. Our primary goal is to elucidate the microbial response patterns along this successional gradient and to decipher the underlying mechanisms by which environmental filtering and microbial interactions jointly regulate soil microbial functional potentials associated with C, N, and S cycling. Specifically, we aim to: (1) characterize the structural and functional shifts of bacterial communities across the three habitats; (2) uncover the community assembly processes and co-occurrence patterns of these microbial communities; and (3) elucidate the relative contributions of microbial diversity and network complexity to soil microbial functional potentials. Correspondingly, we hypothesized that (1) the spatial transition toward barelands significantly decreases microbial diversity, simplifies network complexity, and reduces overall soil microbial functional potential; (2) the transition toward barelands increases the relative contribution of deterministic assembly due to escalating environmental filtering; and (3) microbial diversity exerts a more potent influence than network complexity in regulating the functional potentials of C, N, and S cycling. 2. Methods and Materials 2.1 Study Area and Field Sampling Our study was conducted in the eastern Pamir Plateau (Xinjiang, China), a region characterized by a typical plateau continental climate. The area has a mean annual temperature of -2.8°C and a precipitation of 90 mm [ 31 ]. The terrestrial ecosystems in our study area are predominantly composed of alpine wetlands, grasslands, and barelands, which form a well-preserved and spatially continuous ecological gradient. To maintain temporal consistency, all sampling was conducted between July 1 and 2, 2025, and no extreme weather events were recorded during this period. A total of three study sites, situated at a mean elevation of 3,311 m, were selected across the region (38.85° N, 74.84° E; 37.88° N, 75.23° E; and 38.94° N, 74.58° E), each comprising three adjacent habitat types: alpine wetland, grassland, and bareland (Fig. 1 a). Within each habitat type at each site, we established four independent replicate plots. In each plot, surface soil samples (0–10 cm) were collected using a five-point sampling method and thoroughly mixed to form a composite sample. Sampling tools were sterilized before collection. This sampling design yielded a total of 36 samples (3 sites × 3 habitat types × 4 replicates). Each collected soil sample was divided into two subsamples: one was stored on dry ice and subsequently frozen at − 80°C in the laboratory for DNA extraction, and the other was air-dried for measurement of soil physicochemical properties. 2.2 Soil Physicochemical Analyses and Data Collection Soil pH was measured at 25°C using a Thermo Orion-868 pH meter (Thermo Orion-868, MA, USA) in a 1:2.5 (w/v) suspension prepared with 1 mol/L potassium chloride solution [ 32 ]. Total nitrogen (TN) was determined via combustion using a Flash Smart Elemental Analyzer (Thermo Fisher Scientific, Bremen, Germany) after the samples were air-dried and sieved through a 2-mm mesh [ 33 ]. The contents of total phosphorus (TP) and total potassium (TK) were measured using an Optima 8000 ICP-AES spectrometer (Perkin-Elmer, USA), following sample preparation via acid digestion and tri-acid extraction, respectively [ 34 ]. Total salt content was determined using a gravimetric method: a soil suspension was prepared with deionized water in a 1:5 (w/v) ratio, after which it was filtered, and the filtrate was evaporated to dryness [ 35 ]. The residue was treated with hydrogen peroxide to remove organic matter, and the remaining weight was used to calculate the total water-soluble salt content. Latitude, longitude, elevation, mean annual temperature (MAT), and mean annual precipitation (MAP) of each sampling site were acquired from the WorldClim ( www.worldclim.org , Version-2). 2.3 Molecular Analysis Total DNA was extracted from soil samples using the FastDNA Spin Kit for Soil (MP Biomedicals, Cleveland, OH). DNA concentration was determined using a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, USA). To profile the bacterial community, the V4 region of the 16S rRNA gene was amplified using the primer pair 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [ 36 ]. All PCR amplifications were conducted in triplicate under the following cycling program: an initial denaturation for 3 min at 94°C, followed by 31 cycles consisting of 30 s denaturation at 94°C, 30 s annealing at 53°C, and 30 s extension at 72°C, concluding with a final elongation of 8 min at 72°C. Amplicons were sequenced on the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA) with paired-end 300 bp reads. The raw sequence data were analyzed using QIIME2 (version 2024.2) [ 37 ]. In brief, the q2-demux plugin was employed for demultiplexing and quality control; subsequently, the q2-dada2 plugin facilitated denoising to resolve exact Amplicon Sequence Variants (ASVs). The taxonomic annotation of ASVs was performed using an sklearn classifier pre-trained on the amplified target region based on the SILVA database (version 138) [ 38 ]. All ASVs identified as chloroplasts or mitochondria were discarded from the dataset. Finally, all samples were rarefied to an identical depth based on the minimum read count (22,000 reads), retaining a total of 39,301 bacterial ASVs for further analyses. 2.4 Functional analyses based on PICRUSt2 The functional potential of the microbial communities was predicted from the 16S rRNA gene ASV abundance tables using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States). Functional prediction was performed using default parameters, which inherently included the normalization of 16S rRNA gene copy numbers. The predicted functional profiles were subsequently annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/ ) database to obtain KEGG Ortholog (KO) abundances for downstream functional pathway analysis [ 39 ]. 2.5 Microbial Networks and Null Models In this study, we applied Spiec-Easi method to construct microbial co-occurrence networks based on the abundance matrix. Network graphs were generated using the igraph package, and network topological properties were calculated, including nodes, edges, diameter, density, average degree, average path length, betweenness centralization, and degree centralization for each habitat type [ 40 , 41 ]. To quantify network complexity at the sample level, we extracted the following key topological features from the subnetworks: the number of nodes, edges, average degree, clustering coefficient, average path length, and graph density [ 42 ]. These topological metrics were subsequently integrated into a single comprehensive index representing soil multitrophic network complexity, derived through multidimensional scaling (MDS) analysis [ 43 ]. Prior to computing the final index, the average path length (an indicator of network sparsity) was transformed into its reciprocal. Network stability and robustness were assessed via simulated random node removal, with the rate of change in natural connectivity used as the evaluation metric [ 44 , 45 ]. The vulnerability of an individual node reflects its proportional role in maintaining global network efficiency, while the overarching vulnerability of the entire network is defined by the highest vulnerability score among all its constituent nodes [ 46 ]. To quantitatively evaluate the contributions of distinct taxa to community assembly processes, we employed a phylogenetic-bin-based null model analysis (iCAMP) [ 47 ]. This method divides the observed taxa into phylogenetic groups (bins) and identifies the assembly processes governing each bin based on both phylogenetic and taxonomic dissimilarities. For each phylogenetic bin of each sample pair, we calculated the beta net relatedness index (βNRIbin) and modified Raup–Crick metric (RCbin) to evaluate community assembly. The specific classifications were as follows: deterministic processes were indicated by |βNRIbin| > 1.96, where βNRIbin 1.96 represented heterogeneous selection. For stochastic processes (|βNRIbin| ≤ 1.96), RCbin > 0.95 denoted dispersal limitation, while RCbin < − 0.95 reflected homogenizing dispersal. The remaining fraction with |βNRIbin| ≤ 1.96 and |RCbin| ≤ 0.95 reflected ecological drift. Subsequently, the fraction of each process within individual bins was weighted by the relative abundance of each bin and aggregated to estimate the overall influence of each process at the whole community level. 2.6 Statistical Analysis In this study, we employed the microeco R package, a specialized tool for statistical analysis and visualization [ 48 ]. To characterize bacterial community composition, initial diversity analyses were performed after rarefying the ASV table. Dunn's Kruskal-Wallis test ( P < 0.05) was used to determine significant differences in α-diversity indices among different habitats. Soil microbial diversity was calculated using Observed ASVs, Shannon index, Simpson index, and Phylogenetic diversity (PD) index via the normalization-mean method [ 49 ]. Non-metric Multidimensional Scaling (NMDS) based on weighted UniFrac distances was applied for β-diversity analysis, highlighting spatial distribution patterns of the community structure. Linear discriminant analysis Effect Size (LEfSe) was performed to identify the key bacterial taxa driving the community dissimilarities across the three successional stages. An LDA effect size threshold of 3.5 was used to evaluate the biomarkers of all stages. To investigate the relationships between soil microbial communities and environmental variables, differences in physicochemical factors were assessed using Euclidean distance, while microbial community differences at the sampling sites were evaluated using weighted UniFrac distances. Scatter plots were created to visually illustrate the correlations between soil microbial communities and environmental variables across different successional stages. Furthermore, Mantel tests were performed using the ‘linkET’ package in R to reveal the relationships between soil microbes and soil environmental factors. Random forest models were used to predict species richness and assess the relative importance of each abiotic variable in predicting it. We calculated the importance of each variable using the ‘rfPermute’ package. The soil microbial functional potential was determined by the average values of functional pathways associated with C, N, and S cycling after Z­score transformation [ 30 ]. To elucidate the relationships between soil microbial functional potential, microbial diversity, and network complexity, linear regression analyses were first employed. Subsequently, owing to the strong inter-correlations among various factors, partial correlation analyses were conducted to isolate their direct associations. Ultimately, partial least squares path modeling (PLS-PM) was employed to comprehensively disentangle the effects of both abiotic and biotic variables on the soil microbial functional potential. 3. Results 3.1 Analysis of soil physicochemical properties Comparative analysis of soil physicochemical properties across the three habitat types revealed significant differences in soil total salt content and TN (Fig. S2 and Table S1 ). Specifically, TN content was highest in grasslands and lowest in barelands (i.e., grasslands > wetlands > barelands). In contrast, soil total salt content exhibited an increasing trend from wetlands to barelands (i.e., barelands > grasslands > wetlands). However, no significant differences were observed in soil pH, TP, and TK among the three habitats. 3.2 Changes in microbial diversity and community composition The Venn diagram illustrated the overlap of the ASV-level microbial communities among the three habitat soils (Fig. 1 c). At the ASV level, the wetland soils contained 13,976 unique ASVs (35.56%), the grassland soils hosted 14,266 unique ASVs (36.30%), and the bareland soils hosted 9,665 unique ASVs (24.59%), with only 236 ASVs (0.60%) shared among all three habitats. ASV-based species annotation revealed that bacterial community compositions were strongly influenced by habitat type, with distinct patterns observed after succession from wetlands to barelands (Figs. 1 b and S3a-c). Taxonomic profiling at the phylum level revealed that the bacterial communities were dominated by Proteobacteria, Acidobacteria, and Chloroflexi, with Proteobacteria being the most abundant. The relative abundance of Proteobacteria increased from an average of 24.9% in wetlands to 26.6% in grasslands, but subsequently decreased to 20.9% in barelands (Fig. S3a and Table S2 ). Further classification into bacterial classes showed that Gammaproteobacteria were dominant in wetlands (12.89%), whereas Alphaproteobacteria were highly abundant in grasslands (12.55%), and Actinobacteria were significantly enriched in barelands (11.18%) (Fig. S3b). Further down to the genus level, KD4-96 was the dominant genus in both wetlands (2.08%) and grasslands (1.55%), while JG30-KF-CM45 became predominant in barelands (2.69%) (Fig. S3c). In terms of alpha diversity, wetlands exhibited significantly higher species richness, Shannon, and PD indices compared to barelands (Figs. 1 d, S1, and Table S3). NMDS analysis based on weighted UniFrac distances revealed that habitat succession drove significant differences in bacterial community composition across wetlands, grasslands, and barelands (Fig. 1 e and Table S4). 3.3 Environmental variables influencing microbial communities Linear regression models based on distance matrices revealed that the associations between microbial community dissimilarity and environmental variations differed notably across the three habitats (Fig. S4). In wetlands, bacterial community composition was positively correlated with pH, TP, TK, and TN. In grasslands, community variations were primarily driven by edaphic factors such as soil total salt content and pH. In barelands, variations in community structure were strongly associated with differences in pH, TP, TK, and soil total salt content. Mantel tests revealed that bacterial communities in wetlands were significantly correlated with a wider array of environmental factors relative to those in grasslands and barelands (Fig. 2 a and Table S5). In wetlands, the bacterial community was significantly associated with soil nutrient factors, with TP exhibiting the strongest correlation. In contrast, the bacterial community in grasslands was primarily driven by soil total salt content, while showing no significant correlations with soil nutrient factors, including TN, TP, and TK. For barelands, the bacterial community exhibited a more pronounced response to spatial and climatic variables. When considering all habitats together, soil total salt content was identified as a critical factor regulating shifts in soil bacterial communities along the successional continuum. Furthermore, LEfSe analysis identified key taxonomic biomarkers characterizing each habitat (Fig. 2 b), revealing that a greater number of taxa were enriched in wetlands compared to grasslands and barelands. Specifically, wetlands were enriched with Burkholderiales , Acidobacteriota , and Desulfobacterota , whereas grasslands showed enrichment of Anaerolineae , Bacteroidales , and SBR1031 . Moreover, barelands were characterized by the enrichment of Actinobacteria , Chloroflexia, and Thermomicrobiales . Subsequent correlation analysis revealed that dominant taxa exhibited distinct responses to various environmental factors during successional transitions (Fig. 2 c and Tables S6-8). Additionally, random forest models demonstrated that soil total salt content was the most crucial variable in predicting bacterial species richness across the different habitats (Fig. 2 d). 3.4 Co-occurrence network analysis Co-occurrence networks were constructed to evaluate changes in possible ecological interactions across the different habitat types (Fig. 3 a). Analysis of topological properties further quantified these differences (Fig. 3 b and Table S9). The grassland network exhibited the highest number of nodes and edges, as well as the highest average degree among all networks, indicating the highest level of complexity. In contrast, the bareland network showed the lowest values for these metrics, indicating a simpler community structure. Additionally, the average path length and diameter were highest in the bareland network and lowest in the grassland network, suggesting that information or resource transfer was more efficient within the tightly clustered grassland network compared to the bareland network. We also found that the grassland network had the lowest vulnerability, suggesting that it was more resilient to environmental perturbations (Fig. 3 c). Consistent with this, the stability of the bacterial network was highest in grasslands compared to wetlands and barelands, as evidenced by higher natural connectivity under random node removal, indicating a more tightly connected microbial community (Fig. 3 d). 3.5 Community assembly and driving factors To further explore the underlying assembly mechanisms, null model analyses were conducted to evaluate the relative contributions of deterministic and stochastic processes along the habitat continuum (Fig. 4 a and Table S10). Our findings indicate that community assembly across all studied habitats was primarily governed by stochastic processes, specifically ecological drift and dispersal limitation. However, the relative contribution of deterministic processes was higher in barelands compared to wetlands and grasslands. Notably, the contribution of heterogeneous selection (HeS) was markedly higher in barelands (8.7%) compared to wetlands (1.9%) and grasslands (1.4%), suggesting that selective pressures played a stronger role in structuring soil microbial communities in the bareland ecosystem. Moreover, we identified the key microbial taxa that contributed significantly to these assembly processes (Fig. 4 b and Tables S11-12). Regarding heterogeneous selection, Nodularia PCC 9350 was the most responsive taxon in wetlands, whereas Nodosilinea PCC 7104 was primarily influenced in grasslands. In contrast, Burkholderia-Caballeronia-Paraburkholderia showed the highest sensitivity to heterogeneous selection in barelands. For dispersal limitation, Roseimaritima , JTB255 -marine benthic group, and JG30-KF-CM45 were identified as the key taxa governed by this process in wetlands, grasslands, and barelands, respectively. 3.6 Soil microbial functional potential To further explore the microbial functional potential driving soil nutrient cycling during habitat succession, the key genes and pathways involved in biogeochemical cycles—specifically C, N, and S cycling—were functionally annotated and predicted across the three habitat types (Figs. 5 a-c). Overall, the soil bacterial functional profiles differed among the three habitat types. At KEGG pathway level 3, the relative abundances of pathways related to C cycling were lower in barelands than in the other two habitats (Fig. 5 a). Habitat succession significantly altered a number of important genes involved in N and S cycling. First, the relative abundances of nitrification genes ( amoA/amoB and hao ) were higher in wetlands than in the other two habitats (Fig. 5 b), and the relative abundance of nitrogen fixation gene ( nifH ) increased along the transition from wetlands to grasslands. Notably, the succession to barelands severely decreased the abundance of almost all key genes involved in N cycling. Regarding the S cycle, dissimilatory sulfate reduction genes ( apr and dsr ) were more abundant in wetlands than in grasslands and barelands (Fig. 5 c). In contrast, assimilatory sulfate reduction genes (for example, cysC , cysH , and sir ) were highly enriched in the grasslands. 3.7 Linking bacterial communities and network complexity to soil microbial functional potential To further disentangle the relative contributions of microbial community characteristics to soil microbial functional potential, we evaluated the relationships between microbial diversity, network complexity, and soil microbial functional potential (Figs. 6 a and 6 b). Regression analyses revealed a highly significant negative correlation between network complexity and soil microbial functional potential. Conversely, bacterial diversity exhibited a significant positive correlation with functional potential, although the explanatory power was relatively lower in the simple regression model. To evaluate the direct and indirect effects of environmental factors and microbial properties (α/β-diversity and network complexity) on soil microbial functional potential, we employed PLS-PM. The model exhibited a robust fit (GoF = 0.74; Fig. 6 c, Table S13). Our results indicated that spatial variables had negative direct effects on climatic (path coefficient = − 0.99, P < 0.001) and edaphic variables (path coefficient = − 0.70, P < 0.001). In turn, edaphic variables positively influenced microbial composition (path coefficient = 0.86, P < 0.001), but negatively affected microbial diversity (path coefficient = − 0.86, P < 0.05) and soil microbial functional potential (path coefficient = − 0.83, P < 0.001). Importantly, soil microbial functional potential was strongly and positively driven by microbial diversity (path coefficient = 0.75, P 0.05). To eliminate the confounding influence of microbial diversity, we performed a partial correlation analysis; notably, when microbial diversity was controlled as a covariate, the initially observed significant correlation between network complexity and soil microbial functional potential became non-significant (Pearson's r = − 0.152, P = 0.416). Collectively, these findings emphasize that compared to microbial network complexity, microbial diversity serves as a much better predictor and driver of soil microbial functional potential. 4. Discussion 4.1 Soil microbial community composition and its determinants across wetlands, grasslands, and barelands Our findings showed that soil total salt content significantly increased along the successional gradient from alpine wetlands to grasslands and barelands. This is in line with previous reports showing significant variations in soil salinity across different habitat types [ 50 ]. While grasslands were primarily characterized by mild to moderate salinity, barelands exhibited the highest salinity levels. Furthermore, our results revealed that soil total nitrogen content was significantly higher in grasslands than in alpine wetlands and barelands. This pattern may be attributed to plant root exudates accelerating the decomposition of plant residues, thereby releasing more nitrogen [ 51 ]. Correspondingly, the habitat succession drove profound shifts in microbial community composition. The phylum Proteobacteria was the most abundant in both wetlands and grasslands, whereas bareland soils showed an increased relative abundance of Actinobacteria —taxa known for their oligotrophic nature and metabolic versatility, exhibiting strong tolerance to extreme environments [ 52 ]. The enrichment of these taxa reflects a harsh microbial habitat resulting from the absence of vegetation cover, reduced soil organic matter inputs, and low nutrient availability [ 53 ]. These succession-induced shifts in soil microbial communities were closely coupled with the drastic alterations in underlying soil physicochemical properties. Here, we found that soil bacterial community composition and diversity were robustly correlated with soil total salt content, suggesting that salinity may be a critical factor regulating changes in soil bacterial communities along the wetland-grassland-bareland continuum, a finding consistent with the widely accepted view that salinity plays a regulatory role in the turnover of soil microbes in many ecosystems. In agreement with previous studies, we observed a continuous decline in bacterial diversity along the increasing salinity gradient. Elevated soil salinity increases extracellular osmotic pressure, forcing many bacterial taxa into dormancy or death, thereby explaining the observed decline in diversity [ 54 ]. Additionally, the accumulation of specific ions such as sodium, chloride, and sulfate at high concentrations can exert direct toxic effects on bacteria, acting as an additional mechanism driving the loss of diversity [ 55 ]. Furthermore, these excess salt ions can competitively occupy membrane transporters, restricting the cellular uptake of vital nutrients like nitrogen, phosphorus, and potassium, which ultimately impedes microbial growth and reproduction [ 56 – 58 ]. 4.2 Network complexity and community assembly in response to habitat succession Soil functions depend not only on the abundance and composition of the microbial community but also on the interactions among microorganisms [ 59 , 60 ]. Although co-occurrence correlations do not directly equate to true ecological interactions, these patterns provide crucial insights into potential relationships, where negative and positive associations may indicate competition and facilitation, respectively [ 61 ]. In our study, the co-occurrence networks across all habitat types were primarily characterized by positive associations. The presence of such widespread positive linkages indicates a strong reliance on microbial mutualism in order to endure the severe climatic conditions, including freezing temperatures, strong winds, and hypoxia, typical of alpine ecosystems [ 27 ]. While positive interactions were a common feature, the complexity of these networks shifted profoundly in response to habitat succession. The peak network complexity observed in grasslands is likely attributable to extensive vegetation cover, which creates heterogeneous ecological niches (e.g., the rhizosphere) and supplies abundant organic carbon via root exudates and plant litter, thereby fostering intricate synergistic and competitive interactions among diverse microbial taxa. However, as the ecosystem transitions to barelands, elevated salinity imposes severe constraints on species interactions, leading to a highly simplified network. This aligns well with previous studies demonstrating that high environmental stress can disrupt microbial network structures and reduce their complexity [ 62 , 63 ]. Consistent with the shifts in network complexity, the highest stability and lowest vulnerability were observed in grasslands, indicating that enhanced network complexity likely contributes to maintaining the structural stability of microbial communities. The reduced stability in the bareland network can be attributed to the loss of positive interactions. As previously demonstrated, positive associations likely represent synchronous ecological shifts among taxa in the face of disturbances, thereby acting as a critical mechanism for maintaining steady ecosystem functioning [ 64 ]. Consequently, these findings strongly support the view that complexity begets stability [ 65 ], challenging alternative perspectives which argue that high network complexity induces fragility. It is widely recognized that both deterministic and stochastic processes contribute to microbial community assembly, with their relative importance varying across different environmental conditions [ 19 ]. Recent studies suggest that microbial communities governed by stochastic processes tend to exhibit greater network complexity and stability [ 66 ], which strongly aligns with our findings. Our results demonstrated that stochastic processes dominated the assembly of soil bacterial communities along the wetland-grassland-bareland continuum. This widespread stochasticity is typically observed in environments maintaining strong connections to aquatic habitats with changing hydrology [ 67 ]. These dynamics may increase random microbial colonization and extinction events, thereby overwhelming environmental selection pressures [ 68 , 69 ]. In addition, frequent environmental disturbances in soils, such as freeze-thaw cycles and alternating wet and dry conditions, probably disrupt the successional trajectories of communities and promote stochasticity in the Pamir Plateau region, where large daily temperature variations occur [ 58 ]. Despite the overall dominance of ecological drift, deterministic processes, particularly heterogeneous selection (HeS), were significantly elevated in barelands. The successional transition to barelands modifies the soil microenvironment by increasing soil salinity and depleting nutrients, thereby encouraging HeS. Concurrently, severe water scarcity in barelands restricts hydrological connectivity, thereby exacerbating dispersal limitation by physically impeding microbial mobility [ 70 ]. 4.3 Soil microbial functional potential and its ecological links with soil microbial properties across wetlands, grasslands, and barelands PICRUSt2 functional predictions indicated that habitat succession led to differential changes in microbial community function. Wetlands harbored high abundances of dissimilatory sulfate reduction ( apr , dsr ) genes, aligning with the typical biogeochemical characteristics of anaerobic wetland environments [ 71 , 72 ]. This finding is strongly supported by our LEfSe analysis, which demonstrated that the phylum Desulfobacterota was significantly enriched in wetlands. Members of this specific taxon actively participate in dissimilatory sulfate reduction, ultimately resulting in the production of hydrogen sulfide [ 73 ]. After the succession from wetlands to grasslands, the abundance of nitrogen fixation gene ( nifH ) increased, indicating the critical role of plant-microbe symbioses in grasslands. Plants recruit beneficial microorganisms through root exudates, which then enhance nutrient acquisition through direct mechanisms such as nitrogen fixation [ 74 ]. Additionally, grasslands exhibited the highest abundance of C cycling pathways, likely driven by increased plant residue inputs and root exudates, which facilitate soil organic carbon accumulation and stimulate microbial carbon turnover [ 75 ]. However, the ultimate succession to barelands severely depleted almost all key genes involved in C, N, and S cycling. This widespread functional decline highlights the profound impact of habitat succession on soil microbial functional potential. Recent evidence highlights that within agroecosystems, increased soil microbial network complexity underpins enhanced functional potential by stimulating nutrient cycling and carbon utilization efficiency [ 76 , 77 ]. However, few studies have linked succession-driven edaphic changes with soil microbial functional potential, nor have they thoroughly disentangled the mediating roles of microbial properties, especially in ecologically fragile alpine habitats on the Pamir Plateau. Contrary to previous studies, our PLS-PM and partial correlation analyses demonstrated that along the wetland-grassland-bareland continuum on the Pamir Plateau, microbial diversity served as the paramount biotic predictor of soil microbial functional potential. This pattern may be attributed to the fact that higher bacterial diversity inherently provides a broader and more versatile genetic pool, encompassing diverse biogeochemical pathways. This taxonomic and genetic complementarity ensures that the ecosystem can sustain multi-element cycling despite environmental fluctuations [ 78 ]. Conclusions This study comprehensively elucidated the spatial succession of soil microbiomes along an alpine environmental continuum on the Pamir Plateau. We demonstrated that microbial diversity gradually decreased along the spatial transition from wetlands to barelands, with bareland soils exhibiting significantly lower community stability than the other two habitats. Null model analyses of community assembly identified heterogeneous selection and dispersal limitation as the major drivers of the decline in diversity and stability. Crucially, our findings highlight that grasslands exhibited significantly higher soil microbial functional potential than both wetlands and barelands, a pattern likely driven by robust plant-soil feedbacks. Finally, this study revealed that, rather than network complexity, soil microbial diversity served as the pivotal microbial property mediating soil microbial functional potential. Given the significance of microorganisms in the overall functioning of ecosystems, gaining a deeper understanding of the microbial diversity and functional shifts triggered by alpine habitat succession is crucial for predicting and mitigating the ecological consequences of current and future environmental changes. Declarations Acknowledgments: We extend our gratitude to all researchers involved in the project for their efforts in collecting soil samples on the Pamir Plateau and working on data analysis. Funding statement: The authors declare that this research received no funding. Author contributions: All authors contributed to the study conception and design. W.W. and C.L. performed material preparation, data collection, and data analysis. C.L. and W.W. wrote the first draft of the manuscript, and C.H. provided critical supervision. All authors commented on previous versions of the manuscript, read, and approved the final manuscript. Conflicts of Interest The authors declare no competing interests. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon request. References Pennekamp F, Pontarp M, Tabi A et al (2018) Biodiversity increases and decreases ecosystem stability. Nature 563:109-112. https://doi.org/10.1038/s41586-018-0627-8 De Keersmaecker W, Lhermitte S, Honnay O et al (2014) How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Global Change Biol 20:2149-2161. https://doi.org/10.1111/gcb.12495 Isbell F, Gonzalez A, Loreau M et al (2017) Linking the influence and dependence of people on biodiversity across scales. 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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-9428328","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627787347,"identity":"340d4628-edab-46f6-9ea0-1600098bb01c","order_by":0,"name":"Wenrui Wang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Wenrui","middleName":"","lastName":"Wang","suffix":""},{"id":627787348,"identity":"f6a591cc-e838-4c77-9994-415073b95087","order_by":1,"name":"Changjiang Huang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Changjiang","middleName":"","lastName":"Huang","suffix":""},{"id":627787349,"identity":"af3a05c8-4b26-4c49-b920-0298101033c7","order_by":2,"name":"Congrui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACPmYYi5mx/ccHIM3GTkALG0IL8wHJGSgiuLQgMROkecB6CWlh5zH88HFHbb7BcR4DY5tf2+SBTmX88DEHn8N4jCVnnjluueEwj0Fybt9twzZmBmbJmdvwajFj5m07ZmAA1HI4t+c2I1ALGzMvkVoMmy17btsTq6UGqIUtmZnhx+1EIrSwFUvObDtgIHmY+Rhjb8Pt5DZmxma8fuHnP7zxw8e2OgO+8wfbGH78uW07v7354IePeLRAwWEIxdgGJhsIqgeCOij9hxjFo2AUjIJRMNIAAC9bRnadFxWbAAAAAElFTkSuQmCC","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Congrui","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-15 14:24:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9428328/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9428328/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107769545,"identity":"3362b2cc-32f4-4184-a16f-4bbe0f9402c5","added_by":"auto","created_at":"2026-04-25 03:41:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community composition and diversity across three habitat types. \u003c/strong\u003e(a) Overview of the sampling area. (b) Relative abundance of the main bacterial phyla across the three habitat types. (c) Venn diagram depicting the shared and unique ASVs of soil bacterial communities across wetlands, grasslands, and barelands. (d) The effects of habitat transitions on bacterial alpha diversity (Observed ASVs). Different letters above the violin plots denote significant differences at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05. (e) Non-metric Multidimensional Scaling (NMDS) based on weighted UniFrac distances illustrating the shifts in bacterial community structure across wetlands, grasslands, and barelands. Statistical analysis of community dissimilarity was performed using PERMANOVA (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/a6016aabeaca6ff2015574e2.png"},{"id":107769546,"identity":"2ec1432d-3ba5-4683-ae16-ad784871d7db","added_by":"auto","created_at":"2026-04-25 03:41:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnvironmental drivers of bacterial community composition and diversity across three habitat types. \u003c/strong\u003e(a) Mantel tests assessing the associations between environmental factors and bacterial community composition across wetlands, grasslands, and barelands. Edge width corresponds to Mantel’s \u003cem\u003er\u003c/em\u003e statistic, and edge color denotes the statistical significance. Spearman correlation coefficients for pairwise environmental factors are illustrated by the color gradient. (b) Linear Discriminant Analysis Effect Size (LEfSe) identifying differentially abundant soil microbial taxa. (c) Heatmap showing the correlations between environmental factors and major genera across habitat types. Color gradients represent Pearson's correlation coefficients. Asterisks indicate the statistical significance (*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (d) Random forest model showing the relative contribution of environmental predictors to bacterial species richness. The percentage increase in mean squared error (%IncMSE) quantifies variable importance. \u003cem\u003eP\u003c/em\u003e-values within the bars indicate the statistical significance of the predictors in the model, and the bar colors distinguish significant (blue) from non-significant (gray) factors.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/5df4068a7d39265487d67497.png"},{"id":107769547,"identity":"ffa6a578-414a-44d1-92aa-b8e686faa693","added_by":"auto","created_at":"2026-04-25 03:41:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial co-occurrence networks, complexity, and stability across three habitat types. \u003c/strong\u003e(a) Bacterial co-occurrence networks across different habitat types. The top 18 modules with the highest node counts are shown in different colors, and other modules are colored gray. (b) Differences in multiple network properties, including numbers of nodes and edges, diameter, density, degree centrality, betweenness centrality, average path length and average degree across three habitat types. (c) Differences in vulnerability of soil bacterial networks. (d) Stability of microbial networks across different habitats, assessed by observing the decline in natural connectivity under random node removal (0–80%).\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/5c9526ed59819f91fa923623.png"},{"id":107869734,"identity":"f93fe83f-5e76-4a41-bb9e-7932031991e6","added_by":"auto","created_at":"2026-04-27 07:38:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssembly mechanisms and key microbial drivers across the habitat continuum. \u003c/strong\u003e(a) Donut charts illustrating the relative importance of different ecological processes in shaping soil bacterial communities in wetlands, grasslands, and barelands. (b) Phylogenetic distribution of assembly contributions based on bin-level classification. The central tree branches are shaded according to microbial taxonomy. The three middle heatmap rings (Layers 1-3) display the relative importance of deterministic and stochastic processes for each bin within wetlands, grasslands, and barelands, respectively. The outermost bar chart (Layer 4) indicates microbial relative abundance.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/3d98be0aec12c6091062052b.png"},{"id":107870564,"identity":"778d36a9-fa06-441a-8a7d-b58c396ba6ac","added_by":"auto","created_at":"2026-04-27 07:39:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":69566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariations in functional genes involved in biogeochemical cycling across wetlands, grasslands, and barelands. \u003c/strong\u003e(a) Heatmap illustrating the variations in major carbon pathways across wetlands, grasslands, and barelands. (b) Relative abundance of genes involved in different nitrogen cycling steps. (c) Relative abundance of genes involved in sulfur cycling steps. Yellow and green arrows represent assimilatory and dissimilatory sulfate reduction, respectively. In panels (b) and (c), the three-block heatmaps adjacent to the gene names represent the abundance of that specific gene in wetlands, grasslands, and barelands, respectively (from left to right).\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/902a701b53084206af5ab29e.png"},{"id":107769548,"identity":"bd0f4e34-432f-4ec7-9125-e2a51db5b629","added_by":"auto","created_at":"2026-04-25 03:41:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinks between environmental variables, microbial properties, and soil microbial functional potential. \u003c/strong\u003eLinear regressions showing the relationships between soil microbial functional potential and with (a) network complexity and (b) bacterial diversity. (c) Partial least squares path modeling (PLS-PM) analysis of the relationships between environmental variables, soil microbial factors, and soil microbial functional potential. Positive and negative effects are represented by red and blue arrows, respectively. The thickness of the arrows indicates the magnitude of the path coefficients. Numbers adjacent to arrows are standardized path coefficients. The Goodness-of-Fit was used to assess the model. Significance levels for the path coefficients are indicated by asterisks: *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Binder16.png","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/01eff9a20eb66994d56b0255.png"},{"id":107872947,"identity":"1ff12c87-02b1-4223-94d4-7547f78298c6","added_by":"auto","created_at":"2026-04-27 08:00:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":988749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/20ac3651-9408-4432-ba46-9ec614a50867.pdf"},{"id":107769550,"identity":"adb20f7e-f936-461f-84db-fd37d938edc0","added_by":"auto","created_at":"2026-04-25 03:41:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":757700,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/695796dedd4f5aa6016aaf72.docx"},{"id":107769551,"identity":"98baf654-f497-4c73-abeb-c940117334d8","added_by":"auto","created_at":"2026-04-25 03:41:30","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":159092,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9428328/v1/95c3ad8f8007b78cb7c9e07f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Community Assembly Mechanisms Facilitate Understanding of Microbial Responses to Habitat Succession along a Spatial Environmental Continuum in Alpine Plateaus","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal ecosystems are currently facing dual pressures from anthropogenic disturbances and climate change, posing significant threats to ecosystem services [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In particular, high-altitude and mountainous ecosystems are extremely sensitive to perturbations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], often undergoing profound biogeomorphic successions that manifest as spatial continua across wetlands, grasslands, and barelands. This successional transition is characterized not only by hydrological recession but also by marked changes in vegetation cover and diversity, which potentially reshape the soil environment through plant-soil feedbacks (e.g., litter decomposition and root exudation) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As vital regulators of terrestrial ecosystems, soil microorganisms drive organic matter decomposition, nutrient cycling, and soil structure improvement, thereby sustaining overall ecological balance [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, severe changes in environmental conditions, such as decreased soil water content and shifts in soil properties (e.g., salinity and nutrients), significantly threaten the diversity and structure of soil microbiomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although the structural characteristics of microbial communities in isolated habitats have been well documented, their responses within spatially connected environmental continua remain poorly understood.\u003c/p\u003e \u003cp\u003eTo advance our understanding of belowground ecological responses, it is imperative to elucidate the underlying community assembly processes, given their profound implications for biodiversity and ecosystem functioning [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Generally, microbial community assembly is jointly governed by the interplay of stochastic and deterministic processes [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Deterministic processes underscore the pivotal role of selection, encompassing both environmental filtering by habitat conditions and biotic interactions such as competition and antagonism. In contrast, stochastic processes highlight the significance of probabilistic dispersal, ecological drift, and unpredictable disturbances [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These underlying mechanisms fundamentally shape the diversity and co-occurrence networks of soil microbiomes, thereby explaining the observed differences in taxonomic composition and species association patterns across distinct communities [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For instance, deterministic processes typically dominate in low-diversity communities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], whereas stochastic processes drive the formation of more complex microbial co-occurrence networks that can influence microbial interactions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, it remains poorly understood how the balance between these assembly processes shifts along the alpine wetland-grassland-bareland continuum, specifically whether severe abiotic stress in barelands enhances deterministic selection.\u003c/p\u003e \u003cp\u003eMicroorganisms rarely function in isolation; rather, they form complex ecological networks through various interspecies interactions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Key topological features of these networks serve as robust indicators of community complexity and resilience against external disturbances [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For example, as habitats transition from moist to arid environments, severe water scarcity significantly reduces the node number and average degree of microbial networks, resulting in simplified microbial co-occurrence networks and diminished community stability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, recent studies have not only revealed that biogeomorphic succession restructures soil microbial communities but also highlighted that both microbial diversity and network complexity are crucial predictors of soil functional potential [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For instance, microbial communities regulate the carbon (C), nitrogen (N), phosphorus (P), and sulfur (S) cycling potentials of soil ecosystems through the dual mechanisms of community diversity and network interactions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, a critical knowledge gap remains regarding whether microbial taxonomic diversity or network complexity acts as the primary biotic driver regulating soil functional potentials during successional transitions in extreme alpine environments.\u003c/p\u003e \u003cp\u003eIn this study, we focus on the Pamir Plateau, a region characterized by spatially connected environmental continua comprising wetlands, grasslands, and barelands, making it an ideal natural laboratory for investigating the rules of habitat succession in sensitive alpine regions. Our primary goal is to elucidate the microbial response patterns along this successional gradient and to decipher the underlying mechanisms by which environmental filtering and microbial interactions jointly regulate soil microbial functional potentials associated with C, N, and S cycling. Specifically, we aim to: (1) characterize the structural and functional shifts of bacterial communities across the three habitats; (2) uncover the community assembly processes and co-occurrence patterns of these microbial communities; and (3) elucidate the relative contributions of microbial diversity and network complexity to soil microbial functional potentials. Correspondingly, we hypothesized that (1) the spatial transition toward barelands significantly decreases microbial diversity, simplifies network complexity, and reduces overall soil microbial functional potential; (2) the transition toward barelands increases the relative contribution of deterministic assembly due to escalating environmental filtering; and (3) microbial diversity exerts a more potent influence than network complexity in regulating the functional potentials of C, N, and S cycling.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area and Field Sampling\u003c/h2\u003e\n \u003cp\u003eOur study was conducted in the eastern Pamir Plateau (Xinjiang, China), a region characterized by a typical plateau continental climate. The area has a mean annual temperature of -2.8\u0026deg;C and a precipitation of 90 mm [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The terrestrial ecosystems in our study area are predominantly composed of alpine wetlands, grasslands, and barelands, which form a well-preserved and spatially continuous ecological gradient.\u003c/p\u003e\n \u003cp\u003eTo maintain temporal consistency, all sampling was conducted between July 1 and 2, 2025, and no extreme weather events were recorded during this period. A total of three study sites, situated at a mean elevation of 3,311 m, were selected across the region (38.85\u0026deg; N, 74.84\u0026deg; E; 37.88\u0026deg; N, 75.23\u0026deg; E; and 38.94\u0026deg; N, 74.58\u0026deg; E), each comprising three adjacent habitat types: alpine wetland, grassland, and bareland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Within each habitat type at each site, we established four independent replicate plots. In each plot, surface soil samples (0\u0026ndash;10 cm) were collected using a five-point sampling method and thoroughly mixed to form a composite sample. Sampling tools were sterilized before collection. This sampling design yielded a total of 36 samples (3 sites \u0026times; 3 habitat types \u0026times; 4 replicates).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eEach collected soil sample was divided into two subsamples: one was stored on dry ice and subsequently frozen at \u0026minus;\u0026thinsp;80\u0026deg;C in the laboratory for DNA extraction, and the other was air-dried for measurement of soil physicochemical properties.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Soil Physicochemical Analyses and Data Collection\u003c/h2\u003e\n \u003cp\u003eSoil pH was measured at 25\u0026deg;C using a Thermo Orion-868 pH meter (Thermo Orion-868, MA, USA) in a 1:2.5 (w/v) suspension prepared with 1 mol/L potassium chloride solution [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Total nitrogen (TN) was determined via combustion using a Flash Smart Elemental Analyzer (Thermo Fisher Scientific, Bremen, Germany) after the samples were air-dried and sieved through a 2-mm mesh [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The contents of total phosphorus (TP) and total potassium (TK) were measured using an Optima 8000 ICP-AES spectrometer (Perkin-Elmer, USA), following sample preparation via acid digestion and tri-acid extraction, respectively [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Total salt content was determined using a gravimetric method: a soil suspension was prepared with deionized water in a 1:5 (w/v) ratio, after which it was filtered, and the filtrate was evaporated to dryness [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The residue was treated with hydrogen peroxide to remove organic matter, and the remaining weight was used to calculate the total water-soluble salt content. Latitude, longitude, elevation, mean annual temperature (MAT), and mean annual precipitation (MAP) of each sampling site were acquired from the WorldClim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.worldclim.org\u003c/span\u003e\u003c/span\u003e, Version-2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Molecular Analysis\u003c/h2\u003e\n \u003cp\u003eTotal DNA was extracted from soil samples using the FastDNA Spin Kit for Soil (MP Biomedicals, Cleveland, OH). DNA concentration was determined using a Qubit 3.0 Fluorometer (Thermo Fisher Scientific, USA). To profile the bacterial community, the V4 region of the 16S rRNA gene was amplified using the primer pair 515F (5\u0026prime;-GTGCCAGCMGCCGCGGTAA-3\u0026prime;) and 806R (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. All PCR amplifications were conducted in triplicate under the following cycling program: an initial denaturation for 3 min at 94\u0026deg;C, followed by 31 cycles consisting of 30 s denaturation at 94\u0026deg;C, 30 s annealing at 53\u0026deg;C, and 30 s extension at 72\u0026deg;C, concluding with a final elongation of 8 min at 72\u0026deg;C. Amplicons were sequenced on the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA) with paired-end 300 bp reads.\u003c/p\u003e\n \u003cp\u003eThe raw sequence data were analyzed using QIIME2 (version 2024.2) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In brief, the q2-demux plugin was employed for demultiplexing and quality control; subsequently, the q2-dada2 plugin facilitated denoising to resolve exact Amplicon Sequence Variants (ASVs). The taxonomic annotation of ASVs was performed using an sklearn classifier pre-trained on the amplified target region based on the SILVA database (version 138) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. All ASVs identified as chloroplasts or mitochondria were discarded from the dataset. Finally, all samples were rarefied to an identical depth based on the minimum read count (22,000 reads), retaining a total of 39,301 bacterial ASVs for further analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Functional analyses based on PICRUSt2\u003c/h2\u003e\n \u003cp\u003eThe functional potential of the microbial communities was predicted from the 16S rRNA gene ASV abundance tables using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States). Functional prediction was performed using default parameters, which inherently included the normalization of 16S rRNA gene copy numbers. The predicted functional profiles were subsequently annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/\u003c/span\u003e\u003c/span\u003e) database to obtain KEGG Ortholog (KO) abundances for downstream functional pathway analysis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Microbial Networks and Null Models\u003c/h2\u003e\n \u003cp\u003eIn this study, we applied Spiec-Easi method to construct microbial co-occurrence networks based on the abundance matrix. Network graphs were generated using the igraph package, and network topological properties were calculated, including nodes, edges, diameter, density, average degree, average path length, betweenness centralization, and degree centralization for each habitat type [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To quantify network complexity at the sample level, we extracted the following key topological features from the subnetworks: the number of nodes, edges, average degree, clustering coefficient, average path length, and graph density [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These topological metrics were subsequently integrated into a single comprehensive index representing soil multitrophic network complexity, derived through multidimensional scaling (MDS) analysis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Prior to computing the final index, the average path length (an indicator of network sparsity) was transformed into its reciprocal. Network stability and robustness were assessed via simulated random node removal, with the rate of change in natural connectivity used as the evaluation metric [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The vulnerability of an individual node reflects its proportional role in maintaining global network efficiency, while the overarching vulnerability of the entire network is defined by the highest vulnerability score among all its constituent nodes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTo quantitatively evaluate the contributions of distinct taxa to community assembly processes, we employed a phylogenetic-bin-based null model analysis (iCAMP) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This method divides the observed taxa into phylogenetic groups (bins) and identifies the assembly processes governing each bin based on both phylogenetic and taxonomic dissimilarities. For each phylogenetic bin of each sample pair, we calculated the beta net relatedness index (\u0026beta;NRIbin) and modified Raup\u0026ndash;Crick metric (RCbin) to evaluate community assembly.\u003c/p\u003e\n \u003cp\u003eThe specific classifications were as follows: deterministic processes were indicated by |\u0026beta;NRIbin| \u0026gt; 1.96, where \u0026beta;NRIbin\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;1.96 indicated homogeneous selection and \u0026beta;NRIbin\u0026thinsp;\u0026gt;\u0026thinsp;1.96 represented heterogeneous selection. For stochastic processes (|\u0026beta;NRIbin| \u0026le; 1.96), RCbin\u0026thinsp;\u0026gt;\u0026thinsp;0.95 denoted dispersal limitation, while RCbin\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.95 reflected homogenizing dispersal. The remaining fraction with |\u0026beta;NRIbin| \u0026le; 1.96 and |RCbin| \u0026le; 0.95 reflected ecological drift.\u003c/p\u003e\n \u003cp\u003eSubsequently, the fraction of each process within individual bins was weighted by the relative abundance of each bin and aggregated to estimate the overall influence of each process at the whole community level.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, we employed the \u003cem\u003emicroeco\u003c/em\u003e R package, a specialized tool for statistical analysis and visualization [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. To characterize bacterial community composition, initial diversity analyses were performed after rarefying the ASV table. Dunn\u0026apos;s Kruskal-Wallis test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to determine significant differences in \u0026alpha;-diversity indices among different habitats. Soil microbial diversity was calculated using Observed ASVs, Shannon index, Simpson index, and Phylogenetic diversity (PD) index via the normalization-mean method [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Non-metric Multidimensional Scaling (NMDS) based on weighted UniFrac distances was applied for \u0026beta;-diversity analysis, highlighting spatial distribution patterns of the community structure. Linear discriminant analysis Effect Size (LEfSe) was performed to identify the key bacterial taxa driving the community dissimilarities across the three successional stages. An LDA effect size threshold of 3.5 was used to evaluate the biomarkers of all stages.\u003c/p\u003e\n \u003cp\u003eTo investigate the relationships between soil microbial communities and environmental variables, differences in physicochemical factors were assessed using Euclidean distance, while microbial community differences at the sampling sites were evaluated using weighted UniFrac distances. Scatter plots were created to visually illustrate the correlations between soil microbial communities and environmental variables across different successional stages.\u003c/p\u003e\n \u003cp\u003eFurthermore, Mantel tests were performed using the \u0026lsquo;linkET\u0026rsquo; package in R to reveal the relationships between soil microbes and soil environmental factors. Random forest models were used to predict species richness and assess the relative importance of each abiotic variable in predicting it. We calculated the importance of each variable using the \u0026lsquo;rfPermute\u0026rsquo; package.\u003c/p\u003e\n \u003cp\u003eThe soil microbial functional potential was determined by the average values of functional pathways associated with C, N, and S cycling after Z\u0026shy;score transformation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To elucidate the relationships between soil microbial functional potential, microbial diversity, and network complexity, linear regression analyses were first employed. Subsequently, owing to the strong inter-correlations among various factors, partial correlation analyses were conducted to isolate their direct associations. Ultimately, partial least squares path modeling (PLS-PM) was employed to comprehensively disentangle the effects of both abiotic and biotic variables on the soil microbial functional potential.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of soil physicochemical properties\u003c/h2\u003e \u003cp\u003eComparative analysis of soil physicochemical properties across the three habitat types revealed significant differences in soil total salt content and TN (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Specifically, TN content was highest in grasslands and lowest in barelands (i.e., grasslands\u0026thinsp;\u0026gt;\u0026thinsp;wetlands\u0026thinsp;\u0026gt;\u0026thinsp;barelands). In contrast, soil total salt content exhibited an increasing trend from wetlands to barelands (i.e., barelands\u0026thinsp;\u0026gt;\u0026thinsp;grasslands\u0026thinsp;\u0026gt;\u0026thinsp;wetlands). However, no significant differences were observed in soil pH, TP, and TK among the three habitats.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in microbial diversity and community composition\u003c/h2\u003e \u003cp\u003eThe Venn diagram illustrated the overlap of the ASV-level microbial communities among the three habitat soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). At the ASV level, the wetland soils contained 13,976 unique ASVs (35.56%), the grassland soils hosted 14,266 unique ASVs (36.30%), and the bareland soils hosted 9,665 unique ASVs (24.59%), with only 236 ASVs (0.60%) shared among all three habitats. ASV-based species annotation revealed that bacterial community compositions were strongly influenced by habitat type, with distinct patterns observed after succession from wetlands to barelands (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and S3a-c).\u003c/p\u003e \u003cp\u003eTaxonomic profiling at the phylum level revealed that the bacterial communities were dominated by Proteobacteria, Acidobacteria, and Chloroflexi, with Proteobacteria being the most abundant. The relative abundance of Proteobacteria increased from an average of 24.9% in wetlands to 26.6% in grasslands, but subsequently decreased to 20.9% in barelands (Fig. S3a and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther classification into bacterial classes showed that Gammaproteobacteria were dominant in wetlands (12.89%), whereas Alphaproteobacteria were highly abundant in grasslands (12.55%), and Actinobacteria were significantly enriched in barelands (11.18%) (Fig. S3b). Further down to the genus level, \u003cem\u003eKD4-96\u003c/em\u003e was the dominant genus in both wetlands (2.08%) and grasslands (1.55%), while \u003cem\u003eJG30-KF-CM45\u003c/em\u003e became predominant in barelands (2.69%) (Fig. S3c).\u003c/p\u003e \u003cp\u003eIn terms of alpha diversity, wetlands exhibited significantly higher species richness, Shannon, and PD indices compared to barelands (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, S1, and Table S3). NMDS analysis based on weighted UniFrac distances revealed that habitat succession drove significant differences in bacterial community composition across wetlands, grasslands, and barelands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Environmental variables influencing microbial communities\u003c/h2\u003e \u003cp\u003eLinear regression models based on distance matrices revealed that the associations between microbial community dissimilarity and environmental variations differed notably across the three habitats (Fig. S4). In wetlands, bacterial community composition was positively correlated with pH, TP, TK, and TN. In grasslands, community variations were primarily driven by edaphic factors such as soil total salt content and pH. In barelands, variations in community structure were strongly associated with differences in pH, TP, TK, and soil total salt content.\u003c/p\u003e \u003cp\u003eMantel tests revealed that bacterial communities in wetlands were significantly correlated with a wider array of environmental factors relative to those in grasslands and barelands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Table S5). In wetlands, the bacterial community was significantly associated with soil nutrient factors, with TP exhibiting the strongest correlation. In contrast, the bacterial community in grasslands was primarily driven by soil total salt content, while showing no significant correlations with soil nutrient factors, including TN, TP, and TK. For barelands, the bacterial community exhibited a more pronounced response to spatial and climatic variables. When considering all habitats together, soil total salt content was identified as a critical factor regulating shifts in soil bacterial communities along the successional continuum.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, LEfSe analysis identified key taxonomic biomarkers characterizing each habitat (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), revealing that a greater number of taxa were enriched in wetlands compared to grasslands and barelands. Specifically, wetlands were enriched with \u003cem\u003eBurkholderiales\u003c/em\u003e, \u003cem\u003eAcidobacteriota\u003c/em\u003e, and \u003cem\u003eDesulfobacterota\u003c/em\u003e, whereas grasslands showed enrichment of \u003cem\u003eAnaerolineae\u003c/em\u003e, \u003cem\u003eBacteroidales\u003c/em\u003e, and \u003cem\u003eSBR1031\u003c/em\u003e. Moreover, barelands were characterized by the enrichment of \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eChloroflexia, and Thermomicrobiales\u003c/em\u003e. Subsequent correlation analysis revealed that dominant taxa exhibited distinct responses to various environmental factors during successional transitions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Tables S6-8). Additionally, random forest models demonstrated that soil total salt content was the most crucial variable in predicting bacterial species richness across the different habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Co-occurrence network analysis\u003c/h2\u003e \u003cp\u003eCo-occurrence networks were constructed to evaluate changes in possible ecological interactions across the different habitat types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Analysis of topological properties further quantified these differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and Table S9). The grassland network exhibited the highest number of nodes and edges, as well as the highest average degree among all networks, indicating the highest level of complexity. In contrast, the bareland network showed the lowest values for these metrics, indicating a simpler community structure. Additionally, the average path length and diameter were highest in the bareland network and lowest in the grassland network, suggesting that information or resource transfer was more efficient within the tightly clustered grassland network compared to the bareland network. We also found that the grassland network had the lowest vulnerability, suggesting that it was more resilient to environmental perturbations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Consistent with this, the stability of the bacterial network was highest in grasslands compared to wetlands and barelands, as evidenced by higher natural connectivity under random node removal, indicating a more tightly connected microbial community (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Community assembly and driving factors\u003c/h2\u003e \u003cp\u003eTo further explore the underlying assembly mechanisms, null model analyses were conducted to evaluate the relative contributions of deterministic and stochastic processes along the habitat continuum (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and Table S10). Our findings indicate that community assembly across all studied habitats was primarily governed by stochastic processes, specifically ecological drift and dispersal limitation. However, the relative contribution of deterministic processes was higher in barelands compared to wetlands and grasslands. Notably, the contribution of heterogeneous selection (HeS) was markedly higher in barelands (8.7%) compared to wetlands (1.9%) and grasslands (1.4%), suggesting that selective pressures played a stronger role in structuring soil microbial communities in the bareland ecosystem.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, we identified the key microbial taxa that contributed significantly to these assembly processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and Tables S11-12). Regarding heterogeneous selection, \u003cem\u003eNodularia\u003c/em\u003e PCC 9350 was the most responsive taxon in wetlands, whereas \u003cem\u003eNodosilinea\u003c/em\u003e PCC 7104 was primarily influenced in grasslands. In contrast, \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e showed the highest sensitivity to heterogeneous selection in barelands. For dispersal limitation, \u003cem\u003eRoseimaritima\u003c/em\u003e, \u003cem\u003eJTB255\u003c/em\u003e-marine benthic group, and \u003cem\u003eJG30-KF-CM45\u003c/em\u003e were identified as the key taxa governed by this process in wetlands, grasslands, and barelands, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Soil microbial functional potential\u003c/h2\u003e \u003cp\u003eTo further explore the microbial functional potential driving soil nutrient cycling during habitat succession, the key genes and pathways involved in biogeochemical cycles\u0026mdash;specifically C, N, and S cycling\u0026mdash;were functionally annotated and predicted across the three habitat types (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-c). Overall, the soil bacterial functional profiles differed among the three habitat types. At KEGG pathway level 3, the relative abundances of pathways related to C cycling were lower in barelands than in the other two habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Habitat succession significantly altered a number of important genes involved in N and S cycling. First, the relative abundances of nitrification genes (\u003cem\u003eamoA/amoB\u003c/em\u003e and \u003cem\u003ehao\u003c/em\u003e) were higher in wetlands than in the other two habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), and the relative abundance of nitrogen fixation gene (\u003cem\u003enifH\u003c/em\u003e) increased along the transition from wetlands to grasslands. Notably, the succession to barelands severely decreased the abundance of almost all key genes involved in N cycling. Regarding the S cycle, dissimilatory sulfate reduction genes (\u003cem\u003eapr\u003c/em\u003e and \u003cem\u003edsr\u003c/em\u003e) were more abundant in wetlands than in grasslands and barelands (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In contrast, assimilatory sulfate reduction genes (for example, \u003cem\u003ecysC\u003c/em\u003e, \u003cem\u003ecysH\u003c/em\u003e, and \u003cem\u003esir\u003c/em\u003e) were highly enriched in the grasslands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Linking bacterial communities and network complexity to soil microbial functional potential\u003c/h2\u003e \u003cp\u003eTo further disentangle the relative contributions of microbial community characteristics to soil microbial functional potential, we evaluated the relationships between microbial diversity, network complexity, and soil microbial functional potential (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Regression analyses revealed a highly significant negative correlation between network complexity and soil microbial functional potential. Conversely, bacterial diversity exhibited a significant positive correlation with functional potential, although the explanatory power was relatively lower in the simple regression model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the direct and indirect effects of environmental factors and microbial properties (α/β-diversity and network complexity) on soil microbial functional potential, we employed PLS-PM. The model exhibited a robust fit (GoF\u0026thinsp;=\u0026thinsp;0.74; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, Table S13). Our results indicated that spatial variables had negative direct effects on climatic (path coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and edaphic variables (path coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In turn, edaphic variables positively influenced microbial composition (path coefficient\u0026thinsp;=\u0026thinsp;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but negatively affected microbial diversity (path coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and soil microbial functional potential (path coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Importantly, soil microbial functional potential was strongly and positively driven by microbial diversity (path coefficient\u0026thinsp;=\u0026thinsp;0.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas it exhibited a weak and non-significant negative relationship with network complexity (path coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). To eliminate the confounding influence of microbial diversity, we performed a partial correlation analysis; notably, when microbial diversity was controlled as a covariate, the initially observed significant correlation between network complexity and soil microbial functional potential became non-significant (Pearson's r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.152, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.416). Collectively, these findings emphasize that compared to microbial network complexity, microbial diversity serves as a much better predictor and driver of soil microbial functional potential.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Soil microbial community composition and its determinants across wetlands, grasslands, and barelands\u003c/h2\u003e \u003cp\u003eOur findings showed that soil total salt content significantly increased along the successional gradient from alpine wetlands to grasslands and barelands. This is in line with previous reports showing significant variations in soil salinity across different habitat types [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. While grasslands were primarily characterized by mild to moderate salinity, barelands exhibited the highest salinity levels. Furthermore, our results revealed that soil total nitrogen content was significantly higher in grasslands than in alpine wetlands and barelands. This pattern may be attributed to plant root exudates accelerating the decomposition of plant residues, thereby releasing more nitrogen [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCorrespondingly, the habitat succession drove profound shifts in microbial community composition. The phylum \u003cem\u003eProteobacteria\u003c/em\u003e was the most abundant in both wetlands and grasslands, whereas bareland soils showed an increased relative abundance of \u003cem\u003eActinobacteria\u003c/em\u003e\u0026mdash;taxa known for their oligotrophic nature and metabolic versatility, exhibiting strong tolerance to extreme environments [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The enrichment of these taxa reflects a harsh microbial habitat resulting from the absence of vegetation cover, reduced soil organic matter inputs, and low nutrient availability [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese succession-induced shifts in soil microbial communities were closely coupled with the drastic alterations in underlying soil physicochemical properties. Here, we found that soil bacterial community composition and diversity were robustly correlated with soil total salt content, suggesting that salinity may be a critical factor regulating changes in soil bacterial communities along the wetland-grassland-bareland continuum, a finding consistent with the widely accepted view that salinity plays a regulatory role in the turnover of soil microbes in many ecosystems. In agreement with previous studies, we observed a continuous decline in bacterial diversity along the increasing salinity gradient. Elevated soil salinity increases extracellular osmotic pressure, forcing many bacterial taxa into dormancy or death, thereby explaining the observed decline in diversity [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Additionally, the accumulation of specific ions such as sodium, chloride, and sulfate at high concentrations can exert direct toxic effects on bacteria, acting as an additional mechanism driving the loss of diversity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Furthermore, these excess salt ions can competitively occupy membrane transporters, restricting the cellular uptake of vital nutrients like nitrogen, phosphorus, and potassium, which ultimately impedes microbial growth and reproduction [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Network complexity and community assembly in response to habitat succession\u003c/h2\u003e \u003cp\u003eSoil functions depend not only on the abundance and composition of the microbial community but also on the interactions among microorganisms [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Although co-occurrence correlations do not directly equate to true ecological interactions, these patterns provide crucial insights into potential relationships, where negative and positive associations may indicate competition and facilitation, respectively [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In our study, the co-occurrence networks across all habitat types were primarily characterized by positive associations. The presence of such widespread positive linkages indicates a strong reliance on microbial mutualism in order to endure the severe climatic conditions, including freezing temperatures, strong winds, and hypoxia, typical of alpine ecosystems [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While positive interactions were a common feature, the complexity of these networks shifted profoundly in response to habitat succession. The peak network complexity observed in grasslands is likely attributable to extensive vegetation cover, which creates heterogeneous ecological niches (e.g., the rhizosphere) and supplies abundant organic carbon via root exudates and plant litter, thereby fostering intricate synergistic and competitive interactions among diverse microbial taxa. However, as the ecosystem transitions to barelands, elevated salinity imposes severe constraints on species interactions, leading to a highly simplified network. This aligns well with previous studies demonstrating that high environmental stress can disrupt microbial network structures and reduce their complexity [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent with the shifts in network complexity, the highest stability and lowest vulnerability were observed in grasslands, indicating that enhanced network complexity likely contributes to maintaining the structural stability of microbial communities. The reduced stability in the bareland network can be attributed to the loss of positive interactions. As previously demonstrated, positive associations likely represent synchronous ecological shifts among taxa in the face of disturbances, thereby acting as a critical mechanism for maintaining steady ecosystem functioning [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Consequently, these findings strongly support the view that complexity begets stability [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], challenging alternative perspectives which argue that high network complexity induces fragility.\u003c/p\u003e \u003cp\u003eIt is widely recognized that both deterministic and stochastic processes contribute to microbial community assembly, with their relative importance varying across different environmental conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent studies suggest that microbial communities governed by stochastic processes tend to exhibit greater network complexity and stability [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], which strongly aligns with our findings. Our results demonstrated that stochastic processes dominated the assembly of soil bacterial communities along the wetland-grassland-bareland continuum. This widespread stochasticity is typically observed in environments maintaining strong connections to aquatic habitats with changing hydrology [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. These dynamics may increase random microbial colonization and extinction events, thereby overwhelming environmental selection pressures [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. In addition, frequent environmental disturbances in soils, such as freeze-thaw cycles and alternating wet and dry conditions, probably disrupt the successional trajectories of communities and promote stochasticity in the Pamir Plateau region, where large daily temperature variations occur [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Despite the overall dominance of ecological drift, deterministic processes, particularly heterogeneous selection (HeS), were significantly elevated in barelands. The successional transition to barelands modifies the soil microenvironment by increasing soil salinity and depleting nutrients, thereby encouraging HeS. Concurrently, severe water scarcity in barelands restricts hydrological connectivity, thereby exacerbating dispersal limitation by physically impeding microbial mobility [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3 Soil microbial functional potential and its ecological links with soil microbial properties across wetlands, grasslands, and barelands\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePICRUSt2 functional predictions indicated that habitat succession led to differential changes in microbial community function. Wetlands harbored high abundances of dissimilatory sulfate reduction (\u003cem\u003eapr\u003c/em\u003e, \u003cem\u003edsr\u003c/em\u003e) genes, aligning with the typical biogeochemical characteristics of anaerobic wetland environments [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. This finding is strongly supported by our LEfSe analysis, which demonstrated that the phylum \u003cem\u003eDesulfobacterota\u003c/em\u003e was significantly enriched in wetlands. Members of this specific taxon actively participate in dissimilatory sulfate reduction, ultimately resulting in the production of hydrogen sulfide [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. After the succession from wetlands to grasslands, the abundance of nitrogen fixation gene (\u003cem\u003enifH\u003c/em\u003e) increased, indicating the critical role of plant-microbe symbioses in grasslands. Plants recruit beneficial microorganisms through root exudates, which then enhance nutrient acquisition through direct mechanisms such as nitrogen fixation [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, grasslands exhibited the highest abundance of C cycling pathways, likely driven by increased plant residue inputs and root exudates, which facilitate soil organic carbon accumulation and stimulate microbial carbon turnover [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. However, the ultimate succession to barelands severely depleted almost all key genes involved in C, N, and S cycling. This widespread functional decline highlights the profound impact of habitat succession on soil microbial functional potential.\u003c/p\u003e \u003cp\u003eRecent evidence highlights that within agroecosystems, increased soil microbial network complexity underpins enhanced functional potential by stimulating nutrient cycling and carbon utilization efficiency [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. However, few studies have linked succession-driven edaphic changes with soil microbial functional potential, nor have they thoroughly disentangled the mediating roles of microbial properties, especially in ecologically fragile alpine habitats on the Pamir Plateau. Contrary to previous studies, our PLS-PM and partial correlation analyses demonstrated that along the wetland-grassland-bareland continuum on the Pamir Plateau, microbial diversity served as the paramount biotic predictor of soil microbial functional potential. This pattern may be attributed to the fact that higher bacterial diversity inherently provides a broader and more versatile genetic pool, encompassing diverse biogeochemical pathways. This taxonomic and genetic complementarity ensures that the ecosystem can sustain multi-element cycling despite environmental fluctuations [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study comprehensively elucidated the spatial succession of soil microbiomes along an alpine environmental continuum on the Pamir Plateau. We demonstrated that microbial diversity gradually decreased along the spatial transition from wetlands to barelands, with bareland soils exhibiting significantly lower community stability than the other two habitats. Null model analyses of community assembly identified heterogeneous selection and dispersal limitation as the major drivers of the decline in diversity and stability. Crucially, our findings highlight that grasslands exhibited significantly higher soil microbial functional potential than both wetlands and barelands, a pattern likely driven by robust plant-soil feedbacks. Finally, this study revealed that, rather than network complexity, soil microbial diversity served as the pivotal microbial property mediating soil microbial functional potential. Given the significance of microorganisms in the overall functioning of ecosystems, gaining a deeper understanding of the microbial diversity and functional shifts triggered by alpine habitat succession is crucial for predicting and mitigating the ecological consequences of current and future environmental changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to all researchers involved in the project for their efforts in collecting soil samples on the Pamir Plateau and working on data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this research received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. W.W. and C.L. performed material preparation, data collection, and data analysis. C.L. and W.W. wrote the first draft of the manuscript, and C.H. provided critical supervision. All authors commented on previous versions of the manuscript, read, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePennekamp F, Pontarp M, Tabi A et al (2018) Biodiversity increases and decreases ecosystem stability. 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J Environ Manage 327:116882. https://doi.org/10.1016/j.jenvman.2022.116882\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Habitat Succession, Soil Microbiome, Assembly Mechanisms, Network Complexity, Microbial Function","lastPublishedDoi":"10.21203/rs.3.rs-9428328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9428328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile soil microorganisms mediate important ecosystem functions, their community assembly mechanisms along a spatially connected environmental continuum during alpine habitat succession remain elusive. Here, we profiled the taxonomic and functional characteristics of soil microbiomes across three independent wetland-grassland-bareland continua on the Pamir Plateau. Along this successional gradient, microbial diversity, network complexity, and network stability decreased significantly towards barelands. Null model analyses revealed a pronounced shift in community assembly within barelands, characterized by increased heterogeneous selection and dispersal limitation. This mechanistic shift, driven by severe water scarcity and salt stress, underpins the diminished diversity and network robustness. Notably, soil microbial functional potential peaked in grasslands, likely reflecting enhanced plant-soil feedbacks. By linking community properties to functionality, we demonstrated that microbial diversity, rather than network complexity, serves as the primary biotic driver mediating shifts in soil microbial functional potential. Ultimately, these findings provide a mechanistic understanding of microbial community assembly and functional succession in sensitive alpine ecosystems.\u003c/p\u003e","manuscriptTitle":"Community Assembly Mechanisms Facilitate Understanding of Microbial Responses to Habitat Succession along a Spatial Environmental Continuum in Alpine Plateaus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-25 03:41:24","doi":"10.21203/rs.3.rs-9428328/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T03:30:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T02:06:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99467588791932594593012256091480175918","date":"2026-04-22T09:47:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178246670706999059651414934490339698589","date":"2026-04-19T02:55:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217843406041408961959027430216224937613","date":"2026-04-16T23:56:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T20:58:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T14:45:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T14:45:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2026-04-15T14:16:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"43c8a6f3-b171-416c-918b-9381cb393008","owner":[],"postedDate":"April 25th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T03:30:26+00:00","index":21,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T02:06:30+00:00","index":20,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T03:41:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-25 03:41:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9428328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9428328","identity":"rs-9428328","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}