Seasonal variations in rhizosphere soil bacterial and fungal communities of Quercus franchetii along an elevational gradient in the Yuanmou dry-hot valley, Southwest China

Seasonal variations in rhizosphere soil bacterial and fungal communities of Quercus franchetii along an elevational gradient in the Yuanmou dry-hot valley, Southwest China | 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 Seasonal variations in rhizosphere soil bacterial and fungal communities of Quercus franchetii along an elevational gradient in the Yuanmou dry-hot valley, Southwest China Xuedan XIE, Guanping LI, Jiao WU, Cailan LIU, Haitao YUE, Dacai ZHANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9357713/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Soil microbial communities are essential components and key drivers in maintaining ecosystem function. This study focused on Quercus franchetii in the Yuanmou dry-hot valley of Southwest China, and analyzed the characteristics of rhizosphere soil microbial communities along an elevational gradient in the dry and rainy season using high-throughput sequencing technique. Result Actinobacteriota, Proteobacteria, Acidobacteriota, and Chloroflexi were identified as the dominant bacterial phyla, with Acidothermus , Conexibacter , Mycobacterium , and Bryobacter as the dominant genera. For fungi, Ascomycota and Basidiomycota were identified as the dominant phyla, with Russula , Penicillium , Phialomyces , and Sebacina as the most abundant genera. Elevation was a more important determinant of bacterial α-diversity than season. By contrast, fungal α-diversity was significantly affected by season but not by elevation. Elevation significantly shaped the community structure of both bacteria and fungi. Soil pH was the primary driver of bacterial α-diversity, whereas fungal α-diversity correlated with none of the measured soil physicochemical properties. Soil pH and water content exerted highly significant effects on bacterial community structure, whereas only soil pH had a highly significant impact on fungal community structure. The bacteria-fungi co-occurrence network in the dry season featured a higher proportion of positive correlations. The functions of the bacterial community were primarily chemoheterotrophic and aerobic chemoheterotrophic, with significant seasonal differences in cellulolysis, nitrate reduction, and ureolysis. Symbiotroph and saprotroph‑symbiotroph, pathotroph‑saprotroph were the dominant trophic modes in the fungal community, with significant seasonal differences in ectomycorrhizal fungi and saprotrophic fungi. Conclusions Bacterial community diversity showed higher sensitivity to environmental fluctuations, while fungal diversity exhibited more pronounced seasonal variation. Soil pH was the primary driver of bacterial and fungal diversity. Bacterial functions were dominated by carbon cycling processes, with seasonal differences in cellulolysis, nitrate reduction, and ureolysis. Ectomycorrhizal fungi dominated in the dry season, while saprotrophic fungi prevailed in the rainy season. This research advances our understanding of the microbial ecology of rhizosphere of Q. franchetii , the factors shaping its microbial communities, and the stable operation of plant and soil systems. Quercus franchetii Dry-hot valley Rhizosphere soil Microbial community Dry and rainy seasons Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Soil microbial communities are essential components and key drivers in maintaining ecosystem function [ 1 , 2 ]. Rhizosphere soil microbial communities, in particular, play a critical role in global biogeochemical cycles, including nutrient retention, nutrient cycling, organic matter decomposition, and multiple ecosystem functions linked to plant diversity and decomposition [ 3 – 5 ]. The composition and diversity of soil microbial communities drive the stable operation of plant and soil systems [ 6 ], and interact closely with plants, influencing plant growth, health, and stress resilience [ 7 – 9 ]. As the two major soil microbial taxa, bacteria and fungi are of utmost importance in determining environmental and host functioning [ 3 , 10 ]. Soil microbial communities in different ecosystems exhibit distinct spatiotemporal variations, while the dynamics of the microbiome are crucial for understanding its functions [ 11 , 12 ]. Elevational gradients provide unique insights into the regulatory mechanisms underpinning the spatial distribution of soil microorganisms [ 13 ]. Previous studies have documented various elevational distribution patterns of microbial communities in different ecosystems, including monotonic decreasing or increasing, unimodal [ 14 ], hollow [ 15 , 16 ] and no elevation pattern [ 17 ]. However, a comprehensive understanding of the drivers and ecological implications underlying these patterns remains limited. Thus, further investigation into shifts in the composition and structure of microbial communities along elevational gradients is essential to elucidate the underpinning mechanisms. Those elevational responses of microbial communities are commonly driven by temperature, arid environments and precipitation [ 13 ]. Furthermore, seasonal variations in temperature, precipitation, soil physicochemical factors, plant exudates play a key role in the diversity and structure of soil microbial communities [ 18 – 20 ]. Therefore, it is necessary to integrate seasonal dynamics into studies investigating microbial community changes along elevational gradients to obtain a more holistic understanding of their spatiotemporal patterns. Soil properties strongly drive spatial variations in soil microbial communities [ 21 ]. Soil physicochemical properties are consistently identified as predominant environmental covariates. However, there is no consistent conclusion about the variation of soil microbial communities and its driving factors [ 18 ]. At the global scale, climatic factors, followed by edaphic and spatial patterning, are the best predictors of soil fungal richness and community composition [ 22 ]. Local environmental conditions like organic carbon and soil pH were important in shaping the bacterial community [ 23 ]. Nitrate nitrogen (NN) and available phosphorus (AP) were identified as the primary determinants shaping the bacterial community structure, while NN and soil organic carbon (SOC) as the key factors influencing the fungal community composition [ 24 ]. Soil pH and total nitrogen (TN) were identified as the main driving factors for the variability of EM fungal community in the alpine zone of the European Alps [ 21 ]. Overall, the factors influencing rhizosphere microbial communities vary across different scales, vegetation types, geographical location, habitat and main species. The relationships between microbial communities and their environmental drivers remain poorly elucidated in tropical regions [ 25 ]. Similarly, the soil microbial communities associated with vegetation in arid environments, such as dry-hot valleys, remains poorly characterized. Dry-hot valleys of southwestern China, characterized by seasonal drought, high temperatures, and intense ultraviolet radiation, present a challenging environment that exacerbates risks of land degradation and biodiversity loss [ 26 ]. Quercus franchetii , a dominant drought-tolerant evergreen sclerophyllous tree in these valleys, plays a critical role in maintaining ecosystem resilience, and performs critical ecological functions in sustaining biodiversity and ensuring ecosystem resilience within dry-hot valleys ecosystems [ 27 ]. Natural regeneration of seedlings is severely limited in this habitat [ 28 ], and our investigations have documented increasing tree mortality, indicating that extended seasonal drought and extreme heat events pose serious threats to the survival of Q. franchetii populations in the dry-hot valley. Given that rhizosphere microbial communities are not only sensitive to environmental changes but also enhance plant resilience to harsh conditions, this study investigates the rhizosphere microbial communities of Q. franchetii to elucidate the ecological processes by which it assists host adaptation to this fragile ecosystem. We hypothesized that: (i) The diversity of rhizosphere bacterial and fungal communities would vary significantly with season and elevation, being lower in the dry season than in the rainy season due to drought stress, and follow a pattern similar to plant diversity, where diversity increases along the elevation gradient [ 29 ]. (ii) Soil water content (WC), SOC, and pH would be the primary drivers of diversity. (iii) Both the stability of microbial co-occurrence networks and community functional profiles would differ across seasons and elevations, with the dry season enriching unique taxa that possess drought-resistant functional traits. The findings aim to provide a scientific basis for the conservation and health maintenance of Q. franchetii populations. Materials and Methods The study site was located in the Yuanmou dry-hot valley, Southwest China. This region has a typical dry-hot climate with an average annual temperature of 21.8 ℃, an average annual precipitation of 634.0 mm, the annual evaporation is over 6.1 times the annual precipitation [30]. The region has obvious dry and rainy seasons, and the precipitation during the rainy season is mainly concentrated in July to September, and it accounting for about 92% of the annual precipitation. The vegetation is characterized by a Savanna of valley type, with Q. franchetii being one of the natural dominant tree species. The experimental samples were collected in January (dry season) and August (rainy season) 2024. According to the distribution range of Q. franchetii , five elevations (1200 m, 1400 m, 1600 m, 1800 m, 2100 m) were chosen in the Yuanmou dry-hot valley (Fig. 1, Table 1). The soil sampling method can be found in the Supplementary Appendix 1. Voucher plant specimens were deposited at the Herbarium of Southwest Forestry University and taxonomically identified as Quercus franchetii by Prof. Dacai Zhang of the same university. The voucher IDs for these specimens are SWFC0072428-SWFC0072432. The collections of soil and plant samples complied with institutional, national, and international guidelines. Table 1. Information of sample plots Site NO. Elevation Latitude and longitude Soil Type Slope Aspect Coverage of vegetation DBH (cm) S1200 1244.3 m 25°35′15″N; 101°49′58″E Yermosol-arenosols 14.20° 245° SW 90% 13.25 S1400 1426.8 m 25°45′16″N; 101°41′5″E Red soil-arenosols 10.50° 58° NE 98% 8.67 S1600 1608.8 m 25°43′58N″; 101°41′35″E Red soil-arenosols 19.0° 158° S 70% 6.16 S1800 1823.1 m 25°40′59N″; 101°37′24″E Red soil-arenosols 8.70° 29° NE 95% 4.13 S2100 2108.1 m 25°43′35N″; 101°57′31″E Purple soil-loam 19.0° 174° S 95% 8.03 Note: DBH: diameter at breast heigh, the value represents the mean of 15 sampled trees in each sample plot. Determination of rhizosphere soil physicochemical properties Soil water content (WC) was measured by the oven-drying method (105 ℃ to constant weight), soil pH value was determined potentiometrically using a glass electrode (soil : water = 1 : 2.5), SOC content was quantified via potassium dichromic oxidation-external heating method, TN was digested with sulfuric acid-accelerator followed by Kjeldahl distillation, total phosphorus (TP) was analyzed by NaOH fusion and molybdenum-antimony anti-spectrophotometry, AN was measured using alkali-hydrolyzable diffusion method, AP was extracted with ammonium fluoride-hydrochloric acid solution and determined by Mo-Sb anti-colorimetry [31]. Referring to the second national soil census of China for soil pH and nutrient classification [32, 33], the rhizosphere soil samples were all acidic, and the nutrient contents are generally low (Supplementary Table S1). WC revealed significant differences between dry and rainy season. Sequencing of rhizosphere soil microbial communities The method for DNA extraction polymerase chain reaction (PCR) amplification, and high-throughput sequencing can be found in the Supplementary Appendix 2. Statistical analysis The main and interaction effects of season and elevation on bacterial and fungal α-diversity (assessed using Chao1 and Shannon indices) were analyzed by nonparametric ANOVA with Aligned Rank Transform (ART). Soil microbial β-diversity across seasons and elevation was visualized by non-metric multidimensional scaling (NMDS) based on Bray-Curtis distances. A stress value ≤ 0.1 indicates excellent fit of the ordination to the original dissimilarity data. Permutational multivariate analysis of variance (PERMANOVA) was further applied to test the significance of seasonal and elevational differences. Variations in soil physicochemical properties across groups were compared using the Kruskal-Wallis test. The linkages between soil factors and microbial communities were quantified using Spearman correlation, Mantel tests, and redundancy analysis (RDA), and these analyses was performed by the genescloud tools, a free online platform for data analysis (https://www.genescloud.cn). To investigate microbial co-occurrence patterns across dry and rainy seasons along an elevation gradient, co-occurrence networks were constructed using the igraph and multcomp packages in R version 4.3.3. The Amplicon sequence variants (ASVs) were taxonomically annotated at the genus level, and those with an average relative abundance greater than 0.01% were retained for downstream analysis. These networks were based on Spearman's rank correlations, with a correlation coefficient threshold of 0.6 and a significance level of P < 0.05. Multiple topological properties were computed to characterize each network, including the number of nodes and edges, proportion of positive and negative edges, average path length, network diameter, average clustering coefficient, modularity, and average degree. The bacterial 16S rRNA sequences were analyzed to predict their ecological functions using the Functional Annotation of Prokaryotic Taxa (FAPROTAX) database. The fungal ITS1 sequences were analyzed to predict their functional roles using FUNGuild database. Only ASVs with confidence rankings of "probable" or "highly probable" for analysis. Results Composition of bacteria and fungi in the rhizosphere soil Taxonomic analysis of ASVs revealed that the bacterial community comprises 42 phyla, 135 classes, 321 orders, 544 families, 1109 genera and 3107 species, and the fungal community comprises 13 phyla, 56 classes, 146 orders, 381 families, 801 genera and 1288 species. The dominant bacterial phyla included Actinobacteriota, Proteobacteria, Acidobacteriota and Chloroflexi, which together accounted for over 75% of the total bacteria across all samples, and the relative abundance of Actinobacteriota reached the maximum at the 1200m and 1600m sites (Fig. 2A). The dominant bacterial genera included Acidothermus , Conexibacter , Mycobacterium and Bryobacter (Fig. 2B). The relative abundance of the genus Acidothermus was enriched in the dry season compared to the rainy season. The dominant fungal phyla included Ascomycota and Basidiomycota, which together accounted for over 90% of the total fungi across all samples, and the relative abundance of Ascomycota declined in the dry season, in contrast to a concomitant increase in Basidiomycota (Fig. 2C). The most abundant genera were Russula , Penicillium , Phialomyces and Sebacina (Fig. 2D). Diversity of bacteria and fungi in the rhizosphere soil The results of the ART nonparametric ANOVA indicted that the α-diversity of bacterial and fungal communities associated with Q. franchetii was differentially affected by elevation and season (Supplementary Table S2). The α-diversity of bacteria and fungi in rainy season was consistently higher than those in dry season (Fig. 3). Bacterial α-diversity was significantly influenced by elevation (P < 0.001) and season (P < 0.05), and the elevational pattern exhibited an approximately V-shaped distribution (Fig. 3A-B). Furthermore, the statistical analysis did not detect a significant interaction between elevation and season on the Chao1 and Shannon index (P > 0.05), indicating that the effects of elevation and season on bacterial α-diversity are independent. Fungal α-diversity were mainly influenced by season (P 0.05) (Fig. 3C-D). The peak values of the Chao1 index in both the dry and rainy seasons were observed at the 1200 m site. The maximum values of the Shannon index occurred at the 1400 m and 1600 m sites during the rainy and dry seasons, respectively. For the bacterial community, the NMDS yielded stress values of 0.0633 and 0.0644 (Fig. 4A-B). Samples from the dry and rainy season clustered tightly within the same elevation groups (Fig. 4A), while samples from different elevations indicated almost complete separation (Fig. 4B). For the fungal community, the NMDS yielded stress values of 0.185 and 0.147, representing a reasonable but less robust ordination (Fig. 4C-D). PERMANOVA analysis revealed that, while the composition of bacterial and fungal communities exhibited differences across the dry and rainy seasons, these differences were not significant (P > 0.05), but significant differences were observed among elevations (P < 0.05, Supplementary Table S3). The samples were widely dispersed in dry season (large ellipse), reflecting high heterogeneity of fungi in dry season, whereas the samples clustered tightly in rainy season (small ellipse), indicating relatively consistent community structures. Notably, the 95% confidence ellipse of the rainy season was entirely enclosed within that of the dry season, implying weak seasonal differentiation in fungal community, likely masked by the high variability of dry-season community (Fig. 4C). In contrast, samples from different elevations showed a discernible but incomplete separation (Fig. 4D). Effects of soil physicochemical properties on rhizosphere soil microbial diversity The Mantel tests indicated significant correlations between bacterial α-diversity and soil physicochemical properties (Fig. 5). Both the Chao1 and Shannon indices were strongly correlated with pH (P < 0.01), and significantly higher correlations with TN, AN, and AP (P < 0.05). In contrast, fungal α-diversity demonstrated moderate but non-significant links to all measured soil properties (Fig. 5). The RDA results indicated that the soil physicochemical factors explained 52.62% of the variation in bacterial community composition at the genus level (Fig. 6A). Among these factors, pH (R 2 = 0.52, P < 0.001) and WC (R 2 = 0.74, P SOC > TN > C/N > AP (Supplementary Table S4). The RDA results revealed that the soil physicochemical factors explained 28.15% of the variation in fungal community composition at the genus level (Fig. 6B). Among these factors, pH exerted a highly significant effect (R 2 = 0.57, P AN > TN > AP > TP > C/N (Supplementary Table S4). The RDA results indicated that the soil physicochemical factors explained 56.69% of the variance in the composition of the top 10 dominant bacterial genera (Fig. 6C). Among these factors, pH and AN exerted a highly significant effect (P < 0.001), TN, SOC and AP showed significant effects (P < 0.01). Regarding species-environment correlations, WC was negatively associated with the relative abundance of Acidothermus but positively correlated with that of Bradyrhizobium . Conversely, the nutrients SOC, AN, TN, TP, and AP were all positively associated with the abundances of Bradyrhizobium and Candidatus_Solibacter . The RDA results revealed that the soil physicochemical factors explained 41.48% of the variance in the composition of the top 10 dominant fungal genera (Fig. 6D). Among these factors, pH exerted a highly significant effect (P < 0.001), AN, TN, and SOC showed significant effects (P < 0.01), while TP and AP also had discernible effects (P < 0.05). Regarding species-environment correlations, WC was negatively associated with the relative abundance of Russula but positively correlated with that of Penicillium . Conversely, the nutrients SOC, AN, TN, TP, and AP were all positively associated with the abundances of Lactarius and Sebacina . Co-occurrence network and functional prediction The rhizosphere bacterial network in dry season showed fewer vertices, edges, average degree, clustering coefficient and modularity, but higher proportions of positive edges compared to rainy season (Supplementary Table S5). With increasing elevation, the bacterial networks exhibited a pattern of initially decreasing and then increasing vertices, edges, average degree, and clustering coefficient. Similarly, the fungal network in dry season displayed fewer vertices, edges, average degree, and modularity, but higher proportions of positive edges compared to rainy season (Supplementary Table S5). Fungal networks exhibited a heterogeneous pattern of fluctuation across different elevation. In bacteria–fungi co-occurrence network analysis, the network in dry season displayed fewer vertices, edges, average degree, and clustering coefficient, but higher modularity and proportions of positive edges compared to rainy season (Supplementary Table S5). the networks exhibited a heterogeneous pattern of fluctuation across different elevation. Functional prediction analysis showed that chemoheterotrophy, aerobic chemoheterotrophy and cellulolysis showed higher relative abundance in the rhizosphere bacterial community of Q. franchetii (Fig. 7A). Among these carbon-cycling functions, cellulolysis showed a significantly higher relative abundance in the dry season than in the rainy season (P < 0.01), while for N-cycling functions, the relative abundances of nitrate reduction and ureolysis were significantly lower in the dry season than in the rainy season (P < 0.001). Besides these, the relative abundance of cellulolysis and ureolysis exhibited significant differences among different elevations (P < 0.05) (Supplementary Fig. S1). The most relative abundance of trophic modes in the rhizosphere fungal community of Q. franchetii are symbiotroph, saprotroph-symbiotroph, pathotroph-saprotroph and pathotroph-saprotroph-symbiotroph (Fig. 7B). Symbiotrophs exhibited significantly higher relative abundance in the dry season than rainy season (P < 0.05, Supplementary Fig. S2). The most relatively abundant fungal taxa within these trophic modes were characterized by Russula , Thelephoraceae, Phialomyces and Penicillium . Discussion The composition and diversity of rhizosphere microbial communities In this work, we found that Actinobacteriota, Proteobacteria, Acidobacteriota, and Chloroflexi were the dominant bacterial phyla in the rhizosphere soil of Q. franchetii (Fig. 2 A), while Ascomycota and Basidiomycota were the dominant fungal phyla (Fig. 2 C), which are dominant microbial groups in drylands worldwide [ 22 , 34 , 35 ]. The genus Acidothermus , recognized as an important indicator of soil nutrient properties [ 36 ], thrives under acidic and high-temperature conditions [ 37 , 38 ]. As a dominant genus in the dry season, Acidothermus exhibited enhanced drought tolerance in this study (Fig. 6 B). In contrast, Bradyrhizobium , a genus of nitrogen-fixing bacteria, converts atmospheric nitrogen into NH₄⁺ and provides essential amino acids, nitrate, and proline to enhance plant resilience under drought stress [ 39 , 40 ]. However, Bradyrhizobium itself exhibits a strong dependence on soil moisture in this study (Fig. 6 B). As ectomycorrhizal fungi, Russula and Sebacina significantly enhance host plant drought resistance and nutrient uptake efficiency through common mycorrhizal networks (CMNs) [ 10 , 41 , 42 ]. In this study, the dry-season dominant genera Russula and Sebacina exhibited enhanced drought tolerance (Fig. 2 D). Effects of season and elevation on the diversity and structure of rhizosphere microbial communities The seasonal dynamics of microbial communities provide critical insights into their functional adaptations. We found that α-diversity indices of soil bacterial and fungal communities were higher in the rainy season than in the dry season, with fungal diversity exhibiting particularly significant seasonal variation. Xu et al. (2023) observed that the seasonal variation in soil fungal diversity in the two temperate forests was more significant than that of bacteria. This increase could be: (i) tIn the rainy season, plant growth stimulates root system development and root exudate production, which are important sources of soil carbon and nitrogen [ 43 ], especially the accelerated proliferation of fine root systems enhances the availability of root tissues for fungal symbiosis. (ii) The increase in soil temperature and moisture during the rainy season may be the environmental trigger for breaking dormancy of quiescent fungal spores under water stress, which are typically maintained in a metabolically inactive state under prolonged hydro-stress conditions [ 44 ]. (iii) In the rainy season, the higher WC and SOC may provide more stable resources for fungi. However, they also increase competition among different functional groups (such as saprotrophs and pathogens), thereby enhancing the fluctuation of diversity indices [ 31 ]. However, studies have found that, the richness of fungi and bacteria was higher in the dry season than the rainy season in Colombian tropical forests and páramo ecosystems, and the bacterial Shannon diversity was significantly higher in the dry season than in the rainy season [ 20 ]. This suggests that the diversity and composition of soil microbial communities may be involved in the response to precipitation changes for the host plant’s high resistance to drought. Effects of soil physicochemical factors on rhizosphere microbial communities Soil microbial communities have developed various strategies to adapt to diverse soil physicochemical properties, leading to a strong correlation between their diversity and these environmental conditions [ 45 ]. We found that the diversity and structure of bacterial communities was significantly correlated with pH, while α-diversity of fungi community exhibited no significant correlation with measured soil physicochemical parameter. The previous results by multi-scale investigations have collectively demonstrated that pH was the predominant determinant of both bacterial diversity and structure [ 23 , 46 ]. The bacterial diversity and structure were more influenced by the alterations in soil pH due to the narrow pH ranges for optimal growth of bacteria. For instance, Acidobacteria and Actinobacteria, the bacterial phyla with the highest relative abundance identified in this study, were classified as acid-tolerant microbial taxa [ 37 , 38 ]. However, the fungal diversity was less strongly affected by pH, as they have wider pH tolerance ranges [ 47 ]. As an ecological filter, soil pH indirectly alters the ecological functions of microbial communities by influencing their composition and stability [ 48 ]. Conversely, Microbial communities can actively modify pH through their involvement in fundamental biogeochemical processes, such as ammonification and denitrification [ 45 , 49 ]. WC can regulate the structure and metabolic activity of the soil microbial communities [ 50 , 51 ]. Despite the absence of a significant effect of WC on the composition of the top 10 bacterial and fungal genera, our results are in line with the established consensus that bacterial community exhibit greater sensitivity to water stress than fungal community [ 52 , 53 ]. Bacteria experiencing prolonged drought may enter a lag phase or even die, and their stability is more sensitive to shift of WC [ 54 ]. Although the overall effect of WC on the microbial communities was not significant-likely masked by covarying factors such as elevation and seasonal fluctuations-strong correlations were observed at genus level. Specifically, the dominant bacterial genera Acidothermus and Bradyrhizobium , and the key fungal genera Russula , Penicillium , Lactarius , and Sebacina , were strongly correlated with WC (Fig. 6 C-D). This indicates that WC is a selective force shaping specific components of rhizosphere microbial communities. Furthermore, beyond merely responding to moisture, these microbes likely engage in a feedback loop by actively modifying soil moisture dynamics through the secretion of compounds such as extracellular polymeric substances [ 45 ]. Prediction of ecological function of rhizosphere microbial communities The changes in rhizosphere microbial communities structure caused by season will further affect the ecological function of microbial communities. In this study, the functions of bacterial community were predominantly characterized by chemoheterotrophy, aerobic chemoheterotrophy and cellulolysis. Significant seasonal variations were observed in key metabolic processes including cellulolysis, nitrate reduction, and ureolysis. The significantly higher cellulolytic activity observed in the dry season could potentially result from the synergistic effects of multiple environmental and microbial factors: (i) enhanced soil aeration, (ii) increased plant litter input, and (iii) sustained enzymatic efficiency of drought-adapted cellulolytic bacteria. As keystone mediators of soil biogeochemical cycling, fungal and bacterial communities collectively drive nutrient mineralization, with fungi specializing in lignin degradation and bacteria in cellulose decomposition [ 34 , 55 ]. In the fungal community, the symbiotrophic guild exhibited a significantly higher relative abundance in the dry season, with ectomycorrhizal fungi (eg, Russula , Thelephoraceae) as the primary contributors, while saprotrophic guild exhibited a significantly higher relative abundance in the rainy season, led by unidentified saprotrophs (eg, Phialomyces , Penicillium) . Bacterial growth strongly reduces in drought conditions, whereas fungi display a remarkable resistance to drought via almost unchanged growth rates [ 52 ]. Hydraulic redistribution is likely one of the mechanisms underlying the higher drought resistance of soil fungi [ 56 ]. The sustained microbial activity during drought conditions could have large effects on soil carbon storage [ 57 ]. The reduced bacterial abundance diminished organic matter decomposition capacity, while drought-tolerant symbiotic fungi may be compensated by maintaining hyphal growth rates and increasing their relative abundance, thus facilitating host plant carbon assimilation and preserving ecosystem carbon flux stability. Microbial communities may enhance functional resilience and survivability under environmental stress by enhancing their local microenvironment [ 58 ]. Fungi can resist drought through their extensive hyphal networks, which maintain hydraulic continuity and water and nutrients uptake even under conditions of reduced soil diffusivity [ 52 , 59 ]. With the onset of seasonal rainfall, increased soil moisture content reactivates dormant spore-forming taxa that persisted under preceding drought conditions [ 57 ]. In this study, the functional inferences derived from taxonomic tools possess inherent limitations, as they may overlook functional redundancy and condition-specific metabolic activities. Future studies should integrate microbial culturing with metabolomic analyses to elucidate the functional dynamics within microbial networks. The co-occurrence networks in the dry season exhibited a marked reduction in network size (vertices and edges) and complexity (average degree and average path length) in this work. These changes likely resulted from environmental filtering under water stress, which selectively removed taxa poorly adapted to aridity [ 60 ]. The increased proportion of positive edges from the network in dry season suggests a shift toward a cooperative microbial strategy to mitigate environmental stress. Furthermore, the enhanced modularity observed in both the fungal and fungal-bacterial networks implies that, fungi and bacteria may form more cohesive, cooperative modules, thereby enhancing functional stability and resilience to drought [ 61 ]. Fungal and bacterial community form complex co-occurrence networks through physiological interactions and environmental filtering mechanisms, and these networks regulate nutrient cycling, thereby influencing soil health and plant growth [ 62 ]. However, both fungal-bacterial networks and subnetworks exhibited low modularity, indicating a stochastic network structure with limited stability that is susceptible to collapse under stress [ 61 ]. Previous studies have reported a significant decline in microbial co-occurrence network complexity with increasing elevation, and have also shown that the influence of microbial diversity on multifunctionality is indirectly mediated by network complexity [ 63 ]. In this study, bacterial network complexity along elevational gradient showed an initial decrease followed by an increase. This pattern may be linked to nonlinear variations in environmental factors (such as temperature, vegetation type, and soil properties) across the gradient, with the 1600 m site potentially representing a threshold for certain environmental stressors. In contrast, fungal networks and bacterial-fungal networks displayed heterogeneous fluctuations, suggesting that fungi may respond more sensitively and variably to different environmental filters [ 62 ]. Further comparative analysis of co-occurrence networks was limited by the sample size in this study, and therefore deeper analyses were not pursued. Conclusions The main results of this work reveal that bacterial community abundance responded to both elevation and season. In contrast, fungal community abundance was mainly influenced by season. Owing to their symbiotic associations with host plants and extensive hyphal networks, fungi exhibited a buffering capacity against micro-environmental variations. Elevation significantly shaped the community structure of both bacteria and fungi, creating distinct soil habitats and driving environmental filtering. Soil pH and WC significantly influenced bacterial community composition, whereas pH also affected fungal community composition, albeit with lower explanatory power. The higher heterogeneity in the fungal community in the dry season, together with the trophic shift from ectomycorrhizal dominance in the dry season to saprotrophic prevalence in the rainy season, suggests that certain ectomycorrhizal fungi (e.g., Russula and Thelephoraceae) may play an important role in drought adaptation. Bacterial community were primarily chemoheterotrophic and aerobic chemoheterotrophic, with significant seasonal variations in functions related to cellulolysis, nitrate reduction, and ureolysis. Collectively, these findings advance our understanding of the microbial ecology in the rhizosphere soil of Q. franchetii , elucidate the key factors shaping its microbial communities, and underscore their contributions to the stable functioning of plant-soil systems. Declarations Data availability The total raw sequences in this study have been deposited in the Genome Sequence Archive in National Genomics Data Center (https://ngdc.cncb.ac.cn/gsa) under the accession number CRA040326/CRA040327. Supplementary Information The online version contains supplementary material available. Acknowledgments We gratefully acknowledge the research team led by Dr. Fuqiang Yu at the Kunming Institute of Botany, Chinese Academy of Sciences, for their laboratory support. Our thanks also go to Dr. Dong Liu and Dr. Shanping Wan for their expert guidance. We are thankful to Dr. Dong Liu and Dr. Shanping Wan for their valuable advice and assistance. Authors’ Contributions All authors have made substantial contributions to the work. Xuedan Xie: investigation, data curation, formal analysis, methodology, validation, visualization, writing - original draft, writing - review and editing. Jiao WU, Guanping LI and Cailan LIU: investigation. Haitao YUE: methodology, software. Dacai Zhang: conceptualization, project administration, resources, supervision, funding acquisition. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by Technology Research of Ecological Restoration for Arid River Valley in Yunnan Province (6321A5), the Strategic Biological Resources Capacity Building Project, Chinese Academy of Sciences (KFJ-BRP-017-47). Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Key Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Southwest Forestry University, Kunming, 650224, China 2 Yunnan Key Laboratory of Plant Diversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China References Compant S, Samad A, Faist H, Sessitsch A. A review on the plant microbiome: Ecology, functions, and emerging trends in microbial application. J Adv Res 2019, 19:29-37. https://doi.org/10.1016/j.jare.2019.03.004 Labouyrie M, Ballabio C, Romero F, Panagos P, Jones A, Schmid MW, Mikryukov V, Dulya O, Tedersoo L, Bahram M et al . Patterns in soil microbial diversity across Europe. Nat Commun 2023, 14:3011. https://doi.org/10.1038/s41467-023-37937-4 Bahram M, Netherway T, Frioux C, Ferretti P, Coelho LP, Geisen S, Bork P, Hildebrand F. Metagenomic assessment of the global diversity and distribution of bacteria and fungi. 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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-9357713","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630669199,"identity":"b9fe8a2d-e3a8-4b34-b903-c37afdd96d95","order_by":0,"name":"Xuedan XIE","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xuedan","middleName":"","lastName":"XIE","suffix":""},{"id":630669206,"identity":"2f5fb672-f83a-49eb-8dcc-d21ef9e4c5bf","order_by":1,"name":"Guanping LI","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Guanping","middleName":"","lastName":"LI","suffix":""},{"id":630669215,"identity":"094c72c4-cc83-4c2d-b511-fd5b7cbb7677","order_by":2,"name":"Jiao WU","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"WU","suffix":""},{"id":630669217,"identity":"654a1e86-7758-4dde-a505-7bc4dfaa76d3","order_by":3,"name":"Cailan LIU","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Cailan","middleName":"","lastName":"LIU","suffix":""},{"id":630669221,"identity":"2406032c-0265-453e-b4d4-033b21cd4a1e","order_by":4,"name":"Haitao YUE","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Haitao","middleName":"","lastName":"YUE","suffix":""},{"id":630669223,"identity":"1bd17b48-a51f-4707-b738-606e29b3cf8e","order_by":5,"name":"Dacai ZHANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDCCAwxsIEqOsb2x8eEHUrQYM/ccbjaWIEVLYvuM9DYBHmJ08B0//uzBzx21jL0zH7YxSDDYyek2ENAieSYh3bD3zHFmydmJbQ8KGJKNzQ4Q0GJwg+GYBG/bMTbD2YntBhIMBxK3EdbC2Cb5t+0Yj/3Ng20SPMRpYWaT5m2rkWCcwUikFskzaWzSsm0HDBh7EoGBbECEX0AhJvm2ra6+sf34w4cfKuzkCGqBgsMwdxKnHATqiFc6CkbBKBgFIw8AAC51RrkM+gW2AAAAAElFTkSuQmCC","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Dacai","middleName":"","lastName":"ZHANG","suffix":""}],"badges":[],"createdAt":"2026-04-08 13:38:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9357713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9357713/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182975,"identity":"27b20528-9969-44d8-8414-50db8f16c33e","added_by":"auto","created_at":"2026-04-30 08:59:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1331514,"visible":true,"origin":"","legend":"\u003cp\u003eMap of sampling locations in Yuanmou dry-hot valley, Yunnan, Southwest China. The base map was obtained from the Standard Map Service (GS (2024) 0650), with no alteration to provincial/county boundaries.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/05ae37f6e937acc28f821636.jpeg"},{"id":108179097,"identity":"bcbbea92-18c3-4a08-9dc5-ab9b0286657f","added_by":"auto","created_at":"2026-04-30 08:33:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1427356,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of dominant soil microbial taxa in the rhizosphere of \u003cem\u003eQ. franchetii\u003c/em\u003e. (A) Bacterial phyla. (B) Bacterial genera. (C) Fungal phyla. (D) Fungal genera.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/544fb24123041bb2b60f579c.jpeg"},{"id":108179098,"identity":"12050fda-9ad5-4f5d-a1cd-1ee0c0e98b16","added_by":"auto","created_at":"2026-04-30 08:33:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1361123,"visible":true,"origin":"","legend":"\u003cp\u003eResponses of bacterial and fungal α-diversity to seasonal and elevational changes. (A) Bacterial Chao1 index. (B) Bacterial Shannon index. (C) Fungal Chao1 index. (D) Fungal Shannon index.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/03a2174bede2fee319c6d728.jpeg"},{"id":108179102,"identity":"84951b95-c359-45ef-947d-52a6c64acf2e","added_by":"auto","created_at":"2026-04-30 08:33:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1468485,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial (A-B) and fungal (C-D) community structures visualized by NMDS analysis.\u003c/p\u003e\n\u003cp\u003ePERMANOVA analysis revealed that, while the composition of bacterial and fungal\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/3043de54d992e53e7e6e67c1.jpeg"},{"id":108182892,"identity":"c8ea21e2-c95d-4704-8f14-8443ea4c46be","added_by":"auto","created_at":"2026-04-30 08:59:39","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1583158,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between soil physicochemical properties and microbial α-diversity by Spearman correlation and Mantel test.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/e54e52c37d141888726e7f04.jpeg"},{"id":108179100,"identity":"892d1d28-e108-42a9-9804-111d1393baf3","added_by":"auto","created_at":"2026-04-30 08:33:19","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1499464,"visible":true,"origin":"","legend":"\u003cp\u003eRDA of bacterial and fungal composition at genus level in relation to soil physicochemical factors. (A) All bacterial genera. (B) All fungal genera. (C) Top 10 dominant bacterial genera. (D) Top 10 dominant fungal genera.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/955cc220393e0dc1717fd2bd.jpeg"},{"id":108182898,"identity":"b07c3e3a-7cec-4be7-b7ee-bb9e22216f6f","added_by":"auto","created_at":"2026-04-30 08:59:40","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":439743,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of functional categories in bacterial (A) and fungal (B) communities.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/9b654b33310dcd97aac1915b.jpeg"},{"id":108803610,"identity":"0a67c8eb-955c-4bab-a754-434564c31eb0","added_by":"auto","created_at":"2026-05-08 15:01:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4033659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/a0934ba6-6185-4252-912a-afc470ba3378.pdf"},{"id":108179095,"identity":"4aabb055-1558-46e7-b9ff-b798e409070a","added_by":"auto","created_at":"2026-04-30 08:33:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2396945,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9357713/v1/8bedc945530e2eb8da69e72c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal variations in rhizosphere soil bacterial and fungal communities of Quercus franchetii along an elevational gradient in the Yuanmou dry-hot valley, Southwest China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil microbial communities are essential components and key drivers in maintaining ecosystem function [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rhizosphere soil microbial communities, in particular, play a critical role in global biogeochemical cycles, including nutrient retention, nutrient cycling, organic matter decomposition, and multiple ecosystem functions linked to plant diversity and decomposition [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The composition and diversity of soil microbial communities drive the stable operation of plant and soil systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and interact closely with plants, influencing plant growth, health, and stress resilience [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As the two major soil microbial taxa, bacteria and fungi are of utmost importance in determining environmental and host functioning [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSoil microbial communities in different ecosystems exhibit distinct spatiotemporal variations, while the dynamics of the microbiome are crucial for understanding its functions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Elevational gradients provide unique insights into the regulatory mechanisms underpinning the spatial distribution of soil microorganisms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Previous studies have documented various elevational distribution patterns of microbial communities in different ecosystems, including monotonic decreasing or increasing, unimodal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], hollow [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and no elevation pattern [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, a comprehensive understanding of the drivers and ecological implications underlying these patterns remains limited. Thus, further investigation into shifts in the composition and structure of microbial communities along elevational gradients is essential to elucidate the underpinning mechanisms. Those elevational responses of microbial communities are commonly driven by temperature, arid environments and precipitation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, seasonal variations in temperature, precipitation, soil physicochemical factors, plant exudates play a key role in the diversity and structure of soil microbial communities [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, it is necessary to integrate seasonal dynamics into studies investigating microbial community changes along elevational gradients to obtain a more holistic understanding of their spatiotemporal patterns.\u003c/p\u003e \u003cp\u003eSoil properties strongly drive spatial variations in soil microbial communities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Soil physicochemical properties are consistently identified as predominant environmental covariates. However, there is no consistent conclusion about the variation of soil microbial communities and its driving factors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. At the global scale, climatic factors, followed by edaphic and spatial patterning, are the best predictors of soil fungal richness and community composition [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Local environmental conditions like organic carbon and soil pH were important in shaping the bacterial community [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nitrate nitrogen (NN) and available phosphorus (AP) were identified as the primary determinants shaping the bacterial community structure, while NN and soil organic carbon (SOC) as the key factors influencing the fungal community composition [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Soil pH and total nitrogen (TN) were identified as the main driving factors for the variability of EM fungal community in the alpine zone of the European Alps [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Overall, the factors influencing rhizosphere microbial communities vary across different scales, vegetation types, geographical location, habitat and main species. The relationships between microbial communities and their environmental drivers remain poorly elucidated in tropical regions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, the soil microbial communities associated with vegetation in arid environments, such as dry-hot valleys, remains poorly characterized.\u003c/p\u003e \u003cp\u003eDry-hot valleys of southwestern China, characterized by seasonal drought, high temperatures, and intense ultraviolet radiation, present a challenging environment that exacerbates risks of land degradation and biodiversity loss [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eQuercus franchetii\u003c/em\u003e, a dominant drought-tolerant evergreen sclerophyllous tree in these valleys, plays a critical role in maintaining ecosystem resilience, and performs critical ecological functions in sustaining biodiversity and ensuring ecosystem resilience within dry-hot valleys ecosystems [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Natural regeneration of seedlings is severely limited in this habitat [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and our investigations have documented increasing tree mortality, indicating that extended seasonal drought and extreme heat events pose serious threats to the survival of \u003cem\u003eQ. franchetii\u003c/em\u003e populations in the dry-hot valley. Given that rhizosphere microbial communities are not only sensitive to environmental changes but also enhance plant resilience to harsh conditions, this study investigates the rhizosphere microbial communities of \u003cem\u003eQ. franchetii\u003c/em\u003e to elucidate the ecological processes by which it assists host adaptation to this fragile ecosystem. We hypothesized that: (i) The diversity of rhizosphere bacterial and fungal communities would vary significantly with season and elevation, being lower in the dry season than in the rainy season due to drought stress, and follow a pattern similar to plant diversity, where diversity increases along the elevation gradient [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. (ii) Soil water content (WC), SOC, and pH would be the primary drivers of diversity. (iii) Both the stability of microbial co-occurrence networks and community functional profiles would differ across seasons and elevations, with the dry season enriching unique taxa that possess drought-resistant functional traits. The findings aim to provide a scientific basis for the conservation and health maintenance of \u003cem\u003eQ. franchetii\u003c/em\u003e populations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe study site was located in the Yuanmou dry-hot valley, Southwest China. This region has a typical dry-hot climate with an average annual temperature of 21.8 ℃, an average annual precipitation of 634.0 mm, the annual evaporation is over 6.1 times the annual precipitation [30]. The region has obvious dry and rainy seasons, and the precipitation during the rainy season is mainly concentrated in July to September, and it accounting for about 92% of the annual precipitation. The vegetation is characterized by a Savanna of valley type, with \u003cem\u003eQ. franchetii\u003c/em\u003e being one of the natural dominant tree species. The experimental samples were collected in January (dry season) and August (rainy season) 2024. According to the distribution range of \u003cem\u003eQ. franchetii\u003c/em\u003e, five elevations (1200 m, 1400 m, 1600 m, 1800 m, 2100 m) were chosen in the Yuanmou dry-hot valley (Fig. 1, Table 1). The soil sampling method can be found in the Supplementary Appendix 1. Voucher plant specimens were deposited at the Herbarium of Southwest Forestry University and taxonomically identified as \u003cem\u003eQuercus franchetii\u003c/em\u003e by Prof. Dacai Zhang of the same university. The voucher IDs for these specimens are SWFC0072428-SWFC0072432. The collections of soil and plant samples complied with institutional, national, and international guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eInformation of sample plots\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite NO.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElevation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatitude and longitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAspect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage of vegetation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBH (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eS1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1244.3 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e25\u0026deg;35\u0026prime;15\u0026Prime;N; 101\u0026deg;49\u0026prime;58\u0026Prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eYermosol-arenosols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e14.20\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e245\u0026deg; SW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eS1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1426.8 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e25\u0026deg;45\u0026prime;16\u0026Prime;N; 101\u0026deg;41\u0026prime;5\u0026Prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eRed soil-arenosols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e10.50\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e58\u0026deg; NE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eS1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1608.8 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e25\u0026deg;43\u0026prime;58N\u0026Prime;; 101\u0026deg;41\u0026prime;35\u0026Prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eRed soil-arenosols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e19.0\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e158\u0026deg; S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eS1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1823.1 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e25\u0026deg;40\u0026prime;59N\u0026Prime;; 101\u0026deg;37\u0026prime;24\u0026Prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eRed soil-arenosols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e8.70\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e29\u0026deg; NE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eS2100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2108.1 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e25\u0026deg;43\u0026prime;35N\u0026Prime;; 101\u0026deg;57\u0026prime;31\u0026Prime;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePurple soil-loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e19.0\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e174\u0026deg; S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 577px;\"\u003e\n \u003cp\u003eNote: DBH: diameter at breast heigh, the value represents the mean of 15 sampled trees in each sample plot.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of rhizosphere soil physicochemical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil water content (WC) was measured by the oven-drying method (105 ℃ to constant weight), soil pH value was determined potentiometrically using a glass electrode (soil : water = 1 : 2.5), SOC content was quantified via potassium dichromic oxidation-external heating method, TN was digested with sulfuric acid-accelerator followed by Kjeldahl distillation, total phosphorus (TP) was analyzed by NaOH fusion and molybdenum-antimony anti-spectrophotometry, AN was measured using alkali-hydrolyzable diffusion method, AP was extracted with ammonium fluoride-hydrochloric acid solution and determined by Mo-Sb anti-colorimetry [31]. Referring to the second national soil census of China for soil pH and nutrient classification [32, 33], the rhizosphere soil samples were all acidic, and the nutrient contents are generally low (Supplementary Table S1). WC revealed significant differences between dry and rainy season.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing of rhizosphere soil microbial communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe method for DNA extraction polymerase chain reaction (PCR) amplification, and high-throughput sequencing can be found in the Supplementary Appendix 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main and interaction effects of season and elevation on bacterial and fungal \u0026alpha;-diversity (assessed using Chao1 and Shannon indices) were analyzed by nonparametric ANOVA with Aligned Rank Transform (ART). Soil microbial \u0026beta;-diversity across seasons and elevation was visualized by non-metric multidimensional scaling (NMDS) based on Bray-Curtis distances. A stress value \u0026le; 0.1 indicates excellent fit of the ordination to the original dissimilarity data. Permutational multivariate analysis of variance (PERMANOVA) was further applied to test the significance of seasonal and elevational differences. Variations in soil physicochemical properties across groups were compared using the Kruskal-Wallis test. The linkages between soil factors and microbial communities were quantified using Spearman correlation, Mantel tests, and redundancy analysis (RDA), and these analyses was performed by the genescloud tools, a free online platform for data analysis (https://www.genescloud.cn).\u003c/p\u003e\n\u003cp\u003eTo investigate microbial co-occurrence patterns across dry and rainy seasons along an elevation gradient, co-occurrence networks were constructed using the igraph and multcomp packages in R version 4.3.3. The Amplicon sequence variants (ASVs) were taxonomically annotated at the genus level, and those with an average relative abundance greater than 0.01% were retained for downstream analysis. These networks were based on Spearman\u0026apos;s rank correlations, with a correlation coefficient threshold of 0.6 and a significance level of P \u0026lt; 0.05. Multiple topological properties were computed to characterize each network, including the number of nodes and edges, proportion of positive and negative edges, average path length, network diameter, average clustering coefficient, modularity, and average degree. The bacterial 16S rRNA sequences were analyzed to predict their ecological functions using the Functional Annotation of Prokaryotic Taxa (FAPROTAX) database. The fungal ITS1 sequences were analyzed to predict their functional roles using FUNGuild database. Only ASVs with confidence rankings of \u0026quot;probable\u0026quot; or \u0026quot;highly probable\u0026quot; for analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eComposition of bacteria and fungi in the rhizosphere soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaxonomic analysis of ASVs revealed that the bacterial community comprises 42 phyla, 135 classes, 321 orders, 544 families, 1109 genera and 3107 species, and the fungal community comprises 13 phyla, 56 classes, 146 orders, 381 families, 801 genera and 1288 species.\u003c/p\u003e\n\u003cp\u003eThe dominant bacterial phyla included Actinobacteriota, Proteobacteria, Acidobacteriota and Chloroflexi, which together accounted for over 75% of the total bacteria across all samples, and the relative abundance of Actinobacteriota reached the maximum at the 1200m and 1600m sites (Fig. 2A). The dominant bacterial genera included \u003cem\u003eAcidothermus\u003c/em\u003e, \u003cem\u003eConexibacter\u003c/em\u003e, \u003cem\u003eMycobacterium\u003c/em\u003e and \u003cem\u003eBryobacter\u0026nbsp;\u003c/em\u003e(Fig. 2B). The relative abundance of the genus \u003cem\u003eAcidothermus\u003c/em\u003e was enriched in the dry season compared to the rainy season. The dominant fungal phyla included Ascomycota and Basidiomycota, which together accounted for over 90% of the total fungi across all samples, and the relative abundance of Ascomycota declined in the dry season, in contrast to a concomitant increase in Basidiomycota\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig. 2C). The most abundant genera were \u003cem\u003eRussula\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003ePhialomyces\u003c/em\u003e and \u003cem\u003eSebacina\u003c/em\u003e (Fig. 2D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiversity of bacteria and fungi in the rhizosphere soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the ART nonparametric ANOVA indicted that the \u0026alpha;-diversity of bacterial and fungal communities associated with \u003cem\u003eQ. franchetii\u003c/em\u003e was differentially affected by elevation and season (Supplementary Table S2). The \u0026alpha;-diversity of bacteria and fungi in rainy season was consistently higher than those in dry season (Fig. 3). Bacterial \u0026alpha;-diversity was significantly influenced by elevation (P \u0026lt; 0.001) and season (P \u0026lt; 0.05), and the elevational pattern exhibited an approximately V-shaped distribution (Fig. 3A-B). Furthermore, the statistical analysis did not detect a significant interaction between elevation and season on the Chao1 and Shannon index (P \u0026gt; 0.05), indicating that the effects of elevation and season on bacterial \u0026alpha;-diversity are independent. Fungal \u0026alpha;-diversity were mainly influenced by season (P \u0026lt; 0.01) and not significantly regulated by elevation and their interaction (P \u0026gt; 0.05) (Fig. 3C-D). The peak values of the Chao1 index in both the dry and rainy seasons were observed at the 1200 m site. The maximum values of the Shannon index occurred at the 1400 m and 1600 m sites during the rainy and dry seasons, respectively.\u003c/p\u003e\n\u003cp\u003eFor the bacterial community, the NMDS yielded stress values of 0.0633 and 0.0644 (Fig. 4A-B). Samples from the dry and rainy season clustered tightly within the same elevation groups (Fig. 4A), while samples from different elevations indicated almost complete separation (Fig. 4B). For the fungal community, the NMDS yielded stress values of 0.185 and 0.147, representing a reasonable but less robust ordination (Fig. 4C-D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePERMANOVA analysis revealed that, while the composition of bacterial and fungal communities exhibited differences across the dry and rainy seasons, these differences were not significant (P \u0026gt; 0.05), but significant differences were observed among elevations (P \u0026lt; 0.05, Supplementary Table S3). The samples were widely dispersed in dry season (large ellipse), reflecting high heterogeneity of fungi in dry season, whereas the samples clustered tightly in rainy season (small ellipse), indicating relatively consistent community structures. Notably, the 95% confidence ellipse of the rainy season was entirely enclosed within that of the dry season, implying weak seasonal differentiation in fungal community, likely masked by the high variability of dry-season community (Fig. 4C). In contrast, samples from different elevations showed a discernible but incomplete separation (Fig. 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of soil physicochemical properties on rhizosphere soil microbial diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mantel tests indicated significant correlations between bacterial \u0026alpha;-diversity and soil physicochemical properties (Fig. 5). Both the Chao1 and Shannon indices were strongly correlated with pH (P \u0026lt; 0.01), and significantly higher correlations with TN, AN, and AP (P \u0026lt; 0.05). In contrast, fungal \u0026alpha;-diversity demonstrated moderate but non-significant links to all measured soil properties (Fig. 5).\u003c/p\u003e\n\u003cp\u003eThe RDA results indicated that the soil physicochemical factors explained 52.62% of the variation in bacterial community composition at the genus level (Fig. 6A). Among these factors, pH (R\u003csup\u003e2\u003c/sup\u003e = 0.52, P \u0026lt; 0.001) and WC (R\u003csup\u003e2\u003c/sup\u003e = 0.74, P \u0026lt; 0.001) exerted a highly significant effect, and the remaining soil variables influenced bacterial community structure in the following order: AN \u0026gt; SOC \u0026gt; TN \u0026gt; C/N \u0026gt; AP (Supplementary Table S4). The RDA results revealed that the soil physicochemical factors explained 28.15% of the variation in fungal community composition at the genus level (Fig. 6B). Among these factors, pH exerted a highly significant effect (R\u003csup\u003e2\u003c/sup\u003e = 0.57, P \u0026lt; 0.001), and the remaining soil variables influenced fungal community structure in the following order: SOC \u0026gt; AN \u0026gt; TN \u0026gt; AP \u0026gt; TP \u0026gt; C/N (Supplementary Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe RDA results indicated that the soil physicochemical factors explained 56.69% of the variance in the composition of the top 10 dominant bacterial genera (Fig. 6C). Among these factors, pH and AN exerted a highly significant effect (P \u0026lt; 0.001), TN, SOC and AP showed significant effects (P \u0026lt; 0.01). Regarding species-environment correlations, WC was negatively associated with the relative abundance of \u003cem\u003eAcidothermus\u003c/em\u003e but positively correlated with that of \u003cem\u003eBradyrhizobium\u003c/em\u003e. Conversely, the nutrients SOC, AN, TN, TP, and AP were all positively associated with the abundances of \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u003cem\u003eCandidatus_Solibacter\u003c/em\u003e. The RDA results revealed that the soil physicochemical factors explained 41.48% of the variance in the composition of the top 10 dominant fungal genera (Fig. 6D). Among these factors, pH exerted a highly significant effect (P \u0026lt; 0.001), AN, TN, and SOC showed significant effects (P \u0026lt; 0.01), while TP and AP also had discernible effects (P \u0026lt; 0.05). Regarding species-environment correlations, WC was negatively associated with the relative abundance of \u003cem\u003eRussula\u003c/em\u003e but positively correlated with that of \u003cem\u003ePenicillium\u003c/em\u003e. Conversely, the nutrients SOC, AN, TN, TP, and AP were all positively associated with the abundances of \u003cem\u003eLactarius\u003c/em\u003e and \u003cem\u003eSebacina\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-occurrence network and functional prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rhizosphere bacterial network in dry season showed fewer vertices, edges, average degree, clustering coefficient and modularity, but higher proportions of positive edges compared to rainy season (Supplementary Table S5). With increasing elevation, the bacterial networks exhibited a pattern of initially decreasing and then increasing vertices, edges, average degree, and clustering coefficient. Similarly, the fungal network in dry season displayed fewer vertices, edges, average degree, and modularity, but higher proportions of positive edges compared to rainy season (Supplementary Table S5). Fungal networks exhibited a heterogeneous pattern of fluctuation across different elevation. In bacteria\u0026ndash;fungi co-occurrence network analysis, the network in dry season displayed fewer vertices, edges, average degree, and clustering coefficient, but higher modularity and proportions of positive edges compared to rainy season (Supplementary Table S5). the networks exhibited a heterogeneous pattern of fluctuation across different elevation.\u003c/p\u003e\n\u003cp\u003eFunctional prediction analysis showed that chemoheterotrophy, aerobic chemoheterotrophy and cellulolysis showed higher relative abundance in the rhizosphere bacterial community of \u003cem\u003eQ. franchetii\u003c/em\u003e (Fig. 7A). Among these carbon-cycling functions, cellulolysis showed a significantly higher relative abundance in the dry season than in the rainy season (P \u0026lt; 0.01), while for N-cycling functions, the relative abundances of nitrate reduction and ureolysis were significantly lower in the dry season than in the rainy season (P \u0026lt; 0.001). Besides these, the relative abundance of cellulolysis and ureolysis exhibited significant differences among different elevations (P \u0026lt; 0.05) (Supplementary Fig. S1). The most relative abundance of trophic modes in the rhizosphere fungal community of \u003cem\u003eQ. franchetii\u003c/em\u003e are symbiotroph, saprotroph-symbiotroph, pathotroph-saprotroph and pathotroph-saprotroph-symbiotroph (Fig. 7B). Symbiotrophs exhibited significantly higher relative abundance in the dry season than rainy season (P \u0026lt; 0.05, Supplementary Fig. S2). The most relatively abundant fungal taxa within these trophic modes were characterized by \u003cem\u003eRussula\u003c/em\u003e, Thelephoraceae,\u003cem\u003e\u0026nbsp;Phialomyces\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe composition and diversity of rhizosphere microbial communities\u003c/h2\u003e \u003cp\u003eIn this work, we found that Actinobacteriota, Proteobacteria, Acidobacteriota, and Chloroflexi were the dominant bacterial phyla in the rhizosphere soil of \u003cem\u003eQ. franchetii\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), while Ascomycota and Basidiomycota were the dominant fungal phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), which are dominant microbial groups in drylands worldwide [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The genus \u003cem\u003eAcidothermus\u003c/em\u003e, recognized as an important indicator of soil nutrient properties [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], thrives under acidic and high-temperature conditions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As a dominant genus in the dry season, \u003cem\u003eAcidothermus\u003c/em\u003e exhibited enhanced drought tolerance in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In contrast, \u003cem\u003eBradyrhizobium\u003c/em\u003e, a genus of nitrogen-fixing bacteria, converts atmospheric nitrogen into NH₄⁺ and provides essential amino acids, nitrate, and proline to enhance plant resilience under drought stress [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, \u003cem\u003eBradyrhizobium\u003c/em\u003e itself exhibits a strong dependence on soil moisture in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As ectomycorrhizal fungi, \u003cem\u003eRussula\u003c/em\u003e and \u003cem\u003eSebacina\u003c/em\u003e significantly enhance host plant drought resistance and nutrient uptake efficiency through common mycorrhizal networks (CMNs) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, the dry-season dominant genera \u003cem\u003eRussula\u003c/em\u003e and \u003cem\u003eSebacina\u003c/em\u003e exhibited enhanced drought tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffects of season and elevation on the diversity and structure of rhizosphere microbial communities\u003c/h2\u003e \u003cp\u003eThe seasonal dynamics of microbial communities provide critical insights into their functional adaptations. We found that α-diversity indices of soil bacterial and fungal communities were higher in the rainy season than in the dry season, with fungal diversity exhibiting particularly significant seasonal variation. Xu et al. (2023) observed that the seasonal variation in soil fungal diversity in the two temperate forests was more significant than that of bacteria. This increase could be: (i) tIn the rainy season, plant growth stimulates root system development and root exudate production, which are important sources of soil carbon and nitrogen [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], especially the accelerated proliferation of fine root systems enhances the availability of root tissues for fungal symbiosis. (ii) The increase in soil temperature and moisture during the rainy season may be the environmental trigger for breaking dormancy of quiescent fungal spores under water stress, which are typically maintained in a metabolically inactive state under prolonged hydro-stress conditions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. (iii) In the rainy season, the higher WC and SOC may provide more stable resources for fungi. However, they also increase competition among different functional groups (such as saprotrophs and pathogens), thereby enhancing the fluctuation of diversity indices [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, studies have found that, the richness of fungi and bacteria was higher in the dry season than the rainy season in Colombian tropical forests and p\u0026aacute;ramo ecosystems, and the bacterial Shannon diversity was significantly higher in the dry season than in the rainy season [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This suggests that the diversity and composition of soil microbial communities may be involved in the response to precipitation changes for the host plant\u0026rsquo;s high resistance to drought.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffects of soil physicochemical factors on rhizosphere microbial communities\u003c/h2\u003e \u003cp\u003eSoil microbial communities have developed various strategies to adapt to diverse soil physicochemical properties, leading to a strong correlation between their diversity and these environmental conditions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We found that the diversity and structure of bacterial communities was significantly correlated with pH, while α-diversity of fungi community exhibited no significant correlation with measured soil physicochemical parameter. The previous results by multi-scale investigations have collectively demonstrated that pH was the predominant determinant of both bacterial diversity and structure [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The bacterial diversity and structure were more influenced by the alterations in soil pH due to the narrow pH ranges for optimal growth of bacteria. For instance, Acidobacteria and Actinobacteria, the bacterial phyla with the highest relative abundance identified in this study, were classified as acid-tolerant microbial taxa [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, the fungal diversity was less strongly affected by pH, as they have wider pH tolerance ranges [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. As an ecological filter, soil pH indirectly alters the ecological functions of microbial communities by influencing their composition and stability [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Conversely, Microbial communities can actively modify pH through their involvement in fundamental biogeochemical processes, such as ammonification and denitrification [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. WC can regulate the structure and metabolic activity of the soil microbial communities [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Despite the absence of a significant effect of WC on the composition of the top 10 bacterial and fungal genera, our results are in line with the established consensus that bacterial community exhibit greater sensitivity to water stress than fungal community [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Bacteria experiencing prolonged drought may enter a lag phase or even die, and their stability is more sensitive to shift of WC [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Although the overall effect of WC on the microbial communities was not significant-likely masked by covarying factors such as elevation and seasonal fluctuations-strong correlations were observed at genus level. Specifically, the dominant bacterial genera \u003cem\u003eAcidothermus\u003c/em\u003e and \u003cem\u003eBradyrhizobium\u003c/em\u003e, and the key fungal genera \u003cem\u003eRussula\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003eLactarius\u003c/em\u003e, and \u003cem\u003eSebacina\u003c/em\u003e, were strongly correlated with WC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). This indicates that WC is a selective force shaping specific components of rhizosphere microbial communities. Furthermore, beyond merely responding to moisture, these microbes likely engage in a feedback loop by actively modifying soil moisture dynamics through the secretion of compounds such as extracellular polymeric substances [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of ecological function of rhizosphere microbial communities\u003c/h2\u003e \u003cp\u003eThe changes in rhizosphere microbial communities structure caused by season will further affect the ecological function of microbial communities. In this study, the functions of bacterial community were predominantly characterized by chemoheterotrophy, aerobic chemoheterotrophy and cellulolysis. Significant seasonal variations were observed in key metabolic processes including cellulolysis, nitrate reduction, and ureolysis. The significantly higher cellulolytic activity observed in the dry season could potentially result from the synergistic effects of multiple environmental and microbial factors: (i) enhanced soil aeration, (ii) increased plant litter input, and (iii) sustained enzymatic efficiency of drought-adapted cellulolytic bacteria. As keystone mediators of soil biogeochemical cycling, fungal and bacterial communities collectively drive nutrient mineralization, with fungi specializing in lignin degradation and bacteria in cellulose decomposition [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In the fungal community, the symbiotrophic guild exhibited a significantly higher relative abundance in the dry season, with ectomycorrhizal fungi (eg, \u003cem\u003eRussula\u003c/em\u003e, Thelephoraceae) as the primary contributors, while saprotrophic guild exhibited a significantly higher relative abundance in the rainy season, led by unidentified saprotrophs (eg, \u003cem\u003ePhialomyces\u003c/em\u003e, \u003cem\u003ePenicillium)\u003c/em\u003e. Bacterial growth strongly reduces in drought conditions, whereas fungi display a remarkable resistance to drought via almost unchanged growth rates [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Hydraulic redistribution is likely one of the mechanisms underlying the higher drought resistance of soil fungi [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The sustained microbial activity during drought conditions could have large effects on soil carbon storage [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The reduced bacterial abundance diminished organic matter decomposition capacity, while drought-tolerant symbiotic fungi may be compensated by maintaining hyphal growth rates and increasing their relative abundance, thus facilitating host plant carbon assimilation and preserving ecosystem carbon flux stability. Microbial communities may enhance functional resilience and survivability under environmental stress by enhancing their local microenvironment [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Fungi can resist drought through their extensive hyphal networks, which maintain hydraulic continuity and water and nutrients uptake even under conditions of reduced soil diffusivity [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. With the onset of seasonal rainfall, increased soil moisture content reactivates dormant spore-forming taxa that persisted under preceding drought conditions [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In this study, the functional inferences derived from taxonomic tools possess inherent limitations, as they may overlook functional redundancy and condition-specific metabolic activities. Future studies should integrate microbial culturing with metabolomic analyses to elucidate the functional dynamics within microbial networks.\u003c/p\u003e \u003cp\u003eThe co-occurrence networks in the dry season exhibited a marked reduction in network size (vertices and edges) and complexity (average degree and average path length) in this work. These changes likely resulted from environmental filtering under water stress, which selectively removed taxa poorly adapted to aridity [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The increased proportion of positive edges from the network in dry season suggests a shift toward a cooperative microbial strategy to mitigate environmental stress. Furthermore, the enhanced modularity observed in both the fungal and fungal-bacterial networks implies that, fungi and bacteria may form more cohesive, cooperative modules, thereby enhancing functional stability and resilience to drought [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Fungal and bacterial community form complex co-occurrence networks through physiological interactions and environmental filtering mechanisms, and these networks regulate nutrient cycling, thereby influencing soil health and plant growth [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. However, both fungal-bacterial networks and subnetworks exhibited low modularity, indicating a stochastic network structure with limited stability that is susceptible to collapse under stress [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Previous studies have reported a significant decline in microbial co-occurrence network complexity with increasing elevation, and have also shown that the influence of microbial diversity on multifunctionality is indirectly mediated by network complexity [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In this study, bacterial network complexity along elevational gradient showed an initial decrease followed by an increase. This pattern may be linked to nonlinear variations in environmental factors (such as temperature, vegetation type, and soil properties) across the gradient, with the 1600 m site potentially representing a threshold for certain environmental stressors. In contrast, fungal networks and bacterial-fungal networks displayed heterogeneous fluctuations, suggesting that fungi may respond more sensitively and variably to different environmental filters [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Further comparative analysis of co-occurrence networks was limited by the sample size in this study, and therefore deeper analyses were not pursued.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe main results of this work reveal that bacterial community abundance responded to both elevation and season. In contrast, fungal community abundance was mainly influenced by season. Owing to their symbiotic associations with host plants and extensive hyphal networks, fungi exhibited a buffering capacity against micro-environmental variations. Elevation significantly shaped the community structure of both bacteria and fungi, creating distinct soil habitats and driving environmental filtering. Soil pH and WC significantly influenced bacterial community composition, whereas pH also affected fungal community composition, albeit with lower explanatory power. The higher heterogeneity in the fungal community in the dry season, together with the trophic shift from ectomycorrhizal dominance in the dry season to saprotrophic prevalence in the rainy season, suggests that certain ectomycorrhizal fungi (e.g., \u003cem\u003eRussula\u003c/em\u003e and Thelephoraceae) may play an important role in drought adaptation. Bacterial community were primarily chemoheterotrophic and aerobic chemoheterotrophic, with significant seasonal variations in functions related to cellulolysis, nitrate reduction, and ureolysis. Collectively, these findings advance our understanding of the microbial ecology in the rhizosphere soil of \u003cem\u003eQ. franchetii\u003c/em\u003e, elucidate the key factors shaping its microbial communities, and underscore their contributions to the stable functioning of plant-soil systems.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total raw sequences in this study have been deposited in the Genome Sequence Archive in National Genomics Data Center (https://ngdc.cncb.ac.cn/gsa) under the accession number CRA040326/CRA040327.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the research team led by Dr. Fuqiang Yu at the Kunming Institute of Botany, Chinese Academy of Sciences, for their laboratory support. Our thanks also go to Dr. Dong Liu and Dr. Shanping Wan for their expert guidance. We are thankful to Dr. Dong Liu and Dr. Shanping Wan for their valuable advice and assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have made substantial contributions to the work. Xuedan Xie: investigation, data curation, formal analysis, methodology, validation, visualization, writing - original draft, writing - review and editing. Jiao WU, Guanping LI and Cailan LIU: investigation. Haitao YUE: methodology, software. Dacai Zhang: conceptualization, project administration, resources, supervision, funding acquisition. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Technology Research of Ecological Restoration for Arid River Valley in Yunnan Province (6321A5), the Strategic Biological Resources Capacity Building Project, Chinese Academy of Sciences (KFJ-BRP-017-47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eKey Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Southwest Forestry University, Kunming, 650224, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eYunnan Key Laboratory of Plant Diversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCompant S, Samad A, Faist H, Sessitsch A. 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Soil microbial network complexity predicts ecosystem function along elevation gradients on the Tibetan Plateau. \u003cem\u003eSoil Biol Biochem \u003c/em\u003e2022, 172:108766. https://doi.org/10.1016/j.soilbio.2022.108766\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":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Quercus franchetii, Dry-hot valley, Rhizosphere soil, Microbial community, Dry and rainy seasons","lastPublishedDoi":"10.21203/rs.3.rs-9357713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9357713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSoil microbial communities are essential components and key drivers in maintaining ecosystem function. This study focused on \u003cem\u003eQuercus franchetii\u003c/em\u003e in the Yuanmou dry-hot valley of Southwest China, and analyzed the characteristics of rhizosphere soil microbial communities along an elevational gradient in the dry and rainy season using high-throughput sequencing technique.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eActinobacteriota, Proteobacteria, Acidobacteriota, and Chloroflexi were identified as the dominant bacterial phyla, with \u003cem\u003eAcidothermus\u003c/em\u003e, \u003cem\u003eConexibacter\u003c/em\u003e, \u003cem\u003eMycobacterium\u003c/em\u003e, and \u003cem\u003eBryobacter\u003c/em\u003e as the dominant genera. For fungi, Ascomycota and Basidiomycota were identified as the dominant phyla, with \u003cem\u003eRussula\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003ePhialomyces\u003c/em\u003e, and \u003cem\u003eSebacina\u003c/em\u003e as the most abundant genera. Elevation was a more important determinant of bacterial α-diversity than season. By contrast, fungal α-diversity was significantly affected by season but not by elevation. Elevation significantly shaped the community structure of both bacteria and fungi. Soil pH was the primary driver of bacterial α-diversity, whereas fungal α-diversity correlated with none of the measured soil physicochemical properties. Soil pH and water content exerted highly significant effects on bacterial community structure, whereas only soil pH had a highly significant impact on fungal community structure. The bacteria-fungi co-occurrence network in the dry season featured a higher proportion of positive correlations. The functions of the bacterial community were primarily chemoheterotrophic and aerobic chemoheterotrophic, with significant seasonal differences in cellulolysis, nitrate reduction, and ureolysis. Symbiotroph and saprotroph‑symbiotroph, pathotroph‑saprotroph were the dominant trophic modes in the fungal community, with significant seasonal differences in ectomycorrhizal fungi and saprotrophic fungi.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBacterial community diversity showed higher sensitivity to environmental fluctuations, while fungal diversity exhibited more pronounced seasonal variation. Soil pH was the primary driver of bacterial and fungal diversity. Bacterial functions were dominated by carbon cycling processes, with seasonal differences in cellulolysis, nitrate reduction, and ureolysis. Ectomycorrhizal fungi dominated in the dry season, while saprotrophic fungi prevailed in the rainy season. This research advances our understanding of the microbial ecology of rhizosphere of \u003cem\u003eQ. franchetii\u003c/em\u003e, the factors shaping its microbial communities, and the stable operation of plant and soil systems.\u003c/p\u003e","manuscriptTitle":"Seasonal variations in rhizosphere soil bacterial and fungal communities of Quercus franchetii along an elevational gradient in the Yuanmou dry-hot valley, Southwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 08:33:09","doi":"10.21203/rs.3.rs-9357713/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-13T18:46:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:36:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T08:18:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314727633960679068389212483347767300911","date":"2026-04-23T01:58:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56469097880017648489874556033772046892","date":"2026-04-22T13:21:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T12:48:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T07:31:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T05:51:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-04-17T05:44:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56a4268c-5501-4bc3-982d-a5a4bc52384f","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-13T18:46:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:36:12+00:00","index":41,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T08:18:43+00:00","index":40,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T18:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 08:33:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9357713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9357713","identity":"rs-9357713","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}