Seasonal Dynamics of Soil Microbiome in Response to Dry-Wet Alternation along the Jinsha River Dry-hot Valley

Seasonal Dynamics of Soil Microbiome in Response to Dry-Wet Alternation along the Jinsha River Dry-hot Valley | 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 Dynamics of Soil Microbiome in Response to Dry-Wet Alternation along the Jinsha River Dry-hot Valley Hao Jiang, Xiaoqing Chen, Yongping Li, Jiangang Chen, Li Wei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4643110/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Soil microorganisms play a key role in nutrient cycling, carbon sequestration, and other important ecosystem processes, yet their response to seasonal dry-wet alternation remains poorly understood. Here, we collected 120 soil samples from dry-hot valleys (DHVs, ~1100 m a.s.l.), transition (~2000 m a.s.l.) and alpine zones (~3000 m a.s.l.) along the Jinsha River in southwest China during both wet and dry seasons. Our aims were to investigate the bacterial microbiome across these zones, with a specific focus on the difference between wet and dry seasons. Results Despite seasonal variations, bacterial communities in DHVs exhibit resilience, maintaining consistent community richness, diversity, and coverage. This suggests that the microbes inhabiting DHVs have evolved adaptive mechanisms to withstand the extreme dry and hot conditions. In addition, we observed season-specific microbial clades in all sampling areas, highlighting their resilience and adaptability to environmental fluctuations. Notably, we found similarities in microbial clades between soils from DHVs and the transition zones, including the phyla Actinobacteria, Chloroflexi, and Proteobacteria. The neutral community model respectively explained a substantial proportion of the community variation in DHVs (87.7%), transition (81.4%) and alpine zones (81%), indicating that those were predominantly driven by stochastic processes. Our results showed that migration rates were higher in the dry season than in the wet season in both DHVs and the alpine zones, suggesting fewer diffusion constraints. However, this trend was reversed in the transition zones. Conclusions Our findings contribute to a better understanding of how the soil microbiome responds to seasonal dry-wet alternation in the Jinsha River valley. These insights can be valuable for optimizing soil health and enhancing ecosystem resilience, particularly in dry-hot valleys, in the context of climate change. Altitudinal gradient Dry-hot valley Mountain-valley breeze circulation Seasonal dry-wet cycle Stochastic process Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background The soil microbiome, a diverse community of microorganisms, plays a pivotal role in ecosystem services such as nutrient cycling, carbon sequestration [ 1 – 4 ], and plant growth promotion [ 5 ]. However, its activities are sensitive to environmental factors including temperature fluctuations, precipitation patterns, and extreme events [ 6 – 10 ], potentially challenging soil microbial communities and ecosystem stability [ 11 , 12 ]. Seasonal variations, particularly dry and wet alternations, significantly impact soil microbiome composition and function [ 4 , 13 , 14 ]. During the dry season, the soil microbiome experiences water stress, which can affect microbial activity and nutrient availability. This, in turn, leads to reduced nutrient cycling and decreased plant productivity. Conversely, increased moisture levels during the wet season may facilitate microbial metabolic processes, contributing to nutrient availability and soil fertility. Yet, the deep mechanisms underlying soil microbiome responses to seasonal fluctuations remain poorly understood [ 11 , 15 ]. Consequently, elucidating these differences is imperative for optimizing soil health, enhancing ecosystem services, and mitigating climate change impacts. The dry-hot valleys (DHVs) in southwest China, especially located in the lower reaches of the Jinsha River exhibit unique climate types and geographic regions. Despite serving as a sanctuary for ancient life during global climate change, extreme drought and high temperatures have resulted in severe degradation of the local ecosystem [ 16 ]. The vegetation landscape below 1600 m above sea level (a.s.l.) on both sides of the river valley is characterized by a savanna-like ecosystem (Additional file 1: Supplementary Table S1 , S2; Additional file 2: Supplementary Figure S1 ), with a hot and dry climate that is marked by a clear distinction between wet and dry seasons. Investigations reveal that the valley exhibits a distinctive pattern of vertical vegetation distribution, characterized by a reverse succession from forests to savannas as elevation decreases. Above 3000 m a.s.l., the alpine zones boast lush forest vegetation and ample rainfall. The region spanning from 1600 m to 3000 m a.s.l. serves as the transition zones between DHVs and the alpine zones. Within the transition zones, significant alterations in hydrothermal conditions and vegetation habitats occur, marking the interface between the two distinct ecological zones. Researchers have identified local phenomena, including the foehn effect and mountain-valley breeze circulation, as significant contributors to this occurrence. Particularly noteworthy is the pivotal role played by mountain-valley breeze circulation. In this process, diurnal temperature variations prompt mountain summits to warm more rapidly than valley floors during daylight hours, inducing lower air pressure atop the peaks. Consequently, airflow ascends, transporting moisture from the valley floors up the mountain slopes, thereby instigating the formation of valley breezes. In the evening, the mountain tops cool faster than the valleys, causing the airflow to sink and the cold air to descend along the mountain slopes, forming a mountain breeze. Over time, this process leads to increasing dryness in the valley floor, while moisture conditions remain suitable at higher elevations. Consequently, soil properties closely associated with water availability, such as pH, total nitrogen, total phosphorus, etc., do not exhibit significant seasonal differences in the low-elevation DHVs (Additional file 1: Supplementary Table S3). These findings imply the importance of extending soil microbiome studies beyond low-elevation DHVs to encompass the entire valley ecosystem. The intricate mechanisms governing community assembly represent a central challenge within microbial ecology [ 17 – 19 ]. Views of community assembly have traditionally been based on the contrasting perspectives of the deterministic niche paradigm and stochastic neutral models [ 20 , 21 ]. Based on the niche theory, microbial community assembly is posited as a deterministic process influenced by abiotic factors (e.g., pH and temperature) and biotic factors (e.g., species interaction), reflecting diverse habitat preferences and microbial fitness. Conversely, the neutral theory postulates that microbial community assembly is governed by stochastic processes like birth, death, migration, speciation, and limited diffusion, assuming a stochastic equilibrium between taxon loss and acquisition [ 22 ]. Recent research into the microbial community assembly within DHVs remains sparse, leaving the underlying mechanisms poorly understood. Specifically, it is yet uncertain whether soil microorganisms have developed seasonally specific microbiota and community assembly mechanisms in response to environmental fluctuations within DHVs. Previous studies have addressed the influence of various environmental factors on soil microbial composition, diversity, and function within DHVs, including desertification [ 23 ], land use change [ 13 , 24 , 25 ], vegetation [ 16 , 26 ], and elevational gradients [ 27 ]. However, there exists a notable gap in research concerning the effects of seasonal dry and wet alternation [ 13 , 14 ]. To address this gap, we examined the bacterial microbiome of 120 soil samples collected from DHVs, transition and alpine zones along the Jinsha River during both wet and dry seasons to compare the composition, structure, and function. We hypothesized that: i) microbes inhabiting DHVs have evolved efficient adaptive mechanisms to withstand persistent high temperature and drought, thereby minimizing the impact of seasonal dry and wet alternation on their diversity; ii) season-specific microbial clades contribute to adaptation of microbial taxa to seasonal dry and wet alternation; and iii) stochastic processes primarily govern the assembly of microbial communities in DHVs, whereas deterministic processes prevail in the transition and alpine zones due to substantial environmental disparities between dry and wet seasons . Methods Study area and sampling This study was conducted in DHVs of the Xiaojiang River section, which serves as a first-class tributary of the Jinsha River. To examine the differences in soil microbial community diversity between the dry and wet seasons in different elevations, we established sampling plots (20 m × 20 m) at three different elevations (26°14′-26°15′ N, 103°0′-103°6′ E), based on long-term observation data. We recorded soil characteristics, vegetation composition, and meteorological information (Fig. 1 and Additional file 1: Supplementary Table S1 ). The sampling plots represent three distinct landscape types along the path of mountain-valley breeze circulation. Firstly, the DHV (~ 1100 m a.s.l.) is an area characterized by typical dry and hot conditions. The dominated vegetation in this region consists of savanna-like species, including Dodonaea viscosa , Heteropogon contortus , and Agave sisalana . Secondly, the high mountain area (Alpine zone, ~ 3000 m a.s.l.) is characterized by low temperatures and abundant rainfall. Planted trees, primarily Pinus armandii , are prevalent in this region. Lastly, the transition zone (~ 2000 m a.s.l.) lies between the two aforementioned areas. It is distinguished by sparse vegetation and is often surrounded by white cloud bands, which form as a result of the daytime mountain-valley breeze airflow rising. In August 2019 and April 2020, soil samples were collected from three elevation gradients during the wet and dry seasons, respectively. To account for spatial heterogeneity within each sampling plot, ten soil cores were randomly collected from the upper 20 cm depth, and surface litter was meticulously removed. The soil cores were then pooled and homogenized to create a composite sample. A total of 20 composite soil samples were prepared from each sampling site for each season, resulting in a total of 120 composite samples for both seasons. The soils were sieved (< 2 mm) and separated into two portions: one was air-dried for one month and stored for soil biochemical analyses, and the other was immediately frozen at − 20°C for molecular analyses. Soil pH was determined from the air-dried samples using a soil:solution ratio of 1:2.5. Soil total nitrogen (TN) concentrations were measured using an elemental analyser (Elementar Vario EL, Chengdu, China). Soil total phosphorus (TP) was measured by ICP-OES (Optima 8300, PerkinElmer, USA) as described by Yang et al [ 28 ]. DNA extraction, PCR amplification and amplicon sequencing Genomic DNA was extracted from 0.5 g of each soil sample with the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s instructions and stored at − 20°C until further processing. The DNA extract was assessed on a 1% agarose gel, and DNA concentration and purity were determined with a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with the PCR primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') [ 29 ]. PCR amplification was performed as follows: initial denaturation at 95°C for 3 min, followed by 27 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 45 s, single extension at 72°C for 10 min, and termination at 4°C. The PCR mixtures contained 5 × TransStart FastPfu buffer (4 µL), 2.5 mM dNTPs (2 µL), forward primer (5 µM; 0.8 µL), reverse primer (5 µM; 0.8 µL), TransStart FastPfu DNA Polymerase (0.4 µL), bovine serum albumin (BSA; 0.2 µL), template DNA (10 ng), and up to 20 µL ddH 2 O. PCR reactions were performed in triplicate. The PCR products were extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer’s instructions and were quantified using Quantus™ Fluorometer (Promega, Madison, WI, USA). Purified amplicons were pooled equimolar and were paired-end sequenced on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Sequence processing Raw fastq files were quality-filtered by Trimmomatic [ 30 ] and merged by FLASH [ 31 ] applying the following criteria: (i) Reads were truncated at any site receiving an average quality score < 20 over a 50-bp sliding window. (ii) Sequences with overlap longer than 10 bp were merged according to their overlap with mismatch of no more than 2 bp. (iii) Sequences of each sample were separated according to barcodes (exact matching) and primers (allowing a 2-nucleotide mismatch). Reads containing ambiguous bases were removed. Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using UPARSE [ 32 ]. Mitochondrial and chlorophyll sequences were removed from the OTU table. The taxonomy of each 16S rRNA gene sequence was analyzed by the RDP classifier algorithm ( http://rdp.cme.msu.edu/ ) against the Silva database [ 33 ], using a confidence threshold of 70% and implementation in QIIME [ 34 ]. Statistical analyses Soil physicochemical properties were analyzed using the SPSS 17.0 software (SPSS Inc., Chicago, IL, USA). Significant differences among the means of different treatments were determined by Tukey’s multiple range tests after conducting tests of homogeneity for variances. Differences were considered statistically significant at the P < 0.05 level. To assess alpha-diversity, communities were rarified to the minimum sample sequence number (that is, 22647). The Sobs (community richness), Shannon (community diversity), and Good’s coverage index (community coverage) were calculated using QIIME [ 34 ]. The significances of differences among treatments were compared using the Welch’s t-test. The Bray-Curtis distances between samples were used for principal coordinate analysis (PCoA) to assess the major variance components of the beta-diversity. ADONIS was carried out to evaluate group differences. The Welch’s t-test within STAMP [ 35 ] was used to identify bacterial phyla that showed significant differences in abundance between groups. P -values were adjusted for multiple comparisons using the Bonferroni method. Discriminant taxa were significantly retrieved by linear discriminant analysis (LDA) effect size (LEfSe) for soil bacterial communities between dry and wet seasons [ 36 ]. In order to explore the potential significance of stochastic processes in community assembly, a neutral community model (NCM) was used to examine the association between the detection frequency of OTUs and their relative abundance across the metacommunity [ 37 ]. Within this model, the parameter Nm serves as an estimate of dispersal among communities. Specifically, the Nm parameter determines the correlation between the frequency of occurrence and the regional relative abundance, where N represents the size of the metacommunity and m denotes the migration rate. The parameter R 2 represents the overall goodness of fit to the neutral model. To calculate the 95% confidence intervals for all fitting statistics, bootstrapping was performed with 1000 bootstrap replicates. Moreover, the normalized stochasticity ratio (NST) was calculated to determine the contribution of the stochastic process to the microbial community assembly [ 21 ]. Results Comparison of dry and wet season environmental factors at different elevations During the dry and wet seasons, we documented variations in air temperature, air humidity, soil temperature, and soil moisture at various elevations (as shown in Fig. 1 and Additional file 1: Supplementary Table S2 ). Notably, both DHVs and the transition zones exhibited a distinct synoptic pattern in which drought and high temperatures persisted concurrently for the same period. Soil moisture in DHVs remained consistently low, hovering around 5% for an extended period, and was only higher than 6% in July and August. In contrast, the lowest value of 2.46% occurred in May, which was significantly lower than that in the transition zones (6.41%) and alpine zones (8.1%), indicating extreme arid conditions. In both the wet and dry seasons in DHVs, there were no significant changes in soil pH, TN, TP, and N: P ratio, resulting in a statistically insignificant outcome (Additional file 1: Supplementary Table S3). In contrast, soil TN and TP in the transition zones were significantly lower in the dry season compared to the wet season, but soil pH remained unchanged. Furthermore, in the alpine zones, there were no significant changes in TN and TP, except for a decrease in pH and N: P ratio in the dry season (Additional file 1: Supplementary Table S3). Soil bacterial diversity and composition Bacterial community profiling yielded a total of 6,054,234 sequences ranging from 33,634 to 73,165, which were obtained for the 120 soil samples. After subsampling each to the minimum number of sample sequences, 293170 bacterial OTUs (approximately 2443 per sample) were identified, representing an average Good’s coverage of 95.82% (Additional file 2: Supplementary Figure S1 ). Our analysis of soil bacterial communities at different elevations revealed consistent alpha diversity in DHVs and the alpine zones during the dry and wet seasons. There were no statistically significant differences in community richness, diversity, or coverage. However, seasonal variations had a greater impact on community diversity in the transition zones (Fig. 2 c), with higher diversity observed in the dry season compared to the wet season. The dominant bacterial phyla in the soil were Proteobacteria, Acidobacteria, Actinobacteria, and Chloroflexi, collectively accounting for over 80% of the total abundance in both the dry and wet seasons (Fig. 2 a). Additionally, we assessed the taxonomic composition at the class level, revealing significant variations among soil samples from different elevations and seasons. The dominant classes were Actinobacteria, Alphaproteobacteria, and Acidobacteriia (Fig. 2 b). Moreover, we performed beta diversity analysis to examine the similarity or difference in community composition among samples. PCoA ordinations and Adonis tests demonstrated clear distinctions in bacterial community compositions between the dry and wet seasons for soils obtained from all three elevations (Fig. 2 d, e; Additional file 2: Supplementary Figure S2 ). Specific microbial clades in soil bacteria community We conducted a statistical analysis using Welch’s t-test with Bonferroni correction to compare the differences in soil bacterial phylum composition between the dry and wet seasons at various elevations. The abundance of Actinobacteria consistently exhibited higher levels in the dry season compared to the wet season across all soil samples at different elevations. In DHVs and the transition zones, Chloroflexi abundance was higher in the dry season compared to the wet season (Fig. 2 f, g), while the opposite trend was observed in the alpine zones (Additional file 2: Supplementary Figure S3). The difference in abundance of Proteobacteria between the dry and wet seasons was found to be significant primarily in the transition zones (Fig. 2 g) and alpine zones (Additional file 2: Supplementary Figure S3), but exhibited an opposite pattern of variation. To further identify microorganisms that can effectively differentiate between the dry and wet seasons at different elevations, we employed LEfSe to visualize the distribution of various clades at the phylum to genus levels in soil samples (Fig. 3 ). During the dry season, we observed similarities in microbial clades between soils from DHVs (Fig. 3 a) and the transition zones (Fig. 3 b), including the phyla Actinobacteria, Chloroflexi, and Proteobacteria. However, we found a predominance of phyla Acidobacteria (9), Planctomycetes (4), and Verrucomicrobia (2) from the alpine zones (Fig. 3 c). Additionally, we observed a significant decrease in the abundance of Chloroflexi and an increase in Proteobacteria in the alpine zones, as determined by the same screening criteria (LDA score > 3.5, p < 0.05). In contrast to the dry season, soil samples from the wet season exhibited a higher diversity of microbial clades, including the presence of specific clades such as Acidobacteria, Patescibacteria, and Proteobacteria. Verrucomicrobia (4) was exclusively detected in the transition zones, while WPS-2 (5) was specifically found in the alpine zones. Finally, histograms of the LDA scores (LDA score > 3.5, p < 0.05) implied bacterial clades (class level) showing statistically significant and biologically consistent differences between the dry and wet seasons at different elevations (Fig. 3 d-f). Environmental factors influencing community structure We conducted a linear regression analysis utilizing the first principal axis (PC1) of PCoA to elucidate the relationship between environmental variables and beta-diversity at the phylum level. Out of the 24 potential combinations examined, only five exhibited statistical significance (Fig. 4 ). Notably, nutrient availability in the transition zones exerted an impact on PC1 during both the dry season (N and P) and the wet season (N:P ratio), whereas in the alpine zones, nutrient availability influenced PC1 only during the wet season (N and P). Remarkably, bacterial communities displayed no significant correlation with environmental factors, with the exception of N and P in the transition zones during the dry season. Furthermore, the correlation heatmap revealed the correlation coefficients and statistically significant correlations between the dominant phyla (top 10) of soil bacterial communities and environmental factors (N, P, and N:P ratio) in the transition zones (Fig. 5 a, b) and alpine zones (Fig. 5 c), respectively. Particularly, soil P and N:P ratio exhibited substantial effects on the abundance of Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, and Planctomycetes in the wet season within the transition zones (Fig. 5 b). Ecological assembly of bacterial community A comprehensive investigation was conducted on soil microbial communities at varying elevations and seasons (Fig. 6 and Additional file 2: Supplementary Figure S4), uncovering the significant influence of stochastic processes on the assembly of soil bacterial communities throughout the entire valley (R 2 = 0.613). Notably, within this geographical region, microorganisms face higher diffusion restrictions, with a migration rate (m) of 0.116. In-depth analysis of soil bacterial communities at various elevations revealed that DHVs exhibited the highest fit to the NCM at 0.877, and had lower diffusion restrictions for its microorganisms (m = 0.818). Among the three elevations, the transition zone between DHVs and the alpine zones exhibited the highest level of dispersal limitation for its microorganisms (m = 0.352), indicating greater restrictions on their dispersal. Given that the number of sequences in each sample was consistent (N = 22647), the estimates of intercommunity dispersal, which represent the movement of microorganisms between different communities, were highest in DHVs (Nm = 18,526), followed by the alpine zones (Nm = 11,342), and lowest in the transition zones (Nm = 7,964). Furthermore, an analysis of the disparity in migration rates at different elevations during the dry and wet seasons was undertaken. Intriguingly, migration rates in both DHVs and the alpine zones were found to be higher in the dry season compared to the wet season, suggesting reduced diffusion constraints. In contrast, this pattern was reversed in the transition zones. Overall, NST analysis again quantitatively revealed that stochastic processes played a crucial role in shaping microbial community assembly, with all samples exhibiting an NST value exceeding 50% (Additional file 2: Supplementary Figure S5). Discussion The hydrothermal conditions in the Jinsha River valley exhibit significant variations at different elevations. The lower elevation areas exhibit a persistently arid and high-temperature climate, characterized by minimal fluctuations in both moisture and temperature throughout the dry and wet seasons. This stands in stark contrast to the high elevation forests and the transition zones between them. Numerous studies have highlighted the crucial role of soil microorganisms in the ecological restoration of the dry-hot valley. We propose that soil microorganisms in the low elevation DHVs have developed tolerance mechanisms to effectively cope with seasonal dry-wet alternation and even the extreme dry and hot environments they encounter during long-term adaptation processes [ 4 , 6 , 38 , 39 ]. As expected, the bacterial communities within DHVs exhibited strong stability and adaptability to seasonal dry and wet alternation (Fig. 2 ). Our results indicated that the core bacterial taxa, which accounted for over 80% of the community, remained consistent. These taxa were prominently represented by Proteobacteria, Acidobacteria, Actinobacteria, and Chlorobacteria (Fig. 2 a, b). However, these findings diverge from observations in other regions of the DHVs in southwest China [ 13 , 23 – 25 , 40 ]. It may suggest that although the arid and hot environment exerts a selective pressure on the soil microbial community [ 41 , 42 ], the composition of the core microbial community is intricately linked to soil properties, vegetation diversity, and other environmental factors [ 43 , 44 ]. Within DHVs and the transition zones, the abundance of Actinobacteria and Chloroflexi was increased during the dry season, in contrast to Acidobacteria, which displayed an opposite trend (Fig. 2 f, g). These microbial dynamics are posited to critically influence the microbiome's adaptation to seasonal moisture fluctuations [ 45 ]. Notably, Actinobacteria exhibit robustness to environmental stress, including drought, and are instrumental in soil functionality under challenging conditions [ 10 , 46 , 47 ]. Their symbiotic relationships with plant roots further bolster drought tolerance by facilitating phytohormone production and enhanced nutrient acquisition [ 10 , 42 , 48 ]. In addition, Chloroflexi, a phylum adapted to oligotrophic conditions, demonstrates the capacity to prosper in resource-limited environments [ 49 – 51 ]. Our findings align with the results [ 45 ] in suggesting that Chloroflexi may interact with other soil microorganisms, thereby shaping community structure and function. Taken together, these results underscore the crucial role of Actinobacteria and Chloroflexi in promoting soil resilience and ecosystem stability under the extreme conditions found in DHVs [ 12 ]. Seasonal fluctuations in soil temperature, moisture, and nutrient levels can significantly influence belowground microbial communities. However, these seasonal variations may not be the primary driver of microbiota diversity, as it is influenced more by taxon-specific gene expression [ 8 ]. Our research revealed that soil bacterial communities at various elevations maintained consistent richness, diversity, and coverage throughout both dry and wet seasons, except within the transition zones (see Fig. 2 c). This observation suggests that the regulation of season-specific microbial clades may contribute to the stability of microbial community structure. For example, our study identified an increase in the activity of clades associated with Acidobacteria and Patescibacteria within DHVs and the transition zones during the wet season, which subsequently decreased following a prolonged dry period. In contrast, specific bacterial phyla displayed adaptability by increasing the number of clades (Fig. 3 a-c). Interestingly, some clades exhibited complex regulatory patterns, such as those related to Proteobacteria. Within the lower elevation DHVs, clades associated with Actinobacteria were more abundant during dry seasons compared to wet seasons. Previous studies have highlighted the pivotal role of Actinobacteria in responding to drought stress, including their ability to maintain activity and enter a dormant state under dry conditions. Santos-Medellín et al. [ 52 ] proposed that prolonged drought leads to a significant enrichment of Actinobacteria in the rice rhizosphere microbiome, with their abundance declining upon recovery. Therefore, we hypothesize that Actinobacteria and its related clades play a regulatory role in response to changes in water availability, enhancing the adaptability and functionality of soil bacterial communities during seasonal dry-wet cycles. Acidobacteria, a widely distributed phylum in diverse ecosystems [ 53 , 54 ], demonstrates a remarkable regulatory capacity and adaptive mechanisms to thrive in complex environmental conditions. Kalam et al. [ 55 ] conducted a comprehensive review of current research on Acidobacteria in soil, highlighting its significant ecological importance. Our findings revealed a notable decline in the abundance of Acidobacteria-related microbial clades in DHVs and the transition zones during dry seasons, with a resurgence of activity observed during wet seasons when water availability increased (Fig. 3 a, b). Studies suggest that Acidobacteria may possess specific genes facilitating survival and competitive colonization in the rhizosphere, fostering beneficial interactions with plants [ 56 , 57 ]. Consequently, it is proposed that Acidobacteria-related microbial clades play a pivotal role in the ecological restoration of DHVs. In contrast, fluctuations in the abundance of Proteobacteria-associated clades across different sampling areas did not exhibit consistent patterns during the transition from wet to dry seasons (Fig. 3 a-c), indicating diverse regulatory strategies employed by Proteobacteria-related microbial clades in response to seasonal variations [ 9 , 47 , 58 ]. Notably, seasonal fluctuations in the alpine soil microbial community, while evident, are relatively minor compared to DHVs and the transition zones, possibly due to the limited impact of moisture and temperature changes in alpine regions. This suggests that soil bacterial communities in alpine zones maintain stability under the mild seasonal fluctuations of wet and dry conditions. Thus, our results reveal that soil microbial communities undergo rapid evolution in response to selective pressures imposed by seasonal dry-wet alternations. Adaptive evolution may result in the emergence of novel traits or genetic variants that confer fitness advantages under specific moisture regimes [ 59 – 61 ]. Genetic adaptation to fluctuating environmental conditions contributes to the resilience and stability of soil microbial aggregates over time. Recent advancements in microbial community assembly research have illuminated our understanding of these intricate ecological dynamics. However, the impact of seasonal dry and wet alternation on community assembly within unique savanna-like dry-hot valley ecosystems remains poorly characterized across spatial and temporal scales. This study investigates the influence of environmental factors on microbial communities in these distinct ecosystems. Our results indicate a minimal effect of seasonal dry and wet alternation in valley regions, but varying degrees of alteration in transition and alpine zones. This observation led us to hypothesize that soil microbial community assembly in dry-hot valley ecosystems is predominantly driven by stochastic processes, in contrast to the deterministic processes observed in the transition and alpine zones. Employing the neutral community model (NCM), our analysis emphasizes the dominance of stochastic mechanisms in microbial community assembly within valley ecosystems, particularly in savanna-like dry-hot valleys (Fig. 6 ). This finding partially supports our hypothesis and suggests that stochastic processes play a significant role in shaping microbial communities in these ecosystems. However, the influence of stochastic processes extends to the transition and alpine zones as well. Our findings challenge previous assumptions about the primary influence of environmental factors, such as seasonal dry and wet alternation, on shaping microbial communities across all ecosystem types. Soil pH, a crucial abiotic factor affecting soil microbial communities, could mediate the balance between stochastic and deterministic assembly of bacteria [ 62 ]. In our study, soil pH in DHVs and the transition zones did not vary significantly between dry and wet seasons, except in the alpine zones (Additional file 1: Supplementary Table S1 ). Nevertheless, the potential role of deterministic processes in dry and hot valley regions cannot be discounted. It is well-established that deterministic and stochastic processes coexist in regulating ecological community assembly [ 20 , 21 , 63 ]. Moreover, Thompson et al. [ 64 ] proposed that narrow abiotic niche curves lead to strong species responses to environmental variation (deterministic processes), while broad or flat curves result in weak or absent responses (stochastic processes). Our study suggests that the dry-hot valleys may exhibit characteristics that favor stochastic assembly, potentially influenced by reduced environmental variability or increased dispersal rates [ 65 ]. Conclusions Our study demonstrates the significant influence of seasonal dry and wet alternation on bacterial community diversity within transition zones, surpassing its impact in both DHVs and alpine zones. Notably, we identified season-specific microbial clades across all sampling areas, highlighting their adaptability and responsiveness to dynamic environmental conditions. Stochastic processes emerged as the dominant drivers of soil bacterial community assembly in all three zones. These findings (Fig. 7 ) hold implications for developing strategies to optimize soil health, enhance ecosystem services, and mitigate the effects of climate change in these distinct regions: DHVs, transition zones, and alpine zones. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The raw data was deposited into the NCBI Sequence Read Archive (SRA) database under Accession Number: PRJNA1114417. Competing interests The authors declare no competing interests. Funding The research was supported by the National Natural Science Foundation of China (Grant No. 41925030) and Science and Technology Projects in Sichuan Province, China (Grant No. 2023YFS0367). Author contributions HJ, XC, and YZ designed the research project. HJ, YL, JC, and LW carried out the field work and performed the laboratory and statistical analyses. HJ and YL wrote the initial draft of the paper. All authors read and approved the final version of the manuscript. Acknowledgements We would like to thank the Dongchuan Debris Flow Observation and Research Station (DDFORS), Chinese Academy of Sciences, which provided the field observation data for this study. References Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth's biogeochemical cycles. Science 2008; 320:1034–1039. Philippot L, Chenu C, Kappler A, Rillig MC, Fierer N. The interplay between microbial communities and soil properties. Nat Rev Microbiol. 2024; 22:226–239. Sokol NW, Slessarev E, Marschmann GL, Nicolas A, Blazewicz SJ, Brodie EL, et al. Life and death in the soil microbiome: how ecological processes influence biogeochemistry. Nat Rev Microbiol. 2022; 20:415–430. Canarini A, Schmidt H, Fuchslueger L, Martin V, Herbold CW, Zezula D, et al. 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Linking bacterial community to aggregate fractions with organic amendments in a sandy soil. Land Degrad Dev. 2019; 30:1828–1839. Xu P, Liu Y, Zhu J, Shi L, Fu Q, Chen J, et al. Influence mechanisms of long-term fertilizations on the mineralization of organic matter in Ultisol. Soil Till Res. 2020; 201:104594. Santos-Medellín C, Liechty Z, Edwards J, Nguyen B, Huang B, Weimer BC, et al. Prolonged drought imparts lasting compositional changes to the rice root microbiome. Nat Plants. 2021; 7:1065–1077. Kielak AM, Barreto CC, Kowalchuk GA, van Veen JA, Kuramae EE. The ecology of Acidobacteria: moving beyond genes and genomes. Front Microbiol. 2016; 7:744. Eichorst SA, Trojan D, Roux S, Herbold C, Rattei T, Woebken D. Genomic insights into the Acidobacteria reveal strategies for their success in terrestrial environments. Environ Microbiol. 2018; 20:1041–1063. Kalam S, Basu A, Ahmad I, Sayyed RZ, El-Enshasy HA, Dailin DJ, et al. Recent understanding of soil Acidobacteria and their ecological significance: A critical review. Front Microbiol. 2020; 11:580024. Na X, Cao X, Ma C, Ma S, Xu P, Liu S, et al. Plant stage, not drought stress, determines the effect of cultivars on bacterial community diversity in the rhizosphere of broomcorn millet ( Panicum miliaceum L.). Front Microbiol. 2019; 10:828. Gonçalves OS, Fernandes AS, Tupy SM, Ferreira TG, Almeida LN, Creevey CJ, et al. Insights into plant interactions and the biogeochemical role of the globally widespread Acidobacteriota phylum. Soil Biol Biochem. 2024; 192:109369. Cordero I, Leizeaga A, Hicks LC, Rousk J, Bardgett RD. High intensity perturbations induce an abrupt shift in soil microbial state. ISME J. 2023; 17:2190–2199. Olson-Manning CF, Wagner MR, Mitchell-Olds T. Adaptive evolution: evaluating empirical support for theoretical predictions. Nat Rev Genet. 2012; 13:867–877. Tusso S, Nieuwenhuis BPS, Weissensteiner B, Immler S, Wolf JBW. Experimental evolution of adaptive divergence under varying degrees of gene flow. Nat Ecol Evol. 2021; 5:338–349. Bleuven C, Landry CR. Molecular and cellular bases of adaptation to a changing environment in microorganisms. Proc R Soc B. 2016; 283:20161458. Tripathi BM, Stegen JC, Kim M, Dong K, Adams JM, Lee YK. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 2018; 12:1072–1083. Langenheder S, Szekely AJ. Species sorting and neutral processes are both important during the initial assembly of bacterial communities. ISME J. 2011; 5:1086–1094. Thompson PL, Guzman LM, Meester DL, Horvath Z, Ptacnik R, Vanschoenwinkel B, et al. A process-based metacommunity framework linking local and regional scale community ecology. Ecol Lett. 2020; 23:1314–1329. Evans S, Martiny JB, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017; 11:176–185. Additional Declarations No competing interests reported. Supplementary Files Additionalfile2.docx Additionalfile1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4643110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321794178,"identity":"76c37d71-964c-499a-8441-2f2d9f1bcec2","order_by":0,"name":"Hao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACxmYQ0cDAwM/MwCBBmhbJZmK1QPQBtRgcIFYLczvzs4dfd9jJGR/nTrzxgcFOnoH97AECDmMzN5Y9k2xsdph3s+UMhmTDBp68BAJaGMykJduYE7cd5t0mzcPAnMAgwWNAQAv7N6CW+sTNzUAtfxjqidHCYyb5se1w4gZmoBYGhsNEaSmTZmw7biwB8kuPwXHDNp4c/FoM+49vk/zZVi3H3392440fFdXy/OxnCGhpAAY0D5wLVMyGVz0QyIMc94OQqlEwCkbBKBjZAACh5juP39t2UAAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Jiang","suffix":""},{"id":321794179,"identity":"0c5c8ebb-73a0-48fc-9a64-f4a5b80bf38a","order_by":1,"name":"Xiaoqing Chen","email":"","orcid":"","institution":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqing","middleName":"","lastName":"Chen","suffix":""},{"id":321794180,"identity":"0ccf6024-96f6-473c-b829-08a18d528aec","order_by":2,"name":"Yongping Li","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Li","suffix":""},{"id":321794181,"identity":"e6ce6ce8-7d7e-42bd-b6e3-d382e7b47e40","order_by":3,"name":"Jiangang Chen","email":"","orcid":"","institution":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiangang","middleName":"","lastName":"Chen","suffix":""},{"id":321794182,"identity":"767adef6-e39c-4747-a638-f6c21b6a1e54","order_by":4,"name":"Li Wei","email":"","orcid":"","institution":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wei","suffix":""},{"id":321794183,"identity":"bb07b22c-c8c0-46fd-b040-e3d16a9990c8","order_by":5,"name":"Yuanbin Zhang","email":"","orcid":"","institution":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuanbin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-26 13:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4643110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4643110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60995845,"identity":"07a55d0a-cfdc-443d-9dd0-9346734ae0b0","added_by":"auto","created_at":"2024-07-24 12:10:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":830051,"visible":true,"origin":"","legend":"\u003cp\u003eThe dry-hot valleys of southwest China present a unique and challenging environment for both microorganisms and plants.\u003cstrong\u003e \u003c/strong\u003eThis region, which includes the valleys along\u003cstrong\u003e \u003c/strong\u003ethe Jinsha River, Nu River, and Yuan River, covers more than 16,000 km\u003csup\u003e2 \u003c/sup\u003eand is characterized by an extended dry season lasting from November of the previous year to May of the current year, with low precipitation (about 50-150 mm, accounting for only 10% of the total annual precipitation) and high evaporation. During this period, the area experiences extreme aridity and high temperatures, creating a harsh environment for life. Microorganisms have been identified as pivotal biotic factors that facilitate plant adaptation to these harsh conditions.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/c07fd5be049933e876b8585b.png"},{"id":60995846,"identity":"ac6b0167-0b79-431a-80a9-bddf140cc9f8","added_by":"auto","created_at":"2024-07-24 12:10:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":629805,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of seasonal dry and wet alternation on soil bacterial community structure. The compositional shifts within the\u003cstrong\u003e \u003c/strong\u003ebacterial microbiome are shown at the (\u003cstrong\u003ea\u003c/strong\u003e) phylum and (\u003cstrong\u003eb\u003c/strong\u003e) class taxonomic levels, illustrating the ecological responses to climatic variation. Panel (\u003cstrong\u003ec\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003epresents a comparative analysis of the Shannon diversity index, which indicates the pronounced influence of seasonal dry and wet alternation in the transition zones, contrasting with those in the dry-hot valleys (DHVs) and alpine zones. Principal component analysis (PCoA) and Adonis tests were performed at the phylum level, suggesting the distinct differences in (\u003cstrong\u003ed\u003c/strong\u003e) DHVs and (\u003cstrong\u003ee\u003c/strong\u003e) the transition zones, respectively. An extended error bar plot identifying significant differences between the mean proportions of bacterial taxa between soil samples from (\u003cstrong\u003ef\u003c/strong\u003e) DHVs and (\u003cstrong\u003eg\u003c/strong\u003e) the transition zones, corresponding to the wet (green) and dry (red) seasons, respectively. The corrected \u003cem\u003ep\u003c/em\u003e-values are annotated on the right side of the plot. W1000 (D1000), W2000 (D2000), and W3000 (D3000) represent samples collected from DHVs, transition, and alpine zones during the wet (dry) season, respectively.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/41b0b1fd8cda532bb9da5935.png"},{"id":60995853,"identity":"fbf4e9a3-66f0-4fc5-8809-1c87fc812b32","added_by":"auto","created_at":"2024-07-24 12:10:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":805363,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant taxa significantly retrieved by linear discriminant analysis (LDA) effect size (LEfSe) for soil bacterial communities between dry and wet seasons. The cladogram indicates the taxonomic representation of statistically consistent differences between soil samples from dry (red) and wet (green) seasons in (\u003cstrong\u003ea\u003c/strong\u003e) dry-hot valleys (DHVs), (\u003cstrong\u003eb\u003c/strong\u003e) transition, and (\u003cstrong\u003ec\u003c/strong\u003e) alpine zones, respectively. Below each cladogram, discriminant clades are documented. Histogram of the LDA score determined for differentially abundant taxa (class level) with cut-off LDA score \u0026gt; 3.5, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (\u003cstrong\u003ed-f\u003c/strong\u003e). Negative LDA score (red) highlight the enriched taxa in soil samples from dry seasons and positive LDA score (green) are abundant taxa in soil samples from wet seasons.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/f3735cd2cb2cdf75f4d63789.png"},{"id":60995841,"identity":"c1dfa176-2508-43cf-9cd6-96d4f8857f88","added_by":"auto","created_at":"2024-07-24 12:10:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":351221,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression analysis elucidating the relationship between nutrient availability and phylum-level beta diversity in the dry (red) and wet (green) seasons, respectively.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/4aad245365711517bd8e107f.png"},{"id":60995843,"identity":"18684ea2-1715-4c48-be07-b069da98cd40","added_by":"auto","created_at":"2024-07-24 12:10:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":248590,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap between dominant phyla of soil bacterial community and N, P and N:P ratio in transition and alpine zones during the dry and wet seasons.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/dadf6a3d36b865ffa22aad60.png"},{"id":60995847,"identity":"63fa0142-7a30-44bc-b81a-2046d7c9a3a6","added_by":"auto","created_at":"2024-07-24 12:10:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":744760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFit of the neutral community model (NCM) of bacterial community assembly.\u003c/strong\u003e The solid yellow line represents the best fit to the model, with dashed red and green lines indicating the 95% confidence intervals. OTUs deviate from the model’s predictions, either occurring more or less frequently, are highlighting in distinct colors. The R\u003csup\u003e2\u003c/sup\u003e indicates the goodness of fit to the model, with a higher value denoting a better fit. The migration rate (m) represents the dispersal ability of species within the community. A smaller m-value suggests restricted dispersal and a more localized community, while a higher m-value indicates less restricted dispersal and a more interconnected community.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/34c86697e97118a7813eda0b.png"},{"id":60995851,"identity":"6a1f650d-c4c1-42e0-8a38-d52f7285d86e","added_by":"auto","created_at":"2024-07-24 12:10:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":650159,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of seasonal dynamic of soil microbiome responding to dry-wet alternation along the Jinsha River Dry-hot Valley. The distinctive ecological characteristics of the dry-hot valleys are defined by their attributes: aridity and high temperatures. Seasonal dry and wet alternation led to this phenomenon prominent. This unique climate exhibits a clear vertical distribution, with elevation playing a pivotal role in shaping the environment and vegetation. As one descends from the mountain peaks, the landscape transforms from lush evergreen or coniferous forests into savanna or scrubland. The formation of this ecological landscape is a fascinating subject of scientific study, with a multitude of factors contributing to its uniqueness. Among the various influences, the daily mountain-valley breeze circulation stands out as a prominent and consistent phenomenon. During the day, rapid warming at the mountaintop triggers a decrease in air pressure, inducing moisture-laden valley breezes (indicated by red arrows) that ascend from the valley floor. Conversely, at night, the swift drop in temperature at higher altitudes generates descending air currents, forming mountain breezes (depicted by blue arrows). We propose that the soil microorganisms native to the valley floors of dry-hot valleys (DHVs) have likely evolved specific mechanisms to adapt to these conditions over extended periods. For instance, microbial community assembly, dominated by stochastic processes, may play a significant role. Additionally, the involvement of seasonal-specific microbial clades and cascades in the adaptation process cannot be underestimated, as they potentially influence and are influenced by the unique environmental factors. Of particular interest is the transition zones (1600-3000 m a.s.l.), situated between the DHVs (below 1600 m a.s.l.) and the alpine zones (above 3000 m a.s.l.), where moisture and temperature conditions are becoming favorable. This intermediate area exhibits relatively higher microbial activity compared to the extreme environments of the valley floors and mountain peaks. Therefore, this zone presents itself as a strategic focus for ecological restoration efforts, leveraging the intricate interplay between plant and microbial life.\u003c/p\u003e","description":"","filename":"Picture7.png","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/1b6a99d857b8e0ad3eb78070.png"},{"id":61677436,"identity":"871b3b03-04a4-481d-bfc0-a275762647ee","added_by":"auto","created_at":"2024-08-02 22:29:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5341059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/e74f31c9-7df6-4eb1-91e9-9a9acf45df17.pdf"},{"id":60995850,"identity":"8889a069-2c49-4bac-8b11-0ad68bc4351d","added_by":"auto","created_at":"2024-07-24 12:10:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":693739,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/5c624e6b57bf15528523ca03.docx"},{"id":60996882,"identity":"d4e2ac0b-9298-4e4f-9b74-fc3ebaa4aec3","added_by":"auto","created_at":"2024-07-24 12:18:21","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":23333,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4643110/v1/cbfc6ca4b93628be5b8c805b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Dynamics of Soil Microbiome in Response to Dry-Wet Alternation along the Jinsha River Dry-hot Valley","fulltext":[{"header":"Background","content":"\u003cp\u003eThe soil microbiome, a diverse community of microorganisms, plays a pivotal role in ecosystem services such as nutrient cycling, carbon sequestration [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and plant growth promotion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, its activities are sensitive to environmental factors including temperature fluctuations, precipitation patterns, and extreme events [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], potentially challenging soil microbial communities and ecosystem stability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Seasonal variations, particularly dry and wet alternations, significantly impact soil microbiome composition and function [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. During the dry season, the soil microbiome experiences water stress, which can affect microbial activity and nutrient availability. This, in turn, leads to reduced nutrient cycling and decreased plant productivity. Conversely, increased moisture levels during the wet season may facilitate microbial metabolic processes, contributing to nutrient availability and soil fertility. Yet, the deep mechanisms underlying soil microbiome responses to seasonal fluctuations remain poorly understood [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consequently, elucidating these differences is imperative for optimizing soil health, enhancing ecosystem services, and mitigating climate change impacts.\u003c/p\u003e \u003cp\u003eThe dry-hot valleys (DHVs) in southwest China, especially located in the lower reaches of the Jinsha River exhibit unique climate types and geographic regions. Despite serving as a sanctuary for ancient life during global climate change, extreme drought and high temperatures have resulted in severe degradation of the local ecosystem [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The vegetation landscape below 1600 m above sea level (a.s.l.) on both sides of the river valley is characterized by a savanna-like ecosystem (Additional file 1: Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2; Additional file 2: Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), with a hot and dry climate that is marked by a clear distinction between wet and dry seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInvestigations reveal that the valley exhibits a distinctive pattern of vertical vegetation distribution, characterized by a reverse succession from forests to savannas as elevation decreases. Above 3000 m a.s.l., the alpine zones boast lush forest vegetation and ample rainfall. The region spanning from 1600 m to 3000 m a.s.l. serves as the transition zones between DHVs and the alpine zones. Within the transition zones, significant alterations in hydrothermal conditions and vegetation habitats occur, marking the interface between the two distinct ecological zones. Researchers have identified local phenomena, including the foehn effect and mountain-valley breeze circulation, as significant contributors to this occurrence. Particularly noteworthy is the pivotal role played by mountain-valley breeze circulation. In this process, diurnal temperature variations prompt mountain summits to warm more rapidly than valley floors during daylight hours, inducing lower air pressure atop the peaks. Consequently, airflow ascends, transporting moisture from the valley floors up the mountain slopes, thereby instigating the formation of valley breezes. In the evening, the mountain tops cool faster than the valleys, causing the airflow to sink and the cold air to descend along the mountain slopes, forming a mountain breeze. Over time, this process leads to increasing dryness in the valley floor, while moisture conditions remain suitable at higher elevations. Consequently, soil properties closely associated with water availability, such as pH, total nitrogen, total phosphorus, etc., do not exhibit significant seasonal differences in the low-elevation DHVs (Additional file 1: Supplementary Table S3). These findings imply the importance of extending soil microbiome studies beyond low-elevation DHVs to encompass the entire valley ecosystem.\u003c/p\u003e \u003cp\u003eThe intricate mechanisms governing community assembly represent a central challenge within microbial ecology [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Views of community assembly have traditionally been based on the contrasting perspectives of the deterministic niche paradigm and stochastic neutral models [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on the niche theory, microbial community assembly is posited as a deterministic process influenced by abiotic factors (e.g., pH and temperature) and biotic factors (e.g., species interaction), reflecting diverse habitat preferences and microbial fitness. Conversely, the neutral theory postulates that microbial community assembly is governed by stochastic processes like birth, death, migration, speciation, and limited diffusion, assuming a stochastic equilibrium between taxon loss and acquisition [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Recent research into the microbial community assembly within DHVs remains sparse, leaving the underlying mechanisms poorly understood. Specifically, it is yet uncertain whether soil microorganisms have developed seasonally specific microbiota and community assembly mechanisms in response to environmental fluctuations within DHVs.\u003c/p\u003e \u003cp\u003ePrevious studies have addressed the influence of various environmental factors on soil microbial composition, diversity, and function within DHVs, including desertification [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], land use change [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], vegetation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and elevational gradients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, there exists a notable gap in research concerning the effects of seasonal dry and wet alternation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To address this gap, we examined the bacterial microbiome of 120 soil samples collected from DHVs, transition and alpine zones along the Jinsha River during both wet and dry seasons to compare the composition, structure, and function. We hypothesized that: i) microbes inhabiting DHVs have evolved efficient adaptive mechanisms to withstand persistent high temperature and drought, thereby minimizing the impact of seasonal dry and wet alternation on their diversity; ii) season-specific microbial clades contribute to adaptation of microbial taxa to seasonal dry and wet alternation; and iii) stochastic processes primarily govern the assembly of microbial communities in DHVs, whereas deterministic processes prevail in the transition and alpine zones due to substantial environmental disparities between dry and wet seasons .\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and sampling\u003c/h2\u003e \u003cp\u003eThis study was conducted in DHVs of the Xiaojiang River section, which serves as a first-class tributary of the Jinsha River. To examine the differences in soil microbial community diversity between the dry and wet seasons in different elevations, we established sampling plots (20 m \u0026times; 20 m) at three different elevations (26\u0026deg;14\u0026prime;-26\u0026deg;15\u0026prime; N, 103\u0026deg;0\u0026prime;-103\u0026deg;6\u0026prime; E), based on long-term observation data. We recorded soil characteristics, vegetation composition, and meteorological information (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Additional file 1: Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The sampling plots represent three distinct landscape types along the path of mountain-valley breeze circulation. Firstly, the DHV (~\u0026thinsp;1100 m a.s.l.) is an area characterized by typical dry and hot conditions. The dominated vegetation in this region consists of savanna-like species, including \u003cem\u003eDodonaea viscosa\u003c/em\u003e, \u003cem\u003eHeteropogon contortus\u003c/em\u003e, and \u003cem\u003eAgave sisalana\u003c/em\u003e. Secondly, the high mountain area (Alpine zone, ~\u0026thinsp;3000 m a.s.l.) is characterized by low temperatures and abundant rainfall. Planted trees, primarily \u003cem\u003ePinus armandii\u003c/em\u003e, are prevalent in this region. Lastly, the transition zone (~\u0026thinsp;2000 m a.s.l.) lies between the two aforementioned areas. It is distinguished by sparse vegetation and is often surrounded by white cloud bands, which form as a result of the daytime mountain-valley breeze airflow rising.\u003c/p\u003e \u003cp\u003eIn August 2019 and April 2020, soil samples were collected from three elevation gradients during the wet and dry seasons, respectively. To account for spatial heterogeneity within each sampling plot, ten soil cores were randomly collected from the upper 20 cm depth, and surface litter was meticulously removed. The soil cores were then pooled and homogenized to create a composite sample. A total of 20 composite soil samples were prepared from each sampling site for each season, resulting in a total of 120 composite samples for both seasons. The soils were sieved (\u0026lt;\u0026thinsp;2 mm) and separated into two portions: one was air-dried for one month and stored for soil biochemical analyses, and the other was immediately frozen at \u0026minus;\u0026thinsp;20\u0026deg;C for molecular analyses. Soil pH was determined from the air-dried samples using a soil:solution ratio of 1:2.5. Soil total nitrogen (TN) concentrations were measured using an elemental analyser (Elementar Vario EL, Chengdu, China). Soil total phosphorus (TP) was measured by ICP-OES (Optima 8300, PerkinElmer, USA) as described by Yang et al [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, PCR amplification and amplicon sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from 0.5 g of each soil sample with the E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer\u0026rsquo;s instructions and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further processing. The DNA extract was assessed on a 1% agarose gel, and DNA concentration and purity were determined with a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA).\u003c/p\u003e \u003cp\u003eThe hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with the PCR primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. PCR amplification was performed as follows: initial denaturation at 95\u0026deg;C for 3 min, followed by 27 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 55\u0026deg;C for 30 s, extension at 72\u0026deg;C for 45 s, single extension at 72\u0026deg;C for 10 min, and termination at 4\u0026deg;C. The PCR mixtures contained 5 \u0026times; TransStart FastPfu buffer (4 \u0026micro;L), 2.5 mM dNTPs (2 \u0026micro;L), forward primer (5 \u0026micro;M; 0.8 \u0026micro;L), reverse primer (5 \u0026micro;M; 0.8 \u0026micro;L), TransStart FastPfu DNA Polymerase (0.4 \u0026micro;L), bovine serum albumin (BSA; 0.2 \u0026micro;L), template DNA (10 ng), and up to 20 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. PCR reactions were performed in triplicate. The PCR products were extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer\u0026rsquo;s instructions and were quantified using Quantus\u0026trade; Fluorometer (Promega, Madison, WI, USA). Purified amplicons were pooled equimolar and were paired-end sequenced on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSequence processing\u003c/h2\u003e \u003cp\u003eRaw fastq files were quality-filtered by Trimmomatic [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and merged by FLASH [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] applying the following criteria: (i) Reads were truncated at any site receiving an average quality score\u0026thinsp;\u0026lt;\u0026thinsp;20 over a 50-bp sliding window. (ii) Sequences with overlap longer than 10 bp were merged according to their overlap with mismatch of no more than 2 bp. (iii) Sequences of each sample were separated according to barcodes (exact matching) and primers (allowing a 2-nucleotide mismatch). Reads containing ambiguous bases were removed. Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using UPARSE [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Mitochondrial and chlorophyll sequences were removed from the OTU table. The taxonomy of each 16S rRNA gene sequence was analyzed by the RDP classifier algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) against the Silva database [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], using a confidence threshold of 70% and implementation in QIIME [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eSoil physicochemical properties were analyzed using the SPSS 17.0 software (SPSS Inc., Chicago, IL, USA). Significant differences among the means of different treatments were determined by Tukey\u0026rsquo;s multiple range tests after conducting tests of homogeneity for variances. Differences were considered statistically significant at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level. To assess alpha-diversity, communities were rarified to the minimum sample sequence number (that is, 22647). The Sobs (community richness), Shannon (community diversity), and Good\u0026rsquo;s coverage index (community coverage) were calculated using QIIME [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The significances of differences among treatments were compared using the Welch\u0026rsquo;s t-test. The Bray-Curtis distances between samples were used for principal coordinate analysis (PCoA) to assess the major variance components of the beta-diversity. ADONIS was carried out to evaluate group differences. The Welch\u0026rsquo;s t-test within STAMP [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was used to identify bacterial phyla that showed significant differences in abundance between groups. \u003cem\u003eP\u003c/em\u003e-values were adjusted for multiple comparisons using the Bonferroni method. Discriminant taxa were significantly retrieved by linear discriminant analysis (LDA) effect size (LEfSe) for soil bacterial communities between dry and wet seasons [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In order to explore the potential significance of stochastic processes in community assembly, a neutral community model (NCM) was used to examine the association between the detection frequency of OTUs and their relative abundance across the metacommunity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Within this model, the parameter Nm serves as an estimate of dispersal among communities. Specifically, the Nm parameter determines the correlation between the frequency of occurrence and the regional relative abundance, where N represents the size of the metacommunity and m denotes the migration rate. The parameter R\u003csup\u003e2\u003c/sup\u003e represents the overall goodness of fit to the neutral model. To calculate the 95% confidence intervals for all fitting statistics, bootstrapping was performed with 1000 bootstrap replicates. Moreover, the normalized stochasticity ratio (NST) was calculated to determine the contribution of the stochastic process to the microbial community assembly [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of dry and wet season environmental factors at different elevations\u003c/h2\u003e \u003cp\u003eDuring the dry and wet seasons, we documented variations in air temperature, air humidity, soil temperature, and soil moisture at various elevations (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Additional file 1: Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Notably, both DHVs and the transition zones exhibited a distinct synoptic pattern in which drought and high temperatures persisted concurrently for the same period. Soil moisture in DHVs remained consistently low, hovering around 5% for an extended period, and was only higher than 6% in July and August. In contrast, the lowest value of 2.46% occurred in May, which was significantly lower than that in the transition zones (6.41%) and alpine zones (8.1%), indicating extreme arid conditions. In both the wet and dry seasons in DHVs, there were no significant changes in soil pH, TN, TP, and N: P ratio, resulting in a statistically insignificant outcome (Additional file 1: Supplementary Table S3). In contrast, soil TN and TP in the transition zones were significantly lower in the dry season compared to the wet season, but soil pH remained unchanged. Furthermore, in the alpine zones, there were no significant changes in TN and TP, except for a decrease in pH and N: P ratio in the dry season (Additional file 1: Supplementary Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSoil bacterial diversity and composition\u003c/h2\u003e \u003cp\u003eBacterial community profiling yielded a total of 6,054,234 sequences ranging from 33,634 to 73,165, which were obtained for the 120 soil samples. After subsampling each to the minimum number of sample sequences, 293170 bacterial OTUs (approximately 2443 per sample) were identified, representing an average Good\u0026rsquo;s coverage of 95.82% (Additional file 2: Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Our analysis of soil bacterial communities at different elevations revealed consistent alpha diversity in DHVs and the alpine zones during the dry and wet seasons. There were no statistically significant differences in community richness, diversity, or coverage. However, seasonal variations had a greater impact on community diversity in the transition zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), with higher diversity observed in the dry season compared to the wet season. The dominant bacterial phyla in the soil were Proteobacteria, Acidobacteria, Actinobacteria, and Chloroflexi, collectively accounting for over 80% of the total abundance in both the dry and wet seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eAdditionally, we assessed the taxonomic composition at the class level, revealing significant variations among soil samples from different elevations and seasons. The dominant classes were Actinobacteria, Alphaproteobacteria, and Acidobacteriia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Moreover, we performed beta diversity analysis to examine the similarity or difference in community composition among samples. PCoA ordinations and Adonis tests demonstrated clear distinctions in bacterial community compositions between the dry and wet seasons for soils obtained from all three elevations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, e; Additional file 2: Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSpecific microbial clades in soil bacteria community\u003c/h2\u003e \u003cp\u003eWe conducted a statistical analysis using Welch\u0026rsquo;s t-test with Bonferroni correction to compare the differences in soil bacterial phylum composition between the dry and wet seasons at various elevations. The abundance of Actinobacteria consistently exhibited higher levels in the dry season compared to the wet season across all soil samples at different elevations. In DHVs and the transition zones, Chloroflexi abundance was higher in the dry season compared to the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, g), while the opposite trend was observed in the alpine zones (Additional file 2: Supplementary Figure S3). The difference in abundance of Proteobacteria between the dry and wet seasons was found to be significant primarily in the transition zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg) and alpine zones (Additional file 2: Supplementary Figure S3), but exhibited an opposite pattern of variation.\u003c/p\u003e \u003cp\u003eTo further identify microorganisms that can effectively differentiate between the dry and wet seasons at different elevations, we employed LEfSe to visualize the distribution of various clades at the phylum to genus levels in soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). During the dry season, we observed similarities in microbial clades between soils from DHVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and the transition zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), including the phyla Actinobacteria, Chloroflexi, and Proteobacteria. However, we found a predominance of phyla Acidobacteria (9), Planctomycetes (4), and Verrucomicrobia (2) from the alpine zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Additionally, we observed a significant decrease in the abundance of Chloroflexi and an increase in Proteobacteria in the alpine zones, as determined by the same screening criteria (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;3.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast to the dry season, soil samples from the wet season exhibited a higher diversity of microbial clades, including the presence of specific clades such as Acidobacteria, Patescibacteria, and Proteobacteria. Verrucomicrobia (4) was exclusively detected in the transition zones, while WPS-2 (5) was specifically found in the alpine zones. Finally, histograms of the LDA scores (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;3.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) implied bacterial clades (class level) showing statistically significant and biologically consistent differences between the dry and wet seasons at different elevations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental factors influencing community structure\u003c/h2\u003e \u003cp\u003eWe conducted a linear regression analysis utilizing the first principal axis (PC1) of PCoA to elucidate the relationship between environmental variables and beta-diversity at the phylum level. Out of the 24 potential combinations examined, only five exhibited statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, nutrient availability in the transition zones exerted an impact on PC1 during both the dry season (N and P) and the wet season (N:P ratio), whereas in the alpine zones, nutrient availability influenced PC1 only during the wet season (N and P). Remarkably, bacterial communities displayed no significant correlation with environmental factors, with the exception of N and P in the transition zones during the dry season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the correlation heatmap revealed the correlation coefficients and statistically significant correlations between the dominant phyla (top 10) of soil bacterial communities and environmental factors (N, P, and N:P ratio) in the transition zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b) and alpine zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), respectively. Particularly, soil P and N:P ratio exhibited substantial effects on the abundance of Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, and Planctomycetes in the wet season within the transition zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEcological assembly of bacterial community\u003c/h2\u003e \u003cp\u003eA comprehensive investigation was conducted on soil microbial communities at varying elevations and seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Additional file 2: Supplementary Figure S4), uncovering the significant influence of stochastic processes on the assembly of soil bacterial communities throughout the entire valley (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.613). Notably, within this geographical region, microorganisms face higher diffusion restrictions, with a migration rate (m) of 0.116.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn-depth analysis of soil bacterial communities at various elevations revealed that DHVs exhibited the highest fit to the NCM at 0.877, and had lower diffusion restrictions for its microorganisms (m\u0026thinsp;=\u0026thinsp;0.818). Among the three elevations, the transition zone between DHVs and the alpine zones exhibited the highest level of dispersal limitation for its microorganisms (m\u0026thinsp;=\u0026thinsp;0.352), indicating greater restrictions on their dispersal. Given that the number of sequences in each sample was consistent (N\u0026thinsp;=\u0026thinsp;22647), the estimates of intercommunity dispersal, which represent the movement of microorganisms between different communities, were highest in DHVs (Nm\u0026thinsp;=\u0026thinsp;18,526), followed by the alpine zones (Nm\u0026thinsp;=\u0026thinsp;11,342), and lowest in the transition zones (Nm\u0026thinsp;=\u0026thinsp;7,964). Furthermore, an analysis of the disparity in migration rates at different elevations during the dry and wet seasons was undertaken. Intriguingly, migration rates in both DHVs and the alpine zones were found to be higher in the dry season compared to the wet season, suggesting reduced diffusion constraints. In contrast, this pattern was reversed in the transition zones. Overall, NST analysis again quantitatively revealed that stochastic processes played a crucial role in shaping microbial community assembly, with all samples exhibiting an NST value exceeding 50% (Additional file 2: Supplementary Figure S5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe hydrothermal conditions in the Jinsha River valley exhibit significant variations at different elevations. The lower elevation areas exhibit a persistently arid and high-temperature climate, characterized by minimal fluctuations in both moisture and temperature throughout the dry and wet seasons. This stands in stark contrast to the high elevation forests and the transition zones between them. Numerous studies have highlighted the crucial role of soil microorganisms in the ecological restoration of the dry-hot valley. We propose that soil microorganisms in the low elevation DHVs have developed tolerance mechanisms to effectively cope with seasonal dry-wet alternation and even the extreme dry and hot environments they encounter during long-term adaptation processes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As expected, the bacterial communities within DHVs exhibited strong stability and adaptability to seasonal dry and wet alternation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our results indicated that the core bacterial taxa, which accounted for over 80% of the community, remained consistent. These taxa were prominently represented by Proteobacteria, Acidobacteria, Actinobacteria, and Chlorobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). However, these findings diverge from observations in other regions of the DHVs in southwest China [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It may suggest that although the arid and hot environment exerts a selective pressure on the soil microbial community [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], the composition of the core microbial community is intricately linked to soil properties, vegetation diversity, and other environmental factors [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Within DHVs and the transition zones, the abundance of Actinobacteria and Chloroflexi was increased during the dry season, in contrast to Acidobacteria, which displayed an opposite trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, g). These microbial dynamics are posited to critically influence the microbiome's adaptation to seasonal moisture fluctuations [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Notably, Actinobacteria exhibit robustness to environmental stress, including drought, and are instrumental in soil functionality under challenging conditions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Their symbiotic relationships with plant roots further bolster drought tolerance by facilitating phytohormone production and enhanced nutrient acquisition [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, Chloroflexi, a phylum adapted to oligotrophic conditions, demonstrates the capacity to prosper in resource-limited environments [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our findings align with the results [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] in suggesting that Chloroflexi may interact with other soil microorganisms, thereby shaping community structure and function. Taken together, these results underscore the crucial role of Actinobacteria and Chloroflexi in promoting soil resilience and ecosystem stability under the extreme conditions found in DHVs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeasonal fluctuations in soil temperature, moisture, and nutrient levels can significantly influence belowground microbial communities. However, these seasonal variations may not be the primary driver of microbiota diversity, as it is influenced more by taxon-specific gene expression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our research revealed that soil bacterial communities at various elevations maintained consistent richness, diversity, and coverage throughout both dry and wet seasons, except within the transition zones (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This observation suggests that the regulation of season-specific microbial clades may contribute to the stability of microbial community structure. For example, our study identified an increase in the activity of clades associated with Acidobacteria and Patescibacteria within DHVs and the transition zones during the wet season, which subsequently decreased following a prolonged dry period. In contrast, specific bacterial phyla displayed adaptability by increasing the number of clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c). Interestingly, some clades exhibited complex regulatory patterns, such as those related to Proteobacteria.\u003c/p\u003e \u003cp\u003eWithin the lower elevation DHVs, clades associated with Actinobacteria were more abundant during dry seasons compared to wet seasons. Previous studies have highlighted the pivotal role of Actinobacteria in responding to drought stress, including their ability to maintain activity and enter a dormant state under dry conditions. Santos-Medell\u0026iacute;n et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] proposed that prolonged drought leads to a significant enrichment of Actinobacteria in the rice rhizosphere microbiome, with their abundance declining upon recovery. Therefore, we hypothesize that Actinobacteria and its related clades play a regulatory role in response to changes in water availability, enhancing the adaptability and functionality of soil bacterial communities during seasonal dry-wet cycles. Acidobacteria, a widely distributed phylum in diverse ecosystems [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], demonstrates a remarkable regulatory capacity and adaptive mechanisms to thrive in complex environmental conditions. Kalam et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] conducted a comprehensive review of current research on Acidobacteria in soil, highlighting its significant ecological importance. Our findings revealed a notable decline in the abundance of Acidobacteria-related microbial clades in DHVs and the transition zones during dry seasons, with a resurgence of activity observed during wet seasons when water availability increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). Studies suggest that Acidobacteria may possess specific genes facilitating survival and competitive colonization in the rhizosphere, fostering beneficial interactions with plants [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Consequently, it is proposed that Acidobacteria-related microbial clades play a pivotal role in the ecological restoration of DHVs. In contrast, fluctuations in the abundance of Proteobacteria-associated clades across different sampling areas did not exhibit consistent patterns during the transition from wet to dry seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c), indicating diverse regulatory strategies employed by Proteobacteria-related microbial clades in response to seasonal variations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Notably, seasonal fluctuations in the alpine soil microbial community, while evident, are relatively minor compared to DHVs and the transition zones, possibly due to the limited impact of moisture and temperature changes in alpine regions. This suggests that soil bacterial communities in alpine zones maintain stability under the mild seasonal fluctuations of wet and dry conditions. Thus, our results reveal that soil microbial communities undergo rapid evolution in response to selective pressures imposed by seasonal dry-wet alternations. Adaptive evolution may result in the emergence of novel traits or genetic variants that confer fitness advantages under specific moisture regimes [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Genetic adaptation to fluctuating environmental conditions contributes to the resilience and stability of soil microbial aggregates over time.\u003c/p\u003e \u003cp\u003eRecent advancements in microbial community assembly research have illuminated our understanding of these intricate ecological dynamics. However, the impact of seasonal dry and wet alternation on community assembly within unique savanna-like dry-hot valley ecosystems remains poorly characterized across spatial and temporal scales. This study investigates the influence of environmental factors on microbial communities in these distinct ecosystems. Our results indicate a minimal effect of seasonal dry and wet alternation in valley regions, but varying degrees of alteration in transition and alpine zones. This observation led us to hypothesize that soil microbial community assembly in dry-hot valley ecosystems is predominantly driven by stochastic processes, in contrast to the deterministic processes observed in the transition and alpine zones. Employing the neutral community model (NCM), our analysis emphasizes the dominance of stochastic mechanisms in microbial community assembly within valley ecosystems, particularly in savanna-like dry-hot valleys (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This finding partially supports our hypothesis and suggests that stochastic processes play a significant role in shaping microbial communities in these ecosystems. However, the influence of stochastic processes extends to the transition and alpine zones as well. Our findings challenge previous assumptions about the primary influence of environmental factors, such as seasonal dry and wet alternation, on shaping microbial communities across all ecosystem types. Soil pH, a crucial abiotic factor affecting soil microbial communities, could mediate the balance between stochastic and deterministic assembly of bacteria [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In our study, soil pH in DHVs and the transition zones did not vary significantly between dry and wet seasons, except in the alpine zones (Additional file 1: Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Nevertheless, the potential role of deterministic processes in dry and hot valley regions cannot be discounted. It is well-established that deterministic and stochastic processes coexist in regulating ecological community assembly [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Moreover, Thompson et al. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] proposed that narrow abiotic niche curves lead to strong species responses to environmental variation (deterministic processes), while broad or flat curves result in weak or absent responses (stochastic processes). Our study suggests that the dry-hot valleys may exhibit characteristics that favor stochastic assembly, potentially influenced by reduced environmental variability or increased dispersal rates [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study demonstrates the significant influence of seasonal dry and wet alternation on bacterial community diversity within transition zones, surpassing its impact in both DHVs and alpine zones. Notably, we identified season-specific microbial clades across all sampling areas, highlighting their adaptability and responsiveness to dynamic environmental conditions. Stochastic processes emerged as the dominant drivers of soil bacterial community assembly in all three zones. These findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) hold implications for developing strategies to optimize soil health, enhance ecosystem services, and mitigate the effects of climate change in these distinct regions: DHVs, transition zones, and alpine zones.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data was deposited into the NCBI Sequence Read Archive (SRA) database under Accession Number: PRJNA1114417.\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\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by the National Natural Science Foundation of China (Grant No. 41925030) and Science and Technology Projects in Sichuan Province, China (Grant No. 2023YFS0367).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJ, XC, and YZ designed the research project. HJ, YL, JC, and LW carried out the field work and performed the laboratory and statistical analyses. HJ and YL wrote the initial draft of the paper. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Dongchuan Debris Flow Observation and Research Station (DDFORS), Chinese Academy of Sciences, which provided the field observation data for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFalkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth's biogeochemical cycles. Science 2008; 320:1034\u0026ndash;1039.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhilippot L, Chenu C, Kappler A, Rillig MC, Fierer N. The interplay between microbial communities and soil properties. 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ISME J. 2017; 11:176\u0026ndash;185.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Altitudinal gradient, Dry-hot valley, Mountain-valley breeze circulation, Seasonal dry-wet cycle, Stochastic process","lastPublishedDoi":"10.21203/rs.3.rs-4643110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4643110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil microorganisms play a key role in nutrient cycling, carbon sequestration, and other important ecosystem processes, yet their response to seasonal dry-wet alternation remains poorly understood. Here, we collected 120 soil samples from dry-hot valleys (DHVs, ~1100 m a.s.l.), transition (~2000 m a.s.l.) and alpine zones (~3000 m a.s.l.) along the Jinsha River in southwest China during both wet and dry seasons. Our aims were to investigate the bacterial microbiome across these zones, with a specific focus on the difference between wet and dry seasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite seasonal variations, bacterial communities in DHVs exhibit resilience, maintaining consistent community richness, diversity, and coverage. This suggests that the microbes inhabiting DHVs have evolved adaptive mechanisms to withstand the extreme dry and hot conditions. In addition, we observed season-specific microbial clades in all sampling areas, highlighting their resilience and adaptability to environmental fluctuations. Notably, we found similarities in microbial clades between soils from DHVs and the transition zones, including the phyla Actinobacteria, Chloroflexi, and Proteobacteria. The neutral community model respectively explained a substantial proportion of the community variation in DHVs (87.7%), transition (81.4%) and alpine zones (81%), indicating that those were predominantly driven by stochastic processes. Our results showed that migration rates were higher in the dry season than in the wet season in both DHVs and the alpine zones, suggesting fewer diffusion constraints. However, this trend was reversed in the transition zones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings contribute to a better understanding of how the soil microbiome responds to seasonal dry-wet alternation in the Jinsha River valley. These insights can be valuable for optimizing soil health and enhancing ecosystem resilience, particularly in dry-hot valleys, in the context of climate change.\u003c/p\u003e","manuscriptTitle":"Seasonal Dynamics of Soil Microbiome in Response to Dry-Wet Alternation along the Jinsha River Dry-hot Valley","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 12:10:16","doi":"10.21203/rs.3.rs-4643110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f989c41-e34e-4661-9e6b-0f30decfa202","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-16T09:21:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 12:10:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4643110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4643110","identity":"rs-4643110","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}