Succession Characteristics of Soil Microbial Communities Along Elevational Gradients in the Lhasa River Basin and Analysis of Environmental Driving Factors

Abstract

The Qinghai-Xizang Plateau is among the most ecologically vulnerable and responsive areas worldwide. Studying the characteristics of soil microbial communities along altitudinal gradients on plateaus and revealing the response mechanisms and vertical distribution patterns of microbial communities in alpine ecosystems is of significant academic value for assessing the ecological stability of the Qinghai-Xizang Plateau. This research examines the Lhasa River Basin by employing Illumina NovaSeq high-throughput sequencing to investigate how soil bacterial and fungal communities shift across elevation gradients in the Duilong Qu subbasin. This study also explored the key environmental drivers behind these microbial distribution patterns. The results indicate the following: (1) Key bacterial groups in the Duilong Qu Basin soil include Proteobacteria, Acidobacteria, and Actinobacteria, with Ascomycota, Mortierellomycota, and Basidiomycota as the prevalent fungal phyla. (2) Soil bacterial richness fluctuates with increasing elevation, and diversity exhibits a V-shaped distribution; fungal richness increases monotonically with elevation, whereas diversity shows no altitudinal dependence. (3) Principal coordinate analysis (PCoA) revealed that bacterial community structures exhibit separation trends across different elevations, with high intragroup consistency; fungal community structures at mid-elevations (4000–5000 m) show clustering similarity, whereas those at 3650–5000 m and 5500 m remain highly distinct from those at other elevations. (4) RDA reveals that factors such as accessible phosphorus, potassium, and organic content have a major effect on how bacterial communities are arranged. On the other hand, soil conductivity, along with available and total phosphorus levels, as well as pH, plays a key role in shaping fungal communities. (5) Functional prediction analysis suggests that soil bacteria shift from aerobic and biofilm-forming to facultatively anaerobic, stress-tolerant, and pathogenic traits with increasing elevation. Fungi are predominantly undefined saprotrophs, transitioning from ectomycorrhizal and pathogenic functions to saprotrophic functions at relatively high elevations.

1. Introduction

The Qinghai-Xizang Plateau, known as the “Third Pole”, is the Earth’s highest and most vulnerable cold ecosystem [1,2]. Its distinctive environmental conditions give rise to unique microbial communities that play vital roles in carbon sequestration, hydrological regulation, and the maintenance of ecological barriers [3]. In recent years, the altitudinal distribution of microbial communities and the environmental factors driving these patterns have emerged as key topics in soil microbial ecology. Owing to their ability to compress multiple environmental stressors within limited spatial scales, elevation gradients serve as natural experimental systems for studying climate change. These gradients generate pronounced environmental heterogeneity, providing a valuable framework for investigating microbial adaptation strategies to climatic shifts [4]. The soil microbiota, which is a fundamental component of terrestrial ecosystems [5], includes diverse microbial groups, such as bacteria and fungi [6]. These organisms contribute to ecosystem functioning through their involvement in critical biogeochemical processes, including organic matter decomposition and nutrient cycling. Large-scale studies examining the vertical distribution patterns of soil bacterial and fungal communities, as well as disentangling the relative contributions of various environmental drivers to microbial community assembly, are essential for understanding how high-altitude ecosystems respond to global warming at the microbial level [7].

Ongoing debate surrounds the effect of altitude on soil microbe communities [8], with studies reporting a range of trends, including unimodal distributions [9], monotonically increasing patterns [10,11,12], monotonically decreasing patterns, no significant trend [13], and V-shaped relationships [14,15]. For example, the team led by Singh [9,16] demonstrated a unimodal relationship between soil bacterial diversity and altitude on Mount Fuji, whereas a V-shaped pattern was observed on Mount Halla. Similarly, Bryant et al. [17] reported that the species richness of soil bacterial communities decreased with increasing elevation. Research conducted by Bahrain et al. [18] in the vegetation zones of northern Iran revealed a decline in fungal diversity with elevation, although some studies have reported no significant altitudinal dependence. In addition to elevation, other environmental factors have also been identified as significant drivers of microbial community structure. Li et al. [19], in their study of Dongling Mountain, reported that the soil temperature, pH, C/N ratio, and available phosphorus jointly regulated the vertical distribution of microbial diversity. Corneo et al. [20] highlighted the roles of soil moisture content and metal elements such as Al, Mg, and Mn in shaping microbial communities. In the Changbai Mountain region, the microbial community composition varies significantly along the altitudinal gradient and is strongly correlated with the soil pH, carbon-to-nitrogen ratio, and moisture content [21]. Wang et al. [22] highlighted that soil pH and organic matter were major edaphic attributes shaping a highly homogeneous soil bacterial community compared to wild ecosystems in the Qinghai-Xizang Plateau. However, studies focusing on the coordinated variation in soil bacterial and fungal communities along altitudinal gradients, as well as their environmental drivers, remain limited in the Lhasa River Basin ecosystem—a key component of the Yarlung Tsangpo River system on the Tibetan Plateau.

This study investigated soil microbial communities across five different elevations (3650 m, 4000 m, 4500 m, 5000 m, and 5500 m) within the Duilong Qu tributary basin of the Lhasa River watershed. By leveraging the power of Illumina NovaSeq high-throughput sequencing, this study investigated how soil microbial diversity and community makeup shift across different elevations in the area. Furthermore, the relationships between soil microbial community structure and diversity and various ecological factors, particularly soil characteristics, have been explored. The findings of this study not only enhance our understanding of regional soil ecosystem functions but also provide essential theoretical insights and practical recommendations for protecting ecological security in the Lhasa River Basin and achieving sustainable development goals.

2. Study Area and Methodology

2.1. Overview of the Study Area

As a key component of the Yarlung Zangbo River system, the Lhasa River is the largest river system in the Xizang Autonomous Region in terms of watershed area. The river originates near the main peak of the Nyenchen Tanglha Mountains, with its main course flowing from northeast to southwest. It stretches over a total length of 568 km and drains a watershed area of 31,760 km² [23]. The Duilong Qu, a major tributary of the Lhasa River, originates on the northwestern slope of the Nyenchen Tanglha Peak at an elevation of 7111 m. It spans a total length of 137 km and drains a watershed area of 4988 km². This river basin displays the typical vertical zonation characteristics of plateau regions, with vegetation communities including Kobresia pygmaea and Astragalus adsurgens. Located in a subfrigid plateau zone, the climate of the zone is semiarid monsoonal, showing marked seasonal variation. The annual average temperature ranges from 0 to 17 °C, and the annual precipitation varies between 400 and 500 mm [24].

2.2. Soil Sample Collection

In April 2024, five sampling belts were established at different altitudes, ranging from 3650 m to 5500 m, in the Duilong Qu Basin of the Lhasa River, from Gedaxiang Township in Damxung County to Chengguan District. As shown in Figure 1 and

Table 1. Basic information of sample plots.

Plot	Vegetation Type	Ecological Types	Latitude and Longitude	Elevation (m)
1	Alpine Meadow	Alpine Meadow Soil	29°86′93″ N, 90°21′03″ E	5522.1
2	Alpine Meadow	Alpine Meadow Soil	29°93′38″ N, 90°20′02″ E	4989.7
3	Alpine Desert	Desert soil	29°94′44″ N, 90°28′77″ E	4594.4
4	Natural Grassland	Grassland soil	29°72′70″ N, 91°08′44″ E	3996.8
5	Mixed Forest	Planted forest soil	29°64′11″ N, 91°12′28″ E	3683.9

. These belts were spaced 500 m apart, with three 5 m × 5 m quadrats evenly distributed horizontally within each belt, totaling 15 quadrats. At the center of each quadrat, surface soil samples were collected from the 0–15 cm depth. During sample pretreatment, visible plant and animal residues, as well as stones, were removed, and sterile pouches containing homogenized aliquots were used. The samples were then stored at low temperatures in portable freezers for transport to the laboratory. Subsequent sample processing involved two procedures: air-dried soil samples were subjected to 2 mm and 0.149 mm sieve fractionation to assess their physicochemical characteristics. Another sample was cryopreserved at −80 °C for subsequent soil microbiome profiling via high-throughput sequencing.

2.3. Determination of Soil Physicochemical Properties

The soil physicochemical properties were measured according to the methods outlined in Baosidan’s Soil Agricultural Chemical Analysis [25]. The soil pH was assessed with a Leici pH meter after a 1:5 soil–water mixture was prepared; it was thoroughly agitated and allowed to settle. To determine the available potassium, ammonium acetate extraction was performed, followed by analysis with a flame photometer. For available phosphorus, sodium bicarbonate served as the extracting agent, and concentrations were determined via the molybdenum-antimony colorimetric technique. We determined alkaline nitrogen levels via alkaline diffusion. To obtain a read of total potassium, we fused the samples with sodium hydroxide and then used flame photometry. For total phosphorus, we again employed sodium hydroxide fusion, followed by the molybdenum-antimony colorimetric method for quantification. A carbon–nitrogen analyzer was used to determine total nitrogen and organic matter by combustion.

2.4. Soil Microbial DNA Extraction and Sequencing

Genomic DNA was extracted from 0.25 to 0.5 g of soil via a TGuide S96 magnetic bead-based DNA extraction kit (DP812). The concentration and purity of the nucleic acids were evaluated via a Synergy HTX microplate reader (Gene Company Limited, Hong Kong, China). Bacterial communities were assessed by amplifying the V3–V4 region of the 16S rRNA gene with the forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Fungal communities were characterized by targeting the ITS1 region via the primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′) [26,27]. PCR was performed in a 20 μL reaction volume consisting of 5 μL of purified amplicon, 2.5 μL of each MPPI-a and MPPI-b primer (2 μM), and 10 μL of 2× Q5 HF Master Mix. The thermal cycling conditions included initial denaturation at 98 °C for 30 s, followed by 10 cycles of denaturation at 98 °C for 10 s, annealing at 65 °C for 30 s, and extension at 72 °C for 30 s. A final extension was conducted at 72 °C for 5 min, and the samples were then held at 4 °C. The PCR products were initially verified via 1.8% agarose gel electrophoresis. Subsequently, quality control was conducted with a Qsep-400 capillary electrophoresis system. Libraries that met the quality standards were successfully constructed and subjected to paired-end sequencing on the Illumina NovaSeq 6000 platform. All library preparation and sequencing procedures were carried out by Biomarker Technologies Corporation (Beijing, China).

2.5. Data Analysis

The initial raw sequencing data were subjected to quality control processing with Trimmomatic v0.33 to eliminate substandard reads. The primer sequences were subsequently detected and clipped via Cutadapt v1.8.3 to yield refined, high-quality reads. The DADA2 plugin within QIIME2 2020.6 performs denoising operations, incorporating quality assessment, paired-end read alignment, and chimera screening and elimination. This pipeline ultimately produced amplicon sequence variants (ASVs) for downstream analysis. To determine if altitude had a statistically significant effect on the soil properties, we ran a one-way ANOVA, followed by Duncan’s multiple range test. We also used QIIME to construct taxonomic abundance tables at different classification levels, and the community composition for each sample was visualized with R. QIIME2 2020.6 was also used to compute alpha and beta diversity measures for bacterial and fungal communities. The functional profiles of the bacterial and fungal communities were predicted via the BugBase and FUNGuild databases, respectively. Redundancy analysis (RDA), principal coordinate analysis (PCoA), and Spearman correlation analysis were performed and visualized in R. All the statistical analyses were conducted via SPSS version 27 and Microsoft Excel 2019.

3. Results

3.1. Composition of Soil Microbial Communities at Different Elevations in the Duilong Qu Basin

Illumina NovaSeq high-throughput sequencing generated 1,200,212 high-quality bacterial rRNA gene sequences and 1,199,831 valid fungal ITS sequences. After denoising, a total of 20,914 bacterial ASVs and 7780 fungal ASVs were identified. The rarefaction curves effectively demonstrated the relationship between sequencing depth and microbial ASV richness. As illustrated in the Figure 2, the number of observed bacterial and fungal ASVs increased rapidly and eventually plateaued with increasing sequencing depth across all five elevation gradients. This finding indicates that the sequencing effort was sufficient to capture the vast majority of microbial species within the soil samples, thereby ensuring data robustness. Overall, differences in the plateau heights among the samples suggest that altitude significantly influences species richness of the soil bacterial and fungal communities.

In the Duilong Qu Basin of the Lhasa River, the soil bacterial community comprises 32 phyla, 85 classes, 250 orders, 486 families, 1005 genera, 2731 species, and 20,914 ASVs. The fungal community consists of 16 phyla, 54 classes, 143 orders, 324 families, 786 genera, 1323 species, and 7780 ASVs.

As shown in Figure 3. For the analysis of bacterial community composition, the ten most abundant phyla were selected, resulting in the identification of nine major bacterial groups. Among these phyla, Proteobacteria, Acidobacteria, and Actinobacteria emerged as the dominant phyla. Proteobacteria presented the highest average relative abundance (25.81%), followed by Acidobacteriota (23.76%) and Actinobacteriota (16.46%). The distribution patterns of these dominant phyla were significantly influenced by altitude. Notably, there was a significant correlation between the prevalence of Proteobacteria and rising altitude, indicating a positive link. Conversely, the Actinobacteriota seemed to diminish as the elevation increased. Moreover, the richness of Gemmatimonadota steadily decreased as the altitude increased.

In terms of the fungal community, the ten most abundant taxa were also analyzed, revealing nine major fungal groups. Ascomycota, Mortierellomycota, and Basidiomycota were the predominant phyla. Ascomycota had the highest average relative abundance (74.97%), followed by Mortierellomycota (13.92%) and Basidiomycota (8.28%). In contrast to the bacterial communities, the distributions of Ascomycota and Mortierellomycota did not significantly vary across the altitudinal gradient, indicating a lack of clear elevation-dependent trends.

Heatmaps were generated on the basis of hierarchical clustering of bacterial and fungal phyla via relative abundance data to illustrate community composition patterns.As shown in Figure 4. The bacterial phyla were divided into two distinct clusters: the first comprised Proteobacteria, Bacteroidota, and Myxococcota, whereas the second included Acidobacteriota, Actinobacteria, Chloroflexi, Gemmatimonadota, Methylomirabilota, and Patescibacteria. In terms of the sampling sites, the bacterial community at 5000 m formed a separate cluster, whereas the communities at 3650 m, 4000 m, 4500 m, and 5500 m clustered together. Similarly, fungal phyla were also grouped into two clusters: Mucoromycota, Ascomycota, and Monoblepharomycota formed the first cluster, whereas Basidiomycota, Chytridiomycota, Rozellomycota, Glomeromycota, Olpidiomycota, and Mortierellomycota formed the second cluster. Fungal assemblages at 4500 m differed from a unified cluster at 3650 m, 4000 m, 5000 m, and 5500 m in altitude.

3.2. Diversity of Soil Microbial Communities at Different Elevations in the Duilong Qu Basin

As shown in

Table 2. Alpha diversity of bacteria and fungi at different elevation gradients.

Altitude	Soil Bacteria			Soil Fungi
	ASV	Chao1	Shannon	ASV	Chao1	Shannon
3650 m	5100	2039.16 ± 51.90 a	9.91 ± 0.03 a	1427	611.22 ± 124.20 c	6.30 ± 0.05 b
4000 m	4472	1872.75 ± 27.86 b	9.90 ± 0.01 a	2183	1022.15 ± 11.15 a	6.16 ± 0.04 b,c
4500 m	4762	1965.00 ± 28.98 a	9.04 ± 0.05 c	1376	730.76 ± 6.13 b	6.08 ± 0.09 c,d
5000 m	3516	1591.17 ± 40.12 c	9.58 ± 0.03 b	2148	1031.30 ± 32.17 a	5.98 ± 0.09 d
5500 m	4401	1868.00 ± 63.41 b	9.62 ± 0.03 b	1895	956.17 ± 16.05 a	6.88 ± 0.10 a

Note: The data in the table represent mean data ± SD; differing lowercase letters indicate significant altitude gradient differences (p < 0.05).

. In the bacterial community, the number of amplicon sequence variants (ASVs) changed noticeably with altitude; the highest point was 3650 m, and the lowest was 5000 m. The Chao1 index, a key indicator of species richness, exhibited a similar fluctuating pattern, with the highest value recorded at 3650 m and the lowest at 5000 m. These findings suggest substantial differences in bacterial richness among elevations. In contrast, the Shannon diversity index followed a distinct V-shaped trend, decreasing with increasing altitude before increasing again, peaking at 5500 m. For the fungal community, the ASV count peaked at 4000 m and decreased to its lowest value at 4500 m. Although fungal richness showed some variation along the elevation gradient, the differences were less pronounced than those observed in the bacterial community. The fungal Chao1 index peaked at 5000 m and decreased to its lowest value at 4500 m, showing an overall increase with elevation. The Shannon diversity index exhibited no clear altitudinal trend, although its maximum value was also observed at 5500 m.

3.3. Structural Characteristics of the Soil Microbial Communities at Different Elevations in the Duilong Qu Basin

The soil microbial community structure in the Lhasa River Basin varied significantly across different elevations, reflecting distinct altitudinal responses and clear patterns of community differentiation. As shown in Figure 5. Principal coordinate analysis (PCoA) revealed that primary bacterial community variance via axes 1 and 2—PC1 (52.48%) and PC2 (23.86%)—explained 76.34% of the total variation, indicating that the model effectively captured the main structural differences in bacterial communities along the elevation gradient. The bacterial communities at different altitudes exhibited clear separation trends, whereas the samples within each elevation group presented high internal similarity. For the fungal community, PC1 (42.35%) and PC2 (27.17%) jointly accounted for 69.52% of the total variation, demonstrating that the PCoA model also successfully revealed the main differences in fungal community structure across elevations. Fungal communities at mid-elevations (4000–5000 m) strongly clustered, whereas those at 3650 m and 5500 m remained distinct from those at other elevations.

3.4. Variations in Soil Physicochemical Properties at Different Elevations in the Duilong Qu Basin

As shown in

Table 3. Soil physical and chemical properties at different altitude gradients.

Altitude	pH	EC μs·cm −1	AK mg·kg −1	AP mg·kg −1	AN mg·kg −1	TK g·kg −1	TP g·kg −1	TN g·kg −1	SOM g·kg −1
5500	6.52 ± 0.29 c	147.00 ± 10 a	167.70 ± 59.27 a	26.16 ± 1.49 a	256.70 ± 14.15 a	35.47 ± 0.42 c	0.27 ± 0.02 b	4.23 ± 0.07 a	108.20 ± 1.88 a
5000	6.58 ± 0.25 c	48.30 ± 2.5 b	121.00 ± 2.00 b	5.86 ± 1.58 b	165.7 ± 15.78 b	39.27 ± 0.70 b	0.26 ± 0.02 b	2.00 ± 0.09 b	42.22 ± 1.30 b
4500	6.70 ± 0.07 c	41.00 ± 9.75 b	25.00 ± 1.73 a	10.32 ± 2.52 b	67.70 ± 8.80 c	47.10 ± 2.48 a	0.13 ± 0.04 c	0.96 ± 0.04 c	15.60 ± 0.98 c
4000	7.30 ± 0.37 b	41.30 ± 13 b	28.30 ± 5.13 d	19.79 ± 6.42 a	43.17 ± 4.04 d	44.70 ± 3.25 a	0.25 ± 0.02 b	0.64 ± 0.04 d	11.20 ± 2.50 d
3650	7.90 ± 0.7 a	141.00 ± 3 a	43.30 ± 1.53 c	25.56 ± 3.98a	77.00 ± 18.20 c	35.87 ± 0.50 b,c	0.35 ± 0.026 a	0.90 ± 0.07 c	15.80 ± 1.20 c

Note: The data in the table represent mean data ± SD; differing lowercase letters indicate significant altitude gradient differences (p < 0.05).

.The soil pH clearly decreased with increasing elevation. Electrical conductivity (EC) initially decreased and subsequently increased along the altitudinal gradient, reaching its peak at 5500 m. The soil organic matter (SOM) content increased consistently with elevation, with the highest value also recorded at 5500 m. Total nitrogen (TN) displayed an overall upward trend, reaching a maximum at 5500 m and a minimum at 4000 m. Alkaline nitrogen (AN) increased steadily as elevation rose. Total phosphorus (TP) showed a decreasing-then-increasing pattern, with the highest concentration at 3650 m and the lowest at 4500 m. Available phosphorus (AP) generally declined with increasing elevation, reaching its maximum at 5500 m and minimum at 5000 m. Total potassium (TK) followed a unimodal distribution across elevations. Available potassium (AK) was positively correlated with elevation, peaking at 5500 m.

3.5. Relationships Between the Soil Microbial Community Structure and Environmental Factors

As shown in Figure 6. According to the RDA results for the bacterial community, the first two axes collectively explained 56.22% of the total variance, with RDA1 and RDA2 accounting for 39.86% and 16.36%, respectively. Soil TP, AP, pH, EC, SOM, TN, AN, and AK were positively correlated with RDA1, whereas TK was negatively correlated. Among these variables, SOM, AP, TN, AK, and AN emerged as the key drivers of bacterial community composition. For the fungal community, the first two RDA axes explained 53.35% of the total variation, with RDA1 and RDA2 contributing 33.63% and 19.72%, respectively. Soil pH, EC, TP, SOM, AN, AK, and TN were negatively correlated with RDA1, whereas TK was positively correlated. Among these variables, AP, TK, TP, pH, and EC were the primary environmental variables that significantly affected fungal community composition.

As shown in Figure 7. A correlation analysis revealed a clear link between environmental factors and the makeup of soil microbial communities. We found that the ten most common bacterial phyla were significantly related to the physical and chemical properties of the soil. Total potassium (TK) was significantly positively correlated with Chloroflexi and Gemmatimonadota and negatively correlated with Myxococcota. The acidity of the soil was strongly positively related to the Actinobacteria population. Additionally, key soil nutrients—including organic matter, alkaline nitrogen, total nitrogen, and available potassium—demonstrated favorable associations with Patescibacteria, while inversely affecting Gemmatimonadota levels. The analysis revealed that available phosphorus (AP) had strong positive associations with Myxococcota, Bacteroidota, and Proteobacteria but demonstrated inverse relationships with Acidobacteriota, Chloroflexi, and Methylomirabilota. These correlations were statistically significant, highlighting distinct microbial responses to phosphorus availability in the environment. Total phosphorus (TP) was positively correlated with Actinobacteria and negatively correlated with Chloroflexi. EC was positively correlated with Myxococcota but negatively correlated with Gemmatimonadota and Chloroflexi.

In the top ten fungal phyla, there was a notable positive link between TK and Mucoromycota and Olpidiomycota, while it was inversely associated with Basidiomycota and Chytridiomycota. On the other hand, AK, TN, AN, and SOM presented significant positive relationships with Monoblepharomycota. In addition to AK, several other elements presented favorable associations with the Chytridiomycota group. EC was positively related to both Chytridiomycota and Basidiomycota, but the opposite was true for Mucoromycota. AP had a favorable association with Basidiomycota and an adverse association with Mucoromycota. On the other hand, TP had a notably adverse relationship with Olpidiomycota.

3.6. Functional Prediction of Soil Microbial Communities Along Different Elevations in the Duilong Qu Basin

As shown in Figure 8. Phenotypic functional prediction based on the BugBase database was used to assess the potential functional dynamics of bacterial communities along the altitudinal gradient. At all elevations, the bacterial communities were dominated by biofilm-forming (Forms_Biofilms), Gram-negative (Gram_Negative), and aerobic (Aerobic) groups, which together accounted for more than 60% of the total abundance. This finding highlights the dominant ecological roles of these functional groups in the microbial networks of the Qinghai-Xizang Plateau. Additionally, functions related to mobile genetic elements (Contains_Mobile_Elements) and pathogenicity (Potentially_Pathogenic) were widely distributed across all the sites, with relatively high abundances observed at mid-to-high altitudes (4500 m and 5000 m). Fungal functional traits were annotated via the FUNGuild database. At 3650 m, the fungal community was mainly composed of ectomycorrhizal fungi, with undefined saprotrophs as the secondary group. At 4000 m, undefined saprotrophs became dominant, followed by plant pathogens. At 4500 m, the community was still dominated by undefined saprotrophs, with dung saprotrophs appearing as a secondary group. At 5000 m and 5500 m, undefined saprotrophs remained the most abundant group, with plant pathogens consistently serving as the secondary component.

4. Discussion

4.1. Composition and Diversity of Soil Bacterial Communities at Different Elevations in the Duilong Qu Basin

Altitudinal gradients are critical ecological factors that profoundly influence the spatial distribution of biodiversity. Numerous studies have demonstrated that soil microbial community structure and diversity are markedly affected by changes in hydrothermal conditions associated with elevation [28]. In the present study, the Chao1 index for the soil bacterial communities exhibited a fluctuating trend along the elevation gradient, whereas the Shannon diversity index displayed a distinct V-shaped pattern. The contrasting altitudinal trends of these two indices imply that bacterial diversity responds to elevation through complex mechanisms, making it challenging to define a consistent elevation-related pattern [29]. At 5000 m in the Duilong Qu region of the Lhasa River Basin, both the total number of ASVs and the bacterial richness reached their lowest values, which aligns with the Chao1 index results. This reduction may be linked to the significantly lower level of available phosphorus observed at this elevation, which was strongly correlated with the bacterial community composition. These findings suggest that variations in elevation influence soil microbial communities primarily by modulating the distribution of critical environmental factors such as available phosphorus.

The results of this study reveal substantial variations in the composition of soil microbial communities across different elevations in the Duilong Qu Basin of the Lhasa River. Proteobacteria predominated and displayed a notable positive correlation between prevalence and altitude, which is consistent with the findings of other studies [30]. Its abundance markedly exceeded that of Acidobacteria and Actinobacteria, with these three phyla collectively accounting for more than 95% of the overall bacterial community composition. This pattern diverges from the findings of Chu [31], who reported Actinobacteria as the dominant phylum in relatively flat areas of the Xizang Plateau. These differences may be attributed to the stronger environmental adaptability of Proteobacteria and Acidobacteriota [32]. Proteobacteria consistently dominate the surface soils of the Qinghai-Xizang Plateau [33]. Notably, Proteobacteria possess specialized adaptations to low-temperature environments, allowing them to maintain competitive dominance in high-altitude, cold ecosystems [34,35]. Their functional importance in soil carbon and nitrogen cycling further contributes to the resilience of the microbial community under extreme conditions [36]. Moreover, Acidobacteriota thrive in acidic soil environments, which accounts for their widespread presence in the Duilong Qu Basin’s neutral to slightly acidic soils. On the other hand, Actinobacteria populations decline with increasing elevation, as colder soil temperatures at higher altitudes limit their growth. Furthermore, their numbers are inversely related to critical soil nutrients such as organic matter, nitrogen, and phosphorus.

In this study, available phosphorus (AP), available potassium (AK), and soil organic matter (SOM) were identified as the top three environmental factors influencing the soil bacterial community composition, in descending order of importance, which is consistent with the findings of other studies [37]. Among them, AP was determined to be the most critical driver shaping microbial communities in the Duilong Qu Basin of the Lhasa River. This is likely due to the strong correlations observed between AP and several dominant bacterial phyla, which substantially influence microbial distribution and abundance along the altitudinal gradient. Specifically, AP was significantly associated with Proteobacteria, Bacteroidota, Myxococcota, Methylomirabilota, Chloroflexi, and Acidobacteriota. As Proteobacteria are known for their copiotrophic characteristics, increases in soil organic matter [38] and phosphorus availability are closely associated with their physiological and metabolic activities [39]. These results shed new light on how phosphorus turnover governs the way soil microbes operate, highlighting the vital role that microbial activity tied to phosphorus plays in keeping our agricultural systems healthy and sustainable for the long term [40]. In support of this, a study by Chen Jin et al. [41] in Pinus massoniana forests in central Guizhou revealed that the dominant bacterial phyla were Acidobacteria, Proteobacteria, and Bacteroidota, with community compositions strongly influenced by soil pH, organic carbon, and available phosphorus—consistent with the present study.

Previous studies have demonstrated that Gram-negative bacteria exhibit strong environmental adaptability [42], which may account for their sustained dominance across diverse altitudinal gradients. Additionally, research has shown a significant positive correlation between Gram-positive bacteria and the availability of phosphorus in soil. Interestingly, the pathogenic potential of the bacterial communities displayed an inverted V-shaped distribution along the elevation gradient, with the highest levels observed at mid-altitudes. This pattern could be explained by the increased permeability of plant cell membranes under moderately low temperatures, which facilitates pathogen invasion. However, at relatively high elevations, extremely cold conditions likely inhibit pathogen survival, thereby reducing pathogen prevalence. Altitudinal environmental gradients significantly reshaped the functional organization of bacterial communities. At lower elevations, the community was primarily composed of aerobic and biofilm-forming bacteria. As elevation increased, the composition gradually shifted toward a more complex assemblage dominated by facultative anaerobes, stress-tolerant taxa, and potentially pathogenic groups.

4.2. Composition and Diversity of Soil Fungal Communities in Different Elevations in the Duilong Qu Basin

The investigation revealed a positive correlation between the Chao1 index of soil fungi and altitude, whereas the Shannon diversity index did not exhibit a clear altitudinal trend. This divergence may stem from the highly modular structure of plant–soil symbiotic networks, which contributes to ecosystem stability under environmental fluctuations and enhances resilience to disturbance. The notably lower fungal richness observed at 3650 m can be attributed to several factors: (1) Most fungi thrive in acidic environments; however, the relatively alkaline pH and elevated electrical conductivity at this site may impede the uptake of essential nutrients by fungal communities. (2) Artificial afforestation at lower elevations has altered land use patterns, resulting in ecological niche simplification and reduced diversity of organic substrates available to fungi [43]. (3) Previous soil disturbances likely disrupted the original microecological structure, resulting in a reduction in environmentally fragile fungal species [43].

The fungal community in the Duilong Qu soils of the Lhasa River Basin was dominated primarily by Ascomycota, followed by Mortierellomycota and Basidiomycota. This pattern aligns with the findings of Xu Huan [44], who identified Ascomycota, Basidiomycota, Chytridiomycota, and Entomophthoromycota as the dominant fungal groups in alpine grasslands, underscoring the widespread ecological dominance of these phyla in cold environments. Together, these dominant phyla accounted for more than 99% of the fungal community, with Ascomycota alone comprising approximately 75% to 80% of the fungal community. Owing to their oligotrophic nature, Ascomycota exhibit strong adaptability and competitive advantages in extreme environments, enabling them to dominate fungal communities in high-altitude soils. The relative abundances of Ascomycota and Mortierellomycota did not significantly change along the elevation gradient, likely because of their specialized adaptations to cold and nutrient-poor conditions. However, at 4500 m, a significant decrease in Ascomycota and a corresponding increase in Mortierellomycota was observed. This shift may result from two possible ecological mechanisms: (1) at other elevations, Ascomycota may form symbiotic associations with alpine plants, contributing to nutrient cycling and releasing secondary metabolites that suppress the growth of Mortierellomycota [45]; (2) at 4500 m, Mortierellomycota may engage in mutualistic interactions with specific bacterial taxa, releasing dissolved organic carbon that facilitates nitrogen mineralization, thereby promoting their own proliferation [46]. A reduction in the abundance of Ascomycota could impair soil nutrient cycling efficiency, thereby adversely affecting the overall health and stability of the ecosystem [43]. These dynamics reflect the combined effects of soil nutrient fluxes, plant–microbe symbioses, and microbial competition. Additionally, Basidiomycota exhibited relatively high abundances at both 3650 m and 5500 m. This may be linked to their capacity to decompose complex organic matter. In the afforested low-elevation region, Basidiomycota efficiently degrade high C/N ratio compounds such as lignin from tree residues, whereas at 5500 m, the elevated organic matter content provides ample substrates for their metabolic activities. Chytridiomycota presented the highest relative abundance at 5500 m, indicating notable adaptation to extremely cold conditions in high-altitude alpine environments [47].

A study conducted in Michigan, USA [48], identified the soil pH and moisture content as critical ecological factors regulating the distribution of fungal communities. On the basis of field data collected across five altitudinal gradients in the Duilong Qu Basin of the Lhasa River, the present study employed redundancy analysis (RDA) and revealed that soil electrical conductivity (EC) is the dominant factor shaping fungal community structure. In addition, other soil physicochemical properties—including AP, TP, pH, and TK—also played significant roles in influencing community composition. The analysis indicated a strong positive association (p < 0.01) linking EC with Basidiomycota and Chytridiomycota representation. Notably, phosphorus—typically characterized by low bioavailability in soils—can affect fungal communities by regulating plant root exudation and the decomposition of organic matter. This plant–microbe interaction mechanism has been shown to substantially increase fungal diversity, as also reported by Wang Aiping et al., who underscored the strong link between soil fungi and available phosphorus. Although pH is universally acknowledged as a pivotal determinant of microbial community structure [12,49] and research has revealed a considerable inverse relationship between pH and the prevalence of Mortierellomycota, its direct effect on fungal communities appears to be limited. Instead, pH likely functions indirectly by altering soil physicochemical conditions and influencing vegetation characteristics, thereby shaping fungal community structure and diversity.

According to the results of FUNGuild functional prediction, the soil fungal community structure in the Duilong Qu Basin of the Lhasa River clearly exhibited altitudinal differentiation. At 3650 m, within the artificially afforested zone, the introduction of numerous tree species provided abundant host resources for ectomycorrhizal fungi, resulting in their highest relative abundance. However, as elevation increased and ecosystems transitioned into natural grasslands, the lack of appropriate symbiotic hosts led to a sharp decline in ectomycorrhizal fungi. The distribution of plant pathogenic fungi is closely linked to the presence of host plants. Their abundance remained relatively low at 3650 m and 4000 m but increased significantly between 5000 m and 5500 m. This pattern may be attributed to lower temperatures at higher altitudes, which reduce plant tissue resistance and thus increase vulnerability to fungal invasion. Additionally, the simplified plant community structure at relatively high elevations may offer broader ecological niches for pathogenic fungi. Overall, the vertical distribution of soil fungal functional groups reflects the combined influence of multiple ecological factors. At reduced elevations, plentiful resources and varied flora support the prevalence of ectomycorrhizal fungi. With increasing elevation, however, vegetation succession and shifts in soil properties promote the prevalence of plant pathogens and saprotrophic fungi [50], which have become the dominant functional groups in high-altitude ecosystems.

5. Conclusions

(1)

This research investigated the interplay and ecological roles of soil microbe populations, including bacteria and fungi, across different elevations within the Duilong Qu Basin of the Lhasa River. These findings indicate substantial alterations in the composition of microbial communities at various altitudes. For bacteria, the Chao1 richness index displayed a fluctuating trend, whereas the Shannon diversity index exhibited a distinct V-shaped pattern. In contrast, fungal richness generally increased with altitude, while fungal diversity showed no consistent altitudinal trend. Available phosphorus (AP) was identified as the primary environmental factor influencing bacterial community structure, with soil SOM, TN, AN, and AK also playing significant roles. For fungal communities, EC was the most important factor, followed by total phosphorus (TP), AP, and pH. In terms of functional structure, bacterial communities shifted markedly along the elevation gradient—from being dominated by aerobic and biofilm-forming taxa at lower altitudes to facultative anaerobes, stress-tolerant strains, and potential pathogens at higher altitudes. Conversely, fungal communities gradually transitioned from ectomycorrhizal dominance at low elevations to a community structure increasingly characterized by saprotrophic and plant pathogenic fungi with increasing elevation.

(2)

The current altitudinal sampling design presents certain limitations that may affect the resolution of microbial community response analysis. With only five discrete elevation points, the existing scheme lacks sufficient granularity to capture fine-scale changes along the altitudinal gradient. To overcome these limitations, several improvements are recommended for future research.

First, increasing the sampling density along the elevation gradient is essential. Incorporating 8–10 sampling points at regular intervals of 150–200 m would enhance both the continuity and representativeness of the dataset.

Second, the spatial distribution of the sampling sites should be balanced. Within each elevation zone, 2–3 replicate plots should be established, accounting for variables such as slope aspect, vegetation type, and the degree of anthropogenic disturbance to ensure data robustness and comparability.

Third, soil profile sampling protocols should be refined by including multiple depth layers (e.g., 0–10 cm and 20–30 cm) to comprehensively reflect the vertical distribution of microbial communities. In high-altitude regions where topsoil is shallow, deeper sampling can be considered supplementary.

Fourth, the development of an environmental metadata database is strongly encouraged. Recording site-specific microclimate variables—including temperature, humidity, and light intensity—at the time of sampling would provide valuable contextual information for interpreting microbial–environment interactions.

Finally, incorporating seasonal resampling into the study design would allow for the assessment of temporal dynamics in microbial communities, thereby offering a more complete understanding of their ecological variability and resilience.