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Article

Altitudinal Influences on Soil Microbial Diversity and Community Assembly in Topsoil and Subsoil Layers: Insights from the Jinsha River Basin, Southwest China

by
Zhihong Guo
1,2,
Xiaobo Huang
1,3,4,
Tongli Wang
5,
Jianrong Su
1,3,4 and
Shuaifeng Li
1,3,4,*
1
Institute of Highland Forest Science, Chinese Academy of Forestry, Kunming 650224, China
2
Nanjing Forestry University, Nanjing 210037, China
3
Pu’er Forest Ecosystem Research Station, National Forestry and Grassland Administration of China, Kunming 650224, China
4
Pu’er Forest Ecosystem Observation and Research Station of Yunnan Province, Kunming 650224, China
5
Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Forests 2024, 15(10), 1746; https://doi.org/10.3390/f15101746
Submission received: 27 August 2024 / Revised: 23 September 2024 / Accepted: 29 September 2024 / Published: 3 October 2024
(This article belongs to the Special Issue Soil Microbial Ecology in Forest Ecosystems)
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Abstract

:
Mountain regions play a crucial role in maintaining global biodiversity, with altitude exerting a significant influence on soil microbial diversity by altering plant diversity, soil nutrients, and microclimate. However, differences in microbial community composition between topsoil (0–10 cm deep) and subsoil (10–20 cm deep) remain poorly understood. Here, we aimed to assess soil microbial diversity, microbial network complexity, and microbial community assembly in the topsoil and subsoil layers of the dry–hot Jinsha River valley in southwestern China. Using high-throughput sequencing in soil samples collected along an altitudinal gradient, we found that bacterial diversity in topsoil decreased with increasing altitude, while bacterial diversity in subsoil showed no altitude-dependent changes. Fungal diversity in topsoil also varied with altitude, while subsoil fungal diversity showed no change. These findings suggest that microbial diversity in topsoil was more sensitive to changes in altitude than subsoil. Bacterial community assembly tended to be governed by stochastic processes, while fungal assembly was deterministic. Soil bacterial and fungal network complexity was enhanced with increasing altitude but reduced as diversity increased. Interestingly, the presence of woody plant species negatively affected bacterial and fungal community composition in both soil layers. Soil pH and water content also negatively affected microbial community composition, while organic carbon and total nitrogen positively influenced the microbial community composition. Simultaneously, herb and woody plant diversity mainly affected soil bacterial diversity in the topsoil and subsoil, respectively, while woody plant diversity mainly affected soil fungal diversity in subsoil and soil nutrients had more effect on soil fungal diversity. These findings suggest that altitude directly and indirectly affects microbial diversity in topsoil, subsequently influencing microbial diversity in subsoil through nutrient availability.

1. Introduction

Mountain regions harbor high biodiversity, playing a pivotal role in global biodiversity maintenance. The interplay between climate and geological processes in these areas promotes speciation and species coexistence [1]. Mountain biodiversity can, in part, be attributed to the positive effects of high altitude on habitat heterogeneity [2]. While most biodiversity studies traditionally emphasize elevational shifts in plant diversity [3,4], the biodiversity below ground remains largely unexplored.
Soil microbes represent a critical component of terrestrial biodiversity, orchestrating various ecosystem processes such as primary production, carbon fixation, organic matter decomposition, nutrient cycling, and plant productivity [5]. Recently, attention has turned towards understanding altitudinal effects on soil microbial diversity, community composition, community assembly processes, and biotic and abiotic influences [6]. Thus, altitude has a strong effect on soil microbial diversity. Most studies confirmed that soil microbial diversity decreased significantly along an altitudinal gradient. For instance, studies have shown a decline in soil bacterial diversity with increasing altitude (2400–3400 m) in the Colorado Rocky Mountains [7]. Similar findings occurred in the Changbai Mountains on an altitude gradient ranging from 800 m to 2300 m, where the diversity of soil fungi gradually decreased with increasing altitude [8]. In addition, previous study observed higher soil bacterial diversity at medium altitude (820 m) in the subtropical mountain forests of China [9]. Furthermore, it has been noted that soil microbial community composition was more sensitive to high altitudes (4300–4700 m) than microbial diversity [6]. Understanding the influence of altitude on soil microbial interactions can help predict microbial diversity [10]. However, the impact of soil depth on soil microbial communities along an altitudinal gradient remains unclear.
Simultaneously, altitude significantly affects the soil microbial community assembly process [11], while these processes play a more important role in sustaining biodiversity in the terrestrial ecosystem [12]. Conventionally, deterministic processes are presumed to play a more dominant role in an environmental variation gradient than random processes [13]. Nonetheless, due to minimal environmental selection pressure, ecological drift has a great influence on the soil microbial community resulting in an increase in randomness [14]. Furthermore, studies have shown that drift and diffusion limitation dominate the construction process of soil bacterial communities [15]. Altitudinal gradients bring about variations in drought, which is a significant factor influencing soil microbes and suggests that deterministic processes had a more important role in sustaining soil microbial community assembly. Drought poses a severe challenge in the dry–hot valley of Jinsha River [16], significantly impacting soil microbial communities assembly processes [17], while the effects of drought diminish with increasing altitude [18]. However, the change of soil microbial community assembly along an altitudinal gradient remains uncertain.
Recently, co-occurrence network analysis has emerged as a valuable tool for analyzing interactions between soil microorganisms [19]. A complex ecological relationship of soil microbial community determines their stability and ability to recover after disturbance [20]. Investigations have revealed that the network complexity of soil bacterial and fungal communities tends to decline with increasing altitude in the Tibetan Plateau [21]. Conversely, an altitude-related increase in soil fungal network complexity occurs with increasing altitude in the Sygera Mountains, with soil bacterial taxa exhibiting higher network complexity than fungal taxa [6].
Altitude influences both biotic and abiotic factors, including terrain, plant diversity, and climatic and edaphic variables, which can directly or indirectly affect soil microbial community structure [22]. Soil nutrients, alongside plant diversity and vegetation types, plays especially important roles in affecting soil bacterial diversity along an altitudinal gradient [20]. Furthermore, most studies found that plant diversity affected the soil microbial community more than the soil nutrients [23], while the relative importance of these abiotic and biotic factors in affecting soil microbial diversity still requires a deeper understanding.
The dry–hot valley of the Jinsha River is an ecologically important region in the Hengduan mountains of southwestern China [24]. The region displays serious land degradation due to drought and human disturbance [25], but plant and soil fungal diversity have sustained its fragile ecosystem [26]. The influence of the Foehn effect manifests in the dominance of savanna-like vegetation in the valley, transitioning to coniferous evergreen forests as altitude increases [27]. Therefore, understanding the nuanced alterations in the soil microbial community across an altitudinal gradient remains limited.
To bridge this gap, our study aims to explore soil microbial diversity, community composition, assembly, and network complexity across the topsoil and subsoil layers of the Jinsha River valley along varying altitudes. We hypothesized that (1) soil bacterial and fungal diversity decreases with increasing altitude; (2) soil microbial community assembly shifts from a deterministic to a random process with increasing altitude; and (3) plant diversity plays a more significant role in shaping soil microbial diversity and community composition than soil nutrients.

2. Material and Methods

2.1. Study Area

Our study region was conducted in the mountain (26°03′–26°19′ N, 101°27′–101°49′ E) along Jinsha River of Yongren County, northern of Yunnan Province, China. The altitude range of the study was from 1200 m to 2000 m. This area has a subtropical plateau monsoon climate, with obvious characteristics of dry season and rainy season [26]. The climate is an obvious dry–hot characteristic with the foehn effect. The mean annual temperature and precipitation are 17.8 °C and 834 mm, respectively, while mean maximum and minimum temperatures are 37.7 °C and −3.5 °C, respectively. This region has relatively little rainfall because of relatively high temperature and evaporation rates, and approximately 90% of the precipitation is concentrated in the rainy season from June to October [28]. Vegetation in the region mainly comprises valley-type savanna, sub-tropical evergreen broad-leaved forest, and subtropical coniferous forest. Along the altitudinal gradient, species include sparse shrub grass, Terminalia franchetii, Phyllanthus emblica, Pistacia weinmannifolia, Quercus franchetii, Castanopsis delavayi, and Pinus yunnanensis.

2.2. Field Survey and Soil Sampling

In October 2020, a total of 24 plots (20 m × 20 m) were established along an altitudinal gradient of 1200~2000 m including 8 altitude segments with an interval of 100 m, and 3 representative plots were selected for each altitude segment. Tree, liana, and shrub with a diameter at breast height (DBH) ≥1 cm were recorded. At the same time, five 1 m × 1 m subsamples were set up in each 20 m × 20 m plot for herbaceous plants, and the species, height, cover, and abundance of all woody plants with DBH ≤1 cm and herbaceous plants were recorded. Woody plant cover (WC) and herb plant cover (HC) is calculated based on the vertical projection area of vegetation per unit area. The height, DBH, species name of all plants, and environmental factors (including altitude and geographical location) were measured in each plot. Meanwhile, we collected five soil samples from both the topsoil (depth of 0–10 cm) and subsoil (depth of 10–20 cm) at the four corners and the center of each plot, away the tree roots. Then, a composite soil sample was obtained from the topsoil and subsoil and the plant residues were separated through a 2 mm pore size sieve., which were then divided into two parts: one part was air-dried for soil physicochemical property analysis, and the second part was obtained and stored in a −20 °C car refrigerator, and transported back to the laboratory, then immediately stored at −80 °C for soil DNA extraction. We also used a soil-cutting ring to collect soil samples to measure soil water content (SWC).

2.3. Soil Physical and Chemical Property

Soil physicochemical properties including pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), hydrolysable nitrogen (HN), available phosphorus (AP), available potassium (AK), SWC, and soil bulk density (SBD) were determined using a standard procedure. Soil samples were dried at 105 °C for 24 h; SWC was determined by gravity method, and soil pH was determined by pH meter (soil–water ratio 1:2.5) (FE20K, METTLER TOLEDO, Greisensee, Switzerland). SOC and TN were determined by a 2400 II CHN elemental analyzer (Perkin Elmer Medical Diagnostics Shanghai Co., Ltd., Boston, MA, USA). The molybdenum antimony blue colorimetry and Olsen method were used to determine TP and AP, respectively. The alkaline hydrolysis-diffusion method was used to measure HN.

2.4. DNA Extraction and Sequencing

The soil microbial community was measured using 16S rRNA and internal transcribed spacer (ITS) gene sequencing (Illumina MiSeq platform, Illumina, San Diego, CA, USA) including soil bacteria and fungi. An amount of 10 g of mixed soil subsamples was used to measure soil DNA using the MoBio PowerSoilTM DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA), then purified using the UltraClean Soil DNA Kit (MOBIO Laboratories, Carlsbad, CA, USA) following the manufacturer’s instructions. Bacteria were analyzed using 16S rRNA gene primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), while fungi were quantified using ITS primers ITS1 (5′-CTTGGTCATTTAGAGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′). The Illumina MixSeq2500 platform (Biomarker Technologies, Beijing, China) was used to perform amplicon sequencing. The phyla of the soil bacterial and fungal communities were identified using Uclust [29], which was assigned to operational taxonomic units (OTUs) based on 97% similarity [30].

2.5. Data Analysis

All data were calculated in R 4.3.1. The Shannon index and OTU were used to estimate soil microbial α-diversity including soil bacteria and fungi, and bivariate regression analysis was used to assess differences between microbial α-diversity and altitude in topsoil and subsoil. Differences in microbial α-diversity between topsoil and subsoil were determined by t-test. Non-multidimensional scaling (NMDS) based on Bray–Curtis distance and similarity analysis (ANOSIM) were performed to determine differences in soil microbial community composition at different altitudes and in soil layers using the “vegan” package.
Principal component analysis (PCA) was used to measure different soil nutrients in the topsoil and subsoil. The woody species richness (WSR) indicates the number of woody species with DBH ≥ 1 cm in each plot, and the herb species richness (HSR) was calculated by the sum of all plant species in 5 subsamples of each plot. The effects of WSR, HSR, WC, HC, and soil nutrients on the relative abundance of the top 10 phyla of soil fungi and soil bacteria were analyzed by linear mixed model using the “lme4” package. The effects of WSR, HSR, WC, HC, and soil nutrients on soil bacterial and fungal community composition in different soil layers were investigated using redundancy analysis (RDA) at the operational taxonomic units (OTUs) level [31]. The normalized stochasticity ratio (NST) was used to evaluate the relative contribution of deterministic and stochastic processes in soil microbial community assembly [32]. The value ranges from 0 to 1. When NST < 0.5, it indicates that the deterministic process is dominant, and when NST > 0.5, the random process dominates. Spearman correlation co-efficient matrices were calculated at the genus level using the “Hmisc” package [33].
Microbial network analysis can be utilized to identify keystone species or key microbial groups within microbial communities and explore their roles and functions [34]. The soil microbial co-occurrence network was constructed for each soil depth using the bacterial and fungal OTU. All OTUs with <0.01% of the total relative abundance were removed. All pairwise correlation of the co-occurrence network was analyzed by Spearman correlation using random matrix theory (RMT) [35]. Soil microbial network co-occurrence was constructed using the “WGCNA” package based on Spearman’s correlation metrics, where p > 0.05 and a correlation coefficient of <0.65 were applied. The co-occurrence network was visualized on the Gephi 0.10.1. Based on soil microbial network co-occurrence analysis, topological parameters could be classified into four categories, including module hubs, connectors, network hubs, and peripherals. In addition, topological parameters of the soil microbial network co-occurrence included numbers of nodes and edges, average degree, clustering coefficient, graph density, average path length, and diameter using the subgraph function in the “igraph” package, which were used to calculate soil bacterial and fungal network complexity using multidimensional scaling analysis (MDS) [25,36].
Structural equation modeling (SEM) uses the “lavaan” package to calculate the relationship between microbial potential variables and observed variables, which could provide a systematic understanding of combined relationships between altitude, soil nutrients (pH, SOC, TN, TP, TK, HN, AP, AK, SBD, and SWC), plant cover, plant diversity, and microbial diversity [37]. All data were logarithmically transformed to improve linearity and normality. All bivariate relationships between variables were checked to ensure that a linear relationship was introduced into the model and that all parameters included in the model were not strongly multicollinearity expected for plant cover including woody and herb plant. Therefore, SEM analysis did not include plant cover. The main pathway included the following: (1) altitude, soil nutrient, and plant diversity directly affected soil bacterial and fungal diversity, (2) altitude indirectly affected soil bacterial and fungal diversity through soil nutrient and plant diversity, and (3) soil nutrient indirectly affected soil bacterial and fungal diversity through plant diversity. The maximum likelihood estimation method was used to test its overall goodness of fit, which included the following: (i) Chi-squared (χ2) test with p ≥ 0.05; (ii) goodness-of-fit index (GFI) with GFI ≥0.95, and (iii) root-mean-square error of approximation (RMSEA) with RMSEA ≤0.05.

3. Results

3.1. Changes in Soil Microbial α-Diversity along an Altitudinal Gradient

Significant differences (p < 0.05) were observed in soil bacterial and fungal α-diversity (OTUs and Shannon index) along an altitudinal gradient in the topsoil (Figure 1a,b,f,g). In the topsoil, bacterial α-diversity decreased with increasing altitude, exhibiting a sharp decline starting at 1700 m (Figure 1a,b). Fungal OTUs in the topsoil decreased between 1200 m and 1400 m, then increased until 1700 m, and finally decreased (Figure 1f). However, the Shannon index decreased steadily with increasing altitude (Figure 1g). In contrast to the topsoil, no significant differences (p > 0.05) in soil bacterial and fungal α-diversity along the altitudinal gradient were observed in the subsoil (Figure 1). A direct comparison of topsoil and subsoil revealed no significant difference in bacterial α-diversity (Figure 1e), but greater fungal α-diversity in topsoil (Figure 1j).

3.2. Changes in Soil Microbial Community Composition along an Altitudinal Gradient

NMDS analysis revealed significant differences in bacterial and fungal community composition along an altitudinal gradient between the topsoil and subsoil. The difference of soil bacterial and fungal community composition in the topsoil was greater than the subsoil. In addition, soil bacterial community composition displayed more difference than soil fungal community composition based on stress value (Figure 2). The largest difference in bacterial composition between soil layers was found at 1700 m, while the largest difference in soil fungal community composition was found at 1600 m. ANOSIM confirmed that soil bacterial community composition displayed more differences in the topsoil than the subsoil (Figure 2c), while fungal community composition was not different between soil depth (Stress < 0.1, p > 0.05) (Figure 2f).

3.3. Changes in Soil Main Microbial Phylum along an Altitudinal Gradient

At the phylum level, the top 10 relative abundances of bacterial and fungal phyla were analyzed (Figure 3), where phyla with an average abundance >1% in at least one sample are considered dominant phyla. The topsoil bacterial phylum was mainly composed of Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, Chloroflexi, Verrucomicrobia, Bacteroidetes, Planctomycetes, Firmicutes, and Nitrospirae (Figure 3a), and the subsoil had similar bacterial phylum (Figure 3b). The relative abundance of Proteobacteria in topsoil and subsoil did not change significantly along the altitude gradient. Relative abundances of Acidobacteria in both topsoil and subsoil declined firstly, then increased along the altitude gradient, and displayed the smallest relative abundance at 1600 m elevation (22% of topsoil; 12% of subsoil). The relative abundance of Actinobacteria firstly increased, then decreased with increasing altitude in the topsoil, but decreased steadily with increasing altitude in the subsoil (Figure 3a,b; Table S1). In the topsoil, the relative abundance of Gemmatimonadetes decreased with increasing altitude, while Verrucomicrobia and Planctomycetes increased. In the subsoil, the relative abundance of Verrucomicrobia increased with increasing altitude, while Gemmatimonadetes, Bacteroidetes, and Nitrospirae decreased. The relative abundance of Firmicutes in the subsoil was notably high at a 1500 m altitude (25% of subsoil) (Figure 3a,b; Table S1).
The main fungal phylum in the topsoil and subsoil were Ascomycota, Basidiomycota, Mortierellomycota, and Unclassified fungi. In the topsoil and subsoil, the relative abundance of Ascomycota decreased with increasing altitude, while the relative abundance of Basidiomycota trended upwards. The relative abundance of Mortierellomycota was greatest at an altitude of 1700 m in both the topsoil and subsoil (27% of topsoil; 39% of subsoil) (Figure 3c,d).

3.4. Changes in Soil Community Assembly along the Altitudinal Gradient

Normalized stochasticity ratio analysis of topsoil showed that bacterial community assembly was mainly a stochastic process along the altitudinal gradient, with NST > 0.5 (Figure 4a). In subsoil, the stochastic process dominated bacterial community assembly except for at 1600 m and 1700 m, where the deterministic process (NST < 0.5) was more pronounced (Figure 4b). The deterministic process (NST < 0.5) dominated soil fungal microbial community assembly at all altitudes tested, for both the topsoil and subsoil (Figure 4c,d).

3.5. Changes in Soil Microbial Network Complexity along an Altitudinal Gradient

Bacterial co-occurrence network analysis revealed only one type of connector (Gemmatimonadetes) in the topsoil (Figure 5a, Table S2), while no keystone taxa were identified in the subsoil (Figure 5b, Table S2). In the topsoil, the fungal co-occurrence network showed eight connectors and two module hubs (Ascomycota) (Figure 5c, Table S2), while there were six connectors and one module hub (Ascomycota and Basidiomycota) in the subsoil (Figure 5d, Table S2). The larger number of keystone nodes of soil microbes in topsoil suggests that the stability of soil bacterial and fungal communities is stronger in the topsoil than in the subsoil.
In the topsoil, bacterial network complexity increased with increasing altitude, while fungal network complexity initially increased, then decreased, then increased again with advancing altitude (Figure 5a,c). There was no significant change in soil microbial network complexity with respect to altitude in the subsoil (Figure 5b,d). Soil bacterial and fungal network complexity decreased with increasing bacterial and fungal diversity in both the topsoil and subsoil (Figure 5).

3.6. Factors Affecting the Relative Abundance of Soil Microbial Main Phylum

Soil nutrients and plant diversity had different effects on the relative abundance of bacterial phylum between topsoil and subsoil. PCA analysis displayed that the first axis (PC1) displays SOC and TN in both soil layers, and the second axis (PC2) displays SWC and pH, AP and SWC in the topsoil and subsoil, respectively (Figure S1). In the topsoil, PC1 analysis revealed a significant negative effect on the relative abundance of Nitrospirae, and a positive effect on the relative abundance of Planctomycetes (Figure 6a). PC2 displayed a significant positive effect on the relative abundance of Verrucomicrobia and Planctomycetes, while displaying a negative effect on the relative abundance of Actinobacteria, Gemmatimonadetes, Chlorofexi, and other phyla (Figure 6b). HSR indicated a positive effect on the relative abundance of Chlorofexi and Nitrospirae (Figure 6c). In the subsoil, WSR had a significant positive effect on the relative abundance of Actinobacteria, Bacteroidetes, and Nitrospirae, and a negative effect on the relative abundance of Verrucomicrobia (Figure 6g). PC1 and PC2 showed a positive effect on Acidobacteria (Figure 6e,f). In contrast, HSR significantly negatively affected the relative abundance of Acidobacteria, Verrucomicrobia, and Planctomycetes (Figure 6h). WC and HC have a positive effect on the relative abundance of Planctomycetes in the subsoil (Figure 6e,f).
Soil nutrient and plant diversity also had different effects on the relative abundance of fungal phylum between the topsoil and subsoil. In the topsoil, PC1 and PC2 showed a significant positive effect on the relative abundance of Basidiomycota (Figure 6i,j), while herb species richness had a negative effect on the relative abundance of Basidiomycota (Figure 6l). In the subsoil, PC2 and WSR had a significant negative effect on the relative abundance of Mortierellomycota. In addition, WSR had a negative effect on the relative abundance of Mucoromycota and a positive effect on the relative abundance of Glomeromycota (Figure 6n,o). WC and HC had a positive effect on the relative abundance of Basidiomycota and GS19 in the topsoil (Figure 6q,r).

3.7. Factors Affecting Soil Microbial Community Composition

The RDA analysis was used to explore the effect of soil nutrients and plant attributes on bacterial and fungal community composition. RDA1 and RDA2 accounted for 71.56% and 7.92% of the variation in soil bacterial community composition in the topsoil and subsoil, respectively. WSR significantly affected soil bacterial community composition in the topsoil and subsoil (Figure 7a,b, Table S3). PC2 indicated a more significant effect on soil bacterial community composition in the topsoil, while PC1 indicated a more significant effect on soil bacterial community composition in subsoil.
For the soil fungi, RDA1 and RDA2 accounted for 45.01% and 48.63% of the variation in community composition in the topsoil and subsoil. PC1 and WSR significantly affected fungal community composition in the topsoil and subsoil (Figure 7c,d, Table S3).

3.8. Combined Effects of Soil Nutrients and Plant Diversity on Soil Microbial Diversity

The SEM analysis indicated a better fit for the effect of soil nutrients and plant diversity on bacterial and fungal diversity along the altitudinal gradient in the topsoil and subsoil, which explained 48.7% and 32.5% of the variation in soil bacterial diversity, respectively, and 43.7% and 64.8% of the variation in soil fungal diversity, respectively. Altitude directly affected soil bacterial diversity in topsoil, indirectly affecting soil bacterial diversity through WSR in the subsoil (Figure 8a). HSR directly affected soil bacterial diversity in the topsoil. Soil nutrients displayed an indirect effect through HSR in the topsoil on soil bacterial diversity (Figure 8a,b).
Altitude indirectly affected soil fungal diversity via soil nutrients and plant diversity in the topsoil and subsoil. Soil nutrients directly affected soil fungal diversity in the topsoil and subsoil, WSR significantly negatively affected soil fungal diversity in the subsoil (Figure 8c,d).

4. Discussion

Our study demonstrated that soil bacterial diversity and the fungal Shannon index decreased significantly with increasing altitude in the topsoil, which supported our first hypothesis. These results were consistent with previous research [38]. The reason for this was primarily because soil microbial diversity changes according to abiotic and biotic factors along the altitudinal gradient. Climatic factors also change with increasing altitude, influencing soil microbial community structure [39]. In our study, temperature and HC decreased sharply with increasing altitude in the dry–hot valley, while air humidity and WC increased, explaining changes in vegetation and plant species composition. Higher temperatures increase microbial metabolism and growth [40] and conserve plant diversity, providing more nutrients for microorganisms [23]. In our study, reductions in temperature along the altitudinal gradient may explain reduction in soil microbial diversity. In addition, soil bacterial and fungal communities are strongly and persistently modified by high-intensity drought [25]. In areas with lower natural mean annual precipitation, soil bacteria are more resilient to drought [41], and under long-term drought, the bacterial growth rate increases along with fungal diversity [42].
Our study found that soil bacterial diversity decreased rapidly beyond 1700 m elevation. Interestingly, our survey found that vegetation shifts from a savanna to coniferous forest type around this altitude as the dominant species changes from shrub to Pinus yunnanensis. A significant positive correlation was found between soil moisture and the biomass of Pinus yunnanensis [43]. Previous study found that higher soil bacterial diversity was in the savanna ecosystem [25]. Changes in vegetation type may drive changes in soil microbial communities, thereby affecting the transformation and availability of soil P [44]. In our study, TP content and soil pH decreased significantly after 1700 m (Figure S2). A previous study showed that soil pH was positively correlated with soil bacterial diversity [45], and a global study showed that soil pH was related to differences in bacterial and fungal niches in topsoil [46]. Our study demonstrated that soil bacterial diversity decreased with increasing altitude, possibly due to decreasing soil pH.
With increasing altitude, soil fungal OTUs decreased at first, then increased, and finally decreased in the topsoil. This fluctuation in fungal diversity may be attributed to the indirect effects of nutrients and WSR along the altitudinal gradient in topsoil. Arbuscular mycorrhizal fungi and saprotrophic fungi are found to be positively related to soil nutrients [22]. However, we did not find that the variation in soil fungal communities was associated with a change in species richness, which may be related to the relative abundances of the dominant fungal taxa. Mortierellomycota, a typical soil saprotroph, is drought-sensitive functional group [47]. Basidiomycota can decompose plant litter layers more rapidly than other fungi [48]. In our study, the relative abundance of this fungus initially decreased with advancing altitude, then increased, and finally decreased. Several studies have demonstrated a lack of drought effect on soil fungal community composition with decreasing water availability in the soil [49]. Our study indicated that SWC in this dry–hot valley initially decreases with increasing altitude, then increases, and finally decreases, consistent with changes in soil fungal OTUs in topsoil.
Interestingly, soil bacterial and fungal diversity had no significant change with increasing altitude in the subsoil, consistent with previous studies [8]. Previous study showed that soil depth had a greater impact on soil microbial diversity than soil pH and plant diversity [50]. Indeed, our results showed that soil microbial diversity was more sensitive to altitude in the topsoil than in the subsoil. Another study showed that soil water content in the topsoil is higher than in the subsoil [51]. Greater changes in climate, plant, and soil factors at a soil depth of 0–10 cm than at 10–20 cm lead to more variation in the soil microbial community [52]. Increasing the quality and quantity of plant litter can increase soil microbial diversity and change soil biological community composition [53], and our findings suggested that plant litter affected soil microbial diversity to a greater degree in the topsoil than the subsoil.
Soil fungi decompose more plant-derived organic compounds (such as cellulose) in topsoil than subsoil [54], and organic carbon decomposition is correlated with soil fungal communities in topsoil [55]. In addition, differences in ectomycorrhizal fungi between topsoil and subsoil might lead to different substrate utilization patterns [8], altering soil microbial α-diversity at different soil depths. Soil bacterial and fungal network complexity enhanced along an increasing altitudinal gradient, while they decreased with increasing soil microbial diversity in the topsoil and subsoil. Drought made the soil bacterial co-occurrence network more unstable but had little effect on the fungal co-occurrence network [56]. Therefore, soil microbes exist at both soil depths, but those in topsoil had a close association with nutrient cycling [57].
We found that soil microbial community composition was altered along the altitudinal gradient in both the topsoil and subsoil, but the bacterial community composition showed greater variation than the fungal community composition, particularly in the topsoil. Our findings agree with previous research, showing that altitude directly altered soil bacterial community composition [58]. In our study, the relative abundances of Verrucomicrobia and Actinobacteria gradually decreased along increasing altitudinal gradient. Actinobacteria and Verrucomicrobia show adaptive resistance to drought [56], have higher substrate utilization, and may promote carbon decomposition [59]. Previous study found that members of the Verrucomicrobia and Actinobacteria phyla primarily drove differences in bacterial communities [60]. Soil fungal community composition was significantly different with increasing altitude [61], which may be due to changes in environmental factors, as fungal groups are suitable for different soil habitats, resulting in different community composition [62]. Our results showed that the relative abundances of Mortierellomycota and Basidiomycota changed significantly with altitude, which is expected to significantly influence the soil fungal community under drought conditions.
Quantifying soil microbial community assembly could deepen understanding of the spatiotemporal distribution change of the microbial community [63]. We found that a stochastic process dominated soil bacterial community assembly, while a deterministic process dominated soil fungal microbial community assembly along an altitudinal gradient in the top and subsoil. Previous study found that the stochastic assembly process contributed to soil bacterial community assembly in wetland and desert soils [64]. Under drought conditions, plants reduce the supply of carbon sources to soil bacteria but not fungi [56]. Others have shown that deterministic assembly contributes substantially to shaping the soil fungal community [65,66]. Actually, community assembly process of soil fungi is mainly related to the annual average temperature, annual precipitation, and altitude in Southwest China [65]. Our study found that the number of keystone taxa in the soil fungal community was higher than for bacteria, so the composition of soil fungi appeared more stable than that of bacteria. Therefore, we conclude that the stability of the soil fungal community, dominated by a deterministic assembly process, is higher than that of soil bacterial, the assembly of which is dominated by a stochastic process, along an altitudinal gradient in the dry–hot valley of the Jinsha River.
Our findings showed that altitude indirectly affected soil bacterial and fungal diversity by altering soil nutrient and plant diversity. Specifically, plant diversity directly affected soil bacterial α-diversity, while soil nutrients directly affected soil fungal α-diversity in the topsoil and subsoil. Plant species composition varies across different altitudes along plant cover, leading to distinct vegetation types, thereby establishing the altitude gradient as a pivotal factor influencing the overall pattern of species diversity [67].
Previous studies found that soil microbial diversity was greatest with high soil nutrients and plant biomass [68], and altitude affects plant diversity and soil nutrients [69]. In our study, pH, SOC, and TN decreased, and SWC increased with increasing altitude. pH and SWC mainly negatively affected soil bacterial and fungal community composition, while SOC and TN mainly positively affected soil microbial community composition. Soil factors along an altitudinal gradient can directly or indirectly affect the spatial structure of soil microbial communities [70], and changes in soil pH lead to niche differentiation of the soil microbial community and control their patterns along altitude gradients [46]. SSWC and soil pH are also potential driving factors affecting soil bacterial and fungal diversity [36,71], and soil nitrogen (N) and phosphorus (P) enhance bacterial growth and activity [72]. In our study, soil bacterial α-diversity was primarily affected by plant diversity in the topsoil and subsoil, while soil fungal α-diversity was mainly affected by soil nutrients in the topsoil and subsoil. Moreover, SOC and TN are determinants of soil fungal community composition, which was consistent with previous studies [73].

5. Conclusions

Our study reveals that alterations in altitude affected soil microbial diversity, community composition, main phylum, network complexity, and community assembly in both the topsoil and subsoil in the dry–hot valley. These changes were directly or indirectly influenced by altitude-dependent changes in soil nutrients and plant diversity. Remarkably, drought conditions in the dry–hot valley did not diminish soil bacterial and fungal diversity. Inversely, soil microbial diversity tended to decrease with increasing altitude, with stochastic processes governing soil bacterial community assembly and deterministic processes dominating the soil fungal community assembly. Notably, soil microbial diversity and community composition exhibited more pronounced changes in the topsoil than in the subsoil. These findings provide a novel understanding of the impact of altitude on soil microbes in a dry–hot valley and underscore the differential responses between topsoil and subsoil.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15101746/s1, Figure S1: Principal component analysis of the effects of soil nutrients on soil microbial community structure in topsoil and subsoil. Soil nutrients including pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), hydrolysable nitrogen (HN), available phosphorus (AP), available potassium (AK), soil bulk density (SBD), and soil water content (SWC); Figure S2: Bivariate relationship between altitude, biotic factors and abiotic factors. Biotic factors and abiotic factors including pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), hydrolysable nitrogen (HN), available phosphorus (AP), available potassium (AK), soil bulk density (SBD), soil water content (SWC), woody species diversity (WSR), and herb species diversity (HSR); Table S1: Relative abundance values and standard deviations of main phylum of soil bacteria and fungi between altitudes and topsoil and subsoil; Table S2: The inter-module connectivity (Pi) and intra-module connectivity (Zi) are calculated by network co-occurrence analysis; Table S3: Redundancy analysis of environmental factors on soil bacteria and soil fungi at different soil depths.

Author Contributions

Z.G.: Conceptualization, investigation, methodology, and formal analysis, writing—original draft; S.L.: conceptualization, writing-review, and editing; J.S.: conceptualization and visualization; X.H.: methodology, investigation, and formal analysis; T.W.: investigation and formal analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Applied Basic Research Foundation of Yunnan Province (202401AS070016).

Data Availability Statement

The original sequencing data can be found in the NCBI Sequence Read Archive (SRA) under the accession number: PRJAN1162061; PRJAN1162063.

Acknowledgments

We would like to thank all the editors for their appreciation, as well as the reviewers for their valuable comments, which have been an important support for the maturity of this article.

Conflicts of Interest

The authors declare there are no competing interests.

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Figure 1. Changes in soil bacterial and fungal α-diversity with respect to altitude and soil depth. (a–e) represent soil bacteria; (f–j) represent soil fungi. ns represents no significant difference between different soil depths, ** indicates significant difference between different soil depths (p < 0.01), “ns” indicates that there is no significant difference between different soil depths (p > 0.05).
Figure 1. Changes in soil bacterial and fungal α-diversity with respect to altitude and soil depth. (a–e) represent soil bacteria; (f–j) represent soil fungi. ns represents no significant difference between different soil depths, ** indicates significant difference between different soil depths (p < 0.01), “ns” indicates that there is no significant difference between different soil depths (p > 0.05).
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Figure 2. Non-metric multidimensional scaling (NMDS) analysis based on Bray–Curtis distance and analysis of similarities (ANOSIM) of soil bacterial and fungal community composition along an altitudinal gradient and between topsoil and subsoil. (a–c) represent soil bacteria; (d–f) represent soil fungi. The lower the stress value, the greater the difference. Stress < 0.05 indicates that the result is excellent, Stress < 0.1 indicates that the result is better, and Stress < 0.2 indicates that the result is acceptable.
Figure 2. Non-metric multidimensional scaling (NMDS) analysis based on Bray–Curtis distance and analysis of similarities (ANOSIM) of soil bacterial and fungal community composition along an altitudinal gradient and between topsoil and subsoil. (a–c) represent soil bacteria; (d–f) represent soil fungi. The lower the stress value, the greater the difference. Stress < 0.05 indicates that the result is excellent, Stress < 0.1 indicates that the result is better, and Stress < 0.2 indicates that the result is acceptable.
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Figure 3. Change in relative abundance of soil bacterial and fungal main phylum between different altitudes and topsoil and subsoil. (a,b) represent soil bacteria, and (c,d) represent soil fungi.
Figure 3. Change in relative abundance of soil bacterial and fungal main phylum between different altitudes and topsoil and subsoil. (a,b) represent soil bacteria, and (c,d) represent soil fungi.
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Figure 4. The normalized random ratio (NST) of soil bacterial and fungal community assembly along an altitudinal gradient in the topsoil and subsoil. (a,b) represent soil bacteria, and (c,d) represent soil fungi. The red dotted line represent the cut-off point value (0.5) between deterministic and random processes.
Figure 4. The normalized random ratio (NST) of soil bacterial and fungal community assembly along an altitudinal gradient in the topsoil and subsoil. (a,b) represent soil bacteria, and (c,d) represent soil fungi. The red dotted line represent the cut-off point value (0.5) between deterministic and random processes.
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Figure 5. Soil bacterial and fungal network co-occurrence, diversity, and network complexity along an altitudinal gradient in topsoil and subsoil. (a,b) representing soil bacteria, and (c,d) representing soil fungi.
Figure 5. Soil bacterial and fungal network co-occurrence, diversity, and network complexity along an altitudinal gradient in topsoil and subsoil. (a,b) representing soil bacteria, and (c,d) representing soil fungi.
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Figure 6. Effect of soil nutrient and plant diversity on main bacterial and fungal phylum along an altitudinal gradient in topsoil and subsoil using linear mixed models. (a–f) represent soil bacteria in 0–10 cm, (g–l) represent soil bacteria in 10–20 cm, (m–r) represent soil fungi in 0–10 cm, and (s–x) represent soil fungi in 10–20 cm.
Figure 6. Effect of soil nutrient and plant diversity on main bacterial and fungal phylum along an altitudinal gradient in topsoil and subsoil using linear mixed models. (a–f) represent soil bacteria in 0–10 cm, (g–l) represent soil bacteria in 10–20 cm, (m–r) represent soil fungi in 0–10 cm, and (s–x) represent soil fungi in 10–20 cm.
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Figure 7. Redundancy analysis of environmental factors on soil bacteria and fungi at different depths. Redundancy analysis of environmental factors on 0–10 cm (a) and 10–20 cm (b) soil bacterial communities. Redundancy analysis of environmental factors on 0–10 cm (c) and 10–20 cm (d) soil fungal communities.
Figure 7. Redundancy analysis of environmental factors on soil bacteria and fungi at different depths. Redundancy analysis of environmental factors on 0–10 cm (a) and 10–20 cm (b) soil bacterial communities. Redundancy analysis of environmental factors on 0–10 cm (c) and 10–20 cm (d) soil fungal communities.
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Figure 8. Combined effect of altitude, soil nutrients, and plant diversity on bacterial and fungal diversity in the topsoil and subsoil through structural equation modeling. (a,b) representing soil bacteria, and (c,d) representing soil fungi. The solid line represents a significant effect (p < 0.05), and the dotted line represents no significant effect (p > 0.05). Green represents a significant positive effect, while red indicates a negative effect.
Figure 8. Combined effect of altitude, soil nutrients, and plant diversity on bacterial and fungal diversity in the topsoil and subsoil through structural equation modeling. (a,b) representing soil bacteria, and (c,d) representing soil fungi. The solid line represents a significant effect (p < 0.05), and the dotted line represents no significant effect (p > 0.05). Green represents a significant positive effect, while red indicates a negative effect.
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MDPI and ACS Style

Guo, Z.; Huang, X.; Wang, T.; Su, J.; Li, S. Altitudinal Influences on Soil Microbial Diversity and Community Assembly in Topsoil and Subsoil Layers: Insights from the Jinsha River Basin, Southwest China. Forests 2024, 15, 1746. https://doi.org/10.3390/f15101746

AMA Style

Guo Z, Huang X, Wang T, Su J, Li S. Altitudinal Influences on Soil Microbial Diversity and Community Assembly in Topsoil and Subsoil Layers: Insights from the Jinsha River Basin, Southwest China. Forests. 2024; 15(10):1746. https://doi.org/10.3390/f15101746

Chicago/Turabian Style

Guo, Zhihong, Xiaobo Huang, Tongli Wang, Jianrong Su, and Shuaifeng Li. 2024. "Altitudinal Influences on Soil Microbial Diversity and Community Assembly in Topsoil and Subsoil Layers: Insights from the Jinsha River Basin, Southwest China" Forests 15, no. 10: 1746. https://doi.org/10.3390/f15101746

APA Style

Guo, Z., Huang, X., Wang, T., Su, J., & Li, S. (2024). Altitudinal Influences on Soil Microbial Diversity and Community Assembly in Topsoil and Subsoil Layers: Insights from the Jinsha River Basin, Southwest China. Forests, 15(10), 1746. https://doi.org/10.3390/f15101746

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