Abstract
The alpine ecosystem as one of the most representative terrestrial ecosystems has been highly concerned due to its susceptibility to anthropogenic impacts and climatic changes. However, the distribution pattern of alpine soil bacterial communities and related deterministic factors still remain to be explored. In this study, soils were collected from different altitudes and slope aspects of the Mount (Mt.) Shergyla, Tibetan Plateau, and were analyzed using 16S rRNA gene-based bioinformatics approaches. Acidobacteriota and Proteobacteria were identified consistently as the two predominant phyla in all soil samples, accounting for approximately 74% of the bacterial community. The alpha diversity of the soil bacterial community generally increased as the vegetation changed with the elevated altitude, but no significant differences in alpha diversity were observed between the two slopes. Beta diversity analysis of bacterial community showed that soil samples from the north slope were always differentiated obviously from the paired samples at the south slope with the same altitude. The whole network constituted by soil bacterial genera at the Mt. Shergyla was parsed into eight modules, and Elev-16S-573, Sericytochromatia, KD4-96, TK10, Pedomicrobium, and IMCC26256 genera were identified as the “hubs” in the largest module. The distance-based redundancy analysis (db-RDA) demonstrated that variations in soil bacterial community thereof with the altitude and slope aspects at the Mt. Shergyla were closely associated with environmental variables such as soil pH, soil water content, metal concentrations, etc. Our results suggest that environmental variables could serve as the deterministic factors for shaping the spatial pattern of soil bacterial community in the alpine ecosystems.
Introduction
The alpine ecosystems have attracted extensive attention because they are susceptible to climate changes and human impacts. The Tibetan Plateau is named the world’s “Third Pole,” where the most representative alpine ecosystem has been shaped due to the highest average altitude in the world. Many factors, including topography, precipitation, and altitude, may have significant influences on the soils of alpine ecosystems, leading to the observed changes in microbial community and function (). A growing body of studies has been completed to understand the vegetation and biogeochemical cycling in the soils of the Tibetan Plateau (). Soil microorganisms are well-recognized as the main primary producers, which could be underpinned to support the whole ecosystem, especially for oligotrophic alpine ecosystems. The main factors responsible for shaping soil microbial communities in the Tibetan Plateau deserve to be addressed.
Microbes play the irreplaceable roles in maintaining soil fertility and sustainability, e.g., decomposing organic matters and regulating the cycling of nutrients (). A large variety of the bacterial species assigned to Betaproteobacteria and Acidobacteriota in the forest soils can contribute largely to the carbon cycling via degrading cellulose (). Hence, to find the key factors for shaping the soil microbial communities could be critical in understanding ecosystem function and elucidating their responses to the environmental changes (). It has been highlighted that those environmental variables (e.g., precipitation and soil pH), as competition filtering factors, are more important than dispersal capacity in determining the global distributions of soil bacteria and their encoded functions (). The study on the grassland ecosystem in Chile showed that both land use and climate changes could affect the soil bacterial community functions (). The taxonomic composition of soil microorganisms may also change greatly with altitudes () and latitudes (), probably ascribing to distinct gradients in environmental factors including vegetation, and soil abiotic factors such as pH, carbon, and nitrogen nutrients ().
The taxonomic diversity, composition, and biomass of soil bacteria on the Tibetan Plateau were also closely associated with altitude. reported that the abundance of soil bacteria on Mount (Mt.) Everest significantly decreased along an elevation from 4,000 to 6,550 m. The structures of soil bacterial communities at a subalpine coniferous forest (altitude, 2,800–3,500 m) in Mt. Gonggar substantially differed among the four altitudes (). An elevation gradient along the mountain always leads to great variations in temperature and soil pH, which could be found to collegially affect the composition and function of the bacterial community at Mt. Segrila using phospholipid fatty acids (PLFAs) (; ). Other studies conducted in the same region toward soil archaeal, bacterial, and fungal also reported that altitude and soil pH had close relationships with microbial community structure (,; ). Aforementioned studies were mainly focused on the edaphic factors to address the changes in alpine soil bacterial community with altitude. However, the interactions between aboveground vegetation and soil bacteria in the Tibetan Plateau, as well as the influence of different slopes, also need to be paid more attention to. Knowledge regarding the relationships among bacterial community, soil properties, and vegetation could be essential for comprehensively understanding the biogeographic pattern of alpine soil bacteria along the elevation.
Mt. Shergyla has a typical vertical distribution of vegetations that transit from temperate forest to alpine desert along with the increasing altitude. Soils at Mt. Shergyla are acidic with a pH range of 3–6 due to the prevalence of mountain acid brown soils (). Therefore, Mt. Shergyla is considered a representative alpine region to investigate the relationships of soil bacterial community with the altitude. The objectives of this study were to characterize the taxonomic structure of the soil bacterial community at Mt. Shergyla using 16S rRNA gene sequence-based bioinformatics approaches and to determine the main factors responsible for changes in the diversity and composition of the soil bacterial community with the elevation along windward or leeward slopes.
Materials and Methods
Sampling Location
The Shergyla Mountain that belongs to Nyainqêntanglha Range (29°34′ N, 94°28′ E, greater than 3,000 m a.s.l.) is located in the Nyingchi Prefecture, southeastern Tibet Plateau (Supplementary Figure 1). The average annual precipitation according to the record of the local meteorological station is approximately 676.7 mm, and about 72% occurrence of annual rainfalls happens during the monsoon season (June to September). Monthly average temperature ranged from 0.5°C (January) to 15.8°C (July) (). According to the windward side, sampling sites at Mt. Shergyla are divided into the south-facing (S) and north-facing (N) slopes and the south-facing slope is less influenced by rainfalls compared with another slope. There is a typical montane frigid-temperate forest at Mt. Shergyla with a distinct vertical distribution. Along the altitude span from 3,800 to 4,100 m, the aboveground vegetation is dominated successively by Abies georgei var. smithii (3,800–4,100 m), Sabina saltuaria (4,200–4,300 m), and Rhododendron nivale (4,400–4,500 m).
Soil Sampling
A total of 16 soil samples were collected from the two slopes of Mt. Shergyla every 100 m (altitude, 3,800–4,500 m a.s.l.) in September 2019 (Supplementary Figure 1), and eight soil samples from each of the two slopes. The surface soils (0–10 cm) were taken from six sampling quadrats (2 × 2 m) within a 50 m scope at each of the sampling sites, and then the samples were pooled together. All the samples were sealed in sterilized polyethylene bags, and delivered back to the laboratory on ice bag. Each of the soil samples was divided into two parts. One part was used to extract DNA and determine soil water content (SWC) after rock and visible root debris were removed. Another part was freeze-dried and ground, and afterward screened through 100 mesh. The pretreated soil samples were stored at –20°C until analysis of the basic soil physicochemical properties and metal concentrations.
Analysis of Edaphic Properties
Soil pH was determined in the mixture of fresh soils and deionized water (1:2.5, w/w) using a pH monitor (Mettler Toledo, Switzerland, FE-28-Standard). The contents of total carbon (TC) and inorganic carbon (IC) in the soils were measured using a carbon analyzer (Shimadzu Corp., Kyoto, Japan, SSM-5000 A). The total organic carbon (TOC) content in soil samples was calculated by subtracting the IC content from the TC content. Total nitrogen (TN) content in the soils was tested using an elemental analyzer (Elementar corp., Germany, vario EL cube). The soil SWC was determined by freeze-drying approximately 5 g of the fresh soils for 48 h at –40°C. Following soil extraction using 2 mol⋅L-1 KCl solution [the ratio of soil to KCl solution, 1:10 (w/w)], the contents of NO3+-N and NH4+-N were measured using a continuous flow analyzer (Skalar, Holland, San ++). Elements, including Cr, Co, Ni, Cu, Zn, As, Se, Cd, Sn, Sb, Hg, and Pb, were analyzed using an inductively coupled plasma mass spectrometry (ICP-MS) (Thermo Fisher Scientific, iCAP Qc).
DNA Extraction and 16s rRNA Gene Sequencing
Soil bacterial DNA was extracted using the Fast DNA Spin Kit for Soils (MP Biomedical) following the manufacturer’s instructions. DNA quality and quantity were determined using NanoDrop (Thermo Fisher Scientific) and agarose gel electrophoresis. A pair of primers of 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) specific to the V3–V4 region of bacterial 16S rRNA genes were used for PCR amplification according to the reported procedure (). The PCR amplicons were purified and delivered to company (Magigene Ltd., Guangzhou) for DNA sequencing using an Illumina HiSeq 2500 Platform. All sequencing datasets were deposited in the Sequence Read Archive (SRA) of the NCBI Database with an accession number of PRJNA784354.
Bioinformatics Analysis
After primer removal using q2-cutadapt plugin,1 the raw paired-end reads were then processed using the DADA2 in the QIIME2 software (version 2020.11) with a pipeline of quality control including filtering, trimming, denoising, dereplicating, merging, and chimera removing (). A total of 4,272,911 qualified reads in the 16S rRNA gene sequencing datasets of the Mt. Shergyla soils were kept after the DADA2 processing. Taxonomy was assigned to the sequencing data using a naïve Bayes pre-trained SILVA 132 99% OTU classifier () specific to the 338F-806R primer pairs. The number of different features identified in the soils of Mt. Shergyla was 24,965 covering 441 genera assigned to 39 different phyla.
The alpha diversity indices of bacteria communities in the Mt. Shergyla soils were calculated according to amplicon sequence variants (ASVs) using QIIME 2 software, including Good’s coverage, observed species, Shannon, Chao1, and Simpson indices. Good’s coverage index may explain if the sequencing depth of the 16S rRNA genes is sufficient to reveal the soil bacterial community (). Observed species and Chao1 indices can reflect the diversity of bacterial community in the soil samples. Shannon and Simpson indices may indicate both richness and evenness of soil bacterial community. Beta diversity was reflected by Bray-Curtis dissimilarity that was computed using the “Vegan” package in the R environment (Version4.1.1) ().
Statistical Analysis
All the statistical analysis was performed in the R environment. The relative abundance of the top 15 species at the phylum and genus levels was used to compare the spatial distribution of soil bacterial communities among different sampling sites. Hierarchical clustering analysis (HCA) was employed to group soil samples according to relative abundance of the main soil bacterial genera. Non-metric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) in the “Vegan” package were used to analyze the beta diversity of soil bacterial community according to the Bray-Curtis dissimilarities (). The relationships between the soil bacterial genera and environmental factors were explored using distance-based redundancy analysis (db-RDA) in the “Vegan” package. Prior to the db-RDA, forward selection of all environmental variations was performed according to variance inflation factors for avoiding the occurrence of possibly over-fitting. The significance of the db-RDA was evaluated using the Monte Carlo permutation test. Graphs were plotted using the “ggplot2” package (Version 3.3.5) (). To explore the relationships among soil bacterial genera, a correlation matrix with the correlation ecoefficiency being greater than 0.8 and significance being smaller than 0.05 was obtained, and then network analysis was performed using “Vegan,” “igraph,” and “Hmisc” packages (; ; ). Network visualization was conducted using Gephi.2
Results
Edaphic Characteristics Along Altitude Gradient
Various soil physicochemical parameters at Mt. Shergyla, including metal concentrations, pH, TOC, TN, etc., are tabulated in Supplementary Table 1. Soil physicochemical parameters differed clearly among the different groups, which were categorized by the aboveground vegetation along altitude gradient. In particular, the TOC and SWC in soil samples from Mt. Shergyla generally increased along the elevation with the dominant species shifting from Abies (altitude, 3,800–4,100 m) to Sabina (4,200–4,300 m), and on to Rhododendron (4,400–4,500 m) (Supplementary Figure 2A). Mean Hg concentration in the soils overlaid with Abies was the highest among the aforementioned three groups (Supplementary Figure 2B). In addition, the soils with Sabina were more acidic than those with Abies and Rhododendron (Supplementary Figure 2C).
Soil Bacterial Community Structure
Figure 1A showed that Proteobacteria (mean relative abundance, 40.4%) and Acidobacteriota phyla (33.7%) were preponderant over other phyla in the soils of Mt. Shergyla, followed by Actinobacteriota (8.1%), Chloroflexi (6.4%), Patescibacteria (3.2%), WPS-2 (1.7%), Planctomycetota (1.5%), etc. Remarkably, relative abundance of the Proteobacteria phylum was generally higher at the north slope than at the south slope with the same elevation, but a reverse trend was observed for the Acidobacteriota phylum. Approximately 60% of the total 16S rRNA sequences in this study could be annotated at the genus level. Figure 1B showed that the mean relative abundance of the top 15 identified genera ranged from 0.7 to 7.1%, including Bryobacter (7.1%), Candidatus Solibacter (6.5%), Granulicella (3.3%), IMCC26256 (2.9%), etc. Soil samples could be sorted into three groups according to the identified bacteria genera using an HCA method (Figure 1B). Three samples (S40, S43, and S44) were clustered in Group I, which were featured with higher relative abundance of KD4-96 and IMCC26256 than other samples. Group II was comprised of seven samples with high-relative abundance of Granulicella and Acidipila. Group III included S39, S42, S45, N39, N40, and N45, in which WPS-2 and Bryobacter were relatively more abundant than those in other soils.
FIGURE 1
Alpha Diversity and Beta Diversity of Soil Bacterial Community
Good’s coverage, Chao1, observed species, Simpson, and Shannon indices were used to evaluate the alpha diversity of soil bacteria at Mt. Shergyla (Supplementary Table 3). The Good’s coverage index of the soils was all greater than 0.98, indicating that the sequencing depth of all datasets in this study was sufficient for revealing soil bacterial communities (). Figure 2A showed comparisons in the Chao1 and Shannon indices among soil samples. According to the vegetation, Chao1 and Shannon indices in the soils with Rhododendron were both significantly higher than those with Abies and Sabina (P < 0.05), and no significant differences between soils with Abies and those with Sabina were observed. Regarding slope aspects, no significant differences in the Chao1 and Shannon indices between the two slopes were found. However, soil samples from the south slope had a greater variation in both Chao1 and Shannon indices than those from the north slope.
FIGURE 2
The beta diversity of soil bacteria at Mt. Shergyla was analyzed using the Bray-Curtis dissimilarities-based NMDS approach. Figure 2B showed that soil samples belonging to different groups (Groups I, II, and III) were also clearly separated, which was in good accordance with the HCA results. Soils collected at the north slope were always deviated obviously from the counterpart samples at the south slope with the same altitude. In general, the Bray-Curtis dissimilarity between the paired samples increased with the elevated altitude, e.g., from N38/S38 (0.058) to N44/S44 (0.450). The PERMANOVA was also performed to evaluate the differences in bacterial communities between different groups of soil samples (Supplementary Table 4). Bacterial community structure in the soils of Group I was significantly different from those of Groups II or III (P < 0.05). With respect to vegetation, a significant difference in the bacterial community was also observed between the soils covered with Abies and those with Rhododendron (P < 0.05).
Co-occurrence Patterns of Soil Bacterial Genera
Co-occurrence patterns among bacterial genera in the soils of Mt. Shergyla were characterized using a network inference with strict correlation cut-off values (correlation coefficient, r > 0.8 and P < 0.05). There were 53 nodes (bacterial genera) and 83 edges in Figure 3. The modularity resolution of 0.356 was used, reflecting that the network had a modular structure (
FIGURE 3

Network analysis revealing co-occurrence patterns of soil bacterial genera at Mt. Shergyla. The nodes were colored according to the modules of bacterial genera. A connection represents a strong (Spearman’s correlation coefficient > 0.8) and significant (P < 0.05) correlation. The size of each of nodes is proportional to the number of connections, i.e., degree.
Relationships Between Soil Bacterial Community and the Environmental Factors
The distance-based redundancy analysis (db-RDA) was conducted to explore the relationships between bacterial community and environmental factors in the soils of Mt. Shergyla (Figure 4). Environmental factors investigated in this study could account for approximately 51.3% of the total variance in the soil bacterial community. The differences among soil samples could also be reflected by the projection on the vectors, e.g., the projection points of most of the soil samples from the north slope on the corresponding vector of Acidobacteriota phylum (e.g., Candidatus Solibacter and Bryobacter) were in front of those collected from the south slope, indicating that this phylum had a higher relative abundance at the north slope. The results also exhibited the correlations between each of the bacterial genera and environmental variables, e.g., soil pH was positively correlated to KD4-96 and IMCC26256, but negatively correlated to Candidatus Solibacter, Granulicella, and Bryobacter that belong to Acidobacteriota phylum. Environmental factors, especially pH, SWC, TOC, and metal concentrations (e.g., Hg and Cu), were closely associated with the bacterial community in the soils of Mt. Shergyla (P < 0.05) (Supplementary Table 5). Changes in these environmental factors in the soils of Mt. Shergyla could contribute to great variations in the soil bacterial communities with altitude and mountain slopes.
FIGURE 4

Distance-based redundancy analysis (db-RDA) of bacterial communities and environmental factors in the soils of Mt. Shergyla. The point colors indicate the mountain slopes of sample collection. The point shapes represent the aboveground plant species. The blue and orange arrows represent environmental factors and soil bacterial genera, respectively, and the cosine of the angle within two vector arrows represents the correlation between the vectors.
Discussion
Soil Bacterial Community Structures at Mount Shergyla
Soil bacteria are well-recognized as the major drivers in the biogeochemical cycling of elements and nutrients (e.g., organic matters) in nature, which are crucial for soil formation and fertility (
Proteobacteria was the most abundant phylum in the Mt. Shergyla soils, which is recognized as one of the main bacterial phyla responsible for ammonifying (
At the genus level, there were four dominant genera (Bryobacter, Candidatus Solibacter, Granulicella, and IMCC26256) in the Mt. Shergyla soils, with average relative abundance being greater than 1%. Bryobacter and Candidatus Solibacter are also the two most abundant genera in the alpine soils collected from the Wolong Nature Reserve (
Soil Bacterial Diversity at Mount Shergyla
Both the vegetation and soil nutrients may change largely among sampling locations with different altitudes and slopes, thereby causing a noticeable variation in the soil bacterial diversity (
Soil Bacterial Community in Response to Environmental Factors
Although species compositions were similar in the Mt. Shergyla soils, bacterial community structure still varied greatly among them. To elucidate the differentiation of soil bacterial communities, the relationships between the relative composition of soil bacteria and environmental factors were explored using db-RDA. It was found that altitude, pH, SWC, TOC, and metals (e.g., Hg and Cu) were closely related to the soil bacterial community at Mt. Shergyla. The aforementioned environmental factors exerted differential influences on soil bacterial taxa at Mt. Shergyla. It was demonstrated that the relative abundance of major bacterial phyla can be explained by soil pH, nutrient concentration and to a lesser extent by climatic variables such as mean annual precipitation (
Soil pH is well-considered one of the most important factors for shaping the soil bacterial community (
Heavy metals are ubiquitous in the environments. Many metals such as Zn, Se, and Cu are beneficial to bacteria at a low level (
Conclusion
Based on the bacterial 16S rRNA gene bioinformatics, we examined variations in the soil bacterial composition and diversity with geographic distance (altitude and slope aspects) at Mt. Shergyla, Tibetan Plateau. This study demonstrated that the diversity and compositional patterns of soil bacterial community changed greatly among sampling sites, with Proteobacteria, Acidobacteriota, Actinobacteria, Chloroflexi consistently being the dominant phyla. The impacts of altitude and slope aspects on the soil bacterial community could be explained by the aboveground vegetation and environmental variables such as soil pH, SWC, and metal concentrations at Mt. Shergyla, which might confer environmental filtering of the bacterial community.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA784354.
Author contributions
RY collected all the soil samples. ZZ, KY, LM, and HL carried out these assays. ZZ, KY, LM, ZL, HL, YY, WC, JJ, TL, and BC analyzed the data. BC and ZZ drafted this manuscript. All authors took part in the design of experiments, editing, and revision of the manuscript.
Funding
This work was supported financially by the National Key Research and Development Program of China (No. 2018YFD0900604), National Natural Science Foundation of China (Nos. 21777198 and 22076204), the second Tibetan Plateau Scientific Expedition and Research Program (STEP) (No. 2019QZKK0605), and the Guangdong Special Support Program (No. 2019TQ05H119).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2022.839499/full#supplementary-material
References
1
BahramM.HildebrandF.ForslundS. K.AndersonJ. L.SoudzilovskaiaN. A.BodegomP. M.et al (2018). Structure and function of the global topsoil microbiome.Nature560233–237. 10.1038/s41586-018-0386-6
2
BarkaE. A.VatsaP.SanchezL.Gaveau-VaillantN.JacquardC.KlenkH.-P.et al (2016). Taxonomy, physiology, and natural products of Actinobacteria.Microbiol. Mol. Biol. Rev.801–43. 10.1128/MMBR.00019-15
3
BasuS.KumarG.ChhabraS.PrasadR. (2021). Role of soil microbes in biogeochemical cycle for enhancing soil fertility in New and Future Developments in Microbial Biotechnology and Bioengineering.Amsterdam: Elsevier, 149–157.
4
BolyenE.RideoutJ. R.DillonM. R.BokulichN. A.AbnetC. C.Al-GhalithG. A.et al (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.Nat. Biotechnol.37852–857. 10.1038/s41587-019-0209-9
5
CaoL.LiuH.SunQ. (2017). Distribution characteristics of soil nutrient content in mountain acid brown soil in sejila mountain, southeast tibet.J. West China For. Sci.4656–60. 10.16473/j.cnki.xblykx1972.2017.04.011
6
CastroH. F.ClassenA. T.AustinE. E.NorbyR. J.SchadtC. W. (2010). Soil microbial community responses to multiple experimental climate change drivers.Appl. Environ. Microbiol.76999–1007. 10.1128/AEM.02874-09
7
ChuH.SunH.TripathiB. M.AdamsJ. M.HuangR.ZhangY.et al (2016). Bacterial community dissimilarity between the surface and subsurface soils equals horizontal differences over several kilometers in the western Tibetan Plateau.Environ. Microbiol.181523–1533. 10.1111/1462-2920.13236
8
ClarkeK. R. (1993). Non-parametric multivariate analyses of changes in community structure.Aust. J. Ecol.18117–143. 10.1111/j.1442-9993.1993.tb00438.x
9
CsardiG.NepuszT. (2006). The igraph software package for complex network research.InterJ. Compl. Syst.16951–9.
10
CuiY.BingH.FangL.WuY.YuJ.ShenG.et al (2019). Diversity patterns of the rhizosphere and bulk soil microbial communities along an altitudinal gradient in an alpine ecosystem of the eastern Tibetan Plateau.Geoderma338118–127. 10.1016/j.geoderma.2018.11.047
11
de CastroV. H.SchroederL. F.QuirinoB. F.KrugerR. H.BarretoC. C. (2013). Acidobacteria from oligotrophic soil from the Cerrado can grow in a wide range of carbon source concentrations.Can. J. Microbiol.59746–753. 10.1139/cjm-2013-0331
12
DengX. (2019). Soil microbial in the forest of Wolong Nature Reserve based on high-throughput sequencing technology. Master’s thesis.Chengdu: Chengdu University of Technology.
13
GirardotF.AllegraS.PfendlerS.ConordC.ReyC.GilletB.et al (2020). Bacterial diversity on an abandoned, industrial wasteland contaminated by polychlorinated biphenyls, dioxins, furans and trace metals.Sci. Total Environ.748:141242. 10.1016/j.scitotenv.2020.141242
14
GoodI. J. (1953). The population frequencies of species and the estimation of population parameters.Biometrika40237–264. 10.1093/biomet/40.3-4.237
15
HallinS.PhilippotL.LofflerF. E.SanfordR. A.JonesC. M. (2018). Genomics and ecology of novel N2O-Reducing Microorganisms.Aust. J. Ecol.2643–55. 10.1016/j.tim.2017.07.003
16
HarrellF. E. (2019). Package ‘hmisc’. CRAN2018 2019 Version:4.6-0.
17
HoA.Di LonardoD. P.BodelierP. L. (2017). Revisiting life strategy concepts in environmental microbial ecology.FEMS Microbiol. Ecol.93:6. 10.1093/femsec/fix006
18
HuangR.ZhuH.LiuX.LiangE.GrießingerJ.WuG.et al (2017). Does increasing intrinsic water use efficiency (iWUE) stimulate tree growth at natural alpine timberline on the southeastern Tibetan Plateau?Glob. Planet Chan.148217–226. 10.1016/j.gloplacha.2016.11.017
19
JiangH.ChenY.HuY.WangZ.LuX. (2021). Soil bacterial communities and diversity in alpine grasslands on the tibetan plateau based on 16S rRNA Gene Sequencing.Front. Ecol. Evol.9:45. 10.3389/fevo.2021.630722
20
KaiserK.WemheuerB.KorolkowV.WemheuerF.NackeH.SchoningI.et al (2016). Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests.Sci. Rep.6:33696. 10.1038/srep33696
21
KaspariM.PowersJ. S. (2016). Biogeochemistry and geographical ecology: embracing all twenty-five elements required to build organisms.Am. Nat.188S62–S73. 10.1086/687576
22
KulichevskayaI. S.SuzinaN. E.LiesackW.DedyshS. N. (2010). Bryobacter aggregatus gen. nov., sp. nov., a peat-inhabiting, aerobic chemo-organotroph from subdivision 3 of the Acidobacteria.Int. J. Syst. Evol. Microbiol.60301–306. 10.1099/ijs.0.013250-0
23
LauberC. L.HamadyM.KnightR.FiererN. (2009). Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale.Appl. Environ. Microbiol.755111–5120. 10.1128/AEM.00335-09
24
LeeS. H.KaJ. O.ChoJ. C. (2008). Members of the phylum Acidobacteria are dominant and metabolically active in rhizosphere soil.FEMS Microbiol. Lett.285263–269. 10.1111/j.1574-6968.2008.01232.x
25
LiuK.CaiM.HuC.SunX.ChengQ.JiaW.et al (2019). Selenium (Se) reduces Sclerotinia stem rot disease incidence of oilseed rape by increasing plant Se concentration and shifting soil microbial community and functional profiles.Environ. Pollut.254:113051. 10.1016/j.envpol.2019.113051
26
LladoS.Lopez-MondejarR.BaldrianP. (2017). Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change.Microbiol. Mol. Biol. Rev.81:16. 10.1128/MMBR.00063-16
27
LladóS.ŽifčákováL.VětrovskýT.EichlerováI.BaldrianP. (2015). Functional screening of abundant bacteria from acidic forest soil indicates the metabolic potential of Acidobacteria subdivision 1 for polysaccharide decomposition.Biol. Fertil. Soils52251–260. 10.1007/s00374-015-1072-6
28
LuS.ChenF.ZhouJ.HughesA. C.MaX.GaoW. (2020). Cascading implications of a single climate change event for fragile ecosystems on the Qinghai-Tibetan Plateau.Ecosphere11:e03243. 10.1002/ecs2.3243
29
NancharaiahY. V.LensP. N. (2015). Ecology and biotechnology of selenium-respiring bacteria.Microbiol. Mol. Biol. Rev.7961–80. 10.1128/MMBR.00037-14
30
NewmanM. E. (2006). Modularity and community structure in networks.Proc. Natl. Acad. Sci. USA1038577–8582. 10.1073/pnas.0601602103
31
OksanenJ.BlanchetF. G.FriendlyM.KindtR.LegendreP.McGlinnD.et al (2020). The Vegan Package. Community Ecology Package Version 2.5-7.
32
QuastC.PruesseE.YilmazP.GerkenJ.SchweerT.YarzaP.et al (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools.Nucleic Acids Res.41D590–D596. 10.1093/nar/gks1219
33
RamírezP. B.Fuentes-AlburquenqueS.DíezB.VargasI.BonillaC. A. (2020). Soil microbial community responses to labile organic carbon fractions in relation to soil type and land use along a climate gradient.Soil Biol. Biochem.141:107692. 10.1016/j.soilbio.2019.107692
34
ShenC.GuninaA.LuoY.WangJ.HeJ. Z.KuzyakovY.et al (2020). Contrasting patterns and drivers of soil bacterial and fungal diversity across a mountain gradient.Environ. Microbiol.223287–3301. 10.1111/1462-2920.15090
35
SiG.LeiT.WangJ.ZhangG. (2020). Microbial community structure and enzyme activity along altitude gradients in the shegyla mountains.Fresen. Environ. Bull.294807–4817.
36
SilesJ. A.MargesinR. (2016). Abundance and diversity of bacterial, archaeal, and fungal communities along an altitudinal gradient in alpine forest soils: what are the driving factors?Microb. Ecol.72207–220. 10.1007/s00248-016-0748-2
37
SinghD.Lee-CruzL.KimW.-S.KerfahiD.ChunJ.-H.AdamsJ. M. (2014). Strong elevational trends in soil bacterial community composition on Mt. Halla, South Korea.Soil Biol. Biochem.68140–149. 10.1016/j.soilbio.2013.09.027
38
SrinivasanS.HoffmanN. G.MorganM. T.MatsenF. A.FiedlerT. L.HallR. W.et al (2012). Bacterial communities in women with bacterial vaginosis: high resolution phylogenetic analyses reveal relationships of microbiota to clinical criteria.PLoS One7:e37818. 10.1371/journal.pone.0037818
39
WangG.DuW.XuM.AiF.YinY.GuoH. (2021). Integrated assessment of cd-contaminated paddy soil with application of combined ameliorants: a three-year field study.Bull. Environ. Contam. Toxicol.1071236–1242. 10.1007/s00128-021-03289-2
40
WangJ.-T.CaoP.HuH.-W.LiJ.HanL.-L.ZhangL.-M.et al (2015a). Altitudinal distribution patterns of soil bacterial and archaeal communities along Mt. Shegyla on the Tibetan Plateau.Microb. Ecol.69135–145. 10.1007/s00248-014-0465-7
41
WangJ.-T.ZhengY.-M.HuH.-W.ZhangL.-M.LiJ.HeJ.-Z. (2015b). Soil pH determines the alpha diversity but not beta diversity of soil fungal community along altitude in a typical Tibetan forest ecosystem.J. Soils Sedim.151224–1232. 10.1007/s11368-015-1070-1
42
WickhamH. (2016). ggplot2: Elegant Graphics for Data Analysis.New York, NY: Springer.
43
WuL.NieY.YangZ.ZhangJ. (2016). Responses of soil inhabiting nitrogen-cycling microbial communities to wetland degradation on the Zoige Plateau.China J. Mt. Sci.132192–2204. 10.1007/s11629-016-4004-5
44
XiaZ.BaiE.WangQ.GaoD.ZhouJ.JiangP.et al (2016). Biogeographic distribution patterns of bacteria in typical chinese forest soils.Front. Microbiol.7:1106. 10.3389/fmicb.2016.01106
45
XuM.LiX.CaiX.GaiJ.LiX.ChristieP.et al (2014). Soil microbial community structure and activity along a montane elevational gradient on the Tibetan Plateau.Eur. J. Soil Biol.646–14. 10.1016/j.ejsobi.2014.06.002
46
YangG.PengC.ChenH.DongF.WuN.YangY.et al (2017). Qinghai–Tibetan Plateau peatland sustainable utilization under anthropogenic disturbances and climate change.Ecosyst. Health Sust.3:1263. 10.1002/ehs2.1263
47
YinH.NiuJ.RenY.CongJ.ZhangX.FanF.et al (2015). An integrated insight into the response of sedimentary microbial communities to heavy metal contamination.Sci. Rep.51–12. 10.1038/srep14266
48
ZhangD.ZhangL.ShenJ.WangM. (2018). Soil bacterial and fungal community succession along an altitude gradient on Mount Everest.Shengtai Xuebao382247–2261. 10.5846/stxb201704130657
49
ZhouH.ZhangD.JiangZ.SunP.XiaoH.YuxinW.et al (2019). Changes in the soil microbial communities of alpine steppe at Qinghai-Tibetan Plateau under different degradation levels.Sci. Total Environ.6512281–2291. 10.1016/j.scitotenv.2018.09.336
Summary
Keywords
alpine ecosystem, soils, bacterial community, altitude, slope, environmental variables, Tibetan Plateau
Citation
Zou Z, Yuan K, Ming L, Li Z, Yang Y, Yang R, Cheng W, Liu H, Jiang J, Luan T and Chen B (2022) Changes in Alpine Soil Bacterial Communities With Altitude and Slopes at Mount Shergyla, Tibetan Plateau: Diversity, Structure, and Influencing Factors. Front. Microbiol. 13:839499. doi: 10.3389/fmicb.2022.839499
Received
20 December 2021
Accepted
14 March 2022
Published
04 May 2022
Volume
13 - 2022
Edited by
Ke Yu, Peking University, China
Reviewed by
Weiguo Hou, China University of Geosciences, China; Haijian Bing, Institute of Mountain Hazards and Environment (CAS), China
Updates

Check for updates
Copyright
© 2022 Zou, Yuan, Ming, Li, Yang, Yang, Cheng, Liu, Jiang, Luan and Chen.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Baowei Chen, chenbw5@mail.sysu.edu.cn
†These authors have contributed equally to this work
This article was submitted to Microbiotechnology, a section of the journal Frontiers in Microbiology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.