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Geoderma 338 (2019) 118-127 



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Geoderma 

journal homepage: www.elsevier.com/locate/geoderma 



Diversity patterns of the rhizosphere and bulk soil microbial communities 
along an altitudinal gradient in an alpine ecosystem of the eastern Tibetan c ^ r 
Plateau 

Yongxing Cui ,b ’ 1 , Haijian Bing ’ 1 , Linchuan Fang a ’*, Yanhong Wu , Jialuo Yu’, Guoting Shen , 

Mao Jiang a , Xia Wang a,b , Xingchang Zhang a 

a State Key Laboratory of soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, PR 
China 

b University of Chinese Academy of Sciences, Beijing 100049, PR China 

c Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 
610041, PR China 


ARTICLE INFO 


ABSTRACT 


Handling Editor: Junhong Bai 
Keywords: 

Microbial community 
Rhizosphere 
Altitudinal gradients 
Edaphic factors 
Alpine ecosystems 
Mount Gongga 


The diversity patterns and drivers of soil microbial communities in altitudinal gradients have recently received 
much attention. The rhizosphere is a focus of soil microbial communities, but the patterns and drivers of these 
communities have rarely been studied in alpine ecosystems. We used high-throughput Illumina sequencing to 
examine the community variations of bacteria, archaea and fungi between the rhizosphere and bulk soil along an 
altitudinal gradient in an Abies fabri (Mast.) community on Mount Gongga of the eastern Tibetan Plateau. 
Microbial alpha diversity and community structure varied significantly with altitude but not between the rhi¬ 
zosphere and bulk soil. Soil temperature and the carbonmitrogen ratio were the primary drivers of the structures 
of the bacterial, archaeal and fungal communities, and altitude (geographic distance) contributed a small part 
( < 3%) of the community variation, indicating that various edaphic factors were the key regulators of microbial- 
community variation. This consistency of the microbial communities between the rhizosphere and bulk soil in 
this alpine ecosystem could be attributed to low temperature and high nutrient content. The bacterial, archaeal 
and fungal communities were governed by specific environmental factors (total phosphorus content for bacteria; 
organic-carbon content, dissolved organic-carbon content, NH 4 + -N content and nutrient stoichiometry for ar¬ 
chaea and N0 3 _ -N content for fungi). The distinct environmental responses of the microbial taxa suggested 
metabolic separation and resource preferences of the belowground communities, even within the small-scale 
spatial distances in this alpine ecosystem. Our study suggested that the ecosystem harbored many microbial taxa 
with diverse nutrient preferences and metabolic characteristics and could thus potentially tolerate the soil en¬ 
vironmental variation under a scenario of climate change. 


1. Introduction 

Alpine ecosystems represent one of the most important components 
of the terrestrial system and provide many ecological services (Li et al., 
2018). Climate, vegetation and soil properties in alpine ecosystems vary 
greatly over short spatial distances and altitudinal gradients (McCain, 
2010). These changes will strongly affect the structures and functions of 
soil microbial communities (Shen et al., 2013; Lin et al., 2015). For 
example, geographic distance, soil pH, the carbonmitrogen (C:N) ratio 
and vegetation type have been generally reported as the key drivers of 


the distributional patterns of soil microbes (Chen et al., 2017b; Li et al., 
2018). Soil microorganisms play important roles in regulating biogeo¬ 
chemical cycles and maintaining ecosystem functions (Chen et al., 
2017b). Also, soil microorganisms are more sensitive than plants and 
animals to environmental change (Shen et al., 2015). A better under¬ 
standing of the patterns of geographic distribution and drivers of 
community assembly along environmental gradients of alpine ecosys¬ 
tems is therefore important for elucidating microbial processes, and for 
improving our predictions of the functions of alpine ecosystem in a 
changing climate. 


* Corresponding author. 

E-mail address: flinc629@hotmail.com (L. Fang). 

1 These authors contributed equally to this work. 

https://d 0 i. 0 rg/l 0.1016/j .geoderma.2018.11.047 

Received 12 October 2018; Received in revised form 21 November 2018; Accepted 25 November 2018 

Available online 01 December 2018 

0016-7061/ © 2018 Elsevier B.V. All rights reserved. 
















Y. Cui et al 

The rhizosphere, as a focus of microbial activity, plays an important 
role in microbial assembly because microbial-plant interactions and 
genetic exchanges are frequent there (Tkacz et al., 2015; Cui et al., 
2018; Duan et al., 2018). Bacterial diversity is generally lower in the 
rhizosphere than the bulk soil (Marilley and Aragno, 1999), and mi¬ 
crobial-community compositions differ greatly due to the strongly se¬ 
lective environment of rhizospheres (Kielak et al., 2010; Ai et al., 
2012). Bulk soil has relatively oligotrophic conditions, with low rates of 
nutrient transformation and microbial activity, unlike the more active 
rhizosphere environment (Ai et al., 2012). Most studies of microbial 
communities in the alpine ecosystems, however, have focused on bulk 
soil (Shen et al., 2013; Chen et al., 2017b; Li et al., 2018). Microbial 
communities in alpine ecosystems have more stable habitats than 
communities in agricultural ecosystems with more annually variable 
conditions (Ai et al., 2012; Tkacz et al., 2015). These differences in 
physicochemical and biological properties suggest distinct differences 
in microbial communities between rhizosphere and bulk soil, and the 
responses of microbial communities to the rhizosphere conditions in 
alpine ecosystems may differ greatly from the responses in other eco¬ 
systems. The distributional patterns and drivers of microbial commu¬ 
nities may consequently differ between rhizosphere and bulk soil in 
alpine ecosystems. 

In addition to the influences of external conditions on microbial 
communities, the distinct responses of microbial taxa (bacteria, archaea 
and fungi) to environmental factors would lead to the variation of 
patterns of geographic distribution and differences in the drivers of 
community assembly (Zhang et al., 2017). Mounting evidences suggest 
that soil pH is a key regulator shaping the structures of bacterial and 
archaeal communities (J.T. Wang et al., 2015; Hu et al., 2016), but that 
plant diversity determines the structures of soil fungal communities 
over broad geographic scales (Chen et al., 2017a). Fungal diversity and 
richness decrease as altitude increases in alpine ecosystems such as the 
Tibetan Plateau (Margesin et al., 2009). These studies indicated that 
bacteria, archaea and fungi responded differently to environmental 
conditions. All these studies notably focused on a complete or large- 
scale altitudinal gradient, with relatively large elevational intervals and 
different vegetation types (Shen et al., 2013; Chen et al., 2017b; Li 
et al., 2018). The scale over which biodiversity is sampled will strongly 
influence the patterns observed (Green and Bohannan, 2006), so the 
effects of environmental factors on structuring microbial communities 
in a consistent ecosystem within a small-scale altitudinal gradient re¬ 
main poorly known. 

Metabolic characteristics differ greatly between bacteria and ar¬ 
chaea, and both bacteria and archaea are very abundant and func¬ 
tionally important in terrestrial ecosystems. For example, ammonia- 
oxidizing archaea have unique mechanisms for nitrification, better 
adaptation to low-pH pressures, and strikingly lower ammonia re¬ 
quirements compared with ammonia-oxidizing bacteria (He et al., 
2012; Hu et al., 2013). Fungal breakdown of plant materials rich in 
lignin and cellulose (i.e. lignocellulose) is centrally important to the 
cycling of terrestrial C due to the abundance of lignocellulose in above- 
and belowground systems (Meier et al., 2010). These differences in 
metabolic processes among bacteria, archaea and fungi and their im¬ 
portant ecological functions (Nemergut et al., 2010; Meier et al., 2010), 
indicate that further understanding of the responses of bacterial, ar¬ 
chaeal and fungal taxa to environmental conditions in small-scale al¬ 
titudinal gradients with the same vegetation type is necessary for ac¬ 
curately assessing the altitudinal patterns of distribution and drivers of 
community assembly in alpine ecosystems and can improve the re¬ 
solution and precision of our knowledge. 

Mount Gongga is the highest mountain on the eastern boundary of 
the Tibetan Plateau. It has steep slopes, distinct vegetation and rela¬ 
tively low environmental temperature (He and Tang, 2008). We pre¬ 
viously reported that the soil of this area has abundant organic matter 
and sources of available N and phosphorus (P), with a C:N:P ratio of 
556:22:1 for the O horizon (Bing et al., 2016), which provide abundant 


Geoderma 338 (2019) 118-127 

energy and nutrients for the local microorganisms and plants. These 
conditions provide a natural platform for identifying geographic dis¬ 
tributional patterns and drivers of community assembly along an alti¬ 
tudinal gradient. These conditions are also helpful for assessing po¬ 
tential microbial responses to climate change, with a strategy of space- 
for-time substitution. We investigated the altitudinal distributional 
patterns and driving factors of the bacterial, archaeal and fungal com¬ 
munities in the rhizosphere and bulk soil along an altitudinal gradient 
from 2800 to 3500 m a.s.l. containing the same vegetation type ( Abies 
fabri Mast.) on Mount Gongga. We hypothesized that: (1) the pattern of 
microbial-community diversity would not vary significantly along the 
small-scale altitudinal gradient in an A. fabri community due to the 
small spatial scale and consistent vegetation, (2) the microbial com¬ 
munities would not distinctly vary between the rhizosphere and bulk 
soil because of the low environmental temperature and sufficient re¬ 
sources in the alpine ecosystems, and (3) the driving factors of the 
bacterial, archaeal and fungal taxa would differ due to the differences 
in their metabolic processes, even within a short distance. 

2. Materials and methods 

2.1. Study area and soil sampling 

The study area was in the Hailuogou catchment of Mount Gongga 
(29°30'-30°20'N, 101°30'-102°15'E; 2800-3500 m a.s.l.) (Fig. 1). The 
mountain is in the transition zone of the Tibetan Plateau frigid zone and 
the warm-humid subtropical monsoon zone and is the highest mountain 
in the Hengduan Mountains. The climate in the area is mainly con¬ 
trolled by the Asian monsoon. Mean annual temperature and pre¬ 
cipitation are 4.2 °C and 1947 mm, respectively (Wu et al., 2013). The 
soil has mainly developed from glacial debris and colluvial deposits 
derived from weathered Cenozoic feldspar granite and Permian quartz 
schist. The specific types of soil and vegetation with altitude have been 
described elsewhere (Bing et al., 2016). 

Soil was collected from four altitudes (2800, 3000, 3200, and 
3500 m) in a subalpine dark coniferous forest dominated by A. fabri. 
Three 10 x 10 m plots were established at each altitude in October 
2017. The plots were separated by > 15 m and were considered as true 
replicates (Mariotte et al., 1997). Bulk and rhizosphere soil were col¬ 
lected from each plot. The bulk soil, not directly attached to the root 
systems of A. fabri, was collected by removing several roots and gently 
shaking them to release the soil. The rhizosphere soil, tightly adhered to 
the root surface, was then physically brushed from the root surfaces 
with a sterile soft-bristled paintbrush. Each soil sample was divided into 
two subsamples. One subsample was immediately placed in an ice box, 
transported to the laboratory, and then stored at - 80 °C for the ex¬ 
traction of genomic DNA. The other subsample was passed through a 2- 
mm sieve and air-dried for physicochemical analysis. 

2.2. Soil physiochemical analysis 

The amount of soil moisture was determined by oven-drying 10 g of 
fresh soil at 105 °C for 48 h. Soil pH was measured for a 1:2.5 soikwater 
(w/v) mixture using a meter with a glass electrode (InsMark™ IS126, 
Shanghai, China). Soil organic-C (SOC) content was analyzed using the 
dichromate oxidation method; approximately 0.10 g of air-dried soil 
was digested with 5 ml of 0.8 M K 2 Cr 2 07 and 5 ml of H 2 SC >4 for 5 min at 
170-180 °C and was then titrated using 0.2 M FeS0 4 . Dissolved organic 
C was extracted with 0.5 M K 2 S0 4 and shaken for 60 min at 200 rpm on 
a reciprocal shaker, and the extracts were measured using a Liqui TOCII 
analyzer (Elementar, Germany) (Jones and Willett, 2006). Total N (TN) 
content was measured by the Kjeldahl method (Bremner and Mulvaney, 
1982). In detail, approximately 0.700 g of air-dried soil was digested 
with 1.85 g of a mixed catalyst (100:10:1 K 2 S0 4 :CuS0 4 :Se) and 5 ml of 
H 2 S0 4 for 45 min at 385 °C and was then titrated using 0.02 M HC1. Soil 
N0 3 _ -N and NH 4 + -N contents were measured using a Seal Auto 


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Y. Cui et al 


Geoderma 338 (2019) 118-127 



Fig. 1 . Maps of the Tibetan Plateau and study area (A), and a graph of the sampling sites along the altitudinal gradient on Mount Gongga (B). 


Analyzer after extraction with 2 M KC1 with a 1:5 ratio. Total P (TP) and 
available P (AP) were extracted with H2SO4-HCIO4 and sodium bi¬ 
carbonate (Olsen and Sommers, 1982), respectively, and then de¬ 
termined by the molybdenum blue method using an ultraviolet spec¬ 
trophotometer (Hitachi UV2300). 

2.3. DNA extraction, amplification and MiSeq sequencing 

DNA was extracted from 0.25 g of soil using the FastDNA SPIN Kit 
for Soil (Q-BIOgene, Carlsbad, USA) following the manufacturer's in¬ 
structions. The quality and quantity of the extracted DNA was assessed 
using an automatic microplate reader (BioTek ELX 800, USA). The in¬ 
tegrity of the DNA extracts was confirmed by 1% agarose gel electro¬ 
phoresis. The primers 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 
806R (5'-GGACTACHVGGGTWTCTAAT-3') (Huse et al., 2008) were 
used to amplify the V3-V4 hypervariable region of the bacterial 16S 
rRNA gene. The primers Arch349F (5'-GYGCASCAGKCGMGAAW-3') 
and Arch806R (5'-GGACTACVSGGGTATCTAAT-3') (Takai and 
Horikoshi, 2000) were used to amplify the V3-V4 hypervariable regions 
of the archaeal 16S rRNA gene. The primers ITS1F (5'-CTTGGTCATTT 
AGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3') 
(Gardes and Bruns, 1993) were used to amplify the fungal ITS1 region. 


The PCR reactions were performed in a thermal cycler (ABI GeneAmp 
9700) at a volume of 20 pi and undergone 5 cycling procedure. Suc¬ 
cessful PCR amplification was verified by 2% agarose gel electrophor¬ 
esis. The three PCR products were pooled, purified by gel extraction 
and quantified using the AxyPrepDNA gel extraction kit (AXYGEN 
Corporation, USA) and the QuantiFluor™-ST blue fluorescence quanti¬ 
tative system (Promega Corporation, USA). The purified PCR products 
were then mixed at equimolar ratios for sequencing on an Illumina 
HiSeq PE150 system (Illumina Corporation, USA) by Biomarker Tech¬ 
nologies Co, LTD. 

2.4. Bioinformatics analysis 

Primer sequences were trimmed after the raw sequences were de- 
noised, sorted and separated using Trimmomatic (version 0.33). The 
remaining sequences were filtered for redundancy, and all unique se¬ 
quences for each sample were then clustered into operational taxo¬ 
nomic units (OTUs) at similarities of 97%. Low-abundance OTUs were 
eliminated from the OTU table if they did not present a total of at least 
two counts across all samples in the experiment. The taxonomic identify 
(species level) of representative sequences for each OTU was de¬ 
termined using the Silva reference database (http://www.arb-silva.de) 


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Y. Cui et al 


Geoderma 338 (2019) 118-127 



Fig. 2. Differences in bacterial (A, B and C), archaeal (D, E and F) and fungal (G, H and I) alpha diversity among the altitudes. All data are presented as the 
mean ± standard error. Lowercase letters (a, b and c) indicate that means are significantly different (P < 0.05) among different altitudes within rhizosphere and 
bulk soil; whereas there are no significant differences in microbial alpha diversity between the rhizosphere and bulk soil (P > 0.05). 


for the 16S rRNA genes and the Unite reference database (http://unite. 
ut.ee/index.php) for the ITS using the RDP naive Bayesian classifier 
with the BLAST tool in QIIME (http://qiime.org/index.html). Alpha 
diversity was calculated using the Shannon-Wiener and Simpson's di¬ 
versity indices by the ‘diversity’ function in the R (v.3.3.2) Vegan 
package (version 2.4-4; Oksanen et al., 2013). The relative abundances 
of the microbes were determined as percentages. 

2.5. Statistical analysis 

Two-way ANOVAs were used to analyze the effects of altitude, lo¬ 
cation (bulk soil and rhizosphere) and their interaction on the soil 
properties and microbial alpha diversities (number of OTUs and 
Simpson's diversity and Shannon-Wiener indices). A Pearson correla¬ 
tion analysis assessed the association between microbial alpha diversity 
and environmental factors. A value of P < 0.05 was considered sig¬ 
nificant. The heterogeneity of the variance was tested, and the original 
data were normalized by log-transformation or standardization prior to 
analysis when necessary. These analyses were performed using R 
(v.3.3.2). 

The structures of the bacterial, archaeal and fungal communities 
were visualized by principal coordinates analyses (PCoA) based on 
Bray-Curtis dissimilarity matrices using the Vegan package (Oksanen 
et al., 2013). The difference of microbial-community structure between 
two altitudes was identified by analysis of similarities (ANOSIM). The 
effects of altitude, location and their interaction on the microbial Bray- 
Curtis dissimilarity were tested by a two-way permutational multi¬ 
variate analysis of variance using the Adonis function in the Vegan 
package (https://cran.r-project.org/web/packages/vegan/index.html). 


The most significant factors shaping the structures of the microbial 
communities were determined by a canonical correspondence analysis 
(CCA) and a Monte Carlo permutation test via the Hellinger transferred 
data of microbial species and the data of environmental factors stan¬ 
dardized using the Vegan package. A Mantel test was used to assess the 
correlations of microbial communities and environmental variables and 
geographic distance using the Vegan package. A partial Mantel test in 
the Vegan package was used to control the covarying effects of various 
factors. Significant environmental variables identified by the partial 
Mantel test were selected to construct an environmental matrix for 
conducting a variation-partitioning analysis to determine the relative 
importance of the environmental variables and geographic distance in 
explaining the microbial-community compositions identified by a re¬ 
dundancy analysis using the varpart function in the Vegan package. 

3. Results 

3.1. Soil characteristics along the altitudinal gradient 

Most soil parameters differed significantly between the rhizosphere 
and bulk soil along the altitudinal gradient (Tables S2 and S3). The two- 
way ANOVAs indicated that the SOC, DOC and NH 4 + -N contents, the 
C:P and N:P ratios and soil temperature were significantly higher at 
2800 and 3000 m than at 3200 and 3500 m (P < 0.05). The N 03 ~-N, 
NH 4 + -N and TP contents and the C:N and C:P ratios were strongly af¬ 
fected by both altitude and location and their interaction (P < 0.05). 
The Pearson correlation analysis identified strong correlations among 
soil properties, nutrient stoichiometry and soil temperature (Table S4). 


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Y. Cui et at 


Geoderma 338 (2019) 118-127 



2800m 3000m 3200m 3500m 


Others 

Chlamydiae 

Planctomycetes 

Firmicutes 

Chloroflexi 

Verrucomicrobia 

Gemmatimonadetes 

Bacteroidetes 

Actinobacteria 

Proteobacteria 

Acidobacteria 


Others 

Proteobacteria 

Woesearchaeota_[DHVEG-6] 

Bacteroidetes 

Chlamydiae 

Parvarchaeota 

Parcubacteria 

Planctomycetes 

Euryarchaeota 

Thaumarchaeota 


Others 

Mucoromycota 

Rozellomycota 

Giomeromycota 

Chytridiomycota 

Rotifera 

Unassigned 

Mortierellomycota 

Ascomycota 

Basidiomycota 

Unclassified 


Fig. 3. Relative abundance of the dominant bacterial, archaeal and fungal taxa 
abundance of archaeal phyla (%), (Cj relative abundance of fungal phyla (%). 

3.2. Microbial alpha diversity and community composition 

A total of 4,065,875 high-quality microbial sequences were identi¬ 
fied from all soil samples: 1,482,852 bacterial, 1,112,142 archaeal and 
1,470,881 fungal sequences (Table SI). The bacterial, archaeal and 
fungal sequences were clustered into 1271, 753 and 2043 OTUs, re¬ 
spectively. For the bacteria and archaea communities, the numbers of 
OTUs and the Shannon-Wiener and Simpson's diversity indices in¬ 
dicated significant differences along the altitudinal gradient, but these 
indices did not differ significantly between rhizosphere and bulk soil 
(Fig. 2 and Table S5). In contrast, the fungal alpha diversities were si¬ 
milar among the sampling sites. 

The dominant bacterial phyla at 2800, 3000, 3200 and 3500 m were 
Acidobacteria (54.0, 47.5, 37.8 and 50.1%, respectively) and 
Proteobacteria (31.3, 31.1, 37.1 and 29.3%, respectively) (Fig. 3A). The 
most abundant archaeal phyla at 2800, 3000, 3200 and 3500 m were 
Thaumarchaeota (45.0, 63.3, 56.9 and 63.0%, respectively) and Eur¬ 
yarchaeota (36.7, 23.5, 21.4 and 22.0%, respectively) (Fig. 3B). The 
most abundant fungal phyla at 2800, 3000, 3200 and 3500 m were 
Basidiomycota (40.5, 60.1, 73.4 and 40.8%, respectively) and Ascomy¬ 
cota (31.2, 23.5, 15.3 and 33.9%, respectively) (Fig. 3C). Other phyla 
such as Chytridiomycota and Giomeromycota accounted for only a minor 
fraction of the fungal-community composition. 

3.3. Effect of environmental variables on microbial alpha diversity and 
microbial-community structure 

Altitude rather than location (rhizosphere or bulk soil) affected the 


among the altitudes. (A) Relative abundance of bacterial phyla (%), (B) relative 


bacterial and archaeal alpha diversities (Table S5). The Pearson cor¬ 
relation analysis found that all environmental factors except soil tem¬ 
perature were significantly correlated with bacterial alpha diversities 
(Table S6). The archaeal alpha diversities were significantly correlated 
only with N0 3 ~-N and AP contents, the C:N ratio and pH. The number 
of fungal OTUs was significantly correlated with SOC, DOC, TN and 
NH 4 + -N contents, N:P ratio, soil moisture and temperature. 

The PCoA and ANOSIM found that the structures of the bacterial, 
archaeal and fungal communities differed significantly among the four 
altitudes (Table 2 and Fig. 4). The Adonis analysis also indicated that 
altitude significantly affected microbial-community structure 
(P < 0.001; Table 1). Both the CCA and Monte Carlo permutation test 
indicated that altitude, the C:N ratio and soil temperature concurrently 
affected the structures of the bacterial, archaeal and fungal commu¬ 
nities. Specifically, the correlations indicated that the TP content sig¬ 
nificantly affected bacterial-community structures. SOC, DOC and 
NH 4 + -N contents, and the C:P and N:P ratios regulated archaeal-com- 
munity structures. N0 3 ~-N content had a strong impact on fungal- 
community structures (P < 0.05; Table 3 and Fig. 5). 

3.4. Associations of microbial beta diversity with environmental variables 
and geographic distance 

The partial Mantel test found that environmental factors (e.g., SOC 
and DOC) and geographic distance (i.e., the relative distance between 
the sampling sites) were both significantly correlated with the dissim¬ 
ilarity of the bacterial, archaeal and fungal communities (all P < 0.05; 
Table 4), indicating a significant distance-decay relationship. The 


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Y. Cui et aL 


Geoderma 338 (2019) 118-127 


PCoA - PCI vs PC2 




-20 0 20 40 

PCI-Percent variation explained 13.35% 


(A) 


O BS2800 
A BS3000 
+ BS3200 
X BS3500 
O RS2800 
V RS3000 
B RS3200 
* RS3500 


(B) 


O BS2800 
A BS3000 
+ BS3200 
X BS3500 
O RS2800 
V RS3000 
RS3200 
* RS3500 


(C) 


O BS2800 
A BS3000 
+ BS3200 
X BS3500 
O RS2800 
V RS3000 
E RS3200 
* RS3500 


Fig. 4. Principal coordinates analysis (PCoA) of bacteria (A), archaeal (B) and 
fungal (C) community structures in the rhizosphere and bulk soil among the 
altitudes. 


variation-partitioning analysis found that the environmental factors 
were the main contributors to the dissimilarities of the bacterial, ar¬ 
chaeal and fungal communities, explaining 53.9, 47.9 and 24.9% of the 
variation, respectively (Fig. 6). Geographic distance explained only a 
small percentage of the microbial-community dissimilarity. For ex¬ 
ample, geographic distance explained only 0.6% of the fungal-com¬ 
munity dissimilarity. By comparison, the environmental factors were 
important predictors of fungal beta diversity (Fig. 6). These results in¬ 
dicated that the contemporary environment played a more important 


Table 1 

Two-way permutational multivariate analysis of variance (PERMANOVA) 
(Adonis analysis) showing the effects of altitude, location (rhizosphere and 
bulk), and their interaction on microbial-community structure. 


Factors 

df 

F 

P 

Bacteria 




Altitude 

3 

7.94 

0.001 

Location 

1 

1.25 

0.260 

Altitude * location 

3 

0.46 

0.975 

Residuals 

16 



Total 

23 



Archaea 




Altitude 

3 

6.91 

0.001*** 

Location 

1 

1.36 

0.205 

Altitude * location 

3 

0.61 

0.923 

Residuals 

16 



Total 

23 



Fungi 




Altitude 

3 

3.29 

0.001*** 

Location 

1 

0.41 

0.997 

Altitude * location 

3 

0.35 

1.000 

Residuals 

16 



Total 

23 




Note: df, degrees of freedom. 
*** P < 0.001. 


role than geographic distance in shaping the composition of the mi¬ 
crobial communities. 


4. Discussion 

4.1. Differences in microbial alpha diversity and community structure along 
the altitudinal gradient 

Our results indicated that the alpha diversities and community 
structures of the soil microbes on Mount Gongga varied markedly along 
an altitudinal gradient containing the same type of vegetation, which 
did not support our first hypothesis. The soil properties and spatial 
attributes associated with altitude greatly affected the composition of 
the belowground communities. Other studies have also reported var¬ 
iations of microbial communities along altitudinal gradients (Yang 
et al., 2014; Guo et al., 2015; J.T. Wang et al., 2015). These differences 
in the microbial communities can be ascribed to differences in the en¬ 
vironmental conditions along the gradients. A pattern of decreased 
microbial diversity with altitude emphasizes the importance of en¬ 
vironmental variables (J.T. Wang et al., 2015; Bryant et al., 2008). 
Furthermore, environmental fluctuations are larger in areas at low al¬ 
titude, but areas at high altitude feature many restraining factors such 
as low temperature, nutrient and water availability and thermal energy 
and high viscosity (Jansson and Ta§, 2014). 

Only microbes (psychrotolerant or psychrophilic) with structural 
and functional adaptations to the harsh environmental conditions can 
survive at high altitudes. For example, microbes can evolve multiple 
strategies such as dormancy or the production of specialized proteins to 
survive at low temperatures (Jansson and Ta§, 2014). Our correlation 
analysis between the matrix of environmental variables and community 
structures indicated that microbial-community structure was closely 
correlated with the environmental factors, suggesting that the Bass- 
Becking hypothesis may apply. The Bass-Becking hypothesis states that 
“everything is everywhere, but, the environment selects” and assumes 
that microbes are ubiquitous and that environmental factors contribute 
decisively to structuring microbial-community composition (OMalley, 
2007). The environmental factors in our study had obvious distribu¬ 
tions at the various altitudes (Tables S2 and S3) and greatly influenced 
microbial-community composition (Tables 3 and 4, Figs. 5 and 6). The 
apparent diversity patterns of the various microbial taxa highlighted 
the importance of these soil environmental factors in regulating 


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Y. Cui et al 


Geoderma 338 (2019) 118-127 



Fig. 5. Canonical correspondence analysis (CCA) used to identify the re¬ 
lationships among the bacterial (A), archaeal (B), and fungal (C) populations 
(trilateral), environmental factors and geographic distance. SOC, soil organic- 
carbon content; DOC, dissolved organic-carbon content; TN, total nitrogen 
content; TP, total phosphorus content; AP, available P content; C:N ratio, the 
ratio of SOC to TN; C:P ratio, the ratio of SOC to TP; N:P ratio, the ratio of TN to 
TP; Moisture, soil moisture content. 

microbial distribution. Fungal alpha diversity particularly did not differ 
significantly among the altitudes, possibly due to an unexpectedly ef¬ 
ficient dispersal by known agents, including wind and birds (Egan et al.. 


Table 2 

Analysis of similarities (ANOSIM) showing the differences of the microbial- 
community structure between the altitudes. 


Altitude 

Bacteria 

Archaea 


Fungi 


R 2 

P 

R 2 

P 

R 2 

P 

2800-3000 m 

0.669 

0.007 

0.691 

0.006 

0.713 

0.003 

2800-3200 m 

0.985 

0.002 

0.967 

0.003 

0.963 

0.001 

2800-3500 m 

0.693 

0.003 

0.776 

0.005 

0.994 

0.001*** 

3000-3200 m 

0.735 

0.002 

0.659 

0.003 

0.378 

0.005. 

3000-3500 m 

0.639 

0.03* 

0.591 

0.003 

0.507 

0.005** 

3200-3500 m 

0.9 

0.001 

0.917 

0.002 

0.859 

0.003 


*** P < 0.001. 
** P < 0.01. 

* P < 0.05. 


Table 3 

Relationships of bacterial, archaeal and fungal community compositions with 
environmental variables identified by Monte Carlo permutation tests. 


Factors 

Bacteria 

Archaea 

Fungi 


R 2 

P 

R 2 

P 

R 2 

P 

Altitude 

0.288 

0.038 

0.361 

0.007 

0.294 

0.026 

SOC 

0.053 

0.573 

0.253 

0.049 

0.056 

0.543 

DOC 

0.079 

0.430 

0.312 

0.017 

0.049 

0.588 

TN 

0.015 

0.846 

0.190 

0.108 

0.006 

0.943 

no 3 _ -n 

0.221 

0.076 

0.097 

0.369 

0.299 

0.024 

nh 4 + -n 

0.080 

0.429 

0.286 

0.039 

0.036 

0.667 

TP 

0.270 

0.039 

0.231 

0.082 

0.165 

0.133 

AP 

0.064 

0.520 

0.016 

0.856 

0.079 

0.438 

C:N ratio 

0.384 

0.009 

0.374 

0.011 

0.384 

0.008 

C:P ratio 

0.090 

0.376 

0.309 

0.024 

0.042 

0.630 

N:P ratio 

0.151 

0.168 

0.335 

0.019 

0.097 

0.357 

Moisture 

0.035 

0.679 

0.065 

0.520 

0.041 

0.647 

pH 

0.022 

0.762 

0.006 

0.941 

0.010 

0.921 

Soil temperature 

0.302 

0.026 

0.357 

0.009 

0.307 

0.022 


Note: SOC, soil organic-carbon content; DOC, dissolved organic-carbon content; 
TN, total nitrogen content; TP, total phosphorus content; AP, available P con¬ 
tent; C:N ratio, the ratio of SOC to TN; C:P ratio, the ratio of SOC to TP; N:P 
ratio, the ratio of TN to TP; Moisture, soil moisture content. 

** P < 0.01. 

* P < 0.05. 

Table 4 


Relationships among dissimilarities of the bacterial, archaeal and fungal com¬ 
munities, environmental factors and geographic distance identified by partial 
Mantel tests. 


Variables 

Control for 

Mantel statistic r 

P 

Bacteria 

Environmental factors 

Geographic distance 

0.247 

0.005 

Geographic distance 

Environmental factors 

0.134 

0.047' 

Archaea 

Environmental factors 

Geographic distance 

0.264 

0.002'' 

Geographic distance 

Environmental factors 

0.155 

0.033' 

Fungi 

Environmental factors 

Geographic distance 

0.164 

0.027' 

Geographic distance 

Environmental factors 

0.333 

0.001 


*** P < 0.001. 
** P < 0.01. 

* P < 0.05. 


2014). Also, many fungal species can form spores (Harrison, 2005), so a 
bank of propagules might be formed to efficiently exploit even harsh 
environmental conditions (Davison et al., 2015). 

The microbial alpha diversity and community structures did not 
differ significantly between the rhizosphere and bulk soil, supporting 
our second hypothesis. Other studies of grassland and agricultural soil, 


124 







































Y. Cui et al 


Geoderma 338 (2019) 118-127 


(A) 





Fig. 6. Variation-partitioning analysis showing the percentages of the variance in the bacterial (A), archaeal (B) and fungal (C) communities explained by the 
environmental variables and geographic distance. Environmental variables include SOC, soil organic-carbon content; DOC, dissolved organic-carbon content; N0 3 “- 
N content; NH 4 + -N content; TP, total phosphorus content; C:N ratio, the ratio of SOC to TN; C:P ratio, the ratio of SOC to TP; N:P ratio, the ratio of TN to TP; soil 
temperature. 


however, reported that the diversities of the microbial communities 
were generally lower in the rhizosphere than the bulk soil (Marilley and 
Aragno, 1999; Ai et al., 2012; Guo et al., 2016). Three possible ex¬ 
planations could account for these inconsistent findings. 

Firstly, soil temperature is one of the most important drivers af¬ 
fecting microbial communities (Zhou et al., 2016) and was lower in our 
study area (mean annual temperature of 4.2 °C) than those in the 
grassland and agricultural ecosystems (mean annual temperatures of 
12.6-14.5 °C) (Table S3). Our low soil temperature could create a re¬ 
latively stable habitat and lead to low nutrient fluctuation and micro¬ 
bial metabolic activities in the alpine ecosystem, thus contributing to 
the consistent diversity of the microbial communities between the 
rhizosphere and bulk soil. Secondly, the soil of the A. fabri community 
had high nutrient and moisture contents in both the rhizosphere and 
bulk soil (Table S2), which could meet the needs of the microorganisms. 
Thirdly, the alpine ecosystem would have weaker root activities due to 
the low temperatures, such as lower absorption of nutrients and se¬ 
cretion of organic acids, so the influence of the root systems on the 
rhizosphere microbial communities would be weak (Meng et al., 2017). 
Previous studies found that variations in the composition of microbial 
communities between the rhizosphere and bulk soil could be controlled 
by differences in nutrient availability and root selection pressure 
(Kielak et al., 2010; Ai et al., 2012). We therefore concluded that the 
soil environment (e.g. low temperature and high nutrient content) was 
responsible for this consistency of the microbial communities between 
the rhizosphere and bulk soil in this alpine ecosystem. 

4.2. Contrasting drivers of bacterial, archaeal and fungal communities in 
the alpine ecosystem 

The critical environmental factors varied greatly across the altitu¬ 
dinal gradient in the A. fabri community on Mount Gongga. Altitude, 
soil temperature and the C:N ratio had the most influence on the di¬ 
versity and composition of the microbial communities (Table 3). Alti¬ 
tude was the most important environmental factor, which can affect 
microbial communities not only by regulating the microclimate and the 
availability of nutrients but also by conditioning geographic distances 
(J.T. Wang et al., 2015). Different climatic zones can occur over very 
short geographic distances due to steep environmental gradients with 
varied soil properties (Lin et al., 2015). The altitudinal climatic zone in 
our study area was likely responsible for the variation of the soil mi¬ 
crobial communities. Geographic distance was a notable factor shaping 
the structures of the microbial communities (Table 4 and Fig. 6). Soil 
bacteria have a limited capacity for long-distance dispersal over broad 
geographic scales (X.B. Wang et al., 2015), partly due to burial and the 
cold environment. A previous study found that the dominant archaeal 
taxa MBGA on the Tibetan Plateau, usually abundant in marine 


sediments (Inagaki et al., 2006), was due to the historical contingency 
(geographic distance) of the uplifting of the Tibetan Plateau. The geo¬ 
graphic distance along the altitudinal gradient could therefore also 
greatly affect the patterns of microbial distribution. 

Soil temperature was another important factor regulating microbial- 
community structure. The microbial community structures in the 
Mount Gongga and the permafrost layer of the Tibetan Plateau (Chen 
et al., 2017b; Li et al., 2018) and across a range of ecosystems on an 
intercontinental scale (Zhou et al., 2016) are strongly correlated with 
temperature. It suggests that soil temperature plays an important role in 
shaping microbial community structures. The physiological stresses or 
low temperatures at high altitudes could hinder microbial growth and 
reduce their diversity (McCain, 2010). Zhou et al. (2016) reported that 
temperature caused variations in microbial diversities over broad geo¬ 
graphic scales, mainly by altering the rates of metabolism, growth and 
ecosystem productivity. Alternate freezing and thawing can also alter 
soil microbial communities directly by affecting the metabolic activity 
and reproduction of soil microbes and indirectly by affecting soil phy¬ 
sical properties, such as moisture content and rock weathering (J.T. 
Wang et al., 2015). The differences and changes of air/soil temperature 
caused by seasonal freezing and thawing and by altitudinal variations 
are therefore key ecological factors affecting the structure of soil mi¬ 
crobial communities in alpine ecosystems. 

The C:N ratio was also significantly correlated with microbial- 
community structure, as also reported by several studies (Shen et al., 
2013; Lin et al., 2015; X.B. Wang et al., 2015). Plants interact with soil 
microbial communities by the input of litter and root exudates 
(Knelman et al., 2012; Cui et al., 2018). Plants can determine the 
sources of soil C and N and alter the soil physical and chemical en¬ 
vironments, and thus indirectly affect soil microbial communities 
(Landesman et al., 2014; Li et al., 2018). The effects of the C:N ratio on 
the microbial communities in our study, however, were likely not 
caused by plants due to the consistency of the microbial communities 
between the rhizosphere and bulk soil, suggesting the importance of 
soil nutrient stoichiometry in determining microbial-community struc¬ 
ture. The impacts of the C:N ratio on the microbial communities may be 
attributed to the disruption of the elemental stoichiometric balance and 
homeostasis by the microorganisms (Sinsabaugh et al., 2009). The C:N 
ratio was thus a good predictor of microbial-community variation. Soil 
pH has generally been considered a major factor determining microbial 
diversity and composition (J.T. Wang et al., 2015; Hu et al., 2016; Li 
et al., 2018), but pH did not have a significant effect in our study 
(Table 3), perhaps partly because the range of soil pH at our sampling 
sites was small (3.66-4.39). The lack of neutral to alkaline sites in our 
study area may partially account for the lack of an effect of soil pH on 
the microbial communities. 

Special factors driving the variation were identified for the 


125 














Y. Cui et al 


Geoderma 338 (2019) 118-127 


bacterial, archaeal and fungal communities (e.g., DOC and NH 4 + -N 
contents and C:P and N:P ratios for archaea and N0 3 “-N content for 
fungi) (Table 3 and Fig. 5), supporting our third hypothesis. NH 4 + -N 
and N0 3 - -N are exclusive N resources for bacteria and fungi in agri¬ 
cultural ecosystems and participate in protein synthesis in bacteria and 
fungi, respectively (Bottomley et al., 2012). Fungi can also acquire C 
from the decomposition of plant material rich in lignin and cellulose 
(i.e. lignocellulose) (Meier et al., 2010) and thus need to acquire more 
soil available N (N0 3 ~-N) to achieve a biomass elemental stoichio¬ 
metric balance. Our results further indicated that the archaea and fungi 
in this alpine ecosystem preferred NH 4 + -N and N0 3 ~-N, respectively. 
The archaeal community was also affected by other nutrient properties 
such as SOC, DOC and nutrient ratios. Archaea have a lower capacity 
than bacteria and fungi to decompose litter (Singh et al., 2012), and are 
thus more sensitive to variations in soil nutrients. The different roles of 
the environmental factors in the microbial communities can further 
account for the distinct variations of bacteria and archaea along the 
altitudinal gradient. These results imply the distinct metabolic separa¬ 
tion and resource preferences of the bacteria, archaea and fungi in the 
alpine ecosystem, even over short distances, due to their differential 
responses to the environmental factors. 

4.3. Ecological implications of microbial-community variation in the alpine 
ecosystem 

Our study found highly consistent microbial communities between 
the rhizosphere and bulk soil represented by the alpha and beta di¬ 
versities in this alpine ecosystem, in contrast to generally distinct mi¬ 
crobial communities between rhizospheres and bulk soil in agricultural 
and grassland ecosystems (Ai et al., 2012; Guo et al., 2016). Rhizo¬ 
spheres could thus play a relatively weak role in belowground ecolo¬ 
gical processes and the turnover of soil nutrients at high altitudes where 
temperatures are low. For example, the metabolic activities of roots are 
weaker, sediments are fewer and less low-molecular-weight organic 
matter is secreted by roots in alpine ecosystems than agricultural and 
grassland ecosystems (Meng et al., 2017). These findings suggest that 
the belowground ecological processes mediated by roots may not al¬ 
ways benefit nutrient cycling in alpine ecosystems, impeding our ability 
to predict nutrient turnover in these ecosystems sensitive to climate 
change more than previously thought. The different patterns of mi¬ 
crobial diversity along the altitudinal gradient also suggested that the 
distinctness of the environmental conditions within short distances can 
profoundly influence microbial metabolic and functional differentia¬ 
tion, implying that ecosystem processes driven by microbes would 
differ substantially in different microenvironments. 

The specific responses of the bacterial, archaeal and fungal com¬ 
munities to soil nutrients further indicated the metabolic diversity of 
the microbial taxa. For example, Acidobacteria, the most abundant 
bacterial group, were more abundant at 3200-3500 m than at 
2800-3200 m (Fig. 3). Acidobacteria are generally oligotrophic and 
versatile heterotrophs (Nemergut et al., 2010), featuring low metabolic 
rates under low-nutrient conditions (Ward et al., 2009). Their higher 
abundance in the C-poor soil at high altitudes (SOC and DOC contents 
are lower at higher altitudes) was consistent with this pattern. Proteo- 
bacteria were also abundant, and many of the Proteobacteria sequences 
represented Rhodoplanes and Bradyrhizobium, indicating a potential role 
of N 2 fixation in the active layer of the Tibetan Plateau (Yarwood et al., 
2010; Lin et al., 2015). The breakdown of plant materials rich in lignin 
and cellulose by fungi is a crucial process in terrestrial C cycling (Meier 
et al., 2010). These results indicated that the microbial taxa could 
perform various metabolic functions and have distinct nutrient pre¬ 
ferences in the alpine ecosystem and were thus able to tolerate en¬ 
vironmental stress and maintain belowground ecological functions 
under environmental change. We also demonstrated that this high-al- 
titude ecosystem harbored a rich array of soil microbial phyla with 
varied metabolic characteristics, highlighting the necessity of studying 


overall microbial taxa, including bacteria, archaea and fungi, to gain a 
more complete understanding of microbial ecology in alpine ecosys¬ 
tems. 

5. Conclusions 

This study provides insights into the distributional patterns and 
drivers of microbial communities in rhizosphere and bulk soil along an 
altitudinal gradient containing the same vegetation type and improves 
our understanding of microbial ecology in alpine ecosystems. We found 
significant differences in microbial alpha diversity and microbial-com¬ 
munity structure along the gradient but not between the rhizosphere 
and bulk soil. Environmental factors (explaining 14.6-44% of the var¬ 
iance) and geographic distance (explaining 0.6-2.5% of the variance) 
together accounted for most of the microbial-community variations. 
These findings suggested that edaphic conditions were the main driving 
factors of microbial-community variation, but geographic distance also 
played a non-negligible role in microbial-community composition, even 
along a small-scale altitudinal gradient containing the same vegetation. 
We also identified specific drivers among the bacterial, archaeal and 
fungal taxa, suggesting differences and complex responses of the mi¬ 
croorganisms to environmental changes in this alpine ecosystem. Our 
findings also suggest that microorganisms in alpine ecosystems could be 
less affected by environmental variation and vegetation than micro¬ 
organisms in other ecosystems due to diverse microbial metabolic 
strategies and the weak impact of roots. 

Acknowledgements 

This work was financially supported by the National Natural Science 
Foundation of China (41571314 and 41630751), CAS “Light of West 
China” Program (XAB2016A03) and State Key Research & Development 
Plan Project (2017YFC0504504). 

Appendix A. Supplementary data 

Supplementary information provides additional tables showing the 
effects of altitude, location (rhizosphere and bulk soil) and their in¬ 
teraction on soil parameters, microbial alpha diversity and the corre¬ 
lations between microbial alpha diversity and the environmental vari¬ 
ables. Supplementary data to this article can be found online at https:// 
doi.org/10.1016/j.geoderma.2018.11.047. 

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