AEM Accepted Manuscript Posted Online 26 June 2015 Appl. Environ. Microbiol. doi:10.1128/AEM.00557-15 Copyright © 2015, American Society for Microbiology. All Rights Reserved.
1
Responses of bacterial communities to simulated climate changes in alpine meadow soil of
2
Qinghai-Tibet plateau
3
Junpeng Rui,a§ Jiabao Li,b§ Shiping Wang,c# Jiaxing An,b Wen-tso Liu,d Qiaoyan Lin,c Yunfeng
4
Yang,e Zhili He,b Xiangzhen Lib#
5 6
Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization,
CAS;
7
Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu
8
Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, Chinaa;
9
Key Laboratory of Environmental and Applied Microbiology, CAS; Environmental
10
Microbiology Key Laboratory of Sichuan Province，Chengdu Institute of Biology, Chinese
11
Academy of Sciences, Chengdu 610041, Chinab;
12
Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research,
13
Chinese Academy of Sciences, Beijing 100101, Chinac;
14
Department of Civil and Environmental Engineering, University of Illinois at Urbana-
15
Champaign, Urbana, IL 61801, USAd;
16
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of
17
Environment, Tsinghua University, Beijing 100084, Chinae
18 19
Running Head: Bacterial community structure in alpine meadow soil
20 21
#Address
correspondence
to
Xiangzhen
22
[email protected].
23
§ J.R. and J.L. contributed equally to this work.
Li,
24 1
[email protected]
or
Shiping
Wang,
25
Abstract
26
The soil microbial community plays an important role in terrestrial carbon and nitrogen cycling.
27
However, microbial responses to climate warming or cooling remain poorly understood, limiting
28
our ability to predict the consequences of future climate changes. To address this issue, it is
29
critical to identify microbes sensitive to climate change and key driving factors shifting
30
microbial communities. In this study, alpine soil transplant experiments were conducted
31
downwards or upwards along an elevation gradient between 3200 and 3800 m in the Qinghai-
32
Tibet plateau to simulate climate warming or cooling.
33
experiment, soil bacterial communities were analyzed by pyrosequencing of 16S rRNA gene
34
amplicons. The results showed that the transplanted soil bacterial communities became more
35
similar to those in their destination sites, and more different from those in their “home” sites.
36
Warming
37
Gammaproteobacteria, and Actinobacteria and decreases of Acidobacteria, Betaproteobacteria,
38
and Deltaproteobacteria, while cooling had opposite effects on bacterial communities (symmetric
39
response). Soil temperature and plant biomass contributed significantly to shaping the bacterial
40
community structure. Overall, climate warming or cooling shifted the soil bacterial community
41
structure mainly through species sorting, and such a shift might correlate to important
42
biogeochemical processes such as greenhouse gas emissions. This study provides new insights
43
into our understanding of soil bacterial community responses to climate warming/cooling.
44
Keywords: Bacterial community; Climate change; Cooling; Soil transplant; Alpine soil;
45
Qinghai-Tibet plateau
led
to
increases
in
the
relative
46
2
After a two-year soil transplant
abundances
of
Alphaproteobacteria,
47
Introduction
48
Global climate change greatly affects terrestrial ecosystems, particularly in polar or alpine
49
regions (1). However, due to the complexity of soil microbiome, how global warming affects
50
their diversity, abundance, and structure is poorly understood (2, 3). To show the responses of
51
microorganisms to climate changes, we must identify those microbes sensitive or acclimated to
52
temperature and their relationships to biogeochemical processes, such as greenhouse gas
53
emissions.
54
Warming is able to directly affect soil bacterial physiology (4) and indirectly affect
55
microbes through changing plant and soil properties (5). For example, an increase in temperature
56
may lead to a preponderance of thermally adapted microorganisms (6). Artificial warming is
57
widely used to study the effects of warming on soil ecosystems. Integrated metagenomic and
58
functional analyses of a long-term warming experiment in a grassland ecosystem show that one
59
of the outcomes of climate warming is a shift of the microbial community composition, which
60
most likely leads to the reduced temperature sensitivity of heterotrophic soil respiration (3). Also,
61
multifactorial warming experiments show that warming and precipitation alter the microbial
62
community composition in a constructed natural grass prairie (7). Likewise, another long-term
63
in-situ warming experiment experiences a moderate natural drought, and warming decreases the
64
diversity and significantly alters the community composition in normal precipitation years (8).
65
However, these artificial warming studies seldom compare the sensitivity of a microbial
66
community along the temperature gradient and determine whether cooling exerts an opposite
67
effect to warming on the microbial community structure.
68
In a mesic ecosystem, artificial warming usually causes a decrease in soil moisture content
69
and other concurrent changes (9). These observed changes are different from those in alpine and
3
70
polar regions where climate warming is often accompanied by higher soil moisture caused by
71
glacier and permafrost melting (1). Soil transplant experiments can offer an opportunity to
72
expose the microbial community to natural climate regimes, and provide a valid field experiment
73
platform for the study of microbial responses to climate changes (10-12). Using this method, a
74
previous study observed the structural changes of the “home” and “away” bacterial communities
75
on an unvegetated glacier forefield (11). In a California oak-grassland ecosystem, transplant
76
experiments indicate that the sensitivity of soil microbial communities to climate change is
77
dependent on historical exposure to a range of environmental conditions, such as soil
78
temperature and moisture (10). Transplanting organic surface horizons of boreal soils into
79
warmer regions alters the microbial community but not the temperature sensitivity of
80
decomposition (13). Although the microbial community is generally recognized to be sensitive to
81
disturbance (14), many previous studies using low resolution microbial profiling methods, such
82
as denaturing gradient gel electrophoresis (DGGE), are not able to discern whether particular
83
taxonomic groups are more or less sensitive to environmental factors (10, 11, 15).
84
Alpine grassland accounts for roughly 35% of the Qinghai-Tibet plateau (16), which is
85
recognized as a region very sensitive to climate change (17). Qinghai-Tibet plateau experienced
86
climate warming at three times the global warming rate since 1960 (18). Using an elevation
87
gradient of an alpine ecosystem in this region as an analog of climate gradient can provide a
88
nature model to analyze climatic change on ecosystem structure and function. This approach has
89
been successfully used to document the effects of global change on vegetation (19, 20) and soil
90
biogeochemical processes (21). However, little is known about the responses of soil microbial
91
communities to climate changes in the Qinghai-Tibet plateau, though they are integral in driving
92
biogeochemical processes such as carbon and nitrogen cycling, and greenhouse gas mitigation as
4
93
well as ecosystem services.
94
In this study, we aimed to elucidate the responses of the bacterial community to climate
95
warming or cooling in the alpine meadow ecosystem in Qinghai-Tibet plateau. We hypothesized
96
that (i) alpine soil bacterial community structure would response significantly to climate changes,
97
in which soil temperature and aboveground vegetation contribute significantly to shaping the
98
bacterial community structure; (ii) climate warming and cooling would exert the opposite effects
99
on the shift of dominant bacteria; and (iii) the changes in the relative abundances of certain
100
bacterial groups, e.g. nitrifiers, may be correlated to the flux of relevant greenhouse gases (e.g.,
101
N2O). To test these hypotheses, reciprocal soil transplant experiments were conducted along an
102
elevation gradient in an alpine meadow ecosystem in the Qinghai-Tibet plateau to simulate
103
climate warming or cooling. We analyzed the bacterial phylogenetic compositions after
104
transplanting soil blocks downwards or upwards using a 16S rRNA gene-based pyrosequencing
105
technique, and assessed the relationships between the bacterial community structure and
106
environmental factors (e.g., temperature). The results from this study generally support our
107
hypotheses. This study provides new insights into our understanding of soil bacterial
108
communities in response to climate warming/cooling in alpine meadow ecosystems.
109 110
Materials and methods
111
Study site description
112
The study site was located at the Haibei Alpine Meadow Ecosystem Research Station, Chinese
113
Academy of Sciences (HBAMERS, Qinghai; 37°37’N, 101°12’E), in the Northeastern Qinghai-
114
Tibet Plateau. The mean elevation of the valley bottom is 3200 m. The average precipitation is
115
570 mm and annual mean air temperature is -1.7°C (22). The soil is silty clay loam of Mat Cry-
5
116
gelic Cambisols with a pH of between 7.17 and 7.97 at depths of 10 cm. Detailed soil properties
117
at each site, e.g., total organic carbon (TOC) and total nitrogen (TN), were described in Table S1.
118
Four
119
101°19’52.7’’E), 3600 (37°41’46.0’’N, 101°21’33.4’’E), and 3800 m (37°42’17.7’’N,
120
101°22’09.2’’E) were chosen to set up four field transplant experiment sites in May 2007 (20).
121
The spatial distances between adjacent elevations were 6.2 km (3200-3400 m), 4.2 km (3400-
122
3600 m), and 1.3 km (3600-3800 m), respectively. Based on the field measurement in the
123
growing season (May to September) of 2008 and 2009, mean soil temperature at 5 cm depth was
124
12.91 ± 1.77, 11.84 ± 1.47, 9.77 ± 1.09, and 8.86 ± 1.86 °C at 3200, 3400, 3600, and 3800 m,
125
respectively; thus the temperature differences among the sites ranged from 0.91 to 4.05 °C. The
126
background information about the vegetation in each site was described in detail by Wang et al
127
(20).
128
Experimental design
129
For the soil transplant, an intact soil block (100 cm long × 100 cm wide × 30 cm deep) was
130
divided into 4 equal parts and dug out with attached vegetation. Each part was enveloped by
131
straw ropes and immediately transferred to target sites. The blocks were reinstalled in the target
132
sites by putting 4 equal parts together. Plastic film was used to isolate the soil block from
133
surrounding soil. The soil blocks were transplanted upwards (cooling) to a higher elevation from
134
the valley bottom at 3200 m to 3400 m (sample was named 3200-3400 and so on), 3600 m and
135
3800 m, respectively. Soil blocks from 3800 m were also transplanted downwards (warming) to
136
3600, 3400, and 3200 m after the soils started to thaw in early May 2007. Triplicate soil blocks
137
were transplanted from one elevation to another. Soil blocks were fully randomized throughout
138
the study site. The setup of transplant experiment was described in detail by Wang et al (20). Soil
elevations
of
3200
(37°36’42.3’’N,
6
101°18’47.9’’E),
3400
(37°39’55.1’’N,
139
blocks from 3400 m were transplanted downwards to 3200 m and upward to 3600 m and 3800 m.
140
The translocation of the intact soil block caused minimal damage to the plant roots because 85%
141
of the total root biomass is distributed above 20 cm depth (23). Three blocks from each altitude
142
were also removed and then reinstated at the same holes to produce “home control” blocks that
143
had been handled as similarly as possible to those blocks moved to other elevations. Thus, there
144
were triplicate transfers from each altitude, except for the loss of one 3200-3600 m sample.
145
Soil samples were collected in August 2009. Five soil cores with the depth of 0-10 cm were
146
taken randomly at each plot and pooled. Soil samples were transported to the lab on ice, sieved
147
with 2 mm mesh to remove visible grass roots and stones, stored at -20°C and used for genomic
148
DNA extraction and soil property measurements. Soil and vegetation property measurements,
149
including soil temperature, soil moisture, plant biomass, soil pH, NO3--N, NH4+-N, total organic
150
carbon (TOC), total nitrogen (TN), and released CH4, released CO2, and released N2O in August
151
2009, were described previously (24). The greenhouse gases (CO2, N2O, and CH4) were
152
measured by chamber per plot setting at the 5 cm depth of soil, as previously described (25).
153
DNA extraction, PCR amplification and pyrosequencing
154
Soil genomic DNA was extracted using a FastDNA spin kit for soil (MP Biomedical, Carlsbad,
155
CA, USA) according to the manufacturer’s instructions. To amplify the V4-V5 hypervariable
156
regions of 16S rRNA genes, universal primers 515F (5’- GTGYCAGCMGCCGCGGTA-3’) and
157
909R (5’-CCCCGYCAATTCMTTTRAGT-3’) were used in PCR (26). PCR and other
158
experimental procedures were described in details by Li et al (15). The barcoded amplicons were
159
pooled in an equimolar concentration for 454 pyrosequencing using a GS FLX system (454 Life
160
Sciences, Branford, CT).
161
Pyrosequencing data processing
7
162
The raw sequences were sorted based on unique sample tags, and trimmed for sequence quality
163
using the RDP Pipeline Initial Process (http://pyro.cme.msu.edu/). A total of 170161 high quality
164
and chimera-free reads with an average length of 408 bp were obtained by pyrosequencing in
165
this study. The processed pyrosequences were aligned using the fast, secondary-structure aware
166
infernal aligner in the RDP pyrosequencing pipeline (27). The aligned 16S rRNA gene sequences
167
were used for a chimera check using the Uchime algorithm (28). Resampling to the same
168
sequence
169
(http://www.genomics.ceh.ac.uk/GeneSwytch/Tools.html) was conducted before the downstream
170
analysis. Sequences were clustered by the complete-linkage clustering method incorporated in
171
the RDP platform. Operational taxonomic units (OTUs) were classified using a 97% nucleotide
172
sequence similarity cutoff. Singletons were removed from the OTU table for downstream
173
analysis. Rarefaction, Shannon index, and Chao1 estimator were calculated in RDP at the 97%
174
sequence similarity. The phylogenetic affiliation of each 16S rRNA gene sequence was analyzed
175
by the RDP Classifier at a confidence level of 80%. The original pyrosequencing data are
176
available at the MG-RAST database (accession no. 4565936.3 to 4565973.3).
177
Statistical analysis
178
Overall structural changes of bacterial communities were evaluated by principal coordination
179
analysis (PCoA) in Fast UniFrac (http://bmf.colorado.edu/fastunifrac/). The statistical
180
significance of differences between datasets was assessed by PerMANOVA using the weighted
181
PCoA scores in PAST (http://folk.uio.no/ohammer/past/). Environmental variables providing the
182
highest Spearman’s correlation coefficients with bacterial communities were selected, and
183
variance partitioning analysis (VPA) was performed to quantify the relative contributions of
184
environmental variables by the method described previously (29). The Mantel test was applied to
depth
(2291
sequences
per
8
sample)
using
daisychopper.pl
185
evaluate the correlations between bacterial communities with environmental variables using the
186
Mantel procedure in the R package Vegan. Indicator species analysis at the OTU level was
187
applied
188
project.org/web/packages/indicspecies/index.html), and the untransformed data of bacterial
189
relative abundance at the OTU level were used as input data. Climate, soil, and vegetable
190
properties were normalized and used as environmental variables. The differences in relative
191
abundances of taxonomic units between samples were tested by one-way-analysis of variance
192
(ANOVA). The mean values, standard errors and p values for these statistical analyses were
193
based on 12 treatments in triplicate and one treatment in duplicate.
using
the
R
package
indicspecies
(http://cran.r-
194 195
Results
196
Changes of overall bacterial community structure after reciprocal soil transplant
197
After the reciprocal soil transplant, the principal coordination analysis (PCoA) of the overall
198
composition and structure of soil bacterial communities revealed that most transplanted samples
199
were well separated from their “home” controls using a weighted UniFrac method (Fig. 1). When
200
we used unweighted (presence or absence of an OTU) method, it provided a similar result to the
201
weighted analysis (data not shown), but lower variations explained by the first two axes (PCo1-
202
axis 7.6% and PCo2-axis 4.9%). This indicated that major differences between samples were in
203
the relative abundances of the OTUs rather than in the absence or presence of specific taxa.
204
The weighted PCoA pattern (PCo1 vs. PCo2) showed that most of the samples collected at
205
the same elevation formed a cluster regardless of which elevations where they were originally
206
located (Fig. 1). For example, bacterial communities in soil transplanted from 3200, 3400, and
207
3800 m to 3600 m tended to cluster together. The PerMANOVA test based on the Bray-Curtis
9
208
distance measures showed that the bacterial community structure was significantly (p
1
Responses of bacterial communities to simulated climate changes in alpine meadow soil of
2
Qinghai-Tibet plateau
3
Junpeng Rui,a§ Jiabao Li,b§ Shiping Wang,c# Jiaxing An,b Wen-tso Liu,d Qiaoyan Lin,c Yunfeng
4
Yang,e Zhili He,b Xiangzhen Lib#
5 6
Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization,
CAS;
7
Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu
8
Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, Chinaa;
9
Key Laboratory of Environmental and Applied Microbiology, CAS; Environmental
10
Microbiology Key Laboratory of Sichuan Province，Chengdu Institute of Biology, Chinese
11
Academy of Sciences, Chengdu 610041, Chinab;
12
Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research,
13
Chinese Academy of Sciences, Beijing 100101, Chinac;
14
Department of Civil and Environmental Engineering, University of Illinois at Urbana-
15
Champaign, Urbana, IL 61801, USAd;
16
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of
17
Environment, Tsinghua University, Beijing 100084, Chinae
18 19
Running Head: Bacterial community structure in alpine meadow soil
20 21
#Address
correspondence
to
Xiangzhen
22
[email protected].
23
§ J.R. and J.L. contributed equally to this work.
Li,
24 1
[email protected]
or
Shiping
Wang,
25
Abstract
26
The soil microbial community plays an important role in terrestrial carbon and nitrogen cycling.
27
However, microbial responses to climate warming or cooling remain poorly understood, limiting
28
our ability to predict the consequences of future climate changes. To address this issue, it is
29
critical to identify microbes sensitive to climate change and key driving factors shifting
30
microbial communities. In this study, alpine soil transplant experiments were conducted
31
downwards or upwards along an elevation gradient between 3200 and 3800 m in the Qinghai-
32
Tibet plateau to simulate climate warming or cooling.
33
experiment, soil bacterial communities were analyzed by pyrosequencing of 16S rRNA gene
34
amplicons. The results showed that the transplanted soil bacterial communities became more
35
similar to those in their destination sites, and more different from those in their “home” sites.
36
Warming
37
Gammaproteobacteria, and Actinobacteria and decreases of Acidobacteria, Betaproteobacteria,
38
and Deltaproteobacteria, while cooling had opposite effects on bacterial communities (symmetric
39
response). Soil temperature and plant biomass contributed significantly to shaping the bacterial
40
community structure. Overall, climate warming or cooling shifted the soil bacterial community
41
structure mainly through species sorting, and such a shift might correlate to important
42
biogeochemical processes such as greenhouse gas emissions. This study provides new insights
43
into our understanding of soil bacterial community responses to climate warming/cooling.
44
Keywords: Bacterial community; Climate change; Cooling; Soil transplant; Alpine soil;
45
Qinghai-Tibet plateau
led
to
increases
in
the
relative
46
2
After a two-year soil transplant
abundances
of
Alphaproteobacteria,
47
Introduction
48
Global climate change greatly affects terrestrial ecosystems, particularly in polar or alpine
49
regions (1). However, due to the complexity of soil microbiome, how global warming affects
50
their diversity, abundance, and structure is poorly understood (2, 3). To show the responses of
51
microorganisms to climate changes, we must identify those microbes sensitive or acclimated to
52
temperature and their relationships to biogeochemical processes, such as greenhouse gas
53
emissions.
54
Warming is able to directly affect soil bacterial physiology (4) and indirectly affect
55
microbes through changing plant and soil properties (5). For example, an increase in temperature
56
may lead to a preponderance of thermally adapted microorganisms (6). Artificial warming is
57
widely used to study the effects of warming on soil ecosystems. Integrated metagenomic and
58
functional analyses of a long-term warming experiment in a grassland ecosystem show that one
59
of the outcomes of climate warming is a shift of the microbial community composition, which
60
most likely leads to the reduced temperature sensitivity of heterotrophic soil respiration (3). Also,
61
multifactorial warming experiments show that warming and precipitation alter the microbial
62
community composition in a constructed natural grass prairie (7). Likewise, another long-term
63
in-situ warming experiment experiences a moderate natural drought, and warming decreases the
64
diversity and significantly alters the community composition in normal precipitation years (8).
65
However, these artificial warming studies seldom compare the sensitivity of a microbial
66
community along the temperature gradient and determine whether cooling exerts an opposite
67
effect to warming on the microbial community structure.
68
In a mesic ecosystem, artificial warming usually causes a decrease in soil moisture content
69
and other concurrent changes (9). These observed changes are different from those in alpine and
3
70
polar regions where climate warming is often accompanied by higher soil moisture caused by
71
glacier and permafrost melting (1). Soil transplant experiments can offer an opportunity to
72
expose the microbial community to natural climate regimes, and provide a valid field experiment
73
platform for the study of microbial responses to climate changes (10-12). Using this method, a
74
previous study observed the structural changes of the “home” and “away” bacterial communities
75
on an unvegetated glacier forefield (11). In a California oak-grassland ecosystem, transplant
76
experiments indicate that the sensitivity of soil microbial communities to climate change is
77
dependent on historical exposure to a range of environmental conditions, such as soil
78
temperature and moisture (10). Transplanting organic surface horizons of boreal soils into
79
warmer regions alters the microbial community but not the temperature sensitivity of
80
decomposition (13). Although the microbial community is generally recognized to be sensitive to
81
disturbance (14), many previous studies using low resolution microbial profiling methods, such
82
as denaturing gradient gel electrophoresis (DGGE), are not able to discern whether particular
83
taxonomic groups are more or less sensitive to environmental factors (10, 11, 15).
84
Alpine grassland accounts for roughly 35% of the Qinghai-Tibet plateau (16), which is
85
recognized as a region very sensitive to climate change (17). Qinghai-Tibet plateau experienced
86
climate warming at three times the global warming rate since 1960 (18). Using an elevation
87
gradient of an alpine ecosystem in this region as an analog of climate gradient can provide a
88
nature model to analyze climatic change on ecosystem structure and function. This approach has
89
been successfully used to document the effects of global change on vegetation (19, 20) and soil
90
biogeochemical processes (21). However, little is known about the responses of soil microbial
91
communities to climate changes in the Qinghai-Tibet plateau, though they are integral in driving
92
biogeochemical processes such as carbon and nitrogen cycling, and greenhouse gas mitigation as
4
93
well as ecosystem services.
94
In this study, we aimed to elucidate the responses of the bacterial community to climate
95
warming or cooling in the alpine meadow ecosystem in Qinghai-Tibet plateau. We hypothesized
96
that (i) alpine soil bacterial community structure would response significantly to climate changes,
97
in which soil temperature and aboveground vegetation contribute significantly to shaping the
98
bacterial community structure; (ii) climate warming and cooling would exert the opposite effects
99
on the shift of dominant bacteria; and (iii) the changes in the relative abundances of certain
100
bacterial groups, e.g. nitrifiers, may be correlated to the flux of relevant greenhouse gases (e.g.,
101
N2O). To test these hypotheses, reciprocal soil transplant experiments were conducted along an
102
elevation gradient in an alpine meadow ecosystem in the Qinghai-Tibet plateau to simulate
103
climate warming or cooling. We analyzed the bacterial phylogenetic compositions after
104
transplanting soil blocks downwards or upwards using a 16S rRNA gene-based pyrosequencing
105
technique, and assessed the relationships between the bacterial community structure and
106
environmental factors (e.g., temperature). The results from this study generally support our
107
hypotheses. This study provides new insights into our understanding of soil bacterial
108
communities in response to climate warming/cooling in alpine meadow ecosystems.
109 110
Materials and methods
111
Study site description
112
The study site was located at the Haibei Alpine Meadow Ecosystem Research Station, Chinese
113
Academy of Sciences (HBAMERS, Qinghai; 37°37’N, 101°12’E), in the Northeastern Qinghai-
114
Tibet Plateau. The mean elevation of the valley bottom is 3200 m. The average precipitation is
115
570 mm and annual mean air temperature is -1.7°C (22). The soil is silty clay loam of Mat Cry-
5
116
gelic Cambisols with a pH of between 7.17 and 7.97 at depths of 10 cm. Detailed soil properties
117
at each site, e.g., total organic carbon (TOC) and total nitrogen (TN), were described in Table S1.
118
Four
119
101°19’52.7’’E), 3600 (37°41’46.0’’N, 101°21’33.4’’E), and 3800 m (37°42’17.7’’N,
120
101°22’09.2’’E) were chosen to set up four field transplant experiment sites in May 2007 (20).
121
The spatial distances between adjacent elevations were 6.2 km (3200-3400 m), 4.2 km (3400-
122
3600 m), and 1.3 km (3600-3800 m), respectively. Based on the field measurement in the
123
growing season (May to September) of 2008 and 2009, mean soil temperature at 5 cm depth was
124
12.91 ± 1.77, 11.84 ± 1.47, 9.77 ± 1.09, and 8.86 ± 1.86 °C at 3200, 3400, 3600, and 3800 m,
125
respectively; thus the temperature differences among the sites ranged from 0.91 to 4.05 °C. The
126
background information about the vegetation in each site was described in detail by Wang et al
127
(20).
128
Experimental design
129
For the soil transplant, an intact soil block (100 cm long × 100 cm wide × 30 cm deep) was
130
divided into 4 equal parts and dug out with attached vegetation. Each part was enveloped by
131
straw ropes and immediately transferred to target sites. The blocks were reinstalled in the target
132
sites by putting 4 equal parts together. Plastic film was used to isolate the soil block from
133
surrounding soil. The soil blocks were transplanted upwards (cooling) to a higher elevation from
134
the valley bottom at 3200 m to 3400 m (sample was named 3200-3400 and so on), 3600 m and
135
3800 m, respectively. Soil blocks from 3800 m were also transplanted downwards (warming) to
136
3600, 3400, and 3200 m after the soils started to thaw in early May 2007. Triplicate soil blocks
137
were transplanted from one elevation to another. Soil blocks were fully randomized throughout
138
the study site. The setup of transplant experiment was described in detail by Wang et al (20). Soil
elevations
of
3200
(37°36’42.3’’N,
6
101°18’47.9’’E),
3400
(37°39’55.1’’N,
139
blocks from 3400 m were transplanted downwards to 3200 m and upward to 3600 m and 3800 m.
140
The translocation of the intact soil block caused minimal damage to the plant roots because 85%
141
of the total root biomass is distributed above 20 cm depth (23). Three blocks from each altitude
142
were also removed and then reinstated at the same holes to produce “home control” blocks that
143
had been handled as similarly as possible to those blocks moved to other elevations. Thus, there
144
were triplicate transfers from each altitude, except for the loss of one 3200-3600 m sample.
145
Soil samples were collected in August 2009. Five soil cores with the depth of 0-10 cm were
146
taken randomly at each plot and pooled. Soil samples were transported to the lab on ice, sieved
147
with 2 mm mesh to remove visible grass roots and stones, stored at -20°C and used for genomic
148
DNA extraction and soil property measurements. Soil and vegetation property measurements,
149
including soil temperature, soil moisture, plant biomass, soil pH, NO3--N, NH4+-N, total organic
150
carbon (TOC), total nitrogen (TN), and released CH4, released CO2, and released N2O in August
151
2009, were described previously (24). The greenhouse gases (CO2, N2O, and CH4) were
152
measured by chamber per plot setting at the 5 cm depth of soil, as previously described (25).
153
DNA extraction, PCR amplification and pyrosequencing
154
Soil genomic DNA was extracted using a FastDNA spin kit for soil (MP Biomedical, Carlsbad,
155
CA, USA) according to the manufacturer’s instructions. To amplify the V4-V5 hypervariable
156
regions of 16S rRNA genes, universal primers 515F (5’- GTGYCAGCMGCCGCGGTA-3’) and
157
909R (5’-CCCCGYCAATTCMTTTRAGT-3’) were used in PCR (26). PCR and other
158
experimental procedures were described in details by Li et al (15). The barcoded amplicons were
159
pooled in an equimolar concentration for 454 pyrosequencing using a GS FLX system (454 Life
160
Sciences, Branford, CT).
161
Pyrosequencing data processing
7
162
The raw sequences were sorted based on unique sample tags, and trimmed for sequence quality
163
using the RDP Pipeline Initial Process (http://pyro.cme.msu.edu/). A total of 170161 high quality
164
and chimera-free reads with an average length of 408 bp were obtained by pyrosequencing in
165
this study. The processed pyrosequences were aligned using the fast, secondary-structure aware
166
infernal aligner in the RDP pyrosequencing pipeline (27). The aligned 16S rRNA gene sequences
167
were used for a chimera check using the Uchime algorithm (28). Resampling to the same
168
sequence
169
(http://www.genomics.ceh.ac.uk/GeneSwytch/Tools.html) was conducted before the downstream
170
analysis. Sequences were clustered by the complete-linkage clustering method incorporated in
171
the RDP platform. Operational taxonomic units (OTUs) were classified using a 97% nucleotide
172
sequence similarity cutoff. Singletons were removed from the OTU table for downstream
173
analysis. Rarefaction, Shannon index, and Chao1 estimator were calculated in RDP at the 97%
174
sequence similarity. The phylogenetic affiliation of each 16S rRNA gene sequence was analyzed
175
by the RDP Classifier at a confidence level of 80%. The original pyrosequencing data are
176
available at the MG-RAST database (accession no. 4565936.3 to 4565973.3).
177
Statistical analysis
178
Overall structural changes of bacterial communities were evaluated by principal coordination
179
analysis (PCoA) in Fast UniFrac (http://bmf.colorado.edu/fastunifrac/). The statistical
180
significance of differences between datasets was assessed by PerMANOVA using the weighted
181
PCoA scores in PAST (http://folk.uio.no/ohammer/past/). Environmental variables providing the
182
highest Spearman’s correlation coefficients with bacterial communities were selected, and
183
variance partitioning analysis (VPA) was performed to quantify the relative contributions of
184
environmental variables by the method described previously (29). The Mantel test was applied to
depth
(2291
sequences
per
8
sample)
using
daisychopper.pl
185
evaluate the correlations between bacterial communities with environmental variables using the
186
Mantel procedure in the R package Vegan. Indicator species analysis at the OTU level was
187
applied
188
project.org/web/packages/indicspecies/index.html), and the untransformed data of bacterial
189
relative abundance at the OTU level were used as input data. Climate, soil, and vegetable
190
properties were normalized and used as environmental variables. The differences in relative
191
abundances of taxonomic units between samples were tested by one-way-analysis of variance
192
(ANOVA). The mean values, standard errors and p values for these statistical analyses were
193
based on 12 treatments in triplicate and one treatment in duplicate.
using
the
R
package
indicspecies
(http://cran.r-
194 195
Results
196
Changes of overall bacterial community structure after reciprocal soil transplant
197
After the reciprocal soil transplant, the principal coordination analysis (PCoA) of the overall
198
composition and structure of soil bacterial communities revealed that most transplanted samples
199
were well separated from their “home” controls using a weighted UniFrac method (Fig. 1). When
200
we used unweighted (presence or absence of an OTU) method, it provided a similar result to the
201
weighted analysis (data not shown), but lower variations explained by the first two axes (PCo1-
202
axis 7.6% and PCo2-axis 4.9%). This indicated that major differences between samples were in
203
the relative abundances of the OTUs rather than in the absence or presence of specific taxa.
204
The weighted PCoA pattern (PCo1 vs. PCo2) showed that most of the samples collected at
205
the same elevation formed a cluster regardless of which elevations where they were originally
206
located (Fig. 1). For example, bacterial communities in soil transplanted from 3200, 3400, and
207
3800 m to 3600 m tended to cluster together. The PerMANOVA test based on the Bray-Curtis
9
208
distance measures showed that the bacterial community structure was significantly (p
