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Bacterial communities in the upper soil layers in the permafrost regions on the Qinghai-Tibetan plateau
Applied Soil Ecology 120 (2017) 81–88
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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil
Bacterial communities in the upper soil layers in the permafrost regions on the Qinghai-Tibetan plateau
MARK
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Xiaodong Wua, Haiyan Xub, , Guimin Liub, Xiaoliang Mab, Cuicui Muc, Lin Zhaoa a Cryosphere Research Station on the Qinghai-Tibetan Plateau, State Key Laboratory of Cryosphere Sciences, Northwest Institute of Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, China b School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China c Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Permafrost Soil organic carbon Bacterial community Qinghai-Tibetan plateau Illumina sequencing
The Qinghai-Tibetan plateau (QTP) is the largest middle-low latitude permafrost region on earth, while little is known about the microbial community in this area. Here, we investigated the bacterial community in the upper 30 cm soils in the permafrost regions on the central QTP using Illumina sequencing technology. In these soils, the most abundant phyla were Acidobacteria, Proteobacteria, and Bacteroidetes. The depth was significantly correlated with Acidobacteria, Proteobacteria, Nitrospirae, and Gemmatimonadetes. The soil pH and the gravel content were significantly positively correlated with Bacteroidetes. The active layer thickness was significantly correlated with Bacteroidetes and Arabinonates. Although these factors were closely correlated with the relative abundances of specific bacterial phyla, the overall bacterial community structure was mainly affected by pH, soil organic carbon content, and the mean annual precipitation, while the community structure had no significant relationship with the active layer thickness. Our results suggested that the permafrost region on the QTP had greatly heterogeneous environmental conditions, and the responses of microbial communities to permafrost degradation would also be affected by other factors such as precipitation, soil texture and vegetation.
1. Introduction Soils are complex systems including interactions of biotic and abiotic processes. Soil microorganisms regulate biogeochemical processes in soils and play vital roles in organic matter (SOM) cycling (O’Donnell et al., 2007). SOM is mainly originated from the partial decomposition and transformation of plant debris and litter input by soil microbes (Cindye, 2010). The microbial decomposition of SOM is also the main pathway for the transfer of greenhouse gases from the terrestrial ecosystem to the atmosphere (Chauvin et al., 2015). The soil microbes also play fundamental functions for the maintenance of soil ecosystem integrity. For example, ammonia oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) are involved in ammonia oxidation and nitrification and thus are important drivers of soil nitrogen cycle (Isobe et al., 2012). During the past centuries, human activities greatly changed the global carbon cycle and the climate. The climate warming definitely changed soil temperature, moisture and thus have both direct and indirect effects on the soil microbial communities (Joshi and Shekhawat, 2014). It is of great importance to understand the changes of soil
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Corresponding author. E-mail address: [email protected] (H. Xu).
http://dx.doi.org/10.1016/j.apsoil.2017.08.001 Received 19 May 2017; Received in revised form 2 August 2017; Accepted 4 August 2017 0929-1393/ © 2017 Elsevier B.V. All rights reserved.
microbes in a warming world because they are vital to many basically ecological processes (Bardgett et al., 2008). The circum-arctic permafrost regions store approximately 1330–1580 Pg organic carbon, which is almost twice as the carbon in the atmosphere (Hugelius et al., 2014; Schuur et al., 2015). The SOC pools in the permafrost regions have received worldwide attention because global warming could potentially cause emissions of greenhouse gases (Hugelius et al., 2014; Ping et al., 2015, 2008). In permafrost regions, a shift in the microbial community would greatly change the organic matter decomposition (Xue et al., 2016), and the release of greenhouse gases from the organic matter decomposition is closely associated with soil microbial abundance (Mackelprang et al., 2011). Therefore, investigation of soil microbial community could shed light on the relationship between soil microbes and environmental factors thus improve our knowledge of the effects of climate change on ecosystems (Herold et al., 2014; Nannipieri et al., 2003; Tardy et al., 2014). Many biotic and abiotic factors such as pH (Feng et al., 2014; Kim et al., 2014), soil moisture content (Brockett et al., 2012), nutrient levels (Cleveland and Liptzin, 2007; Stark et al., 2011), organic carbon
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disinfected soil auger and placed in clean, sealable plastic bags. The soil samples were immediately transported to the laboratory in a cooler with ice packs and stored at −80 °C until genomic DNA extraction was carried out. We used a square-meter quadrant and took five samples at each site to investigate the vegetation cover. Additionally, we excavated a shallow soil pit (approximately 50 cm) and measured the soil bulk density using a bulk soil sampler (i.e., a 5-cm diameter and 5-cm high stainless steel cutting ring). We collected soil samples at different depths to calculate the total water content, which was determined by drying the soil at 105 °C for 8 h. The pH values of the air-dried soils were measured using the 1:5 mixture of soil:water. The SOC and total nitrogen (TN) were measured using a Vario EL elemental analyzer (Elementra, Hanau, Germany). To measure the SOC, we pretreated 0.5 g dry soil samples with HCl (10 mL, 1 mol l−1) for 24 h to remove the carbonate. We calculated the mass ratio of C/N. The percent of gravel was measured by weight of rock fragments (> 2 mm) using the oven-dried samples, and soil particle distribution was measured using a combination of wet sieving (Chaudhari et al., 2008) and a laser diffraction instrument (Malvern Mastersizer 2000, Malvern, UK). The proportions of sand, silt and clay were classified by the respective size intervals of 2 mm–50 μm, 50–2 μm, and < 2 μm.
(Zhang et al., 2014), and vegetation cover (Shi et al., 2015) can affect the soil microbial community structure. In the permafrost regions, it has also been found that temperature and the freeze-thaw cycle affected the microbial community (Jansson and Tas, 2014; Stres et al., 2010). These studies summarized the factors that control the soil microbial community. However, these conclusions were mainly based on statistical analyses of measured soil parameters, but little consideration was given to the factors that had no significant relationships with the microbial community. In fact, soil variables usually interacted with each other. For example, the permafrost conditions can affect the soil microbial community since permafrost is associated with many soil variables, such as soil moisture content (Wang et al., 2008), pH, and soil texture (Mu et al., 2016a,b). The low temperature in the permafrost region favors the perseverance and accumulation of organic matter (Mu et al., 2014; Zimov et al., 2006), and the organic matter at different depths usually has a high proportion of labile fractions (Wu et al., 2014). So far, most studies for the soil microbial community have mainly been focused on the upper 10 cm layer (Bowles et al., 2014; Chu et al., 2016; Yue et al., 2015). Although the soil microbes below 0–10 cm soils were abundant and active (Fenchel, 2003), and these microbes at the subsurface and even the deep soil doubtless play an important role in soil biogeochemical cycling and other processes (Fierer et al., 2003; Mackelprang et al., 2011), little is known about the soil microbial community below the upper 10 cm layer. The Qinghai-Tibet Plateau (QTP), accounts for approximately three quarters of the high-latitude permafrost on earth. Under global warming scenarios, the soil environment in the permafrost region on the plateau has become a major concern since its role in maintaining ecosystem function and services (Mu et al., 2015; Wu et al., 2017; Yang et al., 2014). It is well-known that pH, SOC, and C:N ratios are the most important factors that determine the soil bacterial community, and these factors vary considerably at different depths of the upper soil layer (Shang et al., 2016). Therefore, we hypothesized that depth could have a great effect on the microbial community in this area. Based on the interactions of environmental conditions in the permafrost region, we also hypothesized that active layer thickness, soil moisture, soil texture, vegetation cover, and climatic conditions could also affect the soil bacterial community. To test these hypotheses, six plots in the permafrost region of the central QTP were selected and the soil bacterial community and its relationship with soil parameters was investigated.
2.2. Soil DNA extraction We extracted the total soil DNA from each soil sample using a MoBio PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA, USA) per the manufacturer’s instructions, using 0.3 g soil. The concentration of the extracted DNA was determined using a QuBit DNA quantification system (Invitrogen) with the QuBit high sensitivity assay reagents. Then, all the soil DNA samples were stored frozen at −20 °C until used. 2.3. PCR amplification We performed PCR amplification, purification, and sequencing of a region of the 16S rRNA gene (Fierer and Jackson, 2006). The V4 and V5 hypervariable regions of the bacterial 16S ribosomal RNA gene were amplified by PCR. The PCR conditions were 94 °C for 5 min, followed by 35 cycles at 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 30 s, and then a final extension step at 72∘C for 10 min. We used the primer set F515 (5′-GTGCCAGCMGCCGCGG-3′) and R907(5′-CCGTCAATTCMTTTRAGTTT-3′) (Bates et al., 2011). The PCR amplification was performed in 50 μL reactions containing about 10 ng template DNA, 25 μL of PCR Pre-Mixture (TaKaRa) and 0.3 μM of forward and reverse primer. The same volume of 1X loading buffer (contained SYB green) was mixed with the PCR products and visualized with 1.2% agarose gel electrophoresis. We chose samples with a bright strip between 350 and 450 bp for further analysis. The PCR products were purified using a QIAquick Gel Extraction Kit (Qiagen, Chatsworth, CA, USA). We prepared a single composite sample for sequencing by combining approximately equimolar amounts of PCR products from each sample. Sequencing was performed using a Miseq PE250 platform (Majorbio Bio-pharm Technology Co., Ltd., Shanghai, China).
2. Material and methods 2.1. Soil sampling and analysis The soils at 6 sites in the permafrost region of the central QTP were sampled during July, 2014 (Fig. 1). The climatic, permafrost and vegetation conditions for the sampling sites are shown in Table 1. The thawing of frozen ground on the QTP is expected to start in April, and the maximum thaw depths, which equal to the active layer thicknesses, are largely recorded in late September. The thaw depths in July are about 1 m in the central QTP (Zhao et al., 2000, 2010). To exclude the possibility that soil properties would differ considerably within the same site, 1.5 m × 1.5 m plots with relatively uniform microrelief conditions (i.e., without a patchy distribution of vegetation and flat areas) were selected. There is still a possibility that soils have heterogeneities in the same plots. Therefore, to examine the spatial distribution of bacterial community structure and its relationship to environmental variables, we collected five subsamples at each depth, and then pooled into a single soil sample to represent the sample at the certain depth. The soil variables and DNA sequencing analysis were all performed using this sample and thus we can examine their relationships. Samples from the surface 30 cm were collected at a depth interval of 10 cm. The soil samples were collected aseptically using an ethanol-
2.4. Processing of sequencing data Paired-end reads from the original DNA fragments were merged with FLASH, which was designed to merge paired-end pairs of reads when the original DNA fragments were shorter than twice the length of the reads. The sequences were analyzed using QIIME 1.17 (http:// qiime.org/scripts/pick_otus.html). Operational taxonomic units (OTUs) with 97% similarity cutoff were clustered. To compute Alpha Diversity, we rarified the OTU table and calculated four metrics, i.e., the Chao1 metric, the Observed OTUs, which was a count of unique OTUs in the sample, the Ace Shannon, and the Simpson index. We performed the mantel test, redundancy analysis (RDA), and prepared figures using the 82
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Fig. 1. Sampling sites in the permafrost regions on the Qinghai-Tibetan Plateau.
3.2. Sequencing data
Vegan packages in the R software. We deposited the raw reads into the NCBI Sequence Read Archive (SRA) database (Accession Number: SRR5034716).
A total of 24,483 OTUs (at the 3% evolutionary distance) were identified based on 27828 reads for each sample. The Good’s coverage estimator of the OTUs ranged from 98.1% to 98.6%. The rarefaction curves showed that the sequences sufficiently covered the diversity of the bacterial communities in the samples. The OTUs varied, with the lowest value of 894 in the BS0_10 sample, and the highest value of 2214 in the AC10_20 sample, under the land cover of meadow. The mean highest OTU numbers were recorded at XM site under land cover of meadow, which also had the lowest elevation (Table 2). However, the OTU numbers showed considerable variations both among sites and depths, and there is no clear pattern between the OTU numbers and elevation and depth. The total richness estimators, ACE and Chao1, showed similar patterns with that of the OTU numbers (Table 2). The bacterial community diversity, as indicated by Shannon–Wiener index, was significantly positively correlated with the OTU numbers (p < 0.01, Pearson, two-tailed), Chao1 (p < 0.01, Pearson, two-tailed), but negatively correlated with the Simpson index (p < 0.01, Pearson, two-tailed). The Shannon-Wiener indices ranged from 2.44 to 5.85.
3. Results 3.1. Soil properties The pH values at all sites were above 8.0. In general, the surface 10 cm layer had the lowest pH values. The TOC and TN values ranged from 6.8 to 47.6 g kg−1 and 0.47 to 3.44 g kg−1, respectively. The TOC and TN largely decreased with depth. The highest values of TOC, TN, soil moisture were recorded at the meadow of the AC site, and this site also had the lowest pH and bulk density. There were no gravels at the BM and AC sites. The highest value of gravel content was 41.7% at the depth of 20–30 cm at the WS site. The clay content at the sampling sites was generally low, with a mean value of 1.7%. For all the samples, the mean percentage of silt and sand were 9.6% and 80.7%, respectively. The distribution of clay, silt and sand varied considerably, with no obvious patterns among the sites (Fig. 2).
Table 1 Geographiccoordinates, climatic, permafrost and vegetation conditions for the sampling sites.
XM BS WS FM BW AC
Longitude
Latitude
Elevation (°C)
MAT (°C)
MAP (mm)
MGT (°C)
AL (m)
Vegetation cover (%)
Vegetation type
94.008 92.922 92.727 92.895 92.922 91.741
35.712 34.823 34.471 34.729 34.823 31.821
4538 4553 4573 4988 4557 4813
−4.0 −3.8 −1.8 −5.2 −3.8 −1.6
393.0 291.0 305.0 328.0 291.0 350.0
−1.345 −0.069 0.199 −2.194 −0.855 0.902
1.60 2.50 3.20 1.40 2.30 2.70
85 40 25 100 90 95
Meadow Steppe Steppe Meadow Meadow Meadow
MAT, mean annual average temperature, MAP, mean annual average precipitation, MGT, mean annual ground temperature (at 10 cm), AL, active layer thickness (meaured in 2014).
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Fig. 2. Soil properties of the different layers (white: 0–10 cm, light gray: 10–20 cm, dark gray: 20–30 cm) at sampling sites. a: pH, b: total soil organic carbon, c: Total nitrogen, d: soil moisture content, e: soil bulk densities, f: clay content, g: silt content, h: sand content, i: gravel content.
RB41_norank (13.7%), Flavobacterium (13.4%), Subgroup_6_norank (11.0%), and Pseudomonas (5.4%). The genera which accounted for between 2% to 3% were Anaerolineaceae_uncultured (2.7%), Comamonadaceae_unclassified (2.1%), while other genera were less than 2%.
Table 2 Estimated OUT richness, diversity indices and sample coverage of the samples. Samples
OTUs
Ace
Chao1
Shannon
Simpson
coverage
AC0_10 AC10_20 AC20_30 BS0_10 BS10_20 BS20_30 BW0_10 BW10_20 BW20_30 FM0_10 FM10_20 FM20_30 WS0_10 WS10_20 WS20_30 XM0_10 XM10_20 XM20_30
1544 1652 1489 894 1558 1557 1174 1472 1360 1553 1326 1156 1476 1336 1283 1544 1054 1055
1967 2214 2034 1832 2108 2015 1648 1795 1917 2171 1868 1686 1876 2339 1727 2017 1606 2001
1999 2183 1970 1409 2061 2069 1669 1851 1922 2072 1772 1630 1866 1967 1683 2090 1561 1611
5.61 5.78 5.34 2.44 5.47 5.34 4.63 5.63 5.15 5.32 4.64 4.35 5.29 4.57 5.1 5.85 3.19 3.27
0.011 0.008 0.014 0.271 0.013 0.022 0.038 0.013 0.021 0.019 0.044 0.064 0.025 0.051 0.021 0.007 0.262 0.225
0.984 0.981 0.982 0.986 0.981 0.983 0.985 0.986 0.983 0.981 0.983 0.985 0.984 0.982 0.985 0.983 0.985 0.984
3.4. Relationships between environmental factors and the microbial community A mantel test showed that the bacterial community structure, which included all the bacterial phyla, was significantly correlated with pH (r = 0.32, p = 0.04), TOC (r = 0.32, p = 0.04) and the mean annual precipitation (r = 0.24, p = 0.04), while other factors, including soil texture, moisture content, and C:N ratios were not significantly correlated with the bacterial community structure. The significant relationships between the phyla and various environmental factors are shown in Table 3. Depth was significantly positively correlated with Acidobacteria, Nitrospirae, and Gemmatimonadetes, and it was negatively correlated with Proteobacteria. Bacteroidetes was significantly negatively correlated with active layer thickness but positively correlated with pH and the mean annual precipitation. The TOC and TN was positively correlated with Firmicutes. The bulk density, proportion of sand, gravel content, vegetation cover and C:N ratio also had a significant relationship with the diversity of the bacterial phyla. The first and second axis of the RDA explained 52.4% and 34.8% of the total phyla variance. The RDA result showed that samples from the upper 10 cm layer were largely scattered in the right of the chart, and the samples from the 10–20 cm and 20–30 cm layers were mainly distributed in the upper part of the chart. The RDA result demonstrated that TOC, TN and bulk density were strongly correlated with the first axis. Depth also played an important role in the distribution of the bacterial phyla. The second axis was strongly positively correlated with pH, and MAP but negatively correlated with AL, MGT, and the conductivity (Fig. 4).
3.3. Bacterial diversity and community structure The OTUs indicated 40 phyla, 99 classes, 221 orders, 399 families and 682 genera. The phyla varied greatly among the different samples, and the predominant phyla were Acidobacteria (22.3%), Proteobacteria (28.5%), Bacteroidetes (22.7%), Chloroflexi (3.7%), and Actinobacteria (3.6%). These phyla accounted for 88.8% of sequences across all the samples (Fig. 3). Nine other phyla were represented to varying degrees in one or more samples for < 5% of the effective sequences, except for the 26.4% Cyanobacteria in the BS0_10 sample. At the class level, the predominant classes were Actinobacteria (30.4%), Alphaproteobacteria (8.2%), Betaproteobacteria (9.8%), Deltaproteobacteria (2.3%), Gammaproteobacteria (8.1%). These four classes were recorded in all the samples for the phylum Proteobacteria, but the Epsilonproteobacteria was only found in the BW20_30 sample. At the genus level, the microbial community was largely composed of 84
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Fig. 3. Relative abundance of bacteria phyla in different layers at the sampling sites.
4. Discussion
layers in some the permafrost region of the QTP were shallow and the soil parameters showed markedly changes with depth (Dorfer et al., 2013; Wu et al., 2012, 2016). Therefore, it would be reasonable to expect that depth greatly affected the bacterial community. In this study, although no clear evidence indicated that the soil samples in similar layers were clustered, the distribution of the samples from the 0–10 cm soils was largely far away from the samples from the deeper soils. The relationship between depth and the bacterial phyla suggested that Acidobacteria, Nitrospirae, and Gemmatimonadetes increased with depth, while Proteobacteria was less likely to occur in deeper layers. In an arid region on the western QTP, the Gemmatimonadetes was reported to have a higher relative abundance in the subsurface layers, and Acidobacteria was higher in the near-surface layers (Chu et al., 2016). The soil depth was found to be one of the most important soil properties that determined the bacterial community structure in a subarctic area (Kim et al., 2014). Overall, the mechanism of the effect of soil depth on the soil bacterial community requires further study since such studies are scarce. Local factors such as pH and soil moisture content exerted stronger effects on soil microbial community than climatic and latitudinal gradients, which played less important roles in shaping the microbial community (Steven et al., 2013). The soil parameters interacted with each other in the permafrost region. For example, vegetation cover, soil texture, and soil organic matter were closely associated with the permafrost conditions. Permafrost has been recognized to be associated with a lower temperature and a higher soil moisture content, which causes a lower decomposition of soil organic matter, thus the SOC content in the permafrost region is usually higher than in the nonpermafrost region (Bockheim and Munroe, 2014). The permafrost distribution and active layer thickness on the QTP varied greatly with
The richness and diversity indices of the soil bacteria showed similar patterns and were largely significantly correlated with each other. No significant differences were found among the mean values of these indices in any of three layers. Although the bacterial structure significantly correlated with pH and TOC, none of these indices significantly correlated with the soil properties including TOC or pH. These results suggested that bacterial diversity and richness was not significantly different in different soil environments in similar ecosystems (Yang et al., 2015). Acidobacteria (22.3%), Proteobacteria (28.5%), Bacteroidetes (22.7%), Chloroflexi (3.7%), and Actinobacteria (3.6%) were the predominant phyla in the present study. This pattern was in agreement with most of the soils under different climatic conditions (Feng et al., 2014; Rousk et al., 2010; Wallenstein et al., 2007). In this study, although the mean relative abundance of Actinobacteria was 3.6%, the highest value (9.99%) was also comparable with a previous study, which demonstrated that the Actinobacteria in the upper 10 cm layer accounted for approximately 15% of the soil bacterial community in an alkaline soil (Zhang et al., 2014). It has been shown that carbon availability, nutrients content, soil moisture (Drenovsky et al., 2004), and C/N ratios (Wan et al., 2014) affect the soil bacterial community. In the continental region, pH was the most important factor that affected the soil bacterial community (Chu et al., 2016; Fierer and Jackson, 2006; Tripathi et al., 2012). In the present study, the results of the mantel test also confirmed that pH and total organic carbon played an important role in the bacterial community structure. The soil properties in the permafrost region of the QTP varied considerably on a centimeter scale in the upper soil layers, since the soil
Table 3 Pearson’s correlation coefficients between the environmental factors and the bacterial phyla. Depth Acidobacteria Proteobacteria Bacteroidetes Nitrospirae Gemmatimonadetes Firmicutes Armatimonadetes
pH
AL
0.646**
−0.530*
TOC
TN
C:N ratio
Bulk
Sand
Gravel
−0.614**
0.688**
MAT
MAP
Vegetation
*
0.486 −0.538*
0.485*
0.752** 0.536*
0.604**
0.502* 0.548*
−0.617**
0.490*
−0.471*
0.670**
0.572*
0.560*
−0.600**
** p < 0.01; *p < 0.05; n = 209; 2-tailed test. AL, Active layer thickness; TOC, total organic carbon; TN, Total nitrogen; MAT, mean annual air temperature; MAP, mean annual precipitation.
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Fig. 4. Redundancy analysis (RDA) results for the bacterial community structure of the soil samples.
than 2 m. Therefore, the higher pH values associated with a thinner active layer probably suggests that the effects of precipitation on the soil pH overwhelmed the effect of the permafrost in the present study, and thus the active layer thickness acted in an opposite manner on the soil bacterial community with that of the pH and the mean annual precipitation. Permafrost is usually regarded as a key factor that affects the soil properties of a terrestrial ecosystem (Anisimov and Reneva, 2006). The interaction of the permafrost and soil properties indicates that the permafrost should have a great effect on the soil microbial community, because a thicker active layer would increase the soil pH and decrease the organic carbon content. However, the active layer thickness did not have a significant relationship with the bacterial community, but the pH and SOC significantly affected the bacterial community. Therefore, the non-significant relationship between the active layer thickness and the microbial community does not necessarily indicate that the effect of the permafrost on the microbial community was not important. Instead, the conclusion could be drawn that, because the soil environment in the permafrost region of the QTP has considerable heterogeneity, the effects of the permafrost on soil bacterial communities for the upper 30 cm layers could have been masked by other soil variables. Therefore, the response of the microbial community to global warming and permafrost degradation would be affected by other environmental variables such as precipitation, soil texture and vegetation.
geography (Zou et al., 2016) and local conditions (Pang et al., 2009). In the present study, the SOC did not have a significant relationship with the active layer thickness, suggesting that active layer thickness played a different role with that of SOC in shaping the bacterial communities than the SOC. In the present study, soil moisture was closely related with the fine soil particles (i.e., clay and silt) and the vegetation cover, but negatively correlated with the gravel content. This pattern reflects that fine soil particles have a higher water holding capacity (Gómez-Plaza et al., 2001) and consequently provides water for vegetation growth. On the other hand, the vegetation growth interacts with pedogenesis, producing a higher fine soil particle content (Bockheim et al., 2014). Although previous reports showed that these factors, especially vegetation and soil moisture, played an important role in shaping the soil microbial community (Shi et al., 2015; Zhang et al., 2014), they showed no significant relationships with the bacterial community in present study. This result showed the microbial community is sensitive to environmental factors in the permafrost regions of the QTP. In addition to these factors, soil moisture was also correlated with the permafrost, i.e., a shallower active layer thickness meant a higher soil moisture content, which was further associated with a lower pH (Mu et al., 2016a). Therefore, the active layer thickness should be positively correlated with pH, and exerted a similar effect on the bacterial community. However, in the present study, the active layer had an opposite effect with that of pH. Interestingly, the pH had a significantly positive relationship with the mean annual precipitation, which was significantly positively correlated with the soil moisture, and negatively correlated with the active layer thickness. The mean annual precipitation was positively correlated with the latitude, indicating that the precipitation increased from the north to the south. It has been found that the active layer thickness has a similar pattern (Pang et al., 2012). The effect of the water supply from the thawing soil on the soil moisture content became less important as the active layer thickness increased (Yang et al., 2012). The active layer thickness on the QTP is usually greater
5. Conclusions The soil microbial community and its relationship to environmental factors were investigated in the permafrost regions on the QTP. The abundance of Acidobacteria decreased along with depth, with an opposite pattern than the abundance of Proteobacteria. In permafrost regions, the active layer thickness was significantly correlated with Bacteroidetes and Arabinonates. The bacterial community structure was mainly driven by pH, SOC, and the mean annual precipitation, 86
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while the active layer thickness did not have a significant relationship with the bacterial community. The results showed that the pH and TOC had strong effects on the soil microbial community, and depth also affected some bacterial phyla. The relatively less important role of the permafrost on the soil bacterial community indicated that the effect of the permafrost was overwhelmed by other environmental factors. The results suggested that other factors such as precipitation, soil texture and vegetation should be considered in an analysis of changes in the microbial community in response to permafrost degradation under global warming scenarios, because the environmental conditions in the permafrost region of the QTP are very heterogeneous. Acknowledgements The authors gratefully acknowledge to anonymous referees, as well as the editors, for the constructive comments. This work was financially supported by the foundation the National Natural Science Foundation of China (41661013), the State Key Laboratory of Cryosphere Sciences (SKLCS-ZZ-2017) and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (41421061). This work was also supported in part by the foundation from Gansu Provincial Education Department (215218), Gansu Provincial Sic. & Tech. Department (215097) and the Young Teachers Program of Lanzhou Jiaotong University (2015015). References Anisimov, O., Reneva, S., 2006. Permafrost and changing climate: the Russian perspective. AMBIO: J. Hum. Environ. 35, 169–175. Bardgett, R.D., Freeman, C., Ostle, N.J., 2008. Microbial contributions to climate change through carbon cycle feedbacks. ISME J. 2, 805–814. Bates, S.T., Berglyons, D., Caporaso, J.G., Walters, W.A., Knight, R., Fierer, N., 2011. Examining the global distribution of dominant archaeal populations in soil. ISME J. 5, 908–917. Bockheim, J.G., Munroe, J.S., 2014. Organic carbon pools and genesis of alpine soils with permafrost: a review. Arct. Antarct. Alp. Res. 46, 987–1006. Bockheim, J.G., Gennadiyev, A.N., Hartemink, A.E., Brevik, E.C., 2014. Soil-forming factors and soil taxonomy. Geoderma 226, 231–237. Bowles, T.M., Acosta-Martínez, V., Calderón, F., Jackson, L.E., 2014. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem. 68, 252–262. Brockett, B.F.T., Prescott, C.E., Grayston, S.J., 2012. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44, 9–20. Chaudhari, S.K., Singh, R., Kundu, D.K., 2008. Rapid textural analysis for saline and alkaline soils with different physical and chemical properties. Soil Sci. Soc. Am. J. 72, 431–441. Chauvin, C., Dorel, M., Villenave, C., Rogerestrade, J., Thuries, L., Risede, J., 2015. Biochemical characteristics of cover crop litter affect the soil food web, organic matter decomposition, and regulation of plant-parasitic nematodes in a banana field soil. Appl. Soil Ecol. 96, 131–140. Chu, H., Sun, H., Tripathi, B.M., Adams, J.M., Huang, R., Zhang, Y., Shi, Y., 2016. Bacterial community dissimilarity between the surface and subsurface soils equals horizontal differences over several kilometers in the western Tibetan Plateau. Environ. Microbiol. 18, 1523–1533. Cindye, P., 2010. Litter decomposition: what controls it and how can we alter it to sequester more carbon in forest soils. Biogeochemistry 101, 133–149. Cleveland, C.C., Liptzin, D., 2007. C:N:P stoichiometry in soil: is there a Redfield ratio for the microbial biomass? Biogeochemistry 85, 235–252. Dorfer, C., Kuhn, P., Baumann, F., He, J.S., Scholten, T., 2013. Soil organic carbon pools and stocks in permafrost-affected soils on the Tibetan plateau. PLoS One 8, e57024. Drenovsky, R.E., Vo, D.D., Graham, K.J., Scow, K.M., 2004. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430. Fenchel, T., 2003. MIcrobiology: biogeography for bacteria. Science 301, 925–926. Feng, Y., Grogan, P., Caporaso, J.G., Zhang, H., Lin, X., Knight, R., Chu, H., 2014. pH is a good predictor of the distribution of anoxygenic purple phototrophic bacteria in Arctic soils. Soil Biol. Biochem. 74, 193–200. Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U. S. A. 103, 626–631. Fierer, N., Schimel, J.P., Holden, P.A., 2003. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35, 167–176. Gómez-Plaza, A., Martı́nez-Mena, M., Albaladejo, J., Castillo, V.M., 2001. Factors regulating spatial distribution of soil water content in small semiarid catchments. J. Hydrol. 253, 211–226. Herold, N., Schöning, I., Gutknecht, J., Alt, F., Boch, S., Müller, J., Oelmann, Y., Socher,
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Applied Soil Ecology journal homepage: www.elsevier.com/locate/apsoil
Bacterial communities in the upper soil layers in the permafrost regions on the Qinghai-Tibetan plateau
MARK
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Xiaodong Wua, Haiyan Xub, , Guimin Liub, Xiaoliang Mab, Cuicui Muc, Lin Zhaoa a Cryosphere Research Station on the Qinghai-Tibetan Plateau, State Key Laboratory of Cryosphere Sciences, Northwest Institute of Eco-Environment and Resource, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, China b School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China c Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Permafrost Soil organic carbon Bacterial community Qinghai-Tibetan plateau Illumina sequencing
The Qinghai-Tibetan plateau (QTP) is the largest middle-low latitude permafrost region on earth, while little is known about the microbial community in this area. Here, we investigated the bacterial community in the upper 30 cm soils in the permafrost regions on the central QTP using Illumina sequencing technology. In these soils, the most abundant phyla were Acidobacteria, Proteobacteria, and Bacteroidetes. The depth was significantly correlated with Acidobacteria, Proteobacteria, Nitrospirae, and Gemmatimonadetes. The soil pH and the gravel content were significantly positively correlated with Bacteroidetes. The active layer thickness was significantly correlated with Bacteroidetes and Arabinonates. Although these factors were closely correlated with the relative abundances of specific bacterial phyla, the overall bacterial community structure was mainly affected by pH, soil organic carbon content, and the mean annual precipitation, while the community structure had no significant relationship with the active layer thickness. Our results suggested that the permafrost region on the QTP had greatly heterogeneous environmental conditions, and the responses of microbial communities to permafrost degradation would also be affected by other factors such as precipitation, soil texture and vegetation.
1. Introduction Soils are complex systems including interactions of biotic and abiotic processes. Soil microorganisms regulate biogeochemical processes in soils and play vital roles in organic matter (SOM) cycling (O’Donnell et al., 2007). SOM is mainly originated from the partial decomposition and transformation of plant debris and litter input by soil microbes (Cindye, 2010). The microbial decomposition of SOM is also the main pathway for the transfer of greenhouse gases from the terrestrial ecosystem to the atmosphere (Chauvin et al., 2015). The soil microbes also play fundamental functions for the maintenance of soil ecosystem integrity. For example, ammonia oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) are involved in ammonia oxidation and nitrification and thus are important drivers of soil nitrogen cycle (Isobe et al., 2012). During the past centuries, human activities greatly changed the global carbon cycle and the climate. The climate warming definitely changed soil temperature, moisture and thus have both direct and indirect effects on the soil microbial communities (Joshi and Shekhawat, 2014). It is of great importance to understand the changes of soil
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Corresponding author. E-mail address: [email protected] (H. Xu).
http://dx.doi.org/10.1016/j.apsoil.2017.08.001 Received 19 May 2017; Received in revised form 2 August 2017; Accepted 4 August 2017 0929-1393/ © 2017 Elsevier B.V. All rights reserved.
microbes in a warming world because they are vital to many basically ecological processes (Bardgett et al., 2008). The circum-arctic permafrost regions store approximately 1330–1580 Pg organic carbon, which is almost twice as the carbon in the atmosphere (Hugelius et al., 2014; Schuur et al., 2015). The SOC pools in the permafrost regions have received worldwide attention because global warming could potentially cause emissions of greenhouse gases (Hugelius et al., 2014; Ping et al., 2015, 2008). In permafrost regions, a shift in the microbial community would greatly change the organic matter decomposition (Xue et al., 2016), and the release of greenhouse gases from the organic matter decomposition is closely associated with soil microbial abundance (Mackelprang et al., 2011). Therefore, investigation of soil microbial community could shed light on the relationship between soil microbes and environmental factors thus improve our knowledge of the effects of climate change on ecosystems (Herold et al., 2014; Nannipieri et al., 2003; Tardy et al., 2014). Many biotic and abiotic factors such as pH (Feng et al., 2014; Kim et al., 2014), soil moisture content (Brockett et al., 2012), nutrient levels (Cleveland and Liptzin, 2007; Stark et al., 2011), organic carbon
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disinfected soil auger and placed in clean, sealable plastic bags. The soil samples were immediately transported to the laboratory in a cooler with ice packs and stored at −80 °C until genomic DNA extraction was carried out. We used a square-meter quadrant and took five samples at each site to investigate the vegetation cover. Additionally, we excavated a shallow soil pit (approximately 50 cm) and measured the soil bulk density using a bulk soil sampler (i.e., a 5-cm diameter and 5-cm high stainless steel cutting ring). We collected soil samples at different depths to calculate the total water content, which was determined by drying the soil at 105 °C for 8 h. The pH values of the air-dried soils were measured using the 1:5 mixture of soil:water. The SOC and total nitrogen (TN) were measured using a Vario EL elemental analyzer (Elementra, Hanau, Germany). To measure the SOC, we pretreated 0.5 g dry soil samples with HCl (10 mL, 1 mol l−1) for 24 h to remove the carbonate. We calculated the mass ratio of C/N. The percent of gravel was measured by weight of rock fragments (> 2 mm) using the oven-dried samples, and soil particle distribution was measured using a combination of wet sieving (Chaudhari et al., 2008) and a laser diffraction instrument (Malvern Mastersizer 2000, Malvern, UK). The proportions of sand, silt and clay were classified by the respective size intervals of 2 mm–50 μm, 50–2 μm, and < 2 μm.
(Zhang et al., 2014), and vegetation cover (Shi et al., 2015) can affect the soil microbial community structure. In the permafrost regions, it has also been found that temperature and the freeze-thaw cycle affected the microbial community (Jansson and Tas, 2014; Stres et al., 2010). These studies summarized the factors that control the soil microbial community. However, these conclusions were mainly based on statistical analyses of measured soil parameters, but little consideration was given to the factors that had no significant relationships with the microbial community. In fact, soil variables usually interacted with each other. For example, the permafrost conditions can affect the soil microbial community since permafrost is associated with many soil variables, such as soil moisture content (Wang et al., 2008), pH, and soil texture (Mu et al., 2016a,b). The low temperature in the permafrost region favors the perseverance and accumulation of organic matter (Mu et al., 2014; Zimov et al., 2006), and the organic matter at different depths usually has a high proportion of labile fractions (Wu et al., 2014). So far, most studies for the soil microbial community have mainly been focused on the upper 10 cm layer (Bowles et al., 2014; Chu et al., 2016; Yue et al., 2015). Although the soil microbes below 0–10 cm soils were abundant and active (Fenchel, 2003), and these microbes at the subsurface and even the deep soil doubtless play an important role in soil biogeochemical cycling and other processes (Fierer et al., 2003; Mackelprang et al., 2011), little is known about the soil microbial community below the upper 10 cm layer. The Qinghai-Tibet Plateau (QTP), accounts for approximately three quarters of the high-latitude permafrost on earth. Under global warming scenarios, the soil environment in the permafrost region on the plateau has become a major concern since its role in maintaining ecosystem function and services (Mu et al., 2015; Wu et al., 2017; Yang et al., 2014). It is well-known that pH, SOC, and C:N ratios are the most important factors that determine the soil bacterial community, and these factors vary considerably at different depths of the upper soil layer (Shang et al., 2016). Therefore, we hypothesized that depth could have a great effect on the microbial community in this area. Based on the interactions of environmental conditions in the permafrost region, we also hypothesized that active layer thickness, soil moisture, soil texture, vegetation cover, and climatic conditions could also affect the soil bacterial community. To test these hypotheses, six plots in the permafrost region of the central QTP were selected and the soil bacterial community and its relationship with soil parameters was investigated.
2.2. Soil DNA extraction We extracted the total soil DNA from each soil sample using a MoBio PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA, USA) per the manufacturer’s instructions, using 0.3 g soil. The concentration of the extracted DNA was determined using a QuBit DNA quantification system (Invitrogen) with the QuBit high sensitivity assay reagents. Then, all the soil DNA samples were stored frozen at −20 °C until used. 2.3. PCR amplification We performed PCR amplification, purification, and sequencing of a region of the 16S rRNA gene (Fierer and Jackson, 2006). The V4 and V5 hypervariable regions of the bacterial 16S ribosomal RNA gene were amplified by PCR. The PCR conditions were 94 °C for 5 min, followed by 35 cycles at 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 30 s, and then a final extension step at 72∘C for 10 min. We used the primer set F515 (5′-GTGCCAGCMGCCGCGG-3′) and R907(5′-CCGTCAATTCMTTTRAGTTT-3′) (Bates et al., 2011). The PCR amplification was performed in 50 μL reactions containing about 10 ng template DNA, 25 μL of PCR Pre-Mixture (TaKaRa) and 0.3 μM of forward and reverse primer. The same volume of 1X loading buffer (contained SYB green) was mixed with the PCR products and visualized with 1.2% agarose gel electrophoresis. We chose samples with a bright strip between 350 and 450 bp for further analysis. The PCR products were purified using a QIAquick Gel Extraction Kit (Qiagen, Chatsworth, CA, USA). We prepared a single composite sample for sequencing by combining approximately equimolar amounts of PCR products from each sample. Sequencing was performed using a Miseq PE250 platform (Majorbio Bio-pharm Technology Co., Ltd., Shanghai, China).
2. Material and methods 2.1. Soil sampling and analysis The soils at 6 sites in the permafrost region of the central QTP were sampled during July, 2014 (Fig. 1). The climatic, permafrost and vegetation conditions for the sampling sites are shown in Table 1. The thawing of frozen ground on the QTP is expected to start in April, and the maximum thaw depths, which equal to the active layer thicknesses, are largely recorded in late September. The thaw depths in July are about 1 m in the central QTP (Zhao et al., 2000, 2010). To exclude the possibility that soil properties would differ considerably within the same site, 1.5 m × 1.5 m plots with relatively uniform microrelief conditions (i.e., without a patchy distribution of vegetation and flat areas) were selected. There is still a possibility that soils have heterogeneities in the same plots. Therefore, to examine the spatial distribution of bacterial community structure and its relationship to environmental variables, we collected five subsamples at each depth, and then pooled into a single soil sample to represent the sample at the certain depth. The soil variables and DNA sequencing analysis were all performed using this sample and thus we can examine their relationships. Samples from the surface 30 cm were collected at a depth interval of 10 cm. The soil samples were collected aseptically using an ethanol-
2.4. Processing of sequencing data Paired-end reads from the original DNA fragments were merged with FLASH, which was designed to merge paired-end pairs of reads when the original DNA fragments were shorter than twice the length of the reads. The sequences were analyzed using QIIME 1.17 (http:// qiime.org/scripts/pick_otus.html). Operational taxonomic units (OTUs) with 97% similarity cutoff were clustered. To compute Alpha Diversity, we rarified the OTU table and calculated four metrics, i.e., the Chao1 metric, the Observed OTUs, which was a count of unique OTUs in the sample, the Ace Shannon, and the Simpson index. We performed the mantel test, redundancy analysis (RDA), and prepared figures using the 82
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Fig. 1. Sampling sites in the permafrost regions on the Qinghai-Tibetan Plateau.
3.2. Sequencing data
Vegan packages in the R software. We deposited the raw reads into the NCBI Sequence Read Archive (SRA) database (Accession Number: SRR5034716).
A total of 24,483 OTUs (at the 3% evolutionary distance) were identified based on 27828 reads for each sample. The Good’s coverage estimator of the OTUs ranged from 98.1% to 98.6%. The rarefaction curves showed that the sequences sufficiently covered the diversity of the bacterial communities in the samples. The OTUs varied, with the lowest value of 894 in the BS0_10 sample, and the highest value of 2214 in the AC10_20 sample, under the land cover of meadow. The mean highest OTU numbers were recorded at XM site under land cover of meadow, which also had the lowest elevation (Table 2). However, the OTU numbers showed considerable variations both among sites and depths, and there is no clear pattern between the OTU numbers and elevation and depth. The total richness estimators, ACE and Chao1, showed similar patterns with that of the OTU numbers (Table 2). The bacterial community diversity, as indicated by Shannon–Wiener index, was significantly positively correlated with the OTU numbers (p < 0.01, Pearson, two-tailed), Chao1 (p < 0.01, Pearson, two-tailed), but negatively correlated with the Simpson index (p < 0.01, Pearson, two-tailed). The Shannon-Wiener indices ranged from 2.44 to 5.85.
3. Results 3.1. Soil properties The pH values at all sites were above 8.0. In general, the surface 10 cm layer had the lowest pH values. The TOC and TN values ranged from 6.8 to 47.6 g kg−1 and 0.47 to 3.44 g kg−1, respectively. The TOC and TN largely decreased with depth. The highest values of TOC, TN, soil moisture were recorded at the meadow of the AC site, and this site also had the lowest pH and bulk density. There were no gravels at the BM and AC sites. The highest value of gravel content was 41.7% at the depth of 20–30 cm at the WS site. The clay content at the sampling sites was generally low, with a mean value of 1.7%. For all the samples, the mean percentage of silt and sand were 9.6% and 80.7%, respectively. The distribution of clay, silt and sand varied considerably, with no obvious patterns among the sites (Fig. 2).
Table 1 Geographiccoordinates, climatic, permafrost and vegetation conditions for the sampling sites.
XM BS WS FM BW AC
Longitude
Latitude
Elevation (°C)
MAT (°C)
MAP (mm)
MGT (°C)
AL (m)
Vegetation cover (%)
Vegetation type
94.008 92.922 92.727 92.895 92.922 91.741
35.712 34.823 34.471 34.729 34.823 31.821
4538 4553 4573 4988 4557 4813
−4.0 −3.8 −1.8 −5.2 −3.8 −1.6
393.0 291.0 305.0 328.0 291.0 350.0
−1.345 −0.069 0.199 −2.194 −0.855 0.902
1.60 2.50 3.20 1.40 2.30 2.70
85 40 25 100 90 95
Meadow Steppe Steppe Meadow Meadow Meadow
MAT, mean annual average temperature, MAP, mean annual average precipitation, MGT, mean annual ground temperature (at 10 cm), AL, active layer thickness (meaured in 2014).
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Fig. 2. Soil properties of the different layers (white: 0–10 cm, light gray: 10–20 cm, dark gray: 20–30 cm) at sampling sites. a: pH, b: total soil organic carbon, c: Total nitrogen, d: soil moisture content, e: soil bulk densities, f: clay content, g: silt content, h: sand content, i: gravel content.
RB41_norank (13.7%), Flavobacterium (13.4%), Subgroup_6_norank (11.0%), and Pseudomonas (5.4%). The genera which accounted for between 2% to 3% were Anaerolineaceae_uncultured (2.7%), Comamonadaceae_unclassified (2.1%), while other genera were less than 2%.
Table 2 Estimated OUT richness, diversity indices and sample coverage of the samples. Samples
OTUs
Ace
Chao1
Shannon
Simpson
coverage
AC0_10 AC10_20 AC20_30 BS0_10 BS10_20 BS20_30 BW0_10 BW10_20 BW20_30 FM0_10 FM10_20 FM20_30 WS0_10 WS10_20 WS20_30 XM0_10 XM10_20 XM20_30
1544 1652 1489 894 1558 1557 1174 1472 1360 1553 1326 1156 1476 1336 1283 1544 1054 1055
1967 2214 2034 1832 2108 2015 1648 1795 1917 2171 1868 1686 1876 2339 1727 2017 1606 2001
1999 2183 1970 1409 2061 2069 1669 1851 1922 2072 1772 1630 1866 1967 1683 2090 1561 1611
5.61 5.78 5.34 2.44 5.47 5.34 4.63 5.63 5.15 5.32 4.64 4.35 5.29 4.57 5.1 5.85 3.19 3.27
0.011 0.008 0.014 0.271 0.013 0.022 0.038 0.013 0.021 0.019 0.044 0.064 0.025 0.051 0.021 0.007 0.262 0.225
0.984 0.981 0.982 0.986 0.981 0.983 0.985 0.986 0.983 0.981 0.983 0.985 0.984 0.982 0.985 0.983 0.985 0.984
3.4. Relationships between environmental factors and the microbial community A mantel test showed that the bacterial community structure, which included all the bacterial phyla, was significantly correlated with pH (r = 0.32, p = 0.04), TOC (r = 0.32, p = 0.04) and the mean annual precipitation (r = 0.24, p = 0.04), while other factors, including soil texture, moisture content, and C:N ratios were not significantly correlated with the bacterial community structure. The significant relationships between the phyla and various environmental factors are shown in Table 3. Depth was significantly positively correlated with Acidobacteria, Nitrospirae, and Gemmatimonadetes, and it was negatively correlated with Proteobacteria. Bacteroidetes was significantly negatively correlated with active layer thickness but positively correlated with pH and the mean annual precipitation. The TOC and TN was positively correlated with Firmicutes. The bulk density, proportion of sand, gravel content, vegetation cover and C:N ratio also had a significant relationship with the diversity of the bacterial phyla. The first and second axis of the RDA explained 52.4% and 34.8% of the total phyla variance. The RDA result showed that samples from the upper 10 cm layer were largely scattered in the right of the chart, and the samples from the 10–20 cm and 20–30 cm layers were mainly distributed in the upper part of the chart. The RDA result demonstrated that TOC, TN and bulk density were strongly correlated with the first axis. Depth also played an important role in the distribution of the bacterial phyla. The second axis was strongly positively correlated with pH, and MAP but negatively correlated with AL, MGT, and the conductivity (Fig. 4).
3.3. Bacterial diversity and community structure The OTUs indicated 40 phyla, 99 classes, 221 orders, 399 families and 682 genera. The phyla varied greatly among the different samples, and the predominant phyla were Acidobacteria (22.3%), Proteobacteria (28.5%), Bacteroidetes (22.7%), Chloroflexi (3.7%), and Actinobacteria (3.6%). These phyla accounted for 88.8% of sequences across all the samples (Fig. 3). Nine other phyla were represented to varying degrees in one or more samples for < 5% of the effective sequences, except for the 26.4% Cyanobacteria in the BS0_10 sample. At the class level, the predominant classes were Actinobacteria (30.4%), Alphaproteobacteria (8.2%), Betaproteobacteria (9.8%), Deltaproteobacteria (2.3%), Gammaproteobacteria (8.1%). These four classes were recorded in all the samples for the phylum Proteobacteria, but the Epsilonproteobacteria was only found in the BW20_30 sample. At the genus level, the microbial community was largely composed of 84
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Fig. 3. Relative abundance of bacteria phyla in different layers at the sampling sites.
4. Discussion
layers in some the permafrost region of the QTP were shallow and the soil parameters showed markedly changes with depth (Dorfer et al., 2013; Wu et al., 2012, 2016). Therefore, it would be reasonable to expect that depth greatly affected the bacterial community. In this study, although no clear evidence indicated that the soil samples in similar layers were clustered, the distribution of the samples from the 0–10 cm soils was largely far away from the samples from the deeper soils. The relationship between depth and the bacterial phyla suggested that Acidobacteria, Nitrospirae, and Gemmatimonadetes increased with depth, while Proteobacteria was less likely to occur in deeper layers. In an arid region on the western QTP, the Gemmatimonadetes was reported to have a higher relative abundance in the subsurface layers, and Acidobacteria was higher in the near-surface layers (Chu et al., 2016). The soil depth was found to be one of the most important soil properties that determined the bacterial community structure in a subarctic area (Kim et al., 2014). Overall, the mechanism of the effect of soil depth on the soil bacterial community requires further study since such studies are scarce. Local factors such as pH and soil moisture content exerted stronger effects on soil microbial community than climatic and latitudinal gradients, which played less important roles in shaping the microbial community (Steven et al., 2013). The soil parameters interacted with each other in the permafrost region. For example, vegetation cover, soil texture, and soil organic matter were closely associated with the permafrost conditions. Permafrost has been recognized to be associated with a lower temperature and a higher soil moisture content, which causes a lower decomposition of soil organic matter, thus the SOC content in the permafrost region is usually higher than in the nonpermafrost region (Bockheim and Munroe, 2014). The permafrost distribution and active layer thickness on the QTP varied greatly with
The richness and diversity indices of the soil bacteria showed similar patterns and were largely significantly correlated with each other. No significant differences were found among the mean values of these indices in any of three layers. Although the bacterial structure significantly correlated with pH and TOC, none of these indices significantly correlated with the soil properties including TOC or pH. These results suggested that bacterial diversity and richness was not significantly different in different soil environments in similar ecosystems (Yang et al., 2015). Acidobacteria (22.3%), Proteobacteria (28.5%), Bacteroidetes (22.7%), Chloroflexi (3.7%), and Actinobacteria (3.6%) were the predominant phyla in the present study. This pattern was in agreement with most of the soils under different climatic conditions (Feng et al., 2014; Rousk et al., 2010; Wallenstein et al., 2007). In this study, although the mean relative abundance of Actinobacteria was 3.6%, the highest value (9.99%) was also comparable with a previous study, which demonstrated that the Actinobacteria in the upper 10 cm layer accounted for approximately 15% of the soil bacterial community in an alkaline soil (Zhang et al., 2014). It has been shown that carbon availability, nutrients content, soil moisture (Drenovsky et al., 2004), and C/N ratios (Wan et al., 2014) affect the soil bacterial community. In the continental region, pH was the most important factor that affected the soil bacterial community (Chu et al., 2016; Fierer and Jackson, 2006; Tripathi et al., 2012). In the present study, the results of the mantel test also confirmed that pH and total organic carbon played an important role in the bacterial community structure. The soil properties in the permafrost region of the QTP varied considerably on a centimeter scale in the upper soil layers, since the soil
Table 3 Pearson’s correlation coefficients between the environmental factors and the bacterial phyla. Depth Acidobacteria Proteobacteria Bacteroidetes Nitrospirae Gemmatimonadetes Firmicutes Armatimonadetes
pH
AL
0.646**
−0.530*
TOC
TN
C:N ratio
Bulk
Sand
Gravel
−0.614**
0.688**
MAT
MAP
Vegetation
*
0.486 −0.538*
0.485*
0.752** 0.536*
0.604**
0.502* 0.548*
−0.617**
0.490*
−0.471*
0.670**
0.572*
0.560*
−0.600**
** p < 0.01; *p < 0.05; n = 209; 2-tailed test. AL, Active layer thickness; TOC, total organic carbon; TN, Total nitrogen; MAT, mean annual air temperature; MAP, mean annual precipitation.
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Fig. 4. Redundancy analysis (RDA) results for the bacterial community structure of the soil samples.
than 2 m. Therefore, the higher pH values associated with a thinner active layer probably suggests that the effects of precipitation on the soil pH overwhelmed the effect of the permafrost in the present study, and thus the active layer thickness acted in an opposite manner on the soil bacterial community with that of the pH and the mean annual precipitation. Permafrost is usually regarded as a key factor that affects the soil properties of a terrestrial ecosystem (Anisimov and Reneva, 2006). The interaction of the permafrost and soil properties indicates that the permafrost should have a great effect on the soil microbial community, because a thicker active layer would increase the soil pH and decrease the organic carbon content. However, the active layer thickness did not have a significant relationship with the bacterial community, but the pH and SOC significantly affected the bacterial community. Therefore, the non-significant relationship between the active layer thickness and the microbial community does not necessarily indicate that the effect of the permafrost on the microbial community was not important. Instead, the conclusion could be drawn that, because the soil environment in the permafrost region of the QTP has considerable heterogeneity, the effects of the permafrost on soil bacterial communities for the upper 30 cm layers could have been masked by other soil variables. Therefore, the response of the microbial community to global warming and permafrost degradation would be affected by other environmental variables such as precipitation, soil texture and vegetation.
geography (Zou et al., 2016) and local conditions (Pang et al., 2009). In the present study, the SOC did not have a significant relationship with the active layer thickness, suggesting that active layer thickness played a different role with that of SOC in shaping the bacterial communities than the SOC. In the present study, soil moisture was closely related with the fine soil particles (i.e., clay and silt) and the vegetation cover, but negatively correlated with the gravel content. This pattern reflects that fine soil particles have a higher water holding capacity (Gómez-Plaza et al., 2001) and consequently provides water for vegetation growth. On the other hand, the vegetation growth interacts with pedogenesis, producing a higher fine soil particle content (Bockheim et al., 2014). Although previous reports showed that these factors, especially vegetation and soil moisture, played an important role in shaping the soil microbial community (Shi et al., 2015; Zhang et al., 2014), they showed no significant relationships with the bacterial community in present study. This result showed the microbial community is sensitive to environmental factors in the permafrost regions of the QTP. In addition to these factors, soil moisture was also correlated with the permafrost, i.e., a shallower active layer thickness meant a higher soil moisture content, which was further associated with a lower pH (Mu et al., 2016a). Therefore, the active layer thickness should be positively correlated with pH, and exerted a similar effect on the bacterial community. However, in the present study, the active layer had an opposite effect with that of pH. Interestingly, the pH had a significantly positive relationship with the mean annual precipitation, which was significantly positively correlated with the soil moisture, and negatively correlated with the active layer thickness. The mean annual precipitation was positively correlated with the latitude, indicating that the precipitation increased from the north to the south. It has been found that the active layer thickness has a similar pattern (Pang et al., 2012). The effect of the water supply from the thawing soil on the soil moisture content became less important as the active layer thickness increased (Yang et al., 2012). The active layer thickness on the QTP is usually greater
5. Conclusions The soil microbial community and its relationship to environmental factors were investigated in the permafrost regions on the QTP. The abundance of Acidobacteria decreased along with depth, with an opposite pattern than the abundance of Proteobacteria. In permafrost regions, the active layer thickness was significantly correlated with Bacteroidetes and Arabinonates. The bacterial community structure was mainly driven by pH, SOC, and the mean annual precipitation, 86
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while the active layer thickness did not have a significant relationship with the bacterial community. The results showed that the pH and TOC had strong effects on the soil microbial community, and depth also affected some bacterial phyla. The relatively less important role of the permafrost on the soil bacterial community indicated that the effect of the permafrost was overwhelmed by other environmental factors. The results suggested that other factors such as precipitation, soil texture and vegetation should be considered in an analysis of changes in the microbial community in response to permafrost degradation under global warming scenarios, because the environmental conditions in the permafrost region of the QTP are very heterogeneous. Acknowledgements The authors gratefully acknowledge to anonymous referees, as well as the editors, for the constructive comments. This work was financially supported by the foundation the National Natural Science Foundation of China (41661013), the State Key Laboratory of Cryosphere Sciences (SKLCS-ZZ-2017) and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (41421061). This work was also supported in part by the foundation from Gansu Provincial Education Department (215218), Gansu Provincial Sic. & Tech. Department (215097) and the Young Teachers Program of Lanzhou Jiaotong University (2015015). References Anisimov, O., Reneva, S., 2006. Permafrost and changing climate: the Russian perspective. AMBIO: J. Hum. Environ. 35, 169–175. Bardgett, R.D., Freeman, C., Ostle, N.J., 2008. Microbial contributions to climate change through carbon cycle feedbacks. ISME J. 2, 805–814. Bates, S.T., Berglyons, D., Caporaso, J.G., Walters, W.A., Knight, R., Fierer, N., 2011. Examining the global distribution of dominant archaeal populations in soil. ISME J. 5, 908–917. Bockheim, J.G., Munroe, J.S., 2014. Organic carbon pools and genesis of alpine soils with permafrost: a review. Arct. Antarct. Alp. Res. 46, 987–1006. Bockheim, J.G., Gennadiyev, A.N., Hartemink, A.E., Brevik, E.C., 2014. Soil-forming factors and soil taxonomy. Geoderma 226, 231–237. Bowles, T.M., Acosta-Martínez, V., Calderón, F., Jackson, L.E., 2014. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem. 68, 252–262. Brockett, B.F.T., Prescott, C.E., Grayston, S.J., 2012. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44, 9–20. Chaudhari, S.K., Singh, R., Kundu, D.K., 2008. Rapid textural analysis for saline and alkaline soils with different physical and chemical properties. Soil Sci. Soc. Am. J. 72, 431–441. Chauvin, C., Dorel, M., Villenave, C., Rogerestrade, J., Thuries, L., Risede, J., 2015. Biochemical characteristics of cover crop litter affect the soil food web, organic matter decomposition, and regulation of plant-parasitic nematodes in a banana field soil. Appl. Soil Ecol. 96, 131–140. Chu, H., Sun, H., Tripathi, B.M., Adams, J.M., Huang, R., Zhang, Y., Shi, Y., 2016. Bacterial community dissimilarity between the surface and subsurface soils equals horizontal differences over several kilometers in the western Tibetan Plateau. Environ. Microbiol. 18, 1523–1533. Cindye, P., 2010. Litter decomposition: what controls it and how can we alter it to sequester more carbon in forest soils. Biogeochemistry 101, 133–149. Cleveland, C.C., Liptzin, D., 2007. C:N:P stoichiometry in soil: is there a Redfield ratio for the microbial biomass? Biogeochemistry 85, 235–252. Dorfer, C., Kuhn, P., Baumann, F., He, J.S., Scholten, T., 2013. Soil organic carbon pools and stocks in permafrost-affected soils on the Tibetan plateau. PLoS One 8, e57024. Drenovsky, R.E., Vo, D.D., Graham, K.J., Scow, K.M., 2004. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430. Fenchel, T., 2003. MIcrobiology: biogeography for bacteria. Science 301, 925–926. Feng, Y., Grogan, P., Caporaso, J.G., Zhang, H., Lin, X., Knight, R., Chu, H., 2014. pH is a good predictor of the distribution of anoxygenic purple phototrophic bacteria in Arctic soils. Soil Biol. Biochem. 74, 193–200. Fierer, N., Jackson, R.B., 2006. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U. S. A. 103, 626–631. Fierer, N., Schimel, J.P., Holden, P.A., 2003. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35, 167–176. Gómez-Plaza, A., Martı́nez-Mena, M., Albaladejo, J., Castillo, V.M., 2001. Factors regulating spatial distribution of soil water content in small semiarid catchments. J. Hydrol. 253, 211–226. Herold, N., Schöning, I., Gutknecht, J., Alt, F., Boch, S., Müller, J., Oelmann, Y., Socher,
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