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Keywords:

  • archaea;
  • biogeography;
  • community structure;
  • soil profile;
  • soil type;
  • soil pH

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

To understand the distribution and diversity of archaea in Chinese soils, the archaeal communities in a series of topsoils and soil profiles were investigated using quantitative PCR, T-RFLP combining sequencing methods. Archaeal 16S rRNA gene copy numbers, ranging from 4.96 × 106 to 1.30 × 108 copies g−1 dry soil, were positively correlated with soil pH, organic carbon and total nitrogen in the topsoils. In the soil profiles, archaeal abundance was positively correlated with soil pH but negatively with depth profile. The relative abundance of archaea in the prokaryotes (sum of bacteria and archaea) ranged from 0.20% to 9.26% and tended to increase along the depth profile. T-RFLP and phylogenetic analyses revealed that the structure of archaeal communities in cinnamon soils, brown soils, and fluvo-aquic soils was similar and dominated by Crenarchaeota group 1.1b and 1.1a. These were different from those in red soils, which were dominated by Crenarchaeota group 1.3 and 1.1c. Canonical correspondence analysis indicated that the archaeal community was primarily influenced by soil pH.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Since the recognition of Archaea as the third domain of life and a second prokaryotic kingdom distinct from Bacteria, increasing studies have shown their widespread distribution on Earth, with the help of molecular techniques. Euryarchaeota and Crenarchaeota are two major phyla of archaea which are abundant in extreme environments, such as deep-sea hydrothermal vents (Takai et al., 2001), thermal springs (Weidler et al., 2007), and hypersaline basins (van der Wielen et al., 2005). Diverse groups of archaea affiliating with the Crenarchaeota were also widely found in moderate environments, such as forest soils (Bintrim et al., 1997), ocean (Fuhrman et al., 1992), freshwaters (Llirós et al., 2010), and marine sediments (Santoro et al., 2008). Marine Crenarchaeota, estimated at 1028 cells in the ocean, might account for one-third of all prokaryotic cells in the ocean (Karner et al., 2001). By combining lipid- and DNA-based approaches, it was estimated that at least 87% of intact polar membrane lipids in marine sediments was attributed to archaea (Lipp et al., 2008). The Crenarchaeota group accounted for 1–5% of the prokaryotes in cultivated soils and grassland soils (Ochsenreiter et al., 2003) and could reach 12% in acid forest soils (Kemnitz et al., 2007).

Archaea play important roles in the global carbon and nitrogen cycles. The isolation of autotrophic ammonia-oxidizing archaea from the ocean and hot springs revealed that archaea possess the homologues of alpha and beta subunits of the bacterial ammonia monooxygenase enzyme and might be important contributors to ammonia oxidation (Könneke et al., 2005; Hatzenpichler et al., 2008). Further molecular investigations have confirmed their widespread occurrence in terrestrial ecosystems and their potential roles in nitrification, one of the most important processes in the nitrogen cycle (Leininger et al., 2006). Recent phylogenetic analyses based on r-proteins, other core genes, and comparative genomics strongly proposed that the mesophilic crenarchaeota could be assigned to a novel archaea phylum, Thaumarchaeota (Brochier-Armanet et al., 2008, 2011; Pester et al., 2011).

Archaeal communities and their potential roles in the soil ecosystem may be affected by a number of soil and environmental factors (Hansel et al., 2008). Vegetation (Rooney-Varga et al., 2007), trophic status (Nicol et al., 2004), spatial distance (Green & Bohannan, 2006), altitude (Zhang et al., 2009), depth profile (Hansel et al., 2008), and pH (He et al., 2007; Nicol et al., 2008) may all result in changes in the abundance and composition of archaeal communities in soil environment. For instance, the relative abundance of archaea in the prokaryotes was found to increase with depth in an acidic forest soil (Kemnitz et al., 2007). Pesaro & Widmer (2002) found that the abundance of Euryarchaeota and Crenarchaeota decreased with soil depth. One of the archaeal groups, Crenarchaeota group 1.1a, was widely distributed in water environments (Herfort et al., 2009), but only accounted for a small proportion in soil environments (Nicol et al., 2005) and was sensitive to freezing (Pesaro et al., 2004). Group 1.1b was usually detected in soils with pH above 5, while group 1.1c preferred acidic soils and was even detected in pH below 3 (Nicol et al., 2005, 2007). These studies were confined to specific sites, making it difficult to achieve a systematic understanding of the archaeal community distribution in soils across large-scale regions.

Although important progresses on some functional groups of archaea like thaumarchaeal ammonia oxidizers in aquatic and soil environments have been made (He et al., 2007; Llirós et al., 2010; Auguet et al., 2011), our knowledge of the spatial distribution of archaeal communities and their relationship with soil and environmental conditions in terrestrial environments, especially in soils, is still limited. In a survey across large-scale elevational gradient soils, no significant correlation was observed between environmental or spatial variables and archaeal diversity (Oline et al., 2006). While at the global scale, archaeal distribution showed well-defined community patterns along a broad environmental gradients and habitat types and was mainly driven by salinity (Auguet et al., 2010). Our previous studies revealed that difference in soil bacterial diversity over a regional scale was mainly affected by soil type and depth profile (Ge et al., 2008). It is unknown whether there are similar spatial patterns for archaeal communities across a regional scale in China. Therefore, the abundance and community composition of archaea across a latitude gradient from Northern to Southern China were investigated in this study with the aims to (1) understand the spatial distribution patterns of archaea at a regional scale and (2) explore the driving environmental factors that shape the archaeal community to shed light on the biogeography of archaea in the terrestrial ecosystem.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Description of the sites and sampling

Soil samples from the topsoil (0–20 cm) and deeper in the profile (0–100 cm, separated into six layers) were collected from different sites across a latitude gradient from Northern to Southern China. The sites were located in Beijing (BJ), Tianjin (TJ), Qingdao (QD), Zhengzhou (ZZ), Taoyuan (TY), and Qiyang (QY) (Table 1, Fig. 1). The soils from north to south belong to four typical soil types, that is cinnamon soils (ustic cambosols), brown soils (udic agrosols), fluvo-aquic soils (aquic inceptisol), and red soils (udic ferrosols) (Table 1). After being transported to the laboratory on ice, soil samples were passed through a 2.0-mm sieve, stored at 4 °C for general soil analysis and subsamples were stored at −80 °C for DNA extraction.

image

Figure 1. Soil sample locations as shown in a Chinese map.

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Table 1. Soil samples used in this study and the sampling site information
Sample nameLand useDepth (cm)aSiteLocationTextureSoil type
  1. a

    Depth, 0–20 cm means topsoils; 0–100 cm means the soil profiles with six layers, i.e. 0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm.

BJ-1Fallow0–20Miyun, BJ

116°48′–116°54′E

 40°25′–40°29′N

Sandy loamCinnamon soils  (Ustic cambosols)
BJ-2Fallow0–20
BJ-3Fallow0–20
BJ-4Woodland0–20
BJ ProfileFallow0–100
TJ-1Fallow0–20Jixian, TJ

117°28′–117°31′E

 40°04′–40°07′N

Silt loam
TJ-2Fallow0–20
TJ-3Fallow0–20
TJ-4Fallow0–20
TJ ProfileFallow0–100
QD-1Fallow0–20QD, Shandong  Province

120°31′–120°37′E

 36°17′–36°18′N

Sandy loamBrown soils  (Udic agrosols)
QD-2Cropland0–20
QD-3Cropland0–20
QD-4Woodland0–20
QD-5Fallow0–20
QD-6Cropland0–20
QD-7Cropland0–20
QD ProfileFallow0–100
ZZ-1Fallow0–20ZZ, Henan Province

113°39′E

 34°43′–34°53′N

LoamFluvo-aquic soils  (Aquic inceptisols)
ZZ-2Cropland0–20
ZZ-3Woodland0–20
ZZ-4Cropland0–20
ZZ ProfileFallow0–100
TY-1Grassland0–20TY, Hunan Province

111°26′E

 28°55′N

Silty clayRed soils (Udic ferrosols)
TY-2Woodland0–20
TY-3Cropland0–20
TY-4Woodland0–20
TY ProfileFallow0–100
QY ProfileFallow0–100QY, Hunan Province

111°52′E

 26°45′N

Soil chemical analysis

Soil pH was determined with a soil to water ratio of 1 : 2.5. Soil organic carbon (SOC) was determined using the K2Cr2O7 oxidation method (Bremner, 1996). Total nitrogen (TN) was determined using the Dumas method by an Element Analyser (Vario EL III; Elementar, Hanau, Germany) (McGill et al., 2007). Soil nitrate (inline image) was extracted with 2 M KCl and determined by a Continuous Flow Analyser (SAN++; Skalar, Breda, Holland). All these analyses were carried out in triplicate.

Soil DNA extraction and real-time PCR

Triplicate fresh soil samples (0.5 g) were extracted using a MoBio UltraClean™ soil DNA isolation kit (MO BIO Laboratories, San Diego, CA) according to the manufacturer's protocol. Real-time PCR was performed on an iCycler iQ 5 thermocycler (Bio-Rad Laboratories, Hercules, CA). The number of archaeal 16S rRNA gene was determined in 25-μL reaction mixtures containing SYBR® Premix Ex Taq™ (TaKaRa Bio, Otsu, Shiga, Japan) and the primer pairs A364aF (5′-CGGGGYGCASCAGGCGCGAA-3′) and A934bR (5′-GTGCTCCCCCGCCAATTCCT-3′) (Kemnitz et al., 2005). Real-time PCR assay was carried out as follows: 2 min at 94 °C, followed by 40 cycles of 20 s at 94 °C, 30 s at 63 °C and 30 s at 72 °C, plate read at 83 °C. The number of bacterial 16S rRNA gene was quantified by Taqman assays in 25-μL reaction mixtures containing Premix Ex Taq™ (TaKaRa Bio) and the primer pairs BACT1369F (5′-CGGTGAATACGTTCYCGG-3′), PROK1492R (5′-GGWTACCTTGTTACGACTT-3′), and the probe TM1389F (5′-CTTGTACACACCGCCCGTC-3′) (Suzuki et al., 2000). The amplification condition was as follows: 10 s at 95 °C for initial denaturation, 40 cycles of 15 s at 95 °C, 1 min at 56 °C. Two microlitres of 10-fold diluted DNA was used as template with a final content of 1–10 ng in each reaction mixture.

Standard curves for real-time PCR assays were developed using primer pairs Ar4F (Hershberger et al., 1996)/Ar958R (DeLong, 1992) and primer pairs 27F/1492R (Lane, 1991) to amplify archaeal and bacterial 16S rRNA genes from templates, respectively. The obtained PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI). Plasmids from the positive clone with the target gene insert were extracted for sequencing and used as standards for calibration curves. The plasmid concentration was determined on a Nanodrop® ND-1000 UV-Vis Spectrophotometer (NanoDrop Technologies, Wilmington, DE) and used for the calculation of standard copy numbers. Ten-fold serial dilutions of a known copy number of the plasmid were subject to real-time PCR assay in triplicate to generate an external standard curve.

Amplification of archaeal 16S rRNA gene and terminal restriction fragment length polymorphism (T-RFLP) analysis

The PCR amplification for T-RFLP analysis was carried out using the archaeal primer pairs A364aF/A934bR (Kemnitz et al., 2005) as mentioned above with the 5′ end of the A934bR primer labeled with 6-carboxyfluorescein (FAM). The 50-μL reaction mixture contained 4 μL of DNA template, 5 μL of 10× buffer, 1 μL of 25 mM bovine serum albumin (BSA), 3 μL of 25 mM MgCl2, 4 μL of 2.5 mM dNTPs, 1 μL of each primer (10 μM), and 0.5 μL of Taq polymerase (5 U μL−1) (TaKaRa Bio). The thermal profile for amplification was as follows: 2 min at 94 °C, followed by 30 cycles of 20 s at 94 °C, 30 s at 59 °C and 30 s at 72 °C, and finally 5 min at 72 °C. The FAM-labeled PCR product was purified using a Universal DNA Purification Kit (Tiangen, Beijing, China). Purified PCR products from three replicates of DNA extract were mixed at the equal quantity ratio, and totally 500 ng of PCR product was digested at 37 °C for 3 h by Hha I (TaKaRa Bio), followed by deactivation at 95 °C for 10 min. A 50-μL digestion product was precipitated overnight at −20 °C after adding 150 μL of ethanol (100%, v/v) and 5 μL of 3 M sodium acetate (pH 5.2). The pellets were washed with ethanol (70%, v/v) and dissolved in distilled water. A portion of the purified products was mixed with deionized formamide and the internal standard of GeneScan-1000 ROX (Applied Biosystems, Foster City, CA). The mixtures were denatured for 3 min at 95 °C, and the DNA fragments were size-separated using a 3130xl Genetic Analyzer (Applied Biosystems). T-RFLP patterns were produced using the GeneMapper software (version 3.7; Applied Biosystems), and peaks at positions between 50 and 550 bp (the length of the sequences in the phylogenetic tree) were selected because most T-RFs fall in this range. The relative abundance of a T-RF was calculated by dividing the peak height of the T-RF by the total peak height of all T-RFs in the profile. The peaks with heights ≤ 1% of the total peak height were not included for further analyses.

Cloning and sequencing

T-RFLP analysis showed that the archaeal 16S rRNA gene T-RF patterns were quite similar among samples from BJ, TJ, QD, and ZZ and among TY and QY samples, and the samples from soil profiles at 40–60 cm depth contained the most abundant T-RFs types. Therefore, PCR products of the archaeal 16S rRNA gene retrieved from ZZ and TY soil samples at depth 40–60 cm were used for clone library construction. The PCR amplification used the same primer pairs (without FAM) as for T-RFLP. The PCR products were purified and ligated into the pGEM-T Easy Vector (Promega). Ligation mix was then transformed into Escherichia coli JM109 competent cells following the manufacturer's instructions. After re-amplification with the vector-specific primers T7 and SP6, 48 positive clones were randomly selected from each clone library for sequencing. The coverage of the clone libraries was checked by rarefaction analysis using the software program Analytic Rarefaction 1.3 (Steven Holland, Stratigraphy Lab, University of Georgia [http://strata.uga.edu/software/win/aRarefactWin.exe]).

Phylogenetic analysis and sequences in silico digestion

The sequences were chimera checked in rdp database (http://rdp.cme.msu.edu) and aligned with blast search program (NCBI, http://blast.ncbi.nlm.nih.gov/Blast.cgi), and similarity analysis was performed with DNAStar version 5.0 (DNASTAR Inc., Madison, WI) and dnaman version 4.0 (Lynnon Biosoft, Pointe-Claire, QC, Canada) (Cole et al., 2009). For each clone library, sequences sharing a 98% 16S rRNA gene nucleotide sequence identity were defined as one operational taxonomic unit (OTU) and only one representative sequence of the same OTU was used for phylogenetic tree construction. Phylogenetic analyses were conducted using mega version 4.0, and the neighbor-joining tree was constructed using Kimura 2-parameter distance with 1000 replicates to produce Bootstrap values (Tamura et al., 2007).

Sequences were subject to in silico digestion analysis using Software MapDraw version 5.0, one of seven programs in the Lasergene suite (DNASTAR Inc.). The obtained results were a series of sequence fragments divided at the first restriction enzyme sites (GCGC) from the primer A934bR.

Nucleotide sequence accession numbers

The sequences of the 16S rRNA gene clones used in phylogenetic tree construction were deposited in the GenBank nucleotide sequence database under accession numbers HM051111 to HM051130 for TY soil profile at depth 40–60 cm and HM051131 to HM051153 for ZZ soil profile 40–60 cm.

Statistical analysis

Prior to statistical analysis, GPS coordinates were converted to UTM coordinates in meters. The 16S rRNA gene copy numbers were log-transformed to normalize the distribution for analysis. Detrended correspondence analysis was firstly used and the longest gradient ranged from 3 to 4, and both canonical correspondence analysis (CCA) and redundancy analysis worked well. CCA with manual forward selection was performed to analyze the variance of species (characterized by T-RFs with interspecies distances) and the environmental and spatial factors (soil pH, TN, SOC, inline image, latitude, longitude, altitude and depth of the soil profiles). Additionally, these spatial data and environmental variables were standardized (mean = 0, standard deviation = 1). The variables were rejected as > 0.05 in the selection procedure by Monte Carlo permutation test. Then, partial CCA was performed to partition the variance on archaeal community caused by soil environment or spatial variables. CCA and partial CCA were performed using canoco version 4.5 (Centre for Biometry Wageningen, The Netherlands) (ter Braak & Šmilauer, 2002). Spearman's correlations and one-way anova analyses were performed using spss version 13.0 (IBM Co., Armonk, New York). The probability level < 0.05 was considered to be statistically significant.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Soil chemical properties

The soil pH varied widely in the topsoils from the different sites in the following order: ZZ > TJ > BJ > QD > TY (Table 2). The pH also varied with soil depth (Fig. 2). The ZZ profile had the highest pH (8.0–8.6), followed by the TJ (7.0–7.3), BJ (6.0–6.7), and QD (5.5–6.3). The TY and QY soils had the lowest pH (4.4–5.0) (Fig. 2). SOC and TN contents decreased with soil depth but showed significant differences among different soils (Fig. 2, Table 2).

image

Figure 2. Soil chemical properties along the profile horizons: (a) pH; (b) SOC; (c) TN; (d) Nitrate (inline image). (The soil profile was divided into six layers: 0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm.).

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Table 2. Basic chemical characteristics of topsoil samples
Soil typeSample namepHa, b (H2O)TNa, b (g kg−1)SOCb (g kg−1)inline imageb (mg kg−1)
  1. a

    Values are mean of three replicates with a deviation range of 0.00–0.08.

  2. b

    Mean ± SD (n = 3). Values within the same soil types followed by the same letter do not differ at < 0.05.

Cinnamon soilsBJ-16.9c1.16e16.0 ± 0.3e2.46 ± 0.64e
BJ-26.8d0.69g11.7 ± 0.5f2.59 ± 0.49de
BJ-36.9c0.83f13.0 ± 0.2ef2.21 ± 0.17e
BJ-45.7e2.05b37.8 ± 0.1b3.58 ± 0.02cd
TJ-17.1b2.88a44.6 ± 1.8a2.73 ± 0.14de
TJ-27.2a1.57d22.2 ± 1.4d4.82 ± 0.55ab
TJ-37.2a1.11e14.2 ± 2.9ef4.32 ± 0.66bc
TJ-47.1b1.82c27.4 ± 2.9c5.56 ± 0.33a
Brown soilsQD-15.3a0.68e11.1 ± 0.8ab2.64 ± 0.63d
QD-25.0c0.54f9.65 ± 0.37cd13.6 ± 0.8b
QD-35.1b0.72d10.4 ± 0.2c12.3 ± 0.3c
QD-45.1b0.69e11.4 ± 0.3ab3.15 ± 0.67d
QD-54.9d0.77c12.2 ± 0.2a1.94 ± 0.04d
QD-64.8e0.83b9.15 ± 0.37d12.3 ± 1.0c
QD-74.6f0.99a11.1 ± 0.7ab43.5 ± 0.5a
Fluvo-aquic soilsZZ-18.2a0.74c9.92 ± 1.56c2.60 ± 0.15a
ZZ-27.8c1.11b19.6 ± 1.5a3.59 ± 0.39a
ZZ-38.1b0.68d13.3 ± 0.6b2.58 ± 0.90a
ZZ-47.6d1.32a18.9 ± 1.1a2.60 ± 0.31a
Red soilsTY-14.2a1.14b21.9 ± 1.8ab6.28 ± 0.33c
TY-24.0b1.11b17.4 ± 2.3c4.44 ± 0.27d
TY-34.0b1.26a24.3 ± 0.3a10.6 ± 0.6b
TY-44.0b1.05b14.5 ± 3.4c12.7 ± 1.0a

Abundance of archaea

The archaeal 16S rRNA gene copy numbers in the topsoils ranged between 4.96 × 106 and 1.30 × 108 copies g−1 dry soil, and bacterial 16S rRNA gene copy numbers varied from 4.77 × 108 to 7.94 × 109 copies g−1 dry soil (Fig. 3). The archaeal abundance was the lowest in the QD soil (brown soils) but was similar in the other three soil types (Fig. 3). Bacteria abundance in the topsoils showed a similar pattern as archaea. Within each soil type, the archaeal or bacterial 16S rRNA gene abundance was not significant among the different land-use patterns (Fig. 3). Bacterial abundance was significantly higher than archaeal abundance in all the soil samples (Fig. 3 and Supporting information, Table S1).

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Figure 3. Abundance of archaea and bacteria in topsoils along a latitude gradient from Northern to Southern China (please refer to Table 1 for the sample names).

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The archaeal and bacterial 16S rRNA gene copy numbers decreased with soil depth in all the soil profiles. However, the percentage of archaea in prokaryotes increased with soil depth in the TJ, QD, ZZ, and TY soil profiles, ranging from 0.20% in TY at the depth of 0–10 cm to 9.26% in ZZ at the depth of 80–100 cm (Table 3). A significant positive correlation was observed between archaeal and bacterial 16S rRNA gene copy numbers along the depth profile (= 0.53, = 36, < 0.01).

Table 3. Ratio of soil archaeal 16S rRNA gene copy numbers to prokaryotic 16S rRNA gene abundancea in the soil profiles
Depth (cm)BJ profile (%)TJ profile (%)QD profile (%)ZZ profile (%)TY profile (%)QY profile (%)
  1. a

    Prokaryotic abundance: total 16S rRNA gene copy numbers of archaea and bacteria.

  2. b

    Values are mean of three replicates with a deviation range of 0.06–3.90%. Different lowercase letters a, b, c following the data means significant difference at < 0.05 within the same column.

0–101.74ab2.42a1.30b4.37b0.20c0.33c
10–200.99abc4.81a2.19b4.40b0.52c2.37a
20–401.67a4.95a5.08a3.64b1.07c1.26b
40–600.48c5.36a4.42a6.39ab3.87bc1.72b
60–801.39ab6.10a2.26b6.86ab1.90ab1.50b
80–1000.82bc4.06a5.72a9.26a3.04a1.26b

Archaeal 16S rRNA gene copy numbers were positively correlated with soil pH in all the soil samples and were positively correlated with SOC and TN in the topsoils. Bacterial 16S rRNA gene copy numbers showed no significant correlation with soil pH but were positively correlated with SOC and TN in all the soil samples (Table S2 and S3).

Community structure of archaea

T-RFLP, cloning, and sequencing analyses were used to characterize the community structure and diversity of the soil archaeal communities. In total, 12 T-RFs were detected in all the soil types (Figs 4 and 5). In the topsoils, T-RFs patterns in the cinnamon soils (BJ and TJ samples), brown soils (QD samples), and fluvo-aquic soils (ZZ samples) were similar and were dominated by T-RFs 192 and 537 bp with the relative abundance ranging from 30.0% to 88.7% and 4.8% to 62.8%, respectively (Fig. 4). The dominant T-RFs in the red soils (TY and QY samples) were T-RFs 231 and 192 bp of which their relative abundance varied between 3.9–71.1% and 23.4–50.5%, respectively (Fig. 4). The T-RF 162 bp was present in most soil samples with a proportion of 1.4–32.3%. Interestingly, the T-RF 92 bp was present in cinnamon, brown, and fluvo-aquic soils but was absent in most of the red soil samples. The T-RF 231 was only present in the red soils with high proportions (Fig. 4). Therefore, the archaeal 16S rRNA gene profiles in the red soils were different from the other soils. Within each soil type, there were no clear changes in T-RF types and proportions among different land uses or depth profile (Figs 4 and 5).

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Figure 4. T-RFLP patterns of archaeal 16S rRNA gene in different topsoils: (a) Cinnamon soils; (b) Brown soils; (c) Fluvo-aquic soils; (d) Red soils. Data are means of three replicates.

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image

Figure 5. T-RFLP patterns of archaeal 16S rRNA gene along the depth profiles: (a) BJ profile; (b) TJ profile; (c) QD profile; (d) ZZ profile; (e) TY profile; (f) QY profile. Data are means of three replicates.

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To characterize the diversity of archaeal 16S rRNA gene based on T-RFLP analysis, each 48 clones of archaeal 16S rRNA gene retrieved from the ZZ (fluvo-aquic soils) and TY (red soils) profiles at 40–60 cm depth (which contained the most abundant T-RFs types) were sequenced. After chimera check analyses, 46 sequences from the ZZ soil and 42 sequences from the TY soil were analyzed by the in silico digestion. Most detected T-RFs by T-RFLP analysis could be found in the digestion of sequences analysis. The rarefaction curves were far away from the plateau, and the archaeal communities should contain other unidentified phylogenetic groups if larger clone libraries were analyzed (Fig. S1). Of the 46 sequences from the ZZ sample, 33% of sequences corresponding to the T-RF 192 bp and 11% of sequences corresponding to the T-RF 92 bp were affiliated with the Crenarchaeota group 1.1b (Fig. 6). Twenty-eight percent of the sequences corresponded to T-RF 537 bp and fell within the Crenarchaeota group 1.1a, indicating that the Crenarchaeota group 1.1b and 1.1a dominated the archaeal community in the ZZ sample (Fig. 6). The identified dominant T-RFs (192, 537 and 92 bp) were consistent with the T-RFLP analysis. The remaining sequences (26%) grouped within the Euryarchaeota Thermoplasmales and were mainly represented by T-RF 162 bp (Fig. 6). Of the 42 sequences from the TY sample, 48% were affiliated with Crenarchaeota group 1.3% and 38% with Crenarchaeota group 1.1c. Another 9.5% of the sequences corresponding to T-RF 537 bp fell within Crenarchaeota group 1.1a associated, which was defined by Nicol et al. (2007) (Fig. 6). These results showed that the TY soil had a different archaeal community in relation to the ZZ soil, according to the above T-RFLP analysis. Therefore, the dominant T-RFs 192, 537 and 92 bp in cinnamon soils, brown soils and fluvo-aquic soils could be assigned to Crenarchaeota group 1.1b, 1.1a and 1.1b, respectively, and the dominant T-RFs 231 and 192 bp in the red soils could be assigned to Crenarchaeota group 1.3 and 1.1c.

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Figure 6. Phylogenetic tree of archaeal 16S rRNA gene sequences retrieved from the Taoyuan red soil and Zhengzhou Fluvo-aquic soil at depth 40–60 cm. Designation of the clones includes the following information: site (TY, ZZ), clone code, nucleotide sequence accession number, followed by the length of terminal restriction fragments and the number of clones with similarities above 98% to the listed sequence in the clone library in the parentheses. Clones in bold indicated the dominant T-RFs detected by T-RFLP analysis. Bootstrap values (> 50%) were indicated at branch points. The scale bar represented 5% estimated sequence divergence.

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Relationship of archaeal communities and environmental factors

Using the forward selection CCA analysis of T-RFLP fragments, the archaeal community distribution in the different soil samples and their relationships with environmental and spatial factors (i.e. soil pH, inline image, latitude, longitude, depth profile) were analyzed (Fig. 7a). SOC and TN were rejected in the CCA analysis, because their inflation factors were larger than 20, which implied that the variables were redundant with other variables in the datasets. The variance in the relationship between species (T-RFs) and environmental and spatial factors were explained by the two axes, and the total percentage reached 84.7%, passing the Monte Carlo test with 999 permutations (< 0.01) (Table S4). The soil archaeal communities mainly distributed along the soil pH gradient. Soil depth profile was the second impact variable, followed by longitude and latitude. The variation in the species–environment relationship explained by pH, depth profile, longitude and latitude was 27%, 10%, 4% and 4%, respectively. Further partial CCA analysis indicated that soil environmental factors explained 21.9% of the archaeal communities’ variance, whereas spatial factors accounted for 10.7% of the variance, and the overlap between them was 9.5% (Fig. 7b). To determine the effect of land use and soil type on archaeal community distribution, only the data from topsoils were subjected to CCA analysis as performed earlier. The results showed that the soil archaeal communities mainly distributed along the soil pH gradient and divided into four groups corresponding to the four soil typologies. Land use and spatial factors were covariant with other variables and were thus rejected in the analysis of archaeal community distribution among different topsoils (Fig. S2). Further analyses showed no significant effects of land use on archaeal distribution within each soil type. Therefore, the distribution of archaea was mainly determined by soil pH and affected by depth profile and spatial sites, but not by the land use (Fig. S3).

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Figure 7. Relations between archaeal community diversity and environmental and spatial factors. (a) The canonical correspondence analysis (CCA). Percentage values on axes represent cumulative percentage variation of species–environment relation explained by consecutive axes. (b) Variation partition using partial CCA.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The widespread distribution of archaea in terrestrial ecosystems implied their potential contribution to global biogeochemical cycles (Schleper et al., 2005). In present study, archaea were detected in all 59 soil samples, which represented four major soil types in China. Archaea accounted for 0.20–9.26% of total prokaryotes in the tested soils. These values are similar to those from a previous study in forest soils (Kemnitz et al., 2007). Archaea were also detected in 87% of the 146 soils in a pyrosequencing survey in which archaea occupied 2% of the prokaryotes (Bates et al., 2011). Using fluorescent in situ hybridization (FISH) microscopic counts, Sandaa et al. (1999) reported that archaea accounted for 1–6% of total cell numbers in soils. Our results obtained using real-time quantitative PCR are in good agreement with these studies using different quantitative methods, showing the widespread presence of archaea in various environments.

Although both archaeal and bacterial abundance decreased with soil depth, the relative abundance of archaea increased because of the more rapid decrease in bacterial abundance with soil depth. This trend with soil depth is in agreement with previous observations in saline sediments (Swan et al., 2010). Concentrations of SOC and TN decreased with soil depth and showed a positive correlation with bacterial abundance but not with archaeal abundance. This is in line with previous observations that bacteria was highly responsive to soil nutritional changes and could not endure the conditions of energy stress as archaea (Valentine, 2007). Therefore, as the soil nutrient status declined with soil depth, bacterial abundance declined rapidly, while archaea better survived in these less fertile deeper soil profiles. Furthermore, other than bacterial abundance showing no significant correlation with soil pH, archaeal abundance positively correlated with soil pH but there was no significant difference among land-use patterns in this study. Firstly, soil pH would affect the chemical form, concentration and availability of substrates and thus influence cell growth and activity (Kemmitt et al., 2006), and also produced strong selective pressure for structuring the archaeal community. Secondly, SOC content and TN content were ultimately influenced by soil pH for its impacts on rates of decomposition and on the solubility of compounds. Altogether, soil characteristics reflected by pH, SOC, and TN mainly influenced the archaeal and bacterial abundance. However, it is difficult to specify which environmental variable really shaped this distribution pattern, because the soil microenvironments were unavoidably affected by other factors such as spatial and climate factors (Bates et al., 2011).

The archaeal communities recovered in the present study affiliated within Crenarchaeota group 1.1a, 1.1b, 1.1c, 1.3 and Euryarchaeota Thermoplasmales. Crenarchaeota group 1.1b and 1.1a, represented by T-RFs 192 and 92, and T-RF 537, respectively, were the dominant archaeal groups in the cinnamon, brown, and fluvo-aquic soils. Previous studies have also shown that Crenarchaeota, especially group 1.1b, was the main archaeal population in terrestrial soils (Pesaro & Widmer, 2002; Nicol et al., 2005; Hansel et al., 2008). The archaeal communities in the red soils were distinct from the other three soils and were dominated by Crenarchaeota group 1.3 and 1.1c represented by T-RF 231, T-RFs 222 and 192, respectively. The Crenarchaeota group 1.1c was detected exclusively in the red soils (i.e. TY and QY samples with the lowest pH). This group has previously been shown to prefer acidic habitats (Nicol et al., 2005, 2007; Kemnitz et al., 2007). These findings demonstrated that different archaeal communities were developed in response to the variance of soil environments. This could be further proved by larger clone libraries analyses with more confident information.

Thermoplasmales have been described as thermophilic organisms for a long time but were recently assumed to be mesophiles for they were observed in various temperate soils (Pesaro & Widmer, 2002; Kemnitz et al., 2005, 2007; Auguet et al., 2010). In our study, Thermoplasmales, represented by 162 bp T-RF, were detected in most of soil samples, which supports for this hypothesis.

The CCA analysis based on T-RFLP data indicated that the archaeal communities were mainly influenced by soil factors, followed by spatial factors. Soil pH was the primary environmental factor influencing archaeal communities followed by soil depth profile. Additionally, the point of the above could be supported by the observation that the red soils had a distinct archaeal community structure from other three soil types. CCA analysis for topsoils within each soil type also showed that archaeal communities were mainly distributed along the soil pH gradient and divided into four types with soil types, while land use and spatial factor were rejected as the covariant variables. Soil pH could affect microbial communities by affecting the chemical form, concentration and availability of substrates (Kemmitt et al., 2006), and also produced strong selective pressures structuring the microbial community. Kemnitz et al. (2007) observed that soil depth was a predominant variable for driving bio-patterns of archaeal communities in sediment and forest soils. Correspondingly, the results indicated that soil depth profile could be used to explain the variance of archaeal communities as a selected spatial factor in present study. However, Angel et al. (2010) reported that the difference in the community composition of bacteria and archaea along a steep precipitation gradient forest soils could be explained by ecosystem type represented by precipitation gradient and the vegetation cover. Organic carbon and TN had a strong impact on the community of Euryarchaeota and the relative abundance of Crenarchaeota group 1.1b (Pesaro & Widmer, 2002). These varied conclusions were typically drawn from studies based on soil samples with relatively narrow habitats or along a single environmental gradient. To get a more comprehensive insight into the distribution and biogeography of archaea, further studies using samples from different habitats and at a larger spatial scale are therefore needed in the future.

In conclusion, our results indicated that archaeal abundance varied significantly in the different soil types. Crenarchaeota were the main archaeal communities in these Chinese soils, and the structure of archaeal communities were mainly influenced by soil pH. Some archaeal groups preferring specific habitats were also observed, which would support the concept of niche partitioning for terrestrial archaeal groups. The study provided new insights into the distribution characteristics of archaeal community in typical Chinese soils across a large spatial scale.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We greatly appreciate the support of the following colleagues in soil sampling: Prof. Shen A-Lin from Henan Academy of Agricultural Sciences and Prof. Shi Yan-Xi from Qingdao Agricultural University. This work was financially supported by the National Natural Science Foundation of China (41025004, 41020114001, 40871129) and the CAS/SAFEA International Partnership Program for Creative Research Teams of ‘Ecosystem Processes and Services’.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
fem1280-sup-0001-FigureS1.docxWord document136KFig. S1. Rarefaction analysis of archaeal 16S rRNA gene clones from the Taoyuan (TY) soil and Zhengzhou (ZZ) soil at depth 40–60 cm.
fem1280-sup-0002-FigureS2.docxWord document364KFig. S2. Archaeal community distribution among different topsoils using CCA.
fem1280-sup-0003-FigureS3.docxWord document115KFig. S3. Archaeal community distribution among different land-use patterns using CCA.
fem1280-sup-0004-TableS1.docxWord document13KTable S1. Archaeal and bacterial 16S rRNA gene copy numbers of soil profiles.
fem1280-sup-0005-TableS2.docxWord document12KTable S2. Correlation analyses between archaeal abundance and soil properties in topsoils.
fem1280-sup-0006-TableS3.docxWord document12KTable S3. Correlation analyses between archaeal abundance and soil properties in the soil profiles.
fem1280-sup-0007-TableS4.docxWord document13KTable S4. The parameters of canonical correspondence analysis (CCA) in Figs 7a and S2.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.