RESEARCH ARTICLE
Soil characteristics more strongly influence soil bacterial
communities than land-use type
Eiko E. Kuramae1,2, Etienne Yergeau3, Lina C. Wong1, Agata S. Pijl1, Johannes A. van Veen1,4
& George A. Kowalchuk1,2
1
Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands; 2Institute of Ecological
Science, Free University Amsterdam, Amsterdam, The Netherlands; 3Biotechnology Research Institute, National Research Council of Canada,
Montréal, QC, Canada; and 4Institute of Biology, Leiden University, Leiden, The Netherlands
Correspondence: Eiko E. Kuramae,
Department of Microbial Ecology,
Netherlands Institute of Ecology (NIOOKNAW), Wageningen, The Netherlands.
Tel.: +31 317 473 502; fax: +31 317 473
675; e-mail: [email protected]
Received 14 December 2010; revised 15
August 2011; accepted 20 August 2011.
Final version published online 19 September
2011.
DOI: 10.1111/j.1574-6941.2011.01192.x
MICROBIOLOGY ECOLOGY
Editor: Kornelia Smalla
Keywords
phosphorus; firmicutes; phylochips; C : N
ratio.
Abstract
To gain insight into the factors driving the structure of bacterial communities
in soil, we applied real-time PCR, PCR-denaturing gradient gel electrophoreses,
and phylogenetic microarray approaches targeting the 16S rRNA gene across a
range of different land usages in the Netherlands. We observed that the main
differences in the bacterial communities were not related to land-use type, but
rather to soil factors. An exception was the bacterial community of pine forest
soils (PFS), which was clearly different from all other sites. PFS had lowest bacterial abundance, lowest numbers of operational taxonomic units (OTUs), lowest soil pH, and highest C : N ratios. C : N ratio strongly influenced bacterial
community structure and was the main factor separating PFS from other fields.
For the sites other than PFS, phosphate was the most important factor explaining the differences in bacterial communities across fields. Firmicutes were the
most dominant group in almost all fields, except in PFS and deciduous forest
soils (DFS). In PFS, Alphaproteobacteria was most represented, while in DFS,
Firmicutes and Gammaproteobacteria were both highly represented. Interestingly, Bacillii and Clostridium OTUs correlated with pH and phosphate, which
might explain their high abundance across many of the Dutch soils. Numerous
bacterial groups were highly correlated with specific soil factors, suggesting that
they might be useful as indicators of soil status.
Introduction
Soilborne microorganisms are key to numerous biological
processes such as nutrient cycling, plant nutrition, disease
suppression, water purification, and soil structure maintenance (Filip, 2002). Even though our knowledge of the
particular organism (species) involved in these key functions is limited, bacteria are known to be influenced by a
range of biotic and abiotic factors, such as vegetation
(Marschner et al., 2001; Kowalchuk et al., 2002; Weinert
et al., 2011), soil characteristics (Hansel et al., 2008; Wu
et al., 2008), soil texture (Schutter et al., 2001), land use
(Kennedy et al., 2005; Yergeau et al., 2007), geographic
distance (Yergeau et al., 2007), and pH (Fierer & Jackson,
2006; Lauber et al., 2008).
Soils are physically, chemically, and biologically heterogeneous, thereby providing a wide range of niches to susª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
tain microbial diversity. More recently, it was shown that
small changes in soil pH can have important impacts on
microbial succession in abandoned Dutch chalk grasslands (Kuramae et al., 2010). In some ecosystems, soil
properties overide the effects of land management (Schutter et al., 2001; Lauber et al., 2008).
The aforementioned soil characteristics are strongly
affected by land usage. For instance, intensive agriculture
causes physical degradation, such as erosion and compaction, and chemical changes, such as pollution from pesticides. Furthermore, differences in nutrient pools owing to
fertilization may lead to organic matter depletion and
induce changes in nutrient cycling (Pretty & Shah, 1997;
Doran & Zeiss, 2000). All these factors, in turn, have profound effects on soil microorganisms, and it is therefore
expected that soils with diverse management systems will
contain dissimilar bacterial communities.
FEMS Microbiol Ecol 79 (2012) 12–24
13
Effect of soil factors on bacterial community structure
In order to increase crop yield, inorganic fertilizers
and animal manure are often added to agricultural soils;
especially in the Dutch agriculture systems including
pasture, tremendous amount of fertilizers have been
applied over the past decades. The addition of organic
manure has been shown to promote soil microbial activities (Enwall et al., 2007), and the addition of mineral
fertilizers has been shown to have an impact on microbial community diversity (Zhong et al., 2010). In the
face of current anthropogenic pressure on soil ecosystems, for instance owing to agricultural intensification
and climate change, there is a need to better understand
the effects of these factors in order to predict and mitigate the impacts of such changes. However, reliable predictions of the potential consequences of perturbations
on soil microorganisms and subsequent ecosystem feedbacks are hampered by the lack of baseline knowledge
about the distribution, density, and ecology of soil-borne
microbial communities. The objective of this study was
to investigate the relative importance of various soil factors and land-use regimes on soilborne microbial community composition. To this end, we examined
microbial community structure across a range of soils
and land-use types throughout the Netherlands with a
combination of real-time PCR, PCR-denaturing gradient
gel electrophoreses (DGGE) approaches, and phylogenetic microarray analysis (PhyloChips). These three
methods were chosen because they give different information on microbial community structure based on the
SSU rRNA gene, namely quantification of bacterial and
fungal abundance by real-time PCR, bacterial community profile by PCR-DGGE, and bacterial community
composition by PhyloChips. Resulting community patterns were then related to soil and land-use characteristics in order to identify the most important drivers of
soil-borne bacterial community structure and to identify
the bacterial classes, orders, and families that best correlate with specific soil factors.
Material and methods
Experimental design, sampling, and soil
analyses
Twenty-five fields, which represent six of the most
important land usages in the Netherlands (two deciduous forests, three pine forests, two natural grasslands,
six pastures, six conventional arable land, and six
organic arable land), were sampled (Supporting Information, Fig. S1) in May 2007. All sampling was conducted within a 2-day period, in the absence of notable
weather events, in order to minimize the effects of sampling time. In each field, a central point was selected,
FEMS Microbiol Ecol 79 (2012) 12–24
and subsequently, four sampling points at 20 m from
the central point were chosen to obtain five samples
per field (A, B, C, D, and E). Each sample (A, B, C,
D, and E) was comprised of five subsamples (A1, A2,
A3, A4, A5; B1, B2, B3, B4, B5, etc) consisting of soil
cores (8 cm diameter 9 30 cm deep) taken randomly
within a two-meter radius of each of the five sample
points A, B, C, D, and E (see Fig. S2). Soil samples
were sieved through a 4-mm mesh to remove stones,
roots, and plant material. Equal amounts of each of the
five subsamples of a sample were pooled to obtain a
composite sample replicate per field, thereby yielding
five biological replicates per field (A, B, C, D, and E).
Part of each sample was stored at 80 °C for DNA
extraction, while the rest kept at 4 °C for physical and
chemical analyses. For physical and chemical analyses,
equal amounts of each of the five replicates per field
were pooled.
Physicochemical characterization of total C, total N,
phosphate, organic matter, pH, As, CaCO3, Cd, Cr, Cu,
Hg, Ni, Pb, Zn, soil texture, and soil moisture was carried
out by BLGG (Bedrijfslaboratorium voor Grond en Gewasonderzoek, Wageningen, the Netherlands, https://blgg.
agroxpertus.nl/).
DNA extraction
DNA was extracted separately on each of the five replicates per field using the MoBio Power Soil Extraction kit
(MoBio, Carlsbad, CA) with bead-beating (Retsch
MM301; Retsch GmbH, Germany) at 5.5 m s 1 for
10 min. Total DNA concentration was quantified on a
ND-1000 spectrophotometer (Nanodrop Technology,
Wilmington, DE).
PCR-denaturing gradient gel electrophoresis
Bacterial 16S rRNA gene-specific PCR and subsequent
DGGE were carried out as previously described, using a
D-Code Universal Mutation Detection System (Bio-Rad,
Hercules, CA) (Yergeau et al., 2007). Banding patterns
were normalized with respect to standards of known
composition as well as samples loaded across multiple
gels. The validity between comparisons was tested by
examining the grouping of samples run across multiple
gels, which revealed tight grouping of replicates as
opposed to grouping according to gel (not shown). Community profile banding patterns were analyzed using the
IMAGE MASTER 1D program (Amersham Biosciences, Roosendaal, The Netherlands) as described by Yergeau et al.
(2007). The resulting binary matrices based on 66 DGGEdetected band positions were used in statistical analyses
as ‘species’ presence/absence matrices.
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
14
Real-time PCR
Real-time PCR quantifications for bacteria were performed using primers and cycling conditions for 16S
rRNA genes as previously described (Fierer et al., 2006),
while 18S rRNA gene copies for fungi were quantified
according to Lueders et al. (2004). Real-time PCR quantifications were carried out on soil DNA using ABsolute
QPCR SYBR Green mixes (AbGene, Epsom, UK) on a
Rotor-Gene 3000 (Corbett Research, Sydney, NSW, Australia) as previously described (Yergeau et al., 2007).
Sample preparation for PhyloChip analysis
For PhyloChip analyses, the five replicate DNA extractions from a single field were pooled, providing a single
representative DNA sample per field. However, in order
to examine within-site variation, seven fields (1F, 4F, 8F,
10F, 13F, 16F, and 25F) representing the different landuse types in this study were assessed in three of the five
field replicates. 16S rRNA gene amplification was carried
out by bacterial-specific primers, 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTACGACTT-3′). PCR amplifications were carried out
with 19 Ex Taq buffer (Takara Bio Inc, Japan), 0.8 mM
dNTP, 0.02 units mL 1 Ex Taq polymerase, 0.4 mg mL 1
BSA, and 1.0 mM of each primer. Three independent
PCR amplifications were carried out with annealing
temperatures of 48, 51.9, and 58 °C, with an initial denaturation step at 95 °C for 3 min, followed by 25 amplification cycles with denaturation at 95 °C for 30 s,
annealing for 30 s, and extension at 72 °C for 60 s, followed by a final extension at 72 °C for 7 min. PCR products were pooled, and a 2-lL subsample was quantified
on 2% agarose gel. The volume of the pooled PCR products was reduced to less than 40 lL with micrometer
YM100 spin filters (Millipore, Billerica, MA). Regression
analysis confirmed that the quantity of PCR amplicon
applied to the array was not correlated with any organism
abundances as estimated by fluorescence intensity of
hybridization (data not shown).
Phylochip processing, scanning, and
normalization
The pooled PCR products described earlier were spiked
with known concentrations of amplicons derived from
yeast and prokaryotic metabolic genes. This amplicon
mix was fragmented and biotin-labeled using the GeneChip DNA labeling reagent (Brodie et al., 2006). Subsequently, the labeled DNA was denatured at 99 °C for
5 min and hybridized to custom-made Affymetrix Gene-
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
E.E. Kuramae et al.
Chips (16S PhyloChips G2) (DeSantis et al., 2007) at
48 °C and 60 rpm for 16 h in a hybridization chamber.
PhyloChip washing and staining were performed according to standard Affymetrix protocols (Brodie et al., 2006).
Each PhyloChip was scanned and recorded as a pixel
image, and initial data acquisition and intensity determination were carried out using AFFYMETRIX software (GeneChip microarray analysis suite, version 5.1).
To account for variation in scanning intensity from
array to array, the intensities resulting from the internal
standard probe sets were natural-log-transformed. Adjustment factors for each PhyloChip were calculated by fitting
a linear model with the least-squares method. Resulting
PhyloChip adjustment factors were subtracted from each
probe set’s natural log of intensity.
Background subtraction, noise calculation, and spot
detection and quantification were carried out essentially
as previously reported (Brodie et al., 2006). A probe pair
was considered positive if the difference in intensity
between the perfect match and mismatch probes was at
least 130 times the square noise value (N). A taxon was
considered present in the sample when 90% or more of
its assigned probe pairs for its corresponding probe set
were positive [positive fraction (PosFrac) 0.90]. Operational taxonomic unit (OTU) richness was simply the
number of OTU considered positive for a given sample.
Relative OTU intensities were calculated by dividing the
average signal of the probes aiming at a given OTU by
the total average signal for all the OTUs that were identified as present. The relative abundance values were used
directly for most analyses or summed up to the Phylum
level.
Statistical analyses
Nonmetric multidimensional scale (NMDS) was used to
visualize the differences among different land-use types
and soil physicochemical properties owing to microbial
community composition. Data from each molecular
approach for microbial community analysis, namely realtime PCR, DGGE-PCR, and PhyloChips, were analyzed
separately with respect to soil properties. Analysis of similarity (ANOSIM) (PRIMER v.5 software) was used to assess
significant differences with respect to land use. This nonparametric permutation procedure uses the rank similarity
matrix underlying an ordination plot to calculate an R test
statistic. For real-time PCR and PhyloChip data, Bray–Curtis distance index was calculated, whereas for DGGE data,
Hellinger distance index was used. Bray–Curtis is a popular
similarity index for abundance data, whereas Hellinger
distance is used to quantify the similarity between two
probability distributions.
FEMS Microbiol Ecol 79 (2012) 12–24
15
Effect of soil factors on bacterial community structure
Bacterial community structure was related to soil factors using canonical correspondence analyses (CCA) in
Canoco (ter Braak & Šmilauer, 2002). DGGE-PCR presence–absence data were used as ‘species’ data, while soil
data were included in the analysis as ‘environmental’ variables. Variables having the most significant influence on
the microbial community structure were chosen by forward selection with a P < 0.010 baseline. The variables
selected this way were then included in a model for which
significance was tested with 999 permutations. The CCAs
for real-time PCR and PhyloChips were basically the
same used for the CCA analysis of DGGE-PCR data,
except that absolute values and normalized intensities
were used as ‘species’ for real-time PCR and PhyloChips,
respectively.
Pearson’s correlations were calculated between soil factors (C : N ratio, As, CaCO3, Cd, Cr, Cu, Hg, Pb, Zn,
phosphate, total C, total N, organic matter, soil moisture,
clay, sand, silt, and soil pH) and normalized OTU
hybridization intensities using the ‘multtest’ package in R
(version 2.6.0; The R Foundation for Statistical Computing). P values were corrected for multiple testing, using
the false discovery rate controlling procedure (Benjamini
& Hochberg, 1995).
Results
Soil characteristics
Soil physicochemical characteristics varied according to
land use (Table 1). Pine forest soils (PFS) showed lower
pH and higher C : N ratios than other soils. Furthermore, arable fields and pastures had higher pH values
and higher phosphate than deciduous forest and natural
grassland fields. Natural grasslands and pastures had two
times more total N than arable fields and pine forest
and 1/3 more total N than deciduous forest soils. Natural grasslands field 26F had higher total carbon and
organic matter than other fields. Pasture field 19F had
more silt, clay, total N, Cr, Ni, and Zn than all other
fields.
Bacterial and fungal abundance
Real-time PCR targeting small subunit rRNA genes was
used to quantify the relative abundance of bacteria and
fungi across the range of soils sampled. In general, bacterial abundance was lower in PFS and higher in deciduous
forest soils (Table 2). Fungal abundance was lower in natural grasslands, and relatively high in forest soils, especially in sample 23F. Fungal abundance was significantly
correlated with phosphate, while bacterial abundance was
not significantly correlated with any measured soil physiFEMS Microbiol Ecol 79 (2012) 12–24
cochemical factors (Table S1). The fungal abundance was
not significantly correlated with total numbers of OTUs
given in the PhyloChips (Table S2).
The total bacterial and total fungal abundance as determined by real-time PCR did not separate the fields
according to land use by NMDS analysis (Fig. 1a*). The
NMDS analysis gave a good representation of the data
(stress = 0.06613, Bray–Curtis distance index). These data
were analyzed by a second method, CCA, in order to
confirm observed differences, and this additional analysis
showed no clustering of the fields according to land use
(Fig. 1a**).
DGGE analysis
A total of 66 band positions were detected across all samples, and almost no variability in banding patterns was
observed between replicates (Fig. S3). NMDS analysis of
DGGE data showed no clear separation of the fields
according to land use (Fig. 1b*). The stress value, based
on Jaccard similarity, was 0.21471, indicating that an
additional analysis method is necessary for data interpretation. We therefore also performed CCA analysis
(Fig. 1b**) with DGGE data and soil physicochemical
data, the results of which clearly showed that the main
differences in bacterial structure were between PFS and
field 23F, and all other soils. C : N ratio, sand, and pH
were the underlying soil variables most responsible for
the variation as shown on the second axis of the ordination.
OTU richness detected by PhyloChips
PhyloChip analyses detected a total of 2869 different
OTUs across the 25 soils examined. Of these OTUs, 362
were detected in all samples. The numbers of OTUs per
site ranged from 2207 in the pasture 21F field to 867
OTUs in pine forest field 4F (Table 3). On average, arable
soils (conventional and organic), natural grasslands, pasture and deciduous forest soils had 38% to 42% more
OTUs than PFS (Table 3). This was mainly attributable
to lower numbers of OTUs belonging to the phyla Bacterioidetes, Firmicutes, Nitrospira, and classes Beta proteobacteria and Gammaproteobacteria in PFS. Most OTUs
detected belonged to Firmicutes (16–18% of the total) in
almost all fields, except in pine forest and deciduous forest soils (Table 3). In pine forests, members of the Alphaproteobacteria (17%) were most detected, while in
deciduous forest soils, members of Firmicutes and Gammaproteobacteria had similar contributions (17%) to the
total bacterial communities. The total numbers of OTUs
detected were positively correlated with pH and Zn and
negatively correlated with C : N ratio (Table S2).
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
pH
Conventional arable field
7F
5.1 1309
8F
5.8 1267
9F
5.5 1985
16F 7.3 1217
17F 7.4 1741
18F 7.4 1292
Organic arable field
10F 5.8 1338
11F 6.2
966
12F 6.5 1275
13F 7.4 1440
15F 7.3 1367
27F 6.2
714
Pasture
1F
6.2 2142
2F
6.1 2498
3F
7.6 1858
19F 6.0 5489
21F 6.6 2491
28F 5.9 4610
Natural grassland
25F 5.5 2029
26F 5.7 4990
Pine forest
4F
3.7 1703
5F
4.1 1281
6F
3.8
814
Deciduous forest
23F 3.7 2805
24F 6.3 1431
Field
Total N
(mg kg 1)
2.5
2.1
3.8
1.1
2.3
1.2
2
1.9
1.4
1.4
1.2
0.9
2.7
2.3
2.8
4.8
2.3
4.1
2.9
8.3
3.8
2.7
2.0
5.0
1.6
152
76
296
137
160
164
337
197
130
310
196
234
30
59
17
31
10
49
78
Total C
(g 100
g 1)
82
90
81
102
62
106
Total P
(mg P2O5
100 g 1)
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
17.8
11.2
22.3
21.1
24.6
14.3
16.6
12.6
9.2
15.1
8.7
9.2
8.9
14.9
19.7
11.0
9.7
8.8
12.6
19.1
16.6
19.1
9.0
13.2
9.3
C:N
ratio
9.0
3.0
6.4
4.6
3.0
5.3
16.3
5.8
4.0
6.1
9.4
4.4
8.3
3.6
2.9
2.7
2.4
2.2
1.2
3.9
3.5
8.3
1.7
4.1
1.6
Organic
matter
(%)
0
0
0
0
0
0
0
0.2
0
0
0.2
0
0
0
0
0.1
3.8
0.8
0
0.1
0
0.2
0.2
0.7
2.4
CaCO3
(%)
28
16
17
14
14
25
55
14
16
19
29
17
27
13
11
17
18
15
13
11
17
16
16
15
15
Moisture
(%)
4.3
4.3
0.3
1.5
1
1.8
9.5
4.1
1.1
2.2
36.7
21
32.1
1.8
0.9
13.4
8.2
7.5
4.1
0.5
0.6
1.2
11.6
5.6
16.1
Clay
(%)
9.8
6.9
4.3
9.8
5.5
6.4
22.6
16.1
6.8
24.3
51.1
36.8
44.4
10.2
6
29.4
39.8
35.2
8.8
6.1
10.9
4.7
27.2
15.6
37.9
Silt
(%)
Table 1. Soil physical and chemical values of 25 fields representing six land-use types in the Netherlands
85.9
88.8
95.4
88.8
94.0
91.9
68.0
79.7
91.9
73.3
12.1
42.1
23.6
88.0
93.1
57.4
52.0
57.2
87.0
93.4
88.4
94.1
61.2
78.7
46.0
Sand
(%)
0.1
0.2
0.1
0.2
0
0.2
0.6
0.3
0.2
0.3
0.5
0.5
0.5
0.2
0
0.2
0.2
0.2
0.2
0
0.2
0.1
0.1
0.1
0.1
Cd (mg
kg 1)
8.2
11.0
0
3.5
0
7.2
17.0
5.5
6.9
6.8
52.0
28.0
42.0
7.2
3.6
19.0
26.0
19.0
5.3
0
7.6
4.0
19.0
13.0
29.0
Cr (mg
kg 1)
0
12.0
0
0
0
6.2
6.4
21.0
13.0
15.0
27.0
47.0
26.0
5.3
6.4
15.0
8.2
26.0
0
6.3
11.0
11.0
0
0
21.0
Cu (mg
kg 1)
3.8
6.5
0
0
0
3.5
8.0
0
0
0
33.0
24.0
34.0
0
0
8.4
10.0
9.9
0
0
0
0
6.7
6.6
14.0
Ni (mg
kg 1)
23.0
37.0
15.0
13.0
10.0
9.2
26.0
37.0
9.7
14.0
32.0
42.0
40.0
17.0
6.1
21.0
15.0
21.0
11.0
0
15.0
35.0
16.0
6.1
14.0
Pb (mg
kg 1)
12
79
0
0
0
12
42
43
28
34
130
94
112
25
19
71
40
36
24
16
28
13
29
22
41
Zn (mg
kg 1)
5.0
4.4
1.8
2.4
1.6
3.4
7.2
26.0
2.2
2.5
14.0
14.0
14.0
2.5
1.1
10.0
13.0
8.8
3.6
0
1.4
1.9
7.5
6.8
11.0
As (mg
kg 1)
0.09
0.12
0.04
0.04
0
0.06
0.08
0.05
0
0.04
0.08
0.18
0.1
0.03
0
0.09
0.04
0.05
0
0
0.03
0.13
0.04
0.04
0.03
Hg
(mg
kg 1)
16
E.E. Kuramae et al.
FEMS Microbiol Ecol 79 (2012) 12–24
17
Effect of soil factors on bacterial community structure
Table 2. Mean real-time PCR quantification of bacterial 16S rRNA
genes and fungal 18S rRNA genes for 25 fields
Field #
Land use
Bacterial 16S
rRNA
108 copies g
soil FW*
7F
Arable field conventional
8F
9F
16F
17F
18F
10F
Arable field organic
11F
12F
13F
15F
27F
1F
Pasture
2F
3F
19F
21F
28F
25F
Natural grassland
26F
4F
Pine forest
5F
6F
23F
Deciduous forest
24F
Mean (SEM)
9.31
9.27
12.02
7.57
22.40
8.39
11.40
10.06
9.69
8.73
11.78
11.80
15.13
16.85
17.11
9.90
26.77
16.03
9.75
16.59
7.84
7.22
6.74
32.16
20.66
Arable field conventional
Arable field organic
Pasture
Natural grassland
Pine forest
Deciduous forest
11.49
10.58
16.97
13.17
7.27
26.41
1
Fungal 18S
rRNA
107 copies g
soil FW
1
10.82
6.83
8.71
5.32
7.06
3.19
16.17
7.74
7.14
4.63
9.78
11.20
10.83
9.02
7.69
1.94
9.87
3.44
1.43
1.34
7.15
4.96
4.52
19.09
4.97
(2.27)
(0.52)
(2.24)
(3.42)
(0.31)
(7.35)
6.99
9.44
7.13
1.39
5.54
12.03
(1.08)
(1.63)
(1.48)
(0.04)
(1.10)
(4.78)
*, fresh weight. The real-time PCR quantification of bacteria and fungi
of each field is a mean of five replicates per field.
The means were calculated for each land-use type, and the standard
error of the means (SEM) is indicated in parenthesis.
Clostridiales, Lactobacillales (Firmicutes), Rhizobiales, Rhodobacterales, Sphingomonadales (Alphaproteobacteria),
Burkholderiales (Betaproteobacteria), Aeromonadales, Chromatiales, Enterobacteriales, Pseudomonadales, Xanthomonadales (Gammaproteobacteria), and Verrucomicrobiales
(Verrucomicrobia) were found only in agricultural soils
and not in pine forest or in natural grassland soils
(Table 4).
More detailed analyses were conducted by correlating
OTUs intensities given in the PhyloChip with 19 soil physicochemical characteristics (Table 1) measured across all
soils. A total of 670 OTUs significantly correlated with at
least one of the following soil factors: pH, C : N ratio,
phosphate, soil texture (sand, silt, and clay), Cr, Zn, and
soil moisture. These OTUs were distributed over most of
the common phyla and classes detected in soil such as
Acidobacteria, Actinobacteria, Bacterioidetes, Chloroflexi,
Cyanobacteria, Firmicutes, Nitrospira, Planctomyces, Spirochaeta, Verrucomicrobia, Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria.
A total of 425 OTUs were correlated with pH (421
positively and three negatively), 150 OTUs positively correlated with phosphate, 147 OTUs with C : N ratio (124
negatively and 23 positively), 75 OTUs positively correlated with sand, 60 OTUs negatively correlated with silt,
78 OTUs negatively correlated with clay, 88 OTUs negatively correlated with Cr, and 10 OTUs negatively correlated with Zn. Numerous OTUs were co-correlated with
pH, C : N ratio, and phosphate.
Because the C : N ratio and pH of PFS were very different from the other fields, we also carried out CCA
analysis of microarray data and soil data excluding those
samples. The CCA results did not group the fields according to land-use or soil types (Fig. S4). Based on this analysis, the main factor that appeared to influence bacterial
community structure was the phosphate content, independently of the land-use type.
Discussion
The NMDS analysis of the PhyloChip data did not
show clear clustering of samples based on land use
(stress = 0.04673), except for pine forest and natural
grassland (Fig. 1c*). CCA analysis clearly grouped pine
forest sites and natural grassland sites into distinct clusters, but the remaining land-use fields did not form
groups according to land-use type. The main factor that
grouped the PFS was the C : N ratio, while natural grassland communities seemed to be most influenced by soil
moisture (Fig. 1c**). Particular OTUs belonging to
Acidobacteria-4, Acidobacteria-6, and Acidobacteria-7 subgroups (Acidobacteria), Actinomycetales (Actinobacteria),
Flavobacteriales, Sphingobacteriales (Bacterioidetes), Bacillales,
FEMS Microbiol Ecol 79 (2012) 12–24
Effects of land use on bacterial communities
A combination of molecular approaches was used in this
study, namely general fungal and bacterial quantity (realtime PCR), fingerprinting (PCR-DGGE), and highthroughput phylogenetic microarrays (PhyloChips), to
assess the bacterial community structure of six different
common land usages in the Netherlands. Although the
three different approaches have different levels of robustness and resolution, none of them revealed a grouping of
fields according to the land use only, with the exception
of the three PFS (PCR-DGGE and PhyloChips) and one
natural grassland (PhyloChips). High C : N ratio, low
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
18
E.E. Kuramae et al.
(a*)
(a**)
(b*)
(b**)
(c*)
(c**)
ENV. Variables
Samples
pH, and low phosphate concentration in pine forest
might explain this distinction, because these PFS were
more acidic and had lower phosphate than the soils with
other soil usages. Soil pH is known to affect microbial
community structure (Fierer & Jackson, 2006; Baker
et al., 2009; Lauber et al., 2009; Kuramae et al., 2010).
The largest numbers of OTUs that correlated with soil
physicochemical factors were positively correlated with
pH; only three OTUs affiliated to Gammaproteobacteria
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
Fig. 1. NMDS* and CCA** of sampling sites,
soil factors and (a) bacterial and fungal realtime PCR quantification, (b) PCR-DGGE
fingerprinting, (c) normalized intensities given
in PhyloChips for 25 soil samples representing
six different land uses across the Netherlands.
The red arrows in CCA (a**, b**, c**) are
significant soil factors.
and Actinobacteria were more abundant in soils with low
pH. Indeed, 425 (63%) of the OTUs correlated with soil
factors were correlated with soil pH; 154 (23%) of the
OTUs that correlated with pH also showed a significantly
negative correlation with C : N ratio, which are
represented by some taxa belonging to Acidobacteria (subgroup Acidobacteria-4, subgroup Acidobacteria-6, class Acidobacteriales), Bacterioidetes (Bacterioidales, Flavobacteriales,
and Sphingobacteriales), Alphaproteobacteria (SphingomonaFEMS Microbiol Ecol 79 (2012) 12–24
Conventional arable field
7F
8F
9F
16F
17F
18F
Mean
Organic arable field
10F
11F
12F
13F
15F
27F
Mean
Pasture
1F
2F
3F
19F
21F
28F
Mean
Natural grassland
25F
26F
Mean
Pine forest
4F
5F
6F
Mean
Deciduous forest
23F
24F
Mean
Total numbers of OTUs detected
Field
52
62
69
42
50
65
56.7
65
41
44
54
75
65
57.3
57
59
58
39
46
22
35.7
49
73
61
88
1605
1590
1816
1395
1600
1765
1628.5
1948
1415
1698
1551
2207
1699
1753.0
1741
1505
1623
867
1232
936
1011.7
1560
2137
1848.5
Total numbers of OTU
44
32
40
76
46
79
52.8
Acidobacteria
1545
1340
1411
2169
1677
2040
1697
Actinobacteria
FEMS Microbiol Ecol 79 (2012) 12–24
172
247
209.5
331
96
152
108
118.7
189
150
169.5
224
195
204
207
256
222
218.0
198
192
209
178
179
204
193.3
188
163
175
241
225
230
203.7
Bacteroidetes
87
152
119.5
220
32
63
48
47.7
124
109
116.5
168
87
133
120
164
113
130.8
106
98
134
105
128
122
115.5
105
101
100
169
129
155
126.5
Chlorobi
7
10
8.5
10
5
6
4
5.0
8
9
8.5
7
6
7
6
10
8
7.3
6
6
7
4
6
6
5.8
6
4
6
9
7
9
6.8
Chloroflexi
30
43
36.5
53
26
31
18
25.0
37
37
37
35
20
33
24
45
34
31.8
30
28
37
28
32
35
31.7
29
22
26
41
27
45
31.7
Cyanobacteria
47
49
48
61
28
41
30
33.0
47
40
43.5
48
45
48
43
50
45
46.5
46
44
49
33
45
51
44.7
44
47
43
49
45
50
46.3
Firmicutes
259
359
309
508
107
174
126
135.7
295
222
258.5
351
250
318
198
405
322
307.3
319
298
321
240
278
306
293.7
305
254
259
372
233
389
302.0
Gemmatimonadetes
6
9
7.5
9
5
7
5
5.7
8
8
8
9
7
9
9
9
8
8.5
8
9
9
7
9
8
8.3
7
6
7
9
9
9
7.8
Natronoanaerobium
4
4
4
5
3
3
2
2.7
2
4
3
4
4
2
5
5
4
4.0
4
4
4
5
4
4
4.2
4
2
3
4
5
4
3.7
Nitrospira
4
8
6
14
1
5
2
2.7
12
14
13
6
2
7
7
9
9
6.7
4
7
6
7
6
3
5.5
4
2
3
11
7
10
6.2
Planctomycetes
15
28
21.5
46
10
12
12
11.3
19
14
16.5
21
14
18
15
34
16
19.7
14
12
21
12
13
16
14.7
13
10
12
26
21
28
18.3
Alphaproteobacteria
222
305
263.5
403
158
194
154
168.7
219
198
208.5
267
208
246
236
292
240
248.2
209
226
236
195
218
251
222.5
227
179
206
298
235
265
235.0
122
175
148.5
220
59
81
79
73.0
156
147
151.5
154
109
135
130
183
136
141.2
140
147
153
106
120
145
135.2
119
116
118
169
139
167
138.0
Betaproteobacteria
Table 3. Total numbers of OTUs detected in PhyloChips and the total numbers of OTUs per phylum or class of bacterial phylum in 25 fields
Deltaproteobacteria
74
107
90.5
146
66
84
57
69.0
101
99
100
99
77
80
87
115
102
93.3
85
88
95
81
84
86
86.5
83
73
78
111
90
104
89.8
Epsilonproteobacteria
34
42
38
57
35
38
25
32.7
33
37
35
39
38
37
38
42
38
38.7
39
37
40
36
39
40
38.5
38
39
38
48
39
43
40.8
Gammaproteobacteria
279
338
308.5
454
97
154
143
131.3
265
203
234
285
206
230
225
317
172
239.2
209
204
270
202
247
275
234.5
192
172
178
350
271
268
238.5
Spirochaetes
33
37
35
44
17
29
15
20.3
35
28
31.5
36
13
30
34
35
34
30.3
30
27
31
21
33
31
28.8
30
25
26
36
35
35
31.2
Unclassified
25
39
32
57
18
25
17
20.0
34
31
32.5
32
22
28
28
42
30
30.3
25
25
29
23
27
28
26.2
25
24
22
34
27
37
28.2
Verrucomicrobia
27
36
31.5
48
10
21
13
14.7
30
29
29.5
28
16
27
24
37
33
27.5
21
19
30
21
24
30
24.2
21
15
18
39
23
31
24.5
Effect of soil factors on bacterial community structure
19
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
20
E.E. Kuramae et al.
Table 4. Taxa and numbers of OTUs detected in soils of pine forests and field 23F, of natural grasslands (fields 25F and 26F), and of the
remaining fields (Others)
Phylum
Class
Order
Acidobacteria
Acidobacteria
Acidobacteria-4
Acidobacteria-6
Acidobacteria-7
Acidobacteria
Actinobacteria
Acidobacteriales
Actinobacteria
BRC1
Chloroflexi
BD2-10 group
Bacteroidetes
Flavobacteria
Sphingobacteria
Unclassified
Unclassified
Anaerolineae
Cyanobacteria
Dehalococcoidetes
Cyanobacteria
Deferribacteres
Dictyoglomi
DSS1
Firmicutes
Unclassified
Deferribacer
Dictyoglomi
Unclassified
Bacilli
Bacteroidetes
Clostridia
Mollicutes
NC10
Nitrospira
OP8
Unclassified
NC10-2
Nitrospira
Unclassified
Holophagales
Acidimicrobiales
Actinomycetales
Coriobacteriales
Rubrobacterales
Unclassified
Unclassified
Bacteroidales
Flavobacteriales
Sphingobacteriales
Unclassified
Unclassified
Chloroflexi-1a
Unclassified
Unclassified
Chloroplasts
Oscillatoriales
Unclassified
Unclassified
Dictyoglomales
Unclassified
Bacillales
Lactobacillales
Clostridiales
Acholeplasmatales
Anaeroplasmatales
Mycoplasmatales
Unclassified
Unclassified
Nitrospirales
Unclassified
dales), Gammaproteobacteria (Alteromonadales), and Verrucomicrobia (Verrucomicrobiales). When removing the
pine forest fields from the analysis, due to their very different characteristics of C : N ratio, pH, and phosphate
as compared to the remaining fields, the CCA analysis
still did not group the fields according to land-use or soil
types and the main factor that appeared to influence bacterial community structure independently of land use was
the phosphate content. The similarity of bacterial community structures in some pasture fields, conventional
arable fields, and organic arable fields is probably
explained by the fact that these fields have similar pH
and phosphate content. Strikingly, soil clay or sand
textures did not have a large impact on soil bacterial
community structure; phosphate content was even more
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
Pine
forests, 23F*
Natural
grassland*
Others*
0
0
0
0
0
3
1
0
0
1
0
0
0
0
0
1
1
0
0
0
0
0
1
1
0
0
1
3
0
0
0
1
0
0
0
1
0
0
0
1
0
0
1
0
0
0
5
2
0
0
0
0
1
1
0
0
1
0
0
1
3
3
10
0
0
0
0
0
5
1
2
3
8
1
0
3
36
1
1
3
1
4
12
17
1
0
0
0
0
2
2
0
0
0
0
30
15
24
7
1
1
0
1
1
0
important when the pine forest fields were excluded from
the analysis. Phosphate was also observed to be an important driver of soilborne microbial community succession
in chronosequences of abandoned chalk grasslands with
neutral pH (Kuramae et al., 2011).
Fungal abundance was highest in deciduous forest soils
but not in PFS. The higher abundance of fungi in forest
soils is in line with results from Jangid et al. (2008). Interestingly, PFS had very low fungal abundance compared
with other soils, including deciduous forests. It may be that
the difference in the types of complex organic matter in
pine forests contributed to the selection of distinct groups
of fungi in these soils. Fungal abundance was also low in
natural grasslands. This type of Dutch natural grassland
called ‘blauwgrasslands’ is characterized by frequent water
FEMS Microbiol Ecol 79 (2012) 12–24
21
Effect of soil factors on bacterial community structure
Table 4. Continued
Phylum
Class
Order
Pine
forests, 23F*
OP9/JS1
Planctomycetes
Proteobacteria
JS1
Planctomycetacia
a-Proteobacteria
Unclassified
Planctomycetales
Acetobacterales
Azospirillales
Bradyrhizobiales
Caulobacterales
Consistiales
Devosia
Ellin314/wr0007
Rhizobiales
Rhodobacterales
Sphingomonadales
Unclassified
Verorhodospirilla
Burkholderiales
Hydrogenophilales
MND1 clone group
Neisseriales
Nitrosomonadales
Rhodocyclales
Desulfobacterales
Myxococcales
Desulfuromonadales
Unclassified
Desulfuromonadales
Syntrophobacterales
Campylobacterales
Aeromonadales
Chromatiales
Enterobacteriales
Methylococcales
Oceanospirillales
Pseudomonadales
Unclassified
Vibrionales
Xanthomonadales
Unclassified
Spirochaetales
Unclassified
Verrucomicrobiales
0
0
1
0
1
0
0
0
0
0
0
0
2
0
1
0
0
0
0
0
1
1
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
b-Proteobacteria
d-Proteobacteria
e-Proteobacteria
c -Proteobacteria
Spirochaetes
TM7
Verrucomicrobia
Unclassified
Spirochaetes
Unclassified
Verrucomicrobiae
Natural
grassland*
2
2
0
0
0
2
3
0
0
1
0
1
1
0
3
1
2
1
0
2
2
0
0
1
4
5
2
0
1
4
0
1
1
0
0
0
0
2
0
1
Others*
0
6
1
1
3
4
0
1
2
12
14
14
3
1
5
0
0
0
1
3
0
2
1
0
0
0
4
9
5
9
1
1
5
3
2
8
2
2
1
6
*OTUs detected at least in three fields of pine forests and field 23F (four fields in total), in two fields of natural grasslands (two fields in total),
and in 14 fields of others (18 fields in total).
logging, creating episodic anaerobic conditions that might
explain the low abundance of fungi. Our pine forest samples were rich in humic acids, which are known to inhibit
PCR amplification steps in various molecular analyses. To
eliminate the possible influence of such PCR-inhibiting
compounds, extracts were tested for amplification efficiency by spiking test reactions with known quantities of
tester DNAs and comparing product yield with and without environmental DNA extract. No PCR inhibition was
observed. DNA yields (17–25 ng lL 1), DNA quality (260/
280 nm ratio ranging from 1.7 to 1.9), and real-time PCR
FEMS Microbiol Ecol 79 (2012) 12–24
results were also highly consistent across the five replicates
examined per field.
Effects of soil factors on bacterial community
structure
PCR-DGGE and PhyloChips analyses revealed that the
C : N was an important factor contributing to the differences in bacterial community structure between PFS and
all other soils. Fierer & Jackson (2006) and Fields et al.
(2006), who used PCR T-RFLP, found pH to be a highly
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
22
significant predictor of soil microbial diversity and richness in samples across North America and South America. In contrast, we found C : N ratio to be a very strong
predictor of bacterial community composition in the
Dutch soil systems studied here. The high C : N ratio in
PFS might indicate the presence of more complex organic
compounds and N limitation, two factors that may be
unfavorable for bacterial and fungal growth. High C : N
ratio is typical of soil systems with a high amount of
recalcitrant organic matter that is decomposed very
slowly. Although these three fields of pine forest are
located in different places in the Netherlands (North,
Center, and South), they showed remarkably similar bacterial communities, perhaps due to the selective pressures
elicited by pine leaf litter. Another remarkable result
was that PFS had the lowest number of bacterial taxa
(PhyloChips) and lower bacterial 16S rRNA gene copies
(real-time PCR) than the other 22 fields across the Netherlands. This fact is explained by a combination of high
C : N ratio and low pH. Similar to findings of Fierer
et al. (2006) and Fierer & Jackson (2006), the total numbers of OTUs in Dutch soils were positively correlated
with soil pH. Soil pH was not an independent variable,
being significantly correlated with C : N ratio (Pearson’s
r = 0.76, P < 0.05).
Representative bacterial groups
Firmicutes, mainly Bacilli and Clostridia, represented the
most dominantly detected group in most of the soils target in this study, with the noted exception of the PFS.
These results are consistent with previous studies that
reported a large proportion of Firmicutes in chalk and
slightly acidic grasslands, as determined by this same
PhyloChip platform (Kuramae et al., 2010) and quantitative dot blot hybridization (Felske et al., 1998, 2000),
respectively. However, the high abundance of members of
Firmicutes in Dutch soils is in stark contrast to most
studies that have examined soil microbial communities,
including soils from North America and South America,
Europe, and Antarctica (Zarda et al., 1997; Chatzinotas
et al., 1998; Kobabe et al., 2004; Caracciolo et al., 2005;
Stein et al., 2005; Fierer & Jackson, 2006; Janssen, 2006;
Yergeau et al., 2009). These other 16S rRNA gene-based
analyses of soil communities have typically found Alphaproteobacteria, Acidobacteria, and Actinobacteria to be the
most dominant groups, as opposed to the Bacteroidetes,
Firmicutes, and Planctomycetes, which have typically been
found to be less abundant. Firmicutes have, however, been
observed as being dominate among bacterial communities
in forest soil of Kashmir, India (Ahmad et al., 2009), and
in ornithogenic soils in the Ross Sea region of Antarctica
(Aislabie et al., 2009). Interestingly, many of the OTUs
ª 2011 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
E.E. Kuramae et al.
within the Bacilli and Clostridia classes of Firmicutes were
correlated with pH and phosphate, which might explain
their high abundance in Dutch soils, which are generally
characterized by high phosphate levels that have accumulated over decades of external inputs of inorganic and
organic fertilizers (van Bruchem et al., 1999) especially
between 1950 and 1980.
The detailed analysis of PhyloChips and soil physicochemical properties may allow for the selection of candidate indicators of Dutch soil conditions at finer
taxonomical level than phylum and class. Taxa belonging
to Alphaproteobacteria (Acetobacterales, Bradyrhizobiales,
and Rhizobiales), Deltaproteobacteria (Bdellovibrionaceae,
Dessulfobulbaceae, Nitropiraceae, and Syntrophobacteraceae), Betaproteobacteria (Alcaligenaceae, Burkholderiacea,
and Oxalobacteracea), Gammaproteobacteria (Chromatiales), Chloroflexi (Anaerolineae), and Actinobacteria might
be possible indicators of soils with high C : N ratios
(> 18) and low pH (< 4.0). Likewise, some taxa
within the Alphaproteobacteria (Sphingomonadales and
Rhodobacterales), Gammaproteobacteria (Alteromonadales),
Betaproteobacteria (Comamonadaceae), Deltaproteobacteria
(Synthrophaceae), Bacteroidetes (Flavobacteria, Sphingobacteria, and Prevotellaceae), and Acidobacteria (Acidobacteria
subgroups 4, 6, 7, 9) are indicative of fields with high pH
(> 6.5) and low C : N ratio (< 12.5). Several of these
associations are highly coherent with previously published
results. For example, Rhizobiales and Sphingomonadales
have previously been observed to respond differentially to
the rhizosphere inputs of different plants (Haichar et al.,
2008), and we found that these groups were associated
with either natural (Rhizobiales) or agricultural (Sphingomonadales) ecosystems. Furthermore, the abundance of
Bacteroidetes was shown by Nemergut et al. (2008) to
increase in fertilized soil, and Percent et al. (2008) found
this group to be positively correlated with soil pH. In the
present study, we also found that several taxa within the
Bacteroidetes were more abundant in agricultural ecosystems, as compared to deciduous forest soils. Our results
regarding Acidobacteria subgroups 4, 6, 7, and 9 showed
these subgroups to be strongly correlated with soil pH,
which is in agreement with the findings of Jones et al.
(2009).
In summary, our study showed that soil physicochemical factors, in particular C : N ratio, phosphate, and pH,
were the main factors explaining the variation in bacterial
communities, as opposed to the independent impact of
vegetation type and land-use practices. Exceptions were
the pine forest and natural grassland sites. Furthermore, in
comparing the molecular approaches used in this study, the
high-throughput microarray approach proved to be more
informative than real-time PCR and PCR-DGGE, as the
PhyloChip approach allowed for community assessment
FEMS Microbiol Ecol 79 (2012) 12–24
Effect of soil factors on bacterial community structure
at several taxonomical levels, facilitating a fine-scaled and
detailed assessment of microbial community composition
patterns. Using this approach, we were not only able to
discern the important drivers of soilborne bacterial communities, but also able to detect numerous bacterial
groups that were indicative of specific environmental conditions across a range of Dutch soils.
Acknowledgements
We thank Y.M. Piceno (Lawrence Berkeley National Laboratory, Berkeley, USA) for laboratory assistance, G.L.
Andersen for valuable consultation and guidance with
PhyloChip analyses (Lawrence Berkeley National Laboratory, Berkeley, USA), BLGG (Wageningen, The Netherlands) for soil physicochemical analysis, and Bart
Pietersen (BDS-BioDetection System), Remy Hillekens
(NIOO-KNAW), and Tjalf de Boer (Vrij University of
Amsterdam) for help with soil sampling. This work was
supported by the Bsik program of ‘Ecogenomics’ (http://
www.ecogenomics.nl/) Publication number 5092 of the
NIOO-KNAW, Netherlands Institute of Ecology.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Fig. S1. Map of sampling sites with different land use.
Fig. S2. Schematic representation of soil sampling per
field.
Fig. S3. Bacterial PCR-DGGE band patterns between replicates (A, B, C, D, E) and between fields (F1, F2, F3, F4,
F7, F8, F9, F10, F11).
Fig. S4. Canonical correspondence analysis of normalized
intensities given in PhyloChips, sampling sites, and significant soil factors (red arrows) for 25 soils sampled across
the Netherlands.
Table S1. Pearson’s correlation between bacterial and
fungal abundance quantified by real-time PCR and soil
physical and chemical factors.
Table S2. Pearson’s correlation between total numbers of
OTUs given in the PhyloChips and soil physicochemical
factors and fungal abundance of 25 different fields.
Please note: Wiley-Blackwell are not responsible for the
content or functionality of any supporting materials supplied by the authors. Any queries (other than missing
material) should be directed to the corresponding author
for the article.
FEMS Microbiol Ecol 79 (2012) 12–24