Soil Biology and & Biochemistry
Soil Habitat and Horizon Properties Impact
Bacterial Diversity and Composition
Himaya M. Michel
Dep. of Plant and Soil Sci.
Mississippi State Univ.
117 Dorman Hall
Mississippi State, MS 39759
Mark A. Williams*
Dep. of Horticulture
Virginia Polytechnic Inst. and State Univ.
301 Latham Hall
Blacksburg, VA, 24061.
Little is known about how bacterial communities differ between soil horizons and in response to horizon development. Soil horizons are distinct habitats that continue to develop and change with age. It was hypothesized that as
soil horizons aged and differentiated, that microbial community composition and structure between the horizons
would become more different. Clone libraries of the 16S rRNA (ribosomal ribonucleic acid) gene were constructed
for soils from the A and B horizons along a soil-age gradient of 5,000, 45,000, and 77,000 yr. Results showed that
bacterial communities (16S rRNA genes) in both horizons and across the soil development gradient were dominated by similar groups: Acidobacteria, Alpha-Proteobacteria, and Planctomycetes. Bacterial richness (Simpson’s 1/D)
was greater in the A (154–337) than the B (29–139) horizon and tended to increase with horizon development.
The effect of horizon on the fatty acid-based microbial community structure and physiology indicated a clear horizon effect. Despite the much greater richness in the A than the B horizon, cluster analysis of the 16S rRNA genes
of the dominant bacterial taxa indicated that communities in developmentally immature soils (5,000) had similar
communities in the A and B horizons, sharing ~75% of the sequences (>97% sequence similarity). As the soils
(45,000 and 77,000 yr) developed further, the communities between the A and B horizons diverged, sharing 50%
or less of the sequences in the most abundant operational taxonomic units (OTUs). Horizon development during
soil genesis seems to be an important determinant of bacterial community composition and structure.
Abbreviations: FAME, fatty acid methyl ester; OTU, operational taxonomic unit; rRNA, ribosomal
ribonucleic acid; SOM, soil organic matter.
S
oil is a structured and heterogeneous system (Nannipieri et al., 2003) with distinctive gradients at multiple scales from the nanometer to the landscape levels
and beyond. In particular, horizon development with soil depth provides an easily
observable change in important soil characteristics that are known to have a profound effect on soil properties. The unique physicochemical and biological characteristics of these habitats likely harbor distinct microbial assemblages. Bacterial
communities change appreciably along gradients in soil characteristics such as
organic matter, texture, and vegetation (Hansel et al., 2008; Zhou et al., 2002;
Nusslein and Tiedje, 1999; Kuske et al., 2002); however, only a few studies have
looked closely at the changes in community composition with depth (e.g., Hansel
et al., 2008; Steenwerth et al., 2008; Ekelund et al., 2001; Girvan et al., 2003).
Microbial communities change with depth, and the types of microorganisms at
depth are not always found in surface soils. Hence, an understanding of how microbial
diversity changes with depth within a soil profile can provide a better estimate of the
global extent of biodiversity (Roesch et al., 2007). Several studies have demonstrated
that changes in microbial community that do occur across soil depth gradients are
related to soil characteristics such as C content or root growth (Agnelli et al., 2004;
LaMontagne et al.; 2003; Fritze et al., 2000; Bruneau et al., 2005). Yet, the impact that
soil habitat properties have on the structure of microbial communities across a gradiSoil Sci. Soc. Am. J. 75:1440-1448
Posted online 23 June 2011
doi:10.2136/sssaj2010.0171
Received 14 Apr. 2010.
*Corresponding author ([email protected]).
© Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by
any means, electronic or mechanical, including photocopying, recording, or any information storage
and retrieval system, without permission in writing from the publisher. Permission for printing and for
reprinting the material contained herein has been obtained by the publisher.
1440
SSSAJ: Volume 75: Number 4 • July–August 2011
Table 1. Selected characteristics of the two soil horizons across soil age.
5,000 yr
45,000 yr
77,000 yr
A Horizon
B Horizon
A Horizon
B Horizon
A Horizon
B Horizon
Depth†
0–5
55–65
3–12
40–50
5–15
40–50
pH
3.7
4.0
3.5
4.1
3.4
4.0
Ca, μg g–1
31
29
7.2
0.1
0.70
0.1
Mg, μg g–1
1.9
2.2
3.2
1.2
2.7
1.0
P, μg g–1
93
84
8.8
1.1
8.4
15
Mineralizable C, μg g–1
83
78
97
69
95
76
Fatty acid, μg g–1
421
430
617
133
431
370
Clay+Silt, % dry wt.
2.0
2.2
3.7
4.1
6.5
5.3
SOM, % dry wt.
1.6
1.0
1.5
0.8
1.1
1.0
† The exact delineation between the two horizons, A and B, was wavy and diffuse. Samples were taken from the zone of greatest contrast between
the horizons.
ent of soil development and between soil horizons, has not been investigated. Indeed, most studies have focused on the effect of depth
rather than changes associated with soil horizonation.
The primary goal of this research was to understand how soil
microbial communities change between two vertically adjacent
horizons along a gradient of soil development. The study presented
here describes bacterial diversity and composition in two contiguous soil horizons sampled along an eolean chronosequence of ages
5,000, 45,000, and 77,000 yr. We investigated bacterial community
diversity, composition, and structural changes between horizons in
a very well-drained and drought-prone set of dunes characterized by
low nutrients and low vegetative diversity (Ivester and Leigh, 2003).
It was hypothesized that the communities in the B horizon would
be different from the A horizon and that these differences between
communities would become greater with soil development and age.
MATERIALS AND METHODS
Study Area and Soil Sampling
The study area is located in the subtropical Altamaha and Ohooppe
River Valley dunes of southeast Georgia, USA (31°55´12˝ N, 82°6´36˝ W).
Classified as Typic quartzipsamment, these soils are >95% sand, droughty,
intact, and predominantly unperturbed with distinct horizons. These sites
are unfavorable for agriculture and have been minimally influenced by recent human activity. The 5,000-yr soil is about 3.5 km from the 45,000 site
while the latter is about 30 km apart from 77,000-yr soil (Ivester and Leigh,
2003). The plant communities in the youngest site are composed predominantly of short statured (<5m) Quercus hemisphaerica and Quercus virginiana. The two older sites are dominated by trees 8 to 12 m in height. The
45,000-yr site is dominanted by Q. virginiana and Quercus laevis, and the
77,000-yr site is dominated by Pinus spp. and Q. hemisphaerica.
Horizons A and B were delineated by visual examination of organic
matter in the surface, the general color change (hue, value, chroma) within
the soil profile and evidence of clay translocation to the underlying horizon. A riverbank cut for each soil age was used to help identify horizons. Soils were then collected from the zone of greatest contrast between
each horizon at the apex of each dune ridge. An auger was used to further
confirm the nature of the horizons and then samples were taken with a
Hoffer (JBK Manufacturing, Beavercreek, OH) corer. Samples were separated into horizons and immediately placed into sterile Whirlpak bags.
The process was repeated to obtain ~350 g of soil at three locations across
SSSAJ: Volume 75: Number 4 • July–August 2011
a 9-m transect. The corer was wiped with 70% ethanol (v/v) after each
collection. After sampling, soils were immediately homogenized through
repeated mixing. A 50-g subsample was placed into a sterile 60-mL polypropylene screw top vial and stored in a cooler packed with dry ice. Upon
arrival in the laboratory, soils were thawed for 10 to 20 min. Extraneous
roots and organic matter were removed using tweezers cleansed with ethanol. Soil was stored at –80°C until DNA extraction.
Soil organic matter (SOM) content was determined by mass loss
after ignition (560°C) while cumulative CO2–C (mineralizable C) was
measured after incubating 100 g of soil at 50% water-holding capacity
in 1-L canning jars for 30 d. Temperature and soil moisture were maintained at 25°C and 50% water-holding capacity, respectively. Soil texture
was analyzed using a hydrometer according to Gee and Bauder (1986).
Exchangeable cations were analyzed by inductively coupled plasma spectrometry following Mehlich-3 extraction (CH3COOH, NH4NO3,
HNO3, NH4F, and EDTA) of samples (Elrashidi et al., 2003). Soil pH
was determined with 1:2, soil/0.01 mol L−1 CaCl2 solution. Some physicochemical characteristics of the study site are presented in Table 1.
Soil DNA Extraction and Preparation
of the 16S rRNA Gene Libraries
Community DNA was directly extracted from 10 g of soil using PowerMax Soil DNA Isolation Kit (MO Bio Laboratories Inc.,
Carlsbad, CA) following the manufacturer’s specifications. Soil was shaken for 45 min in an orbital shaker incubator set at 60°C and 100 rpm.
Construction and storage of the 16S rRNA clone libraries were done
following the procedure described by Tarlera et al. (2008). Briefly, Taq
Ready-To-Go Beads from GE-Healthcare (Buckinghamshire, UK) were
set up following the manufacturer’s protocol using 27 F and 1492 R
primers with the following polymerase chain reaction (PCR) conditions: 3 min denaturation at 95.0°C followed by 15 cycles of 1.0 min at
94.0°C, 45 s at 58.0°C, 2 min at 72.0°C and final extension of 4.0 min at
72.0°C. Cloning was done using the TOPO TA Cloning Kit (Invitrogen,
Carlsbad, CA) according to the manufacturer’s specifications. Five white
colonies were randomly picked per plate and the clone libraries were
stored in LB agar with 10% glycerol (v/v) at –80°C until sequencing.
Sequencing and Editing
The sequencing plates were shipped on dry ice to the University
of Georgia’s sequencing facility (Athens, GA). Chromatograms were
1441
trimmed and edited using the CodonCode Aligner (LI COR Inc.,
Dedham, MA). Sequences were aligned using Clustal W for chimera check by Mallard and Pintail programs (Ashelford et al., 2005).
Before analyses, sequences were aligned using Greengenes (De Santis
et al., 2006) and were submitted to Genbank with accession numbers
EU043524- EU044592; GQ918495- GQ919012.
Library Comparisons, Diversity Estimates,
and Phylogenetic Assignment
The distance matrices for communities within each soil habitat, which were used in comparing the libraries with LIBSHUFF v1.2
(Singleton et al., 2001), were calculated using the Jukes-Cantor algorithm in the DNADIST from the PHYLIP package. In multiple comparisons, Bonferroni correction was used to correct for experiment-wise
error. The OTUs were determined using the average neighbor algorithm
in DOTUR (Schloss and Handelsman, 2005). Consequent estimates for
the fraction of OTUs shared between two communities were determined
using SONS (shared OTUs and similarity) (Schloss and Handelsman,
2006). Rarefaction analyses and estimates of richness, evenness, Shannon
index, Simpson’s reciprocal index and Chao1 were done on OTUs at 0.01
evolutionary distance (about 99% sequence similarity). Analysis of the
distribution of abundant taxa within libraries was assessed at an evolutionary distance of ≤0.03. Taxonomic assignment of OTUs was done using the Ribosomal database project (RDP) classifier and sequence match
query (Cole et al., 2009). Cluster analysis on the most abundant OTUs,
those with nine or more clones, was done following general relativization of the data using the PC-ORD Version 4 software package by MjM
Software Design (McCune and Mefford, 1999). Ward’s method was used
to provide group linkage and the distance measure was Euclidean. Ward’s
linkage method has been shown to be an effective, useful tool and one of
the few space-conserving methods, which allows for an effective and representative depiction of the clustering results (McCune and Grace, 2002;
Blackwood et al., 2003). Its incompatibility with Sorensen’s, a generally
preferred distance matrix for community analysis, is a recognized drawback of Ward’s method. However, it this is thought to be offset by the latter’s reliability and effectiveness as a clustering method. While Euclidean
distance has been shown to perform poorly for community data under
certain circumstances, this problem is thought to be relieved by the relativization of the data (McCune and Grace, 2002).
Fatty Acid Methyl Ester Analysis
Fatty acid methyl ester (FAME) extractions were conducted according to the method utilized by Grigera et al. (2006), with a few modifications. Briefly, 3 g of soil were placed in a 40-mL scintillation vial,
followed by the addition of 15 mL of 0.2 mol L−1 KOH (in methanol)
to each sample and sonicated for 1 h in a sonicating bath. Three milliliters of 1 mol L−1 acetic acid were then added to neutralize the KOH,
and the samples were vortexed for 1 min. Ten milliliters of hexane were
added, and the samples were vortexed again for 1 min. Deionized water, 5 mL, was then added for better phase separation. The samples were
vortexed a third time for 1 min and then centrifuged at 747 RCF for
15 min. Five milliliters of the hexane layer were transferred into a 7-mL
disposable glass tube using a pipette. The samples were completely dried
under a gentle stream of 99.999% UHP N2 gas. The remaining residue
1442
Table 2. P values from one-way ANOVA on the effects of horizon and age on the measured soil properties.
Soil property
Horizon effect (n = 2)
pH
0.005**
Ca
0.816 ns†
Mg
0.092 ns
P
0.930 ns
Mineralizable C
0.028*
Fatty acid
0.182 ns
Silt + clay
0.906 ns
SOM
0.036*
* Significant at α = 0.05.
** Significant at α = 0.01.
† ns, not significant.
Age effect (n = 3)
0.945 ns
0.004**
0.957 ns
0.001**
0.941 ns
0.969 ns
0.012*
0.836 ns
was then redissolved in 0.5 mL of hexane and placed in a 2-mL gas chromatography vial for analysis of FAMEs on a gas chromatograph-mass
spectrometer using an INNOWAX capillary column (30 m, 0.25 I.D.,
0.25-μm film; Palo Alto, CA). Cluster analysis of the 22 most abundant
FAMEs was performed as described for the most abundant OTUs.
Statistical Analysis
One-way ANOVA was done to separately test horizon and age effects on the soil properties. A Mantel test was conducted to determine the
correlation between the two matrices constructed of (i) soil properties and
(ii) the relative abundance of the most abundant OTUs. Regression and
p-values between individual soil properties (e.g., P) and the multivariate
ordination scores of the relative OTU abundances were determined using
PC-ORD version 4 software package (McCune and Mefford, 1999).
RESULTS
The soils, regardless of depth, were strongly acidic. However,
one-way ANOVA detected a significantly lower pH in the A
compared with the B horizon (Tables 1 and 2). The SOM and
mineralizable C were also significantly greater in the A than the
B horizons (Tables 1 and 2). Calcium and P contents declined
significantly with soil age, and both silt + clay content were
greater in the two older compared with the youngest soil.
Bacterial community diversity and composition of the sand
dune soils were determined by constructing duplicate 16S rRNA
gene clone libraries for each of the three replicate soils and each of
the two horizons. A total of 1043 clones were thus obtained from 12
libraries. To test for the reproducibility of our methods in extracting
and cloning microbial DNA, the replicate libraries were subjected
to LIBSHUFF analysis. LIBSHUFF is a statistical tool that tests
whether two or more libraries are derived from the same population
(Singleton et al., 2001). Results showed that bacterial communities
in replicate libraries were not significantly different, indicating that
our methods for DNA extraction and cloning were reproducible.
Replicate libraries were then pooled for the subsequent sequence
analyses. LIBSHUFF results from comparison of libraries between
horizons A and B for each soil age and the comparisons between soil
ages within the same horizon all showed that bacterial communities
were significantly different (P = 0.002). Further analyses were done
to determine the specific nature of these differences.
SSSAJ: Volume 75: Number 4 • July–August 2011
Table 3. Diversity indices for the clone libraries by soil horizon and age.
5,000 yr
45,000 yr
77,000 yr
A
B
A
B
A
B
Diversity
index† horizon horizon horizon horizon horizon horizon
N‡
165
156
159
186
193
180
S§
121
67
120
113
158
124
Shannon 4.66
3.71
4.63
4.46
4.97
4.63
Evenness 0.97
0.88
0.97
0.94
0.98
0.96
1/D¶
185.4
28.8
154.1
86.90
336.9
138.9
Chao1
315.5
172.1
436.9 271.00
690.6
386.6
95% CI# 229–469 112–311 290–711 198–406 460–1098 267–607
† Calculations based on the operational taxonomic units (OTUs)
determined by Distance based on OTU and richness (DOTUR;
Schloss & Handelsman, 2005) at an evolutionary distance of <0.01.
‡ Number of clones in the library.
§ Number of operational taxonomic units.
¶ Simpson’s reciprocal index.
# CI, confidence interval.
Bacterial diversity was analyzed by placing clones into
groups using ≤ 0.01 evolutionary distance (D) (~99% 16S
rRNA sequence similarity). These results were similar to the
trends found when sequences were grouped at D ≤ 0.03 (data
not shown). The Shannon and Simpson’s reciprocal indices
showed higher diversity in horizon A than B across soil ages
(Table 3). The Shannon index is more sensitive to changes in the
abundance of rare groups while Simpson’s is weighted by domi-
nance (Hill et al., 2003). The richness values were corroborated
by rarefaction (Fig. 1) and the Chao1 estimator (Table 3). Chao
1 values showed that only 28 to 42% of the OTUs predicted by
this estimator were actually observed. Based on all indicators, the
diversity and richness of the bacterial communities were greater in the A compared with the B horizon. At a broad level of
bacterial classification (phylum and class levels, >85 and > 75%
sequence similarity, respectively), both A and B horizons were
dominated by similar bacterial groups (Fig. 2). Acidobacteria
was the most abundant phylum (54.6% of the total clones) and a
substantial portion of the sequences were distributed within the
Planctomycetes (11.1%), Alpha- (9.3%), Beta- (3.3%), Delta(1.5%), and Gamma- (4.1%) Proteobacteria and the unclassified
bacteria (11.1%). The remainder of the clones (~ 8%) were composed of the least dominant phyla: Firmicutes, Bacteroidetes,
Actinobacteria, Gemmatimodetes, Verrucomicrobia, Nitrospira,
and OP10. Three of the rare groups (OP10, Bacteroidetes, and
Gemmatimodetes) were found in the A layer; Nitrospira were
only seen in the B layer while Verrucomicrobia, Actinobacteria,
Firmicutes and Delta-Proteobacteria occurred in both horizons.
To describe bacterial species abundance and composition
between the two horizons and across soil ages, we determined the
OTUs using DOTUR at D ≥ 0.03 (Schloss and Handelsman,
2005). Reflecting 32% of the total clones, OTUs composed of nine
or more clones were taken to describe the most abundant OTUs.
The distribution of the abundances of these taxonomic units in
Fig. 1. Rarefaction curves of clone libraries with operational taxonomic units (OTUs) defined at an evolutionary distance of 0.01. Diagnonal line
represents the 1:1 relationship where each screened clone is unique.
SSSAJ: Volume 75: Number 4 • July–August 2011
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Fig. 2. Phylogenetic distribution of the 16S rRNA gene sequences of horizons (a) A and (b) B or the 5,000 (Black), 45,000 (Light Gray), and 77,000 (Dark
Gray) sites, respectively. The percentage of clones from each soil type is presented. Phylogenetic assignments were based on the comparisons to sequences
in the Ribosomal Database Project (RDP) using similarity cutoff values of 80 and 85% for phylum and class designations, respectively. The “other group”
included sequences affiliated to OP10, Verrucomicrobia, Actinobacteria, Bacteriodetes, Gemmatimonadetes, Actinobacteria, and Nitrospira.
each soil is shown in Table 4. Changes in bacterial composition
and abundance were observed based on both soil age and horizon.
Cluster analysis showed the most abundant OTUs of 5KA and
5KB were closely grouped together while phylotypes from A and
B horizons of the two older soils clustered separately (Fig. 3a). The
mole% of fatty acids indicated that communities from the A horizon were relatively similar to each other but different from those
found in the B horizon (Fig. 3b). In contrast, B horizon communities showed considerably more variation among each other. A
Mantel test of the array of habitat properties (e.g., P, Ca, pH) did
Table 4. Distribution and phylogenetic affiliation of the most abundant operational taxonomic units (OTUs)†.
No. of clones distributed among treatments
5,000 yr
45,000 yr
77,000 yr
Similarity A horizon B horizon A horizon B horizon A horizon B horizon
Total
%
5KB-03 Chloroflexi bacterium (AM749752)
93.0
13
13
5KA-10 Acidobacterium (EF0118355)
99.9
5
4
9
5KA-02 Firmicutes (EU043850)
99.9
6
17
23
5KA-12 Planctomycete (AF465657)
95.1
8
15
23
5KA-08 Acidobacterium (EF515910)
98.8
5
17
22
5KA-31 Eubacterium (AJ292577)
98.5
5
5
2
9
5KA-25 Alpha proteobacterium (AY395368)
98.5
6
10
4
5
25
5KA-11 Bradyrhizobium (AF216780)
99.5
6
1
3
3
2
3
18
5KA-01 Acidobacterium (DQ451441)
98.6
4
1
1
13
2
4
25
5KA-06 Acidobacterium (EF018355)
97.1
1
1
7
2
11
5KA-09 Acidobacterium (EF018936)
98.8
2
3
2
1
1
9
5KB-13 Holophaga sp. (AJ519377)
99.1
2
4
4
10
77KA-03 Unclassified bacterium (AY913277)
98.8
2
11
2
15
5KA-04 Acidobacterium (DQ451440)
98.1
3
1
3
4
11
45KA-02 Acidobacterium (FJ466205)
96.3
6
1
1
5
13
45KA-09 Acidobacterium (AY963341)
97.9
5
13
5
23
45KA-04 Singuilisphaera bacterium (FJ466406)
98.6
11
5
16
45KB-02 Acidobacterium (AY963316)
98.0
2
8
1
11
77KB-06 Acidobacterium (EF032756)
97.7
1
5
7
13
45KB-34 Acidobacterium (EU276509)
98.0
6
8
14
45KB-60 Acidobacterium (EF494335)
96.0
13
1
14
45KB-78 Acidobacterium (EU680450)
99.4
8
1
9
† OTUs with equal to or greater than nine members.
‡ Phylogenetic assignment of closest GenBank sequence (accession number in parenthesis). Except for 5KA-11, the closest matches were all
uncultured clones.
Clone
name‡
1444
Ribosomal
Database Project
query phylogenetic group
SSSAJ: Volume 75: Number 4 • July–August 2011
Fig. 3. Hierarchical cluster analysis of (a) most abundant operational taxonomic units (OTUs) (n ≥ 9) using Ward’s method and relative Euclidean
distance and (b) mol (%) distribution of the soil extractable fatty acids. The distance axis is a similarity index.
not show significant correlation with bacterial community change
between horizons as soils age (t = 1.71; r = 0.454; p = 0.086).
However, tests of individual correlations between soil properties
and community change showed a significant correlation between
soil P and community structure change (r = 0.76, p = 0.005).
DISCUSSION
This study set out to answer several questions regarding the
distribution of bacterial taxa in soil habitats. We hypothesized
that soil horizons would show large and significant differences in
the types of bacterial taxa because they provide well defined and
different habitats that influence microbial survival, colonization,
and growth. We also expected that as the vertically adjacent A and
B horizons differentiated as a consequence of soil development
that their microbial communities would also become increasingly different. Selected soil characteristics measured in this study
showed typical trends during soil development such as the drastic drop of Ca and P in older soils compared with the young soil
(Table 1). This is a phenomenon of nutrient loss through leaching
during soil development, which is consistent with the results from
previous researchers (Crews et al., 1995; Richardson et al., 2004;
Lichter, 1998). Also, the observed increase in clay and silt in the
mature soils was indicative of increased weathering and translocation of these fractions as the soil matured (Leigh, 1996).
We characterized the bacterial community composition and
diversity using the 16S rRNA clone library techniques in which
1043 clones were generated. Although Chao1 predicted that only
about 28 to 42% of the actual diversity was observed, the focus of
our analysis on the dominant members (n > 9) of the community
provided confidence in the patterns of community change that we
detected. A previous study along this chronosequence found that
SSSAJ: Volume 75: Number 4 • July–August 2011
community composition was reproducible at three locations along
a 30-m transect (Tarlera et al., 2008). These findings suggest but
do not prove that our efforts could capture the community variability found among the samples collected from the study sites.
Nevertheless, the design of the study across three ages and using two
horizons allowed for comparison of these effects and the testing of
the hypothesis that as the horizons aged and differentiated chemically, that the communities would, likewise, become more different.
Though bacterial community composition was not strongly
associated with the full suite of soil habitat properties as described
by a Mantel test, bacterial community change did correlate with P
content as the soils developed and aged. This provides an example
of how soil habitat differentiation could select for bacterial communities that can best survive and grow in that habitat. In this
regard, the relatively young and undeveloped A and B horizons
were more similar to each other than the horizons found in the
two more mature soils, and 83% of the RNA sequences associated
with the most abundant OTUs were held in common between
them. In contrast, only 30 and 47% of the RNA sequences associated with the most abundant OTU were shared between the A
and B horizons of the 45,000- and 77,000-yr soils, respectively
(Table 4). The incipient nature of soil development in the young
soils, and the lack of differentiation between the A and B horizons
could explain the similarity between the two bacterial communities. As the soils developed over 45,000 and 77,000 yr, the A and
B horizons became increasingly chemically and physically differentiated. In support of the hypothesis, the bacterial communities
also become more different between the two horizons (Table 4,
Fig. 3) as the soils developed and the horizons differentiated,
One intriguing aspect about the selection of bacteria in the
soil habitats can be viewed from the context of scale. Bacterial
1445
communities from the same diagnostic horizons of the two older
soils (45,000 and 77,000 yr), which are >30 km apart (Ivester
and Leigh, 2003), were more similar than their horizon counterparts (Table 4, Fig. 3), which are vertically adjacent to each
other and separated by <50 cm (Table 1). The A horizons shared
82% and the B horizons shared 80% of the sequences in the most
abundant OTU within the A and B horizons of the 45,000- and
77,000-yr soils, respectively (Table 4). These results suggest that
soil habitat characteristics are more important than spatial proximity for predicting microbial communities in heterogeneous
landscapes, a result that is consistent with previous research
(Girvan et al., 2003; Fierer and Jackson, 2006).
Changes in microbial community structure with soil depth
have previously been reported (Zhou et al., 2002; Feng et. al.,
2003; Fierer et al., 2003; Allison et al., 2007; Hansel et al.,
2008). However, one unique aspect of the work reported here
is the ability to suggest that there are important differences in
the types of bacteria that inhabit vertically adjacent soil horizons
that arise as soils develop. Other studies have also attempted to
relate changes in soil microbial community structure to important habitat characteristics to determine whether soil properties
such as P (Grayston et al., 2004; Bünemann et al., 2004; Allison
et al., 2007), particle size (Sessitsch et al., 2001), and C (Zhou
et al., 2002; Fierer et al., 2003) might be potential drivers of
community structure. In addition to the variables we measured
(Table 2), there may be several competing factors or stressors
that we did not measure that could also be used to explain the
composition in the bacterial communities. For example, temperature and water availability almost certainly differ between the
two horizons and could impact the composition and structure
of the soil microbial community. It is also notable that vegetation changed across the chronosequence. However, not enough
data were collected to explicitly link changes in vegetation with
bacterial community assembly, but a more detailed study of vegetation changes across the chronosequence would be valuable for
discerning possible plant-microbe associations.
Several other observations about the bacterial communities
and their relationships to the soil habitat are noticeable. For one,
the dominance of the Acidobacteria in the sand dune soils, representing ~55% of the clones, is in agreement with the postulation
that Acidobacteria are a dominant group in soils with low resource
availability (Fierer et al., 2007). The dominance of this phylotype,
however, should not be interpreted as suggesting that these sandy
soils harbor low amounts of bacterial diversity. Indeed, despite the
difficulties of comparing diversity indices based on different bacterial population sample sizes, it appears that these low C and low pH
soils have relatively similar amounts of diversity and richness when
compared with other soil systems (Upchurch et al., 2008; Jangid et
al., 2008). Another interesting observation was the relatively high
degree of community dominance (majority of sequenced clones in
few OTUs) and low richness in the most undeveloped soil horizon
(5KB). This suggests that among the soils sampled, this habitat may
be the least favorable for broad bacterial colonization. One striking
difference between the horizons of the 5K soil is the dominance
1446
of clone 5KB-03 in the B but not the A horizon. The phylum
Chloroflexi provides the closest match to this clone. This is a highly
diverse and ubiquitous group of bacteria, which can have photoheterotrophic and/or chemolithotrophic growth under mesophilic
or moderately thermophilic conditions (Yamada and Sekiguchi,
2009). Reasons for their occurrence in the sublayer of the soil profile cannot be directly determined in this study. However, solar
radiation would likely be completely attenuated in the B horizon,
suggesting that the Chloroflexi-like bacteria that were detected are
primarily oligotrophic heterotrophs. Lastly, detection of a clone
closely resembling Bradyrhizobium (Table 4) found in our samples
in all treatments agrees with the physiological nature of this bacterium where they are known to be acid-tolerant (Garrity et al.,
2004) and may suggest occurrence of N fixation in these nutrient
impoverished systems. However, no plant species found across all
sites are known to enter into relationships with bacterial members
of the genus Bradyrhizobia. This bacterial taxa may have occurred
in these soils as a free-living oligotroph. In grassland soils, slowgrowing oligotrophic bacteria were found to be closely related to
Bradyrhizobium japonicum; however, they did not show characteristics indicative of symbiotic N fixation (Saito et al., 1998).
In these sandy and highly porous landscapes that are found on
dry dune ridges, the source of the bacterial inoculums for the soils
is likely from aerial deposition on the soil surface. Compared with
soils with more clay and silt, the movement of microorganisms
from the A to the B horizons could have occurred readily by percolating water (Trevors et al., 1990; Ripp et al., 2001; Mosaddeghi
et al., 2008). Transport can also occur via growing roots (Parke et
al., 1986) and by soil faunal movement through the soil (Dighton
et al., 1997). Yet, despite the potential of persistent movement,
there was still an increasing differentiation between the soil bacterial communities in the mature, compared with the incipient, soil.
If it is true that the source of the microbial recruits in the B horizon
must first pass through the A horizon, it is intriguing to note the
abundance of bacterial groups that are unique only to the B horizons and those that are only found in the A horizons (Table 4).
This observation suggests the possible selection for bacteria that
are adapted to the inherent conditions of each horizon.
Horizon but not age effects were observed for the FAMEbased analyses of soil microbial communities suggesting a somewhat different response than the RNA gene based characterizations.
Fatty acid methyl ester profiles are an index of the broad taxonomic
characteristics of the soil microbial communities including the
eukaryotes (Cavigelli et al., 1995). While fatty acids are sensitive
indicators of community structural change, they also provide information on physiological adaptations. It is often difficult to separate
the impacts of physiology and community structure using cellular
fatty acids because fatty acid composition is both a reflection of an
organism’s response to the environment and a function of its genotype. No particular FAME stood out as an important indicator of
the differences between the A and B habitats (data not shown).
This may reflect broad levels of community and physiological differences between the A and B habitats. Both temperature and water
dynamics are likely to differ significantly between the horizons at
SSSAJ: Volume 75: Number 4 • July–August 2011
different depths but are likely to be similar for the same horizons
found at different locations and may help to explain why FAME
analysis indicated that the same horizons were relatively similar to
one another regardless of the maturity of the soil.
CONCLUSIONS
This study characterized the differences in bacterial community composition within two soil horizons (A and B) in relatively young and mature soils. We found that the surface A horizon
contained a more diverse and rich bacterial community than the
B horizon and that in mature soils, the soil habitat in the two vertically adjacent soil horizons possessed different types of bacteria.
Distinctive in nature, both of the A and B horizon habitats appear
to accumulate niches during soil development that allow for the
colonization and survival of different microorganisms. The importance of the soil habitat as a selective factor for bacterial community
assembly appears to be strong despite the high potential for migration between the sandy horizons. The occurrence of shared OTUs
between horizons may be indicative of this vertical migration.
However, vertical migration is not the only mechanism of microbial community assembly in underlying horizons since communities
found in the B horizon are not a mere subset of the communities
observed in the overlaying A horizon. By the same token, it is also
noteworthy that similar soil habitats in remote locations can have
similar microbial communities. These observations demonstrate
the importance that the soil habitat characteristics have on microbial communities. This study contributes to the growing body of
evidence that soils and their properties play an important role in
selection, and perhaps the evolution of microbial communities.
ACKNOWLEDGMENTS
This research was funded, in part, by grants from NSF and USDA-NRI.
We thank the kind efforts of anonymous reviewers and the Associate
Editor, M.C., of SSSAJ.
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