RESEARCH ARTICLE
Altering the mineral composition of soil causes a shift in microbial
community structure
Jennifer K. Carson1, Deirdre Rooney2, Deirdre B. Gleeson1 & Nicholas Clipson2
1
Soil Biology Group, School of Earth and Geographical Sciences M087, University of Western Australia, Crawley, WA, Australia; and 2Microbial Ecology
Group, School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland
Correspondence: Jennifer K. Carson,
Soil Biology Group, School of Earth and
Geographical Sciences M087, University of
Western Australia, 35 Stirling Hwy, Crawley
6009, WA, Australia. Tel.: 161 8 6488 3595;
fax: 161 8 6488 1050; e-mail:
[email protected]
Received 12 January 2007; revised 5 April 2007;
accepted 9 April 2007.
First published online August 2007.
DOI:10.1111/j.1574-6941.2007.00361.x
Editor: Karl Ritz
Keywords
ARISA; microbial community structure;
minerals; soil.
Abstract
This study tests the hypothesis that altering the mineral composition of soil
influences microbial community structure in a nutrient-deficient soil. Microcosms
were established by adding mica (M), basalt (B) and rock phosphate (P) to soil
separately, and in combination (MBP), and by planting with Lolium rigidum,
Trifolium subterraneum or by leaving unplanted. The effects of mineral and plant
treatments on microbial community structure were assessed using automated
ribosomal intergenic spacer analysis. Bacterial community structure was significantly affected by both mineral (global R = 0.73 and P o 0.001) and plant (global
R = 0.71 and P o 0.001) treatments, as was the fungal community structure:
mineral (global R = 0.65 and P o 0.001) and plant (global R = 0.65 and
P o 0.001) treatments. All pairwise comparisons of bacterial and fungal communities between different mineral treatments and between different plant treatments
were significantly different (P o 0.05). This study has shown that mineral addition
to soil microcosms resulted in substantial changes in both bacterial and fungal
community structure, dependent on the type of mineral added and the plant
species present. These results suggest that the mineral composition of soil may be
an important factor influencing the microbial community structure in soil.
Introduction
Microbial communities in soil are extremely diverse and
perform key functions, including cycling of carbon and
nutrients, maintaining soil productivity and water quality,
dissolution of minerals, decomposing contaminants and
controlling atmospheric composition and climate. Microbial community structure in soil is affected by physicochemical properties of soil such as pH, nutrients, texture and
structure, and biotic factors such as plant species, plant
diversity and root exudation (Brodie et al., 2002; Kennedy
et al., 2005a; Cookson et al., 2006). Analyses of the factors
influencing soil microbial communities have generally overlooked the influence of the underlying parent material and
subsequent mineralogy. There have been a number of recent
studies indicating that geology may have played a role in
microbial community development (Certini et al., 2004;
Rogers & Bennett, 2004; Gleeson et al., 2005, 2006).
Early studies showed that microorganisms colonizing
mineral surfaces were influenced by mineral chemistry, with
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a variety of microcosm studies using microscopy to analyse
microbial populations on silicate glass and aluminosilicate
surfaces (Thorseth et al., 1995; Ullman et al., 1996; Barker
et al., 1998; Welch et al., 1999). Recent studies using
molecular fingerprinting techniques linked microbial community structure to the chemical composition of mineral
surfaces, with specific bacterial and fungal ribotypes (or
species) being related to the presence of particular chemical
elements (Gleeson et al., 2005, 2006). However, none of
these studies were performed in a soil environment and
questions remain regarding the extent to which mineralogy
exerts a selective pressure on indigenous soil microbial
communities.
Most soil microorganisms live attached to surfaces and
are therefore likely to be influenced by mineral chemistry
(Hazen et al., 1991; Holm et al., 1992; Banfield et al., 1999).
There are few studies examining soil microorganism–
mineral interactions in situ; however, Certini et al. (2004)
do report a different microbial community structure in rock
fragments compared with the surrounding soil. Studies
FEMS Microbiol Ecol 61 (2007) 414–423
415
Mineral–microorganism interactions in soil
examining the effect of mineral additions to soil have
centred on their potential as slow-release fertilizers for
pastures, focusing on their effect on soils and plant growth
but largely ignoring effects on soil microorganisms (Hughes
& Gilkes, 1994; Hinsinger et al., 1996; Bolland et al., 2001;
Priyono & Gilkes, 2004). Mineral additions to soil are likely
to affect microbial communities, both through the direct
effect of mineral chemistry but also indirectly though effects
on physicochemical conditions in soil (Hinsinger et al.,
1996; Gillman et al., 2002; Longanathan et al., 2002; Priyono
& Gilkes, 2004) and increased plant growth (Sanz Scovino &
Rowell, 1988; Bolland et al., 1995; Coroneos et al., 1996;
Bakken et al., 2000). In nutrient-poor soils (like those found
in Western Australia), minerals containing limiting nutrients may exert an even greater influence on microbial
community structure by providing substrates for microbial
growth.
The work reported here tests the hypothesis that altering
the mineral composition of soil (via mineral addition)
significantly influences microbial community structure in a
nutrient-deficient soil. Microcosms were established by
adding mica (M), basalt (B) and rock phosphate (P) to soil
separately, and in combination (MBP), and by planting with
Lolium rigidum, Trifolium subterraneum or leaving unplanted. Microcosms representing rhizosphere soil were
used as in this soil microbial biomass, microbial diversity
and mineral dissolution are increased compared with bulk
soil (Hinsinger et al., 1992; Hinsinger & Gilkes, 1997). Two
plant species were compared as different plant species are
known to influence mineral dissolution (Hinsinger &
Gilkes, 1997; Wang et al., 2000) and microbial community
structure in the rhizosphere (Kennedy et al., 2005b; Lejon
et al., 2005). To examine mineral and plant effects on
microbial community structure, a molecular community
profiling approach [automated ribosomal intergenic spacer
analysis (ARISA)] was used, which allows for discrimination
of microbial communities to the species and possibly the
strain level (Ranjard et al., 2000, 2001; Kennedy et al., 2005a;
Lejon et al., 2005).
Materials and methods
especially phosphorus. Because the soil was largely composed of silica, the addition of minerals to the soil in this
study represented an alteration in the mineral composition
of the soil, its nutrient content and the diversity of mineral
substrates available for the soil microbial community. The
site was a managed pasture, with the dominant grass species
being Italian ryegrass (Lolium multiflorum) with some
annual ryegrass (Lolium rigidum cv Wimmera) and lotus
minor (Lotus subbiflorus).
Microcosms
Microcosms were prepared by weighing 80 g dry soil (sieved
to o 2 mm) into lined 105 mL round pots. The experimental design consisted of two factors: mineral (five levels, fixed)
and plant (three levels, fixed), with four replicates. Mineral
treatments consisted of a control with no mineral addition,
additions of either mica (M), basalt (B) or rock phosphate
(P) separately and a final treatment containing all minerals
(MBP). Minerals were sieved to o 250 mm and mixed with
the soil at rates of 5 g kg soil1 for mica (M) and basalt (B)
(Coroneos et al., 1996; Hinsinger et al., 1996; Bolland &
Baker, 2000) and at 1.7 g kg soil1 for rock phosphate (P)
(Bolland et al., 1995). The elemental composition of each
mineral, as well as bulk soil (before the minerals were added)
was determined by X-ray fluorescence (Philips PW1404
XRF) (Table 1). To confirm that the minerals were not a
source of microorganisms for the microcosms, DNA was
extracted from each mineral and the internal-transcribed
spacer (ITS) region was amplified according to the methods
described below for the microcosm soils. Pots were planted
with annual ryegrass (Lolium rigidum cv. Concord), subterranean clover (Trifolium subterraneum cv. Trikkala) or
remained unplanted. Microcosms had a high plant density
to ensure that all soil was influenced by plant roots and
could therefore be considered rhizosphere.
Microcosms were incubated in a temperature-controlled
glasshouse (20/15 1C day/night) in a randomized block
design from February to April, 2005. Nutrients were added
to permit adequate plant growth in the highly nutrientdeficient soil. The nutrients contained in each mineral were
omitted from microcosms containing that mineral (mica K,
basalt Ca and Mg, rock phosphate P and Ca). Microcosms
Soil
Soil (0–10 cm) was collected in January 2005 from a site
130 km south of Perth, Western Australia (latitude 32160 0 ,
longitude 115150 0 ). The soil is a Haplic Podzol (FAO, 1998);
the 0–10 cm layer (o 2 mm) had 99.8% sand, 3.5% organic
carbon and a pH of 4.7 (1 : 5 soil/water). The parent material
is wind blown, siliceous marine sand (McArthur, 2004) and
the soil contains few minerals other than silica (SiO2) (Table
1). As a result, the soil has a low capacity to retain cations
(6.4 cmol kg soil1) and is naturally deficient in nutrients,
FEMS Microbiol Ecol 61 (2007) 414–423
Table 1. X-ray fluorescence analysis (XRF) of the major element composition (wt%) of each mineral
Mineral
Al2O3 SiO2 Fe2O3 CaO K2O MgO Na2O P2O5 SO3
Soil
0.0
Mica
11.6
Basalt
16.6
Rock
1.6
phosphate
92.1 0.1
70.8 5.1
53.7 12.0
7.5 2.5
0.1
1.5
9.5
44.2
0.0
3.8
0.4
0.1
0.0
3.1
5.1
0.3
0.0
0.0
2.4
0.0
0.0
0.5
0.3
30.7
0.0
0.3
0.3
1.5
Detection limits are typically 0.01 wt%.
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416
were watered daily to field capacity (24% water content
w/w, 10 kPa) with deionized water and destructively
sampled on day 78.
Plant biomass, soil and microbial activity
Plant root and shoot material were removed, dried at 70 1C
and weighed. Soil was sieved to o 2 mm to remove the
roots and stored at 4 1C for microbial activity, microbial
biomass, pH (1 : 5 soil/water, Rayment & Higginson, 1992)
and moisture analysis, and at 20 1C for molecular analyses. Dehydrogenase activity was measured as described by
Alef (1995). Respiration was determined after preincubating
20 g soil for 7 days at 25 1C and measuring the CO2 content
of 10 mL of headspace gas using an infrared gas analyser
(Series 225, Analytical Development Company, Hoddesdon,
England). Microbial biomass C was measured using fumigation extraction (Brookes et al., 1985; Sparling & Zhu, 1993).
For each replicate, 10 g of soil was fumigated with chloroform for 7 days and extracted for 1 h in 40 mL 0.5 M K2SO4.
Total organic carbon in the extracts was measured using
a total organic carbon analyser (TOC 5000A, Shimadzu),
and microbial biomass C was calculated according to the
method of Joergensen (1996).
Total soil DNA extraction
Total soil DNA was extracted using the MoBio PowerSoilTM
DNA isolation kit (Carlsbad, CA) with the following modifications: 0.5 g soil was used; samples were homogenized
using a Mini-BeadBeater-8 (Biospec Products Inc., Bartlesville, OK) at 3200 r.p.m. for 2 min.
Bacterial and fungal community fingerprinting
using ARISA
Bacterial ARISA PCR was performed using the method of
Gleeson et al. (2006). Briefly, the intergenic spacer (ITS)
region between the 16S and 23S rRNA genes was amplified
using the primer set S-D-Bact-1522-b-S-20 (eubacterial
rRNA small subunit, 5 0 -TGCGGCTGGATCCCCTCCTT-3 0 )
and L-D-Bact-132-a-A-18 (eubacterial rRNA large subunit,
5 0 -CCGGGTTTCCCCATTCGG-3 0 ) (Normand et al., 1996).
Amplified sequences contained the ITS plus c. 130 bp of the
23S rRNA gene.
Fungal ARISA was performed using the method of
Gleeson et al. (2005). Briefly, the fungal intergenic spacer
region containing the two ITSs and the 5.8S rRNA gene
(ITS1-5.8S-ITS2) was amplified using the primer set ITS1-F
(CTTGGTCATTTAGAGGAAGTAA) (Gardes & Bruns, 1993)
and ITS4 (TCCTCCGCTTATTGATATGC) (White et al.,
1990). Amplified sequences contained the two ITS regions
and the 5.8S gene a section of the 28S rRNA gene.
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J.K. Carson et al.
In each case, forward primers were labelled with Beckman
Coulter fluorescent dye D4 (Proligo) and PCR reactions
were carried out according to Gleeson et al. (2005, 2006).
After quantification of PCR products by gel electrophoresis,
aliquots (1–2 mL) of PCR products were mixed with 38.4 mL
deionized formamide, 0.2 mL of Beckman Coulter size
standard 600 (dye D1) and 0.4 mL of custom-made marker
(containing ribotypes ranging from 600 to 1200 bp in
intervals of 20 bp, and from 1000 to 1200 in intervals of
50 bp, all labelled with Beckman Coulter dye D1) (Bioventures, Murfreesboro, TN).
ARISA ribotype analysis
Analysis of intergenic spacer profiles was performed using
a Beckman Coulter (CEQ8000) automated sequencer and
BECKMAN COULTER CEQ 8000 fragment analysis software, algorithm v 2.1.3. (Gleeson et al., 2005). Only amplicons with a
fluorescence 4 1% of the total fluorescence were included
in the analyses. ARISA amplicons that differed by o 0.5 bp
in different profiles were considered to be identical. Amplicon lengths that were present in each of two replicates were
used to produce a representative ARISA profile of each
bacterial community. In order to make comparisons of
microbial communities more reliable, a modification of an
analytical procedure proposed by Dunbar et al. (2001) was
used. The Shannon diversity index was calculated for
bacterial and fungal communities from each replicate using
Primer 6 (Primer-E Ltd, UK).
Statistical analyses
Differences between mineral and plant treatments in microbial, soil and plant measurements, number of ribotypes
and Shannon diversity index were tested by two-way ANOVA
and the least significant difference was calculated for
the 95% confidence interval using GENSTAT version 8.2
(Rothamsted).
Multivariate statistical analyses were performed on bacterial and fungal ARISA profiles using Primer 6 (Primer-E
Ltd) and permutational multivariate analysis of variance
(PERMANOVA) (Anderson, 2001). To visualize the effect of
mineral treatments on bacterial and fungal community
profiles, nonmetric multidimensional scaling (MDS) plots
for each plant treatment were created (Primer 6) using
Bray–Curtis similarity matrices of untransformed data. In
MDS plots, the distance between points reflects how similar
the community profiles of those samples are. Thus, communities with the greatest similarity are represented by the
points that are closest together and the communities that are
most dissimilar are represented by the points furthest from
each other. The degree to which the plot matches the
underlying similarity matrix is shown by the stress (Kruskal’s stress), with values o 0.1 representing ordinations
FEMS Microbiol Ecol 61 (2007) 414–423
417
Mineral–microorganism interactions in soil
with little risk of misinterpreting the data (Clarke, 1993).
MDS was performed using 10 random starting configurations of sample points, and in all cases, two-dimensional
solutions are presented.
Analysis of similarity (ANOSIM) was performed (Primer 6)
to determine whether the effects of mineral and plant
treatments on bacterial and fungal community structure
that were observed in the MDS plot were statistically
significant. ANOSIM is a statistical method that is used to
detect treatment effects on multivariate data as ANOVA does
for univariate data (Clarke & Warwick, 2001). ANOSIM was
performed on Bray–Curtis similarity matrices constructed
using untransformed relative abundance data of either
bacterial or fungal ribotypes. The ANOSIM R statistic is
calculated by comparing the mean distances within treatments with distances between treatments and ranges from
0 to 1 (where R = 1 indicates that populations from different
treatments are completely separated from each other (dissimilar), and where R = 0 indicates that populations are
completely random). The p statistic indicates the probability
an R statistic that large would be determined by chance, and
is calculated by randomizing the samples to determine
whether the calculated R value is greater than any randomly
generated R value (Clarke & Warwick, 2001).
To complement the ANOSIM, a PERMANOVA was also used to
determine the effect of mineral and plant treatments on
microbial community structure. Unlike ANOSIM, PERMANOVA is
able to determine whether the interaction between the
mineral and plant treatments was significant. PERMANOVA
constructs an F-ratio from sums of squared distances within
and between groups that is analogous to Fisher’s F-ratio
(Anderson, 2001).
BEST analysis was performed (Primer 6) to determine the
correlation between bacterial and fungal ARISA profiles and
normalized microcosm variables (plant, soil and microbial
activity measurements). Correlations were also determined
between bacterial and fungal ARISA profiles from separate
plant treatments and microcosm variables. BEST determines
the rank correlation between the underlying similarity
matrices for species data and environmental variables
using the Spearman coefficient (r). As r increases from
0 to 1, the correlation between the species data and
environmental variables increases from no correlation
to complete correlation (Clarke & Ainsworth, 1993; Clarke
& Warwick, 2001)
Results
Minerals
The elemental composition of each mineral and bulk soil
(before minerals were added) is shown in Table 1. There was
FEMS Microbiol Ecol 61 (2007) 414–423
no amplifiable DNA on the minerals, confirming that they
were not a source of microorganisms for the microcosms.
Microbial activity
The effects of mineral and plant treatments on the size and
activity of the microbial community were measured using
microbial biomass carbon (C), dehydrogenase activity and
soil respiration (Table 2). Microbial biomass C was not
significantly affected by mineral treatment, but may have
been significantly affected by plant treatment (P = 0.053,
Table 3). Within each plant treatment, dehydrogenase
activity in treatments where minerals were added separately
did not differ significantly from each other, and was lower
than in the treatment containing all minerals (MBP).
Soil respiration was significantly affected by mineral
treatment (P = 0.018, Table 3) and was lower in the mica
(M) and basalt (B) treatments than all the other mineral
treatments. Soil respiration was significantly affected by
plant species (P o 0.001, Table 3) and was higher in the
T. subterraneum treatment (mean 1.05 mg C g soil1 h1) than
in unplanted (mean 0.57 mg C g soil1 h1) and L. rigidum
(mean 0.66 mg C g soil1 h1) treatments. For both unplanted
and T. subterraneum soil, respiration in the control was
not significantly different from the treatment containing
all minerals.
Soil and plants
Dissolved organic carbon (DOC) was higher in the rock
phosphate (P) treatment (mean 117 mg C g soil1) than in
the other mineral treatments, which did not differ significantly from each other (mean 94 mg C g soil1) (Table 2).
DOC was lower in the L. rigidum treatment (mean
77 mg C g soil1) than in the unplanted and T. subterraneum
treatments (means 107 and 112 mg C g soil1, respectively).
Although the overall variation in soil pH was small, only
ranging from 4.5 to 4.8 (Table 2), the effect of mineral and
plant treatments on pH was significant (P = 0.005, Table 3).
In the unplanted treatment, mineral addition had no effect
on soil pH compared with the control. However, in the
L. rigidum treatment, soil pH was higher in soils with
mineral additions than in the control. In the T. subterraneum
treatment, soil pH was higher in the treatments containing
basalt, rock phosphate and all minerals than in the control
or the treatment containing mica.
The effect of mineral treatment on plant, shoot and root
biomass depended on the plant species (Table 2). For
T. subterraneum, plant biomass was the highest in the
treatments containing P and all minerals and for L. rigidum
it was the highest in the P treatment and the control. Shoot
biomass followed similar trends (data not shown). For both
plant species, plant and root biomass were the lowest in the
treatment containing M. In T. subterraneum root biomass
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418
J.K. Carson et al.
Table 2. Microbial, soil and plant properties for mineral treatments within each plant treatment: control (no minerals added), mica (M), basalt (B),
rock phosphate (P) and MBP (all minerals added)
Plant
Mineral
Unplanted
Control
M
B
P
MBP
Control
M
B
P
MBP
Control
M
B
P
MBP
T. subterraneum
L. rigidum
LSD
Microbial Biomass C
(mg C g1 soil)
Dehydrogenase activity
(mg TPF g1 soil)
Soil respiration
(mg C g1 soil)
DOC
(mg C g1 soil)
Soil pH
(water)
245 (11)
259 (7)
262 (20)
248 (28)
224 (32)
300 (34)
288 (27)
249 (29)
281 (30)
306 (9)
259 (11)
249 (25)
262 (21)
226 (17)
245 (24)
77
54 (5)
49 (2)
48 (3)
41 (4)
62 (7)
83 (1)
73 (2)
74 (1)
65 (1)
86 (2)
63 (5)
100 (5)
96 (4)
92 (4)
111 (2)
11
0.87 (0.07)
0.34 (0.08)
0.48 (0.15)
0.64 (0.09)
0.51 (0.10)
1.17 (0.23)
0.77 (0.05)
0.82 (0.08)
1.17 (0.29)
1.33 (0.20)
0.80 (0.14)
0.50 (0.06)
0.69 (0.06)
0.64 (0.03)
0.64 (0.03)
0.43
110 (5)
94 (6)
97 (7)
128 (9)
109 (6)
107 (11)
106 (7)
121 (11)
126 (9)
100 (3)
75 (5)
66 (3)
78 (3)
67 (7)
67 (7)
23
4.8 (0.03)
4.8 (0.10)
4.7 (0.02)
4.8 (0.03)
4.8 (0.06)
4.5 (0.06)
4.5 (0.02)
4.7 (0.03)
4.6 (0.03)
4.7 (0.01)
4.5 (0.02)
4.8 (0.05)
4.6 (0.01)
4.7 (0.02)
4.8 (0.05)
0.14
Biomass
Plant (mg)
Root (mg)
–
–
–
–
–
–
–
–
–
–
301 (21)
273 (9)
314 (4)
298 (7)
337 (15)
774 (13)
578 (25)
542 (w)
665 (14)
683 (17)
52
958 (52)
835 (19)
963 (17)
1060 (29)
1143 (50)
1363 (31)
1116 (42)
1135 (w)
1325 (16)
1240 (28)
114
LSD, least significant difference (P o 0.05) determined by two-way ANOVA.
w
(n = 1) (TPF, triphenyl formazan).
Mean ( SE), n = 4.
DOC, dissolved organic carbon.
Table 3. Probability of F values from two-way ANOVA of microbial, soil and plant properties
Biomass
Source of
variation
Microbial
Biomass C
Dehydrogenase
activity
Soil
respiration
DOC
Soil pH
Plant
Mineral
Plant
Mineralplant
0.951
0.053
0.817
o 0.001
o 0.001
o 0.001
0.018
o 0.001
0.642
0.001
o 0.001
0.691
o 0.001
o 0.001
0.005
o 0.001
o 0.001
0.007
No. ribotypes
Shannon diversity
Root
Bacteria
Fungal
Bacterial
Fungal
o 0.001
o 0.001
o 0.001
0.006
o 0.001
o 0.001
0.271
0.276
0.021
0.032
0.002
0.014
0.142
0.317
0.129
DOC, dissolved organic carbon.
was the highest in the treatment containing all minerals and
in L. rigidum root biomass was the highest in the control.
Bacterial and fungal community profiles
ARISA was used to generate ribotype profiles for each
treatment, consisting of the individual ARISA amplicons
present and their relative abundances. In this study, each
ARISA amplicon indicated one sequence polymorphism, or
ribotype. A total of 246 unique bacterial ribotypes and 124
unique fungal ribotypes were detected across all samples,
and multivariate statistical analysis was performed to determine the main factors contributing to variation within the
microcosm.
In the L. rigidum planted microcosms, mineral treatment
did not have a significant effect on the number of ribotypes
or Shannon diversity index for either bacterial or fungal
communities (Table 4). In both the T. subterraneum and the
unplanted treatments, bacterial ribotype number and Shannon diversity index of the bacterial community were the
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lowest in the treatment containing mica and the control, and
the highest in the combined mineral treatment. Ribotype
number and Shannon diversity index of fungal communities
were less affected by mineral treatments than the bacterial
communities. For fungal communities in the L. rigidum and
T. subterraneum treatments, there was no effect of mineral
treatment on ribotype number and Shannon diversity index.
In the unplanted treatment, fungal ribotype number and
Shannon diversity index were the lowest in the P treatment
and the highest in the B treatment and the control.
In the MDS ordinations (Figs 1 and 2), the replicates from
each mineral treatment clustered together, indicating that
within plant treatments, bacterial and fungal ribotype
profiles of replicates from mineral treatments were more
similar to each other than to replicates from other mineral
treatments (Clarke, 1993), that is, that each mineral treatment resulted in a different microbial community structure.
The stress of each of these plots did not exceed 0.1,
indicating that there was little risk of misinterpreting the
data using these ordinations (Clarke, 1993). For bacterial
FEMS Microbiol Ecol 61 (2007) 414–423
419
Mineral–microorganism interactions in soil
Table 4. Number of bacterial and fungal ribotypes, and Shannon diversity index for mineral treatments: control (no minerals added), mica (M), basalt
(B), rock phosphate (P) and MBP (all minerals added)
No. of ribotypes
Shannon diversity
Plant
Mineral
Bacterial
Fungal
Bacterial
Fungal
Unplanted
Control
M
B
P
MBP
Control
M
B
P
MBP
Control
M
B
P
MBP
3.5 (0.8)
2.0 (0.5)
17.0 (2.9)
6.0 (1.1)
13.0 (2.7)
10.3 (2.5)
9.5 (3.3)
13.0 (4.3)
27.3 (1.0)
27.3 (1.0)
11.0 (5.0)
10.8 (0.4)
7.8 (2.0)
9.5 (1.8)
5.0 (1.1)
8.5
8.5 (0.9)
4.3 (0.9)
6.5 (2.3)
1.5 (0.6)
6.8 (0.8)
3.5 (0.8)
3.0 (0.7)
3.0 (0.6)
6.5 (1.5)
5.0 (1.0)
6.5 (1.1)
5.0 (0.9)
3.8 (1.1)
5.5 (1.4)
4.5 (0.8)
3.7
0.92 (0.24)
0.53 (0.26)
2.14 (0.15)
1.15 (0.16)
2.11 (0.15)
1.61 (0.19)
1.56 (0.32)
1.89 (0.44)
2.67 (0.24)
2.53 (0.09)
1.12 (0.57)
1.79 (0.15)
1.23 (0.36)
1.61 (0.26)
1.10 (0.17)
0.92
1.65 (0.16)
0.71 (0.22)
1.19 (0.36)
0.42 (0.22)
1.04 (0.25)
0.77 (0.22)
0.71 (0.21)
0.68 (0.15)
1.15 (0.22)
1.29 (0.14)
1.44 (0.13)
1.18 (0.19)
0.82 (0.30)
1.09 (0.32)
1.32 (0.17)
0.74
T. subterraneum
L. rigidum
LSD
LSD, least significant difference (P o 0.05) determined by two-way ANOVA.
Mean ( SE), n = 4.
communities in unplanted soil (Fig. 1a), replicates of the
mica treatment clustered together separately to the other
mineral treatments, indicating that mica addition resulted
in a shift in community structure compared with all other
mineral treatments. When T. subterraneum was planted,
there was no obvious clustering of bacterial communities
from different mineral treatments (Fig. 1b), indicating that
community structure was not strongly influenced by any
one mineral treatment. When L. rigidum was planted, the
community structure of the treatment containing P was
most different from all other mineral treatments. Fungal
community structure exhibited slightly different characteristics from the bacterial equivalent. In unplanted soil,
treatments containing M and MBP exhibited a community
structure similar to the control, whereas the P and B
treatments resulted in a shift in fungal community structure
(from the control). With T. subterraneum planted only the
treatment containing P showed a shift in community
structure compared with the other mineral treatments. For
L. rigidum, the fungal community structure varied across all
mineral treatments with no obvious clustering.
ANOSIM was used to investigate the effect of mineral and
plant treatments on both bacterial and fungal community
structure. Bacterial community structure was affected by
both mineral (R = 0.73) and plant (R = 0.71) treatments, as
was fungal community structure: mineral (R = 0.65) and
plant (R = 0.65) treatments (in each case P o 0.001).
The larger R values for bacterial community structure
indicate that the mineral and plant treatments had greater
effects on bacterial community structure than on the fungal
community structure. All the pairwise comparisons between
FEMS Microbiol Ecol 61 (2007) 414–423
different mineral treatments and between different plant
treatments were significantly different (P o 0.05), and
the R statistics are shown in Table 5. These demonstrate that
each mineral treatment resulted in a different microbial
community structure and confirms the results of the MDS
ordination.
PERMANOVA analysis for both bacterial and fungal community structure showed a significant interaction between
mineral and plant treatments (P = 0.001). For bacterial
community structure, all pairwise comparisons of mineral
treatments (within plant treatments) were significantly
different (P o 0.05). For fungal community structure, pairwise comparisons of mineral treatments (again within each
plant treatment) were all significantly different (P o 0.05),
except for: M and control in bare soil (P = 0.08) and in
T. subterraneum (P = 0.14); M and B in T. subterraneum
(P = 0.07).
Correlation coefficients calculated by BEST for bacterial
and fungal communities were relatively low. However, for
planted treatments correlations were higher than for the
unplanted treatment (Table 6). This analysis showed that the
overall bacterial community structure was best correlated
with soil pH, dehydrogenase activity and root weight while
the overall fungal community structure was best correlated
with dehydrogenase activity and root weight.
Discussion
This study showed that the addition of minerals to pasture
soil microcosms resulted in substantial changes in both
bacterial and fungal community structure, dependent upon
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420
J.K. Carson et al.
Fig. 1. Nonmetric MDS plots of individual bacterial community profiles
for (a) unplanted (stress = 0.01), (b) Trifolium subterraneum (stress = 0.09)
and (c) Lolium rigidum (stress = 0.01). Control, ; mica, m; basalt, ’; rock
phosphate, ; all minerals added, &. The MDS plots are derived from a
Bray–Curtis similarity matrix constructed with untransformed abundance
of bacterial ribotypes.
Fig. 2. Nonmetric MDS plots of individual fungal ribotype/community
profiles for (a) unplanted (stress = 0.01), (b) Trifolium subterraneum
(stress = 0.09) and (c) Lolium rigidum (stress = 0.01). Control, ; mica,
m; basalt, ’; rock phosphate, ; all minerals added, &. The MDS plots
are derived from a Bray–Curtis similarity matrix constructed with untransformed abundance of fungal ribotypes.
the type of mineral applied and the plant species present.
The absence of amplifiable DNA in the minerals shows that
the changes in microbial community composition were not
due to microorganisms introduced into the soil with the
minerals. This suggests that soil mineral composition may
be a factor determining microbial community structure in
soils.
The significant effects of mineral addition on microbial
community structure revealed by the ANOSIM may have been
due to differences in the elemental composition of the
minerals. Recent studies have indicated that mineral composition influences microbial community structure (Certini
et al., 2004; Gleeson et al., 2005, 2006). Gleeson et al. (2005,
2006) demonstrated that different minerals within a pegmatitic granite outcrop supported distinct bacterial and fungal
populations and that microbial community structure was
driven by the elemental composition of the mineral substrates, in particular Al, Si, K and Ca. In this study, it is also
reported that minerals of different chemical composition
influence microbial community structure, but in a soil
environment. The addition of minerals to the silica-based
soil altered the mineral composition of the soil and may
have caused the shifts in microbial community composition.
Certini et al. (2004) also reported that rock fragments in soil
supported a different microbial community from fine earth,
although this study was based on microbial community
structure as determined using phospholipid fatty acid
analysis and community-level physiological profiles.
Mineral addition may have influenced microbial community structure in soil via the release of limiting nutrients into
the soil upon dissolution. In an aquifer limiting in P and
Fe, microorganisms preferentially colonized manufactured
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FEMS Microbiol Ecol 61 (2007) 414–423
421
Mineral–microorganism interactions in soil
glasses containing apatite or goethite rather than glasses
without these minerals, suggesting that microorganisms
were directly benefiting from mineral dissolution (Rogers &
Bennett, 2004). The soil used in the present study contained
low amounts of nutrients (Table 1), exchangeable cations
and phosphorus. The minerals contained larger amounts of
these nutrients than the soil and differed from each other in
their nutrient concentrations: mica had a higher K content,
basalt a higher Mg content and rock phosphate a higher
phosphorus and Ca content (Table 1). In this study,
increased nutrient content of the soil due to mineral
addition may have caused the observed shifts in microbial
community composition.
Mineral addition may also have affected microbial community structure indirectly via increases in plant growth and
soil pH. BEST analysis revealed that in this study, bacterial
community structure was correlated with shoot biomass
and soil pH and fungal community structure was correlated
with root biomass. Mineral addition to soil can increase
shoot biomass as plants respond to the nutrients released by
Table 5. ANOSIM R statistics for pairwise comparisons of mineral and
plant treatment effects on microbial community structure: control
(no minerals added), mica (M), basalt (B), rock phosphate (P) and MBP
(all minerals added)
Pairwise comparison
Bacterial
Fungal
Unplanted/T. subterrnaneum
Unplanted/L. rigidum
T. subterrnaneum/L. rigidum
Control/M
Control/B
Control/P
Control/MBP
M/B
M/P
M/MBP
B/P
B/MBP
0.710
0.654
0.788
0.694
0.587
0.750
0.691
0.774
0.748
0.741
0.802
0.816
0.519
0.733
0.700
0.531
0.649
0.667
0.667
0.590
0.667
0.750
0.611
0.694
All comparisons were significant (P = 0.001).
mineral dissolution (Sanz Scovino & Rowell, 1988; Bolland
et al., 1995, 2001; Hinsinger et al., 1996; Bolland & Baker,
2000). This can alter C return to the soil through roots and
increase microbial biomass and activity (Vale et al., 2005).
Soil pH can increase when silicate minerals and rock
phosphate are added to soil and dissolve (Hildebrand &
Schack-Kirchner, 2000; Gillman et al., 2002; Longanathan
et al., 2002; Priyono & Gilkes, 2004). Previous studies have
found that bacterial community structure is strongly influenced by changes in soil pH, while fungal community
structure is influenced less (Brodie et al., 2002; Kennedy
et al., 2004, 2005a). The low correlations found between
community structure and biotic and abiotic variables
(P o 0.420) indicate that changes in microbial community
structure may be influenced by a number of interacting
variables and not solely by any single factor.
The microbial community profiles in this study were
assessed using ARISA and the results need to be interpreted
with caution. Biases may be introduced during DNA extraction and PCR amplification, and there are difficulties in
standardizing the amount of DNA analysed per replicate
(Crosby & Criddle, 2003; Egert & Friedrich, 2003). Recent
studies on bacterial community profiling found that TRFLP,
a method similar to ARISA, gave a quantitative view of the
community not affected by PCR bias, particularly when
nondegenerate primers were used, such as in this study
(Lueders & Friedrich, 2003; Banning et al., 2005). In this
study, all samples were subject to the same biases and
comparisons were made on a relative basis after standardization of ARISA fragment peak heights.
The present study shows that when mineral substrates
rich in K, Mg, Ca or P soils are added to nutrient-poor soils,
distinct microbial responses are elicited. It is generally
accepted that soil physicochemical characteristics influence
microbial community structure (Brodie et al., 2002; Kennedy et al., 2005a; Cookson et al., 2006), and this study
suggests that soil mineralogy may also play a significant
role. Caution must be used when extrapolating controlled
laboratory experiments to field conditions, as important soil
Table 6. Correlations between microbial community structure and environmental variables for the microcosms found using BEST
Community
Plant
Correlation
Variables
Bacterial
Unplanted
T. subterraneum
L. rigidum
Overall
Unplanted
T. subterraneum
L. rigidum
Overall
0.070
0.322
0.308
0.230
0.158
0.420
0.309
0.186
Soil pH, dehydrogenase activity
Soil pH, plant biomass, root biomass
Root biomass, shoot biomass
Soil pH, dehydrogenase activity
Soil respiration, dehydrogenase activity
Dehydrogenase activity
Root biomass
Dehydrogenase activity, root biomass
Fungal
The environmental variables used were microbial biomass carbon (C), dehydrogenase activity, respiration, pH, dissolved organic carbon, plant weight,
shoot weight and root weight. Environmental variables were correlated with untransformed microbial relative abundance data in a Bray–Curtis similarity
matrix.
FEMS Microbiol Ecol 61 (2007) 414–423
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422
microbial community parameters such as biomass have
been shown to differ under field and glasshouse conditions
(Ibekwe & Kennedy, 1998). Nevertheless, this study demonstrates that mineralogy may be a significant factor in
determining microbial community structure in soils.
Acknowledgements
Louise Campbell, Michael Smirk and Gary Cass are thanked
for their technical assistance. Seeds were supplied by
Symonds Seeds and minerals were supplied by EcoGrowth
International. The authors thank Daniel Murphy, Natasha
Banning and Anke Herrmann for helpful comments on the
manuscript. This work was funded by the Rural Industries
Research and Development Corporation (RIRDC).
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