Influence of Soil Characteristics on the Diversity of Bacteria in
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APPLIED AND ENVIRONMENTAL MICROBIOLOGY, July 2010, p. 4744–4749 Vol. 76, No. 14
0099-2240/10/$12.00 doi:10.1128/AEM.03025-09
Copyright © 2010, American Society for Microbiology. All Rights Reserved.
Influence of Soil Characteristics on the Diversity of Bacteria in the
Southern Brazilian Atlantic Forest䌤†
H. Faoro,1 A. C. Alves,2 E. M. Souza,1 L. U. Rigo,1 L. M. Cruz,1 S. M. Al-Janabi,1
R. A. Monteiro,1 V. A. Baura,1 and F. O. Pedrosa1*
Department of Biochemistry and Molecular Biology, Universidade Federal do Paraná, CP 19046, 81531-990 Curitiba, PR, Brazil,1
and Laboratory of Artificial Intelligence and Computer Science, University of Porto, Porto, Portugal2
Received 15 December 2009/Accepted 13 May 2010
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The Brazilian Atlantic Forest is one of the 25 biodiversity hot spots in the world. Although the diversity of
its fauna and flora has been studied fairly well, little is known of its microbial communities. In this work, we
analyzed the Atlantic Forest ecosystem to determine its bacterial biodiversity, using 16S rRNA gene sequenc-
ing, and correlated changes in deduced taxonomic profiles with the physicochemical characteristics of the soil.
DNAs were purified from soil samples, and the 16S rRNA gene was amplified to construct libraries. Compar-
ison of 754 independent 16S rRNA gene sequences from 10 soil samples collected along a transect in an altitude
gradient showed the prevalence of Acidobacteria (63%), followed by Proteobacteria (25.2%), Gemmatimonadetes
(1.6%), Actinobacteria (1.2%), Bacteroidetes (1%), Chloroflexi (0.66%), Nitrospira (0.4%), Planctomycetes (0.4%),
Firmicutes (0.26%), and OP10 (0.13%). Forty-eight sequences (6.5%) represented unidentified bacteria. The
Shannon diversity indices of the samples varied from 4.12 to 3.57, indicating that the soils have a high level
of diversity. Statistical analysis showed that the bacterial diversity is influenced by factors such as altitude,
Ca2ⴙ/Mg2ⴙ ratio, and Al3ⴙ and phosphorus content, which also affected the diversity within the same lineage.
In the samples analyzed, pH had no significant impact on diversity.
The Brazilian Atlantic Forest is one of the 25 biodiversity more susceptible to variation in soil properties and to disturb-
hot spots in the world. Altogether, these hot spots contain ing factors (33). Seasonal, physical, and physicochemical fac-
more than 60% of the total terrestrial species of the planet tors can be relevant to the structure and diversity of microbial
(17). The Atlantic Forest is a dense ombrophilous forest with communities. For example, seasonal changes in vegetation and
several variations, including coastal (3 to 50 m), submontane temperature led to replacement of dominant groups in a wheat
(50 to 500 m), montane (500 to 1,200 m), and high montane field (25) and in grassland soils (16). The particle size also has
(1,200 to 1,400 m) forests, creating a vegetation gradient rang- an influence on the bacterial diversity of soils. The clay fraction
ing from shrubs to well-developed montane forest (4). The has a more diverse bacterial community than do silt or sand
Serra do Mar is a mountainous system that shelters the main fractions (23). Finally, analyses of communities from North
remainder of the Atlantic Forest following the Brazilian east and South American soils showed that pH plays a major role in
coast, from north to south along the coastal line, and it is bacterial diversity, with less diverse communities associated
divided into diverse sections of high and low blocks, which have with a lower pH (9).
regional denominations. Human activity can also change the microbial diversity of
The most important law-protected conservation area of the soils, both qualitatively and quantitatively. Analyses of micro-
Brazilian Atlantic Forest is located in the Serra do Mar of the bial communities on coral atolls in the central Pacific Ocean
southern state of Paraná. This conservation area (⬃5,000 km2) under different degrees of human impact showed that the
shelters 72% of the fauna and flora species that occur in least-impacted atoll had autotrophs and heterotrophs equally
Paraná and was declared a Biosphere Reserve by UNESCO in distributed in the community, whereas the most-impacted atoll
1992. Much is known about the diversity of its fauna and flora, had a dominance of heterotrophs and about 10 times more
but little is known of its microbial diversity, particularly the soil microbial cells and virus-like particles in the water column,
microbial diversity and the soil characteristics that influence it. including a large percentage of potential pathogens (7). A
The soil microbial diversity is vast, and it is estimated that
comparison between bacterial communities in forest and
⬎99% of species remain unidentified (1, 28). Acidobacteria and
pasture soil showed that there is a less diverse and more
Proteobacteria are the most abundant groups in soil (15). How-
restricted community in pasture soils. The vegetation shift
ever, the Proteobacteria lineage is more diverse and stable than
from forest to pasture resulted in changes to G⫹C% con-
the Acidobacteria lineage, suggesting that the latter group is
tents of soil bacterial DNA and amplified rRNA gene re-
striction analysis (ARDRA) profiles (18). Similar changes
* Corresponding author. Mailing address: Department of Biochem- occurred with communities of soils submitted to agroindus-
istry and Molecular Biology, Universidade Federal do Paraná, CP trial treatments and pollutants (3, 30).
19046, 81531-990 Curitiba, PR, Brazil. Phone: 55(41)3361-1581. Fax: In this work, we used a culture-independent approach based
55(41)3361-1578. E-mail: fpedrosa@ufpr.br.
† Supplemental material for this article may be found at http://aem
on 16S rRNA gene sequences to survey the bacterial commu-
.asm.org/. nity of the Atlantic Forest soils and determined the physico-
䌤
Published ahead of print on 21 May 2010. chemical factors affecting its bacterial biodiversity.
4744VOL. 76, 2010 EFFECTS OF SOIL CHARACTERISTICS ON BACTERIAL DIVERSITY 4745
TABLE 1. Bacterial diversity of the Brazilian Atlantic Forest soila sity of Washington [http://evolution.genetics.washington.edu/phylip.html]). Dis-
tance matrices were used as input for the DOTUR program (22), which was used
Altitude No. of No. of to cluster sequences into operational taxonomic units (OTUs) (identities of
Library H⬘ E
(m)b reads OTUs ⱖ95%).
MA01 874 77 64 4.08 0.94 Biodiversity evaluation. Sequences with identities of ⱖ95% were assumed to
MA02 900 78 66 4.12 0.95 belong to the same OTU (5, 19). The bacterial diversity was evaluated (13) by the
MA03 896 70 48 3.79 0.97 Shannon diversity index (H⬘), calculated by the DOTUR program (22). Rarefac-
MA04 810 74 58 3.96 0.92 tion curves, ACE estimators, and Shannon indices for high- and low-altitude
MA05 604 83 59 3.87 0.87 groups were also calculated using DOTUR. Evenness (E) was calculated by the
MA06 375 80 54 3.84 0.88 equation E ⫽ H⬘/ln S, where S (species richness) is the total number of OTUs.
MA07 161 71 51 3.81 0.96 The similarities in the compositions of the clone libraries were examined by
MA08 95 69 43 3.57 0.95 using the S-LibShuff program (21). Graphical analyses were done using the
MA09 44 81 51 3.60 0.92 LibShuff program (24). The LibShuff program generates homologous and het-
MA10 29 70 47 3.67 0.95 erologous coverage curves (CX and CXY, respectively), at any level of sequence
similarity or evolutionary distance (D), from two 16S rRNA gene clone libraries
a
Sequences with identities of ⱖ95% were assumed to belong to the same (X and Y). To determine if the coverage curves CX(D) and CXY(D) are signifi-
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OTU. Indices were calculated from the number and abundance of species in each cantly different, the distances between the two curves are first calculated by using
soil sample by using DOTUR (22). H⬘, Shannon index; E, evenness index. the Cramér-von Mises test. The two libraries were considered significantly dif-
b
Referenced to the average level of the sea. ferent when the P value was ⬍0.05.
Statistical methods. Statistical analyses of the biological diversity indices and
physicochemical characteristics of soil were performed for samples with high
(MA01 to MA04) and low (MA07 to MA10) levels of diversity. An independent
MATERIALS AND METHODS two-sample Student t test and the Mann-Whitney test were performed to screen
Soil sampling. Atlantic Forest soil samples were collected along the PR 410 for variables with statistically significant differences between the two groups of
highway in the State of Paraná, Brazil, which transverses 28.5 km of an area of samples. The Hodges-Lehman (HL) estimator of the difference in central ten-
Atlantic Forest. GPS coordinates (Gardin) of the collection point for each dency between the two groups was calculated for all biological and physicochem-
sample were recorded (see Table S1 in the supplemental material). For sample ical variables. Principal component analysis (PCA) was carried out on the mean,
collection, the site was cleaned superficially to remove plants and decomposing centered with unit variance scaled data by a matlab routine developed in-house.
organic matter. The soil in a circle of approximately 50 cm in diameter, from 0 Data were visualized in the form of the principal component score plots and
to 20 cm in depth, was thoroughly mixed, and soil samples (approximately 500 g) loading plots. Partial least-square discriminant analysis (PLS-DA) was per-
were then collected, transferred to sterilized Falcon tubes, and stored on ice. formed to determine which variables were correlated with the biodiversity and to
Collection tools were washed in water, followed by disinfection with 70% alcohol validate the results obtained with the unsupervised PCA model. Validation of
and 2% sodium hypochlorite and, finally, were washed thoroughly with sterile statistical data was performed using jackknifing and cross-validation tests. The
water. A total of 10 soil samples were collected from sites in the submontane (50 model predictive value was assessed by the Q2 parameter (10), indicating how
to 500 m of altitude) and montane (500 to 1,200 m) forest (4) (Table 1). The well the model predicts new data by using leave-one-out cross-validation.
following physicochemical parameters of the collected soil were determined: pH, Nucleotide sequence accession numbers. The obtained 16S rRNA gene se-
Al3⫹, H⫹ ⫹ Al3⫹, Ca2⫹, Mg2⫹, K⫹, total bases (SB; ⫽ Ca2⫹ ⫹ Mg2⫹ ⫹ K⫹), quences were deposited in the GenBank database under accession no. EF135620
effective cation exchange capability (T; ⫽ SB ⫹ H⫹ ⫹ Al3⫹), phosphorus level, to EF136358 and GU071058 to GU071072.
carbon content, base saturation (V), aluminum saturation (m), Ca2⫹/Mg2⫹ ratio,
and clay content (see Table S2 in the supplemental material). Soil analyses were
performed by the Laboratory of Soil Analyses of the Department of Soils of the
RESULTS
Universidade Federal do Paraná, using standard methods (27).
Atlantic Forest soil physical and chemical properties. The
Soil DNA extraction, 16S rRNA gene amplification, and cloning. After collec-
tion, the soil samples were stored on ice for no more than 4 h before DNA physicochemical properties of the soil samples are shown in
extraction. Soil DNA was extracted using an UltraClean soil DNA kit (MoBio Table S2 in the supplemental material. All of the samples had
Laboratories) following the manufacturer’s instructions. Briefly, soil (0.5 g) was a low pH (ⱕ4.50) and high aluminum saturation level (⬎50%).
added to a tube containing 2 ml of bead suspension and vigorously mixed. The The base saturation (V%) was low (⬍50%), and thus the soil
mixture was treated with an inhibitor removal solution, and then the DNA was
purified on silica columns. 16S rRNA gene amplification was performed using
was classified as infertile or dystrophic. The organic matter
the universal primers for the Bacteria domain: 27F (5⬘-AGAGTTTGATCCTG content (C) was high only in sample MA01 (⬎50 g/dm3). The
GCTCAG) and 1492R (5⬘-ACGGCTACCTTGTTACGACTT) (31). The PCR other samples had low organic matter contents (⬍50 g/dm3).
mixture (20 l) contained 2 U of Taq DNA polymerase, 4 pmol of each primer, The amount of clay was also determined and varied from 150
a 200 M concentration of each deoxynucleoside triphosphate (dNTP), approx-
to 500 g per kg of soil.
imately 10 ng of extracted soil DNA, and PCR buffer (200 mM Tris-HCl, pH 8.4,
500 mM KCl). The thermocycler program was as follows: 1 cycle at 95°C for 5 Sequence identification and diversity characterization. PCR
min, followed by 20 sequential cycles of 94°C for 1 min, 62°C for 1 min, and 72°C products were obtained for all DNA samples, using primers
for 1 min and a final step at 72°C for 5 min. The PCR products were cloned using 27F and 1492R, and were used to construct 10 libraries of soil
the pGEM-T Easy vector system (Promega) according to the manufacturer’s organism 16S rRNA gene amplicons in pGEM-T Easy (Pro-
instructions.
Plasmid DNA extraction and sequencing. Plasmid DNA was purified in 96-
mega). Ninety-six clones of each library were isolated and used
well plates by the alkaline lysis method (20). The V1-V2 region of cloned 16S as templates for sequencing reactions. Among the 960 tem-
rRNA genes (⬃300 bp at the 5⬘ end of the 16S rRNA gene) was sequenced with plates, 754 complete sequences of the V1-V2 region were ob-
the forward primer Y1 (5⬘-TGGCTCAGAACGAACGCTGGCGGC) and the tained, and they varied from 234 to 341 bp in length. All of the
reverse primer Y2 (5⬘-CCCACTGCTGCCTCCCGTAGGAGT) (32) in a Mega-
reads used in the assembly of the contigs had a Phred quality
bace 1000 automatic sequencer, using a DYEnamic ET dye terminator cycle
sequencing kit (GE Healthcare). index of at least 30.
Sequence assembly and analysis. The Phred program was used for base calling The partial 16S rRNA gene sequences were compared to the
(8). The Phrap program was used to assemble the reads into the 16S rRNA RDP II database through the RDPquery program (Fig. 1).
partial gene sequence. Finally, the Consed program (11) was used to view and Approximately 63% (473 sequences) of the sequences were
edit the sequence assembly. The final sequences were compared with the Ribo-
somal Database Project II (6), using the SeqMatch tool. Partial 16S rRNA gene
grouped in the phylum Acidobacteria. The Proteobacteria phy-
sequences were aligned using ClustalW (26), and the alignment was used to lum was ranked second, with 25.2% (190 sequences) of the
construct distance matrices with the DNAdist program (J. Felsenstein, Univer- sequences, which were distributed as follows: Alphaproteobac-4746 FAORO ET AL. APPL. ENVIRON. MICROBIOL.
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FIG. 1. Bacterial phyla in Brazilian Atlantic Forest soil.
teria (52.1%), Betaproteobacteria (20%), Deltaproteobacteria diversity (MA05 and MA06), and those with a low level of
(16.3%), and Gammaproteobacteria (11.5%). Other phyla diversity (MA07 to MA10). To evaluate this separation, we
found were Actinobacteria (1.2%), Bacteroidetes (1%), Chlo- grouped sequences from libraries according to altitude, i.e.,
roflexi (0.66%), Firmicutes (0.26%), Gemmatimonadetes high altitude (MA01 to MA04 [between 900 and 800 m above
(1.6%), Nitrospira (0.4%), Planctomycetes (0.4%), and OP10, a sea level]) and low altitude (MA07 to MA10 [between 160 and
thermophilic bacterium phylum (0.13%). Forty-eight se- 30 m above sea level]), and compared them using the LibShuff
quences (6.5%) matched the 16S rRNA genes of unclassified, program. Graphic analyses of homologous and heterologous
usually uncultured, bacteria and could not be grouped with coverage curves generated by LibShuff (Fig. 2A) indicated that
sequences of known bacteria phyla. the bacterial community in the first group was different from
The number of OTUs (sequences with identities of ⱖ95%) that in the second group in the interval of evolutionary dis-
differed from sample to sample. The MA01 and MA02 samples tances from 0.0 (100% of identity and 0% of differences) to 0.3
had the highest species richness (S), with 64 OTUs in 77 (70% of identity and 30% of differences). This result suggests
sequences and 66 OTUs in 78 sequences, respectively. These that the genetic diversity between these two groups occurs not
two samples also showed the highest Shannon indices, of 4.02 only at lower taxonomic ranks but also at higher taxonomic
and 4.12, respectively (Table 1). The other samples had lower levels (Fig. 2A). The separation in the two groups was also
species richness and Shannon indices. The evenness index var- evident when we analyzed the tendency curves for rarefaction
ied from 0.97 to 0.87, suggesting that the species were equally (Fig. 2B), Shannon indices (Fig. 2C), and ACE estimators (Fig.
represented in the analyzed samples, without dominance of 2D) on DOTUR plots. The high-altitude group had higher
specific bacterial phylotypes (Table 1). Shannon indices and OTU numbers (95% 16S rRNA gene
Sequences from the 10 libraries were compared using the sequence similarity) than the low-altitude group. Also, the
S-LibShuff program to evaluate their degrees of similarity. rarefaction curve for the high-altitude group is less saturated
Analyses of homologous coverage curves (see Table S3 in the than that for the low-altitude group, indicating that more phy-
supplemental material) indicated that libraries for samples lotypes could be recovered from the first than from the second
MA01 to MA05 had similar bacterial communities (P ⬎ 0.05). group of libraries. The LibShuff and DOTUR results suggest
These libraries were grouped in a cluster and were different that the high-altitude group had a different, more diverse,
from the libraries for samples MA06 to MA10. Similarly, li- richer microbial community than that of the low-altitude
braries for samples MA07 to MA10 also seemed to have sim- group.
ilar communities. On the other hand, the MA06 library was Microbial diversity is significantly different in high- and
different from all the others (P ⬍ 0.05). low-altitude soil samples. To understand the impact of altitude
A linear regression plot considering the Shannon index of and the physicochemical characteristics of soil on microbial
each library versus the altitude of the sampling site (see Fig. S1 biodiversity, the groups were compared for differences in mean
in the supplemental material) revealed that sample clustering and central tendency, using an independent two-sample Stu-
may be influenced by the altitude of the collection points and dent t test and the Mann-Whitney test, respectively. Table S4
can be divided into three groups: those with a high level of in the supplemental material shows the results for the biolog-
diversity (MA01 to MA04), those with an intermediate level of ical diversity indices (richness [S], evenness [E], and ShannonVOL. 76, 2010 EFFECTS OF SOIL CHARACTERISTICS ON BACTERIAL DIVERSITY 4747
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FIG. 2. High-altitude samples have more diverse microbial communities than low-altitude samples. Sequences from the MA01 to MA04
libraries were grouped in the high-altitude, high-diversity cluster, and sequences from MA07 to MA10 were grouped in the low-altitude,
low-diversity cluster. (A) Phylogenetic diversity in the high- and low-altitude clusters was compared using the LibShuff program. Homologous (E)
and heterologous (F) coverage curves for 16S rRNA gene sequence libraries are shown. Solid lines indicate the value of (CX ⫺ CXY)2 for the
original samples at each value of D. D is equal to the Jukes-Cantor evolutionary distance, determined by the DNADIST program of PHYLIP.
Broken lines indicate the 950th value (or P ⫽ 0.05) of (CX - CXY)2 for the randomized samples. (B, C, and D) DOTUR graphic analyses comparing
the groups according to rarefaction curves (B), Shannon indices (C), and ACE estimators (D).
index [H]), and Table S5 in the supplemental material shows model perfectly discriminates samples with low levels of biodi-
the results for the physicochemical characteristics of the soil versity from those with high levels of biodiversity. In order to
samples. Microbial diversity of soil samples at low and high determine which variables are important for discriminating
altitudes was also compared using the Wilcoxon-Mann-Whit- between groups, the loadings of the third principal component
ney two-sample rank sum test. The effect of the altitude (dif- were plotted (Fig. 3B). The variables associated with higher
ference between groups) was quantified using the HL estima- biodiversity levels have larger magnitudes in the same direc-
tor, which is consistent with the Wilcoxon test. The results tion as the high-biodiversity samples in the score plots. Higher
showed an association between the altitude level and soil mi- altitudes and Ca2⫹/Mg2⫹ ratios were found to be associated
crobial diversity. On the other hand, there was no statistically with higher levels of biodiversity, while higher levels of Al3⫹
significant effect of a particular soil parameter. Hence, the and phosphorus were associated with lower levels of biodiver-
results suggest that the difference found in the biodiversity sity.
between groups may be explained by interactions between the To identify the physicochemical characteristics that play a
physicochemical soil characteristics. A PCA model was devel- major role in discriminating between low- and high-biodiver-
oped to explore this hypothesis. sity soil samples, PLS-DA was performed. The PLS-DA model
PCA reveals a perfect separation between soil samples with achieved a very high predictive value (Q2Y ⫽ 0.8) and attained
high and low levels of microbial biodiversity. PCA was per- an out-of-sample prediction accuracy of 100%. The signifi-
formed to visualize the interdependence between the variables cance of the PLS regression coefficients was estimated using
that could explain the differences between the groups of high- the one-sample Student t test on all variables (see Table S6 in
and low-biodiversity soil samples. The score plots of the first the supplemental material). The samples with higher levels of
and third principal components show a perfect separation be- biodiversity were confined to a very small and dense cluster,
tween samples of each group (Fig. 3A). A PCA model with while the low-biodiversity samples were spread over the space
only three components captures over 90% of the variance of defined by the scores of the first three latent variables (see Fig.
the soil samples. The third principal component of the PCA S2A in the supplemental material). There are also other vari-4748 FAORO ET AL. APPL. ENVIRON. MICROBIOL.
inant phylum in Atlantic Forest soil samples was Acidobacteria
(63%), followed by the Proteobacteria (25.2%). These two
groups are frequently the most numerous in soil samples. In a
meta-analysis of 16S rRNA gene sequences from distinct soils,
Janssen (15) determined that the most abundant bacterial
phyla were Proteobacteria (39%) and Acidobacteria (19%), fol-
lowed by Verrucomicrobia, Bacteroidetes, Chloroflexi, Plancto-
mycetes, Gemmatimonadetes, and Firmicutes (15). Except for
Verrucomicrobia, all of these phyla were represented in Atlan-
tic forest soils, although in different proportions.
The profile of the bacterial community found in Atlantic
Forest soils is similar to that found in European forests in
eastern Austria (12). In a spruce-fir-beech forest, the
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Acidobacteria phylum was dominant (35%), followed by the
Alphaproteobacteria (27%) and Verrucomicrobia (10%) phyla.
In the Kolmberg oak-hornbeam forest, the Acidobacteria were
also dominant (28%), followed by the Verrucomicrobia (24%)
and Bacteroidetes (11%) phyla. While these similarities occur
at the phylum level, it is very unlikely that they also occur at the
species level. The dominance of Acidobacteria is common in
forest soils, while a dominance of Proteobacteria occurs in dis-
turbed soils (18), possibly because Acidobacteria species are
slow-growing bacteria fit to nutrient-limited environments such
as pristine forest soils (29). When the soil nutrient content is
altered, Acidobacteria organisms are replaced by fast-growing
bacteria. The main difference between the Brazilian Atlantic
Forest and European forests was the apparent absence of
Verrucomicrobia phylum sequences in the Atlantic Forest soils,
suggesting that this group is much less represented or absent in
the latter environment.
A similar study of the Brazilian Amazon Rainforest (2)
revealed a different bacterial community from that found in
the Atlantic Forest. The dominant bacterial phylum in the
Amazon Rainforest soil (pH 5.0) was the Firmicutes/Clostrid-
FIG. 3. PCA. (A) First and third principal component scores show- ium phylum (22%), followed by Acidobacteria/Fibrobacterium
ing complete class separation between high and low levels of soil
bacterial diversity. (B) First and third principal component loadings. (18%), Planctomycetes (16%), and Proteobacteria (12%). In
Loadings with higher magnitudes have more impact on the model. The contrast, in the Brazilian Atlantic Forest, the Firmicutes/Clos-
variables that significantly increase biodiversity are altitude and the tridium phylum was much less represented. Similar to the case
Ca2⫹/Mg2⫹ ratio. The variables Al3⫹ and phosphorus significantly for the Atlantic Forest soil, sequences from the thermophilic
decrease biodiversity.
OP10 phylum were also found in the Amazon Rainforest soil.
This phylum, initially found in the Obsidian Pool, a 75 to 95°C
hot spring at the Yellowstone Caldera (14), has frequently
ables contributing to reduce the biodiversity in the discrimi- been identified in soil 16S rRNA gene libraries (15), but little
nant model; for example, a similar decrease in biodiversity can is known about its role in soil. One hypothesis to be explored
be achieved by increasing any of the variables Al3⫹, clay, and is the presence of nonthermophilic species in this group.
phosphorus because they have very similar contributions to the Statistical analyses showed that physicochemical character-
PLS regression coefficients (22%, 20%, and 17%, respectively) istics have specific contributions to soil biodiversity. The vari-
(see Fig. S2B in the supplemental material). On the other ability in samples with a high level of biodiversity in the PLS
hand, an identical increase in the altitude increases the biodi- score space was relatively small, and there were more variables
versity indicator variable by 40%, while the Ca2⫹/Mg2⫹ ratio contributing significantly to reducing biodiversity. This sug-
increases the biodiversity indicator by only 13.5%. These re- gests that a decrease in microbial biodiversity of the soil sam-
sults show a perfect separation between the low- and high- ples is associated with a complex interaction of multiple fac-
biodiversity soil samples and provide evidence to support the tors, while an increase in biodiversity is associated mainly with
hypothesis that interdependencies between soil characteristics altitude and, to a lesser extent, the Ca2⫹/Mg2⫹ ratio. The
are associated with the biodiversity in soil samples. influence of abiotic factors was also evident for the dominant
lineages. The LibShuff analysis of high- and low-altitude sam-
DISCUSSION ples indicated that the communities are different at evolution-
ary distances of 0% (species level) to 30% (phylum level).
In this work, we investigated the microbial biodiversity in Since Acidobacteria and Proteobacteria are the dominant
Atlantic Forest soil and the factors that influence it. The dom- groups, this result suggests that there is variation within lin-VOL. 76, 2010 EFFECTS OF SOIL CHARACTERISTICS ON BACTERIAL DIVERSITY 4749
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ACKNOWLEDGMENTS 4224.
24. Singleton, D. R., M. A. Furlong, S. L. Rathbun, and W. B. Whitman. 2001.
We thank the Brazilian Research Council (CNPq/MCT Programa Quantitative comparisons of 16S rRNA gene sequence libraries from envi-
Instituto do Milênio) and Fundação Araucária of the State of Paraná, ronmental samples. Appl. Environ. Microbiol. 67:4374–4376.
Brazil, for financial support. 25. Smit, E., P. Leeflang, S. Gommans, J. van den Broek, S. van Mil, and K.
We thank Julieta Pie and Roseli Prado for technical support. Wernars. 2001. Diversity and seasonal fluctuations of the dominant members
of the bacterial soil community in a wheat field as determined by cultivation
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