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1
Influence of Soil Characteristics on the Diversity of Bacteria in the Southern 1
Brazilian Atlantic Forest. 2
3
1Faoro, H.,
2A. C. Alves,
1E. M. Souza,
1L. U. Rigo,
1L. M. Cruz,
1S. M. Al-Janabi,
1R. 4
A. Monteiro, 1V. A. Baura,
1F. O. Pedrosa.* 5
6
1Department of Biochemistry and Molecular Biology – Universidade Federal do Paraná, 7
CP 19046, 81531-990 Curitiba, PR, Brazil. FAX-55(41)3361-1578. 8
2Laboratory of Artificial Intelligence and Computer Science. University of Porto. 9
Portugal. 10
*Corresponding author: [email protected] 11
12
Keywords: Brazilian Atlantic Forest, bacterial biodiversity, soil, 16S rRNA, microbial 13
ecology 14
Copyright © 2010, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.03025-09 AEM Accepts, published online ahead of print on 21 May 2010
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Abstract 15
The Brazilian Atlantic Forest is one of the 25 biodiversity hot-spots in the world. 16
Although the diversity of its fauna and flora has been fairly studied, little is known of its 17
microbial communities. In this work we analyzed the Atlantic Forest ecosystem to 18
determine its bacterial biodiversity using 16S rRNA gene (16S rDNA) sequencing and 19
correlated changes in deduced taxonomic profiles with the physico-chemical 20
characteristics of the soil. DNA was purified from soil samples and the 16S rRNA gene 21
amplified to construct libraries. Comparison of 754 independent 16S rRNA gene 22
sequences from 10 soil samples collected along a transect in an altitude gradient showed 23
the prevalence of Acidobacteria (63%) followed by Proteobacteria (25.2%), 24
Gemmatimonadetes (1.6%), Actinobacteria (1.2%), Bacteroidetes (1%), Chloroflexi 25
(0.66%), Nitrospira (0.4%), Planctomycetes (0.4%), Firmicutes (0.26%) and OP10 26
(0.13%). Forty eight sequences (6.5%) represented unidentified bacteria. The Shannon 27
diversity index of the samples varied from 4.12 to 3.57, indicating that the soils have a 28
high diversity. Statistical analysis showed that the bacterial diversity is influenced by 29
factors, such as the altitude, Ca2+
/Mg2+
ratio, Al3+
and phosphorus content, which also 30
affected the diversity within the same lineage. In the samples analyzed, pH had no 31
significant impact on the diversity. 32
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INTRODUCTION 33
The Brazilian Atlantic Forest is one of the 25 biodiversity hot-spots in the world. 34
Altogether, these hot-spots contain more than 60% of the total terrestrial species of the 35
planet (18). The Atlantic Forest is a dense ombrophilous forest with several variations 36
including coastal (3 to 50 meters), submontane (50 to 500 meters), montane (500 to 37
1,200 meters) and high montane (1,200 to 1,400 meters) forests, creating a vegetation 38
gradient ranging from shrubs to well-developed montane forest (4). The Serra do Mar is 39
a mountainous system that shelters the main remainder of the Atlantic Forest following 40
the Brazilian east coast, from north to south along the coastal line, and it is divided into 41
diverse sections of high and low blocks, which have regional denominations. 42
The most important law protected Conservation Area of the Brazilian Atlantic 43
Forest is located in the Serra do Mar of the Southern state of Paraná. This Conservation 44
Area (~5,000 km2) shelters 72% of the fauna and flora species that occur in Paraná and 45
was declared a Biosphere Reserve by the UNESCO in 1992. Much is known about the 46
diversity of its fauna and flora, but little is known of its microbial diversity, particularly 47
the soil microbial diversity and the soil characteristics that influence it. 48
The soil microbial diversity is vast and it is estimated that more than 99% 49
remain unidentified (1, 28). Acidobacteria and Proteobacteria are the most abundant 50
groups in soil (16). However, the Proteobacteria lineage is more diverse and stable 51
when compared to Acidobacteria lineage, suggesting that the latter group is more 52
susceptible to variation of soil properties and disturbing factors (34). Seasonal, physical 53
and physico-chemical factors can be relevant to the structure and diversity of microbial 54
communities. For example, seasonal changes in vegetation and temperature lead to 55
replacement of dominant groups in a wheat field (29) and in grassland (17) soils. The 56
particle size also has influence on the bacterial diversity of soils. The clay fraction had a 57
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more diverse bacterial community than silt or sand fractions (22). Finally, analyses of 58
communities from North and South America soils showed that pH plays a major role in 59
bacterial diversity with less diverse communities associated to lower pH (9). 60
Human activity can also change the microbial diversity of soils both 61
qualitatively and quantitatively. Analyses of microbial communities on coral atolls in 62
central Pacific under different degree of human impact showed that the less impacted 63
atoll had autotrophs and heterotrophs equally distributed in the community whereas the 64
most impacted had a dominance of heterotrophs and about 10 times more microbial 65
cells and virus-like particles in the water column, including a large percentage of 66
potential pathogens (7). The comparison between bacterial community of forest and 67
pasture soil showed that there is a less diverse and more restrict community in pasture 68
soils. The vegetation shift from forest to pasture resulted in change of G+C% content 69
soil bacterial DNA and ARDRA profile (19). Similar changes occurred with 70
communities of soils submitted to agroindustrial treatments and pollutants (3, 31). 71
In this work we used a culture-independent approach based on 16S rRNA gene 72
sequences to survey the bacterial community of the Atlantic Forest soils and determined 73
the physico-chemical factors affecting its bacterial biodiversity. 74
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Experimental Procedure 75
76
Soil sampling 77
Atlantic Forest soil samples were collected along the PR 410 highway in the State of 78
Paraná, Brazil, which transverses 28.5 km of an area of Atlantic Forest. GPS 79
coordinates (GPS Model Gardin) of the collection point of each sample were recorded 80
(Table S1). For sample collection the site was cleaned superficially to remove plants 81
and decomposing organic matter. The soil from circle of approximately 50 cm in 82
diameter, from 0 to 20 cm in depth, was thoroughly mixed and soil samples 83
(approximately 500 g) were then collected, transferred to sterilized Falcon tubes and 84
stored on ice. Collection tools were washed in water, followed by disinfection with 70% 85
alcohol and 2% sodium hypochlorite and, finally, washed thoroughly with sterile water. 86
A total of 10 soil samples were collected from sites in the submontane (50 to 500 meters 87
of altitude) and montane (500 to 1,200 meters) forest (4) (Table 1). The following 88
physico-chemical parameters of the collected soil were determined: pH, Al3+
, (H+ + 89
Al3+
), Ca2+
, Mg2+
, K+, total bases (Ca
2+ + Mg
2+ + K
+), effective cation exchange 90
capability (SB + H+ + Al
3+), phosphorus, carbon content, base saturation (V), aluminum 91
saturation (m), Ca2+
/Mg2+
and clay content. Soil analyses were performed by the 92
laboratory of Soil Analyses of the Department of Soils of the Universidade Federal do 93
Paraná using standard methods (Table S2). 94
95
Soil DNA extraction, 16S rRNA gene amplification and cloning 96
After collection, the soil samples were stored on ice for no more than 4 hours 97
before DNA extraction. Soil DNA was extracted using the “UltraClean Soil DNA Kit” 98
(MoBio Laboratories) following the manufacturer’s instructions. Briefly, soil (0.5 g) 99
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was added to a tube containing 2 ml of bead suspension and vigorously mixed. The 100
mixture was treated with an inhibitor removal solution and then the DNA was purified 101
on silica columns. The 16S rRNA gene amplification was performed using the universal 102
primers for the Bacteria domain 27F – 5’AGAGTTTGATCCTGGCTCAG and 1492R - 103
5’ACGGCTACCTTGTTACGACTT (33). The PCR mixture (20 µl) contained 2U of 104
Taq DNA polymerase, 4 pmol of each primer, 200 µM of each dNTP, approximately 10 105
ng of extracted soil DNA and PCR buffer (200 mM Tris-HCl pH 8.4, 500 mM KCl). 106
The thermocycler program was: 1 cycle at 95°C for 5 min, followed by 20 sequential 107
cycles at 94°C for 1 min, 62°C for 1 min and 72°C for 1 min, and a final single step at 108
72°C for 5 min. The PCR products were cloned using the pGEM-T Easy Vector System 109
(Promega) according to the manufacturer’s instructions. 110
111
Plasmid DNA extraction and sequencing 112
Plasmid DNA was purified by the alkaline lysis method (21) in 96 well plates. 113
The V1-V2 region of cloned 16S rDNA (~ 300bp at the 5’ end of the 16S rRNA gene) 114
was sequenced with the forward primer Y1 (5’-115
TGGCTCAGAACGAACGCTGGCGGC) and reverse primer Y2 (5’-116
CCCACTGCTGCCTCCCGTAGGAGT-) (32) in a Megabace 1000 automatic 117
sequencer using the DYEnamic™ ET Dye Terminator Cycle Sequencing Kit (GE 118
HealthCare). 119
120
Sequence assembly and analysis 121
The Phred program was used for base calling (8). The Phrap 122
(http://www.phrap.com) program was used to assemble the reads into the 16S rRNA 123
partial gene sequence. Finally, the Consed program (12) was used to view and edit the 124
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sequence assembly. The final sequences were compared with the Ribosomal Database 125
Project II (6) using the SeqMatch tool. Partial 16S rRNA gene sequences were aligned 126
using ClustalW (26) and the alignment used to construct distance matrices with 127
DNAdist program (11). Distance matrices were used as input for the DOTUR program 128
(24) to cluster sequences in OTUs (identity ≥ 95%). The obtained 16S rRNA gene 129
sequences were deposited in the GenBank database under the accession no. EF135620 130
to EF136358 and GU071058 to GU071072. 131
132
Biodiversity evaluation 133
Sequences with identity ≥ 95% were assumed to belong to the same OTU (20, 134
5). The bacterial diversity was evaluated (14) by the Shannon diversity index (H’) 135
calculated by the DOTUR program (24). Rarefaction curve, ACE estimator and 136
Shannon index for high and low altitude groups were also calculated using DOTUR. 137
Evenness (E) was calculated by the equation: E = H’/ln S, where S (species richness) is 138
the total number of OTUs. 139
The similarity in the composition of the clone libraries was examined by using 140
the S-LibShuff program (23). Graphical analyses were done using the LibShuff program 141
(25). The LibShuff program generates, from two 16S rRNA gene clone libraries (X and 142
Y), homologous and heterologous coverage curves (CX and CXY, respectively) at any 143
level of sequence similarity or evolutionary distance (D). To determine if the coverage 144
curves CX(D) and CXY(D) are significantly different, the distances between the two 145
curves were first calculated by using the Cramér-von Mises test. The two libraries were 146
considered significantly different when P < 0.05. 147
148
149
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Statistical methods 150
Statistical analysis of the biological diversity indices and physico-chemical 151
characteristics was performed in samples with high (MA01-MA04) and low diversity 152
(MA07-MA10). An independent two sample Student t-test and a Man-Whitney test 153
were performed to screen for variables with statistically significant differences between 154
the two groups of samples. The Hodges Lehman estimator of the difference in central 155
tendency between the two groups was calculated for all biological and physico-chemical 156
variables. Principal Component Analysis (PCA) was carried out on mean centred with 157
unit variance scaled data with a matlab routine developed in-house. Data were 158
visualized in the form of the principal component score plots and loading plots. Partial 159
Last Square Discriminant Analysis (PLS-DA) was performed to determine which 160
variables were correlated with the biodiversity and to validate the results obtained with 161
the unsupervised PCA model. Validation of statistical data was performed using 162
Jackknifing and cross-validation tests. The model predictivity was assessed by the Q2 163
parameter (10) indicating how well the model predicts new data using leave on out 164
cross validation. 165
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RESULTS 166
167
Atlantic Forest soil physical and chemical properties. 168
The physico-chemical properties of the soil samples are shown in Table S2. All 169
the samples had a low pH (pH ≤ 4.50) and high aluminum saturation (> 50%). The base 170
saturation (V%) was low (< 50%), thus the soil was classified as infertile or dystrophic. 171
The organic matter content (C) was high only in sample MA01 (> 50 g/dm3). The other 172
samples had a low content of organic matter (< 50 g/dm3). The amount of clay was also 173
determined and varied from 150 to 500 g per kilo of soil. 174
175
Sequence identification and diversity characterization 176
PCR products using primers 27F and 1492R were obtained for all DNA samples 177
and used to construct ten libraries of soil 16S rDNA amplicons in pGEM-T Easy 178
(Promega). Ninety-six clones of each library were isolated and used as templates for the 179
sequencing reaction. Out of the 960 templates, 754 complete sequences of the V1-V2 180
region were obtained, which varied from 234 to 341 bp in length. All the reads used in 181
the assembly of the contigs had a Phred quality index of at least 30. 182
The partial 16S rRNA sequences were compared to the RDP II database through 183
the RDPquery program (Figure 1). Approximately 63% (473) of the sequences were 184
grouped with that of the phylum Acidobacteria. The Proteobacteria phylum was ranked 185
second with 25.2% (190) of the sequences, which were distributed as follows: α-186
Proteobacteria (52.1%), β-Proteobacteria (20%), δ-Proteobacteria (16.3%) and γ-187
Proteobacteria (11.5%). Other phyla found were: Actinobacteria (1.2%), Bacteroidetes 188
(1%), Chloroflexi (0.66%), Firmicutes (0.26%), Gemmatimonadetes (1.6%), Nitrospira 189
(0.4%), Planctomycetes (0.4%) and OP10, a thermophilic bacterium phylum (0.13%). 190
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Forty-eight sequences (6.5%) matched with 16S rRNA gene of unclassified, usually 191
uncultured, bacteria and could not be grouped with sequences of known bacteria phyla. 192
The number of OTUs (Operational Taxonomic Units) (identity ≥ 95%) differed 193
from sample to sample. The MA01 and MA02 samples had the highest species richness 194
(S) with 64 OTUs in 77 sequences and 66 OTUs in 78 sequences, respectively. These 195
two samples also showed the highest Shannon indices, 4.02 and 4.12, respectively 196
(Table 1). The other samples had lower species richness and Shannon indices. The 197
evenness index varied from 0.97 to 0.87 suggesting that the species were equally 198
represented in the analyzed samples without dominance of specific bacterial phylotypes 199
(Table 1). 200
Sequences from the ten libraries were compared using the S-LibShuff program 201
to evaluate their degree of similarity. Analyses of homologous coverage curves (Table 202
S3) indicated that libraries from samples MA01 to MA05 had a similar bacterial 203
community (P > 0.05). These libraries were grouped in a cluster and were different from 204
the libraries of samples MA06 to MA10. Similarly, libraries of samples MA07 to MA10 205
also seem to have similar communities. On the other hand, the MA06 library was 206
different from all the others (P < 0.05). 207
A linear regression considering Shannon index of each library versus altitude of 208
the sampling site (Figure S1) revealed that sample clustering may be influenced by the 209
altitude of the collection points and can be divided in three groups: high diversity 210
(MA01-MA04), intermediary diversity (MA05-MA06) and low diversity (MA07-211
MA10). To evaluate this separation we grouped sequences according to the altitude 212
from libraries: high altitude (MA01 to MA04 between 900 and 800 meters above sea 213
level) and low altitude (MA07 to MA10 between 160 and 30 meters above sea level) 214
and compared them using the LibShuff program. Graphic analyses of homologous and 215
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heterologous coverage curves generated by LibShuff (Figure 2A) indicated that the 216
bacterial community in the first group is different from that of the second group in the 217
interval of evolutionary distances from 0.0 (100% of identity and 0% of differences) to 218
0.3 (70% of identity and 30% of differences). This result suggests that the genetic 219
diversity between these two groups not only occurs at lower taxonomic ranks, but also 220
at higher taxonomic levels (Figure 2A). The separation in two groups was also evident 221
when we analyzed the tendency curve for the rarefaction (Figure 2B), Shannon index 222
(Figure 2C) and ACE estimator (Figure 2D) on DOTUR plots. The high altitude group 223
had higher Shannon index and OTU number when compared with low altitude group at 224
95% of 16S rRNA gene sequence similarity. Also, the rarefaction curve for the high 225
altitude group is less saturated when compared with the low altitude group indicating 226
that more phylotypes can be recovered from the first than from the second group of 227
libraries. The LibShuff and DOTUR results suggest that the high altitude group has a 228
different, more diverse and rich microbial community compared to the low altitude 229
group. 230
231
Microbial diversity is significantly different in high and low altitude soil samples 232
To understand the impact of the altitude and physico-chemical characteristics of 233
soil on the microbial biodiversity, the groups were compared for differences in mean 234
and central tendency using, respectively, an independent two sample Student t-test and a 235
Man-Whitney test. Table S4 shows the results for the biological diversity indices 236
(Richness - S, Evenness - E and Shannon - H) and Table S5 shows the results for the 237
physico-chemical characteristics of the soil samples. Microbial diversity of soil samples 238
at low (A) and high (B) altitudes was also compared using the Wilcoxon-Mann-239
Whitney two-sample rank-sum test. The effect of the altitude (difference between 240
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groups) was quantified using the Hodges-Lehmann (HL) estimator, which is consistent 241
with the Wilcoxon test. The results showed an association between the altitude level and 242
soil microbial diversity. On the other hand, there was no statistically significant effect of 243
a particular soil parameter. Hence, the results suggest that the difference found in the 244
biodiversity between groups may be explained by interactions between the physico-245
chemical soil characteristics. A PCA model was developed to explore this hypothesis. 246
247
Principal component analysis reveals a perfect separation between soil samples of 248
high and low microbial biodiversity 249
The principal component analysis (PCA) was performed to visualize the 250
interdependence between the variables that could explain the differences between the 251
groups of high and low biodiversity soil samples. The score plots of the first and third 252
principal component show a perfect separation between samples of each group (Figure 253
3A). A PCA model with only three components captures over 90% of the variance of 254
the soil samples. The third principal component of the PCA model perfectly 255
discriminates samples of low from those of high biodiversity. In order to determine 256
which variables are more important to discriminate between groups, the loadings of the 257
third principal component were plotted (Figure 3B). The variables that are associated 258
with higher biodiversity have larger magnitude in the same direction of the higher 259
biodiversity samples in the scores plots. Higher altitude and Ca2+
/Mg2+
ratio were found 260
to be associated with higher biodiversity, while higher levels of Al3+
and phosphorus 261
were associated with lower biodiversity. 262
To identify the physico-chemical characteristics that play a major role in 263
discriminating between low and high biodiversity soil samples a partial least squares 264
discriminant analysis (PLS-DA) was performed. The PLS-DA model achieved a very 265
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high predictivity (Q2Y=0.8) and attained an out-of-sample prediction accuracy of 266
100%. The significance of the PLS regression coefficients was estimated using the one 267
sample Student t-test on all variables (Table S6). The samples having higher 268
biodiversity are confined to a very small and dense cluster, while the low biodiversity 269
samples are spread over the space defined by the scores of the first three latent variables 270
(Figure S2A). There are also more variables contributing to reduce the biodiversity in 271
the discriminant model: a similar decrease of biodiversity can be achieved by increasing 272
any of the variables Al3+
, clay or phosphorus because they have very similar 273
contributions to the PLS regression coefficients (respectively, 22%, 20%, and 17%) 274
(Figure S2B). On the other hand, an identical increase in the altitude increases the 275
biodiversity indicator variable by 40% while Ca2+
/Mg2+
ratio only increase the 276
biodiversity indicator by 13.5%. These results show a perfect separation between the 277
lower and higher biodiversity soil samples and provide evidence to support the 278
hypothesis that interdependencies between the soil characteristics are associated with 279
the biodiversity in soil samples. 280
281
DISCUSSION 282
283
In this work we investigated the microbial biodiversity of Atlantic Forest soil 284
and the factors that influence it. The dominant phylum in Atlantic Forest soil samples 285
was Acidobacteria (63%) followed by the Proteobacteria (19%). These two groups are 286
frequently the most numerous in soil samples. In a meta-analyses of 16S rRNA 287
sequences from distinct soils Janssen et al. (2006) determined that the most abundant 288
bacterial phyla were Proteobacteria (39%) and Acidobacteria (19%), followed by 289
Verrucomicrobia, Bacteroidetes, Chloroflexi, Planctomycetes, Gemmatimonadetes and 290
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Firmicutes (16). Excepted by Verrumicrobia, all these phyla were represented in 291
Atlantic forest soils, although in different proportions. 292
The profile of the bacterial community found in Atlantic Forest soils is also 293
similar to that found in European forests at eastern Austria (13). In the Spruce-fir-beech 294
Forest the Acidobacteria phylum was dominant (35%) followed by α-Proteobacteria 295
(27%) and Verrucomicrobia phyla (10%). In the Kolmberg oak-hornbeam forest the 296
Acidobacteria were also dominant (28%) followed by Verrucomicrobia (24%) and 297
Bacteroidetes (11%) phyla. Despite the similarity at the phylum level, it is very unlikely 298
that these similarities also occur at the species level. The dominance of Acidobacteria is 299
common in forest soils, while dominance of Proteobacteria occurs in disturbed soils 300
(19), possibly because Acidobacteria are slow-growing bacteria fit to nutrient-limited 301
environments such as pristine forest soils (30). When the soil nutrient content is altered, 302
Acidobacteria is substituted by fast-growing bacteria. A main difference between the 303
Brazilian Atlantic forest and European forests was the apparent absence of 304
Verrucomicrobia phylum sequences in the Atlantic Forest soils, suggesting that this 305
group is much less represented or absent in the latter environment. 306
A similar study of the Brazilian Amazon Rain Forest (2) revealed a different 307
bacterial community from that found in the Atlantic Forest. The dominant bacterial 308
phylum in the Amazon Forest soil (pH 5.0) was the Firmicutes/Clostridium (22%) 309
followed by Acidobacteria/Fibrobacterium (18%), Planctomyces (16%) and 310
Proteobacteria (12%). In contrast, in the Brazilian Atlantic Forest 311
Firmicutes/Clostridium phylum was much less represented. Similarly to the Atlantic 312
forest soil, sequences from thermophilic OP10 phylum were also found in the Amazon 313
Forest. This phylum, initially found in the Obsidian Pool, a 75 to 95°C hot spring at the 314
Yellowstone caldera (15), has been frequently identified in soil 16S rRNA gene libraries 315
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(16), but little is known about its role in soil. One hypothesis to be explored is the 316
presence of a non-thermophilic species in this group. 317
Statistical analyses showed that physico-chemical characteristics have specific 318
contributions to soil biodiversity. The variability of samples having high biodiversity in 319
the PLS scores space is relatively smaller and there are more variables contributing 320
significantly to reduce the biodiversity. This suggests that the decrease of microbial 321
biodiversity of the soil samples is associated with a complex interaction of multiple 322
factors, while the increase in biodiversity is mainly associated with altitude and to a 323
lesser extent the Ca2+
/Mg2+
ratio. The influence of abiotic factors is also evident at the 324
dominant lineages. The LibShuff analysis for high and low altitude samples indicated 325
that the communities are different at evolutionary distances from 0% (species level) to 326
30% (phylum level). Since the Acidobacteria and Proteobacteria are the dominant 327
groups, this result suggests that there is a variation within the lineages between the high 328
and low altitude groups. This intra-lineage variation is probably related to physico-329
chemical characteristics of the soil (34). Considering that there is not a large variation in 330
pH, other physico-chemical (Ca+2
/Mg+2
ratio, phosphorus and Al+3
content) and spatial 331
factors (altitude) are acting on biodiversity. 332
Altitude is relevant to variables that affect the ecosystem such as temperature 333
and oxygen availability. The results showed that altitude is statistically correlated with 334
the Shannon index (r = 0.77, α = 0.05) and is also significantly different between the 335
high and low diversity groups of samples. The effect of altitude may be related with 336
vegetation change and/or human activity at low altitudes, which is at the limits of the 337
Conservation area, when compared to high altitude levels of the Serra do Mar. These 338
factors may result in complex alterations of the soil physico-chemical properties and, 339
consequently, the bacterial diversity. 340
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To correctly evaluate a microbial ecosystem it is necessary to integrate the 341
influence of biotic and abiotic factors on the community structure and biodiversity. 342
Recently, analysis of soil samples from different ecosystems across North and South 343
America showed that bacterial diversity could be predicted by a single variable, the soil 344
pH (9). However, here we show that in the acidic soils of the Brazilian Atlantic Forest 345
the bacterial diversity is influenced by additional factors, such as the Ca2+
/Mg2+
ratio, 346
altitude and Al3+
and phosphorus content, which also affected the diversity within the 347
same lineage. Thus characterization of the abiotic properties is important to understand 348
the factors that affect the bacterial diversity and provide a clearer view of how the 349
communities change. 350
351
ACKNOWLEGMENTS 352
We would like to thank the Brazilian Research Council (CNPq/MCT Programa 353
Instituto do Milênio) and Fundação Araucária of the State of Paraná, Brazil for financial 354
support. We thank Julieta Pie and Roseli Prado for technical support. 355
356
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Figure 1: Bacterial phyla in the Brazilian Atlantic Forest soil. 458
459
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Table 1: Bacterial diversity of the Brazilian Atlantic Forest soil: Sequences with 460
identity ≥ 95% were assumed to belong to the same OTU. Indices were calculated from 461
the number and abundance of species in each soil sample by DOTUR (24). H’- Shannon 462
index; E - Evenness index. 463
Library aAltitude Reads OTUs H' E
MA01 874 77 64 4.08 0.94
MA02 900 78 66 4.12 0.95
MA03 896 70 48 3.79 0.97
MA04 810 74 58 3.96 0.92
MA05 604 83 59 3.87 0.87
MA06 375 80 54 3.84 0.88
MA07 161 71 51 3.81 0.96
MA08 95 69 43 3.57 0.95
MA09 44 81 51 3.60 0.92
MA10 29 70 47 3.67 0.95
aAltitude referenced to the average level of the sea 464
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Figure 2: High altitude samples have more diverse microbial communities. 465
Sequences from MA01-04 libraries were grouped in the high altitude/diversity cluster 466
and sequences from MA07-10 were grouped in the low altitude/diversity cluster. (A) 467
phylogenetic diversity in the high and low clusters was compared using the LibShuff 468
program. Homologous ( ) and heterologous ( ) coverage curves for 16S rRNA gene 469
sequence libraries are shown. Solid lines indicate the (CX - CXY)2 for the original samples 470
at each value of D. D is equal to the Jukes-Cantor evolutionary distance determined by 471
the DNADIST program of PHYLIP. Broken lines indicate the 950th
value (or P=0.05) 472
of (CX - CXY)2
for the randomized samples. (B, C and D) DOTUR graphic analyses 473
comparing the groups according the rarefaction curve (B), Shannon index (C) and ACE 474
estimator (D). 475
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Figure 3: Principal Component Analysis. (A) First and third principal component 477
scores showing complete class separation between high and low soil bacterial diversity. 478
(B) First and third principal component loadings. Loadings with higher magnitude have 479
higher impact on the model; variables having a significant effect increasing biodiversity 480
are altitude and Ca2+
/Mg2+
. The variables Al
3+ and phosphorus have a significant effect 481
decreasing biodiversity. 482
(A) 483
484
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