Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests

Abstract

Soil bacteria provide a large range of ecosystem services such as nutrient cycling. Despite their important role in soil systems, compositional and functional responses of bacterial communities to different land use and management regimes are not fully understood. Here, we assessed soil bacterial communities in 150 forest and 150 grassland soils derived from three German regions by pyrotag sequencing of 16S rRNA genes. Land use type (forest and grassland) and soil edaphic properties strongly affected bacterial community structure and function, whereas management regime had a minor effect. In addition, a separation of soil bacterial communities by sampling region was encountered. Soil pH was the best predictor for bacterial community structure, diversity and function. The application of multinomial log-linear models revealed distinct responses of abundant bacterial groups towards pH. Predicted functional profiles revealed that differences in land use not only select for distinct bacterial populations but also for specific functional traits. The combination of 16S rRNA data and corresponding functional profiles provided comprehensive insights into compositional and functional adaptations to changing environmental conditions associated with differences in land use and management.

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Scientific RepoRts | 6:33696 | DOI: 10.1038/srep33696
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Driving forces of soil bacterial
community structure, diversity, and
function in temperate grasslands
and forests
Kristin Kaiser1, Bernd Wemheuer1, Vera Korolkow1, Franziska Wemheuer2, Heiko Nacke1,
Ingo Schöning3, Marion Schrumpf3 & Rolf Daniel1
Soil bacteria provide a large range of ecosystem services such as nutrient cycling. Despite their
important role in soil systems, compositional and functional responses of bacterial communities to
dierent land use and management regimes are not fully understood. Here, we assessed soil bacterial
communities in 150 forest and 150 grassland soils derived from three German regions by pyrotag
sequencing of 16S rRNA genes. Land use type (forest and grassland) and soil edaphic properties
strongly aected bacterial community structure and function, whereas management regime had
a minor eect. In addition, a separation of soil bacterial communities by sampling region was
encountered. Soil pH was the best predictor for bacterial community structure, diversity and function.
The application of multinomial log-linear models revealed distinct responses of abundant bacterial
groups towards pH. Predicted functional proles revealed that dierences in land use not only select
for distinct bacterial populations but also for specic functional traits. The combination of 16S rRNA
data and corresponding functional proles provided comprehensive insights into compositional and
functional adaptations to changing environmental conditions associated with dierences in land use
and management.
Soil bacteria play an important role in biogeochemical cycles1,2. ey control soil processes such as decompo-
sition3 and mineralization, including the associated release of greenhouse gases such as carbon dioxide (CO2),
nitrous oxide (N2O), and methane (CH4)4,5 into the atmosphere. Moreover, several soil bacteria promote plant
growth and productivity2,6. As soil represents a highly dynamic and complex environment, bacterial communi-
ties living in this ecosystem are inuenced by a multitude of dierent biotic and abiotic factors. Previous studies
showed that soil pH is a major driver of these communities7–9. Lauber and colleagues8 observed that the overall
bacterial community composition in dierent soils from across South and North America was signicantly cor-
related with soil pH. is was conrmed by a study of bacterial communities in German grassland and forest
soils9. Other studies investigating the eect of edaphic parameters on soil bacteria found that these communities
were inuenced by the availability of nutrients such as carbon, nitrogen10,11, and soil moisture in grasslands12 and
forests13.
In recent years, the impact of land use intensication on bacterial community diversity and composition, e.g.
by fertilization in grasslands, has been frequently investigated14–17. In a study by Herzog et al.15, composition and
diversity of entire and active bacterial communities were altered by fertilizer application. Lauber et al.16 analyzed
soil bacterial communities across dierent land use types such as grasslands and forests. For soil bacteria in forest
systems, soil disturbance and organic matter removal18,19 as well as the dominant tree species20 have been shown
to inuence community composition. is provides evidence that land use intensication can alter soil bacte-
rial community composition. However, most studies have focused on a limited number of soil samples in one
region. erefore, the response of bacterial communities in grasslands and forests to land use intensication and
1Department of Genomic and Applied Microbiology, Institute of Microbiology and Genetics, Georg-August-
University Göttingen, Grisebachstr. 8, D-37077 Göttingen, Germany. 2Department of Crop Sciences, Georg-August-
University Göttingen, Grisebachstr. 6, D-37077 Göttingen, Germany. 3Max Planck Institute for Biogeochemistry,
Hans-Knoell-Str. 10, D-07745 Jena, Germany. Correspondence and requests for materials should be addressed to
R.D. (email: rdaniel@gwdg.de)
Received: 18 May 2016
Accepted: 31 August 2016
Published: 21 September 2016
OPEN

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environmental changes is not yet fully understood. Large comparative studies are required to unravel the diverse
interactions between bacteria and their environments, and how changes in community composition might reect
changes in bacterial functioning.
e aim of the present study was to identify key drivers of bacterial community composition, diversity, and
functions in forest and grassland soils. In addition, we aimed at clarifying in which way soil bacterial communities
respond to management regime, and if changes are merely a product of the edaphic properties. In this study, 300
soil samples were taken from the three German Biodiversity Exploratories Schoreide-Chorin, Hainich-Dün
and the Schwäbische Alb21. Two previous studies focusing on subsets of samples taken in the Biodiversity
Exploratories showed that bacterial diversity was inuenced by land use intensity22 and land use type9. Bacterial
communities were assessed by pyrotag sequencing targeting the bacterial 16S rRNA gene. Additionally, func-
tional proles were calculated from obtained 16S rRNA gene data23. We focused on three main hypotheses:
(1) soil bacterial communities exhibit distinct biogeographic patterns, (2) respond dierently to soil conditions
and land use intensication, and (3) bacterial community composition, diversity and functioning are shaped in a
similar way within the same land use system.
Results and Discussion
General characteristics of the soil samples. Soil samples showed signicant dierences with respect to
soil texture and edaphic properties (Table1, Supplementary Material Tables S1 and S2). Forest soils were more
acidic, had a higher C:N ratio and smaller clay amount than grassland soils. Forest soil samples derived from
the dierent exploratories exhibited signicant dierences in all measured edaphic properties. e Schoreide-
Chorin forest soils were more acid and had higher C:N ratios compared to the Hainich-Dün and Schwäbische
Alb soils, which did not dier signicantly. In addition, Schoreide-Chorin forest soils also exhibited the lowest
gravimetric water content, clay and silt amount of all exploratories.
Grassland soil samples derived from the dierent exploratories also exhibited signicant dierences between
all measured edaphic properties. e Hainich-Dün grasslands soil had the highest pH values, lowest gravimetric
water content and highest silt amount compared to the Schoreide-Chorin and Schwäbische Alb soil, which did
not dier signicantly. e Schoreide-Chorin grassland soils exhibited the highest C:N ratio and sand amount
compared to the other two exploratories. Clay amount was lowest in the Schoreide-Chorin grassland soils,
followed by the Hainich-Dün soils. e highest clay amounts were determined for the Schwäbische Alb grassland
soils. Signicant dierences in soil parameters between the dierent management regimes were not recorded
(ANOVA, P > 0.5 in all cases).
Soil bacterial communities. Composition and diversity of soil bacterial communities were assessed by
pyrotag sequencing of 16S rRNA genes. Aer quality ltering, denoising, and removal of potential chimeras and
non-bacterial sequences, approximately 2,700,000 high quality sequences with an average read length of 525 bp
were obtained for further analyses. All sequences were classied below phylum level. Based on richness estima-
tor data (Michaelis-Menten t; Supplementary Material Table S3) 78–88% of the operational taxonomic units
(OTUs) at 80% identity (phylum level) and 27–55% of the OTUs at 97% identity (species level) were covered by
the surveying eort (for rarefaction curves, see Supplementary Material Figs S1 and S2).
Obtained sequences clustered into 203,530 OTUs (97% identity) and were assigned to 51 bacterial phyla, 574
orders and 1,215 families. e dominant phyla and proteobacterial classes (> 1% of all sequences across all samples)
were Actinobacteria (23.75% ± 8.55%), Alphaproteobacteria (20.43% ± 5.21%), Acidobacteria (18.39%% ± 9.19%),
Deltaproteobacteria (7.22% ± 2.84%). Bacteroidetes (5.15% ± 2.60%), Chloroflexi (5.09% ± 2.10%),
Betaproteobacteria (4.64% ± 2.38%), Gammaproteobacteria (4.32% ± 1.23%), Gemmatimonadetes (1.88% ± 0.92%),
Firmicutes (1.18% ± 3.20%), and Nitrospirae (1.14% ± 1.10%). ese phylogenetic groups were present in all sam-
ples and accounted for more than 95% of all sequences analyzed in this study (Fig.1). ese results are consistent
with previous studies on grasslands24 and temperate beech forests25. e most abundant phylotype (3.99% ± 2.44)
is an uncultured member of the Subgroup 6 of the Acidobacteria. e ve most abundant phylotypes that could
be assigned to a genus are Bradyrhizobium (2.66% ± 1.45%), Candidatus Solibacter (2.00% ± 1.86%), Haliangium
(1.39% ± 0.74%), Var ii b a c t e r (1.36% ± 0.58%) and Gaiella (1.34% ± 1.31%) of all sequences, respectively.
Land use Exploratory n pH C:N ratio
Gravimetric
water content (%) Clay (g kg−1)Silt (g kg−1)Sand (g kg−1)
Forest
All plots 150 4.5 ± 1.1A13.8 ± 3.2A33.5 ± 18.1 289.0 ± 203.3A440.5 ± 247.1 67.5 ± 386.7
Schoreide-Chorin 50 3.4 ± 0.1a18.1 ± 2.8a12.0 ± 4.3a48.5 ± 18.9a74.0 ± 49.2a875.0 ± 60.6a
Hainich-Dün 50 4.6 ± 0.9b12.8 ± 1.1b33.5 ± 6.4b307.0 ± 99.3b634.5 ± 95.6b54.5 ± 17.5b
Schwäbische Alb 50 5.2 ± 0.8b12.9 ± 0.9b52.5 ± 10.0c501.0 ± 104.8c445.0 ± 107.6c42.5 ± 46.0b
Grassland
All plots 150 6.7 ± 0.7B10.3 ± 0.9B31.5 ± 39.4 425.0 ± 192.4B418.0 ± 159.3 74.5 ± 228.2
Schoreide-Chorin 50 6.4 ± 0.9a10.4 ± 1.1a54.5 ± 60.5a159.5 ± 87.0a317.0 ± 191.7a489.5 ± 220.8a
Hainich-Dün 50 7.1 ± 0.9b10.1 ± 0.5b22.0 ± 5.4b452.0 ± 130.3b489.5 ± 122.7b53.5 ± 23.1b
Schwäbische Alb 50 6.2 ± 0.5a10.2 ± 0.7b41.0 ± 11.1a571.0 ± 134.0c386.0 114.6a41.0 ± 45.0b
Table 1. Edaphic properties among dierent land uses and exploratories (median ± SD). Signicant
dierences between study regions are indicated by lowercase letters and between forest and grassland by capital
letters according to Dunn’s test (P < 0.05).

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Biogeographic variations of soil bacterial diversity and community composition. Diversity (rep-
resented by the Shannon index H’) and community structure of soil bacteria (PERMANOVA, P < 0.001) diered
between the three Biodiversity Exploratories. e Hainich-Dün exploratory harbored the most diverse bacte-
rial community (H’ = 10.22) compared to Schoreide-Chorin (H’ = 9.72) and the Schwäbische Alb (H’ = 9.92).
Furthermore, grassland soils are signicantly more diverse than forest soils (H’ = 10.12 and H’ = 9.48, respectively,
with P < 0.001), which supports previous ndings of Nacke and colleagues9, who reported that bacterial commu-
nities were more diverse in grasslands at phylum level. As samples derived from forests soils were more acidic
than grassland soil samples (P < 0.001), the dierence in pH might explain the dierence in diversity (Table 1).
e most dominant bacterial orders of the complete dataset diered in their distribution across the three explor-
atories. ese dierences most likely arose from dierrences in edaphic properties in the exploratories. erefore,
we tested for correlation of environmental factors by NMDS analysis based on Bray-Curtis dissimilarities. Fitting the
edaphic properties to the ordination revealed the pH as the strongest driver of the community. Additional canonical
correspondence analysis (CCA) using pH as constrain showed that pH explains 26% of the variation in community
structure (P < 0.001, Supplementary Figure S3). We additionally found a separation of soil bacterial communities
by sampling region (PERMANOVA, P < 0.001) and the two land use types grassland and forest (PERMANOVA,
P < 0.001) (Supplementary Figure S4). erefore, we were interested in a detailed analysis of the factors driving the
changes in the structure of bacterial communities in each exploratory. We further split the data between grasslands
and forests due to the strong separation between the community structure of both land use types.
Figure 1. Abundances of bacterial orders in Schoreide-Chorin, Hainich-Dün and Schwäbische Alb
grassland and forest soils. Mean abundances of the most abundant bacterial orders (> 1% of the total bacterial
community) for each exploratory and land use are given. Rare: sum of bacterial orders contributing < 1% to the
total bacterial community per exploratory.

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Scientific RepoRts | 6:33696 | DOI: 10.1038/srep33696
Key drivers of bacterial communities. To identify the key drivers of soil bacterial community structure
for each land use type in each exploratory, we performed NMDS analysis for the six subsets. e soil pH was the
only property, which aected the community structure in each subset (Fig.2). Another property inuencing the
community structure in grasslands and forests was soil texture (amount of clay, sand and/or silt), which repre-
sents pore size, water and gas uxes, and nutrient availability26,27. Moreover, soil texture is important for niche
separation and protection from predation28.
In grassland soils, the C:N ratio influenced bacterial community structure in the Schwäbische Alb and
Schoreide-Chorin, but not in the Hainich-Dün. is is supported by a PFLA-based study on soil bacterial com-
munities, in which edaphic properties such as soil texture, pH, and C and N concentration were involved in struc-
turing soil bacterial communities10. e land use intensity index (LUI) was only correlated with the Schwäbische
Alb grassland community. However, the LUI only accounts for the amount and not for the source of fertilization.
In the Schwäbische Alb grasslands, most plots received organic fertilizer (manure, dung), whereas fertilization
in the Hainich-Dün and Schoreide-Chorin was predominated by mineral fertilizer application. ese ndings
support a recent study, in which soil microbial communities of farming systems receiving organic fertilizer were
dierent compared to those of conventional, minerally fertilized systems and control soils29. In agreement with
Geisseler and Scow30, clear trends suggesting bacterial community structural shis due to long-term mineral
fertilizer application, were not found in our survey.
In forest soils, the tree species was correlated with bacterial community structure in all exploratories,
while the silvicultural management index (SMI) only signicantly inuenced the community structure in the
Schoreide-Chorin (Fig. 2). Soil bacterial communities under broadleaved (Fagus and Quercus) and coniferous
(Pinus and Picea) trees formed distinct patterns. is is in accordance with results of previous studies9,20. Nacke
et al.9 analyzed a subset of soil samples derived from the Schwäbische Alb and found that the bacterial commu-
nity structure was dierent under beech (Fagus) and spruce (Picea). is is consistent with a study comparing
bacterial communities under coniferous and broadleaved trees20. We did not observe a dierence between the
two broadleaved tree species, although dierences in soil community structure between broadleaved trees have
been described for Fagus versus Tilia and Acer31. ese eects might be partly due to the reduced soil acidication
and higher turnover rates of the leaf litter of Tilia and Acer32. Coniferous tree species such as spruce (Picea abies)
and pine (Pinus sylvestris) are known to signicantly decrease the soil pH (reviewed in ref. 33) due to the special
chemical structure of evergreen litter or capture of atmospheric acidic compounds34. is would result in an indi-
rect pH eect on soil bacteria. Additionally, this might be one of the reasons why tree species play an important
role in the structuring of bacterial communities in all forest samples analyzed.
According to our hypothesis that bacterial community structure and diversity would be aected in similar
ways under the same land use, we compared the bacterial diversity, represented by the Shannon index (H’),
between the dierent management regimes (Supplementary Material Table S4). Dierences in diversity were
detected for the tree species in the Schwäbische Alb and Schoreide-Chorin.
Interestingly, the management regimes in grasslands (meadow, pasture, mown pasture) and forests (unman-
aged forest, age-lass forest, selection forest) exhibited no signicant eect on bacterial diversity (PERMANOVA,
P < 0.05). is is in contrast to a previous study by Will et al.22, who found a higher bacterial diversity in grass-
land soils of low land use intensity in the Hainich-Dün. In contrast, Tardy et al.17 investigated bacterial diversity
along gradients of land use intensity and observed the highest bacterial diversity in moderately managed soils.
e authors suggest that this eect is related to the stress response of the bacterial community. In highly stressed
environments, as under high land use intensity, diversity decreases due to the dominance of competitive species
and competitive exclusion, while in unstressed environments diversity decreases due to the dominance of adapted
species through selection. In accordance with our hypothesis, we could nd soil conditions such as pH that con-
sistently drive bacterial community structure as well as diversity, while management regimes and therefore land
use intensity have no signicant inuence. In addition, we could show that pH is the best predictor of bacterial
communities.
Bacterial functioning in grassland and forest soils. We further hypothesized that bacterial function-
ing was driven in a similar manner as bacterial community structure and diversity. To clarify this hypothesis,
we focused on pathways involved in the cycling of carbon, nitrogen, phosphorus, and sulfur (Fig.3) and com-
pared the relative abundances of key enzyme-encoding genes between the two land uses grassland and forest.
Abundances of the enzyme-encoding genes were derived from a novel bioinformatic tool Taxa4Fun23. Tax4Fun
transforms the SILVA-based OTUs into a taxonomic prole of KEGG organisms, which is normalized by the
16S rRNA copy number (obtained from NCBI genome annotations). As soils harbor unknown or uncultured
organisms, not all 16S sequences can be mapped to KEGG organisms. Spearman correlation analysis of func-
tional proles derived from whole metagenome sequencing and proles deduced from 16S rRNA gene sequences
revealed a median of the correlation coecient of 0.8706 for soils23. is indicated that Tax4Fun provides a good
approximation to functional proles obtained from metagenomic shotgun sequencing approaches. is is espe-
cially valuable to deduce functional proles for a large number of samples derived from complex environments,
as achieving representative coverage for each sample of a large sample set by metagenome shotgun sequencing
would be a daunting task.
Most key enzyme-encoding genes involved in the cycling of C, N, S, and P are either more abundant in grass-
land or forest soils (Mann-Whitney test, P < 0.05, Supplementary Material Table S5). For example, genes that
encode acid phosphatases were observed at 1.4-fold higher abundances in the functional prole of the forest soils
than in the grassland soils, while alkaline phosphatases showed the opposite trend. We assume that this eect
could be attributed to the dierence in pH between the land use types, as we showed that pH is the best predictor
for bacterial communities. e genes encoding urease were 1.2-fold more abundant in the grassland. e avail-
ability of urea was higher in the grassland samples, as these are partly fertilized with manure or dung or were

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grazed by animals. Chitinase genes also showed a 1.2-fold higher abundance in grasslands compared to forest
soils. is might result from the higher abundance of Actinobacteria in grassland soils, as this group is known
Figure 2. NMDS plots split by region and land use. NMDS plots based on Bray Curtis dissimilarities of
grassland (a,c,e) and forest (b,d,f) bacterial communities. Environmental parameters that are signicantly
(P < 0.05) correlated are indicated as arrows (C:N: carbon: nitrogen ratio; water: gravimetric water content;
sand: sand amount; silt: silt amount; clay: clay amount; LUI: land use intensity index in grasslands; SMI:
silvicultural management index in forests). (a) Schoreide-Chorin grassland samples; (b) Schoreide-Chorin
forest samples; (c) Hainich-Dün grassland samples; (d) Hainich-Dün forest samples; (e) Schwäbische Alb
grassland samples; (f) Schwäbische Alb forest samples. Note that the NMDS axes have dierent scales for each
ordination.

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to harbor a high number of chitinase genes35. Genes involved in polyaromatic hydrocarbon (PAH, here lignin)
degradation are more abundant in grasslands. In forest systems, this process is primarily performed by lignino-
lytic fungi (mainly saprotrophic basidiomycetes), which are able to degrade wooden biomass36. One key enzyme
for aerobic methane oxidation, methanol dehydrogenase, was notably more abundant in forest soils. Methane
oxidation in forest soils is the largest biological sink for atmospheric methane4 and therefore plays a critical role
in the ux of this greenhouse gas. Additionally, nitrous-oxide (N2O) reductase, which catalyzes the last step in
denitrication and reduces N2O to N2, is also more abundant in forest soils (data not shown). ese results indi-
cate that temperate forest ecosystems not only play a crucial role in the regulation and removal of methane, but
also of the greenhouse gas nitrous oxide.
Interestingly, the key enzyme of nitrogen xation, the nitrogenase, is less abundant in grassland than in forest
soils. In this study, only bulk soil was sampled and therefore presumably only free-living nitrogen-xing bacteria
could be detected. It is possible, that nitrogen xation by free-living bacteria plays a greater role in forest systems,
whereas symbiotic and rhizospheric bacteria, which were not covered by the study, carry out the major part of
nitrogen xation in grassland systems.
e obtained results suggest that the dierent land uses grassland and forest not only select for distinct bacte-
rial populations, but also for specic functional traits within their bacterial communities. As the grasslands and
forests analyzed in the present study are long-term established systems, it would be interesting to evaluate if a
similar adaptation is also present in younger systems.
Soil pH is the best predictor of bacterial communities. In the present study, pH was the only fac-
tor, which inuenced the bacterial community regardless of exploratory and land use. Furthermore, it not only
aected bacterial community structure, but also the functional prole of the soil bacteria. As already mentioned,
CCA analysis revealed that pH explains 26% of total variance in the community prole (Supplementary Figure S3).
us, the pH was the strongest predictor for bacterial community structure.
We hypothesized that bacterial community structure and functioning would be shaped in a similar man-
ner. Environmental correlations with the Tax4Fun-derived functional prole were tested by NMDS based on
Bray-Curtis dissimilarities (Fig.4). e results are similar to those obtained for the community structure. e
pH played an important role in shaping the functional prole and explained 32% of the variance (tested by CCA,
P < 0.001, Supplementary Figure S5). is supports our hypothesis that structure and functions of bacterial com-
munities are shaped by similar mechanisms. e functional prole also showed a separation between grassland
and forest systems.
Additionally, we found that pH is the strongest predictor of soil bacterial diversity (P < 0.001, R2 = 0.4)
(Fig.5). It has already been shown that diversity of soil bacterial communities in the exploratories is positively
correlated with pH9,22. However, our results indicate a more complex relationship between pH and diversity.
Diversity was lowest at low pH, then increased and appeared to be stable between pH 5 and 7 and increases again
under slightly alkaline conditions. is is in contrast to Fierer and Jackson7 and Lauber et al.8, who described a
peak of soil bacterial diversity in near neutral soils.
Multinomial regression models revealed multiple responses of bacterial orders to soil pH. To
better understand the complex relationship of single bacterial groups and soil pH, we applied multinomial regres-
sion models on the 30 most abundant orders of the dataset (Supplementary Material Figure S6). Four general
Figure 3. Relative abundances of key enzymes in grassland and forest. Key genes for nitrogen, sulfur, and
methane metabolism, carbon xation pathways, cellulose, xylan, lignin and polyaromatic-hydrocarbon (PAH)
degradation, acid and alkaline phosphatases and urease were combined. eir mean abundance (relative to the
mean in the complete dataset) in grasslands soil was plotted against the mean abundance in forest soils. Size and
color of the circles indicate the mean abundance in the complete dataset. Low abundance: small blue circles;
medium abundance: medium yellow circles; high abundance: large red circles. e enzymes included in the
analysis are given in Supplementary Material Table S5.

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responses were observed: (1) decrease in abundance with increasing pH (Acidobacteriales, acidobacterial sub-
group 3, Frankiales, Corynebacteriales), (2) increase in abundance with increasing pH (acidobacterial subgroup 6,
Gaiellales, Acidimicrobiales, Propionibacteriales), (3) narrow pH range with high abundance (Rhizobiales,
Rhodospirillales), and (4) relatively constant abundance across pH range (Bacillales, Gemmatimonadales,
Sphingobacteriales) (Fig.6). In their publication on niche theory, Austin and Smith37 described pH as a direct
physiological gradient acting on organisms, resulting in unimodal, or skewed unimodal response curves
restricted by growth limiting conditions at one end, and competition at the other end. is is supported by our
observation of few highly abundant orders at low pH and many less abundant orders in near neutral soils. e
ability to grow at low pH values is known as ATR (acid tolerance response) and confers a competitive advantage
compared to other bacteria in soils.
To test which mechanisms are involved in acid tolerance of soil bacteria, we chose those genes reported to
be involved in acid tolerance in Rhizobia38 and Gram positive bacteria39 that were present in the functional
prole. Additionally, we analyzed the genes present of the KEGG pathway for biosynthesis of unsaturated fatty
acids (ko01040) as well as 3-trans-2-decenoyl isomerase. is enzyme is involved in the generation of unsatu-
rated fatty acids and was shown to increase acid tolerance in Streptococcus mutans by changing cell membrane
composition40. We found that the genes for biosynthesis of unsaturated fatty acids were highly abundant in low
pH samples (pH 3–4), while decenoyl isomerase did not follow this trend (Fig.7). erefore, this gene might
not be generally involved in acid tolerance in soil. Additionally, most genes involved in alkali production, two
-0.050.000.0
50
.1
0
-0.10 -0.05 0.00 0.05
NMDS1
pH
C:N
clay
silt
sand
water
H‘
NMDS2
AEW
HEW
SEW
AEG
HEG
SEG
Figure 4. NMDS based on Bray Curtis dissimilarities of the functional prole. Statistically signicant
correlations of soil characteristics (C:N: carbon: nitrogen ratio; water: gravimetric water content; sand: sand
amount; silt: silt amount; clay: clay amount) and the Shannon index (H’) were indicated by arrows. Grassland
soil samples are represented by brown squares, forest samples by green triangles. Samples from dierent regions
are distinguished by color shading (SEG: Schoreide-Chorin grassland; SEW: Schoreide-Chorin forest; HEG:
Hainich-Dün grassland; HEW: Hainich-Dün forest; AEG: Schwäbische Alb grassland; AEW: Schwäbische Alb
forest).
8
9
10
11
345678
pH
H‘
Figure 5. Relationship between soil bacterial diversity, represented by the Shannon index (H’) and soil
pH. Points indicate observed Shannon indices for each sample, while the line represents the non-linear cubit
regression tted to the data (adjusted R2 = 0.5337, P < 0.001).

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component systems and repair of macromolecules were more abundant in the low pH samples compared to more
neutral samples. Several genes involved in DNA repair were probably also involved in the ATR of soil-inhabiting
bacteria, as well as levansucrase, a gene involved in biolm formation. Our results suggest that bacteria can apply
an active mechanism to cope with stressful pH conditions. Alkali production increases the pH in the immediate
environment, improving bacterial survival chances. Additionally, macromolecule repair-enzymes protect and
repair DNA and proteins, and bacteria seem to enhance pH tolerance by altering their cell wall components or
protect themselves within biolms.
Conclusion
During the last years, several studies targeting soil microbial communities and their driving forces came to the
same conclusion that soil pH is the major driver of bacterial communities. is statement, however, falls short
as it provides no direct answer about the complex interaction of soil bacteria with pH. We showed that soil
bacteria respond dierently to changing pH conditions, being adapted to certain pH ranges or even stable over
a broad pH range. Obtained data suggest that this adaptation is attributed to dierent mechanisms including
Figure 6. Response curves of selected bacterial orders towards pH. Each line represents the predicted
abundance changes along the measured pH gradient, based on predictions derived from multinomial regression
models. A detailed version of this graph including the 30 most abundant orders is available as Supplementary
Material Figure S4.
Figure 7. Heatmap based on mean abundances of genes putatively involved in ATR. Only genes with KEGG
orthologs and present in the functional prole are shown. e KEGG pathway for biosynthesis of unsaturated
fatty acids is included, also on the basis of the genes with KEGG orthologs in the functional prole. White: low
relative abundance; yellow: mean relative abundance; red: high relative abundance.

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Scientific RepoRts | 6:33696 | DOI: 10.1038/srep33696
alkali production and alteration of cell wall components. In addition to soil pH, it is generally assumed that
land use intensity drives bacterial community composition and diversity. However, the present study demon-
strated that land use intensity plays a minor role, or that its eect is concealed by the tree species eect in forest.
Biogeographic variations and the corresponding changing edaphic properties resulted in distinct patterns of soil
bacteria, which explains regional dierences and also the distinct patterns of bacterial communities in grasslands
and forests. is is in line with our rst and second hypothesis.
Large comparative studies are required to unravel the diverse interactions between bacteria and their envi-
ronments, and how changes in community structure might reect changes in bacterial functioning. With a total
of 300 samples representing dierent land uses and gradients of land use intensity, this study provides compre-
hensive insights into soil bacterial communities present in temperate systems. Taking the enormous size and
diversity of soil microbial communities into account, functional information on soil bacterial communities has
been limited as it was so far mainly derived from small-scale comparative metagenomic approaches with a rather
low coverage. However, the ability to focus on functional genes and enzymes oers novel insights in the nutrient
cycling potential of soil bacterial communities. Consequently, the application of novel bioinformatic and statis-
tical approaches, such as Tax4Fun and multinomial log-linear models, in microbial ecology resulted in a more
holistic understanding of the links between bacteria and their environment.
Materials and Methods
Study regions. e present study was conducted as part of the German Biodiversity Exploratories initiative,
which is a project investigating large-scale and long-term relationships of biodiversity and land use in Central
European grasslands and forests21. Its unique design allows detailed analysis of bacterial communities along a
regional north-south gradient in Germany. e study is based on 300 plots in three study regions (exploratories).
ey are located in the Schoreide-Chorin, the Hainich-Dün and the Schwäbische Alb. Each study region covers
the land use types forest and grassland. Grassland plots are 50 m × 50 m and forest plots are 100 m × 100 m in size.
e grassland land use intensity-gradient was represented by three dierent management regimes (mead-
ows, pastures and mown pastures) that are non-fertilized or fertilized. Fertilization always represents higher
land use intensity. e land use intensity index (LUI41) combines and equally weights the three components
of land use in grasslands: (1) fertilization, (2) mowing, and (3) grazing. To account for interannual variation
in management practices, the LUI was calculated from 2006 (start of the experiment) to 2011 (sampling year)
(Supplementary Table S1). It is therefore used as an index for long-term management and thereby allows the
evaluation of long-term eects on bacterial communities.
In forests, the land use intensity-gradient was represented by dierent forest management systems (age class
forest, selection forest and unmanaged forest). Additionally, forest plots were dominated by one of the following
tree species: (1) European beech (Fagus sylvatica), (2) sessile/pedunculate oak (Quercus petrea/Quercus robur),
(3) Scots pine (Pinus sylvestris) or (4) Norway spruce (Picea abies). e silvicultural management index (SMI) was
used to assess the impact of management intensity in forest systems (Supplementary Table S1). is index inte-
grates three characteristics of forest stands: (1) tree species, (2) stand age and (3) aboveground, living and dead
wooden biomass42. Detailed information on land use, the applied management, dominant tree species, soil type
and fertilization for every experimental plot is provided in Supplementary Material Table S2.
Sampling and soil properties. Soil samples were collected from all 300 experimental plots in May 2011. In
brief, plots were sampled along two 36 m transects in forests and along two 18 m transects in grasslands. e top
10 cm of the soil layer were taken from 14 locations along the two transects in each plot with a split tube auger of
5 cm diameter. At forest sites, the litter layer was removed with a metal frame (15 × 15 cm) prior to sampling. e
soil cores were pooled and sieved to remove stones > 0.5 cm and roots.
Ten grams of the pooled soil samples were used to determine the gravimetric water content, which repre-
sents the water content of the respective sample at the sampling time. e subsamples were weighted and dried
at 105 °C to a constant weight. Air-dried soil samples sieved to < 2 mm were used for the determination of soil
texture, soil pH, and carbon (C) and nitrogen (N) concentrations as described previously43. Detailed information
on soil characteristics is given in Supplementary Material Table S1.
DNA extraction, amplication of 16S rRNA genes and pyrosequencing. Total microbial com-
munity DNA was isolated from approximately 0.25 g soil per sample using the MoBio Power Soil DNA isolation
kit (MoBio laboratories, Carlsbad, CA, USA) following the manufacturer’s recommendations. is method was
recently shown to perform equally well over a range of dierent soils44. It produces similar amounts of DNA
and 16S rRNA gene copies for each soil tested and does not overestimate any of the abundant phyla detected
throughout the soils. erefore, extraction biases were limited and comparability given for all DNA extractions.
DNA concentrations were quantified using a NanoDrop ND-1000 UV-Vis Spectrophotometer (NanoDrop
Technologies, USA) as recommended by the manufacturer.
e V3-V5 region of the 16S rRNA gene was amplied by PCR. e PCR reaction mixture (50 μ l) contained
10 μ l 5-fold reaction buer, 200 μ M of each of the four deoxyribonucleoside triphosphates, 2% DMSO, 2% BSA,
0.2 μ M of each of the primers, 0.5 U of Phusion High delity DNA polymerase (ermo Scientic, Waltham, MA,
USA) and approximately 50 ng of isolated DNA as template. e V3-V5 region was amplied with the following
set of primers containing the Roche 454 pyrosequencing adaptors and a unique MID per sample (underlined):
V3for 5′-CCATCTCATCCCTGCGTGTCTCCGACTCAG-MID-TACGGRAGGCAGCAG-3′
45 and V5rev
5′-CCTATCCCCTGTGTGCCTTGGCAGTCTCAG-MID-CCGTCAATTCMTTTGAGT-3′
46. e following
thermal cycling scheme was used: initial denaturation at 98 °C for 3 min, 25 cycles of denaturation at 98 °C for
10 s, annealing at 58 °C for 30 s, and extension at 72 °C for 30 s followed by a nal extension at 72 °C for 10 min. All
samples were amplied in triplicate, pooled in equal amounts and puried by gel electrophoresis using peqGOLD

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Scientific RepoRts | 6:33696 | DOI: 10.1038/srep33696
Gel Extraction kit as recommended by the manufacturer (Peqlab Biotechnologie GmbH, Erlangen, Germany).
PCR products were quantied using the Quant-iT dsDNA HS assay kit and a Qubit uorometer (Invitrogen
GmbH, Karlsruhe, Germany) as recommended by the manufacturer. e Göttingen Genomics Laboratory deter-
mined the 16S rRNA gene sequences employing the Roche GS-FLX+ pyrosequencer with Titanium chemistry
(Roche, Mannheim, Germany).
Analysis of pyrosequencing data. Pyrosequencing-derived 16S rRNA gene sequences were processed
using the QIIME soware package version 1.847. Following the extraction of raw data, reads shorter than 300 bp,
with long homopolymer stretches (> 8 bp), or primer mismatches (> 3) were removed. Subsequently, sequences
were denoised employing Acacia version 1.53b48. Cutadapt49 was employed to truncate remaining primer
sequences. Chimeric sequences were removed using UCHIME implemented in USEARCH version (8.0.1623)
rst in de novo and subsequently in reference mode using the SILVA SSURef 123 NR database as reference data-
base50,51. Aerwards, processed sequences were clustered with UCLUST version 1.2.22q in operational taxonomic
units (OTUs) at 97% and 80% genetic identity representing species and phylum level, respectively52. OTUs were
classied by BLAST alignment against the most recent SILVA database (see above). Rarefaction curves, alpha
diversity indices (Chao1, Shannon, Simpson) and Michaelis-Menten-Fit were determined using QIIME accord-
ing to Wemheuer et al.53. e analysis was performed by using 5,311 sequences per sample (Supplementary
Material Table S3). Non-metric multidimensional scaling plots were generated based on Bray Curtis dissimilari-
ties or weighed UniFrac distances in R using the metaMDS function to visualize dierences in bacterial commu-
nity composition.
Statistical analyses. All statistical analyses were conducted employing R version 3.154. e results of all
statistical tests were regarded signicant with P ≤ 0.05, and only signicant results are shown and described
throughout the manuscript. e median is used throughout the manuscript instead of the mean value, except
stated otherwise. For all statistical analysis, the dataset calculated for 97% identity (species level) was used.
e Mann-Whitney-test and non-parametric Kruskal-Wallis one-way analysis of variance (ANOVA) were
used due to the non-normal distribution of the data. ey were performed to test for dierences in soil param-
eters and bacterial diversity between land use systems, exploratories and management regimes. e eects of
environmental parameters onto the variance of bacterial communities were analyzed using the envt function as
described previously55. Canonical correspondence analysis (CCA) on single soil properties was carried out using
the cca function and subsequently tested for signicance applying the permu.test function with 1000 permuta-
tions. All these functions are contained in the vegan package56. Response curves of bacterial orders toward pH
were calculated employing a multinomial log-linear model (function multinom contained in the nnet package).
Functional proles were predicted from obtained 16S rRNA gene data using Tax4Fun23. Genes involved in
acid tolerance (ATR) and encoding key enzymes in nutrient cycling were identied in the resulting proles using
their KEGG orthologs. e heatmap, based on the ATR-involved genes was calculated using the heatmap.2 func-
tion of the gplots package57. Dierences in the abundances of key genes involved in nutrient cycling were ana-
lyzed employing the Mann-Whitney test in R. e mean abundances of genes in grasslands and forests (relative
to mean abundance in complete dataset) were plotted against each other using ggplot of the ggplot2 package58.
Sequence data deposition. Sequence data were deposited in the Sequence Read Archive (SRA) of the
National Center for Biotechnology Information (NCBI) under the accession number SRP065604.
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Acknowledgements
We thank the managers of the three exploratories, Kirsten Reichel-Jung, Swen Renner, Katrin Hartwich, Sonja
Gockel, Kerstin Wiesner, and Martin Gorke for their work in maintaining the plot and project infrastructure;
Christiane Fischer and Simone Pfeiffer for giving support through the central office, Michael Owonibi for
managing the central data base, and Markus Fischer, Eduard Linsenmair, Dominik Hessenmöller, Jens Nieschulze,
Daniel Prati, Ingo Schöning, François Buscot, Ernst-Detlef Schulze, Wolfgang W. Weisser and the late Elisabeth
Kalko for their role in setting up the Biodiversity Exploratories project. e work has been (partly) funded by
the Deutsche Forschungsgemeinscha (DFG) Priority Program 1374 “Infrastructure-Biodiversity-Exploratories”
(Grant ID DA374/4-1 and Grant ID DA374/6-1). In addition, we acknowledge support by the Open Access
Publication Funds of the Göttingen University. Field work permits were issued by the responsible state
environmental oces of Baden-Württemberg, üringen, and Brandenburg (according to § 72 BbgNatSchG).
We further thank Andrea Thürmer of the Göttingen Genomics Laboratory for assistance with sequencing.
Additionally, we would like to thank Peter Schall and Christian Ammer for the calculation of the SMI.
Author Contributions
R.D. and H.N. conceived the study and planned the experiments. K.K. and V.K. performed the experiments.
K.K. and B.W. analyzed data. I.S. and M.S. contributed data. K.K., B.W., F.W. and R.D. wrote the manuscript. All
authors interpreted the results and reviewed the manuscript.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Kaiser, K. et al. Driving forces of soil bacterial community structure, diversity, and
function in temperate grasslands and forests. Sci. Rep. 6, 33696; doi: 10.1038/srep33696 (2016).
is work is licensed under a Creative Commons Attribution 4.0 International License. e images
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