Ecological Drivers of Biogeographic Patterns of Soil Archaeal Community
Citation: Zheng Y-M, Cao P, Fu B, Hughes JM, He J-Z (
Ecological Drivers of Biogeographic Patterns of Soil Archaeal Community
Yuan-Ming Zheng 0
Peng Cao 0
Bojie Fu 0
Jane M. Hughes 0
Ji-Zheng He 0
Gabriele Berg, Graz University of Technology (TU Graz), Austria
0 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences , Beijing , China , 2 Environmental Futures Centre, Griffith School of Environment, Griffith University , Nathan, Queensland , Australia
Knowledge about the biogeography of organisms has long been a focus in ecological research, including the mechanisms that generate and maintain diversity. In this study, we targeted a microbial group relatively underrepresented in the microbial biogeographic literature, the soil Archaea. We surveyed the archaeal abundance and community composition using real-time quantitative PCR and T-RFLP approaches for 105 soil samples from 2 habitat types to identify the archaeal distribution patterns and factors driving these patterns. Results showed that the soil archaeal community was affected by spatial and environmental variables, and 79% and 51% of the community variation was explained in the non-flooded soil (NS) and flooded soil (FS) habitat, respectively, showing its possible biogeographic distribution. The diversity patterns of soil Archaea across the landscape were influenced by a combination of stochastic and deterministic processes. The contribution from neutral processes was higher than that from deterministic processes associated with environmental variables. The variables pH, sample depth and longitude played key roles in determining the archaeal distribution in the NS habitat, while sampling depth, longitude and NH4+-N were most important in the FS habitat. Overall, there might be similar ecological drivers in the soil archaeal community as in macroorganism communities.
Funding: This work was supported by the Natural Science Foundation of China (41230857 and 41025004) and the CAS/SAFEA International Partnership Program
for Creative Research Teams of Ecosystem Processes and Services. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Biogeography is the study of the distribution of biodiversity over
space and time . For decades, knowledge about the
biogeography of organisms has been a focus in ecological research,
including the mechanisms that generate and maintain diversity,
i.e., speciation, extinction, dispersal and species interactions . It
has been shown that macroorganisms have obvious zonal
distributions along gradients of water and energy . Whether
or not microorganisms follow similar distribution patterns to
macroorganisms is still a matter of debate , although we have
benefited from recent advances in molecular biological techniques
which make it possible to examine geographic distributions of
microbes [1,5,6]. Some interesting studies on bacterial
communities have been undertaken through comparative studies across
different habitats . While at a global range, archaea
distribution were either mainly driven by salinity along a broad
environmental gradient and habitat types , or precipitation
gradient and vegetation cover . However, there is insufficient
information on soil Archaea from various locations and habitats,
even though the diversity and composition of archaeal
communities are thought to have a direct influence on a wide range of
ecosystem processes [1214,15].
Moreover, a question, which prevails in the macroecology, is
whether natural communities obey general predictable processes
through species sorting in a spatially heterogeneous environment
[16,17], or communities are structured by neutral drift in species
densities [18,19]. This question mainly concerns the processes that
structure ecological communities and the ecological mechanisms
driving these patterns, defined as niche theory and neutral theory.
The basic assumption of niche theory is that species differ in their
traits to avoid competition and enable them to co-exist within
communities for long periods of time . This theory
emphasizes the species-specific differences in explaining patterns
in community organization and biodiversity. It predicts that
species relative abundances will follow geometric series, the broken
stick or some other niche-based models [23,24]. In contrast,
neutral theory emphasizes the equivalence of species in a
community and the importance of stochastic events such as
dispersal, local extinction and speciation [18,19,25]. No single
species is at a competitive advantage or disadvantage, and
exclusion does not occur [19,26,27]. Consequently, stochastic
drift and changes in species composition will be only related to the
geographic distances as a result of dispersal limitation . Both
theories have gained support from empirical studies [17,2934].
And thus, the synthesis of biogeographical theory with microbial
ecology should be developed .
Here we address the question: what is the relative importance of
stochastic and deterministic processes in structuring distribution
patterns of Archaea in different environments? The biogeography
of macroorganisms is much better studied and ecologists who
study microorganisms and those who study macroorganisms have
been interacting more often in recent years [1,3638], particularly
in understanding mechanisms of community assembly. Nowadays,
many efforts attempt to characterize the global microbial
distribution patterns and the underlying driving mechanisms.
For instance, the Earth Microbiome Project (EMP) is proposed to
map the spatiotemporal variability of microbial across the globe
and the hypothesis is that certain environmental features are
correlated with specific combinations of microbial species .
However, the truth is the number of such studies available is
limited because few microbial biogeographic studies report the
geographic distance between their samples, or directly test for a
distance effect relative to a contemporary environmental effect, or
significant influences from longitude and latitude when test for the
distance effect in their studies [1,4042]. Thus, the relative
importance of stochastic and deterministic processes in structuring
distribution patterns of Archaea in different environments is still
The legacy of historical separation, which means the dispersal
limitation, will exert a dominant influence on the microbes
compared with environmental factors as long as the research scale
is big enough . Also, ecologists often focus on the importance of
temporal and spatial scales in their investigation. This leads to the
second question: what is an appropriate spatial scale to explore the
biogeography of microorganisms and its driving mechanisms? It is
thought to be at the intermediate spatial scale (103000 km) that
the influence of both historical contingencies and contemporary
ecological factors on microbial biogeography is most likely to be
detected , especially comparing the relative contribution of
environmental or spatial variables .
Chinas climates range from tropical to alpine, and various soil
habitats have developed under different bioclimatic conditions
within this vast area. This natural variety of different conditions
may help to probe into the soil microbial biogeography. Our
previous studies have investigated differences in soil bacterial
diversity, which are mainly driven by historical contingencies, such
as locations and soil depth . In the present study, we collected
105 soil samples from two typical habitats, non-flooded soil
(natural soil, NS) and flooded soil (paddy soil, FS), in China, which
were separated at intermediate scales to clarify the biogeography
of archaeal communities and examine the dominant ecological
mechanism (niche or neutral theories) structuring microbial
communities of Archaea. The FS habitat was attributed with
quite different conditions from the ambient environment (air,
water and soil). Our classification of two habitats might be helpful
to test whether the FS habitat could be a well-isolated habitat
, which meant the microorganisms inhabiting it were adapted
to the conditions quite different from the ambient environment
(the surrounding non-flooded soil) and geographical isolation
might be one of the important components of microbial diversity,
or at least compared the influences of two habitats on
microorganisms. Molecular community profiling - terminal restriction
fragment length polymorphism (T-RFLP) and quantitative
realtime PCR was used to characterize the abundance, diversity, and
distribution of archaeal communities to conduct a comparative
study to distinguish the mechanisms in the individual habitats.
Afterwards, the relationships between the soil archaeal
communities and the spatial heterogeneity of the environmental factors
were analyzed based on ecological models and multivariate
statistical methods to tackle the mechanisms that generate the
spatial patterns of microbial biodiversity.
Description of the Site and Sampling Design
In this study, two habitats were involved in the investigation,
i.e., non-flooded soil (natural soil, NS) and flooded soil (FS) (In this
study, no specific permissions were required for the locations/
activities since the investigation fields did not belong to the protect
areas and private lands, and the field studies did not involve
endangered or protected species). There were 6 sites chosen for the
NS and 13 sites for the FS habitat along a latitudinal gradient from
the north to the south of China considering the longitudinal
variation at the same time. Fifty-nine of the NS samples were from
our previous work , and three new ones from BJ, TJ and QY
were added. Thus totally sixty-two NS samples were obtained.
The distance between any two sites ranged from 6 m to
1,873,226 m (Fig. 1). One hundred and five samples were
obtained in total, and site information is listed in Table S1
(Supplementary material). Each soil sample was passed through a
2.0mm sieve, and then stored at 4uC until analysis of soil
characteristics. A subsample was taken from each sample and
stored at 280uC for DNA extraction.
Soil Chemical Analysis
Soil pH was determined with a soil to water ratio of 1: 2.5. Soil
organic carbon (SOC) was determined using the K2Cr2O7
oxidation method . Total N (TN) was determined using the
Dumas method with an Element Analyzer (Vario EL III,
Elementar, Hanau, Germany) . Soil nitrate (NO3N) and
ammonium (NH4+-N) were extracted with 2 M KCl and
determined with a Continuous Flow Analyzer (SAN++, Skalar,
Breda, Holand). All results are listed in Table S2 (Supplementary
Archaeal Abundance and Community Analysis
Soil DNA was extracted using MoBio UltraCleanTM soil DNA
isolation kits (MO BIO Laboratories, Inc., San Diego, CA, USA)
according to the manufacturers protocol. Real-time PCR was
performed on an iCycler iQ 5 thermocycler (Bio-Rad
Laboratories, Inc., Hercules, CA, USA). The total number of archaeal 16S
rRNA gene copies was determined according to the protocol of
Cao et al . The amplification efficiency for all qPCR reactions
ranged from 91.7% to 94.9%. The specificity of amplification
products was verified by melting curve analysis and standard
agarose gel eletrophoresis. Standard curves for the qPCR assays
were generated as described previously, using primer pairs Ar4F
/Ar958R  to amplify the 16S rRNA gene from soil DNA
. The PCR products were cloned into the pGEM-T Easy
Vector (Promega, Madison, WI, USA). Plasmids from the positive
clones with the targeted gene insert were extracted for sequencing
and used as standards for the calibration curve. The plasmid
concentration was 51.21 ng?mL21, determined on a NanodropH
ND-1000 UV-Vis Spectrophotometer (NanoDrop Technologies,
Wilmington, DE, USA). Then, the plasmids were ten-fold serial
diluted and used as templates with a final content of 1.0261027 to
1.02 ng in 25-ml reaction mixtures.
Archaeal community analysis used terminal restriction fragment
length polymorphism (T-RFLP), and the process was described
previously . The PCR amplification for T-RFLP analysis was
carried out using the archaeal primer pairs A364aF/A934bR 
with the 59 end of the A934bR primer labeled with
6carboxyfluorescein (FAM). The purified FAM-labeled PCR
product was digested by Hha I (TaKaRa Bio, Otsu, Shiga,
Japan). The mixtures of the purified products and the internal
standard GeneScan-1000 ROX (Applied Biosystems, Foster City,
CA, USA) were denatured for 3 min at 95uC, and the DNA
fragments were size separated using a 3130xl Genetic Analyzer
(Applied Biosystems, Foster City, CA, USA). T-RFLP profiles
were produced using the GeneMapper software (version 3.7; ABI,
USA), and peaks at positions between 50 to 550 bp were selected
Figure 1. Soil sample locations as shown in a Chinese map. Different colors and numbers of sectors in the pie diagrams represent the soil type
and the number of samples at each site. Coffer = cinnamon soil (ustic cambosols), orange = brown soil (udic agrosols), green = fluvo-aquic soil (aquic
inceptisol), red = red soil (udic ferrosols), and blue = paddy soil.
because most T-RFs fall in this range and also to avoid T-RFs
caused by primer-dimers. The relative abundance of a T-RF was
calculated by dividing the peak height of the T-RF by the total
peak height of all T-RFs in the profile. In addition, we also
calculated the relative abundance of a T-RF using peak area, and
there was no significant difference comparing with the results of
peak height. The peaks with height #1% of the total peak height
were not included in further analyses.
Prior to analysis, GPS coordinates were converted to UTM
coordinates in meters for principal coordinates of neighbor
matrices (PCNM) and Mantel test (vegan library in R software,
2010). These spatial data and other environmental variables
(Table S2) were standardized (mean = 0, standard deviation = 1)
for the redundancy analysis (RDA) with manual forward selection
(CANOCO 4.5, CANOCO 4.5, Centre for Biometry Waginingen,
The Netherlands). Log normalized T-RFLP profiles data were
subjected to the PCNM with variation partitioning, Mantel test
and RDA analyses. PCNM with variation partitioning and Mantel
tests based on distance dissimilarity matrices. RDA analyses
directly used log normalized T-RFLP profiles data.
The results of real-time PCR were firstly converted into cell
numbers based on the average number of 16S rRNA gene copies
for Archaea (1.77) . The number of distinct T-RFs was
used as an estimate of species richness, so the proportion of T-RFs
within a sample combined with real-time PCR results represented
the abundance of T-RFs species; this is not a taxonomic definition
of the true number of species within a sample, and the term T-RF
species was applied in recognition of that fact. The most widely
used a, b-diversity indices, i.e., Shannon-Wiener index, Simpson
index, Evenness index and Bray-Curtis dissimilarity index, were
calculated in the R statistical language. For a diversity measure,
the mean value per site (e.g., n = 10 for BJ) was used for further
analysis. For the b-diversity measure, the mean pair-wise measure
between samples for each site was used. Sites with only single
sample were omitted from all analysis of b-diversity. The Welch
test was used to compare archaeal abundance, T-RFs species
richness, Shannon-Wiener index, Simpson index, Evenness index
and Bray-Curtis dissimilarity index between the two habitats, NS
a- diversity indices
b- dissimilarity indices
and FS (SPSS 13.0, IBM Co., Armonk, New York, USA). P,0.05
was considered to be significant.
In order to test the sampling efficiency, T-RFs accumulation
curves were computed using specaccum in R (vegan library in R
software, 2010) . Three kinds of methods were used to analyze
the effects of environmental variables and spatial structure on
archaeal community variation: variance partitioning using
extracted PCNM as spatial predictors employing RDA, correlation
of archaeal -diversity and the change of spatial and
environmental variables using the Mantel test, and manual forward
selection RDA to explain the proportion of the variance in
archaeal community explained by each significant variable. For
the Mantel test, the dissimilarity index of spatial and
environmental variables between each pair of sites was calculated using
Euclidean distances when tested by Mantel analysis and the
importance of each variable was evaluated using spearman
correlation coefficient. PCNM with variation partitioning analysis
partitioned the variation represented by adjusted R2 values (Ra2)
into four fractions as Dumbrell described: (a) variation explained
by environmental variables and not spatially structured, (b)
variation explained by environmental variables with spatial
structure, (c) spatially structured variation not explained by the
environmental variables and (d) residual variation . Thus 12 of
the 34 extracted PCNM variables that significantly (a = 0.05)
explained the spatial structure of archaeal community in NS, and
8 of 24 PCNM variables could be used to explain the spatial
structure in FS (all the PCNM variables were examined by
forward selection based on 10 000 permutation test). The PCNM
analysis used PCNM and vegan libraries in the R statistical
language. Mantel analysis was based on 10 000 randomizations of
the original data. Finally, the relationship between archaeal
community and both environmental and spatial variables was
analyzed using RDA with manual forward selection using 10 000
Monte Carlo permutation tests.
Archaeal Abundance and Alpha, Beta Diversity
The soil pH, SOC and TN were significantly different among
the sampling sites. Soil pH varied widely in NS with the following
order: ZZ. TJ. BJ. QD. QY. TY (Table S2). The pH also
varied between 5.63 and 9.08 among different sites in FS (Table
S2). Soil samples of PJ had the highest pH (9.0860.14) while TY
soils had the lowest pH (4.4260.34). SOC and TN varied with
different site and ranged from 4.19 to 62.2 and 0.33 to 8.87,
respectively (Table S2). The archaeal abundance ranged from
8.726106 to 4.126107 cells g21 dry soil in NS, which was
significantly higher than in FS which ranged from 3.726105 to
1.606106 cells g21 dry soil (P = 0.01) (Table 1). In total, 12 and 19
T-RFs were detected in NS and FS, respectively. In NS, the
dominated T-RFs were 162 bp, 192 bp, 231 bp and 537 bp with
the relative abundance ranging from 0.00% to 35.5%, 11.3% to
88.7%, 0.00% to 71.1% and 0.00% to 62.8%, respectively. The
dominated T-RFs in FS were 81 bp, 86 bp, 117 bp, 162 bp,
192 bp, 207 bp, 227 bp and 539 bp varied between 1.0925.6%,
1.3243.2%, 1.4110.5%, 1.1021.9%, 9.8486.2%, 1.0614.1%,
1.1842.0% and 1.8163.1%, respectively. As to the archaeal
community biodiversity, T-RFs species richness (P = 0.00),
Shannon-Wiener index (P = 0.00) and Simpsons index (P = 0.01) were
significantly lower in NS than in FS, but there were no significant
differences for evenness (P = 0.32) or b-dissimilarity indices
(P = 0.19) between the two habitats (Table 1). For the NS habitat,
the maxima of the richness, Shannon-Wiener and Simpson indices
were found in soil at TY, while the minima were found at TJ. For
the FS habitat, the maxima of the richness, Shannon-Wiener and
Simpson indices were attained at NC, while the minimum values
at ShY (Table 1).
The Influence of Spatial and Environmental Effects on
T-RFs species accumulation curves showed that they had
asymptotic trends for the data from both NS and FS (Supplementary
material Fig. S1). Therefore, further sampling would be unlikely to
qualitatively affect the results. PCNM with variation partitioning
analysis has been shown to be a successful method to examine the
relative contribution of spatial structure and environmental
variables to the variation of ecological communities .
There was 79% of the archaeal community variation explained in
the NS habitat using adjusted R2 values (Ra2) (a+b+c; F = 2.11;
P,0.001) (Fig. 2A). 5% of the explained variation in community
composition was attributed to environmental variables (a) and
24% to spatially structured variation (c). The majority of variation
explained was spatially structured environmental variables (b,
50%). In FS, analysis of variation partitioning explained 51% of
the variation of archaeal communities (a+b+c; F = 2.05; P,0.001)
(Fig. 2B). The explained variation in community composition was
attributed to environmental variables (a+b) and spatially structured
variation (b+c), which was 23% and 37%, respectively. This
indicated that the majority of variation in archaeal community
composition was explained by spatial structure in the FS habitat.
Mantel tests showed that environmental variables were
positively correlated with b-diversity measured by Bray-Curitss
dissimilarity index (r = 0.22, P = 0.002). b-diversity of NS was
positively correlated with pH, then profile depth (cm), longitude
(m), NH4+-N, and altitude (m) (Table 2). However, in the FS
habitat, b-diversity was significantly correlated only with depth
(Table 2). Combined with the results of PCNM analysis,
bdiversity of the archaeal community was predominantly controlled
by spatial structure both in NS and FS habitats.
In NS and FS habitats, RDA significantly explained 98.1% and
97.3% respectively of the species-environment relationship across
the first two canonical axes under the full model that included all
environmental variables. With manual forward selection of
environmental variables using Monte Carlo permutation tests,
RDA of NS revealed that soil pH, depth and longitude were
significantly related to the archaeal community composition
(Table 2), which accounted for 28%, 14% and 6% of the variation
in community composition, respectively. Latitude was excluded,
and the axis 1 explained 97.7% of the species-environment
relationship, which was much higher than our previous results
. Depth, longitude and NH4+-N were significant variables in
the FS habitat (Table 2), accounting for 20%, 11% and 7% of the
variation in community composition, respectively. It showed that
these variables explained 99.6% and 99.5% of the variation within
the speciesenvironment relationship across the first two canonical
axes in different soil habitat (Table 3; Fig. 3). No other
environmental variable was significantly related to archaeal
community composition (P.0.05 in all the cases).
In our study, real-time PCR and T-RFLP were used to analyze
the abundance and diversity of archaeal communities. We found
large differences between the NS and the FS habitats using
abundance and a-diversity indices as indicators. These differences
were similar to the comparative study on soils from forest and
floodplain by Kemnitz et al. [50,58]. Both the Shannon-Wiener
index and the Simpson index are influenced by the richness and
the evenness of T-RFs species in the sample. Larger values of each
of these indices indicate the higher diversity . Thus the FS
habitat was more diverse than the NS habitat, which might
indicate that FS would be more stable when experiencing
disturbance due to its more complex structure . However,
there was no significant difference in b-diversity of archaeal
communities between the NS and FS habitats based on the results
of this study. This was mainly because of the big differences within
each habitat, i.e., the differences between the two groups was as
great as the differences within each group. These results also
suggested that archaeal communities varied with location or site.
b-diversity needs to be explored using more complex multivariate
statistical methods, as suggested by Legendre et al. .
In the NS habitat, soil pH, depth of sampling and longitude
were found to be responsible for the regulation of archaeal
community, with soil pH the most important factor like our
previous results , suggesting strong selective pressures. In
contrast, longitude, sampling depth and concentration of NH4+-N
Manual forward selection
Manual forward selection
were the most important variables in the FS habitat, with
longitude the most important. Longitude, reflecting geographic
distance, played a dominant role in both NS and FS habitats,
which indicated the effects from dispersal limitation. Furthermore,
in NS and FS habitats, explained variances accounted for 79%
and 51% of total variation, respectively (a+b+c, Fig. 2). These
Figure 3. Redundancy analysis (RDA) for the relationship between environmental or geographic variables and archaeal community
composition. (A) non-flooded soil (NS) and (B) flooded soil (FS).
Cumulative percentage variance of species data
Cumulative percentage variance of species-environment relation
explained proportions were mainly attributed to the stochastic
processes, while the deterministic processes were relatively weak.
The independent contribution of spatial variables was 24% and
28%, while the contribution of environmental variables was only
5% and 14% for the NS and FS habitat, respectively. Therefore, at
the scale examined here, stochastic processes seem to be more
important in structuring the community composition of Archaea,
indicating some similarities with phytoecology . To some
extent it could be concluded that soil archaea community is
controlled by spatial factors to certain extent, although only
longitude was significant in our study.
It was notable that the portion of variation undetermined in FS
was 49% (fraction d in Fig. 2). Although the underlying processes
could not be identified from the available data, analyses implied
that they could be (at least partly) independent of the measured
environmental variables (which obviously were not exhaustive,
and did not include all of the possible environmental variables in
nature), and a fair amount of variation was due to local effects of
unmeasured (biotic or abiotic) controlling variables, or to spatial
structures that have been missed because they required more
complex functions to be described . Another interpretation is
that it might be due to stochastic processes. The latter explanation
has theoretical connection to the neutral theory of macroecology
that assumes the dynamics of populations are primarily driven by
ecological drift and dispersal with or without limitation, and are
not habitat dependent. Dispersal has a spatial signature and
produces variation in fractions (c) and (d) whereas the effect of drift
comes out in fraction (d) . Indeed, the contemporary factors
chosen might be arbitrary sometimes. This would impact the
statistical results significantly. For example, there was no
relationship between pH and the archaeal community in FS,
which contrasts not only with NS, but also with other studies [6,8].
For the FS habitat, some other variables might be considered, such
as reduction potential (Eh), to improve the explanation of variation
in community composition, whereas for the NS habitat, the
current set of variables produced satisfying results.
There was a clear difference between the two habitat types in
the proportion of the variation accounted for by the combined
effects of environmental and spatial variables. This proportion was
as high as 50% in NS showing the environmental variables
depended on the spatial structure, while in FS, there was no such
obvious combined effect. Since in PCNM analysis, fraction (c) was
related with the pure effects of neutral processes and fraction (a)
represented the pure effects of niche differentiation, fraction (b)
should indicate the interaction of niche and neutral theories in
driving the soil archaeal biogeography. This is probably because
niche and neutral processes are not diametrically opposed to each
other and a community is likely determined by the interplay of the
two processes, as ecologists in macroecology are acknowledging
[7,9,54,62]. Thus, different studies showed high variations in the
driving patterns of organisms , even between microorganisms.
Microbial ecological theory needs more exploration to distinguish
the contribution of local niche-based processes and dispersal
As has been known for some time, factors driving
macroorganism distributions are water-energy related. Biogeographic patterns
of plants are dependent on longitude, latitude and/or altitude. In
our study, because of wide variation in water-energy in the NS
habitat, niche differentiation was significant [44,62]. It has been
suggested that species tend to differ in their traits in order to avoid
competition and enable them to co-exist within communities for
long periods of time . In contrast, flooded conditions could
make the FS habitats located in the different sites more
homogeneous. Microorganisms in paddy fields might be less
affected by environmental factors, especially considering that
sampling was in summer. Therefore, weak niche differentiation
possibly resulted in stronger effects of spatial structure. Conversely,
the flooded environment may have made the FS habitats more
isolated from one another as Papke et al.  described, which
would have reinforced the effects of dispersal limitation. The fast
metabolic rate and short generation time of microorganisms might
also result in stochastic processes being more important in
determining community assemblages . This phenomenon
caused by the unique land use pattern of paddy fields is potentially
very useful and worthy of exploring further in the future.
In addition, as many environmental factors as possible should
be included, especially in the preliminary investigation. Factors
involved with the ecological drivers are complicated. Sometimes
we could hardly distinguish the original variables from derived
variables. Many studies pointed out that pH was a significant
factor in driving the microbial distribution patterns [46,53,63].
However, it is still controlled by other soil properties, such as soil
minerals. So whether this means that we should substitute pH with
more original variables such as soil mineral composition? The
same question will be arisen in the spatial variables selection.
Whether we should only use longitude and latitude, or we should
add moisture and temperature, which are considered the
representative factor related to longitude and latitude,
respectively? If moisture and temperature should be involved in the
calculation of ecological driving processes, they should be regarded
as spatial variables or environmental variables? So in future
studies, we should pay more attention to the systematic
classification of variables which are related to the effect analysis
of spatial and environmental variables contribution to the
In conclusion, we applied ecological theories for
macroorganisms to explore microorganism communities. Uniquely for a
microbial community by a case study of archaeal communities
across China, we partitioned the relative importance of
deterministic and stochastic processes, and explained the patterns by niche
or neutral theory. We suggest that the biogeographic patterns of
soil Archaea and ecological mechanisms driving distributions were
explained by the niche and neutral theories jointly in different
terrestrial ecosystems, as well as found in macro ecological studies.
Figure S1 T-RFs species accumulation curves. T-RFs
species accumulation is shown as archaeal T-RFs species
abundance data sampled per non-flooded soil (NS) (A) or per
flooded soil (FS) (B). Data points mean estimated T-RFs species
richness (6 SE) using rarefaction in the R statistical language.
Descriptive information of the soil samples.
Basic chemical properties of the examined
We greatly appreciate the supports from Prof. Shen Alin (Henan Academy
of Agricultural Sciences) and Prof. Shi Yanxi (Qingdao Agricultural
University) in soil sampling.
Conceived and designed the experiments: YMZ PC JZH. Performed the
experiments: YMZ PC. Analyzed the data: YMZ PC JZH. Contributed
reagents/materials/analysis tools: YMZ PC JZH. Wrote the paper: YMZ
PC BF JMH JZH.
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