Soil bacterial community succession during long-term
ecosystem development
KAMLESH JANGID,* WILLIAM B. WHITMAN,? LEO M. CONDRON,? BENJAMIN L. TURNER?
and MARK A. WILLIAMS?
*Microbial Culture Collection, National Centre for Cell Science, Pune 411007, Maharashtra, India, ?Department of
Microbiology, 527 Biological Science, University of Georgia, GA 30602, USA, ?Agriculture and Life Sciences, Lincoln
University, PO Box 84, Canterbury 7647, New Zealand, ?Smithsonian Tropical Research Institute, Apartado 0843-03092,
Balboa, Ancon, Republic of Panama, ?Rhizosphere and Soil Microbial Ecology/Biogeochemistry, Virginia Polytechnic and State
University, 301 Latham Hall, Blacksburg, VA 24060, USA
Abstract
The physicochemical and biological gradients of soil and vegetative succession along the
Franz Josef chronosequence in New Zealand were used to test whether bacterial commu-
nities show patterns of change associated with long-term ecosystem development. Py-
rosequencing was conducted on soil-derived 16S rRNA genes at nine stages of ecosystem
progression and retrogression, ranging in age from 60 to c. 120 000 years since glacial
retreat. Bray?Curtis ordination indicated that the bacterial communities showed clear
patterns of change that were closely aligned with ecosystem development, pedogenesis
and vegetative succession (Mantel test; r = 0.58; P < 0.001). Eighty per cent (80%) of the
explained variability in bacterial community structure was observed during the first c.
1000 years of development, when bacterial richness (Simpson?s 1/D) declined from 130 to
30. The relatively high turnover of soil bacterial communities corresponded with an inte-
grative ?plant?microbial successional feedback? model that predicts primarily negative
feedbacks between plants and soil bacterial communities during progression and early
pedogenesis. Positive feedbacks, similar to those of the plant community, could explain
the long periods of community stability during later retrogressive stages of ecosystem
development. This hypothesized model provides a consistent description linking below-
ground communities to ecosystem development and succession. The research, using
deep sequencing technology, provides the first evidence for soil bacterial community
change associated with the process of long-term ecosystem development. How these bac-
terial community changes are linked to the processes of primary ecosystem succession is
not known and needs further investigation.
Keywords: 16S rRNA pyrosequencing , bacterial diversity , Franz Josef chronosequence , soil
development , soil nutrients , vegetative succession
Received 25 July 2012; revision received 13 March 2013; accepted 14 March 2013
Introduction
Microbial ecologists are in the nascent stages of devel-
oping theories to describe and predict patterns of soil
microbial community composition and structure across
meaningful ecological scales. In this regard, chronose-
quences of primary developing ecosystems are natural
experiments that can be used to study ecological rela-
tionships (Walker et al. 2010). Ecological succession and
ecosystem development have for decades been used
to provide fundamental descriptions of ecosystem pro-
cesses (McIntosh & Odum 1969; Walker & Syers 1976),
but little is known about belowground microbial commu-
nities and their relationship to the process of succession.
Studies of soil bacterial community change associated
with retreating glaciers during early (c. 100 years) ecosys-
tem development have shown that bacterial communities
Correspondence: Mark A. Williams, Fax: 540 231 3083;
E-mail: markwill@vt.edu
? 2013 John Wiley & Sons Ltd
Molecular Ecology (2013) 22, 3415?3424 doi: 10.1111/mec.12325
can be dynamic (Nemergut et al. 2007; Schutte et al. 2009;
Wu et al. 2012; Zumsteg et al. 2012) but difficult to pre-
dict. How soil bacterial communities change during eco-
system development over longer timescales has not been
studied during glacial retreat, but could provide clues to
the linkages, mechanisms and feedbacks that regulate
soil community assembly during the process of long-
term ecosystem development.
Gradients of primary development have been used to
understand fundamental ecological processes associated
with ecosystem change. Conceptually, the aboveground
plant community can be viewed as the engine of pri-
mary ecosystem development. As the process of vegeta-
tive succession proceeds, it is often constrained by the
availability of nitrogen. At the same time, root growth
helps to liberate available forms of many key nutrients,
setting the stage for the accumulation of soil organic
matter during early progressive stages of ecosystem
development. This early transformation of the ecosys-
tem is fundamental and typically driven by early mutu-
alistic relationships between plants (Myrica, Alnus spp.)
and symbiotic bacteria (e.g. Rhizobia, Frankia spp.) that
can fix nitrogen (Menge & Hedin 2009; Chaia et al.
2010; Walker et al. 2010). The progressive stage of eco-
system biomass accrual is eventually, in the absence of
disturbance, followed by a retrogressive stage of bio-
mass decline. This stage occurs as a result of long-term
losses and re-allocation of key nutrients, especially
phosphorus, into biologically resistant forms, which
ultimately limits ecosystem productivity (Peltzer et al.
2010). As such, the developing soil and vegetative
gradient are defined by an interactive set of dynamic
biological and chemical feedbacks that are fundamental
to the process of succession and ecosystem develop-
ment. The extent to which soil bacterial communities
are patterned by these changes and feedbacks during
long-term ecosystem development is not known.
Plants clearly impact the composition and structure
of belowground microbial taxa (Kourtev et al. 2002,
2003; Bonanomi et al. 2005; Singh et al. 2007), and
microorganisms, in turn, impact the occurrence and sur-
vival of plants (Kardol et al. 2006). Relatedly, microbial
communities have been studied during ecosystem
development (Jumpponen et al. 2002; Tscherko et al.
2004; Nicol et al. 2005). However, many unanswered
questions remain about the broader extent of direct and
indirect community-level linkages between plants and
microbes and how they fit into the interactive model of
ecosystem development and primary ecological succes-
sion (Wardle et al. 2004; Peltzer et al. 2010).
The 120 000-year-old Franz Josef soil chronosequence
was sampled to test whether bacterial communities show
patterns of succession during ecosystem development.
Within this framework, vegetative succession and pedo-
genesis were related to changes in bacterial community
structure and diversity. It was hypothesized that change
in bacterial community structure would closely follow
the developing gradient of pedogenesis and vegetative
succession during ecosystem development.
Materials and methods
Site description
A series of schist greywacke sediments formed by the
outwash of retreating glaciers have formed a 120 000-year
chronosequence of developing soils across the western
South Island of New Zealand (Almond et al. 2001). The
yearly annual average temperature is 10.8 ?C, and annual
rainfall totals 3500?6500 mm. The 47 dominant woody
plant species changed appreciably along the chronose-
quence (Richardson et al. 2004). The dominant plant
species and their occurrence along the chronosequence
collectively represent ~80% of the woody plant cover
(Table S1, Supporting information). Bray?Curtis ordina-
tion of these data based on canopy cover (%) is shown in
Fig. 1a. Vegetative succession was dominated by ever-
green angiosperms during the early stages, while conifers
become increasingly common during the latter stages, con-
tributing ~60% of the vegetative plant canopy cover on the
two oldest sites. Plant biomass peaks at c. 5000 years,
highlighting the unimodal progressive and retrogressive
stages of plant succession (Richardson et al. 2004).
Soil sampling
Five 5-m radius replicate plots along a 50-m transect
were setup for each of 9 soil ages (60, 130, 280, 530,
1000, 5000, 12 000, 60 000 and 120 000 years). This
design allowed for the collection of 5 independent repli-
cates (n = 5) from nine plots for a total of 45 soil sam-
ples. Within each replicate, 5-m radius plot, a set of five
cores was collected using a 6.5-cm-diameter corer from
the centre of the plot and from 2 m in each cardinal
direction from the plot centre and pooled. These indi-
vidually pooled soil samples from each replicated plot
were collected in plastic bags and stored on ice before
transportation to the laboratory (Allison et al. 2007).
Soils were then sieved through a 4-mm mesh and
stored frozen (
20 ?C) before DNA-based analysis.
DNA extraction and pyrosequencing of bacterial
16S rRNA genes
Total community DNA was extracted from 0.5 g of soil
using ZR Soil Microbe DNA kit (Zymo research, Orange,
CA, USA) with minor modifications in the manufac-
turer?s protocol as described in Garcia et al. (2011) and
? 2013 John Wiley & Sons Ltd
3416 K. JANGID ET AL.
stored at 
80 ?C. Overall, PCR amplification of the bac-
terial 16S rRNA V3 region, purification and processing
for pyrosequencing was carried out using barcoded
primers and conditions as described by Garcia et al.
(2011). Briefly, each 25 lL PCR contained 1.25 lL (20?
50 ng) of DNA, 12.5 pmol of each primer and 22.5 lL of
Platinum
 PCR SuperMix High Fidelity (Invitrogen).
Samples were initially denatured at 95 ?C for 3 min,
then amplified by using 20 cycles of 94 ?C for 30 s,
annealing at 50 ?C for 30 s and extension at 72 ?C for
1 min. Samples that did not amplify were further puri-
fied to remove PCR inhibitors using OneStepTM PCR
Inhibitor Removal kit (Zymo Research) and the Power-
Clean
 DNA Clean-Up kit (MoBio Laboratories, Inc.).
For a few samples with very low amplification, 25 cycles
were used. Our analysis of such variable cycle samples
revealed that doing this did not affect the estimated
diversity as long as the total number of sequences used
for estimating the diversity was equal. Following gel
quantification of amplicons, products from the replicates
of each developmental age were pooled in equimolar
concentrations and gel was eluted using ZymocleanTM
Gel DNA Recovery kit (Zymo Research). The eluted
amplicons were quantified on the Experion
 System
(Bio-Rad) and pooled in equimolar concentrations to
form a single composite sample for pyrosequencing. This
amplicon pool was further purified using the Agencourt
AMPure XP system (Beckman Coulter Genomics) and
submitted to the Environmental Genomics Core Facility
at the University of South Carolina for pyrosequencing
with Roche
 GS FLX sequencing (Branford, CT, USA),
yielding 76 555 reads (260-bp average length).
Processing of 16S rRNA gene data
A standardized two-step pipeline was established that
used a combination of QIIME version 1.1.0 (Caporaso
et al. 2010) and MOTHUR version 1.11.0 (Schloss et al.
2009). First, QIIME was used to quality trim the 16S
rRNA gene sequences to >200-bp length, sort them into
individual libraries based on the 8-nt barcodes and
followed by denoising (Reeder & Knight 2010). The de-
noised data were then imported into MOTHUR for fur-
ther processing. In MOTHUR, sequences were aligned
against the SILVA reference database, filtered, preclu-
stered and checked for chimeras. Chimera-Slayer analy-
sis detected 730 potentially chimeric sequences, of which
316 and 132 sequences, respectively, showed >90% and
100% bootstrap support. According to the programs
instructions, some of these 132 sequences were manually
tested against the entire NCBI database, and none were
confirmed to be chimeric as they showed close homol-
ogy with the same genera for the 3? and 5? regions.
Hence, it was concluded that the majority of these
potentially chimeric sequences were false positives, and
the entire data set was used to calculate the distance
matrix. Finally, operational taxonomic units (OTUs)
were formed using the average neighbour method at an
evolutionary distance (D) = 0.03, followed by classifica-
tion of representative sequences from OTUs using the
SILVA reference taxonomy.
The experimental strategy used to sample bacterial
communities across five replicates showed consistently
low variability (see Fig. 1b) for each plot except in one
case. A replicate from the youngest 60-years soil con-
tained low amounts of DNA that resulted in reduced
amplification of the 16S rRNA genes. The replicate was
removed from subsequent analyses because of low
quantity and quality of DNA.
A
xi
s 
1 
(5
2%
)
0.0
0.2
0.4
0.6
0.8
1.0
Years of Ecosystem development
102 103 104 105
A
xi
s 
1 
(5
8%
)
0.0
0.2
0.4
0.6
0.8
1.0
(a) Plant community
(b) Bacterial community
de
c
a
b
c
d cd
d
b
d
a
b
c
d
e
e e e
Fig. 1 Bray?Curtis ordination of the age-related (60?
120 000 years) change in the 12 dominant (Table S1, Suppor-
timg information) plant taxa based on per cent cover (a) and
the change in the bacterial community based on relative abun-
dance of the 250 most abundant OTUs (b). Percentages on the
y-axis denote an assessment of the variance explained by
the multivariate data reduction. The OTUs were formed using
the average neighbour algorithm in MOTHUR at a distance of
0.03. Each symbol represents the average of five age-related
replicate samples. Significant differences based on multire-
sponse permutation procedure (MRPP) are noted with different
lower case letters (P < 0.01).
? 2013 John Wiley & Sons Ltd
SOIL BACTERIAL COMMUNITIES AND ECOSYSTEM DEVELOPMENT 3417
Statistical and sequence analyses
Bray?Curtis ordination (using Sorenson distance) of the
250 most abundant OTUs (D < 0.03) and the 47 woody
plants was performed using the PC-ORD software ver-
sion 4 (MjM Software, Gleneden Beach, OR, USA) as
advised by McCune & Grace (2002). Data were trans-
formed by treatment using the ?general relativization?
function to remove the potentially strong influence that
absolute abundance can have on community data. The
multiresponse permutation procedure (MRPP), a non-
parametric test, was used to assess differences in bacte-
rial community structure between soil ages. Mantel
tests were conducted using PC-ORD to determine
whether correlations existed between community, vege-
tative and soil descriptive data (Table S2, Supporting
information). Multivariate statistics (Mantel, MRPP)
were considered significantly different using an
a < 0.01. For pairs of samples, coefficients of similarity
(Sc) were calculated for both plant and bacterial com-
munities, using the method of Whittaker (1972):
Sc = Ss/(Sa+Sb
Ss), where Ss is the number of taxa
shared between samples, and Sa and Sb are the number
of taxa in the first and second sample, respectively.
Results
Description of the 16S rRNA data
The 16S rRNA gene sequence possessed an 260-bp
average length and was submitted to the NCBI
Sequence Read Archive according to MIMS standards
(SRP006445.2). These formed 4775 OTUs at D = 0.03
(Table 1). Each soil age was represented by between
7155 and 11 248 sequences forming 488 to 1420 OTUs
per soil age. The most abundant OTUs were repre-
sented by 6167 sequences, accounting for ~8% of the
entire sequence data set. The top 20 and 250 OTUs rep-
resented 48% and ~83% of the entire sequence data set,
respectively.
Bacterial community diversity along the ecosystem
development gradient
All of the indices for community diversity (Simpson?s
index, Shannon index and Chao1) declined along the
sequence, showing statistically significant (Mantel) rela-
tionships with age (Table 1). Variation in sample size
can affect the calculation of alpha-diversity indices;
however, the effect on b-diversity would be minimal if
a random subsample of equal size was taken. To
assess the effect of size differences among the libraries
on the calculated beta-diversity, the indices (Simpson?s
1/D, Chao1) were also calculated from random subs-
amples of 100, 200 and 300 sequences from each of the
replicate soil samples. These indices showed the same
trends as those calculated with the complete libraries,
indicating that the variation in sample size did not
bias the results. Thus, all quality sequences were
included in the subsequent analyses to maximize sam-
ple coverage. Moreover, the decline in diversity across
the chronosequence was also supported by the rarefac-
tion curves, which are independent of sample size
(Fig. 2).
Table 1 Diversity indices for the 16S rRNA sequences according to site age
Diversity
index? 60 years 130 years 280 years 530 years 1000 years 5000 years 12 000 years 60 000 years 120 000 years Reg**
N? 7155 8579 7779 7961 11 284 8311 8826 7480 9180
S? 1377 1420 978 953 1035 625 764 488 668 0.81*
Goods
coverage
0.90 0.91 0.93 0.94 0.95 0.96 0.96 0.96 0.96 0.80*
Richness
(Ace)
3364 3871 2614 2502 2866 1483 1821 1431 2320 0.62*
Shannon
(H)
5.99 5.72 5.20 4.99 4.77 4.42 4.46 4.09 4.27 0.84*
1/D? 130 83 61 38 38 30 28 26 29 0.63*
Chao1 2508 2686 1851 1709 1998 1090 1411 1031 1414 0.72*
?Calculations based on the operational taxonomic units (OTUs) determined at an evolutionary distance of 0.03.
?Number of sequences collected.
?Number of OTUs.
?Simpson?s reciprocal index.
**Regression between diversity index and ecosystem age using a log-linear model. Significant results are noted by an asterisk (*)
(P < 0.01).
? 2013 John Wiley & Sons Ltd
3418 K. JANGID ET AL.
Bacterial community structure and its association with
soil, vegetation and ecosystem properties
Bray?Curtis ordination was used to provide a metric of
bacterial community relatedness and explained ~58% of
the variability in the original data set using one dimension
(Fig. 1b; McCune & Grace 2002). The 250 most abundant
bacterial OTUs changed considerably across the chronose-
quence during the early stages (Fig. 1b; <1000 years) and
were significantly correlated (Mantel; P < 0.001) with
changing levels of phosphorus and pH throughout ecosys-
tem development. The bacterial communities shared only
40% of their dominant members in the youngest (60 years)
compared with older soils (>1000 years), as calculated by
the method of Whittaker (1972). Bacterial community
structure changed much less during the latter stage of eco-
system development. Thus, a multiresponse permutation
procedure identified significant differences in community
structure only during early but not late ecosystem devel-
opment (Fig. 1b). The fit of a log-linear model indicated
the presence of two main stages of ecosystem develop-
ment defined by a transition c. 530?1000 years.
While there was a significant correlation (Mantel test;
r = 0.58; P < 0.001) between bacterial and plant community
change, a closer inspection of the data indicated that the cor-
relation was strongest during early ecosystem development
(Fig. 1a). Bacterial community structure varied but remained
relatively unchanged during late ecosystem development.
Plant community change also slowed during latter ecosys-
tem development. The dominant members in the woody
plant community in the youngest site did not overlap with
those from the 530-years sites, and the plant communities
continued to change with ecosystem development. How-
ever, Weinmannia racemosa and Dacrydium cupressinum were
dominant and found consistently throughout the latter
stages of the chronosequence (>1000 years), contributing to
the observed similarities in plant community structure based
on Bray?Curtis ordination.
In terms of the phylogenetic composition, rRNA genes
related to Actinomycetes, Alphaproteobacteria, Acido-
bacteria, Planctomyces and Betaproteobacteria accounted
for ~82% of the sequences, representing 36%, 25%, 11%,
5% and 5% of the total sequences, respectively. The rela-
tive abundance of the three largest taxa as represented by
rRNA genes (Actinomycetes, Alphaproteobacteria and
Acidobacteria) was fairly constant across the gradient
(Fig. 3). Frankia, a genus of Actinomycetes that is capable
of nitrogen fixation, was highly abundant during the
earliest stages of ecosystem development (c. 60 years),
correlating with the high abundance of its putative plant
host, Coriaria (Table S1, Supporting information). The 16S
rRNA genes most closely associated with Bacteroidetes,
Firmicutes (Bacilli) and other groups such as Verrucomi-
crobia (data not shown) each accounted, on average, for
 2% of the sequences. However, these least dominant
phyla were typically prone to change across the ecosys-
tem gradient. Betaproteobacteria- and Bacteroidetes-
related rRNA genes showed significant declines during
soil and ecosystem development, almost disappearing
completely in the oldest soils. Bacilli-related rRNA genes
were abundant early but much less abundant during the
latter stages of pedogenesis.
Discussion
Patterns of soil bacterial community change during
primary ecosystem succession
Bacterial community dynamics during ecosystem devel-
opment near retreating glaciers have previously been
# of sequences sampled
0 2000 4000 6000 8000 10 000
# 
O
TU
0
200
400
600
800
1000
1200
1400
1600
60y 130y
280y
530y 1000y
5Ky
120Ky
60Ky
12Ky
Fig. 2 Rarefaction curves of the 16S
rRNA gene libraries. The OTUs were
formed using the average neighbour
algorithm in MOTHUR at a distance of
0.03. K = 1000.
? 2013 John Wiley & Sons Ltd
SOIL BACTERIAL COMMUNITIES AND ECOSYSTEM DEVELOPMENT 3419
studied during the earliest decades of pedogenesis
(Nemergut et al. 2007; Schutte et al. 2009). These studies
have reported rapid changes in bacterial communities
that are variable but sometimes correlated with soil
properties (Wu et al. 2012; Zumsteg et al. 2012). The
long-term nature of the Franz Josef chronosequence
greatly extends the temporal extent of change that can
be studied to understand bacterial community linkages
to the process of succession during ecosystem develop-
ment (Wardle et al. 2004; Peltzer et al. 2010). Even with
large differences in chronosequence age, bacterial com-
munity change during development at Franz Josef and
the younger sequences together indicate that commu-
nity variation is greatest during the earliest years and
then slows with ecosystem development. The very high
bacterial community turnover during very early devel-
opment (less than a decade) tends to involve fewer
discrete patterns of structural change (Nemergut et al.
2007; Wu et al. 2012; Zumsteg et al. 2012), which might
indicate that young stages are more prone to the natural
stochasticity associated with colonization. At Franz
Josef, in contrast, there was a clear pattern of change
during the early stages of development (up to c.
1000 years). Bacterial communities thus become more
predictable, perhaps a reflection of the stabilizing effect
of belowground habitat development during primary
ecosystem development.
Bacterial community change in the dune sands of
northern Michigan (Wilderness Park) and southern
Georgia, USA (Altamaha), showed some similar pat-
terns to Franz Josef (Tarlera et al. 2008; Williams et al.
2013); however, the periods of correlated change
occurred over c. 500 years in Michigan (Wilderness
Park) and thousands of years in Georgia, suggesting
that ecosystems might follow developmental timing that
is specific to the conditions of a chronosequence, such
Gamma-proteobacteria
0
2
4
6
Actinomycetes
%
 R
el
at
iv
e 
ab
un
da
nc
e
0
10
20
30
40
50
Acidobacteria
0
5
10
15
20
Planctomycetes
0
2
4
6
8
10
Beta-proteobacteria
%
 R
el
at
iv
e 
ab
un
da
nc
e
0
2
4
6
8
10
12
Alpha-proteobacteria
0
10
20
30
40
Bacteroidetes
0
2
4
6
8
%
 R
el
at
iv
e 
ab
un
da
nc
e
Firmicutes
0
2
4
6
Bacilli
0
2
4
r
2 = 0.41
P = 0.08
 = 0.71
P = 0.01
 = 0.04
P = 0.27
 = 0.27
P = 0.19
 = 0.19
P = 0.27
 = 0.32
P = 0.14
 = 0.59
P = 0.02
 = 0.41
P = 0.09
 = 0.10
P = 0.44
Years of development
10
1 2 3
10
4
10
5
10
1 2 3
10
4
10
5
10
1 2
10 10 10 10
10 10
3
10
4
10
5
r
2
r
2
r
2
r
2
r
2
r
2 r2 r2
Fig. 3 Relationship between percentage relative abundance of nine individual bacterial phyla across the chronosequence during eco-
system development (60?120 000 years). Each point in the graph is the average (n = 5) of the percentage abundance of each phyla at
each stage of development. Regression coefficient and P-value for each phylum are shown. Relative abundance of bacterial phyla
across the FJ chronosequence.
? 2013 John Wiley & Sons Ltd
3420 K. JANGID ET AL.
as parent material or climate (Griffiths et al. 2011; De
Vries et al. 2012). Despite this, bacterial community
dynamics showed a number of consistent patterns at
higher taxonomic levels (phyla, class) across biomes.
For example, Betaproteobacteria and Bacteroidetes
showed similar patterns of decline as the ecosystems
aged. These changes are patterned after the process of
ecosystem development and primary succession,
applied up to now, mainly to above-ground plant com-
munities (Wardle et al. 2004; Bardgett & Wardle 2010).
Nutrients and soil properties as drivers of bacterial
community change during ecosystem development
Soil development along the Franz Josef chronosequence
illustrates patterns that are typical of a broad range of
developmental ecosystems (Stevens 1968; Crews et al.
1995; Vitousek & Farrington 1997; Lichter 1998; Turner
et al. 2007). Nitrogen levels show a very typical increase
and plateau early during primary succession, similar to
carbon (Allison et al. 2007; Menge & Hedin 2009; Menge
et al. 2012). Although it may limit colonization and eco-
system productivity early, the quick accumulation of N
in soil at Franz Josef during the first c. 530 years helps
to reduce or eliminate N limitation. Before this, N limi-
tation probably drives the colonization process, select-
ing for dominance by specific types of bacteria and
plants. Indeed, early ecosystem development at Franz
Josef was described by a classic N-limited response
whereby co-colonization and dominance of a plant?
microbial mutualistic association was likely responsible
for importing large amounts of N and setting the stage
for a productive developing ecosystem. At Franz Josef,
the symbionts are the plant Coriaria (Menge & Hedin
2009) and a bacterium closely related to the N-fixing
symbiont Frankia. Nitrogen is clearly important to eco-
system productivity during very early ecosystem devel-
opment, consistent with bacterial community changes
that appear synchronized to nitrogen accumulation
during this same period.
Phosphorus showed patterns of decline that are typi-
cal of long-term ecosystem development (Crews et al.
1995; Allison et al. 2007; Turner et al. 2012). At Franz
Josef, phosphorus was highly correlated with change in
the structure and diversity of bacterial communities,
possibly indicating a link between phosphorus and bac-
terial community change (Beauregard et al. 2010; DeFor-
est & Scott 2010; Wakelin et al. 2012). However, this
same relationship with phosphorus was not observed in
the much younger c. 4000-year-old Wilderness Park
ecosystem (Williams et al. 2013).
A slightly more complex model incorporates the eco-
system paradigm of N and P as key limiting nutrients
for biological activity during primary ecosystem succes-
sion. Ecosystem development can be viewed in two
fairly distinct stages described by ecosystem progres-
sion and retrogression, based largely on transitions
from N to P limitation with age (Wardle et al. 2004;
Peltzer et al. 2010). There are a number of other impor-
tant transitions, such as vegetative change, that occur
concurrently. The tipping point whereby the ecosystem
shifts from progression (nutrient sufficient) to retrogres-
sion (nutrient insufficient) at Franz Josef has been iden-
tified somewhere c. 5000?12 000 year. (Richardson et al.
2004). If bacterial communities follow this similar uni-
modal model, bacterial and other belowground micro-
bial communities would reach a state of relative ?feast?
during midecosystem development, when P levels are
still relatively high and N levels are accumulating that
would be preceded and followed by periods of ?fam-
ine?. If the bacterial community was responding to the
increasingly favourable conditions early, then it would
be logical to expect that the bacterial community would
similarly decline or change again during the retrogres-
sive nutrient decline stages of late ecosystem develop-
ment. However, the bacterial community did not show
a similar pattern linked to progression and retrogres-
sion. Belowground bacterial community structure may
be indirectly related to the effects of progression and
retrogression through plant community dynamics,
which have been previously linked to nutrient limita-
tion and stress during the development of ecosystems
(Richardson et al. 2004).
Pedogenesis and bacterial community change during
ecosystem development
Over the past several years, pH and bacterial commu-
nity change have been shown to be well correlated
across broad geographic landscapes (Lauber et al. 2009;
Rousk et al. 2010). Although pH correlates well with
bacterial community change at Franz Josef and Wilder-
ness Park (Williams et al. 2013), it is not well correlated
with similar bacterial community changes (e.g. declin-
ing bacterial richness and diversity) at the uniformly
acidic (pH < 4.5) Altamaha sequence (Tarlera et al.
2008). It is thus not clear which processes can simulta-
neously account for the patterns of community change
between these three developmental ecosystems. Pedo-
genesis is described by complex but predictable chemi-
cal and physical changes that correlate with these
numerous dynamics during ecosystem development. A
pedogenic model describing bacterial communities has
the advantage of being a well-described mechanism of
change during ecosystem succession (Walker & Syers
1976; Vitousek & Farrington 1997). The pedogenic
model is further supported by other studies that have
observed covariance between bacterial communities and
? 2013 John Wiley & Sons Ltd
SOIL BACTERIAL COMMUNITIES AND ECOSYSTEM DEVELOPMENT 3421
pedogenesis-related changes such as soil type, organic
C content and texture (B
a
ath & Anderson 2003; Girvan
et al. 2003; H?ogberg et al. 2007).
Pedogensis may adequately describe soil bacterial
community dynamics during the early stages of ecosys-
tem development. However, pedogenesis continues
during the advanced stages of ecosystem development,
while bacterial community structure remains relatively
unmodified. Pedogenesis may reach a critical develop-
mental point whereby changes in soil properties have
less of an effect on bacterial communities. The changes
in the bacterial communities themselves may also be
resilient to further pedogenic change following c.
1000 year, although it is not clear why this would
occur.
Fungal to bacterial ratios using phospholipid fatty
acids declined two-fold during ageing across the Franz
Josef sequence (Allison et al. 2007). Declining fungal to
bacterial ratios have been linked to declining (five-fold)
bacterial and increasing (six-fold) fungal community
activity (Rousk et al. 2010), suggesting that declining
ratios provide an indication of the extent that these
microbes contribute to community processes. The estab-
lished and unvarying structure of the bacterial commu-
nity during latter ecosystem development was thus
consistent with the declining belowground role that
bacterial communities play relative to fungi during
succession. Very low bacterial activity could slow the
turnover of bacterial communities and, assuming high
survival rates, support invariant and structurally stable
communities that are resilient to immigration and soil-
environmental change.
The successional plant?microbial feedback hypothesis
and bacterial community change during ecosystem
development
The dynamics of the bacterial community were partly
related to progression and retrogression and to the
shifting contents of nutrients such as nitrogen and
phosphorus during ecosystem development. The pro-
cess of pedogenesis was also shown to covary with bac-
terial community change, particularly during the early
rapid changes of ecosystem development. Although
these mechanisms can be used to explain bacterial
community dynamics and show merit for incorporating
communities into various ecosystem development para-
digms, testing of these hypotheses requires further
investigation.
A hypothesis described by Kardol et al. (2006)
explains that plant communities interact differently
with belowground biota depending on the stage of
plant succession and ecosystem development. They and
others have shown evidence that negative plant?micro-
bial feedbacks encourage the replacement of plant spe-
cies during early succession (Kulmatiski et al. 2008).
Pathogens, in particular, were hypothesized to accrue in
response to early successional species, and this process
facilitates plant species replacement (Van der Putten
et al. 1993, 2001, 2009). This model of rapid vegetative
turnover is consistent with the patterns of maximum
turnover of soil bacterial communities during the first c.
1000 year. Similarly, positive plant?bacterial community
feedbacks would be consistent with a stabilizing effect
on plant and bacterial communities during latter eco-
system development. Although this explanation has not
been discussed explicitly in the context of soil bacterial
communities (Tarlera et al. 2008; Michel & Williams
2011), the relative stability of bacterial community struc-
ture during the advanced stages of ecosystem develop-
ment mirrors the slower turnover of plant communities.
The widespread application of the successional plant?
microbial feedback hypothesis needs further verifica-
tion.
The plant?microbial successional feedback model is
consistent with the correlation between vegetative and
bacterial community change during ecosystem develop-
ment. However, this does not have to be the result of
direct species?species interactions much like the Coria-
ria?Frankia mutualism observed early during ecosystem
development. Rather, it could result from broad
changes in plant community functional types (Bardgett
& Wardle 2010) that support the growth of specific bac-
terial communities. It is also worth noting that plant
and bacterial communities can indirectly influence one
another through a number of mechanisms, which influ-
ence soil weathering and pedogenesis (Leyval & Berth-
elin 1990; Banfield et al. 1999; Bonanomi et al. 2005;
Lambers et al. 2009; Knelman et al. 2012). Bacterial
community and vegetative succession show patterns
reminiscent of one another and thus deserve further
study to understand the potential feedbacks between
them during ecosystem development.
Conclusions
The research, using deep sequencing technology provides
the first observations for soil bacterial community change
associated with the process of long-term ecosystem devel-
opment. The results indicate that belowground bacterial
communities are linked to the processes of primary eco-
system succession. The ?pedogenesis?, ?progression?retro-
gression? and ?plant?microbial successional feedback?
hypotheses provide interrelated mechanisms that explain
and incorporate bacterial community change into the par-
adigm of ecosystem development and succession. The
consistency in bacterial community structure observed
during the advanced stages of ecosystem development
? 2013 John Wiley & Sons Ltd
3422 K. JANGID ET AL.
provides a glimpse into the potential stability of bacterial
communities over long time periods. Further research is
needed on how to best integrate soil bacterial community
dynamics and stability into models of ecosystem develop-
ment and succession.
References
Allison VJ, Condron LM, Peltzer DA, Richardson SJ, Turner BL
(2007) Changes in enzyme activities and soil microbial com-
munity composition along carbon and nutrient gradients at
the Franz Josef chronosequence, New Zealand. Soil Biology &
Biochemistry, 39, 1770?1781.
Almond PC, Moar NT, Lian OB (2001) Reinterpretation of the
glacial chronology of South Westland, New Zealand. New
Zealand Journal of Geology and Geophysics, 44, 1?15.
B
a
ath E, Anderson TH (2003) Comparison of soil fungal/bacte-
rial ratios in a pH gradient using physiological and PLFA-
based techniques. Soil Biology & Biochemistry, 35, 955?963.
Banfield JF, Barker WW, Welch SA, Taunton A (1999) Biologi-
cal impact on mineral dissolution: application of the lichen
model to understanding mineral weathering in the rhizo-
sphere. Proceedings of the National Academy Sciences of the
United States of America, 96, 3404?3411.
Bardgett RD, Wardle DA (2010) Aboveground?Belowground
Linkages. Biotic Interactions, Ecosystem Processes, and Global
Change. Oxford University Press, New York.
Beauregard MS, Atul-Nayyar CH, St-Arnaud M (2010) Long-
term phosphorus fertilization impacts soil fungal and
bacterial diversity but not AM fungal community in alfalfa.
Microbial Ecology, 59, 379?389.
Bonanomi G, Giannino F, Mazzoleni S (2005) Negative plant?
soil feedback and species coexistence. Oikos, 111, 311?321.
Caporaso JG, Kuczynski J, Stombaugh J et al. (2010) QIIME
allows analysis of high-throughput community sequencing
data. Nature Methods, 7, 335?336.
Chaia E, Wall L, Huss-Danell K (2010) Life in soil by the
actinorhizal root nodule endophyte, A review. Symbiosis, 51,
201?226.
Crews TE, Kitayama K, Fownes JH et al. (1995) Changes in soil
phosphorus fractions and ecosystem dynamics across a long
chronosequence in Hawaii. Ecology, 76, 1407?1424.
De Vries FT, Manning P, Tallowin JRB et al. (2012) Abi-
otic drivers and plant traits explain landscape-scale
patterns in soil microbial communities. Ecology Letters,
15, 1230?1239.
DeForest JL, Scott LG (2010) Available organic soil phosphorus
has an important influence on microbial community compo-
sition. Soil Science Society of America Journal, 74, 2059?2066.
Garcia SL, Jangid K, Whitman WB, Das KC (2011) Transition
of microbial communities during the adaptation to anaerobic
digestion of carrot waste. Bioresource Technology, 102, 7149?
7256.
Girvan MS, Bullimore J, Pretty JN, Osborn AM, Ball AS (2003)
Soil type is the primary determinant of the composition of
the total and active bacterial communities in arable soils.
Applied and Environmental Microbiology, 69, 1800?1809.
Griffiths RI, Thomson BC, James P, Bell T, Bailey M, Whiteley
AS (2011) The bacterial biogeography of British soils. Envi-
ronmental Microbiology, 13, 1642?1654.
H?ogberg M, H?ogberg P, Myrold D (2007) Is microbial commu-
nity composition in boreal forest soils determined by pH,
C-to-N ratio, the trees, or all three? Oecologia, 150, 590?601.
Jumpponen A, Trappe JM, Cazares E (2002) Occurrence of
ectomycorrhizal fungi on the forefront of retreating Lyman
Glacier (Washington, USA) in relation to time since deglacia-
tion. Mycorrhiza, 12, 43?49.
Kardol P, Bezemer TM, Van Der Putten WH (2006) Temporal
variation in plant?soil feedback controls succession. Ecology
Letters, 9, 1080?1088.
Knelman JE, Legg TM, O?Neill SP et al. (2012) Bacterial com-
munity structure and function change in association with
colonizer plants during early primary succession in a glacier
forefield. Soil Biology & Biochemistry, 46, 172?180.
Kourtev PS, Ehrenfeld JG, Haggblom M (2002) Exotic plant
species alter the microbial community structure and function
in the soil. Ecology, 83, 3152?3166.
Kourtev PS, Ehrenfeld JG, Haggblom M (2003) Experimental
analysis of the effect of exotic and native plant species on
the structure and function of soil microbial communities. Soil
Biology & Biochemistry, 35, 895?905.
Kulmatiski A, Beard KH, Stevens JR, Cobbold SM (2008) Plant-
soil feedbacks: a meta-analytical review. Ecology Letters, 11,
980?992.
Lambers H, Mougel C, Jaillard B, Hinsinger P (2009) Plant-
microbe-soil interactions in the rhizosphere: an evolutionary
perspective. Plant and Soil, 321, 83?115.
Lauber CL, Hamady M, Knight R, Fierer N (2009) Pyrose-
quencing-based assessment of soil pH as a predictor of soil
bacterial community structure at the continental scale.
Applied and Environment Microbiology, 75, 5111?5120.
Leyval C, Berthelin J (1990) Weathering of a mica by roots and
rhizospheric microorganisms of pine. Soil Science Society of
America Journal, 55, 1009?1016.
Lichter J (1998) Rates of weathering and chemical depletion in
soils across a chronosequence of Lake Michigan. Geoderma,
85, 255?282.
McCune B, Grace JB. (2002) Analysis of Ecological Communities.
MjM Software Design, Gleneden Beach, Oregon.
McIntosh RP, Odum EP (1969) Ecological succession. Science,
166, 403?404.
Menge DNL, Hedin LO (2009) Nitrogen fixation in different
biogeochemical niches along as 120,000-year chronosequence
in New Zealand. Ecology, 90, 2190?2201.
Menge DNL, Hedin LO, Pacala SW (2012) Nitrogen and phos-
phorus limitation over long-term ecosystem development in
terrestrial ecosystems. PLoS ONE, 7, e42045.
Michel HM, Williams MA (2011) Soil habitat and horizon prop-
erties impact bacterial diversity and composition. Soil Science
Society of America Journal, 75, 1440?1448.
Nemergut DR, Anderson SP, Cleveland CC et al. (2007) Micro-
bial community succession in an unvegetated recently degla-
ciated soil. Microbial Ecology, 53, 110?122.
Nicol GW, Tscherko D, Embley TM, Prosser JI (2005) Primary
succession of soil Crenarchaeota across a receding glacier
foreland. Environmental Microbiology, 7, 337?347.
Peltzer DA, Wardle DA, Allison VJ et al. (2010) Understanding
ecosystem retrogression. Ecological Monographs, 80, 509?529.
Reeder J, Knight R (2010) Rapid denoising of pyrosequencing
amplicon data: exploiting the rank-abundance distribution.
Nature Methods, 7, 668?669.
? 2013 John Wiley & Sons Ltd
SOIL BACTERIAL COMMUNITIES AND ECOSYSTEM DEVELOPMENT 3423
Richardson S, Peltzer D, Allen R, McGlone M, Parfitt R (2004)
Rapid development of phosphorus limitation in temperate
rainforest along the Franz Josef soil chronosequence. Oecolo-
gia, 139, 267?276.
Rousk J, Baath E, Brookes PC et al. (2010) Soil bacterial and
fungal communities across a pH gradient in an arable soil.
The International Society of Microbial Ecology Journal, 4, 1340?
1351.
Schloss PD, Westcott SL, Ryabin T et al. (2009) Introducing
MOTHUR: open-source, platform-independent, community-
supported software for describing and comparing microbial
communities. Applied and Environmental Microbiology, 75,
7537?7541.
Schutte UME, Abdo Z, Bent SJ et al. (2009) Bacterial succession
in a glacier foreland of the high arctic. The International
Society of Microbial Ecology Journal, 3, 1258?1268.
Singh BK, Munro S, Potts JM, Millard P (2007) Influence of grass
species and soil type on rhizosphere microbial community
structure in grassland soils. Applied Soil Ecology, 36, 147?155.
Stevens PR (1968) A chronosequence of soils near the Franz josef
Glacier. Thesis, Lincoln College, University of Canterbury,
New Zealand.
Tarlera S, Jangid K, Ivester AH, Whitman WB, Williams MA
(2008) Microbial community succession and bacterial diver-
sity in soils during 77,000 years of ecosystem development.
FEMS Microbiology Ecology, 64, 129?140.
Tscherko D, Hammesfahr U, Marx MC, Kandeler E (2004)
Shifts in rhizosphere microbial communities and enzyme
activity of Poa alpina across an alpine chronosequence. Soil
Biology & Biochemistry, 36, 1685?1698.
Turner BL, Condron LM, Richardson SJ, Peltzer DA, Allison VJ
(2007) Soil organic phosphorus transformations during pedo-
genesis. Ecosystems, 10, 1166?1181.
Turner BL, Condron LM, Wells A, Anderson KM (2012) Soil
nutrient dynamics during podzol development under low-
land temperate rainforest in New Zealand. Catena, 97, 50?62.
van der Putten WH, Bardgett RD, De Ruiter PC et al. (2009)
Empirical and theoretical challenges in aboveground-below-
ground ecology. Oecologia, 161, 1?14.
van der Putten WH, Van Dijk C, Peters BAM (1993) Plant spe-
cific soil-borne diseases contribute to succession in foredune
communities. Nature, 362, 53?56.
Van der Putten WH, Vet LEM, Harvey JA, W?ackers FL (2001)
Linking above- and belowground multitrophic interactions
of plants, herbivores, pathogens, and their antagonists.
Trends in Ecology and Evolution, 16, 547?554.
Vitousek PM, Farrington H (1997) Nutrient limitation and soil
development: experimental test of a biogeochemical theory.
Biogeochemistry, 37, 63?75.
Wakelin S, Mander C, Gerard E et al. (2012) Response of soil
microbial communities to contrasted histories of phosphorus
fertilization in pastures. Applied Soil Ecology, 61, 40?48.
Walker TW, Syers JK (1976) The fate of phosphorus during
pedogenesis. Geoderma, 15, 1?19.
Walker LR, Wardle DA, Bardgett RD, Clarkson BD (2010) The
use of chronosequences in studies of ecological succession
and soil development. Journal of Ecology, 98, 725?736.
Wardle DA, Walker LR, Bardgett RD (2004) Ecosystem proper-
ties and forest decline in contrasting long-term chronose-
quences. Science, 305, 509?513.
Whittaker RH (1972) Evolution and measurement of species
diversity. Taxon, 21, 3?251.
Williams MA, Jangid K, Shanmugam SG, Whitman WB
(2013) Bacterial communities in soil mimic patterns of veg-
etative succession and ecosystem climax but are resilient
to change between seasons. Soil Biology & Biochemistry, 57,
749?757.
Wu X, Zhang W, Liu G et al. (2012) Bacterial diversity in the
foreland of the Tianshan no. 1 glacier, China. Environmental
Research Letters, 7, 014038.
Zumsteg A, Luster J, Goransson H et al. (2012) Bacterial,
archeal, and fungal succession in the forefield of a receding
glacier. Microbial Ecology, 63, 552?564.
M.W. conceived the research. K.J. performed research.
M.W., K.J. and B.W. wrote the grant and received fund-
ing from NSF. K.J. and M.W. developed figures and
analyzed data. B.T. and L.C. collected samples. M.W.
wrote the manuscript. All authors provided edits and
intellectual expertise to manuscript.
Data accessibility
DNA sequences: NCBI SRA: SRP006445.2. Sample col-
lection metadata, Barcode Information: Associated with
NCBI SRA submission. Final DNA sequence assembly:
uploaded as online supporting information.
Supporting information
Additional supporting information may be found in the online
version of this article.
Table S1 Dominant woody vegetation at each stage of ecosys-
tem development across the Franz Josef chronosequence.
Table S2 Concentrations of Mehlich-3 extractable cations and
descriptive soil variables in the mineral soil (0?10 cm depth)
across the Franz Josef chronosequence.
? 2013 John Wiley & Sons Ltd
3424 K. JANGID ET AL.