Environmental Microbiology (2010)
doi:10.1111/j.1462-2920.2010.02392.x
Ribosomal tag pyrosequencing of DNA and RNA from
benthic coral reef microbiota: community spatial
structure, rare members and nitrogen-cycling guilds
emi_2392
Eric Gaidos,1* Antje Rusch2 and Melissa Ilardo3
Department of Geology and Geophysics, University of
Hawaii, Honolulu, HI, USA.
2
Department of Microbiology, Southern Illinois University,
Carbondale, IL, USA.
3
Princeton University, Princeton, NJ, USA.
1..15
Introduction
1
Summary
Ribosomal tag libraries based on DNA and RNA in
coral reef sediment from Hawaii show the microbial
community to be dominated by the bacterial phyla
Proteobacteria, Firmicutes and Actinobacteria, the
archaeal order Nitrosopumilales and the uncultivated divisions Marine Group III (Euryarchaeota) and
Marine Benthic Group C (Crenarchaeota). Operational taxonomic units (OTUs) number in the high
thousands, and richness varies with site, presence
or absence of porewater sulfide (sediment depth),
and nucleotide pool. Rank abundance curves of
DNA-based libraries, but not RNA-based libraries,
possess a tail of low abundance taxa, supporting the
existence of an inactive ‘rare’ biosphere. While bacterial libraries from two oxic samples differ markedly, those from two anoxic (sulfidic) samples are
similar. The four dominant bacterial OTUs are
members of genera that include pathogens, but
are found in marine environments, and include facultative anaerobes, i.e. dissimilatory nitrate reducers
and denitrifiers. This may explain their abundance
in both oxic and anoxic samples. A numerous
archaeon is closely related to the lithoautotrophic
ammonia oxidizer Nitrosopumilus maritimus. Known
bacterial ammonia oxidizers are essentially absent,
but bacterial nitrite oxidizers are abundant. Although
other studies of this reef found evidence for anaerobic ammonia oxidizers, primer bias rendered that
clade invisible to this study.
Received 16 July, 2010; accepted 1 November, 2010. *For correspondence. E-mail [email protected]; Tel. (+1) 808 956 7897; Fax
(+1) 808 956 5512.
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd
The introduction of molecular techniques to environmental microbiology triggered a sea change in our view of
microbial diversity, evolution and ecology, one previously
based solely on microscopy and cultivation. Salient revelations include the very small fraction of taxa that have
been cultivated, the large number of extant bacterial divisions (now over 100), and the partitioning of prokaryotes
into the domains Archaea and Bacteria (DeLong and
Pace, 2001). Increasingly larger libraries of ribosomal and
metabolic gene sequences continue to reveal more diversity at the molecular level (Venter et al., 2004; Schloss
and Handelsman, 2006; Rusch et al., 2007). Massively
parallel and less biased1 technologies such as pyrosequencing can sequence short (~100 bp) but phylogenetically informative (Liu et al., 2007) fragments of ribosomal
genes on a scale that more fully describes the structure of
microbial communities. This approach has been applied
to a variety of environments (Edwards et al., 2006; Sogin
et al., 2006; Huber et al., 2007; Acosta-Martinez et al.,
2008; Andersson et al., 2008; Huse et al., 2008; Gaidos
et al., 2009; Jones et al., 2009). These studies detected
‘rare’ taxa that escaped inclusion in smaller clone libraries
(Sogin et al., 2006), and may include cultivated organisms
(Pedros-Alio, 2007). This rare microbial biosphere may
act as a dormant ‘seed bank’ with which a community
responds robustly to environmental change or the extinction of taxa (Pedros-Alio, 2007; Jones et al., 2009).
Most sequence-based studies of microbial communities have used libraries constructed by amplifying DNA
extracted from samples. These extracts can include
DNA from active cells, inactive but viable cells, defunct
or ‘dead’ cells, and extracellular DNA from lysed or
degraded cells. The last may be especially prevalent in
sediments and soils where mineral grains can efficiently
bind and retain DNA (Corinaldesi et al., 2005; LevyBooth et al., 2007). As such, DNA pools represent a
complex integration of microbial activity and growth over
1
During pyrosequencing, different template strands are
amplified separately by the polymerase chain reaction (PCR)
and do not compete for primers or polymerase, and in principle this produces less biased product than a single PCR
reaction on a pool of template.
2 E. Gaidos, A. Rusch and M. Ilardo
the recent past. The RNA pool, in contrast, represents
more recent activity as it is governed by the rapid rate of
intracellular RNA turnover and the short lifetime of extracellular RNA compared with DNA (Novitsky, 1986). An
expectation is that sequence libraries constructed from
reverse transcripts of RNA (complementary DNA, cDNA)
should interrogate the active moiety of a microbial community (Frias-Lopez et al., 2008). The representation,
i.e. number of tags, of a taxon in an RNA-based library
relative to that in a DNA-based library is, in principle, a
measure of relative activity. Comparisons of parallel
pyrosequencing tag libraries based on DNA and RNA
can address whether taxa that are abundant are actually
active cells, and whether ‘rare but active’ taxa exist. This
methodology was demonstrated in studies of the bacterial, archaeal and eukaryotic groups in a soil community
(Urich et al., 2008) and bacteria in lakes (Jones and
Lennon, 2010).
We used this approach to study the microbial community in the coarse-grained carbonate sands of Checker
Reef, a coralline patch reef in Kane’ohe Bay, Hawaii
(Fig. S1). Molecular-based surveys have revealed
diverse bacteria and archaea associated with reef corals
and sponges (Rohwer et al., 2002; Fieseler et al., 2004;
Kellogg, 2004; Webster et al., 2004; Wegley et al., 2004;
Bourne and Munn, 2005; Yokouchi et al., 2006; Dinsdale
et al., 2008; Siboni et al., 2008). Free-living microbes
also inhabit the permeable carbonate framework and
coarse-grained sediment of reefs, a unique benthic environment with dilated, heterogeneous and variable redox
gradients (Tribble et al., 1992; Falter and Sansone, 2000;
Sørensen et al., 2007; Rusch et al., 2009), suboxic
zones where concentrations of oxygen and sulfide are
both < 10 mM (Sørensen et al., 2007), and gradients in
porewater nitrate, nitrite and ammonium indicative of
microbial nitrogen cycling (Capone et al., 1992; Haberstroh and Sansone, 1999; Falter and Sansone, 2000;
Stimson and Larned, 2000; Miyajima et al., 2001;
Rasheed et al., 2002; Al-Rousan et al., 2004; Rusch
et al., 2009). This microbial benthos remains comparatively unexplored and has been called ‘the blackest of
the black boxes’ in the reef ecosystem (Hatcher, 1997),
but several studies have illuminated the importance of
this box to organic matter remineralization and recycling
of nutrients (Ducklow, 1990; Ferrier-Pages et al., 2000;
Wild et al., 2005). Efficient recycling of nutrients, especially nitrogen, in reef and lagoon sediments is a leading
explanation for high reef productivity in the low-nutrient
setting of the tropical ocean (Skyring and Chambers,
1976; Lewis, 1977; Valiela, 1984; Tribble et al., 1990;
Capone et al., 1992; Sansone et al., 1993; Rasheed
et al., 2002; Wild et al., 2005).
Earlier studies of sediment from these patch reefs using
DNA- and RNA-based clone libraries and fluorescent
in situ hybridization (FISH) of intact cells revealed a rich
and diverse microbial community whose structure correlates with gradients in porewater chemistry (Sørensen
et al., 2007; Rusch et al., 2009). Estimated richness
(number of unique sequences) was highest in nucleotide
pools from anoxic samples, and RNA-based libraries of
40–60 sequences consisted entirely of singletons (no
duplicates), and did not overlap with other, DNA-based
libraries. From this, a lower limit of ~3000 unique phylotypes was inferred, indicating that Sanger sequencing of
clone libraries is inadequate for scoping the taxonomical
richness of these communities.
As part of the International Census of Marine
Microbes (ICoMM) (Sogin and de Leeuw, 2004) and the
Census of Marine Life, we constructed and analysed 10
bacterial and archaeal tag libraries by pyrosequencing
the V6 hypervariable region of the 16S (small-subunit)
rRNA gene from both sediment DNA and RNA (cDNA)
pools. These pools were extracted from pairs of oxic
samples (from the sediment–water interface) and anoxic
samples (from 50 cm sediment depth) obtained at two
similar sites separated by 5 m. This sampling brackets
the sediment depth range over which our previous work
found a redoxocline and gradients in major nitrogen
species (NO3-, NO2-, NH4+) (Sørensen et al., 2007;
Rusch et al., 2009). Our objectives were to: (i) describe
the communities in terms of the distribution of tags
among the major bacterial and archaeal phyla/divisions,
(ii) determine how taxonomic richness, diversity and
composition vary between DNA and RNA pools, oxic
and anoxic samples, and sites, (iii) investigate whether
there is a ‘rare’ biosphere in the DNA pool and if this is
also reflected in the RNA pool, (iv) compare the
numbers of tags assigned to operationally defined taxa
in RNA-based libraries relative to that in DNA-based
libraries as proxies for activity and abundance, and (v)
identify members of guilds involved in the reef nitrogen
cycle.
Some operational definitions: A ‘tag’ is a single read
with a specific V6 sequence. One V6 sequence may be
represented by more than one tag. A ‘library’ is the total
collection of tags from a pool. A ‘phylotype’ is a unique
ribosomal sequence that represent one bacterial or
archaeal lineage, or a group of closely related strains.
Phylotypes can be assigned to operational taxonomic
units (OTUs) using a sequence distance criterion and
clustering algorithm. ‘Species’, ‘genus’, etc. are classical
taxonomic assignments whose interpretation in microbiology is often controversial (Doolittle and Papke, 2006).
‘Richness’ is the observed or predicted total number of
OTUs in a library or pool, and ‘evenness’ describes the
distribution of tags among those OTUs. ‘Diversity’ in the
context of this work is a combination of richness and
evenness.
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
Pyrosequencing tags of benthic coral reef microbes 3
Table 1. Statistics of V6 pyrosequencing tag libraries.
Library
Site
Porewater
chemistry
Pool
Primer set
Reads
Filtered
reads
Unique
sequences
OTUs
(10%)
1
2
3
4
9
10
11
12
6
7
A
B
A
B
A
B
A
B
A
B
Oxic
Oxic
Anoxic
Anoxic
Oxic
Oxic
Anoxic
Anoxic
Oxic
Anoxic
DNA
DNA
DNA
DNA
RNA
RNA
RNA
RNA
DNA
DNA
Bacterial
Bacterial
Bacterial
Bacterial
Bacterial
Bacterial
Bacterial
Bacterial
Archaeal
Archaeal
11 876
18 367
16 529
14 251
5 688
17 215
18 698
11 609
8 557
11 261
8 960
14 689
13 225
11 102
4 291
12 350
12 653
7 811
7 002
10 587
1518
5830
4101
3277
951
1689
1680
1657
1149
1663
847
4555
2906
2725
564
1037
1082
1005
620
807
Libraries from anoxic samples are shaded and those from RNA pools are italicized.
Results
Library statistics
Assay PCR reactions of both DNA and cDNA from all four
samples (oxic and anoxic from two sites) with the ICoMM
bacterial primers resulted in product of the appropriate
length. No reactions with archaeal primers and cDNA
template gave acceptable product, and no pyrosequencing reactions were attempted with these templates/primer
combinations. Reactions were successful with all bacterial templates, but with the archaeal primers only two (oxic
and anoxic samples from separate sites) were successful.
A total of 114 233 and 19 818 reads were generated with
bacterial and archaeal primers respectively. Of these,
85 081 bacterial (74.5%) and 17 589 archaeal (88.8%)
passed the pipeline filters that removed reads that contained errors, were of mitochondrial, chloroplast or nonribosomal origin, or that could not be confidently assigned
to either domain (Huse et al., 2007; 2010). Acceptable
reads comprised 19 170 unique archaeal or bacterial
sequences. All sequence data are available from
GenBank’s Sequence Read Archive (SRA number
SRP001221). Numbers of reads, filtered reads, unique
sequences and OTUs (10% distance criterion) in each of
the 10 libraries are given in Table 1. The number of reads
varies by a factor of 3.4 between libraries, and the smallest and largest number of unique sequences and OTUs
are found in our smallest and largest libraries respectively.
Rarefaction curves of number of OTUs versus sampling effort do not asymptote, and in none of our bacterial
tag libraries does sampling reach saturation (Fig. S2 in
Supporting information). This obviously also holds for
unique sequences and OTUs defined by a smaller distance criterion. ACE and Chao1 estimates of OTU richness are consistent within pools and are roughly a factor
of 2 greater than the captured richness (Table 2). Estimates of OTU richness vary by a factor of 7–8 between
libraries. Each library represents a different level of
sequencing effort, and the accuracy of both the estimator
and the estimated richness should increase with library
size (Schloss and Handelsman, 2006). To compare
libraries without this effect, we constructed 1000 synthetic libraries from each real library, all the size of the
smallest library (library 9 for bacteria and library 6 for
archaea), by sampling with replacement. We estimated
the ACE richness, and averaged the values (cf. Gilbert
Table 2. Single library OTU (10% distance) richness and diversity estimates.
OTU richness
OTU diversity
Library
ACE
Chao1
ACE*
Shannon H
Simpson D
1
2
3
4
9
10
11
12
6
7
1 722 (1 644, 1 809)
10 056 (9 873, 10 246)
4 353 (4 260, 4 451)
4 301 (4 203, 4 405)
1 270 (1 227, 1 314)
1 822 (1 758, 1 890)
2 528 (2 435, 2 628)
1 966 (1 897, 2 041)
1 419 (1 354, 1 489)
2 409 (2 294, 2 534)
1610 (1414, 1874)
7548 (7238, 7895)
4471 (4253, 4724)
4319 (4096, 4580)
1099 (947, 1312)
1698 (1543, 1901)
2218 (1951, 2568)
1813 (1627, 2056)
1174 (1016, 1395)
1512 (1336, 1748)
834
9211
5717
5401
1104
2000
2176
2001
1239
1674
4.86 (4.82, 4.91)
7.67 (7.65, 7.69)
6.97 (6.94, 7.00)
6.98 (6.95, 7.00)
4.46 (4.39, 4.52)
4.79 (4.75, 4.84)
5.44 (5.41, 5.47)
5.13 (5.09, 5.19)
4.45 (4.40, 4.50)
5.13 (5.10, 5.16)
0.043 (0.040, 0.046)
0.0011 (0.0011, 0.0012)
0.0048 (0.0044, 0.0052)
0.0038 (0.0035, 0.0041)
0.054 (0.050, 0.058)
0.055 (0.052, 0.058)
0.016 (0.016, 0.017)
0.049 (0.044, 0.052)
0.063 (0.059, 0.067)
0.016 (0.016, 0.017)
Libraries from anoxic samples are shaded and those from RNA pools are italicized. ACE* is average ACE from 1000 synthetic libraries re-sampled
at size of smallest bacterial or archaeal library. Values in parentheses delimit the 95% confidence intervals. See Table 1 for information about each
library.
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
4
E. Gaidos, A. Rusch and M. Ilardo
has a pronounced peak of highly abundant OTUs (insert
of Fig. 1), although the detailed shape of the peak varies.
DNA-based libraries, with the exception of library 1 (constructed from the oxic sample of site A), have remarkably
similar tails of less abundant and ‘rare’ taxa, whereas
RNA-based libraries lack them.
Library comparisons
Fig. 1. Rank abundance of OTUs in DNA-based libraries (heavy
lines) and RNA-based libraries (light lines) from different samples.
Although the two kinds of libraries have similar distributions at high
abundance (inset), the RNA-based libraries lack a pronounced ‘tail’
of much less abundant OTUs. A fractional abundance of 10-4
means that an OTU appears about once in a library.
et al., 2009). The averages of the ACE richness estimates of the equal-size (resampled) libraries are not dramatically different from those of the real libraries
(Table 2), except for the case of library 1 when compared
with library 9. Resampling, even at the same library size,
can only lose taxa, not gain them, and this process
invariably underestimates richness, as is seen for libraries 6 and 9. Even accounting for varying sample size, all
other libraries sample higher-richness pools than do
libraries 1 and 9 (oxic sample from site A).
The ACE estimator suggests there may be ~104 bacterial OTUs within our richest pool: DNA from the oxic
sample of site B. Although the richness of each RNA pool
is less than that of the corresponding DNA pool (Table 2),
neither the ACE nor the Chao1 indicators support a direct
correlation between the richness of DNA and RNA pools
(Pearson’s product–moment correlation P = 0.78 and
0.50 respectively). The Shannon and Simpson calculators
also indicate that the RNA-based libraries are less diverse
than their DNA-based counterparts (Table 2). Library 2,
from the DNA pool of the oxic sample from site B, is the
most diverse as well as the richest, whereas library 9,
from the RNA pool of the oxic sample from site A, is the
least diverse. The DNA-based archaeal library from the
anoxic sample was significantly richer and less diverse
than that from the oxic sample (Table 2).
The rank logarithmic abundance of the OTUs in each of
our eight bacterial libraries is plotted in Fig. 1. Each library
According to the Cramer-von Mises test, and at a significance of P < 10-3, each library is distinguishable from
every other library. An unweighted pair group method
with arithmetic mean (UPGMA) tree constructed from
Czekanowski–Bray–Curtis distances shows that the
DNA-based libraries from the two anoxic samples (3 and
4) are most alike, and the two RNA-based libraries (11
and 12) are most alike as well (Fig. 2A). Furthermore, the
DNA- and RNA-based libraries from the oxic sample at
site B (2 and 10) more closely resemble the corresponding libraries from the anoxic samples than they do the
libraries from the oxic sample from site A (1 and 9). Oddly,
the DNA- and RNA-based libraries from oxic sample A
cluster with the RNA- and DNA-based libraries, respectively, of the other samples. A majority consensus tree of
1000 bootstrap replicates provides strong statistical
support for these relationships (Fig. 2B).
Library composition
For an individual OTU in a given library, the normalized
number of RNA-based (cDNA) tags is correlated with
the normalized number of DNA-based tags (Pearson’s
product–moment correlation = 0.55, P < 2 ¥ 10-16, see
also Fig. S3), but there is also significant scatter. Figure 3
describes the overall abundance of OTUs, represented by
DNA-based tags, versus their activity in oxic and anoxic
samples, represented by the significance of the ratio of
RNA-based tags to DNA-based tags. It shows that more
active OTUs are also more abundant, and that the most
abundant organisms, four in particular, were active in both
oxic and anoxic sediment. Those OTUs that are only
significantly active under one redox state or the other do
not have an especially high overall (DNA) abundance. A
few OTUs assigned to uncultivated divisions OP11 and
TM7 are abundant in our RNA-based libraries but sparse
in our DNA-based libraries. The opposite is true for OTUs
in OP8.
The distributions of tags among bacterial phyla/
divisions of tags each of the eight libraries among
phyla/divisions are shown in Fig. 4. Proteobacteria and
Actinobacteria dominate every library (overall 40% and
13% respectively): other significant groups include the
Firmicutes (10%), Acidobacteria (6%), candidate divisions
TM7 (3.8%) and OP11 (3.7%), Chloroflexi (2.6%), Planc-
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
Pyrosequencing tags of benthic coral reef microbes 5
A
B
opumilales in the proposed phylum Thaumarchaeota
(Brochier-Armanet et al., 2008). The four most abundant
OTUs comprise nearly 38% of all archaeal tags
(Table S1). The most numerous (11.6%) is assigned to
order Nitrosopumilales. The next most abundant (9.3%) is
assigned to Marine Group III in the Euryarchaeota. The
third-ranked taxon (8.7%) is assigned to the Marine
Benthic Group C of the Crenarchaeota (which includes
the former Terrestrial Miscellaneous Crenarchaeotal
Group), and the fourth-ranked taxon (8.1%) is an unclassified member of the Crenarchaeota. Lone tags (represented by a single tag in our summed libraries) comprise
29% of the 5437 bacterial OTUs, but only 1.8% of all
bacterial tags. The distribution of these tags among phyla/
divisions resembles, but is not identical to that of the more
abundant sequences (Fig. 4). Proteobacteria and Actinobacteria dominate, and Planctomycetes tags are comparatively more likely to appear as ‘rare’ taxa, but no
phylum is exceptionally over-represented in the rare
biosphere.
Discussion
Community composition and richness
The distribution of bacterial tags among phyla (Fig. 4) is
broadly consistent with sequence data from six previous
clone libraries constructed with DNA and RNA from
Fig. 2. (A) UPGMA tree based on Czekanowski–Bray–Curtis
distances and (B) majority consensus tree (1000 bootstraps)
showing similarities between tag libraries from different pools and
samples.
tomycetales (2.4%), Bacteroidetes (2.2%) and candidate
division OP8 (2%). The four most abundant OTUs comprise 20% of the tags in our summed bacterial libraries: a
member of the genus Ralstonia in the Betaproteobacteria
(10.7%), one of genus Staphylococcus in the Firmicutes
(4.0%), and two members of the order Actinomycetales in
the Actinobacteria (2.8% and 2.0%) (Table S1).
The distribution of archaeal tags among the major,
widely accepted groups is also shown in Fig. 4. Archaeal
OTUs are roughly equally divided between the Crenarchaeota and Euryarchaeota, and are mostly in the Marine
Benthic Group C and Marine Group III respectively
(Fig. 4). In the library from the oxic sample, 35% of all tags
are also assigned to the Marine Group I order Nitros-
Fig. 3. Overall abundance of bacterial OTUs versus their activity in
oxic and anoxic samples. ‘Abundance’ is the average fractional
number of an OTU’s tags in all four DNA-based libraries. ‘Aerobic
activity’ and ‘anaerobic activity’ are, for the oxic and anoxic
samples, respectively, the ratio of the number of tags in the
RNA-based library to the DNA-based library, divided by the
standard deviation in that ratio expected from finite counting
statistics. Normalizing by the standard deviation gives the
significance of the ratio, and avoids the problem of OTUs with very
small numbers of tags producing widely varying ratios.
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
6
E. Gaidos, A. Rusch and M. Ilardo
Fig. 4. Distribution of tags by phylum/division
among bacterial (left) and archaeal (right)
libraries. ‘Rare’ refers to those bacterial OTUs
that have only one tag in our summed
libraries.
Checker Reef and Moku o loe, another, nearby patch reef
(Fig. S1) (Sørensen et al., 2007; Rusch et al., 2009). The
most abundant phylum in both the V6 tag and clone
libraries is the Proteobacteria (40% and 22–49% respectively), and six of the seven most abundant phyla in the
clone libraries (> 5% averaged over all libraries) are
among the nine most abundant overall in the tag libraries.
The single exception is the ‘Marine Carbonate Cluster’,
which appeared in DNA libraries from Moku o loe reef but
not RNA libraries from Checker Reef or our tag libraries. It
is phylogenetically related to the Cyanobacteria and may
be of organellar origin. The taxonomic distribution of
archaeal V6 tags agrees with previous clone library
results where 15 of 16 sequences fell into Marine Group I,
Marine Group III or the Terrestrial Miscellaneous Crenarchaeotal Group (now Marine Benthic Group C) (Sørensen
et al., 2007).
Rarefaction curves (Fig. S2) indicate that, even with
OTUs constructed by clustering sequences within a 10%
distance, none of our libraries sample a pool to saturation,
and that even massively parallel pyrosequencing has
failed to encompass the richness of these communities
(Magurran, 2004). This does not necessarily mean that
estimates of richness are inaccurate, only that we are
missing many taxa. Kemp and Aller (2004) concluded
that, to adequately describe a community with low evenness, the minimum library size should be an order of
magnitude larger than the Chao1 richness estimator.
Among our libraries, the modal ratio is 4.9 but varies from
1.9 (library 2) to 7.3 (library 10). By this metric, many of
our libraries are inadequate to accurately describe the
complete structure of the community. Nevertheless, two
different estimators of richness (Chao1 and ACE) give
consistent results (Table 2). Further, the rough agreement
between the ACE richness of the actual libraries and
those of the scaled, re-sampled libraries shows that
sample size does not greatly affect our results, with the
possible exception of library 1.
These richness estimates reinforce previous conclusions of high OTU richness based on analyses of clone
libraries from these sediments (Sørensen et al., 2007;
Rusch et al., 2009), as well as sediment from the Great
Barrier Reef, Australia (Uthicke and McGuire, 2007), and
living corals (Reis et al., 2009; de Castro et al., 2010;
Lins-de-Barros et al., 2010). With the exception of those
from the oxic sample from site A, our DNA pools are richer
than those previously reported in pyrosequencing studies
of marine water masses and hydrothermal vent fluids
(Sogin et al., 2006; Gilbert et al., 2009; Kirchman et al.,
2010), and comparable to the communities in terrestrial
soils (Torsvik et al., 1990; Schloss and Handelsman,
2006; Roesch et al., 2007; Schutte et al., 2010). However,
the number of predicted OTUs depends on sequence
filtering and the exact clustering scheme, not just
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
Pyrosequencing tags of benthic coral reef microbes 7
sequence distance cut-off (Huse et al., 2010), and quantitative comparisons between biomes would require a
uniform analysis.
Community structure and the ‘rare biosphere’
Excepting library 1, all DNA-based bacterial tag libraries
are richer and more diverse than their RNA-based counterparts (Table 2). The origin of this contrast is readily
apparent in the rank abundance curves (Fig. 1). While
DNA- and RNA-based libraries have similar distributions
among high abundance taxa, only the former has an
extended tail of much less abundant or ‘rare’ taxa that is
characteristic of DNA-based libraries from many environments (Sogin et al., 2006; Pedros-Alio, 2007; Elsahed
et al., 2008; Galand et al., 2009). These ‘rare’ taxa may
constitute significant evolutionary novelty (Elsahed et al.,
2008) and include some of the easily cultivated organisms
that often fail to appear in smaller clone libraries (PedrosAlio, 2007). PCR amplification using primers specific to
low-abundance tag sequences usually recovered nearly
full-length ribosomal RNA genes, demonstrating that they
are not anomalies (Neufeld et al., 2008). Because sampling has not proceeded to saturation, the appearance of
a ‘lone’ tag in our libraries offers only an upper limit to the
actual abundance of that taxon, and no lower limit can be
assigned. Lone tags may represent an extension of the
abundance distribution of OTUs such that their actual
abundance is approximately one-half of that of a taxon
with two tags, or they may represent a chance few from a
much larger pool of extremely rare taxa.
Community differences between samples
The libshuff method tests the null hypothesis that two
libraries are statistically indistinguishable based on distances between their members in sequence space
(Singleton et al., 2001). It is more discriminating than
OTU-based approaches (Schloss, 2008), and has been
used to distinguish different coral-associated microbial
communities (Hong et al., 2009; de Castro et al., 2010;
Lins-de-Barros et al., 2010). Our finding that all libraries
can be distinguished from one another is not unexpected
as the large size of pyrosequencing tag libraries allows
even small differences to be discerned. We then used an
OTU abundance-based metric, the Czekanowski–Bray–
Curtis distance (Czekanowski, 1909; Bray and Curtis,
1957), to calculate the difference between bacterial libraries and cluster them. The Czekanowski–Bray–Curtis distance has been used to compare 16S sequence libraries
constructed from other environments (Boyd et al., 2007;
Flores-Mireles et al., 2007; Tarlera et al., 2008), including
coral endosymbiotic dinoflagellates (Stat et al., 2009).
While others metrics use sequence data directly
(Lozupone et al., 2007; Schloss, 2008), and may impose
or infer a phylogenetic tree and measure distances along
its branches (Andersson et al., 2008), these other
approaches make more sense from an evolutionary rather
than an ecological perspective. For microbes, the relationship between evolutionary distance, physiological differences and ecosystem role is extremely complex and little
explored.
A UPGMA tree based on our Czekanowski–Bray–
Curtis distances (Fig. 2A), and supported by a bootstrapped majority consensus tree (Fig. 2B), visually
represents what is obvious from an inspection of Fig. 4:
the DNA-based libraries from the two anoxic samples
are very alike, and the two RNA-based libraries are alike
as well. The similarity of the libraries from the oxic
sample at site B to those from the two anoxic samples
might be explained if the near-surface sample experienced anoxic conditions in the past. For both the DNA
and RNA pools to resemble those of the anoxic
samples, this must not have been long before sampling,
and it must have been persistent. The community in the
oxic sample from site A is distinguishable from the other
three samples not only in terms of overall structure, but
also by its low richness and diversity; it is dominated by
a few major taxa. Whether this is characteristic of the
oxic regions of these sediments is, unfortunately, not
possible to say. The contrasting results between sites
separated by only 5 m is a cautionary tale about basing
conclusions on single samples or sites.
Identity and activity of abundant OTUs
The four most abundant bacterial OTUs are affiliated with
the genera Ralstonia, Staphylococcus and Corynebacterium. Members of Ralstonia have gained infamy as plant
pathogens, but affiliated 16S sequences have been
recovered from coastal waters (Fera et al., 2007) and a
hypersaline lagoon (Clementino et al., 2008). The tag
sequence (63 bp) is an identical match to a single clone
sequence generated from DNA from the nearby Moku o
loe patch reef (Sørensen et al., 2007). Staphylococcus
is another pathogen-containing genus, but affiliated
sequences have been obtained from corals (Chiou et al.,
2010), and strains have been isolated from sea anemones (Du et al., 2010), mangroves (Grisi and Gorlach-Lira,
2010), recreational marine beaches (Roberts et al.,
2009; Soge et al., 2009; Abdelzaher et al., 2010), deltaic
sediments (Chikere et al., 2009) and microbial mats
(López-Cortés et al., 2007). Actinobacteria, including
Corynebacterium, have been found in sponges (Sun
et al., 2010) and coral reef waters and sediments
(Kannapiran et al., 2008), and a Corynebacterium has
been isolated from the mucus of a scleractinian coral
(Ben-Dov et al., 2009).
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
8
E. Gaidos, A. Rusch and M. Ilardo
The most abundant OTUs in our DNA-based libraries
also tend to be highly abundant in the corresponding
RNA-based libraries. The success of these taxa may
reflect adaptation to a range of conditions in the reef
sediment. At least some members of these genera are
facultative anaerobes (see below), and would be adapted
to fluctuating porewater redox chemistry. Figure 3 shows
that the four dominant bacterial taxa, as represented by
fraction of tags in all DNA-based libraries, are active (as
represented by the ratio of RNA- to DNA-based tags)
under both oxic and anoxic conditions. Taxa that are
active under only one condition or another are not especially abundant. On the other hand, the presence of these
pathogen-containing genera is often ascribed to human
impact, and it is tempting to explain their dominance in the
reef sediment to past or present anthropogenic disturbance. Treated wastewater discharge to Kaneohe Bay
from the 1950s to the 1970s led to eutrophication, algal
blooms and degradation of the coral reefs. Diversion of
the most of the wastewater to deep ocean outfalls has
been accompanied by a documented recovery of the
reefs (Hunter and Evans, 1995). Permeable reef sediments may concentrate and enhance anthropogenic
microbial populations by filtering large volumes of surface
water and providing a large surface area for growth. A
comparison with the microbial communities of other, more
pristine reefs is required to evaluate this hypothesis.
Operational taxonomic units assigned to TM7, OP11
and OP8, candidate bacterial divisions with no cultivated
representatives, are abundant in some of our libraries.
The genome of a TM7 member has been partially
sequenced via culture-independent techniques (Marcy
et al., 2007), and these three groups may prefer reducing
environments (Hugenholtz et al., 2001; Harris et al.,
2004). Curiously, the TM7 and OP11 taxa are abundant in
the RNA-based libraries but not the DNA-based libraries,
and they do not appear in previous clone libraries from
DNA and cDNA (Sørensen et al., 2007; Rusch et al.,
2009). In contrast, candidate division OP8 is abundant in
the DNA-based libraries but not the RNA-based libraries,
and appears in a previous, DNA-based clone library from
this environment (Sørensen et al., 2007). All of the taxa
have comparatively high (but < 0.2) GAST numbers, but
this may simply reflect comparatively poor representation
of these divisions in the reference ribosomal sequence
database (Huse et al., 2008). The OP8 taxa may represent inactive or defunct cells. The distribution of TM7 and
OP11 tags among libraries is more difficult to explain, but
could be the result of two systematic effects: DNA versus
RNA extraction bias, or reverse transcription errors that
have caused incorrect assignments.
The most numerous archaeal OTU (11.6% of the
summed libraries) is assigned to the Nitrosopumilales
order of Marine Group I. Marine Group I had been previ-
ously assigned to the Crenarchaeota, but a new phylum,
Thaumarchaeota, has been proposed for it and some
other mesophilic archaea such as Cenarchaeales
(Brochier-Armanet et al., 2008). Our most numerous tags
identically match full-length 16S rRNA sequences recovered from these reefs whose closest (94% identity) cultivated relative is Nitrosopumilus maritimus SMC1. The
high abundance of tag in the oxic sample relative to the
one from the anoxic sample is consistent with this aerobic
metabolism (see below). The next most abundant (9.3%)
OTU is assigned to Marine Group III in the Euryarchaeota.
The third-ranked taxon (8.7%) is assigned to the Marine
Benthic Group C of the Crenarchaeota (which includes
the former Terrestrial Miscellaneous Crenarchaeotal
Group). The fourth-ranked taxon (8.1%) is an unclassified
member of the Crenarchaeota. There are no cultivated
members of these divisions and their metabolisms can
only be guessed at.
Guilds involved in the reef nitrogen cycle
Reef sediments host gradients in porewater ammonium,
nitrite and nitrate (Capone et al., 1992; Haberstroh and
Sansone, 1999; Falter and Sansone, 2000; Miyajima
et al., 2001; Rasheed et al., 2002; Atkinson and Falter,
2003; Rusch et al., 2009) as a result of nitrogen remineralization and cycling, including aerobic ammonia
oxidation (nitrosification), nitrification, denitrification and
anaerobic ammonia oxidation (ANAMMOX). Members of
guilds involved in these reactions have been described in
previous studies of this site (Rusch et al., 2009), and our
tag libraries paint a more complete picture of microbial
nitrogen cycling.
Aerobic ammonia oxidation. The most abundant
archaeal tag in our libraries is related to the isolate N.
maritimus, a lithoautotrophic, aerobic ammonia oxidizer
and member of Marine Group I. Marine Group I
sequences similar, but not identical to N. maritimus were
found in previous DNA- and RNA-based clone libraries
(Sørensen et al., 2007; Rusch et al., 2009) and Marine
Group I-affiliated cells were found using FISH (Rusch
et al., 2009). Nitrosopumilus maritimus-like sequences
dominate 16S rRNA gene clone libraries constructed from
bacterial DNA in the mucus of Australian and Red Sea
corals (Siboni et al., 2008). If coral mucus is a significant
source of the particulate organic matter in reef sediments,
then it is unsurprising that mucus-associated ammoniaoxidizing archaea (AOA) are also present. In contrast,
known ammonia-oxidizing bacteria (AOB) are only marginally present. A probe targeting the ammonia-oxidizing
betaproteobacterial order Nitrosomonadales hybridized to
5% of DAPI-stained cells, but very few betaproteobacterial clones were found in a RNA-based library (Rusch
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
Pyrosequencing tags of benthic coral reef microbes 9
et al., 2009), and no Nitrosomonadales sequences were
found in a DNA-based clone library from a nearby reef
(Sørensen et al., 2007). Only two V6 tags (both from
library 2) were assigned to the Nitrosomonadales. Based
on an analysis by probeCheck (Pruesse et al., 2007) that
allows for one mismatch, the discrepancy between FISH
and sequences cannot be explained by non-specific
hybridization of the Nso190 probe (Mobarry et al., 1996).
Our libraries and cell pools were not constructed contemporaneously, raising the interesting possibility of community shifts between AOB and AOA dominance.
Nitrification. The product of aerobic ammonia oxidation is
nitrite, a substrate for nitrifying bacteria that produce
nitrate (and ANAMMOX bacteria, see below). Nitrifying
bacteria have been identified in the subphyla Alphaproteobacteria and Gammaproteobacteria, the phylum Nitrospirae, and the genus Nitrospina, which has been
provisionally placed in the Deltaproteobacteria (Spieck
and Bock, 2005). Up to 5% of sequences in 16S rRNA
clone libraries and DAPI-stained cells from sediments
from this same reef were affiliated with the Nitrospirae and
their abundance was positively correlated with NO3- concentration (Rusch et al., 2009). V6 tags affiliated with
Nitrospirae and the Nitrospina constitute only 0.7% of all
bacterial tags, and are primarily found in libraries from the
oxic A and anoxic A and B samples. A single tag assigned
to the nitrite-oxidizing genus Nitrococcus (Gammaproteobacteria) was found in library 2.
Denitrification. The dissimilatory reduction of nitrate to
nitrite, and thence to N2O and N2, may be an important
energy source for microorganisms in the suboxic zones of
reef sediments. Denitrification can be responsible for a
large fraction of organic matter mineralization in coral reef
sediments, and represent a significant loss of fixed nitrogen in the reef ecosystem (Alongi et al., 2007). A wide
range of archaea and bacteria, especially Proteobacteria,
possess genes for nitrate reduction or have been demonstrated to carry it out (Ye and Thomas, 2001; Philippot,
2002). All three of the bacterial genera to which the most
abundant OTUs have been assigned (Ralstonia, Staphylococcus, Corynebacterium) have members capable of
respiratory nitrate reduction, or complete denitrification
(Gregory et al., 2003; Nishimura et al., 2007; Cramm,
2009). This, plus their activity in both oxic and anoxic
sediments, makes these OTUs prime candidates for bacterial denitrification in these reefs.
ANAMMOX. The anaerobic oxidation of ammonium with
nitrite is a metabolism that, so far is known, is exclusively
a metabolism of a single clade in the Planctomycetes, a
deeply branching phylum of bacteria with intracellular
membrane structures resembling eukaryotic nuclei
(Fuerst, 2005). ANAMMOX accounts for a significant fraction of N2 evolution (and hence nitrogen loss) in marine
low-oxygen zones: ANAMMOX activity has been previously documented in marine and brackish sediments
(Thamdrup and Dalsgaard, 2002; Tal et al., 2005; Trimmer
et al., 2005). Planctomycetes is well represented in DNAand RNA-based clone libraries and intact cell pools from
these reefs (Sørensen et al., 2007; Rusch et al., 2009),
both clone library- and FISH-based searches that specifically target the ANAMMOX clade in these sediments have
been successful, and ANAMMOX activity has been documented using stable isotopes (J.M. Beman, pers. comm.).
Planctomycetes-related V6 tags appear in all eight bacterial libraries (0.8–6%), and are most abundant in libraries
from the DNA pool of the oxic site B sample. However,
none of the tags are assigned to any of the recognized
ANAMMOX genera (Anammoxoglobus, Brocadia, Kuenenia, Jettenia, Scalindua). We ascribe this absence to
primer bias: the ICoMM primer cocktail targets none of the
21 good-quality sequences from Ribosomal Database
Project (RDP) release 10.12 (Cole et al., 2007) belonging
to these genera. Unfortunately, ANAMMOX bacteria
remain a poorly illuminated corner of the microbial black
box of this reef ecosystem.
Our pyrosequencing study of these coral reef sediments reinforces and expands conclusions from our previous work. In particular, we found that the diversity of the
microbial community approaches 104 OTUs, comparable
to terrestrial soils, and contains a ‘rare’ component that is
apparent in DNA- but not RNA-based libraries. We discovered marked heterogeneity in the community in sediments from the sediment surface, but not at depth, a
patchiness suggested by our previous geochemical and
molecular microbiological studies. We identified candidate
members of nitrogen-transforming guilds that may be
responsible for nitrogen cycling in the reef. Especially
interesting is the potential dominant role of Nitrosopumilus
archaea, rather than bacteria, in ammonia oxidation, a
possible shunt of fixed nitrogen away from primary producers and towards denitrifiers.
Coral reefs are under threat from overharvesting, invasive species, eutrophication, rising sea surface temperatures and ocean acidification (Bellwood et al., 2004;
Carpenter et al., 2008; De’ath et al., 2009). Changes in
coral-associated microbial communities have been
related to coral disease (Frias-Lopez et al., 2002; 2004;
Cervino et al., 2004; Bourne, 2005; Breitbart et al., 2005;
Klaus et al., 2005), but the impacts of anthropogenic disturbances on the benthic microbial community and any
concomitant changes in biogeochemical cycling are
unknown. A pyrosequencing-based analysis of the
benthic microbial communities in a pristine Hawaiian reef,
and a comparison with the results presented here would
be a first step towards understanding such impacts.
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
10 E. Gaidos, A. Rusch and M. Ilardo
Experimental procedures
Field site and sampling
Samples of coarse-grained carbonate sands were obtained
from the interior flat of Checker Reef in Kane’ohe Bay
(21°25′30″N, 157°47′30″W, Fig. S1) on the Hawaiian island
of O’ahu. This patch reef consists of an irregular wall of living
coral bounding an interior flat of unlithified or partially lithified
coralline sands and rubble, and has been described elsewhere (Tribble et al., 1992; Hunter and Evans, 1995). Surface
water temperature in Kane’ohe Bay varies annually from
19°C to 28°C and the salinity is near oceanic. The reef
experiences tides and waves with < 0.6 m amplitude (Tribble
et al., 1992; Jokiel et al., 1993). Water depth was 0.7 m at the
time of sampling. Two similar locations separated by 5 m
were sampled at the surface and a depth of 50 cm using the
techniques described in Sørensen and colleagues (2007).
Porewater at 50 cm depth at both locations was sulfidic
(smell test), consistent with previous measurements of the
location of the redoxocline (Sørensen et al., 2007; Rusch
et al., 2009). Samples were placed on ice and returned to the
laboratory where they were stored within the hour at -80°C.
All DNA and RNA extractions were performed within 48 h of
sampling. DNA extractions were preformed with the MO-BIO
UltraClean kit. RNA was extracted using the technique from
Biddle (Biddle et al., 2006), followed by DNA digestion. cDNA
was constructed using the iScript kit (Bio-Rad). Suitability of
these DNA and cDNA pools as PCR template were tested by
real-time PCR (Bio-Rad iCycler/MyiQ) with the ICoMM primer
sets (Huber et al., 2007) and inspecting melt curves as well
as gel electrophoresis of the short (~100 bp) product.
become issues at large GAST distances (Huse et al., 2008).
Libraries of sequences and OTUs were further analysed in
MOTHUR 11.0 (Schloss et al., 2009) by identifying unique
sequences with unique.seqs, aligning sequences with
align.seqs and the Greengenes core set of aligned
sequences (DeSantis et al., 2006), and removing common
gaps using filter.seqs. Sequences were clustered into OTUs
using dist.seqs and cluster and a 10% distance cut-off criterion. This choice of cut-off is very approximately equivalent to
the distance between bacterial ‘species’ and was motivated
by an examination of the distribution of distances between
randomly selected pairs of sequences (Fig. S4). For ease of
computation we calculated Levenshtein distance d (equal to
the number of substitutions, deletions and insertions to
equate to sequences), ignored the contribution to distance
due to differences in length and divided by an average
sequence length of 61.5 nt. A distance cut-off of 10% captures some of the diversity within formally described genera,
but almost none of the diversity between genera: 36% of
pairs within the same genus had d < 10%, whereas only ~3%
of pairs in different genera, and <1% of pairs in families or
orders did. The peak at 2–3 bp could include sequences
produced by uneven termination of the sequencing or imperfect matching to the distal primer (Huse et al., 2008). Thus, in
terms of the classical bacterial taxonomy, these OTUs should
be considered as groups that rank intermediate between
strains and genera. We adopted a 10% distance criterion for
archaeal OTUs as well. Clustering outcome also depends
sensitively on how the distance is calculated, e.g. nearest
neighbour, average or most distant, and, following Huse and
colleagues (2010), we used the average distance.
Taxonomy
Sequence library generation
V6 tag libraries were generated by pyrosequencing with a
454 GS-FLX machine at the Marine Biological Laboratory,
Woods Hole, MA. Reactions were performed with the
ICoMM cocktails of primers designed to maximize coverage
of known phylotypes (Huber et al., 2007) (http://vamps.
mbl.edu/resources/primers.php). Reactions were run with
bacterial primers on both DNA and cDNA, and with archaeal
primers on DNA, from all four samples. All reactions with
bacterial primers but only two of the four archaeal templates
(one oxic, one anoxic) produced sufficient product for pyrosequencing. Taxonomic coverage by primer sets was evaluated
using PRIMROSE (Ashelford et al., 2002) and the Ribosomal
Database Project release 10, update 12 (Cole et al., 2007),
and excluding sequences of suspect quality.
Pipeline processing and clustering
Sequences were processed using the Visualization and
Analysis of Microbial Population Structure (VAMPS) pipeline
with improved filtering and clustering as previously described
(Sogin et al., 2006; Huse et al., 2007; 2008; 2010). A total of
102 670 tags passed the pipeline filters. We used the 5 April
2010 version of the pipeline-processed data for analysis. We
also removed all tags with Global Alignment for Sequence
Taxonomy (GAST) distances > 0.2 as phylogenetic assignment and contamination by non-ribosomal sequences
The VAMPS pipeline assigned, where possible, a taxonomy
down to the level of genus based on the Bayesian RDP
Classifier (Wang et al., 2007), and a GAST value describing
confidence in that assignment to each unique sequence
(Huse et al., 2008). The RDP-based taxonomic assignment in
the pipeline analysis was inadequate for the Archaea.
Instead, assignment was performed using the following procedure: we developed an archaeal taxonomic scaffold of 67
curated sequences from NCBI and RDP. These sequences
represent every archaeal order and group identified as incertae sedis. For each OTU in our archaeal libraries, we identified the most numerous unique tag sequence. Using RDP
SeqMatch (Cole et al., 2007), we identified the best match to
that tag among good-quality ribosomal sequences with
>1200 bp. We used the sequence with the highest Sab score
(the ratio of number of matching to total 7-mers), and the
longest sequence, if there are multiple highest scoring
sequences. We downloaded these sequences and classified
them using MOTHUR’s classify.seqs Bayesian routine (Wang
et al., 2007), with a cut-off of 60% and the default k-mer size
of 8. Finally, we assigned each OTU and its tags according to
the taxonomy, if resolved, of its representative long RDP
sequence. OTUs were not assigned either if a sufficiently
close (Sab > 60%) match was not found, or if that sequence
was not assigned any affiliation with at least 60% probability.
FASTA template and taxonomy files for the scaffold
sequences are available upon request.
© 2010 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology
Pyrosequencing tags of benthic coral reef microbes 11
Library analysis
Library richness and diversity were calculated using
MOTHUR’s summary.single routine, the Chao1 (Chao,
1987), and ACE (Chao and Lee, 1992; Chazdon et al., 1998)
estimators of richness, and Shannon’s ‘H’ (Shannon, 1948),
and Simpson’s D (Simpson, 1949) estimators of diversity.
Rarefaction curves were calculated using rarefaction.single
with 10 000 iterations. Bacterial libraries were compared
using the Cramer-von Mises test implemented in the libshuff
command of MOTHUR, with 1000 Monte Carlo realizations.
Distances between libraries were computed according to
the relative abundance of tags among OTUs using the
Czekanowski–Bray–Curtis metric (Czekanowski, 1909; Bray
and Curtis, 1957), and those distances clustering by the
UPGMA (Sneath and Sokal, 1973), both in the tree.shared
function of MOTHUR. One thousand artificial sets of libraries
were created by sampling with replacement, trees computed
with MOTHUR in batch mode, and a majority consensus tree
constructed using Phylip (Felsenstein, 1989). Annotation of
abundant OTUs were performed using the BLAST (Altschul
et al., 1990) and Entrez tools at the National Center for Biological Information (NCBI) internet portal. Standard statistics
were calculated and plots constructed using R, and UPGMA
and consensus trees were plotted using the R package APE
(R Development Core Team, 2010).
Acknowledgements
Pyrosequencing was provided by the ICoMM, with financial
support from a W. M. Keck foundation award to the Marine
Biological Laboratory at Woods Hole. M.I. was supported by
the Princeton Environmental Institute. We thank Sue Huse,
Linda Amaral Zettler and Mitch Sogin for helpful discussions
and crucial technical assistance above and beyond the call of
duty, Sarah Yasui for performing NCBI database searches,
and two anonymous reviewers for their comments.
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Supporting information
Additional Supporting Information may be found in the online
version of this article:
Fig. S1. Location of our sample site on Checker Reef in
Kane’ohe Bay, O’ahu, Hawaii. Our previous studies investigated both Checker and adjacent Moku o loe (Coconut
Island) reefs. Based on LANDSAT imagery modified by the
Hawaii State GIS Program.
Fig. S2. Rarefaction curves of bacterial tag libraries based
on DNA (heavy lines) and RNA (light line) pools of different
samples. In none of the libraries did sampling achieve
saturation.
Fig. S3. OTU abundance normalized by library size in RNAbased versus DNA-based libraries. Each OTU is represented
by four points representing four samples. The discretization in
the lower left is a result of finite library size. For clarify, the
errors due to finite counting statistics are plotted for the points
associated for the anoxic sample from site A.
Fig. S4. The distributions of distances between pairs of V6
bacterial tag sequences randomly selected from our complete tag libraries. Here, distance is calculated as the Levenshtein distance, minus the contribution from sequence length
difference, and normalized by the average sequence length
(61.5 bp). The black curve is the distribution for all pairs that
have been assigned the same genus in the classic bacterial
taxonomy, and thus may represent different strains or
species. The red curve represents sequences assigned to
different genera but within the same family, etc. The curves
for higher taxonomic units appear to approach a normal distribution, probably because the hypervariable region ‘saturates’ at evolutionary/taxonomic distances.
Table S1. Most abundant bacterial and archaeal OTUs. The
accession number is that of the highest Sab hit in an SeqMatch search of long (> 1200 bp) ribosomal sequences in
RDP.
Please note: Wiley-Blackwell are not responsible for the
content or functionality of any supporting materials supplied
by the authors. Any queries (other than missing material)
should be directed to the corresponding author for the article.
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