APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Apr. 2009, p. 1801–1810
0099-2240/09/$08.00⫹0 doi:10.1128/AEM.01811-08
Copyright © 2009, American Society for Microbiology. All Rights Reserved.
Vol. 75, No. 7
Diversity and Stratification of Archaea in a Hypersaline Microbial Mat䌤†
Charles E. Robertson,1 John R. Spear,2 J. Kirk Harris,3 and Norman R. Pace1*
Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, Colorado 80309-03471; Division of
Environmental Science and Engineering, Colorado School of Mines, Golden, Colorado 804012; and Department of Pediatrics,
University of Colorado Denver, Aurora, Colorado 800453
Received 5 August 2008/Accepted 16 December 2008
The Guerrero Negro (GN) hypersaline microbial mats have become one focus for biogeochemical studies of
stratified ecosystems. The GN mats are found beneath several of a series of ponds of increasing salinity that
make up a solar saltern fed from Pacific Ocean water pumped from the Laguna Ojo de Liebre near GN, Baja
California Sur, Mexico. Molecular surveys of the laminated photosynthetic microbial mat below the fourth
pond in the series identified an enormous diversity of bacteria in the mat, but archaea have received little
attention. To determine the bulk contribution of archaeal phylotypes to the pond 4 study site, we determined
the phylogenetic distribution of archaeal rRNA gene sequences in PCR libraries based on nominally universal
primers. The ratios of bacterial/archaeal/eukaryotic rRNA genes, 90%/9%/1%, suggest that the archaeal contribution to the metabolic activities of the mat may be significant. To explore the distribution of archaea in the
mat, sequences derived using archaeon-specific PCR primers were surveyed in 10 strata of the 6-cm-thick mat.
The diversity of archaea overall was substantial albeit less than the diversity observed previously for bacteria.
Archaeal diversity, mainly euryarchaeotes, was highest in the uppermost 2 to 3 mm of the mat and decreased
rapidly with depth, where crenarchaeotes dominated. Only 3% of the sequences were specifically related to
known organisms including methanogens. While some mat archaeal clades corresponded with known chemical
gradients, others did not, which is likely explained by heretofore-unrecognized gradients. Some clades did not
segregate by depth in the mat, indicating broad metabolic repertoires, undersampling, or both.
hydrogen sulfide (H2S) (HS⫺, ⬍1.6 ␮M) occurs between 3 and
6 mm, and below that is high hydrogen sulfide (⬎2 ␮M)
formed by the reduction of seawater sulfate. At night, with the
cessation of photosynthesis, the entire mat becomes anoxic.
These steep variations in chemistry with depth in the mat are
expected to foster the development of a diversity of microorganisms, and indeed, molecular surveys based on small-subunit
rRNA (16S rRNA) gene sequences have identified a spectacular diversity of bacteria among the microbial constituents of
the mat, with thousands of novel species, few with described
close relatives (29). In contrast to the diversity of bacteria,
studies of eukaryotes using a similar approach encountered
only a simple composition, a few diverse sequences dominated
by two kinds indicative of nematodes. (15).
The archaeal diversity in pond 4 mat and similar settings has
received little attention. The usual focus of saltern investigations has been on crystallizer ponds rather than concentrator
ponds such as pond 4, and numerous extremely halophilic
archaea have been discovered in saturated brines (2, 44, 48).
The pond 4 mat is distinct from the crystallizer environment,
covered by brine ⬃1 m deep with only ⬃80‰ salinity, ⬍⬃3⫻
seawater. Studies of archaea in the pond 4 mat have usually
been related to methanogenesis, which is generally considered
a minor metabolic theme of the mat (3, 47). Consequently, the
aims of this study were to describe the archaeal diversity in the
pond 4 mat and to determine how that diversity is distributed
with respect to depth in the mat and previously measured
chemical gradients (16, 28, 29). The results uncover substantial
unrecognized archaeal diversity and very likely indicate the
occurrence of so-far-undetected chemical gradients in the mat.
This study completes the first survey of the three domains of
life in the GN pond 4 mat.
Photosynthetic microbial mats occur worldwide and serve as
models for microbial community interactions. Fossil microbial
mats in the form of stromatolites are one of the earliest distinguishable life forms in the rock record (3.4 billion years)
(53) and are often studied to gain insights into the development of life on Earth (3, 11) and the influence that early life
may have had on the development of the planet’s atmosphere,
geosphere, and hydrosphere (20).
A large contemporary microbial mat system that has received the attention of microbiological studies is found in the
solar saltern operated by the Exportadora de Sal SA in Guerrero Negro (GN), Mexico. The GN saltern, fed from Pacific
Ocean seawater pumped from the Laguna Ojo de Liebre,
occupies ⬃100 km2 and consists of ⬃12 precrystallizer ponds
with increasing salinity due to evaporation. Many of the ponds
in this saltern contain persistent microbial mats. More than a
million metric tons of biomass (⬃17 km2 by ⬃6 cm by 1.2
gm/cm3) covers the floor of hypersaline pond 4 in the form of
a laminated photosynthetic microbial mat that is 4 to 6 cm
thick. The subjective, macroscopic appearance of the pond 4
mat is stable from year to year, and the mat has been a subject
of numerous microbiological and biogeochemical studies (3, 8,
15, 19, 24, 29, 30, 38, 47, 49, 56). Chemical measurements of
the mat (8, 29) show that the top 2 to 3 mm forms an oxic zone
in daylight due to oxygenic photosynthesis. A zone of low
* Corresponding author. Mailing address: Department of Molecular, Cellular, and Developmental Biology, University of Colorado,
Boulder, CO 80309-0347. Phone: (303) 735-1864. Fax: (303) 492-7744.
E-mail: Norman.Pace@colorado.edu.
† Supplemental material for this article may be found at http://aem
.asm.org/.
䌤
Published ahead of print on 29 December 2008.
1801
1802
ROBERTSON ET AL.
APPL. ENVIRON. MICROBIOL.
FIG. 1. Relative proportions of the three domains of life seen in the GN pond 4 mat and a comparison of bacterial and archaeal species
richnesses. (A) The pie chart shows the relative proportions of each domain as detected in 557 16S rRNA sequences obtained with universal
primers from core samples of the pond 4 mat taken in 2001. The archaea are further subdivided into the two main phylogenetic clades, the
Euryarchaeota and the Crenarchaeota. (B) Measured (Sobs) and estimated (SChao1) (32) species richnesses versus sequence identity are shown
for bacterial and archaeal sequences found with all primer pairs in the 2001 mat bulk samples. Rarefaction was used to compensate for the
differences in numbers of observed bacteria (1,584) and archaea (282).
MATERIALS AND METHODS
Sample collection. Bulk core samples were obtained from pond 4 (near pond
5) (Global Positioning System location, N27 41.245 W113 55.027) in October
2001 as described previously (29). Core samples for sectioning were taken from
the same pond 4 location in November 2005. Samples were immediately frozen
in liquid nitrogen for transport to the laboratory.
DNA extraction, PCR, cloning, and sequencing. DNA extraction using a beadbeating protocol, PCR, cloning, and sequencing were performed as described
previously (29). DNAs were amplified with universal primers (515F/1391R) and
archaeon-specific primers (333FA/1391R and 4FA/1391R). The 2005 mat core
samples were sectioned into 23 layers, with 1-mm slices for 10 mm and 3-mm
slices in deeper strata. The 10 strata analyzed consisted of the first 6-mm samples
and four DNA pools constructed from equimolar mixtures of DNA extracted
from multiple layers (see Table S1 in the supplemental material). Amplification
was conducted with the archaeon-specific primer 23FA (5⬘-ATT CCG GTT GAT
CCT GC-3⬘) and primer 1391R.
Sequence, statistical, and phylogenetic analyses. Sequences were assembled
with XplorSeq (17). Bellerophon (23) and Mallard (1) were used to screen all
sequences for potential chimeras. Sequences judged to be potentially chimeric by
either software package were discarded. The phylogenetic relationships of the
sequences were determined by maximum-parsimony insertion into ARB (31)
dendrograms provided by the Greengenes (9) (public sequences of ⱖ1,250 nucleotides [nt]) and SILVA (39) (ⱖ900 nt) internet portals after sequence alignment with NAST (10) (for Greengenes) or the SINA Web aligner (for SILVA).
(We find that the SILVA database, which includes sequences up to ⱖ900 nt,
provides a broader representation of reported archaeal sequences than does the
Greengenes database.) Hypervariable regions in the sequences were not considered in the parsimony insertion calculations by masking them with lanemaskPH
(included with the Greengenes database, January 2008) or the SILVA 93 pos_var_Archaea_93 mask. The validities of the resulting phylogenetic relationships
were verified by generating 1,000 maximum-likelihood bootstrap trees with
RAxML (50) on a distance-based subsampling (3% minimum distance between
sequences) of the archaeal branch of the SILVA 93 ARB dendrogram after
parsimony insertion of the sequences from this study. The distance-based subsampling was accomplished with SeqClusterX (C. E. Robertson, B. Holland, and
D. Knox, unpublished), which selected cluster-representative sequences after
complete linkage clustering. The bootstrap trees were evaluated and annotated,
and branches with less than 70% support were collapsed with Phylovis (C. E.
Robertson and D. Knox, unpublished). Phylovis was also used to prepare the
rough drafts of the phylogenetic-tree figures, which were fully annotated and
finalized with Adobe Illustrator. In order to obtain a rough estimate of the
phylogenetic distance within a cluster of sequences from this study, we defined a
metric termed the maximum intraclade distance (ICD). The ICD was obtained
by identifying the largest distance in the uncorrected hypervariability-masked
distance matrix produced by ARB’s neighbor-joining distance matrix computation function invoked on the sequences of interest. Operational taxonomic units
(OTUs) were computed with the sortx module of XplorSeq (17), which uses a
radial sorting algorithm with average-neighbor linkage to assign sequences to
clusters. Species diversity, richness, coverage, and evenness statistics were computed through rarefaction of the resampled sortx 99% OTU clustering data using
the biodiv module of XplorSeq (17).
Nucleotide sequence accession numbers. All nonchimeric sequences were
submitted to GenBank, which assigned accession numbers EU731010 to
EU732045. The mapping of accession numbers (including accession numbers for
a few archaeal sequences described previously by Ley et al. [29]) to sampling
date, clone name, stratum in the mat, and phylogenetic classification is provided
in Tables S2 and S3 of the supplemental material.
RESULTS
Archaeal abundance and distribution in the pond 4 mat. In
order to assess the bulk relative abundances of representatives
of the three phylogenetic domains in this environment, we
determined rRNA gene sequences derived from mat cores
taken in 2001 and amplified with the (nominally) universal
primer set 515F/1391R by PCR. These primers, in principle,
capture rRNA genes from all archaea, bacteria, and eukaryotes, although some refractory examples are known (13,
14). Collectively, 557 sequences were determined and sorted
phylogenetically into domain-level groups. The phylogenetic
distribution among the sequences is summarized in Fig. 1A.
Since the genomes of different organisms can contain different
numbers of rRNA genes, these ratios do not necessarily correspond directly to the specific numbers of individual cells in
this environment. The results do, however, provide an overall
perspective on the phylogenetic composition of archaeal rRNA
genes in the pond 4 mat. The ratios of the rRNA sequences in
the mat may also provide a rough bulk assessment of the
VOL. 75, 2009
DIVERSITY OF ARCHAEA IN A HYPERSALINE MICROBIAL MAT
1803
relative contributions of representatives of the three phylogenetic domains to the metabolic functions of the mat.
Although few identical sequences were encountered in libraries from 2001 and 2005, similar phylogenetic kinds of organisms were encountered (see Table S2 in the supplemental
material), consistent with previous observations of the relative
stability of the mat. However, too few archaeal sequences (only
⬃50) were examined in the libraries made with universal PCR
primers to warrant more than general comparisons.
To gain a more-detailed perspective on the specific distribution of archaeal sequences in the mat, PCR-based archaeonspecific clone libraries were prepared from each of 10 strata of
the mat (see Materials and Methods). An additional 817 archaeal rRNA sequences were determined, approximately
equally distributed among the strata. Although still undersampled (see below), we could use these sequences with previously determined bacterial sequences (29) to estimate statistically the comparative diversities of archaea and bacteria in
the mat and the relative numbers of OTUs at different degrees
of sequence identity. We define species-level diversity at 99%
sequence identity over the alignment masked to exclude highly
variable sequences (lanemaskPH or pos_var_Archaea_93). This
corresponds to approximately 97% identity in raw rRNA sequence alignments, which include hypervariable regions, approximately to the level of intraspecies sequence identity (18).
As shown in the observed and estimated (Chao 1) (6) species
richness curves in Fig. 1B, while the archaea in the bulk mat
represent substantial species-level diversity, they are less diverse than the resident bacteria, with severalfold-lower numbers of OTUs at all levels of sequence similarities. The Chao 1
predictions of diversity based on the observations show that
both bacteria and archaea are undersampled by severalfold in
these analyses, the usual situation with complex microbial communities. Nonetheless, we have presumably sampled the most
abundant populations.
The archaeal sequences are not distributed uniformly in the
mat. As shown in Fig. 2A, the observed and projected species
richness of archaeal sequences decreases severalfold from the
top of the mat into the deeper, highly reduced zones. This is
accompanied by differentiation in the kinds of organisms that
are detected. In Fig. 2B, we compare the distribution in the
mat of members of the Crenarchaeota and Euryarchaeota, generally considered the two broad-relatedness groups that constitute the Archaea. Figure 2B shows that euryarchaeote rRNA
FIG. 2. Species richness and diversity of archaea vary by depth in the
mat. (A) The observed (Sobs) and estimated (SChao1) (32) species richnesses of archaea decrease with depth in the mat. Rarefaction was used to
compensate for differences in the numbers of sequences per layer (see
Table S3 in the supplemental material) in the 817 archaeal sequences
from the 2005 mat core samples. (B) The number of 99% OTUs versus
depth for the Euryarchaeota and Crenarchaeota. Euryarchaeota dominate the diversity of archaeal species in the mat and are almost the
exclusive archaea in the top 2 mm. Crenarchaeota are consistently
present in the mat below 3 mm and begin to dominate diversity below
27 mm. (C) Simpson’s reciprocal diversity index (32) indicates the
highest diversity in the top 3 mm of the mat, at which point it decreases
rapidly to a depth of 5 mm and remains relatively constant through to
the deepest sample. Good’s coverage index (32) shows approximate
inverse proportionality to diversity and indicates that all layers of the
mat may benefit from sequencing beyond what was attempted in this
study. Indices are shown for 99% OTUs computed on the 817 archaeal
sequences from the 2005 mat core samples.
1804
VOL. 75, 2009
DIVERSITY OF ARCHAEA IN A HYPERSALINE MICROBIAL MAT
1805
FIG. 3. Phylogenetic trees of members of the Euryarchaeota and Crenarchaeota of the pond 4 mat. Bootstrap support values (percent) are
indicated at the bases of branches. Branches with less than 70% bootstrap support were collapsed to better reveal the statistically supported
structure of the trees. GenBank sequences reported elsewhere that are present in each clade are indicated with accession numbers along with the
distance to the closest sequence in this study. Clones show total numbers of clones from all samples and primer pairs, numbers of clones from
primers 4FA/515F/333FA/1391R, and numbers of clones from primer 23FA/1391R. The ICDs were obtained by the identification of the largest
uncorrected distance in the sequence distance matrix calculated by the ARB neighbor-joining distance matrix calculation function applied to the
sequences of interest, masked for hypervariability, within a specific clade. (A) The Euryarchaeota. The Euryarchaeota clade names were selected
in order of analysis except for GNMethanos (cluster with classic methanogens), GNHalos (cluster with the halobacteria), and GNThrm, which
cluster with sequences in the Greengenes taxonomy Thermoplasmata/E2/terrestrial and Thermoplasmata/E2/aquatic group (III). (B) The Crenarchaeota. The Crenarchaeota clade names were selected based upon similarity to previously published sequences and are prefixed as follows: DV,
deep-sea hydrothermal vent; MB, marine benthic; MV, mud volcano. MBCrenUn is a synthetic clade formed from all of the nonclade sequence
singletons and doubletons found within the MBCren branch of the tree shown in B.
genes are most abundant in the upper few mm, the oxic zone
during the day, where rRNA genes of the crenarchaeota are
comparatively rare. In contrast, crenarchaeote rRNA genes
increase in diversity and numbers with depth. Calculations of
archaeal species diversity and the predicted coverage of the
analysis with depth in the mat are shown in Fig. 2C. Diversity
declines precipitously at 3 to 4 mm into the mat, which approximately coincides with the oxic-anoxic boundary. Good’s
coverage is approximately inversely proportional to diversity in
this case, so substantial archaeal diversity remains to be documented throughout the mat.
Phylogenetic analysis of mat constituents. We aligned the
sequences with sequences drawn from public databases and
used the program RAxML to perform likelihood phylogenetic
analyses with large data sets, all as detailed in Materials
and Methods. The results of the phylogenetic analysis of
euryarchaeal and crenarchaeal sequences are compiled in the
two dendrograms of Fig. 3, respectively. Only a few of the
sequences have specific relationships to described organisms;
most are only remotely related to cultured examples of
archaea. As identified in Fig. 3, however, many of the main
groups of sequences are more or less closely related to archaeal sequences previously detected in other environmental
rRNA sequence surveys. Tables 1 and 2 summarize the environments and studies where sequences representative of the
main GN groups were encountered previously and their relationships with the GN sequences. Sediments and other stringently anoxic environments seem to be primary sources of
sequences similar to those analyzed here.
Currently, there is no recognized classification scheme for
1806
ROBERTSON ET AL.
APPL. ENVIRON. MICROBIOL.
TABLE 1. GN archaeal clades represented in previous studies of diverse environmentsa
Group prefix
Study
GenBank
accession no.
Description and location
Clone and/or taxonomic
designation
Eury5
A
Simmering-water sediments, Iheya Basin
AB019749
pISA18, DHVE5
MBCren
A
B
C
F
Black smoker chimney, Myojin Knoll
Marine sediments, Skan Bay, Alaska
Microbial mats, Black Sea
Deep-sea sediments, Atlantic ocean
AB019720
DQ522936
AJ578139
AF119128
pMC2A36, DHVC1
SBAK-shal-20, MBGB1
BS-SR-B9, MBGB
CRA8-27cm, MBGB
Eury6
A
A
G
E
Simmering-water sediments, Iheya Basin
Black smoker chimney, Myojin Knoll
Solvent-contaminated aquifer, Michigan
Gas-associated water, Japan
AB019755
AB019753
AF050612
DQ841229
pISA48, DHVE6
pMC2A35, DHVE6
WCHD3-30
MOB4-9
Eury4
A
Black smoker chimney, Myojin Knoll
AB019747
pMC2A17, DHVE4
DVCren
A
B
B
B
C
Black smoker chimney, Myojin Knoll
Shallow marine sediments, Skan Bay
Deep marine sediments, Skan Bay
Shallow marine sediments, Skan Bay
Microbial mats, Black Sea
AB019718
DQ640141
DQ640136
DQ522930
AJ578148
pMC2A15, DHVC1
SBAK-shal-12, MBGB-2
SBAK-deep-12, MBGB-2
SBAK-shal-08, MBGB-2
BS-SR-H5, MBGB
GNMethanos
E
D
J
Gas-associated water, Japan
Hypersaline microbial mat, Israel
Methanohalophilus portucalensis strain SF1
DQ841237
DQ103680
M59132
MOB7-2
ArcB03, Methanosarcinales
Named isolate
GNThrmAq
B
C
D
Marine sediments, Skan Bay
Microbial mats, Black Sea
Hypersaline microbial mat, Israel
DQ640149
AJ578149
DQ103673
SBAK-mid-31, MBG-D
BS-S-316, MBG-D
ArcG12, MBG-D
GNThrmTer
H
E
B
Hydrothermal sediments, Guaymas Basin
Gas-associated water, Japan
Marine sediments, Skan Bay
AY835423
DQ841244
DQ640155
7C08, Thermoplasmatales
MOB7-9
SBAK-shal-02, MBG-D
MVCren
I
Anaerobic rice paddy soil, Japan
AB243805
NRP-N
GHalos
D
K
Hypersaline microbial mat, Israel
“Halorubrum jeotgali” strain A30
DQ103677
EF077634
ArcF12, Halobacteriales
Named isolate
a
Clade name prefixes from this study matched with long archaeal sequences (ⱖ1,250 nt) found in GenBank.
clades, relatedness groups, of uncultured archaea. The designations that we use are specified in the dendrograms (Fig. 3),
which also identify the maximum ICD of the GN component of
the particular relatedness groups. This provides some view
of the extent of variation among the GN sequences that fall
into the particular group. An ICD of only a few percent indicates little phylogenetic diversity among the GN organisms
TABLE 2. Environmental survey studies which report clusters of
archaea similar to the clusters found in this study
Study
No. of groups
represented
in the mat
Reference
A
B
C
D
E
F
G
H
I
J
K
5
4
3
3
3
1
1
1
1
1
1
52
26
27
48
35
55
14
12
45
43
4
detected, perhaps species-level variation, organisms expected
to have generally similar physiologies. A total of ⬎10 to 15%
ICD indicates substantial phylogenetic depth among the organisms detected by the sequences. Most (⬎90%) of the mat
sequences are representatives of only four large phylogenetic
groups, designated with the prefixes Eury4, Eury5, Eury6, and
MBCren in Fig. 3. All these groups have large ICD values,
19% to 33%, which is indicative of the potential in each of
these groups for substantial physiological as well as phylogenetic diversity.
Figure 4 illustrates the distribution in the mat of the specific
euryarchaeal and crenarchaeal sequence clusters identified in
Fig. 3. The marked stratification of many of the sequence
groups suggests the occurrence of microenvironmental conditions that enrich for the particular clades. For instance, the
most abundant euryarchaeal rRNA sequences in the mat, representing the Eury5J clade, are stratigraphically tightly confined to the uppermost few mm, suggesting a dependence on
chemistry or perhaps some syntrophic relationship restricted to
the oxic or photic layer. On the other hand, sequences representative of the Eury4AA group are the most abundant
euryarchaeal sequences in the middle layers of the mat, well
below the oxic stratum. The Eury4AA phylogenetic group in-
VOL. 75, 2009
DIVERSITY OF ARCHAEA IN A HYPERSALINE MICROBIAL MAT
cludes sequences from other diverse sources, including hydrothermal vents, e.g., GenBank accession number AB019747
(52), and Kalahari Shield subsurface water, e.g., accession
number DQ336956 (4).
Additional fine structures in the distributions of the different
major phylogenetic groups in the strata of the mat are evident
in Fig. 4A and B. Figure 4C shows the distribution of some of
the minor euryarchaeotes, including the GNMethanos group.
The GNMethanos sequences, although not abundant in the
mat, are sufficiently closely related to characterized methanogens (97 to 99% identity in rRNA sequences) that we can infer
the potential for methanogenesis in these environmental organisms.
DISCUSSION
Archaeal diversity in the GN mat. The ecology of the GN
pond 4 mat is driven by diurnal photosynthesis in the upper
mm of the mat and by the reduction of seawater sulfate and
fermentations in the deeper mat. Based on microscopy and
culture studies, the GN and similar microbial mats had been
considered relatively “simple ecosystems” (7, 33, 34, 36, 37, 40,
51). However, rRNA sequence surveys have revealed instead
that the pond 4 mat teems with complex microbial diversity
(28–30). In this study, we explore the distribution and phylogenetic diversity of archaea in the mat.
To assess the overall genetic contribution of archaea to the
pond 4 mat biota, we analyzed rRNA sequences derived from
PCR libraries prepared using nominally universal primers.
The phylogenetic distribution of the sequences for bacteria/
archaea/eukaryotes in the mat, ⬃90/9/1, indicates that the
functions of the mat, although dominated by bacteria, are
likely influenced by archaeal metabolisms, an example of which
is the detection of small amounts of methane in gases collected
from the mat (3). Although bacterial diversity is much greater
than archaeal diversity, archaeal and bacterial diversities are
approximately proportional to depth. Maximum archaeal diversity occurs within the top few mm (the oxic zone), consisting
mainly of members of the Euryarchaeota with no cultured representation. Euryarchaeotes dominate the archaeal diversity to
a depth of ⬃26 mm, far into the anoxic, high-hydrogen sulfide
zone of the mat. In contrast, few crenarchaeal sequences are
found above 2 mm, but numbers and prevalences increase with
depth to become the most numerous and diverse archaeal
sequences in the mat below ⬃27 mm.
Phylogenetic stratification and chemical gradients. The archaeal constituents of the pond 4 mat are stratified, as long
observed for the bacterial constituents (7). The basis for the
stratifications of phyla is presumably metabolic, the requirement of organisms for particular conditions or nutrients. Different phyla tend to occupy different strata, which indicates the
occurrence of many such specifically favorable chemical gradients, although little is known about the nature of such microenvironments. The mat community overall is driven by photosynthesis, but light penetrates only a few mm into the mat.
Consequently, most of the mat volume is supported by fermentations of photosynthetic products or by the metabolism of
hydrogen derived from fermentations, such as in methanogenesis or sulfate reduction. The photic zone is expected to have
the highest metabolic rate, and indeed, previous measurements
1807
of ATP concentrations throughout the mat found the highest
concentrations in the top few mm, indicating that it is the most
biochemically active stratum of the mat (29).
One clear chemical boundary in the mat is the daily interface
between the oxic and anoxic zones, the low-sulfide zone 3 to 5
mm into the mat. This interface is populated by a rich diversity
of euryarchaeal phyla, individually tightly stratified across this
interface (Fig. 4A and C). The metabolic properties of these
euryarchaeotes are mainly not known, although a few members
of the GNMethanos group, are specifically related to known
methanogens (Fig. 4A) and can probably conduct methanogenesis (below). In contrast to the prevalence of euryarchaeota
in the upper mat, crenarchaeotes apparently avoid the highenergy upper layers of the mat and are found primarily in the
deep anoxic zone. The main occurrence of crenarchaeotes at
depth in the mat may reflect the energy-poor state of that
portion of the mat. ATP concentrations in the deeper zones of
the mat strata are ⬍10% of the concentration at the highly
metabolically active mat surface, indicating relatively low metabolic activity in the deep zones of the mat (29). The crenarchaeotes found in the deepest layer of the mat are phylogenetically nearest to crenarchaeotes that were previously
reported to prevail in other extremely low-nutrient environments, in the deep sea and benthic sediments (5, 25, 46, 52, 55).
The prevalence of crenarchaeotes in the deepest, most-energypoor region of the mat is consistent with the model for archaeal dominance of energy-poor environments suggested previously by Valentine (54).
rRNA phylogenetics and metabolism. Extrapolation from an
rRNA-based phylogeny of environmental sequences to physiological properties of organisms that correspond to the sequences is possible only if the environmental organisms are
closely related to characterized microbes. The GN sequences
are only distantly related to previously described organisms, so
few specific properties of the GN archaea can be inferred.
Thus, this survey and others show the need for a more-comprehensive study of the properties of archaea in general; our
understanding of the phylogenetic and biochemical diversity
among these organisms is abysmally poor. Environmental sequence surveys point to a vast reservoir of untapped biodiversity capable of important metabolisms that likely play significant roles in elemental cycling.
None of the crenarchaeal groups and only two clades of the
Euryarchaeota, which collectively represent less than 3% of
all sequences, contain close cultured representatives. The
GBHalos clade contains just three such sequences, which are
98 to 99% identical to those of Halorubrum sp., Halobacterium
sp., or Haloplanus sp., all classic salt-tolerant euryarchaeaotes.
The GNMethanos relatedness group contains sequences that
are 98 to 99% identical to Methanohalophilus sp., a well-characterized halotolerant methanogen that produces methane
from H2 and CO2 using methanol or methylamine as a substrate (22, 41–43). This high level of rRNA sequence identity
predicts that the environmental organisms can also conduct
methanogenesis. Although representatives of this group occur
throughout the mat, they are found predominantly in the surface mm, concentrated particularly at the oxic-anoxic boundary
(Fig. 4C). The restriction of methanogens to the layers just
below the surface of the mat indicates a balance between the
presence of oxygen during the day, which poisons methano-
ROBERTSON ET AL.
A
3.5
3.0
Total Sequences (%)
2.5
2.0
1.5
1.0
0.5
0.0
0.5
1.5
2.5
3.5
4.5
5.5
8.0
16.0
28.0
41.5
B
6.0
Total Sequences (%)
1808
5.0
4.0
3.0
2.0
1.0
0.0
0.5
1.5
2.5
3.5
4.5
5.5
8.0
16.0
28.0
41.5
APPL. ENVIRON. MICROBIOL.
VOL. 75, 2009
DIVERSITY OF ARCHAEA IN A HYPERSALINE MICROBIAL MAT
C
FIG. 4. Phylogenetic clades versus depth in the mat for the sectioned core samples from 2005. (A) The Euryarchaeota. The height of
the three-dimensional cones represents the percentage of total sequences (817) for each depth sample point for each euryarchaeal clade
(solid filled circles represent 0%). The order of the clades was selected
to minimize visual interference among the data points. Although most
numerous and diverse at the top of the mat, the Euryarchaeota are
present in all layers sampled. (B) The Crenarchaeota. As in A, the
height of the cones is the percentage of total sequences from the 2005
sectioned core samples. The Crenarchaeota show the highest abundance in the deeper layers of the mat. The largest crenarchaeal clade,
MBCrenUn, is composed of many species but does not readily segregate by depth distribution or phylogenetic bootstrap support. (C) Additional detail showing minority euryarchaeal clades found in the top 5
mm of the mat, including the mat methanogen clade GNMethanos.
genesis, and bacterial sulfate reduction in the deeper layers,
which can outcompete methanogenesis for hydrogen and acetate substrates (21). Recognizable methanogens represent only
a small proportion of the archaea detected, but it is possible
that some of the GN archaea with no characterized close
relatives also conduct methanogenesis. However, the relative
rarity of methanogens as observed by sequences in the mat is
consistent with data from studies that showed that the pond 4
mat produces only small amounts of methane under field conditions (3, 47). The depth distribution of some of the archaeal
phylogenetic clades tends to be narrowly defined despite the
fact that these same clades have relatively large ICDs, which
might predict broad biochemical potential and thereby wider
distribution in the mat. The Eury5J group, for instance, has an
ICD of 15%, far more diverse than the genus level, but is
restricted to the upper mm of the mat, avoiding even the
oxic-anoxic boundary where most of the euryarchaeal species
reside. On the other hand, other clades with ICDs of ⬎10 to
15% (e.g., Eury5K) show more than one depth maximum or
are broadly distributed throughout the mat (e.g., MBCrenUn)
(see below), consistent with the occurrence of many species
with differing biochemistries in these clades. Despite efforts,
diverse clades with broad distributions in the mat could not be
resolved into stratified phyla. This indicates that some of the
clades detected have representatives with multiple metabolic
repertoires, which allow occupancy in different strata of the
1809
mat. It is also possible that the mat composition is sufficiently
undersampled that adequate bootstrap support (70% bootstrap support for in-clade) for some stratified phyla does not
emerge from phylogenetic analyses.
An example of a clade that may fall into this last category is
the large MBCrenUn (marine benthic crenarchaeota, unaffiliated) clade shown in Fig. 4B. MBCrenUn is composed of
numerous sequence singletons and doubletons that are all on
the same branch of the tree in Fig. 3B as clades DVCrenA and
MBCrenB through MBCrenI with 77% bootstrap support but
are not specifically associated with those groups. Thus, the
MBCrenUn group is part of a larger phylogenetic radiation
that also includes clades that dominate the lower levels of the
mat, in some cases in a stratified manner. Representatives of
the MBCrenI, MBCrenD, and MBCrenH clades are abundant
only in the lowest layer of the mat. This would be consistent
with a previous proposal that the MBCrenB group contains
new forms of sulfate reducers (27).
Conclusion. This study provides perspective on the diversity
of archaea that occur in moderately hypersaline, stratified microbial mats in general and builds a phylogenetic framework
for the GN pond 4 mat in particular. We document substantial
novel phylogenetic diversity of archaea, placed in specific, although little-understood, ecological contexts. In addition to
the utility in the identification of novel environmental organisms, the sequences are also the basis of tools, such as hybridization probes and PCR primers, with which to explore these
main constituents of this complex and highly productive community further.
ACKNOWLEDGMENTS
This research was supported by funds to N.R.P. from the Astrobiology
Institute of NASA and by an Agouron postdoctoral fellowship to J.R.S.
We thank representatives of Exportadora de Sal of Guerrero Negro,
Baja California Sur, Mexico, for access to sites and assistance. We also
thank the GN mat researchers at the NASA Ames Research Center
for field support, permitting, and logistical assistance.
REFERENCES
1. Ashelford, K. E., N. A. Chuzhanova, J. C. Fry, A. J. Jones, and A. J.
Weightman. 2006. New screening software shows that most recent large 16S
rRNA gene clone libraries contain chimeras. Appl. Environ. Microbiol.
72:5734–5741.
2. Baati, H., S. Guermazi, R. Amdouni, N. Gharsallah, A. Sghir, and E. Ammar. 2008. Prokaryotic diversity of a Tunisian multipond solar saltern. Extremophiles 12:505–518.
3. Bebout, B. M., S. P. Carpenter, D. J. Des Marais, M. Discipulo, T. Embaye,
F. Garcia-Pichel, T. M. Hoehler, M. Hogan, L. L. Jahnke, R. M. Keller, S. R.
Miller, L. E. Prufert-Bebout, C. Raleigh, M. Rothrock, and K. Turk. 2002.
Long-term manipulations of intact microbial mat communities in a greenhouse collaboratory: simulating earth’s present and past field environments.
Astrobiology 2:383–402.
4. Benson, D. A., I. Karsch-Mizrachi, D. J. Lipman, J. Ostell, and D. L.
Wheeler. 2006 GenBank. Nucleic Acids Res. 34:D16–D20.
5. Biddle, J. F., J. S. Lipp, M. A. Lever, K. G. Lloyd, K. B. Sorensen, R.
Anderson, H. F. Fredricks, M. Elvert, T. J. Kelly, D. P. Schrag, M. L. Sogin,
J. E. Brenchley, A. Teske, C. H. House, and K. U. Hinrichs. 2006. Heterotrophic Archaea dominate sedimentary subsurface ecosystems off Peru. Proc.
Natl. Acad. Sci. USA 103:3846–3851.
6. Colwell, R. K., and J. A. Coddington. 1994. Estimating terrestrial biodiversity
through extrapolation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 345:101–118.
7. D’Amelio D’Antoni, E., Y. Cohen, and D. J. DesMaris. 1989. Comparative
functional ultrastructure of two hypersaline submerged cyanobacterial mats:
Guerrero Negro, Baja California Sur, Mexico, and Solar Lake, Sinai, Egypt.
In Y. Cohen and E. Rosenberg (ed.), Microbial mats: physiological ecology
of benthic microbial communities. American Society for Microbiology,
Washington, DC.
8. Decker, K. L., C. S. Potter, B. M. Bebout, D. J. Marais, S. Carpenter, M. Discipulo,
T. M. Hoehler, S. R. Miller, B. Thamdrup, K. A. Turk, and P. T. Visscher. 2005.
1810
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
ROBERTSON ET AL.
Mathematical simulation of the diel O, S, and C biogeochemistry of a hypersaline
microbial mat. FEMS Microbiol. Ecol. 52:377–395.
DeSantis, T. Z., P. Hugenholtz, N. Larsen, M. Rojas, E. L. Brodie, K. Keller,
T. Huber, D. Dalevi, P. Hu, and G. L. Andersen. 2006. Greengenes, a
chimera-checked 16S rRNA gene database and workbench compatible with
ARB. Appl. Environ. Microbiol. 72:5069–5072.
DeSantis, T. Z., Jr., P. Hugenholtz, K. Keller, E. L. Brodie, N. Larsen, Y. M.
Piceno, R. Phan, and G. L. Andersen. 2006. NAST: a multiple sequence
alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids
Res. 34:W394–W399.
Des Marais, D. J. 2003. Biogeochemistry of hypersaline microbial mats
illustrates the dynamics of modern microbial ecosystems and the early evolution of the biosphere. Biol. Bull. 204:160–167.
Dhillon, A., M. Lever, K. G. Lloyd, D. B. Albert, M. L. Sogin, and A. Teske.
2005. Methanogen diversity evidenced by molecular characterization of
methyl coenzyme M reductase A (mcrA) genes in hydrothermal sediments of
the Guaymas Basin. Appl. Environ. Microbiol. 71:4592–4601.
Dojka, M. A., J. K. Harris, and N. R. Pace. 2000. Expanding the known
diversity and environmental distribution of an uncultured phylogenetic division of bacteria. Appl. Environ. Microbiol. 66:1617–1621.
Dojka, M. A., P. Hugenholtz, S. K. Haack, and N. R. Pace. 1998. Microbial
diversity in a hydrocarbon- and chlorinated-solvent-contaminated aquifer
undergoing intrinsic bioremediation. Appl. Environ. Microbiol. 64:3869–
3877.
Feazel, L. M., J. R. Spear, A. B. Berger, J. K. Harris, D. N. Frank, R. E. Ley,
and N. R. Pace. 2008. Eucaryotic diversity in a hypersaline microbial mat.
Appl. Environ. Microbiol. 74:329–332.
Fike, D. A., C. L. Gammon, W. Ziebis, and V. J. Orphan. 2008. Micron-scale
mapping of sulfur cycling across the oxycline of a cyanobacterial mat: a
paired nanoSIMS and CARD-FISH approach. ISME J. 2:749–759.
Frank, D. N. 2008. XplorSeq: a software environment for integrated management and phylogenetic analysis of metagenomic sequence data. BMC
Bioinformatics 9:420.
Goebel, B. M., and E. Stackebrandt. 1994. Cultural and phylogenetic analysis
of mixed microbial populations found in natural and commercial bioleaching
environments. Appl. Environ. Microbiol. 60:1614–1621.
Green, S. J., C. Blackford, P. Bucki, L. L. Jahnke, and L. Prufert-Bebout.
2008. A salinity and sulfate manipulation of hypersaline microbial mats
reveals stasis in the cyanobacterial community structure. ISME J. 2:457–470.
Hoehler, T. M., B. M. Bebout, and D. J. Des Marais. 2001. The role of
microbial mats in the production of reduced gases on the early Earth. Nature
412:324–327.
Holmer, M., and E. Kirstensen. 1994. Coexistance of sulfate reduction and
methane production in an organic-rich sediment. Mar. Ecol. Prog. Ser.
107:117–184.
Hong, S. H., J. Bunge, S. O. Jeon, and S. S. Epstein. 2006. Predicting
microbial species richness. Proc. Natl. Acad. Sci. USA 103:117–122.
Huber, T., G. Faulkner, and P. Hugenholtz. 2004. Bellerophon: a program to
detect chimeric sequences in multiple sequence alignments. Bioinformatics
20:2317–2319.
Jorgensen, B. B., Y. Cohen, and D. J. Des Marais. 1987. Photosynthetic
action spectra and adaptation to spectral light distribution in a benthic
cyanobacterial mat. Appl. Environ. Microbiol. 53:879–886.
Karner, M. B., E. F. DeLong, and D. M. Karl. 2001. Archaeal dominance in
the mesopelagic zone of the Pacific Ocean. Nature 409:507–510.
Kendall, M. M., G. D. Wardlaw, C. F. Tang, A. S. Bonin, Y. Liu, and D. L.
Valentine. 2007. Diversity of Archaea in marine sediments from Skan Bay,
Alaska, including cultivated methanogens, and description of Methanogenium boonei sp. nov. Appl. Environ. Microbiol. 73:407–414.
Knittel, K., T. Losekann, A. Boetius, R. Kort, and R. Amann. 2005. Diversity
and distribution of methanotrophic archaea at cold seeps. Appl. Environ.
Microbiol. 71:467–479.
Kunin, V., J. Raes, J. K. Harris, J. R. Spear, J. J. Walker, N. Ivanova, C. von
Mering, B. M. Bebout, N. R. Pace, P. Bork, and P. Hugenholtz. 2008.
Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat. Mol. Syst. Biol. 4:198.
Ley, R. E., J. K. Harris, J. Wilcox, J. R. Spear, S. R. Miller, B. M. Bebout,
J. A. Maresca, D. A. Bryant, M. L. Sogin, and N. R. Pace. 2006. Unexpected
diversity and complexity of the Guerrero Negro hypersaline microbial mat.
Appl. Environ. Microbiol. 72:3685–3695.
Liu, Z., C. Lozupone, M. Hamady, F. D. Bushman, and R. Knight. 2007.
Short pyrosequencing reads suffice for accurate microbial community analysis. Nucleic Acids Res. 35:e120.
Ludwig, W., O. Strunk, R. Westram, L. Richter, H. Meier, Yadhukumar, A.
Buchner, T. Lai, S. Steppi, G. Jobb, W. Forster, I. Brettske, S. Gerber, A. W.
Ginhart, O. Gross, S. Grumann, S. Hermann, R. Jost, A. Konig, T. Liss, R.
Lussmann, M. May, B. Nonhoff, B. Reichel, R. Strehlow, A. Stamatakis, N.
Stuckmann, A. Vilbig, M. Lenke, T. Ludwig, A. Bode, and K.-H. Schleifer.
2004. ARB: a software environment for sequence data. Nucleic Acids Res.
32:1363–1371.
Magurran, A. E. 2004. Measuring biological diversity. Blackwell Publishing
Company, Malden, MA.
APPL. ENVIRON. MICROBIOL.
33. Minz, D., S. Fishbain, S. J. Green, G. Muyzer, Y. Cohen, B. E. Rittmann, and
D. A. Stahl. 1999. Unexpected population distribution in a microbial mat
community: sulfate-reducing bacteria localized to the highly oxic chemocline
in contrast to a eukaryotic preference for anoxia. Appl. Environ. Microbiol.
65:4659–4665.
34. Minz, D., J. L. Flax, S. J. Green, G. Muyzer, Y. Cohen, M. Wagner, B. E.
Rittmann, and D. A. Stahl. 1999. Diversity of sulfate-reducing bacteria in
oxic and anoxic regions of a microbial mat characterized by comparative
analysis of dissimilatory sulfite reductase genes. Appl. Environ. Microbiol.
65:4666–4671.
35. Mochimaru, H., H. Yoshioka, H. Tamaki, K. Nakamura, N. Kaneko, S.
Sakata, H. Imachi, Y. Sekiguchi, H. Uchiyama, and Y. Kamagata. 2007.
Microbial diversity and methanogenic potential in a high temperature natural gas field in Japan. Extremophiles 11:453–461.
36. Nubel, U., M. M. Bateson, M. T. Madigan, M. Kuhl, and D. M. Ward. 2001.
Diversity and distribution in hypersaline microbial mats of bacteria related to
Chloroflexus spp. Appl. Environ. Microbiol. 67:4365–4371.
37. Nubel, U., F. Garcia-Pichel, M. Kuhl, and G. Muyzer. 1999. Quantifying
microbial diversity: morphotypes, 16S rRNA genes, and carotenoids of oxygenic phototrophs in microbial mats. Appl. Environ. Microbiol. 65:422–430.
38. Omoregie, E. O., L. L. Crumbliss, B. M. Bebout, and J. P. Zehr. 2004.
Determination of nitrogen-fixing phylotypes in Lyngbya sp. and Microcoleus
chthonoplastes cyanobacterial mats from Guerrero Negro, Baja California,
Mexico. Appl. Environ. Microbiol. 70:2119–2128.
39. Pruesse, E., C. Quast, K. Knittel, B. M. Fuchs, W. Ludwig, J. Peplies, and
F. O. Glockner. 2007. SILVA: a comprehensive online resource for quality
checked and aligned ribosomal RNA sequence data compatible with ARB.
Nucleic Acids Res. 35:7188–7196.
40. Risatti, J. B., W. C. Capman, and D. A. Stahl. 1994. Community structure of
a microbial mat: the phylogenetic dimension. Proc. Natl. Acad. Sci. USA
91:10173–10177.
41. Roberts, M. F., M. C. Lai, and R. P. Gunsalus. 1992. Biosynthetic pathways
of the osmolytes Nε-acetyl-␤-lysine, ␤-glutamine, and betaine in Methanohalophilus strain FDF1 suggested by nuclear magnetic resonance analyses. J.
Bacteriol. 174:6688–6693.
42. Robertson, D. E., M. C. Lai, R. P. Gunsalus, and M. F. Roberts. 1992.
Composition, variation, and dynamics of major osmotic solutes in Methanohalophilus strain FDF1. Appl. Environ. Microbiol. 58:2438–2443.
43. Rouviere, P., L. Mandelco, S. Winker, and C. R. Woese. 1992. A detailed
phylogeny for the Methanomicrobiales. Syst. Appl. Microbiol. 15:363–371.
44. Sahl, J. W., N. R. Pace, and J. R. Spear. 2008. A comparative molecular
analysis of endoevaporatic microbial communities. Appl. Environ. Microbiol. 74:6444–6446.
45. Sakai, S., H. Imachi, Y. Sekiguchi, A. Ohashi, H. Harada, and Y. Kamagata.
2007. Isolation of key methanogens for global methane emission from rice
paddy fields: a novel isolate affiliated with the clone cluster rice cluster I.
Appl. Environ. Microbiol. 73:4326–4331.
46. Sinninghe Damsté, J. S., W. I. Rijpstra, E. C. Hopmans, F. G. Prahl, S. G. Wakeham, and S. Schouten. 2002. Distribution of membrane lipids of planktonic Crenarchaeota in the Arabian Sea. Appl. Environ. Microbiol. 68:2997–3002.
47. Smith, J. M., S. J. Green, C. A. Kelley, L. Prufert-Bebout, and B. M. Bebout.
2008. Shifts in methanogen community structure and function associated
with long-term manipulation of sulfate and salinity in a hypersaline microbial
mat. Environ. Microbiol. 10:386–394.
48. Sorensen, K. B., D. E. Canfield, A. P. Teske, and A. Oren. 2005. Community
composition of a hypersaline endoevaporitic microbial mat. Appl. Environ.
Microbiol. 71:7352–7365.
49. Spear, J. R., R. E. Ley, A. B. Berger, and N. R. Pace. 2003. Complexity in
natural microbial ecosystems: the Guerrero Negro experience. Biol. Bull.
204:168–173.
50. Stamatakis, A. 2006. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics
22:2688–2690.
51. Summons, R. E., L. L. Jahnke, J. M. Hope, and G. A. Logan. 1999. 2-Methylhopanoids as biomarkers for cyanobacterial oxygenic photosynthesis. Nature 400:554–557.
52. Takai, K., and K. Horikoshi. 1999. Genetic diversity of archaea in deep-sea
hydrothermal vent environments. Genetics 152:1285–1297.
53. Tice, M. M., and D. R. Lowe. 2004. Photosynthetic microbial mats in the
3,416-Myr-old ocean. Nature 431:549–552.
54. Valentine, D. L. 2007. Adaptations to energy stress dictate the ecology and
evolution of the Archaea. Nat. Rev. Microbiol. 5:316–323.
55. Vetriani, C., H. W. Jannasch, B. J. MacGregor, D. A. Stahl, and A. L.
Reysenbach. 1999. Population structure and phylogenetic characterization of
marine benthic Archaea in deep-sea sediments. Appl. Environ. Microbiol.
65:4375–4384.
56. Visscher, P. T., L. K. Baumgartner, D. H. Buckley, D. R. Rogers, M. E.
Hogan, C. D. Raleigh, K. A. Turk, and D. J. Des Marais. 2003. Dimethyl
sulfide and methanethiol formation in microbial mats: potential pathways for
biogenic signatures. Environ. Microbiol. 5:296–308.