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AQUATIC MICROBIAL ECOLOGY
Aquat Microb Ecol
Vol. 64: 267–273, 2011
doi: 10.3354/ame01527 Published online September 20
INTRODUCTION
Biodiversity is often defined as the variability among
living organisms within ecological systems (Harper &
Hawksworth 1995, Magurran 2004) and is generally
calculated using traditional indices such as richness
and evenness. However, population geneticists have
developed methods that characterize the diversity of
populations or groups using phylogenetic or taxonomic
differences (Faith 1994, Clarke & Warwick 1998).
Novel diversity indices have been introduced that
reflect this variability by characterizing the related-
ness or distinctness of organisms within a community
(Nixon & Wheeler 1990, Vane-Wright et al. 1991, Faith
1992, Solow et al. 1993). In addition to being indepen-
dent of sample size (Price et al. 1999), the advantage of
utilizing phylogenetic distance as opposed to standard
diversity estimates in microbial communities is that the
functional contribution of a community may depend
less on species counts and more on the phylogenetic
diversity represented (our Fig. 1; Clarke & Warwick
1998). The introduction of these methods stems from
limitations of traditional diversity indices where each
organism is counted equivalently despite high phylo-
genetic divergence (Fig. 1). One potential result of neg -
lecting the phylogenetic difference between communi-
© Inter-Research 2011 · www.int-res.com*Email: jacob.parnell@usu.edu
Phylogenetic distance in Great Salt Lake microbial
communities
J. Jacob Parnell1,*, Giovanni Rompato1, Todd A. Crowl2,5, Bart C. Weimer3,
Michael E. Pfrender4
1Center for Integrated BioSystems and Department of Biology, and 2Department of Watershed Sciences and
Ecology Center, Utah State University, Logan, Utah 84322, USA
3School of Veterinary Medicine, Department of Population Health and Reproduction,
University of California at Davis, Davis, California 95616, USA
4Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana 46556, USA
5Present address: National Science Foundation, Division of Environmental Biology, Arlington, Virginia 22230, USA
ABSTRACT: Investigations of community composition often rely on metrics based on the abundance
of taxonomic groups to estimate biodiversity. Although traditional measures of biodiversity, such as
richness and evenness, can be used in a comparative fashion to evaluate differences among commu-
nities in both temporal and spatial contexts, these measures generally omit a phylogenetic perspec-
tive of the evolutionary diversity represented in a community. Using Fast UniFrac, we examined
PhyloChip data from 9 microbial communities throughout the Great Salt Lake, Utah, USA, for
changes in phylogenetic distance. We found a significant correlation (p < 0.001) between the
decreased community phylogenetic distance and increased salt concentration. Despite significant
differences in composition, communities in locations with a similar salt concentration had a similar
phylogenetic distance. This trend was confirmed by analyzing the biodiversity of 89 published micro-
bial communities classified as extreme (n = 20) and non-extreme (n = 69). Although we found no sig-
nificant statistical difference in traditional diversity estimates, such as Chao1 and abundance-based
coverage estimate (ACE), between environments, the phylogenetic distance within extreme commu-
nities is significantly lower than in non-extreme communities. A smaller phylogenetic distance within
more extreme communities may imply evolutionary conservatism and specialization.
KEY WORDS: Biodiversity · Hypersaline · Extremophile · Phylogenetic distance · Ecology
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ties is that 2 communities may be considered equally
diverse when, in fact, one community is more phyloge-
netically and functionally diverse than the other (Mar-
tin 2002, Hamady et al. 2010). For example, consider
the representative communities in Fig. 1. All of the
communities have the same number of species, and, at
the highest taxonomic resolution (e.g. species or geno-
type), evenness is also identical. However, the ge-
netic — and consequently functional — difference be-
tween these 4 communities is quite distinct. This
problem becomes particularly troubling when using
biodiversity to infer community function. For example,
an over-abundance of closely related groups of species
that are functionally redundant (Faith 1994) can lead to
a disparity between traditional estimates and func-
tional diversity.
In a previous study, we examined the richness and
the taxonomic dispersion between the genus-to-
species ratio and the species-to-genotype-ratio (Par-
nell et al. 2009). We found a significant loss of geno-
typic diversity in extreme environments that had
experienced disturbances, and we hypothesized that
specialization in extreme environments drives the
maintenance of genotypic diversity. In the present
study we tested this hypothesis by analyzing the phy-
logenetic distance of 9 microbial communities in the
Great Salt Lake, Utah, USA, that differ widely in salin-
ity (Parnell et al. 2010). In addition, we examined 89
published community datasets wherein we asked
whether extreme environments (defined by the origi-
nal authors) harbor more closely related groups than
would be expected for non-specialized communities.
MATERIALS AND METHODS
Case study. We used phylogenetic data from a previ-
ous study (Parnell et al. 2010) collected along a salinity
gradient, including additional sampling points from the
Great Salt Lake (GSL), Utah, USA (Fig. 2). One sample
was collected near freshwater inlets into the GSL in
Farmington Bay (FB; 41° 03’ 31.30’’ N, 112°14’ 04.98’’ W).
Three samples were collected from each of 2 sites
in the south arm of GSL: Sites A (41° 18’48.6’’N,
112° 40’ 59’’ W) and B (41° 07’16.9’’N, 112° 33’ 03.5’’ W);
these samples were taken at the surface (A and B
surface), within the water column (A and B column),
and and at the bottom (A and B bottom) near the sedi-
ments (ca. 3 m depth). Another surface sample was col-
lected near Antelope Island (AI; 41° 02’ 22.37’’N,
112° 16’ 42.33’’W). One sample was collected from
Aquat Microb Ecol 64: 267–273, 2011268
Fig. 1. Representative phylogenetic trees of microbial com-
munities. Despite the same values for richness in each, Com-
munity A will most likely have the greatest functional diver-
sity, and functional diversity will decrease (A > B > C > D) as
the phylogenetic relatedness increases (A < B < C < D). Figure
redrawn from Clarke & Warwick (2001)
Fig. 2. Sample site locations along a salinity gradient in the
Great Salt Lake, Utah, USA. Farmington Bay (FB) is the least
saline. South arm samples — AI (near Antelope Island) and
the 6 samples taken from Sites A and B — are from locations
with inter mediate salinity; the samples taken from Sites A and
B include depth samples. The north arm (NA) sample, col-
lected near Rozel Point, is near salt saturation. Black lines
indicate causeway structures
Parnell et al.: Phylogenetic distance in Great Salt Lake
the salt-saturated brine of the north arm (NA;
41° 25’ 56.13’’N, 112° 39’ 48.31’’W) of GSL near Rozel
Point. The least extreme environment was near the
freshwater inlet into the lake, where salt concentrations
are approximately twice that of marine environments.
The microbial community collected from the waters
of the southern arm of the lake inhabits an environ-
ment with an intermediate (~15%) salt concentration.
The north end of the lake, the site for collection of
the extremophilic hypersaline community, was at salt
saturation.
Total DNA was extracted from the hypersaline
waters of the GSL as described by Griffiths et al. (2000)
using modified hexadecyltrimethylammonium bro-
mide (CTAB) extraction buffer (Zhou et al. 1996).
Bead-beating was used to lyse cells, and DNA was
extracted with chloroform (Griffiths et al. 2000). The
extracted community DNA was purified through a
Sephacryl®S-300 column (Parnell et al. 2010).
To assess microbial diversity we used the 16S Phylo-
genetic Array (PhyloChip) that contained probes for
8741 bacterial and archaeal taxa (Brodie et al. 2007).
Hybridization of the PhyloChip is achieved using
slightly modified Affymetrix protocols. The 16S rRNA
was amplified by PCR with Bacteria-specific primers
(8F: 5’-AGA GTT TGA TCC TGG CTC AG-3’; 1512R:
5’-ACG GCT ACC TTG TTA CGA CTT-3’) or
Archaea-specific primers (F: 5’-GAC GGG CGG TGT
GTC A-3’; R: 5’-GCG GAT CCG CGG CCG CTG CAG
ATC-3’) (Parnell et al. 2010). To minimize the primer
bias, PCR amplification was performed with a temper-
ature gradient from 48 to 58°C for the annealing tem-
perature. The PCR products from the different amplifi-
cation reactions were collected, purified (QIAquick,
Qiagen) and quantified. Fragmentation, labeling, and
hybridization were done as mentioned previously
(Parnell et al. 2010).
Presence/absence values were determined using
probe pair scores by Phylotrac analysis (www.phylo-
trac.org). Probe pairs scored as positive met 2 criteria:
(1) the intensity of fluorescence from the perfect match
probe has to be greater than 1.3 times the intensity
from the mismatch control; and (2) the difference in
intensity (perfect match – mismatch) has to be at least
500 times greater than the squared noise value (> 500
N2; see Brodie et al. 2006). Phylotrac data were im -
ported into Fast UniFrac (http://bmf2.colorado.edu/
fastunifrac) as per Hamady et al. (2010) for community
comparisons. Phylogenetic classifications of PhyloChip
data were weighted by class, order and family for sub-
sequent community comparisons (Clarke & Warwick
2001).
Meta analysis. Community biodiversity information
was obtained by downloading the 16S rRNA sequence
information of 89 randomly selected microbial commu-
nities (each containing between 100 and 726 se -
quences) from the ribosomal database project (http://
rdp.cme.msu.edu) as mentioned previously (Parnell et
al. 2009). Microbial communities were from a wide
range of globally distributed environmental settings
amounting to over 18 000 total sequences (see Table S1
in the supplement at www.int-res.com/articles/suppl/
a064p267_supp.pdf). After collecting microbial com-
munity datasets, we divided the datasets into 2 cate-
gories based on the environmental characteristics of
each community as originally defined by the authors
(Table 1). Briefly, datasets were categorized as ‘ex -
treme’ (n = 20) based on the description of the environ-
ments from which the community data were collected
(Table S1): environments with high pressure (i.e. deep
ocean), extreme temperatures, high salinity, low pH, or
environments that were contaminated with solvents;
communities with relatively normal environmental
parameters (n = 69) were termed ‘non-extreme’. The
average sample size for extreme and non-extreme
communities was not significantly different, minimiz-
ing sampling issues. We recognize the fact that ex -
treme and non-extreme environments are not discrete,
but rather a continuum, and omitted communities
whose category would be considered uncertain; some
extremophilic environmental details are in cluded in
Table 1.
Each microbial community was analyzed with
DOTUR (Schloss & Handelsman 2005) for biodiversity
using the Simpson index (Chazdon et al. 1998, Hughes
et al. 2001, Magurran 2004), the Shannon evenness
index (Magurran 2004), and the abundance-based
coverage estimate (ACE) (Chazdon et al. 1998, Hughes
et al. 2001, Magurran 2004).
Data on microbial biodiversity, including evenness,
richness and phylogenetic distance components, were
examined using descriptive and inductive analyses for
a difference in extreme environments. In order to
normalize residuals, Simpson index data were trans-
formed using the negative natural log (Rosenzweig
1995). Likewise, in order to compensate for hetero -
269
Non-extreme n Extreme n
Fresh water 6 Oligotrophic (BOD <1 ppm) 2
Marine water 11 Radiation (>50 µCi g–1) 1
Sediments 4 Low pH (<4.5) 2
Soils 26 Low temperature (< 5°C) 5
Microbiome 13 Contaminated 5
Waste treatment 9 Hypersaline (> 7%) 3
High pressure 2
69 20
Table 1. Summary of environmental conditions supporting
microbial communities (n = 89) analyzed in this study. BOD:
biological oxygen demand
Aquat Microb Ecol 64: 267–273, 2011
scedasticity and to normalize residuals, we used nat-
ural log-transformed ACE values. We compared the
variance within extreme and non-extreme communi-
ties using Student’s t-test for independent samples in
order to compensate for the different sample sizes. Sta-
tistical analyses and graphical output were performed
using JMP8 software (SAS).
The Simpson diversity index is a composite value
that captures both evenness and richness characteris-
tics of community assemblages (Magurran 2004) and is
a robust measure for statistical analyses. In addition,
the Simpson diversity index is relatively insensitive
to undersampling (Chao & Shen 2003). Evenness of
microbial communities was determined using the
Shannon evenness measure as described by Magurran
(2004). The ACE was calculated following Hughes et
al. (2001) and Magurran (2004). In addition to approxi-
mating richness using the ACE, we verified richness
patterns using the Chao1 estimate of richness as
described by Magurran (2004).
We used a quantitative measure of genetic diversity
similar to that using the branch length for the phylo -
genetic tree (Faith 1992, 1994). Specifically, the
genetic distance for each community was determined
by the average distance for all members within the
community calculated from distance matrix.
RESULTS AND DISCUSSION
Case study
In order to determine how the degree of environ-
mental extremity affects phylogenetic distance, we
compared 9 microbial communities examined previ-
ously along a salinity gradient in the Great Salt Lake,
Utah, USA. In this case study, the phylogenetic dis-
tance was reached using a qualitative approach
(Clarke & Warwick 2001) due to the qualitative nature
of the phylogenetic data (Parnell et al. 2010). The
microbial community richness ranged from 1114 iden-
tified organisms (Archaea and Bacteria) at the lowest
salinity (FB; approximately 8% NaCl) to 145 organisms
at salt saturation (NA).
UniFrac clustering demonstrates the influence of
salt concentration on community composition (Fig. 3A);
this separation of communities was confirmed using
principal coordinate analysis (Fig. 3B). Despite signifi-
cant differences in individual communities within sim-
ilar salt concentrations (all south arm samples; inter-
mediate salt) using the Fast UniFrac p-test (corrected
p < 0.05 for all community comparisons except for
Abottom vs. Bsurface and Acolumn vs. Bbottom) and
the UniFrac significance test (corrected p < 0.05 for
community comparisons: Abottom vs. Bcolumn, Abot-
tom vs. Bsurface, Bbottom vs. Bsurface, and Bcolumn
vs. Bsurface), the phylogenetic distance within these
communities was similar. We found a significant corre-
lation (p < 0.001) between higher salinity environments
and lower phylogenetic distance (Fig. 4). Difference in
potential community function with respect to taxo-
nomic richness is illustrated in archaeal communities
throughout the salinity gradient. Archaeal communi-
ties in the south arm are represented by both Crenar-
270
Fig. 3. Statistical grouping of microbial communities from the
Great Salt Lake, Utah, USA, using Fast UniFrac. (A) Cluster
analysis, and (B) and principal coordinate analysis of micro-
bial communities in increasingly saline environments. The
sampling sites A and B are shown in Fig. 2. AI: Antelope Is-
land; FB: Farmington Bay; NA: North Arm. ‘column’ refers to
samples taken within the water column; ‘bottom’ refers to
samples taken near the sediments (ca. 3 m depth)
Fig. 4. Correlation between phylogenetic distance of micro-
bial communities and salt concentration of sampling sites
throughout the Great Salt Lake
chaeota and Eury archaeota with a large number of
methanogenic and halophilic groups, respectively. Al -
though the NA sample contained much more archaeal
richness than any other sample, all types were within
the family Halobacteriaceae (no members of the Cre-
narchaeota were detected), suggesting evolutionary
specialization to extreme conditions.
Meta analysis
The microbial communities from extreme environ-
ments (n = 20) had a mean richness estimate of 427 to
484 OTUs, depending on the index used (Chao-ACE).
Although this estimate appears to be lower than the
richness estimate for non-extreme environments (741
to 817), the variability between communities within the
same category is high, making this difference not sig-
nificant (Chao, p = 0.08; ACE, p = 0.12). Similarly, the
Simpson index (ln-transformed) appears lower in
extreme environments (4.58 vs. 5.21 in non-extreme
environments), but this difference is also not statisti-
cally significant (p > 0.05). Rarefaction curves of the
communities analyzed indicate that the sample size
effect is minimized, as shown previously (Parnell et al.
2009). It should be noted that this study does not con-
trol for the different PCR primers or conditions used in
individual cases.
Although traditional estimates did not show signifi-
cantly less community diversity in extreme environ-
ments, compared with non-extreme environments, the
phylogenetic distance is significantly lower (p = 0.03).
If specialists are significantly clustered phylogeneti-
cally, then the mean phylogenetic distance falls lower
than the null distribution (Silvertown et al. 2006). Com-
munity ecology studies have shown that resource limi-
tations scale positively with phylogenetic similarity
due to increased species packing (Tello & Stevens
2010). Similarly, in extreme environments, where other
limitations exist, the phylogenetic distance of these
communities suggests a higher tendency toward
closely related organisms (Fig. 5). The effect of harsh
environmental conditions on phylo genetic diversity
indicates that closely related species might have toler-
ances to similar environmental stressors and thus be
more likely to occur within the same community than
to occur with less-related species (e.g. Webb 2000).
Both extreme and non-extreme categories fit a
normal distribution (Fig. 5) of phylogenetic distance
among communities; however, the communities of ex -
treme environments appear to have some multi-modal-
ity. By subdividing the extreme categories into groups
of temperature, salinity, pH, and contamination, we
found that the communities near the mean consisted of
contaminated sediments, hypersaline and low-temper-
ature environments, and high-pressure (deep ocean)
sediments. The high phylogenetic divergence of these
communities may suggest a convergent adaptation to
extreme environments (Webb et al. 2002) —that sev-
eral different phylogenetic groups have evolved differ-
ent mechanisms to overcome a similar stress. An exam-
ple of this convergent evolutionary strategy is seen in
the adaptation of halophilic organisms to life in high
salt concentrations, where at least 2 vastly different
Parnell et al.: Phylogenetic distance in Great Salt Lake 271
Fig. 5. Distribution of phylogenetic distance between microbial communities in (A) non-extreme and (B) extreme environments.
Shadow histograms show the distribution of communities (x-axis) with respect to phylogenetic distance (y-axis). The red line
indicates normal distribution; normal quantile plots illustrate how closely data follow normal distribution and suggest that com-
munities in both non-extreme and extreme environments follow a normal distribution. Box-plots illustrate that datasets from non-
extreme and extreme environments are not skewed and delineate the upper and lower quartiles, diamonds designate 95% confi-
dence intervals. Communities in extreme environments are colored as follows: oligotrophic = red; radiation = dark blue;
contaminated = green; hypersaline = orange; acidic = black; high pressure = yellow; low temperature = light blue
mechanisms are involved in regulating osmotic pres-
sure (Oren 2002). Low pH, high radiation, and
resource-limited (oligotrophic) environments corre-
spond to higher phylogenetic similarity. In previous
studies, this close grouping of phylogeny has sug-
gested that community organization (i.e. the role of
competition) can be deduced from the ecological simi-
larity within a closely related group (Webb 2000) and
implies habitat selection for ecologically similar, phylo-
genetically related species (Webb et al. 2002), result-
ing in a conserved trait within the pool of species in the
community. It is unclear whether the type of extreme
environment plays a role in the phylogenetic diver-
gence of the community.
CONCLUSION
Population ecology studies have shown that different
organisms make unequal contributions to diversity and
ecosystem function due to the amount of variability
within genetic or morphological characteristics (May
1990, Humphries et al. 1995, Crozier 1997, Norberg et
al. 2001, Allen et al. 2009). Phylogenetic distance (often
referred to as phylogenetic diversity or taxonomic di-
versity) measures the average phylogenetic distance
between individual organisms within a community and
has been successfully applied to microbial communi-
ties, demonstrating a potential to distinguish ecological
differences (Martin 2002). As an example, we show
that higher archaeal taxonomic richness (at the species
level) in the salt-saturated brine of GSL corresponds
with specialization rather than with high functional di-
versity. Communities in the south arm with lower rich-
ness have higher phylogenetic distance and greater
potential functional diversity.
Understanding microbial biodiversity and its rela-
tionship to ecosystem function is a central component
of microbial ecology and one of the key questions in
science (Huber et al. 2007). In order to address this
question we need novel metrics that can link biodiver-
sity with evolutionary history and community struc-
ture. Phylogenetic diversity is an important aspect of
measuring the total microbial biodiversity of an ecosys-
tem; in the case of the communities examined here,
phylogenetic distance is the only significantly different
measure between extreme and non-extreme microbial
biodiversity. Used in conjunction with traditional biodi-
versity estimates, phylogenetic diversity is a useful tool
for understanding how communities are structured.
The work described in the present study is obviously
restricted in its coverage and is limited by the datasets
examined. Due to the variable nature of the communi-
ties examined, a direct comparison of specific phyloge-
netic groups is not appropriate. However, phylogenetic
distance is a well established method of evaluating
ecological and evolutionary mechanisms that promote
species diversity and co-existence in community ecol-
ogy (Losos 1996, Webb et al. 2002). In the present
study, smaller phylogenetic distance within extreme
communities (rather than within non-extreme commu-
nities) implies evolutionary conservatism in the spe-
cialist group (Silvertown et al. 2006). In this light, a
phylogenetic perspective of studying microbial com-
munities provides a new approach to competition and
the maintenance of diversity.
Acknowledgements. Funding for this research was provided
by a National Science Foundation grant DEB-021212487 to
M.E.P., a grant from United States Department of Agriculture
CSREES 2006-34526-17001 and support from the Utah Agri-
cultural Experiment Station at Utah State University as jour-
nal paper number 8158. T.A.C. was partially supported while
serving at the National Science Foundation, Virginia, USA.
Any opinion, findings, and conclusions or recommendations
expressed in this document are those of the authors and do
not necessarily reflect the views of the National Science
Foundation.
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Editorial responsibility: Jed Fuhrman,
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Submitted: February 7, 2011; Accepted: June 10, 2011
Proofs received from author(s): September 2, 2011
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