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Comparative microbial ecology of sediment-associated microbial communities from anthropogenically and endogenously metal impacted systems

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Title:
Comparative microbial ecology of sediment-associated microbial communities from anthropogenically and endogenously metal impacted systems
Creator:
Sackett, Joshua David ( author )
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English
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Anthropogenic soils -- Colorado ( lcsh )
Microbial metabolism ( lcsh )
Bacteria -- Physiology ( lcsh )
Anthropogenic soils ( fast )
Bacteria -- Physiology ( fast )
Microbial metabolism ( fast )
Colorado ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Microorganisms, particularly the Bacteria, are differentially impacted by metal toxicities, and will respond very quickly to changes in their environment, making them ideal bioindicators of environmental health. In this study, we evaluated the sediment associated bacterial diversity of fifty seven samples collected from twenty four anthropogenically and endogenously metal impacted, geographically distinct sites in the Colorado Mineral Belt, and elucidated the factors that correlated with observed differences in the bacterial community structure. Overall, the geochemistry of all sites distinguished anthropogenic from endogenous sources of metal impact. Anthropogenic samples, on average, had higher concentrations of total recoverable and dissolved sodium and magnesium, and lower concentrations of aluminum and zinc, compared to the endogenous samples. Bacterial communities from both anthropogenically and endogenously metal impacted sites were characterized using Illumina high throughput amplicon sequencing of the V4 region of the 16S rRNA gene. Overall, bacterial communities were remarkably diverse, with endogenously metal impacted sediments having higher diversity compared to anthropogenic sediments. The Actinobacteria and Betaproteobacteria dominated anthropogenic samples, and the Acidobacteria and Deltaproteobacteria dominated endogenous samples. Clustering of bacterial communities based on membership and structure presence absence and relative abundance of particular taxa, respectively also distinguished samples based on their source of metal impact. Analysis of similarity ANOSIM tests indicated a significant difference between bacterial community structure based on source of metal impact weighted UniFrac RANOSIM 0.746, p 0.001 . Mantel tests indicated that total recoverable magnesium concentrations accounted for 54per cent of variance in community structure of all bacterial communities in the study. Dissolved aluminum concentrations accounted for 71per cent of the variation in all communities with an anthropogenic source of metal, and dissolved aluminum concentrations also accounted for 41per cent of the variation in bacterial communities with endogenous sources of metal impact. This study provides one of the first direct comparisons between microbial community structures of sediments based on source of metal impact. This study is also one of the first comprehensive characterizations of bacterial communities from naturally occurring iron fen systems.
Thesis:
Thesis (M.S.)--University of Colorado Denver.
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Includes bibliographic references.
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Department of Integrative Biology
Statement of Responsibility:
by Joshua David Sackett.

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Auraria Library
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Full Text
COMPARATIVE MICROBIAL ECOLOGY OF SEDIMENT-ASSOCIATED MICROBIAL
COMMUNITIES FROM ANTHROPOGENICALLY AND ENDOGENOUSLY METAL
IMPACTED SYSTEMS
by
JOSHUA DAVID SACKETT
B.S., University of Colorado Denver, 2012
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado
in partial fulfillment
of the requirements for the degree of
Master of Science
Biology
2015


2015
JOSHUA DAVID SACKETT
ALL RIGHTS RESERVED


This thesis for the Master of Science degree by
Joshua David Sackett
has been approved for the
Biology Program
by
Timberley M. Roane, Chair
Christopher S. Miller
Michael Wunder
July 24, 2015


Sackett, Joshua David (M.S., Biology)
Comparative Microbial Ecology of Sediment-Associated Microbial Communities
from Anthropogenically and Endogenously Metal impacted Systems
Thesis directed by Associate Professor Timberley M. Roane.
ABSTRACT
Microorganisms, particularly the Bacteria, are differentially impacted by
metal toxicities, and will respond very quickly to changes in their environment,
making them ideal bioindicators of environmental health. In this study, we
evaluated the sediment-associated bacterial diversity of fifty-seven samples
collected from twenty-four anthropogenically and endogenously metal impacted,
geographically distinct sites in the Colorado Mineral Belt, and elucidated the factors
that correlated with observed differences in the bacterial community structure.
Overall, the geochemistry of all sites distinguished anthropogenic from endogenous
sources of metal impact. Anthropogenic samples, on average, had higher
concentrations of total recoverable and dissolved sodium and magnesium, and
lower concentrations of aluminum and zinc, compared to the endogenous samples.
Bacterial communities from both anthropogenically and endogenously metal
impacted sites were characterized using Illumina high-throughput amplicon
sequencing of the V4 region of the 16S rRNA gene. Overall, bacterial communities
were remarkably diverse, with endogenously metal impacted sediments having
higher diversity compared to anthropogenic sediments. The Actinobacteria and
Betaproteobacteria dominated anthropogenic samples, and the Acidobacteria and
Deltaproteobacteria dominated endogenous samples. Clustering of bacterial
IV


communities based on membership and structure (presence/absence and relative
abundance of particular taxa, respectively) also distinguished samples based on
their source of metal impact. Analysis of similarity (ANOSIM) tests indicated a
significant difference between bacterial community structure based on source of
metal impact (weighted UniFrac Ranosim = 0.746, p = 0.001). Mantel tests indicated
that total recoverable magnesium concentrations accounted for ~54% of variance in
community structure of all bacterial communities in the study. Dissolved aluminum
concentrations accounted for ~71% of the variation in all communities with an
anthropogenic source of metal, and dissolved aluminum concentrations also
accounted for ~41% of the variation in bacterial communities with endogenous
sources of metal impact.
This study provides one of the first direct comparisons between microbial
community structures of sediments based on source of metal impact. This study is
also one of the first comprehensive characterizations of bacterial communities from
naturally occurring iron fen systems.
The form and content of this abstract are approved. I recommend its publication.
Approved: Timberley M. Roane
v


ACKNOWLEDGEMENTS
The completion of this thesis would not be possible without the support of so
many people. First, I would like to thank my research advisor, Dr. Timberley M.
Roane, for sharing your knowledge and expertise with me, for supporting and
encouraging me, for remaining dedicated to the project and to my success, and for
being a spectacular mentor and role model. I would also like to thank my graduate
committee members, Drs. Christopher Miller and Michael Wunder, for your
mentorship, training, time, and sharing your knowledge with me. I would not be
where I am today without these three individuals.
I would like to thank the following people for their role in formulating the
project and securing funding, for their aid in the field, and for their continued
dedication to this project: Robyn Blackburn, Colorado Fish and Wildlife Service;
Brian Lloyd, Director of the Abandoned Mines Program, United States Forest Service
Region 2; Benn Stratton, Hydrologist, Grand Mesa, Uncompahgre, and Gunnison
National Forests; Jean Wyatt, Remedial Project Manager, United States
Environmental Protection Agency Region 8; and Cathleen Zillich, Tres Rios
Abandoned Mine Land Specialist, Bureau of Land Management.
I would like to thank the following individuals and facilities for their role in
biological and chemical sample analyses, and logistical support: Jeffery Boon,
University of Colorado Shared Analytical Services Laboratory; Rocky Mountain
vr


Biological Laboratory; United States Environmental Protection Agency Region 8
Laboratory; and the University of Colorado BioFrontiers Institute.
I would like to give a special thanks to the following individuals: Sladjana
Subotic, for your assistance in the field and in the laboratory, and for being an
amazing friend and colleague throughout this journey; to Adrienne Narrowe, for
taking the time to assist me with bioinformatic analysis and sharing your knowledge
with me; to my parents, David and Natalie Sackett, for your encouraging me to
continue learning and growing every day, and to many other friends, family
members, and colleagues who have helped and supported me along the way. I am
grateful for all of these people for this amazing opportunity to learn and grow as an
individual and as a scientist.
Lastly, I would like to recognize the United States Forest Service Region 2
Abandoned Mines Program for funding this exciting project. Without their financial
support, this study would not have been possible.
vn


TABLE OF CONTENTS
CHAPTER
I. LITERATURE REVIEW...................................................1
Problem Statement.................................................1
Background Information............................................4
Metal Classifications........................................4
Metal Bioavailability........................................5
Cellular Uptake and Toxicity of Metals.......................7
Metal Resistance Mechanisms..................................9
Types of Metal Impacted Systems.............................15
Generation of Acid Mine Drainage and Acid Rock Drainage.....17
Microorganisms as Bioindicators.............................23
16S rDNA High-Throughput Sequencing.........................26
Microbial Communities of Acidic, Anthropogenically
Metal Impacted Systems......................................33
Microbial Communities of Naturally Occurring Acidic,
Metal Impacted Systems......................................37
II. SITE DESCRIPTIONS..................................................39
Iron Springs Mining District.....................................39
Chattanooga Fen..................................................42
Mount Emmons Fen.................................................43
viii


Microbiology of Fens.............................................44
III. CONDUCTED STUDY....................................................46
Introduction.....................................................46
Methods..........................................................48
Field Site Descriptions......................................48
Sample Naming Conventions....................................49
Sampling Procedures..........................................50
Metal Analyses...............................................51
DNA Extraction, PCR Amplification, and Next-Generation
Sequencing...................................................52
Phylogenetic and Statistical Analyses........................53
Results..........................................................55
Diversity Analyses of Bacterial Communities..................55
Bacterial Community Structure of Anthropogenically
and Endogenously Metal Impacted Systems......................64
Environmental Chemistry of Anthropogenically
and Endogenously Metal Impacted Sites........................68
Correlations Between Bacterial Community Structure and
Geochemical Variables........................................83
Discussion.......................................................93
Geochemistry.................................................93
Bacterial Community Analysis and Biodiversity................94
Microbial Community Structure and Environmental
Parameters...................................................98
Concluding Remarks..............................................101
IX


IV. SUMMARY AND FUTURE DIRECTIONS
103
Specific Findings and Discussion...........................103
Future Directions..........................................106
SUPPLEMENTAL TABLES AND FIGURES....................................108
REFERENCES.........................................................120
x


CHAPTER I
LITERATURE REVIEW
Problem Statement
This study began as an attempt to identify individual bacterial taxa that
correlate with metal toxicity under environmental conditions. While this remains a
long-term goal of the project, the study presented here aims to characterize the
bacterial community structure of anthropogenically (acid mine drainage) and
endogenously (naturally occurring) metal impacted sites, and to elucidate the
factors that correlate with any observed differences in bacterial community
structure. To this end, the work presented here provides the initial microbiological
and geochemical characterization of anthropogenically and endogenously metal
impacted sites.
Current methods of prioritization of metal contaminated sites have several
limitations. One limitation is the sheer number of metal contaminated sites
requiring monitoring, remediation, and/or mitigation. It is implausible, and highly
cost-prohibitive, to conduct these activities at all metal contaminated sites, so an
effective and biologically relevant method for prioritizing these sites is needed.
Second, traditional methods of determining metal concentrations, both total
recoverable metal concentrations and dissolved metal concentrations, are
1


problematic as they fail to identify the biologically relevant, or bioavailable, fraction
of the metal concentration that is capable of interacting with biological systems.
Lastly, the use and interpretation of biological indicators of environmental quality,
mostly the presence of particular plant and animal species, has proven complicated
as the abundance and distribution of these indicators can fluctuate in a somewhat
unpredictable manner, and these indicator species do not necessarily respond
quickly to changes in environmental conditions. In an effort to circumvent these
issues, we propose using bacterial community structure as an indicator of metal
impact. While the implications of metal toxicity remain to be determined, this study
has found metal source as an important driver of community structure in metal
impacted systems. Bacteria were chosen as potential bioindicators due to their
small size, their large surface-to-volume ratio, their metabolic flexibility and rapid
physiological response to changes in their environment (e.g. metal concentration
fluctuations), and due to the ability to monitor not only changes in the
presence/absence of organisms within the community but to also use variations in
the relative abundance of particular groups of organisms as possible indicators.
Bacteria have been used as biological indicators of environmental impact in a
variety of situations, such as using the presence of coliforms (enteric bacteria
associated with fecal contamination in water systems) to assess water quality, and
more recently, the use of next-generation sequencing technologies to assess
microbial community structure and identify potential bioindicators in a cultivation-
independent manner. The relative abundance of the Gammaproteobacteria
correlated positively with polycyclic aromatic hydrocarbon concentrations in
2


amended agricultural soil mesocosm experiments (Niepceron et al. 2013). The
relative abundance of the Alphaproteobacteria and Epsilonproteobacteria correlated
positively with Zn concentrations in metal impacted soils, the Deltaproteobacteria
and Actinobacteria correlated negatively with Zn concentrations, and the relative
abundance of the Chloroflexi correlated positively with soil mercury concentrations
(Zhu et al. 2013). These studies highlight the utility of using cultivation-
independent methods of bioindicator identification and their use in assessing
environmental health.
The objectives of this study were to: 1) describe the sediment-associated
bacterial diversity of anthropogenically (acid mine drainage) and endogenously
(acid rock drainage) metal impacted, geographically distinct sites in the Colorado
Mineral Belt, and 2) to elucidate the factors that correlate with observed differences
in the bacterial community structure. To do this, high-throughput sequencing
methods were used to evaluate the microbial community structures of metal
impacted systems, in conjunction with laboratory-based chemical analyses, to
determine dissolved and total recoverable metal concentrations in water samples,
and field-based site assessments (e.g. pH, conductivity, temperature, dissolved
oxygen). The results of this study will be used in continuing research into the
identification and elucidation of specific bacterial indicators of environmental metal
impact and toxicity. The results of this study will also contribute to our
understanding of iron fen microbiology, an understudied metal impacted wetland
system.
3


Background Information
Metal Classifications
Metals may be classified based on toxicity to biological systems (Gadd and
Griffiths 1977; Rouch, Lee, and Morby 1995; Nies 1999; Bruins, Kapil, and Oehme
2000; Vails and de Lorenzo 2002; Gadd 2010; Rathnayake etal. 2010; Olaniran,
Balgobind, and Pillay 2013). Heavy metals, classified as metals with atomic
densities of 5 g/cm3 or greater, include aluminum (Al), arsenic (As), cadmium (Cd),
gold (Au), lead (Pb), mercury (Hg), and silver (Ag) (Gadd and Griffiths 1977;
Rathnayake et al. 2010; Olaniran, Balgobind, and Pillay 2013). Heavy metals have
no widespread physiological use in organisms and are toxic to all cell types. In
contrast, the essential metals, including chromium (Cr), cobalt (Co), copper (Cu),
iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), and
zinc (Zn), are primarily toxic when intracellular concentrations of these metals
surpass what is physiologically required by the cell. Although overall metal
concentrations in an environment may be high, the toxicity of a metal to
microorganisms is dependent on the bioavailability of that metal; that is, the metal
concentration that is biologically reactive (John and Leventhal 1995). Assessing the
bioavailable metal concentration of any metal remains one of the greatest
challenges to understanding environmental metal toxicity issues due to the lack of
an agreed upon method of analytically or biologically determining the bioavailable
concentration of a metal.
4


Metal Bioavailability
While many definitions of bioavailable metal concentrations exist, and are
often specific to the biological system being examined (e.g. plant, animal, or
microbial), this study defines the bioavailable concentration of a metal as the
fraction of the total metal concentration that can readily interact with cell receptors
or disrupt physiological processes intracellularly following direct uptake of the
metal from the environment (Bolan et al. 2010; Gomez-Sagasti et al. 2012; Pauget et
al. 2012). Total extractable metal concentrations from sediments often do not
adequately reflect the concentration of metals that can interact with
microorganisms, and therefore are poor indicators of metal toxicity, especially in
sediments and waters where complex metal-chemical and metal-surface
interactions occur (Giller, Witter, and McGrath 2009).
Bioavailable metal concentrations are a function of a variety of
physicochemical parameters that influence metal chemistry, including pH, anion
concentrations (hydroxides, sulfates, phosphates, carbonates, etc.), soil particle size
and composition, and organic matter content (Giller, Witter, and McGrath 2009;
Chodak etal. 2013; Larsson etal. 2013; de Santiago-Martin etal. 2013; de Santiago-
Martin et al. 2014), and is a function of the chemical reactivity of a metal. Factors
that cause stabilization, such as metal precipitation or formation of large metal
complexes, decrease metal solubility and bioavailability. Humic substances and
decomposing organic matter can interact with dissolved metals to form
organometallic complexes, which reduce the bioavailability (and therefore toxicity)
of the metal to soil microorganisms. Generally, as sediment pH becomes more
5


acidic, precipitated metallic compounds (organometallics, metal salts, etc.) will
dissociate, mobilizing the metal cation and increasing the metals bioavailability.
While not a direct relationship, increased bioavailability requires metal solubility.
Those factors that are known to influence metal solubility are therefore assumed to
also influence metal bioavailability.
Due to the multitude of factors that influence metal bioavailability; due to the
inherent fluctuations in an open environment system (including seasonality,
weather, and environmental disturbances); and due to the lack of standardized
analytical and biological methods to assess metal bioavailability in environmental
samples, it is difficult to accurately predict the bioavailable metal concentration
over discrete and indiscriminate time courses (Wang et al. 2007; Bolan et al. 2010;
Pauget et al. 2012). However, bioindicator organisms, such as the use of genetically-
engineered Bacillus subtilis, Escherichia coli, and Staphylococcus aureus to detect
nanomolar concentrations of bioavailable arsenite (As(III)), arsenate (As(V)),
antimonite (Sb(III)), and cadmium (Cd2+), may be used in vitro to determine the
bioavailable concentration of specific metals from environmental samples, as these
organisms fluoresce in response to concentrations of these metals (Tauriainen et al.
1997; Liao and Ou 2005).
There is also increased interest in the identification of in situ bioindicator
organisms inherent to the system being studied to allow scientists to track
ecosystem health and metal bioavailability in a minimally invasive manner without
the use of genetically engineered organisms. Identification of in situ bioindicators of
metal toxicity, at the species level or community level, will allow scientists to detect
6


fluctuations in bioavailable metal concentrations with minimal disturbances to the
environmental system being studied.
Cellular Uptake and Toxicity of Metals
Heavy and essential metals interact with cells via two main mechanisms. The
first mechanism involves non-specific transport across the membranes via transport
proteins, which results in a rapid influx of metal ions into the cell until
chemiosmotic equilibrium is reached (Nies 1999; Maier, Pepper, and Gerba 2009;
Gadd 2010; Lemire, Harrison, and Turner 2013; Olaniran, Balgobind, and Pillay
2013). The second mechanism involves highly substrate-specific inducible
transport via proteins driven by ATP hydrolysis; a metabolically expensive
mechanism often utilized during times of high stress.
In Gram-negative bacteria, metals (such as Mn2+, Fe3+, Co2+, Zn2+, Ni2+, Cu2+,
Cd2+ and Ga3+) often are non-speciflcally transported across the outer membrane via
porin proteins (Lemire, Harrison, and Turner 2013). To transport metals across the
cell membrane according to the established chemiosmotic gradient, both Gram-
positive and Gram-negative bacteria can utilize ATP-independent proteins to non-
speciflcally uptake metals, such as the corA protein (specific to Gram-negatives),
which transports most cations, and HoxN family proteins, which transport Ni2+ (Nies
1999).
ATP-dependent transport systems include P-type ATPases and ABC-type
transporters, which allow for the uptake of metal ions against a concentration
gradient (Nies 1999). These transport systems are metal- or compound-specific,
7


however, due to the similarity in ionic radii of divalent metals, transporters will
non-specifically allow for the uptake of multiple divalent cations. For example,
manganese (Mn2+) ion transporters are capable of transporting cadmium (Cd2+) ions
(ionic radii = 0.83 A and 1.03 A, respectively, assuming 6-fold coordination) (Maier,
Pepper, and Gerba 2009). Oxyanions, such as chromate and arsenate, are similar in
structure to sulfate and phosphate molecules, respectively, and are commonly
transported into the cell via sulfate and phosphate transport proteins (Nies 1999).
Lastly, metals can enter the cell through siderophores low molecular weight
metal-chelating proteins that are expelled from the cell and re-enter the cell via
siderophore-specific ATPases (Lemire, Harrison, and Turner 2013).
Once inside the cell, metals can damage nucleic acid and protein structure,
induce genetic mutations, act as ligands or displace metal cofactors in proteins,
inhibit membrane fluidity and function, and induce oxidative stress (Rouch, Lee, and
Morby 1995; Bruins, Kapil, and Oehme 2000; Rathnayake etal. 2010; Lemire,
Harrison, and Turner 2013). The damage induced by non-essential metals and
essential metals in high concentrations may result in inhibition of biochemistry or
cell death unless the cells are capable of reducing the bioavailable metal
concentration in their cytosol and immediate extracellular environment. In
response to these detrimental effects, bacteria have evolved resistance mechanisms
to tolerate, and sometimes even detoxify, elevated concentrations of bioavailable
metals.
8


Metal Resistance Mechanisms
Some bacteria have evolved mechanisms for surviving in environments
containing elevated levels of bioavailable metals, either through horizontal gene
transfer or mutations in the genome. Sabiy, Ghozlan, and Abou-Zeid (1997)
illustrated the variability in metal tolerance in bacterial isolates from seawater
collected in the Eastern Harbor of Alexandria, Egypt. Of the 81 aerobic
heterotrophic isolates in the study, two isolates were deemed resistant to all eight
metals tested (As, Cd, Co, Cd, Hg, Ni, Pb, and Zn), five were resistant to seven of those
metals, and four were resistant to only one of the metals. Within each category (e.g.
number of metals isolates were resistant to), the isolates had varying levels of
resistance to each metal tested, which indicates that, even within communities,
there are differential responses and levels of resistance to elevated levels of toxic
metals, and multiple metal resistance mechanisms are likely invoking these variable
responses in the community.
Multiple metal resistance mechanisms allow for some microorganisms to
survive, and even thrive, in environments containing elevated concentrations of
bioavailable metals (Vails and de Lorenzo 2002; Gadd 2010; Blindauer 2011). El
Aafi et al. (2012) reported that a rhizobacterium isolate, Serratia sp. MSMC541,
resisted up to 13.3 mM As, 2.2 mM Cd, 2.3 mM Cu, 9 mM Pb, and 30 mM Zn with no
observed decrease in growth rate compared to a no-metal control culture.
Furthermore, bioaccumulation assays found that strain MSMC541 biosorbed
cadmium, copper, lead, and zinc at parts-per-thousand concentrations onto the cell
surface, and intracellular bioaccumulation for these metals ranged from
9


approximately 500 ppm to over 2000 ppm, which indicates that both passive and
active metal resistance mechanisms are at play to protect the bacterium from the
effects of elevated metal concentrations.
Furthermore, Cupriavidus metallidurans contains two plasmids, the pMOL28
and pMOL30 plasmids (171,459 bp and 233,720 bp, respectively), that encode for
various metal resistance proteins (Monchy et al. 2007). These metal resistance
genes, concentrated in genomic islands on each plasmid, encode for proteins
involved in resistance to Co2+, Cr6+, Hg2+, and Ni2+ (on pMOL28), and to Ag+, Cd2+,
Co2+, Hg2+, Pb2+, and Zn2+ (on pMOL30). Transcriptomic analysis, using microarrays,
of C. metallidurans cultures exposed to heavy metals individually (final
concentrations: 0.4 mM Pb2+, 0.6 mM Ni2+, 0.6 mM Cd2+, 2 mM Co2+, 0.8 mM Zn2+, 5
pM Hg2+, or 0.1 mM Cu2+) identified intriguing global responses to elevated
concentrations of individual heavy metals. For example, mer genes (involved in
mercury reduction) were activated in response to Cd2+ and Pb2+ concentrations; cop
genes (involved in copper efflux) were activated in response to Cd2+, Ni2+, and Zn2+;
and cnr genes (involved in cobalt and nickel efflux) were upregulated in response to
Cd2+ and Cu2+. However, as discussed above, not all microorganisms employ
multiple metal resistance mechanisms to survive in environments contaminated
with multiple heavy metals, and those that are resistant to multiple heavy metals
may have varying levels of resistance. Because of the variable responses
microorganisms have to elevated metals in their environment, those organisms that
are sensitive to changes in metal concentrations may be used as bioindicators of
metal toxicity.
10


Known resistance mechanisms involve both intracellular and extracellular
strategies, including siderophore production, extracellular sequestration, ATP-
dependent efflux systems, metallothionein production, and enzymatic
transformation of reactive metal species via reduction-oxidation mechanisms
(Figure 1.1).
Suppression of influx,
Efflux mechanisms decreased transport
Volatilization
O
O
A
Impermeability
Intracellular chelation
(eg. metallothionein,
y-Glu-Cys peptides)
s' \
Enzymatic detoxification
(e g arsenite reductase,
mercuric reductase)
( Organetlar localization (eukaryotes) *

Intracellular precipitation
I wiydiieitdi
Precipitation or binding within
walls and outer layers and to
surfaces and extracellular
material; metal and mineral
nanoparticle deposition;
biomineralization; redox
transformations
8
a
Ol
Release of
metal-
complexing
agents and
metabolites
Figure 1.1: General metal resistance mechanisms employed by cells, both
Prokaryotic and Eukaryotic. Note: organellar localization is specific to eukaryotic
organisms, as bacteria and archaea do not have membrane-bound organelles.
Source: Gadd 2010.
Extracellular resistance mechanisms generally involve the production and
export of metal-chelating compounds (siderophores), or the production of a
negatively-charged polymeric substance (exopolysaccharide [EPS]) composed of
carbohydrates, proteins, and nucleic acids (Vails and de Lorenzo 2002; Salehizadeh
and Shojaosadati 2003; Teitzel and Parsek 2003; Ren, Xie, and Xing 2009;
11


Andersson, Dalhammar, and Kuttuva Rajarao 2011; Park et al. 2011a; Schalk,
Hannauer, and Braud 2011; Ahmed and Holmstrom 2014). Siderophores have a
high affinity for binding ferric iron and transporting the iron back into the cell.
However, siderophores, such as pyoverdine and pyochelin (both produced by
Pseudomonas aeruginosa isolates), also have a high affinity for binding Ag+, Al3+,
Cd2+, Co2+, Cr2+, Cu2+, Ga3+, Hg2+, Mn2+, Ni2+, Pb2+, Sn2+, Tb3+, Tl+, and Zn2+, although
the affinity for these other metals is less than the affinity for ferric iron (Braud et al.
2009). Extracellular chelation of metals reduces bioavailable concentrations and
prevents the cations from diffusing across membranes through non-specific porins.
EPS layers provide a mechanism of metal resistance for many bacteria (Vails
and de Lorenzo 2002; Salehizadeh and Shojaosadati 2003; Teitzel and Parsek 2003;
Ren, Xie, and Xing 2009; Andersson, Dalhammar, and Kuttuva Rajarao 2011). The
overall negative charge of EPS electrostatically attracts metal cations. The
"stickiness that the carbohydrates and proteins give to the layers of EPS, which are
typically used for adhesion to surfaces, aids in binding metals and other substances,
preventing or inhibiting the transport of metals into the cell and decreasing the
bioavailability of the metals. The composition of EPS varies from cell to cell based
on genetic variability, environmental factors, and phase of growth, all of which
contribute to the permeability and metal sorption capacity of the EPS.
Metallothioneins, a family of low molecular weight cysteine-rich metal
chelating stress response proteins, are commonly expressed by bacteria to protect
against the elevated concentrations of metals in the cytosol (Blindauer et al. 2002;
Vails and de Lorenzo 2002; Bolan et al. 2010; Naik, Pandey, and Dubey 2012).
12


Bacterial metallothioneins, known to bind divalent cadmium, lead, and zinc,
complex metals inside the cell, thereby reducing the bioavailable concentration of
the metal intracellularly (Blindauer et al. 2002; Blindauer 2011; Naik, Pandey, and
Dubey 2012). However, due to the relatively short half-life of metallothioneins and
the metabolic expense of producing them, metallothioneins provide only a short-
term response to elevated intracellular toxic metal concentrations (Klaassen, Liu,
and Choudhuri 1999).
Perhaps one of the most effective mechanisms of metal resistance among the
bacteria is enzymatic detoxification/transformation of metal species via
reduction-oxidation reactions (Nies 1999; Beliaev etal. 2001; Vails and de Lorenzo
2002; Tao et al. 2008; Park et al. 2011a; Park et al. 2011b). Enzymatic
transformation may result in decreased reactivity of the metal cation, which, in turn,
decreases its potential to exert toxic effects on the cell. For example, some
dissimilatory iron (III) reducing bacteria are capable of reducing soluble uranium
(VI) to insoluble uranium (IV) by replacing hexavalent uranium with ferric iron as
the terminal electron acceptor (Lovley et al. 1991). Also, a variety of bacteria
commonly found in soil, such as Pseudomonas and Bacillus species, express
reductase enzymes capable of reducing chromium (VI) to chromium (III), a less
soluble form of chromium, under either aerobic or anaerobic conditions (Wang and
Shen 1995).
Metal volatilization, an enzymatic reduction mechanism, has been effective
at reducing bioavailable cytosolic concentrations of mercury, selenium, and arsenic
in bacteria (Monsieurs et al. 2011; Slyemi and Bonnefoy 2012; Kagami et al. 2013;
13


Majumder et al. 2013). The mer and ars operons (groups of genes, under the control
of a single promoter, that are co-transcribed into a single polycistronic mRNA), for
example, confer resistance to mercury and arsenic, respectively, via enzymatic
reduction and volatilization out of the cell. The mer operon specifically codes for a
mercury reductase (merA), that reduces mercuric compounds, e.g. HgCh, to
elemental mercury (Hg) (Monsieurs et al. 2011). In its gaseous form, elemental
mercury diffuses out of the cell. The ars operon functions similarly to the mer
operon with the exception of arsenic ions being methylated to either mono-, di-, or
trimethylarsine (volatile) instead of being reduced to their elemental state (Slyemi
and Bonnefoy 2012; Majumder et al. 2013).
When intracellular metal concentrations exceed what is physiologically
required, or when non-biologically active metals are present inside the cell, bacteria
may begin expressing ATP-dependent efflux pumps to expel metal ions from the
cytosol and regain homeostasis (Silver 1996; Nies 1999; Argiiello, Gonzalez-
Guerrero, and Raimunda 2011; Raimunda et al. 2011). Some ATPases are metal-
specific, such as the copper-specific CopA P-type ATPase or the arsenite-specific
ArsA A-type ATPase, whereas other ATPases are capable of effluxing multiple
metals, such as the ZntA P-type ATPase, which effluxes Cd2+, Pb2+, and Zn2+ (Liu et al.
2006; Argiiello, Gonzalez-Guerrero, and Raimunda 2011). This mechanism
effectively reduces the intracellular bioavailable concentration of metals, but does
not reduce the reactivity of the metals or the potential for the metals to re-enter the
cell.
14


Types of Metal Impacted Systems
Microorganisms may employ a variety of these metal resistance mechanisms
to withstand elevated bioavailable concentrations of toxic metals in the
environment. The source of metal impact in the environment may be from
anthropogenically-facilitated actions, such as mining activities or industry, or from
naturally occurring phenomena, such as from erosion events or groundwater
interactions with sulfide-rich minerals. These two types of metal impacted systems
are described in detail below.
Anthropogenically metal impacted systems result from the exposure of
sulfide-rich minerals to oxidative conditions at the earths surface, such as by
mining activity or other earth-moving activities (Hogsden and Harding 2012).
Anthropogenic metal impact may also result from industrial releases of metal-rich
and/or acidic fluids. These releases oftentimes have broad implications for
environmental quality and ecosystem health, sometimes resulting in environmental
disasters requiring intervention and remediation.
In addition to the environmental release of metals from anthropogenic
activities, natural or endogenous sources of metal impact, such as with the
formation of acid rock drainage (ARD), are also present in the environment. Fens
are naturally occurring and can be metal impacted because of ARD formation. While
no set definition exists describing a fen system, the majority of descriptions define
fens as oligotrophic (nutrient-poor), minerotrophic (mineral-rich water often
originating from springs) peatlands fed by mineral-rich groundwater upwellings
15


with minimal contribution from precipitation or surface runoff (Welsch etal. 1995;
Marshall, Finnamore, and Blades 1999) (Figure 1.2).
Fens can be further classified based on the chemistry of the groundwater
feeding them. Poor fens are characterized by acidic pH <5.5 with low
concentrations of calcium carbonate. Rich fens are characterized by basic pH >8
and high concentrations of calcium carbonate. Transition fens, types of fens with
qualities of both rich and poor fens, have circumneutral pH and may have elevated
levels of calcium carbonate (Windell etal. 1986; Marshall etal. 1999; Chimner,
Lemly, and Cooper 2010; Omelkova et al. 2013).
While most fens can be classified using the above descriptions, one rare class
of fens, called iron fens, are both minerotrophic and acidic (pH <5), typically
exhibiting high concentrations of dissolved Ca2+ and Mg2+ (Chimner, Lemly, and
Cooper 2010). In nature, iron fens occur in volcanic watersheds with highly
mineralized pyrite deposits (Cooper, Nydick, and Lemly 2008). The groundwater
feeding these fens oxidizes sulfide minerals, which results in the production of
sulfuric acid and subsequent dissolution of calcium, magnesium, iron, and other
metals characteristic of the parent material underlying the fen (Simon 2004;
Chimner, Lemly, and Cooper 2010). Iron fens, due to their unique chemistry,
typically provide habitats that allow for disjunct plant and animal species to thrive.
Rocky mountain iron fens, such as the Chattanooga Fen and Mt. Emmons Fen
(discussed in Chapter 2), are home to several disjunct species, including the sundew
[Drosera rotundifolia), a rare species of sphagnum moss (Sphagnum balticum), and
16


several species of dragonflies and other arthropods (Mattson 2000; Chimner, Lemly,
and Cooper 2010}.
Fens receive both surface and subsurface water and have both surface and subsurface outflows.
As a result, fens tend to reflect the chemistry of the underlying geology and can be quite alkaline
when fed from limestone sources.
Figure 1.2: Characterization of a fen. Fens are fed primarily by subsurface
groundwater flow. The mineralogy of the parent material determines the general
chemical nature of the fen system. Adapted from: Welsch et al. 1995.
Generation of Acid Mine Drainage and Acid Rock Drainage
Acid mine drainage (AMD] and acid rock drainage (ARD] formation involve
the oxidation of sulfide-containing minerals, such as pyrite (FeS2), chalcopyrite
(CuFeS2), galena (PbS), and sphalerite (ZnS) (Figure 1.3} (Johnson 1998; Baker and
Banfield 2003; Akcil and Koldas 2006; Kuang et al. 2013}. AMD generation is the
result of anthropogenic exposure of sulfide-containing minerals to oxidative
conditions at earths surface, such as those associated with mining activities where
metal-laden ore material is brought to the soil surface for processing, while ARD
generation occurs when sulfide-containing minerals are oxidized or dissolved
(under neutral to acidic conditions} as a result of natural (endogenous} processes,
including hydrologic regimes, erosion, and other natural phenomena. Although
AMD is often a more significant concern than ARD due to the increased surface area
17


of minerals exposed to weathering and oxidative conditions, both AMD and ARD
result in the introduction of metal-rich, acidic effluent.
Acid Mine Drainage
Here's a look at what AMD Is and how it affects
the surrounding environment.
During mining, pyrite is exposed Water drains out of the mine,
to oxygen. _
_ Dissolved metals react with oxygen and
Ground water seeps into the mine. fall out of solution into the stream water,
turning a bright color.
Oxygen, water and pyrite react to
form suffurlc add ana in turn @ Aquatic animals and plants
dissolve metals from the rocks. are killed by the drainage.
Figure 1.3: AMD generation and its effects on the local ecosystems macrobiota.
Source: www.post-gazette.com
In both anthropogenically and endogenously metal impacted systems,
oxidation and dissolution of minerals is dependent on the mineralogy, and variation
in mineralogy may lead to variation in the chemistry (e.g. pH, mineral content] of
AMD and ARD effluent. Abiotically, once sulfide-containing minerals are exposed to
oxidative conditions, the sulfide mineral is oxidized resulting in the dissolution of
the metals, sulfate, and hydrogen. The reactions for iron sulfide oxidation are
shown below:
(1] 2FeS2 (s) + 702 + 2H20 ^ 2Fe2+ (aq) + 4 S042' (aq) + 4H+
(2] 4Fe2+ + 02 + 4H+ ^ 4Fe3+ + 2H20
18


(3) Fe3+ (aq) + 3H20 ^ Fe(OH)3 (s) + 3H+
(4) FeS2 (s) + 14Fe3+ + 8H20 ^ 15Fe2+ (aq) + 2 S042' (aq) + 16H+
Variations of these reactions occur for other metal sulfides as well. Metal sulfide
oxidation results in increased acidity, which results in dissolution of metal sulfides.
However, although metals tend to become more soluble with decreasing pH, ferric
iron (Fe3+) can precipitate out of solution at pH values between 2.3 and 3.5 as iron
hydroxide (Fe(OH)3) or jarosite (KFe3+3(0H)6(S04)2), resulting in the orange-
colored precipitate present at many AMD and ARD sites (Figure 1.4).
Figure 1.4: Gold Finch Mine effluent showing the orange precipitate (iron
hydroxide and other iron oxides) characteristic of iron-rich acid mine drainage.
Photo credit: Timberley Roane.
19


Microorganisms, in particular the bacteria and archaea, are significant
contributors to the genesis of AMD and ARD due to their high rates of ferrous iron
oxidation (Fe2+ Fe3+) (Singer and Stumm 1970). Many taxa of bacteria inhabiting
AMD/ARD systems produce acidic metabolic byproducts from carbon fixation,
nitrification, heterotrophic metabolisms, and other biochemical pathways, all of
which result in the acidification of micro- and macro-scale environments,
contributing to the dissolution of acid-soluble metal sulfides (Table 1.1) (Johnson
1998; Schippers and Sand 1999; Baker and Banfield 2003; Vera, Schippers, and Sand
2013). Many microorganisms in AMD/ARD systems also engage in dissimilatory
oxidation reactions, resulting in the oxidation of sulfur and iron compounds, and
contributing to the AMD/ARD generation and dissolution of sulfide- and pyrite-
containing minerals (Figure 1.5). A variety of organisms are capable of iron and/or
sulfur oxidation, including members of the Proteobacteria, Nitrospirae, Firmicutes,
and Actinobacteria phyla in bacteria; and the Sulfolobales and Thermoplasmales
(Fe2+-oxidizing species only) phyla in archaea (Vera, Schippers, and Sand 2013).
Some notable well-studied examples of iron- and sulfur-oxidizing bacteria include
Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, and Leptospirillum
ferrooxidans, each of which solubilize and oxidize metal sulfides (Figure 1.5)
(Schippers and Sand 1999; Akcil and Koldas 2006; Almeida et al. 2009; Gadd 2010;
Slyemi and Bonnefoy 2012; Korehi, Blothe, and Sitnikova 2013; Kuang etal. 2013;
Vera, Schippers, and Sand 2013).
20


Table 1.1: Bacterial and archaeal taxa known to dominate anthropogenically and
endogenously metal impacted systems.________________________________________
Taxon Phylum Source of Metal Impact
Acidiphilium (genus) Alphaproteobacteria Both
Acidisphaera (genus) Alphaproteobacteria Both
Acidithiobacillus (genus) Proteobacteria Both
Acidobacteria (phylum) Acidobacteria Endogenous
Acidocella (genus) Alphaproteobacteria Both
Acinetobacter (genus) Gammaproteobacteria Endogenous
Actinobacteria (phylum) Actinobacteria Endogenous
Chloroflexi (phylum) Chloroflexi Endogenous
Ferroplasma (genus) Euryarchaeota Anthropogenic
Gallionellaceae (family) Betaproteobacteria Endogenous
Gemmatimonas (genus) Gemmatimonadetes Anthropogenic
Legionella (genus) Gammaproteobacteria Anthropogenic
Leptospirillum (genus) Nitrospirae Anthropogenic
Sphingomonas (genus) Alphaproteobacteria Anthropogenic
Sulfobacillus (genus) Firmicutes Anthropogenic
Thermogymnomonas (genus) Euryarchaeota Anthropogenic
21


3 Thiosulfate mechanism Jj Polysulfide mechanism
Figure 1.5: Diagram showing the biogenic solubilization of metal sulfides (MS) via
the thiosulfate mechanism (a) or polysulfide mechanism (b) by iron(II)-oxidizing
bacteria, such as Acidithiobacillus ferrooxidans (Af), Acidithiobacillus thiooxidans
(At), and Leptospirillum ferrooxidans (If). Main electron acceptors for each reaction
are indicated to the right of each arrow. In both pathways, iron(II)-oxidizing
bacteria generate iron(III), which reacts with the metal sulfide, resulting in
reduction to iron(II) and release of the metal cation and soluble sulfur compounds
(thiosulfate in (a) and hydrogen sulfide in (b)). In the polysulfide mechanism (b),
acid-soluble metal sulfides are attacked by hydrogen ions produced during
generation of sulfuric acid. Compounds in boxes indicate the main reaction products
that accumulate in the absence of sulfur- and iron-oxidizing bacteria. Biotic
contributions to reactions capable of proceeding abiotically are indicated by
organism abbreviations in parentheses. Source: Vera et al. 2013, adapted from
Schippers & Sand 1999.
Iron- and sulfur-oxidizing bacteria and archaea contribute to the bioleaching
(biological solubilization) of metals via two main reaction mechanisms (Figure 1.5)
(Schippers and Sand 1999; Vera, Schippers, and Sand 2013). The thiosulfate
pathway (Figure 1.5a) describes the dissolution of acid-insoluble metal sulfides,
including pyrite (FeS2), molybdenite (M0S2), and tungstenite (WS2). In this
pathway, iron-oxidizing bacteria oxidize ferrous iron (Fe2+) to ferric iron (Fe3+),
22


which then reacts with the metal sulfide via a reduction reaction, resulting in the
dissolution of the metal sulfide into the metal cation and soluble thiosulfate group.
The thiosulfate group is then further oxidized (via abiotic and biotic reactions) to
sulfuric acid.
Acid-soluble metal sulfides, including chalcopyrite (CuFeS2), galena (PbS),
hauerite (MnS2), orpiment (AS2S3), realgar (AS4S4), and sphalerite (ZnS), are
solubilized via the polysulfide mechanism (Figure 1.5b) (Schippers and Sand 1999;
Vera, Schippers, and Sand 2013). Iron-oxidizing organisms oxidize ferrous iron
(Fe2+) to ferric iron (Fe3+), which then reacts with the metal sulfide via a reduction
reaction, resulting in the release of the metal cation and hydrogen sulfide. Metal
sulfides may also be solubilized by hydrogen ions produced by the biotic reduction
of FhSn and elemental sulfur to sulfuric acid. The hydrogen sulfide produced by the
dissolution of metal sulfides is then oxidized, abiotically or biotically, to elemental
sulfur and/or FhSn. In the presence of sulfur-oxidizing organisms, the elemental
sulfur and FhSn are oxidized to sulfuric acid.
Microorganisms as Bioindicators
Because microorganisms, in particular the bacteria and archaea, have high
surface area-to-volume ratios, interact intimately with their immediate
environment, and can respond very quickly to environmental changes,
microorganisms offer promise for use as bioindicators of environmental
contamination and ecosystem health (McArthur 2001; Rublee, Henrich, and
Marshall 2009; Gadd 2010). Rublee etal. (2009) describe how selective pressures
23


in an environment, such as nutrient limitation or build-up of toxic compounds, will
elicit a change in microbial community composition. The correlation between the
environmental change and shift in community structure allows for the identification
of potential microbiological outcomes or indicators associated with the
environmental change.
Traditionally, bioindicator organisms have been identified and assessed
using culture-dependent cultivation methods (McArthur 2001). This traditional
method of bioindicator identification required that the organism was cultivable
and easily identifiable in the laboratory. The advantage of using the traditional
method for identifying bioindicators is that the organisms identified may be
biochemically characterized, and therefore a better understanding of the indicators
growth and behavior could be attained. However, culturing-dependent methods of
bioindicator identification are limited to organisms capable of growth under
laboratory conditions. It is estimated that only 1-10% of all bacterial species are
capable of growth under laboratory conditions, presenting a significant challenge
when attempting to elucidate the comprehensive microbial community structure
from culturing alone, or for identification of bioindicators from environmental
samples (Riesenfeld, Schloss, and Handelsman 2004; Hirsch, Mauchline, and Clark
2010). However, with the advent of modern molecular biology techniques (e.g.
high-throughput sequencing), cultivating organisms is no longer a requirement for
identifying bioindicator organisms, and this has renewed interest in examining
microbial community responses to environmental disturbances.
24


Modern methods of bioindicator identification are based primarily on
high-throughput sequencing of DNA isolated from environmental samples. This
approach allows researchers to elucidate the overall structure of microbial
communities by sequencing DNA marker genes, such as the 16S rRNA gene, and gain
insights into the overall community profile (Riesenfeld, Schloss, and Handelsman
2004). This DNA sequencing approach allows for identification and characterization
of microbial communities in a culture-independent manner, providing a platform for
identifying environmental bioindicator organisms.
The use of high-throughput DNA sequencing to identify bioindicator
organisms is still a relatively new application of the technology. Using next-
generation sequencing, the relative abundance of Proteobacteria lineages was
identified as a potential bioindicator for naphthenic acid contamination (Yergeau et
al. 2012). In that study, the relative abundance of Proteobacteria, specifically the
Betaproteobacteria class, correlated positively with increasing concentrations of
naphthenic acids found in oil sands mining activity associated with the Athabasca
River watershed; and the relative abundance of Cyanobacteria correlated negatively
with increasing concentrations of total petroleum hydrocarbons, total straight-chain
hydrocarbons, and total aromatic hydrocarbons. Similarly, Niepceron et al. (2013)
have proposed the Gammaproteobacteria as a potential bioindicator of multiple
polycyclic aromatic hydrocarbon contamination. In that study, the relative
abundance of Gammaproteobacteria was positively correlated with concentrations
of polycyclic aromatic hydrocarbons (up to 300 mg/kg) in artificially spiked
agricultural soil mesocosms. Lastly, Zhu et al. (2013) have shown through
25


redundancy analysis (a statistical method that correlates relative abundance species
data with environmental factors) that the Alphaproteobacteria and
Epsilonproteobacteria correlate positively with cadmium and zinc concentrations,
the Deltaproteobacteria and Actinobacteria correlate negatively with these metals,
and the Chloroflexi correlate positively with mercury concentrations from
industrially-polluted sediments from the Xiangjiang River, China. These studies
highlight the utility of using next-generation sequencing technologies to
characterize microbial communities and identify potential bioindicators for
environmental contamination and overall ecosystem health.
16S rDNA High-Throughput Sequencing
Illumina sequencing technology allows for massively parallel sequencing of
DNA fragments, resulting in upwards of 25-million paired-end reads per run from
the MiSeq sequencer (Illumina). For microbial census studies, targeted sequencing
strategies are often used to sequence segments of the 16S rRNA gene (a gene that
has been highly conserved in Prokaryotes throughout evolution) from mixed-DNA
samples. Specifically, variable regions within the gene (regions of the 16S rRNA
gene whose sequence varies from species to species) are targeted for sequencing
and allow for taxonomic identification. A general overview of the next-generation
16S rRNA gene amplicon sequencing process is provided below (Figure 1.6).
26


Environmental,
sample
Nucleic acid
extraction/purification
16S rRNA sequencing
PCR amplify
16S rRNA gene
i
Sequence
_AAAr _nA'V
_AAAT _MAT~
-JMT JlAflT
JWVT _JIAAT
l
Group sequences into OTUs v
Compare OTU sequences
to databases
i ;
Identification of:
Species
Relative abundance of
species within sample
o
o
o
Figure 1.6: Broad procedural overview of 16S rRNA gene amplicon sequencing
using next-generation sequencing technologies. Operation taxonomic units (OTUs)
refer to DNA sequences >97% similar (similarity threshold is user-defined). Image
adapted from:
https://www.neb.eom/~/media/NebUs/Page%20Images/Tools%20and%20Resou
rces/Feature%20Articles/FA_Microbiome_Figurel.jpg
Multiple samples can be combined and sequenced in a single sequencing run
(Caporaso et al. 2011). Using this strategy, the V4 regions of most 16S rRNA genes
present in a sample are amplified using specific primers. The forward primer, which
anneals to the conserved region of the 16S rRNA upstream of the V4 region, also
contains the 5 Illumina adaptor sequence (which hybridizes to the flow cell during
sequencing), a primer pad and primer linker (which do not have homology with the
27


region of the 16S rDNA adjacent to the V4 region and are used to link the Illumina
adaptor with the forward primer and for annealing of sequencing primers during
sequencing). The reverse primer contains the 3 complement of the Illumina
adaptor, a 12-nucleotide molecular barcode (used to identify which sample a
specific sequence read belongs and allows for multiple samples to be sequenced in a
single sequencing run), and a reverse primer pad and linker (Figure 1.7). Reactions
are performed in triplicate in an attempt to reduce reaction-specific PCR biases.
Triplicate reactions were pooled and cleaned to remove genomic DNA, excess
primers, dNTPs, and other reaction components, leaving only the amplicon of
interest. All samples are then pooled in equimolar concentrations, creating a
sequencing library.
28


+ strand
Target gene:
3
.......onplicon...... ATTAGAKACCCBDCTAGTCC ATACAGGTGAGCACCTTGTA...
rc.....amplicon...... TAATCTWTGGGVHCATCAGG TATGTCCACTCGTGGAACAT... strand
S
Amplification primers with annealing sites:
5
...CTTCCACTTAAATGAGACTT GTGCCAG04GCCGCGGTAA
...GAAGGTGAATTTACTCTGAA CACGGTCGKCGGCGCCATT
3
...CTTCCACTTAAATGAGACTT GTGCCAGCMGCCGCGGTAA ............................................ampUcon.......... ATTAGAWACCCBOGTAGTCC ATACAGGTGAGCACCTTGTA. ..
4---------TAATCTWTGGGVHCATCAGG CCGACTGACTGATTGCGTGCGATCTAGAGCATACGGCAGAAGACGAAC 5*
Rev. primer Rcv.bnker Rev.Pad RC RC of + strand J'
Forward PCR primer constnjct barcode iriumma Adapter
strand S'llluminaA Forward primer
5 aatgccgcgaccacccagacctacctcgctctcc[aco)ccc(;CG(;taa--------------
...GAAGGTGAATTTACTCTGAA CACGGTCGKCGGCGCCATT ....................................rc......ampUcon..........TAATCTWTGGGVHCATCAGG TATGTCCACTCGTGGAACAT...
Amplification products:
AATGATACGGCGACCACCGAGACGTACGTACGGTGTGCCAGCMGCCGCGCTAA .........................................onplicon....... ATTAGAWACCCBDGTAGTCCGGGTACGTACGTAACGCACGCTAGATCTCGTATGCCGTCTTCTGCTTG
TTACTATGCCGCTGGTGGCTCTGCATGCATGCCACACGGTCGKCGGCGCCATT .....................................rc. .wrplicon...... TAATCTWTGGGVHCATCAGGCCCATGCATGCATTGCGTGCGATCTAGAGCATACGGCAGAAGACGAAC
Sequencing primers with annealing sites:
AATGATACGGCGACCACCGAGACGTACGTACGGTGTGCCAGCMGCCGCGGTAA ..
amplicon...... ATTAGAWACCCBDGTAGTCCGGGTACGTACGTAACGCACGCTAGATCTCGTATGCCGTCTTCTGCTTG
4---------TAATCTKTGGCVHCATCAGGCCCATGCATGCA g.
Read 2 sequencing primer
Read I sequencing primer Index sequencing primer
5'ACGTACGTACGGTGTGCCAGCMGCCGCGGTAA---- 5 ATTAGAWACCCBDGTAGTCCGGCTGACTGACT-----
TTACTATGCCGCTGGTGGCTCTGCATGCATGCCACACGGTCGKCGGCGCCATT ............................rc.. omplicon..... TAATCTVITGGGVHCATCAGGCCGACTGACTGATTGCGTGCGATCTAGAGCATACGGCAGAAGACGAAC
Figure 1.7: Protocol for amplification and barcoding of 16S V4 rDNA for Illumina
sequencing. Conserved regions (highlighted in blue) flanking the V4 region of the
16S rDNA (V4 regions labeled amplicon and rc amplicon above) targeted by
complementary forward and reverse primers. Illumina adaptors (green text [used
for hybridization to the sequencing flow cell]), primer pads (blue text), primer
linkers (red text), and internal Golay barcodes (purple text [reverse primer only])
are not homologous to the 16S rDNA (highlighted in red). The locations of
annealing sites for the forward sequencing primer (Read 1), reverse sequencing
primer (Read 2), and index sequencing primer (used to sequence the internal Golay
barcode to each amplicon) are indicated in the bottom panel. Source: Caporaso etal.
2011
Prior to the actual sequencing process, the sequencing library is denatured
using sodium hydroxide, diluted in hybridization buffer, and loaded into the reagent
cartridge. The diluted, denatured library is then dispensed onto the sequencing
flow cell and the Illumina adaptors of the amplicon strands hybridize to the
oligonucleotides bound to the flow cells surface (Figure 1.8). DNA strands are then
amplified via a process called bridge amplification, resulting in the formation of up
29


to 25 million unique clusters, each containing thousands of copies of a single
amplicon, which are then ready for sequencing.
! '
I
1
i
i
i
Figure 1.8: Cluster generation on the Illumina MiSeq sequencing flow cell. The
Illumina adaptors of the denatured DNA fragments hybridize to the lawn of
homologous oligos on the surface of the flow cell. The DNA molecules are then
amplified using a process called bridge amplification, which results in the
generation of up to 25 million unique clusters. Source: Illumina Inc.
During sequencing, a forward, a reverse (Read 1 and 2, respectively), and an
indexing sequencing run are performed. The forward sequencing primer binds to
the reverse complement of the 5 primer pad, primer linker, and primer of each
amplicon; the reverse sequencing primer binds to the reverse complement of the 3
reverse primer pad, primer linker, and primer of each amplicon; and the indexing
primer binds to the reverse primer, primer linker, and primer pad of each amplicon
(Figure 1.8). During the forward and reverse sequencing runs, each performed
separately, multiple cycles, consisting of fluorescently labeled reversibly terminable
nucleotides being flowed across the flow cell and incorporated into the synthesis
strand of DNA, are performed (Figure 1.9). After each cycle, a laser excites each
30


cluster on the flow cell, and the fluorophore of the most recently incorporated
nucleotide fluoresces. The wavelength of light emitted by the fluorophore is
captured by a camera and interpreted as a specific nucleotide. The blocking group
and fluorophore attached to the last-incorporated nucleotide are cleaved, and the
process is repeated for the next nucleotide. With Alumina MiSeq technology, the
maximum read length is 300 base pairs, so the forward and reverse sequencing
cycles may each be repeated up to 300 times. The indexing read proceeds in a
similar fashion to the forward and reverse reads described previously, except only
twelve cycles are completed due to the length of the Golay barcode (12 nucleotides).
During the first several sequencing cycles, X-Y coordinates of all clusters on the flow
cell are determined, which allow for forward and reverse reads to be combined, and
for sample IDs to be assigned to each cluster when comparing sequencing
coordinates of the reverse and forward reads to the coordinates of the indexing
read.
31


T
Figure 1.9: During sequencing, all clusters on the flow cell are sequenced
simultaneously. DNA is sequenced using sequencing-by-synthesis technology in
which fluorescently labeled, reversibly terminable nucleotides are incorporated into
the synthesis strand one by one. Laser excitation in between cycles of nucleotide
incorporation results in the fluorescence of the fluorophores incorporated into each
cluster. A camera interprets the wavelength of light each fluorophore emits as a
specific nucleotide. The blocking group and fluorophore attached to the last-
incorporated nucleotide are cleaved, and the process is repeated for the next
nucleotide. Source: Illumina Inc.
To analyze sequencing data generated from high-throughput 16S rRNA gene
sequencing, bioinformatic and statistical tools, such as QIIME (Quantitative Insights
Into Microbial Ecology) and R, respectively, can be used (Caporaso etal. 2010; R
Core Team 2014). QIIME is commonly used to quality filter sequencing reads,
assign taxonomy to reads, and perform phylogenetic and statistical analyses, such as
calculating alpha- and beta-diversity indices (diversity within samples and among
samples, respectively). R is a powerful statistical and visualization programming
language used to analyze virtually all data types. For sequencing data, R may be
used to calculate multivariate statistics, including principal component analysis and
32


multiple regression analysis, and to generate heatmaps and other plots to observe
patterns among samples, among many other applications.
Microbial Communities of Acidic, Anthropogenically Metal impacted Systems
Many factors influence the structure of microbial communities in acidic metal
impacted systems, none of which are mutually exclusive. Conductivity, pH, metal
concentrations, nutrient availability (particularly organic carbon and total
nitrogen), and temperature all influence microbial community structure and gross
metabolic capability in these systems. For example, a recent study characterized the
microbial communities of southern Poland forest soils impacted by high
concentrations of copper, lead, and zinc from industrial pollution and pH levels
ranging from 3.4 to 5.6 (Chodak et al. 2013). Multiple regression analysis indicated
that total nitrogen concentration was the strongest limiting factor in the production
of microbial biomass (adjusted R2 = 0.443). Basal respiration rates of the
communities were strongly correlated with organic carbon content, organic carbon
to sulfur ratios, and toxicity levels (adjusted R2 = 0.834). Lastly, community
diversity, determined by 16S rRNA gene pyrosequencing, was very strongly
correlated with soil pH, total nitrogen content, and toxicity level (adjusted R2 =
0.908), and higher pH seemed to select for a high abundance of Chloroflexi,
Gemmatimonadetes, Verrucomicrobia, Deltaproteobacteria, and Firmicutes compared
to more acidic samples. Interestingly, metal concentrations had a weak effect on
community diversity indices, but phospholipid fatty acid analysis showed that heavy
metal pollution did affect fatty acids, characteristic of Gram-positive organisms, and
33


indicated that Gram-positive organisms may be more susceptible to heavy metal
pollution than Gram-negatives.
pH has been found to be a significant driver of microbial community
structure in a variety of systems. Focusing on metal impacted environments, a
study comparing the microbial communities of 59 acid mine drainage water
samples taken from Southeastern China (determined by 16S rRNA gene
pyrosequencing), found that pH alone (which ranged from 1.9 to 4.1) accounted for
23% of the variation seen in the phylogenetic diversity among all samples (Kuang et
al. 2013). The Betaproteobacteria increased in relative abundance as pH increased,
with the most drastic increase seen around pH 2.4 and above. At pH below 2.4, the
Alphaproteobacteria, Euryarchaeota, Gammaproteobacteria, and Nitrospira were the
most abundant taxa. Lastly, multivariate regression tree analysis, which compares
the phylogenetic diversity of all samples to the environmental parameters collected,
indicated that pH, total organic carbon concentrations, sulfate, ferrous iron, and
ferric iron concentrations explained 70% of the variation in the presence and
abundance of organisms among all samples.
Kuang et al. (2013) reported similar trends when they analyzed 66
worldwide AMD samples obtained from the literature. For those 66 samples, pH
and sulfate concentration had a stronger effect on community structure and
diversity than did spatial isolation, which indicated that environmental factors are
likely influencing microbial community structure, and that the observed variation in
community structure is not due to geographic location. Nitrospira and
Euryarchaeota were dominant in samples with a pH below 1.9 in the global dataset,
34


which corroborated the results from the southeastern China samples collected and
analyzed by Kuang et al. (2013).
Chen et al. (2013) also saw a pH-dependent trend in microbial community
structure. In their study, six tailings samples from the Fankou lead/zinc mine in
Shaoguan, Guangdong Province, China, were collected along an acidification transect
(including samples showing no signs of oxidation, slight signs of oxidation, and
strong signs of oxidation). Microbial community profiling through pyrosequencing
of the V4 region of 16S rRNA gene found that tailings samples with no sign of
oxidation (pH 7.5) were dominated by the genera Hydrogenophaga, Thiobacillus,
Thiovirga, and Comamonas. In slightly acidic tailings (pH 6.4), the microbial
communities were dominated by Thiobacillus, Legionella, Gemmatimonas, and
Sphingomonas. In tailings exhibiting strong signs of oxidation (pH ranging 1.8 and
2.1), the communities were dominated by Ferroplasma species, showing a shift in
dominance from Bacterial to Archaeal taxa. Lastly, in an orange-colored oxidized
tailings sample (pH 2.4), a mix of both Bacteria and Archaea was seen, with
Ferroplasma, Acidithiobacillus, Leptospirillum, Sulfobacillus, and Thermogymnomonas
being the dominant genera.
Multivariate regression tree analysis revealed that pH and moisture content
were the strongest influencers of community structure in these six communities,
with the Betaproteobacteria dominating in samples with pH above 4.3, and
Euryarchaeota dominated samples with pH below 4.3 and moisture content above
10% (Chen et al. 2013). Although this study was limited due to only six samples
being characterized, all from the same tailings pile with differing amounts of
35


oxidation, the results of this study reinforce the observation that pH is a strong
factor influencing microbial community structure in acidic environments.
pH appeared to be the most significant factor influencing microbial
community structure at the Shuimuchong copper tailing impoundment near
Tongling City, Anhui Province, China (Liu et al. 2014). In this study, 90 soil samples
were collected at random within a 10 km2 area surrounding the tailing
impoundment. Microbial communities, determined by high-throughput 454
pyrosequencing of the V4 region of 16S rRNA genes present in each sample, were
analyzed in conjunction with physicochemical parameters associated with each soil
sample, including pH, nutrient concentrations, and total metal concentrations.
Linear regression analysis indicated that pH alone was responsible for
approximately 69.7% of the variation in the relative abundance of the
Euryarchaeota among all samples. pH was also a significant factor describing the
phylogenetic diversity of the Gammaproteobacteria (r2 = 0.741). Principal
coordinate analysis of weighted UniFrac distances for all 90 samples indicated that
pH was responsible for 66% of the variation of both the presence and abundance of
organisms. No other physicochemical parameter could individually account for
more than 50% of the variance in diversity metrics tested in the study, and were
considered insufficient in explaining the diversity of the communities. From this
study, it was clear that pH was an important factor driving the structure of the
microbial communities.
36


Microbial Communities of Naturally Occurring Acidic, Metal impacted Systems
Due to the exigency surrounding anthropogenically-induced input of
elevated metals in the environment, and the sheer number of anthropogenically
metal impacted sites worldwide, these systems have received far more attention
from the scientific world than naturally metal impacted sites (e.g. naturally
occurring spring systems, acidic fens, some rivers, etc.). For these same reasons, the
microbiology of naturally metal impacted systems is poorly elucidated and remains
an active area of research. However, recent studies have begun to disentangle the
differences in community structure between naturally occurring and
anthropogenically-induced acidic, metal impacted environments. While pH seems
to be one of the most significant factors influencing microbial community structure
in AMD systems, microbial communities of naturally occurring acidic, metal-rich
environments are influenced by a variety of physicochemical parameters.
Terminal restriction fragment length polymorphism (T-RFLP) and
sequencing of DNA extracted from naturally occurring aluminum, iron/manganese,
and sulfate rock coatings from Karkevagge, a cold-climate valley located in the
Northern Caledonide Mountains of Sweden, has shown high bacterial diversity and
substrate-specific community specialization (Marnocha and Dixon 2013). The study
found 910 OTUs unique to sulfate crusts, 804 OTUs unique to aluminum glazes, and
608 OTUs unique to iron/manganese films, with 32 OTUs (including Acidiphilium,
Acidisphaera, Acidobacterium, Acinetobacter, and Methylobacterium taxa) found in
all three environments. However, differences in community membership among the
three site types are due to the presence of lowly abundant and/or rare taxa and not
37


typically due to the presence of a particular taxon in high abundance. Interestingly,
the Acidocella, Acidithiobacillus, Acidisphaera, Thiobacillus, and Acidiphilium genera
found in varying relative abundances in the three rock coating types are also
commonly found in AMD systems, indicating a parallel between anthropogenically
and naturally occurring metal impacted systems (Baker and Banfield 2003; Chen et
al. 2013).
A recent study described the microbiology of sediments associated with the
Paint Pots, a naturally acidic (pH~3) spring system in Kootenay National Park,
Canada, impacted by spring water concentrations of 35.8 mg/L Zn, 461gg/L Pb, and
82.7 gg/L As (Grasby et al. 2013). Three of the four sediment samples analyzed
were dominated by Proteobacteria, which accounted for approximately 30-50% of
the community composition. Members of the Chloroflexi, Acidobacteria, and
Actinobacteria were also abundant in these three communities. Members of the
candidate phylum WPS-2, Chloroflexi, and Proteobacteria dominated a fourth
sample, taken from the Paint Pot mound, and collectively accounted for
approximately 62% of the organisms sequenced in that sample. Interestingly,
known iron-oxidizing bacteria commonly found in high abundances in AMD sites
were found in low proportions in the Paint Pot communities. However, members of
the Gallionellaceae family (an iron-oxidizing bacterium often associated with AMD in
less acidic environments) were found in low abundance in the Paint Pot microbial
communities. Although only four samples were analyzed, these data indicate that
there are distinct differences between AMD and ARD microbial community
structures.
38


CHAPTER II
SITE DESCRIPTIONS
During the summer and fall of 2013, fifty-seven sediment samples were
collected from three regions encompassing both anthropogenic and endogenous
sources of metal impact:
Iron Springs Mining District
Chattanooga Fen
Mt. Emmons Fen
Tables 2.1-2.3 below list and describe sampling locations for each of the three study
regions.
Iron Springs Mining District
The Iron Springs Mining District is home to several abandoned mine sites
that discharge acidic, metal-rich water into the Howard Fork River running through
the town of Ophir, Colorado (Figure 2.1, Table 2.1). Metal concentrations in the
mine drainages typically exceed allowable concentrations as determined by the
Colorado Department of Public Health and Environment (CDPHE) Regulation 31.
These discharges also have noticeable effects on ecosystem health, including water
and sediment discoloration, overall turbidity, and the absence of macrobiota
(aquatic insects and fish) in a segment of the Howard Fork River most affected by
39


these discharges (Robyn Blackburn, Colorado Fish and Wildlife Service, personal
communication 2013).
Figure 2.1: Colorado state map showing the location of mineral reserves (green).
Iron Springs sites are located in the southeast corner of San Miguel County (blue
square), Chattanooga Fen sites are located in northern San Juan County (red
square), and Mt. Emmons Fen sites are located in central northern Gunnison County
(black square). Source: geosurvey.state.co.us
The Iron Springs Mining District is dominated by Permian Period ore-
deficient sandstone and siltstone (composed of feldspars and quartz) of the Cutler
Formation, and Middle Tertiary Period intrusive plutonic rock rich in base metal
sulfides (e.g.: copper, tin, zinc, and other non-precious metals) and precious metals
(mainly silver) (Green 1992; Nash 2002). Large, naturally occurring ferricrete (iron-
rich cemented rock) deposits litter the valley floor, and were likely used by
prospectors to locate metal-rich veins in the earth. Prospecting efforts proved
40


lucrative, and this area was extensively mined for silver, gold, and lead, and, to a
lesser extent, iron and tungsten, from 1877 through the 1970s and 1980s, producing
an estimated 873,000 tons of ore (Nash, 2002).
Table 2.1: Iron Springs sampling site abbreviations, descriptions, and GPS
coordinates.
Site Abbreviation Site Description Latitude Longitude
ISCA01 Caribbeau Mine drainage exiting mountain above collapsed mine adit. Upstream of large tailings pile. 37.856156 -107.846186
ISCA02 Caribbeau Mine drainage upstream of confluence with first pond/wetland system. Downstream of large tailings pile. 37.856728 -107.845953
ISCA03 Caribbeau Mine drainage downstream of wetland Bfstem and upstream of Fioward Fork confluence. 37.857186 -107.845847
ISCB01 Iron bog drainage eastern tributary flow originating from underground. 37.858263 -107.809694
ISCB02 Iron bog drainage western tributary flow originating from underground. 37.858263 -107.809943
ISCB03 Iron spring large acidic peat pond just east of the Iron Bog, containing fallen trees and non- decomposing organics. 37.858272 -107.810269
ISCS01 Carbonero Mine tailings cap seep pond at southwest end of tailing cap. 37.8557 -107.821756
ISHF01 Howard Fork downstream of Chapman Gulch and upstream of the iron bog area and mine discharges. 37.855567 -107.804417
ISHFIBE01 Howard Fork/iron bog eddy just downstream of the confluence of Howard Fork and the iron bog drainage. 37.856371 -107.811055
ISND01 Pond directly below New Dominion Mine adit discharge on western end of wetlands. 37.908667 -107.821933
ISND02 High-flowing seep discharging into Howard Fork and originating from New Dominion Mine adit drainage wetlands. 37.855671 -107.822135
ISNDCS01 Seep originating towards north end of Carbonero tailings, fed by discharge from New Dominion ponds, that flows around tailings pile into Howard Fork 37.855738 -107.821786
ISNDGP01 New Dominion Mine green pond near Carbonero Mine tailings cap at south end of New Dominion Mine wetlands. 37.856183 -107.821633
ISNDMD01 New Dominion Mine adit drainage accessed just south of Ophir Pass Road outside of drainage pipe. 37.857633 -107.822722
41


Chattanooga Fen
The Chattanooga Fen system is located at the top of Red Mountain Pass, just
south of Ouray, Colorado, near the old township of Chattanooga (Figure 2.1, Table
2.2). The geology of the area is characterized by Quaternary Period glacial drifts,
Tertiary Period andesite (Si02-rich extrusive igneous rock), and Middle Tertiary
Period intrusive plutonic rock (Green 1992).
This fen system, carbon dated to be around 600 years old, lies just below the
Gold Finch Mine (Chimner and Cooper 2006). While the majority of the water
associated with this fen system is estimated by the United States Forest Service to
be from subsurface sources, portions of the Chattanooga Fen receive above-surface
mine effluent from the Gold Finch Mine. The Chattanooga Fen system is
characterized by rich fens (pH > 6.4) and acidic iron fen ponds, with pH values
ranging from 3.4 to neutrality. This site is of interest due to the rarity of iron fens in
general, while also providing an ideal environment to compare both anthropogenic
and endogenous sources of metal contribution on microbial community structure.
Table 2.2: Chattanooga Fen sampling site abbreviations, descriptions, and GPS
coordinates.
Site Abbreviation Site Description Latitude Longitude
CFA01 Fen pond, affected by Gold Finch Mine drainage, downhill (east) from CFU2. 37.868433 -107.725117
CFA02 Fen pond, affected by Gold Finch Mine drainage, south of CFA01. 37.867050 -107.725100
CFGF01 Gold Finch Mine drainage at constructed portal, just north of frontage road. 37.868233 -107.726800
CFU01 Fen pond unaffected by Gold Finch Mine drainage, just downhill [east] from frontage road and north of Gold Finch Mine. 37.870833 -107.725717
CFU02 Fen pond unaffected by Gold Finch Mine drainage, downhill feast] from CFU1. 37.871000 -107.725317
42


Mount Emmons Fen
The Mt. Emmons Fen is located several miles northwest of Crested Butte,
Colorado, on Mt. Emmons in the Gunnison National Forest (Figure 2.1, Table 2.3),
just downslope from the Keystone Mine (Mattson 2000). The fen lies atop
Quaternary Period glacial drifts, Cretaceous Period sedimentary rock (sandstone
and other rock of the Mesaverde Formation) and shale (of the Mancos Formation),
and Middle Tertiary Period intrusive igneous rock (including the nearby,
mineralized molybdenum porphyry deposit underlying Mt. Emmons) (Green 1992).
In addition to molybdenum, deposits of gold, silver, lead, and copper have been
found in the intrusive igneous rock of the West Elk Mountains.
Multiple geologic fractures and other hydrogeologic characteristics allowed
for the formation and persistence of the Mt. Emmons Fen over thousands of years.
Noticeable surface releases of groundwater, called mounds, are evident throughout
the fen system. Based on peat deposition rates, the Mt. Emmons Fen is said to be
~8,000 years old, which coincides with the global deglaciation event following the
Pleistocene Epoch (2,588,000 to 11,700 years ago) (Mattson 2000). This expansive
fen system is fed by convective groundwater flow and spring discharges over a large
geographic area, giving rise to unique environments suitable to uniquely adapted
organisms, such as a rare species of sundew (Drosera rotundifolia) found in the
marshy areas around the fen. The age of the fen, the unique biology of this
environment, and the moderately acidic waters (pH ~4.0) make this fen particularly
unique, classifying this system as a Resource Category 1 area by the United States
43


Forest Service (signifying the habitat's irreplaceability and active protection) (Fall
1997; Mattson 2000).
Table 2.3: Mt. Emmons Fen sampling site abbreviations and descriptions. GPS
coordinates have been omitted due to the sensitivity of this environment at the
request of the United States Forest Service.________________________
Site Abbreviation Site Description
MTEF01 Mt. Emmons Fen pond just west of source mound.
MTEF02 Mt. Emmons Fen midpond, west of MTEF1
MTEF03 Mt. Emmons Fen pond outfall, at western edge of fen pond.
MTEFP01 MTEFP02 Smaller, isolated greenish pond northeast of main fen pond. Smaller, isolated red pond southeast of MTEFP1.
Microbiology of Fens
The microbiology of naturally occurring, acidic fens remains poorly
understood. T-RFLP was used to characterize the soil microbial communities of two
drained circumneutral fens with varying levels of soil organic carbon content in the
Ljubljana Marsh, Ljubljana, Slovenia (Kraigher etal. 2006). The 16S rRNA gene
clone library constructed for the fen sample elucidated that the Proteobacteria and
Acidobacteria, accounted for 53% and 23%, respectively. Although this fen system
is not metal impacted, nor acidic, it was one of the first studies to characterize the
microbial communities associated with fen sediments.
In another study characterizing fen microbiology, quantitative PCR and
denaturing gradient gel electrophoresis (DGGE) were used to characterize the
abundance, activity, and diversity of ammonia-oxidizing archaea (AOA) and
ammonia-oxidizing bacteria (AOB) from peat soil of the Schloppnerbrunnen acidic
fen (pH 4.6-4.9) in Germany (Herrmann, Hadrich, and Kiisel 2012). The abundance
of AOA was four orders of magnitude higher than the abundance of AOB in most
44


samples. Further experimentation showed that acidic pH and low ammonium
concentrations favored AOA over AOB. However, this study did not characterize the
overall community structure of the fen, and did not perform chemical analyses to
determine metal concentrations in the peat or surface waters. These studies of fen
microbiology highlight our severely limited knowledge of fen microbiology. Our
objectives, to characterize microbial communities associated with
anthropogenically and endogenously metal impacted systems (which includes two
fen systems), and to identify bioindicators of metal toxicity and ecosystem health,
will enhance our limited knowledge of fen microbiology and further evaluate the
differences in bacterial community structure between anthropogenically and
endogenously metal impacted systems.
45


CHAPTER III
CONDUCTED STUDY
Introduction
Abiotically, acid mine drainage (AMD] and acid rock drainage (ARD]
generation results from the exposure of sulfide-containing minerals to oxidative
conditions and liquid water; however, the route of exposure to oxidative conditions
can vary. AMD typically results from anthropogenic-facilitated exposure of rock
material, such as in mining, to surface oxygen and water. ARD formation results
from the natural, or endogenous, exposure of sulfide-containing minerals to
oxidative or acidic conditions via hydrologic regimes, erosion, and other natural
phenomena (Johnson 1998; Baker and Banfield 2003; Akcil and Koldas 2006; Kuang
etal. 2013]. Sulfide mineral oxidation results in dissolution of metals and
generation of metal ions/complexes (whose solubility is determined by pH] and
sulfuric acid, which catalyzes further oxidation of metal sulfides (Schippers and
Sand 1999; Vera, Schippers, and Sand 2013].
Bacteria and Archaea play an integral role in biogeochemical processes
leading to AMD and ARD generation due to their ubiquity in nature, high surface-to-
volume ratios, intimate interactions with their immediate environment, diverse
metabolisms, and high rates of ferrous iron oxidation (Fe2+ Fe3+] (Singer and
Stumm 1970; Rublee, Henrich, and Marshall 2009; Gadd 2010]. Bacteria and
46


Archaea inhabiting AMD/ARD systems produce acidic metabolic byproducts from
carbon fixation, nitrification, heterotrophic metabolisms, and other biochemical
pathways, all of which result in the acidification of micro- and macro-scale
environments, contributing to the dissolution of acid-soluble metal sulfides
(Johnson 1998; Schippers and Sand 1999; Baker and Banfield 2003; Vera, Schippers,
and Sand 2013). Many microorganisms in AMD/ARD systems also engage in
microbial dissimilatory redox reactions, resulting in the oxidation of sulfur and iron
compounds, contributing to the AMD/ARD generation and dissolution of sulfide-
and pyrite-containing minerals. A variety of organisms are capable of iron and/or
sulfur oxidation, including members of the Proteobacteria, Nitrospirae, Firmicutes,
and Actinobacteria phyla in bacteria; and the Sulfolobales and Thermoplasmales
(Fe2+-oxidizing species only) phyla in archaea (Vera, Schippers, and Sand 2013).
Some notable well-studied examples of chemolithoautotrophic iron- and sulfur-
oxidizing bacteria include Acidithiobacillus ferrooxidans, Acidithiobacillus
thiooxidans, and Leptospirillum ferrooxidans, each of which oxidize metal sulfides
and are commonly found in AMD environments (Schippers and Sand 1999; Akcil
and Koldas 2006; Almeida et al. 2009; Gadd 2010; Slyemi and Bonnefoy 2012;
Korehi, Blothe, and Sitnikova 2013; Kuang et al. 2013; Vera, Schippers, and Sand
2013).
Although AMD is often a more significant concern than ARD due to the
increased surface area of minerals exposed to weathering and biological oxidation,
both AMD and ARD result in the introduction of metal-rich, acidic effluents,
generated by the same biogeochemical processes, into the environment. While acid
47


mine drainage microbiology has been studied extensively in highly acidic
environments, such as Iron Mountain, CA (pH <1) (Edwards, Gihring, and Banfield
1999) and others (Zhang et al. 2007; Chen et al. 2013; Giloteaux et al. 2013; Kuang
et al. 2013), the microbiology of acid rock drainage sites remains relatively
unexplored. Acidic, oligotrophic, minerotrophic iron fens (peatlands fed by
groundwater upwellings with negligible contribution from precipitation or surface
runoff) are examples of understudied ARD impacted sites. In this study, we
evaluated the sediment-associated bacterial diversity of both anthropogenically
metal impacted (mining; AMD) and endogenously metal impacted (iron fen; ARD),
geographically distinct sites within the Colorado Mineral Belt, and correlated
environmental factors with observed differences in bacterial community structure
and diversity.
Methods
Field Site Descriptions
The Iron Springs Mining District is located in Ophir, Colorado. The now-
abandoned mines in the district produced an estimated 873,000 tons of ore rich in
silver, gold, lead, and other non-precious metals (Nash 2002). Currently, the mines
are releasing metal-rich, acidic water into the Howard Fork River, requiring
environmental mitigation, and offer a unique opportunity to study microbial
community dynamics prior to, during, and following remediation efforts underway
by the United States Environmental Protection Agency.
48


The Chattanooga Fen system is located at the top of Red Mountain Pass south
of Ouray, Colorado, near the old mining township of Chattanooga. The Chattanooga
Fen, carbon dated to be around 600 years old, is characterized by naturally acidic,
metal-rich waters originating from groundwater flow both above surface and in
parent material (Chimner and Cooper 2006). The system is also home to the
abandoned Gold Finch Mine, which releases iron-rich mine drainage from a
constructed portal into the southeastern edge of the fen system. Due to the
anthropogenic impact on a naturally occurring iron fen system, and the presence of
fen ponds unimpacted by the Gold Finch Mine, this site allowed for comparative
microbial community analysis between these two system types.
The Mt. Emmons Fen is located east of Crested Butte, Colorado, on Mt.
Emmons in the West Elk Mountain Range. This fen is fed by groundwater discharge
via above-surface and subsurface groundwater flow through highly fractured parent
material (Mattson 2000). The expansive iron fen, estimated to be 8,000 years old
based on peat deposition rates, is characterized by acidic, metal-rich water, and
presents a unique opportunity to study the microbiology of a naturally metal
impacted system.
Sample Naming Conventions
All sample names begin with a sample prefix based on the system from which
each sample was collected. Samples collected from Iron Springs begin with the
prefix IS, samples from the Chattanooga Fen begin with CF, and samples from the
Mt. Emmons Fen begin with MTEF. Sample name prefixes are followed by site-
49


specific abbreviations and numbers (See Tables 2.1 2.3). Lastly, when samples are
divided up by month of sampling, a one-letter suffix is added to the sample name to
denote the month that each sample was collected (J June, A August, and S -
September). For example, CFU01J and CFU01S correspond to the Chattanooga Fen
Unaffected pond sample, collected at the same location in June and September.
Sampling Procedures
Samples for water chemistry and microbial community analysis were
obtained from twenty-four locations in June 2013, August 2013, and from nine
locations in September 2013 (Table 3.1). Surface waters were used for chemical
analyses instead of sediments due to the lack of structure in sediments. Specifically,
500 mL of raw surface water samples, co-located with sediment samples, were
collected for total recoverable metal analysis (described below), acidified to pH <2.0
with ultrapure 1:1 HNO3, and stored on ice in the dark during transport to the
analytical chemistry laboratory. An additional 250 mL of surface water was filtered
through a Nalgene 0.45 pm cellulose nitrate membrane filter (Thermo Scientific,
Waltham, MA), acidified to pH <2.0 with ultrapure 1:1 HNO3, and stored on ice in the
dark for dissolved metal analysis. Composite surface sediment samples
(approximately 250 grams of surface sediment encompassing the top ~5cm) at all
sampling locations were collected and stored on dry ice in the dark during
transport. Conductivity, dissolved oxygen, pH, and temperature measurements at
the sediment surface were obtained at the time of sample collection using a Thermo
Scientific Orion 5-Star Multiparameter Meter Kit (Thermo Fisher Scientific, Inc.,
50


Waltham, MA). Due to the United States government shutdown in
September/October, 2013, Iron Springs samples were not collected at this time
point.
Table 3.1: Scheme showing time course for sampling all regions and indicating
which physicochemical variables were measured at time of sample collection. Note:
Iron Springs samples were not collected in September 2013 due to the government
shutdown.
Sampte Region Sampling Date Number of Sites Sampled Sediment Collected PH Measured Conductivity Measured Temperature Measured Dissolved Oxygen Measured
Iron Springs June 2013 14
August 2013 14
September 2013 0 X X X X X
Chattanooga Fen June 2013 5
August 2013 5
September 2013 4
Mt. Emmons Fen June 2013 5
August 2013 5
September 2013 5
Metal Analyses
Iron Springs water samples were analyzed for total recoverable metal
concentrations at the EPA Region 8 Laboratory (Golden, Colorado) using
Inductively-Coupled Plasma Mass Spectrometry (ICP-MS) and following EPA
method 200.8. Dissolved metal analyses for Iron Springs samples were conducted
at the EPA Region 8 Laboratory (Golden, Colorado) using Inductively Coupled
Plasma Optical Emission Spectrometry (ICP-OES) following EPA method 200.7.
Chattanooga Fen and Mt. Emmons Fen samples were analyzed for total recoverable
51


metal concentrations in sediment, total recoverable metal concentrations in water,
and dissolved metal concentrations in water at the University of Colorado Denver
Shared Analytical Services Lab (CU Denver SASL) using a Thermo Jarrell Ash ICAP
61 inductively-coupled plasma optical emission spectrometer (Thermo Jarrell Ash
Corporation, Franklin, MA) and following EPA method 200.8. Water hardness
(mg/L CaCOs) for each water sample collected was calculated using the following
equation:
mg/L CaCC>3 = (2.497 [Ca, mg/L]) + (4.118 [Mg, mg/L])
where the concentrations of calcium and magnesium correspond to the dissolved
fraction from water.
DNA Extraction, PCR Amplification, and Next-Generation Sequencing
Total genomic DNA was isolated from 10.0 0.2 grams sub-samples of
mechanically homogenized saturated composite sediment from each site using the
MO BIO PowerMax Soil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA)
and following manufacturers protocol. 16S rRNA genes from each sample were
amplified in triplicate, pooled, and cleaned. The V4 variable region of the 16S rRNA
gene was amplified using primers F515 (5 GTG CCA GCM GCC GCG GTA A 3) and
806R (5 GGA CTA CHV GGG TWT CTA AT 3), each containing 5 overhangs
necessary for Illumina high-throughput sequencing, and the reverse primer
containing a 12-nucleotide sample-specific Golay barcode sequence (Caporaso etal.
2012). The polymerase chain reaction (PCR) reaction mixture contained 10 pL 5
PRIME Hot Master Mix (5 PRIME, Gaithersburg, MD) (final reaction concentrations:
52


0.5 U Taq DNA polymerase, 22.5 mM KC1,1.25 mM Mg2+, and 100 pM of each dNTP),
200 nmol/L of each primer, 200 nanograms (IX) bovine serum albumen (BSA)
(New England BioLabs, Inc., Ipswich, MA), molecular biology grade water (Thermo
Fisher Scientific, Inc., Waltham, MA) and template DNA to a total volume of 25 pL.
Reactions were subjected to a 3-minute denaturation step at 94C, 30 cycles of a 45
second denaturation at 94C, a 60-second annealing step at 50C, and a 90-second
extension step at 72C, followed by a ten-minute final extension step at 72C.
Successful amplification was verified via agarose gel electrophoresis (1%
agarose gel) and visualized with ethidium bromide to confirm that the PCR product
size obtained coincided with expected product size. Successfully amplified reactions
were pooled and purified using the MO BIO UltraClean PCR Cleanup Kit (MO BIO
Laboratories, Inc., Carlsbad, CA), following included protocol. DNA concentrations
from cleaned-up reaction concentrates were quantified using the Qubit Broad-
Range dsDNA Assay Kit and Qubit 3.0 Fluorometer (Thermo Fisher Scientific, Inc.,
Waltham, MA), following included protocol. DNA from each quantified sample was
pooled in equimolar ratios, concentrated, and quantified again using Qubit. The
library pool of all samples, along with aliquots of the forward, reverse, and index
sequencing primers, were sent to the University of Colorado BioFrontiers Institute
(Boulder, Colorado) for Illumina MiSeq 2x150 paired-end sequencing.
Phylogenetic and Statistical Analyses
Phylogenetic analysis was performed with QIIME (Quantitative Insights Into
Microbial Ecology) (Caporaso et al. 2010). Overlapping paired-end sequences were
53


joined with the join_paired_ends.py command, using the fastq-join method with
default parameters (Aronesty 2011). Joined sequences were filtered based on
Phred quality scores, and reads with all bases with a Phred score >19
(corresponding to a sequencing error rate of 1% per base) were retained.
Operational Taxonomic Units (OTUs) were picked using an open reference OTU
picking algorithm, which clusters reads together based on 97% or greater sequence
similarity, and assigns taxonomy to OTUs using the May 2013 Greengenes bacterial
and archaeal 16S rRNA database (DeSantis et al. 2006). Chimeric sequences were
identified using ChimeraSlayer and removed from the dataset (Haas et al. 2011).
Alpha diversity metrics, including OTU richness, Chaol estimated OTU
richness, Shannon index, and Faiths Phylogenetic Diversity (PD), and beta diversity
metrics, including UniFrac analyses, were determined using QIIME and rarefying the
dataset to a depth of 100,000 sequences per sample to account for variation in
sequencing depth (Lozupone et al. 2011). FigTree vl.4.0 was used to analyze
bootstrap-supported hierarchical clustering of abundance-weighted and
abundance-unweighted UniFrac distances for each sample
(http://tree.bio.ed.ac.uk/software/figtree/). Mantel tests were conducted in QIIME
to determine the correlation between environmental variables and bacterial
community UniFrac distances, using 999 permutations. Analysis of similarity
(ANOSIM) tests were conducted in QIIME to determine the statistical significance of
binning site-specific microbial communities by a particular categorical variable (e.g.
date sampled), using 999 permutations, t-tests were performed to determine if the
mean value for one group was significantly different from that of another group for
54


parameters that followed a Gaussian distribution. Mann-Whitney U Tests were
conducted to determine if median values were statistically significantly different
between two groups for data that did not follow a normal distribution (e.g.: phylum
relative abundances). Principal component analysis, conducted in R using the
gplots, GUniFrac, Heatplus, RColorBrewer, and vegan packages (R Core Team 2014;
Oksanen et al. 2015), was performed to examine the variation in geochemical
profiles for all sites. Redundancy analyses were performed to evaluate how much of
the variation in microbial community structure and relative abundance of phyla in
all samples was explained by geochemical parameters. For all graphical and
statistical analyses conducted involving dissolved or total recoverable metal
concentrations, concentrations that were below the detection limit of the
instrument were normalized to a value of zero.
Results
Diversity Analyses of Bacterial Communities
Some OTUs were commonly observed in all samples or in all samples of sites
identified as anthropogenically or endogenously metal impacted (Figure 3.2). This
type of OTU analysis reported here is for reporting purposes only, as the abundance
of these OTUs varied dramatically from sample to sample (Tables 3.2-3.4). Analyses
about community diversity are shown in Tables 3.5-3.7 and Figures 3.1-3.5.
Of the 222,111 OTUs detected in more than 27 million quality-filtered reads
(Supplemental Table S2), ten OTUs were common to all fifty-seven samples
collected (Table 3.2), and the per-sample abundance of these ten taxa collectively
55


ranged from ~0.5% to ~60% in anthropogenically metal impacted sites, and ~1% to
~35% in endogenously metal impacted systems. Three OTUs present in all fifty-
seven samples had an average relative abundance >1% and belonged to OS-K (class
of Acidobacteria], GaUionella (genus), and Stramenopiles.
Eleven OTUs were common to all anthropogenically metal impacted samples
(Table 3.3). In contrast, 198 OTUs were common to the endogenously metal
impacted sites. While 198 OTUs were common to endogenously metal impacted
sites, only seven OTUs were present in >1% average relative abundance (Table 3.4).
One OTU, which corresponded to OS-K, a class of Acidobacteria, was the most
abundant (7.5%) in endogenous samples. OTUs identified as belonging to
Stramenopiles, MBNT15 (order), Bacteroidales (order), Desulfobacteraceae (order),
and Geothrix (genus) had an average relative abundance of >1% in endogenous
samples.
Table 3.2: Ten OTUs were common to all sites in this study. The range of
abundance, mean abundance, and standard deviation are presented here.
Taxonomic Assignment Phylum Relative Abundance Range(%) Mean Relative Abundance r%i Standard Deviation
OS-K (class) Acidobacteria <0.1-27.4 3.902 7.126
GaUionella [genus] Proteobacteria [3] <0.1-59.4 3.803 11.908
Stramenopiles forderl Cyanobacteria <0.1-22.0 2.893 6.114
Stramenopiles [order] Cyanobacteria <0.1-37.0 1.260 4.995
Geothrix feenusl Acidobacteria <0.1-13.3 0.807 1.933
Desulfobacteraceae [family] Proteobacteria [5] <0.1-3.2 0.652 0.870
Betaproteobacteria fclassl Proteobacteria (0) <0.1-2.7 0.474 0.676
SBIal4 [order] Proteobacteria [3] <0.1-4.1 0.439 0.788
SBIal4 forderl Proteobacteria <0.1-8.0 0.408 1.170
SBIal4 [order] Proteobacteria [3] <0.1-1.1 0.194 0.215

Total 14.832 13.778
56


Table 3.3: Eleven OTUs were common to all anthropogenically metal impacted
samples. The range of abundance, mean abundance, and standard deviation are
presented here.____________________________________________________________
Taxonomic Assignment Phylum Relative Abundance Range(%} Mean Relative Abundance f%) Standard Deviation
Gallionella [genus] Proteobacteria [|3] <0.1-59.4 7.738 16.203
Stramenopiles [order] Cyanobacteria <0.1-37.0 2.313 7.021
SBIal4 forder) Proteobacteria fB) <0.1-8.0 0.583 1.643
Anaeromyxobacter [genus] Proteobacteria [5] <0.1-2.6 0.503 0.809
Geothrix Glen us) Acidobacteria <0.1-5.1 0.438 1.038
Betaproteobacteria [class] Proteobacteria [3] <0.1-2.7 0.434 0.736
SBIal4 forder) Proteobacteria CD <0.1-4.1 0.395 0.903
SBIal4 [order] Proteobacteria [3] <0.1-1.1 0.225 0.252
OS-K fclass) Acidobacteria <0.1-1.3 0.095 0.284
Desulfobacteraceae [family] Proteobacteria [5] <0.1-1.6 0.072 0.307
Stramenopiles forder) Cyanobacteria <0.1 0.4 0.042 0.097
Total 12.836 15.879
Table 3.4: Top ten most abundant OTUs common to endogenously metal impacted
sites. 198 total OTUs were detected in all endogenously metal impacted samples.
The range of abundance, mean abundance, and standard deviation are presented
here.
Taxonomic Assignment Phylum Relative Abundance Range(%) Mean Relative Abundance (%] Standard Deviation
OS-K [class] Acidobacteria 0.1-27.4 7.578 8.544
Stramenopiles [order] Cyanobacteria <0.1-22.0 5.647 7.666
MBNT15 forder) Proteobacteria f5) <0.1-9.2 2.718 2.523
Bacteroidales [order] Bacteroidetes 0.2-3.6 1.338 0.725
Desulfobacteraceae [family) Proteobacteria f5) <0.1-3.2 1.212 0.874
Geothrix [genus] Acidobacteria <0.1-13.3 1.164 2.483
Desulfobacteraceae [family) Proteobacteria fS) <0.1-8.1 1.052 1.666
Desulfobacca [genus] Proteobacteria [5] 0.2-3.0 0.808 0.633
Deltaproteobacteria fclass) Proteobacteria fS) 0.1-2.0 0.789 0.415
Syntrophaceae [family] Proteobacteria [3] <0.1-1.8 0.672 0.560

Total (198 OTUs] 42.373 10.968
To compare bacterial community compositions among all 57 samples, beta-
diversity analyses were conducted using bootstrap-supported Unweighted Pair
Group Method with Arithmetic Mean (UPGMA] clustering of pairwise abundance-
57


unweighted and weighted UniFrac distances (Figures 3.1 and 3.2, respectively). The
resulting abundance-unweighted dendrogram, which accounted for
presence/absence of OTUs but not abundance of OTUs, revealed delineation
between two resulting sample clusters (bootstrap support of 100% at the first
node)(Figure 3.1). This delineation separated all samples into two clusters sample
locations or sites with anthropogenic sources of metal impaction (highlighted in
red) and sample locations corresponding to an endogenous source of metal
(highlighted in blue). Approximately 65% of the variation in overall community
membership observed between the two sources of metal (anthropogenic and
endogenous) was accounted for by metal source (unweighted UniFrac Ranosim =
0.6476, p < 0.001, 999 permutations).
Of the 222,111 OTUs detected in the entire dataset, 126,720 OTUs were
found only in endogenous samples and accounted for 11.6% of the relative OTU
abundance in the endogenous sample dataset. Conversely, 70,753 OTUs were
unique to anthropogenic sample locations, accounting for 9.7% of the relative OTU
abundance in the anthropogenic sample dataset. Also, with a few exceptions,
microbial communities from the same sample location, but taken at different time
points (June, August, September), clustered together, which indicated that bacterial
community membership was temporally stable over the examined time points.
When relative abundance was considered, accounting for both
presence/absence of OTUs and community structure (relative abundance of OTUs),
a similar pattern in site-specific community partitioning was observed. With 100%
bootstrap support, samples clustered into two distinct clusters which identified
58


samples with an anthropogenic metal source (highlighted in red) and samples with
an endogenous source of metal (highlighted in blue)(Figure 3.2). This structuring of
samples accounted for nearly 75% of the variance in bacterial community structure
observed between the two groupings (weighted UniFrac Ranosim = 0.746, p < 0.001,
999 permutations). With the exception of the MTEFP01 samples, which clustered in
the anthropogenic metal source cluster in the abundance-unweighted dendrogram
(Figure 3.1) and in the endogenous metal source in the abundance-weighted
dendrogram (Figure 3.2), all samples were assigned to the same clusters in the
abundance-weighted analysis as in the unweighted analysis. Again, microbial
communities from the same sample location taken at different time points tended to
cluster together. Also note that branch lengths in Figure 3.2 are shorter than the
branch lengths in Figure 3.1, indicating that OTU abundance both describes the
similarities among the observed community structures within and among sample
locations over time. Branch length directly correlates with divergence in
community membership/structure; so shorter branch lengths corresponded with a
lower divergence and higher similarity in community structure.
59


CFAQ1A
BSC0O3J
ISC803A
^ CFAQ2A
CfAffiS
CFA02J
CFA01S
CFAD1J
4 CFUQ2J
CFUQ2A
^ CHJQ2S
CFU01J
9 CFUD13
5 CFU01A
| MTEF02S
MTEF01A
__| tnffPC2s
a WTEFPQ2A
MTB^PQ3J
-----MlfcHkU
WTEFD1S
MTEF01J
WTEF03S
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MTEFB2A
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3
- ISCSOIA
^ tSNDGPOIA
| MIU-KJ1S
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-"I MTB^POTJ
JCFGFOU
C
*CFGF01A
K3C802A
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BHF1BED1A
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- BHHBSMJ
^ I5HF01A
- tsHratj
ISNDMDD1A
fl ISNDMDG2J
I ISNDQ2A
>~i ISCACQJ
ISCAB3A
-| ISCAC1A
1 BCAD1J
[ ISCAQ2A
| BCA02J
Q.G5
I--------1--------1------------------1----------------1--------1--------1--------1
0.0 0.1 0.2 0.3 0.4
Figure 3.1: Bootstrap-supported Unweighted Pair Group Method with Arithmetic
mean (UPGMA] hierarchical clustering of abundance-unweighted UniFrac distances
for all 57 bacterial communities analyzed. Percent bootstrap support for each node
is indicated on the figure, based on random selection of 100,000 sequences from
each sample replicated 100 times. Clusters highlighted in red identify samples with
an anthropogenic source of metal impact and clusters highlighted in blue identify
samples with an endogenous source of metal impact.
60


-I took
-1100%
-5
-itKSOIJ
ttSCSQlA
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4SSC802J
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sscsou
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MT^POtJ
CFUD2S
CFUD1S
CRJD1J
CFUQ2J
, CFUQ2A
- CFUD1A
CFAD2J
CfAOIJ
CFA02A
- CFA02S
tfcVTEFll2S
- SC0D3J
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CFA01A
- MTEFD3J
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- MTEFP02A
WTB^PQZJ
MTEFD1A
' WIEFD1J
- UTERUS
-MTEF02A
< MTEF03A
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Q.04
I----1-----1---------1----1----1---------1----1-----1----1-----1--------1-----1----1
0.0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Figure 3.2: Bootstrap-supported Unweighted Pair Group Method with Arithmetic
mean (UPGMA} hierarchical clustering of abundance-weighted UniFrac distances for
all 57 bacterial communities analyzed. Percent bootstrap support for each node is
indicated on the figure, based on random selection of 100,000 sequences from each
sample replicated 100 times. Clusters highlighted in red identify samples with an
anthropogenic source of metal impact and clusters highlighted in blue identify
samples with an endogenous source of metal impact.
Alpha diversity calculations are reported in Table 3.5 and Figure 3.3. The
average observed OTU richness, Chaol estimate, and Faiths PD indices were higher
in endogenously metal impacted samples than in anthropogenically metal impacted
samples (unpaired two-tailed t-test p<0.01, Table 3.6}. Although not statistically
significant, the average Shannon index for endogenous samples was higher than for
anthropogenic samples (unpaired two-tailed t-testp=0.118, Table 3.6}.
61


Table 3.5: Alpha diversity metrics for all sites. Observed OTU richness, Chaol
estimate, Shannon index, and Faiths phylogenetic diversity (Faiths PD) are
averages of ten random samples taken of 100,000 sequences per sample.
Sample Site Date Observed OTU Richness Chaol Estimate Shannon Index Faiths PD
ISCA01 6.25.13 5467.7 9607.728 8.876 407.503
8.5.13 4603.6 6900.415 8.317 374.351
ISCA02 6.25.13 7225.8 13062.081 9.463 476.929
8.5.13 6642.8 9981.231 9.720 465.380
ISCA03 6.25.13 8690.3 16732.833 10.233 557.558
8.5.13 9166.2 14960.188 10.266 639.748
ISCS01 6.25.13 1840.2 3819.597 4.856 142.595
8.5.13 2343.9 3930.636 6.036 172.884
ISCB01 6.25.13 4017.5 7865.347 7.469 274.038
8.5.13 4040.5 5679.549 8.526 260.242
ISCB03 6.25.13 7263.9 13564.745 8.570 562.287
8.6.13 10008.4 17372.251 9.654 707.882
ISND01 6.25.13 8011.8 13900.064 9.807 562.817
8.5.13 8018.1 12715.335 9.995 568.364
ISND02 6.25.13 3520.4 4951.430 8.802 284.548
8.5.13 8555.7 14457.839 9.883 602.223
ISHF01 6.25.13 8315.2 13890.077 9.662 545.275
8.5.13 9827.7 15302.504 10.513 607.872
ISHFIBE01 6.25.13 10146.9 18114.150 10.470 623.931
8.6.13 5719.3 9348.585 8.166 408.726
ISNDCS01 6.25.13 4498.3 8668.248 8.327 384.102
8.6.13 6647.0 10606.306 8.870 514.811
ISNDGP01 6.25.13 1408.3 2536.102 5.136 141.671
8.6.13 1203.8 2275.666 4.632 107.932
ISNDMD02 6.25.13 4917.7 8723.745 6.308 401.535
8.5.13 5566.9 8789.619 6.637 435.064
ISCB02 6.25.13 2118.0 4413.382 6.514 173.850
8.5.13 2682.7 4575.758 6.536 189.133
CFA01 6.26.13 9431.1 18871.772 8.666 719.810
8.6.13 8496.1 14184.815 9.451 641.138
9.28.13 7138.1 10815.25 9.460 592.110
CFA02 6.26.13 9084.6 16239.975 9.204 690.103
8.6.13 15307.3 33303.904 9.270 1046.890
9.28.13 8311.1 12720.331 8.564 661.143
CFGF01 6.26.13 1410.6 2233.532 4.611 144.981
8.6.13 1533.2 2461.009 3.927 153.314
CFU01 6.26.13 6108.0 11907.627 7.663 488.484
8.6.13 4566.2 6951.850 7.619 394.812
9.28.13 3896.0 6179.188 7.558 340.562
CFU02 6.26.13 6840.0 13101.581 7.553 544.560
8.6.13 4138.4 5779.126 7.660 372.418
9.28.13 4186.0 6650.403 7.529 359.876
MTEF01 6.27.13 10442.3 21226.094 9.755 775.160
8.7.13 8621.7 14197.934 9.120 651.440
9.29.13 9582.5 15028.913 10.237 733.887
MTEF02 6.27.13 11353.9 22377.894 10.113 828.716
8.7.13 9156.5 13881.770 10.156 708.458
9.29.13 10258.1 18096.172 9.823 736.646
MTEF03 6.27.13 7528.9 15383.166 8.798 601.098
8.7.13 7814.0 12122.856 9.768 639.441
9.29.13 9556.2 18304.146 9.712 758.709
MTEFPOl 6.27.13 2466.0 4691.146 6.233 213.363
8.7.13 3002.6 4818.336 6.521 253.021
9.29.13 4178.2 7842.766 6.549 312.939
MTEFP02 6.27.13 6188.8 10802.457 8.801 519.857
8.7.13 6576.9 10929.424 8.341 545.928
9.29.13 6527.9 10947.172 8.658 535.654
62


Figure 3.3: Bar plot showing the Observed OTU Richness, Chaol Richness Estimate, Faith's Phylogenetic Diversity Index, and
Shannon Index for all samples and time points. June values are represented in red, August values in yellow, and September
values in blue.
£9
Faith's PD
M *. CT> 00 O
O O O O O
o o o o o o
l_l____I__I___I_I
# OTUs
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ooooooo
oooooooo
ISCA01
ISCA02
ISCA03
ISCS01
ISCB01
ISND01
ISND02
ISHF01
ISHFIBE01
ISNDCS01
ISNDGP01
ISNDMD01
ISCB02
CFGF01
ISCB03
CFA01
CFA02
CFU01
CFU02
MTEF01
MTEF02
MTEF03
MTEFP01
MTEFP02
W

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05
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(Q

3

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g
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S
a

x
ISCA01
ISCA02
ISCA03
ISCS01
ISCB01
ISND01
ISND02
ISHF01
ISHFIBE01
ISNDCS01
ISNDGP01
ISNDMD01
ISCB02
CFGF01
ISCB03
CFA01
CFA02
CFU01
CFU02
MTEF01
MTEF02
MTEF03
MTEFP01
MTEFP02
Shannon Index # OTUs
-t -k ro ro cj ui oyi outo
B88S8S
l M ^ 0) 00 O 1 1 1 1 1 o o o o o o o 1 1 1 1 1 1 1
ISCA01
ISCA02
ISCA03
ISCS01
ISCB01
ISND01
ISND02
ISHF01
ISHFIBE01
ISNDCS01
ISNDGP01
ISNDMD01
ISCB02
CFGF01
ISCB03
CFA01
CFA02
CFU01
CFU02
MTEF01
MTEF02
MTEF03
MTEFP01
MTEFP02
B
S
5

(A
S
0>
3
3
O
3
3
a.

x
ISCA01
ISCA02
ISCA03
ISCS01
ISCB01
ISND01
ISND02
ISHF01
ISHFIBE01
ISNDCS01
ISNDGP01
ISNDMD01
ISCB02
CFGF01
ISCB03
CFA01
CFA02
CFU01
CFU02
MTEF01
MTEF02
MTEF03
MTEFP01
MTEFP02
Observed OTU Richness Chaol Richness Estimate


Table 3.6: Alpha diversity metrics for samples identified as having anthropogenic
or endogenous sources of metal. Observed OTU richness, Chaol estimate, Shannon
Index, and Faiths phylogenetic diversity (Faiths PD) are averages of ten random
samples taken of 100,000 sequences per sample. Averages for each community
subtype are reported, with standard deviation in parentheses. Two-tailed t-tests
were performed for all indices to test if the mean value for each system type was
significantly different._______________________________________________________________
Observed OTU Richness Chaol Estimate Shannon Index Faiths PD
Anthropogenic Metal Source 5,291 [2,848] 8,947 [4,839] 7.590 [2.059] 379.335 [177.044]
Endogenous Metal Source 7,518 [2,838] 13,389 [6,134] 8.536 [1.145] 584.014 [188.435]
Two-tailed t- testp-value p<0.01 p<0.01 p=0.118 p<0.01
Bacterial Community Structure of Anthropogenically and Endogenously Metal
Impacted Systems
Bacterial communities of anthropogenically and endogenously metal
impacted systems, while dominated by many of the same groups of organisms,
differed in the relative abundance of major phyla (Supplemental Figures SI and S2,
Supplemental Table SI). The Betaproteobacteria dominated anthropogenically
metal impacted sites, which comprised an average of 19% of these bacterial
communities, followed by the Alphaproteobacteria, which comprised an average of
17.3% of the overall community structure (Figure 3.4). In contrast, the
Betaproteobacteria and Alphaproteobacteria accounted for an average of 5.7% and
4.5%, respectively, of endogenously metal impacted bacterial communities.
Endogenously metal impacted bacterial communities were dominated by the
Deltaproteobacteria, which accounted for an average of 20.7% of the total bacterial
community structure, and by the Acidobacteria, which accounted for an average of
16.7% of the total bacterial community structure. In anthropogenically metal
64


impacted systems, the Deltaproteobacteria and Acidobacteria accounted for an
average of 6.1% and 8.3%, respectively, of total bacterial community structure. This
pattern of differential relative abundance of organisms between the two system
types was seen in nearly all of the dominant bacterial phyla/classes detected (Figure
3.5). Interestingly, some taxa appear to have a wide relative abundance range (e.g.:
the Betaproteobacteria in anthropogenic sites), and the relative abundance of other
taxa remains relatively consistent among all samples in both system types (e.g.:
Chlorobi and other phyla).
To determine if the relative abundances of these dominant phyla were
statistically higher or lower in a particular sample subset (anthropogenic or
endogenous samples), Mann-Whitney U-Tests were performed. Of the dominant
taxa detected, the Actinobacteria, Alphaproteobacteria, Bacteroidetes,
Betaproteobacteria, Gammaproteobacteria, and Gemmatimonadetes had higher
median relative abundances in anthropogenically metal impacted systems than in
endogenously metal impacted systems (p-values <0.02 for all taxa) (Table 3.7).
Endogenously metal impacted systems had higher median relative abundances of
Acidobacteria, Chlorobi, Chloroflexi, Deltaproteobacteria, Nitrospirae, and
Spirochaetes (p-values <0.002) (Table 3.7).
65


Average Phylum-Level Relative Abundances of Major Taxa in
Anthropogenic and Endogenous Samples
June Anthro All Anthro August Endo All Endo
Sample Subset
Other
Nitrospirae
Spirochaetes
Chlorobi
Planctomyeetes
Gemmatimonadetes
Verrucomicrobia
Chloroflexi
Actinobacteria
Unassigned
Deltaproteobacteria
Gammaproteobacteria
Cyanobacteria
Bacteroidetes
Acidobacteria
Alphaproteobacteria
Betaproteobacteria
Figure 3.4: Taxonomic bar chart showing the average relative abundance of major
bacterial and archaeal taxa detected in anthropogenic and endogenously metal
impacted sample subsets. "Other refers to all remaining taxa each composing less
than ~1% of relative abundance.
66


Distributor of Dominant Phyla in Anthropogenic and Endogenous Sites
o
Phyta
Figure 3.5: Boxplots representing the distribution of the relative abundance (%) of dominant phyla
in anthropogenically (red] and endogenously (blue) metal impacted sites. The Proteobacteria
phylum has been further subdivided into Classes.
CT\
"J
111


Table 3.7: Phylum-level diversity (median percent relative abundance median
absolute deviation) in anthropogenic and endogenously metal impacted systems.
Mann-Whitney U tests were performed for median comparisons between
anthropogenic and endogenous phylum/class level relative abundances (P-values
reported below). The Proteobacteria have been further subdivided into classes.
Phylum/Class Anthropogenic Endogenous P-value
Acidobacteria 8.0 2.1 14.2 8.3 0.002
Actinobacteria 4.1 1.7 1.2 0.4 <0.001
Alghaproteobacteria 17.6 6.2 3.1 1.8 <0.001
Bacteroidetes 8.0 1.9 5.8 2.5 0.017
Betaproteobacteria 15.2 7.0 4.1 1.5 <0.001
Chlorobi 0.6 0.4 2.4 0.7 <0.001
Chlorofiexi 2.1 1.0 5.5 1.3 <0.001
Cyanobacteria 6.0 4.6 2.7 2.4 0.307
Deltaproteobacteria 5.3 2.8 20.1 5.7 <0.001
Gammaproteobacteria 5.0 2.1 0.8 0.3 <0.001
Gemmatimonadetes 1.4 1.4 0.3 0.1 0.002
Nitrospirae 0.1 0.1 1.9 1.0 <0.001
Planctomfcetes 1.8 0.9 1.2 0.3 0.655
Spirochaetes 0.2 0.1 2.5 0.8 <0.001
Verrucomicrobia 2.4 1.3 2.7 0.9 0.151
Environmental Chemistry of Anthropogenically and Endogenously Metal
Impacted Sites
Environmental parameters (pH, conductivity, temperature, and dissolved
oxygen content) for all samples are presented in Table 3.8. Two-tailed Mann-
Whitney U tests indicated that median pH (p<0.001), conductivity (p<0.001),
dissolved oxygen (p<0.032), and hardness (p<0.001) were higher in
anthropogenically metal impacted samples than samples with an endogenous metal
source. Generally, the pH of anthropogenically metal impacted sites increased from
June to August (Figure 3.6). The pH associated with sample sites affected by an
endogenous source of metal impact was variable; the Chattanooga Fen samples and
ISCB03 varied by less than one pH unit. No consistent pattern in pH was observed
in the Mt. Emmons Fen sites throughout the study as some sites saw an increase in
pH and others saw a decrease in pH throughout summer.
68


Table 3.8: Environmental parameters (pH, conductivity, temperature, and dissolved
oxygen] for each sampling site and sampling date.
Sample Site Sample Date PH Conductivity (pS/cm) Temperature (C) Dissolved Oxygen (mg/L) Hardness (mg CaCOi/Ll
ISCA01 6.25.13 7.3 1095 7.9 8.8 708.7
8.6.13 7.1 829 7.8 7.7 670.7
ISCAO'J 6.25.13 7.2 1034 8.4 8 692.3
8.6.13 7.8 830 8 8.5 684.1
ISCAOlf 6.25.13 6.6 1068 13.1 7.9 708.2
8.6.13 7 814 7.9 10 670.4
ISCS01 6.25.13 3.2 1529 14.9 5.1 685.2
8.5.13 4.2 947 21.2 6.6 591.7
ISCliOl 6.25.13 4.5 945 13.4 1.4 577.1
8.5.13 4.8 745 10.4 1.1 544.3
1SND01 6.25.13 7.3 1380 11 8.1 959.6
8.5.13 6.2 1051 13.3 6 946.7
ISND02 6.25.13 6.4 1403 14.3 6.6 958.8
8.5.13 6.5 1040 18.7 5.9 968.8
ISIIF01 6.25.13 5.7 335 8.9 7.8 199.6
8.5.13 8.3 296 10.9 7.9 210.7
ISIIFIHE01 6.25.13 6.1 700 17.4 6.3 346.5
8.5.13 6.4 479 14.6 7.5 351.8
ISNDCS01 6.25.13 4.6 1563 20.3 5.2 996.6
8.5.13 4.1 1036 18 4.5 947.5
ISNDCil0'1 6.25.13 3.6 1427 14.2 2.2 639.2
8.5.13 3.6 998 22.4 5 719.0
1SNDMD01 6.25.13 6.8 1424 9.5 8.1 955.1
8.6.13 7.4 1072 8.7 8.9 967.5
ISCI102 6.25.13 4.7 1018 14 3.8 615.0
8.5.13 5.9 802 8.4 3.9 614.6
Cl'Cl'Ol 6.26.13 6.5 429 7.2 4.4 179.9
8.6.13 6.8 319 7.1 5.1 191.6
isc:iio:i 6.25.13 4.1 826.6 18.5 6.4 358.2
8.5.13 4.3 533 19.8 3.5 350.1
CFU01 6.26.13 5.8 246.9 20.6 4.8 77.2
8.6.13 5.4 170 17.9 5.1 70.1
9.28.13 5 139.1 17 4.7 53.9
c:ruo2 6.26.13 5.3 203.6 19.7 3.7 85.6
8.6.13 4.3 172.5 17.6 1.7 73.1
9.28.13 4.8 159.2 10.7 6.4 57.7
CI-'AOl 6.26.13 5.7 387 19.3 5.8 175.2
8.6.13 5.2 277.2 14.3 7.9 186.5
9.28.13 5.7 290.3 13 6.2 158.2
c:r,\02 6.26.13 6.2 409 21.6 5 204.0
8.6.13 6.2 277.3 17.7 5.9 166.6
9.28.13 6.3 222.1 13.4 5.5 126.5
MTEF01 6.27.13 3.7 309 18.4 4.5 52.6
8.7.13 3.7 252.3 11.1 6.4 43.2
9.29.13 5.9 211.3 4.4 0.1 42.6
mti:f<>2 6.27.13 4.1 204 18 5 39.4
8.7.13 4.1 208 14.3 5.1 44.8
9.29.13 4 182.8 5.6 6.7 38.5
mteioh 6.27.13 4.9 197.5 19.2 4.5 38.1
8.7.13 4.6 210.1 14.7 5.4 41.0
9.29.13 4.4 213.3 5.9 9.6 40.0
MTEFPOl 6.27.13 3.5 377 18.8 2.3 69.7
8.7.13 4.1 308 13.6 3.5 61.3
9.29.13 5 309 5.8 6.1 54.0
MTEFP02 6.27.13 3.5 310 18 2.9 53.2
8.7.13 3.7 278 14.3 4.1 50.0
9.29.13 5 270.8 4.5 6.5 41.7
69


pH of All Sampling Locations and Time Points
Sample Site
June
August
A September
Figure 3.6: Scatterplot showing the change in pH at the sediment surface at all
sampling locations for all sampling events (June pH values as red squares, August
pH values as blue diamonds, and September pH values as green triangles).
Anthropogenic sampling locations are on the left (ISCA01 through CFGF01), and
endogenous samples are on the right (ISCB03 through MTEFP02). Samples from
locations exhibiting an anthropogenic source of metal impact were not sampled in
September.
Total recoverable metal concentrations from surface waters (TRW), which
included both dissolved and suspended metals bound to particulates, for all samples
are presented in Table 3.9a and 3.9b. Two-tailed Mann-Whitney U tests indicated
that median total recoverable aluminum (p=0.023), arsenic (p<0.001), barium
(p<0.001), cadmium (p<0.001), nickel (p<0.001), thallium (p<0.001), and zinc
(p<0.001) concentrations from surface water were higher in samples exhibiting an
endogenous source of metal than sites exhibiting an anthropogenic source of metal.
70


Anthropogenic samples had higher total recoverable calcium (p<0.001),
copper (p=0.034), iron (p<0.001), magnesium (p<0.001), manganese (p=0.018), and
sodium (p<0.001) concentrations when compared to endogenous sites. The
heatmap and average linkage hierarchical clustering of samples based on Bray-
Curtis distances for total recoverable metal concentrations from surface water in
Figures 3.7a, 3.7b, and 3.8 further illustrate the temporal variability of total
recoverable metal concentrations among sample locations, with anthropogenic
samples chemistry appearing more variable than endogenous samples chemistry.
The heatmaps also revealed that the Mt. Emmons Fen sites had higher levels of
aluminum, zinc, iron, and magnesium concentrations compared to all other sites.
71


Table 3.9a: Total recoverable metal concentrations from surface water samples of anthropogenically metal impacted
sites. All concentrations are in pg/L.
Sample Site Date A1 As Ba Ca Cd Cu Fe Mg Mn Na Ni Pb T1 Zn
ISCA01 6.25.13 6250 <50 <0.5 279000 12 132 5240 10800 1410 4570 <5 57 <50 104
8.5.13 202 <50 <0.5 270000 <5 52 7310 9930 1610 4610 6 <50 <50 166
ISCA02 6.25.13 164 <50 4 261000 <5 3233 5490 9300 1400 4580 <5 <50 <50 102
8.5.13 <20 <50 <0.5 266000 <5 43 6360 10000 1670 4660 <5 <50 <50 150
ISCA03 6.25.13 158 <50 4 264000 <5 28.8 4610 9360 1340 4590 <5 <50 <50 99
8.5.13 <20 <50 <0.5 254000 <5 40 6060 9550 1500 4500 <5 <50 <50 160
ISCS01 6.25.13 11800 <50 9 244000 19 194 36800 14900 16300 7850 11 <50 <50 16200
8.5.13 13900 <50 <0.5 208000 49 382 12400 11300 9860 6090 28 <50 <50 18300
ISCB01 6.25.13 7670 <50 6 208000 <5 308 4540 11600 1140 6420 <5 <50 <50 430
8.5.13 7850 <50 22 194000 <5 281 16300 11500 1320 6270 12 <50 <50 538
ISND01 6.25.13 121 <50 <0.5 356000 <5 <5 2500 10500 1130 8360 <5 <50 <50 55
8.5.13 <20 <50 <0.5 362000 <5 <5 2820 10800 1130 8540 <5 <50 <50 <5
ISND02 6.25.13 126 <50 <0.5 366000 <5 <5 <5 11100 38.7 8760 <5 <50 <50 89
8.5.13 217 <50 <0.5 367000 <5 <5 <5 11000 235 8740 <5 <50 <50 279
ISHF01 6.25.13 726 <50 22 71100 <5 <5 381 5560 156 2930 <5 <50 <50 33
8.5.13 814 <50 23 73200 <5 <5 <5 6050 196 3290 <5 <50 <50 <5
ISHFIBE01 6.25.13 5920 <50 15 133000 1 91 4950 8060 515 4560 <5 <50 <50 191
8.6.13 1980 <50 <0.5 125000 <5 76 2130 7830 418 4240 <5 <50 <50 169
ISNDCS01 6.25.13 166 <50 <0.5 354000 <5 <5 686 10800 143 8720 <5 <50 <50 158
8.6.13 1040 <50 21 365000 <5 54 16300 11500 550 8740 <5 110 <50 503
1SNDGP01 6.25.13 13800 <50 12 285000 5 271 15400 13800 1570 8820 27 <50 <50 1860
8.6.13 12400 <50 <0.5 263000 13 225 15600 12700 1590 7820 48 <50 <50 4230
ISNDMD01 6.25.13 117 <50 <0.5 337000 <5 11 6200 10400 1250 8600 <5 <50 <50 <5
8.5.13 <20 <50 <0.5 369000 <5 <5 6470 10800 1350 8600 <5 <50 <50 <5
ISCB02 6.25.13 8200 <50 5 229000 <5 277 15900 11700 1150 6300 <5 <50 <50 374
8.5.13 8630 <50 <0.5 225000 <5 292 26400 11800 1220 6330 <5 <50 <50 422
CFGF01 6.26.13 870 62 30 66062 <5 22 15634 6493 1557 1893 24 <50 63 329
8.6.13 924 149 23 72820 29 22 14715 6904 1643 2043 48 115 96 403


Table 3.9b: Total recoverable metal concentrations from surface water samples of endogenously metal impacted sites.
All concentrations are in pg/L.
Sample Site Date Al As Ba Ca Cd Cu Fe Mg Mn Na Ni Pb T1 Zn
ISCB03 6.25.13 1970 <50 31 127000 <5 46 666 9340 1150 6360 <5 <50 <50 236
8.6.13 1850 <50 32 122000 <5 37 759 9130 1190 6150 5 <50 <50 325
CFU01 6.26.13 3918 126 42 23953 <5 25 342 5503 531 1927 9 <50 <50 651
8.6.13 3232 <50 47 21003 <5 14 475 4596 417 <50 <5 <50 <50 736
9.28.13 2885 76 48 17145 <5 24 174 3581 326 1104 13 <50 <50 675
CFU02 6.26.13 3289 121 51 24896 6 28 298 5640 522 1829 18 <50 51 354
8.6.13 2580 75 35 21182 7 7 412 4675 411 1542 <5 <50 <50 349
9.28.13 2290 317 32 19759 24 18 356 4024 372 1339 34 120 130 288
CFA01 6.26.13 916 88 34 66249 8 32 403 6580 1489 1991 26 <50 70 304
8.6.13 669 76 21 66162 17 15 268 6119 1525 1753 13 77 <50 393
9.28.13 587 <50 20 53306 <5 <5 558 5465 1240 1625 <5 <50 <50 302
CFA02 6.26.13 178 81 14 70805 15 16 587 7648 956 2586 20 77 61 80
8.6.13 31 <50 17 60717 <5 <5 723 6587 1101 1926 <5 <50 <50 48
9.28.13 155 80 21 43527 16 14 274 4814 596 1649 17 75 61 61
MTEF01 6.27.13 11966 381 37 15706 59 13 3662 3359 1010 870 49 59 78 13025
8.7.13 9257 357 31 14797 43 15 6234 3087 966 911 39 104 81 8219
9.29.13 7395 318 32 11739 42 20 2377 2693 816 1039 54 130 110 5348
MTEF02 6.27.13 6901 277 41 12076 41 11 3170 2452 856 1012 25 80 78 6784
8.7.13 7416 189 39 12621 5 17 2538 2837 884 961 17 <50 <50 5372
9.29.13 7047 290 32 11457 37 14 2205 2536 809 935 37 106 82 5263
MTEF03 6.27.13 7015 268 45 12594 34 28 3051 2580 894 1074 31 <50 75 6747
8.7.13 7174 277 35 12924 30 14 2986 2781 871 1012 33 75 76 5556
9.29.13 7220 318 39 12534 36 35 2147 2726 857 962 47 104 117 5198
MTEFP01 6.27.13 20807 688 55 20295 60 10 1621 3889 1219 1042 75 91 107 21648
8.7.13 18479 600 43 19780 46 12 994 3665 1075 947 70 82 98 16254
9.29.13 16752 581 41 16283 71 16 662 3280 970 1025 67 117 116 13106
MTEFP02 6.27.13 11812 422 44 17033 71 17 2505 3800 972 820 66 119 93 14761
8.7.13 8792 272 43 13140 22 5 2262 2993 838 718 15 <50 <50 9391
9.29.13 9690 346 38 12357 46 15 1789 3068 792 796 51 77 77 8608
"j
00


Approximate Metal Concentration (mg/L)
0 100 200 300 400
CFMm
CFMtA
CFM2A
SHFOIJ
3**01A
CFA02J
CFGF01J
crorou
crwi
CTM)1S
*caou
C01A
C401A
ISNDOM1J
OMDOrotA
tSCAOZA
4CA01A
4CAQ7J
4CA01J
4NOOM
tSMDOtA
iSNOMOOIA
ONDCMU
Figure 3.7a: Heatmap of the total recoverable metal concentrations (mg/L) in
water (TRW) for metals determined in all samples. The dendrograms on the x and y
axes were generated using average linkage hierarchical clustering of Bray-Curtis
distances for metal concentrations (x-axis) and sampling sites (y-axis).
74


Approximate Metal Concentration (pg/L)
0 100 200 300 400
MTEFF013
WTEfPOIA
MTEFPOIJ
WTEFP02J
mtefou
MTEfPare
MTEFP02A
MTEF01A
KTTEF03A
WTEF02A
IfTCfOSS
MTEfOTS
MTEFOIS
IftEFOJJ
HfT6F02J
ocsou
ISC301A
(ScaotA
JSC8C2A
(SNDGPQU
tSfOGPOU
lSM)CSOtA
CfCFOU
CFGF01A
ISMFVEOU
(SNDOtJ
(SNOOIA
SSMMD01A
ISHOQ2J
ISMDQ2A
iSNOCSOIJ
rs^eeoiA
ISCAOIJ
ISC801J
tSCXOA
CSCA02A
iSCAOtA
ISCA03J
tSCAOZJ
CFA02J
CFM2A
CFA91J
CFAQtA
CFM1S
CFA02S
IS^OU
tS*OtA
CFU02S
CFII02A
cfuois
CFUQ2J
CFU01J
CFU01A
y
z
Figure 3.7b: Heatmap of the total recoverable metal concentrations (gg/L) for
metals analyzed in all samples. Dendrograms showing average linkage hierarchical
clustering of Bray-Curtis distances for metals and samples are on the x- and y-axes,
respectively. Note: Calcium concentrations have been omitted to illustrate the
differences in concentrations of the remaining metals among sites.
75


Log Average Total Recoverable Metal Concentrations for Anthropogenic
and Endogenous Sources of Metal Impact by Sampling Date

a
0)
u
S
o
u
M
o
June -
Anthropogenic
August -
Anthropogenic
June -
Endogenous
August -
Endogenous
September -
Endogenous
Figure 3.8: Scatterplot of the log average total recoverable metal concentrations
(gg/L) for anthropogenic and endogenous sources of metal by sampling date.
Anthropogenic sample concentrations are designated with squares, endogenous
sample concentrations with Xs. June sample averages are indicated in red, August
in blue, and September in green. Calcium concentrations are in mg/L.
Dissolved metal (DM) concentrations from surface waters for all sites and
sampling time points are presented in Table 3.10a and 3.10b. Two-tailed Mann-
Whitney U tests indicated that median dissolved aluminum (p=0.018), barium
(p<0.01), cadmium (p<0.01), nickel (p<0.01), lead (p =0.013), and zinc (p=0.021)
were higher in anthropogenic sites compared to endogenous sites. Higher median
concentrations of dissolved calcium (p<0.01), copper (p=0.014), iron (p=0.021),
magnesium (p<0.01), and sodium (p<0.01) were seen in AMD sites compared to
76


ARD sites. Figures 3.9a and 3.9b show the heatmaps and average linkage
hierarchical clustering of Bray-Curtis distances for dissolved metal concentrations
for all samples. The dendrograms indicate a similar site clustering pattern as seen
in the heatmap for total recoverable metal concentrations (Figures 3.7a and 3.7b),
with samples from the same system clustering more closely together than samples
taken from separate systems. Again, the Mt. Emmons Fen sites were characterized
by elevated concentrations of dissolved aluminum and zinc compared to the
remaining sites. Regardless of when samples were collected, metal concentrations
(total recoverable and dissolved concentrations) were generally higher in
anthropogenic sites versus endogenous sites (Figures 3.8 and 3.10).
77


Table 3.10a: Dissolved metal concentrations from surface water samples of anthropogenically metal impacted sites. All
concentrations are in gg/L.
Sample Site Date Al Ba Ca Cd Cu Fe Mg Mn Na Ni Pb Zn
ISCA01 6.25.13 212 <0.5 268000 <5 <5 3490 9590 1480 4640 <5 <50 108
8.5.13 210 <0.5 253000 <5 <5 4030 9450 1540 4290 <5 <50 129
ISCA02 6.25.13 <20 <0.5 262000 <5 <5 2820 9240 1460 4440 <5 <50 <5
8.5.13 <20 <0.5 258000 <5 <5 2850 9680 1540 4370 <5 <50 <5
ISCA03 6.25.13 <20 <0.5 268000 <5 <5 <5 9460 1420 4520 <5 <50 <5
8.5.13 <20 <0.5 253000 <5 <5 <5 9380 1470 4240 <5 <50 <5
ISCS01 6.25.13 11900 <0.5 249000 20 195 40300 15400 18500 7960 11 <50 18600
8.5.13 14000 <0.5 218000 49 380 12800 11500 10200 6090 25 <50 19100
ISCB01 6.25.13 7380 <0.5 212000 <5 294 4910 11600 1220 6240 <5 <50 505
8.5.13 7240 <0.5 199000 <5 255 10700 11500 1310 6140 <5 <50 530
ISND01 6.25.13 <20 <0.5 367000 <5 <5 1510 10500 1200 8420 <5 <50 <5
8.5.13 <20 <0.5 362000 <5 <5 <5 10400 1090 7930 <5 <50 <5
ISND02 6.25.13 <20 <0.5 366000 <5 <5 <5 10900 40 8490 <5 <50 58
8.5.13 <20 <0.5 370000 <5 <5 <5 10900 229 8310 <5 <50 273
ISHF01 6.25.13 125 22 70700 <5 <5 126 5610 156 2930 <5 <50 19
8.5.13 <20 24 74500 <5 <5 <5 5990 196 3050 <5 <50 <5
ISHFIBE01 6.25.13 813 15 128000 <5 65 929 7820 445 4290 <5 <50 194
8.6.13 453 <0.5 126000 <5 51 <5 7750 403 4110 <5 <50 143
ISNDCS01 6.25.13 <20 <0.5 380000 <5 <5 <5 11600 161 9020 <5 <50 158
8.6.13 209 <0.5 361000 <5 <5 4840 11200 672 8370 <5 <50 537
ISNDGP01 6.25.13 10700 11 238000 <5 203 12800 10900 1310 6840 22 <50 1680
8.6.13 12300 <0.5 267000 13 221 15600 12700 1610 7480 46 <50 4230
ISNDMD01 6.25.13 102 <0.5 365000 <5 <5 4360 10600 1280 8300 <5 <50 <5
8.5.13 <20 <0.5 370000 <5 <5 4100 10600 1300 8210 <5 <50 <5
ISCB02 6.25.13 7900 <0.5 227000 <5 267 882 11700 1210 6110 <5 <50 426
8.5.13 7990 <0.5 227000 <5 264 1460 11600 1220 6060 <5 <50 429
CFGF01 6.26.13 559 30 61542 <5 25 36 6363 1478 1892 23 <50 332
8.6.13 721 29 66329 21 36 54 6304 1464 1875 46 174 377
"j
CD


Table 3.10b: Dissolved metal concentrations from surface water samples of endogenously metal impacted sites. All
concentrations are in pg/L.
Sample Site Date Al Ba Ca Cd Cu Fe Mg Mn Na Ni Pb Zn
ISCB03 6.25.13 1910 31 128000 <5 50 681 9380 1220 6330 <5 <50 256
8.6.13 1770 32 125000 <5 33 <5 9210 1200 6000 <5 <50 304
CFU01 6.26.13 3917 32 21673 9 9 130 5608 514 432 25 <50 603
8.6.13 3146 42 20683 12 14 127 4484 430 1699 10 <50 785
9.28.13 2778 38 15824 <5 6 158 3486 319 1204 <5 <50 645
CFU02 6.26.13 3217 47 25141 <5 24 156 5531 513 582 15 <50 361
8.6.13 2571 42 21796 18 33 269 4536 394 472 29 154 375
9.28.13 2160 36 16818 7 23 314 3806 317 1285 23 74 254
CFA01 6.26.13 870 38 59097 10 31 46 6712 1398 1969 28 <50 279
8.6.13 780 30 64219 18 36 58 6346 1454 480 40 162 393
9.28.13 640 22 53543 <5 5 333 5957 1340 <50 13 <50 318
CFA02 6.26.13 56 25 68554 13 15 45 7976 946 2828 16 <50 53
8.6.13 46 15 55869 5 7 39 6582 426 471 12 <50 36
9.28.13 201 24 43027 14 29 150 4637 533 480 12 127 55
MTEF01 6.27.13 11971 42 15383 63 45 2773 3435 970 <50 77 182 13358
8.7.13 8924 29 12435 19 <5 413 2960 915 966 20 <50 7230
9.29.13 7437 35 12692 43 31 2664 2658 812 535 30 183 6121
MTEF02 6.27.13 6936 41 11564 39 32 114 2555 798 952 42 133 6697
8.7.13 7307 38 13221 33 33 587 2869 845 940 48 165 5930
9.29.13 7287 29 11139 26 5 1748 2595 814 1007 24 <50 4957
MTEF03 6.27.13 7157 35 11065 24 <5 136 2550 857 1094 21 <50 6124
8.7.13 7224 37 11770 15 21 422 2814 826 906 28 <50 5151
9.29.13 6885 36 11825 20 22 1877 2540 802 621 21 <50 4865
MTEFP01 6.27.13 22635 57 21002 41 15 1464 4195 1256 814 66 <50 21733
8.7.13 18747 45 18399 45 14 832 3729 1133 1103 82 75 15776
9.29.13 16913 44 16267 45 16 499 3240 962 692 36 <50 12685
MTEFP02 6.27.13 11905 43 15068 52 5 2309 3772 980 859 52 <50 13663
8.7.13 10304 40 14160 33 12 1340 3557 934 548 61 <50 10211
9.29.13 9405 40 11772 37 23 1709 2997 752 676 44 80 8385


Approximate Metal Concentration (mg/L)
0 100 200 300 400

8
E
3
<5
CD
E
.2
E
*
o
1
3
z
E .2 E 2 Zinc E 3 E .2 Iron 1
a <0 o ? 3 < I 9 s S 1 c 2
CFA02A
C7A0T8
C#Of01A
CFA01A
CTGF01J
CtMIJ
*01J
I9#01A
etnas
Cf2S
s*ae BOTaeoiA
ocsosj
I5C8COA
OMDGPOIJ
acsoiA
OC301A
SCAOtA
tscAtaj
OCA82A
acAoaj
MCA01J
(SWGP01A
ISCS01J
SOCS01A
SNOMDOtA
QHDQ2J
SND01J
ONDQ2A
ccvuj
MTEFP02J
MTEfOIJ
WTCFPQ2A
UTtFPOIS
irrtfpoiA
WTEFPOU
MTEF03A
WTCFQ2A
WTEF03J
WT&Q2J
UTCF038
VTEFQ
MTfFOIS
WTEFPOeS
WTEF01A
CFUQ2A
CFUHJ
CFUOtA
CFUQ2J
CFU02S
cmojs
Figure 3.9a: Heatmap of the dissolved metal concentrations in water (DM) for metals
determined in all samples. The dendrograms on the x and y axes were generated using
average linkage hierarchical clustering of Bray-Curtis distances for metal
concentrations (x-axis) and sampling sites (y-axis).
80


Approximate Metal Concentration (pg/L)
0 100 200 300 400
MTEFP01S
MTEFP01A
MTEFP02J
MTEF01J
MTEFP01J
WTEFP02S
UTEF01A
8FTEFP02A
MTEFMS
WTEF02S
MTEF01S
WTCF03A
MTEF02A
MTEF03J
UTEFQ2J
ISC30U
ISC301A
CFA02S
CFA02A
CFGF01J
CFQF01A
CFA01J
CFA01A
CFM1S
ivnacou
iSHFieeoiA
CFA02J
iSHFOti
iSHFOtA
CFU02A
CFU01A
CFU098
CFU01S
CFUQ2J
CFU01J
Cft02A
tscsou
ncsoiA
I9FOOP01J
ISNOOAOIA
iscAou
ISCA01A
WCAC8J
I8CA02A
tSCSOSA
(SN001J
l*OOtA
ISND02J
I9NO02A
ISMOCfOIJ
SMOUOOIJ
tSfCMXMA
K)C#01A
s
I
5
8
s
8
?
2
i
s
E
2
£
2
Figure 3.9b: Heatmap of the dissolved metal concentrations (gg/L) for metals analyzed
in all samples. Dendrograms showing average linkage hierarchical clustering of Bray-
Curtis distances for metals and samples are on the x- and y-axes, respectively. Note:
calcium concentrations have been omitted to illustrate the differences in concentrations
of the remaining metals among sites.
81


Log Average Dissolved Metal Concentrations for Anthropogenic
and Endogenous Sources of Metal Impact by Sampling Date
Metal
June -
Anthropogenic
August -
Anthropogenic
June -
Endogenous
August -
Endogenous
September -
Endogenous
Figure 3.10: Scatterplot of the log average dissolved metal concentrations (gg/L) for
anthropogenic and endogenous sources of metal by sampling date. Anthropogenic
sample concentrations are designated with squares, endogenous sample concentrations
with Xs. June sample averages are indicated in red, August in blue, and September in
green. Calcium concentrations were converted to mg/L whereas all other metal
concentrations are in gg/L.
Site clustering, based on all geochemical data, and overall stability of site-specific
geochemical profiles, was further evaluated by principal component analysis (Figure
3.11). Principal component 1 (PCI) and principal component 2 (PC2) explained 66.9%
of the variation among the environmental data of all samples. Also observed was a
separation between anthropogenic and endogenous sample geochemical profiles along
the first principal component axis.
82


ISCS01J
co
ISCS01A!
o
CL

MTEFP01J
MTEFfc^
Anthro
Endo
-2
r
-1
PC1 (44.6%)
Figure 3.11: Principal component analysis (PCA) of site-specific geochemistry profiles
for all samples. Sites exhibiting an anthropogenic source of metal impact are in red, and
sites exhibiting an endogenous source of metal impact are in blue. TRW refers to total
recoverable concentrations of a specific metal, and DM refers to the dissolved
concentrations of a specific metal. The first and second principal components
accounted for 44.6% and 22.3%, respectively, of the variance in the geochemistry data.
Correlations Between Bacterial Community Structure and Geochemical Variables
Redundancy analyses were performed to examine the influence of geochemistry
on bacterial community structure (Figure 3.12) and on the relative abundance of phyla
among all sample sites examined (Figure 3.13). The arrows associated with each
83


geochemical variable described the direction of correlation with each axis, with
concentrations of each variable increasing along the direction of the arrow. When an
arrow pointed toward a specific community (Figure 3.12) or phylum (Figure 3.13), this
indicated a positive correlation between the two parameters. Negative correlations
also existed where the environmental variable vector pointed in the opposite direction
of a microbial community. To evaluate the correlation between overall community
structure (pairwise UniFrac distances for each pair of site) and environmental
variables, Mantel tests were performed (Supplemental Tables S3-S5).
84


o
Figure 3.12: Redundancy analysis of site-specific bacterial communities (points) and environmental
variables (vectors), based on the OTU table rarefied to 10,000 sequences per sample. Sites exhibiting an
anthropogenic source of metal impact are in red, and sites exhibiting an endogenous source of metal impact
are in blue. TRW refers to total recoverable concentrations of a specific metal, and DM refers to the
dissolved concentrations of a specific metal. The first and second ordination axes accounted for 20.0% and
8.9%, respectively, of the variance in the data.
oo
Ln


Axis 2 (20.2%)
00
d
Betaproteobacteria
Dettaproteobacteria
d
o
o
d
o
i
9
DM8^RWBa
Alphaproteobacteria
-0.6 -0.4 -0.2 0.0 0.2 0.4
Axis 1 (39.1%)
Figure 3.13: Redundancy analysis of the relative abundance of dominant phyla (average relative abundance >1% in
all samples) from all samples (points) and environmental variables (green vectors). TRW refers to total recoverable
concentrations of a specific metal, and DM refers to the dissolved concentrations of a specific metal. The first and
second ordination axes accounted for 39.1% and 20.2%, respectively, of the variance in the data.
oo
CT\


When all samples and all time points were considered, pH, conductivity,
dissolved oxygen, temperature, and water hardness correlated significantly with
microbial community structure (Supplementary Table S3). Specifically, water hardness
(r = 0.41875, p = 0.001), conductivity (r = 0.39397, p = 0.001), and pH (r = 0.32516, p =
0.001) had strong correlations with microbial community structure. Generally, these
patterns were observed temporally for all samples (with the exception of September,
due to our lack of anthropogenic sites). In June, temperature correlated with overall
community structure (r = 0.41807, p = 0.001). In August, pH (r = 0.30465, p = 0.002)
and conductivity (r = 0.30022, p = 0.001) each correlated with community structure.
Microbial communities from samples exhibiting an anthropogenic source of
metal impact correlated with pH (r = 0.51462, p = 0.001), dissolved oxygen
concentrations (DO) (r = 0.26509, p = 0.008), and temperature (r = 0.24326, p = 0.011)
overall (Supplemental Table S3). These patterns were consistent temporally, with pH
correlating with anthropogenic microbial community structure at each time point (June
r = 0.67251, p = 0.001; August r = 0.39524, p = 0.011).
Microbial communities from endogenously metal impacted systems correlated
weakly with most of these five variables, and temporal variations in correlations were
observed. In June, DO (r = 0.41553, p = 0.011) and water hardness (r = 0.37125, p =
0.032) correlated strongly with endogenous microbial community structure. In August,
none of these five factors had statistically significant correlations with community
structure (p>0.05). In September, pH correlated strongly with endogenous microbial
community structure (r = 0.36534, p = 0.049). Overall, water hardness and pH were the
only statistically significant correlations of these five variables with endogenous
87


microbial community structure from all time points combined (r = 0.26840, p = 0.001;
and r = 0.18230, p = 0.005, respectively). Dissolved and total recoverable metal
concentrations also correlated with microbial community structure (Supplementary
Tables 3 and 4).
Total Recoverable Metal Concentrations
Total recoverable metal concentrations from water correlated with the microbial
communities and often coincided with patterns observed with dissolved metals (Table
3.11). In June, total iron (r = 0.49877, p = 0.001), magnesium (r = 0.44583, p = 0.001),
and barium (r = 0.43548, p = 0.001) correlated most strongly with the microbial
community structure of all samples of all the total metals analyzed. In August, total
magnesium (r = 0.45483, p = 0.001), aluminum (r = 0.39087, p = 0.001), and copper (r =
0.37489, p = 0.001) concentrations correlated most strongly with overall microbial
community structure. Overall, when all samples and all time points were considered,
total recoverable magnesium (r = 0.53978, p = 0.001), barium (r = 0.0.48756, p =
0.001), and iron (r = 0.42365, p = 0.001) concentrations correlated most strongly with
overall microbial community structure.
Microbial communities that exhibited an anthropogenic source of metal impact
correlated strongly with total recoverable metal concentrations. In June, total iron (r =
0.72958, p = 0.001), zinc (r = 0.68669, p = 0.001), and manganese (r = 0.64116, p =
0.004) concentrations correlated strongly with anthropogenic microbial community
structure. In August, total aluminum (r = 0.77823, p = 0.001), copper (r = 0.64220, p =
0.002), and nickel (r = 0.45780, p = 0.017) concentrations correlated with
88


anthropogenic microbial community structure. Overall, when all sampling time points
were considered for anthropogenically metal impacted samples, total recoverable
aluminum (r = 0.68010, p = 0.001), copper (r = 0.55200, p = 0.001), and zinc (r =
0.52485, p = 0.001) concentrations correlated stronger than any other dissolved metals.
Microbial communities that exhibited an endogenous source of metal impact had
similar correlations with total recoverable metal concentrations as anthropogenic
samples. In June, total arsenic (r = 0.44218, p = 0.007), thallium (r = 0.39235, p =
0.021), and aluminum (r = 0.37645, p = 0.025) concentrations correlated most strongly
(out of all total recoverable metals analyzed in the study) with microbial community
structure. In August, total aluminum (r = 0.42214, p = 0.004), zinc (r = 0.40640, p =
0.005), and arsenic (r = 0.35660, p = 0.015) correlated most strongly with endogenous
microbial community structure. In September, total recoverable iron (r = 0.34742, p =
0.039) correlated most strongly with microbial community structure. Magnesium (r =
0.33433, p = 0.055) and aluminum (r = 0.33279, p = 0.115) correlated strongly with
microbial community structure in September, however, they are not statistically
significant. Overall, when all time points were considered for endogenously metal
impacted microbial communities, total arsenic (r = 0.41784, p=0.001), aluminum (r =
0.41404, p = 0.001), and barium (r = 0.30833, p = 0.001) correlated most strongly with
community structure.
Dissolved Metal Concentrations
Dissolved metal concentrations also correlated with the microbial communities.
In June, dissolved barium (r = 0.47863, p = 0.001), magnesium (r = 0.43388, p = 0.001),
89


and iron (r = 42806, p = 0.002) correlated most strongly of all the dissolved metals
analyzed with the microbial community structure of all samples (Table 3.11). In
August, dissolved magnesium (r = 0.45678, p = 0.001), barium (r = 0.38362, p = 0.001),
and aluminum (r = 0.27986, p = 0.001) concentrations correlated most strongly with
overall microbial community structure. Overall, when all samples and all time points
were considered, dissolved magnesium (r = 0.53145, p = 0.001), barium (r = 0.48756, p
= 0.001), and calcium (r = 0.41299, p = 0.001) concentrations correlated most strongly
with microbial community structure.
Microbial communities that exhibited an anthropogenic source of metal impact
correlated strongly with dissolved metals throughout the study. In June, dissolved
aluminum (r = 0.74068, p = 0.001), cadmium (r = 0.72918, p = 0.001), and iron (r =
0.70474, p = 0.004) concentrations correlated strongly with anthropogenic microbial
community structure. A similar pattern was observed in August, with dissolved
aluminum (r = 0.67515, p = 0.001), copper (r = 0.67257, p = 0.001), and iron (r =
0.52570, p = 0.006) exhibiting the strongest correlations of all dissolved metals.
Overall, when all sampling time points were considered, dissolved aluminum (r =
0.71201, p = 0.001), copper (r = 0.61945, p = 0.001), and iron (r = 0.57417, p = 0.001)
concentrations correlated stronger than any other dissolved metals.
Microbial communities exhibiting an endogenous source of metal impact had
similar correlations with dissolved metal concentrations as anthropogenic samples. In
June, dissolved aluminum (r = 0.39032, p = 0.026), calcium (r = 0.36757, p = 0.038), and
magnesium (r = 0.35178, p = 0.032) concentrations correlated most strongly (out of all
dissolved metals analyzed in the study) with microbial community structure. In August,
90


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PAGE 22

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PAGE 31

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