ii

© 2019 Mary Grace Bass

ALL RIGHTS RESERVED

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For Mimi -

I could not have done this without you.

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank Dr. Colin Jackson for allowing me to research in his lab. I am especially thankful for the amount of patience he has shown me throughout my research and writing of this thesis. You have inspired me since the beginning of my time at Ole Miss, and I am honored that you have been part of my journey. I would also like to thank Dr. Wayne Gray and Dr. Jason Hoeksema for their unwavering support throughout this process. I appreciate you both. Eric Weingarten, you are an actual hero. You have my deepest thanks for the countless hours spent answering my questions, solving problems, and listening to all of the other things going on in my life. This would not have been possible without you and your movie scores. Work hard. Dream big. These are two of the lessons I brought to Oxford from home. Mom and Dad, you have supported and encouraged me every step of the way – allowing me to be the true Rebel of the Bass Family. Noah Kippenbrock and Kayla Clark, I could not have completed this work without you both. Whether it was extracting DNA in our laboratory or editing a section of my thesis, your encouragement and support has been invaluable. Thank you both for being so good to me. Jackson Lab forever. Sally McDonnell Barksdale Honors College, you have challenged me. You have changed me. Thank you providing me more opportunities and growth than I could have ever imagined. I will forever be grateful. Thank you, Ole Miss, for the education of a lifetime.

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ABSTRACT

Compositional Differences in Bacterial Communities in Fresh and Saltwater Wetlands of the Gulf Coast

Wetlands are important reservoirs for biodiversity and ecosystem services such as

nutrient cycling. However, the anthropogenic stressors of sea-level rise and

eutrophication threaten these habitats. In this study, I examined the bacterial communities

at six locations along the US Gulf Coast between Louisiana and Florida, focusing on how

these communities were affected by salinity, depth, and site location.

At each of six locations, five 30cm-deep soil cores were taken from a tidal fresh

marsh and either a tidal brackish marsh or tidal saltmarsh. Bacterial DNA was extracted

from both the surface layer and the root layer of each soil core. Illumina MiSeq was used

to sequence the V4 region of the 16S rRNA gene. NMDS ordination and analysis of

similarity were calculated from a Bray-Curtis dissimilarity matrix to determine sample

differences and their environmental drivers.

Wetland type had a significant effect on sediment microbial composition with

fresh marsh, saltmarsh, and brackish marsh all differing. Ranking wetlands by salinity in

5 ppt increments revealed that the only non-significant comparisons were between the

three lowest salinity groups and two moderate salinity groups. This pattern was further

refined by NMDS ordination, which showed distinct clustering of communities by

salinity and, generally, tighter grouping of samples in higher salinity wetlands and wider

distribution in lower salinity wetlands. Salinity, depth in sediment, and site all had

vi

significant effects on sediment bacterial community composition. This study represents

one of the largest surveys of the wetland microbiome both spatially and in number of

samples. The results reflect previous data from these sites that showed that site was the

most influential factor in determining enzyme activity, followed by salinity. It is

reasonable to predict that in these and similar sites, sea-level rise will cause shifts in the

sediment microbiome and its activities.

vii

TABLE OF CONTENTS

List of Figures And Tables ............................................................................................ VIII

List of Abbreviations ........................................................................................................ IX

Introduction ......................................................................................................................... 1

Methods............................................................................................................................... 5

Results ............................................................................................................................... 10

Discussion ......................................................................................................................... 19

References ......................................................................................................................... 23

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LIST OF FIGURES AND TABLES

Figure 1: Map of Sites ......................................................................................................... 8

Table 1: Summary of Mothur Commands .......................................................................... 9

Table 2: Ten Most Abundant OTUs ................................................................................ 14

Figure 2: Proportion of Major Bacterial Phyla ................................................................. 15

Figure 3: Proportion of Proteobacteria Subphyla ............................................................. 16

Figure 4: Observed Species Richness ............................................................................... 17

Figure 5: Non-metric Multidimensional Scaling (NMDS) Plot ....................................... 18

ix

LIST OF ABBREVIATIONS

ANOSIM: Analysis of Similarities

DNA: Deoxyribose Nucleic Acid

NMDS: Non-metric Multidimensional Scaling

OTU: Operational Taxonomic Unit

PCR: Polymerase Chain Reaction

RNA: Ribonucleic Acid

rRNA: Ribosomal Ribonucleic Acid

1

Introduction

Wetland microbiology has become an important area of study, especially

regarding concerns of microbial diversity and the conditions that influence it. Wetlands

provide particular insights into processes such as nutrient cycling and erosion control

whereas focusing on the microorganisms within these ecosystems allows for the

characterization of the wetlands based on features such as metabolic activity and oxygen

gradients (Ansola et al. 2014). Little is known of the environmental factors that determine

the structure and composition of the bacterial communities of coastal wetlands, even

though these wetlands and the bacterial communities within them are important

components of coastal systems. Coastal systems rely on the wetlands within these regions

to provide maintenance to fisheries, protection to the coasts, and habitats for other species

(Runting et al. 2017). In particular, the Northern Gulf of Mexico ecoregion accounts for

approximately 60% of the United States’ tidal marshes and contributes to flood and

erosion control, water quality, and carbon sequestration (Ward 2017, EPA 2019).

Wetland sediments can contain great bacterial diversity; however, scientists are

unsure of what factors influence this diversity (Wang et al. 2012). Environmental features

such as pH, particle size, nitrates, and nitrites have been related to the structure of the

overall microbial community among wetlands (Li et al. 2019), and these factors may

interact. Salinity is a factor that may be particularly important in the context of global

climate change and sea-level rise (Xiao et al. 2014), and studies have focused on the

2

effects of salinity on wetland microbial diversity or on understanding how

microorganisms resist high salt concentrations through altering expression of select genes

and metabolites (Canfora et al. 2014). Microcosms have been used to observe changes in

bacterial community structure in response to salinity and bacterial diversity has been

found to increase until a certain salinity threshold is reached, a result of the addition of

new species to the community and shifts towards different bacterial phylum (Jackson &

Vallaire 2009).

While salinity may be an important factor in determining the sediment

microbiome of coastal wetlands, few studies have focused on examining the microbial

communities of wetlands along a gradient of salinities. Rather, studies on these systems

have examined how microbial communities differ between fresh, salt, and brackish

marshes. Freshwater marshes have < 0.5 ppt salinity, and while they are found upstream

from brackish marshes, they can still be tidally influenced. Freshwater marshes are

typically associated with rivers, which gives these marshes greater tidal range, and have a

stable, long term water system that allows for species diversity and productivity

(Meyerson et al. 2000). Brackish marshes are defined as wetlands with salinities ranging

from slight to moderate, or 5 to 20 ppt (Craft et al. 2009). Saltmarshes have a salinity of

18 to 30 ppt, which is a moderate to sea water range (“Natural Resources Conservation

Service.” n.d.).

Global climate change and associated sea level rise can lead to tidal marsh

submergence and the movement of saltwater marshes inward. In Louisiana, saltwater

intrusion is a primary cause of wetland deterioration and sea level rise has caused saline

water to intrude into brackish and fresh marshes, which in turn, has led to movement of

3

vegetation away from the saline marshes (McKee & Mendelssohn 1989). In regions such

as the northern Gulf of Mexico, saltwater intrusion occurs during tropical storms and

hurricanes, which can lead to alterations in the salinity levels and the physiology of plant

species. Recent studies have focused on how vegetation in these regions modifies the

microbial community present; however, the impacts of salinity and nutrient levels must

be accounted for as well, which is difficult to measure (Jackson & Vallaire 2009).

Stressors such as intense rainfall and water level can also influence the salt accumulations

in addition to the ion concentrations found within the sediment and root zones. These

periods of rainfall can remove ions from the root zone of wetlands influence plant growth

and the microbial community present (Franklin et al 2017). There is a need to examine

how a range of salinities from freshwater to saltwater impacts the wetland sediment

microbial community.

In one of the largest surveys to date, I investigated how the sediment bacterial

communities of 12 Gulf Coast wetlands between Louisiana and Florida, including a range

of sites from freshwater to brackish to saltmarsh, were influenced by salinity. Many of

the bacterial species present in wetlands have unknown physiologies and metabolic

activity, which makes culturing difficult (Dedysh 2011). Thus, I used culture-independent

techniques based on PCR (Polymersease Chain Reaction) amplification of a portion of

the bacterial 16S rRNA (ribosomal RNA) gene to characterize these wetland sediment

communities. Next generation sequencing was used to sequence these 16S rRNA gene

fragments. While the specific location of each wetland was an important influence on the

sediment microbiome, each site showed a clear separation of the bacterial communities in

freshwater and saltmarsh sediment. Wetlands, with the goal of examining how salinity

4

and site-specific factors, might influence coastal wetland sediment bacterial community

structures.

5

Methods

Sample Acquisition and Processing

Six locations were sampled along the coast of the northern Gulf of Mexico

between June 14 and June 21, 2018: near Cocodrie, LA, Weeks Bay, LA, Grand Bay,

MS, St. Marks, FL, Apalachicola, FL, and Cedar Key, FL (Figure 1). Each location

consisted of a tidal freshmarsh and a nearby brackish or saltmarsh, giving 12 wetland

sites total. Five soil cores were collected from within each wetland site at five randomly

chosen areas within each site. Cores were taken using a sterilized (70% ethanol) 38 cm x

2 cm soil corer, and soil was collected separately from the top and the bottom of each

core in order to characterize the surface and root level bacterial communities. Thus, the

final experiment design consisted of six locations x marsh types (salt and brackish or

fresh) x depth (surface or root zone) x five replicate cores.

Soil from each sample was passed through a sterilized (70% ethanol) 1mm pore

size sieve to remove larger debris. Samples were then stored in sterile 15 mL tubes and

stored on ice until return to the laboratory at the University of Mississippi. To account for

possible differences in storage time because of sampling on different dates and travel

time, samples were processed in the order collected and all were maintained on ice for a

standardized time of 21 days. At that point, approximately 1 g of soil was transferred to a

1.5 mL microcentrifuge tube and frozen prior to DNA extraction.

6

DNA Extraction and 16S rRNA Gene Sequencing

Samples were thawed, and DNA was extracted from both the surface layer and the root

layer of each soil core using Qiagen DNeasy PowerSoil DNA Isolation Kits. The

bacterial microbiome was examined by Illumina MiSeq sequencing of the V4 region of

the 16S rRNA gene (Kozich et al. 2013). This process used dual-index barcoding and the

primers and procedures of Kozich et al. (2013) and Stone and Jackson (2016). Briefly, 1

µl of extracted DNA was combined with 1 µl of each barcoded primer at a 10 µM

concentration and 17 µl of AccuPrime Pfx SuperMix. PCR reactions were conducted

under the following protocol: 95°C hot start for 2 min, 30 cycles of 95°C (20 s), 55°C (15

s), 72°C (2 min), followed by a final elongation at 72°C for 10 min. Amplicon

concentration was normalized using a SequalPrep Normalization Plate Kit and amplicons

pooled. Final sequencing was completed at the University of Mississippi Medical Center

Molecular and Genomics Core Facility using the Illumina MiSeq platform.

Sequence Data Analysis

The bioinformatics software mothur (Schloss et al., 2009) was used for the

analysis of FASTQ files, which were obtained from the sequencing process (Table 1).

Data processing generally followed the command order set forth by Schloss et al. (2011).

Sequences were aligned to the Silva 16S rRNA database (Quast et al. 2013) and

classified against the Ribosomal Database Project database (Maidak et al. 2000).

Chimeras were then removed using UCHIME, and sequences classified as chloroplasts,

mitochondria, Archaea, Eukarya, or unclassified were removed so that the final dataset

consisted solely of valid bacterial sequences. The bacterial sequences were grouped into

7

operational taxonomic units (OTUs) based on sequence 97% similarity. The overall

composition of each bacterial community was assessed, and beta diversity metrics used to

examine differences between the communities under different salinity conditions.

8

Figure 1: Five soil cores were taken separately from a fresh and a saline marsh in close

proximity at each of the six sites shown along the southern Gulf Coast of the United

States. Sampling took place between June 14 and June 21, 2018. Samples were stored on

ice.

9

Table 1: Summary of commands within the mothur software packaged used for 16S

rRNA sequence data obtained from soil samples.

Command Function

make.contigs Processes FASTQ files

screen.seqs Filters the sequences that do not meet the

specified criteria

unique.seqs Removes identical sequences

count.seqs Counts the numbers of unique sequences

within each sample

align.seqs Aligns sequences to the SILVA database

filter.seqs Removes incorrect sequences

pre.cluster Combines nearly identical sequences

chimera.uchime Identifies the potential chimeric sequences

remove.seqs Removes chimeric sequences

classify.seqs Classifies the sequences against the

Greengenes database

remove.lineage Removes unwanted lineages such as

eukarya, archea, chloroplast, mitochondria

cluster.split Groups sequences into OTUs

make.shared Determines the amount of times each

OTU is found within samples

count.groups Determines the number of sequences in

each sample

classify.otu Identifies the OTUs present in samples

dist.shared Creates a matrix based on the presence

and abundance of OTUs

nmds Gives the coordinates for comparisons on

a plot via. non-metric multidimensional

scaling

10

Results

A total of 2,719,728 bacterial 16S rRNA gene sequences were recovered from the

sediment samples. There was an uneven distribution of sequences across the soil samples.

Cocodrie, LA, a shallow freshwater marsh sample had the most sequences with 110,210,

whereas Weeks Bay, AL, a deep freshwater sample gave the fewest sequences with

1,464. OTUs were classified based on 97% similarity and yielded 4,210 distinct OTUs.

Across all samples, the ten most abundant OTUs were identified (in order from most

abundant to least abundant) as unclassified Desulfobacteraceae, Gp17 (Acidobacteria),

Unclassified Betaproteobacteria, Unclassified Deltaproteobacteria, Gp18 (Acidobacteria)

Clostridium sensu stricto (Firmicutes), Unclassified Ignavibacteriaceae (Ignavibacteria),

Unclassifed Myxoccocales (Deltaproteobacteria), Unclassified Alteromondaceae

(Gammaproteobacteria), and Unclassified Anaerolineaceae (Chloroflexi) (Table 2).

Across all samples, Proteobacteria was the most abundant bacterial phylum with a

mean percentage of 34.4% of the sequences recovered. Other major phyla included

Acidobacteria, Chloroflexi, Bacteroidetes, Planctomycetes, Verrucomicrobia, and

Cyanobacteria. Although Proteobacteria was the most abundant phyla among the

samples, the percentages of the other major phyla varied among samples (Figure 2). It

was observed that samples collected at the root level had higher percentages of

unclassified bacteria. Saltmarsh surface samples had the highest percentage of

Bacteroidetes. These samples were composed of 9% Bacteroidetes, which was double the

11

amount found within the other three marshes. When comparing between freshmarsh and

saltmarshes, freshmarshes had about 10% of Acidobacteria within the samples, whereas

saltmarsh samples had 7% of Acidobacteria (Figure 2). When analyzing the

Proteobacteria phylum, subphylum Deltaproteobacteria accounted for 29.9% of the

Proteobacteria across all of the samples, although sequences classified as

Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, and

Epsilonproteobacteria were also detected, as well as some unclassified Proteobacteria

sequences (Figure 3). Saltmarshes typically had the largest amount of

Alphaproteobacteria with samples collected at the surface level containing the highest

percentage of 24%. Saltmarsh surface samples also had the highest percentage of

Gammaproteobacteria at 30%. Freshmarshes showed the highest composition of

Epsilonproteobacteria and Betaproteobacteria (Figure 3). When comparing between the

depths of the freshmarsh samples, the root level had higher compositions of

Epsilonproteobacteria, yet the surface level samples had higher composition of

Alphaproteobacteria and Gammaproteobacteria (Figure 3).

The alpha diversity of each sample was determined through measuring sobs, or

observed species richness, based on the unique OTUs found within the sample (Figure 4).

A higher score of sobs indicates a more diverse community. When comparing the sobs

scores, the four out of the five most diverse samples were collected from the surface level

with the exception being the most diverse sample, which was collected from the root

level (Figure 4). The average was taken for each of the four conditions. It was determined

that saltmarsh surface samples had the highest average sobs score of 370, and freshmarsh

root samples had the lowest average sobs score, of 349. Observed species richness varied

12

at a particular site, for example when comparing the four conditions in the Cedar Key,

FL, site, the freshwater samples had the lowest sobs count of all of the samples; however,

the saltwater samples had the two of the top five highest sobs counts (Figure 4). When

analyzing the Cocodrie, LA, site, there was separation by depth level. The root level

samples had lower richness when compared to the surface level samples (Figure 4).

Non-metric multidimensional scaling (NMDS) was used to visualize and compare

samples based on Bray-Curtis dissimilarity. NMDS provides each sample a coordinate,

which is based on the similarities to other samples. The sites with similar microbial

communities will be closer in proximity to each other, and the samples with more unique

communities will be further apart on the plot. In order to fully visualize the relationship

of sites, brackish marshes were further divided into intermediate marshes (1-5 ppt) and

brackish marshes (5-20 ppt) to observe a more accurate distribution. There was distinct

separation between the saltmarsh and freshmarsh conditions (Figure 5). Spearman’s rank

correlation coefficient was calculated in order to determine the bacterial taxa responsible

for separating the samples on the NMDS plot. The main saltmarsh taxa driving this

separation were Gp17 (Acidobacteria) with 2,404 sequences, Myxococcales

(Deltaproteobacteria) with 1,921 sequences, Anaerolineaceae (Chloroflexi) with 1,770

sequences, Gp23 (Acidobacteria) with 1,757 sequences, and Psychromonas

(Gammaproteobacteria) with 1,652 sequences. The intermediate and brackish samples

were distributed throughout the plot, which showed similarities to freshmarsh or

saltmarsh conditions depending on location. Specifically, the Grand Bay, MS, samples

were classified as brackish marsh sites and were found in a cluster far apart from the

saltmarsh sites and closer to the freshmarsh sites; however, the Cocodrie, LA, brackish

13

marsh samples were found in a cluster closer to the saltmarsh sites (Figure 5). The

differences between brackish, freshmarsh, and saltmarsh conditions were found to be

statistically significant according to ANOSIM (R=0.206, p=<0.001). When comparing

between pairs of sites, brackish and freshmarsh conditions were statistically significant

according to ANOSIM (R=0.179, p=<0.001), and freshmarsh and saltmarsh conditions

were also found to be statistically significant according to ANOSIM (R=0.323, <0.001).

The brackish marsh and saltmarsh conditions were also found to be statistically

significant with according to ANOSIM (R=0.205, p=0.002).

14

Table 2: The ten most abundant OTUs found among all samples. These OTUs are listed

from most abundant to least abundant. Size indicates the number of species. Taxonomy

refers to classification of the specific bacterium.

OTU Size Taxonomy 00001 3240 Proteobacteria,

Unclassified Desulfobacteraceae

00003 2404 Acidobacteria, Gp17 00004 2311 Proteobacteria,

Unclassified Betaproteobacteria

00005 2232 Proteobacteria, Unclassified Deltaproteobacteria

00008 2028 Acidobacteria, Gp18 00009 1980 Firmicutes, Clostridium

sensu stricto 00010 1961 Ignavibacteriae,

Unclassified Ignavibacteriaceae

00011 1921 Proteobacteria, Unclassified Myxococcales

00012 1823 Proteobacteria, Unclassified Alteromonadaceae

00013 1748 Chloroflexi, Unclassified Anaerolineaceae

15

Figure 2: Proportion of major bacterial phyla in sediment taken from coastal wetland sites

at different depths and salinity ranges. Freshmarsh surface represents samples taken from

freshwater marshes at the surface level. Freshmarsh root accounts for samples taken from

freshwater marshes at the root level. Saltmarsh surface accounts for samples taken from

saltmarshes at the surface level, while saltmarsh root represents samples taken from

saltmarshes at the root level.

16

Figure 3: Proportion of Proteobacteria subphyla associated with sites at various depths

and salinity ranges. Freshmarsh surface represents samples taken from fresh marshes at

the surface level. Freshmarsh root accounts for samples taken from fresh marshes at the

root level. Saltmarsh surface accounts for samples taken from saltmarshes at the surface

level, while saltmarsh root represents samples taken from saltmarshes at the root level.

17

Figure 4: Alpha diversity of bacterial communities measured through observed species

richness, or sobs. Each bar on the plot represents the average number of species found

within a particular site, depth, and salinity. Samples were taken from each of the six sites,

root and surface level, and saltmarshes and fresh marshes. Example: Cocodrie

Freshmarsh Surface sample was taken from the Cocodrie, LA, site from the freshmarsh

condition at the surface level.

18

Figure 5: NMDS plot representing the similarities between bacterial communities

associated with sediment samples obtained from freshmarsh sites (red), intermediate

marsh sites (orange), brackish marsh sites (blue), and saltmarsh sites (purple). Each site is

represented by a specific shape for all of the samples collected within the site. Points

located in closer proximity within the plot represent more similar community

composition based on the relative abundance of OTUs.

19

Discussion

In this study, I investigated how the microbiomes of wetlands differ with salinity.

The microbial communities within fresh water sites were compared to those of brackish

and saltwater sites at six locations along the U.S. Gulf of Mexico. This experiment aimed

to characterize the bacterial communities present within different salinity conditions to

examine how these microbiomes could change in response to events such as sea level rise

and saltwater intrusion. The sequence data from each sample was analyzed to observe the

major phyla and subphyla present in samples, the number of sequences and OTUs within

each sample, and measure alpha and beta diversity.

One of the limitations of examining the microbial community of natural

environments is that many microorganisms cannot be cultured in the laboratory without

specialized cultivation techniques. Bacterial species can have unknown physiologies and

metabolic activity, which makes culturing difficult in order to explore the bacterial

diversity among wetlands (Dedysh 2011). This has led to the emergence of culture-

independent techniques to study microbial communities, typically based around the 16S

rRNA gene, a highly conserved marker that can be used for gene amplification. These

techniques have been applied to study wetland sediments, for example to compare

between sediment types, and observe diversity against a salinity gradient (Jiang et al.

2013). However, 16S rRNA gene sequencing has been used only rarely across broad

geographic scales, especially in the context of variation in environmental conditions such

as salinity.

20

16S rRNA gene sequencing recovered sequences from eight different phyla in this

study. Proteobacteria was the major phylum present within all samples, and

Deltaproteobacteria was the most abundant subphylum with the Proteobacteria,

accounting for 30% of all of the recovered sequences. Previous studies have similarly

found Deltaproteobacteria to be the dominating subphylum within all of the treatments

(Jackson & Vallaire 2009). When comparing other subphyla, and patterns with salinity

the proportions of Betaproteobacteria were lower in more saline sites. This differs from a

prior study that manipulated salinity and found that the proportion of Betaproteobacteria

increased at elevated salinity levels (Jackson & Vallaire 2009). When comparing the

proportions of other major phyla by salinity, the Acidobacteria showed some variation.

Saltmarsh bacterial communities were composed of 7% Acidobacteria, whereas this

phylum accounted for 10% of the freshmarsh sediment bacterial community.

Acidobacteria have previously been reported as being a major phylum present in

freshwater wetland sediments (Jackson & Vallaire 2009, Menon et al. 2013).

The most abundant OTUs found within the samples were identified as

unclassified members of the Desulfobacteraceae (Deltaproteobacteria) family and

unclassified members of the Gp17 order, which is part of the Acidobacteria phylum.

Members of the Desulfobacteraceae family are sulfate reducers found within all types of

marshes that are responsible for oxidizing organic substrates into carbon dioxide or

incompletely oxidizing substrates into acetate (Kuever 2014). Previous studies have

shown how uncultured Desulfobacteraceae account for major acetate assimilation in

coastal marine sediment (Dyksma et al. 2018). Bacteria within the Gp17 order have

shown high abundance in soils containing high levels of nutrients, which would lead to

21

these bacteria thriving in environments rich in nutrients, specifically carbon (Naether et

al. 2012), such as wetland sediments.

Using ordination techniques, at the site level there were weak but discernable

clusters of samples. However, when each site was observed independently by salinity,

there was a sharp separation between freshmarsh and saltmarsh in terms of microbial

composition. This was driven by differences in the proportions of specific taxa and

included some that are salt-tolerant and others that are important in organic matter

degradation (Kim & Liesack 2015, Garris et al. 2018). The main taxa driving the

differences between saltmarsh and freshmarsh sites were Gp17 (Acidobacteria),

Myxococcales (Deltaproteobacteria), Anaerolineaceae (Chloroflexi), Gp23

(Acidobacteria), and Psychromonas (Gammaproteobacteria). Among the taxa driving the

separation, bacteria with sulfate-reducing properties were found specifically in

saltmarshes. In addition to the taxa that were more prevalent in saltmarsh sites, there was

one freshwater taxon helping separate the saltmarsh and freshmarsh sites.

Anaerolineaceae (Chloroflexi), a methanogenic bacterium, was found in both saltmarshes

and freshmarshes. Characteristics of the bacterial taxa found within the types of marshes

reflect how carbon is processed in those systems through different types of respiration or

fermentation. In saltmarshes, seawater contains sulfate, which allows for the presence of

sulfate-reducing bacteria (Bahr et al. 2005). In freshwater marshes, the amount of sulfate

is limited, so fermenting bacteria are more prevalent (Lamers et al. 2002).

With saltwater intrusion occurring more frequently within coastal wetlands, the

amount of fresh and brackish marshes will begin to diminish (Weston et al. 2011).

Increased salinity could change the composition of the microbial community in wetland

22

sediments that, in turn, would affect nutrient cycling and carbon processing. Fresh

marshes typically serve as carbon sinks, which absorb carbon dioxide from the

atmosphere, and saltmarshes tend to produce more greenhouse gases that become stored

within the marsh (Chmura et al. 2003). When considering the threat of saltwater intrusion

into fresh marsh habitat the results of this study demonstrate the possibility that marsh

migration will cause a concurrent shift in the wetland microbiome, the ecological impacts

of which need to be considered.

23

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