ii
© 2019 Mary Grace Bass
ALL RIGHTS RESERVED
iii
For Mimi -
I could not have done this without you.
iv
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.
v
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
viii
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
References
Ansola, G., Arroyo, P., & Sáenz de Miera, Luis E. (2014). Characterisation of the soil
bacterial community structure and composition of natural and constructed
wetlands. Science of the Total Environment, 473-474, 63-71.
doi:10.1016/j.scitotenv.2013.11.125
Bahr, M., Crump, B. C., Klepac‐Ceraj, V., Teske, A., Sogin, M. L., & Hobbie, J. E.
(2005). Molecular characterization of sulfate‐reducing bacteria in a New England
salt marsh. Environmental Microbiology, 7(8), 1175-1185.
Canfora, L., Bacci, G., Pinzari, F., Lo Papa, G., Dazzi, C., & Benedetti, A. (2014).
Salinity and bacterial diversity: To what extent does the concentration of salt
affect the bacterial community in a saline soil? Plos One, 9(9), e106662.
doi:10.1371/journal.pone.0106662
Chmura, G. L., Anisfeld, S. C., Cahoon, D. R., & Lynch, J. C. (2003). Global carbon
sequestration in tidal, saline wetland soils. Global Biogeochemical Cycles, 17(1-
7), 1111-n/a. doi:10.1029/2002GB001917
Craft, C., Clough, J., Ehman, J., Joye, S., Park, R., Pennings, S., . . . Machmuller, M.
(2009). Forecasting the effects of accelerated sea-level rise on tidal marsh
ecosystem services. Frontiers in Ecology and the Environment, 7(2), 73-78.
doi:10.1890/070219
24
Dedysh, S. N. (2011). Cultivating uncultured bacteria from northern wetlands:
Knowledge gained and remaining gaps. Frontiers in Microbiology, 2, 184.
doi:10.3389/fmicb.2011.001
Dyksma, S., Lenk, S., Sawicka, J. E., & Mußmann, M. (2018). Uncultured
gammaproteobacteria and desulfobacteraceae account for major acetate
assimilation in a coastal marine sediment. Frontiers in Microbiology, 9, 3124.
doi:10.3389/fmicb.2018.03124
EPA (2019). “Coastal Wetlands.” Retrieved from https://www.epa.gov/wetlands/coastal-
wetlands
Franklin, R. B., Morrissey, E. M., & Morina, J. C. (2017). Changes in abundance and
community structure of nitrate-reducing bacteria along a salinity gradient in tidal
wetlands. Pedobiologia - Journal of Soil Ecology, 60, 21-26.
doi:10.1016/j.pedobi.2016.12.002
Garris, H. W., Baldwin, S. A., Taylor, J., Gurr, D. B., Denesiuk, D. R., Van Hamme, J.
D., & Fraser, L. H. (2018). Short-term microbial effects of a large-scale mine-
tailing storage facility collapse on the local natural environment. PloS One, 13(4),
e0196032. doi:10.1371/journal.pone.0196032
Jackson, C. R., & Vallaire, S. C. (2009). Effects of salinity and nutrients on microbial
assemblages in louisiana wetland sediments. Wetlands, 29(1), 277-287.
doi:10.1672/08-86.1
Jiang, X., Peng, X., Deng, G., Sheng, H., Wang, Y., Zhou, H., & Tam, N. F. (2013).
Illumina sequencing of 16S rRNA tag revealed spatial variations of bacterial
25
communities in a mangrove wetland. Microbial Ecology, 66(1), 96-104.
doi:10.1007/s00248-013-0238-8
Kim, Y., & Liesack, W. (2015). Differential assemblage of functional units in paddy soil
microbiomes. Plos One, 10(4), e0122221. doi:10.1371/journal.pone.0122221
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., & Schloss, P. D. (2013).
Development of a dual-index sequencing strategy and curation pipeline for
analyzing amplicon sequence data on the MiSeq illumina sequencing
platform. Applied and Environmental Microbiology, 79(17), 5112-5120.
doi:10.1128/AEM.01043-13
Kuever, J. (2014). The family Desulfobacteraceae. The prokaryotes: Deltaproteobacteria
and epsilonproteobacteria, 45-73.
Lamers, L. P., Falla, S. J., Samborska, E. M., van Dulken, I. A., Hengstum, G. V., &
Roelofs, J. G. (2002). Factors controlling the extent of eutrophication and toxicity
in sulfate‐polluted freshwater wetlands. Limnology and oceanography, 47(2),
585-593.
Li, W., Lv, X., Ruan, J., Yu, M., Song, Y., Yu, J., & Dong, M. (2019). Variations in soil
bacterial composition and diversity in newly formed coastal wetlands. Frontiers
in Microbiology, 9. doi:10.3389/fmicb.2018.03256
Maidak, B. L., Cole, J. R., Lilburn, T. G., Parker Jr, C. T., Saxman, P. R., Stredwick, J.
M., . . . Tiedje, J. M. (2000). The RDP (ribosomal database project)
continues. Nucleic Acids Research, 28(1), 173-174. doi:10.1093/nar/28.1.173
26
McKee, K. L., & Mendelssohn, I. A. (1989). Response of a freshwater marsh plant
community to increased salinity and increased water level. Aquatic Botany, 34(4),
301-316. doi:10.1016/0304-3770(89)90074-0
Menon, R., Jackson, C. R., & Holland, M. M. (2013). The influence of vegetation on
microbial enzyme activity and bacterial community structure in freshwater
constructed wetland sediments. Wetlands, 33(2), 365-378. doi:10.1007/s13157-
013-0394-0
Meyerson, L. A., Saltonstall, K., Windham, L., Kiviat, E., & Findlay, S. (2000). A
comparison of Phragmites australisin freshwater and brackish marsh
environments in North America. Wetlands Ecology and Management, 8(2-3),
89-103.
Naether, A., Foesel, B. U., Naegele, V., Wüst, P. K., Weinert, J., Bonkowski, M., . . .
Friedrich, M. W. (2012). Environmental factors affect acidobacterial
communities below the subgroup level in grassland and forest soils. Applied
and Environmental Microbiology, 78(20), 7398-7406.
doi:10.1128/AEM.01325-12
Natural Resources Conservation Service. (n.d.). Retrieved from
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/plantmaterials/technical/publ
ication s/?cid=stelprdb1044268
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., . . . Glöckner, F. O.
(2013). The SILVA ribosomal RNA gene database project: Improved data
processing and web-based tools. Nucleic Acids Research, 41(1), D590-D596.
doi:10.1093/nar/gks1219
27
Runting, R. K., Lovelock, C. E., Beyer, H. L., & Rhodes, J. R. (2017). Costs and
opportunities for preserving coastal wetlands under sea level rise. Conservation
Letters, 10(1), 49-57. doi:10.1111/conl.12239
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., . . .
Weber, C. F. (2009). Introducing mothur: Open-source, platform-independent,
community-supported software for describing and comparing microbial
communities. Applied and Environmental Microbiology, 75(23), 7537-7541.
doi:10.1128/AEM.01541-09
Schloss, P. D., Gevers, D., & Westcott, S. L. (2011). Reducing the effects of PCR
amplification and sequencing artifacts on 16s rRNA-based studies. Plos
One, 6(12), e27310. doi:10.1371/journal.pone.0027310
Stone BWG, Jackson CR (2016) Biogeographic patterns between bacterial
phyllosphere communities of the Southern Magnolia (Magnolia grandiflora)
in a small forest. Microbial Ecology 71:954–961
Wang, Y., Sheng, H., He, Y., Wu, J., Jiang, Y., Tam, N. F., & Zhou, H. (2012).
Comparison of the levels of bacterial diversity in freshwater, intertidal
wetland, and marine sediments by using millions of illumina tags. Applied and
Environmental Microbiology, 78(23), 8264- 8271. doi:10.1128/AEM.01821-12
Ward, C. H. (Ed.). (2017). Habitats and Biota of the Gulf of Mexico: Before the
Deepwater Horizon Oil Spill: Volume 1: Water Quality, Sediments, Sediment
Contaminants, Oil and Gas Seeps, Coastal Habitats, Offshore Plankton and
Benthos, and Shellfish (Vol. 1). Springer.
28
Weston, N. B., Vile, M. A., Neubauer, S. C., & Velinsky, D. J. (2011). Accelerated
microbial organic matter mineralization following salt-water intrusion into tidal
freshwater marsh soils. Biogeochemistry, 102(1-3), 135-151.
Xiao, H., Huang, W., Johnson, E., Lou, S., & Wan, W. (2014). Effects of sea level
rise on salinity intrusion in St. Marks River estuary, Florida, U.S.A. Journal of
Coastal Research, 68, 89-96. doi:10.2112/SI68-012.1