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Nitrate as an electron acceptor in microbial decomposition of salt marsh sediment organic matter
and implications for carbon storage
by Ashley Bulseco-McKim
B.S. in Marine Science, University of Hawaii at Hilo
M.S. in Marine Science and Technology, University of Massachusetts Boston
A dissertation submitted to
The Faculty of
the College of Science of
Northeastern University
in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
July 16, 2018
Dissertation directed by
Jennifer L. Bowen
Associate Professor of Marine and Environmental Sciences
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Dedication
To my parents,
for showing me how to be relentless.
To my brothers,
for teaching me how to follow my heart.
And to my husband,
for getting me to laugh more than I ever have before.
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Acknowledgements
I could not have had more supportive, encouraging advisors throughout my academic
career thus far. First and foremost, I thank Jennifer Bowen for teaching me how to be resilient,
both as a scientist and as a human. Her ability to think critically and quickly are skills I will
always strive to match. More importantly, Jen has helped me persevere through times of extreme
anxiety and self-doubt, and for that I will be forever grateful. I also thank my co-advisor, Anne
Giblin, whose kindness and willingness to help are unmatched by any other individual I have
ever met. She is an exceptional role model for women in science everywhere, and helped to
shape the clarity of my work. Thank you to the rest of my committee at Northeastern University,
Randall Hughes, Aron Stubbins, and Amy Mueller, who contributed insightful thought to my
dissertation. I would also like to thank my UMass committee, including Bob Chen and Crystal
Schaaf, who both played a critical role in the initial development of my proposal and critical
thinking. Lastly, a sincere mahalo to my undergraduate advisor, Tracy Wiegner, for being my
biggest cheerleader throughout the years.
This journey would not have been possible without the constant support from the Bowen
Lab family, including Annie Murphy, Chris Lynum, Andrea Unzueta Martinez, Joe Vineis,
Kerry McNally, Kenly Hiller-Bittrolff, Sarah Feinman, John Angell, Patrick Kearns, and Brian
Donnelly. In particular, I’d like to thank Annie Murphy for always being my voice of reason
both in and out of the laboratory, and Chris Lynum for knowing exactly what to say without the
need for words. Throughout my graduate school career, I’ve had the pleasure to meet and work
with brilliant students, including Michael Greenwood, Khang Tran, Sean Osborne, Ross
Ackerman, Itxaso Garay, Emma Riccardi, and Matthew Smith. I also thank former members of
the Hannigan Lab, who were always there for me during my early days of graduate school,
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including Alan Stebbins, Katie Flanders, Eric Wilcox-Freeburg, Jeremy Williams, Bryanna
Broadaway, Alex Eisen-Cuadra, Steve Nye, Nicole Henderson, and Aaron Honig.
I would like to express my gratitude to the immense amount of people who have helped
me during this journey. I thank several individuals from the Marine Biological Laboratory,
including Jane Tucker, Sam Kelsey, Inke Forbrich, Joe Vallino, Tyler Messerschmidt, Suzanne
Thomas, Hap Garritt, Rich McHorney, Marshall Otter, Zoe Cardon, and others who, without
question, never hesitated to train me on various analyses or tolerate my taking up space in the
laboratory. In particular, I would like to thank Jane for her immeasurable patience and support as
I anxiously navigated my doctoral studies. After leaving science in question of my career path, it
was involvement with the TIDE project and constant encouragement from Linda Deegan, David
Johnson, and Jimmy Nelson, that rekindled my passion for research. I am still here because of
these individuals, along with other TIDE members that have helped along the way, including
David Behringer, Bethany Williams, Hillary Sullivan, Caitlin Bauer, and Serina Wittyngham.
I am lucky to have worked with and crossed paths with such influential people. My
involvement with the Coastal & Estuarine Research Federation as the student member-at-large
has provided me with invaluable experience that I believe has shaped me into the scientist I am
today. Throughout my internships and undergraduate study, mentors such as Alan Shanks, Erin
“Ezzy” Cooper, Stephanie Schroeder, Jan Hodder, Chris Langdon, Matthew Gray, Itchung
Cheung, and Jon Sun, all influenced my form of thought. Relationships formed during my time
in Hawaii with individuals, such as Barb Bruno from C-MORE, Sherwood Maynard from the
MOP program, and classmates from UH Hilo, continue to shape me today. I would also like to
thank several brilliant women in science, who inspire me daily, perhaps without even knowing:
Robyn Hannigan, Ellen Douglas, Torrie Hanley, Jessica Carilli, Cascade Sorte, Amanda Glazier,
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Jamie Dombach, Casey Lyons, Kristin Osborne, Bonnie Blalock, and so many others. Lastly,
thanks to my closest of friends, Nick and Barbarajean Fountoulakis, who have been by my side
through thick and thin.
Words cannot describe how thankful I am to have such a supportive family. My parents,
Dylan and Georgeen, faced such adversity to provide me with every opportunity possible and I
would not be here today without their hard work and relentless encouragement. My brothers,
Brandon and Connor, continue to inspire me daily as they pursue their individual passions. My
step son, Caleb, is one of the strongest kids I know, preserving through constant challenges. I
thank the Marvells, who instilled in me curiosity and healthy skepticism early on in life. And
lastly, I would like to thank my husband, Shaun, whose support and kindness have been
unwavering since we first met. His love keeps me grounded in this crazy world, and I am certain
this journey would have been much less enjoyable without him.
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Abstract of Dissertation
Atmospheric carbon dioxide (CO2) concentrations continue to rise as a result of fossil
fuel burning and land-use changes, thereby contributing to increases in global temperature, ocean
acidification, and sea level rise. Sequestering some of this excess CO2 in blue carbon habitats,
such as salt marshes, mangroves, and seagrasses, has been proposed as a mitigation strategy due
to their ability to efficiently store carbon. Salt marshes, in particular, store carbon at rates that are
orders of magnitude greater than terrestrial forests due to large inputs of organic matter (OM)
from primary production concurrent with slow decomposition rates; the balance between the two
ultimately determines the burial of OM and carbon storage over time. As nitrogen loading to
coastal waters continues to rise, primarily in the form of nitrate (NO3-), it is unclear what effect it
will have on carbon storage capacity of these systems. This uncertainty is largely driven by the
dual role NO3- can play in biological processes, where it can either serve as a nutrient for primary
production or a powerful electron acceptor fueling heterotrophic microbial metabolism.
Distinguishing between the two is critical, since the former could promote carbon storage by
enhancing fixation, while the latter could potentially deplete this service by stimulating microbial
decomposition.
Using a combination of controlled flow through experiments and field surveys, my
dissertation sought to: 1) determine the importance of NO3- as an electron acceptor in OM
decomposition across different sediment depths, 2) assess whether chronic NO3- enrichment
affected OM burial, and, since microbes are largely responsible for controlling long term carbon
storage, 3) examine microbial community diversity, structure, activity, and assembly of deep salt
marsh sediments spanning over 3000 years of accretion between two sites: an experimentally
enriched marsh and its paired reference marsh. To carry out these objectives, I applied a
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comprehensive set of tools, including 1) biogeochemical measurements of dissolved inorganic
carbon and nutrient concentrations, 2) OM quality measurements, such as % carbon, % nitrogen,
lipid biomarkers concentration, and Fourier Transform-Infrared Spectroscopy, as well as 3)
sequencing of the 16S rRNA gene, its product 16S rRNA, and shotgun metagenomics.
In controlled flow through experiments where I exposed sediment of varying depths and
OM lability to 500 µM NO3-, I observed a 40-45% increase in OM decomposition in response to
NO3- when compared to a seawater control. This pattern persisted at sediment depths typically
considered to be less labile. NO3- altered both the microbial community and its associated
functional potential, selecting for taxa belonging to groups known to reduce NO3- and oxidize
more complex forms of OM, and increasing the abundance of nitrogen cycling genes.
Stimulation in OM decomposition in response to NO3- was not as pronounced in sediments from
sites that had been chronically exposed to NO3-, with the lowest effect size occurring at a site
exposed to sewage effluent for 40 years, suggesting the effect of NO3- on OM decomposition is
limited. These results demonstrate that NO3- can serve as an electron acceptor in microbial
metabolism and may expand the OM pool available to microbial oxidation, effectively reducing
overall carbon storage potential in salt marsh systems, however, OM that is buried under high
NO3- conditions may be more stable over time.
In a field survey examining microbial community diversity, structure, activity, and
assembly of deep salt marsh sediments spanning over 3000 years of accretion between an
experimentally enriched marsh and its paired reference marsh, I found that both microbial
diversity and gene abundance decreased with depth, potentially due to resource limitation, and
observed high rates of inactivity in deeper sediments. Depth and associated changes in OM
explained changes in microbial community structure in shallow (0-50 cm) sediments, but this
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pattern became much less apparent in deeper sediments beyond the rooting zone (60+ cm), likely
due to more stochastic assembly at depth. The only difference between the reference and
enriched marshes occurred in deeper sediments, suggesting that the effect of nutrient enrichment
is not detectable over longer time scales of carbon storage; instead, these differences may be
attributed to stochastic processes resulting from energy limitation in deep subsurface marsh
sediments. Overall, my dissertation highlights the role of NO3- as an electron acceptor in OM
decomposition, and underscores the need to better understand the microbes mediating carbon
storage and how they will respond to nutrient enrichment.
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Table of Contents
Dedication 2
Acknowledgements 3
Abstract of Dissertation 6
Table of Contents 9
List of Figures 11
List of Tables 13
Introduction: 14
Chapter 1: Nitrate addition stimulates microbial decomposition of organic matter 30
in salt marsh sediments
Abstract 30
Introduction 31
Materials and Methods 34
Results 42
Discussion 46
Tables 66
Figures 68
Supplemental Material 77
Chapter 2: Chronic exposure to nutrient enrichment lessens the effect of additional 86
nitrate on organic matter decomposition despite changes to
microbial community structure and activity
Abstract 86
Introduction 86
Materials and Methods 91
Results 101
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Discussion 106
Tables 125
Figures 128
Supplemental Material 138
Chapter 3: Stochastic processes shape microbial communities in deep salt marsh 145
sediments
Abstract 145
Introduction 146
Materials and Methods 151
Results 158
Discussion 164
Tables 183
Figures 185
Supplemental Material 194
Appendix: Nitrate reduction pathways and functional potential in response to 220
nutrient enrichment
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List of Figures
Chapter 1
Figure 1. Dissolved inorganic carbon production over time across three depths 68
Figure 2. Cumulative dissolved inorganic carbon in response to treatment and depth 69
Figure 3. Nitrate reduction rates and cumulative nitrate reduction across depths 70
Figure 4. Sulfide production rates and cumulative production, sulfur storage, and 71
cumulative sulfate reduction across three depths
Figure 5. Ammonium production rates and ammonium production across three depths 72
Figure 6. Ratio of dissolved inorganic carbon to ammonium production in response to 73
nitrate across three depths
Figure 7. Fourier Transform-Infrared Spectra examining organic matter functional 74
groups in response to nitrate across three depths
Figure 8. Microbial community structure and diversity in response to nitrate by depth 75
Figure 9. Heatmap of order-level taxa relative abundance in response to nitrate 76
Figure S1. Schematic of flow through reactor 82
Figure S2. Bromide breakthrough curve 83
Figure S3. Quantitative PCR of 16S rRNA gene by depth and treatment 84
Figure S4. Relative abundance of 20 bacterial orders present across all samples 85
Chapter 2
Figure 1. Map of sites from a gradient of prior nutrient enrichment 128
Figure 2. Boxplot of Index II values and mid-IR spectra of each site along nutrient 129
enrichment gradient
Figure 3. Microbial community structure and order-level relative abundance of 130
taxa most different among sites from nutrient enrichment gradient
Figure 4. Dissolved inorganic carbon production rate over time in response to nitrate 131
Figure 5. Cumulative dissolved inorganic carbon production in response to nitrate 132
Figure 6. Nitrate and sulfate reduction across sites 133
Figure 7. Mid-IR spectra and total dissolved inorganic carbon as a function of Index II 134
Figure 8. Weighted UniFrac similarity across sites in response to nitrate 135
Figure 9. Order-level relative abundance of taxa most different between treatments 136
Figure 10. Order-level activity assessed by 16S rRNA/16S rRNA gene in response to 137
per site
Figure S1. Bromide breakthrough curve 143
Figure S2. Ammonium production over time from sites across a nutrient enrichment 144
gradient
Chapter 3
Figure 1. Map of core locations within reference and enriched sites 185
Figure 2. Organic matter characteristics and age of reference and enriched sites 186
Figure 3. Shannon diversity and 16S rRNA gene/16S rRNA by depth 187
Figure 4. Weighted UniFrac of total microbial community by depth 188
Figure 5. Relative abundance of top 100 ASVs in shallow sediments at class-level 189
Figure 6. Order-level relative abundance of total microbial community by depth 190
Figure 7. Order-level relative abundance of active microbial community by depth 191
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Figure 8. Relative abundance of top 100 ASVs in deep sediments at class-level 192
Figure 9. Standardized effect size of mean pairwise distances by depth and site 193
Figure S1. Weighted UniFrac of active microbial community by depth 219
Appendix
Figure 1. Denitrification and dissimilatory nitrate reduction rates over time per depth 238
Figure 2. Cumulative denitrification and dissimilatory nitrate reduction per depth 239
Figure 3. Relative contribution of denitrification and dissimilatory nitrate reduction to 240
total nitrate consumption rates per depth
Figure 4. Non-metric multidimensional scaling plot of subsystems-level functional 241
annotation by treatment and depth
Figure 5. Heatmap of N-cycling gene abundance in response to nitrate 242
Figure 6. Boxplot of total fatty acids and sub-classes per treatment and depth 243
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List of Tables
Chapter 1
Table 1. Functional group assignments for Fourier Transform-Infrared Spectroscopy 66
Table 2. Organic matter characteristics before and after flow through experiment 67
Table S1. Flow property characteristics of flow through experiment 79
Table S2. Top 30 bacterial orders important in discriminating between treatments 80
Chapter 2
Table 1. Environmental characteristics of three sites along a nutrient enrichment gradient 125
Table 2. Denitrification and dissimilatory nitrate reduction rates per site 126
Table 3. Organic matter characteristics and gene abundance in response to nitrate 127
Table S1. Functional group assignments for Fourier Transform-Infrared Spectroscopy 138
Table S2. Flow property characteristics of flow through experiment 139
Table S3. Relative abundance of order-level taxa most important in discriminating 140
between treatments
Table S4. Taxonomic information for groups that exhibited change in activity 141
Chapter 3
Table 1. Organic matter characteristics from reference and enriched marshes 183
Table 2. Radiocarbon dating for cores from reference and enriched marshes 184
Table S1. Linear mixed effects model results examining the effect of site and depth 194
on organic matter characteristics
Table S2. Linear mixed effects model results examining effect of site, depth, and 195
organic matter characteristics on Shannon diversity
Table S3. Linear mixed effects model results examining effect of site, depth, and 196
organic matter characteristics on total microbial community structure
Table S4. Linear mixed effects model results examining effect of site, depth, and 197
organic matter characteristics on active microbial community structure
Table S5. Top 100 16S rRNA gene ASVs from shallow sediments for both reference 198
and enriched sites
Table S6. Top 100 16S rRNA ASVs from shallow sediments for both reference 201
and enriched sites
Table S7. Taxonomic information for ASVs from 16S rRNA gene most important in 204
explaining patterns by depth in shallow sediments
Table S8. Taxonomic information for ASVs from 16S rRNA most important in 205
explaining patterns by depth in shallow sediments
Table S9. Top 100 16S rRNA gene ASVs from deep reference sediments 207
Table S10. Top 100 16S rRNA gene ASVs from deep enriched sediments 210
Table S11. Top 100 16S rRNA ASVs from deep reference sediments 213
Table S12. Top 100 16S rRNA ASVs from deep enriched sediments 216
Appendix
Table 1. Fatty acid sub-class source and lipid number 236
Table 2. Number of basepairs and quality-filtered sequences from MG-RAST 237
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Introduction
General Introduction
Salt marshes are coastal and estuarine wetlands situated at the intersection between the
land and sea that thrive in protected areas (Redfield 1972) with low wave energy (Allen 2000).
These systems are vital components of coastal systems and provide a number of ecosystem
services, such as shoreline protection (Costanza et al. 2008), nutrient filtration (Valiela & Teal
1974), and support of marine and coastal food webs (Teal 1962). One important ecosystem
service is their ability to store carbon at an average rate of 218 ± 24 g C m-2 yr-1, which is orders
of magnitude greater than terrestrial forests (Mcleod et al. 2011). The ability of salt marshes to
sequester carbon is due to a combination of high rates of primary production and slow
decomposition of anoxic sediments (Reddy & Patrick Jr. 1975). The balance between the two
ultimately determines the rate of organic matter burial. Consequently, salt marshes, and other
“blue carbon” systems (seagrasses and mangroves; Nelleman et al. 2009) have become a major
focus of coastal restoration projects (Warren et al. 2002, Macreadie et al. 2017) as a strategy to
mitigate increasing concentrations of atmospheric carbon dioxide that could otherwise worsen
climate change.
Despite providing an efficient means to store carbon, salt marshes face a number of
anthropogenic-driven threats that can diminish, and potentially reverse, this ecosystem service.
Nitrogen (N) inputs, for instance, have been increasing at an alarming rate (Galloway et al. 2008,
2017), leading to the alteration of coastal biogeochemical cycles via increased primary
production and associated symptoms of eutrophication (Nixon 1995, Rabalais et al. 2009). Salt
marshes have been traditionally viewed as more resilient to the adverse effects of excess N
loading due to their high capacity for removing nutrients (Valiela & Cole 2002), either by
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assimilation into plant biomass (Valiela & Teal 1974) or conversion to gaseous products (NO,
N2, or N2O) via denitrification (Kaplan et al. 1979, Seitzinger 1988, Hopkinson & Giblin 2008)
or anaerobic ammonium oxidation (anammox; Dalsgaard et al. 2005, Koop-Jakobsen & Giblin
2009a); however, we do not fully understand how much N loading salt marshes can withstand,
and whether this can affect the carbon storage capacity of these highly productive systems.
Nutrient enrichment can increase aboveground biomass (Valiela et al. 1975, Kaplan et al.
1979, Morris 1991, Langley et al. 2013), which may facilitate sediment accretion (Morris et al.
2002), augment carbon sink potential, and consequently help salt marshes keep pace with sea
level rise (Kirwan & Megonigal 2013). In other systems, however, nutrient addition can decrease
belowground biomass, potentially leading to loss of sediment stability (Darby & Turner 2008).
There are two mechanisms by which this could occur that are not necessarily mutually exclusive.
The first mechanism is that wetland plants decrease allocation of root material needed to forage
for nutrients as ambient nutrient supply increases, thus allowing them to divert energy away from
belowground production towards aboveground production and photosynthesis (Levin et al.
1989). Consequently, less root biomass is present to stabilize sediments. The second mechanism
is that increased N concentrations leads to increased microbial respiration (Wigand et al. 2009)
and decomposition of belowground organic matter (Deegan et al. 2012). If the addition of N
stimulates subsurface microbial respiration, which oxidizes organic matter to fuel heterotrophic
processes, then the carbon storage capacity of salt marshes could drastically diminish,
highlighting the need to understand these two mechanisms in response to nutrient enrichment.
One factor that may influence which mechanism underlies responses to experimental
marsh fertilization may be the form of N that is applied. Many studies apply N in its reduced
form, as ammonium (NH4+) or urea, or a mix of oxidized and reduced forms (NH4NO3);
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although the primary form of N delivery to the coasts is as nitrate (NO3-), not NH4
+ (Cloern
2001, Galloway et al. 2008). NO3- , however, plays a dual role in nature, either serving as a
nutrient in support of primary production or by acting as an energetically favorable electron
acceptor in microbial oxidation of organic matter through various anaerobic respiration
processes, including denitrification (Kaplan et al. 1979, Seitzinger 1988, Hamersley & Howes
2005) and dissimilatory nitrate reduction to ammonium (An & Gardner 2002, Gardner et al.
2006, Giblin et al. 2013). Distinguishing between the two is critical, since the former could
promote carbon storage by enhancing fixation, and the latter could potentially destroy this
service by stimulating microbial decomposition.
In a long-term, multi-investigator nutrient enrichment experiment conducted in Plum
Island Sound in northeastern Massachusetts, USA (Deegan et al. 2007, 2012), researchers
applied N in the form of NO3- into flooding waters (as opposed to dry application on the marsh
surface, as is typically done in marsh fertilization experiments) to closely mimic realistic nutrient
delivery to salt marsh systems. In contrast to previous salt marsh enrichment studies, the plant
community demonstrated only a mild response to nutrients (Johnson et al. 2016). There was no
significant response of either aboveground biomass or shifts in plant species composition due to
nutrient enrichment, which the authors attributed to the more realistic enrichment conditions and
the form of N used. There may be more energetic costs for plants associated with assimilating
NO3- when compared to NH4
+ (Lambers et al. 1998), resulting in lower uptake kinetics
(Mendelssohn & Morris 2000) and rates (Mozdzer et al. 2011), despite elevated porewater NO3-
concentrations in this enriched system (Johnson et al. 2016). It is possible that instead, the
subsurface microbial community is using much of this excess NO3- to fuel heterotrophic
metabolisms in the absence of oxygen.
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There are several lines of evidence supporting the stimulation of microbial respiration in
response to NO3-. In a creek-scale isotope (15NO3
-) addition experiment comparing N-cycling
between an experimentally enriched marsh and its paired reference marsh, only 7 and 1.5% of
added NO3- was being assimilated by plants and benthic microalgae, respectively, indicating that
< 10% of added NO3- was being used to support primary production (Drake et al. 2009). This
suggests that this excess NO3- must be contributing to some other process. In the same
experimental marshes, Koop-Jakobsen & Giblin (2010) applied a novel push-pull method in
conjunction with isotope pairing (Koop-Jakobsen & Giblin 2009b) to measure N-cycling in the
sediment rhizosphere and found that nutrient addition stimulated denitrification and dissimilatory
nitrate reduction to ammonium, both of which use NO3- as an electron acceptor. Lastly, Deegan
et al. (2012) found that, due to a combination of lower belowground biomass and increased
microbial respiration, nutrient enrichment weakened geomorphic stability, resulting in creek
bank collapse. This degradation may not only decrease the ability of salt marshes to store carbon,
but may also release this carbon that would otherwise be stored for decades (Deegan et al. 2012),
contributing to greenhouse gas release and climate change in unknown ways. These studies
emphasize the role of NO3- in organic matter decomposition, however, in these broad field
surveys, a mechanistic understanding of the role NO3- plays in marsh carbon and nitrogen cycling
cannot be explicitly tested due to the challenge of disentangling competing processes in situ. The
goal of my dissertation, therefore, was to test how NO3- addition affected microbial
decomposition of salt marsh organic matter.
Decomposition of Organic Matter in Salt Marsh Ecosystems:
Belowground decomposition plays a major role in salt marsh ecosystems by recycling
nutrients and controlling carbon dynamics through mineralization of organic material (Howarth
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& Hobbie 1982), ultimately determining rates of marsh accretion and carbon storage. The
microbial degradation of organic matter in salt marshes, however, is slow due to frequent
inundation, since compounds tend to diffuse ten thousand times slower through water than air.
Thus, when microbes rapidly utilize oxygen in the surface sediments, slower diffusion rates lead
to its insufficient replacement and subsequent depletion. This imbalance results in anoxia below
the top few millimeters of salt marsh sediments (Teal & Kanwisher 1961). Once organisms
deplete oxygen, microbial catabolic activity switches to anaerobic respiration, which uses
alternative electron acceptors that are generally less efficient at oxidizing some types of organic
matter (Reddy & Patrick 1975). As a result of these factors, under typical conditions,
decomposition processes tend to be slower, relative to primary production, and organic matter
accumulates, contributing to salt marsh maintenance (DeLaune & Patrick 1980). However,
whether this continues to be true under high nutrient conditions remains unclear.
The microbial decomposition of organic matter depends on the redox potential and
energy yield of the reaction, availability of electron acceptors, quality of organic carbon supply
(electron donator), and other physiochemical parameters (e.g. temperature, water-table level, and
pH; (Brinson et al. 1981, McLatchey & Reddy 1998). Redox potential (Eh) describes the
tendency of a pair of chemical species to undergo a transfer of electrons through a redox
reaction, with one accepting (reduction) and one donating electrons (oxidation). When ordering
electron acceptor half reactions by redox potential, oxygen is at the top with an Eh of +1.27 V
and carbon dioxide is at the bottom with an Eh of +0.21. The higher the Eh value, the stronger
the electron acceptor, and the greater the potential for electron transfer; this pattern explains the
order in which chemical species will reduce available electron acceptors (Berner 1980, Stumm &
Morgan 1996, Canfield et al. 2005). The electron tower, however, does not sufficiently explain
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why organisms use one electron acceptor over another. In reality, the order in which microbes
reduce alternative electrons once anoxia occurs depends partly on the energy yield, or Gibb’s
free energy change (ΔG˚), evolved during the redox reaction. Microbes preferentially reduce
electron acceptors that yield the most energy, leading to a predictable sequence of utilization –
starting with oxygen, and proceeding through manganese, nitrate, iron, sulfate, and carbon
dioxide (Canfield 1993). In salt marsh ecosystems, microbes use carbon rich sediments to fuel
their heterotrophic metabolisms in the absence of oxygen by reducing various electron acceptors,
with nitrate (Kaplan et al. 1979, Hopkinson & Giblin 2008) and sulfate (Jorgensen 1977,
Howarth & Giblin 1983) being the most prominent.
Nitrate and sulfate are more important than manganese and iron in salt marsh sediments
because electron acceptor concentration and carbon quality also control oxidation of organic
matter. Sulfate reduction acts as the dominant metabolic strategy in salt marshes (> 50%;
Jorgensen 1977, Howarth 1984) regardless of its low energy yield (Stumm & Morgan 1996) due
to its large supply from tidal flushing (Howarth & Teal 1979) as well as high proportions of
sulfide that are oxidized back to sulfate (Jorgensen 1982). Nitrate, on the other hand, is more
thermodynamically favorable, releasing more free energy per mole of carbon oxidized (ΔG°H2 =
-420 kJ) than reducing SO42- (ΔG°H2 = -98.9 kJ) (Canfield et al. 2005). Further, this NO3
- can
have multiple fates as a respiratory substrate. In marshes, the two most common fates are
denitrification, where the NO3- is converted to a gaseous end-product that leaves the system, or
dissimilatory nitrate reduction to ammonia (DNRA), where the NO3- is converted to another
bioavailable form, ammonia, and remains in the system (Kaplan et al. 1979, Seitzinger 1988, An
& Gardner 2002, Giblin et al. 2013). Increased NO3- availability, which is typically limiting in
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coastal systems (Ryther & Dunstan 1971), may therefore affect the microorganisms using these
resources, and consequently alter essential carbon and nitrogen cycle processes.
Salt marsh microbial communities
To assess how carbon storage responds to NO3- loading, we need to gain a better
understanding of the microbes that inhabit salt marsh sediments. Recent molecular advances
have significantly improved our ability to understand biogeochemical processes in salt marsh
ecosystems by providing a novel link between the microbial community and the processes they
mediate (Zak et al. 2006). These methods have allowed us to recognize that microbes are the key
organisms responsible for the cycling of carbon and nitrogen in marsh sediments (Benner et al.
1984, Falkowski et al. 2008). We know virtually nothing, however, about which microbes are
performing what services. This is, in part, due to high rates of dormancy in salt marsh sediments.
Previous work (Kearns et al. 2016) found that while N fertilization had no effect on total
microbial diversity (i.e. “who is there”), nutrient enriched marshes demonstrated a significant
loss in potentially active microbial diversity (i.e. “who is actively performing functions”). These
significant changes in the active microbial community, emphasize the need to move beyond
simply identifying microbial taxa through analysis of DNA. We need to examine both the active
community and explore the presence of protein coding genes through metagenomics to more
directly draw linkages between microbial community structure and ecosystem function.
Dissertation overview
In the first two chapters of my dissertation, I used controlled flow through reactor (FTR)
experiments modified from Pallud et al. (2006, 2007) that are uniquely designed to make rate
measurements at steady-state. In contrast to sediment slurries, which are commonly used in
biogeochemical flux measurements, these FTRs allow for the extraction of meaningful kinetic
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rate measurements that can be used to quantitatively describe microbial processes in response to
environmental perturbation, such as nutrient enrichment. The system itself consists of two
Plexiglas® plates sealed with O-rings to prevent leakage, between which I loaded homogenized
salt marsh sediment and manipulated pore water conditions using a peristaltic pump system. By
carefully maintaining a uniform flow rate at steady state, I was able to infer rates of microbial
respiration by monitoring the change in outflow concentrations.
In my first chapter, I used these FTRs to investigate the effect of NO3- exposure on
microbial community structure and decomposition of organic matter at three different depths: 0-5
cm (recently deposited organic matter), 10-15 cm (within the rooting zone), and 20-25 cm
(beyond the rooting zone) in salt marsh sediments (Valiela & Teal 1974, Valiela et al. 1976). My
objective was to determine if the addition of NO3- would stimulate decomposition of salt marsh
sediments due to its role as an energetically favorable electron acceptor in heterotrophic
microbial respiration, with the hypothesis that microbial respiration would increase in response
to NO3-, but to a lesser extent in deeper sediments that contain less labile organic matter. Results
indicated a significant increase in microbial respiration, particularly denitrification, in response
to nitrate, with the most pronounced effects occurring in sediments that ranged from 150 to 200
years old. This is significant because organic matter at this depth is typically thought to be more
resistant to microbial decomposition, suggesting that the capacity of salt marshes to store carbon
may decrease with eutrophication. Additionally, NO3- addition significantly altered the microbial
community and decreased alpha diversity, selecting for taxa belonging to groups known to
reduce nitrate and oxidize more complex forms of organic matter. Together, these results suggest
the presence of a pool of organic matter that microbes can respire only with NO3- present, and
demonstrate that carbon stored for decades in salt marsh sediments are still vulnerable to
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microbial degradation under high nutrient enrichment. This work contributes to our knowledge
base by providing mechanistic evidence for the importance of NO3- as an electron acceptor in
systems with high N loading.
The second chapter of my dissertation built on the first by investigating whether chronic
nutrient enrichment exerted any influence on the effect of additional NO3- exposure on organic
matter decomposition by either causing shifts in the microbial community, altering organic
matter chemistry, or some combination of the two. I hypothesized that sites already receiving
high NO3- inputs for long durations would not demonstrate as much stimulation in organic matter
decomposition in response to further NO3- exposure, because whatever carbon compounds
remain will be relatively resistant to degradation. Any available organic matter will have already
been oxidized due to the consistent availability of NO3- as an electron acceptor fueling
heterotrophic respiration. To test my hypothesis, I exposed sediments from three salt marshes
varying in time and intensity of prior NO3- exposure to additional NO3
- and monitored changes in
biogeochemical parameters and microbial community structure and activity. I found that, while
NO3- addition stimulated decomposition in all sediments when compared to a seawater control,
the effect was smaller at a chronically enriched site receiving sewage effluent for approximately
40 years, despite significant changes to both microbial community structure and activity. This
work suggests that long term nutrient enrichment may lead to less overall carbon storage;
however, the fraction of organic matter that does become buried is more stable when compared
to less eutrophic systems as evidenced by lower microbial respiration rates in response to
additional NO3-. These results highlight the need to consider the effects of chronic nutrient
enrichment when quantifying carbon storage potential in salt marsh systems, especially when
determining effective strategies for effective management and restoration.
23
Finally, my third chapter aimed to explore the microbes present in deep salt marsh
sediments at a larger spatial scale. Salt marshes are critical for storing carbon at rates that are
orders of magnitude greater than their terrestrial counter parts, yet, we know very little about the
microbes that mediate this ecosystem service at depth. There is also considerable evidence that
nutrient enrichment promotes dormancy in salt marsh bacteria (Kearns et al. 2016) and enhances
fungal diversity and abundance (Kearns et al. 2018) in the surface, however it is unclear what
effect NO3- addition has in deeper sediments where long term carbon storage occurs. To address
these knowledge gaps, I collected deep salt marsh cores from two marshes, one that has been
experimentally nutrient-enriched, and its paired reference site, and analyzed organic matter
characteristics and microbial community structure, abundance, and diversity. I found that both
microbial diversity and gene abundance decreased with depth, potentially due to resource
limitation, with evidence for significant rates of inactivity in deeper sediments. Depth and
associated changes in organic matter explained a large portion of microbial community structure
in shallower sediments and was driven by shifts in rare taxa. However, in deeper sediments
beyond the rooting zone, changes to the community could no longer be attributed to parameters I
measured. This pattern was likely due to more stochastic assembly at depth. Overall, this work
provides novel information on the microbes mediating carbon cycling in these critical
ecosystems and highlights the need to further examine these highly diverse microbial
communities at depth.
In conclusion, my dissertation work contributes three major findings to the literature: 1)
In addition to serving as a nutrient, NO3- can serve as an electron acceptor in metabolism and
may expand the organic matter pool available to microbial oxidation, effectively reducing overall
carbon storage potential in salt marsh systems 2) the effect of NO3- on organic matter
24
decomposition is limited, however, because any accessible forms of organic matter that become
available under high nutrient enrichment will eventually be depleted and 3) the effect of nutrient
enrichment is not detectable over longer time scales of carbon storage. Overall, my work
highlights the need to further understand the genetic machinery behind the microbes mediating
carbon storage in salt marsh sediments, how they respond to NO3- addition, and what this means
for the critical ecosystem services salt marshes provide.
25
References
Allen J (2000) Morphodynamics of Holocene salt marshes: a review sketch from the Atlantic and
Southern North Sea coasts of Europe. Quat Sci Rev 19:1155–1231
An S, Gardner WS (2002) Dissimilatory nitrate reduction to ammonium (DNRA) as a nitrogen
link, versus denitrification as a sink in a shallow estuary (Laguna Madre/Baffin Bay,
Texas). Mar Ecol Prog Ser 237:41–50
Benner R, Newell SY, Maccubbin AE, Hodson RE (1984) Relative contributions of bacteria and
fungi to rates of degradation of lignocellulosic detritus in salt-marsh sediments. Appl
Environ Microbiol 48:36–40
Berner R (1980) Early Diagenesis. Princeton University Press, Princeton, NJ
Brinson MM, Lugo AE, Place J (1981) Primary productivity, decomposition, and consumer
activity in freshwater wetlands. Annu Rev Ecol Syst 12:123–161
Canfield D (1993) Organic matter oxidation in marine sediments. In: Wollast R., Mackenzie
F.T., Chou L. (eds), Interactions of C, N, P, S Biogeohemical Cycles and Global
Change. NATO ASI Series (Series I: Global Environmental Change), vol 4. Springer,
Berlin, Heidelberg
Canfield DE, Thamdrup B, Kristensen E (2005) Aquatic Geomicrobiology. Elsevier Academic
Press, Boston, MA
Cloern JF (2001) Our evolving conceptual model of the coastal eutrophication problem. Mar
Ecol Prog Ser 210:223–253
Costanza R, Perez-maqueo O, Martinez ML, Sutton P (2008) The value of coastal
wetlands for hurricane protection. Ambio 37:241-248
Dalsgaard T, Thamdrup B, Canfield DE (2005) Anaerobic ammonium oxidation (anammox) in
the marine environment. Res Microbiol 156:457–64
Darby FA, Turner RE (2008) Effects of eutrophication on salt marsh root and rhizome biomass
accumulation. Mar Ecol Prog Ser 363:63–70
Deegan LA, Bowen JL, Drake D, Fleeger JW, Friedrichs CT, Galván KA, Hobbie JE, Hopkinson
C (2007) Susceptibility of salt marshes to nutrient enrichment and predation removal.
Ecol Appl 17:42–63
Deegan LA, Johnson DS, Warren RS, Peterson BJ, Fleeger JW, Fagherazzi S, Wollheim WM
(2012) Coastal eutrophication as a driver of salt marsh loss. Nature 490:388–392
DeLaune R, Patrick Jr W (1980) Rate of sedimentation and its role in nutrient cycling in a
26
Lousiana salt marsh. Estuar Wetl Process 11:401–412
Drake ADC, Peterson BJ, Galván KA, Deegan LA, Hopkinson C, Johnson M, Lemay LE, Picard
C, Peterson J, Deegan A, Drake C, Michael J (2015) Salt marsh ecosystem
biogeochemical responses to nutrient enrichment: A paired 15N tracer study. Ecology
90:2535–2546
Falkowski PG, Fenchel T, Delong EF (2008) The microbial engines that drive Earth’s
biogeochemical cycles. Science 320:1034–1039
Galloway JN, Leach AM, Erisman JW, Bleeker A (2017) Nitrogen: the historical progression
from ignorance to knowledge with a view to future solutions. Soil Res 55:417–424
Galloway JN, Townsend AR, Erisman JW, Bekunda M, Cai Z, Freney JR, Martinelli LA,
Seitzinger SP, Sutton MA (2008) Transformation of the nitrogen cycle: Recent trends,
questions, and potential solutions. Science 320:889-892
Gardner WS, McCarthy MJ, An S, Sobolev D, Sell KS, Brock D (2006) Nitrogen fixation and
dissimilatory nitrate reduction to ammonium (DNRA) support nitrogen dynamics in
Texas estuaries. Limnol Oceanogr 51:558–568
Giblin A, Tobias C, Song B, Weston N, Banta G, Rivera-Monroy V (2013) The importance of
dissimilatory nitrate reduction to ammonium (DNRA) in the nitrogen cycle of coastal
ecosystems. Oceanography 26:124–131
Hamersley MR, Howes BL (2005) Coupled nitrification–denitrification measured in situ in a
Spartina alterniflora marsh with a 15NH4+ tracer. Mar Ecol Prog Ser 299:123–135
Hopkinson CS, Giblin AE (2008) Nitrogen dynamics of coastal salt marshes. In: Capone D,
Bronk D, Mulholland M, Carpenter E (eds) Nitrogen in the Marine Environment, 2nd
edition. Academic Press, Burlington, MA, p 991–1036
Howarth RW (1984) The ecological significance of sulfur in the energy dynamics of salt
marsh and coastal marine sediments. Biogeochemistry 1:5–27
Howarth RW, Giblin A (1983) Sulfate reduction in the salt marshes at Sapelo Island, Georgia.
Limnol Oceanogr 28:70–82
Howarth R, Hobbie J (1982) The regulation of decomposition and heterotrophic microbial
activity in salt marsh soils: A review. In: VS K (ed) Estuarine Comparisons. Academic
Press, Inc, New York, NY, p 183–208
Howarth RW, Teal JM (1979) Sulfate reduction in a New England salt marsh. Limnol Oceanogr
24:999–1013
Johnson DS, Warren RS, Deegan LA, Mozdzer TJ (2016) Saltmarsh plant responses to
27
eutrophication. Ecol Appl 26:2647–2659
Jorgensen BB (1977) The sulfur cycle of a coastal marine sediment (Limfjorden, Denmark).
Limnol Oceanogr 22:814–832
Jorgensen BB (1982) Mineralization of organic matter in the sea bed the role of sulfate
reduction. Nature 296:643–645
Kaplan W, Valiela I, Teal JM (1979) Denitrification in a salt marsh ecosystem. Limnol Oceanogr
24:726–734
Kearns PJ, Angell JH, Howard E, Deegan LA, Stanley RH, Bowen JL (2016) Nutrient
enrichment induces high rates of dormancy and decreases diversity of active bacterial
taxa. Nat Commun 7: 12881
Kearns P, Bulseco-McKim A, Hoyt H, Angell J, Bowen J (2018) Nutrient enrichment alters salt
marsh fungal communities and promotes putative fungal denitrifiers. Microb Ecol In
press.
Kirwan ML, Megonigal JP (2013) Tidal wetland stability in the face of human impacts and sea-
level rise. Nature 504:53–60
Koop-jakobsen K, Giblin AE (2009) New approach for measuring denitrification in the
rhizosphere of vegetated marsh sediments. Limnol Oceanogr Meth 7:626–637
Koop-Jakobsen K, Giblin AE (2009) Anammox in tidal marsh sediments: The role of salinity,
nitrogen loading, and marsh vegetation. Estuar Coasts 32:238–245
Koop-Jakobsen K, Giblin AE (2010) The effect of increased nitrate loading on nitrate reduction
via denitrification and DNRA in salt marsh sediments. Limnol Oceanogr 55:789–802
Lambers H, Chapin III S, Pons T (1998) Plant Physiological Ecology. Springer, Berlin Germany
Langley J, Mozdzer TJ, Shepard KA, Hagerty SB, Patrick Megonigal J (2013) Tidal marsh plant
responses to elevated CO2, nitrogen fertilization, and sea level rise. Glob Chang Biol
19:1495–1503
Levin SA, Mooney HA, Field C (1989) The dependence of plant root:shoot ratios on internal
nitrogen concentration. Ann Bot 64:71–75
Macreadie PI, Nielsen DA, Kelleway JJ, Atwood TB, Seymour JR, Petrou K, Connolly RM,
Thomson ACG, Trevathan-Tackett SM, Ralph PJ (2017) Can we manage coastal
ecosystems to sequester more blue carbon? Front Ecol Environ 15:206–213
McLatchey G, Reddy K (1998) Regulation of organic matter decomposition and nutrient
release in a wetland soil. J Environ Qual 27:1268–1274
28
Mcleod E, Chmura GL, Bouillon S, Salm R, Björk M, Duarte CM, Lovelock CE, Schlesinger
WH, Silliman BR (2011) A blueprint for blue carbon: toward an improved understanding
of the role of vegetated coastal habitats in sequestering CO2. Front Ecol Environ 9:552–
560
Mendelssohn I, Morris J (2000) Ecophysiological controls on the growth of Spartina alterniflora.
In: Weinstein N, Kreeger D (eds) Concepts and controversies in tidal marsh ecology.
Kluwer, Dordrecht, p 59–80
Morris JT (1991) Effects of nitrogen loading on wetland ecosystems with particular reference to
atmospheric deposition. Annu Rev Ecol Syst 22:257–279
Morris JT, Sundareshwar PV, Nietch CT, Kjerfve B, Cahoon DR (2002) Responses of coastal
wetlands to rising sea level. Ecology 83:2869–2877
Mozdzer TJ, Kirwan M, McGlathery KJ, Zieman JC (2011) Nitrogen uptake by the shoots of
smooth cordgrass Spartina alterniflora. Mar Ecol Prog Ser 433:43–52
Nelleman C, Corcoran E, Duarte C, Valdes L, Young C De, Foncesa L, Grimsditch G (2009)
Blue carbon. A rapid response assessment. United Nations Environment Programme.
Birkelant: GRID-Arendal
Nixon S (1995) Coastal marine eutrophication: A definition, social causes, and future concerns.
Ophelia 41:199–219
Pallud C, Cappellen P Van (2006) Kinetics of microbial sulfate reduction in estuarine sediments.
Geochim Cosmochim Acta 70:1148–1162
Pallud C, Meile C, Laverman a. M, Abell J, Cappellen P Van (2007) The use of flow-through
sediment reactors in biogeochemical kinetics: Methodology and examples of
applications. Mar Chem 106:256–271
Rabalais NN, Turner RE, Dı RJ, Justic D (2009) Global change and eutrophication of coastal
waters. ICES J Mar Sci 66:1528–1537
Reddy K, Patrick Jr. W (1975) Effect of alternate aerobic and anaerobic conditions on redox
potential, organic matter decomposition, and nitrogen loss in a flooded soil. Soil Biol
Biochem 7:87–94
Redfield AC (1972) Development of a New England salt marsh. Ecol Monogr 42:201–
237
Ryther JH, Dunstan WM (1971) Nitrogen, phosphorus, and eutrophication in the coastal marine
environment. Science 171:1008–1013
29
Seitzinger SP (1988) Denitrification in freshwater and coastal marine ecosystems: Ecological
and geochemical significance. Limnol Oceanogr 33:702–724
Stumm W, Morgan J (1996) Aquatic Chemistry: An Introduction to Emphasizing Chemical
Equilibria in Natural Waters, 2nd edition. Wiley Interscience, New York, NY
Teal JM (1962) Energy flow in the salt marsh ecosystem of Georgia. Ecology 43:614–624
Teal J, Kanwisher J (1961) Gas exchange in a Georgia salt marsh. Limnol Oceanogr 6:388–399
Valiela I, Cole ML (2002) Comparative evidence that salt marshes and mangroves may protect
seagrass meadows from land-derived nitrogen loads. Ecosystems 1:92–102
Valiela I, Teal JM (1974) Nutrient limitation in salt marsh vegetation. In: Mold RJ, Queen WH
(eds) Ecology of Halophytes. Elsevier, College Park, MD
Valiela I, Teal J, Sass W (1975) Production and dynamics of salt marsh vegetation and the
effects of experimental treatment with sewage sludge. Biomass, production and species
composition. J Appl Ecol 12:973–981
Valiela I, Teal JM, Persson NY (1976) Production and dynamics of experimentally enriched salt
marsh vegetation: Belowground biomass. Limnol Oceanogr 21:245-252
Warren RS, Fell PE, Rozsa R, Brawley AH, Orsted AC, Olson ET, Swamy V, Niering WA
(2002) Salt marsh restoration in Connecticut: 20 years of science and management.
Restor Ecol 10:497–513
Wigand C, Brennan P, Stolt M, Holt M, Ryba S (2009) Soil respiration rates in coastal
marshes subjest to increasing watershed nitrogen loads in Southern New England, USA.
Wetlands 29:952–963
Zak DR, Blackwood CB, Waldrop MP (2006) A molecular dawn for biogeochemistry. Trends
Ecol Evol 21:288–95
30
Chapter 1: Nitrate addition stimulates microbial decomposition of organic matter in salt
marsh sediments
In collaboration with: Anne E. Giblin, Jane Tucker, Anna E. Murphy, Jonathan Sanderman, and
Kenly Hiller-Bittrolff
Abstract
Salt marshes store carbon at rates that are more than an order of magnitude greater than
their terrestrial counterparts, helping to mitigate negative consequences of climate change. As
nitrogen loading to coastal waters continues to rise, primarily in the form of nitrate, it is unclear
what effect it will have on carbon storage capacity of these highly productive systems. This
uncertainty is largely driven by the dual role nitrate can play in biological processes, where it can
serve as either a nutrient that stimulates primary production or a thermodynamically favorable
electron acceptor fueling heterotrophic metabolism. Here, I used a controlled flow through
reactor experiment to test the role of nitrate as an electron acceptor, and its effect on organic
matter decomposition and the associated microbial community in salt marsh sediments. I
observed a significant increase in organic matter decomposition in response to nitrate and found
that this pattern persisted even at sediment depths typically considered to be less labile. Nitrate
addition significantly altered the microbial community and decreased alpha diversity, selecting
for taxa belonging to groups known to reduce nitrate and oxidize more complex forms of organic
matter. Fourier Transform-Infrared Spectroscopy data further supported these results, suggesting
that nitrate facilitated decomposition of complex organic matter compounds into more labile
forms. Taken together, these results suggest the existence of organic matter pools that only
become accessible with nitrate and would otherwise remain stable. The existence of such pools
could have important implications for carbon storage, since greater decomposition rates may
result in less overall burial of organic-rich sediment. Given the extent of nitrogen loading along
31
our coastlines, it is imperative that we better understand the resilience of salt marsh systems to
nutrient enrichment, especially if we hope to rely on salt marshes, and other blue carbon systems,
for long-term carbon storage.
Introduction
Carbon dioxide (CO2) concentrations continue to rise as a result of fossil fuel burning and
land-use changes, thereby contributing to increases in global temperature, ocean acidification,
and sea level rise. While a number of mitigation strategies have been proposed, recent emphasis
has been placed on sequestering CO2 in blue carbon habitats (Dargusch & Thomas 2012), which
include salt marshes, mangroves, and seagrass meadows (Nelleman et al. 2009, Mcleod et al.
2011). Salt marshes are particularly efficient at storing carbon due to high levels of primary
production, the ability to trap organic rich sediments (Chmura et al. 2003), and low rates of
microbial decomposition due to largely anaerobic conditions below the first few millimeters of
the surface (Reddy & Patrick Jr. 1975). They can bury carbon at a rate more than an order of
magnitude greater than that of their terrestrial counterparts, over time scales of thousands of
years (Duarte et al. 2005, Mcleod et al. 2011). As such, they have become a major focus of
coastal restoration projects (Warren et al. 2002, Macreadie et al. 2017).
Salt marshes face several anthropogenically-driven threats that can diminish, and
potentially reverse, their capacity to store carbon. Here, I focus on the role of coastal nitrogen
(N) inputs, which continue to increase in many systems due to fertilizer production, agricultural
and urban runoff, enriched groundwater, and atmospheric deposition (Galloway et al. 2017).
While salt marshes can remove some of this anthropogenic N before entry into the coastal ocean,
either by assimilation into plant biomass (Valiela & Teal 1974) or conversion to gaseous
32
products (NO, N2, N2O) via denitrification or anammox (Kaplan et al. 1979, Hopkinson & Giblin
2008, Koop-Jakobsen & Giblin 2009), it is unclear how much N loading salt marshes can
withstand without having negative implications for carbon storage. In general, salt marshes are
more resilient to N loading when compared to other coastal systems because of their ability to
efficiently remove N (Valiela & Cole 2002). There is considerable evidence that nutrient
enrichment stimulates aboveground primary production (Kaplan et al. 1979, Morris et al. 2002,
Vivanco et al. 2015), which facilitates sediment trapping and marsh accretion (Morris et al.
2002) and augments the carbon sink potential by adding biomass. Other studies have also
observed increased belowground production in response to elevated N (Pastore et al. 2017). In
some systems, however, responses to N enrichment diminished carbon storage capacity,
including lost root biomass, increased belowground microbial respiration, and changes in species
composition, all of which can result in lower sediment stability and potential marsh collapse
(Deegan et al. 2012, Langley et al. 2013). Due to these complexities, the exact response of the
marsh carbon storage capacity to increased N loading remains unclear.
One plausible explanation for conflicting observations among marsh fertilization
experiments may be the form of N that is applied. Many studies cover small spatial scales and
apply N in its reduced form, ammonium (NH4+) or urea; although some use a mix of oxidized
and reduced forms, ammonium nitrate (NH4NO3). In contrast, much of the N delivered to the
coastal zone occurs in its oxidized form, nitrate (NO3-) (Galloway et al. 2008). In addition to
supporting primary production through assimilation by marsh vegetation, benthic microalgae,
and phytoplankton, NO3- can also serve as an energetically favorable electron acceptor to fuel
microbial oxidation of organic matter (OM) through various anaerobic respiration processes,
including denitrification (Kaplan et al. 1979, Hamersley & Howes 2005) and dissimilatory
33
nitrate reduction to ammonium (DNRA; Rysgaard et al. 1996, An & Garnder 2002, Giblin et al.
2013). Sulfate (SO42-) is another important electron acceptor in salt marsh sediments, accounting
for up to 70-90% of total sediment respiration (Howarth 1984, Howarth & Teal, 1979) due to its
virtually unlimited supply from incoming seawater. However, these two electron acceptors are
different thermodynamically in that reducing NO3- releases more free energy (ΔG°H2 = -420 kJ)
than reducing SO42- (ΔG°H2 = -98.9 kJ) (Canfield et al. 2005). Increased NO3
- availability, which
is typically limiting in coastal systems (Ryther & Dunstan 1971), may therefore affect the
microorganisms using these resources, and consequently alter the ecosystem functions they
mediate.
The mechanisms by which this change in function could occur include: 1) a shift in total
microbial community structure to an alternative state better fit for a high NO3- environment
through change in electron acceptor availability 2) alteration of metabolic capacity of the
existing microbial community to N-cycling metabolisms due to high physiological plasticity, or
3) some combination of the two (Meyer et al. 2004, Allison & Martiny 2008, Shade et al. 2012).
Considering the fundamental role microbes play in carbon decomposition, and more indirectly,
long-term carbon storage (Benner et al. 1984, Falkowski et al. 2008), it is essential that we tease
apart which of these mechanisms control microbial and ecosystem response to NO3- addition.
Regardless of the mechanism, prior studies in salt marsh systems suggest functional responses to
NO3- do occur (Koop-Jakobsen & Giblin 2010, Deegan et al. 2012). When compared to SO4
2-
reducers, NO3- reducers, as well as other microbes adapted to high N environments (Treseder et
al. 2011) may oxidize more complex forms of OM (Achtnich et al.1995), potentially resulting in
decomposition of OM that would have otherwise remained stable.
34
To better quantify the role of marshes in long-term carbon storage it is critical to
understand how these systems respond to increasing NO3- concentrations. In this study, I
investigate whether NO3- addition increases decomposition of salt marsh OM. To explicitly
address this question, I implemented a controlled flow through reactor (FTR) experiment, where
I exposed salt marsh sediments to elevated levels of NO3-. I hypothesized that the addition of
NO3- would stimulate the decomposition of OM when compared to unamended sediments, and
that these experiments would reveal the presence of a “NO3- accessible” pool of OM that
microbes could only oxidize in the presence of this more favorable electron acceptor. I also
examined whether depth and age of OM would play a role in the salt marsh sediment response to
NO3- addition. Specifically, I hypothesized that there would be little difference in decomposition
between the NO3- and unamended treatments in shallow sediments, since the OM there would be
recently deposited and relatively labile, making it accessible for both SO42- and NO3
- reduction.
Further, I hypothesized that there would be an overall reduction in decomposition in deeper
sediments, where OM lability decreases and becomes less amenable to microbial oxidation, but
that there would be a greater stimulation of decomposition at depth in the NO3- treatment
compared to the unamended sediments. Lastly, I hypothesized that these changes in metabolic
function would result from a shift in the microbial community towards taxa better adapted to use
NO3- in metabolic functions, such as denitrification and DNRA.
2. Materials and methods
2.1 Sample collection
I assessed the effect of NO3- on the decomposition of sediment OM of varying ages by
collecting samples along a depth gradient from salt marsh sediments located in West Creek, part
35
of a marsh complex located in Plum Island Sound, MA (42.759 N, 70.891 W). West Creek is a
relatively pristine reference site monitored as part of a long-term nutrient enrichment experiment
called the TIDE project (Deegan et al. 2007). I collected three replicate cores (5 cm diameter and
30 cm deep) from the tall ecotype of Spartina alterniflora, a habitat that floods daily and is
underwater approximately 35% of the time (Deegan et al. 2007). I sectioned each core into
shallow (0-5 cm), mid (10-15 cm), and deep (20-25 cm) sediments and homogenized sections
under anoxic conditions. I chose these depths to include OM of varying quality, ranging from
relatively newly deposited OM (shallow), to older OM found both within (mid) and beyond
(deep) the rooting zone (Valiela & Teal 1974, Valiela et al. 1976). Based on accretion rates taken
from nearby sites, I estimate that these sediments range from 50 to 100 years in age (Forbrich et
al. 2018). Before proceeding, I removed any root material visible to the naked eye from the
homogenized cores, standardized across all cores by total time searching. I then split each
sectioned depth into a plus-NO3- and an unamended treatment (filtered seawater). This resulted in
three replicates for each treatment at each depth.
2.2 Flow through reactors and experimental design
The flow-through reactor experimental system (Fig. S1) is a modified version of the
system described in Pallud et al. (2007) and Pallud & Van Cappellen (2006). In contrast to
whole-core batch incubations or sediment slurries, flow-through reactors provide biogeochemical
rate measurements at steady-state conditions and prevent dissolved metabolic byproducts from
accumulating in the system. Each flow-through reactor has a volume of 31.81 cm3 and consists of
two Plexiglas® caps that are radially scored for uniform flow. I confirmed unilateral,
homogenous flow in each reactor using the conservative tracer, bromide, in breakthrough
36
experiments (see supplemental methods for details and supplemental Table S1 and Fig. S2 for
flow property results).
Under anoxic conditions I loaded each reactor with homogenized sediment, and randomly
assigned each reactor a treatment, plus-NO3- (+NO3
- in 0.2 µm filtered seawater) or unamended
(0.2 µm filtered seawater only, representing natural salt marsh conditions). To prepare the two
treatment reservoirs, I filtered (0.2 µm) water collected from Woods Hole, MA, sparged each
with N2 gas for approximately 20 minutes until they reached anoxic conditions, and spiked the
NO3- reservoir with 500 µmol L-1 additional K15NO3
- (Cambridge Isotope Laboratories, Andover,
MA). 500 µmol L-1 is high when compared to natural conditions, ranging from approximately 2-
5 times higher than porewater concentrations found in nutrient enriched marshes (e.g. Negrin et
al. 2011, Peng et al. 2016). However, my goal was to compare sediment OM decomposition
under NO3- enriched and unamended conditions rather than to compare to field rates. Therefore,
it was critical that NO3- be available to microbes through the entire thickness of sediment in each
reactor of the enriched treatment. I initially added 350 µmol L-1 for the first 25 days, but found
that all the NO3- was being consumed. At this point, I increased the concentration top 500 µmol
L-1.
Half of the reactors received the plus-NO3- treatment and half received the unamended
treatment, both at a targeted flow rate of approximately 0.08 mL min-1 (see Table S1 for
measured flow rate) using peristaltic pumps rigged with 0.89 mm (inner-diameter) MasterFlex
FDA viton tubing (Cole Parmer, IL, USA). I then carried out a 92-day experiment under anoxic
conditions in a glove bag flushed with nitrogen. Once the FTRs reached steady state at the 10-
day mark, I collected samples from both the reservoirs and the effluent throughout the
experiment to measure changes in biogeochemical parameters and to monitor flow rate. To
37
assess changes in OM composition and microbial community structure, I homogenized and
aliquoted bulk sediment from the start of the experiment (pre) and from sediment in each reactor
at the end of the experiment. I dried bulk sediments overnight at 65ºC before freezing at -20°C,
and immediately flash froze additional aliquots of sediments in liquid nitrogen for nucleic acid
extraction and stored them at -80°C until further analysis.
2.3 Biogeochemical and OM analyses
I collected water samples approximately every 10 days from both the plus-NO3- and the
unamended reservoir along with all reactor outflows to measure biogeochemical processes
resulting from microbial activity. Samples for DIC, sulfide and gases were collected in glass
tubes placed in-line in the outflow with no head space. To assess total microbial respiration, I
measured dissolved inorganic carbon (DIC; CO2 + HCO3 + CO32-) on an Apollo SciTech AS-C3
DIC analyzer (Newark, DE) following methods in Dickson & Goyet (1994). I measured nitrate +
nitrite (NO3- + NO2
-) via chemiluminescence on a Teledyne T200 NOx analyzer (Teledyne API,
San Diego, CA) following methods outlined in Cox (1980), and measured ammonium (NH4+)
and sulfide colorimetrically on a Shimadzu 1601 spectrophotometer (Kyoto, Japan) following
protocols from Solorzano (1969) and Gilboa-Garber (1971), respectively. To calculate
production and consumption rates of each analyte (DIC, 3-, NH4
+, and sulfide) over time, I
calculated the difference in concentration between the inflow (reservoir) and the outflow
(effluent), corrected for flow rate in L hr-1, and divided by reactor volume (31.81 cm-3) for each
sampling point. Because I was not able to measure changes in SO42- due to high seawater
concentrations and proportionally minor changes resulting from experimental conditions, I
determined that sulfate reduction was occurring through the production of sulfide (HS-) and
38
calculated total sulfate reduction rates (SRR) by taking the sum of HS- produced and total S
storage measured at the end of the experiment (described below). I also calculated the DIC:NH4+
ratio to draw general inferences about OM pools being decomposed based on C:N stoichiometry.
To assess geochemical changes in OM, I dried samples at 65°C and fumed samples with
12N HCl before performing elemental composition analysis (percent carbon and nitrogen) on a
Perkin Elmer 2400 Series Elemental Analyzer (Perkin Elmer, Billerica, MA) using acetanilide as
a standard. I dried additional samples at 105°C overnight to obtain water content and used these
data to calculate bulk density of each reactor assuming a volume of 31.81 cm3. Lastly, I obtained
percent sulfur (%S) by combusting dried samples at 1350°C and measuring sulfur dioxide (SO2)
production on a LECO S635 S analyzer (LECO Corporation, Saint Joseph, MI).
To further characterize changes in OM as a result of NO3- addition, I used Fourier-
Transform-Infrared Spectroscopy (FT-IR), a technique that provides rapid, detailed information
about the relative abundance of chemical functional groups. To prepare samples for FT-IR
analysis, I finely ground sediment dried at 40°C for 48 hours. I ran each sample on a Bruker
Vertex 70 Fourier Transform Infrared Spectrometer (Bruker Optics Inc., Billerica, MA) outfitted
with a Pike AutoDiff diffuse reflectance Accessory (Pike Technologies, Madison, WI) and
obtained data as pseudo-absorbance (log[1/reflectance]) in diffuse reflectance mode. I collected
data at a 2 cm-1 resolution with 60 co-added scans per spectrum at the mid-IR range, from 4000-
400 cm-1, using a mirror for background correction. Resulting raw spectra were transformed
using a calculated two-point linear tangential baseline using Unscrambler X (Camo Software,
version 10.1, Woodbridge, NJ) and then assigned peaks according to Margenot et al. (2015) and
Parikh et al. (2014).
39
2.4 Nucleic acid extraction, amplification, and amplicon sequencing
I extracted genomic DNA from approximately 0.25 g wet sediment using the MoBio®
PowerSoil DNA Isolation Kit (MoBio Technologies, CA, USA) following manufacturer’s
instructions, and eluted the DNA into a 35 µL final volume. I amplified in triplicate the V4
region of the 16S rRNA gene using the general bacterial primer-pair 515F (5’-
GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’)
(Caporaso et al. 2011) with Illumina adaptors (Caporaso et al. 2012) and individual 12-bp GoLay
barcodes on the reverse primer, using the following reaction: 10 µl 5-Prime Hot Master Mix
(Quanta Bio, Beverly, MA), 0.25 µl of 20 M forward and reverse primers, 13.5 µL DEPC-
treated water, and 1 µl of DNA template. PCR cycling conditions follow those outlined by the
Earth Microbiome Project (Caporaso et al. 2011). Although these primers are biased against the
SAR11 group (Apprill et al. 2015), these bacterioplankton are aerobic (Giovannoni 2017), and
should not play a large role in the microbial community associated with my anoxic experimental
conditions. Prior to sequencing, I gel-purified the pooled PCR product using a Qiagen®
QIAquick gel purification kit (Qiagen, Valencia, CA) and quantified the resulting purified
product using a Qubit® 3.0 fluorometer (Life Technologies, Thermo Fisher Scientific, Waltham,
MA). After pooling to equimolar concentrations, I performed sequencing on the Illumina MiSeq
(Illumina, San Diego, CA) platform using a 300-cycle kit and V2 chemistry. All reads are
deposited in the NCBI Sequence Read Archive under accession number TBD.
I quantified the abundance of 16S rRNA gene copies by performing quantitative PCR
(qPCR) in triplicate using 357F and 519R primers (Turner et al. 1999) and the following 20 µl
reaction: 10 µl Brilliant III Ultrafast SYBR Green qPCR Master Mix (Agilent Technologies), 0.5
µl of each primer (20 µM), 8 µl of DEPC-treated water, and 1 µl DNA template. I ran qPCR on
40
an Aria Mx Real Time PCR instrument (Agilent Technologies) using a program optimized for
16S rRNA gene targets, which consisted of an initial denaturation step at 95ºC for 3 minutes, and
40 cycles of 95ºC for 5 seconds and 60ºC for 10 seconds. I acquired data at the end of each cycle
and conducted a melt curve to confirm the size of the target gene amplification product. I ran
standard curves with each sample batch using stock from purified 16S rRNA gene product that I
quantified on a tape station (Agilent Technologies), resulting in an R2 > 0.95 and qPCR
efficiency >90%.
2.5 Statistical analyses
To investigate changes in DIC production over time, I performed a linear regression on
each core using time as the explanatory variable. I integrated between sampling points to
calculate the cumulative flux across the length of the experiment and tested for significant
differences in DIC, NH4+ production, sulfur storage, and total SO4
2- reduction, as a function of
treatment, depth, and their interaction using a two-way ANOVA. For both NO3- consumption
and sulfide production, both of which were only detectable in one of the two treatments, I
assessed differences among depths using a one-way ANOVA. To account for differences in bulk
carbon supply on DIC production, I also calculated total carbon loss by taking the proportion of
carbon released as DIC divided by the total mass of carbon per reactor using sediment
characteristic data (e.g. water content and %C).
To assess changes in %C, %N, bulk density, and %S throughout the experiment, I
calculated the difference between initial and final sediments per core and compared these relative
changes among treatment and depth using a two-way ANOVA. I performed principal coordinate
analysis (PCoA) across the entirety of the FT-IR spectra and used a PERMANOVA with 999
41
permutations to test for significant differences by treatment and depth using Manhattan distance
to construct the resemblance matrix from the FT-IR data. To further visualize trends in these
data, I also plotted the Pearson’s correlation coefficients against wavenumber to determine which
spectral bands best explained the distribution of sample scores in the PCoA based on functional
group assignments in Table 1. Lastly, I calculated a relative recalcitrance index according to the
following equation:
Eq. 1 Index II = 2924 + 2850 + 1650 + 1470 + 1405 + 920 + 840
3400 + 1270 + 1110 + 1080
where each value represents a wavenumber (Table 1) corresponding to either a carbon
(numerator) or oxygen-bonded (denominator) functional group. Higher Index II values are
typically associated with greater OM recalcitrance (Ding et al. 2002, Veum et al. 2014). I used a
two-way ANOVA to compare Index II values to infer relative recalcitrance as a function of
treatment and depth.
To investigate bacterial community composition, I analyzed sequence data in QIIME 2
(version 2017.12; Caporaso et al. 2010, QIIME 2 Development Team). I demultiplexed a total of
1,521,493 16S rRNA gene sequences across all samples and inferred amplicon sequence variants
(ASVs) using the DADA2 plugin (Callahan et al. 2016) with a maxEE of 2 and the consensus
chimera removal method. Quality filtering resulted in an average of 37,381 (± 6,693) sequences
per sample. I then assigned taxonomy with the Greengenes 16S rRNA sequence database
(version 13-8; McDonald et al. 2012) and removed ASVs occurring only once (singletons) and
any sequences matching chloroplasts and mitochondria. After aligning sequences using MAFFT
v7 (Katoh & Standley 2013), I performed beta diversity analysis with weighted UniFrac
(Lozupone et al. 2011) on ASV tables normalized to 22,999 sequences (which was my lowest
42
sequencing depth), and tested for significant differences among treatments and depth using
PERMANOVA with 999 permutations. To examine within sample diversity, I calculated a
Shannon diversity index with these normalized data and tested for differences across treatment
and depth using a two-way ANOVA. I ran a random forest model from the randomForest R
package (v4.6-12; Liaw & Wiener 2002) using 10,001 trees on a filtered feature table containing
ASVs present at least 100 times (186 ASVs total) to identify taxa most important in classifying
between plus-NO3- and unamended treatments, and confirmed model results by examining the
out-of-bag error rate (a method that uses bootstrap aggregation to assess performance without a
training set) and leave-one-out cross-validation with 999 permutations in the caret R package
(v6.0-73; Kuhn 2016). Lastly, I compared 16S rRNA gene abundance using a two-way ANOVA
with depth and treatment as fixed effects. I conducted all statistical analyses in R (R Core Team
2012) unless otherwise stated and used an alpha of 0.05 for all significance testing.
3. Results
3.1 Biogeochemical rates
Across all depths, the addition of NO3- resulted in higher DIC production rates (microbial
respiration; Fig. 1) and total cumulative production (Fig. 2) compared to the unamended
treatment, both over time and at the end of the experiment. In both treatments, total DIC
production decreased with depth (Fig. 2), with shallow sediments exhibiting significantly greater
microbial respiration than mid and deep sediments. While DIC production rates decreased over
the duration of the experiment in the shallow, unamended sediments (linear regression; p=0.002,
F(1,18) = 12.87, R2 = 0.38), no such pattern existed in the plus-NO3- treatment.
43
I measured NO3- consumption and sulfide production in reactor effluent (plus total S in
sediment) to assess nitrate reduction rates (NRR) and sulfate reduction rates (SRR), respectively.
Background NO3- concentrations in the incoming seawater were consistently low (0.6-1.2 µM).
Although all of this nitrate was removed throughout the experiment in the unamended treatment
the low initial NO3- resulted in negligible NRR. In the plus-NO3
- treatments, NRR ranged from
14.6-87.2 µmol cm-3, accounting for 86.7% ± 0.05, 98.4% ± 0.08, and 101.9% ± 0.09 of DIC
production in shallow, mid, and deep sediments respectively. There was not a significant
difference in total NRR with depth (Fig. 3), although the shallow sediments demonstrated
elevated NRR in a manner comparable to DIC production (Fig. 2).
Sulfide production occurred at all depths in the unamended treatment and was
significantly higher in shallow compared to deep sediments (Fig. 4B), but was undetectable in
the plus-NO3- treatment. However, changes in S storage indicated that SO4
- reduction was
occurring in all treatments (Fig. 4C), although increases were smaller in the plus-NO3- treatment
than in the unamended sediments (Fig. 4D). In the unamended treatment, SO4- reduction
accounted for 76.8% ± 1.4, 64.2% ± 9.3, and 59% ± 30.8 of total DIC production in shallow,
mid, and deep sediments respectively. When combined with NRR in the plus-NO3- treatment, I
could explain 97.7% ± 5.8, 110.1% ± 15.1, and 107.6% ± 8.9 of total DIC production. There was
a significant interaction between treatment and depth on sulfate reduction, with the unamended
treatment exhibiting more sulfate reduction than the plus-NO3- treatment in shallow sediments,
and unamended shallow sediments exhibiting more sulfate reduction than the unamended deep
sediments (Fig. 4D).
While there was no significant difference in NH4+ production between treatments,
shallow sediments produced significantly more NH4+ when compared to mid and deep sediments
44
in both the plus-NO3- and unamended treatments (Fig. 5). In addition, the DIC:NH4
+ ratio was
significantly higher in the plus-NO3- treatment, while the unamended treatment remained
consistently low across all depths (Fig. 6) and was similar to the C:N value of sediments from the
start of the experiment (13.66 ± 0.69).
3.2 Organic matter
I compared change in C, N, molar C:N, and S in the plus-NO3- and unamended sediments
versus sediments collected prior to the experiment (Table 2). There was no significant difference
in pre- versus post-experiment carbon or molar C:N between treatments; however, there was a
change in N by depth and total S by depth and treatment, with significantly greater S
concentrations in the unamended treatment and lower concentrations in the deep sediments (Fig.
4C).
The proportion of carbon lost as DIC throughout the experiment ranged only from 0.76 to
3.47% of the total carbon in each reactor, so it is not surprising that I did not detect significant
changes in most bulk sediment properties between treatments. To observe more precise changes
in OM, I applied FT-IR spectroscopy and explored relative shifts in chemical functional groups
related to decomposition processes. A principal coordinates analysis (PCoA; Fig. 7A) of the
whole FT-IR spectra using Manhattan distances indicated separation by depth along the first
coordinate axis (explaining 77.6% of the variance) and treatment along the second coordinate
axis (explaining 22.2% of the variance), both of which were significant according to
PERMANOVA analysis (Fig. 7A). Pairwise comparisons of mean Manhattan distances further
indicated that each depth was significantly different from the rest (shallow-mid, p=0.006,
t=3.029; mid-deep, p=0.009, t=2.72; shallow-deep, p=0.001, t=5.94), but when examining
treatment, only pre- and unamended sediments were significantly different from each other
45
(p=0.019, t=2.20). I next plotted the Pearson’s correlation coefficient against wavenumber,
which accounted for 99.8% of total variance in the PCoA. This allowed me to identify three
functional groups that exhibited the most influence on observed separation along the primary and
secondary axes (Fig. 7B): Three groups that were important in differentiating among treatments
included the lignin-like compounds at 840 and 1650 cm-1 (Artz et al., 2008), aliphatic carbon at
1470 cm-2, and polysaccharides at 1080 cm-1.To further explore whether a change in carbon
quality occurred during the incubation, I calculated Index II (Eq. 1), where higher values imply
greater recalcitrance. In both the plus-NO3- and unamended treatments, Index II was significantly
greater than in initial sediments when compared to after the incubation, although there was no
difference between the two treatments (Fig. 7C), nor was there a difference by depth.
3.3 Microbial community composition and abundance in response to nitrate
There was no difference in 16S rRNA gene abundance by treatment (p=0.385) or depth
(p=0.233) according to a two-way ANOVA (Fig. S3). A principal coordinates analysis
constructed from Weighted UniFrac similarities revealed a significant effect of both treatment
and depth on microbial community composition (Fig. 8A). There was a clear separation in
community similarity along the primary axis (43.20% of the variance explained) due to NO3-
addition, and a separation driven primarily by differences between shallow and mid/deep
sediments (Fig. 8A) along the secondary axis (20.82% of the variance explained). To determine
the effect of NO3- addition on alpha diversity, I calculated the Shannon Index and found a
significant effect of both treatment and depth, but not the interaction of the two factors. Across
all depths, alpha diversity was significantly lower in the plus-NO3- treatment when compared to
the unamended treatment (Fig. 8B).
46
A random forest model, using 10,000 trees and 186 predictor variables derived from the
most abundant ASVs, correctly classified microbial communities as belonging to either the plus-
NO3- or unamended treatment 100% of the time with a 0% out-of-bag error rate. Leave-one-out
cross-validation confirmed model performance, with a Cohen’s kappa statistic of 100%, which
compares observed accuracy to expected accuracy due to random chance. The top 30 ASVs most
important in discriminating between treatments accounted for 45.2% of total sequences and
included taxa from Phyla Bacteroidetes, Proteobacteria, Chlorobi, Caldithrix, Chloroflexi,
Planctomycetes, Acidobacteria, Gemmatimonadetes, Verrucomicrobia, and candidate group
WWE1 (Table S2; Fig. 9). Out of these 30 ASVs, classes from Flavobacteria,
Gammaproteobacteria, Alphaproteobacteria, and Ignavibacteria were more abundant in the plus-
NO3- treatment, while the unamended treatment was much more diverse, including classes from
Deltaproteobacteria, Bacteroidia, Caldithrix, Anaerolineae, Cloacamonae, BPC102, Gemm-2,
Phycisphaerae, Epsilonproteobacteria, Alphaproteobacteria, Verrucomicrobiae, and
Betaproteobacteria. I also tested the random forest model without excluding rare taxa (but still
removing singletons) to see if these rare ASVs would have a disproportionate influence on the
dataset. This also resulted in 100% classification rate and 0% out-of-bag error, but only
accounted for an additional 1.9% of all sequences (ASVs 31-41 listed in Table S1).
4. Discussion
4.1 DIC production rates decreased with depth
This study used a controlled FTR experiment to test the effect of OM quality and NO3-
addition on microbial respiration. I found that DIC production decreased as a function of depth,
with shallow sediments exhibiting significantly greater microbial respiration rates than mid and
47
deep sediments (Fig. 2). This pattern was particularly evident in the unamended treatment, where
DIC production also decreased throughout the duration of the experiment in the shallow
sediments (Fig. 1A). Decreasing DIC production with depth likely resulted from changes in OM
lability that also occurred with depth. Microbes preferentially degrade the most labile plant and
microalgae derived OM in surface sediments; the less labile components that remain accumulate
over time, are more recalcitrant, and ultimately become buried under newly deposited organic
matter and sediment (Cowie & Hedges 1994, Wakeham et al. 1997). While microbes can still
degrade these less labile organic compounds at depth, it occurs at a much slower rate (Westrich
& Berner 1984), resulting in decreased decomposition rates. Initial bulk sediment carbon and
molar C:N in this experiment did not differ significantly among the different depths (Table 1);
although the FT-IR spectra indicated a significant difference in functional groups (Fig. 7A) at
different depths driven primarily by polysaccharide depletion (Fig. 7B), which suggests
decreasing lability in deeper sediments.
Decreasing DIC production with depth can also be explained, in part, due to decreasing
availability of thermodynamically favorable electron acceptors in anoxic marsh sediments.
Energetically-favorable electron acceptors, such as NO3-, are preferentially reduced at the surface
and are therefore depleted in deeper sediments (Canfield et al. 2005). While SO42- is rarely
limiting in most marsh systems due to its high concentration in seawater and delivery via
incoming tides (Jørgensen 1977, Howarth & Teal 1979), SO42- reduction is a much less
energetically favorable metabolic pathway, releasing less free energy per mole of carbon
oxidized compared to NO3- reduction. Since there is less energy available to degrade OM that has
accumulated with time, rates of decomposition generally decrease with depth (Canfield et al.
2005, Arndt et al. 2013); though the presence of roots and bioturbation can alter this pattern
48
(Aller & Aller 1998, Kostka et al. 2002, Canfield & Farquhar 2009). This is consistent with the
decrease in DIC production (Fig. 2) and sulfide production (Fig. 4B) I observed in the deeper
unamended sediments. Further, DIC production decreased as a function of time in the surface
unamended treatment (Fig. 1), suggesting that after first oxidizing the more labile OM
compounds, only more recalcitrant, less available OM remained, leading to decreased
decomposition rates. This result corroborates other studies that find a strong relationship between
OM degradability and SRR, with decreasing OM lability resulting in lower decomposition rates
regardless of SO42- availability (Westrich & Berner 1984, Canfield 1989).
4.2 Evidence for a nitrate accessible pool of OM
The addition of NO3- resulted in significantly greater DIC production across all depths,
most notably in deeper sediments, where OM is older and less labile. While NO3- is a
thermodynamically favorable terminal electron acceptor that fuels high rates of denitrification
and DNRA in salt marshes, it is typically coupled with nitrification at oxic interfaces or rooting
zones (Hamersley & Howes 2005; Howes et al. 1981), and hence limited at depth where it
cannot be internally regenerated. By experimentally adding NO3- here, similar to what might
occur in coastal environments under high N loading, I thereby increased NO3- availability. In
doing so, I increased rates of NO3- reduction (Fig. 3) and OM oxidation (Fig. 1-2), and
consequently increased rates of decomposition. These results suggest the existence of a “NO3--
accessible” OM pool and emphasize that the definition of “recalcitrant” can differ depending on
both OM lability and electron acceptor availability. OM that is considered recalcitrant under
SO42--only conditions may no longer be stable under NO3
- availability.
High NO3- conditions may stimulate the microbial community to break down these
otherwise stable, less labile OM compounds by providing more energy for metabolic processes.
49
Higher DIC:NH4+ ratios in the plus-NO3
- treatment provide support for this claim. In general,
more DIC relative to NH4+ production indicates that microbes are using OM with higher C:N
ratios (Canfield et al. 2005). In the plus-NO3- treatment, particularly at depth, the DIC:NH4
+ ratio
was much higher, suggesting that microbial communities may be accessing a different OM pool
compared to unamended sediments, which remained consistently low and very similar to the
average sediment C:N ratio from the start of the experiment (Fig. 5). It is noteworthy that the
ratio of DIC:NH4+ in the plus-NO3
- and unamended treatment were similar in shallow sediments,
where OM is more labile and appears to be accessible to both NO3- and SO4
2- reducers. As this
ratio diverges between treatments with depth, it provides further evidence for the existence of
this separate “NO3--accessible” OM pool that microbes can access once NO3
- limitation is
released. There are other processes by which this increasing pattern in DIC:NH4+ can emerge,
including differences in microbial biomass and N uptake or anammox (Thamdrup et al. 2002,
Dalsgaard et al. 2005, Schmid et al. 2007). My NH4+ data seem to suggest, however, that there
were no significant differences in uptake or regeneration between treatments, given that
cumulative NH4+
production across the entire experiment was the same (Fig. 5). Further, I have
stable isotope 29N2 production data (Bulseco-McKim et al. In Prep) showing that anammox was
negligible in this experiment, agreeing with other studies conducted in salt marsh sediments
(Koop-Jakobsen & Giblin 2009). I therefore conclude that this increase in DIC:NH4+ ratio may
be explained, at least in part, by the oxidation of a higher C/N pool of OM at depth in the plus-
NO3- treatment.
FT-IR spectral data also suggest that microbes in the plus-NO3- treatment were accessing
a different pool of OM than microbes in the unamended treatment. A PCoA of the whole FT-IR
spectra from 4000-400 cm-1 indicated a significant difference in the OM chemistry among depths
50
and between the unamended and pre sediments, but not the plus-NO3- treatment (Fig. 7A). This
result suggests that decomposition not only caused a shift in the OM signature when compared to
pre-treatment sediments, but also, that the plus-NO3- and unamended OM composition shifted in
different ways. In addition, higher Index II values in both the plus-NO3- and unamended
treatments compared to the pre-treatment sediments show that microbial OM oxidation resulted
in more recalcitrant OM (Fig. 7C), a result that I could not detect in the bulk sediment properties
(Table 2).
Remarkably, these data also suggest that after incubation, the OM from the unamended
treatments was more recalcitrant than the OM from the plus-NO3- treatment (Fig. 7C), even
though the amount of C mineralized was less, which supports the pattern observed in the PCoA
of OM composition (Fig. 7A). One possible explanation for this finding is that NO3- addition
might facilitate decomposition of more complex OM (e.g. large cyclic compounds such as
cellulose), either through fermentation or hydrolysis, which would result in more labile, low-
molecular-weight substrates (Beauchamp et al. 1989). Rather than a strict predictable sequence
following thermodynamic theory, which asserts that electron acceptors with higher redox
potential are exclusively reduced first (Zehnder & Stumm 1988), these results suggest that NO3-
supports co-metabolism by providing more labile OM compounds for competing microbial
functional groups (Achtnich et al. 1995). Further, the fact that the pre- and plus-NO3- treatments
were not significantly different from each other suggests that there is less selective utilization of
OM in response to NO3-. In the unamended treatment, SO4
2- reducers may only have access to a
limited supply of low-molecular-weight substrates (Canfield et al. 2005), therefore creating a
more recalcitrant OM pool over time (Fig. 1, 3) and a significant shift in overall chemical
composition (Fig. 7A). With the addition of NO3-, however, these data suggest that microbes are
51
accessing a wider range of compounds; thus, despite greater decomposition rates (Fig. 2), there
was less of a shift in the overall chemical composition of the remaining OM (Fig. 7A). Similar
results have been observed in both terrestrial and oceanic studies, with N addition resulting in the
selection for microbes that responded to the N supply and that could decompose recalcitrant
carbon compounds more efficiently (Campbell et al. 2010, Treseder et al. 2011, Allison et al.
2013). Another possible explanation for a more labile signature in the plus-NO3- treatment is a
greater supply of extracellular DNA from greater microbial biomass (e.g. Dell’Anno & Danavaro
2005), however since NH4+ production rates and 16S rRNA gene abundance were both similar
between treatments (Fig. 5), this is likely not the case.
Rather than acting as an electron acceptor, another consequence of NO3- addition could
be the release of the microbial community from nutrient-limitation, which may also result in
increased DIC production rates due to higher growth rates. However, most anaerobic microbes
are not nutrient-limited because their growth-per-unit substrate-intake is much lower than with
aerobic respiration (Canfield et al. 2005), which is why typical anoxic porewater nutrient
concentrations are higher than those found in oxic sediments. If NO3- addition were affecting
growth rates via alleviation of N limitation in the plus- NO3- treatment, I would also expect to see
a spike in microbial biomass. qPCR of the 16S rRNA gene (supplemental Fig. S3) suggests this
is not occurring, with no significant differences between the plus-NO3- and unamended
treatments. However, I was only able to sample sediments for qPCR at the beginning and end of
the experiment, so I cannot comment on any microbial population growth dynamics that may
have occurred throughout the experiment. Lastly, the majority of DIC produced in the plus-NO3-
treatment can be accounted for by NRR (86.7% ± 0.05, 98.3% ± 0.08, and 101.9% ± 0.09 for
52
shallow, mid, and deep sediments, respectively), suggesting that most of the consumed NO3- can
be attributed to dissimilatory processes.
4.3. NO3- addition effects on microbial community structure
I hypothesized that the end result of NO3- addition would be 1) to fundamentally alter the
resident microbial community through a change in the competitive landscape or 2) to alter the
function of the existing community through metabolic plasticity of the microbes present (Allison
& Martiny 2008), with both scenarios resulting in shifts in the dominant metabolic pathways.
Through 16S rRNA gene sequencing, I found evidence for a combination of the two. While I
observed a core microbiome that existed in both the plus-NO3- and unamended treatment (Fig.
S4), including microbial taxa typically present in these particular salt marsh sediments (e.g.
Kearns et al. 2016), I also found a significant shift in microbial community structure (Fig. 8A)
and decreases in alpha diversity (Fig. 8B) in response to NO3-. This suggests that NO3
- addition
selects for taxa that are more competitive in a high N environment, and that this community is
fundamentally different from both pre- and unamended sediments.
Through random forest classification analysis, I identified 30 ASVs most important in
correctly classifying between plus-NO3- and unamended treatments (Fig. 9; Table S2). Out of
these 30 ASVs, ~70% were from the class Gammaproteobacteria, a widely diverse group of
gram-negative bacteria that increase in abundance as a result of fertilization (e.g. Campbell et al.
2010). Many of these ASVs were putatively assigned to orders known to reduce nitrate
(Kiloniellales; Wiese et al. 2009) oxidize sulfur/sulfide (Thiotrichales, Chromatiales; Garrity et
al. 2005, Thomas et al. 2014), ferment OM (Ignavibacteriales, Rhodospirallales; (Biebl &
Pfening 1981, Iino et al. 2010), and degrade high-molecular-weight (HMW) compounds
(Flavobacteriales, Thiotrichales, Alteromonadales). Some members of these groups can also use
53
long-chain alkanes (Fernández-Gómez et al. 2013, Guibert et al. 2016) and are stimulated in the
presence of HMW dissolved organic matter (McCarren et al. 2010, Mahmoudi et al. 2015).
These shifts in the community provide evidence for selection of taxa more adept at using nitrate
or oxidizing more complex OM. In contrast, ASVs more abundant in the unamended treatment
included orders that are ubiquitous in soil and mangrove sediments (Verrucomicrobiae,
Caldithrixales; Miroshnichenko et al. 2010, Freitas et al. 2012), that can reduce sulfate
(Desulfobacterales, Desulfarculales; Bahr et al. 2005), and that exhibit properties associated with
iron metabolism (Campylobacterales, Rhizobiales (Eppinger et al. 2004, Reese et al. 2013).
While I cannot make definitive statements regarding the exact function associated with these
taxa, identifying the taxa most responsive to NO3- addition is a step forward in understanding the
mechanistic response of microbial communities to nutrient enrichment.
4.4. Assumptions and limitations of FTR experiments
I chose a high concentration of NO3- (500 µM NO3
- ) to assure non-limiting
concentrations at a reasonable flow rate (Pallud et al. 2007). I designed this experiment
specifically to assess the potential of NO3- to mobilize carbon pools that were not being oxidized
by SO42- reduction, rather than to simulate realistic environmental conditions. In the
environment, NO3- will almost always be limiting except in the most eutrophic conditions or in
situations of continuous replacement; therefore, I cannot extrapolate the rates of decomposition
observed in this experiment to field conditions. However, I can conclude from my data that
adding NO3- may stimulate decomposition of older, more recalcitrant sediment OM. There exist
scenarios where NRR may dominate anaerobic respiration, such as freshwater wetlands or
wastewater treatment plants, but it is much less likely to occur naturally in salt marshes where
NO3- is typically limiting and there is unlimited supply of sulfate from tidal water. This study
54
suggests that, by adding NO3-, NRR does not necessarily become the dominant process, but
instead allows for the decomposition of more recalcitrant sediment that could not be mobilized
under conditions of sulfate alone.
Further, the use of FTRs eliminates the complexity involved with plant-microbe
feedbacks and competition for NO3- by benthic microalgae and phytoplankton. While these
interactions are important, the aim of this experiment was to directly assess microbial processes.
I also assumed that in this experiment, SO42- was the only electron acceptor being supplied in the
unamended treatment aside from the very small background concentration of NO3- (0.6-1.2 µM)
in the seawater I used. I do not believe that this affected the treatment differences. Since
background SO42- concentrations are so high in seawater (~28 mM), I was not able to detect
small changes at the µM level that occurred in the FTRs and had to instead infer SRR from rates
of sulfide production and changes in sediment S concentrations. These changes are likely due to
pyrite or FeS formation; although I cannot rule out the production of organic sulfur (Luther et al.
1986). Although I did not monitor the influent oxygen concentrations, I conducted the entire
experiment in an anoxic glove chamber, so oxygen should not have been present for either oxic
respiration or nitrification. In both treatments, it is possible that iron and manganese oxides were
available as electron acceptors, especially in the shallow sediments, which could have
contributed to DIC production; however, the nitrate and DIC balance suggests this was not
important in the plus-NO3- treatment. The balance was not as close in the unamended treatment
but SRR was still the dominant process. Finally, since this experiment only lasted ~90 days, I
cannot determine how large the NO3--accessible OM pool is, whether NO3
- reducers are solely
responsible for the stimulation, or if they also stimulate SO42- reducers through co-metabolism.
55
Extrapolating to the ecosystem-level from small-scale laboratory experiments is
challenging; but these FTRs are specifically designed to isolate meaningful parameters and to
allow for the extraction of kinetic rate measurements of specific microbial processes, which can
then be used to inform predictive models designed for unraveling sediment biogeochemistry
across various spatial and temporal scales (see Algar & Vallino 2014, Vallino 2011).
4.5. Implications of N-loading on salt marsh carbon storage capacity
My results show that NO3- addition stimulates DIC production and consequently,
decomposition of OM in salt marsh sediments. I observed this response even in deep sediments,
where we traditionally assume OM to be fairly recalcitrant to microbial degradation. I
hypothesize that by adding NO3- and providing a more energetically favorable electron acceptor
to the system, I am shifting the microbial community towards taxa better suited for a high NO3-
environment, and consequently changing the accessible OM pool from one that is stable and
recalcitrant to SO42- reducers, to one that is bioavailable under high NO3
- conditions. These
results suggest that comparable additions of NO3- to salt marshes could also enhance OM
decomposition in situ.
These results could have important implications for salt marsh carbon storage potential.
The effect of adding NO3- that I demonstrate here, would depend on the specific hydrology of the
marsh system. If NO3- -rich flooding waters penetrate into deep sediments, it could accelerate
decomposition of stored carbon. Not only could this decrease carbon storage potential, it could
also result in decreased belowground marsh stability (e.g. Deegan et al. 2012) and lead to greater
CO2 production. Additionally, marsh systems currently experiencing high NO3- conditions may
store less OM over time, leading to less overall carbon storage; although the OM that is buried
may be more recalcitrant, since a larger portion will already be oxidized. What this means for
56
carbon storage potential of marshes at a larger scale is unclear, since NO3- can also stimulate OM
production by acting as a nutrient, with such production offsetting respiration. Total marsh
carbon storage capacity depends heavily on the balance between these two processes. Lastly, by
stimulating N-cycling processes, we may also increase the potential for nitrous oxide (N2O)
production, a greenhouse gas with 263 times the global warming potential of CO2 (Neubauer &
Megonigal 2015), as a result of incomplete denitrification; although I did not measure N2O
fluxes in this study. Considering the degree of eutrophication in US estuaries (Bricker et al.
2008), and how NO3- addition alters processes that control OM, it is important to incorporate our
understanding of these processes when assessing the resilience of salt marsh systems to changing
climate and increasing anthropogenic pressures. This is especially critical if we hope to rely on
salt marshes for long-term carbon storage.
Acknowledgements
I would like to thank Joseph Vallino at Marine Biological Laboratory for his invaluable
contribution to the design of the flow through reactor system. I also thank researchers of the
TIDE project (NSF OCE0924287, OCE0923689, DEB0213767, DEB1354494, and OCE
1353140) for maintenance of the long-term nutrient enrichment experiment, as well as
researchers of the Plum Island Ecosystems LTER (NSF OCE 0423565, 1058747, 1637630). I
would also like to acknowledge Sam Kelsey, Khang Tran, Michael Greenwood, and members of
the Bowen lab for their assistance in the field and laboratory, as well as Inke Forbrich, Nat
Weston, and Gary Banta for their thoughtful comments on this research. This work was funded
by an NSF CAREER Award to JLB (DEB1350491) and a Woods Hole Oceanographic Sea
Grant award to AEG and JJV (Project No. NA140AR4170074 Project R/M-65s). Additional
57
support was provided by a Ford Foundation pre-doctoral fellowship award to ABM. The views
expressed here are those of the authors and do not necessarily reflect the views of NOAA or any
of its sub-agencies.
58
References
Achtnich C, Bak F, Conrad R (1995) Competition for electron donors among nitrate reducers,
ferric iron reducers, sulfate reducers, and methanogens in anoxic paddy soil. Biol Fertil soils
19:65–72
Algar CK, Vallino JJ (2014) Predicting microbial nitrate reduction pathways in coastal
sediments. Aquat Microb Ecol 71:223–238
Aller RC, Aller JY (1998) The effect of biogenic irrigation intensity and solute exchange on
diagenetic reaction rates in marine sediments. J Mar Res 56:905–936
Allison SD, Lu Y, Weihe C, Goulden ML, Adam C, Treseder KK, Martiny JBH, Allison SD, Lu
Y, Weihe C, Goulden ML, Martiny AC, Treseder KK, Martiny JBH (2013) Microbial
abundance and composition influence litter decomposition response to environmental
change. Ecology 94:714–725
Allison SD, Martiny JBH (2008) Resistance, resilience, and redundancy in microbial
communities. Proc Natl Acad Sci USA 105:11512–11519
An S, Gardner WS (2002) Dissimilatory nitrate reduction to ammonium (DNRA) as a nitrogen
link, versus denitrification as a sink in a shallow estuary (Laguna Madre/Baffin Bay,
Texas). Mar Ecol Prog Ser 237:41–50
Apprill A, Mcnally S, Parsons R, Weber L (2015) Minor revision to V4 region SSU rRNA 806R
gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol
75:129–137
Arndt S, Jørgensen BB, LaRowe DE, Middelburg JJ, Pancost RD, Regnier P (2013) Quantifying
the degradation of organic matter in marine sediments: A review and synthesis. Earth-
Science Rev 123:53–86
Bahr M, Crump BC, Klepac-Ceraj V, Teske A, Sogin ML, Hobbie JE (2005) Molecular
characterization of sulfate-reducing bacteria in a New England salt marsh. Environ
Microbiol 7:1175–85
Beauchamp E, Trevors J, JW P (1989) Carbon sources for bacterial denitrification. Adv Soil Sci
10:113–142
Benner R, Newell SY, Maccubbin AE, Hodson RE (1984) Relative contributions of bacteria and
fungi to rates of degradation of lignocellulosic detritus in salt-marsh sediments. Appl
Environ Microbiol 48:36–40
Biebl H, Pfening N (1981) Isolation of members of the family Rhodospirillaceae. In: Starr M,
Stolp H, Truper H, Balows A, Schlegel H (eds) The Prokaryotes. Springer Berlin
Heidelberg, Berlin, p 267–268
59
Bricker SB, Longstaff B, Dennison W, Jones A, Boicourt K, Wicks C, Woerner J (2008) Effects
of nutrient enrichment in the nation’s estuaries: A decade of change. Harmful Algae 8:21–
32
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2:
High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583
Campbell BJ, Polson SW, Hanson TE, Mack MC, Schuur EAG (2010) The effect of nutrient
deposition on bacterial communities in Arctic tundra soil. Environ Microbiol 12:1842–1854
Canfield D (1989) Sulfate reduction and oxic respiration in marine sediments: implications for
organic carbon preservation in euxinic environments. Deep Sea Res Part A Oceanogr Res
Pap 36:121–138
Canfield DE, Farquhar J (2009) Animal evolution , bioturbation , and the sulfate concentration of
the oceans. Proc Natl Acad Sci USA 106:8123-8127
Canfield DE, Thamdrup B, Kristensen E (2005) Aquatic Geomicrobiology. Elsevier Academic
Press, Boston, MA
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña
AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE,
Lozupone CA, Mcdonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ,
Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows
analysis of high- throughput community sequencing data. Nat Meth 7:335–336
Caporaso JG, Lauber CL, Walters W a, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J,
Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R (2012) Ultra-high-
throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.
ISME J 6:1621–1624
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N,
Knight R (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences
per sample. Proc Natl Acad Sci USA 108:4516-4522
Chmura GL, Anisfeld SC, Cahoon DR, Lynch JC (2003) Global carbon sequestration in tidal,
saline wetland soils. Global Biogeochem Cy 17:1-12
Cox RD (1980). Determination of nitrate and nitrite at the parts per billion level by
chemiluminescence. Anal Chem 52:332-335
Cowie GL, Hedges JI (1994) Biochemical indicators of diagenetic alteration in natural organic
matter mixtures. Nature 369:304-307
Dalsgaard T, Thamdrup B, Canfield DE (2005) Anaerobic ammonium oxidation (anammox) in
60
the marine environment. Res Microbiol 156:457–64
Dargusch P, Thomas S (2012) A critical role for carbon offsets. Nat Clim Chang 2:470
Deegan LA, Bowen JL, Drake D, Fleeger JW, Friedrichs CT, Galván KA, Hobbie JE, Hopkinson
C (2007) Susceptibility of salt marshes to nutrient enrichment and predation removal. Ecol
Appl 17:42–63
Deegan LA, Johnson DS, Warren RS, Peterson BJ, Fleeger JW, Fagherazzi S, Wollheim WM
(2012) Coastal eutrophication as a driver of salt marsh loss. Nature 490:388–392
Dell’Anno A, Danavaro R (2005) Extracellular DNA plays a key role in deep-sea ecosystem
functioning. Science 309:2179
Dickson A, Goyet C (1994) Handbook of methods for the analysis of the various parameters of
the carbon dioxide system in sea water (Version 2). DOE (US Department of Energy)
ORNL/CDIAC-74. Carbon Dioxide Information and Analysis Center, Oak Ridge, TN
Ding G, Novak JM, Amarasiriwardena D, Hunt PG, Xing B (2002) Soil organic matter
characteristics as affected by tillage management. Soil Sci Soc Am J 66:421-429
Duarte CM, Middelburg JJ, Caraco N, Major NC (2005) Major role of marine vegetation on the
oceanic carbon cycle. Biogeosciences 1:659-679
Eppinger M, Baar C, Raddatz G, Huson DH, Schuster SC (2004) Comparative analysis of four
campylobacterales. Nat Rev Microbiol 2:872–885
Falkowski PG, Fenchel T, Delong EF (2008) The microbial engines that drive Earth’s
biogeochemical cycles. Science 320:1034–1039
Fernández-Gómez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, Pedrós-Alió C
(2013) Ecology of marine bacteroidetes: A comparative genomics approach. ISME J
7:1026–1037
Forbrich I, Giblin AE, Hopkinson CS (2018) Constraining marsh carbon budgets using long-term
C burial and contemporary atmospheric CO2 fluxes. J Geophys Res Biogeosci 123: 867-
878
Freitas S, Hatosy S, Fuhrman JA, Huse SM, Mark Welch DB, Sogin ML, Martiny AC (2012)
Global distribution and diversity of marine Verrucomicrobia. ISME J 6:1499–1505
Galloway JN, Leach AM, Erisman JW, Bleeker A (2017) Nitrogen: the historical progression
from ignorance to knowledge with a view to future solutions. Soil Res 55:417–424
Galloway JN, Townsend AR, Erisman JW, Bekunda M, Cai Z, Freney JR, Martinelli LA,
Seitzinger SP, Sutton MA (2008) Transformation of the nitrogen cycle: Recent trends,
61
questions, and potential solutions. Science 320:889–892
Garrity G, Bell J, Lilburn T (2005) Thiotrichales ord. nov. In: Brenner D (ed) Bergey’s Manual
of Systematic Bacteriology, 2nd edition. Boston, MA
Giblin A, Tobias C, Song B, Weston N, Banta G, Rivera-Monroy V (2013) The Importance of
dissimilatory nitrate reduction to ammonium (DNRA) in the nitrogen cycle of coastal
ecosystems. Oceanography 26:124–131
Gilboa-Garber (1971) Direct spectrophotometric determination of inorganic sulfide in biological
materials and in other complex mixtures. Anal Biochem 43:129–133
Giovannoni SJ (2017) SAR11 Bacteria: The Most Abundant Plankton in the Oceans. Ann Rev
Mar Sci 9:231–255
Guibert LM, Loviso CL, Borglin S, Jansson JK, Dionisi HM, Lozada M (2016) Diverse bacterial
groups contribute to the alkane degradation potential of chronically polluted Subantarctic
coastal sediments. Microb Ecol 71:100–112
Hamersley MR, Howes BL (2005) Coupled nitrification–denitrification measured in situ in a
Spartina alterniflora marsh with a 15NH4+ tracer. Mar Ecol Prog Ser 299:123–135
Hopkinson CS, Giblin AE (2008) Nitrogen dynamics of coastal salt marshes. In: Capone D,
Bronk D, Mulholland M, Carpenter E (eds) Nitrogen in the Marine Environment, 2nd
edition. Academic Press, Burlington, MA, p 991–1036
Howarth RW (1984) The Ecological significance of sulfur in the energy dynamics of salt marsh
and coastal marine sediments. Biogeochemistry 1:5–27
Howarth RW, Teal JM (1979) Sulfate reduction in a New England salt marsh. Limnol Oceanogr
24:999–1013
Howes BL, Dacey JWH, Teal JM (1985) Annual carbon mineralization and belowground
production of Spartina alterniflora in a New England salt marsh. Ecology 66:595–605
Howes BL, Howarth RW, Teal JM (1981) Oxidation-reduction potentials in a salt marsh: Spatial
patterns and interactions with primary production. Limnol Oceanogr 26:350-360
Iino T, Mori K, Uchino Y, Nakagawa T, Harayama S, Suzuki KI (2010) Ignavibacterium album
gen. nov., sp. nov., a moderately thermophilic anaerobic bacterium isolated from microbial
mats at a terrestrial hot spring and proposal of Ignavibacteria classis nov., for a novel
lineage at the periphery of green sulfur bacteria. Int J Syst Evol Microbiol 60:1376–1382
Imhoff J (2005) Bergey’s Manual of Systematic Bacteriology. In: Brenner D, Krieg N, Staley J,
Garrity G (eds) Bergey’s Manual of Systematic Bacteriology, 2nd edition. New York, NY
62
Jorgensen BB (1977) The sulfur cycle of a coastal marine sediment (Limfjorden, Denmark).
Limnol Oceanogr 22:814–832
Kaplan W, Valiela I, Teal JM (1979) Denitrification in a salt marsh ecosystem. Limnol Oceanogr
24:726–734
Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7:
Improvements in performance and usability. Mol Biol Evol 30:772–780
Kearns PJ, Angell JH, Howard E, Deegan LA, Stanley RH, Bowen JL (2016) Nutrient
enrichment induces high rates of dormancy and decreases diversity of active bacterial taxa.
Nat Commun 7:12881
Koop-Jakobsen K, Giblin AE (2009) Anammox in tidal marsh sediments: The role of salinity,
nitrogen loading, and marsh vegetation. Estuar Coasts 32:238–245
Koop-Jakobsen K, Giblin AE (2010) The effect of increased nitrate loading on nitrate reduction
via denitrification and DNRA in salt marsh sediments. Limnol Oceanogr 55:789–802
Kostka JE, Gribsholt B, Petrie E, Dalton D, Skelton H, Kristensen E (2002) The rates and
pathways of carbon oxidation in bioturbated saltmarsh sediments. Limnol Oceanogr
47:230–240
Kuhn M (2015) A Short introduction to the caret package. R Found Stat Comput:1–10
Langley J, Mozdzer TJ, Shepard KA, Hagerty SB, Patrick Megonigal J (2013) Tidal marsh plant
responses to elevated CO2, nitrogen fertilization, and sea level rise. Glob Chang Biol
19:1495–1503
Liaw A, Wiener M (2002) Classification and Regression by randomForest. R news 2:18–22
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: An effective
distance metric for microbial community comparison. ISME J 5:169–172
Luther G, Church T, Scudlark J, Cosman M (1986) Inorganic and Ooganic sulfur cycling in salt-
marsh pore waters. Science 232:746–749
Macreadie PI, Nielsen DA, Kelleway JJ, Atwood TB, Seymour JR, Petrou K, Connolly RM,
Thomson ACG, Trevathan-Tackett SM, Ralph PJ (2017) Can we manage coastal
ecosystems to sequester more blue carbon? Front Ecol Environ 15:206–213
Mahmoudi N, Robeson MS, Castro HF, Fortney JL, Techtmann SM, Joyner DC, Paradis CJ,
Pfiffner SM, Hazen TC (2015) Microbial community composition and diversity in Caspian
Sea sediments. FEMS Microbiol Ecol 91:1–11
Margenot AJ, Calderón FJ, Bowles TM, Parikh SJ, Jackson LE (2015) Soil organic matter
63
functional group composition in relation to organic carbon, nitrogen, and phosphorus
fractions in organically managed tomato fields. Soil Sci Soc Am J 79:772–782
McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, Chisholm SW, DeLong
EF (2010) Microbial community transcriptomes reveal microbes and metabolic pathways
associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci USA
107:16420–16427
McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, Andersen GL,
Knight R, Hugenholtz P (2012) An improved Greengenes taxonomy with explicit ranks for
ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618
Mcleod E, Chmura GL, Bouillon S, Salm R, Björk M, Duarte CM, Lovelock CE, Schlesinger
WH, Silliman BR (2011) A blueprint for blue carbon: toward an improved understanding of
the role of vegetated coastal habitats in sequestering CO2. Front Ecol Environ 9:552–560
Meyer AF, Lipson DA, Martín AP, Schadt CW, Schmidt SK (2004) Molecular and metabolic
characterization of cold-tolerant alpine soil Pseudomonas sensu stricto. Appl Environ
Microbiol 70:483–489
Miroshnichenko ML, Kolganova T V., Spring S, Chernyh N, Bonch-Osmolovskaya EA (2010)
Caldithrix palaeochoryensis sp. nov., a thermophilic, anaerobic, chemo-organotrophic
bacterium from a geothermally heated sediment, and emended description of the genus
Caldithrix. Int J Syst Evol Microbiol 60:2120–2123
Morris JT, Sundareshwar PV, Nietch CT, Kjerfve B, Cahoon DR (2002) Responses of coastal
wetlands to rising sea level. Ecology 83:2869–2877
Negrin V, Spetter C, Asteasuain R, Perillo G, Marcovecchio J (2011) Influence of flooding and
vegetation on carbon, nitrogen, and phosphorus dynamics in the pore awter of a Spartina
alterniflora salt marsh. J Environ Sci 23:212–221
Nelleman C, Corcoran E, Duarte C, Valdes L, Young C De, Foncesa L, Grimsditch G (2009)
Blue carbon. A rapid response assessment. United Nations Environment Programme.
Birkelant: GRID-Arendal
Neubauer SC, Megonigal JP (2015) Moving beyond global warming potentials to quantify the
climatic role of ecosystems. Ecosystems 18:1000–1013
Pallud C, Cappellen P Van (2006) Kinetics of microbial sulfate reduction in estuarine sediments.
Geochim Cosmochim Acta 70:1148–1162
Pallud C, Meile C, Laverman a. M, Abell J, Cappellen P Van (2007) The use of flow-through
sediment reactors in biogeochemical kinetics: Methodology and examples of applications.
Mar Chem 106:256–271
Parikh SJ, Goyne KW, Margenot AJ, Mukome FND, Calderon FJ (2014) Soil chemical insights
64
provided through vibrational spectroscopy. Adv Agron 126:1-148
Pastore MA, Megonigal JP, Langley JA (2017) Elevated CO2 and nitrogen addition accelerate
net carbon gain in a brackish marsh. Biogeochemistry 133:73–87
Peng X, Ji Q, Angell JH, Kearns PJ, Yang HJ, Bowen JL, Ward BB (2016) Long term
fertilization alters the relative importance of nitrate reduction pathways in salt marsh
sediments. J Geophys Res Biogeosci 121: 2082-2095
QIIME 2 Development Team (2018) QIIME 2. https://qiime2.org/
Reddy K, Patrick Jr. W (1975) Effect of alternate aerobic and anaerobic conditions on redox
potential, organic matter decomposition, and nitrogen loss in a flooded soil. Soil Biol
Biochem 7:87–94
Reese BK, Witmer AD, Moller S, Morse JW, Mills HJ (2013) Molecular assays advance
understanding of sulfate reduction despite cryptic cycles. Biogeochemistry 118:307–319
Rysgaard S, Risgaard-Peterson N, Sloth N (1996) Nitrification, denitrification, and nitrate
ammonification in sediments of two coastal lagoons in Southern France. Hydrobiologia
117:133–141
Ryther JH, Dunstan WM (1971) Nitrogen, phosphorus, and eutrophication in the coastal marine
environment. Science 171:1008–1013
Schmid MC, Risgaard-Petersen N, Vossenberg J Van De, Kuypers MMM, Lavik G, Petersen J,
Hulth S, Thamdrup B, Canfield D, Dalsgaard T, Rysgaard S, Sejr MK, Strous M, Op Den
Camp HJM, Jetten MSM (2007) Anaerobic ammonium-oxidizing bacteria in marine
environments: Widespread occurrence but low diversity. Environ Microbiol 9:1476–1484
Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, Huber DH, Langenheder S,
Lennon JT, Martiny JBH, Matulich KL, Schmidt TM, Handelsman J (2012) Fundamentals
of microbial community resistance and resilience. Front Microbiol 3:1–19
Solórzano L (1968) Determination of ammonia in natural waters by the phenolhypochlorite
method. Limnol Oceanogr 14:799-801
Thamdrup B, Dalsgaard T (2002) Production of N2 through anaerobic ammonium oxidation
coupled to nitrate reduction in marine sediments. Appl Environ Microbiol 68:1312–1318
Thomas F, Giblin AE, Cardon ZG, Sievert SM (2014) Rhizosphere heterogeneity shapes
abundance and activity of sulfur-oxidizing bacteria in vegetated salt marsh sediments. Front
Microbiol 5:309
Treseder KK, Kivlin SN, Hawkes C V. (2011) Evolutionary trade-offs among decomposers
determine responses to nitrogen enrichment. Ecol Lett 14:933–938
65
Turner S, Pryer KM, Miao VPW, Palmer JD (1999) Investigating deep phylogenetic
relationships among cyanobacteria and plastids by small subunit rRNA sequence
analysis. J Eukaryotic Micro 46:327-338
Valiela I, Cole ML (2002) Comparative evidence that salt marshes and mangroves may protect
seagrass meadows from land-derived nitrogen loads. Ecosystems 5:92–102
Valiela I, Teal JM (1974) Nutrient limitation in salt marsh vegetation. In: Reimold RJ, Queen
WH (eds) Ecology of Halophytes. Academic Press, New York, NY, p 547–563
Valiela I, Teal JM, Persson NY (1976) Production and dynamics of experimentally enriched salt
marsh vegetation: Belowground biomass. Limnol Oceanogr 21:245-252
Vallino JJ (2011) Differences and implications in biogeochemistry from maximizing entropy
production locally versus globally. Earth Syst Dyn 2:69–85
Veum KS, Goyne KW, Kremer RJ, Miles RJ, Sudduth KA (2014) Biological indicators of soil
quality and soil organic matter characteristics in an agricultural management continuum.
Biogeochemistry 117:81–99
Vivanco L, Irvine IC, Martiny JBH, Vivanco L, Irvine I, Martiny JBH (2018) Nonlinear
responses in salt marsh functioning to increased nitrogen addition. Ecology 96:936–947
Wakeham SG, Lee C, Hedges JI, Hernes PJ, Peterson ML (1997) Molecular indicators of
diagenetic status in marine organic matter. Geochim Cosmochim Acta 61:5363-5369
Warren RS, Fell PE, Rozsa R, Brawley AH, Orsted AC, Olson ET, Swamy V, Niering WA
(2002) Salt marsh restoration in Connecticut: 20 years of science and management. Restor
Ecol 10:497–513
Westrich JT, Berner R a. (1984) The role of sedimentary organic matter in bacterial sulfate
reduction: The G model tested. Limnol Oceanogr 29:236–249
Wiese J, Thiel V, Gärtner A, Schmaljohann R, Imhoff JF (2009) Kiloniella laminariae gen. nov.,
sp. nov., an alphaproteobacterium from the marine macroalga Laminaria saccharina. Int J
Syst Evol Microbiol 59:350–356
Zehnder A, Stumm W (1988) Geochemistry and biogeochemistry of anaerobic habitats. In:
Zehnder A (ed) Biology of Anaerobic Microorganisms. Wiley, p 1–38
66
Tables
Table 1. Functional group assignments based on Parikh et al. (2014) and modified from
Margenot et al. (2015) to evaluate FT-IR spectra using Index II metric. ν = stretching vibration;
νas = asymmetric stretching vibration; νs = symmetric stretching vibration; δ = bending vibration.
Band (cm-1) Assignment
3400 ν(N-H), ν(O-H)
2924 aliphatic νas(C-H)
2850 aliphatic νs(C-H)
1650 aromatic ν(C = C)
1470 aliphatic δ(C-H)
1405 aliphatic δ(C-H)
1270 phenol νas(C-O), carboxylic acid ν(C-O)
1110 polysaccharide νs(C-O)
1080 polysaccharide νs(C-O)
920 aromatic δ(C-H)
840 aromatic δ(C-H), less substituted
67
68
Fig. 1. Average (±SE) dissolved inorganic carbon (DIC) production over time (days) across three
depths that correspond to different ages of marsh organic matter (panels A-C; n = 3).
69
Fig. 2. Average (± SE) cumulative dissolved inorganic carbon (DIC) production in µmol cm-3 for
nitrate and unamended treatments at each depth. Boxes represent 25% to 75% quartiles. The
solid black line is the median value, and the whiskers are upper and lower extremes. Black dots
represent values for each individual reactor. A Two-way ANOVA indicates a significant effect
of treatment (p<0.001, F1,14=21.73) and depth (p<0.001, F2,14=48.33) on total DIC production,
but there was no significant interaction between the two. Letters represent statistically different
DIC production by depth from a Tukey’s HSD test corrected for multiple comparisons test and
asterisks indicate a significant difference between treatments.
70
Fig. 3. (A) Average (±SE) nitrate reduction rates over time (days) and (B) total nitrate reduction
at each depth in the nitrate amended treatment (nitrate was below detection in the unamended
sediments). One-way ANOVA indicated no significant difference in nitrate reduction as a
function of depth.
71
Fig. 4. (A) Average (±SE) sulfide production rates over time (days) and (B) total sulfide
production. One-way ANOVA indicated shallow sediments exhibited significantly greater
sulfide production than mid and deep sediments (p=0.0124, F2,6=9.96). (C) A two-way ANOVA
indicated that total sulfur storage was greater in the unamended treatment across all depths (as
indicated by an asterisk; p=0.0243, F1,14=7.637) and lowest in the deep sediments (p=0.0369,
F2,14=4.214). (D) There was a significant interaction between treatment and depth on total sulfate
reduction (sulfide + sulfur production) (p=0.029, F2,12=4.806). Letters represent statistically
different sulfate reduction from a Tukey’s HSD test corrected for multiple comparisons.
72
Fig. 5. Average (±SE) ammonium production rates over time (days) in the nitrate (A) and
unamended (C) treatments, and total ammonium production in µmol cm-3 across depth for nitrate
(B) and unamended (D) treatments. While there was no effect of treatment, a one-way ANOVA
indicated a significant effect of depth (nitrate: p<0.001, F2,6=37.47; unamended: p=0.005,
F2,6=14.09), as indicated by a Tukey’s HSD test corrected for multiple comparisons.
73
Fig. 6. The ratio of DIC to ammonium production calculated per core was greater in the plus-
NO3- treatment when compared to unamended sediments, while depth was insignificant,
according to a two-way ANOVA (p=0.007, F1,14=10.11). The dotted line indicates the average
C:N ratio of sediments from this experiment (13.66 ± 0.69).
74
Fig. 7. (A) Principal coordinates analysis (PCoA) of Fourier Transform-Infrared Spectra (FT-IR)
indicates significant differences by treatment (PERMANOVA; p=0.017, F2,18 = 3.314) and depth
(p=0.001 F2,18 = 16.598). (B) Pearson’s correlation coefficients plotted against wavenumber
representing regions most discriminating across two axes shown in A. Dotted lines indicate
functional group assignments listed in Table 1, with 840-920 and 1650 cm-1= aromatic carbon
and lignin-type signatures, 1080 cm-1 = polysaccharides, and 1470 and 2850-2924 cm-1 =
aliphatic carbon. (C) A two-way ANOVA of Index II values (eq. 1) indicated a significant effect
of treatment but not depth, with a higher recalcitrance index in plus-NO3- and unamended
treatments when compared to initial sediments.
75
Fig. 8. (A) Principal coordinates analysis constructed based on weighted UniFrac for pre-
experiment (green), nitrate (yellow), and unamended (blue) sediments. Shape indicates sample
depth: shallow (circle), mid (triangle), and deep (square). Results from a PERMANOVA indicate
significant differences in community composition by treatment (p=0.001, F(2,22)=11.1095) and
by depth (p=0.006, F(2,22)=3.0287). (B) Shannon diversity index. A two-way ANOVA revealed a
significant effect of both treatment (p<0.001, F(2,22)=71.207) and depth (p=0.044, F(2,22)=3.613),
as indicated by a Tukey’s HSD test corrected for multiple comparisons, but no effect of the
interaction between the two.
76
Fig. 9. Heatmap showing relative abundance of top 30 ASVs (45.2% of sequences) most
important in correctly discriminating between plus-NO3- (top 9 rows) and unamended treatments
(bottom 9 rows) according to a random forest classification model. Lighter colors indicate less
abundant taxa, while darker colors indicate more abundant taxa. Colored circles represent the
taxonomic class of each ASV. Additional taxonomic information can be found in Supplemental
Table S2.
77
Supplemental Methods
I used bromide (Br-) as a conservative tracer to confirm ideal flow conditions and
constrain transport parameters by measuring outflow concentrations as a function of time (Pallud
et al., 2007; Roychoudhury et al., 1998). I added 2 mM sodium bromide stock (NaBr-; Acros
organics) to each reservoir to increase the final concentration of Br- by 2 mM above the Br-
concentration in the reservoir seawater. I began sampling the effluent at approximately 2-hour
intervals for a 30-hour period. To analyze Br- concentrations in seawater, I followed methods
outlined in Presley (1971). Briefly, to a 500 µL sample, I added, in sequence, 5 mL phenol-red
buffer (500 mL sodium acetate buffer: 30 g sodium acetate (Fisher Scientific) + 7 mL glacial
acetic acid in 1000 mL DI water; 25 mL phenol red reagent; 0.08 g phenol sulfonephthalein
(Sigma-Aldrich) in 10 mL 0.1 N sodium hydroxide (Fisher Scientific) diluted in 500 mL DI
water), 1 ml 0.005 N N-chlorotosylamide (Sigma-Aldrich) for 30 seconds, and 2.5 mL 0.05 N
sodium thiosulfate (Sigma-Aldrich) for 5 seconds. I then immediately read absorbance at 595 nm
on a Shimadzu 1601 spectrophotometer.
To carefully measure flow conditions in the reactors, I examined the time at which the
initial bromide concentration was detected in the outflow (Fig. S2). I determined the linear flow
velocity (µ) using the following equation from Roychoudhury et al. (1998):
Eq. 1: μ = 𝑄
𝜑𝐴
Where Q is the measured flow rate in units of volume of solution per unit of time, A is the cross-
sectional area of the flow through reactor, and φ is the sediment porosity. I also calculated
residence time in the reactor with the following equation:
78
Eq. 2: 𝜏 = 𝜑𝑉
𝑄
where the function τ is the water residence time, and V is the reactor volume (31.81 cm3). I then
plotted the ratio between initial and final bromide concentrations over time (Fig. S2) and divided
the total number of hours until breakthrough by the residence time to attain the number of
porewater volume replacements required before reaching steady state (Roychoudhury et al.
1998). I performed all calculations in R (R Core Team).
References
Pallud C, Meile C, Laverman a. M, Abell J, Cappellen P Van (2007) The use of flow-through
sediment reactors in biogeochemical kinetics: Methodology and examples of applications.
Mar Chem 106:256–271
Pallud C, Cappellen P Van (2006) Kinetics of microbial sulfate reduction in estuarine sediments.
Geochim Cosmochim Acta 70:1148–1162
Presley BJ (1971) Techniques for analyzing interstitial water samples. Part 1: Determination of
selected minor and major inorganic constituents. Initial Report. Institute of Geophysics &
Planetary Physics 869:1749-1755
Roychoudhury AN, Viollier E, Cappellen P Van (1998) A plug flow-through reactor for studying
biogeochemical reactions in undisturbed aquatic sediments. Appl Geochemistry 13:269–280
79
Supplemental Tables
Table S1. Flow property information, including average flow rate (±SD), linear flow velocity as
calculated by Eq. 1, porosity, and residence time as calculated by eq. 2.
Sample Flow rate
(mL min-1)
Linear flow velocity
(mL hr-1 cm-2) Porosity
Residence
time (hr)
Plus-NO3- Treatment
Shallow
Core 1 0.067 (0.016) 0.422 0.60 4.74
Core 2 0.067 (0.016) 0.422 0.64 4.73
Core 3 0.072 (0.017) 0.412 0.64 4.85
Mid
Core 1 0.076 (0.015) 0.437 0.66 4.57
Core 2 0.068 (0.001) 0.414 0.62 4.83
Core 3 0.071 (0.012) 0.387 0.69 5.17
Deep
Core 1 0.077 (0.010) 0.448 0.64 4.46
Core 2 0.067 (0.010) 0.398 0.63 5.03
Core 3 0.069 (0.022) 0.400 0.65 5.00
Unamended Treatment
Shallow
Core 1 0.080 (0.021) 0.474 0.64 4.22
Core 2 0.080 (0.025) 0.462 0.66 4.32
Core 3 0.081 (0.025) 0.453 0.67 4.42
Mid
Core 1 0.082 (0.012) 0.451 0.69 4.43
Core 2 0.077 (0.013) 0.440 0.66 4.54
Core 3 0.079 (0.014) 0.453 0.66 4.42
Deep
Core 1 0.081 (0.013) 0.450 0.68 4.44
Core 2 0.066 (0.024) 0.397 0.62 5.04
Core 3 0.070 (0.037) 0.400 0.66 5.00
80
Table S2. Taxonomic information of 30 ASVs present at least 100 times that were most
important in discriminating between treatments in order of mean decreasing accuracy according
to Random Forest. I also reran the model including all taxa (excluding singletons) and obtained
the same classification result. Discriminating taxa that were rare and not included in the initial
analysis are listed as ASV 31-41 in the table and account for an additional 1.9% of sequences.
ASV Phylum Class Order Family
1 Bacteroidetes Flavobacteria Flavobacteriales NA
2 Proteobacteria Gammaproteobacteria Chromatiales NA
3 Proteobacteria Gammaproteobacteria Alteromonadales Colwelliaceae
4 Proteobacteria Gammaproteobacteria NA NA
5 Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
6 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
7 Bacteroidetes Bacteroidia Bacteroidales NA
8 WWE1 Cloacamonae Cloacamonales MSBL8
9 Proteobacteria Gammaproteobacteria Alteromonadales Alteromonadaceae
10 Caldithrix Caldithrixae Caldithrixales Caldithrixaceae
11 Proteobacteria Alphaproteobacteria Kiloniellales Kiloniellaceae
12 Proteobacteria Gammaproteobacteria Oceanospirillales Oceanospirillaceae
13 Proteobacteria Gammaproteobacteria Oceanospirillales NA
14 Chloroflexi Anaerolineae SBR1031 A4b
15 Planctomycetes Phycisphaerae MSBL9 NA
16 Chloroflexi Anaerolineae OPB11 NA
17 Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
18 Proteobacteria Epsilonproteobacteria Campylobacterales NA
19 Acidobacteria BPC102 MVS-40 NA
20 Chloroflexic Anaerolineae SBR1031 NA
21 Proteobacteria Deltaproteobacteria IndB3-24 NA
22 Proteobacteria Gammaproteobacteria Thiotrichales NA
23 Gemmatimonadetes Gemm-2 NA NA
24 Chloroflexi Anaerolineae Anaerolineales Anaerolinaceae
25 Proteobacteria Alphaproteobacteria Rhizobiales NA
26 Proteobacteria Gammaproteobacteria Thiotrichales Thiotrichaceae
27 Verrucomicrobia Verrucomicrobiae Verrucomicrobiales Verrucomicrobiaceae
28 Chlorobi Ignavibacteria Ignavibacteriales lheB3-7
29 Proteobacteria Betaproteobacteria NA NA
30 Proteobacteria Gammaproteobacteria NA NA
31 Chloroflexi Anaerolineae O4D2Z37 NA
32 Chlorobi NA NA NA
33 Gemmatimonadetes Gemm-2 NA NA
34 WWE1 Cloacamonae Cloacamonales MSBL8
35 KSB3 NA NA NA
36 OP8 OP81 HMMVPog-54 NA
37 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae
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38 Chlorobi Ignavibacteria Ignavibacteriales IheB3-7
39 Acidobacteria BPC102 MVS-40 NA
40 Spirochaetes Leptospirae Leptocpirales Sediment-4
41 WWE1 Cloacamonae Cloacamonales SHA-116
82
Supplemental Figures
Fig. S1. Flow through reactor schematic modeled after Pallud et al. (2006; 2007). (A) There are
three individual reactors, each with independent in- and outflows, per block. (B) Each reactor is
radially scored to promote regular, unilateral flow, and encased with a 0.45 µM filter on either
side. (C) These reactors are held together by two Plexiglas® plates and secured with 8 screws
positioned along plate edges. All indicated measurements are in centimeters.
83
Fig. S2. Bromide breakthrough curve with the ratio of [Br-]initial/[Br-]final on the y-axis for plus-
NO3- (n = 9) and unamended (n = 9) treatments confirmed uniform and regular flow in each
reactor. Dotted line indicates a ratio of 1, where the initial and final bromide concentration are
equal, indicating breakthrough. Average breakthrough time was approximately 10 hours at a
targeted flow rate of 0.08 mL min-1 with 2.14 ± 0.13 pore volume replacements required before
reaching steady state.
84
Fig. S3. A boxplot of 16S rRNA gene abundance showed no difference by treatment (p=0.385)
or site (p=0.233) according to a two-way ANOVA.
85
Fig. S4. Stacked bar graph showing relative abundance of 20 bacterial orders present in all
samples, contributing to 45.94% of total sequences.
86
Chapter 2: Chronic exposure to nutrient enrichment lessens the effect of additional nitrate
on organic matter decomposition despite changes to microbial community structure and
activity
In collaboration with: Anna E. Murphy, Anne E. Giblin, Jane Tucker, and Jonathan Sanderman
Abstract
Salt marshes can sequester carbon at rates that are an order of magnitude greater than
terrestrial forests, but this ecosystem service is under threat from nutrient enrichment, which can
stimulate decomposition of organic matter. Despite efforts to mitigate nitrogen loading, salt
marshes continue to experience chronic enrichment, the consequences of which remain unclear.
To investigate the effect of chronic nutrient exposure on salt marsh organic matter
decomposition, I collected sediments from three sites across a range of prior nitrate exposure: a
relatively pristine marsh, a marsh enriched at ~70 µmol L-1 for 13 years, and a marsh enriched
between 100-1000 µmol L-1 for 40 years. The most chronically enriched site contained more
recalcitrant organic matter and a diverse assemblage of microbial taxa associated with the
nitrogen cycle. I also performed a controlled enrichment experiment to test whether these site
differences influenced the functional response to additional nitrate exposure. I found significant
changes to microbial community structure and activity, with the chronically enriched site having
lower rates of microbial respiration and nitrate reduction. These results suggest that long term
nutrient enrichment could lead to less carbon storage overall, but the portion of organic matter
that is buried is less vulnerable to decomposition in response to further nitrate addition. Thus, it
is important to consider the extent of nutrient enrichment when developing strategies to protect
and restore salt marshes for their carbon storage potential.
Introduction
87
The extent to which salt marshes store carbon (C), one of the many valuable ecosystem
services provided by these habitats, depends largely on the balance between inputs from primary
production and losses from organic matter (OM) decomposition, with the latter ultimately
determining how much C is buried over long time-scales (Burdige 2007). While several studies
document decomposition rates in response to shifting environmental conditions, such as
temperature (Conant et al. 2011, Kirschbaum 1995, Lehmann & Kleber 2015) and sea level rise
(Kirwan et al. 2013), there is uncertainty about the effect that nitrogen (N) has on the balance
between primary production and decomposition. Over the last few centuries, coastal habitats
have experienced increased N loading due to runoff from sewage disposal, urban and agricultural
activities, and atmospheric N deposition (Galloway et al. 2017). Tremendous efforts at
mitigating N loading to coastal waters have improved conditions in some regions (Zedler 2000,
Warren et al. 2002), however, worldwide, many salt marshes continue to experience chronic
nutrient enrichment (Bricker et al. 2008, Deegan et al. 2012). The implications of this nutrient
enrichment for C storage in salt marshes remain unclear, with some studies documenting an
increase in primary production (Valiela et al. 1975, Morris et al. 1991, Langley et al. 2013,
Morris et al. 2013), and others indicating decreased belowground biomass (Langley et al. 2009,
Watson et al. 2014), increased respiration of OM (Wigand et al. 2009, Watson et al. 2014), and
decreased sediment stability (Deegan et al. 2012, Mueller et al. 2018).
The primary form of N enrichment in coastal waters is in the oxidized form of nitrate
(NO3-) (Galloway et al. 2008), which is typically the nutrient that limits primary production
(Nixon et al. 1986, Ryther & Dunstan 1971). Increased N loading partially alleviates this
limitation and promotes primary production; however, in addition to fueling primary production,
NO3- can also serve as an electron acceptor in heterotrophic microbial metabolisms. Sulfate
88
reduction (SO42-) is typically the dominant metabolic process in salt marsh sediments due to
widespread anoxia and a large supply of ions from seawater (Howarth & Teal 1979, Howarth
1984). NO3- reduction, however, is a more thermodynamically favorable process than SO4
2-
reduction, releasing more free energy per mole of C oxidized (Canfield et al. 2005). NO3-
addition could therefore increase the rate of OM decomposition by increasing microbial
respiration. Further, because of the additional energy yielded by NO3- respiration, there is a
“NO3--accessible” pool of OM that becomes accessible only under high NO3
- availability
(Bulseco-McKim et al. In review). What remains unclear, however, is whether a legacy of NO3-
enrichment can diminish that NO3--accessible pool of OM, thereby fundamentally altering marsh
C storage.
If NO3- addition does stimulate OM decomposition during the burial process, then
sediments from chronically NO3- enriched sites may store less C than sites without chronic NO3
-
enrichment. Additionally, C that is buried in the accreting marsh may be less labile because
microbes will have already oxidized part, or all, of this NO3- accessible OM pool. The result
would be the accumulation of OM with lower substrate quality and more complex chemical
composition, resulting in sediments that are more resistant to future decomposition (Cowie &
Hedges 1994, Middelburg 1989, Wakeham et al. 1997). In a meta-analysis conducted across 900
terrestrial litter decomposition studies, chronically enriched sites exhibited decreased stimulation
of litter decay in response to nutrient addition, due to altered leaf chemistry and shifts in the
decomposer community (Knorr et al. 2005). Different OM fractions follow an asymptotic pattern
in rates of decomposition under N loading, with early stages exhibiting faster decomposition
rates than late stages (Hobbie et al. 2012). Thus, it is clear that OM quality plays an important
89
role in determining the effect of N on decomposition rates in terrestrial systems. How these
processes play out in vegetated marine sediments, however, remains unclear.
Increasing N concentrations could also alter the microbial community, causing a shift in
OM lability, and thereby also affecting the amount of OM that is decomposed. Large supplies of
exogenous N can select for N-adapted microbes that can produce more extracellular enzymes
and oxidize complex, recalcitrant OM (Treseder et al. 2011, Bulseco-McKim et al. In review).
Nutrient enrichment may also influence the metabolic strategy of the microbial community, with
increased N supply resulting in shifts in the relative proportion of dissimilatory nitrate reduction
to ammonium (DNRA) and denitrification (DNF) (Koop-Jakobsen & Giblin 2010). The shift
between these two pathways can be important as DNF results in N loss while DNRA conserves
N in the ecosystem (Burgin & Hamilton 2007). High NO3- conditions appear to favor DNF
(Tiedje 1988, Giblin et al. 2013), and NO3- limitation and labile OM appear to favor DNRA
(Burgin & Hamilton 2007, Algar & Vallino 2014, Hardison et al. 2015), though it is unclear how
nutrient enrichment will alter these controls or to what extent. Nutrient enrichment can also alter
the community structure of denitrifying microbes (Bowen et al. 2013, Angell et al. 2018, Graves
et al. 2016) and can increase both the diversity and abundance of putative fungal denitrifiers
(Kearns et al. 2018), potentially translating to more OM oxidation when compared to systems
without exogenous sources of NO3-.
We lack an understanding of whether these changes to the microbial community and
associated metabolisms persist through time. Legacy effects can persist in soil microbial
communities (Evans & Wallenstein 2012, Bernhard et al. 2015, Giauque & Hawkes 2016) and
affect enzyme activity (Averill et al. 2016) for several years after changes to the environment
(Cuddington 2011). It is possible, however, that microbial communities can remain resilient
90
against environmental changes through increased dormancy, a bet-hedging strategy where
microbes enter an inactive state under unfavorable conditions (Lennon & Jones 2011). During a
long-term nutrient enrichment experiment (Deegan et al. 2007), increased N had no effect on the
total salt marsh sediment bacterial community (Bowen et al. 2011) but significantly altered the
active bacterial community and decreased active diversity by inducing dormancy (Kearns et al.
2016). This suggests that salt marsh microbial communities possess a genetic reservoir of traits
that can respond to future changes in the environment. Understanding how the microbial
community changes in response to increased N supply, whether or not chronic nutrient
enrichment leaves behind a legacy of effects, and how this legacy translates to OM
decomposition, is critical to a better understanding of long term C storage in salt marsh systems.
My objective was to examine salt marsh sediments from sites exposed to a range of NO3-
concentrations, varying both in extent (below detection to up to 1000 µmol L-1) and duration (no
exposure to 40-years of chronic enrichment), and to characterize OM quality and the associated
microbial community. I hypothesized that sediments from sites with greater NO3- enrichment
would exhibit lower OM quality and harbor microbes better adapted to a high N environment. To
examine if these site differences influenced functional response to further nutrient enrichment, I
then performed a controlled flow through experiment, where I added 500 µmol L-1 NO3- and
measured metabolic response and changes to the associated microbial community relative to a
seawater control. I hypothesized that 1) NO3- addition would stimulate sediment microbial
respiration due to the presence of a more energetically favorable electron acceptor, 2) this
stimulation would be less pronounced in chronically enriched sediments due to a less labile OM
pool, and 3) NO3- addition would alter the microbial community to a lesser extent in chronically
91
enriched sites due to the presence of taxa already conditioned to survive in a high N
environment.
Methods
Sample collection
I collected sediment cores (5 cm diameter, 30 cm deep) from the tall ecotype of S.
alterniflora in three salt marshes varying in time and intensity of prior NO3- exposure. My sites
(Fig. 1) included (1) West Creek (reference), a relatively pristine reference marsh, (2) Sweeney
Creek (13-year enriched), which was experimentally enriched with 70 µM NO3- dissolved into
flooding tide waters for 13 years as part of the TIDE project (Deegan et al. 2007, 2012), and (3)
Greenwood Creek (40-year enriched), which has received UV-sterilized wastewater effluent
from a nearby wastewater treatment plant for 40 years, with NO3- concentrations reaching as
high as 1000 µM (Graves et al. 2016). To focus on legacy effects that occur within deeper
sediments where C storage occurs (Chmura et al. 2003), I sectioned and homogenized four cores
from each site at 20-25 cm depth.
Organic matter analyses
To compare OM characteristics among sites of varying NO3- legacy, I subsampled
sediments from each core under anoxic conditions, immediately flash froze them in liquid
nitrogen for nucleic acid extraction, and stored the sediments at -80ºC until further analysis. I
froze another subsample of sediment at -20ºC for OM characterization and set aside the
remaining sediment in anoxic jars for the decomposition experiment, described below. I
performed elemental composition analysis (%C and %N) using a Costech Elemental Analyzer
4010 (Costech Analytical Technologies, Valencia, CA) on the initial sediments that were dried at
92
65ºC and fumed with 12N hydrochloric acid. I used the same dried samples to measure %S by
combusting them at 1350ºC and measuring sulfur dioxide production on a LECO S635 S
Analyzer (LECO corporation, Saint Joseph, MI). I then dried additional samples at 105ºC to
obtain water content and used these values to calculate bulk density. I performed a one-way
ANOVA with core replicate as a random effect to compare resulting values for %OM, %C, %N,
molar C:N, and %S across sites.
I also used Fourier Transform-Infrared Spectroscopy (FT-IR) to more precisely examine
differences in OM across sites. This spectroscopic technique provides detailed information about
the relative abundance of chemical functional groups important to decomposition, thereby
allowing for inferences regarding certain OM properties such as recalcitrance. To prepare
samples for FT-IR, I finely ground sediments that were dried at 40ºC for 48 hours. I ran each
sample on a Bruker Vertex 70 FT-IR (Bruker Optics Inc., Billerica, MA) with a Pike AutoDiff
diffuse reflectance accessory (Pike Technologies, Madison, WI) and obtained data as pseudo-
absorbance (log[1/reflectance]) in diffuse reflectance mode. I collected scans at the mid-IR range
(4000-400 cm-1), at a 2 cm-1 resolution, with 60 co-added scans per spectrum, and used a mirror
for background correction and potassium bromide (KBr) and Harvard Forest soils as standards.
To baseline correct the data, I transformed each raw spectrum using a calculated two-point linear
tangential baseline in Unscrambler X (Camo Software, version 10.1, Woodbridge, NJ) and
assigned peaks (Table S1) according to Parikh et al. (2014) and Margenot et al. (2015). I also
calculated a relative recalcitrance index, routinely used to describe soils and sediments (Ding et
al. 2002, Veum et al. 2014), using the following equation:
Eq. 1 Index II = 2924 + 2850 + 1650 + 1470 + 1405 + 920 + 840
3400 + 1270 + 1110 + 1080
93
where each value represents a wavenumber (Table S1) corresponding to either a C (numerator)
or oxygen-bonded (denominator) functional group. Higher Index II values are typically
associated with greater levels of sediment OM recalcitrance (Ding et al. 2002; Veum et al. 2014).
I compared Index II values across sites using a one-way ANOVA.
Nucleic acid extraction, amplification, and sequencing
To assess differences in microbial community structure among my sites, I extracted
genomic DNA from approximately 0.25 g sediment using the MoBio® PowerSoil DNA
Isolation Kit (MoBio Technologies, CA, USA) following manufacturer’s instructions, and eluted
the DNA into a 35 µL final volume. To extract RNA, I used a method modified from Mettel et
al. (2010) according to Kearns et al. (2016). I added 700 µL PBL buffer (5 mM tris-
hydrochloride [pH 5.0], 5 mM ethylenediaminetetraacetic acid disodium salt, 0.1% [wt/vol]
sodium dodecyl sulfate, and 6% [vol/vol] water-saturated phenol), to approximately 0.5 g
sediment, and 0.5 g of 0.17 mm glass beads. After vortexing at maximum speed for 10 minutes, I
spun the samples at 20,000 x g for 30 seconds and transferred the supernatant to a new tube. To
resuspend the remaining sediment and glass beads, I added 700 µL TPM buffer (50 mM tris-
hydrochloride [pH 5.0], 1.7% [wt/vol] polyvinylpyrrolidone, 20 mM magnesium chloride), and
vortexed at maximum speed for an additional 10 minutes. I spun the sediment at 20,000 x g for
an additional 30 seconds, and pooled the supernatant with the supernatant from the previous step.
To each sample, I added an equal volume of phenol:chloroform:isoamyl alcohol (25:24:1 v/v/v),
mixed by vortexing at maximum speed for 30 seconds, and spun at 20,000 x g for 30 seconds. I
then transferred the aqueous layer to a new tube and precipitated nucleic acids using 0.7x
volumes of 100% isopropanol and 0.1x volumes of sodium acetate [pH 5.7]. After spinning at
20,000 x g for 30 minutes, I discarded the supernatant, and washed the resulting pellet using 70%
94
ethanol. I loaded the washed RNA onto an Illustra Autoseq G-50 Spin Column (GE Healthcare),
and spun at 650 x g for 7 seconds, and eluted three times with 200 µL 1.5 M NaCl (pH 5.5). I
precipitated the flow through with 0.7x volumes of 100% isopropanol and 0.1x volumes of
sodium acetate (pH 5.7), spun at 20,000 x g, and resuspended the resulting pellet in 50 µL di-
ethyl pyrocarbonate (DEPC) treated water. I checked for DNA contamination in the RNA using
general bacterial primers 515F and 806R (Bates et al. 2011), and removed contamination using
DNase I (New England BioLabs, Ipswich, MA). Lastly, I reverse transcribed 2 µL RNA to
cDNA using random hexamer primers and an Invitrogen Superscript III cDNA synthesis kit for
RT-PCR (Life Technologies, Carlsbad, CA, USA).
After confirming the presence of DNA using SYBR Safe (Thermo Fisher Scientific,
Waltham, MA), I amplified in triplicate the V4 region of the reverse transcribed 16S rRNA and
the 16S rRNA gene using the general bacterial primer-pair 515F (Bates et al. 2011; 5’-
GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’-GACTACHVGGGTWTCTAAT-3’) with
Illumina adaptors (Caporaso et al. 2012) and individual 12-bp GoLay barcodes on the reverse
primer, using the following reaction: 10 µl 5-Prime Hot Master Mix (Quanta Bio, Beverly, MA),
0.25 µl of 20 µM forward and reverse primers, 13.5 µL DEPC-treated water, and 1 µl of DNA of
cDNA template. I gel purified PCR products using a Qiagen® QIAquick gel purification kit
(Qiagen, Valencia, CA) and quantified the purified product using a Qubit® 3.0 fluorometer (Life
Technologies, Thermo Fisher Scientific, Waltham, MA). After pooling to equimolar amounts, I
performed sequencing on an Illumina MiSeq (Illumina, San Diego, CA) platform using the
paired-end 250 bp 500 cycle kit with V2 chemistry.
I analyzed sequence data in QIIME 2 (version 2018.2) and demultiplexed a total of
2,871,972 sequences from the 16S rRNA gene and its gene product, 16S rRNA. Using the
95
DADA2 plugin (Callahan et al. 2016), I inferred amplicon sequence variants (ASVs) with a
maxEE of 2 and the consensus chimera removal method. I then assigned taxonomy against the
Greengenes 16S rRNA sequence database (version 13-8; McDonald et al. 2012) and removed
any sequences matching chloroplasts and mitochondria in addition to ASVs occurring only once
(singletons). Following quality filtering, I had a total of 2,094,824 sequences with an average of
29,095 sequences per sample that I aligned using MAFFT v.7 (Katoh & Standley 2013).
I examined differences in the microbial community from each site by conducting a
canonical correspondence analysis (CCA; ter Braak & Verdonschot 1995) on ASV tables
normalized to 16,426 sequences (my lowest sequencing depth for the 16S rRNA gene) and tested
for significance among sites using the ‘anova.cca’ function with 9999 permutations and 100
steps in the VEGAN package (v4.6-12; Okansen et al. 2017). I then identified environmental
factors driving community structure using the envfit vector fitting function in the VEGAN
package after screening for multicollinearity by excluding any factors with a variance inflation
factor (VIF) value exceeding 5. To identify which taxa differed most among sites, I performed a
differential abundance analysis in QIIME 2 using the ANCOM (analysis of composition of
microbiomes) plugin (Mandal et al. 2015), which compares relative abundance between groups
by calculating the Aitchison’s log-ratio of the relative abundance of a single taxon against that of
the remaining taxa (Aitchison 1986) and has stringent controls against false discovery (Weiss et
al. 2017). I applied ANCOM on a genus-level table filtered with ASVs that appeared at least 100
times in the dataset and used pseudo-count values to make all counts non-zero.
To quantify the abundance of 16S rRNA gene copies, I performed quantitative PCR
(qPCR) in triplicate using 357F and 519R primers (Turner et al. 1999) on an Aria Mx Real Time
PCR instrument (Agilent Technologies). Each 20 µl reaction consisted of 10 µl of Brilliant III
96
Ultrafast SYBR Green qPCR Master Mix (Agilent Technologies), 0.5 µl of each primer (20
µM), 8 µl of DEPC-treated water, and 1 µl DNA template. I then used the qPCR program
optimized for 16S rRNA gene targets, which consisted of an initial denaturation step at 95ºC for
3 minutes followed by 40 cycles of 95ºC for 5 seconds and 60ºC for 10 seconds, with data
acquisition at the end of each cycle. A melt curve was conducted to confirm the purity of the
target gene amplification product. Standards were prepared from purified 16S rRNA gene
amplicons, which were quantified and assessed for size with a tape station (Agilent
Technologies). Standard curves, run with each batch of samples, resulted in R2 >0.95 and qPCR
efficiency >90%. I compared abundance of 16S rRNA gene copies across sites using a one-way
ANOVA.
Decomposition experiment
I next conducted a decomposition flow through reactor (FTR) experiment to assess how
sediments from each site described above would respond to further NO3- addition. My FTR
experimental system (Bulseco-McKim et al. In review) is a modified version of the system
described in Pallud et al. (2006, 2007). In contrast to whole-core incubations or sediment
slurries, using an FTR system provides biogeochemical rate measurements under steady-state
conditions, prevents the accumulation of dissolved metabolic byproducts, and allows for the
isolation of microbial activity from other environmental conditions that may obscure
measurements in the field (e.g. influence of plant communities or tidal flux). Each FTR consists
of two polyvinyl chloride radially-scored caps that ensure uniform flow and that are sealed with
O-rings to prevent leakage. To assess flow characteristics in the reactors, which were 31.81 cm3
in volume, I performed breakthrough experiments using bromide. Flow property characteristics
can be found in Fig. S1 and Table S2.
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Under anoxic conditions, I loaded the FTRs with homogenized sediment collected from
20-25 cm at each site and randomly assigned reactors a treatment, either plus-NO3- (+NO3
- in 0.2
µm filtered seawater) or unamended (0.2 µm filtered seawater only, representing natural marsh
conditions). To prepare the two treatment reservoirs, I filtered water collected from Woods Hole,
MA (0.2 µm pore size), sparged the reservoirs with N2 gas until they reached anoxic conditions,
and spiked the plus-NO3- reservoir with 500 µmol L-1 additional K15NO3
- (Cambridge Isotope
Laboratories, Andover, MA). Half of the reactors from each site received the plus-NO3-
treatment and half received the unamended treatment, both at a targeted flow rate of 0.08 mL
min-1 under continuously anoxic conditions. To determine nitrate reduction pathways in the plus-
NO3-treatment, I followed the fate of the 15N tracer into various end products (28, 29, 30N2). I
collected samples approximately every 10 days from both the reservoir and effluent throughout
the 100-day experiment once the FTRs reached steady state, which took approximately 10 days.
To assess changes that occurred in the bulk sediment as a result of experimental conditions, I
homogenized sediment from each FTR at the end of the experiment. I immediately sub-sampled
sediments for nucleic acid extraction and OM analysis following methods described above.
Biogeochemical analyses
I collected water samples from both the plus-NO3- and unamended treatment effluent, as
well as each reservoir, to quantify biogeochemical processes resulting from microbial activity in
the decomposition experiment. I measured dissolved inorganic C (DIC; CO2 + HCO3 + CO32-) as
an indicator of total microbial respiration on an Apollo SciTech AS-C3 DIC analyzer (Newark,
DE) and NO3- consumption on a Teledyne T200 NOx analyzer (Teledyne API, San Diego, CA)
using chemoluminescent methods (Cox 1980). I measured ammonium (NH4+) and sulfide (HS-)
colorimetrically on a Shimadzu 1601 spectrophotometer (Kyoto, Japan) following protocols
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from Solorzano (1969) and Gilboa-Garber (1971), respectively. To quantify analyte consumption
or production rate over time, I followed eq. 2 (Pallud et al. 2006):
Eq. 2 R =(Cout−Cin)Q
V
where R is the consumption or production rate of interest, Cout and Cin are the effluent and
reservoir analyte concentrations, respectively, Q is the measured flow rate in L hour-1, and V is
the FTR volume (31.81 cm-3). I then calculated a cumulative flux for each analyte by integrating
between each measured point throughout the experiment. Since background SO42- concentrations
are typically high in seawater (~28 mmol L-1), I determined SO42- reduction rates (SRR) by
calculating the sum of the total production of hydrogen sulfide (HS-) and total sulfur (S) at the
end of the experiment.
To determine the relative contribution of each NO3- reduction pathway, I made dissolved
gas measurements of N2 on a membrane inlet mass spectrometer (Kana et al. 1994) connected to
an inline furnace set to 500ºC and copper column to remove oxygen interference (Eyre et al.
2002, Lunstrum & Aoki 2016). I monitored the production of 29N2 and 30N2 from added 15NO3-
tracer as a measure of denitrification (DNF) and calculated rates using the following equation
from Nielson et al. (1992):
Eq. 3 D15 = p29+2p30
Where D15 is denitrification from 15NO3- and p29 and p30 represent production of 29N2 and 30N2,
respectively. Because I only added NO3- in the form of 15NO3
-, and ambient concentrations of
14NO3- were largely below detection, I did not calculate D14. I also considered production of
14NO3- from nitrification, which is a largely aerobic process (Herbert 1999), as negligible since
this experiment was conducted under strictly anoxic conditions. To measure DNRA, I bubbled
water samples with helium for 10 minutes to remove any N2 and converted 15NH4+ produced
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from DNRA to 29N2 and 30N2 using sodium hypobromite following the OX/MIMS method (Yin
et al. 2014). I calculated DNRA rates as:
Eq. 4 DNRA15 = p29+2p30
Where DNRA15 is DNRA from 15NH4+ and p29 and p30 represent production of 29N2 and 30N2,
respectively. These measurements represent denitrification and DNRA occurring in the FTRs
that result from adding 500 µmol L-1 15NO3-. I did not make these measurements in the
unamended treatment where I did not add any 15NO3-. Isotope tracer measurements were
completed at 9, 12, and 13-weeks after the start of the experiment, with no DNRA measurements
available on week 12 due to sample limitation.
Statistical analyses for decomposition experiment
To examine differences in DIC production across treatments and site, I performed a
repeated measures ANOVA. I compared cumulative fluxes of DIC production, and NO3- and
SO42- reduction (HS- production + S storage), across treatment and site using a two-way
ANOVA and calculated an effect size to measure the magnitude of response to NO3-. To further
explore the influence of initial bulk C supply on DIC production, I calculated total C loss by
taking the proportion of C released as DIC divided by the total mass of C per reactor using
sediment bulk density and %C. I performed a mass balance between DIC production and
NRR/SRR, calculated the proportion of NRR explained by DNF and DNRA in the plus-NO3-
treatment, and performed a one-way ANOVA comparing DNF and DNRA across sites. I also
calculated a DNF:DNRA ratio to assess the contribution of DNRA relative to DNF and
compared this ratio across sites using a one-way ANOVA.
To examine changes in OM characteristics (%C, %N, and %S) that occurred as a result of
the decomposition experiment, I compared the plus-NO3- and unamended treatments within site
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using a student’s t-test. I performed a principal coordinate analysis (PCoA) across the FT-IR
spectra (4000-400 cm-1) and tested for differences among treatment and site using a
PERMANOVA with 999 permutations on a resemblance matrix constructed using Manhattan
distance. To better understand which spectral bands and associated functional groups were
responsible for observed patterns in the PCoA, I plotted the Pearson’s correlation coefficients
against wavenumber, with peaks exhibiting the greatest absolute change influencing the PCoA
most. Lastly, I performed a linear regression between DIC production and Index II values across
all treatments and sites.
To analyze the response of the microbial community to NO3- addition in the
decomposition experiment, I performed beta diversity analysis on a 16S rRNA gene ASV table
normalized to 16,427 sequences per sample, which was my lowest sampling depth, and tested for
significant differences among treatments and site using PERMANOVA with 999 permutations in
the vegan package (Oksanen et al. 2017). To further assess the effect of NO3- across sites, I
compared the weighted UniFrac dissimilarity values between plus-NO3- and unamended
treatments and tested for significance using a one-way ANOVA. I then ran a random forest
model with 10,000 trees with the randomForest R package (v4.6-12; Liaw & Wiener 2002) using
ASVs that occurred at least 100 times, to identify which taxa were most important in
discriminating between the plus-NO3- and unamended treatment within each site. This allowed
us to identify which taxa were most significantly associated with each treatment, and whether
those distinguishing taxa differed among sites. To do this, I calculated the difference in relative
abundance between the plus-NO3- and unamended treatments on a per core basis on the top ten
most important ASVs to examine how these taxa changed in response to the NO3- addition.
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Lastly, because prior studies showed that microbes in salt marsh and soil habitats exhibit
high rates of dormancy (Kearns et al. 2017) and abundant relic DNA (Carini et al. 2016),
particularly in response to nutrient enrichment (Kearns et al. 2016), I assessed the effect of NO3-
on microbial activity at each site. To accomplish this, I calculated a 16S rRNA:16S rRNA gene +
1 ratio on ASVs that appeared at least 100 times across all samples and defined any ratio >1 as
active at the ASV level (Jones & Lennon 2010). I then identified taxa that changed most in
activity by comparing the activity ratio in the plus-NO3- and unamended treatments within
replicate cores that started with the same initial microbial community using permutation tests
against a null distribution. There are caveats associated with the use of a 16S rRNA:16S rRNA
gene ratios, such as variations in rRNA production, growth, and gene copy (Blazewicz et al.
2013; Steven et al. 2017; Papp et al. 2018); however, by comparing the putative activity ratio
between treatments only within a given taxon, these biases are minimized.
Results
Initial site characterization
There were no significant differences (Table 1) in %OM, %C, or molar C:N of initial
sediments across sites (Fig. 1); however, %N was different (p=0.003, F2,8=21.91) with higher
values at both the 13-year and 40-year enriched site. %S was greater in the reference site when
compared to the 40-year enriched site (p=0.010, F2,9 = 10.98) and Index II was higher at the 40-
year enriched site when compared to both the reference and 13-year enriched sites (Fig. 2A;
p<0.001, F2,9=20.99). These differences were also evident in the baseline corrected spectra (Fig.
2B), where the 40-year enriched sediments demonstrated polysaccharide depletion (1080 and
1110 cm-1) and enrichment of aromatic compounds (1650 cm-1). 16S rRNA gene abundance
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varied among sites (p=0.026, F2,8=5.943) with a significantly greater copy number at the
reference site when compared to 40-year enriched site (Table 1). A PERMANOVA revealed a
significant effect of site on microbial community structure (Fig. 3A; p=0.001, pseudo-
F2,9=1.991), with %S and Index II playing a significant role (p < 0.05) at the reference and 40-
year enriched site, respectively. ANCOM analysis (Fig. 3B) showed that the top 20 ASVs most
differentially abundant among sites accounted for 13.57, 10.26, and 7.57% of the total number of
sequences for the reference, 13-year, and 40-year enriched sites, respectively. The orders
Desulfobacterales, Desulfarculales, and Bacteroidales were similar between the reference and
13-year enriched sites, but the 40-year enriched site was quite different, exhibiting greater
overall diversity. Nitrosomonadales were only present in the 40-year enriched site, and orders
Saprospirales, Cytophagales, and an unclassified Acidobacteria were present only in the enriched
sites and absent in the reference site.
Total microbial respiration
During the decomposition experiment, DIC production rate in the unamended treatment
averaged 21.67 ± 2.55, 19.41 ± 0.91, and 14.71 ± 2.53 µmol cm-3 hr-1 for the reference, 13-year,
and 40-year enriched sites, respectively (Fig. 4). NO3- addition resulted in significantly higher
DIC production rates when compared to unamended sediments across all sites, averaging 26.48 ±
5.51, 24.57 ± 3.88, and 18.57 ± 3.70 µmol cm-3 hr-1 for the same sites, respectively. Cumulative
DIC was higher in the plus NO3- sediments across all sites (Fig. 5; p=0.002, F1,20=12.246), and
higher overall at the reference and 13-year enriched sites when compared to the 40-year enriched
site (Fig. 5; p<0.001, F(2,14)=48.33) regardless of treatment. There was no significant interaction
between treatment and site, however, the effect size (Cohen’s d) in response to NO3- was 1.65,
1.90, and 0.78 for the reference, 13-year, and 40-year enriched sites, respectively, corresponding
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to ~74, 79, and 43% non-overlap between each treatment distribution. The proportion of C lost
as DIC was 1.41% ± 0.10, 1.08% ± 0.04, and 0.72% ± 0.06 in the plus-NO3- treatment, and
1.01% ± 0.11, 0.89% ± 0.12, and 0.58% ± 0.01 for the unamended treatment, for the reference,
13-year enriched, and 40-year enriched sites, respectively. There were no significant trends by
treatment or site when examining NO3- reduction rate (p=0.39; Fig. 6A) and cumulative nitrate
reduction (p=0.149; Fig. 6B) or sulfide production rate (p=0.40; Fig. 6C) and cumulative sulfate
reduction (p=0.359; Fig. 6D).
When examining the ratio of DIC production:NO3- consumption in the plus-NO3
-
treatment, average per site was 0.61 ± 0.10, 0.55 ± 0.17, and 0.56 ± 0.12 for the reference, 13-
year, and 40-year enriched sites, respectively, which is considerably less than the ratio that
would be predicted by DNF (1.25) or heterotrophic DNRA (2) stoichiometry (Canfield et al.
2005, Giblin et al. 2013). When including any SO4- reduction that occurred in the plus-NO3
-
treatment, this ratio was even lower (0.56 ± 0.04, 0.55 ± 0.08, 0.53 ± 0.06), indicating that not all
DIC could be accounted for as a result of NO3- and SO4
- reduction. In contrast, the DIC
production:SO4- reduction ratio in the unamended treatment was much closer to, but exceeded,
the stoichiometrically predicted value of 2 (2.53 ± 1.31, 3.71 ± 1.36, and 6.08 ± 7.61 for
reference, 13-year, and 40-year enriched sites, respectively).
The sum of DNF and DNRA accounted for ~44, 40, and 61% of total NRR on week 9
and ~60, 76, and 44% of NRR on week 13 at the reference, 13-year, and 40-year enriched sites,
respectively (Table 2). Overall DNF rates were significantly greater at the 13-year enriched site
when compared to the 40-year enriched site (p=0.010, F2,26=5.531), and DNRA rates were
greatest at the reference site (p=0.002, F2,19=9.174), with significant differences between the 13-
year and 40-year enriched sites, and 40-year and reference sites (but not the 13-year and
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reference sites) (Table 2). The DNF:DNRA ratio was 3.01 ± 0.68 (Reference), 3.03 ± 0.52 (13-
year enriched), and 11.49 ± 3.63 (40-year enriched), indicating that DNF was always the
dominant nitrate reduction process. The 40-year enriched site, however, exhibited less than half
the amount of DNRA relative to DNF when compared to the reference and 13-year enriched site
(p=0.008, F2,15=6.764).
Organic Matter Characteristics
A student’s t-test indicated a significant difference in %C between plus-NO3- and
unamended sediments in the reference marsh (p=0.002, t=-5.219, df=6; Table 3), with all other
bulk sediment properties (%OM, %N, and %S) showing no change as a result of nutrient
enrichment or site. This was not surprising considering only 0.5-1.5% of C was lost as DIC
through respiration. A PCoA of the whole FT-IR spectra allowed for a more refined examination
of OM characteristics (Fig. 7A), demonstrating a clear separation by site along the primary axis
(explaining 84.5% of the variation). There was no effect of treatment (p=0.687), however,
PERMANOVA indicated a significant effect of site (p=0.001, pseudo-F2,27=48.779), with these
differences largely resulting from decreases in polysaccharides (C-O band at 1080 cm-1) and
increases in aromatic (C=C band at 1650 cm-1), aliphatic (asymmetric and symmetric stretching
vibration C-H bands at 2924 cm-1 and 2850 cm-1, respectively), and amide C (N-H and C=N
bands at 1575 cm-1; Fig. 7B). A linear regression showed that DIC production significantly
decreased with increasing recalcitrance as indicated by greater Index II values (Fig. 7C; p<0.001,
F1,10=26.42, R2=0.698).
Microbial community in response to NO3-
A PCoA (Fig. 8A) constructed from weighted UniFrac of the microbial communities
sampled at the end of the decomposition experiment indicated that microbial communities
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differed according to site (PERMANOVA: p=0.001, F=4.11). NO3- addition significantly altered
microbial community structure (p=0.001, pseudo-F=5.041) by shifting the similarities along the
primary axis, however the difference between treatments across sites was not significant (Fig.
8B; p=0.213). Random forest modeling correctly discriminated between the plus-NO3- and
unamended treatments 62.5, 87.5, and 87.5% of the time for the reference, 13-year enriched, and
40-year enriched sites, respectively (kappa-statistic = 25, 75, and 75%). This allowed me to
identify the top ten ASVs most important in correctly classifying between treatments (Fig. 9;
Table S3, which accounted for 7.19, 24.09, and 8.18% of the dataset. Orders Desulfarculales
(~3.4%) and Myxococcales (~0.63%) increased most in relative abundance at the reference site,
Thiotrichales (~2.5%) increased most at the 13-year enriched site, and Chromatiales increased
most at both the 13-year (~9.89%) and 40-year enriched (0.01%) sites, in response to NO3-
enrichment. Desulfobacterales decreased in response to NO3- at both the 13-year and 40-year
enriched sites, demonstrating a ~6.9 and ~10.6% decrease in relative abundance.
According to permutation tests comparing the 16S rRNA:16S rRNA gene ratios between
plus-NO3- and unamended treatments, there were 17, 12, and 15 taxa out of 1895 ASVs that
exhibited a significant change in activity at the reference, 13-year enriched, and 40-year enriched
sites, respectively, when compared to a null distribution (Fig. 10; Table S4). Orders
Thiotrichales, Chromatiales, Rhodothermales, Kiloniellales, Oceanospirillales, and
Alteromonadales were consistently more active (greater 16S rRNA:16S rRNA gene ratio) under
high NO3- conditions, while Syntrophobacterales, Desulfobacterales, Desulfarculales,
Campylobacterales, Clostridiales, Desulfovibrionales, and Candidate Phylum GN15 were more
active in the unamended treatment. Some orders, such as Myxococcales, were active in both
treatments.
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Discussion
Nutrient-enriched sites contain less labile OM and N-adapted microbes
There were no significant differences in bulk %OM, %C, %N, and C:N ratio (Table 1),
however, sediments from the 40-year enriched site demonstrated significantly higher Index II
values compared to the 13-year enriched and reference sites (Fig. 2A). This metric, which is
commonly used to evaluate soil quality (Ding et al. 2002, Veum et al. 2014), increases with
greater recalcitrance due to the accumulation of C relative to O-containing functional groups
(Chefetz et al. 1998; Ding et al. 2002; Veum et al. 2014; Margenot et al. 2015). One explanation
for this pattern is the presence of a NO3--accessible pool of OM that is reactive only under
conditions of high NO3- (e.g. Bulseco-McKim et al. In review). Under this scenario, sites
experiencing chronic nutrient enrichment would demonstrate greater rates of decomposition
during initial deposition, thereby resulting in the accumulation of less labile OM over time. I
observed this pattern in the 40-year enriched site, despite the fact that it contained greater overall
%C. This pattern is further supported by the FT-IR spectra (Fig. 2B), where the depletion of
polysaccharides and enrichment of more aromatic forms of C in the 40-year enriched site suggest
that microbes are oxidizing more labile OM and leaving behind compounds that are more
chemically complex, such as lignin.
Another explanation for differences in OM quality may be due to changes to the
microbial community that result from shifting environmental conditions. In this study, I observed
significant site-specific differences in both microbial abundance and community structure (Table
1; Fig. 3A). In reference sediments, %S was a significant driver of community structure,
suggesting that in the absence of NO3-, S-metabolism is the dominant metabolic strategy in
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reference sediments, as is typical in salt marshes (Howarth & Teal 1979, Howarth 1984). Out of
the top 20 ASVs that were most differentially abundant among sites, groups belonging to orders
known to reduce SO42- (Klepac-Ceraj et al. 2004, Bahr et al. 2005), including Desulfobacterales,
Desulfarculales, and other unclassified Deltaproteobacteria, were most abundant at the reference
site. The relative abundance of these orders was lower at both the 13-year and 40-year enriched
sites, suggesting that taxa associated with SO42- are not as dominant at the chronically enriched
sediments in this study when compared to reference sediments.
The divergence in the microbial community could also be due to salinity differences
(Table 1), as increases in taxonomic groups associated with SO42- reduction tend to occur at sites
with greater salinity (Howarth & Teal 1979). However, the most significant salinity-induced
changes to microbial communities seem to occur at ~5 ppt (Weston et al. 2010) when
metabolisms shift from methanogenesis to SO4- reduction. Salinity was lower at the 40-year
enriched site, but was also more variable, when compared to the reference and 13-year enriched
sites. Regardless of this difference, the same taxa associated with SO4- reduction decreased in
both the 13-year and 40-year enriched site, suggesting that salinity was less important than
nutrient enrichment in driving this pattern (Fig. 3B). Further, the 13-year enriched site had a
higher salinity than the reference site (Table 1) so the abundant ASVs associated with SO4-
reduction were highest at the site with intermediate salinity, further indicating that nutrient
enrichment, not salinity, was the major driver of microbial community structure across sites.
These shifts have important implications for OM quality, as SO42- reducers can typically only
decompose low-molecular weight compounds, leaving behind fractions that may only be
accessible to microbes under high NO3- conditions (Canfield 1989, Westrich & Berner 1984).
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Index II was a significant driver of microbial community structure at the 40-year enriched
site (Fig. 3A), where total microbial abundance was an order of magnitude lower than the 13-
year enriched and reference sites (Table 1). This is likely due to the energy required to oxidize
more recalcitrant OM, or because competition over complex forms of OM results in lower
overall microbial abundance (Table 1). I identified several taxa that were more abundant in the
enriched sites and that belong to groups known to oxidize complex forms of OM (Fig. 3B). Taxa
from the group Anaerolineae contained the broadest range of carbohydrate hydrolytic genes in a
metagenomic study that reconstructed 82 bacterial genomes from estuarine sediment, suggesting
an important role in degrading complex C compounds (Baker et al. 2015). In addition, groups
from the Phylum Acidobacteria, also observed in this study, were widely distributed in soils
where they metabolized more refractory forms of OM (Hartman et al. 2008), providing further
evidence that microbes found in the enriched sites can oxidize less labile forms of OM that
resulted from greater decomposition rates during initial OM burial.
Several taxa, including those from the orders Cytophagales, Rhodocyclales, Chlorobi,
and Nitrosomonadales, were significantly more abundant in both enriched sites, suggesting they
might be important in N-cycling. Metagenomic sequencing of marine sediments revealed that
Cytophagales harbored high abundances of the nosZ gene, a key gene in the final step of
denitrification (Rasigraf et al. 2017). This is consistent with other metagenomic studies in salt
marsh sediments that observed elevated abundance of nosZ and atypical nosZ (Graves et al.
2016) and other genes associated with denitrification within the Cytophagales group (Didi-
Andreote et al. 2016). Both Rhodocyclales and Chlorobi include taxa with the capacity for
DNRA (Saito et al. 2008, Rasigraf et al. 2016) and were found in high concentrations in sites
receiving wastewater effluent (Lu & Lu 2014). Nitrosomonadales was also detected in degraded
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wetland systems (Wu et al. 2013) either acting as chemolithoautotrophic or mixotrophic
ammonia oxidizers (Garrity et al. 2005). The significant increase in these groups in the enriched
sites of this study suggest that these taxa are better suited to take advantage of high N supply
and, through their N removal capacity, may enhance ecosystem resiliency by limiting
susceptibility to additional nutrient disturbance.
The nitrate accessible pool of OM is smaller at nutrient enriched sites
I hypothesized that if chronically enriched sites contained taxa more adapted to high N
conditions and less labile OM, then these communities would not respond as strongly to further
NO3- addition since buried OM that is available for oxidation would be more recalcitrant.
Although the addition of NO3- resulted in significantly greater DIC production compared to the
unamended treatment at all of my sites (Fig. 4, 5); the effect size was lowest at the 40-year
enriched site, supporting my initial hypothesis. Lower microbial respiration rates suggest that the
NO3--accessible pool of OM is smaller at the 40-year enriched site, presumably because some of
it was oxidized during burial. In the reference site, where more of the NO3--accessible OM pool
was still intact, there was greater decomposition of these deep sediments. A significant shift in
%C resulting from NO3- addition (Table 3), a higher decomposition rate (Fig. 4, 5) when
compared to the enriched sites, and the FT-IR spectral data all support the idea that chronic
enrichment may have already chemically altered the OM. These data suggest that the legacy of
nutrient enrichment may be more important in shaping OM quality than acute exposure and that
these long-term shifts in OM influence the response to additional NO3- exposure by increasing
recalcitrance (Fig. 7C).
When examining the mass balance between DIC:NO3- and SO4
2- reduction in the plus-
NO3- treatment, the ratios were considerably lower than the predicted stoichiometry for both
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DNF and heterotrophic DNRA. This indicates that either 1) NO3- is being consumed by
assimilation or other processes that I would not detect through DIC production or 2)
chemoautotrophic processes are fixing DIC via their metabolism, thus drawing down the DIC
pool. Although a small amount of assimilation is likely occurring, I saw no significant
differences in cell abundance between the plus- NO3- and unamended treatments at the end of
the experiment, suggesting assimilation into greater cell biomass is unlikely to account for the
discrepancy in the ratios (Table 3). Further, anaerobic microbes are seldom nutrient-limited
because their growth-per-unit substrate-intake is much lower than for aerobic microbes (Canfield
et al. 2005). Chemoautotrophic processes, on the other hand, could include NO3- reduction
coupled to reduced S and iron (Burgin & Hamilton 2007, Giblin et al. 2013), both of which are
abundant in these sediments. Both processes fix DIC and would result in lower DIC production
relative to NO3- consumption than expected by stoichiometry.
Relative contributions of DNF and DNRA to nitrate reduction
The competing dissimilatory NO3- reduction pathways, DNF and DNRA, showed patterns
consistent with what would be predicted based on resource availability (Algar & Vallino 2014). I
found that the 13-year enriched marsh had significantly higher DNF rates than either the
reference or 40-year enriched sites (Table 2). DNF is favored over DNRA when the ratio of NO3-
availability to C lability is high because it provides more free energy per mole of C oxidized than
DNRA (Tiedje 1988, Giblin et al. 2013). Koop-Jakobsen & Giblin (2010) also found that
nutrient enrichment increased DNF by 16-fold in creek sediment from this same site when
compared to the reference site, although DNRA also increased by 10-fold. The more recalcitrant
nature of the sediments in the 40-year enriched marsh may explain why DNF rates were lower at
this site in spite of high nutrient enrichment (Fig. 2A). DNRA was significantly higher at the
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reference and 13-year enriched marshes when compared to the 40-year enriched marsh (Table 2).
DNRA tends to dominate under conditions of high labile OM and NO3- limitation, since this
process can efficiently transfer 8 electrons per mole of NO3- reduced as opposed to the 5
electrons transferred in DNF (Tiedje 1988, Burgin & Hamilton 2007). What is most interesting is
the DNF:DNRA ratio was highest at the 40-year marsh (Table 2), suggesting that a combination
of greater recalcitrance (Fig. 2A) and higher NO3- concentrations favored DNF relative to DNRA
(Algar & Vallino 2014). Identifying the dominant NO3- reduction pathway is important because
each pathway plays a considerably different role in both N and OM cycling. While DNF
efficiently removes bioavailable N from the system, DNRA retains it, making it available for
autotrophic use (Tiedje 1988, Giblin et al. 2013). Furthermore, both DNF and DNRA can be
either heterotrophic or chemoautotrophic, with the latter augmenting C stores in the sediment.
Therefore, understanding the controls over NO3- reduction pathways is critical for both N
management and restoration applications.
NO3- driven shifts in microbial community structure and activity
Initial sediments from the 13-year and 40-year enriched sites contained taxa associated
with N-cycling, so I hypothesized that further NO3- addition would not alter the microbial
community structure compared to the reference site. Instead, I observed a significant shift in
microbial community structure in response to short-term NO3- addition at all sites. Regardless of
treatment, samples clustered by site, with reference and 13-year enriched site demonstrating
more similar community structure than the 40-year enriched site. This large-scale, site-specific
selection is commonly observed in microbial communities (Martiny et al. 2006, Hanson et al.
2012). More interestingly, the microbial community structure shifted in response to NO3- across
all sites, with no difference among sites in the intensity of this shift, as evidenced by equivalent
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dissimilarity values between plus-NO3- and unamended treatments (Fig. 8B). This contradicts my
hypothesis that communities from the chronically enriched site would exhibit a decreased
response and suggests that regardless of the initial community structure, additional NO3- in this
experiment shifted the microbial community.
Random forest analysis allowed me to identify ASVs that were most important in
correctly classifying between treatments (Fig. 9). Across all sites, taxa belonging to groups
known to oxidize sulfur (Thiotrichales, Chromatiales; Garrity et al. 2005, Imhoff et al. 2005,
Thomas et al. 2014), carry out steps in denitrification (Myxococcales, Oceanospirillales), and
degrade hydrocarbons and other chemically recalcitrant substrates (Clostridiales, Marine
Crenarchaeota Group-B10; Biddle et al. 2006, Goldfarb et al. 2011, Mason et al. 2012), were all
important in distinguishing the plus-NO3- treatment. In contrast, groups known to reduce sulfate
(Desulfobacterales; Bahr et al. 2005), degrade aromatic compounds (Burkholderiales; Pérez-
Pantoja et al. 2012), and that are commonly found in deep, anoxic sediments
(Dehalococcoidales, Thermoplasmata-CCA47; Oni et al. 2015), were important in distinguishing
the unamended sediments.
The taxa important in discriminating between treatments were determined via analysis of
the 16S rRNA gene, which includes cells that are intact, recently dead, and dormant (Nielsen et
al. 2007). Salt marsh sediments can have a proportion of cells that are inactive that is as high as
90%, particularly in response to nutrient enrichment (Kearns et al. 2016). This limits my ability
to assess changes to the portion of the microbial community that is actively carrying out critical
ecosystem functions. Thus, I examined 16S rRNA and identified taxa whose activity changed
most between the plus-NO3- and unamended treatments. Consistent with my random forest
analysis of the total microbial community, in the plus-NO3- treatment I observed increases in
113
activity in orders known to reduce NO3- (Kiloniellales, Myxococcales; Wiese et al. 2009),
oxidize sulfur (Chromatiales, Thiotrichales; Garrity et al. 2005, Imhoff et al. 2005, Thomas et al.
2014), and degrade high-molecular-weight (HMW) compounds (Acidimicrobiales,
Flavobacteriales, Thiotrichales; Guibert et al. 2016, Hartman et al. 2009, Mahmoudi et al. 2015,
McCarren et al. 2010). Increases in S oxidizer activity in response to NO3- is particularly
interesting, as these groups of bacteria can use NO3- to carry out both DNF and DNRA
autotrophically. This fixation of C could, in part, explain the stoichiometric discrepancy in the
DIC:NO3- ratios I observed in my decomposition experiment (Fig. 4-6). Further, only the
reference and 13-year enriched sites demonstrated increased activity of sulfur oxidizers, whereas
the 40-year enriched site included more groups known to degrade HMW OM, corresponding
well with the significantly lower DNF and DNRA rates at the 40-year enriched site (Table 2),
where OM was considerably more recalcitrant (Table 1: Fig. 2A).
Conclusions
I showed that reference and 13-year enriched marshes exhibited similar OM
characteristics and microbial community structure, and that the 40-year enriched marsh
contained more recalcitrant OM and a unique microbial community. After exposing these
sediments to additional NO3- in a controlled FTR experiment, the reference site exhibited the
greatest rates of DIC production compared to the unamended control. In contrast, sediments from
the 40-year enriched site demonstrated a lower response to NO3-, despite significant changes to
both microbial community structure and activity. Taken together, this work suggests that long
term nutrient enrichment may lead to less overall C storage; however, the fraction of OM that
does become buried may be more stable when compared to less eutrophic systems. These results
114
highlight the need to consider the effects of chronic nutrient enrichment when determining C
storage potential in salt marsh systems
Acknowledgements
I would like to thank Joseph Vallino at Marine Biological Laboratory for his thoughtful
contribution to this experiment and Tom Goodkind at the University of Massachusetts Boston for
flow through reactor design and construction. I also thank researchers of the TIDE project (NSF
OCE0924287, OCE0923689, DEB0213767, DEB1354494, and OCE 1353140) for maintenance
of the long-term nutrient enrichment experiment and researchers at the Plum Island Ecosystems
LTER (NSF OCE 0423565, 1058747, 1637630). I would also like to thank Sam Kelsey at the
Marine Biological Laboratory and Alan Stebbins at University of Massachusetts Boston
Environmental Analytical Facility (NSF 09-42371 and DBI:MRI-RI2 to Robyn Hannigan and
Alan Christian) for assistance in the laboratory. This work was funded by an NSF CAREER
Award to JLB (DEB1350491) and a Woods Hole Oceanographic Sea Grant award to AEG and
JJV (Project No. NA140AR4170074 Project R/M-65s). Additional support was provided by a
Ford Foundation pre-doctoral fellowship award to ABM. All sequence data from this study is
available in the Sequence Read Archive under accession number TBD. The views expressed here
are those of the authors and do not necessarily reflect the views of NOAA or any of is
subagencies.
115
References
Aitchison J (1986) The statistical analysis of compositional data. J Roy Stat Soc B Met 44:139-
177
Algar CK, Vallino JJ (2014) Predicting microbial nitrate reduction pathways in coastal
sediments. Aquat Microb Ecol 71:223–238
Angell J, Peng X, Ju Q, Craick I, Jayakumar A, Kearns PJ, Ward BB, Bowen JL (2018)
Community composition of nitrous oxide-related genes in salt marsh sediments exposed
to nitrogen. Front Micro 9:170
Averill C, Waring BG, Hawkes CV (2016) Historical precipitation predictably alters the shape
and magnitude of microbial functional response to soil moisture. Glob Change Biol
22:1957-1964
Bahr M, Crump BC, Klepac-Ceraj V, Teske A, Sogin ML, Hobbie JE (2005) Molecular
characterization of sulfate-reducing bacteria in a New England salt marsh. Environ
Microbiol 7:1175–1185
Baker BJ, Lazar CS, Teske AP, Dick GJ (2015) Genomic resolution of linkages in carbon,
nitrogen, and sulfur cycling among widespread estuary sediment bacteria. Microbiome
3:14
Bates ST, Berg-Lyons D, Caporaso JG, Walters WA, Knight R, Fierer N (2011) Examining the
global distribution of dominant archaeal populations in soil. ISME J 5:908–917
Bernhard AE, Dwyer C, Idrizi A, Bender G, Zwick R (2015) Long-term impacts of disturbance
on nitrogen-cycling bacteria in a New England salt marsh. Front Microbiol 6:46
Biddle JF, Lipp JS, Lever MA, Lloyd KG, Sorensen KB, Anderson R, Fredricks HF, Elvert M,
Kelly TJ, Schrag DP, Sogin ML, Brenchley JE, Teske A, House CH, Hinrichs K-U
(2006) Heterotrophic Archaea dominate sedimentary subsurface ecosystems off Peru.
Proc Natl Acad Sci USA 103:3846–3851
Blazewicz SJ, Barnard RL, Daly RA, Firestone MK (2013) Evaluating rRNA as an indicator of
microbial activity in environmental communities: limitations and uses. ISME J 7:2061-
2068
Bowen JL, Byrnes JEK, Weisman D, Colaneri C (2013) Functional gene pyrosequencing and
network analysis: An approach to examine the response of denitrifying bacteria to
increased nitrogen supply in salt marsh sediments. Front Microbiol 4:1–12
Bowen JL, Ward BB, Morrison HG, Hobbie JE, Valiela I, Deegan LA, Sogin M (2011)
Microbial community composition in sediments resists perturbation by nutrient
enrichment. ISME J 5:1540-1548
116
Bricker SB, Longstaff B, Dennison W, Jones a., Boicourt K, Wicks C, Woerner J (2008) Effects
of nutrient enrichment in the nation’s estuaries: A decade of change. Harmful Algae
8:21–32
Bulseco-McKim A, Giblin AE, Tucker J, Murphy AE, Sanderman J, Hiller-Bittrolf K, Bowen JL
(In Review) Nitrate addition stimulates microbial decomposition of organic matter in salt
marsh sediments. Glob Change Biol
Burdige DJ (2007) Preservation of organic matter in marine sediments: Controls, mechanisms,
and an imbalance in sediment organic carbon budgets? Chem Rev 107:467–485
Burgin AJ, Hamilton SK (2007) Have we overemphasized the role of denitrification in aquatic
ecosystems? A review of nitrate removal pathways. Front Ecol Environ 5:89–96
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2:
High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583
Canfield D (1989) Sulfate reduction and oxic respiration in marine sediments: implications for
organic carbon preservation in euxinic environments. Deep Sea Res Part A Oceanogr Res
Pap 36:121–138
Canfield DE, Thamdrup B, Kristensen E (2005) Aquatic Geomicrobiology. Elsevier Academic
Press, Boston, MA
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J,
Fraser L, Bauer M, Gormley N, Gilbert J a, Smith G, Knight R (2012) Ultra-high-
throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.
ISME J 6:1621–1624
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N (2016) Relic DNA is
abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol 2:1–6
Chefetz B, Adani R, Genevini P, Tambone F, Hadar Y, Chen Y (1998) Humic acid
transofrmation during composting of municipal solid waste. J Environ Qual 27:794-800
Chmura GL, Anisfeld SC, Cahoon DR, Lynch JC (2003) Global carbon sequestration in tidal,
saline wetland soils. Glob Biogeochem Cycles 17:1-12
Conant RT, Ryan MG, Ågren GI, Birge HE, Davidson EA, Eliasson PE, Evans SE, Frey SD,
Giardina CP, Hopkins FM, Hyvönen R, Kirschbaum MUF, Lavallee JM, Leifeld J,
Parton WJ, Megan Steinweg J, Wallenstein MD, Martin Wetterstedt JÅ, Bradford MA
(2011) Temperature and soil organic matter decomposition rates - synthesis of current
knowledge and a way forward. Glob Chang Biol 17:3392–3404
Cowie GL, Hedges JI (1994) Biochemical indicators of diagenetic alteration in natural organic
matter mixtures. Nature 369:304
117
Cox RD (1980). Determination of nitrate and nitrite at the parts per billion level by
chemiluminescence. Anal Chem 52:332-335
Cuddington K (2012) Legacy effects : The persistent impact of ecological interactions. Biol
Theory 6:203–210
Deegan LA, Bowen JL, Drake D, Fleeger JW, Friedrichs CT, Galván KA, Hobbie JE, Hopkinson
C (2007) Susceptibility of salt marshes to nutrient enrichment and predation removal.
Ecol Appl 17:42–63
Deegan LA, Johnson DS, Warren RS, Peterson BJ, Fleeger JW, Fagherazzi S, Wollheim WM
(2012) Coastal eutrophication as a driver of salt marsh loss. Nature 490:388–392
Dini-Andreote F, Brossi MJ de L, Elsas JD van, Salles JF (2016) Reconstructing the genetic
potential of the microbially-mediated nitrogen cycle in a salt marsh ecosystem. Front
Microbiol 7:1–13
Ding G, Novak JM, Amarasiriwardena D, Hung PG, Xing B (2002) Soil organic matter
characteristics as affected by tillage management (2002) Soil Sci Soc Am 66:421-429
Evans SE, Wallenstein MD (2018) Soil microbial community response to drying and rewetting
stress: does historical precipitation regime matter? Biogeochemistry 109:101–116
Eyre BD, Rysgaard S, Dalsgaard T, Christensen PB (2002) Comparison of isotope pairing and
N2:Ar methods for measuring sediment denitrification – assumption, modifications, and
implications. Estuaries 25:1077-1087
Galloway JN, Leach AM, Erisman JW, Bleeker A (2017) Nitrogen: the historical progression
from ignorance to knowledge with a view to future solutions. Soil Res 55:417–424
Galloway JN, Townsend AR, Erisman JW, Bekunda M, Cai Z, Freney JR, Martinelli LA,
Seitzinger SP, Sutton MA (2008) Transformation of the nitrogen cycle: Recent trends,
questions, and potential solutions. Science 320:889-892
Garrity G, Bell J, Lilburn T (2005) Thiotrichales ord. nov. In: Brenner D (ed) Bergey’s Manual
of Systematic Bacteriology, 2nd edition. Boston, MA
Giauque H, Hawkes C V (2016) Historical and current climate drive spatial and temporal
patterns in fungal endophyte diversity. Fungal Ecol 20:108–114
Giblin A, Tobias C, Song B, Weston N, Banta G, Rivera-Monroy V (2013) The importance of
dissimilatory nitrate reduction to ammonium (DNRA) in the nitrogen cycle of coastal
ecosystems. Oceanography 26:124–131
Gilboa-Garber N (1971). Direct spectrophotometric determination of inorganic sulfide in
118
biological materials and in other complex mixtures. Anal Biochem 43:129–133
Global Administrative Areas (2015) GADM database of Global Administrative Areas, version
2.8. www.gadm.org
Goldfarb KC, Karaoz U, Hanson CA, Santee CA, Bradford MA, Treseder KK, Wallenstein MD,
Brodie EL (2011) Differential growth responses of soil bacterial taxa to carbon substrates
of varying chemical recalcitrance. Front Microbiol 2:1–10
Graves CJ, Makrides EJ, Schmidt VT, Giblin AE, Cardon ZG, Rand DM (2016) Functional
responses of salt marsh microbial communities to long-term nutrient enrichment. Appl
Environ Microbiol 82:2862–2871
Guibert LM, Loviso CL, Borglin S, Jansson JK, Dionisi HM, Lozada M (2016) Diverse bacterial
groups contribute to the alkane degradation potential of chronically polluted Subantarctic
coastal sediments. Microb Ecol 71:100–112
Hanson CA, Fuhrman JA, Horner-Devine MC, Martiny JBH (2012) Beyond biogeographic
patterns: Processes shaping the microbial landscape. Nat Rev Microbiol 10:497–506
Hardison AK, Algar CK, Giblin AE, Rich JJ (2015) Influence of organic carbon and nitrate
loading on partitioning between dissimilatory nitrate reduction to ammonium (DNRA)
and N2 production. Geochim Cosmochim Acta 164:146–160
Hartman WH, Richardson CJ, Vilgalys R, Bruland GL (2008) Environmental and anthropogenic
controls over bacterial communities in wetland soils. Proc Natl Acad Sci USA
105:17842–17847
Herbert RA (1999) Nitrogen cycling in coastal marine ecosystems. FEMS Microbio Rev 23:563-
590
Hijmans RJ, van Etten J (2012) Raster: Geographic analysis and modeling with raster data. R
package version 2.0-12
Hobbie SE, Eddy WC, Buyarski CR, Adair EC, Ogdahl ML, Weisenhorn P, Monographs SE,
August N, Hobbie SE, Eddy WC, Buyarski CR, Adair EC, Ogdahl ML (2016) Response
of decomposing litter and its microbial community to multiple forms of nitrogen
enrichment. Ecol Monogr 82:389–405
Howarth RW (1984) The ecological significance of sulfur in the energy dynamics of salt marsh
and coastal marine sediments. Biogeochemistry 1:5-27
Howarth RW, Teal JM (1979) Sulfate reduction in a New England salt marsh. Limnol
Oceanogr 26:350-360
Imhoff J (2005) Bergey’s Manual of Systematic Bacteriology. In: Brenner D, Krieg N, Staley J,
119
Garrity G (eds) Bergey’s Manual of Systematic Bacteriology, 2nd edition. New York,
NY
Jones SE, Lennon JT (2010) Dormancy contributes to the maintenance of microbial diversity.
Proc Natl Acad Sci USA 107:5881-5886
Kana TM, Darkangelo C, Hunt MD, Oldham JB, Bennett GE, Cornwell JC (1994) Membrane
inlet mass spectrometer for rapid high-precision determination of N2, O2, and Ar in
environmental water samples. Anal Chem 66:4166-4170
Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7:
Improvements in performance and usability. Mol Biol Evol 30:772–780
Kearns PJ, Angell JH, Howard EM, Deegan LA, Stanley RHR, Bowen KL (2016) Nutrient
enrichment induces dormancy and decreases diversity of active bacteria in salt marsh
sediments. Nat Commun 7:12881
Kearns P, Bulseco-McKim A, Hoyt H, Angell J, Bowen J (2018) Nutrient enrichment alters salt
marsh fungal communities and promotes putative fungal denitrifiers. Microb Ecol. In
press.
Kirschbaum M (1995) The temperature dependence of soil organic matter decomposition, and
the effect of global warming on soil organic C storage. Soil Biol Biochem 27:753-760
Kirwan ML, Langley JA, Guntenspergen GR, Megonigal JP (2013) The impact of sea-level rise
on organic matter decay rates in Chesapeake Bay brackish tidal marshes. Biogeosciences
10:1869–1876
Klepac-Ceraj V, Bahr M, Crump BC, Teske AP, Hobbie JE, Polz MF (2004) High overall
diversity and dominance of microdiverse relationship in salt marsh sulphate-reducing
bacteria. Environ Microbiol 6:686–698
Knorr M, Frey SD, Curits PS (2005) Nitrogen additions and litter decomposition: A meta-
analysis. Ecology 86:3252-3257
Koop-Jakobsen K, Giblin AE (2010) The effect of increased nitrate loading on nitrate reduction
via denitrification and DNRA in salt marsh sediments. Limnol Oceaogr 55:789-802
Langley J, Mozdzer TJ, Shepard KA, Hagerty SB, Patrick Megonigal J (2013) Tidal marsh plant
responses to elevated CO2, nitrogen fertilization, and sea level rise. Glob Chang Biol
19:1495–1503
Lehmann J, Kleber M (2015) The contentious nature of soil organic matter. Nature 528:60–68
Lennon JT, Jones SE (2011) Microbial seed banks: The ecological and evolutionary implications
of dormancy. Nat Rev Microbiol 9:119–130
120
Liaw A, Wiener M (2002) Classification and regression by randomForest. R news 2:18–22
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: An effective
distance metric for microbial community comparison. ISME J 5:169–172
Lu XM, Lu PZ (2014) Characterization of bacterial communities in sediments receiving various
wastewater effluents with high-throughput sequencing analysis. Microb Ecol 67:612–
623
Lundstrum A, Aoki LR (2016) Oxygen interference with membrane inlet mass spectrometry may
overestimate denitrification rates calculated with isotope pairing technique. Limnol
Oceanogr: Meth 14:425-431
Mahmoudi N, Robeson MS, Castro HF, Fortney JL, Techtmann SM, Joyner DC, Paradis CJ,
Pfiffner SM, Hazen TC (2015) Microbial community composition and diversity in
Caspian Sea sediments. FEMS Microbiol Ecol 91:1–11
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD (2015) Analysis of
composition of microbiomes: A novel method for studying microbial composition.
Microb Ecol Health D 26:27663
Margenot AJ, Calderon FJ, Bowles TM, Parikh SJ, Jackson LE (2015) Soil organic matter
functional group composition in relation to organic carbon, nitrogen, and phosphorus
fractions in organically managed tomato fields. Soil Sci Soc Am J 79:772-782
Marin-Spiotta E, Gruley KE, Crawford J, Atkinson EE, Miesel JR, Greene S, Cardona-Correa C,
Spencer RGM (2014) Paradigm shifts in soil organic matter research affect
interpretations of aquatic carbon cycling: transcending disciplinary and ecosystem
boundaries. Biogeochemistry 117:279-297
Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, Horner-Devine
MC, Kane M, Krumins JA, Kuske CR, Morin PJ, Naeem S, Øvreås L, Reysenbach AL,
Smith VH, Staley JT (2006) Microbial biogeography: Putting microorganisms on the
map. Nat Rev Microbiol 4:102–112
Mason OU, Hazen TC, Borglin S, Chain PS, Dubinsky EA, Fortney JL, Han J, Holman HY,
Hultman J, Lamendella R, Mackelprang R, Malfatti S, Tom LM, Tringe SG, Woyke T,
Zhou J, Rubin EM, Jansson JK (2012) Metagenome, metatranscriptome and single-cell
sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J 6:1715–
1727
McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, Chisholm SW, DeLong
EF (2010) Microbial community transcriptomes reveal microbes and metabolic pathways
associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci USA
107:16420–16427
121
McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, Andersen GL,
Knight R, Hugenholtz P (2012) An improved Greengenes taxonomy with explicit ranks
for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618
Mettel C, Kim Y, Shrestha PM, Liesack W (2010) Extraction of mRNA from soil. Appl Environ
Microbiol 76:5995–6000
Middelburg JJ (1989) A simple rate model for organic matter decomposition in marine
sediments. Geochim Cosmochim Acta1 53:1577–1581
Morris JT (1991) Effects of nitrogen loading on wetland ecosystems with particular reference to
atmospheric deposition. Annu Rev Ecol Syst 22:257–279
Morris JT, Sundberg K, Hopkinson CS (2013) Salt marsh primary production and its responses
to relative sea level and nutrients in estuaries at Plum Island, Massachusetts, and North
Inlet, South Carolina, USA. Oceanography 26:78–84
Mueller P, Schile-Beers LM, Mozdzer TJ, Chmura GL, Dinter T, Kuzyakov Y, Groot AV De,
Esselink P, Smit C, D’Alpaos A, Ibanez C, Lazarus M, Neumeier U, Johnson BJ,
Baldwin AH, Yarwood SA, Montemayor DI, Yang Z, Wu J, Jensen K, Nolte S (2018)
Global change effects on decomposition processes in tidal wetlands : implications from a
global survey using standardized litter. Biogeosciences 15:3189–3202
Nielsen KM, Johnsen PJ, Bensasson D, Daffonchio D (2007) Release and persistence of
extracellular DNA in the environment. Environ Biosaf Res 6:37–53
Nixon SW, Oviatt CA, Frithsen J, Sullivan B (1986) Nutrients and the productivity of estuarine
and coastal marine ecosystems. J Limnol Soc S Afr 12:43-71
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'hara B,
Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2017) Vegan: Community
ecology package version 2.4-3
Oni OE, Schmidt F, Miyatake T, Kasten S, Witt M, Hinrichs KU, Friedrich MW (2015)
Microbial communities and organic matter composition in surface and subsurface
sediments of the Helgoland mud area, North Sea. Front Microbiol 6:1–16
Pallud C, Cappellen P Van (2006) Kinetics of microbial sulfate reduction in estuarine sediments.
Geochim Cosmochim Acta 70:1148–1162
Pallud C, Meile C, Laverman a. M, Abell J, Cappellen P Van (2007) The use of flow-through
sediment reactors in biogeochemical kinetics: Methodology and examples of
applications. Mar Chem 106:256–271
Papp K, Hungate BA, Schwartz E (2018) Microbial rRNA synthesis and growth compared
122
through quantitative stable isotope probing with H218O. Appl Environ Microbiol 84:1-11
Parikh SJ, Goyne KW, Margenot AJ, Mukome FND, Calderon FJ (2014) Soil chemical insights
provided through vibrational spectroscopy. Adv Agron 126:1-148
Peng X, Ji Q, Angell JH, Kearns PJ, Yang HJ, Bowen JL, Ward BB (2016) Long-term
fertilization alters the relative importance of nitrate reduction pathways in salt marsh
sediments. J Geophys Res Biogeochem 121:2082-2095
Pérez-Pantoja D, Donoso R, Agulló L, Córdova M, Seeger M, Pieper DH, González B (2012)
Genomic analysis of the potential for aromatic compounds biodegradation in
Burkholderiales. Environ Microbiol 14:1091–1117
Rasigraf O, Schmitt J, Jetten MSM, Lüke C (2017) Metagenomic potential for and diversity of
N-cycle driving microorganisms in the Bothnian Sea sediment. Microbiology 6:1–13
Reese BK, Witmer AD, Moller S, Morse JW, Mills HJ (2013) Molecular assays advance
understanding of sulfate reduction despite cryptic cycles. Biogeochemistry 118:307–319
Ryther JH, Dunstan WM (1971) Nitrogen, phosphorus, and eutrophication in the coastal marine
environment. Science 171:1008–1013
Saito T, Ishii S, Otsuka S, Nishiyama M, Senoo K (2008) Identification of novel
betaproteobacteria in a succinate-assimilating population in denitrifying rice paddy soil
by using stable isotope probing. Microbes Environ 23:192–200
Steven B, Hesse C, Soghigian J, Gallegos-Graves LV, Dunbar J (2017) Simulated rRNA/DNA
ratios show potential to misclassify active populations as dormant. Appl Environ
Microbiol 83:1-11
Solórzano L (1968) Determination of ammonia in natural waters by the phenolhypochlorite
method. Limnol Oceanogr 14:799-801
ter Braak CJF, Verdonschot PFM (1995) Canonical correspondence analysis and related
multivariate methods in aquatic ecology. Aquat Sci 57:255–289
Thomas F, Giblin AE, Cardon ZG, Sievert SM (2014) Rhizosphere heterogeneity shapes
abundance and activity of sulfur-oxidizing bacteria in vegetated salt marsh sediments.
Front Microbiol 5:309
Tiedje J (1988) Ecology of the denitrification and dissimilatory nitrate reduction to ammonium.
In: Zehnder A (ed) Biology of Anaerobic Microorganisms. Wiley and Sons, New York.
NY, p 179–244
Treseder KK, Kivlin SN, Hawkes CV (2011) Evolutionary trade-offs among decomposers
determine responses to nitrogen enrichment. Ecol Lett 14:933-938
123
Turner S, Pryer KM, Miao VPW, Palmer JD (1999) Investigating deep phylogenetic
relationships among cyanobacteria and plastids by small subunit rRNA sequence
analysis. J Eukaryotic Micro 46:327-338
Valiela I, Teal J, Sass W (1975) Production and dynamics of salt marsh vegetation and the
effects of experimental treatment with sewage sludge. Biomass, production and species
composition. Br Ecol Soc 12:973–981
Veum KS, Goyne KW, Kremer RJ, Miles RJ, Sudduth KA (2014) Biological indicators of soil
quality and soil organic matter characteristics in an agricultural management continuum.
Biogeochemistry 117:81-99
Wakeham SG, Lee C, Hedges JI, Hernes PJ, Peterson ML (1997) Molecular indicators of
diagenetic status in marine organic matter. Geochim Cosmochim Acta 61:5363–5369
Warren RS, Fell PE, Rozsa R, Brawley AH, Orsted AC, Olson ET, Swamy V, Niering WA
(2002) Salt marsh restoration in Connecticut: 20 years of science and management.
Restor Ecol 10:497–513
Watson EB, Oczkowski AJ, Wigand C, Hanson AR (2014) Nutrient enrichment and precipitation
changes do not enhance resiliency of salt marshes to sea level rise in the Northeastern
US. Clim Change 125:501–509
Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, Lozupone C, Zaneveld JR,
Vázquez-Baeza Y, Birmingham A, Hyde ER, Knight R (2017) Normalization and
microbial differential abundance strategies depend upon data characteristics. Microbiome
5:1–18
Weston NB, Vile MA, Neubauer SC, Velinsky DJ (2010) Accelerated microbial organic matter
mineralization following salt-water intrusion into tidal freshwater marsh soils.
Biogeochemistry 102:135–151
Westrich JT, Berner RA. (1984) The role of sedimentary organic matter in bacterial sulfate
reduction: The G model tested. Limnol Oceanogr 29:236–249
Wigand C, Brennan P, Stolt M, Holt M, Ryba S (2009) Soil respiration rates in coastal
marshes subject to increasing watershed nitrogen loads in southern New England, USA.
Wetlands 29:952–963
Wilson CA, Hughes ZJ, FitzGerald DM, Hopkinson CS, Valentine V, Kolker AS (2014)
Saltmarsh pool and tidal creek morphodynamics: Dynamic equilibrium of northern
latitude saltmarshes? Geomorphology 213:99–115
Wu L, Hui L, Wang X, Li J, Yu J, Zhao J (2013) Abundance and composition of ammonia-
oxidizing bacteria in degraded wetland. Soil Res 51:554–560
124
Yin GL, Hou L, Liu M, Liu Z, Gardner WS (2014) A novel membrane inlet mass sepctrometer
method to measure 15NH4+ for isotope-enrichment experiments in aquatic ecosystems.
Environ Sci Technol 48:9555-9562
Zedler JB, Kercher S (2005) Wetland resources: Status, Trends, Ecosystem Services, and
Restorability. Annu Rev Environ Resour 30:39–74
125
Tables
Tab
le 1
. A
ver
age
(± S
EM
) ch
arac
teri
stic
s of
each
stu
dy s
ite
pri
or
to s
tart
of
the
dec
om
posi
tion e
xper
imen
t.
Dif
fere
nt
lett
ers
ind
icat
e si
gnif
ican
ce (
α =
0.0
5)
acco
rdin
g t
o a
Tukey’s
HS
D t
est.
126
Tab
le 2
. A
ver
age
± S
E r
ates
(nm
ol
cm-3
hr-1
) fo
r den
itri
fica
tion (
DN
F)
and d
issi
mil
atory
nit
rate
red
uct
ion t
o
amm
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(D
NR
A)
po
tenti
al m
easu
red a
s 2
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30N
2 p
roduct
ion f
rom
15N
O3
- addit
ion a
long w
ith t
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F:D
NR
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rati
o, n
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ctio
n r
ates
(N
RR
) in
nm
ol
cm-3
hr-1
, an
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ange
of
pro
port
ion o
f N
RR
acc
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d f
or
by D
NF
+
DN
RA
. D
iffe
rent
lett
ers
ind
icat
e si
gnif
ican
ce (
α =
0.0
5)
by s
ite
acco
rdin
g t
o a
Tu
key’s
HS
D t
est.
N
A =
no d
ata.
127
Tab
le 3
. A
ver
age
(± S
EM
) bulk
den
sity
, (%
) org
anic
mat
ter,
% c
arbon,
% n
itro
gen
, m
ola
r C
:N, %
sulf
ur
(N=
4),
and 1
6S
rR
NA
gen
e co
pie
s p
er g
ram
wet
wei
ght
mea
sure
d a
t th
e en
d o
f th
e dec
om
po
siti
on e
xper
imen
t. D
iffe
rent
lett
ers
indic
ate
signif
ican
ce (
α =
0.0
5)
acco
rdin
g t
o a
Tukey’s
HS
D.
128
Figures
Fig. 1. Location of my study sites in northeastern Massachusetts, USA: West Creek (Reference;
42.759 N, 70.891 W), Sweeney Creek (13-year enriched; 42.722 N, 70.847 W), and Greenwood
Creek (40-year enriched; 42.690 N, 70.819 W). Maps were generated by downloading data from
the Database of Global Administrate Areas (GADM; Global Administrative Areas) using the
raster package in R (Hijmans & Jacob van Etten 2012).
129
Fig. 2. (A) Boxplot of Index II, a measure of recalcitrance, across sites. Boxes represent 25% to
75% quartiles. The solid black line is the median value, and the whiskers are upper and lower
extremes. Black dots represent values for each individual reactor (n = 4), and asterisk indicates
significant difference between groups. (B) Baseline corrected mean mid-IR spectra ± SE of each
site before the start of the experiment.
130
Fig. 3. (A) Constrained correspondence analysis and (B) top 20 most differentially abundant
ASVs among sites from the field experiment according to ANCOM analysis.
131
Fig. 4. Average (±SE) dissolved inorganic carbon (DIC) production over time (days) across three
sites that correspond to different levels and durations of chronic nitrate exposure (panels A-C; n
= 4).
132
Fig. 5. Cumulative DIC production. Boxes represent 25% to 75% quartiles. The solid black line
is the median value, and the whiskers are upper and lower extremes. Black dots represent values
for each individual reactor (n = 4). Letters represent statistically different DIC production across
sites from a Tukey’s HSD test corrected for multiple comparisons and asterisks indicate a
significant difference between treatments.
133
Fig. 6. (A) Average (± SE) nitrate reduction rate over time and (B) total nitrate reduction across
sites. (C) Average (± SE) sulfide production rate over time and (D) total sulfate reduction across
sites.
134
Fig. 7 (A) principal coordinates analysis of Mid-IR spectra; (B) Pearson’s correlation
coefficients plotted against wavenumber representing regions most discriminating between the
two axes shown in A; and (C) Total DIC production per site as a function of the recalcitrance
index (Index II). Dotted lines in (B) indicate functional group assignments as follows: 840-920
and 1650 cm-1= aromatic C and lignin-type signatures, 1080 cm-1 = polysaccharides, and 2850-
2924 cm-1 = aliphatic C.
135
Fig. 8. (A) PCoA based on weighted UniFrac similarity after the experiment according to site
(color) and treatment (shape). (B) Average pairwise weighted UniFrac dissimilarities between
plus-NO3- and unamended treatment.
136
Fig. 9. Stacked bar plots of the top ten ASVs most important in discriminating between plus-
NO3- and unamended treatments aggregated at the order level from (A) reference (B) 13-year
enriched and (C) 40-year enriched sites as a result of random forest analysis. Orders marked with
an asterisk indicate importance in at least two sites. Inlaid box in (C) represents relative
abundance of remaining 9 taxa after order Desulfobacterales is removed from analysis. See Table
S3 for additional taxonomic information.
137
Fig. 10. ASV’s whose activity ratio was significantly different (p<0.05) between plus-NO3- and
unamended treatments per site aggregated at the order level. Darker colors indicate more active
taxa, with white boxes represent inactive taxa (ratio < 1). See Table S4 for additional taxonomic
information.
138
Supplemental Tables
Table S1. Functional group assignments based on Parikh et al. (2014) and modified from
Margenot et al. (2015) to evaluate FT-IR spectra using Index II metric. ν = stretching vibration;
νas = asymmetric stretching vibration; νs = symmetric stretching vibration; δ = bending vibration.
Band (cm-1) Assignment
3400 ν(N-H), ν(O-H)
2924 aliphatic νas(C-H)
2850 aliphatic νs(C-H)
1650 aromatic ν(C = C)
1470 aliphatic δ(C-H)
1405 aliphatic δ(C-H)
1270 phenol νas(C-O), carboxylic acid ν(C-O)
1110 polysaccharide νs(C-O)
1080 polysaccharide νs(C-O)
920 aromatic δ(C-H)
840 aromatic δ(C-H), less substituted
139
Table S2. Flow property information, including average flow rate (±SD), porosity, and linear
flow velocity.
Sample Flow Rate (mL
min-1) Porosity
Linear Flow Velocity
(mL hr-1 cm-2)
Plus-NO3-
Reference
Core 1 0.090 (0.007) 0.44 0.701
Core 2 0.091 (0.001) 0.43 0.742
Core 3 0.074 (0.003) 0.44 0.513
Core 4 0.083 (0.002) 0.48 0.591
13-year enriched
Core 1 0.088 (0.004) 0.46 0.677
Core 2 0.091 (0.001) 0.48 0.682
Core 3 0.089 (0.013) 0.51 0.477
Core 4 0.080 (0.004) 0.43 0.770
40-year enriched
Core 1 0.073 (0.001) 0.59 0.458
Core 2 0.082 (0.006) 0.39 0.698
Core 3 0.086 (0.003) 0.40 0.683
Core 4 0.082 (0.005) 0.62 0.484
Unamended Treatment
Reference
Core 1 0.082 (0.015) 0.42 0.803
Core 2 0.085 (0.011) 0.41 0.834
Core 3 0.060 (0.002) 0.40 0.693
Core 4 0.076 (0.012) 0.45 0.692
13-year enriched
Core 1 0.082 (0.002) 0.42 0.792
Core 2 0.087 (0.003) 0.42 0.827
Core 3 0.065 (0.004) 0.50 0.679
Core 4 0.087 (0.009) 0.41 0.729
40-year enriched
Core 1 0.071 (0.003) 0.57 0.484
Core 2 0.072 (0.001) 0.36 0.855
Core 3 0.073 (0.002) 0.41 0.797
Core 4 0.079 (0.005) 0.60 0.517
140
Table S3. Top ten ASVs most important in discriminating between plus-NO3- and unamended
treatments at each site, listed in decreasing order of importance. Asterisks indicate ASVs shared
between at least two sites and bolded indicate taxa that increased in relative abundance with
NO3- addition.
Site Kingdom Phylum Class Order
Δ Rel
Abun.
Ref. Archaea Euryarchaeota Thermoplasmata CCA47* 0.11%
Bacteria Proteobacteria Deltaproteobacteria Myxococcales 0.63%
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales -0.01%
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales 3.42%
Bacteria Chloroflexi Anaerolineae envOPS12 -0.79%
Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales -0.44%
Bacteria Proteobacteria Deltaproteobacteria Myxococcales 0.04%
Archaea Crenarchaeota MCG B10 0.20%
Bacteria Tenericutes Mollicutes unclassified 0.26%
Bacteria SAR406 AB16 Arctic96B-7 0.11%
13-year Bacteria Proteobacteria Gammaproteobacteria Oceanospirillales 0.34%
enriched Bacteria WS3 unclassified unclassified -0.16%
Bacteria Proteobacteria Gammaproteobacteria Thiotrichales 2.51%
Bacteria Chloroflexi Anaerolineae SBR1031 -0.04%
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales* -6.96%
Bacteria Plancotymycetes unclassified unclassified -0.02%
Bacteria Proteobacteria Deltaproteobacteria unclassified -0.01%
Bacteria Proteobacteria Gammaproteobacteria Chromatiales 9.89%
Bacteria Chloroflexi Dehaloccoidetes Dehalococcoidales -0.05%
Archaea Euryarchaeota Thermoplasmata CCA47* -0.06%
40-year Bacteria Clamydiae Chlamydiia Chlamydiales -0.04%
enriched Bacteria TM6 unclassified unclassified -0.01%
Bacteria Firmicutes Clostridia Clostridiales 0.03%
Bacteria Proteobacteria Betaproteobacteria Burkholderiales -0.02%
Bacteria Plancotymycetes Agg27 unclassified 0.02%
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales*
-
10.62
%
Bacteria Chloroflexi Anaerolineae DRC31 0.02%
Bacteria Bacteroidetes unclassified unclassified -0.13%
Bacteria FCPU426 unclassified unclassified -0.08%
Bacteria Proteobacteria Gammaproteobacteria Chromatiales 0.01%
141
Table S4. Taxa whose activity level (± SE), as assessed by 16S rRNA:16S rRNA gene ratios
averaged across each core, significantly differed (p<0.05) between plus-NO3- and unamended
treatments at the end of the decomposition experiment on a per site basis. Bolded text indicates
greater ratios under that experimental condition.
Site Phylum Class Order Nitrate Unamen. p-value
Ref. Proteobacteria Gammaproteobacteria Thiotrichales
24.16
(18.13) 0 (0) 0.025
Proteobacteria Alphaproteobacteria NA
14.12
(12.9) 0 (0)
0.033
Proteobacteria
Gammaproteobacteria Thiotrichales
11.86
(19.4) 0 (0) 0.022
Proteobacteria
Betaproteobacteria NA
1.05
(0.70) 0 (0) 0.025
Proteobacteria
Gammaproteobacteria Alteromonadales
1.04
(0.96) 0 (0) 0.023
Proteobacteria
Gammaproteobacteria Chromatiales
1.00
(0.38) 0 (0) 0.031
Proteobacteria
Gammaproteobacteria Chromatiales
0.88
(0.13)
5.97
(8.70) 0.019
Chloroflexi
Anaerolineae GCA004
0.69
(0.25)
1.87
(0.90) 0.032
Proteobacteria
Betaproteobacteria NA
0.49
(0.40)
1.59
(0.39) 0.032
Proteobacteria Deltaproteobacteria Desulfobacterales
0.40
(0.53)
9.28
(14.48) 0.038
Proteobacteria
Deltaproteobacteria Desulfovibrionales
0.29
(0.23)
1.15
(0.91) 0.03
Proteobacteria
Deltaproteobacteria Myxococcales
0.19
(0.26)
13.45
(7.93) 0.028
Firmicutes
Clostridia Clostridiales
0.11
(0.14)
5.47
(3.36) 0.025
Proteobacteria Alphaproteobacteria NA
0.11
(0.06)
2.59
(4.28) 0.035
Proteobacteria Deltaproteobacteria Myxococcales 0 (0)
13.73
(25.51) 0.045
Poribacteria
NA NA 0 (0)
22.56
(21.04) 0.019
Proteobacteria
Deltaproteobacteria NA 0 (0)
6.80
(10.15) 0.022
13-year Proteobacteria Alphaproteobacteria Kiloniellales
8.34
(4.24) 0 (0)
0.031
Proteobacteria Deltaproteobacteria DTB120
7.65
(13.57) 0 (0)
0.021
Planctomycetes Planctomycetia Pirellulales
56.00
(20.07)
13.00
(15.10) 0.036
Proteobacteria Gammaproteobacteria Thiotrichales
38.93
(76.72) 0 (0)
0.027
Proteobacteria Gammaproteobacteria Chromatiales
175.10
(349.27) 0 (0)
0.03
Proteobacteria Gammaproteobacteria Oceanospirillales
17.48
(24.30) 0 (0)
0.032
142
Proteobacteria Deltaproteobacteria Desulfobacterales
39.25
(46.13)
107.25
(11.59) 0.029
GN04 GN15 NA
0.24
(0.29)
8.59
(14.94) 0.034
Proteobacteria Deltaproteobacteria Desulfarculales
0.22
(0.45)
23.09
(26.58) 0.03
GN04 GN15 NA
0.18
(0.21)
9.15
(17.23) 0.033
Proteobacteria Betaproteobacteria SBla14
0.10
(0.12)
4.70
(8.86) 0.028
Proteobacteria Epsilonproteobacteria Campylobacterales
0.05
(0.10)
13.44
(26.38) 0.032
40-year Bacteroidetes Rhodothermi Rhodothermales
9.10
(10.36) 0 (0)
0.025
Bacteroidetes Rhodothermi Rhodothermales
3.31
(0.66) 0 (0)
0.021
Actinobacteria Acidimicrobiia Acidimicrobiales
2.52
(4.33) 0 (0)
0.036
Proteobacteria Deltaproteobacteria Myxococcales
15.94
(14.59) 0 (0)
0.021
Proteobacteria Alphaproteobacteria Kiloniellales
11.61
(19.59) 0 (0)
0.032
Proteobacteria Deltaproteobacteria Desulfuromonadales
1.58
(0.37)
0.59
(0.22) 0.021
Proteobacteria Zetaproteobacteria Mariprofundales
1.52
(0.41) 0 (0)
0.029
Bacteroidetes Flavobacteriia Flavobacteriales
1.08
(0.66) 0 (0)
0.03
Proteobacteria Deltaproteobacteria Syntrophobacterales
9.04
(9.79)
56.25
(28.04) 0.023
Firmicutes Clostridia Clostridiales
0.82
(0.64)
9.22
(10.55) 0.023
Proteobacteria Alphaproteobacteria Rhizobiales
0.32
(0.41)
42.94
(48.13) 0.022
Proteobacteria Epsilonproteobacteria Campylobacterales 0 (0)
5.11
(8.59) 0.024
Proteobacteria Deltaproteobacteria Myxococcales 0 (0)
29.5
(18.63) 0.019
Proteobacteria Deltaproteobacteria Desulfobacterales 0 (0)
3.28
(3.83) 0.028
Proteobacteria Deltaproteobacteria Desulfobacterales 0 (0)
6.93
(5.02) 0.029
143
Supplemental Figures
Fig. S1. Bromide breakthrough curve with the ratio of [Br-]initial/[Br-]final on the y-axis to confirm
uniform and regular flow in each reactor. Dotted line indicates a ratio of 1, where the initial and
final bromide concentration are equal, indicating breakthrough.
144
Fig. S2. Average ± SE rates of ammonium production for (A) plus-NO3
- and (B) unamended
treatment throughout experiment.
145
Chapter 3: Stochastic processes shape microbial communities in deep salt marsh sediments
In collaboration with: Thomas J. Mozdzer and Donald C. Barber
Abstract:
Salt marshes can sequester carbon at rates that are an order of magnitude greater than
terrestrial counterparts due to slow rates of decomposition. Microbes mediate this critical
ecosystem service, yet we know virtually nothing about their distribution and interaction with
buried organic matter in deep salt marsh sediments. Further, there is evidence that nutrient
enrichment stimulates organic matter decomposition and alters surface sediment microbial
communities, though it is unclear if this pattern holds in deeper salt marsh sediments, where long
term carbon storage occurs. To address these critical knowledge gaps, I collected three-meter-
deep cores spanning ~3000 years of sediment accumulation as part of a long-term nutrient
enrichment experiment at the Plum Island LTER. I characterized sediment organic matter in
parallel with high throughput sequencing of the 16S rRNA gene/16S rRNA to assess microbial
community diversity, abundance, structure, and assembly along a depth gradient between an
experimentally nutrient-enriched marsh and its paired reference marsh. I found that both
microbial diversity and gene abundance decreased with depth, with diverging patterns between
the 16S rRNA gene and 16S rRNA in deeper sediments that suggest significant rates of
microbial inactivity at depth. Depth and associated changes in organic matter explained a large
portion of microbial community structure in shallower sediments, with patterns driven by shifts
in rare taxa. However, in deeper sediments beyond the rooting zone, changes to the community
could no longer be attributed to parameters I measured, likely due to a transition from
deterministic to stochastic assembly at depth. The only detectable differences between the
reference and enriched marshes occurred in deeper sediments, suggesting that these differences
146
resulted from stochastic processes rather than experimental nutrient enrichment. Overall, this
work highlights the stability of salt marsh sediments, and provides novel information on the
microbes mediating carbon cycling in these critical ecosystems.
Introduction:
Salt marshes, and other vegetated coastal habitats (e.g. seagrasses and mangroves),
contribute to ~50% of carbon storage in marine sediments, despite occupying only a small
portion of coastal area (Duarte et al. 2013, Najjar et al. 2018). These “blue carbon” systems
efficiently store carbon due to high aboveground productivity (Mendelssohn & Morris 2002),
and because rates of decomposition are inhibited by anoxic, water-logged soils. As such, they
bury organic matter (OM) over millennial time scales without becoming saturated (Zedler &
Kercher 2005, Mcleod et al. 2011). Microbes largely mediate the amount of carbon decomposed
and/or buried in these deep sediments (Benner at al. 1984, Sutton-Grier et al. 2011), and while
considerable work has gone into understanding microbial distribution and activity in deep
subseafloor marine sediments (Inagaki et al. 2015, Oni et al. 2015, Starnawski et al. 2016, Walsh
et al. 2016, Petro et al. 2017, Marshall et al. 2018), lake sediments (Vueillemin et al. 2018), and
terrestrial soils (Fierer et al. 2003, Hartmann et al. 2009, Eilers et al. 2012, Carini et al. 2016),
very little is known regarding factors that control microbial communities in deep salt marsh
sediments. Given the increased prevalence of salt marsh restoration to promote carbon storage
and other beneficial ecosystem services (Warren et al. 2002, Macreadie et al. 2017, Narayan et
al. 2017), it is critical that we better understand what controls the assembly, structure, and
function of microbial communities in deep salt marsh sediments and examine how their
distribution changes under shifting environmental conditions.
147
There are many factors that can control the vertical distribution of subsurface salt marsh
sediment microbial communities. OM quality and quantity, since it serves as an electron donor
fueling heterotrophic microbial metabolisms, may select for specific microbes based on the
complexity of its chemical composition (Walsh et al. 2016). At the surface, easily degradable
OM is preferentially oxidized at higher rates due to the lower energetic demand it imparts on the
decomposer community (Cowie & Hedges 1994, Wakeham et al. 1997), with net rates of
heterotrophic respiration decreasing with depth (Middelburg 1989, Walsh et al. 2016). The less
labile forms of OM left behind accumulate and eventually become buried (Hedges et al. 2000).
As a result, total microbial biomass, diversity, and decomposition rates tend to decrease with
increasing sediment depth because microbes still need to meet their energetic demands but with
more recalcitrant OM (Burdige 2007, Middelburg 1989, Parkes et al. 1994, Westrich & Berner
1984). While some microbial groups contain special adaptations for complex OM oxidation (e.g.
Chloroflexi, candidate divisions JS1, OP9, and archaeal members of the Miscellaneous
Crenarchaeota Group (MCG) and Marine Benthic Group B (MBG; Biddle et al. 2006, Inagaki et
al. 2006, Teske et al. 2008, Kubo et al. 2012), competition for what limited resources remain
typically leads to smaller and less diverse microbial communities.
Another factor that controls microbial patterns in the subsurface environment is the
availability of electron acceptors that fuel heterotrophic metabolisms and the geochemical
zonation that occurs as a consequence. Microbes preferentially reduce electrons that yield the
most energy first (greater Gibb’s free energy; ΔGº), leading to a predictable sequence of
metabolic processes that starts with oxygen reduction, and proceeds through manganese, nitrate,
iron, and sulfate reduction, as well as methanogenesis (Froelich et al. 1979, Thamdrup et al.
1994, Canfield et al. 2005). In salt marshes, oxygen is rapidly oxidized within the top few
148
millimeters of the surface (Teal & Kanwisher 1961) unless there are physical modifications to
sediments resulting from either bioturbation (Aller 1994, Aller & Aller 1998, Kristensen &
Holmer 2001) or active diffusion of gaseous oxygen by macrophytic roots (Lee et al. 1999,
Holmer et al. 2002). As a result, in salt marsh sediments, microbial metabolism quickly switches
to anaerobic respiration, which is generally less efficient at oxidizing complex OM (Reddy &
Patrick 1975).
The anaerobic decomposition of OM is important for salt marsh carbon storage and it
also plays a role in structuring the microbial community, either by selecting for taxa that can best
use available electron acceptors, or through shifting the functional capacity of facultative
microbes. For example, sulfate is the dominant electron acceptor in salt marsh sediments due to
its very high concentration in seawater. Sulfate fuels the metabolic processes of sulfate-reducing
microbes and can support up to 70-90% of total sediment respiration (Howarth & Teal 1979,
Howarth 1984). However, nitrate can be another important electron acceptor in salt marshes,
because after oxygen, it is the most energetically favorable electron acceptor and it fuels
ecologically important processes such as denitrification (Kaplan et al. 1979, Valiela & Teal
1979, Seitzinger 1988, Sousa et al. 2012) and dissimilatory nitrate reduction to ammonium
(DNRA; Koop-Jakobsen & Giblin 2010, Giblin et al. 2013). Although nitrate is typically limiting
in coastal waters (Ryther & Dunstan 1979), nutrient enrichment from fertilizer production,
agricultural and urban runoff, enriched groundwater, and atmospheric deposition may increase
its availability to sediment microbes (Galloway et al. 2017). Increased supplies of nitrate can
stimulate respiration of belowground OM by providing additional reducing capacity to the
system (Bulseco-McKim et al. In review), which can lead to lower sediment stability and
potential marsh collapse (Darby & Turner 2008, Deegan et al. 2012, Mueller et al. 2018).
149
Previous studies, however, focused on decomposition in surface sediments, but the effect of
nitrate addition on deeper sediments is unclear.
Microbial communities that are responsible for decomposition in both surface and deep
marsh sediments can be assembled by both deterministic and stochastic processes (Stegen et al.
2012, Mittelbach & Schemske 2015, Petro et al. 2017). Deterministic processes result when
abiotic and biotic factors facilitate environmental filtering of the taxa present (Fierer & Jackson
2006, Nemergut et al. 2013). Stochastic processes, in contrast, result in microbial distribution
patterns indistinguishable from random chance. These processes include drift (random
birth/death events and unpredictable disturbances), speciation/diversification, and dispersal
events (Vellend et al. 2010, Petro et al. 2017). While both deterministic and stochastic processes
can act simultaneously on microbial community structure (Dumbrell et al. 2010, Stegen et al.
2012), their relative importance is unclear. Some studies have found deterministic processes to
dominate in deep sediments where limited resources select for specific taxa (Stegen et al. 2013),
while others have observed increasing stochasticity with sediment depth (Chu et al. 2016,
Tripathi et al. 2017). Disentangling the relative contribution of these processes is critical to
understanding the mechanism of microbial community assembly in marsh sediments and
ultimately, the role that community plays in long-term marsh carbon storage.
There are a number of challenges to linking shifts in subsurface sediment microbial
communities to changes in environmental parameters, including the presence of relic DNA
(Carini et al. 2016) and the proportion of inactive or dormant cells (Lennon & Jones 2011).
Extracellular nucleic acids from dead microbes may persist in soils for extended periods of time
(Levy-Booth et al. 2007), contributing to the estimate that total soil DNA can be up to 80% relic
DNA (Carini et al. 2016, Lennon et al. 2018). Consequently, a large portion of sequence data
150
from sediments may inaccurately represent the microbial community, since much of what is
present is not actively contributing to ecosystem processes. The extent to which relic DNA
skews diversity estimates remains unclear (Lennon et al. 2018). Prior work in salt marshes
documented high rates of inactivity, likely a result of a combination of relic DNA and microbial
dormancy (Kearns et al. 2016). Given the resource limitation in deep salt marsh sediments,
microbial communities likely also exhibit high rates of inactivity, thus obscuring our ability to
document how changes in microbial community structure feedback on ecosystem function.
Analysis of transcripts of the small subunit of prokaryotic ribosomes (16S rRNA) can partially
address this issue due to their correlation with protein synthesis (Kerkhof & Kemp 1999).
Although there are several caveats involved with this approach (Blazewicz et al. 2013, Steven et
al. 2017, Papp et al. 2018), it is essential we disentangle how these active members of the
microbial community, which are directly responsible for critical ecosystem processes, vary in
deep marsh sediments.
I examined OM characteristics and performed high throughput sequencing of the 16S
rRNA gene (total community) and its product, 16S rRNA (potentially active community), on six
deep salt marsh cores collected from two marshes in the Plum Island Ecosystem LTER. My
objectives were to 1) examine differences in OM characteristics with depth and between sites 2)
characterize both the total and potentially active community at these two sites, 3) determine the
assembly mechanisms of the microbial community in deep marsh sediments, and 4) examine
whether these mechanisms differ as a result of experimental nutrient enrichment. I hypothesized
that OM characteristics would vary along a depth gradient and that they would be different
between the two sites as a result of nutrient enrichment. Further, I hypothesized that the
microbial community, particularly the active portion, would vary concurrently with these
151
changes in OM composition. I also hypothesized that the overall microbial community diversity,
biomass, and activity would decrease with depth as there would be more competition for limited
resources in deeper sediments, resulting in deterministic selection for adapted taxonomic groups.
Methods:
Study sites and sample collection
I collected samples at salt marshes associated with the TIDE project, a long-term nutrient
enrichment experiment at the Plum Island Ecosystem LTER (Deegan et al. 2007) in
Northeastern, Massachusetts, USA (42.759 N, 70.891 W) (Fig. 1). In the Spartina patens habitat,
I collected three sediment cores each from the Sweeney Creek salt marsh (“enriched”), which
has experimentally received nitrogen in the form of 70 µM nitrate for 13 years and from its
paired reference site, West Creek (“reference”) using a Russian peat corer until reaching the
point-of-refusal. Core depths were 90, 100, and 240 cm, and 80, 150, and 280 cm for enriched
and reference marshes, respectively. I sectioned each core at 2 cm intervals and from each
section, I subsampled sediment for OM analyses, which I stored at -20°C. I also subsampled
sediment for nucleic acid extraction by homogenizing each 2 cm interval in a 50 mL falcon tube,
flash freezing the sediments in liquid nitrogen, and transferring them to a -80°C freezer for
storage until further analysis.
Organic matter analyses
From each homogenized 2 cm section, I subsampled sediment to determine bulk OM
characteristics. I calculated bulk density by dividing the sediment mass by the total volume of
each section and I determined %OM through loss-on-ignition (LOI) by drying sediment in
crucibles for 24 hours at 105ºC followed by 4 hours at 550ºC in a muffle furnace. From each 10
152
cm interval I dried sediment at 60°C for 48 hours, fumed finely ground sediment with 12N
hydrochloric acid (HCl) for three days, and performed elemental analysis (%C and %N) on a
Perkin Elmer 2400 Series II CHN Analyzer (Perkin Elmer, Waltham, MA) using aspartic acid
(Thermo Scientific, Camrbidge, UK) as a standard after correcting the mass for loss of
carbonate. To prepare samples for carbon isotope analysis (δ13C) of bulk organic matter, I freeze-
dried sediments in a VirTisTM benchtop freeze dryer (SP Scientific, Warminster, PA) and then
finely ground the sediments using a RetschTM ball mill (Verder Scientific, Newtown, PA). I ran
samples at Bryn Mawr College using cavity ring-down (CRDS) laser spectroscopy following
methods outlined in Balslev-Clausen (2013) on a PicarroTM G2201-i CRDS (Picarro Inc, Santa
Clara, CA) with combustion carried out at 980ºC on a Costech ECS 4010 elemental analyzer
(Costech Inc, Valencia, CA) using N2 as the carrier gas and USGS40 (glutamic acid) as a
standard. I report isotope values in δ notation according to the following equation:
Eq. 1 δX = [(Rsample/Rstandard) − 1] × 1000
standardized to Vienna Pee Dee Belemnite (VPDB) with reproducibility of δ13C values <0.2‰
based on repeat analysis of USGS40.
Age dating
I collected four rhizome samples from the two deepest cores at each site (Core 3 at the
reference site and Core 1 at the enriched site) for radiocarbon (14C) analysis to produce an age
date model for each site. All radiocarbon analyses were conducted at the National Ocean
Sciences Accelerator Mass Spectrometry (NOSAMS) facility located at the Woods Hole
Oceanographic Institution (Woods Hole, MA). The data were modeled using the Clam package
in R (Classical Age-Depth Modelling of Cores from Deposits; Blauuw 2010).
Nucleic acid extraction
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I extracted genomic DNA from approximately 0.25 g sediment using the MoBio®
PowerSoil DNA Isolation Kit (MoBio Technologies, CA, USA) following manufacturer’s
instructions, and the DNA was eluted into a 35 µL final volume. To extract RNA, I used a
method modified from Mettel et al. (2010), which is specifically designed to handle samples with
high humic acid content. To approximately 0.5 g of sediment, I added 700 µL PBL buffer (5 mM
tris-hydrochloride [pH 5.0], 5 mM ethylenediaminetetraacetic acid disodium salt, 0.1% [wt/vol]
sodium dodecyl sulfate, and 6% [vol/vol] water-saturated phenol), followed by 0.5 g of 0.17 mm
glass beads. After vortexing at maximum speed for 10 minutes, I spun the samples at 20,000 x g
for 30 seconds, and transferred the supernatant to a new tube. I then resuspended the remaining
sediment and glass beads with 700 µL TPM buffer (50 mM tris-hydrochloride [pH 5.0], 1.7%
[wt/vol] polyvinylpyrrolidone, 20 mM magnesium chloride) and vortexed the suspension at
maximum speed for an additional 10 minutes. I spun the sediment at 20,000 x g for 30 seconds
and pooled the supernatant with the supernatant from the previous step. To each tube, I added an
equal volume of phenol:chloroform:isoamyl alcohol (25:24:1 v/v/v), mixed by vortexing at
maximum speed for 30 seconds, and spun at 20,000 x g for 30 seconds. I then transferred the
aqueous layer to a new tube and precipated nucleic acids using 0.7 volumes of 100% isoproponal
and 0.1 volumes sodium acetate [pH 5.7]. After spinning at 20,000 x g for 30 minutes, I
discarded the supernatant, and washed the resulting pellet using 70% ethanol. I loaded the
washed RNA onto an Illustra Autoseq G-50 Spin Column (GE Healthcare) prepared with 500 µl
Q-Sepharose (GE Healthcare; Marlborough, MA), spun the column at 650 x g for 7 seconds, and
eluted from the column three times with 200 µL 1.5 M NaCl (pH 5.5). To precipitate the flow
through, I added 0.7 volumes of 100% isopropanol and 0.1 volumes sodium acetate (pH 5.7),
spun at 20,000 x g, and resuspended the resulting pellet in 50 µL di-ethyl pyrocarbonate-treated
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(DEPC) water. I checked for DNA contamination in the RNA using general bacterial primers
515F and 806R (Bates et al. 2011) and removed contamination using DNase I (New England
BioLabs, Ipswich, MA). Lastly, I reverse transcribed 2 µL of RNA to cDNA using random
hexamer primers and an Invitrogen Superscript III cDNA synthesis kit for RT-PCR (Life
Technologies, Carlsbad, CA, USA).
Illumina library preparation and sequencing
After confirming the presence of DNA using SYBR Safe (Thermo Fisher Scientific,
Waltham, MA), I amplified DNA and cDNA in triplicate using 12 bp Golay barcoded primers
that target the V4 region of the 16S rRNA gene and contain adaptors for Illumina sequencing
(Caporaso et al. 2012). The PCR reaction was as follows: 12.5 µl Phusion High-Fidelity PCR
Master Mix with HF buffer (Thermo Fisher Scientific, Waltham, MA), 0.25 µl of 0.20 µM 515F
(5’-GTGCCAGCMGCCGCGGTAA-3’) forward primer, 0.25 µl of 0.20 µM 806R (5’-
GGACTAC HVGGGTWTCTAAT-3’) reverse primer, 11 µL of DEPC-treated water, and 1 µl of
DNA or cDNA template. I acknowledge the systematic bias of these primers against the SAR11
clade (Apprill et al. 2015), however, salt marsh sediments typically become anoxic within 2-3
mm of the surface, so these strictly aerobic bacterioplankton should not play a large role in the
microbial community associated with this study (Giovannoni 2017). I gel purified PCR products
using the Qiagen® QIAquick gel purification kit (Qiagen, Valencia, CA) and quantified the
purified product using a Qubit® 3.0 fluorometer (Life Technologies, Thermo Fisher Scientific,
Waltham, MA). After pooling to equimolar amounts, I sequenced the nucleic acids using the
Illumina MiSeq (Illumina, San Diego, CA) platform with paired-end 250 bp V2 chemistry.
Quantitative PCR
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From each 20 cm interval, I performed quantitative PCR (qPCR) in triplicate using 357F
and 519R primers (Turner et al. 1999) to quantify the abundance of the 16S rRNA gene and 16S
rRNA, on an Aria Mx Real Time PCR instrument (Agilent Technologies). I used the following
reaction: 10 µL of Brilliant III Ultrafast SYBR Green qPCR Master Mix (Agilent Technologies),
0.5 µL of 20 µM of each forward and reverse primer, 8 µL of DEPC-treated water, and 1 µL of
nucleic acid template (either DNA or cDNA). I ran a 16S rRNA-targeted program, consisting of
an initial denaturation step at 95ºC for 3 minutes followed by 40 cycles of 95 ºC for 5 seconds
and 60 ºC for 10 seconds. I acquired data at the end of each cycle and conducted a melt curve to
confirm that the target gene was amplified. Standards were prepared from purified 16S rRNA
gene product, which I quantified and assessed for size with a tape station (Agilent Technologies).
I ran standard curves with each batch of samples, resulting in R2 > 0.95 and qPCR efficiency >
95%. To calculate gene copies per gram wet weight (g ww-1), I multiplied my results by the mass
of sediment extracted and divided that value by the total elution volume during the
extraction/reverse transcription process.
Nucleic acid sequence processing and analysis
I joined paired-end reads using fast-q join (Aronesty et al. 2011) with default parameters
and performed downstream analysis in QIIME2 (version 2018.2; Caporaso et al. 2010, QIIME 2
Development Team). After demultiplexing and quality filtering following parameters
recommended by Bokulich et al. (2013), I inferred amplicon sequence variants (ASVs) using
DADA2 (Callahan et al. 2016) with a maxEE of 2 and the consensus chimera removal method.
To assign taxonomy I used the Naïve Bayes classifier q2-feature-classifier plugin, trained on the
Greengenes 99% OTUs database (version 13-8; McDonald et al. 2012), and filtered out all
sequences matching chloroplasts and mitochondria, as well as sequences occurring only once
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(singletons). I was left with a total of 5,732,572 sequences for final analysis; 3,629,037
sequences for the 16S rRNA gene and 2,103,535 sequences for 16S rRNA (RNA) that I aligned
using MAFFT v.7 (Katoh & Standley 2013).
Statistical Analyses
To characterize OM across depth and site, I applied linear mixed effects models fit by
maximum likelihood, with core as a random effect, using the lme4 package in R (Bates et al.
2015). I included a null model, a model fit by site or depth only, and an additive and
multiplicative model between these two parameters. I determined the most parsimonious model
by comparing the Akaike’s Information Criterion (AIC) values and performed model selection
using the bbmle package in R (Bolker et al. 2014), which ranks each model according to its
Akaike weight (wi). This metric represents the likelihood of the model normalized by the sum of
all models with values closest to 1 representing the best fit (Johnson & Omland 2004). I
compared models by calculating the difference in AIC (ΔAIC) (Richards 2005).
I normalized the amplicon sequence variant (ASV) tables generated from DADA2 to the
lowest sequencing depth and computed the Shannon diversity index using 10,000 restarts in
QIIME 2 on the 16S rRNA gene and 16S rRNA separately to examine the within-sample
diversity. To test the role of depth on Shannon diversity, I performed an analysis of covariance
(ANCOVA) including site as a fixed effect. I also used a linear mixed effects model fit by
maximum likelihood, with core as a random effect, to investigate the effect of OM characteristics
on Shannon diversity, again examining wi and ΔAIC to assess model appropriateness. To
examine patterns in gene copy number along a depth gradient, I fit a linear model to the 16S
rRNA gene data, and I fit an exponential model to the 16S rRNA data, and compared slopes
between sites using ANCOVA.
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I then calculated beta diversity to explore between sample diversity using weighted
UniFrac in QIIME 2 on 16S rRNA gene and 16S rRNA ASV tables normalized to the lowest
sequencing depth (Lozupone et al. 2005). I tested for differences in community structure as a
function of depth and site with a PERMANOVA using 999 permutations and repeated the
analysis on tables separated into two depth categories: 0-50 cm (shallow) and 60+ cm (deep). To
better visualize changes in microbial community structure, I plotted the first PCoA axis as a
function of depth and I calculated average dissimilarity between sites, also as a function of depth,
for the top 50 cm. To explore which environmental parameters, other than depth, were
responsible for explaining variation in the microbial community structure, I used a linear mixed
effects model to test the effect of depth, %C, %N, and C:N on PCoA axis 1 for both the shallow
and deep regions of the core, with separate analyses per site in the deeper sediments. Lastly, I
analyzed the correlation between all environmental parameters and microbial community
structure with Mantel tests (Mantel 1967) on Euclidean distance using 999 permutations in the
VEGAN package in R (Okansen et al. 2017) on the full dataset and separately based on the two
depth categories.
To gain a better understanding of the dominant taxa in both shallow and deep sediments,
I performed the following steps separately for each depth category. First, I examined the top 100
most abundant ASVs for the total and active community in the top 50 cm. Next, I performed a
random forest regression against depth with 10,000 trees in the randomForest R package (v4.6-
12; Liaw & Wiener 2002) using ASVs that accounted for at least 0.5% of the dataset as predictor
variables, and estimated model performance using leave-one-out cross-validation in the Caret R
package (v6.0-71; Kuhn et al. 2016). From this model, I was able to identify which taxa were
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most important in explaining the variance observed within each depth increment by separately
ranking them in decreasing order of % mean squared error explained.
Finally, I was interested in determining how patterns of microbial community assembly
changed with depth. One way to infer the relative influence of deterministic versus stochastic
processes is to examine phylogenetic community composition in relation to environmental
conditions (Fine & Kembel 2011, Stegen et al. 2012). Assuming that more closely related taxa
are more ecologically similar (Andersson et al. 2010, Phillipot et al. 2010), then a community
whose phylogenetic composition does not significantly differ by chance (i.e. phylogenetically
overdispersed) is considered stochastic. In contrast, if deterministic processes dominate, then
observed taxa should be more closely related than expected by chance (i.e. phylogenetically
constrained). To test this, I constructed a phylogenetic tree using FastTree (Price et al. 2010) in
QIIME 2 and calculated the standardized effect size of the mean pairwise distance (SESMPD) with
999 runs and null model = ‘taxa.labels’ using the Picante package in R (Kembel et al. 2010).
This metric is analogous to -1 times the net relatedness index (-NRI), which quantifies the
phylogenetic distance from the root to terminal leaves (Webb et al. 2008). Positive values (> 0)
indicate phylogenetic overdispersion (greater phylogenetic distance than expected according to a
null model) and negative values (< 0) indicate phylogenetic clustering (smaller phylogenetic
distances than expected according to a null model). All statistical analyses were conducted in R
(R Core Team) unless otherwise stated.
Results
Organic matter characteristics and age date modeling
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After log-transforming bulk density data, a linear mixed effects model showed that depth
best explained patterns for both the reference and enriched site (wi=0.43; Fig. 2A, G; Table 1,
S1). At 250 cm, bulk density more than doubled to 2.03 g cm-3, which coincided with a decrease
in LOI from 19% to 3.1% at the same depth (Fig. 2B, H; Table 1). Besides this section at around
250 cm, LOI was variable down core, and the interactive effect between depth and site best
explained this variation (wi=1.0; Table S1). Values of %N and %C also demonstrated variable
patterns down core, ranging from 0.2 to 1.4% and 0.1 to 1.3% N and 2.2 to 19.2 and 1.9 to
22.6% C at the reference and enriched sites, respectively. There was a slight peak in %C and %N
at ~40 cm in all cores, and a decrease between ~150-180 cm at the enriched marsh; but these fell
within the range of values at each site (Fig. 2C, D, I, J). An additive effect of depth and site best
explained both %C (wi=0.59) and %N (wi=0.58; Table S1), where the reference site had lower
%C by an average of 2.6% ± 0.89 and lower %N by an average 0.095% ± 0.046 when compared
to the enriched site. Site best explained patterns in C:N, which was fairly consistent down core
(wi=0.56; Table 1, S1), with the enriched site exhibiting a C:N that was, on average, 2.25 ± 2.2
higher than the reference marsh (Fig. 2E, K; Table 1). At both sites, δ13C values indicated a C4
plant carbon source except in the fertilizer marsh at 170 cm depth and from 240+ cm where a
more negative δ13C value suggests a transition to a C3 plant source (Fig. 2F, L). Radiocarbon
dating indicated that the deepest cores at the reference and enriched sites were 2290 and 3260
years, respectively (Table 2). Classic age-depth modeling indicated that corresponding depths at
each site were comparable in age by ± 200 years.
Alpha diversity and gene abundance
Shannon diversity of the total community (16S rRNA gene) decreased with depth linearly
by approximately 30% (Fig. 3A; p < 0.001, F1,94=77.91, R2 = 0.44) with no difference between
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reference and enriched sites (p=0.894). Shannon diversity of the inferred active community (16S
rRNA) showed a similar, though less pronounced pattern, with values decreasing with depth
(Fig. 3B; p<0.001, F1,99=36.7, R2 = 0.26) and no difference between sites (p=0.261). Linear
mixed effects modeling revealed that depth was the best predictor of observed changes in
Shannon diversity for both the 16S rRNA gene (wi=0.61) and 16S rRNA (wi=0.62; Table S2).
Abundance of the 16S rRNA gene and its product, 16S rRNA, also exhibited decreasing
patterns with depth. Average 16S rRNA gene copies (per g ww-1) decreased linearly with depth
while remaining within the 108-109 range (Fig. 3C). ANCOVA results indicated that, in addition
to the effect of depth, there was also a difference in slopes between sites (p=0.006, F1,50=8.163),
with a smaller rate of change at the reference site (p<0.001, F1,22 = 8.326, R2 = 0.24, y=-0.0021x
+ 9.4) when compared to the enriched site (p<0.001, F1,27=13.92, R2=0.32, y=-0.003x + 9.3).
Abundance of 16S rRNA, on the other hand, declined by an order of magnitude in the top 10 cm
following an exponential decay (p<0.001, F1,54=22.45, R2 = 0.28) and reached values 3-4 orders
of magnitude lower in the deepest sediments when compared to the surface with equivalent
slopes between sites (Fig. 3D; p=0.126).
Microbial community structure
A principal coordinate analysis (PCoA) constructed from Weighted UniFrac of the total
microbial community (via analysis of the 16S rRNA gene) revealed extensive changes with
depth (Fig. 4A); however, a PERMANOVA indicated there was no effect of site on microbial
community structure (p=0.22, pseudo-F=1.36, n = 98). The first PCoA axis explained 41% of the
variance in the community structure, and when plotted against depth (Fig. 4B), indicated that the
microbial community changed rapidly through the first 50 cm (red line), at which point depth no
longer explained much variation. Considering the dynamic changes occurring in the top 50 cm, I
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examined dissimilarity values between sites at each depth interval from 0-50 cm (Fig. 4C) and
found that while the two sites were very similar at the surface, they became increasingly different
with depth.
As a result of these different patterns, I split the dataset into two depth categories, 0-50
cm (shallow) and 60+ cm (deep) and repeated the above analyses. A PERMANOVA revealed
that the community structure between sites was not significantly different in the shallow
sediments (p=0.422, pseudo-F=0.845, n=36), however, it was different in the deep sediments
(p=0.005, pseudo-F=3.818, n=62). This pattern was also true for the potentially active
community (via analysis of the16S rRNA; Supplemental Fig. S1), with a significant difference
between sites in the deep sediments (p=0.034, pseudo-F=2.438, n=65) but not in the shallow
sediments (p=0.143, pseudo-F=1.716, n=36). In the shallow sediments, a linear mixed effects
model revealed that additive models with either depth and %N (16S rRNA gene; Table S3) or
depth and %C (16S rRNA; Table S4) best explained the variation observed in the first PCoA
axis. Since there was a significant difference between sites in the deeper sediments, I performed
linear mixed models within each site separately. In the deep reference sediments, the null
intercept model best explained variation in the first PCoA axis for both the 16S rRNA gene
(Table S3) and 16S rRNA (Table S4). Similarly, the null intercept model was most appropriate
for the 16S rRNA gene at the enriched site (Table S3), however, C:N best explained variation in
the first PCoA axis in the 16S rRNA (Table S4). Finally, A Mantel test revealed a significant
correlation between microbial community structure and a matrix of parameters for the shallow
(p=0.001, r=0.35) and the deep sediments (p=0.001, r=0.35) for the 16S rRNA gene.
Since there were no differences among sites in the microbial community of the shallow
sediments, I pooled by site to examine the most abundant taxa in the 16S rRNA gene (Fig. 5A)
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and 16S rRNA (Fig. 5B). I found that taxa exhibited tremendous variability over the top 50 cm.
Out of the top 100 most abundant ASVs in the 16S rRNA gene, which accounted for 35% of the
total number of sequences, several classes decreased in abundance with depth including
Gammaproteobacteria and Ignavibacteria, while classes Actinobacteria, Bacteroidia, Nitrospira,
Parvarchaeota, and candidate phyla AC1 and OP9 were relatively more abundant in the deeper
sediments. To further explore the taxa driving shifts in the top 50 cm, I ran a random forest
regression model using 1920 and 2057 ASVs (as predictor variables) to explain the variance in
the 16S rRNA gene and 16S rRNA, respectively. I found that they explained 63.8% and 64.5%
(root-mean-square error; RMSE = 10.28 and 9.65) of the variance observed with depth, with
unique ASVs becoming more abundant at different depths.
The model identified 35 ASVs for the 16S rRNA gene, spread across 16 taxonomic
classes, that significantly contributed to the variance explained in the regression model. These 35
ASVs accounted for only ~5.5% of sequences from the top 50 cm dataset. Of these 35 ASVs, the
orders Anaerolineales, Marinicellales, Chromatiales, and an unclassified Chloroflexi decreased
and unclassified Deltaproteobacteria, unclassified Phycisphaerae, Candidate division AC1
(B04R032), Dehalococcoidales, Bacteroidales, and the Miscellaneous Crenarchaeotal Group
(MCG) increased with depth. Nitrospirales, unclassified Thermoplasmata, and Mariprofundales
peaked at intermediate depths (Fig. 6; Supplemental table S7). The model also identified 41
unique ASVs, distributed across 16 classes, in the 16S rRNA that significantly contributed to the
variance within the top 50 cm. These sequences accounted for only ~10% of sequences from the
top 50 cm dataset. Gammaproteobacteria, an unclassified Anaerolineae, candidate phyla TG3,
and Alphaproteobacteria decreased within the first 10-20 cm, while Dehalococcoidales,
unclassified Chlorobi, unclassified Deltaproteobacteria, and Bacteroidia all increased in relative
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abundance. Spirochaetes, Brachyspirales, Leptospirae, Ignavibacteria, and candidate phylum
GN15 peaked at intermediate depths (Fig. 7; Supplemental table S8).
The top 100 ASVs in the deep sediments (60+ cm) represented a total of 37 unique
taxonomic groups aggregated at the class level, accounting for ~69 and 60% of the total and
active community at the reference site (Fig. 8A, C), and ~67 and 55% of the total and active
community at the enriched site (Fig. 8B, D). Random forest classification of the two sites in the
deep sediments had a classification accuracy of 90.3% (kappa = 0.80) for the 16S rRNA gene
and 81.5% (kappa = 0.62) for 16S rRNA. This was in contrast to the shallow sediments, where
classification success was much lower (50 and 61% for the 16S rRNA gene and 16S rRNA,
respectively). The taxa most important in differentiating between the reference and enriched sites
included classes Anaerolineae, Bacteroidia, Brachyspirae, Caldithrixae, Dehalococcoidetes,
Parvarchaea, Phycisphaerae, S085 from phylum Chloroflexi, and Candidate phyla GN04 (clade
GN15). Bacteroidia and Dehalococcoidetes were also important in differentiating the potentially
active community, as was Deltaproteobacteria, Gammaproteobacteria, and Candidate Phyla OP8
and AC1 (clade B04R032).
At both sites, the total community exhibited clear patterns with depth. Acidomicrobiia,
Alphaproteobacteria, and Nitrospira disappeared within the first 50-100 cm, while Chloroflexi,
and Candidate phyla AC1 and OP8 did not become important until 100-150 cm (Fig. 8A, B;
Table S9, 10). Further, some groups were present throughout the depth gradient, including
Parvarchaea, Clostridia, Dehalococcoidetes, and Candidate phylum OP1. In contrast, there were
fewer taxonomic groups that comprised the top 100 ASVs in the potentially active dataset, with
Deltaproteobacteria and Chloroflexi accounting for a much larger portion compared to the total
community (Fig. 8C, D; Table S11, 12).
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Patterns of microbial community assembly
In the 16S rRNA gene (Fig. 9A), SESMPD values were strongly negative at the surface,
indicating phylogenetic clustering, and exhibited similar patterns for both the reference and
enriched sites. Values became positive, indicating phylogenetic overdispersion, at ~30 cm, and
then varied between -8.3 to 9.7 down to the deepest depths, staying mostly positive with some
exceptions. The pattern in 16S rRNA (Fig. 9B) was less pronounced, with positive excursions
occurring more sporadically and less often throughout the depth profile.
Discussion
Organic matter profiles exhibit local-scale shifts rather than long-term patterns
OM data exhibited patterns characteristic of New England salt marshes (Morris et al.
2016, Forbrich et al. 2018), demonstrating long term carbon storage and OM stability down core.
Bulk density varied significantly with depth (Fig. 2A, G), but much of this variation occurred
deeper than 240 cm where the average at both sites approximately doubled. This transition
provides evidence for the presence of marine sediments as a result of transgression and is further
supported by a negative excursion in δ13C values, suggesting a shift from C3 to C4 plants.
Presumably, this marks the establishment of the Spartina marsh ~3000 years ago, which is
comparable to values reported by Kirwan et al. (2011). Across all other depths, average bulk
density values I measured also agree with other studies conducted within similar marsh sites in
Plum Island Sound (Morris et al. 2016, Forbrich et al. 2018). I found that %OM also varied by
depth, but that this variation by depth differed between the reference and enriched sites. This
pattern is likely driven by the higher %OM values in Core 3 at the reference site, which is
located near a mosquito ditch and a salt marsh panne (Fig. 1). Prior to ditching that occurred in
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the 1930’s (Adamowicz & Roman 2005, Wilson et al. 2014), this sampling location was located
furthest from the natural creek bank, potentially resulting in less inorganic sediment delivery
from the tides. Further, proximity to salt marsh pannes can influence sediment profiles, resulting
in heterogeneous peat deposition strata (Wilson et al. 2014, Spivak et al. 2017). Both %C (Fig.
2D, J) and %N (Fig. 2C, I) were lower at the reference marsh compared to the enriched marsh,
but this is likely due to spatial differences resulting from inherently different characteristics by
site as opposed to consequences from nutrient enrichment.
Decreases in diversity and cell abundance reflect resource limitation
For both the total and potentially active microbial communities, I observed significant
decreases in Shannon diversity and abundance of the 16S rRNA gene and 16S rRNA with depth.
On the surface, Shannon diversity of the 16S rRNA gene was similar to high levels of diversity
typically observed in salt marshes (Kearns et al. 2016) and other terrestrial soils (Fierer et al.
2007). Significant decreases by depth suggest the presence of an ecological filter, where only a
subset of microbes present at the surface can thrive at depth, a pattern that is widely observed in
both marine (Oni et al. 2015, Walsh et al. 2016) and terrestrial subsurface environments
(Hartmann et al. 2009, Eilers et al. 2012). The availability of OM with depth and the increasing
need to oxidize more recalcitrant forms to gain energy, is a likely factor driving these patterns, as
fewer taxa are physiologically adapted to use these complex forms of OM, and those that are
must compete for resources, resulting in lower diversity. Electron acceptor availability may be
another selecting factor, driving competition among groups that use the same pool of available
electron acceptors. In sediment cores from permafrost zones in Alaska, depth, pH, electrical
conductivity, total organic carbon, total nitrogen, and methane all significantly influenced
diversity. In this study, depth best explained the variation in diversity, potentially due to the
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relatively stable nature of the OM in my cores (Table S2). The potentially active microbial
community demonstrated a lower Shannon diversity at the surface when compared to the total
community (Fig. 3B), suggesting that a portion of the DNA was inactive. Similar to the 16S
rRNA gene, depth best predicted patterns in diversity in the active community (Table S2).
Abundance of the 16S rRNA gene decreased with depth at both sites (Fig. 3C). likely
driven by limited resources, with decreasing OM quantity and quality determining the amount of
energy available for growth. Consequences of these energetic limitations could include slower
growth rates or community turnover time (Jørgensen & Marshall 2016), fewer cells due to
energy allocation towards cell repair rather than cell division (Langerhaus et al. 2012), and/or
smaller community size due to selection for taxa that can survive under such conditions (Petro et
al. 2017), all of which would contribute to the lower microbial abundance I observed. Unlike
abundances of the 16S rRNA gene, abundance of 16S rRNA decreased most within the top 10
cm (Fig. 3D), and then followed an exponential decay, reaching values 3-4 orders of magnitude
lower at depth (Fig. 3D). This deviation between the 16S rRNA gene and 16S rRNA copy
number indicates that relic DNA and inactive cells are abundant at depth. Relic DNA can persist
in sediment for long periods of time, potentially altering our interpretation of the ecosystem scale
effects of shifts in the microbial community (Carini et al. 2016, Vuillemin et al. 2017). Lennon et
al. (2018), however, suggest that relic DNA contributes minimally to estimates of diversity due
to degradation being proportional to total abundance. Further, microbes can enter a metabolic
resting state of dormancy under unfavorable environmental conditions (Lennon & Jones 2011).
Since they are not metabolically active, these organisms are not necessarily exposed to forces of
selection, which could result in a difference between the 16S rRNA gene and 16S rRNA
abundance profiles.
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Deterministic processes are important in shallow (0-50 cm) sediments
I found no differences in the microbial community by site (Fig. 4A), but both the total
and active community changed significantly with depth in the top 50 cm (Fig. 4B, S1). The lack
of differentiation between the reference and enriched site suggests that the environmental
conditions resulting from nutrient enrichment had no measurable influence on microbial
community structure when compared to the reference site. Alternatively, the experimental
nutrient enrichment may not have percolated deeper than the surface sediments. I collected cores
in the high marsh from Spartina patens habitat, which receives tidal inundation only 3-4 times a
month (Johnson et al. 2016). It is also unlikely, given the bulk density of these sediments (Fig.
2A, G), that nutrient enrichment could penetrate into deeper sediments. I found that variation in
the first PCoA axis was best explained by depth and %N in the 16S rRNA gene and by depth and
%C in the 16S rRNA, suggesting that the nature of OM in the top 50 cm, in part, dictated
microbial community structure. Further, Mantel tests indicated a significant relationship between
microbial community structure and a full matrix of environmental parameters, further indicating
that other parameters, in addition to depth, played a role in determining microbial community
structure in the top 50 cm. At the surface, I observed phylogenetic clustering (Fig. 9) indicating
that highly selective processes dominated control over microbial community structure (Fine &
Kembel 2011, Stegen et al. 2012). I would expect this pattern where strong plant-microbe
associations tend to dictate microbial community structure (Burke et al. 2002), such as in the
microbial communities in salt marsh surface sediments and in the rooting zone below. This could
explain the similarity among all cores from both sites in surface sediments in both the 16S rRNA
gene and 16S rRNA, because the effect of plant species and other related parameters outweighed
any other selective forces (Fig. 4, S1).
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By applying random forest regression modeling, I was able to identify ASVs that were
most important in explaining the variance in microbial community structure by depth in the
shallow sediments. Groups from orders Gammaproteobacteria, Betaproteobacteria, and
Nitrospira in the total community (Fig. 6), and Alphaproteobacteria in the potentially active
community (Fig. 7), all decreased within the top 50 cm. Proteobacterial taxa are typically
copiotropic and found to decrease with depth as nutrient concentrations, including organic rich
carbon, decrease (Hansel et al. 2008, Will et al. 2010, Eilers et al. 2012). Nitrospira belong to
groups known to oxidize nitrite and they possess enormous metabolic flexibility (Watson et al.
1986, Daims et al. 2015). Their decreasing pattern with depth and ultimate disappearance > 40
cm suggests that they are closely associated with plant roots where microaerophilic regions of
oxygen allow for tight coupling between nitrification and denitrification (Hamersley & Howes
2005, Koop-Jakobsen & Giblin 2010), which supports the deterministic nature of the sediments
throughout the top 50 cm.
In contrast, orders Deltaproteobacteria, Dehalococcoidetes, Bacteroidia, Phycisphaerae,
and Crenarchaeota all increased within the top 50 cm, suggesting these groups survive better in
in deeper sediments. Deltaproteobacteria are known to contain groups that contribute to sulfur
cycling (Bahr et al. 2005). These bacterial groups are widely detected in salt marsh sediments
where sulfate reduction accounts for a large portion of microbial respiration (Howarth & Teal
1979, Howarth 1984), and can become especially dominant at depths where other more
energetically favorable electron acceptors, such as nitrate, are depleted. Further, groups from
Dehalococcoidia and Crenarchaeota can oxidize a wide range of OM substrates, possessing
genes capable of degrading aromatic hydrocarbons (Vigeron et al. 2014, Wasmund et al. 2014),
suggesting these groups are better selected for depths where OM is less labile. Several ASVs,
169
despite driving the patterns I observed across the top 50 cm, were < 1% in relative abundance
indicating that rare taxa had a disproportionate influence on microbial community structure and
were important in discriminating among unique communities along a depth gradient where
deterministic processes determine their success (Shade et al. 2014, Shade & Gilbert 2015).
Stochastic processes are important in deeper (60+ cm) sediments
There was no significant difference in microbial community structure between sites near
the surface (0-50 cm), however dissimilarity values increased with depth (Fig. 4C), resulting in
significant differences in deeper sediments (60+ cm). Since the difference between sites occurred
in sediments that were deeper than 50 cm and 800+ years old, it is unlikely these patterns by site
resulted from nutrient enrichment; rather, it is possible that stochastic processes, or differences
during the specific time frame measured, structured microbial communities in these deeper
sediments. This is supported by SESMPD values that shifted from largely negative to
predominantly positive at this depth (Fig. 9), indicating a transition from deterministic to
stochastic assembly (Fine & Kembel 2011, Stegen et al. 2012). As depth increased and OM
characteristics changed, communities demonstrated phylogenetic overdispersion, suggesting that
stochastic processes such as drift, dispersal, and/or diversification exerted a greater force on
controlling community dynamics. This is in contrast to my expectation that selection by
environmental filtering and competition would increase as resources and niche space (i.e. OM
and electron acceptor availability decline) became more limited, and it contrasts with findings
from other studies conducted in subsurface communities (Stegen et al. 2013).
These results suggest, instead, that deep salt marsh sediments with fairly consistent OM
properties provide a unique environment in which stochastic processes dominate. Contrary to my
hypothesis, competition over resources can result in phylogenetic overdispersion, where taxa
170
occupying a limited range of niche spaces must diversify to survive (Koeppel & Wu 2014).
Further, the influence of stochastic processes may be amplified as a result of the stable nature of
these sediments. In systems with low diversity, drift and diversification play a disproportionate
role in controlling assembly processes (Chase & Meyers 2011), and systems with lower biomass
can experience a greater response to dispersal (Hoehler & Jørgensen 2013, Holyoak et al. 2015).
I observed both of these characteristics in deep sediments, and although I cannot determine
whether diversification or dispersal drive these processes, it is clear that stochastic processes
become more important in deeper sediments. The fact that the null intercept model best
explained variation in microbial community structure for the deeper sediments (Table S3, S4),
further supports this claim. However, C:N was a better predictor of community structure for the
active community at the enriched site, potentially suggesting that OM quality imparted stronger
selection on the active portion of the community at that site.
Conclusions
I found that OM quality was consistent with depth, exhibiting properties of long term
carbon storage potential characteristic of salt marshes and other blue carbon systems. Microbial
diversity and gene abundance decreased with depth, likely due to resource limitation, but these
patterns could not be linked to any other environmental parameter measured in this study. It is
likely that C:N was not a sensitive enough metric to capture changes in OM quality, and more
high resolution techniques are required to assess OM quality and composition. I also found that
while deterministic processes dominated microbial community assembly at the surface, it gave
way to stochastic assembly at depth, perhaps due to the stable nature of deep salt marsh
sediments. This work highlights the patterns of diversity, structure, and assembly of microbial
communities in relation to OM in deep salt marsh sediments, though we still lack a fundamental
171
understanding of functional capacity of these microbes at depth. Future work should aim to apply
meta-omic techniques to gather more information about the genetic machinery present in
microbes driving carbon cycling in salt marsh sediments.
Acknowledgements
I would like to thank researchers of the TIDE project (NSF OCE0924287, OCE0923689,
DEB0213767, DEB1354494, and OCE 1353140) for maintenance of the long-term nutrient
enrichment experiment, as well as researchers of the Plum Island Ecosystems LTER (NSF OCE
0423565, 1058747, 1637630). I would also like to acknowledge Annie Murphy, Joseph Vineis,
Khang Tran, Michael Greenwood, and members of the Bowen lab for contributions in the
laboratory, as well as Anne Giblin, Jane Tucker, and Inke Forbrich for their thoughtful
comments on this research. This work was funded by an NSF CAREER Award to JLB
(DEB1350491). Additional support was provided by a Ford Foundation pre-doctoral fellowship
award to ABM.
172
References
Adamowicz SC, Roman CT (2005) New England salt marsh pools: A quantitative analysis of
geomorphic and geographic features. Wetlands 25:279–288
Aller R (1994) Bioturbation and remineralization of sedimentary organic matter; effects of redox
oscillation. Chem Geol 114:331–345
Aller RC, Aller JY (1998) The effect of biogenic irrigation intensity and solute exchange on
diagenetic reaction rates in marine sediments. J Mar Res 56:905–936
Andersson AF, Riemann L, Bertilsson S (2010) Pyrosequencing reveals contrasting seasonal
dynamics of taxa within Baltic Sea bacterioplankton communities. ISME J 4:171–181
Apprill A, Mcnally S, Parsons R, & Weber L (2015). Minor revision to V4 region SSU rRNA
806R gene primer greatly increases detection of SAR11 bacterioplankton. Aqua Mic Ecol
75:129–137
Aronesty E (2011) Command-line tools for processing biological sequencing data. Ea-utils:
FASTQ processing utilities. http://code.google.com/p/ea-utils
Bahr M, Crump BC, Klepac-Ceraj V, Teske A, Sogin ML, Hobbie JE (2005) Molecular
characterization of sulfate-reducing bacteria in a New England salt marsh. Environ
Microbiol 7:1175–1185
Balsev-Clausen D, Dahl TW, Saad N, Rosing MT (2013) Precise and accurate δ13C analysis of
rock samples using Flash Combustion-Cavity Ring Down Laser Spectroscopy. J Anal
Atom Spectrom 28:516-523
Bates ST, Berg-Lyons D, Caporaso JG, Walters WA, Knight R, Fierer N (2011) Examining the
global distribution of dominant archaeal populations in soil. ISME J 5:908–917
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using
lme4. Journal of statistical software 67:1-48
Benner R, Newell SY, Maccubbin AE, Hodson RE (1984) Relative contributions of bacteria and
fungi to rates of degradation of lignocellulosic detritus in salt-marsh sediments. Appl
Environ Microbiol 48:36–40
Biddle JF, Lipp JS, Lever MA, Lloyd KG, Sorensen KB, Anderson R, Fredricks HF, Elvert M,
Kelly TJ, Schrag DP, Sogin ML, Brenchley JE, Teske A, House CH, Hinrichs K-U
(2006) Heterotrophic Archaea dominate sedimentary subsurface ecosystems off Peru.
Proc Natl Acad Sci USA 103:3846–3851
Blaauw M (2010) Methods and code for ‘classical’ age-modelling of radiocarbon sequences.
Quaternary Geochronology 5:512-518
173
Blazewicz SJ, Barnard RL, Daly RA, Firestone MK (2013) Evaluating rRNA as an indicator of
microbial activity in environmental communities: limitations and uses. ISME J 7:2061-
2068
Bokulich N a, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills D a, Caporaso JG
(2013) Quality-filtering vastly improves diversity estimates from Illumina amplicon
sequencing. Nat Methods 10:57–59
Bolker B, RDC Team (2014) bbmle: Tools for general maximum likelihood estimation. R
package version 01.20
Bulseco-McKim A, Giblin AE, Tucker J, Murphy AE, Sanderman J, Hiller-Bittrolf K, Bowen J
(In Review) Nitrate addition stimulates microbial decomposition of organic matter in salt
marsh sediments. Glob Chang Biol
Burdige DJ (2007) Preservation of organic matter in marine sediments: Controls, mechanisms,
and an imbalance in sediment organic carbon budgets? Chem Rev 107:467–485
Burke DJ, Hahn D, Hamerlynck EP (2002) Interactions among plant species and
microorganisms in salt marsh sediments. Appl Environ Microbiol 68:1157–1164
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2:
High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583
Canfield DE, Thamdrup B, Kristensen E (2005) Aquatic Geomicrobiology. Elsevier Academic
Press, Boston, MA
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña
AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE,
Lozupone CA, Mcdonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ,
Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows
analysis of high- throughput community sequencing data. Nat Meth 7:335–336
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J,
Fraser L, Bauer M, Gormley N, Gilbert J a, Smith G, Knight R (2012) Ultra-high-
throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms.
ISME J 6:1621–1624
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N (2016) Relic DNA is
abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol 2:1–6
Chase JM, Myers JA (2011) Disentangling the importance of ecological niches from stochastic
processes across scales. Philosph Trans R Soc B Biol Sci 366:2351–2363
Chmura GL, Anisfeld SC, Cahoon DR, Lynch JC (2003) Global carbon sequestration in tidal,
saline wetland soils. Glob Biogeochem Cycles 17:1-12
174
Chu H, Sun H, Tripathi BM, Adams JM, Huang R, Zhang Y, Shi Y (2016) Bacterial community
dissimilarity between the surface and subsurface soils equals horizontal differences over
several kilometers in the western Tibetan Plateau. Environ Microbiol 18:1523–1533
Cowie GL, Hedges JI (1994) Biochemical indicators of diagenetic alteration in natural organic
matter mixtures. Nature 369:304-307
Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, Jehmlich N, Palatinszky M,
Vierheilig J, Bulaev A, Kirkegaard RH, Bergen M Von, Rattei T, Bendinger B, Nielsen
PH, Wagner M (2015) Complete nitrification by Nitrospira bacteria. Nature 528:504–509
Darby FA, Turner RE (2008) Effects of eutrophication on salt marsh root and rhizome biomass
accumulation. Mar Ecol Prog Ser 363:63–70
Deegan LA, Bowen JL, Drake D, Fleeger JW, Friedrichs CT, Galván KA, Hobbie JE, Hopkinson
C (2007) Susceptibility of salt marshes to nutrient enrichment and predation removal.
Ecol Appl 17:42–63
Deegan LA, Johnson DS, Warren RS, Peterson BJ, Fleeger JW, Fagherazzi S, Wollheim WM
(2012) Coastal eutrophication as a driver of salt marsh loss. Nature 490:388–392
Duarte CM, Losada IJ, Hendriks IE, Mazarrasa I, Marba N (2013) The role of coastal plant
communities for climate change mitigation and adaptation. Nat Clim Change 3:961-968
Duarte CM, Middelburg JJ, Caraco N, Major NC (2005) Major role of marine vegetation on the
oceanic carbon cycle. Biogeosciences 2:1–8
Dumbrell AJ, Nelson M, Helgason T, Dytham C, Fitter AH (2010) Relative roles of niche and
neutral processes in structuring a soil microbial community. ISME J 4:337–345
Eilers KG, Debenport S, Anderson S, Fierer N (2012) Digging deeper to find unique microbial
communities: The strong effect of depth on the structure of bacterial and archaeal
communities in soil. Soil Biol Biochem 50:58–65
Fierer N, Jackson RB (2006) The diversity and biogeography of soil bacterial communities. Proc
Natl Acad Sci USA 103:626–631
Fierer N, Breitbart M, Nulton J, Salamon P, Lozupone C, Jones R, Robeson M, Edwards RA,
Felts B, Rayhawk S, Knight R, Rohwer F, Jackson RB (2007) Metagenomic and small-
subunit rRNA analyses reveal the genetic diversity of bacteria, archaea, fungi, and
viruses in soil. Appl Environ Microbiol 73:7059–7066
Fierer N, Schimel JP, Holden PA (2003) Variations in microbial community composition
through two soil depth profiles. Soil Biol Biochem 35:167–176
175
Fine PVA, Kembel SW (2011) Phylogenetic community structure and phylogenetic turnover
across space and edaphic gradients in western Amazonian tree communities. Ecography
34:552–565
Forbrich I, Giblin AE, Hopkinson CS (2018) Constraining marsh carbon budgets using long-term
C burial and contemporary atmospheric CO2 fluxes. J Geophys Res Biogeosci 123: 867-
878
Froelich P, Klinkhammer G, Bender M, Luedtke N, Heath G, Cullen D, Dauphin P (1979) Early
oxidation of organic matter in pelagic sediments of the eastern equatorial Atlantic:
suboxic diagenesis. Geochim Cosmochim Acta 43:1075–1090
Galloway JN, Leach AM, Erisman JW, Bleeker A (2017) Nitrogen: the historical progression
from ignorance to knowledge with a view to future solutions. Soil Res 55:417–424
Giblin A, Tobias C, Song B, Weston N, Banta G, Rivera-Monroy V (2013) The importance of
dissimilatory nitrate reduction to ammonium (DNRA) in the nitrogen cycle of coastal
ecosystems. Oceanography 26:124–131
Giovannoni SJ (2017). SAR11 bacteria: The most abundant plankton in the oceans. Ann Rev
Mar Sci 9:231–255
Hamersley MR, Howes BL (2005) Coupled nitrification–denitrification measured in situ in a
Spartina alterniflora marsh with a 15NH4+ tracer. Mar Ecol Prog Ser 299:123–135
Hansel CM, Fendorf S, Jardine PM, Francis CA (2008) Changes in bacterial and archaeal
community structure and functional diversity along a geochemically variable soil profile.
Appl Environ Microbiol 74:1620–1633
Hartmann M, Lee S, Hallam SJ, Mohn WW (2009) Bacterial, archaeal and eukaryal community
structures throughout soil horizons of harvested and naturally disturbed forest stands.
Environ Microbiol 11:3045–3062
Hedges JI, Eglinton G, Hatcher PG, Kirchman DL, Arnosti C, Derenne S, Evershed RP, Kögel-
Knabner I, de Leeuw JW, Littke R, Michaelis W (2000) The molecularly-uncharacterized
component of nonliving organic matter in natural environments. Organic Geochem 31:945-
58
Hijmans RJ, van Etten J (2012) Raster: Geographic analysis and modeling with raster data. R
package version 2.0-12
Hoehler TM, Jørgensen BB (2013) Microbial life under extreme energy limitation. Nat Rev
Microbiol 11:83–94
Holmer M, Gribsholt B, Kristensen E (2002) Effects of sea level rise on growth of Spartina
anglica and oxygen dynamics in rhizosphere and salt marsh sediments. Mar Ecol Prog
176
Ser 225:197–204
Holyoak M, Leibold MA, Holt RD (2005) Metacommunities: Spatial Dynamics and Ecological
Communities. The University of Chicago Press, Chicago
Howarth RW (1984) The Ecological significance of sulfur in the energy dynamics of salt marsh
and coastal marine sediments. Biogeochemistry 1:5–27
Howarth RW, Teal JM (1979) Sulfate reduction in a New England salt marsh. Limnol Oceanogr
24:999–1013
Inagaki F, Hinrichs K-U, Kubo Y, Bowles MW, Heuer VB Exploring deep microbial life in coal-
bearing sediment down to ~2.5 km below the ocean floor (2015) Science 349:16–21
Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends Ecol Evol
19:101–108
Johnson DS, Warren RS, Deegan LA, Mozdzer TJ (2016) Saltmarsh plant responses to
eutrophication. Ecol Appl 26:2647–2659
Jørgensen BB, Boetius A (2007) Feast and famine - Microbial life in the deep-sea bed. Nat Rev
Microbiol 5:770–781
Jørgensen BB, Marshall IPG (2016) Slow microbial life in the seabed. Ann Rev Mar Sci
8:311–332
Kaplan W, Valiela I, Teal JM (1979) Denitrification in a salt marsh ecosystem. Limnol Oceanogr
24:726–734
Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7:
Improvements in performance and usability. Mol Biol Evol 30:772–780
Kearns PJ, Angell JH, Howard EM, Deegan LA, Stanley RHR, Bowen JL (2016) Nutrient
enrichment induces dormancy and decreases diversity of active bacteria in salt marsh
sediments. Nat Commun 7:1–9
Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blombger SP,
Webb CO (2010). Picante: R tools for integrating phylogenies and ecology.
Bioinformatics 26:1463-1464
Kerkhof L, Kemp P (1999) Small ribosomal RNA content in marine Proteobacteria during non-
steady-state growth. FEMS Microbiol Ecol 30:253-260
Kirwan ML, Murray AB, Donnelly JP, Corbett DR (2011) Rapid wetland expansion during
European settlement and its implication for marsh survival under modern sediment
delivery rates. Geology 39:507–510
177
Koeppel AF, Wu M (2014) Species matter: The role of competition in the assembly of
congeneric bacteria. ISME J 8:531–540
Koop-Jakobsen K, Giblin AE (2010) The effect of increased nitrate loading on nitrate reduction
via denitrification and DNRA in salt marsh sediments. Limnol Oceanogr 55:789–802
Kristensen E, Holmer M (2001) Decomposition of plant materials in marine sediment exposed to
different electron acceptors (O2, NO3-, and SO4
2-), with emphasis on substrate origin,
degradation kinetics, and the role of bioturbation. Geochim Cosmochim Acta 65:419–433
Kubo K, Lloyd KGF, Biddle J, Amann R, Teske A, Knittel K (2012) Archaea of the
Miscellaneous Crenarchaeotal Group are abundant, diverse and widespread in marine
sediments. ISME J 6:1949-1965
Kuhn M (2015) A Short introduction to the caret package. R Found Stat Comput:1–10
Langerhuus AT, Røy H, Lever MA, Morono Y, Inagaki F, Jørgensen BB, Lomstein BA (2012)
Endospore abundance and d: L-amino acid modeling of bacterial turnover in holocene
marine sediment (Aarhus Bay). Geochim Cosmochim Acta 99:87–99
Lee RW, Kraus DW, Doeller JE (1999) Oxidation of sulfide by Spartina alterniflora roots.
Limnol Oceanogr 44:1155–1159
Lennon JT, Jones SE (2011) Microbial seed banks: The ecological and evolutionary implications
of dormancy. Nat Rev Microbiol 9:119–130
Lennon JT, Muscarella ME, Placella SA, Lehmkuhl BK (2018) How, when, and where relic
DNA affects microbial diversity. MBio 9:1–14
Levy-Booth DJ, Campbell RG, Gulden RH, Hart MM, Powell JR, Klironomos JN, Pauls KP,
Swanton CJ, Trevors JT, Dunfield KE (2007) Cycling of extracellular DNA in the soil
environment. Soil Biol Biochem 39:2977–2991
Liaw A, Wiener M (2002) Classification and regression by randomForest. R news 2:18–22
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: An effective
distance metric for microbial community comparison. ISME J 5:169–172
Macreadie PI, Nielsen DA, Kelleway JJ, Atwood TB, Seymour JR, Petrou K, Connolly RM,
Thomson ACG, Trevathan-Tackett SM, Ralph PJ (2017) Can we manage coastal
ecosystems to sequester more blue carbon? Front Ecol Environ 15:206–213
Mantel N (1967) The detection of disease clustering and a generalized regression approach.
Cancer Res 27:209-220
Marshall IPG, Karst SM, Nielsen PH, Jørgensen BB (2018) Metagenomes from deep Baltic Sea
178
sediments reveal how past and present environmental conditions determine microbial
community composition. Mar Genomics 37:58–68
McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, Andersen GL,
Knight R, Hugenholtz P (2012) An improved Greengenes taxonomy with explicit ranks
for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618
Mcleod E, Chmura GL, Bouillon S, Salm R, Björk M, Duarte CM, Lovelock CE, Schlesinger
WH, Silliman BR (2011) A blueprint for blue carbon: toward an improved understanding
of the role of vegetated coastal habitats in sequestering CO2. Front Ecol Environ 9:552–
560
Mendelssohn I, Morris J (2000) Ecophysiological controls on the growth of Spartina alterniflora.
In: Weinstein N, Kreeger D (eds) Concepts and controversies in tidal marsh ecology.
Kluwer, Dordrecht, p 59–80
Mettel C, Kim Y, Shrestha PM, Liesack W (2010) Extraction of mRNA from soil. Appl Environ
Microbiol 76:5995–6000
Middelburg J (1989) A simple rate model for organic matter decomposition in marine sediments.
Geochim Cosmochim Acta 53:1577–1581
Mittelbach GG, Schemske DW (2015) Ecological and evolutionary perspectives on community
assembly. Trends Ecol Evol 30:241-247
Morris JT, Barber DC, Callaway JC, Chambers R, Hagen SC, Hopkinson CS, Johnson BJ,
Megonigal P, Neubauer SC, Troxler T, Wigand C (2016) Contributions of organic and
inorganic matter to sediment volume and accretion in tidal wetlands at steady state.
Earth’s Futur 4:110–121
Mueller P, Schile-Beers LM, Mozdzer TJ, Chmura GL, Dinter T, Kuzyakov Y, Groot AV De,
Esselink P, Smit C, D’Alpaos A, Ibanez C, Lazarus M, Neumeier U, Johnson BJ,
Baldwin AH, Yarwood SA, Montemayor DI, Yang Z, Wu J, Jensen K, Nolte S (2017)
Global change effects on decomposition processes in tidal wetlands: Implications from a
global survey using standardized litter. Biogeosciences 15:3189–3202
Najjar RG, Herrmann M, Alexander R, Boyer EW, Burdige DJ, Butman D, Cai WJ, Canuel EA,
Chen RF, Friedrichs MA, Feagin RA (2018). Carbon budget of tidal wetlands, estuaries,
and shelf waters of eastern North America. Global Biogeochem Cy 32:389-416
Narayan S, Beck MW, Wilson P, Thomas CJ, Guerrero A, Shepard CC, Reguero BG, Franco G,
Ingram JC, Trespalacios D (2017) The value of coastal wetlands for flood damage
reduction in the Northeastern USA. Sci Rep 7:1–12
Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, Knelman JE, Darcy
JL, Lynch RC, Wickey P, Ferrenberg S (2013) Patterns and processes of microbial
179
community assembly. Microbiol Mol Biol Rev 77:342–356
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'hara B,
Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2017) Vegan: Community
ecology package version 2.4-3
Oni OE, Schmidt F, Miyatake T, Kasten S, Witt M, Hinrichs KU, Friedrich MW (2015)
Microbial communities and organic matter composition in surface and subsurface
sediments of the Helgoland mud area, North Sea. Front Microbiol 6:1–16
Papp K, Hungate BA, Schwartz E (2018) Microbial rRNA synthesis and growth compared
through quantitative stable isotope probing with H218O. Appl Environ Microbiol 84:1-11
Parkes RJ, Cragg B, Roussel E, Webster G, Weightman A, Sass H (2014) A review of
prokaryotic populations and processes in sub-seafloor sediments, including biosphere:
Geosphere interactions. Mar Geol 352:409–425
Petro C, Starnawski P, Schramm A, Kjeldsen KU (2017) Microbial community assembly in
marine sediments. Aquat Microb Ecol 79:177–195
Philippot L, Andersson SGE, Battin TJ, Prosser JI, Schimel JP, Whitman WB, Hallin S (2010)
The ecological coherence of high bacterial taxonomic ranks. Nat Rev Microbiol 8:523–
529
Price MN, Dehal PS, Arkin AP (2010) FastTree 2 - Approximately maximum-likelihood trees
for large alignments. PLoS ONE 5:e9490
QIIME 2 Development Team (2018) QIIME 2. https://qiime2.org/
Reddy K, Patrick Jr W (1975) Effect of alternate aerobic and anaerobic conditions on redox
potential, organic matter decomposition and nitrogen loss in a flooded soil. Soil Biol
Biochem 7:87–94
Richards SA (2005) Testing ecological theory using the information-theoretic approach :
Examples and cautionary results. Ecology 86:2805–2814
Ryther JH, Dunstan WM (1971) Nitrogen, phosphorus, and eutrophication in the coastal marine
environment. Science 171:1008–1013
Seitzinger SP (1988) Denitrification in freshwater and coastal marine ecosystems: Ecological
and geochemical significance. Limnol Oceanogr 33:702–724
Shade A, Gilbert JA (2015) Temporal patterns of rarity provide a more complete view of
microbial diversity. Trends Microbiol 23:335–340
Shade A, Jones SE, Gregory Caporaso J, Handelsman J, Knight R, Fierer N, Gilbert JA (2014)
180
Conditionally rare taxa disproportionately contribute to temporal changes in microbial
diversity. MBio 5:1–9
Sousa AI, Lillebø AI, Risgaard-Petersen N, Pardal MA, Caçador I (2012) Denitrification: an
ecosystem service provided by salt marshes. Mar Ecol Prog Ser 448:79-92
Spivak AC, Gosselin K, Howard E, Mariotti G, Forbrich I, Stanley R, Sylva SP (2017) Shallow
ponds are heterogeneous habitats within a temperate salt marsh ecosystem. J Geophys
Res Biogeosci 122:1371–1384
Starnawski P, Bataillon T, Ettema TJG, Jochum LM, Schreiber L, Chen X, Lever MA, Polz MF,
Jørgensen BB, Schramm A, Kjeldsen KU (2017) Microbial community assembly and
evolution in subseafloor sediment. Proc Natl Acad Sci USA 114:2940–2945
Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, Rockhold ML, Konopka
A (2013) Quantifying community assembly processes and identifying features that
impose them. ISME J 7:2069–2079
Stegen JC, Lin X, Konopka AE, Fredrickson JK (2012) Stochastic and deterministic assembly
processes in subsurface microbial communities. ISME J 6:1653–1664
Steven B, Hesse C, Soghigian J, Gallegos-Graves LV, Dunbar J (2017) Simulated rRNA/DNA
ratios show potential to misclassify active populations as dormant. Appl Environ
Microbiol 83:1-11
Sutton-Grier AE, Keller JK, Koch R, Gilmour C, Megonigal JP (2011) Electron donors and
acceptors influence anaerobic soil organic matter mineralization in tidal marshes. Soil
Biol Biochem 43:1576–1583
Teal J, Kanwisher J (1961) Gas exchange in a Georgia salt marsh. Limnol Oceanogr 6:388–399
Teske A, Sorensen KB (2008) Uncultured archaea in deep marine subsurface sediments: have we
caught them all? ISME 2:3-18
Thamdrup B, Fossing H, Jorgensen BB (1994) Manganese, iron and sulfur cycling in a coastal
marine sediment, Aarhus bay, Denmark. Geochim Cosmochim Acta 58:5115–5129
Tripathi BM, Kim M, Kim Y, Byun E, Yang JW, Ahn J, Lee YK (2018) Variations in bacterial
and archaeal communities along depth profiles of Alaskan soil cores. Sci Rep 8:1–11
Turner S, Pryer KM, Miao VPW, Palmer JD (1999) Investigating deep phylogenetic
relationships among cyanobacteria and plastids by small subunit rRNA sequence
analysis. J Eukaryotic Micro 46:327-338
Wakeham SG, Lee C, Hedges JI, Hernes PJ, Peterson ML (1997) Molecular indicators of
diagenetic status in marine organic matter. Geochim Cosmochim Acta 61:5363–5369
181
Walsh EA, Kirkpatrick JB, Pockalny R, Sauvage J, Spivack AJ, Murray RW, Sogin ML,
D’Hondt S (2016) Relationship of bacterial richness to organic degradation rate and
sediment age in subseafloor sediment. Appl Environ Microbiol 82:4994–4999
Warren RS, Fell PE, Rozsa R, Brawley AH, Orsted AC, Olson ET, Swamy V, Niering WA
(2002) Salt marsh restoration in Connecticut: 20 years of science and management.
Restor Ecol 10:497–513
Wasmund K, Schreiber L, Lloyd KG, Petersen DG, Schramm A, Stepanauskas R, Jørgensen BB,
Adrian L (2014) Genome sequencing of a single cell of the widely distributed marine
subsurface Dehalococcoidia, phylum Chloroflexi. ISME J 8:383–397
Watson SW, Bock E, Valois FW, Waterbury JB, Schlosser U (1986) Nitrospira marine gen. nov.
sp.:a chemolithotrophic nitrite-oxiziding bacterium. Arch Microbiol 144:1-7
Webb CO, Ackerly DD, Kembel SW (2008) Phylocom: Software for the analysis of
phylogenetic community structure and trait evolution. Bioinformatics 24:2098–2100
Westrich JT, Berner RA (1984). The role of sedimentary organic matter in bacterial sulfate
reduction: The G model tested. Limnol Oceanogr 29:236–249
Will C, Thürmer A, Wollherr A, Nacke H, Herold N, Schrumpf M, Gutknecht J, Wubet T,
Buscot F, Daniell R (2010) Horizon-specific bacterial community composition of german
grassland soils, as revealed by pyrosequencing-based analysis of 16S rRNA genes. Appl
Environ Microbiol 76:6751–6759
Wilson CA, Hughes ZJ, FitzGerald DM, Hopkinson CS, Valentine V, Kolker AS (2014)
Saltmarsh pool and tidal creek morphodynamics: Dynamic equilibrium of northern
latitude saltmarshes? Geomorphology 213:99–115
Valiela I, Teal JM (1974) Nutrient limitation in salt marsh vegetation. In: Mold RJ, Queen WH
(eds) Ecology of Halophytes. Elsevier, College Park, MD
Vellend M (2010) Conceptual synthesis in community ecology. Q Rev Biol 85:183–206
Vigneron A, Cruaud P, Roussel EG, Pignet P, Caprais JC, Callac N, Ciobanu MC, Godfroy A,
Cragg BA, Parkes JR, Nostrand JD Van, He Z, Zhou J, Toffin L (2014) Phylogenetic and
functional diversity of microbial communities associated with subsurface sediments of
the Sonora Margin, Guaymas Basin. PLoS ONE 9:9–13
Vuillemin A, Ariztegui D, Horn F, Kallmeyer J, Orsi WD, Anselmetti F, Corbella H, Francus P,
Lücke A, Maidana NI, Ohlendorf C, Zolitschka B, Schabitz F, Wastegård S (2018)
Microbial community composition along a 50 000-year lacustrine sediment sequence.
FEMS Microbiol Ecol 94:1–14
182
Zedler JB, Kercher S (2005) Wetland resources: Status, trends, ecosystem services, and
restorability. Ann Rev Env Resources 30:39-74
183
Tables
Table 1. Average ± SE for bulk density (g cm-3), %OM, %N, %C, C:N ratio, and δ13C (‰) for
the reference and enriched marshes (n=3)
Reference Enriched
Bulk Density 0.33 (0.15) 0.35 (0.29)
%OM 23.2 (9.50) 25.9 (10.30)
%N 0.64 (0.23) 0.72 (0.24)
%C 10.64 (4.15) 12.93 (4.77)
C:N 18.86 (3.32) 21.11 (3.85)
δ13C -15.2 (1.00) -16.4 (3.17)
184
Table 2. Sediment depth intervals and age date results using radiocarbon dating.
Site Depth (cm) δ14C Age (years) Error (± years)
Reference 81-82 18.08 > Modern 1
97-98 -181.22 1540 15
129-130 -210.47 1830 15
163-164 -241.54 2160 25
213-214 -254.21 2290 20
Enriched 67-68 -89.65 690 15
113-114 -192.76 1660 15
179-180 -256.07 2310 15
253-256 -338.67 3260 20
185
Figures
Fig. 1. Location of my study sites in northeastern Massachusetts, USA: West Creek (Reference;
42.759 N, 70.891 W) and Sweeney Creek (Enriched; 42.722 N, 70.847 W). Shapes indicate core
replicates per site (squares = 1, diamonds = 2, triangles = 3). Maps were generated by
downloading data from the Database of Global Administrate Areas (GADM; Global
Administrative Areas) using the raster package in R (Hijmans & Jacob van Etten 2012).
186
Fig. 2. Organic matter characteristics along a depth gradient for cores collected from reference
(blue, top) and enriched (green, bottom) sites. Shapes indicate core replicate (squares = 1,
diamonds = 2, triangles = 3), and age date along right y-axis is calculated from 14C radiocarbon
dating. Panels A and G report bulk density values, B and H report % organic matter, C and I, and
D and J, report %N and %C respectively, E and K report C:N and F and L report the δ13C values.
187
Fig. 3. Shannon diversity decreases with depth at both sites for the 16S rRNA gene (A) and 16S
rRNA (B). Log 16S rRNA gene abundance decreases with depth linearly (C), while Log 16S
rRNA decreases with depth following an exponential pattern (D). Note the difference in scale in
the Y axis of Figures C and D.
188
Fig. 4. (A) Principal coordinate analysis (PCoA) constructed from weighted Unifrac of the total
community colored by depth, with no effect of site according to a PERMANOVA. (B) The first
PCoA axis versus depth shows the microbial community structure changes drastically with depth
in the top 50 cm, marked with the dotted red line, but then becomes relatively stable 60+cm. (C)
Boxplots of dissimilarity between sites for each depth interval between 0-50 cm.
189
Fig. 5. Bar plots showing relative abundance of top 100 ASVs aggregated at the class-level in
shallow (0-50 cm) sediments for the (A) total and (B) active microbial community combined for
both sites, accounting for an average of ~35 and 40% of the total dataset respectively. Black lines
indicate family-level distinctions within each order. Additional taxonomic information can be
found in Supplemental Table S5-6.
190
Fig. 6. Barplots showing relative abundance of the 35 ASVs most important in explaining the
variance in the total (DNA) microbial community structure along the top 50 cm depth gradient
according to a random forest regression model. Data are aggregated at the class level,
represented by faceted boxes, and colors within each box represent different orders within each
class. Black lines indicate family-level distinctions within each order. Additional taxonomic
information can be found in Supplemental Table S7.
191
Fig. 7. Barplots showing relative abundance of the 35 ASVs most important in explaining the
variance in the active (RNA) microbial community structure along a depth gradient according to
a random forest regression. Data are aggregated at the class level, represented by faceted boxes,
and colors within each box represent different orders. Black lines indicate family-level
distinctions within each order. Additional taxonomic information can be found in Supplemental
Table S8.
192
Fig. 8. Bar plots showing relative abundance of top 100 ASVs aggregated at the class-level in
deep (60+ cm) sediments for the total community at the reference (A) and enriched (B) site (~69
and 67% of total dataset) and the potential active community at the reference (C) and enriched
(D) site (~60 and 55% of total dataset). Black lines indicate family-level distinctions within each
order, and a blank bar indicates that no sequence data are available at that depth. Additional
taxonomic information can be found in Supplemental Table S9-12.
193
Fig. 9. Standardized effect size of mean pairwise distances (SESMPD), equivalent to -Net
relatedness Index, along a depth gradient for (A) the 16S rRNA gene and (B) 16S rRNA.
Negative values (<1) indicate phylogenetic clustering and positive (>1) values indicating
phylogenetic overdispersion, with the red dotted line indicating 50cm.
194
Supplemental Tables
Table S1: Model selection results for linear mixed effects models assessing the response of bulk
density, %OM, %N, %C, and C:N ratio to depth and site (fixed effects) and core replicate
(random effect). Each model includes a null intercept model, reduced models (depth or site), or a
full model (additive or multiplicative relationships between depth and site). Models that are
bolded represent best fit as indicated by an Akaike weight (wi) closest to 1.
Response Variable Model df ΔAIC Weight
Bulk density Bulk density = 1 + (1|Core) 3 234.7 <0.001
Bulk density = Site + (1|Core) 4 235.3 <0.001
Bulk density = Depth + (1|Core) 4 0.0 0.43
Bulk density = Depth + Site + (1|Core) 5 0.4 0.34
Bulk density = Depth*Site + (1|Core) 6 1.2 0.23
%OM %OM = 1 + (1|Core) 3 185.2 <0.001
%OM = Site + (1|Core) 4 184.6 <0.001
%OM = Depth + (1|Core) 4 13.3 0.001
%OM = Depth + Site + (1|Core) 5 12.8 0.002
%OM = Depth*Site + (1|Core) 6 0.0 0.9970
%C %C = 1 + (1|Core) 3 5.5 0.038
%C = Site + (1|Core) 4 3.4 0.109
%C = Depth + (1|Core) 4 4.6 0.059
%C = Depth + Site + (1|Core) 5 0.00 0.589
%C = Depth*Site + (1|Core) 6 2.1 0.204
%N %N = 1 + (1|Core) 3 8.7 0.008
%N = Site + (1|Core) 4 8.6 0.008
%N = Depth + (1|Core) 4 2.0 0.214
%N = Depth + Site + (1|Core) 5 0.0 0.581
%N = Depth*Site + (1|Core) 6 2.2 0.190
C:N Ratio Molar C:N Ratio = 1 + (1|Core) 3 3.3 0.111
Molar C:N Ratio = Site + (1|Core) 4 0.0 0.564
Molar C:N Ratio = Depth + (1|Core) 4 4.7 0.053
Molar C:N Ratio = Depth + Site + (1|Core) 5 2.0 0.205
Molar C:N Ratio = Depth*Site + (1|Core) 6 4.2 0.068
195
Table S2: Model selection results for linear mixed effects models assessing the response of
Shannon diversity to depth, %C, %N, and C:N ratio treating core as a random effect. Models that
are bolded represent best fit as indicated by an Akaike weight (wi) closest to 1.
Response Variable Model df ΔAIC Weight
16S rRNA gene Shannon = 1 + (1|Core) 3 56 <0.001
Shannon Diversity Shannon = Depth + (1|Core) 4 0.0 0.368
Shannon = %C + (1|Core) 4 57.4 <0.001
Shannon = %N + (1|Core) 4 54.7 <0.001
Shannon = C:N + (1|Core) 4 56 <0.001
Shannon = Depth + %C + (1|Core)* 5 1.9 0.141
Shannon = Depth + %N + (1|Core) 5 2.2 0.122
Shannon = Depth + C:N + (1|Core)* 5 0.6 0.271
Shannon = Depth + %C + %N + (1|Core) 6 3.8 0.055
Shannon = Depth + %C + %N + C:N + (1|Core) 7 4.3 0.043
16S rRNA Shannon = 1 + (1|Core) 3 19.9 <0.001
Shannon Diversity Shannon = Depth + (1|Core) 4 0.0 0.406
Shannon = %C + (1|Core) 4 22 <0.001
Shannon = %N + (1|Core) 4 21.7 <0.001
Shannon = C:N + (1|Core) 4 22 <0.001
Shannon = Depth + %C + (1|Core) 5 2.1 0.145
Shannon = Depth + %N + (1|Core)* 5 1.8 0.163
Shannon = Depth + C:N + (1|Core)* 5 1.6 0.186
Shannon = Depth + %C + %N + (1|Core) 6 4 0.056
Shannon = Depth + %C + %N + C:N + (1|Core) 7 4.4 0.044
Shannon = 1 + (1|Core) 3 19.9 <0.001
196
Table S3: Model selection results for linear mixed effects models assessing the response of
PCoA axis 1 to depth, %C, %N, and C:N ratio for the 16S rRNA gene treating core as a random
effect. Models that are bolded represent best fit as indicated by an Akaike weight (wi) closest to
1.
Response Variable Model df ΔAIC Weight
Shallow (0-50 cm) PCoA 1 = 1 + (1|Core) 3 27.4 <0.001
PCoA 1 = Depth + (1|Core) 4 0 0.315
PCoA 1 = %C + (1|Core) 4 29.3 <0.001
PCoA 1 = %N + (1|Core) 4 28.8 <0.001
PCoA 1 = C:N + (1|Core) 4 29.6 <0.001
PCoA 1 = Depth + %C + (1|Core)* 5 1.3 0.164
PCoA 1 = Depth + %N + (1|Core) 5 0 0.321
PCoA 1 = Depth + C:N + (1|Core) 5 2.7 0.084
PCoA 1 = Depth + %C + %N + (1|Core) 6 2.5 0.092
PCoA 1 = Depth + %C + %N + C:N + (1|Core) 7 5.3 0.023
Deep (60+ cm) PCoA 1 = 1 + (1|Core) 3 0 0.3611
Reference only PCoA 1 = Depth + (1|Core)* 4 1.7 0.153
PCoA 1 = %C + (1|Core) 4 2.6 0.0994
PCoA 1 = %N + (1|Core) 4 2.2 0.1176
PCoA 1 = C:N + (1|Core) 4 2.7 0.0934
PCoA 1 = Depth + %C + (1|Core) 5 4.1 0.0469
PCoA 1 = Depth + %N + (1|Core) 5 3.6 0.0589
PCoA 1 = Depth + C:N + (1|Core) 5 3.8 0.0529
PCoA 1 = Depth + %C + %N + (1|Core) 6 6.9 0.0116
PCoA 1 = Depth + %C + %N + C:N + (1|Core) 7 8.5 0.0051
Deep (60+ cm) PCoA 1 = 1 + (1|Core) 3 0 0.3701
Enriched only PCoA 1 = Depth + (1|Core) 4 2.6 0.103
PCoA 1 = %C + (1|Core) 4 2 0.1383
PCoA 1 = %N + (1|Core)* 4 1.7 0.1611
PCoA 1 = C:N + (1|Core) 4 2.5 0.1075
PCoA 1 = Depth + %C + (1|Core) 5 4.6 0.0365
PCoA 1 = Depth + %N + (1|Core) 5 4.3 0.0424
PCoA 1 = Depth + C:N + (1|Core) 5 5.2 0.0276
PCoA 1 = Depth + %C + %N + (1|Core) 6 7.3 0.0098
PCoA 1 = Depth + %C + %N + C:N + (1|Core) 7 9.1 0.0038
197
Table S4: Model selection results for linear mixed effects models assessing the response of
PCoA axis 1 to depth, %C, %N, and C:N ratio for 16S rRNA treating core as a random effect.
Models that are bolded represent best fit as indicated by an Akaike weight (wi) closest to 1.
Response Variable Model df ΔAIC Weight
Shallow (0-50 cm) PCoA 1 = 1 + (1|Core) 3 48.6 <0.001
PCoA 1 = Depth + (1|Core) 4 3.1 0.099
PCoA 1 = %C + (1|Core) 4 48.8 <0.001
PCoA 1 = %N + (1|Core) 4 49.9 <0.001
PCoA 1 = C:N + (1|Core) 4 45.5 <0.001
PCoA 1 = Depth + %C + (1|Core) 5 0 0.458
PCoA 1 = Depth + %N + (1|Core) 5 2 0.167
PCoA 1 = Depth + C:N + (1|Core) 5 3 0.102
PCoA 1 = Depth + %C + %N + (1|Core) 6 2.4 0.141
PCoA 1 = Depth + %C + %N + C:N + (1|Core) 7 5.3 0.033
Deep (60+ cm) PCoA 1 = 1 + (1|Core) 3 0 0.3149
Reference only PCoA 1 = Depth + (1|Core) 4 1 0.1904
PCoA 1 = %C + (1|Core) 4 2.6 0.0876
PCoA 1 = %N + (1|Core) 4 2.5 0.0924
PCoA 1 = C:N + (1|Core) 4 2.8 0.0774
PCoA 1 = Depth + %C + (1|Core) 5 2.9 0.0727
PCoA 1 = Depth + %N + (1|Core) 5 2.9 0.0743
PCoA 1 = Depth + C:N + (1|Core) 5 2.9 0.0739
PCoA 1 = Depth + %C + %N + (1|Core) 6 6.4 0.013
PCoA 1 = Depth + %C + %N + C:N + (1|Core) 7 9.1 0.0034
Deep (60+ cm) PCoA 1 = 1 + (1|Core) 3 1.2 0.222
Enriched only PCoA 1 = Depth + (1|Core) 4 3.8 0.06
PCoA 1 = %C + (1|Core) 4 2.9 0.095
PCoA 1 = %N + (1|Core) 4 3.6 0.067
PCoA 1 = C:N + (1|Core) 4 0 0.397
PCoA 1 = Depth + %C + (1|Core) 5 5.7 0.023
PCoA 1 = Depth + %N + (1|Core) 5 6.4 0.016
PCoA 1 = Depth + C:N + (1|Core) 5 2.8 0.099
PCoA 1 = Depth + %C + %N + (1|Core) 6 7.6 0.009
PCoA 1 = Depth + %C + %N + C:N + (1|Core) 7 7.1 0.011
198
Table. S5. Unique taxonomic information for top 100 ASVs from shallow (0-50cm) total
microbial community for both sites combined
Kingdom Phylum Class Order Family
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Proteobacteria Epsilonproteobacteria Campylobacterales Helicobacteraceaea
Bacteria Proteobacteria Epsilonproteobacteria Campylobacterales Helicobacteraceae
Bacteria Chlorobi Ignavibacteria Ignavibacteriales Ignavibacteriaceae
Bacteria Chlorobi Ignavibacteria Ignavibacteriales Ignavibacteriaceae
Bacteria Chlorobi Ignavibacteria Ignavibacteriales Ignavibacteriaceae
Bacteria Chlorobi Ignavibacteria Ignavibacteriales Ignavibacteriaceae
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Archaea Euryarchaeota Thermoplasmata E2 DHVEG-1
Archaea Crenarchaeota MBGB na na
Archaea Crenarchaeota MBGB na na
Unassigned na na na na
Archaea Crenarchaeota MCG B10 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Bacteria Actinobacteria Acidimicrobiia Acidimicrobiales koll13
Bacteria Caldithrix Caldithrixae Caldithrixales BA059
Bacteria Caldithrix Caldithrixae Caldithrixales BA059
Bacteria Spirochaetes Brachyspirae Brachyspirales A0-023
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Chloroflexi Anaerolineae SB-34 na
Bacteria Chloroflexi Anaerolineae SB-34 na
Bacteria Chloroflexi Anaerolineae GCA004 na
Bacteria Chloroflexi Anaerolineae GCA004 na
199
Bacteria Chloroflexi Anaerolineae GCA004 na
Bacteria Chloroflexi Anaerolineae CFB-26 na
Bacteria Chloroflexi Anaerolineae CFB-26 na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria AC1 na na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria WS2 SHA-109 na na
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Gammaproteobacteria Thiotrichales Thiotrichaceae
Bacteria Proteobacteria Betaproteobacteria Gallionellales Gallionellaceaeb
Bacteria Proteobacteria Betaproteobacteria MND1 na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaec
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaec
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaec
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobulbaceae
Bacteria Proteobacteria na na na
Bacteria Proteobacteria Deltaproteobacteria MBNT15 na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales na
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceaed
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceaed
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceaee
200
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria TPD-58 na na na
Bacteria Caldithrix Caldithrixae Caldithrixales Caldithrixaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Nitrospirae Nitrospira Nitrospirales Thermodesulfovibrionaceae
Bacteria na na na na aGenus Sulfurimonas bGenus Gallionella cGenus Desulfococcus dGenus Rhodoplanes eGenus Hyphomicrobium
201
Table. S6. Unique taxonomic information for top 100 ASVs from shallow (0-50cm) active
microbial community for both sites combined.
Kingdom Bacteria Class Order Family
Archaea Crenarchaeota MBGB na na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Chlorobi Ignavibacteria Ignavibacteriales Ignavibacteriaceae
Bacteria na na na na
Bacteria Proteobacteria Epsilonproteobacteria Campylobacterales Helicobacteraceaea
Bacteria Proteobacteria Gammaproteobacteria na na
Bacteria Proteobacteria Betaproteobacteria MND1 na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Thiotrichales Thiotrichaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria na na
Bacteria Proteobacteria Gammaproteobacteria na na
Bacteria Proteobacteria Gammaproteobacteria na na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Ectothiorhodospiraceae
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales na
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
202
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaec
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria Syntrophobacterales Syntrophaceaed
Bacteria Proteobacteria Deltaproteobacteria Syntrophobacterales Syntrophaceaed
Bacteria Proteobacteria Deltaproteobacteria Syntrophobacterales Syntrophaceaed
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobulbaceae
Bacteria Proteobacteria Deltaproteobacteria AF420338 na
Bacteria Proteobacteria Deltaproteobacteria AF420338 na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
203
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Firmicutes Clostridia Clostridiales Clostridiaceaee
Bacteria GN04 GN15 na na
Bacteria Spirochaetes Brachyspirae Brachyspirales A0-023
Bacteria LCP-89 SAW1_B44 na na
Bacteria na na na na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria Chloroflexi Anaerolineae GCA004 na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes GIF9 na aGenus Sulfurimonas bGenus Desulfococcus cGenus Desulfosarcina dGenus Desulfobacca eGenus Clostridium
204
Table. S7. Top ASVs (n=35) identified from a random forest regression model for the total
microbial community (DNA), ordered by decreasing importance.
Kingdom Phylum Class Order Family
Bacteria Caldithrix Caldithrixae Caldithrixales na
Bacteria Chlamydiae Chlamydiia Chlamydiales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales GZKB119
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales SB-1
Archaea Euryarchaeota Thermoplasmata E2 DHVEG-1
Archaea Crenarchaeota MCG B10 na
Bacteria Proteobacteria Zetaproteobacteria Mariprofundales Mariprofundaceae
Bacteria GN04 na na na
Bacteria Chloroflexi Anaerolineae Anaerolineales Anaerolinaceae
Bacteria Chloroflexi Anaerolineae GCA004 na
Bacteria Chloroflexi S085 na na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria AC1 B04R032 na na
Bacteria WS2 SHA-109 na na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Marinicellales Marinicellaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae
Bacteria Proteobacteria na na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Nitrospirae Nitrospira Nitrospirales Thermodesulfovibrionaceae
205
Table S8. Top ASVs (n=41) identified from a random forest regression model for the active
microbial community (RNA), ordered by decreasing importance.
Kingdom Phylum Class Order Family
Bacteria OD1 na na na
Bacteria OD1 na na na
Bacteria OD1 ABY1 na na
Bacteria SAR406 AB16 noFP_H7 na
Bacteria LCP-89 SAW1_B44 na na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Archaea Euryarchaeota Methanobacteria Methanobacteriales WSA2
Archaea Euryarchaeota Thermoplasmata E2 DHVEG-1
Archaea Euryarchaeota Thermoplasmata E2 20c-4
Archaea Crenarchaeota MCG B10 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Bacteria Actinobacteria Acidimicrobiia Acidimicrobiales koll13
Bacteria GN04 MSB-5A5 na na
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria CD12 na na na
Bacteria na na na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria OP1 MSBL6 na na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria Proteobacteria Gammaproteobacteria Marinicellales Marinicellaceae
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
206
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Nitrospirae Nitrospira Nitrospirales Thermodesulfovibrionaceae
Bacteria BHI80-139 na na na
Bacteria na na na na
207
Table S9. Unique taxonomic information for top 100 ASVs from deep sediment total microbial
community (60+ cm) for the reference marsh.
Kingdom Phylum Class Order Family
Bacteria OD1 na na na
Bacteria OD1 na na na
Bacteria OD1 na na na
Bacteria OD1 na na na
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria OP8 OP8_1 HMMVPog-54 na
Bacteria SAR406 AB16 noFP_H7 na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales SB-1
Archaea Euryarchaeota Methanobacteria Methanobacteriales WSA2
Archaea Euryarchaeota Thermoplasmata E2 DHVEG-1
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG pGrfC26 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Bacteria Spirochaetes Brachyspirae Brachyspirales A0-023
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
208
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Anaerolineae na na
Bacteria Chloroflexi Anaerolineae Anaerolineales Anaerolinaceae
Bacteria Chloroflexi S085 na na
Bacteria Chloroflexi Dehalococcoidetes na na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria AC1 SHA-114 na na
Bacteria AC1 na na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
209
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceaea
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae CCM11a na
Bacteria na na na na
Bacteria na na na na aPseudomonas veronii bGenus Desulfococcus
210
Table S10. Unique taxonomic information for top 100 ASVs from deep sediment total microbial
community (60+ cm) for the enriched marsh.
Kingdom Phylum Class Order Family
Bacteria OD1 na na na
Bacteria OD1 na na na
Bacteria OD1 na na na
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria OP8 OP8_1 HMMVPog-54 na
Bacteria SAR406 AB16 noFP_H7 na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Chlorobi BSV26 C20 na
Archaea Euryarchaeota Methanobacteria Methanobacteriales WSA2
Archaea Crenarchaeota MBGB na na
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG B10 na
Archaea Crenarchaeota MCG pGrfC26 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Archaea Parvarchaeota Parvarchaea WCHD3-30 na
Bacteria Actinobacteria Acidimicrobiia Acidimicrobiales koll13
Bacteria GN04 MSB-5A5 na na
Bacteria GN04 GN15 na na
Bacteria GN04 GN15 na na
Bacteria OP3 na na na
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
211
Bacteria Chloroflexi Ellin6529 na na
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Anaerolineae na na
Bacteria Chloroflexi Anaerolineae GCA004 na
Bacteria Chloroflexi S085 na na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria AC1 SHA-114 na na
Bacteria AC1 na na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceaea
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaeb
212
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Nitrospirae Nitrospira Nitrospirales Thermodesulfovibrionaceae
Bacteria na na na na
Bacteria LD1 na na na aPseudomonas veronii bGenus Desulfococcus
213
Table S11. Unique taxonomic information for top 100 ASVs from deep sediment active
microbial community (60+ cm) for the reference marsh.
Kingdom Bacteria Class Order Family
Archaea Euryarchaeota ANME-1 na na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales SB-1
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria Planctomycetes Planctomycetia Pirellulales Pirellulaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria SAR406 AB16 noFP_H7 na
Bacteria na na na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria Actinobacteria Actinobacteria Actinomycetales Intrasporangiaceae
Bacteria Actinobacteria Actinobacteria Actinomycetales Micrococcaceaea
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae
Bacteria Proteobacteria Gammaproteobacteria Vibrionales Pseudoalteromonadaceaeb
Bacteria Proteobacteria Gammaproteobacteria na na
Bacteria Proteobacteria Betaproteobacteria Burkholderiales Oxalobacteraceaec
Bacteria Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceaed
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceaee
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Thiotrichales Thiotrichaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae
Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae
Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae
Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Erythrobacteraceaef
214
Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceaeg
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceaeh
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaei
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaei
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria AF420338 na
Bacteria Proteobacteria Deltaproteobacteria AF420338 na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales Haliangiaceae
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria NKB19 na na na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales na
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria na na na na
Bacteria Spirochaetes [Brachyspirae] [Brachyspirales] A0-023
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria Spirochaetes Spirochaetes Spirochaetales Spirochaetaceae
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
215
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP8 OP8_1 na na
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Anaerolineae OPB11 na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na aMicrococcocus luteus bGenus Pseudoalteromonas cGenus Ralstonia dVariovorax paradoxus eGenus Enhydrobacter fGenus Erythrobacter gGenus Spingomonas hGenus Skermanella iGenus Desulfococcus
216
Table S12. Unique taxonomic information for top 100 ASVs from deep sediment active
microbial community (60+ cm) for the enriched marsh.
Kingdom Bacteria Class Order Family
Archaea Euryarchaeota Thermoplasmata E2 20c-4
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Bacteroidia Bacteroidales na
Bacteria Bacteroidetes Flavobacteriia Flavobacteriales Weeksellaceaea
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria AC1 B04R032 na na
Bacteria Planctomycetes Planctomycetia Pirellulales Pirellulaceae
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria Planctomycetes Phycisphaerae MSBL9 na
Bacteria AC1 na na na
Bacteria SAR406 AB16 noFP_H7 na
Bacteria SAR406 AB16 noFP_H7 na
Bacteria na na na na
Bacteria OP1 MSBL6 na na
Bacteria OP1 MSBL6 na na
Bacteria Actinobacteria Actinobacteria Actinomycetales Micrococcaceaeb
Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceaec
Bacteria Proteobacteria Gammaproteobacteria Vibrionales Vibrionaceaed
Bacteria Proteobacteria Betaproteobacteria Burkholderiales Burkholderiaceaee
Bacteria Proteobacteria Betaproteobacteria Burkholderiales Oxalobacteraceaef
Bacteria Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae
Bacteria Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceaeg
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceaeh
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceaei
Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceaej
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Thiotrichales Thiotrichaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales Chromatiaceae
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Gammaproteobacteria Chromatiales na
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Methylobacteriaceaek
Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Rhodobiaceael
Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae
217
Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Erythrobacteraceae
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaem
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaem
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaem
Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceaem
Bacteria Proteobacteria Deltaproteobacteria AF420338 na
Bacteria Proteobacteria Deltaproteobacteria AF420338 na
Bacteria Proteobacteria Deltaproteobacteria Myxococcales na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria na na
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Actinobacteria Actinobacteria WCHB1-81 At425_EubF1
Bacteria Firmicutes Clostridia Clostridiales Clostridiaceaen
Bacteria na na na na
Bacteria Tenericutes Mollicutes na na
Bacteria Firmicutes Bacilli Bacillales Staphylococcaceaeo
Bacteria na na na na
Bacteria GN04 MSB-5A5 na na
Bacteria LCP-89 SAW1_B44 na na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
Bacteria OP9 JS1 SB-45 na
218
Bacteria OP8 OP8_1 HMMVPog-54 na
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria CD12 na na na
Bacteria Chloroflexi Anaerolineae na na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales na
Bacteria Chloroflexi Dehalococcoidetes Dehalococcoidales Dehalococcoidaceae
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
Bacteria Chloroflexi Dehalococcoidetes GIF9 na
aGenus Cloacibacterium bMicrococcus luteus cGenus Yersinia dGenus Vibrio eGenus Burkholderia fGenus Ralstonia gVariovorax paradoxus hGenus Enhydrobacter iAcinetobacter johnsonii jGenus Acinetobacter kGenus Methylobacterium lGenus Afifella mGenus Desulfococcus nClostridium bowmanii oGenus Staphylococcus
219
Supplemental Figures
Fig. S1. (A) Principal coordinate analysis (PCoA) constructed from weighted Unifrac of the
active community (RNA) colored by depth, with no effect of site according to a PERMANOVA.
(B) The first PCoA axis versus depth shows the microbial community structure changes
drastically with depth in the top 50 cm, but then becomes relatively stable 60+cm.
220
Appendix: Nitrate reduction pathways and functional potential in response to nutrient
enrichment
In collaboration with: Joseph Vineis, Anna E. Murphy, Amanda C. Spivak, Anne E. Giblin, Jane
Tucker
Background and Objectives:
Salt marshes are efficient at removing excess nutrients from that land that could have
detrimental effects in coastal waters, such as harmful algal blooms, anoxia/hypoxia, and
increased turbidity (Anderson et al. 2002, Diaz & Rosenberg 2008). Situated at the intersection
between the land and sea, salt marshes can intercept nutrients either by assimilation into plant
biomass (Valiela & Teal 1974) or through competing nitrogen (N) cycling processes that use
nitrate (NO3-) as an electron acceptor, such as denitrification (DNF) or dissimilatory nitrate
reduction to ammonium (DNRA). Partitioning which microbial NO3- reduction processes are
occurring is critical for understanding the fate of N in coastal systems, because while DNF
results in the removal of biologically available N in the form of N2 gas (Kaplan et al. 1979,
Seitzinger 1988), DNRA retains this N in the system as NH4+ (An & Gardner 2002, Gardner et
al. 2006, Giblin et al. 2013), which can then stimulate primary production or be transformed
back to NO3- through nitrification. High NO3
- conditions appear to favor DNF (Giblin et al. 2013,
Tiedje 1988), while NO3- limitation and labile organic matter appear to favor DNRA (Algar &
Vallino 2014, Burgin & Hamilton 2007, Hardison et al. 2015), however, the relative contribution
of these controls are not well understood. Being able to distinguish controls over competition
among these microbial pathways is important, as they produce different byproducts with
important implications for water quality. Further, it is unclear how partitioning among these
processes will change with increasing nutrient enrichment, as N loading continues to occur in
221
coastal waters with largely unknown consequences for salt marsh systems (Deegan et al. 2012,
Galloway et al. 2017).
One informative way to understand controls on these important rates is to concurrently
study the microbial community responsible for these processes, along with the fluxes they
produce, under changing conditions. The microbial consortia inhabiting salt marsh sediments
mediate much of the biogeochemical transformations involved in N cycling (Falkowski et al.
2008, Jetten 2008, Kuypers et al. 2018). Despite this critical role, comparatively little is known
about the microbes that facilitate these biogeochemical pathways and how they will respond to
nutrient enrichment. Metagenomics analysis allows us to investigate the genetic diversity and
functional potential of the resident microbial community (Franzosa et al. 2015). The technique
does not rely on culturing member organisms, which is notoriously challenging, and avoids
biases involved in Polymerase Chain Reaction (PCR) amplification that may limit sequence
information we can obtain using techniques such as 16S rRNA sequencing (Zhou et al. 2015). It
also has the potential to reveal phylogenetic and genomic novelty, providing insight into
previously unknown lineages of subsurface microbes we know surprisingly little about.
I used a controlled flow through experiment to examine competing NO3- reduction
pathways and assessed shifts in functional potential in response to nutrient enrichment in salt
marsh sediments across a depth gradient varying in organic matter lability. I hypothesized that
NO3- reduction rates would be greatest in surface sediments, where organic matter is most labile,
and that the contribution of DNRA relative to denitrification would decrease as organic matter
becomes more recalcitrant. I also hypothesized that the addition of NO3- would result in a shift in
the functional potential of the microbial community when compared to an unamended control,
resulting in greater abundance of genes associated with the N cycle. Consequently, this shift in
222
the functional potential would coincide with degradation of more complex forms of organic
matter. The outcome of this work not only provides information on the controls over competing
NO3- reduction processes, but also adds to our understanding of the response of microbes to N
enrichment and its effect on carbon storage.
Methods:
Sample collection & experimental design
To test my hypotheses, I collected sediment along a depth gradient in the tall ecotype of
Spartina alterniflora at West Creek, a relatively pristine marsh complex located in Plum Island
Sound, MA (42.759 N, 70.891 W) that is monitored as part of a long-term enrichment
experiment called the TIDE project (Deegan et al. 2007). I collected three replicate cores (5 cm
diameter, 30 cm deep) and sectioned them under anoxic conditions into shallow (0-5 cm), mid
(10-15 cm), and deep (20-25 cm) depths, thus representing a range in OM quality from newly
deposited material to sediments ranging from 50-100 years in age (Wilson et al. 2014, Forbrich
et al. 2018). After homogenizing sediment under anoxic conditions and removing as much root
material as possible, I split each core section into a plus-NO3- and an unamended treatment
(filtered seawater), resulting in three replicates for each treatment and depth combination.
A more detailed description of the methodology I followed for this experiment can be
found in chapter 1 of this thesis. Briefly, I loaded flow through reactor (FTRs; modified from
Pallud et al. (2006; 2007) with homogenized sediment under anoxic conditions, and randomly
assigned each reactor a treatment, either plus-NO3- (+NO3
- in 0.2 µm filtered seawater) or
unamended (0.2 µm filtered seawater only, representing natural marsh conditions). I prepared
each reservoir with filtered water from Woods Hole, MA by sparging with N2 gas for
223
approximately 20 minutes and spiking the plus-NO3- reservoir with an additional 500 µmol L-1
K15NO3- (Cambridge Isotope Laboratories, Andover, MA). For the first 25 days of the
experiment, I only added 350 µmol L-1 15NO3-, but since NO3
- was being fully consumed, I
increased the added concentration to 500 µmol L-1 to ensure it was never limiting. Half of the
reactors (n = 9) received the plus-NO3- treatment and half (n = 9) received the unamended
treatment, both at a targeted flow rate of 0.08 mL min-1 using MasterFlex FDA viton tubing
(Cole Parmer, IL, USA). The experiment lasted for 92 days, throughout which I collected water
samples to assess microbial respiration and NO3- reduction pathways. I also collected sediment
from before (pre; n = 9) and after (post; n = 18) the experiment to assess OM characteristics and
microbial community functional potential by flash-freezing sediment and storing at -80ºC until
further analysis.
Metabolism measurements
I collected water samples from both the plus-NO3- and unamended treatment effluent, as
well as each corresponding reservoir, to assess biogeochemical processes as a result of microbial
activity in each reactor. I measured dissolved inorganic carbon (DIC; CO2 + HCO3 + CO32-) as
an indicator of total microbial respiration on an Apollo SciTech AS-C3 DIC analyzer (Newark,
DE) and NO3- consumption as an indicator of assimilation/dissimilation on a Teledyne T200
NOx analyzer (Teledyne API, San Diego, CA) using chemoluminescent methods (Cox 1980). I
also collected water samples in airtight cut-off volumetric pipettes, preserved a portion with zinc
chloride (ZnCl2) for denitrification measurements, and froze ~50 mL sample at -20ºC for DNRA
measurements.
To determine the relative contribution of each NO3- reduction pathway, I made dissolved
gas measurements of N2 on a membrane inlet mass spectrometer (Kana et al. 1994) connected to
224
an inline furnace set to 500ºC and copper column to remove oxygen interferences (Eyre et al.
2002; Lunstrum & Aoki 2016). I monitored the production of 29N2 and 30N2 from added 15NO3-
tracer as a measure of denitrification (D15) and calculated rates using the following equation from
Nielson et al. 1992:
Eq. 1 D15 = p29+2p30
where p29 and p30 represent production of 29N2 and 30N2, respectively. Because I only added
NO3- in the form of 15NO3
-, and ambient concentrations of 14NO3- were very low to below
detection (as high as 0.6-1.2 µM), I did not calculate D14. Furthermore, I ignored the production
of 14NO3- from nitrification, which is a largely aerobic process (Herbert 1999), since this
experiment was conducted under strictly anoxic conditions. To measure DNRA, I bubbled water
samples with helium for 10 minutes to remove any N2 and converted 15NH4+ produced from
DNRA to 29N2 and 30N2 using sodium hypobromite following OX/MIMS methodology as
outlined in Yin et al. (2014). I then calculated DNRA as the following:
Eq. 2 DNRA15 = p29+2p30
It is important to note that I am not attempting to calculate ambient rates nor making these
measurements in the unamended treatment, where I did not add any 15NO3-.
Once I made measurements of each analyte, I calculated either production (DIC, N2 gas)
or consumption (NO3-) as a rate following eq. 3 (Pallud et al. 2006):
Eq. 3 R =(Cout − Cin)Q
V
where R is the consumption or production rate of interest, Cout and Cin are the effluent and
reservoir analyte concentrations, respectively, Q is the measured flow rate in L hour-1, and V is
the FTR volume (31.81 cm-3). From this, I also calculated the cumulative flux by integrating
between each measured time point across the length of the experiment.
225
Lipid Extraction and analysis
I extracted and analyzed lipid biomarker compounds from the sediments after the
experiment using a modified method from Bligh & Dyer (1959). To extract total fatty lipids, I
mixed ~3 g wet sediment with a chloroform:methylene chloride:phosphate buffer
(MeOH:CHCl3:PBS) saline mixture (2:1:0.8) and heated to 80ºC in a microwave-accelerated
reaction system (MARS6). I then partitioned samples with a 1:1:0.9 ratio of MeOH:CHCl3:PBS,
removed the organic phase, and concentrated the samples under N2. To separate each sample into
fractions of neutral and glycolipids (F1/2) and phospholipids or PLFAs (F3/4; Guckert et al.
1985), I used a silica gel column and eluted with chloroform, acetone, and methanol,
respectively. I dried the PLFAs under N2 and saponified them with 0.5 M sodium hydroxide
(NaOH) at 70ºC for 4 hours following Osburn et al. (2011). I then acidified the samples with 3
mL 3N hydrochloric acid (HCl) before extracting 3x with hexane. To methylate the PLFA
extract, I added acidic methanol (95:5 methanol:HCl) and heated overnight at 70 ºC to form fatty
acid methyl esters (FAME). I analyzed FAMEs on an Agilent 7890 gas chromatograph mass
spectrometer (Agilent, Santa Clara, CA) with a flame ionization detector located at Woods Hole
Oceanographic Institution using a DB-5 column with methyl heneicosanoate (Supelco 37
Component FAME mix) as an internal standard following methods in Canuel et al. (2007) and
references therein. I designated fatty acids as A:BωC, where A is the number of carbon atoms, B
represents the number of double bonds, and C indicates the \ position of the double bond relative
to the aliphatic end of the molecule as designated by “ω” (Canuel et al. 1995). I then calculated
the µg of fatty acid per total grams of organic carbon. An overview of the compounds I analyzed
in this study can be found in Table 1.
DNA extraction, library preparation, and sequencing
226
I extracted genomic DNA from approximately 0.25 g wet sediment using the MoBio®
PowerSoil DNA Isolation Kit (MoBio Technologies, CA, USA) following manufacturer’s
instructions, and eluted the DNA into a 35 µL final volume. I then confirmed DNA quality
(260/280) and concentration (ng/µL) using a NanoDrop (ThermoFisher Scientific, Waltham,
MA). To shear DNA at a 270 bp target size, I transferred ~100 ng DNA into a V2 8-microtube
strip and ran it on a Covaris ME220 focused-ultra sonicator (Covaris Inc., Woburn, MA) at 1000
cycles/burst, 20% duty factor, and 70% peak power for 88 seconds per sample.
I prepared 27 metagenomic libraries using the NuGEN Ovation Ultralow System V2
(NuGEN, San Carlos, CA), performing end repair and barcode ligation with the recommended
PCR cycling conditions (25ºC for 30 min and 70ºC for 10 min) and purification methods
(Agencourt AMPure XP; Beckman Coulter, Pasadena, CA). I then amplified the final library
under the following conditions: 72ºC for 2 min, 95ºC for 3 min, 9 cycles (98ºC for 20 sec, 65ºC
for 30 sec, 72ºC for 30 sec), and 72ºC for 1 min, and size-selected on a per-sample basis at 390
bp using a PippinPrep (Sage Science, Beverly, MA). After confirming a target insert size of 270
bp on an Agilent 4200 TapeStation (Agilent Technologies Inc, Santa Clara, CA), I quantified
each library using a KAPA library quantification kit (Roche Sequencing, Pleasanton, CA) and
performed sequencing on an Illumina NextSeq Hi-Output 2x150 Illumina flow cell (Illumina Inc,
San Diego, CA) at the Marine Biological Laboratory Keck Facility (Woods Hole, MA).
Sequence analysis and annotation
I joined paired end reads using illumina-utils with a P=0.1, which allows for 1 error in
every ten bases, and used a PHRED score of 30 to remove any regions with low quality reads,
resulting in 93% of raw reads retained after quality filtering (Table 2). I submitted merged reads
227
to the MG-RAST server (Meyer et al. 2008) and performed functional annotation using SEED
subsystems (Aziz et al. 2008) with an 80% minimum cutoff identity.
Statistical analyses
I examined rates of denitrification and DNRA over time and calculated cumulative rates
by integrating between each measured point across the length of the experiment. To compare
cumulative rates by depth as the factor, I ran a one-way ANOVA using core replicate as a
random effect for each process separately. I then divided DIC by the sum of denitrification and
DNRA and plotted values as boxplots by depth to better understand the contribution of these
processes to total microbial respiration. To assess how much each process compared to NO3-
reduction, I plotted denitrification and DNRA as stacked bar plots against NO3- consumption,
which was calculated as the difference between the reactor effluent and treatment reservoirs.
Using all subsystem annotations present in the dataset with at least 80% cutoff identify, I
constructed a non-dimensional scaling plot and tested for significance by depth and treatment
using ‘adonis’ in the Vegan package in R (Oksanen et al. 2017; R Core Team). Finally, to
examine the response of functional potential to nutrient enrichment, I constructed a heat map
showing normalized abundance of genes associated with N cycling for the unamended and plus-
NO3- treatment, which were clustered using unweighted pair group method with arithmetic mean
(UPGMA) on Euclidean distances (Michener & Sokal 1957). Lastly, I examined differences
among total fatty acids and various sub-classes (Table 1) by treatment and depth.
Results and general conclusions:
Denitrification and DNRA were higher in shallow sediments when compared to mid and deep
sediments (Fig. 1, 2A-B): Denitrification rates were similar half way throughout the experiment,
228
which suggests that NO3- addition stimulated microbial metabolism to nearly the same rates
despite differences in organic matter lability that occurs with depth. These rates then diverged
~week 10, likely as a result of decreased organic matter lability over time. DNRA, on the other
hand, exhibited stimulated rates in the shallow sediments; however, rates were lower in the mid
and deep sediments, likely because organic matter was less labile at those depths. When
examining cumulative rates, both denitrification (p=0.05, F2,6=6.48) and DNRA (p<0.001,
F2,6=27.29) were highest in the shallow sediments when compared to the mid and deep
sediments. This is likely a reflection of organic matter lability, where compounds available for
microbial oxidation tend to decrease with depth (Middelburg 1989, Cowie & Hedges 1994).
The relative contribution of DNRA to DIC production was greatest in the shallow sediments
(Fig. 2C): The stoichiometry of DIC to NO3- reduction is 1.25 and 2 for denitrification and
heterotrophic DNRA, respectively (Canfield et al. 2005, Giblin et al. 2013). By dividing DIC by
the sum of measured rates of denitrification and DNRA, I can get a better sense for the relative
contribution of these processes to total microbial respiration. I found that, across all depths, this
ratio averaged 1.42 ± 0.08 (shallow), 1.42 ± 0.15 (mid), and 1.33 ± 0.10 (deep). Statistically
equivalent ratios across depth (p=0.84) suggest that, in conjunction with lower denitrification and
DNRA rates in deeper sediments, DIC also decreased such that the ratio remained the same.
DNF and DNRA account for the majority of nitrate consumption throughout the experiment (Fig.
3): Across all depths, denitrification and DNRA rates account for the majority of NO3-
consumption, suggesting minimal contribution of assimilative processes in this experiment. This
supports the notion that NO3- can play a large role as an electron acceptor fueling heterotrophic
229
microbial metabolism, which in part explains why some marsh fertilization experiments observe
decreases in belowground biomass (Darby & Turner 2008, Deegan et al. 2012).
Functional potential differed by treatment, with NO3- enrichment resulting in significantly
different genetic composition (Fig. 4): In addition to the evidence I have that NO3- leads to
significant shifts in microbial community structure (Fig. 8 of chapter 1 and Fig. 8 of chapter 2),
particularly towards taxa associated with the N cycle (Fig. 9 of chapter 1 and Fig. 9 of chapter 2),
these data also show that community functional potential changes as well. Metagenomes from
the nitrate-enriched samples were significantly different from those in the unamended and pre
sediments (p=0.001, pseudo-F = 30.50). It is also interesting that there seems to be separation by
depth (p=0.001, pseudo-F = 8.88), with the shallow sediments from pre-incubation exhibiting
different functional composition when compared to mid and deep sediments from the pre and
unamended treatments. This suggests that experimental conditions may have resulted in
homogenization of the microbial community.
Out of those genes that were altered in response to NO3- addition, many of them that increased
are associated with N cycling (Fig. 5): A heatmap of genes associated with N cycling exhibited
clear differences in relative abundance when comparing the plus-NO3- and unamended
treatments. While genes encoding for enzymes associated with some maintenance functions,
such as ammonium transport, were similar across treatments, other genes associated with
dissimilatory N reduction, such as nitrite reductases (nir), nitric oxide reductases (nor), and
nitrous oxide reductases (nos) all increased in abundance in the plus-NO3- treatment (Kuypers et
al. 2018). This suggests that the addition of NO3- fundamentally altered the genetic make-up of
230
these microbial communities, increasing functional potential to carry out N transformations.
Future work should consider whether these changes in gene abundance correlate with
biogeochemical measurements of ecosystem function, which is a central goal in microbial
ecology.
It is also interesting to note that genetic potential is most similar by depth in the
unamended sediments, but this pattern does not hold in the plus-NO3- treatment. The addition of
NO3- appears to alter the microbial community such that organic matter lability is not as critical
in defining composition. This supports the idea that NO3- acts as an electron acceptor in
microbial metabolism, and allows for the decomposition of more complex organic matter, as
proposed in chapter 1 of my dissertation. Overall, this work provides a high resolution
characterization of the microbes responsible for N cycling, particularly in response to nutrient
enrichment.
Lower fatty acid concentrations in plus-NO3- treatment suggest enhanced microbial organic
matter degradation (Fig. 6): Fatty acids are a class of lipid biomarkers that demonstrate high
source fidelity and, depending on its source, exhibits a range of chemical reactivity (Canuel et al.
1995). By examining different sub-classes of fatty acids under various environmental conditions,
I can assess both sediment organic matter quality and decomposition in relation to its source. To
test the effect of nutrient enrichment on organic matter decomposition, I quantified phospholipid-
linked fatty acids (PFLAs), which are indicators of recently viable cells, between the plus-NO3-
and unamended treatments at the end of the FTR experiment.
I found that total fatty acid concentration was lower in the plus-NO3- treatment in shallow
and deep sediments (Fig. 6A), suggesting more overall organic matter loss at these depths when
231
compared to the unamended control. This pattern was not evident in the mid sediments. For both
the algal (PUFA and SCFA; Fig. 6G,B) and microbial-derived (MUFA and 10-Methyl C16:0; Fig.
6F, I) fatty acid compounds, concentrations were lower in the plus-NO3- treatment. A similar
pattern also occurred for the less labile compounds that were detritus-based (LCFA; Fig. 6H) and
Spartina derived (C18:2+C18:3; Fig. 6D), although this difference only existed in the deepest
sediments for the LCFAs. Overall, these results suggest that by adding NO3- and providing a
more energetically favorable electron acceptor for microbial metabolism, I am stimulating
organic matter decomposition. In particular, as a result of this stimulation, microbes may be
accessing more complex organic matter that would otherwise remain stable under ambient
nutrient conditions, resulting in decreased capacity for carbon storage in salt marsh systems.
232
References
Algar CK, Vallino JJ (2014) Predicting microbial nitrate reduction pathways in coastal
sediments. Aquat Microb Ecol 71:223–238
An S, Gardner WS (2002) Dissimilatory nitrate reduction to ammonium (DNRA) as a nitrogen
link, versus denitrification as a sink in a shallow estuary (Laguna Madre/Baffin Bay,
Texas). Mar Ecol Prog Ser 237:41–50
Anderson DM, Glibert PM, Burkholder JM (2002) Harmful algal blooms and eutrophication:
nutrient sources, composition, and consequences. Estuaries 25:704–726
Aziz RK, Bartels D, Best A, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass
EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, Overbeek RA, McNeil LK,
Paarmann D, Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O,
Vonstein V, Wilke A, Zagnitko O (2008) The RAST Server: Rapid annotations using
subsystems technology. BMC Genomics 9:1–15
Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J
Biochem Physiol 37:911–917
Burgin AJ, Hamilton SK (2007) Have we overemphasized the role of denitrification in aquatic
ecosystems? A review of nitrate removal pathways. Front Ecol Environ 5:89–96
Canfield DE, Thamdrup B, Kristensen E (2005) Aquatic Geomicrobiology. Elsevier Academic
Press, Boston, MA
Canuel E, Cloern J, Ringelberg D, Guckert JB, Rau GH (1995) Molecular and isotopic tracers
used to examine sources of organic matter and its incorporation into the food webs of San
Francisco Bay. Limnol Oceanogr 40:67–81
Cox RD (1980). Determination of nitrate and nitrite at the parts per billion level by
chemiluminescence. Anal Chem 52:332-335
Cowie GL, Hedges JI (1994) Biochemical indicators of diagenetic alteration in natural organic
matter mixtures. Nature 369:304
Darby FA, Turner RE (2008) Effects of eutrophication on salt marsh root and rhizome biomass
accumulation. Mar Ecol Prog Ser 363:63–70
Deegan LA, Bowen JL, Drake D, Fleeger JW, Friedrichs CT, Galván KA, Hobbie JE, Hopkinson
C (2007) Susceptibility of salt marshes to nutrient enrichment and predation removal.
Ecol Appl 17:42–63
Deegan LA, Johnson DS, Warren RS, Peterson BJ, Fleeger JW, Fagherazzi S, Wollheim WM
(2012) Coastal eutrophication as a driver of salt marsh loss. Nature 490:388–392
233
Diaz RJ, Rosenberg R (2008) Spreading dead zones and consequences for marine ecosystems.
Science 321:926–929
Eyre BD, Rysgaard S, Dalsgaard T, Christensen PB (2002) Comparison of isotope pairing and
N2:Ar methods for measuring sediment denitrification – assumption, modifications, and
implications. Estuaries 25:1077-1087
Falkowski PG, Fenchel T, Delong EF (2008) The microbial engines that drive Earth’s
biogeochemical cycles. Science 320:1034–1039
Forbrich I, Giblin AE, Hopkinson CS (2018) Constraining marsh carbon budgets using long-term
C burial and contemporary atmospheric CO2 fluxes. J Geophys Res Biogeosci 123: 867-
878
Franzosa EA, Hsu T, Sirota-Madi A, Shafquat A, Abu-Ali G, Morgan XC, Huttenhower C
(2015) Sequencing and beyond: integrating molecular “omics” for microbial community
profiling. Nat Rev Microbiol 13:360–72
Galloway JN, Leach AM, Erisman JW, Bleeker A (2017) Nitrogen: The historical progression
from ignorance to knowledge with a view to future solutions. Soil Res 55:417–424
Gardner WS, McCarthy MJ, An S, Sobolev D, Sell KS, Brock D (2006) Nitrogen fixation and
dissimilatory nitrate reduction to ammonium (DNRA) support nitrogen dynamics in
Texas estuaries. Limnol Oceanogr 51:558–568
Giblin A, Tobias C, Song B, Weston N, Banta G, Rivera-Monroy V (2013) The importance of
dissimilatory nitrate reduction to ammonium (DNRA) in the nitrogen cycle of coastal
ecosystems. Oceanography 26:124–131
Guckert JB, Antworth CP, Nichols PD, White DC (1985) Phospholipid, ester-linked fatty acid
profiles as reproducible assays for changes in prokaryotic community structure of
estuarine sediments. FEMS Microbiol Lett 31:147–158
Hardison AK, Algar CK, Giblin AE, Rich JJ (2015) Influence of organic carbon and nitrate
loading on partitioning between dissimilatory nitrate reduction to ammonium (DNRA)
and N2 production. Geochim Cosmochim Acta 164:146–160
Herbert RA (1999) Nitrogen cycling in coastal marine ecosystems. FEMS Microbio Rev 23:563-
590
Jetten MSM (2008) The microbial nitrogen cycle. Environ Microbiol 10:2903–9
Kana TM, Darkangelo C, Hunt MD, Oldham JB, Bennett GE, Cornwell JC (1994) Membrane
inlet mass spectrometer for rapid high-precision determination of N2, O2, and Ar in
environmental water samples. Anal Chem 66:4166–4170
234
Kaplan W, Valiela I, Teal JM (1979) Denitrification in a salt marsh ecosystem. Limnol Oceanogr
24:726–734
Kuypers MMM, Marchant HK, Kartal B (2018) The microbial nitrogen-cycling network. Nat
Rev Microbiol 16:263–276
Lundstrum A, Aoki LR (2016) Oxygen interference with membrane inlet mass spectrometry may
overestimate denitrification rates calculated with isotope pairing technique. Limnol
Oceanogr: Meth 14:425-431
Meyer F, Paarmann D, D’Souza M, Etal. (2008) The metagenomics RAST server—a public
resource for the automatic phylo- genetic and functional analysis of metagenomes. BMC
Bioinformatics 9:386
Michener CD, Sokal RR (1957) A quantitative approach to a problem in classification. Evolution
11:130–162
Middelburg JJ (1989) A simple rate model for organic matter decomposition in marine
sediments. Geochim Cosmochim Acta1 53:1577–1581
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'hara B,
Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2017) Vegan: Community
ecology package version 2.4-3
Osburn MR, Sessions AL, Pepe-Ranney C, Spear JR (2011) Hydrogen-isotopic variability in
fatty acids from Yellowstone National Park hot spring microbial communities. Geochim
Cosmochim Acta 75:4830–4845
Pallud C, Cappellen P Van (2006) Kinetics of microbial sulfate reduction in estuarine sediments.
Geochim Cosmochim Acta 70:1148–1162
Seitzinger SP (1988) Denitrification in freshwater and coastal marine ecosystems: Ecological
and geochemical significance. Limnol Oceanogr 33:702–724
Tiedje J (1988) Ecology of the denitrification and dissimilatory nitrate reduction to ammonium.
In: Zehnder A (ed) Biology of Anaerobic Microorganisms. Wiley and Sons, New York.
NY, p 179–244
Valiela I, Teal JM (1974) Nutrient limitation in salt marsh vegetation. In: Mold RJ, Queen WH
(eds) Ecology of Halophytes. Elsevier, College Park, MD
Wilson CA, Hughes ZJ, FitzGerald DM, Hopkinson CS, Valentine V, Kolker AS (2014)
Saltmarsh pool and tidal creek morphodynamics: Dynamic equilibrium of northern
latitude saltmarshes? Geomorphology 213:99–115
235
Yin G, Hou L, Liu M, Liu Z, Gardner WS (2014) A novel membrane inlet mass spectrometer
method to measure 15NH4+ for isotope-enrichment experiments in aquatic ecosystems.
Environ Sci Technol 48:9555–9562
Zhou J, Zhili H, Yang Y, Deng Y, Tringe SG, Alvarez-Cohen L (2015) High-throughput
metagenomic technologies for complex microbial community analysis: open and closed
formats. CEUR Workshop Proc 1542:33–36
236
Tables
Table 1. Sub-class groups used in this study
Source Subclass Abbreviation Lipid Number
Algal & microbial Short Chain Fatty
Acids SCFA C12:0+C14:0
Sediment heterotrophic
bacteria
Branched Chain
Fatty Acids BCFA
Iso- and anteiso-
C13:0+C15:0+C17:0+C19:0
Sediment microbial and
bacterial OM (sulfate
reducers)
Short Chain Fatty
Acid
10-Methyl
C16:0 10-Methyl C16:0
Microbial OM Monounsaturated
Fatty Acid MUFA C16:1
Microbial OM Monounsaturated
Fatty Acid MUFA C16:1+C17:1+C18:1+C19:1
Spartina derived OM Polyunsaturated
Fatty Acid PUFA C18:2+C18:3
Labile algal OM &
epiphytic diatoms
Polyunsaturated
Fatty Acid PUFA C20:4+C20:5
Detritus derived from
plants
Long Chain Fatty
Acids LUFA C24:0+C26:0+C28:0+C30:0
237
Table 2. Number of basepairs and quality-filtered sequences per metagenome submitted to the
MG-RAST server for annotation
Sample Name Treatment Depth # Basepairs # Sequences
1SPRE_merged Pre Shallow 8.06E+09 3.35E+07
2SPRE_merged 5.74E+09 2.30E+07
3SPRE_merged 4.20E+09 1.76E+07
1MPRE_merged Mid 4.95E+09 2.00E+07
2MPRE_merged 5.51E+09 2.23E+07
3MPRE_merged 5.49E+09 2.25E+07
1DPRE_merged Deep 5.09E+09 2.05E+07
2DPRE_merged 8.47E+09 3.39E+07
3DPRE_merged 7.82E+09 3.11E+07
N1SPOST_merged Nitrate Shallow 5.43E+09 2.19E+07
N2SPOST_merged 4.32E+09 1.85E+07
N3SPOST_merged 5.47E+09 2.30E+07
N1MPOST_merged Mid 5.55E+09 2.21E+07
N2MPOST_merged 4.68E+09 1.91E+07
N3MPOST_merged 5.88E+09 2.42E+07
N2DPOST_merged Deep 5.30E+09 2.16E+07
N3DPOST_merged 7.29E+09 3.03E+07
S1SPOST_merged Unamended Shallow 5.38E+09 2.13E+07
S2SPOST_merged 5.52E+09 2.23E+07
S3SPOST_merged 4.96E+09 2.03E+07
S2MPOST_merged Mid 1.14E+10 4.64E+07
S3MPOST_merged 5.29E+09 2.19E+07
S1DPOST_merged Deep 5.73E+09 2.25E+07
S2DPOST_merged 5.32E+09 2.31E+07
S3DPOST_merged 6.16E+09 2.67E+07
238
Figures
Fig. 1. Average ± SE denitrification (DNF) and dissimilatory nitrate reduction to ammonium
(DNRA) for shallow (A,D), mid (B,E), and deep (C,F) sediments.
239
Fig. 2. (A) Cumulative denitrification (B) DNRA and (C) ratio of cumulative DIC to the sum of
cumulative DNF+DNRA, with the red line representing the stoichiometrically expected ratio of
1.25 and 2 for DNF and DNRA, respectively. Boxes represent 25% to 75% quartiles. The solid
black line is the median value, and the whiskers are upper and lower extremes. Black dots represent
values for each individual reactor (n = 3). Letters represent statistically different DIC production
across sites from a Tukey’s HSD test corrected for multiple comparisons test and asterisks indicate
a significant difference between treatments.
240
Fig. 3. Average ± SE denitrification and DNRA rates over time (weeks) for (A) shallow (B) mid
and (C) deep sediments. Average ± SE nitrate consumption rates indicated by black line.
241
Fig. 4. Non-metric multidimensional scaling plot of subsystems-level functional annotations by
treatment (color) and depth (shape).
242
Fig. 5. Heatmap showing normalized abundance of genes associated with nitrogen cycling for
the unamended and plus-NO3- treatment, which were clustered using unweighted pair group
method with arithmetic mean (UPGMA) on Euclidean distances. Shapes represent sample depth.
Lighter colors represent lower abundance while darker colors represent higher abundance.
243
Fig. 6. Boxplots colored by treatment of (A) total fatty acids and various subclasses of
phospholipid-linked fatty acid (PLFA) subclasses representing (B) short-chain fatty acids
(SCFA; C12:0+C14:0) (C) mono-unsaturated fatty acid (C16:1) (D) polyunsaturated fatty acids
(C18:2+C18:3) (E) branched chain fatty acid (BCFA; iso- and anteiso-C13:0, C15:0, C17:0, C19:0) (F)
mono-unsaturated fatty acids (MUFA) (G) Poly-unsaturated fatty acids (PUFA; C20:4+C20:5) (H)
Long-chain fatty acids (LCFA; C24:0+C26:0+C28:0+C30:0) (I) 10-methyl C16:0.