Factors that affect the composition and activity of salt marsh microbial communities: the effect

of nutrient loading and diel cycles

by Patrick J. Kearns B.S. in Biology, 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

January 24, 2016

Dissertation directed by

Jennifer L. Bowen Associate Professor of Marine and Environmental Science

ii

Dedication

For mom, Jackie, dad, and A.J. for nurturing me and my curiosity. For John, Sarah, and Jen for getting me through it all.

For Ericka, my future.

iii

Acknowledgments I would sincerely like to thank all of the incredible people who have assisted me in not

only completing my dissertation, but also guiding me down the path that I am on today. First and

foremost, I would like to thank my advisor Jen Bowen for her guidance, compassion, friendship,

and her uncanny ability to always know what I wanted to hear or see to guide me along through

good and bad times. I’ve thoroughly enjoyed our journey that has been filled with good times,

good beer, amazing science, and conversations about science that have been among the most

exciting (and nerdy) of my life. I directly credit my development as a scientist to her mentorship

and I do not believe there are words to express how grateful and happy I am to have worked with

her for the past six years. In addition to Jen’s guidance, I would not be where I am today without

Rob Stevenson for giving me my first job in science, nurturing my curiosity, and always planting

new and novel ways to think about systems in my brain. I would also like to thank Mike Shiaris

for not only all his help over the years, but also setting me down the path of microbial ecology.

I’d also like to thank my Northeastern committee members Randall Hughes, David Kimbrio,

Katie Lotterhos, and Doug Woodhams for all their help and guidance.

While I have had many lab mates over the years, I would not have been able to complete

my work nor be the person I am today without the friendship and influence of John Angell and

Sarah Feinman. John and Sarah made graduate school bearable, fun, crazy at times, exciting,

food filled, and just an absolutely incredible experience. Sarah and John have profoundly shaped

my views of the world and their compassion and friendship have greatly changed me as a person.

In addition to John and Sarah I’d like to thank some particularly wonderful undergrads including

Dakota, Helen, Ian, Khang, Nhu, Maddie, Michael, Jen, and Sarah for their assistance in the lab

on parts of my dissertation and other projects. My ‘minons’ have shaped me into a better mentor

and have made me a more productive, focused person. Finally, I’d like to extend my gratitude to

Kenly Hiller and Ashley Bulseco-McKim for all their help, laughs, and making me think about

chemistry.

I have had the pleasure to collaborate with world-class scientists at WHOI, the MBL,

UMass Boston, Princeton, VIMS, and Villanova. In no particular order I’d like to thank Nat

West, Liam Revell, Marc Hensel, Jillian Dunic, Linda Deegan, Anne Giblin, David Johnson,

Jimmy Nelson, Laura Meyerson, Bess Ward, Andrew Babbin, Nick Peng, Tom Mozder, Rachel

Stanley, Amanda Spivak, Brandon LaBumbard, Mary O’Connor, Robin Elahi, Evan Howard,

iv

Jay Lennon, and Melanie Vile. These scientists have greatly expanded my knowledge of the

world around me and have made permanent marks on my scientific career. I’d like to give a

special thank you to Jarrett Byrnes for his amazing math mind, his willingness to always lend a

hand, and his sly ability to imprint the importance of statistics and programming permanently

into my brain. In addition to the scientists that I have interacted with and have shaped my

research, I would also like to extend my gratitude to those who made my work possible and

significantly easier through their actions. These people include Dave Behringer, Hillary

Morrison, David Dawson, Alexa McPherson, Rick Kesseli, Michie Yasuda, Thom Thera and

Illumina Inc., Yvonne Vaillancourt and her workers, Jeff Dusenberry, members of the TIDE

project, and members of the PIE-LTER.

Finally, I’d like to thank my family for their continued support and, at times, putting up

with freezers full of bugs. My mother for always making me think critically about any and

everything. For instilling science, steadiness, commitment, and reason into me from a very

young age and for molding me into the man I am today. Jaclyn for her friendship and support

through thick and thin. For instilling in me the patience of a saint and a strength to face down all

challenges, big or small. My father for always pushing me and encouraging me to be the best I

could be through hard work. Finally, I’d like to thank my fiancé Ericka for her love, compassion,

encouragement, and her smile. I look forward to our future filled with love, family, amazing

food, and sharing all of that with you mi amor. This dissertation embodies all the hard work and

confidence these people have instilled in me over the years.

v

Abstract of dissertation

Microorganisms, including bacteria and fungi, are the most abundant and species-rich

group of organisms on the planet. Their actions drive ecosystem processes and they confer key

ecosystem services that are of benefit to ecosystems worldwide. In addition to natural variation,

ecosystems are experiencing increased anthropogenic stressors which have profound effects on

both macro- and microscopic organisms alike. To better grasp how ecosystems will respond to

current and future changes we need to better understand how microbial communities and their

activity will be affected. In this dissertation I address the role of anthropogenic changes (excess

nitrogen) and natural changes (diel cycles) on microbial communities in salt marshes

ecosystems.

In chapter one, I examined how long-term fertilization (>10 years) alters bacterial

community composition and activity in salt marsh sediments. High-throughput sequencing of the

16S rRNA gene and 16S rRNA from sediments in two distinct salt marsh habitats (tall Spartina

alterniflora and S. patens) spanning nearly ten years of fertilization (2005, 2006, 2013, and

2014) revealed no effect of fertilization on overall bacterial community composition. The active

community, however, changed dramatically as a result of fertilization, resulting in a highly

homogenized active community that displayed lower active bacterial diversity and a higher

percentage of dormant taxa. My results suggest that dormancy is a prevalent strategy in marsh

sediments that can act to preserve microbial diversity in response to future environmental

change.

In chapter two, I analyzed the salt marsh fungal community response to excess nitrogen

across multiple habitats (mudflat, S. alterniflora, S. patens, and short S. alterniflora). Fungal

composition was altered by both nitrogen loading and habitat, with nutrient enrichment

significantly increasing both fungal diversity and abundance. Further, fertilization led to the

proliferation of taxa closely related to fungal denitrifiers, suggesting that nutrient enrichment

enhanced fungal denitrification. Organic matter in fertilized marshes showed greater signs of

decomposition, suggesting nitrate reduction contributed to decomposition of marsh peat and the

liberation of new carbon sources, which in turn promoted higher fungal diversity and biomass.

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My results highlight the important role of fungi to decomposition of marsh organic matter and

their response to long-term fertilization may have implications for carbon storage in marshes.

In my final chapter, I analyzed how both dynamic and static conditions in a salt marsh

pond affect composition and activity of bacteria over two diel cycles. Sediment communities,

which experience static conditions, displayed no net change in composition or abundance of both

the total and active community over the 48-hr experiment. Active communities in the pond

water, where abiotic conditions rapidly cycle on short time scales (<4hr), varied in concordance

with changes in their abiotic condition despite no net change in community composition. The

active pond water communities displayed a surprising dominance by members of the rare

biosphere (bacteria with <1% total abundance) suggesting rare taxa that possess the ability to

withstand large fluctuations in abiotic conditions are better adapted to those conditions and,

surprisingly, do not change the overall community composition. The results from my

dissertation highlight the influence of changes in abiotic conditions in structuring microbial

communities and their activity. Microbial communities mediate numerous ecosystem processes

and changes to the environment, both natural and abiotic, determine their composition.

Therefore, understanding how alterations to their composition, diversity, and abundance affect

their metabolic output will be key to understanding ecosystem response to future change.

vii

Table of Contents Dedication ii Acknowledgments iii Abstract of Dissertation v Table of contents vii List of figures ix List of tables x Introduction 1 Literature Cited 8 Chapter 1: Nutrient enrichment induces dormancy and decreases diversity of active bacteria in salt marsh sediment Abstract 13 Introduction 13 Methods 17 Results 25 Discussion 29 Acknowledgements 32 Literature Cited 32 Tables and Figures 37 Supplemental Materials 63 Chapter 2: Nutrient enrichment alters salt marsh fungal communities and increases decomposition of salt marsh sediments Abstract 48 Introduction 48

viii

Methods 50 Results 55 Discussion 57 Acknowledgements 61 Literature Cited 61 Tables and Figures 68 Supplemental Materials 76 Chapter 3: The effect of short-term, diel changes in environmental conditions of active microbial communities in a salt marsh pond Abstract 78 Introduction 78 Methods 81 Results 84 Discussion 88 Acknowledgements 91 Literature Cited 91 Tables and Figures 97 Supplemental Materials 104

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List of Figures Chapter 1 Figure 1. Effect of nitrogen enrichment on active and total bacterial community composition. 37 Figure 2. The effect of nitrogen enrichment on the percentage of dormant taxa and active bacterial diversity. 38 Figure 3. Order-level taxonomic composition of fertilized and reference sediments assessed by the 16S rRNA: 16S rRNA gene ratio. 39 Figure 4. Significantly different taxa between fertilized and reference sediments. 41 Figure 5. Effect of nutrient enrichment of net ecosystem metabolism. 42 SI Figure 1. The effect of fertilization of bacteria diversity in salt marsh sediments. 43 SI Figure 2. Titration of the 16S rRNA: 16S rRNA gene ratio for fertilized and reference sediments. 44 SI Figure 3. Weighted UniFrac similarity comparing different extraction and sample preservation methods. 45 Chapter 2 Figure 1. The effect of nutrient enrichment and habitat on fungal community composition. 69 Figure 2. The effect of nutrient enrichment and habitat on fungal abundance and diversity. 70 Figure 3. The effect of habitat and nutrient enrichment on taxonomic composition of fungal communities. 71 Figure 4. Significantly different fungal taxa between fertilized and reference marshes. 72 Figure 5. The effect of nutrient enrichment on the percentage of fungal denitrifiers. 73 Figure 6. Regressions of carbon against fungal community composition, diversity, and abundance. 74 Figure 7. The effect of nutrient enrichment and habitat on sediment carbon content, decomposition, and lability. 75 SI Figure 1. Schematic of a typical Plum Island marsh depicting the 4 habitats sampled. 76 SI Figure 2. The effect of habitat and nutrient enrichment on FTIR spectra. 77 Chapter 3 Figure 1. Environmental parameters in the pond sediment and water over the 48hr experiment. 97 Figure 2. The effect of diel cycles on total and active bacterial communities. 98 Figure 3. Changes in the percentage of active taxa in the sediment and water communities. 99 Figure 4. Taxonomic composition of active sediment and water communities over the 48hr experiment. 100 Figure 5. Heatmap of the 16S ratio for the most active water taxa of the 48hr experiment. 101 Figure 6. Plot of the relative abundance of the top 1000 OTUs in 16S rRNA relative to the 16S rRNA gene for sediment and water communities. 102 Figure 7. Plot of the 16S rRNA gene rank of the top 380 taxa versus the relative abundance in 16S rRNA for sediment and water communities. 103

x

SI Figure 1. qPCR results for sediment and water communities. 104 SI Figure 2. Active and total bacterial diversity for sediment and water communities. 105 SI Figure 3. Taxonomic composition of the total sediment community over the 48hr experiment. 106 SI Figure 4. Taxonomic composition of the total water community over the 48hr experiment. 107 SI Figure 5. Heatmap of the 16S ratio for the most active sediment taxa of the 48hr experiment. 108

List of Tables Chapter 1 SI Table 1. Results of Kruskal-Wallis test comparing the abundance of taxa between fertilized and reference sediments. 46 Chapter 2 Table 1. List of FTIR functional wavelengths. 68

1

Introduction

Microbes, including bacteria, fungi, other microeukaryotes, and viruses represent the

majority of biomass and species diversity on the planet (Hugenholtz et al. 1998). With millions

of species and well over 1025 individuals, microbes represent a dominant biological force

(Kallmeyer et al. 2012). Microbes play key roles in all aspects of ecosystems and play a

particularly important role in biogeochemical cycling of the elements essential for life, especially

carbon, sulfur, oxygen, and nitrogen (Falkowski et al. 2008). The actions of microbes in soils and

waters drive fluxes of key biological elements and fuel critical ecosystem processes, including

primary and secondary production (Falkowski et al. 2008). In addition to their role in nutrient

cycling and as ecosystem engineers, microbes also affect macroscopic life through intricate

symbioses (Mazmanian et al. 2008) and play key roles in host health (Cho and Blaser 2012).

While microbes are ubiquitous across the planet, microbial communities vary in both space and

time (Louzupone and Knight 2007) but these communities can also display surprising

consistency in their abundance and composition over decadal time periods (Kallmeyer et al.

2012, Kearns et al. 2016). The composition of microbes, in particular the set of genes a

community carries (metagenome), dictates the metabolic and ecosystem services microbes can

provide (Falkowski et al. 2008).

Studies examining microbial communities demonstrate a sensitivity of microbes to

perturbations in their environment (Allison and Martiny 2008) and their sensitivity to

environmental change is likely due to the narrow niche space most microbes occupy (Kirchman

2015). Studies examining the structure-function relationship of microbial communities has

revealed mixed results. Work by Cavigelli and Robertson (2000) and Reed and Martiny (2013)

demonstrate a tight relationship between the structure or change in structure of microbial

communities and the functional output of those communities. These studies are similar to those

done with macroscopic communities that report structure-function relationships (Thompson et al.

2012). While several studies demonstrate a link between microbial composition and function,

numerous other studies show no net effect of environmental change on function despite changes

in community composition (Bottomley et al. 2004, Balser & Firestone 2005, Langenheder et al.

2005). The disconnect between structure and function is typically ascribed to the presence of

functional redundancy in microbial communities. Functional redundancy allows microbial

communities to perform identical functions despite changes in community structure, likely due to

2

the overlapping metabolic capabilities of microbes occupying the same physical space (Shade et

al. 2012). While microbial communities can change in response to perturbations, communities

can display resistance to change despite changes in geochemical output, as demonstrated in salt

marsh sediments (Koop-Jakobsen and Giblin 2010, Bowen et al. 2011). When communities are

resistant to environmental change their composition remains unperturbed, but their function may

change due to changes in their active communities. The ability of microbial communities to

display resistance and resilience to change is commonly conferred by lower abundance taxa that

occupy similar niche space as dominant taxa allowing them to adapt to novel environmental

conditions (Lynch and Neufeld 2015).

In most habitats, microbial communities are typically dominated by only a few highly

abundant taxa (<10), despite their diversity (Sogin et al. 2006, Pedrós-Alió 2012). Thus,

microbial communities typically contain a large number of low abundance taxa, collectively

known as the rare biosphere, with abundances less than one percent in the total community

(Sogin et al. 2006; Lynch and Neufeld 2015). While macroscopic communities typically contain

organisms specifically adapted to local conditions, in the rare biosphere microbial communities

can contain taxa with metabolic and physiological requirements drastically different than would

be predicted based on local conditions. For example, Wissuwa et al. (2016) isolated thermophilic

bacteria from arctic tundra and sediments, where temperatures rarely achieve the optima for

thermophilic growth. Rare taxa are important contributors to activity of bacterial communities in

coastal waters (Campbell et al. 2011) and their enhanced activity can contribute to pulses of

ecosystem function under different abiotic conditions (Aanderund et al. 2015). Further, rare taxa

can become more abundant under conditions conducive to their growth and enhanced cell

division can significantly alter community composition (Shade et al. 2014). Overall most rare

taxa remain inactive or dormant and act as a seed bank of microbial diversity (Lennon and Jones

2011) that can respond to future changes in the environment. While dormant taxa such as

sporulated Firmicutes can remain dormant for hundreds of years (de Rezende et al. 2013, Müller

et al. 2014), the dormancy and activity of taxa is often cyclical or occasional as conditions

change within ecosystems (Salazar-Villegas et al. 2016). The bank of inactive taxa allows

microbial communities to respond to new conditions and to maintain ecosystem function,

allowing for the preservation of ecosystem services under stressful conditions.

3

Microbial dormancy is a bet-hedging strategy microbes employ to help them persist in

the environment under unfavorable environmental conditions or conditions not conducive to

their growth or reproduction (Lennon and Jones 2011). Dormancy is a strategy used by all three

domains of life, however, the importance of dormancy in eukaryotic lineages appears to be

significantly less than bacterial lineages (Jones and Lennon 2010). The proportion of dormant

taxa varies across ecosystems (20-50% of taxa) and their ubiquity can impede our ability to

detect changes in microbial communities and to relate community composition to associated

changes in ecosystem function. The controls over dormancy in ecosystems are poorly

understood, however dormancy can be a response to nutrient limitation in temperate lakes (Jones

and Lennon 2010) and changes in abiotic factors such as salinity can affect percentages of

dormant taxa (Aanderund et al. 2016). Further, pulses of ecosystem limiting factors, such as

water to desert soils, increases activity and perhaps alleviates dormancy (Frossard et al. 2015).

To assess levels of dormancy and document changes in active microbial communities, recent

work targets 16S rRNA in addition to the 16S rRNA gene (e.g. Campbell et al 2011). 16S rRNA

is used to assess dormancy because production of 16S rRNA tends to be positively correlated

with protein synthesis and thus activity of microbial communities (Kerkhof and Kemp 1999,

Deutscher et al 2006). Although there are some caveats in using this approach to directly predict

microbial growth (Blazewicz et al 2013), it is a reasonable proxy for protein synthesis potential,

indicating either microbial growth or other metabolic activity.

My dissertation examines the effect of changes in abiotic conditions on microbial

communities in a salt marsh complex in the Plum Island Ecosystems Long-Term Ecological

Research Site in Rowley, MA, USA. Salt marshes are critically important coastal ecotypes lying

at the interface of the land and the sea. Marshes are among the most productive ecosystems on

the planet (McLeod et al. 2011) and due to their high levels of primary productivity they provide

numerous ecological and economic services, including carbon sequestration, coastal protection

from storm surges, habitat for numerous species of animals, and removal of anthropogenic

nutrients (Millennium Ecosystem Assessment 2005). However, despite their importance,

marshes have experienced significant declines in recent decades likely due to anthropogenic

stressors including sea level rise, excess nutrients, alterations to hydrology, and invasive species

(Craft 2007, Craft et al. 2009, Deegan et al. 2012, DeLaune and White 2012). These ecosystem

stressors can alter the ability of a marsh to deliver key ecosystem services (e.g. remove N; Drake

4

et al. 2009), however, there are many unknowns about how marshes respond to global changes,

in particular, how their associated microbial communities respond to environmental change.

With densities of microbes of 109 per gram of sediment, marshes represent a substantial reservoir

of microbial diversity (Rublee and Dornseif 1978, Goodfriend 1988, Bowen et al. 2011), and

microbial activity affects rates of carbon sequestration and remineralization (Kirwan and Blum

2011), nutrient removal (Koop-Jakobsen and Giblin 2010), and the growth and function of plants

(Philippot et al. 2013; Hughes et al. 2014). Because of their instrumental role in salt marsh

ecosystem processes, understanding how stressors, both anthropogenic and natural, affect

microbial community structure and function is paramount to understanding how these

ecosystems will respond to future global change.

In chapter one I examine how long-term fertilization influences the composition and

activity of salt marsh bacterial communities. Numerous studies examining the influence of

excess nitrogen on microbial communities demonstrate a significant effect of excess nutrients

(Alison and Martiny 2008, Leff et al. 2015). Salt marsh microbial communities, however, are

resistant to excess nutrients (Bowen et al. 2009a, 2011) despite the observed alteration to

geochemical cycling (Koop-Jakobsen and Giblin 2010), activity (Bowen et al. 2009b), and

below-ground decomposition (Deegan et al. 2012). I hypothesized that the lack of response of

the total microbial community to excess N was due to high rates of dormancy present in marsh

sediments. Further, I hypothesized that long-term fertilization would alter the active microbial

community, leading to a homogenized group of active taxa. To test my hypotheses, I sequenced

the 16S rRNA gene and 16S rRNA to assay the total and active microbial communities

respectively. I sequenced nucleic acids from sediment samples collected monthly (May-October)

from 2005-2006 and 2013-2014 from two distinct marsh habitats (tall Spartina alterniflora and

S. patens) in fertilized (n=2) and reference (n=2) marshes. Consistent with previous work

(Bowen et al. 2011), my results suggest that fertilization does not alter the total microbial

community, despite over ten years of exposure to excess N. The active community, however,

was altered by fertilization, leading to an increase in active taxa that are better adapted to higher

N loads, including the overwhelming domination of Cyanobacteria and one specific sulfate

reducing bacteria (order Desulfobacterales). On average, marshes contain a large reservoir of

dormant taxa (~55%), which is often found in systems with rapidly changing abiotic conditions,

suggesting that dynamic environmental conditions promote dormancy as an ecological strategy.

5

Fertilized sediments, by contrast, displayed a large increase in dormant taxa (up to 90%), which

resulted in lower active microbial diversity. My results suggest that despite the resistance of the

total microbial community to change by excess N, the active community, and therefore the

metabolic output of marsh microbes, is dramatically altered due to the activity of a small number

of taxa. The dormancy of taxa in fertilized creeks allows for the maintenance of bacterial

diversity better able to respond to future global change.

In chapter 2, I followed up my analysis of how nutrient supply alters bacterial community

structure by examining the role of long-term fertilization in structuring fungal communities, their

diversity, and abundance across different marsh habitats. Terrestrial fungal communities can be

structured by carbon availability (Tedersoo et al. 2014) and excess nitrogen (Leff et al. 2015),

with excess nitrogen typically decreasing the diversity of fungi (Lin et al. 2012). Marine fungi,

particularly in coastal habitats, are poorly characterized both taxonomically and functionally

(Hawksworth, and Rossman 1997, Picard 2017), and we lack a fundamental understanding of the

factors that structure fungal communities. To better understand these factors, I examined fungal

communities and their response to excess nitrogen across four distinct marsh habitats (mudflat,

tall S. alterniflora, S. patens, short S. alterniflora; Fig. 1) using high-throughput sequencing and

quantitative PCR (qPCR) of the fungal internal transcribed spacer region (ITS).

Figure 1- A schematic of a typical marsh in the Plum Island Ecosystems marsh complex. Nitrogen loads (g N m-2 y-1) are indicated for reference (blue) and fertilized (red) marshes below

each habitat. Modified from Johnson et al. (2016). MHW=mean high water. MLLW=mean lower low water.

6

In addition to analyzing fungal abundance and community structure, I also used Fourier

Transform Infrared Spectroscopy (FTIR) to analyze organic matter quality. I hypothesized that

fungal communities would vary as a function of habitat, and that nutrient enrichment would have

no net effect on fungal communities as has been previously shown for marsh bacterial

communities (Bowen et al. 2009, 2011). Further, I hypothesized that excess nitrate would favor

the use of nitrate for anaerobic metabolism, resulting in enhanced decomposition of marsh

organic matter. My results indicate that fungal community composition, diversity, and abundance

varied by habitat in both fertilized and reference marshes. In contrast to results from terrestrial

soils (Lin et al. 2012), however, fertilization, significantly increased fungal diversity and

abundance, especially in low marsh habitats. Analysis of the taxonomic composition of fertilized

sediments revealed that many of the fungal taxa responding to excess N are closely related to

taxa that are denitrifying fungi (Maeda et al. 2015), suggesting that the increased supply of

nitrate likely stimulated fungal denitrification. FTIR analysis revealed that regions of the marsh

receiving the greatest exposure to nitrate (mudflat and tall S. alterniflora) displayed enhanced

decomposition and decreased lability of residual carbon, while carbon in the high marsh (S.

patens and short S. alterniflora) was not different between reference and nutrient enriched

marshes. My results highlight the important role of habitat and the associated abiotic conditions

in structuring fungal communities and their importance in decomposition of organic matter in

marsh sediments. Fertilization altered fungal communities through the proliferation of taxa that

are likely able to use nitrate as an anaerobic electron acceptor or that are more capable of

assimilating nitrogen into biomass. The enhanced use of nitrate in anaerobic metabolisms

resulted in enhanced decomposition in low marsh habitats (mudflat and tall S. alterniflora),

which likely resulted the release of additional carbon sources allowing for more fungal biomass

and higher fungal diversity. Further, the addition of nitrate did not alter carbon composition in

the high marsh habitats (S. patens and short S. alterniflora) suggesting primary producers replace

the decomposed carbon through their substantial root biomass.

In my final chapter I examine the role dynamic conditions play in the activity of bacterial

communities in a salt marsh pond. Salt marsh ponds are shallow ponds that form on the marsh

platform and are likely increasing as a result of anthropogenic sea level rise (Raposa et al. 2016).

Marsh ponds are typically isolated from tidal flooding except during the highest tides. During

their isolation, the water in marsh ponds experience dynamic changes in abiotic conditions on

7

short time scales (<4 hr) while the sediments remain stable. Lennon and Jones (2011)

demonstrated that percentages of dormant taxa are higher in systems that are both spatially and

temporally variable. Thus, I hypothesized that the dynamic conditions in the pond water would

promote dramatic changes in bacterial activity, while the stable conditions in the underlying

sediment would promote stable active bacterial communities. Further, I hypothesized that

dynamic conditions would promote greater inactivity overall, but would lead to enhanced

activity of taxa better adapted to conditions in the pond. I demonstrate that while both the total

and active communities remained stable in the sediments, active pond water communities

dramatically changed over short time scales with no net change in the total community. In the

water the most active taxa were rare taxa (<1% total abundance) demonstrating a surprising role

of the rare biosphere in the activity of the pond community. Finally, the percentage of inactive

taxa in the water (>55%) was higher than the sediment (~50%) suggesting that changing

conditions within the overlying water drive the majority of taxa into inactivity, including

numerically dominant taxa. The results from my third chapter underscore the rapid pace at which

microbial communities can respond to changes in their environment as well as the importance of

abiotic conditions in affecting the inactivity of microbial taxa. Further, the overwhelming

abundance of rare taxa in the active pond water communities and the high percentage of inactive

taxa suggests that rare taxa may be better adapted to dynamic conditions and the use of

dormancy may be higher in dynamic systems.

Microbial communities are a ubiquitous group of taxa that exert powerful controls over

ecosystem processes worldwide. Microbial communities are sensitive to changes in

environmental conditions (Alison and Martiny 2008) and changes in their composition and

activity can have profound effects on ecosystem function. While microbes are important

contributors to ecosystems, a substantial proportion of microbial taxa can be inactive and

therefore not contributing to ecosystem function. My dissertation examined the interplay

between environmental change, dormancy, community composition, and activity of microbial

taxa. Long-term nutrient enrichment differentially affected bacterial and fungal communities in

salt marsh sediments. Fungal community composition was changed as a result of increased

anthropogenic nitrogen while bacterial communities remained resistant, despite changes in

activity. The discrepancy between bacterial and fungal lineages may be due the differential use

of dormancy between bacteria and fungi (Jones and Lennon 2010). Conversely, fungi may be

8

more sensitive to environmental change than bacteria. Despite the domain-level differences in

response to N, the changes in activity of bacterial and fungal communities due to excess N has

led to a drastically altered marsh landscape. Fertilized marshes display an enhanced degree of

decomposition and accelerated carbon cycling relative to reference marshes. Further, my results

indicate a switch in the primary anaerobic electron acceptor from sulfate to nitrate in fertilized

creeks resulting in enhanced decomposition, which has been previously documented (Koop-

Jakobsen and Giblin 2010). The altered carbon cycle in fertilized marshes likely alter the ability

of the marsh to store carbon and the enhanced decomposition of marsh peat may be altering

marsh geomorphology (Deegan et al. 2012). The enhanced decomposition of marsh organic

matter is tied to the increase in activity and abundance of heterotrophic bacterial and fungal taxa

better adapted to higher nitrogen, which suggests a domain-independent ability for enhanced

growth under higher nitrogen loads.

My work highlights the important role of dormancy to salt marsh bacterial communities.

Salt marshes are highly dynamic ecosystems, experiencing changes in abiotic conditions on very

rapid time scales. The dynamic conditions appear to promote dormancy as an ecological strategy,

allowing communities to remain stable during perturbations and over decadal time scales.

Further, dormancy acts as a mechanism to preserve bacterial diversity under environmental

change. Salt marshes provide critical ecosystem services to coastal areas and their ability to do so

is directly tied to their microbial communities. My work links dormancy and changes in fungal

communities to alterations of marsh carbon cycling under nitrogen enrichment and the long-term

nitrogen supply to coastal systems may profoundly affect the ability of marshes to store carbon.

Despite the constant press of anthropogenic perturbations received by coastal systems, dormancy

of bacterial communities allows for the preservation of species and metabolic diversity, allowing

communities to continue to provide critical ecosystem services under current and future global

change.

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13

Chapter 1: Nutrient enrichment induces dormancy and decreases diversity of active bacteria in salt marsh sediment

Abstract

Microorganisms control key nitrogen biogeochemical pathways, thus changes in

microbial diversity, community structure, and activity can affect ecosystem function and

response. Factors that control the proportion of active cells in the environment and how that

proportion varies as a function of global change drivers requires clarification. In light of the

increasing supply of anthropogenic nitrogen to ecosystems worldwide understanding how

increasing availability of nutrients alters active microbial communities is imperative. Here we

show that a decade of nitrogen additions to salt marshes, one of the most N enriched ecosystems

in the world, causes a net shift in the active microbial community despite no net change to the

total community. The shift in the active community causes the proportion of dormant microbial

taxa to double, from 45 to 90%, and induces diversity loss in the active portion of the bacterial

community. Our results suggest that perturbations to salt marshes can drastically alter active

microbial communities, however salt marsh microbial communities may remain broadly resilient

by maintaining total diversity through increased dormancy.

Introduction

Human activities have increased the amount of reactive nitrogen (N) in the biosphere.

Excess bioavailable N entering ecosystems can elicit many deleterious effects (Conley et al.

2009) including decreased biodiversity (Isbell et al. 2013, Leff et al. 2015). Numerous studies

have indicated the important coupling between biodiversity and ecosystem function (Hooper et

14

al. 2012), though the emphasis is typically on macro-organismal diversity. Microorganisms,

however, exert strong controls over ecosystems through the mediation of key biogeochemical

cycles, including the N cycle. Despite their importance to ecosystem function, the relationship

between microbial community composition, diversity, and ecosystem function is not clearly

elucidated (Finlay et al. 1997). Resolving the responses of microbial communities, in particular

the active taxa that maintain ecosystem function, is essential for predicting how ecosystems will

respond to global changes such as increased anthropogenic N supply.

Analysis of the 16S rRNA gene, which is commonly used to assess bacterial community

structure, accounts for all cells, including cells that are active, dormant, and recently dead, as

well as extracellular DNA (Nielsen et al. 2007). Relic DNA from dead cells has been shown to

mask ecologically important patterns in soil environments (Carini et al. In press). A large number

of inactive cells might also mask shifts in the active microorganisms that are responsible for

critical ecosystem services. Dormancy is a bet-hedging strategy where microbes enter a low

metabolic or inactive state when they encounter unfavorable environmental conditions (Lennon

and Jones 2011). Microbes can persist in this state until environmental conditions favor their

successful growth. Molecular analyses suggest that dormant microbial taxa can account for ~20-

50% of the microbial community depending on the ecosystem and the heterogeneity of the

environment (Lennon and Jones 2011). It is unclear how high proportions of dormant taxa affect

microbial function, however, dormancy acts as a genomic reservoir allowing for the preservation

of genetic diversity in the presence of unfavorable conditions, and may therefore provide an

important long-term strategy for maintaining ecosystem function (Lennon and Jones 2011).

Coastal nutrient supply, especially by nitrogen (N) in the form of nitrate (NO3-), has

resulted in salt marshes being one of the most nutrient enriched ecosystems in the world, with

15

some systems experiencing nutrient enrichment greater than 500 kg N km-1yr-1 (Pardo et al.

2011). Salt marsh area has significantly declined across the globe and recent evidence suggests

that excess nutrients accelerate marsh collapse (Deegan et al. 2012). Nutrient-induced loss of

marsh area is hypothesized to occur through a decrease in belowground plant biomass and an

increase in belowground bacterial respiration, resulting in decreased stability of the marsh edge

and loss of marsh area. However, evidence from 16S rRNA gene analysis suggests that N-

enrichment has a minimal effect on salt marsh sediment total bacterial community structure

(Bowen et al. 2011). The lack of response in the total bacterial community is surprising given the

observed shifts in biogeochemical function and respiration as a result of nutrient enrichment

(Deegan et al. 2007, Koop-Jakobsen and Giblin 2010) and additional work that clearly connects

changes in ecosystem function with changes in bacterial community structure (Reed and Martiny

2013).

We hypothesize that the apparent lack of bacterial community response to N-enrichment

is a result of a high degree of bacterial dormancy in salt marsh sediments and that the active

community of bacteria will shift in response to increased N supply. Salt marsh sediments are

highly dynamic habitats, where microorganisms are exposed to variable light, oxygen, salinity,

water, carbon, and nutrients that can change in minutes to hours. Because of these widely

changing conditions, we predict that dormant taxa may account for a substantial portion of the

microbial community in marsh sediments. We hypothesize that excess N will favor a small

number of taxa that are able to respond to increased N availability and ultimately result in an

increase in the portion of dormant cells.

Here we test our hypotheses by using analysis of the 16S rRNA gene and the gene

product, 16S rRNA, to assess the response of the total and active microbial communities,

16

respectively, to N-enrichment. Further, we quantified the extent of bacterial dormancy in salt

marsh sediments and the effect of nutrient enrichment on active bacterial diversity. Although the

relative abundance of 16S rRNA cannot be used as an exact proxy for bacterial growth

(Blazewicz et al. 2013) it does indicate cells that have the potential for growth and cells that are

metabolically active, though perhaps not dividing (Kerkhof and Kemp 1999). We examined the

effect of experimental long-term nutrient enrichment (Deegan et al. 2007, 2012, Johnson et al.

2016) on these communities in low and high marsh habitats in a New England salt marsh located

at the Plum Island Ecosystems Long-Term Ecological Research Site. Our long-term

experimental marsh has been enriched with 70 µM nitrate on the incoming tides seasonally since

2004. Another marsh that we sampled was enriched for one year in 2005 and again from 2009-

2015. In each year enriched marshes received 15-fold more N than reference marshes (Deegan et

al. 2007, Johnson et al. 2016).

New England salt marshes are tidal grasslands characterized by low and high marsh

habitats that receive different amounts of tidal flooding. Low marsh is dominated by the tall

ecotype of Spartina alterniflora and is flooded on every semi-diurnal tide. The low marsh habitat

receives considerably more N delivered by tidal water than the high marsh, which is dominated

by Spartina patens and is flooded by only 30% of high tides (Johnson et al. 2016). We collected

triplicate sediment samples from both habitats monthly in each marsh (May-October; 2005,

2006, 2013, 2014) to examine short-term and long-term trajectories in community structure and

activity. After extracting DNA and RNA, we amplified and sequenced the 16S rRNA gene and

16S rRNA to describe the total and active bacterial communities. To examine how changes in the

active microbial community might translate to ecosystem scale processes we also measured

17

whole ecosystem metabolism by examining dissolved oxygen concentrations in creek water of

one enriched and one reference creek.

Methods

Study Site Description

This experiment was conducted at the Plum Island Ecosystems Long-Term Ecological

Research Station (PIE-LTER) in Northeastern Massachusetts, USA (42.759 N, 70.891 W).

Nested within the LTER, a long-term (>10 y), large-scale (60,000 m2 per treatment marsh

system) nutrient enrichment experiment called the TIDE Project (Deegan et al. 2007, 2012,

Johnson et al. 2016) is ongoing. One marsh system received nutrient enrichment with target

enrichment of 70-100 μM nitrate in the creek water on the rising tide continuously throughout

the growing season since 2004 and an additional creek was fertilized in 2005 and then again

from 2009 to the present. Two additional creeks received no fertilizer and serve as reference

marshes.

Sample Collection

We collected samples monthly over the course of the growing season (May-October) in

2005, 2006, 2013, and 2014 at low tide. All samples were collected within 1 m of permanent

transects. Sediment was collected within two dominant macrophytes: Spartina patens (SP) and

the tall ecotype of Spartina alterniflora (TSA). Surface sediments (top 2 cm) were collected in

triplicate with sterile, cut-off 30 mL syringes and homogenized in a sterile 50 mL centrifuge

tube. Samples were aliquoted into either empty cryovials or cryovials containing 1 mL RNAlater

(Invitrogen, Grand Island, NY, USA). Samples put into RNAlater were mixed by vigorous

18

shaking prior to storage. All samples were stored on dry ice for less than 1 hour until they were

flash frozen in liquid nitrogen.

Nucleic Acid Extraction

DNA was extracted from ~0.25 g of sediment using the MoBio® PowerSoil Total DNA

Isolation Kit (Carlsbad, CA, USA) following the manufacturer's instructions. RNA was extracted

from ~0.5 g sediment following the protocol of Mettel et al. (2010) with modifications. First, to

remove residual RNAlater, sediments were spun at 20,000 x g for 1 minute and the resulting

supernatant was discarded. To each sample 700 µL of PBL buffer (5 mM Tris-HCl [pH 5.0], 5

mM Na2EDTA, 0.1% [wt/vol] sodium dodecyl sulfate, and 6% [vol/vol] water-saturated phenol),

along with 0.5 g 0.17 mm glass beads were vortexed at maximum speed for 10 minutes. Samples

were then spun at 20,000 x g for 30 seconds and the supernatant was transferred to a clean tube.

The remaining sediment and glass beads were resuspended in 700 µL TPM buffer (50 mM Tris-

HCl [pH 5.0], 1.7% [wt/vol] polyvinylpyrrolidone, 20 mM MgCl2) and vortexed at maximum

speed for an additional 10 minutes. Sediment was then spun at 20,000 x g for 30 seconds and the

supernatant was pooled with the previous supernatant. An equal volume of

phenol:choloroform:isoamyl alcohol (25:24:1 v/v/v) was added to each sample and mixed by

vortexing at maximum speed for 30 seconds. Samples were then spun at 20,000 x g for 30

seconds, the aqueous layer was transferred to a fresh tube, and nucleic acids were precipitated

with 0.7 volumes of 100% isopropanol and 0.1 volumes 3 M sodium acetate (pH 5.7). Samples

were spun at 20,000 x g for 30 minutes, the supernatant was discarded, and the resulting pellet

was washed with 70% ethanol and allowed to air dry. The washed RNA was loaded onto an

Illustra Autoseq G-50 Spin Column (GE Healthcare, Pittsburgh, PA, USA), which contained 500

19

µl prewashed Q-Sepharose (GE Healthcare). Samples were spun at 650 x g for 7 seconds and

then eluted 5 times with 80 µl of 1.5 M NaCl (pH 5.5). The flow through was transferred to a

clean tube, precipitated with 0.7 volumes 100% isopropanol and 0.1 volumes sodium acetate (pH

5.7), and spun at 20,000 x g for 30 minutes. The resulting pellet was washed with 70% ethanol,

allowed to air dry, and then resuspended in 50 µl di-ethyl pyrocarbonate (DEPC) treated water.

RNA samples were checked for DNA contamination using general bacterial primers

515F and 806R (Caporaso et al. 2011) and any DNA contamination was removed using DNase I

(New England BioLabs, Ipswich, MA, USA) following the manufacturer's instructions. We then

reverse transcribed RNA to cDNA using the Invitrogen Superscript RT III cDNA synthesis kit

following the manufacturer's instructions for random hexamers. Proper cDNA synthesis was

verified by PCR with general bacterial primers, with product checked on a 1.5% agarose gel

stained with ethidium bromide.

PCR and Sequencing

We quantified DNA and RNA concentrations with Picogreen and Ribogreen (Invitrogen)

kits respectively following the manufacturer's instructions and DNA and cDNA were normalized

to 3 ng μl-1 for all PCR reactions. Samples were then prepared for sequencing on the Illumina

MiSeq (Caporaso et al. 2011). We first used general bacterial primers 515F and 806R (Caporaso

et al. 2011), with appropriate Illumina adaptors, and individual 12-bp GoLay barcodes attached

to the reverse primers. We amplified each sample in triplicate using previously described PCR

conditions (Caporaso et al. 2011). Samples were verified with gel electrophoresis and target

bands were excised and purified with the Qiagen QIAquick gel extraction kit (Qiagen, Valencia,

CA, USA). Samples were quantified fluorometrically using a Qubit 2.0 (ThermoFisher,

20

Waltham, MA, USA) and were pooled in equal molar concentrations for sequencing on the

Illumina MiSeq platform for paired-end 151 bp sequencing. All sequencing was performed at the

University of Massachusetts Boston using V2 chemistry.

Sample Preservation and Extraction Method Verification

Due to the nature in which the samples were preserved (RNAlater and dry ice) and how

the nucleic acids were extracted (MoBio PowerSoil DNA Isolation kit and following Mettel et al.

2010), we sought to understand the effect of our preservation and extraction methodology. While

studies have shown RNAlater to be an effective method to preserve rRNA and mRNA for

sequencing (Ottesen et al. 2011), these studies focused on water samples and the efficacy of

RNAlater in preserving sediment samples has not been investigated. To test our methodology,

we extracted three salt marsh sediment samples preserved in either RNAlater or flash frozen in

liquid nitrogen. Additionally, we verified our extraction methods against a commercially

available co-extraction kit (MoBio PowerSoil Total RNA Isolation Kit) following the

manufacturer’s instructions. The 16S rRNA gene and rRNA were sequenced and the resulting

sequence data was processed as described below. To determine any significant differences

between methods we compared weighted UniFrac similarity (Supplemental Fig. 3) values using

a one-way ANOVA. There were no significant differences as a result of the different extraction

or sediment preservation methods (Supplemental Fig. 3). It is also worth noting that the

synchronous patterns in active microbial community structure in the reference marshes over a

decade provide confidence that the long-term storage of sediments in RNALater™ did not

adversely affect the nucleic acids (Fig. 1C).

Sequence and Data Analysis

21

Paired end reads were joined using fastq-join (Aronesty 2011) with default parameters.

Joined reads were then demultiplexed and quality filtered in QIIME (Caporaso et al. 2010)

following methods outlined by previously (Bokulich et al. 2013) Sequences were screened for

chimeras using de novo mode in UCHIME (Edgar et al. 2011) and the resulting chimeric

sequences were discarded. After quality filtering, a total of 25.31 million rRNA and rRNA gene

sequences were included in the final analysis. Operational taxonomic units (OTUs) were

clustered at 97% sequence similarity using swarm (Mahé et al. 2014) in QIIME and OTUs

appearing only once (singletons) across the dataset were discarded. A representative sequence

was chosen from the most abundant sequence for each OTU and taxonomy was assigned using

BLAST against the GreenGenes database (version 13.5). Additionally, we filtered out all

sequences matching chloroplasts. Representative sequences for each OTU were aligned with

PyNast (Caporaso et al. 2010b) and a phylogenetic tree was constructed using fasttree (Price et

al. 2010).

Beta diversity was calculated on normalized OTU tables using weighted UniFrac

(Lozupone and Knight 2005). For comparison, we also analyzed beta diversity using Bray Curtis

similarity, which does not take the phylogenetic information of the OTUs into account when

calculating similarity and the results are consistent with UniFrac results. To assess differences in

community composition, we used Adonis (Anderson 2001) with 10,000 permutations

implemented in QIIME. Adonis tests significance of categorical variables using a permutational

multivariate analysis of variance by fitting linear models to distance matrices and assessing

model fit with an F-test. To assess the degree of dormancy we calculated the 16S rRNA:16S

rRNA gene +1 ratio for each taxa in each sample and defined a taxon as active with a ratio >1

(Jones and Lennon 2010). Work has shown 16S rRNA:16S rRNA gene ratios can vary among

22

taxonomic groups (Blazewicz et al. 2013) and the ratio used here, and previously (Campbell et

al. 2011), may produce many false positives for active taxa due to the variation in the ratio and

biases associated with methodology (Blazewicz et al. 2013, Carini et al. In press). To provide

further support for our dormancy conclusions we addressed this issue by increasing the ratio of

16rRNA to the 16S rRNA gene between 1 and 50 to assess the effects of the ratio on the

calculated rates of dormancy (Supplemental Fig. 2). We tested how levels of dormancy varied

with fertilization using a one-way ANOVA in R (R Core Team 2012).

Shannon Diversity was calculated on rarified OTU tables normalized to the lowest

sampling depth (32,501) in QIIME (10,000 restarts with steps of 100). To assess the diversity of

active taxa, we calculated the 16S rRNA to 16S rRNA gene ratio for each order and defined an

order as active when its ratio was greater than 1. To determine significant changes in diversity

between fertilization regimes and over time we used multiple one-way ANOVAs after testing

that the data met the relevant assumptions. Finally, to assess significant differences in relative

OTU abundance and relative abundance of microbial orders due to fertilization we used a

Kruskal-Wallis test in QIIME and R, defining significance with a Bonferroni corrected p-value

<0.001. Due to the large number of OTUs present in the dataset, we only compared taxa present

100 times when examining differential frequencies of taxa. Due to the conservative nature of

Bonferroni correction, we also examined the results of a false detection method (Benjamini-

Hochberg test) and found that they produced identical results.

Net ecosystem metabolism in tidal creeks

YSI ™ 6 series water property sondes (measuring conductivity, temperature, and oxygen

saturation state) and Onset ® HOBO pressure loggers (for water depth) were deployed in

23

summer of 2012 in the bed of each tidal creek roughly 2 m below the tall-form Spartina

alterniflora zone, at 115 m (N-enriched) and 129 m (reference) upstream from the nearest

confluence with another primary creek. Sonde data were pre- and post-calibrated, and

independently cross-calibrated against in situ oxygen concentration. Concentration is determined

from saturation state using the oxygen solubility function (Garcia and Gordon 1992). Only six

days of sensor data are analyzed here because a bank collapse covered the sensor in the fertilized

creek preventing further data analysis. Water depth and creek geometry were used to construct

tidally varying water balances. Modeled freshwater inputs (Ganju et al. 2012), tidal porewater

exchange (Nuttle and Hemond 1998), and spring-neap driven drainage of the marsh platform

(Gardner and Gaines 2008) are small compared to advective fluxes during the deployment period

(~1% of total flux each) and are not included.

Dissolved oxygen concentrations and the water budget are used to build a non-steady

state oxygen mass balance for each tidal creek (Nidzieko et al. 2014). The oxygen balance

includes terms for gas exchange and the balance between photosynthesis and respiration i.e. net

ecosystem metabolism in oxygen units (NEM), and is solved for a control volume at the

sampling location. We extend the control surfaces into the marsh sediments to include any

oxygen consumption by other terminal metabolic products (e.g. sulfide; Howarth 1984, Larsen et

al. 2015) in the resulting estimate of NEMO2. We chose a gas exchange parameterization that

incorporates short-term variability in both wind speed and current velocity in a shallow, limited

fetch channel (Borges et al. 2004). Gas exchange flux is smaller in magnitude (25-45% lower) if

the current and wind speed or current only parameterizations are used (Borges et al. 2004,

Nidzieko et al. 2014). All four estimates are consistent with in situ noble gas fluxes during the

experimental period at each site.

24

Average and standard errors of NEMO2 (per unit length of creek) over the hour of high

tide at both creeks are plotted in Fig. 5A, when the tall-form Spartina alterniflora zone is flooded

and interacting with the creek water column. Morning high tides may be more heterotrophic in

this particular zone because of oxygen deficit built up overnight or light and temperature

enhanced respiration prior to peak photosynthesis; this trend is consistent with the longer time

series available at the reference creek. The difference between the NEMO2 in the N-enriched and

reference creeks is plotted in Fig 5B. The generally higher daytime and lower nighttime NEMO2

in the N-enriched creek is consistent with both enhanced oxygen production and respiration in

the N-enriched creek.

There are two potential sources of positive bias that may explain NEMO2 values greater

than zero for the three consecutive nighttime spring tides between 23:00 and 01:00: First, we do

not constrain bubble injection (Morris and Whiting 1985, Hamme and Emerson 2006) and the

dissolution of air trapped in the surface sediments as the tide rises; our mass balance would

erroneously attribute oxygen added in this way to biological production. This process is likely to

affect both creeks in a similar way and thus would not affect the NEM difference between the

creeks (which is what is relevant to the arguments presented here). Second, the reference creek

connects to an adjacent drainage during the highest tides (marked with an arrow on Fig. 5A & B)

and therefore the volume transport model likely is not accurate at these highest tides. This factor

would only affect the reference creek. Based on two additional weeks of O2 measurements at the

reference creek only (not shown), we can deduce from the difference of typical night-time high

tide NEMO2 (n=8) and peak flooding NEMO2 (n=3) that this second factor is likely equal to

approximately 0.35 mmol O2 m-1 min-1. Thus the errors caused with drainage during high tide

would decrease the difference between reference and enriched creek but for 2 out of the 3 peak

25

flooding time points (marked with black arrows), the nighttime NEM difference between

reference and enriched creeks would still be significant.

Results

Total and Active Community Composition

Our results revealed a sharp division between the active and total microbial communities,

suggesting the structure of the active community does not reflect the total community (Fig. 1A,

permutational MANOVA; F=19.43, p<0.001). There was no significant effect of N-enrichment

on total community structure (Fig. 1B; p> 0.67) or diversity (Supplementary Fig. 1; p> 0.43),

although we found a significant effect of habitat on total bacterial community structure

(F=19.51, p<0.001). These results extend previous findings that the total salt marsh bacterial

community is resistant to perturbation by nutrient enrichment (Bowen et al. 2011) and indicate

that this resistance has persisted despite a decade of N-enrichment. Additionally, our results

demonstrate a surprisingly low amount of variability in the total microbial community,

suggesting long-term stability of sediment communities despite perturbations, a phenomenon

that was also observed in other New England marshes receiving excess nutrients for over 40

years (Bowen et al. 2011).

Analysis of the potentially active bacterial community shows a markedly different

pattern than the total bacterial community and indicates that long-term nutrient enrichment

spatiotemporally standardizes the active community, overriding the importance of both habitat

and season as structuring forces (Fig. 1C). In reference marshes, habitat (PERMANOVA;

p<0.01, F=9.05) and seasonality (p<0.03, F=24.24) structured active bacterial communities and

these patterns were repeatable over a decade. Active bacterial communities in N-enriched

26

marshes (Fig. 1C), however, were significantly different from reference marshes and were no

longer influenced by habitat-specific factors (Fig. 1C; p<0.001, F=7.75). Further, the seasonal

patterns in the active microbial community were initially present in the early years of fertilization

(Fig. 1C), however, after a decade of fertilization these patterns were no longer present.

Changes in Dormancy and Diversity

To assess the role of dormancy we calculated the proportion of dormant taxa in each

sample using the 16S rRNA:16S rRNA gene ratio and defining any taxon with a ratio ≤ 1 as

dormant (Jones and Lennon 2010). There are caveats to this approach (Blazewicz et al. 2013), so

to ensure our interpretation was robust, we also increased the ratio required from a ratio of one to

50 (Aanderud et al. 2015; Supplemental Fig. 2). Regardless of the ratio threshold we used to

define dormancy (Supplemental Fig. 2), the results show a consistent pattern that the dormant

taxa in N-enriched sediments accounted for a significantly higher proportion of the bacterial

community than in reference sediments (Fig. 2A; ANOVA, p<0.001, F=41.94). Dormant taxa

remained ~45% of the community in reference sediments (Fig. 2A) but in N enriched sediments

the proportion of dormant taxa increased over time (Fig 2A) to ~90% of the community after a

decade of N-enrichment.

The increase in dormancy corresponded to a decrease in potentially active bacterial

diversity. Reference marsh sediments showed no temporal trends in Shannon Diversity and

displayed significantly higher active diversity than N-enriched sediments (Fig. 2B&C; F=12.49,

p<0.01). Furthermore, bacterial communities in N-enriched S. alterniflora displayed a significant

loss in active diversity over time (p<0.001, F=12.43; Fig. 2B). By contrast, although active

diversity in S. patens appears to decrease over time (Fig. 2C), the variation in diversity was

higher and the decrease was not significant. The greater response observed in the low marsh S.

27

alterniflora habitat is likely results from the fact that it is inundated for a longer period of time

than S. patens and therefore receives a greater N supply (Johnson et al. 2016).

Taxonomic Compositional Changes

Due to the observed sharp decline in active bacterial diversity, we assessed which taxa

responded to N-enrichment by examining the ratio of 16S rRNA to the 16S rRNA gene +1 (Fig.

3). Implicit in this calculation is the assumption that if a sequence is present in the 16S rRNA of

a sample it must also be present at least one time on the 16S rRNA gene, but was not detected

due to incomplete sequencing. Three of the five most abundant bacterial orders we identified

(Desulfobacterales, unclassified Cyanobacteria, and Oscillatoriales) had considerably higher

ratios in N-enriched compared to reference marshes. Despite having a few taxa with very high

16S rRNA:16S rRNA gene ratios, N-enriched marshes contained fewer abundant active taxa

(n=23) than nearby reference marshes (n=41). Furthermore, N-enriched marshes contained a

long tail of taxa that had an activity ratio considerably lower than one, indicating that these taxa

were largely dormant in N-enriched marshes but remained active in reference marshes. Our

results suggest, given the large portion of inactive taxa with very low ratios of 16S rRNA: 16S

rRNA gene in N-enriched marshes, that many of these taxa are highly abundant members of the

total community but are inactive due to N-enrichment enhancing the competitive ability of some

taxa at the expense of many others.

Among the most abundant active taxa, defined as those that were present at least 100

times, seven bacterial orders differed significantly (Kruskal-Wallis test, Bonferoni corrected

p<0.001) between N-enriched and reference marshes (Fig. 4, Supplemental Table 1). Retrieved

sequences from N-enriched sediments contained large numbers of anaerobic sulfate reducers

from the Deltaproteobacterial order Desulfobacterales as well as numerous autotrophic

28

Cyanobacteria from the order Oscillatoriales. Whole genome analysis of Desulfobacterium

autotrophicum indicates the order Desulfobacterales may be metabolically diverse due to large

genomes and numerous transposable elements relative to other sulfate reducing bacteria

(Strittmatter et al. 2009), which may allow faster response to environmental perturbations,

including nutrient enrichment. In reference marshes anoxygenic, or potentially anoxygenic

phototrophs (Chromatiales, Chlorobi, Rhizobiales, Rhodocyclales) were significantly more

abundant than in fertilized marshes. By contrast, cyanobacteria dominated the phototrophs in

fertilized marshes (Fig. 3), with unclassified Cyanobacteria and the cyanobacterial order

Oscillatoriales, responding favorably to nutrient enrichment. Oscillatoriales are common bloom-

forming autotrophs that display broad genetic, phenotypic, and habitat diversity, which may

explain their ability to respond to higher N loads despite many members possessing the capacity

to fix N (Marquardt et al. 2007). While N-fixation has been reported in salt marshes (Valiela and

Teal 1979), which are characterized by high concentrations of particle-bound and pore water

ammonium, enhanced N-enrichment to surface sediments may allow Oscillatoria in fertilized

marshes to forego N-fixation and increase their activity relative to Oscillatoria in reference

marshes.

Changes in Ecosystem Metabolism

To determine whether there were any effects on ecosystem function resulting from the

proliferation of Desulfobacterales, unclassified Cyanobacteria, and Oscillatoriales, we measured

whole ecosystem metabolism using dissolved oxygen concentrations in the water column of one

pair of reference and N-enriched marsh tidal creeks (Fig. 5). During high tide, when sediments in

the low marsh habitat were submerged, the N-enriched creek demonstrated both enhanced

autotrophy during the day and enhanced heterotrophy at night compared to the reference creek

29

(Fig. 5). Previous work also suggested that when salt marsh bacterial activity was enhanced by

N-enrichment, it was in response to increased phototrophic productivity (Bowen et al. 2009).

Taken together these data suggest a tight coupling whereby Cyanobacteria in marsh sediments

consume excess nitrate and release labile carbon compounds that stimulate the activity of

Desulfobacterales. Thus, there may be alteration of the carbon cycle in nutrient enriched marshes

due to enhancement of both bacterial photosynthesis and respiration and the role these changes

play in maintaining marsh geomorphology is a critical concern (Deegan et al. 2012).

Discussion

Microbial dormancy in these nutrient enriched salt marsh sediments is higher than

reported values for soil systems (Lennon and Jones 2011), lake systems (Jones and Lennon

2010), and coastal waters (Campbell et al. 2011) using comparable methodology. These results

confirm the assertion that dynamic abiotic conditions may promote dormancy, but also highlight

the important role that dormancy plays in the capacity of microbial communities to withstand

environmental perturbations. Dormancy can be a response to nutrient limitation and there is

evidence that alleviation of nutrient limitation can reduce rates of dormancy in lake systems

(Jones and Lennon 2010). Salt marshes are N limited systems (Nixon et al. 1986) yet despite N-

enrichment, our results indicate that salt marsh microbial communities increase the proportion of

dormant taxa in response to N additions, which may suggest a differential response of water and

soil/sediment communities. We hypothesize that the high rates of dormancy we observed in the

nutrient enriched marshes are a result of enhanced competition whereby bacteria better adapted

to higher N-loads outcompete other bacteria for nutrients and energy sources, leading to an

increase in dormancy among other taxa present in the community. In a sense, nutrient addition to

30

marsh sediments may induce blooms of Desulfobacterales and Oscillatoriales, much like

estuaries often have algal blooms in response to excess N (Anderson et al. 2002). Other nutrient

enrichment induced changes to the marsh, including changes in the geomorphology (Deegan et

al. 2012) of the marsh creeks and changes in faunal abundances (Johnson and Short 2013) could

also influence the proportion of active taxa in marsh sediments. Regardless, our data suggest that

despite the dramatic effect of nutrient enrichment on the active microbial community, the total

bacterial community remained unaffected, allowing marshes to maintain a reservoir of genetic

diversity that provides a buffer such that the highly diverse bacterial community can respond to

future environmental change.

In contrast to terrestrial grasslands where edaphic conditions are comparably more

stable, salt marshes are highly dynamic systems, with environmental conditions changing on

rapid time scales (minutes to hours). Our results demonstrate a surprising stability of the total

microbial community to N-enrichment (Fig. 1B), however, a recent analysis of terrestrial

grasslands systems (Leff et al. 2015) showed consistent shifts in the total microbial community in

response to nutrient enrichment from grasslands across the globe. Our results suggest differential

control mechanisms on the structure of microbial communities between soils and water-saturated

sediments. This difference may be related to community adaptation to intrinsic environmental

variability such that sediment microbial communities respond by altering the dormant versus

active community, thereby allowing them to respond to rapid changes in their environment,

while soil microbial communities alter the total community structure.

Nutrient enriched salt marshes demonstrate a marked loss in active microbial diversity

(Fig. 2b,c), suggesting a net shift in the metabolic functioning of microbial populations, despite

no net change in total community composition. Given the stark decline in active bacterial

31

diversity we expect to see a decrease in functional diversity as well as a truncation of ecosystem

function, as was previously shown in pelagic communities (Gilbert et al. 2010, McCarren et al.

2010). While relationships between biodiversity and ecosystem function have been shown in a

variety of organisms such as plants (Hooper et al. 2012), studies examining the relationship

between diversity and function in microbial communities have been equivocal. Reed and

Martiny (2013) demonstrated a net shift in community composition correlated with enhanced

respiration, however, this correlation was not observed in a similar study (Waldrop and Firestone

2006). Given the decrease in diversity of the active microbial community and the ecosystem-

scale enhancement in both autotrophy and heterotrophy in response to N-enrichment, we

conclude that N-enrichment to salt marshes likely alters metabolic function and thus, ecosystem

function of these critically important coastal habitats.

Increased N additions to ecosystems worldwide have fundamentally altered microbial

communities and the biogeochemical functions they mediate (Galloway et al. 2008, Hooper et al.

2012). In coastal salt marshes, N-enrichment promotes an active bacterial community that is

spatiotemporally homogenous compared to reference marshes. A decade of nutrient enrichment

in marsh sediments resulted in the highest reported proportion of dormant bacterial taxa and

induced diversity loss in the active portion of the bacterial community, despite no apparent

change in the total bacterial community. We suggest that the highly dynamic nature of salt marsh

sediments promotes bacterial dormancy as an ecological strategy for the maintenance of

microbial diversity. Our results suggest a unique feature of salt marsh bacterial communities in

that the active diversity of the community can be dramatically diminished, while still maintaining

a genetic reservoir of traits that can respond to future environmental changes. This work

underscores the importance of examining both total and active bacterial communities in future

32

studies as their responses to global change drivers such as nutrient enrichment are likely to

diverge.

Acknowledgments

would also like to thank researchers of the TIDE Project (NSF OCE0924287, OCE0923689,

DEB0213767, DEB1354494, and OCE 1353140) for maintaining the study site and the N-

enrichment experiment. The TIDE project provided the YSI sondes, Jimmy Nelson did pre- and

post-calibrations on the sondes, and Will Kearney and Sergio Fagherazzi provided the creek

geometry transects for the water budget. We would also like to thank various members of the

Bowen lab for their help in field collections. Thom Thera and Illumina tech support provided

tech support for sequencing. Bioinformatics could not have been done without the use of the

supercomputing facilities managed by Jeff Dusenberry and the Research Computing Department

at the University of Massachusetts Boston. The Plum Island LTER (NSF LTER OCE 0423565

and NSF OCE 1058747) provided critical intellectual and study site support. Finally, we would

like to thank Anne Giblin, David Johnson, James Nelson, Sarah Feinman, Andrew Babbin, Jay

Lennon, and an anonymous reviewer for helpful comments and discussions on the manuscript.

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Figures and Tables

Figure 1. Principal coordinates analysis of weighted UniFrac similarity for (A) the total and

active communities, (B) the total community as assessed by the 16S rRNA gene, and (C) the

active community assessed by 16S rRNA. These data derive from samples collected within one

meter of a permanent transect line that bisected two marsh habitats in four marsh creeks and

were collected monthly from May to October in 2005, 2006, 2013, and 2014 (N=192).

Fert.=fertilized, ref=reference.

38

Figure 2. Percent of dormant taxa in N-enriched (red) and reference (blue) marshes (A) and

Shannon Diversity of the potentially active community, as assed by 16S rRNA for tall Spartina

alterniflora (TSA; B), and Spartina patens (SP; C). Boxes represent 25-75% quartiles, and the

solid black line is the median value.

39

Figure 3. Order-level taxonomic composition of fertilized (red) and reference (blue) sediments

assessed by the log10 16S rRNA:16S rRNA gene ratio for the microbial orders comprising 90%

of all sequences. Orders colored blue are significantly more abundant in reference sediments

(Kruskal-Wallis test, Bonferroni corrected p<0.001) while red orders are significantly more

abundant in N-enriched sediments. Black line is a 16S rRNA:16S rRNA gene ratio of 1. Taxa

with a ratio higher than one are considered active and taxa with ratios lower than one are

considered dormant. Points are the mean and error bars are standard error of the mean.

40

41

Figure 4. Significantly different operational taxonomic units (OTUs) in the potentially active

communities between fertilized (red) and reference (blue) sediments present at least 100 times

assed by a Kruskal-Wallis test (Bonferoni corrected p<0.0001). The class of each OTU is

indicated by the colored circle. More taxonomic information about each OTU can be found in

Supplemental Table 1. Points are the mean and error bars are standard error of the mean.

42

Figure 5. High-tide only dissolved oxygen based net ecosystem metabolism (NEM) in the

fertilized (red) and reference (blue) tidal creeks (A) and the difference (black) between the

fertilized and reference creeks (B) plotted against time of day for each of the 12 high tides during

a six-day period in August 2012. Each point represents the mean NEM or difference rates

evaluated every 10 minutes over the hour of high tide, with one standard error bar for the means.

Note the mean difference rate over each hour of high tide is more tightly constrained than the

mean for either creek. Shading denotes the period between dusk and dawn, and black arrows

identify the time range of three tides that flood the high marsh platform and may have systematic

positive biases in NEM, particularly in the reference creek (see Methods).

43

Chapter 1: Supplementary Information

Supplementary Fig. 1. Box plots of average Shannon Diversity in the total community as assed

by the 16S rRNA gene. Boxes are 25th and 75th quantiles with the medians in bold of samples

collected each year from reference marshes (blue) and fertilized marshes (red). Although the

total diversity of bacteria in S. patens is lower than that of S. alterniflora, there is no difference

in diversity as a result of fertilization, in sharp contrast with what was observed in the diversity

of the active portion of the community.

44

Supplementary Fig. 2. Percent dormant taxa assessed by the ratio of 16S rRNA to the 16S

rRNA gene. Data were generated by increasing the ratio required for a taxon to be considered

active from greater than 1 to 50. At all ratios, fertilized creeks had significantly higher dormant

taxa than reference sediments, as assessed by a one-way ANOVA.

45

Supplementary Fig. 3 Weighted UniFrac similarity comparing community similarity for 16S

rRNA (A) and the 16S rRNA gene (B) for different extraction methods and sample preservation

techniques.

46

Supplementary Table 1- taxonomic information of the 32 significantly different (between

fertilized and reference samples) OTUs present at least 100 times from figure 3 as determined by

BLASTn. Bonferroni corrected p-values are significant at <0.001 as determined by a Kruskal-

Wallis test.

Phylum Class Order Family Bonferroni P

OTU1 Proteobacteria Deltaproteobacteria Desulfobacterales NA 1.25E-14

OTU2 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 6.51E-10

OTU3 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 1.15E-02

OTU4 Cyanobacteria Oscillatoriophycideae Oscillatoriales Phormidiaceae 1.68E-15

OTU5 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae 8.90E-13

OTU6 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae 4.87E-05

OTU7 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 7.80E-11

OTU8 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 3.34E-03

OTU9 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae 1.02E-06

OTU10 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae 1.50E-02

OTU11 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae 2.23E-09

OTU12 Cyanobacteria Oscillatoriophycideae Oscillatoriales Phormidiaceae 2.47E-13

OTU13 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 2.80E-14

OTU14 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae 4.26E-09

OTU15 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 4.14E-10

OTU16 Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae 7.03E-11

OTU17 Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae 1.67E-09

OTU18 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 1.28E-02

OTU19 Cyanobacteria Oscillatoriophycideae Oscillatoriales NA 7.71E-14

OTU20 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 2.75E-06

OTU21 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 5.19E-09

OTU22 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae 1.21E-08

47

OTU23 Proteobacteria Alphaproteobacteria Rhodospirillales Rhodospirillaceae 2.11E-05

OTU24 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 2.40E-06

OTU25 Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae 1.73E-15

OTU26 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae 8.47E-06

OTU27 Planctomycetes Planctomycetia Pirellulales Pirellulaceae 7.47E-19

OTU28 Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae 8.93E-17

OTU29 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 3.46E-12

OTU30 Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae 7.18E-14

OTU31 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 7.79E-03

OTU32 Proteobacteria Deltaproteobacteria Desulfarculales Desulfarculaceae 1.26E-02

48

Chapter 2: Nutrient enrichment alters salt marsh fungal communities and increases

decomposition of salt marsh sediments

Abstract:

Enrichment of ecosystems with excess nutrients, including nitrogen, is occurring at an alarming

rate and has fundamentally altered ecosystems worldwide. Salt marshes, which lie at the land/sea

interface, are highly effective at removing anthropogenic nutrients through the action of

macrophytes and through microbial processes in coastal sediments. The response of salt marsh

bacteria to excess nitrogen has been documented, however, the role of fungi and their response to

excess nitrogen is poorly understood. Here, we document the response of salt marsh fungal

communities to long-term excess nitrate in four distinct marsh habitats within the marsh complex

at the Plum Island Ecosystems Long-Term Ecological Research site in northeastern

Massachusetts, USA. We show that salt marsh fungal communities varied as a function of salt

marsh habitat, with both fungal abundance and diversity increasing with carbon quantity.

Nutrient enrichment significantly altered fungal communities in all habitats through an increase

in fungal abundance and, notably, the proliferation of taxa that have the capacity for

denitrification. Nutrient enrichment also appears to promote decomposition of marsh peat in low

marsh surface sediments, which suggests that fungi, in addition to bacteria, play an important

role in anaerobic decomposition of marsh organic matter, with consequences for long-term

carbon storage.

Introduction:

Enrichment of coastal waters by excess nutrients, in particular nitrogen (N) in the form of

nitrate (NO3-), is occurring at an accelerated pace and is altering global biogeochemical cycles

(Galloway et al., 2008). The addition of excess N to coastal systems, which are typically N

limited (Nixon et al., 1986), can have numerous deleterious effects including increased harmful

algal blooms (Paerl, 1997), decreased species diversity (Elahi et al., 2015), altered

biogeochemical cycling (Koop-Jakobsen & Giblin, 2010), and expanded areas of hypoxia or

anoxia (Rabalais et al., 2002). Salt marshes, which are coastal grassland ecosystems, are highly

49

effective at removing excess nitrogen from coastal areas through trapping nitrogenous

compounds in sediments (Brin et al., 2010), macrophyte uptake (Valiela et al., 1973), and

through denitrification, the microbially-mediated conversion of NO3- to a gaseous end product

(Valiela et al., 1973, Hopkinson & Giblin 2008, Zumft, 1997). Relative to burial and uptake by

macrophytes, denitrification can account for a substantial portion of fixed N loss from marsh

sediments (Valiela and Teal, 1979) and, at times, can account for the majority of nitrogen loss

from marsh ecosystems (Howes et al., 1996).

In addition to removing anthropogenic nutrients, salt marshes are also highly effective at

storing carbon (McLeod et al. 2011). The sequestration of carbon by salt marshes and other

coastal systems, termed “Blue Carbon”, is orders of magnitude greater per hectare than

sequestration in terrestrial systems due to their high productivity, ability to trap organic material,

and anoxic and sulfidic sediments that impede decomposition (Mcleod et al., 2011). The ability

of salt marshes to store carbon for extended periods of time is a balance between the amount of

carbon they fix and the amount of carbon that is removed through decomposition and export.

Therefore, factors such as water supply, nutrient availability, and the redox status of the

sediments, all influence whether salt marshes are net carbon sources or sinks (Kirwan and Mudd,

2012). Multiple human drivers, including nutrient enrichment, can alter the forces that determine

the strength of marshes as a carbon sink. Increased supply of anthropogenic nutrients can

stimulate primary productivity of nutrient limited macrophytes (Fox et al., 2012), decrease

belowground macrophyte biomass (Deegan et al., 2012), stimulate decomposition of existing

organic matter (Deegan. et al., 2012), alter biogeochemical cycling (Hammersley & Howes,

2005; Koop-Jakobsen & Giblin 2010; Peng et al., 2016), and alter the structure of the active

microbial community (Kearns et al., 2016). Thus, understanding the ecosystem-wide response to

excess nutrients is important for predicting how the carbon carrying capacity of marshes will

change by future nutrient enrichment.

The activity of salt marsh bacteria and the controls over their decomposition processes is

well understood (Hammersley & Howes, 2005; Koop-Jakobsen & Giblin, 2010; Bowen et al.

2011; Peng et al., 2016; Kearns et al. 2016), however, we lack a solid understanding of salt

marsh fungi and the factors that influence their composition and activity with respect to carbon

cycling. Fungi are among the most diverse groups of eukaryotic organisms on the planet with a

50

projected five million plus species globally (O'Brien et al., 2005; Blackwell, 2011; Taylor et al.,

2014). Fungal taxa in terrestrial systems are well characterized and their roles as decomposers of

organic matter, plant and animal pathogens and parasites, and symbionts are well documented

(Kirk et al., 2008). Of the described ~100,000 fungal taxa (Kirk et al., 2008), however, less than

one percent have been characterized from marine environments (Kis-Papo, 2005; Richards et al.,

2012). Work on marine fungi has focused on deep ocean sediments (Nagano et al., 2010),

hydrothermal vents (Burgaud et al., 2015), oxygen minimum zones (Stoeck et al., 2006), and

planktonic marine fungi (Wang et al., 2014). In the coastal zone fungi, primarily saprotrophs,

appear to be structured by carbon supply (Dini-Andreote et al., 2016), but changes in salinity,

nutrient availability, and temperature are also important determinants of marine fungal

community structure (Taylor & Cinliffe, 2016). Since fungi are critical to carbon cycling and

because they appear to be abundant in coastal systems (Dini-Andreote et al., 2016; Taylor &

Cunliffe, 2016), it is imperative that we understand how coastal marine fungi respond to global

change drivers, and ultimately, how that translates into changes in the carbon storage capacity of

coastal habitats.

Here, we studied two salt marsh creeks experimentally enriched with nutrients (Deegan et

al., 2007) and two near-by reference creeks that received no excess nutrient supply. In each

marsh we collected samples from four habitats within each site to understand the distribution of

fungi throughout the marsh and we also examined the effect of nutrient loading on fungal

community composition and diversity by comparing results from nutrient enriched and reference

marshes. We hypothesized that salt marsh fungal community structure and abundance would

vary as a function of habitat due to differences in abiotic conditions. Further, we hypothesized

that, like bacterial community composition (Bowen et al., 2009, 2011; Kearns et al., 2016), salt

marsh fungal community composition would be resistant to perturbation by increased nutrient

supply. Our results demonstrate marsh fungal communities do vary as a function of habitat,

however, unlike what we observed with bacterial communities, nutrient enrichment played an

important role in structuring fungal community composition, particularly in habitats receiving

the highest N exposure. Further, excess nitrogen stimulated the decomposition of organic matter

and lowered organic matter quality, with important consequences for salt marsh carbon storage.

51

Methods

Study site description and Sample Collection

Our experiment was carried out at the Plum Island Ecosystems Long-term Ecological

Research Site (PIE-LTER) located in Northeastern Massachusetts, USA (42.759 N, 70.891 W).

Within the LTER, a long-term nutrient enrichment experiment called the TIDE project has been

ongoing for 13 years (Deegan et al., 2007). One marsh creek has received nutrient enrichment in

the form of NO3- dissolved in creek water on the rising tide continuously throughout the growing

season (May-September) since 2004. A second marsh creek received nutrients in 2005 and then

again from 2009 to the present. Two additional creeks, which have similar biophysical properties

as the nutrient enriched creeks, receive no excess nutrients and serve as reference creeks.

Nutrient enriched creeks receive approximately 15 times more N than reference creeks (Deegan

et al., 2007).

We collected samples in May, July, and September of 2013 and 2014 at low tide within

one meter of permanent transects at all four marsh creeks. We collected sediment from four

distinct marsh habitats (SI Fig. 1): mudflat (MF), tall Spartina alterniflora (TSA), S. patens (SP),

and short S. alterniflora (SSA). The four marsh habitats sampled represent a wide range of

abiotic conditions due to their different elevations above mean sea level. The mudflat at tall S.

alterniflora habitats, referred to as the low marsh, are lower in elevation and have longer

inundation times. The S. patens and short S. alterniflora habitats, referred to as the high marsh,

are higher in elevation and are only inundated on the highest high tides. As a result of these

differences in elevation, these habitats have distinct differences in salinity, nutrient availability,

carbon content, pH, redox conditions, soil temperature, soil water content, and plant above and

belowground biomass. At each sampling time and location we collected surface sediments (top

2-cm) in triplicate with sterile cut-off 30-cc syringes, homogenized the sediments in a sterile 50-

mL centrifuge tube, and aliquoted them into cryovials for storage on dry ice. The remaining

sediment in the 50-mL centrifuge tube was stored on dry ice for sediment organic matter

analyses. All samples for molecular analyses were kept at -80°C until extraction and all nutrient

analysis samples were kept at -20°C until processing.

52

Nucleic acid extraction, PCR, and sequencing

DNA was extracted from approximately 0.25 g of sediment using the MoBio PowerSoil®

Total DNA Isolation Kit (Carlsbad, CA, USA) following the manufacturer’s instructions. DNA

extracts were verified with gel electrophoresis. Fungal communities were amplified in triplicate

with primers ITS1F and ITS2 (Walters et al., 2016) targeting the first fungal internal transcribed

spacer region (ITS). Primer constructs were similar to those described previously (Kozich et al.,

2013) and contained overhang sequences to allow for downstream addition of dual indexes and

Illumina adapters. PCR was performed in triplicate 25 μL reactions with 12.5 μL Phusion Hot

Start Master Mix (New England Biolabs, Ipswich, MA, USA), 0.25 μL of each primer (20 pmol

μL-1), 1-4 ng of DNA, and 11 μL of PCR grade water. Samples were amplified with the

following cycling conditions: 94°C for 3m followed by 35 cycles of 94°C for 30s, 50°C for 60s,

72°C for 90s, and a final extension at 72°C for 10m. PCR reactions were checked with gel

electrophoresis and samples were purified with the Qiagen QiaQuick Gel Purification Kit

(Valencia, CA, USA). A second 8-cycle PCR was performed with the Illumina Nextera XT2 kit

following the manufacturer’s instructions to ligate dual indicies and Illumina adaptors for each

sample. Following the second PCR, samples were purified with a Qiagen PCR purification kit,

quantified with a Qubit fluorometer (Thermofisher, Waltham, MA, USA), and pooled in equal

molar concentrations for paired-end sequencing on an Illumina MiSeq. All sequencing was

performed at the University of Massachusetts Boston using V2 chemistry.

Fungal abundance

To determine fungal abundance, we performed quantitative PCR (qPCR) using the primer

pair ITS1F and ITS2 (Walters et al., 2016). DNA and standards prepared from purified ITS PCR

product were quantified with a Qubit fluorometer and serially diluted (Thermofisher) and all

samples were normalized to 3 ng µL-1. Each sample was amplified in triplicate, along with

standards and internal controls on a Stratagene MX-3005p quantitative thermocycler (Stratagene,

La Jolla, CA, USA). ITS genes were amplified in triplicate 25 µL reactions using 0.25 µL of

each primer, 12.5 µL of Qiagen QuantiTect SYBR Green PCR Master Mix, 1 µL of DNA

template, and 11 µL of PCR grade water using conditions described above. Proper product

53

formation was verified with melt curves and gel electrophoresis. All standard curves possessed a

high degree of linearity (R2 > 0.99) and qPCR efficiency ranged from 95-101%.

Elemental analyses and Fourier Transform Infrared Spectroscopy

Elemental composition (percent nitrogen and carbon) was measured on sediments dried

at 50°C for three days on a Perkin Elmer 2400 Series Elemental Analyzer following standard

procedures. We also used Fourier Transform Infrared Spectroscopy (FTIR) to characterize

organic matter quality and to indicate the difference in extent of decomposition as a result of

nutrient enrichment. This technique provides rapid, detailed information about the relative

abundance of organic matter functional groups. To prepare samples for FTIR analysis, we oven-

dried (50ºC) and finely ground the sediments using a mortar and pestle. We analyzed the samples

in diffuse reflectance mode on a Bruker Vertex 70 FTIR spectrometer (Bruker Optics Inc.,

Billerica, MA) outfitted with a Pike Auto Diffuser Accessory (Pike Technologies, Madison, WI)

and obtained the data as pseudo-absorbance (log[1/Reflectance]). Since we were interested in the

MidIR range, we collected at a 2 cm-1 resolution, with 64 co-added scans per spectrum, from

4000 to 400 cm-1 using potassium bromide (KBr) for background correction (Essington, 2004).

We imported the resulting spectra using Unscrambler X (Camo Software (version 10.1),

Woodbridge, NJ) and corrected the data using a calculated two-point linear tangential baseline.

To reduce the dimensionality of the data and compare spectra across site and habitat we

visualized the relationship among carbon species with a correspondence analysis using the vegan

package (Oksanen et al., 2012) in R (R Core Team, 2012). To better understand the difference in

carbon due to habitat and nutrient enrichment, we calculated the ratio of hydroxylamines

(wavelength 3402 cm-1, Table 1) to multiple wavelengths of aromatic carbon (wavelengths

1650/920/840 cm-1; Table 1; Parikh et al., 2014). Higher hydroxylamine:aromatic carbon ratios

indicate more labile carbon. To estimate the extent of decomposition in each habitat and as a

result of nutrient enrichment, we calculated Index I (Veum et al., 2014), which takes into account

seven distinct wavelengths (Table 1). The numerator corresponds to wavelengths associated with

aromatic carbon and the denominator corresponds to wavelengths associated with aliphatic

carbon and each number represents the spectra values measured at the wavelength identified in

the equation:

54

����� � = 1650 + 920 + 840

2924 + 2850 + 1470 + 1405

Index I assesses decomposition because as organic matter is decomposed, it accumulates less

labile aromatic carbon through decomposition of labile aliphatic carbon. Thus, as decomposition

increases, the value for aromatic carbon goes up and the value for aliphatic carbon goes down,

thus increasing the value of Index I (Chefetz et al., 1998; Hsu & Lo, 1999). Increases in Index I

between reference and nutrient enriched marshes would indicate greater decomposition of

organic matter. We assessed significant differences in the ratio of hydroxylamine to aromatic

carbon and in Index I with a repeated measures ANOVA and multiple comparisons were

corrected with a Tukey HSD test.

Sequence processing and analysis

A total of 1.37 million paired-end reads were joined in QIIME (version 1.9.1; Caporaso

et al., 2010) with fastq-join (Aronsety, 2011) using default parameters. Sequences were then

demultiplexed and quality filtered following Bokulich et al., (2013). Reads were checked for

chimeras using USEARCH (Edgar, 2010) in reference and de novo modes and all chimeric

sequences were discarded. To improve ITS analyses, we removed ribosomal fragments from our

sequences using itsX (Bengtsson-Palme et al., 2013). Following quality filtering, 1.2 million

reads remained. Operational taxonomic units (OTUs) were picked against the UNITE database

(version 7; Abarenkov et al., 2010) at 97% sequence identity (Walters et al., 2016) using

UCLUST (Edgar, 2010). OTUs appearing only once and OTUs matching protists were discarded

from the dataset.

We calculated beta diversity on normalized OTU tables (8,000 sequences per sample)

using Bray-Curtis similarity and results were visualized with a principal coordinates analysis

(PCoA). Significant differences in community composition among habitats and between nutrient

enrichment regimes were tested with a non-parametric permutational MANOVA (Anderson,

2002) with 10,000 permutations. To further assess the effect of nutrient enrichment on different

habitats, we compared Bray-Curtis similarity values between reference and nutrient enriched

55

marsh creeks and assessed significance with an ANOVA in R (R Core Team 2012), correcting

for multiple comparisons with a Tukey HSD test. Alpha diversity was calculated on a rarefied

OTU table (10,000 restarts, steps of 100) normalized to the lowest sequencing depth. We

calculated Shannon Diversity and assessed significant differences with an ANOVA in R. To

assess the role of carbon content on fungal community structure, diversity, and abundance we

used an analysis of covariance (ANCOVA) including habitat as random effect and fertilization as

a fixed effect. Using an ANOVA we checked that the covariate (habitat) and the resulting slope

was linear and did not significantly differ from the independent variable’s slope. Further, the

covariate was independent of fertilization. To assess which OTUs were responding to nitrogen

additions we calculated significant differences in OTU frequencies between reference and

nutrient enriched marshes with a Kruskal-Wallis test and defined taxa as significantly different

with a Benjamini-Hochberg corrected p-value <0.01. Finally, we compared our fungal dataset to

previously described fungal denitrifiers from Meada et al., (2015) to determine the extent of

fungal denitrifiers in our dataset.

Results

Community composition, diversity, and abundance

A principal coordinates analysis (Fig. 1A) of Bray-Curtis similarity revealed a significant

effect of both habitat (PERMANOVA, p<0.001, F(3,92)=29.43) and nutrient enrichment (p<0.001,

F(1,94)=31.42) on fungal community composition. Fungal communities associated with all four

habitats experienced significant shifts in community composition as a result of nutrient

enrichment, however, habitats in the low marsh that receive the highest nitrogen exposure

(mudflat and tall S. alternifora) experienced the greatest effect, as indicated by the low similarity

ratio between nutrient enriched and reference marshes (Fig. 1B). Diversity of fungi, as measured

by the Shannon Diversity Index, ranged from two in in reference mudflat habitats to upwards of

four in high marsh habitats (Fig. 2A). Shannon Diversity in nutrient enriched creeks was

significantly higher than in reference creeks in all four habitats (ANOVA, p<0.001,

F(1,94)=29.12). Quantification of fungal abundance using qPCR displayed a similar pattern to

Shannon Diversity (Fig. 2B). The abundance of fungi in reference creeks ranged from ~105 ITS

copies g-1 sediment in the mudflat to upwards of 108 ITS copies g-1 sediment in the high marsh

56

sediments (SP and SSA, Fig. 2B). Nutrient enrichment, however, significantly increased fungal

abundance (p<0.001, F(1,94)=34.56) in all habitats except for the mudflat, which was also very

close to the significance threshold (p=0.07, F(1,23)=19.1).

Taxonomic composition

Salt marsh sediments were dominated by fungal taxa from the phylum Ascomycota

(~84%) with a small presence from the phyla Basidiomycota (~5%) and Chytridiomycota (~2%;

Fig. 3). Additionally, 8% of fungal taxa could not be assigned to a known fungal phylum. All

habitats were dominated by a consistent presence of several families including

Teratospheariaceae, Mycosphaerellaceae, Physalacriaceae, Lasiosphaeriaceae, and from the

orders Capnodiales and Rhytismatales. Taxonomic composition in all habitats shifted in the

nutrient enriched marshes (Fig. 3), consistent with the beta diversity analyses (Fig. 1).

Taxonomic response to NO3- addition and enhancement of fungal denitrifiers

We calculated significant differences in abundance of OTUs between fertilized and

reference marshes using a Kruskal-Wallis test (Fig. 4). Our analysis revealed 23 OTUs with

significantly different abundances between nutrient enriched and reference marshes, with the

majority (n=16) more abundant in nutrient enriched marshes. Many taxa whose abundance

increased with nutrient enrichment, such as taxa from the order Hypocreales and class

Sordariomycetes, were in low abundance in reference creeks. Further, many of the taxa that

increased in abundance in the nutrient enriched creeks were closely related to recently identified

fungal denitrifiers (Meada et al., 2015). We screened our dataset against ITS data from cultured

fungi that are known to denitrify (Fig. 5; Meada et al. 2015). The percent of fungal denitrifiers

was very nearly zero in most reference marsh habitats, but was significantly higher (ANOVA,

p<0.001, F(3,43)=24.01) in reference TSA sediments. Nutrient enrichment significantly increased

relative abundance of fungal denitrifiers in all habitats (p<0.001, F(1,94)=29.43).

Environmental correlates of composition, abundance, and diversity

We used an analysis of covariance (ANCOVA) to examine how fungal composition,

diversity, and abundance varied as a result of carbon content in the sediment, a factor that

essentially scales with habitat (Fig. 6). ANCOVA of the first principal coordinate values from

the PCoA (Fig. 1A) as a function of the percent carbon in the sediment revealed a strong

correlation (R2=0.88) in reference marshes. Axis 1 values in nutrient enriched marshes, however,

were less strongly correlated with carbon (R2=0.56; Fig. 6A). Regressions in both fertilized

57

(F=19.53, p=0.01) and reference (F=96.43, p<0.01) marshes were significant, however, the

slopes of the lines were significantly different (F=9.23, p=0.003). ANCOVA of percent carbon

against Shannon Diversity (Fig. 6B) in reference (R2=0.67) and fertilized (R2=0.09) marshes

indicated a tight and significant (p<0.01) correlation between carbon and fungal diversity in

reference (p<0.01) and fertilized (p=0.02) marshes. Further, the slopes were significantly

different between fertilized and reference marshes (F=41.43, p<0.001). Fungal abundance (Fig.

6C) significantly (p<0.01) correlated (R2=0.90) with carbon in reference marshes, however, this

correlation while still significant (p=0.02) was not as strong in fertilized marshes (R2=0.46).

Additionally, regressions between fertilized and references marshes were significantly different

(F=31.43, p<0.01)

Fourier Transform Infrared Spectroscopy (FTIR)

Fourier Transform Infrared Spectroscopy (FTIR) was used to assess changes in organic

matter quality. A correspondence analysis derived from the full spectra of data from all four

habitats (SI Fig. 2) revealed a large separation (Fig. 7A; PERMANOVA, F(1,47)=24.46, p<0.01)

between nutrient enriched and reference samples in the low marsh (mudflat and tall S.

alterniflora), but no such effect was observed for the high marsh S. patens and short S.

alterniflora sediments. Index I (Fig. 7B), a metric signifying the extent of carbon decomposition,

indicated that nutrient enriched mudflat (F(2,22)=29.01, p<0.01) and tall S. alterniflora sediments

(F(2,22)=34.01, p<0.001) displayed significantly more decomposition than reference sediments.

The ratio of hydroxylamines to aromatic compounds (Fig. 7C), a ratio that increases with the

increased lability of carbon, indicated that the lability of carbon was significantly lower in

nutrient enriched low marsh habitats (F(2,46)=20.44, p<0.01), but no effect on carbon lability in

the high marsh.

Discussion

Salt marshes provide critical ecosystem services to coastal communities, including long-

term storage of carbon. The ability of salt marshes to store carbon, however, is subject to

anthropogenic threats including the supply of excess nutrients. Anthropogenic changes to salt

marshes can have profound effects on the microorganisms that ultimately control carbon

decomposition and storage. In this study, we investigated the composition, diversity, and

58

abundance of salt marsh fungal communities and their response to excess nitrogen in four

distinct habitats in a New England salt marsh (SI Fig. 1). Fungal communities in reference

marshes varied across salt marsh habitats (Fig. 1A, Fig. 3), reflecting the differences in abiotic

conditions that result from subtle changes in marsh elevation among habitats. We identified (Fig.

3) several fungal groups, primarily from the orders Capnodiales, Rhytismatales, Sordariales, and

Myriangiales, that were present across all habitats, suggesting a tolerance to a wide range of

abiotic conditions. Further, recent work in coastal sediments (Picard, 2017), marine planktonic

communities (Taylor & Conliffe, 2016), and a European salt marsh (Dini-Andreote et al., 2016)

documented the importance of these groups, suggesting they are key fungal taxa in coastal

ecosystems. Members of these orders are predominantly saprotrophs (Kirk et al., 2008) and are

likely decomposing portions of the large standing stock of living and dead Spartina that

dominate salt marshes (Valiela et al., 1976; Benner et al., 1984; Johnson et al., 2016).

Our study documented a similar, yet distinct response of salt marsh fungi to fertilization

and changes in abiotic conditions than terrestrial fungi. Salt marsh fungal communities were

strongly structured by habitat and the associated abiotic conditions, supporting previous work in

marshes (Dini-Andreote et al., 2016). Soil fungal communities have been shown to be structured

at broad (Leff et al. 2015) and narrow geographical scales by changes in soil acidity, climate,

plant community composition, organic matter content, and nutrient availability. The addition of

excess N, however, significantly altered community composition of marsh fungi; a response

consistently observed in soil fungal communities (Treseder, 2004; Allison et al., 2008; Lin et al.,

2012; Leff et al. 2015; Zhou et al., 2016) and their associated activity (Allison et al., 2008). A

common change in soil fungal communities in response to excess N is the loss of endophytic

fungi due to the decreased need of the plant host in a nutrient rich environment. Of the taxa that

responded to N-enrichment, only one taxa from the genus Lulwona (Fig 4), which responded

negatively to N additions, are known to be endophytes (Torta et al., 2015). The lack of a

wholescale response of endophytic fungi in marsh sediments may be due to the mild response of

marsh plants to N additions (Johnson et al., 2016) relative to terrestrial plants (Wei et al., 2013;

Borer et al., 2014) or perhaps a primer bias limiting our ability to detect endophytic lineages or

our lack of knowledge about marine endophytic fungi. In addition to changes in community

composition and diversity, we observed increased decomposition (Deegan et al., 2012) and

59

accelerated carbon cycling (Kearns et al., 2016) in response to excess N in marsh systems. By

contrast, several studies of soil fungi have demonstrated depressed rates of fungal decomposition

under excess N (van Diepen et al., 2017) likely as a result of a shift away from lignin

decomposition to cellulose decomposition and perhaps interactions with bacteria. The

discrepancy between decomposition under excess N between underscores a differential response

between soil and marine communities with important implications for carbon storage in marine

environments.

Long-term exposure to excess NO3- significantly altered the composition of fungi across

all habitats, resulting in more diverse and abundant fungal communities (Fig. 2). Members of the

class Sordariomycetes, in particular from the order Hypocreales, which were in low abundance

(<1%) in references marshes, responded the most dramatically to excess NO3- (Fig. 4 and 5).

Their proliferation in nutrient enriched marshes suggests these taxa are better able to use excess

NO3-, either through assimilation into biomass or in dissimilatory processes as an electron

acceptor under anaerobic conditions. Several taxa from the class Dothideomycetes, in particular

from the order Capnodiales, significantly decreased their abundance coincident with excess NO3-

. The decrease in their abundance suggests they are likely outcompeted by taxa better adapted to

higher nitrogen loads, as previously shown for active salt marsh bacteria (Kearns et al., 2016)

and macrophytes (Levine et al., 1998; Pennings et al., 2005; Fox et al., 2012). Further, nutrient

addition can also modulate genomic content and size of microorganisms (Leff et al. 2015) which

may provide adaptations under copiotrophic environments such as those observed in nutrient

enriched creeks. While nutrient enrichment can directly affect the activity of fungi, it likely also

indirectly affects fungi through alterations to salt marsh primary producers (cyanobacteria,

benthic microalgae, macrophytes) and changes in soil chemistry (e.g. pH) highlighting the

response of fungal communities in marsh sediments is likely a multifaceted process.

Decomposition of organic matter is typically slow in oxygen-depleted salt marsh

sediments because the dominant anaerobic metabolisms (sulfate reduction and fermentation)

decompose marsh carbon at a slower rate than in oxygenated soils (Kristensen and Ahmed,

1995). The addition of NO3-, a more energetically favorable electron acceptor, stimulates

denitrifying bacteria (Koop Jakobsen & Giblin 2010) and fungi that can decompose less labile

carbon that was inaccessible to sulfate reducing and fermenting bacteria (Fig. 7). Decomposition

60

by denitrifiers likely alters the forms of carbon available (Solomon et al., 2007) and opens more

niche space for additional fungal taxa (Ekschmitt et al, 2008), thereby increasing overall fungal

diversity (Fig. 2). The increase in fungal denitrifiers (Fig. 5) and the alteration of organic matter

was most pronounced in the low marsh (mudflat and tall S. alterniflora), which receives excess

NO3- on every flooding tide. The high marsh habitats receive excess NO3

- only when flooded

(~30% of the time; Johnson et al., 2016) and the response in these habitats was more muted in

than in the low marsh habitats, implying that both the form (oxidized versus reduced nitrogen)

and the duration of exposure play important roles in determining how anthropogenic nutrient

supply alters the organic matter quality. The overall amount of organic matter did not

significantly vary between fertilized and reference marshes within a habitat, suggesting enhanced

decomposition did not alter the overall amount of carbon in surface sediments (top 2cm).

However, the Plum Island marsh accretes sediment at a rate of ~3mm yr-1 (Fagherazzi et al.,

2012) suggesting input of organic matter either through new sediment or by plant deposition

(Fox et al., 2012) may offset any direct effects of enhanced decomposition we can observe in

surface sediments. Further work is needed to characterize sediments deeper than 2 cm, where the

majority of carbon is stored in marshes, and determine how excess nitrate effects deep, long-term

carbon storage.

Our results demonstrate that excess N stimulates fungal denitrifiers (Fig. 4 and 5).

Denitrification is typically the dominant N-removal pathway in coastal systems (Hopkinson &

Giblin, 2008; Brin et al., 2010) and rates of denitrification increase under excess nitrogen

(Hammersely & Howes, 2005; Koop-Jakobsen & Giblin, 2010; Peng et al., 2016).

Denitrification is an important ecosystem service, but incomplete denitrification can result in the

release in nitrous oxide (N2O), a potent greenhouse gas (Ravishankara et al., 2009). Currently, all

known fungal denitrifiers lack the nosZ gene (Meada et al., 2015) which catalyzes the reduction

of N2O to N2 (Zumft, 1997). Stimulating fungal denitrifiers (Fig. 4 and 5) through excess NO3-

supply may therefore result in a concomitant increase in N2O production. Decreased nosZ

abundance in N-enriched marshes (Kearns et al., 2015) and increased N2O emissions following

short-term (Moseman-Valtierra et al., 2011) and long-term exposure (Ji et al., 2015) to excess

nitrogen also suggests that nutrient enriched salt marshes may be a net source of N2O. Further,

results from agricultural soils demonstrate increase N2O emissions from fertilized fields,

61

however, the capacity of soils to be a sink or source of N2O is a balance between abiotic

conditions (i.e. pH) and biotic interactions (i.e. the presence of N2O scavengers; Jones et al.,

2014). Our results indicate that increased nutrient supply alters the carbon storage capacity of

salt marshes through enhanced decomposition through denitrification. If the stimulation of fungal

denitrification also increases N2O flux, this has important implications for the role of nutrient

enrichment in greenhouse gas emissions as well as carbon storage in coastal systems.

Acknowledgements: We would like to thank researchers of the TIDE Project (NSF

OCE0924287, OCE0923689, DEB0213767, DEB1354494, and OCE 1353140) for maintaining

the site and nutrient enrichment experiment. We would also like to thank Sarah Feinman and

members of the Bowen lab for their help in field collections. FTIR analysis was performed at the

Woods Hole Research Center in the lab of Jonathan Sanderman. Additional support was received

from the Plum Island LTER (NSF LTER OCE 0423565 and NSF OCE 1058747). This work was

funded by NSF award DEB 1350491 and DEB 1353140 to JLB and an NSF Research

Experience for Undergraduates Award DBI-1359241 to Dr. Rachel Skvirsky. All sequence data

from this study is available in the Sequence Read Archive under accession number SRP100756.

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Tables and figures

Table 1- Wavelengths and functional assignments used in this study based on Parikh et al.,

(2014). δ = bending vibration, ʋ= stretching vibration, ʋas =asymmetric stretching vibration, and

ʋs = symmetric stretching vibration.

Wavelength, cm-1 Assignment

3402 O-H and v N-H, hydroxyl amine

2924 aliphatic ʋas(C-H)

2850 aliphatic ʋs(C-H)

1650 aromatic ʋ(C = C)

1470 aliphatic δ(C-H)

1405 aliphatic δ(C-H)

920 aromatic δ(C-H)

840 aromatic δ(C-H), less substituted

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Figure 1: Principal coordinates analysis (A) based on Bray-Curtis similarity values for fertilized

(red) and reference marshes (blue). Boxplot of paired Bray-Curtis similarity values (B) for all

four habitats comparing similarity between nutrient enriched and reference samples. Boxes

represent 25-75% quartiles and the solid black line indicates the median value. Letters in (B)

indicate significantly different categories (p<0.05) assessed by a Tukey HSD test.

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Figure 2: Box and whisker plots of fungal Shannon Diversity (A) and log10 ITS abundance (B)

assessed by quantitative PCR for fertilized (red) and reference (blue) habitats. Boxes represent

25-75% quartiles and the solid black line indicates the median value. Letters indicate

significantly different (p<0.05) categories as assessed by a Tukey HSD test.

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Figure 3: Stacked bar plot of the average relative abundance of the top 20 fungal families in the

four marsh habitats across reference and fertilized creeks. Data shown comprises 90% of all

sequences and the category ‘other’ contains all remaining taxa. k= kingdom, c=class, o=order,

f=family.

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Figure 4: Log10 abundance of significantly different OTUs (Kruskal-Wallis test, Benjamini-

Hochberg corrected p< 0.05, abundance >50) between fertilized and reference sediments. OTUs

are identified to the highest possible taxonomic resolution. k=kingdom, p=phylum, o=order, and

f=family.

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Figure 5: Box and whisker plot of the percentage of fungal denitrifiers present in fertilized (red)

and reference (blue) habitats. Fungal denitrifiers were recently identified by Maeda et al., (2015).

Boxes represent 25-75% quartiles and the solid black line indicates the median value. Letters

indicate significantly different (p<0.05) categories as assessed by a Tukey HSD test.

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Figure 6: Biplot of the principal coordinates axis 1 values from Fig. 1 versus percent carbon for

reference (blue) and fertilized (red) marshes (A). Plots of Shannon Diversity (B) and log10 ITS

copy number (C) for reference marshes. Colored lines in A-C are ANCOVA regressions and the

equations and statistics for each line are denoted in the panel.

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Figure 7: Correspondence analysis (A) of FTIR spectra for fertilized (red) and reference (blue)

habitats. Box and whisker plots of Index I which is a metric of decomposition (B) and the ratio

of hydroxyl amines to three wavelengths of aromatic carbon (C) which is a metric of carbon

lability. Boxes represent 25-75% quartiles and the solid black line indicates the median value.

Letters in (B) and (C) indicate significantly different (p<0.05) categories as assessed by a Tukey

HSD test.

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Chapter 2: Supplemental Information

SI Figure 1: Schematic of a typical Plum Island marsh depicting the 4 habitats sampled in this

study. Modified from Johnson et al., (2016).

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SI Figure 2: Plot of average Fourier Transform Spectroscopy spectra of fertilized (red) and

reference (blue) for mudflat (A), tall Spartina alterniflora (B), S. patens (C), and short S.

alterniflora (D) habitats.

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Chapter 3: The effect of short-term, diel changes in environmental conditions on active

microbial communities in a salt marsh pond

Abstract

Microbial communities play key roles in biogeochemical cycles across the planet and their

composition and the factors that structure them are well documented. However, how changes in

abiotic conditions affect the active proportion of the microbial community that is responsible for

the delivery of key ecosystem services is poorly understood. Salt marshes, in particular salt

marsh ponds, are highly dynamic habitats with abiotic conditions in the pond water that fluctuate

on daily cycles. To determine how diurnally driven changes in abiotic conditions affect active

microbial communities we sampled a single salt marsh pond every four hours over two diel

cycles, sampling two dynamically different habitats, the pond sediment and overlying water. We

assessed abiotic conditions and the total and active microbial communities using high-throughput

sequencing of the 16S rRNA gene and 16S rRNA. Sediments displayed no discernable pattern

and low variation in abiotic conditions, leading to stable active microbial communities.

However, the cyclical, rapidly changing abiotic conditions in the overlying water resulted in

large swings in the active microbial community and in the percent of inactive taxa. Our data

suggest that changes in environmental condition over short time periods alter the structure of

active microbial communities. Further, our data show that the most abundant active taxa in the

overlying water were rare (<1% total abundance) suggesting that under environmental change,

rare taxa can disproportionally contribute to the activity of microbial communities.

Introduction

Microbial communities face considerable physiological challenges from changing abiotic

conditions and the way microbes respond to these changes can have profound effects on

ecosystem function. Broadscale spatial and temporal drivers often control microbial composition

(Lozupone and Knight 2007, Gilbert et al. 2012), however, variation in local environmental

conditions can confer changes in microbial community composition, activity, and geochemical

output (Alison and Martiny 2008, Shade et al. 2012, Griffiths and Philippot 2013, Reed and

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Martiny 2013). Under some circumstances, however, microbial community composition may

remain resistant to perturbations (Bowen et al. 2011, Kearns et al. 2016), despite differences in

microbial activity or geochemistry (Bowen et al. 2011, Kearns et al. 2016, Koop-Jakobson and

Giblin 2010). While variation in community composition can lead to changes in function (Reed

and Martiny 2013), the correlation between composition and function can also be disconnected

(Frossard et al. 2013, Bowen et al. 2014, Balser and Firestone 2005, Strickland et al. 2009) due

to offsets in timing between gene expression and associated function, or because the end product

of microbial activity can be consumed prior to being detected (i.e. cryptic cycling). Functional

redundancy, where multiple organisms perform the same function, can also result in a change in

community structure with no net change in function (Alison and Martiny 2008). Finally, inactive

or dormant microbes may mask our ability to determine microbial responses to a changing

environmental conditions (e.g. Bowen et al. 2011, Kearns et al. 2016).

Dormancy is a strategy some microorganisms use to persist in their environment during

unfavorable conditions (Lennon and Jones 2011). Dormant microbes exist in a wide range of

ecosystems, however their proportion of the total bacterial community differs (Lennon and Jones

2011) and can limit our ability to detect community response to environmental change.

Ecosystems that have high proportions of dormant taxa, such as soils and sediments, possess a

large degree of spatial and temporal heterogeneity (Lennon and Jones 2011, Kearns et al. 2016).

Additional controls on the proportion of inactive or dormant cells include changes in abiotic

conditions such as salinity (Aanderud et al. 2016), and excess nutrients (Kearns et al. 2016),

however nutrient limitation also appears to increase dormancy in some systems (Jones and

Lennon 2010, Aanderud et al. 2016). Ultimately, the controls over dormancy and the role of

environmental change on the proportion of dormant cells remains unclear, resulting in a pressing

need to understand the role of the environment in maintenance and resuscitation of dormant taxa.

Salt marshes provide a dynamic natural experiment to test hypotheses regarding how

varying environmental conditions influence the activity of micro-organisms. Salt marshes are

highly productive, tidally flooded coastal grassland ecosystems that provide numerous services

to both the land and sea (Teal 1962, McLeod et al. 2011, Deegan 1993). Due to their low

elevation relative to mean tidal height (Nicholls et al. 1999), salt marshes are highly susceptible

to changes in sea level. Increases in sea levels can cause net shifts in marsh plant communities as

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well as increased numbers of ponds, unvegetated areas that have lower elevation than the

surrounding marsh platform (Redfield 1972, Reed 2002, Day et al. 2011). Salt marsh ponds can

exchange water with the coastal ocean on very high flooding tides, however, ponds often

experience extended periods of disconnect from tidal flooding. During their isolation, the water

in marsh ponds can experience oscillations of oxygen and other constituents over short periods of

time (Smith and Able 2003, Day et al. 2011). Conversely, the sediments underlying ponds

remain relatively unperturbed on those time steps. Salt marsh ponds are very shallow (often <20

cm water depth), so sediments and overlying water represent highly connected, yet distinct

systems that are ideal for studying how environmental change alters microbial activity over short

time scales.

The activity of microbial cells has often been assessed using transcripts of the small

subunit of prokaryotic ribosomes (16S rRNA) due to its correlation with protein synthesis

(Kerkhof and Kemp 1999, Deuscher et al. 2006) and it has also been used to assess dormancy in

environmental samples (Jones and Lennon 2010). Despite the caveats to this approach

(Blazewicz et al. 2013), 16S rRNA can be useful as a relative measure of active microbial

community structure and how it varies over time or as a result of experimental manipulation

(Jones and Lennon 2010, Campbell et al. 2011, Aanderud et al. 2016). To assess the effect of

cyclical diurnal changes in environmental conditions on active microbial community

composition we sampled the sediment and overlying water in a salt marsh pond every four hours

over two diel cycles (48 hours). The abiotic conditions within the water change both cyclically

and rapidly and the sediment abiotic conditions, while variable, did not systematically change

allowing us to test the hypothesis that that cyclically dynamic abiotic conditions in the overlying

water (temperature, oxygen, salinity, pH, reactive nitrogen species) caused by high daytime

primary productivity, would drive strong changes in active community composition, while static

conditions in the sediment would promote stable active communities. We sequenced the 16S

rRNA gene and 16S rRNA to determine the effect of diel cycles on total and active community

composition respectively. Results indicate that there were distinct oscillations in abiotic

conditions in the overlying water column but no such oscillating pattern in the sediment pore

water abiotic conditions, despite variability in sediment abiotic conditions. Active water column

microbial communities displayed wide shifts in abundance and changes in community structure.

Conversely, the sediment communities, where abiotic conditions remained relatively constant,

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displayed no shift in the abundance or community structure of active taxa in response to diel

cycles. These results suggest that diel cycles structure the active portion of water column

communities and that dynamic conditions can promote rapid changes in the structure of active

microbial communities.

Methods

Study Site and Sample Collection

We sampled a salt marsh pond located at the Plum Island Ecosystems Long Term

Ecological Research Site in Rowley, MA, USA (42.759 N, 70.891 W). The pond (located at

42.741 N, -70.831 W) was ~20 cm in depth and approximately 7120 m2 in area. During the

course of the experiment and two days prior to our sampling, the pond was isolated from tidal

flooding due to low tidal amplitude. Sampling began at 10 AM on June 30, 2014 and ended at 10

AM on July 2, 2014, capturing two full diel cycles. On the days sampled, the sun rose between

5:09 and 5:15am and set between 8:25 and 8:30pm. The weather was clear with nominal wind

speeds (<1 m s-1) and the air temperature ranged from a low of 17°C to a high of 31°C (mean=

24°C)..To account for spatial variability within the pond, we sampled three different locations

that were approximately 15 meters apart and at each site, we sampled within 1 meter of a central

point.

Every two hours over the course of two days, we collected environmental parameters

(dissolved oxygen, salinity, pH, temperature) with a Hanna HI9828 multiparameter meter

(Geoscientific ltd, Vancouver, British Columbia, Canada) and we collected and filtered 50-mL of

water from each location for nutrient analysis. The water was passed through a 0.46 µm

Whatman® GF/F filter into sterile 50-mL centrifuge tubes and stored on dry ice. Additionally,

every four hours one liter of water was filtered onto a 0.22 µm Millipore SterivexTM filter from

each site and flash frozen in liquid nitrogen, leading to a total of 39 water samples. We collected

39 surface sediment (top 2-cm) samples from all three locations within the pond using a 15-cm

inner-diameter PVC corer. Sediments from each site were individually homogenized in a sterile

centrifuge tube and subsamples were placed into cryovials for storage on liquid nitrogen. The

remaining sediment was stored on dry ice for determination of carbon and nitrogen content.

Nutrient Analyses

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Nitrate, ammonium, and phosphate concentrations in sediment pore water and the

overlying water column were analyzed on a Lachat Flow 118 Injection Analyzer (Hach,

Loveland, CO, USA) based on QuikChem methods 31-107-04-1-E 119, 31-107-06-1-B, E31-

115-01-1-W, and E31-114-27-1-E respectively at the UMass Boston Environmental Analytical

Facility. The lower limit of detection for the methods was 0.60 µM, 1.45 µM, 0.25, and 2.5 µM

respectively. Due to the low volume of sediment pore water, nitrate concentrations were

measured by chemiluminescence after vanadium reduction (Garside 1982, Braman and Hendrix

1989) on a Teledyne NOx analyzer (Thousand Oaks, CA, USA). Elemental composition (percent

nitrogen and carbon) was measured on sediments dried at 50°C for three days, using a Perkin

Elmer 2400 Series Elemental Analyzer (Waltham, MA, USA) following standard procedures. To

understand the variability of abiotic conditions within the pond water and porewater, we

calculated the coefficient of variation.

Nucleic Acid Extraction and Reverse Transcription

Nucleic acids from sediments were extracted with the MoBio PowerSoil Total RNA

Isolation Kit (Carlsbad, CA, USA) from ~1.5 g of sediment following manufacturer’s

instructions. Nucleic acids were extracted from water filters using the MoBio PowerWater Kit

following manufacturer’s instructions. RNA was checked for DNA contamination with general

bacterial primers and any DNA contamination was removed using DNAse I (New England

BioLabs, Ipswich, MA, USA) following the manufacturer’s instructions. RNA was reverse

transcribed to cDNA using the Invitrogen Superscript RT III cDNA synthesis kit (Carlsbad, CA,

USA) following manufacturer’s instructions for random hexamers. cDNA synthesis was

confirmed with PCR using general bacterial primers and verified on a 1.5% agarose gel stained

with ethidium bromide.

PCR and Sequencing

DNA and RNA was quantified with Picogreen and Ribogreen (Invitrogen) kits

respectively and nucleic acids were normalized to 1 ng µl-1 for all PCR reactions. Samples were

prepared for sequencing on an Illumina MiSeq following the protocol outlined in Caporaso et al.

(2011). We used general bacterial primers 515F and 806R (Caporaso et al. 2011), with Illumina

adaptors and individual 12-bp GoLay barcodes attached to reverse primers, to amplify a section

of the V4 hypervariable region of the 16S rRNA gene. PCR reactions were performed in

triplicate, verified with gel electrophoresis, and fragments were excised and purified using the

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Qiagen QIAquick gel extraction kit (Qiagen, Valencia, CA, USA). Samples were quantified

fluorometrically on a Qubit (ThermoFisher, Waltham, MA, USA) and pooled in equal molar

concentrations for sequencing on the Illumina MiSeq (San Diego, CA, USA) platform for paired-

end 151 bp sequencing. All sequencing was performed at the University of Massachusetts

Boston using V2 chemistry.

Quantitative PCR

Quantative PCR (qPCR) was performed to assess 16S rRNA copy number for both the

active and total microbial communities. All DNA, cDNA, and standards prepared from purified

PCR product we quantified with a Qubit (ThermoFisher). DNA from each sample was

normalized to 3 ng µl-1 and serial dilutions of standards were prepared. DNA and cDNA were

amplified in triplicate reactions, along with standards and internal controls on a Strategene MX-

3005p quantitative thermocycler (Stratagene, La Jolla, CA, USA) with the primer pair 357F and

519R (Biddle et al. 2008) using conditions described previously (Bowen et al. 2011). All qPCR

reactions were verified for proper product formation with gel electrophoresis and melt curves.

All standard curves had R2 values >0.99 and qPCR efficiency ranged from 90-103%. To test

significant differences in abundance of 16S rRNA we used an ANOVA in R (R Core Team

2011).

Sequence and Data Analysis

Paired end reads were joined with fastq-join (Aronsety 2011) using default parameters

and sequences were demultiplexed and quality filtered in QIIME (version 1.9; Caporaso et al.

2010) following the guidelines recommended by Bokulich et al. (2013). Sequences were checked

for chimeras using USEARCH in de novo mode and subsequently removed (Edgar 2011).

Following quality filtering, we retained a total of 3.87 million sequences. Operational taxonomic

units (OTUs) were clustered at 97% sequence identity with Swarm (Mahé et al. 2013) in QIIME

and OTUs appearing only once across the dataset were discarded. Taxonomy was assigned using

uclust (Edgar et al. 2011) against the GreenGenes database (version 13.5). All sequences

matching archaea and chloroplasts were filtered from the dataset. Sequences were aligned with

PyNast (Caporaso et al. 2010) and a phylogenetic tree was constructed with fasttree (Price et al.

2010).

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We calculated beta diversity on normalized OTU tables using weighted UniFrac

(Lozupone and Knight 2005) in QIIME. Significant differences in groups were assessed with a

non-parametric MANOVA (Anderson 2002) with 10,000 permutations. Alpha diversity was

calculated on a rarefied OTU table (10,000 restarts, steps of 50) normalized to the lowest

sampling depth (11,596 sequences). To assess heterogeneity in the sediment communities and

determine if overdispersion was impeding our ability to detect changes in sediment communities

we performed a regression-based overdispersion test (Cameron and Trivedi 1990) in R. We

calculated Shannon Diversity and phylogenetic diversity and assessed significant differences in

diversity with a one-way ANOVA in R (R Core Team 2011). To assess the degree of inactivity

among taxa in our samples we calculated the 16S rRNA:16S rRNA gene ratio for each OTU in

each sample and defined a taxon as active with a ratio >1 (Lennon and Jones 2010). To assess

the contribution of the rare biosphere to the active communities of the sediment and water we

used a Chi-squared test to determine the differences in abundance between OTUs in the total

(expected) and active (observed) microbial communities using taxa with average abundance in

the total microbial community of <1%. Further, we calculated non-parametric Spearman

correlations between 16S rRNA gene and 16S rRNA profiles to examine how the abundance of

taxa in the total communities correlates to their abundance in the active community.

Results

Environmental Conditions

Abiotic conditions in the marsh pond sediment porewater (Fig. 1) did not vary

appreciably over the time course of our experiment. The coefficient of variation (the ratio of the

standard deviation to the mean) was typically low for environmental parameters (Fig. 1), ranging

from 0.01 to 0.03, suggesting low variability in environmental conditions in the sediment over

the 48 hour experiment. While there was slight variation in abiotic conditions, this variability

was not due to systematic temporal effects. Abiotic parameters in the overlying water column,

however, did vary with the time of day (Fig. 1). Temperature, pH, and oxygen had high levels

during the day and decreased levels at night. Ammonium and nitrate displayed the inverse

pattern, with high concentrations at night and low or undetectable concentrations during the day.

Water column salinity, while variable, only changed by ~0.5 ppt and phosphate was stable over

time. The coefficient of variation of environmental conditions in the water (Fig. 1) ranged from

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0.9 to 1.97 and was significantly higher than the variation in the sediments (t-test, T=14.90,

p<0.002), providing evidence that environmental conditions in the water were relatively more

temporally dynamic than in the sediment.

Community Composition and Abundance

The total (16S rRNA gene) and active (16S rRNA) communities in the pond sediment

were significantly different from each other (PERMANOVA, F(1,78)=12.97, p<0.001; Fig. 2A),

but neither the total (F(4,35)=4.45, p=0.60) nor potentially active (F(4,35)=6.04, p=0.56) sediment

communities displayed significant short-term (F(5,34)=2.43, p=0.91) temporal or spatial

(F(2,37)=0.90, p=0.64) shifts in composition and the variation that exists is nominal for total (mean

weighted UniFrac similarity=96.04%) and active communities (mean= 94.56%). Further, a

overdispersion test indicated no significant (p>0.56) effects of time or site on sediment

composition. As with sediment communities, water column active and total microbial

communities were significantly different from each other (F(1,78)=19.33, p<0.001; Fig. 2B). The

total community in the overlying water also remained stable over the 48-hour period, displaying

no significant spatial (F(3,36)=9.45, p=0.90) or temporal effects (F(4,34)=11.43, p=0.80), and with

an average weighted UniFrac similarity of 97.5%. The potentially active community, however,

displayed consistent and recurring diurnal changes over the 48 hours of our experiment,

displaying five significantly different clusters (F(4,34)=19.21, p<0.001) corresponding to time of

day with no evidence of spatial effects with average similarity of 52.43%. A plot of the ratio of

the abundance of 16S rRNA:16S rRNA gene, as assessed by qPCR (SI Fig. 1) revealed a

significantly higher ratio in sediment than water communities (ANOVA; F=29.45, p<0.001) but

neither ratio varied significantly over the course of the experiment in the sediment or the water.

Diversity and Activity

Shannon Diversity in the active and total communities remained constant in the sediment

over the 48-hour period (SI Fig. 2). Both the sediment and water column total community had

higher Shannon Diversity than the potentially active community. The water column active

community, however, had significantly different Shannon Diversity values at different times of

day. Patterns in the percent of inactive taxa (16S rRNA:16S rRNA gene <1) varied between

sediment and water (Fig. 3). The percent of inactive taxa in the sediment ranged from 45 to 55%

of the community (Fig. 3A) and did not vary significantly during the duration of this experiment

or between sampling locations. Inactivity in the water column, by contrast, ranged from 50 to

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65% of the community, and was temporally variable (ANOVA; p<0.001, F(4,24)=12.43). The

percentage of inactive taxa was significantly higher in the water than the sediment (ANOVA;

p<0.01, F(1,76)=12.43).

Taxonomic Composition

The taxonomic composition of the total sediment community did not vary over the 48-hr

experiment (SI Fig. 3). This community was comprised of largely anaerobic or facultatively

anaerobic orders including Chromatiales (~17%), Desulfobacterales (7%), Alteromonadales

(12%), Myxococcales (9%), Bacteroidales (8%), and Cytophagales (7.5%). The potentially

active sediment community (Fig. 4A) also did not vary in its structure over time and was largely

composed of several largely anaerobic orders such as Chromatiales (~19%), Alteromonadales

(~7%), Desulfobacterales (~9%), and Myxococcales (8%). Phototrophic taxa such as

Cyanobacteria (~15%) were present in the potentially active sediment microbial community

throughout the course of the experiment.

The taxonomic composition of the total water community (SI Fig. 4) was consistent

through time and was largely composed of the orders Fusobacterales (~12%), Actinomycetales

(~7%), Thiotrichales (10%), Myxococcales (9%), and Altermonadales (11%). A stacked bar plot

of the active communities of the water column displayed dynamic shifts in taxonomic

composition (Fig. 4B). Peak photosynthetic hours (10 AM/2 PM) were dominated by an

unclassified Cyanobacterial order (~60%) in addition to the Alphaproteobacterial order

Rickettsiales (~13%). Four hours later at 6 PM, the water column active microbial community

was dominated by the Gammaproteobacterial orders Virbionales (~70%) and Pseudomonadales

(~10%). At 10 PM, the first time point after sunset, the composition of the active community was

primarily comprised of the orders Vibrionales (~15%), Flavobacterales (~10%), Pseudomondales

(~7%), and Actinomycetales (~22%). When oxygen was at its lowest at 2 AM, the active water

column community was dominated by the order Bacillales (~50%), with a smaller contribution

from the order Vibrionales (~15%). Finally, at 6 AM, the time point that harbored the largest

diversity of active taxa, the community was comprised of the orders Flavobacterales (~13%),

Unclassified Cyanobacteria (~7%), and Burkholderiales (~17%).

Activity of Dominant Taxonomic Groups

We further explored patterns among active taxa by calculating the 16S rRNA:16S rRNA

gene ratio for the most active taxa (16S ratio >15) in the overlying water (Fig. 5 And aggregating

87

the data at the order level. A heatmap of this ratio revealed numerous taxa with activity that was

restricted to few times of the day (unclassified Cyanobacteria, Rickettsiales, Acidimicrobiales,

Bacillales) suggesting their activity was limited by environmental conditions or competition with

other taxa. Other taxa were active more frequently (Vibrionales, Burkholderiales,

Actinomycetales, Marinicellales) suggesting less stringent growth requirements. Further, certain

taxa appear to be active/inactive in concert with one another (unclassified Cyanobacteria and

Rickettsiales) possibly suggesting a symbiotic relationship, niche overlap, or a similar response

to environmental queues. A heat map of the most active sediment taxa (SI Fig. 5) generally

displayed a consistent ratio for the most active taxa over the course of the 48-hr experiment.

Rank Abundance and the Rare Biosphere

To assess the role that abundance of taxa in the total community plays in their

concomitant abundance in sediment and water active communities we created bi-plots comparing

the abundance of OTUs in the 16S rRNA to their abundance in the 16S rRNA gene (Fig. 6). The

abundance of sediment taxa in the 16S rRNA was strongly correlated (Spearman’s ρ=0.85) to

their abundance in the 16S rRNA gene (Fig. 7A), as highlighted by the highly abundant OTUs

from the order Chromatiales (0.15-0.20 relative abundance), which was similar in abundance in

the total and active communities. Water communities, however, displayed a disconnect in the

abundance in the 16S rRNA and 16S rRNA gene (Spearman’s ρ =0.31) and that disconnect was

largely due to several taxa, including those from the order Vibrionales, Bacillales, Rickettsiales,

and unclassified Cyanobacteria, that were in low abundance in the 16S rRNA gene compared to

the 16S rRNA, suggesting a role for rare taxa. We next explored the role of the rare biosphere

by constructing plots comparing 16S rRNA gene rank of OTUs versus 16S rRNA abundance

rank-abundance curves (SI Fig. 7). Rank-abundance plots allows us to both explore the

correlation in abundance in the total and active communities and to determine the contribution of

rare taxa to active community composition. The sediment community rank abundance curve (SI

Fig. 7A) indicated that the most abundant taxa in the total community were also the most active

taxa. Indeed, no taxa in the sediment rare biosphere (<1% total abundance) significantly varied in

abundance between the total and active microbial communities (Chi-squared test, p>0.5).

Communities in the overlying water, however, had vastly different rank-abundance curves

relative to the sediment (Fig. 7B). Typically, the most abundant taxa in the active water column

community were in very low abundance in the total community (<1% total abundance) and of

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the rare taxa, 15% displayed significantly higher abundance in the active community relative to

the total community (Chi-square test, p<0.05) suggesting a greater contribution of the rare

biosphere to the structure of the active water community than the sediment.

Discussion

Our study examined the effect of short-term changes in environmental conditions on the

total and active microbial communities in a salt marsh pond. Our analysis revealed differential

responses of sediment and water column microbial communities to diel changes in

environmental conditions. Sediments, while having significantly different total and active

communities (Fig. 2A), displayed no temporal changes in active microbial community

composition or diversity. The lack of change in the active sediment communities may be due to

the short duration of this study (48 hrs), which may be out of step with the duration of time it

takes abiotic conditions in the sediments to significantly vary. In addition, stochastic assembly

processes (Stegen et al. 2012, Nemergut et al. 2013) may mask our ability to detect changes in

sediment communities. However, the striking stability of the taxonomic composition of the total

and active sediment communities (Fig. 4, SI Fig. 3) and high degree of similarity between

samples suggests this is not the case. Coastal sediments display sharp, shallow gradients in

numerous abiotic condition (Canfield et al. 1993) that can confer gradual changes to microbial

community composition (Armitage et al. 2012). Our sampling method (top 2-cm) integrates over

these gradients and may mask potential fine-scale changes that occur over shallow spatial scales.

However, the sampling technique employed here (top 2 cm of surface sediment) was previously

(Kearns et al. 2016) used to elucidate the dramatic effect of nitrogen enrichment on active salt

marsh sediment bacterial communities, suggesting that these methods should be robust to detect

integrated large-scale changes in the active bacterial community within the pond. Finally, we’ve

documented highly abundant taxa from the Chromatiales in the sediment (~20% relative

abundance) and water (~1.5% relative abundance) suggesting that as in vegetated marsh areas

(Bolhuis and Stal 2011, Kearns et al. 2016), anoxygenic photosynthesizers from the order

Chromatiales play an important role in the ecology of salt marsh ponds.

Conversely, the water column displayed a highly variable active microbial community

(Fig. 2B) and a diel change in active diversity (SI Fig. 2B). Our results suggest that cyclically

changing environmental conditions can confer rapid shifts in the active microbial community,

89

while the relatively unchanging conditions found in the pond sediments promote more consistent

structure across time for both active and total bacterial communities. Changes in active

community composition can be correlated with changes in transcriptional activity (Gilbert et al.

2010) suggesting that relatively static habitats, like pond sediments, likely promote consistent

metabolic processes while the microbial communities in dynamic habitats likely displayed

variable metabolic output due to environmental changes. In the water column, we observed

There were large variations in the percentage of inactive taxa (Fig. 3B; 16S ratio <1) in

the overlying water. Of the 352 microbial orders present within the active community of the

overlying water, 62% (n=217) never achieve a 16S rRNA to 16S rRNA gene ratio (hereafter,

16S ratio) greater than one, suggesting these taxa were likely dormant within this system. Several

orders (n=52) had 16S ratios that oscillated between <1 and >10 suggesting an increase and

decrease in their activity over time in response to environmental conditions. These taxa were

likely not in a true state of dormancy during times when their ratio was less than one since the

cellular investment dormancy takes (Lennon and Jones 2011) is a longer-term ecological

strategy. A final group of taxa (n=82), had 16S ratios that cycle between less than one and five

and their status as inactive (short durations of time with ratios less than one) or dormant (longer

durations of time with ratios less than one) remains unclear because several taxa have been

shown to need a 16S ratio greater than one to divide (Blazewicz et al. 2013). However, cell

division is not the only form of activity a microbe can take, instead microbes may be performing

biogeochemical processes or other cellular processes such as repair rather than dividing

(Kirchman 2016). Our results highlight the difficulty in interpreting 16S ratios from

metagenomic data, in particular for taxa with ratios that change rapidly. Further work on

microbial dormancy is needed to better understand what the 16S ratio means for phylogenetically

widespread taxa and to determine how the 16S ratio varies under different stages of growth such

as division, metabolic activity, inactivity, and dormancy. This will allow us to better interpret not

only the 16S ratio, but also how changes in 16S rRNA community profiles correlate to physical

measurements of microbial activity such as geochemical rates or enzyme-based assays.

Our results indicated that the active microbial community in the overlying water in marsh

ponds was tightly controlled by diel cycles. The transcriptional activity of autotrophic and

heterotrophic microbial taxa due to diel changes, typically driven by light, has been previously

observed (Winter et al. 2004, Ito et al. 2009, Zinser et al. 2009, Tomasch et al. 2011, Ottesen et

90

al. 2014) and their metabolic activity can be tightly coupled in aquatic systems (Gasol et al.

1998). The changes in the taxonomic composition of the active community observed in our

experiment corresponded with changes in environmental conditions, which are, in turn, driven by

the activity of the microbes themselves. In addition to responding to changes in the environment,

active taxa in our study often appeared to respond to the activity of other taxa in addition to

changes in their environment. For example, the activity of taxa highly abundant in the active

community from the Vibrionales order closely followed the activity of Cyanobacteria (Fig. 4 and

5), suggesting members of the Vibrionales may be involved with either predation or consumption

of cyanobacterial metabolic products, something also documented on seasonal cycles (Turner et

al. 2009). Further, taxa from the order Bacillales were only active when oxygen was at its lowest

(10pm-6am) suggesting that their oxygen requirements as anaerobes restricts their activity. The

tight control of diel cycles on the active community composition in the overlying water

highlights the importance of both abiotic conditions and bacterial interactions in determining

microbial community structure and function.

We demonstrate that taxa from the rare biosphere (<1% total abundance)

disproportionally contributed to the composition of the active community in the overlying water

relative to the sediment (SI Fig. 6). Abiotic conditions within the overlying pond water did not

appear to promote the activity of the most abundant taxa, rather, many of the most abundant taxa

in the active community (Cyanobacteria, Vibrionales, Bacillales) were relatively rare in the total

community. The rare biosphere is often thought of as a reservoir of low-abundance taxa that

provides resilience in the face of environmental perturbations (Sogin et al. 2006, Pedrós-Alió

2012). Rare taxa can display dynamic patterns of occurrence along gradients (Shade and Gilbert

2015) as well as dynamic abundances and activities on seasonal timescales (Campbell et al.

2011, Shade et al. 2014). Shade et al. (2014) recently demonstrated that rare taxa can

significantly contribute to community turnover across numerous ecosystems. Further, rare taxa

can display transitory or persistence patterns within and between ecosystems (Shade and Gilbert

2014), which may allow them to respond to novel abiotic conditions and lead to pulses in their

activity and changes in total community composition (Aanderud et al. 2015). Our results

demonstrate that despite the enhanced activity of rare taxa, there was no net change in the total

community, indicating that either these taxa were not producing biomass through their activity or

not enough time had elapsed to alter total community composition. In addition, the high percent

91

of inactive taxa (Fig. 3), which was likely driven by the dynamic nature of the system,

maintained a reserve of taxa (rare and abundant), allowing for future response to additional

changes within the system.

In conclusion, our results highlight the rapid pace at which microbial communities can

respond to changes in their environment. The dynamic environmental conditions observed in the

overlying water promote changes in active taxa, while the relatively static conditions in the

sediment promote stable communities, highlighting the importance of temporal sampling of

active microbial communities in dynamic environments. Our results suggest that in communities

that are in static environments or that have had time to adapt to a perturbation, the most abundant

taxa are also the most active taxa. However, under dynamic conditions or perhaps during a

transition, there is a disconnect between the presence and activity of taxa leading to high degrees

of inactivity of abundant taxa and an increase in activity of rare taxa. While the pond water

column community and the abiotic conditions changed at a rapid pace, the mechanisms that

control activity of the sediment community likely acted over greater time periods, and those

static conditions promoted a stable active microbial community.

Acknowledgements

We would like to thank Evan Howard, Rachel Stanley, and Amanda Spivak for field

assistance. This work was funded by NSF award DEB1350491 and DEB 1353140) to JLB and

an NSF Research Experience for Undergraduates Award DBI-1359241 to Dr. Rachel Skvirsky.

Additional support was received from the Plum Island LTER (NSF LTER OCE 0423565 and

NSF OCE 1058747). We would also like to thank Jeff Dusenberry for assistance and use of the

UMass Boston high performance computing cluster for bioinformatic analyses. Sequences from

this data set can be found in the Sequence Read Archive under accession number SRR4419663.

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Figures

Figure 1- Figure 1- Environmental parameters in the sediment pore water (black) and overlying

water (grey) from ammonium (A), Phosphate (B), salinity (C), pH (D), C:N ratio (E), dissolved

oxygen (F), and temperature (G). Grey bars are night time hours. Points are the average of all

three sites and error bars indicate the standard error of the mean.

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Figure 2- Principal coordinates analysis based on weighted UniFrac similarity for the sediment

(A) and the overlying water (B).

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Figure 3- Box and whisker plot of the percentage of inactive taxa in the sediment (A) and water

(B) communities. Taxa are defined inactive if their 16S rRNA:16S rRNA gene ratio is less than

1. Boxes represent 25-75% quartiles, and the solid black line is the median value. Categories in

(B) are significant based on a Tukey HSD correcting for multiple comparisons and no significant

differences occurred in (A).

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Figure 4- Stacked bar plot of the top 25 most abundant active microbial orders in the sediment

(A) and water (B). The category “other” is the sum of the remaining microbial orders.

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Figure 5- Heat map showing the sum of the 16S rRNA: 16S rRNA gene ratio for 28 most active

microbial orders in the water column.

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Figure 6- Plot of the relative abundance of the top 1000 OTUs (comprising >90% of the data) in

16S rRNA relative to the 16S rRNA gene for sediment (A) and water communities (B). Points

are colored by the time point they are most abundant in. Black lines in (A) and (B) are 1:1 lines.

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Figure 7-plot of the 16S rRNA gene rank of the top 380 OTUs (comprising ~90% of the data)

versus the relative abundance in 16S rRNA for sediment (A) and overlying water communities

(B). Inset of (B) highlights the top 60 OTUs in the water. Vertical red lines indicate taxa with an

abundance in the total community <1% and vertical blue lines are taxa with an abundance in the

total community <0.001%.

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Chapter 3: Supplemental Material

SI Figure 1- Quantitative PCR (qPCR) results depicting the ratio of 16S rRNA:16S rRNA gene

for sediment (black) and water (grey) communities. Points are the mean of three sites and the

error bars are standard error of the mean.

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SI Figure 2- Shannon Diversity values for sediment (A) and water (B) communities. Boxes

represent 25-75% quartiles, the solid black line is the median value, and dots are outliers.

Categories in (B) are significant (p<0.001) based on a Tukey HSD correction for multiple

comparisons.

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SI Figure 3- Stacked bar plot of the top 25 most abundant microbial orders from the sediment

16S rRNA gene. The category other is the sum of the remaining microbial orders. Black bars are

night time hours.

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SI Figure 4- Stacked bar plot of the top 25 most abundant microbial orders from the water 16S

rRNA gene. The category other is the sum of the remaining microbial orders. Black bars are

night time hours.

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SI Figure 5- Heat map showing the sum of the 16S rRNA: 16S rRNA gene ratio for 25 most

active microbial orders in the sediment.