The Wetland Dilemma: Nitrogen Removal at the Expense of Methane Generation
THESIS
Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University
By
Michael R Brooker
Graduate Program in Environmental Science
The Ohio State University
2013
Master's Examination Committee:
Paula Mouser, Advisor, Gil Bohrer, Jay Martin
Copyrighted by
Michael R Brooker
2013
Abstract
Wetlands in the United States were subject to draining or dredging leading to
substantial losses prior to gaining legal protection. Combined with increased fertilization
and drainage tile use on agricultural fields, drainage basins have been affected by
increased nutrient loads. Nitrogen introduction to large water bodies contributes to the development of hypoxic conditions and harming the ecosystem. To solve this issue, reconstruction of wetlands has been suggested as they are known nutrient sinks.
However, wetlands also produce large amounts of the greenhouse gas, methane, giving rise to a dilemma: are the benefits worth the harm? Essentially, denitrification is the initial process which ultimately leads to the conditions necessary for methanogenesis, both being the result of microbial metabolisms present within the sediments.
The potential for methane production from sediments collected at three distinct wetland biomes was investigated. Further processing investigated the methanogenic abilities of the upper and lower fifteen centimeter layers from the two hydric soils.
Environmental indicators including effect of temperature and nutrient availability was
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investigated pertaining to their effect on microbial-source carbon cycling in an incubation experiment. Sediments collected from the same sites were analyzed for their microbial community in order to explain spatial variations of biogeochemical processes.
Potential methane and carbon dioxide fluxes were highest at the open water site.
Deep sediments lacked some innate component in its ability to produce methane. A change in temperature from 20°C to 30°C caused potential methane and carbon dioxide fluxes to double, on average. Carbon, especially acetate, was likely the factor limiting methane production in these sediments. Microbial assemblages showed that the open water site had the highest abundance of methanogenic organisms while the vegetated site showed somewhat higher ratios of organisms supposedly involved in nitrogen cycling.
Isolations of pure cultures from wetland sediments provided several organisms which are serviceable as model-organisms. A methanogen and several dissimilatory nitrate reducers can be used to supply standards for quantification of genetic materials in future studies. They may also provide use in laboratory studies used to predict interactions between functional groups.
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Acknowledgments
This research was supported by the US Geological Survey through the Ohio Water
Resource Center, grant#60030648, as I was supported through positions as a graduate
research/teaching associate position in part funded by the Environmental Science
Graduate Program. My graduate committee (Paula Mouser, Gil Bohrer, and Jay Martin)
provided me with invaluable insight and direction. My advisor, Paula Mouser, trained
me in the techniques needed for field sampling and laboratory analysis. Gil Bohrer gave
me practical knowledge by training me in modeling and giving me the ability to
formulate hypotheses derived from field-data. Numerous lab members (Mengling
Stuckman, Matt Noerpel, Raiyung Xiao, Zuzana Bohrerova) assisted me with training of protocols used for this research. Bill Mitsch and the Wetland Research Center (Jorge
Villa-Betancur, Kay Stefanik, Blanca Bernal, Lynn McCready) allowed me access to
equipment, research sites, and information. Purnima Kumar provided me with a
foundation of knowledge skill, and connections. Matt Mason, Shareef Dabdoub, and
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Terry Camerlengo were instrumental in teaching bioinformatics analysis. I could not have completed my goals without the support of family, especially my parents and Molly.
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Vita
2003...... Northwest High School
2007...... B.S. Microbiology, The Ohio State
University
2013...... M.S. Environmental Science, The Ohio
State University
2011 to present ...... Graduate Teaching/Research Associate,
Department of Environmental Engineering,
The Ohio State University
Publications
Kumar, P. S., Brooker, M. R., Dowd, S. E., and Camerlengo, T. (2011). Target region
selection is a critical determinant of community fingerprints generated by 16S
pyrosequencing. Plos One, 6(6)
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Kumar, P. S., Mason, M. R., Brooker, M. R., and O'Brien, K. (2012). Pyrosequencing
reveals unique microbial signatures associated with healthy and failing dental
implants. Journal of Clinical Periodontology, 39(5)
Brooker, M.R., Bohrer, G., and Mouser, P.J. (in prep). Potential carbon cycling derived
from the microbial component of wetland sediments. Journal of Biogeochemical
Cycles
Fields of Study
Major Field: Environmental Science
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Table of Contents
Abstract ...... ii
Acknowledgments...... iv
Vita ...... vi
List of Tables ...... xii
Chapter 1: Introduction ...... 1
1.1 Wetlands in America ...... 1
1.2 Major Biochemical Processes in Wetlands ...... 4
1.2.1 Aerobic Processes ...... 5
1.2.2 Suboxic Processes...... 8
1.2.3 Anaerobic Processes ...... 10
1.3 Microbial Communities in Wetland Environments ...... 14
1.3.2 Microorganisms Involved in Methane Production ...... 19
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1.3.3 Microorganisms Involved in Methane Oxidation ...... 20
1.4 Environmental Factors Influencing Wetland Microbial Activity ...... 22
1.4.1 Hydrology ...... 22
1.4.2 Temperature ...... 24
1.4.3 Nutrient Availability ...... 25
1.4.4 Vegetation ...... 27
1.4.5 Redox Potential and Sediment pH ...... 29
1.4.6 Incorporation of Factors into Models Estimating Biogeochemical Flux ...... 30
1.5 Conclusions ...... 31
Chapter 2: Factors Affecting the Anaerobic Microbial Respiration Potential of Wetland
Sediments ...... 33
2.1 Abstract ...... 33
2.2 Introduction ...... 34
2.3 Materials and Methods ...... 39
2.3.1 Site Description and Sample Collection ...... 39
2.3.2 Experimental Design and Analysis ...... 42
2.3.3 DNA extraction and processing ...... 44
2.3.4 Data Reduction and Statistical Analyses ...... 45
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2.4 Results ...... 50
2.4.1 Factors Affecting Potential Carbon Fluxes ...... 50
2.4.2 Biogeochemical Trends ...... 55
2.4.3 Microbial Community Dynamics ...... 58
2.5 Discussion ...... 63
2.5.1 Methane and Carbon Dioxide Flux Potentials ...... 63
2.5.2 Factors Affecting Microbial Activity ...... 67
2.6 Conclusions ...... 71
Chapter 3: Isolation of Wetland Microorganisms Related to Nitrogen and Methane
Cycling ...... 72
3.1. Abstract ...... 72
3.2. Introduction ...... 73
3.3 Materials and Methods ...... 77
3.3.1 Preparation of Media ...... 77
3.3.2 Sample Collection and Organism Isolation ...... 81
3.3.3 DNA Extraction and Culture Identification...... 83
3.4 Results ...... 84
3.5 Discussion ...... 87
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3.6 Conclusions ...... 90
Chapter 4: Conclusions ...... 92
References ...... 93
Appendix A: Gas Flux Linear Rates and Data Tables ...... 113
Appendix B: Taxonomic Data from 454-sequencing and Diversity Indices ...... 124
Appendix C: Water Chemistry Analysis Data ...... 146
Appendix D: Sediment Density ...... 157
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List of Tables
Table 1.1 Genera involved in nitrogen biogeochemical cycles of wetlands ...... 16
Table 1.2 Genera involved in carbon biogeochemical cycles of wetlands ...... 17
Table 2.1 Putative functional assignment of taxonomic units detected in 454-sequences obtained from the ORWRP 48
Table 2.2 Full-factorial model statistical analysis...... 55
Table 2.3 Methane flux potentials or chamber measured methane flux ...... 65
Table 3.1 Media used for isolation of pure cultures………………………………...... 80
Table 3.2 Isolated cultures and their identity confirmed through the NCBI database ...... 85
Table A.1 Individual microcosm’s methane and carbon dioxide flux potential temporal rates……………………………………………………………………………………..114
Table A.2 Methane concentrations of headspace samples corrected for dilutions in ppm
CH4 from dormant season experiment ...... 115
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Table A.3 Carbon dioxide concentrations of headspace samples corrected for dilutions in ppm CO2 from dormant season experiment...... 117
Table A.4 Methane concentrations of headspace samples corrected for dilutions in ppm
CH4 from growing season experiment...... 119
Table A.5 Carbon dioxide concentrations of headspace samples corrected for dilutions in ppm CO2 from growing season experiment...... 122
Table B.1 Relative abundance up to level 6 of each taxonomy identified from 454- sequencing analysis……………………………………………………………………..126
Table C.1 Combined nitrate and nitrite measurements from water quality microcosms……………………………………………………………………………..147
Table C.2 Sulfate measurements from water quality microcosms...... 149
Table C.3 Acetate measurements from water quality microcosms...... 151
Table C.4 Phosphate measurements from water quality microcosms...... 153
Table C.5 Total dissolved, non-purgeable organic carbon measurements...... 155
Table D.1 Estimated sediment bulk density (wet weight) measured by addition of sediments to DI water in a volumetric instrument…………………………………….. 158
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List of Figures
Figure 1.1 Schematic of redox zone distribution within wetland sediments ...... 5
Figure 2.1 Site locations of sediment sampling………………………………………….41
Figure 2.2 Carbon flux potentials for dormant season experiment...... 52
Figure 2.3 Carbon flux potentials for growing season experiment...... 53
Figure 2.4 Dissolved carbon of the shallow and deep treatments from the growing season
experiment...... 57
Figure 2.5 Dissolved anion concentrations in shallow and deep treatments from the
growing season experiment...... 58
Figure 2.6 Relative abundances of taxonomic units from 454-sequencing analysis of
sediments collected in the growing season experiment...... 60
Figure 2.7 OTU network of genera from 454-sequencing analysis of sediments collected in the growing season experiment ...... 62
Figure 2.8 Comparison of microcosm methane flux potentials against eddy-flux covariance measurements...... 66
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Figure 2.9 Clone library relative abundances of Sanger-sequencing analysis on sediments collected in the dormant season experiment...... 70
Figure 3.1 Site sampling locations to obtain functional cultures………………………..82
Figure 3.2 Cultures isolated but yet to be identified through genetic analysis...... 87
Figure B.1Visualization of taxonomic distribution for four wetland sediments……….125
Figure B.2 Rarefaction curves of α-diversity indices of the four wetland sites ...... 142
Figure B.3 Rarefaction values calculated by all metrics of the α-diversity of each sample
...... 143
Figure B.4 PCoA chart separating β-diversity by depth for deep (red) and shallow (blue)
samples...... 144
Figure B.5 PCoA chart separating β-diversity by site for open-water (red) and vegetated
(blue) samples...... 144
Figure B.6 Jackknifed β-diversity tree of hierarchael clustering...... 145
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Chapter 1: Introduction
1.1 Wetlands in America
Anthropogenic nutrient sources, predominantly through improvements in modern agricultural fertilization practices, are responsible for doubling the input of atmospheric nitrogen deposition onto terrestrial systems (Vitousek et al., 1997). Engineered controls for removing water from the land rapidly, such as tile drainage networks and trapezoidal ditches, transport runoff laden with nitrogenous compounds (e.g. nitrate). Upon entering drainage basins, increased nutrient loads have the potential to alter the growth rates and dynamics of previously deprived organisms like phytoplankton (Rabalais et al., 2002).
Ultimately, this increased biomass can feed benthic bacteria reducing oxygen levels below what is needed to support higher organisms. Areas such as these are known as
“Dead Zones” with the Midwestern United States contributing to the formation of these zones in Lake Erie and the Gulf of Mexico (Conroy et al., 2011; Rabalais et al., 2002).
The impact can be quite large, for example, the Gulf of Mexico has experienced a Dead
Zone as large as 22,000 km2, disturbing important fisheries (Rabalais et al., 2002).
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Furthering this issue is a substantial loss of wetlands (~50%) in the United States, since the early 1800s as a result of land conversion to agriculture use and population development (Dahl, 1990). Wetland loss has removed important land-surface water buffers providing well-documented benefits of nutrient removal (Millennium Ecosystem
Assessment, 2005). In recognizance of services provided by wetlands, the United States established a policy of “no net loss, or a net gain” through wetland mitigation practices with the Clean Water Act of 1972 (33 USC § 1251 et seq.). Through this policy, wetlands harmed for development must be accounted for by either restoration or creation of new wetlands.
The growing concern for global warming has brought to light the importance of wetlands for storing large pools of carbon via the capture of the second most influential greenhouse gas (GHG), carbon dioxide (Bernal and Mitsch, 2013). Yet, they are major contributors of methane which has a higher global warming potential (GWP) comparative to carbon dioxide at a 25:1 ratio over a 100-year period (IPCC, 2007). Additionally, while their ability to remove nitrogen most often results in dinitrogen flux, there is some occurrence of another potent GHG, nitrous oxide, which also has a much greater GWP than carbon dioxide at 241:1, over a 100-year period (Bowden, 1987; IPCC, 2007). The wetland dilemma stems from whether the net radiative forcing applied by wetlands are negligible compared to the benefits of nutrient removal.
Wetlands are found among many different regions world-wide featuring many different types; bogs, mangroves, marshes, swamps, etc. (Mitsch and Gosselink, 2007).
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Typical wetlands found in Ohio and other midwestern States include marshes
(herbaceous vegetation), swamps (woody vegetation), and vernal pools (seasonally
flooded). Vegetation is often necessary to distinguish wetland type such as trees and
shrubs including Populus deltoides (eastern cottonwood), Salix alba (white willow),
Cephalantus occidentatlis (button bush), and Juncus spp. (rush). Herbaceous, emergent
vegetation tends to inhabit the edge zones of wetlands and include: Typha spp. (cattails),
Schoenoplectus tabernaemontanii (soft-stem bulrush), Phragmites australis (common reed), Carex spp. (sedges), and Pontederia cordata (pickerel weed). Floating-leave plants, such as lilies, are often rooted in deeper-water areas and include: Nelumbo latea
(yellow lotus), Nuphar lutea (yellow pond-lily), and Nymphaea odorata (white water lily). Submerged plants are often in the genus, Potamogeton (pondweed). Deep, unvegetated areas are referred to as open water zones, however, Lemna minor
(duckweed) is a floating plant that may occupy this zone with its roots suspended in the water column.
Wetlands are subjected to saturated or inundated conditions for at least part of the year leading to the formation of hydric soils, one of the criteria for wetland delineation according to the U.S. Army Corps of Engineers (1993). Oxygen is rapidly depleted as its intrusion into the soil matrix is slowed by water present at the surface (Sebacher et al.,
1986). Microorganisms are forced to seek out alternate electron acceptors, under these
low oxygen conditions, for respiration or metabolic energy synthesis. The sequential
order of N > Mn > Fe > S > C describes which compounds are preferential for microbial
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activity, as determined by the entropy of these chemical reactions (Conrad, 1996; Lovley
and Klug, 1983; Stadmark and Leonardson, 2005; Whiticar, 1999). That is not to imply
only a single process occurring in the sediments, but rather which electron acceptor is
dominating microbial respiration. The following sections discuss the processes governing
microbial activity in wetlands and the impact on global biogeochemical cycles.
1.2 Major Biochemical Processes in Wetlands
Wetlands are habitats that are characterized by several zones of differing redox
potentials (Figure 1.1). Oxygen diffuses to the water-soil interface and infiltrates the sediments forming an oxic zone. This oxygen supply is slowed by the presence of water, allowing organisms to use more than is supplied with a suboxic zone forming beneath this layer (Reddy et al., 1986; Sebacher et al., 1986). Facultative microorganisms, including denitrifiers, begin to use alternate electron acceptors (Conrad, 2002). Redox levels decrease as reduction processes utilize the remaining electron acceptors until an
anaerobic zone is formed where methanogenesis can occur.
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Figure 1.1 Schematic of redox zone distribution within wetland sediments
1.2.1 Aerobic Processes
Where wetlands sediments are in contact with the atmosphere, aerobic respiration governs biogeochemical processes. This “zone” is often very shallow – on the order of millimeters – and depends upon available carbon and the degree of atmospheric and hydrologic mixing (Reddy and Patrick, 1986; Sebacher et al., 1986). However, oxidized zones also exist near vegetation as hydrophilic plants are adapted to low oxygen ecosystems by forcing oxygen through aerenchyma to form oxidized rhizospheres
(Potter, 1997). Oxidation of carbon (for instance, glucose) is the most common form of respiration yet reduced electron acceptors may also be oxidized as well (Bowden, 1987;
Ettwig et al., 2010; Hanson and Hanson, 1996). Methane, the most reduced of all carbon 5
forms, is no different and methanotrophy (the oxidation of methane to carbon dioxide) is
necessary for consideration when determining GHG fluxes from wetlands.
Methane oxidation is carried out by the methanotrophs which are obligate
methylotrophs capable of only utilizing C1 compounds (Gottschalk, 1986). The following
formulae represent the methane to carbon dioxide metabolism and enzymatic machinery
of methanotrophs:
+ + CH4 + NADH + H + O2 CH3OH + NAD + H2O
CH3OH + PQQ CH2O + PQQH2
+ + CH2O + NAD + H2O HCOOH + NADH + H
+ + HCOOH + NAD CO2 + NADH + H
Methane and NADH act as the co-substrates for their aerobic respiration. The first reaction is the monooxygenase reaction to form methanol. Methoxatin (PQQ) is the identified enzyme responsible for the methanol dehydrogenase action resulting in the
formaldehyde product. Formaldehyde is oxidized in two final steps using NAD+, first
forming formate before reaching CO2 at the completion of the methanotrophic process.
One of the NADH formed in the final two processes can be used in the first step while the
other and PQQH2 are fed into other areas of the respiratory chain. There were originally
two types of methanotrophs belonging to separate taxonomic subdivisions: type I
(Gammaproteobacter) and type II (Alphaproteobacter) (Mohanty et al., 2006). These groups are distinguished by their phylogeny, physiology, morphology, and biochemistry
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mainly with phospholipid ester-linked fatty acids that are unique to methanotrophs and differ between these two types.
Nitrification is another oxidative process which is a critical to wetland functioning when there is an absence of allochthonous nitrate input (Bowden, 1987). Wetlands are most often limited in the availability of nitrate and nitrite while ammonia is much more plentiful in the sediments (Bowden, 1987). Nitrification uses this ammonia with oxygen to produce nitrite or nitrate. There are two groups: one completes their nitrification process upon the formation of nitrite while the other further oxidizes nitrite to nitrate
(Altmann et al., 2003; Purkhold et al., 2000). Either of these nitrogenous compounds is useful for denitrifying bacteria.
Acetogenesis and the oxidation of fatty acids are two crucial processes occurring in the carbon cycle that potentially lead to methane production in wetlands (Drake, 1994;
Schink, 1997). Acetogens and acetic acid bacteria are capable of producing fatty acids
(e.g. acetate) by the fixation of carbon dioxide or oxidation of carbon substrates, such as ethanol, respectively (Chai et al., 1962; Balch et al., 1977). Syntrophs, on the other hand, oxidize fatty acids to produce carbon dioxide and hydrogen (Schink, 1997). This process only occurs if hydrogen partial pressures are kept low enough (<10-4 atm) to ensure energetic favorability (Schink, 1997). This requires the bonding of these organisms to hydrogen scavengers, such as the methanogens, benefitting both organisms (Schink,
1997). These and other oxidative processes form the suboxic layer, where facultative organisms switch from aerobic to anaerobic metabolism.
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1.2.2 Suboxic Processes
When oxygen is low in the soil or water column, microbial activities including denitrification to N2 or the dissimilatory nitrate reduction to ammonium (DNRA) dominate energetic metabolism (Bowden, 1987). The enzymatic machinery used in denitrification, in most cases, is controlled by the presence of oxygen and nitrate and the facultative nature of these microorganisms (Bowden, 1987). In other words, denitrification proceeds when oxygen is insufficient to complete aerobic respiration and a suitable electron donor exists. With glucose as the carbon source (electron donor) and nitrate as the electron acceptor the process is enthalpically comparable to aerobic respiration (93% energy gain of aerobic respiration) (Gottschalk, 1986). The following reaction shows the chemical equation of denitrification:
- + 0’ C6H12O6 + 4.8 NO3 + 4.8H 6CO2 + 2.4N2 + 8.4H2O ΔG = -2669 kJ (-638 kcal)
There are four reduction processes involved in the complete denitrification process: nitrate reductase, nitrite reductase, nitric oxide reductase, and nitrous oxide reductase, each involving their own unique enzymes (Zumft, 1997). Although nitric oxide had been questioned as an intermediate product, more recent research has proven that it is indeed an intermediate for the denitrification pathway (Zumft, 1997).
The four genes that encode for enzymes responsible for the denitrification process include: nar (nitrate respiration), nir (nitrite respiration), nor (nitric oxide respiration), and nos (nitrous oxide respiration) (Zumft, 1997). The nitrate reductase enzyme, the first enzyme involved in denitrification, is membrane-bound and contains a molybdenum
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cofactor (Moco) (Gottschalk, 1986). In this reaction, nitrate is converted to nitrite and
water through the transfer of two electrons to hydrogen ions. This process is shown
below:
- - + - NO3 + 2e + 2H NO2 + H2O
The process of converting nitrite to nitrous oxide has recently been found to be a two-step process involving the linked enzymes of nir and nor linked together to remove nitric oxide compounds which may be toxic to the organisms (Zumft, 1997). There are two forms of the nir gene: nirK and nirS - with organisms possessing only one of these genes
(Ullah and Zinati, 2006). While the nirK gene is responsible for the copper-containing enzyme, the nirS gene encodes for cytochrome cd1 and is the dominant enzyme found
from environmental soil samples (Zumft, 1997). However, the general chemical reaction
remains the same and is shown below:
- + - - NO2 + 2H + 2e NO + H2O (Gottschalk, 1986)
This is followed by the reduction of nitric oxide to nitrous oxide shown below:
- + - 2NO + 2H + 2e N2O + H2O
The nor gene uses an iron site to reduce two nitric oxide molecules to nitrous oxide by
using an iron site located on the enzyme (Ye et al., 1994).
The final step in the denitrification process is completed by the nos c-type cytochrome (Gottschalk, 1986). This enzyme contains a copper catalytic site that converts nitrous oxide to dinitrogen and water as shown below:
+ - N2O + 2H + 2e N2 + H2O
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Some organisms possess enzymes for all four parts of this process, while others
are responsible for only one or two steps (Zumft, 1997). When denitrification proceeds from nitrate through dinitrogen, the overall requirements are (Gottschalk, 1986):
- - + 2NO3 + 10e + 12H N2 + 6H2O
Denitrification has been found to exist through two other processes that differ
from the traditional method – denitrification linked to methane oxidation as well as
anaerobic ammonium oxidation (anammox) (Ettwig et al., 2010; Mulder et al., 1995).
The former is accomplished by using oxygen from nitrate or nitrite to oxidize methane while reducing the nitrogen compounds to dinitrogen gas. The result of anammox is complicated by which kingdom is performing the activity: Bacteria convert some
ammonia to nitrite, ultimately using that nitrite to oxidize ammonia to N2 gas, but the
Archaea can solely produce nitrite (Purkhold et al., 2000; Tourna et al., 2011). This adds
more nitrogen electron acceptors and can endure suboxic conditions, with anaerobic
zones forming in the deeper sediments.
1.2.3 Anaerobic Processes
Upon the reduction of iron, soil chroma changes achieving a criterion for hydric soil determination and is followed by sulfate reduction (Conrad, 2002). Sulfate reduction
can have a stranglehold on methanogenesis if concentrations of sulfate are high. This
accounts for extremely limited methane emissions observed from coastal wetlands
(Hoehler et al., 1994; Lovley and Klug, 1983). However, at fresh-water concentrations
sulfate only dampens methane production and does not prevent it completely.
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Methanogenesis is the terminal reductive process that occurs in wetland sediments
(Gottschalk, 1986). It involves the formation of the substrate compounds that
methanogens utilize: hydrogen, carbon dioxide, carbon monoxide, formate, methanol,
methyl-amines, and acetate (Ferry, 1993). These compounds are found in the anaerobic layers created via other microorganisms, such as the acetogenic or syntrophic bacteria.
The basis of the syntrophic relationship is that unless hydrogen or other fermentative products are kept at low concentrations, the reactions result in a net loss of energy for the
Syntrophs (Schink, 1997). Methanogens that form these relationships have a high 10-4 atm in the sediments.
Methane generation begins when light, nitrate, and sulfate are limited and a new source of energy is needed (Garcia, 1990). Aquatic sediments, both marine and freshwater, are the major habitats for methanogens (Garcia, 1990). They thrive in environments that span a broad range of conditions including pH, salinity, and temperature and work in dependence upon chemoheterotrophic organisms (Ferry, 1993).
There are two major groups of methanogens which are distinguished by the form
of carbon utilized in their metabolic pathways (Gottschalk, 1986). The
chemolithotrophic methanogens utilize inorganic carbon compounds such as CO, CO2,
and formic acid (HCOOH) (Garcia, 1990). Methylotrophic methanogens, on the other
hand, use methyl-group-containing compounds as methanol, methylamines, and acetate
as their substrates (Garcia, 1990). The following reactions highlight these two
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methanogenic pathways and their net energy gain for typical substrates (Gottschalk,
1986):
0’ CO2 + 4H2 CH4 + 2H2O ΔG = -136 kJ (-32.4 kcal) – chemolithotrophs
0’ CH3-COOH CH4 +CO2 ΔG = -37 kJ (-8.9 kcal) – methylotrophs
Two of the first coenzymes discovered and described are coenzyme F420 and
coenzyme M with a reactive mercapto group which is methylated to form the precursor to
methane, methyl-coenzyme M (Ellefson and Wolfe, 1981; Ermler et al., 1997).
Coenzyme F420 acts as an electron acceptor for hydrogenase and is involved in several
reduction reactions as an electron donor (Ellefson and Wolfe, 1981). Other structures
involved in the methanogenic process are factor F430, methanofuran, and 5,6,7,8-
tetrahydromethanopterin, with the latter two involved in CO2 reduction (Gottschalk,
1986). The mcrA gene, which encodes the α-subunit of the methyl-coenzyme M reductase enzyme responsible for the final step of methane formation has been intensively studied and used as an indicator of methane activity due to it being the terminal enzyme for all methanogenic reactions (Ermler et al., 1997; Freitag et al., 2010).
For the methanogens utilizing H2 and CO2, the metabolic process begins with
CO2. Methanofuran (MF) acts as the first carrier reacting with reducing equivalence to
form formylmethanofuran. The formyl group is transferred to tetrahydromethanofuran
(THMP) and reduced to a methyl group. This is transferred to coenzyme M (CoM) and
the methyl coenzyme M reductase enzyme catalyzes the final reaction releasing methane.
The acetate utilizing methanogens begin with cleaving acetate into the methyl group and
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carbon monoxide. Carbon monoxide is oxidized to carbon dioxide and the reducing
equivalents can be formed from this reaction to allow the final step of forming methane.
The entire reaction is shown below:
CO2 + MF-H MF-CHO + H20
MF-CHO + THMP-H + H2 THMP-CH2OH + MF-H
THMP-CH2OH +H2 THMP-CH3 + H2O
THMP-CH3 + CoM-SH CoM-S-CH3 + THMP-H
CoM-S-CH3 + H2 CH4 + CoM-S-H
The final step gains an ATP and the generated methane is respired to the sediments where
it can directly escape through diffusion across the water column or ebullition. In order to
escape it will need to pass through the oxidized layer before entering the atmosphere.
Methylotrophs alternatively commence with a methylated compound, for example acetate which is attached to an unknown carrier (X) removing the hydroxyl component.
The methyl group is then transferred to the coenzyme M while carbon monoxide is oxidized by carbon monoxide dehydrogenase (Cod) to form carbon dioxide. That hydrogen is then use in the final energy-gaining step of producing methane. This reaction is shown below:
CH3COOH + X-H X-CH3CO + H20
X-CH3CO + CoM-SH CoM-S-CH3 + CO
CoM-S-CH3 + CO + COd + H2O CH4 + CO2 + CoM-S-H
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1.3 Microbial Communities in Wetland Environments
Microbial communities play a determining role in the rates of denitrification,
methanogensis, and methanotrophy in wetlands through competition or cooperation for
available resources. The conditions that exist within the wetland likely shape the
community structure of methanogens and denitrifiers (Cleary et al., 2012; Kim et al.,
2008). Although methanogens are limited to anaerobic soils, it is not to imply they
cannot tolerate exposure to oxygen, and thus should be expected to be present in non-
hydric soils (Kiener et al., 1983). In many cases they remain inactive but viable until saturated conditions persist allowing many upland soil types to have methanogenic abilities (Angel and Conrad, 2012).
It is the overall environmental conditions that can determine the composition and it may have large implications to ecosystem functioning. Diversity within groups such as the methanotrophs can be caused by these factors and this diversity can result in major
differences of expected methane oxidation rates (Mohanty et al., 2006; Nazaries et al.,
2013). Organisms inhabit their niche conditions, including pH, under which it has been
demonstrated organisms, will grow best at the same level as where they were found
(Cavigelli and Robertson, 2000). Therefore, the organisms at each site are likely to be
best suited for the conditions that are present where they are growing.
Taxonomy does not explicitly imply functionality among microorganisms,
detailed studies relating functional genes to taxonomy must be performed to reach that
level of identification (Urich et al., 2008). However, some organisms share more
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phylogenetic similarities than others, such as methanogens and methanotrophs, and are
much easier to determine on a taxonomic basis (Nazaries et al., 2013). Carbon and
nitrogen use is common among all organisms – carbon is the major component of organic
matter, while nitrogen is essential to proteinaceous cell components - but many of the
energy metabolisms are limited to a few taxonomic units. The genera which have been
identified and linked to a function important to denitrification or methanogenesis are provided, while broader descriptions are described below (Table 1.1 and 1.2).
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Genus Reference Genus Reference Nitrification or Anammox Denitrification Anammoxoglobus (Arrigo, 2005) Achromobacter (Prieme et al., 2002) Brocadia (Arrigo, 2005) Alcaligenes (Prieme et al., 2002) Jettenia (Arrigo, 2005) Alicycliphilus (Mechichi et al., 2003) Kuenenia (Arrigo, 2005) Blastobacter (Liu et al., 2003) Nitrobacter (Purkhold et al., 2000) Bowmanella (Jean et al., 2006) Nitrococcus (Purkhold et al., 2000) Bradyrhizobium (Prieme et al., 2002) Nitrosococcus (Purkhold et al., 2000) Brucella (Zehnder, 1988) Nitrosolobus (Purkhold et al., 2000) Dechloromonas (Horn et al., 2005) Nitrosomonas (Purkhold et al., 2000) Flavobacterium (Horn et al., 2005) Nitrososphaera (Tourna et al., 2011) Kingella (Zehnder, 1988)
Nitrosospira (Altmann et al., 2003) Methylomirablis (Ettwig et al., 2012)
Nitrospina (Ionescu et al., 2012) Methylotenera (Kalyuhznaya, 2009) Nitrospira (Purkhold et al., 2000) Paracoccus (Zehnder, 1988) Scalindua (Arrigo, 2005) Paenibacillus (Horn et al., 2005) Pseudomonads (Zehnder, 1988) Ralstonia (Zehnder, 1988) Roseobacter (Prieme et al., 2002) Rhodobacter (Prieme et al., 2002)
Shewenella (Zehnder, 1988)
Sinorhizobium (Zehnder, 1988)
Steroidobacter (Fahrbach, 2008)
Thiobacillus (Zehnder, 1988)
Sulfurimonas (Takai et al., 2006) Table 1.1 Genera involved in nitrogen biogeochemical cycles of wetlands
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Genus Reference Genus Reference Acetogenesis/Syntrophy Methanotrophy Acetobacter (Raspor and Goranovic, 2008) Albibacter (Dedyesh et al.,2003) Acetobacterium (Balch et al., 1977) Clonotrhix (Op den Camp et al., 2009) Acidicaldus (Raspor and Goranovic, 2008) Crenothrix (Op den Camp et al., 2009) Acidiphilium (Raspor and Goranovic, 2008) Hansschlegelia (Dedyesh et al.,2003) Acidisphaera (Raspor and Goranovic, 2008) Methylacidiphilum (Op den Camp et al., 2009) Acidocella (Raspor and Goranovic, 2008) Methylobacter (Dedyesh et al.,2003) Acidomonas (Raspor and Goranovic, 2008) Methylocaldum (Dedyesh et al.,2003) Asaia (Raspor and Goranovic, 2008) Methylocella (Op den Camp et al., 2009) Belnapia (Raspor and Goranovic, 2008) Methylococcaceae (Dedyesh et al.,2003) Clostridium (Schink et al., 2002) Methylococcus (Dedyesh et al.,2003) Craurococcus (Raspor and Goranovic, 2008) Methylocella (Op den Camp et al., 2009) Desulfovibrio (Schink et al., 2002) Methylocystis (Dedyesh et al.,2003) Gluconacetobacter (Raspor and Goranovic, 2008) Methylohaloblus (Op den Camp et al., 2009) Gluconobacter (Raspor and Goranovic, 2008) Methylomicrobium (Dedyesh et al.,2003) (Raspor and Goranovic, 2008) Granulibacter Methylomirablis (Ettwig et al., 2012) Kozakia (Raspor and Goranovic, 2008) Methylomonas (Dedyesh et al.,2003) Leahibacter (Raspor and Goranovic, 2008) Methylopila (Dedyesh et al.,2003) Marinobacter (Gray et al., 2011) Methylosinus (Dedyesh et al.,2003) Muricoccus (Raspor and Goranovic, 2008) Methylosphaera (Dedyesh et al.,2003) Neoasaia (Raspor and Goranovic, 2008) Pleomorphomonas (Dedyesh et al.,2003) Oleomonas (Raspor and Goranovic, 2008) Terasakiella (Dedyesh et al.,2003) Paracraurococcus (Raspor and Goranovic, 2008) Methanogenesis Rhodopila (Raspor and Goranovic, 2008) Methanobacterium (Ferry, 1993) Roseococcus (Raspor and Goranovic, 2008) Methanocalculus (Ferry, 1993) Rubritepida (Raspor and Goranovic, 2008) Methanococcoides (Ferry, 1993) Saccharibacter (Raspor and Goranovic, 2008) Methanococcus (Ferry, 1993) Smithella (Gray et al., 2011) Methanoculleus (Ferry, 1993) Stella (Raspor and Goranovic, 2008) Methanogenium (Ferry, 1993) Strain S (Schink et al., 2002) Methanomicrobium (Ferry, 1993) Swaminathania (Raspor and Goranovic, 2008) Methanopyrus (Ferry, 1993) Syntrophaceae (Schink et al., 2002) Methanoregula (Ferry, 1993) Syntrophobacteraceae (Schink et al., 2002) Methanosaeta (Ferry, 1993) Syntrophomonas (Schink et al., 2002) Methanosarcina (Ferry, 1993) Syntrophorhabdaceae (Schink et al., 2002) Methanosphaera (Ferry, 1993) Teichococcus (Raspor and Goranovic, 2008) Methanosprillium (Ferry, 1993) Thermoanaerobium (Schink et al., 2002) Methanothermobacter (Ferry, 1993) Veillonellaceae (Schink et al., 2002) Methanothrix (Ferry, 1993) Zavarzinia (Raspor and Goranovic, 2008) Methofollis (Ferry, 1993) Mthanobrevibacter (Ferry, 1993)
Mthanocorpusculum (Ferry, 1993) Table 1.2 Genera involved in carbon biogeochemical cycles of wetlands
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1.3.1 Microorganisms Involved in Nitrogen Removal
Denitrifiers are one of the most abundant forms of organisms in the environment, in part due to their facultative nature that takes advantage of aerobic and anaerobic conditions
(Tiedje et al., 1982). The denitrifiers are shaped for their environment with a preference for either the aerobic or anaerobic conditions although suitable for both (Tiedje et al.,
1982). Among these, the most bountiful group is the genus Pseudomonads found in both soils and sediments (Knowles, 1982). Genotype analysis of denitrifiers by identifying functional genes is useful to providing insights to which organisms participate in this microbial activity (Ruiz-Rueda et al., 2009). To date, denitrifiers have been found in the
α-proteobacteria, β-proteobacteria, and γ-proteobacteria (Prieme et al., 2002; Zehnder,
1988).
Anammox also deserves consideration in nitrogen removal since the Bacteria involved are able to produce gaseous forms (Arrigo, 2005). All known Bacteria performing anammox belong to the Planctomycetes phylum. Archaea, specifically in the phylum Thaumarchaeota, perform anammox and have only recently been isolated
(Tourna et al., 2011). As described previously, this process terminates upon oxidizing ammonia to nitrite although this can then be utilized by denitrifying bacteria (Tourna et al., 2011). Therefore, ammonia oxidizing archaeons should only be considered as supplying substrate with the potential for removal if other denitrifiers complete the process.
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Similarly, nitrifiers do not effectively remove nitrogen from the aquatic system,
but are potential contributors to that process (Bowden, 1987). Ammonia is first oxidized
to nitrite by the nitrifiers belonging to the β-proteobacteria and γ-proteobacteria members (Ionescu et al., 2012; Purkhold et al., 2000). Nitrite is further oxidized by the other group of nitrifiers which in turn belong to α-proteobacteria, δ-proteobacteria, γ- proteobacteria, or the Nitrospirae phylum (Altmann et al., 2003). Either of these substrates is suitable for denitrifying organisms.
1.3.2 Microorganisms Involved in Methane Production
The process of methane commences with primary decomposers and secondary decomposers capable of polymeric degradation which provide substrates to what are collectively called the secondary decomposers (Schink, 1997). Secondary decomposers are capable of producing the methanogenic substrates acetate, hydrogen, and carbon dioxide which can be readily consumed by methanogens, or more complex fatty acids requiring a syntrophic partnership (Schink, 1997). Syntrophs and the acetogenic bacteria are distributed across the Firmicutes, α-proteobacteria, or δ-proteobacteria (Gray et al.,
2011; Ma et al., 1991; Schink, 1997; Zhao et al., 1993).
There are 68 species of known methanogens which are found within only three phylogenetic orders, all belonging to the Euryarchaeota phylum (Garcia, 1990).
Methanosarcinae and Methanosaeta are the only two known genera of methanogen that are capable of utilizing acetate, with the latter being obligatory to this substrate (Garcia,
1990). However, many methanogens can utilize both pathways and rely on some
19
combination of the acetate, hydrogen, or methylamine substrates - most chemolithotrophs can utilize formate in place of hydrogen (Garcia, 1990). A genotyping study of rice paddy fields found methanogens from the groups: Methanosarcinae, Methanosaeta,
Methanomicrobiaceae, Methanobacteriaceae, and various rice-cluster groups inhabiting fresh-water hydric soils (Lueders and Friedrich, 2000). Recent findings in the Everglades ecosystem, a vast, subtropical wetland in Florida, discovered similar genera of methanogens, with Methanosaetae being especially dominant due to low acetate concentrations and these organisms’ high affinity for that substrate (Castro et al., 2004).
While the chemolithotrophs represent the majority of known methanogens, this may not be reflected in their distribution in wetlands. Studies of methanogenic pathways have estimated that acetate accounts 66-70% or more of methane generation, suggesting the dominance methylotrophs in these ecosystems (Conrad, 2002; Zeikus, 1977).
However, formate was found to have a greater stimulation effect on tropical wetland sediments in a laboratory based experiment, suggesting the opposite to be true (Smith et al., 2007). Some of this discrimination may be incurred by chemolithotrophs ability to switch pathways, or even the presence of syntrophic partnerships. It is theorized that the ratio of these two groups is determined based upon individual site and conditions, as the thermodynamic-favorability of each pathway is determined by many environmental factors including temperature (Conrad, 2002).
1.3.3 Microorganisms Involved in Methane Oxidation
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Methane oxidation is a process carried out by Bacteria and is found across many aerobic habitats including aerobic wetland sediments. There exist two well-known types
(I and II) which are classified as Proteobacteria, with type I associated with γ- proteobacter and type II with α-proteobacter (Dedysh et al., 2003). A new methanotroph, belonging to the Verrucomicrobia phylum, was recently discovered although much about this group is still unknown (Op den Camp et al., 2009). Finally, indications show that the NC10 phylum is capable of denitrification directly linked to methane oxidation and should be considered both a denitrifying and methane oxidizing bacteria (Ettwig et al., 2010). There is some belief that sulfate reducers can also perform methane oxidation, although it most likely occurs in salt-water and is not yet confirmed
(Hoehler et al., 1994).
Type I and II methanotrophs are classified by their enzyme, methane monooxygenase, and generally thrive under different environmental conditions. Type I contain a membrane-associated version of the enzyme called pMMO and are found in areas with high oxygen to methane concentrations (Hanson and Hanson, 1996). Type II methanotrophs can produce pMMO, but also develop a soluble form, sMMO, that is found in their cytoplasm (Hanson and Hanson, 1996). These methanotrophs thrive in areas of high methane to oxygen ratio and low copper availability (Hanson and Hanson,
1996). Methane production in the subsurface enhances the ability of methanotrophs to proliferate in wetland environments and also provide niches for these bacteria, especially around the oxidize rhizospheres of vegetation (Potter, 1997).
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1.4 Environmental Factors Influencing Wetland Microbial Activity
Environmental factors have been intensively studied for modeling purposes in relation to denitrification and methanogensis rates in wetland environments. These factors include temperature, pH, redox conditions (dependent on hydrology), and nutrient availability. Vegetation can also affect microbial activity whether by changing microbial communities, releasing methane through its leaves, or providing nutrients (Cleary et al.,
2012; Potter, 1997). Temperature is well established as a key factor for microbial activity rates, but this effect is not uniform and is more dependent upon each organism (Farrell and Rose, 1967). In other words, some organisms grow best at higher temperatures
(mesophilic, thermophilic) while others are adapted for cold (psychrophilic). In fact all organisms may be affected differently by the summed interaction of environmental factors making estimation of the biogeochemical cycling rates very complicated.
1.4.1 Hydrology
The presence of water at or near the surface of wetlands has several effects on microbial activity. First, water allows the depletion of oxygen by microorganisms by slowing diffusion into the soils and thus reduces the redox potential (Conrad, 2002;
Reddy et al., 1984). This results in lower overall microbial activity and allows for the accumulation of organic matter (Mitsch and Gosselink, 2007). Water is also necessary for these activities and so saturation can also stimulate microbes (Qiu et al., 2005). For methane production, the water table depth is known to increase rates until the area is covered with ~10 cm, at which point there is a negligible effect (Sebacher et al., 1986).
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Areas near the margins of wetlands that experiencing pulsating hydrology therefore are
expected to emit less methane, as has been demonstrated in a large-scale experiment
(Altor and Mitsch, 2008).
The hydrostatic pressure asserted by water onto the soil interface is what prevents
atmospheric exchange with the sediment derived gases such as methane. Water
drawdowns have been shown to cause noticeable spikes of methane release with the relief
of pressure (Roslev and King, 1996). However, this exposure to oxygen can decrease the
methanogenic activity until the area floods again (D’Angelo and Reddy, 1999).
Methanogenic rates will continue to be relatively low until the duration of the flood is
long enough for soils to be completely reduced, again (D’Angelo and Reddy, 1999).
Water drawdown also introduces oxygen to methanotrophs. This has the potential to reduce some of the methane that does not immediately escape the initial drawdown and
for production from the deeper sediments (Roslev and King, 1996). As oxygen
concentrations in the sediment is determined by water table height, so too are
methanotrophic abilities. Oxygen concentrations decline with increasing soil depth and
this has been found to affect the methanotrophic community with more type II
methanotrophs in deep, anoxic soils and more type I in the shallow depths (Roslev and
King, 1996). This shows just one of the ways in which hydrological conditions affect the microbial community.
The alteration caused on methanogenic populations by the introduction of anoxia is less defined. Using 16s rRNA clones, changes were seen shifting the composition of
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methanogenic communities in oxygen-exposed sediments (Lueders and Friedrich, 2000).
However, that same study showed no changes in the methanogen community using the
terminal restriction fragment length polymorphism (T-RFLP) method. The
Methanosarcina was one of several clones that were found to increase from the initiation
of flooding, albeit with lower concentrations of their usual substrate, acetate (Lueders and
Friedrich, 2000). Similar to the T-RFLP results of the laboratory-based study, field
studies have also found only minor changes to the methanogenic community brought
about by flooding (Kruger et al., 2005). As noted before, methanogens show oxygen-
tolerance, and their ability to withstand this exposure is necessary for their survival in
wetlands experiencing a fluctuating hydrology.
1.4.2 Temperature
Temperature is of great importance to all microorganisms and both methanogens and denitrifiers, with a significant increase in their metabolisms corresponding with increased temperature (e.g., Stadmark and Leonardson, 2007; Westermann and Ahring,
1987). This is unsurprising as many organisms which have been cultured show the best growth rate usually between 30-37°C, with methanogens being no exception (Jones et al.,
1987). The response of these organisms to temperature may be more important beyond a simple rate calculation. For instance, in the cold winter months, functional gene quantification has found lower expression ratio of the nosZ gene compared to other denitrifying genes (Garcia-Lledo et al., 2011). This means there may be more N2O
released compared to N2 gas, against an overall lower denitrification rate. This is an
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example of a hidden consequence which can only be understood by more in-depth research as this would result in more GHG production per nitrogen removed.
The energetics of methane production and its interaction with temperature is one way in which this factor affects the microbial community. Energy gained from either methylotrophic or chemolithotrophic metabolism is based on both substrate availability and temperature (Schink, 1997). Methylotrophy is the thermodynamically favored pathway at lower temperatures environmental temperatures while chemolithotrophy is favored at higher temperatures (Conrad, 2002). Affirming this, chemolithotrophs were found to be the dominant methanogen in the warm, tropical Everglades, responsible for approximately 60% of the methane emissions, while methylotrophy was the dominant pathway in cold, polar regions (Smith et al., 2007; Zeikus, 1977). This has a high probability of affecting the overall microbial ecosystem interactions in defining methanogenic cooperators and competitors.
1.4.3 Nutrient Availability
Primary productivity is very important to all microbes, as it introduces the carbon to the system (Whiting and Chanton, 1993). Ratios of other electron acceptors are also useful indicators of potential for microbial activities as methanogensis, denitrification, and all biogeochemical processes (D'Angelo and Reddy, 1999; Laanbroek, 2010; Thiere et al., 2011). These studies have found that C:N ratios are important factors for both methanogenesis and denitrification. In this case, a high C:N is favorable to
25
methanogenesis whereas a low C:N is preferable to denitrification (Flury et al., 2010;
Warneke et al., 2011).
In wetlands, it has been found that exceeding a 5:1 C:N ratio has very little impact
on denitrification rates (Braker et al., 1998). The competition of nitrate among bacteria revolves around the two pathways of denitrification or DNRA (Tiedje et al., 1982). The probability of which pathway is dominant depends upon which nutrient is limiting: denitrification occurs when carbon is limiting and DNRA occurs when nitrate is limiting
(Tiedje et al., 1982). Overall, the sequence of limiting nutrient factors for denitrification
is: carbon, nitrogen, and then phosphorus (White and Reddy, 2003).
While denitrification rates are typically unaffected by concentration of
phosphorus, the effect of phosphorus on methane emissions is less clear (Ullah and
Zinati, 2006). In some cases high phosphorus concentrations have stimulated methane
production (Thiere et al., 2011). However, root-associated methanogens are sensitive to
phosphorus and high concentrations near vegetation can have the opposite effect (Conrad,
2002). Nitrate provides a substrate that organisms can use to out-compete methanogens and so its concentration must remain low for methanogenic activity. Furthermore nitrate is now known to be coupled directly to methane oxidation and having an effect on methane emissions (Ettwig et al., 2010).
Reduction of methanogenic rates by sulfate reducers at fresh-water concentrations has been identified by the addition of molybdate, which effectively terminated sulfate reduction (Lovley and Klug, 1983). Yet, that does not preclude these two processes from
26
occurring at the same time completely (Garcia, 1990). Furthermore, upper sediment
sulfate reducers have been related to syntrophy and acetogenesis, potentially having a
positive impact on the methane production (Conrad, 2002; Zeikus, 1977). Finally, reduced sulfur in the form of hydrogen sulfide can be toxic to many microorganisms including methanogens resulting in another effect on microbial functioning (Zeikus,
1977). Iron and manganese are also important and have been shown to force sulfate reduction and methanogenesis to deeper sediments (Roden and Wetzel, 1996). While principles of nutrients affecting the microbial activity have been developed, much more clarity is needed.
1.4.4 Vegetation
Vegetation has one of the more dynamic influences on methane emissions from wetlands. Plants are the source of primary productivity that introduces carbon and other nutrients to the wetland (Dannenberg and Conrad, 1999). They also can bring oxygen to their roots, forming oxidized zones known as the rhizosphere (Holzapfelpschorn et al.,
1985). However, oxygen is only forced to the roots by gas flow in the opposite direction requiring plants to emit methane through their aerenchyma and leaves. Oxygen in the
rhizosphere then has the ability to create zones in otherwise anoxic sediments, where
oxidative processes such as nitrification can persist (Bowden, 1997).
Nutrient availability is likely the most influential effect of vegetation, whether it
come from detritus or root exudates, with carbon dioxide isotope directly relating
methanogenesis to photosynthesis (Dannenberg and Conrad, 1999; Thiere et al., 2011).
27
Vegetation can also release sulfur compounds, as well as other as elements, and lead to the increase in sulfate concentrations (Dannenberg and Conrad, 1999). The oxidized
rhizosphere also has the potential to act as a boundary over which oxidative and reductive
processes cycle (Roden and Wetzel, 1996). For instance, iron is often found oxidized
around the roots of vegetation which can again be reduced. This suggests these zones
may be favorable for other reductive processes which out-compete methanogenesis.
Oxygen in the roots can also stimulate the methanotrophs, and unsurprisingly high
abundances of methanotrophs have been found near these areas (Hanson and Hanson,
1996). Finally, vegetation has the ability to dnutrients down through the water column as
it uptakes water during evapotranspiration (Martin et al., 2003). This can introduce many
nutrients from the water column to the microbial ecosystem.
Vegetation has the potential to shape the microbial community, and thus effect
biogeochemical cycling, by its provision of nutrients and oxygen. The β-diversity of wetland microbial communities has shown that vegetation influences the community structure (Cleary et al., 2012). Methanotrophic diversity has been shown to vary across land-use types, from wetlands to forests, and discrepancies within α-diversity may have a rather large impact on methanotrophic rates (Singh et al., 2007; Nazaries et al., 2013).
Wetland field studies have found vegetation-introduced oxygen to influence the diversity of the methanotrophic communities; however, laboratory microcosms simulating oxygen exposure yielded no changes to the community (DeJournett et al., 2007). Therefore, it is not certain that oxygen is strictly the force behind this community shift, and it may be
28
caused by root-exudates or other factors. Denitrification community structure has also
been found to vary across wetland biomes, although these changes were not found to
influence denitrification rates (Song et al., 2011). Although the mechanism may not be identified and the effects unclear, it is obvious that vegetation will ultimately impact the microbes.
1.4.5 Redox Potential and Sediment pH
Redox potential and pH are two metrics which have been used in studies to relate their effect on chemical fluxes from wetlands. In reality, they describe the sediment chemistry. The pH measurement is a relationship of the acid and base concentration which describes whether sediments are acidic, basic, or neutral. Redox potentials are a measure of the oxidative and reductive potential of the sediments.
It is well known that pH has an effect on many biological activities. However, it is logical that microorganisms to be adapted to the natural conditions at their own site.
Diurnal and seasonal pH patterns have been found across many wetland biomes, but there is no clear effect on either denitrification or methanogenic rates (Bubier and Moore,
1994; D'Angelo and Reddy, 1999). This is potentially explained by microorganism homeostasis – the ability to maintain optimal pH within an organism’s cell despite exterior changes (Padan et al., 1981). Yet, acidic peat samples under incubation have been found to produce more methane when grown at pH of 6.0 compared to its indigenous 3.8-4.0 range (Williams and Crawford, 1984). This effect shows that the
29
organisms may have more favorable conditions, but the usefulness of pH as a factor to
incorporate into models depends upon the typical range of pH experienced at the site.
Redox potential is a metric capable of showing the nutrient availability within sediments. Specifically, it indicates the tendency within the sediment for other chemicals to be oxidized or reduced. A high (positive) reading suggests that microbial activity will proceed in oxidizing other substrates in order to gain energy. Conversely, lower
(negative) readings suggest that microbial activity will be primarily reductive. Readings indicate the dominant reductive process occurring: 250 mV for nitrogen; 225 mV for manganese; 100 to -100 mV for iron; -100 to -200 mV for sulfate; and < -200 mV for
carbon (methanogenesis) (Mitsch and Gosselink, 2007). These values are not definitive,
though, as methanogenesis has been found to occur in sediments with measurements well
above -150 mV (Boon et al., 1997). In fact, methanogenesis has been found to occur in
sediments where redox potential indicates oxidative processes should be dominant (>250
mV) (Alewell et al., 2008). There is the possibility that infinitesimal microsites existing
within those sediments would explain how methanogenesis was able to occur despite
those levels. Redox potential may be best described in wetland models as by the water
table depth, as this is what determines oxygen availability and allows reduced sediments
to form (Walter and Heimann, 2000).
1.4.6 Incorporation of Factors into Models Estimating Biogeochemical Flux
Models have shown that hydrology is the most important factor, initially, to
methane flux models (Kettunen, 2003; Walter and Heimann, 2000). Without water, no
30
anaerobic sediments would form near enough to the surface restricting methane flux.
After reaching certain thresholds in the water table depth, temperature begins to affect
models more than other factors (Kettunen, 2003; Walter and Heimann, 2000). Although poorly understood, nutrient availability has shown to be critical in sensitivity analysis and
is a major factor to consider (Kettunen, 2003). Vegetation is often considered in models,
but its many interactions sometimes have conflicting effects. For example, while plants
provide some nutrients that may favor methanogenesis, oxygen introduced to the roots
may allow for methane oxidation or other oxidative processes to out-compete methanogens (Kettunen, 2003; Potter, 1997). Each factor in models does not necessarily have uniform effects as they vary across geographic locations (Walter and Heimann,
2000). Some of this variance is likely caused by factors that are not completely understood including nutrient availability, pH, and the microbial community.
1.5 Conclusions
Wetlands are important ecosystems in many respects that outweigh the societal cost incurred from GHG emissions. The ability to provide habitat for endangered species and protections to human society from flooding cannot be undervalued. However, since greenhouse gas flux and nutrient removal are linked it is important to consider whether those benefits outweigh the detriments and what design principles can be used to optimize the system. This is especially true when wetlands are designed specifically for nutrient removal purposes. To create a better understanding we must bring clarity to the role that various environmental factors have on biogeochemical fluxes.
31
There are many complications when it comes to understand the biogeochemical cycling in wetland sediments. The interaction of many various factors makes it difficult to decipher the effect of individual factors. Although each process has its own described favorable conditions interactions between multiple organisms can limit our understanding. Microbial communities and their influence on rates are often neglected despite that they are performing this activity. Experiments have often restricted their studies to only several groups and ignore the effects of the entire ecosystem. The combined integration of nutrient availability, vegetation, temperature, microbial community, and other factors into models should aide best management practices for wetland restoration and construction. Solving the dilemma will allow reasonable application of wetland creation if necessary to deal with the issue of nutrient loading and hypoxia of large water bodies.
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Chapter 2: Factors Affecting the Anaerobic Microbial Respiration Potential of Wetland
Sediments
2.1 Abstract
Wetlands are a valued ecosystem because of their ability to improve water quality
through pollutant removal, their high biodiversity, the carbon that is sequestered and
stored in their sediments, and their ability to dampen storm hydrographs through water
storage. However, wetlands may also contribute to global warming, most obviously
through microbial methane production. Driving the biochemical processes that enable
methanogenesis is a diverse community of microorganisms under the influence of multiple environmental factors that are poorly constrained. Sediments were collected
from several distinct biomes across a fresh-water, constructed wetland. Depth and
temperature were tested to analyze their effect on biogeochemical cycling. Anaerobic
microcosms were prepared to test the potential methane flux with two controlled
temperatures. Pyrosequencing was used to provide an in-depth investigation of the
microbial community responsible for biogeochemical cycling and water chemistry was
monitored. Unvegetated, open-water derived sediment samples were found to produce
33
more methane than vegetated, edge zone sediments. Deeper sediments lacked the ability
to produce any methane and, with lower carbon dioxide flux potentials, had the lowest
rates of microbial respiration. A 10ºC increase in temperature accelerated the rate of methane production across the wetland site 2-3 fold. Methanogens were most prevalent in sediments collected from open-water zones at 4.5%, while taxonomic units closely related to nitrogen cycling microbes were found at higher abundances in vegetated, edge zone sediments. These results suggest that there is a greater potential for radiative- forcing-neutral greenhouse-gas budgets when constructed wetlands are designed with a higher density of vegetated biomes as compared with open-water zones.
2.2 Introduction
The management of wetland ecosystems is complicated by an environmental
dilemma whereby they provide ecosystem services to human society by acting as
nitrogen and carbon sinks, biodiversity reserves, and flood control; but are also major
contributors of greenhouse gases (Bowden, 1987; Bridgham et al., 2006). Early 20th
century areal losses resulting from drainage or dredging of wetlands in combination with
the excessive use of fertilizers in modern agricultural practices have collectively
increased nitrogen loading to surface waters and subsequent hypoxic conditions in large
drainage basins such as the Laurentian Great Lakes and Gulf of Mexico (Conroy et al.,
2011; Dahl, 1990; Rabalais et al., 2002; Vitousek et al., 1997). Currently protected by
the Clean Water Act of 1972 (2002), altered or destroyed wetlands in the United States
require mitigation or replacement (33 USC § 1251 et seq.). Outside these requirements,
34
the creation of even more wetlands has been proposed to purify surface waters and to
support other ecosystem services, such as habitat for rare species (e.g., Mitsch et al.,
2001).
One of the impediments to prescribing the creation of wetlands for nutrient
removal is whether the design considers the effect of greenhouse gasses (GHG)
generation in its assessment of short- or long-term ecosystem services. Wetlands are large contributors of GHGs, and specifically methane, accounting for more global methane flux than any other type of land surface coverage (IPCC, 2007). Wetlands also serve as a carbon sinks through the uptake and long-term storage of carbon dioxide, but the radiative forcing generated by methane may have a greater global warming effect over the short-term (Mitsch et al., 2012). Understanding the dynamics of methane and carbon dioxide production as it relates to water quality improvements is essential for the optimal design and management of these ecosystems.
The production of methane and nitrous oxide from wetlands is executed by methanogenic and denitrifying microorganisms that operate through a complex food web within the sediments (Conrad, 1996). As a result of their characteristic hydric soils, wetlands experience chemically-reducing conditions with limited oxygen availability that penetrates only millimeters to centimeters from the surface, forcing microorganisms to seek out alternate electron acceptors to support respiration (Reddy et al., 1986).
Nitrogenous compounds, including nitrate and nitrite, are often rapidly utilized under suboxic conditions (Bowden, 1987; Conrad, 1996). After oxygen and oxidized
35
nitrogenous compound depletion, utilization of other electron acceptors follows with
methanogenesis commencing as the terminal decomposition pathway (Gottschalk, 1985).
The extent and rate for which these activities take place are influenced by multiple
environmental factors, including temperature and site biome, and ultimately define the
benefits of nitrogen removal relative to methane production at the site.
Among the environmental factors most impacting microbial processes at wetlands, temperature is known to positively influence denitrification and methanogenesis activity and therefore the production of related GHGs (Altor and Mitsch,
2008b; Hernandez and Mitsch, 2007b; Raich and Potter, 1995). Similarly, the
availability of carbon substrates can effectively limit microbial activity. Dissolved
organic carbon (DOC) and nitrate are of great importance to methanogenesis and
denitrification, respectively, with each being the most critical element to sustained
microbial activity (Liu et al., 2011; White and Reddy, 1999). Nitrogen, in nitrate and ammonium forms, has been related to the suppression of methanogenesis (Dong et al.
2011; Laanbroek, 2009). However, increased nutrient loading of these nitrogen compounds can lead to an increased biochemical oxygen demand, which further drops the redox levels leading to increased methanogenesis (Inamori et al., 2007). It has been put forward that nutrient loads may lead to the release of carbon into dissolved organic forms (e.g. acetate), however, evidence suggest rather that DOC controls the production and release of other nutrients (Worral et al., 2006). There is inept understanding of
36
relationship between full-scale environmental factors and the resultant overall microbial
functioning.
Rates of methanogenesis and denitrification are not simply defined by the activity
of these organisms but are the result of competition, cooperation, and reliance of a
microbial ecosystem. Methanogens, for example, critically need primary decomposers
such as fermenters and acetogens to begin processing carbon into labile forms (Conrad,
2002; Drake, 1994; Schink, 1997). Some methanogens also form symbiotic bonds with a syntrophs, allowing for mutually beneficial transfer of energy resources through intra- species hydrogen transfer. These syntrophs can provide methanogens with major substrate pools necessary for methanogenesis: carbon dioxide with hydrogen
(chemolithotrophy) and, in some cases, acetate (methylotrophy) (Ferry, 1993).
Denitrification has typically been described as the suboxic removal of nitrate through its dissimilatory reduction to dinitrogen gas. Denitrifiers are capable of using an array of carbon substrates and only substitute nitrate as the electron acceptor after oxygen has been depleted (Zehnder, 1988). While denitrifiers generally out-compete other organisms due to high entropic favorability, they rely on other organisms to produce nitrate and nitrite in the absence of allochthonous inputs. Nitrification and anammox
(performed by archaeal members) are two processes which can supply denitrifiers with nitrate or nitrite and concurrently remove accumulated ammonium from wetland sediments (Purkhold et al., 2000; Tourna et al., 2011). Alternately, anammox performed
37
by Bacteria results in nitrogen removal directly to N2 gas; therefore their presence and
activity could similarly be considered with traditional denitrification (Arrigo, 2005).
Due to the complexity and previous limitations in molecular methods, our
understanding of wetland microbial ecosystems is still in its nascence. While the entire
system is important to biogeochemical cycling, many studies have limited their focus to a
few select groups (Altor and Mitsch, 2008; Lueders and Friedrich, 2000; Mohanty et al.,
2006; Smith et al., 2007; Song et al., 2010). Further, diversity within groups may be critical to the rate at which metabolic processes, such as methanotrophy, are occurring
(Nazaries et al., 2013). Whether the quantification of genes can capture this effect is
unknown. It may therefore be useful to take a taxonomic approach to analyze the
microbial constituents.
Models estimating methane flux have determined water table depth and
temperature to be the two most important factors driving methanogenesis in wetland
systems (Kettunen, 2003; Walter and Heimann, 2000). In the absence of stagnant water, anaerobic zones will not form in shallow sediments, and methanogenesis cannot begin.
When the water table reaches a suitable depth for the development of anaerobic conditions, temperature begins to play a dominant role in influencing methane flux
(Kettunen, 2003; Walter and Heimann, 2000). Nutrient loading has the potential to influence methane flux as well, showing a high sensitivity in models (Kettunen, 2003).
Unfortunately, methane flux models developed from site-specific environmental variables do not uniformly translate across many wetland sites (Walter and Heimann,
38
2000). Better parameterization of these models with factors that account for site-specific
differences (e.g. microbial community composition, nutrient availability, or other
physiochemical factors) would allow for a broader application of methane flux models
across wetland sites.
The following study systematically addresses two environmental factors thought to influence methane production from a constructed wetland in a temperate climate: temperature and biome (site). Anaerobic microcosms were used to estimate the maximum
rate of microbial-sourced CO2 and CH4 fluxes from sediments collected from several wetland biomes, or microsites. Temperature, biome, and depth were tested for their influence on respiration and methanogenesis while water chemistry and the microbial community structure were monitored and investigated. We hypothesize that differences in the composition of the microbial communities within biome sediments and depths would influence the maximal potential respiration and methane flux rates in collected sediments, and that higher temperature would accelerate metabolic rates for all communities leading to increased methane flux. Our results suggest that microcosms can serve to predict upper limit methane fluxes, and suggest that best management techniques consider the ratio of vegetated to open-water biomes in construction of future wetlands.
2.3 Materials and Methods
2.3.1 Site Description and Sample Collection
The following study took place at the Wilma H. Schiermeier Olentangy River
Wetland Research Park (ORWRP) on the campus of The Ohio State University,
39
Columbus, OH, USA (40º0’ N and 83º1’ E) (Figure 2.1). Two experimental wetlands were created in 1992, one that was planted with indigenous species (experimental wetland 1) while vegetation was allowed to naturally colonize the other (experimental wetland 2). Water is pumped from the nearby Olentangy River into the wetlands in a rate proportional to the river level. The margins are characterized by a fluctuating hydrology dependent upon pump rates and season while deeper basins (~ 0.2-0.5 m) placed in three central areas are continuously inundated for each wetland. Emergent macrophytes grow near the edge zones while the deeper areas are either unvegetated or host submerged plants. A narrow upland zone (10-50 m) characterized by non-hydric soils separates the two wetlands.
40
Figure 2.1 Site locations of sediment sampling. Images of the sampling sites and the meteorological station performing eddy flux covariance are shown: OW (Top left), UP
(Bottom left), VEG (Top right), eddy-flux meteorological station (Bottom right).
Soil core and water samples were collected from the ORWRP on December 1,
2011 (dormant-season) and June 19, 2012 (growing-season) for laboratory microcosm experiments. In the dormant-season, cores were extracted using a 7-cm diameter soil corer to a depth of 30 cm. Replicate cores were composited into sterile mason jars from three areas located within experimental wetland 1: an open-water, unvegetated area in the deeper basin nearest to the inflow (OW), an edge zone dominated by emergent Typha spp., lateral to the OW zone (VEG), and an upland site (UP). Growing-season cores were also collected from the OW and VEG microsites and sampling from locations near the
41
dormant-season core locations, but not in the exact stops, to prevent the effects of
sediment disturbance from affecting the experiment. Cores were separated at the site into
the shallow (0-15 cm, SH) and deep (15-30 cm, DE) sections. At the time of dormant-
season sampling, Typha spp. dominated the VEG microsite, but had senesced while
another emergent plant, Scirpus spp., had succeeded the Typha spp. and was actively growing during the growing-season sampling event. Cores were transported to the
Environmental Biotechnology Laboratory at OSU and stored at 20ºC until use. Water was collected from the intake pipe of experimental wetland 1 and stored at 4ºC until use.
Samples for DNA analysis were frozen at -80ºC until further processing.
2.3.2 Experimental Design and Analysis
Microcosm bottles containing biome sediments and water were prepared within 3
days of sample collection into sterilized, 125 ml borosilicate glass vials. Sediment (25 g)
was added to the water (75 ml), purged with N2 (99.9985%) gas for 15 minutes in the
fluids, vials were sealed with sterile, rubber stopper and crimped closed with aluminum
seals. Headspace gas was then exchanged with N2 (99.9985%) for 10 minutes.
Laboratory experiments were designed to test the influence of three environmental factors on methane production. Dormant-season experimental factors included collecting sediment from three distinct biomes (OW, VEG, UP) and two temperatures (20ºC and
30ºC). Growing-season experimental factors included sediment collected from two biomes (OW and VEG) separated into two depths (0-15 cm, SH and 15-30 cm, DE), incubated under two temperatures (20ºC and 30ºC). Triplicates were tested for headspace
42
gas analysis for each dormant-season experimental factor (total microcosms, n=18). Five
replicates were used in headspace gas analysis for each growing-season experimental
factor (n=30). Triplicate bottles were sacrificed for each time point for the growing-
season experiment to track aqueous biogeochemistry (n=168). Two carbon substrate
amendments were added to OW-DE microcosms to test their potential as limiting factors for microbial activity. Sodium acetate or CO2 gas was added as 14 mg carbon
equivalence to triplicate microcosms for each incubation temperature (n=12).
Microcosm bottles were sampled 7 times over a period of 77 days. Headspace gas was sampled aseptically by exchanging 3 ml nitrogen with headspace gas using a gas- tight syringe, and transferring the sample to 9 ml vacuum-evacuated GC vials. Headspace samples were diluted with N2 gas (30ml final volume) and stored at 4°C until analysis.
Water samples were taken using sterile aseptic methods, syringe filtered (0.22 µm pore size) and tested for dissolved anions using ion chromatography and non-purgeable dissolved organic carbon (DOC) concentrations. DOC samples were acidified to pH < 2 with HCl, and 0.5 ml was measured by combustion at 625°C using the Shimadzu TOC-V
CSN. Ion chromatography was performed on a Dionex ICS-2100 with an AS-11HC column set to 30°C with a flow rate of 1.5 ml min-1 for 40 min per sample eluted in a 1-
60 mM gradient of KOH. Methane and carbon dioxide concentrations were measured
with 2-ml injections on a Shimadzu GC 14A (Shimadzu, Japan) with a thermal
conductivity detector and flame ionization detector in series. A 1.8 m Porapak Q column
43
was used for separation of samples and helium was the carrier gas (25 ml min-1). Oven,
injection, TCD, and FID temperatures were 40ºC, 40ºC, 200ºC, and 150ºC respectively.
2.3.3 DNA extraction and processing
Nucleic acids were extracted from sediment (~0.5 g) using the PowerSoil DNA
Isolation kit according to the manufacturer’s protocol (MoBio, Carlsbad, CA). 454- pyrosequencing was conducted on growing-season samples using the universal primer pair 515F/806R, covering the v4 region of the 16s gene, targeting both Archaea and
Bacteria (Bates et al., 2011). Individual samples for the four sediments were labeled using a unique barcode attached to the SSU rRNA primer and were analyzed in the same well (Parameswaran et al., 2007). Samples from the dormant-season experiment were analyzed with Sanger sequencing using Archael-specific, Ar109F (5’-
ACKGCTCAGTAACACGT-3’) and Ar912R (5’-CTCCCCCGCCAATTCCTTTA-3’), or the Bacterial-specific, 8F (5’-AGAGTTTGATCMTGGCTCAG-3’) and 912R (5’-
CCGTCAATTCMTTTRAGTTT-3’), primer pairs.
PCR mixtures (50 µL) contained ~30 ng of template DNA, 5 µL of 10x polymerase buffer, 3-4.5mM MgCl2 (Sanger/454-sequencing), 1 µL of bovine serum
albumin (0.5 mg/ml), 200 µM deoxyribonucleoside triphosphates, 25 pmol of forward
and reverse primers, and 1.25 U of Taq polymerase. Reactions were carried out in a
S1000 Thermal Cycler (Bio-Rad Laboratories, Foster City, CA) beginning with a 5-min denaturation at 95ºC followed by 27-30 cycles (Sanger/454-sequencing) of 95ºC (1 min),
52ºC (1 min), 72ºC (1.5 min) and a final elongation step at 72ºC (7.5 min). PCR products
44
were visualized using UV light following gel electrophoresis, and expected-size bands
were isolated using gel extraction (Qiagen). The PCR products were visualized using UV light following gel electrophoresis, and expected-size bands were isolated using gel extraction (Qiagen). Clone libraries were developed from the dormant-season experiment by insertion of the amplified 16s DNA into the TOPO TA vector pCR 2.1 followed by cloning into chemically competent Escherichia coli TOP10 cells, according to the manufacturer’s instruction (Invitrogen, Carlsbad, CA). Clones were selected from each site and amplified with M13 forward and reverse primers provided by the manufacturer.
Amplicons were prepared for sequencing using the emPCR Kit II unidirectional library sequencing protocol and was analyzed on a 454 Life Sciences Genome Sequencer
FLX (Roche Diagnostics, Indianapolis, IN, USA) at OSU’s Plant-Microbe Genomics
Facility.
2.3.4 Data Reduction and Statistical Analyses
Gas concentrations (ppm) for both CO2 and CH4 were converted to concentration
by mass – in terms of carbon – using the ideal gas law. Carbon production rates were
determined between days 21-77 of the experiment, when methane production stabilized,
except for ACETATE samples which were instead calculated between days 2-21. Linear temporal rates of change of carbon concentration (C) were calculated (dC/dt) and converted to carbon flux potentials (CFP) by using sediment wet-weight density (ρ),
mass used in constructing microcosms (m), and the core length during collection (L):
45
dC ∗ ρ CDFP=dt ∗ L m
The same equation was applied to the temporal rate of change of methane (MFP) and carbon dioxide (CDFP). This provided the potential methane and carbon dioxide fluxes in units of [mg C m-2 hr-1].
Statistical analysis using ANOVA was performed using the JMP software package (version 9.0.0, SAS Institute Inc. Carey, NC). A full factorial Standard Least
Squares model was used to test for significance of the various factors across the experiment for non-amended samples using the EMS method with the F-test for significance determination. Marginal significance was defined to the level α < 0.10 and statistical significance was defined as α < 0.05 because of limited replication and compensation of errors incurred through dilution. The factors of biome and depth were treated as random effects with interactions between factors also treated as random. The triple interaction between biome, depth, and temperature was not included because of limited replication in the experiment. The amended and non-amended open-water deep- core samples were tested with a similar but separate model. A post-hoc Tukey’s test was performed following each model to differentiate the levels of mean flux potentials. Linear regression tests were performed between methane and carbon dioxide flux within microcosms.
46
DNA sequencing analyses were processed through the virtual QIIME (version
1.6.0) pipeline following standard methods (Caporaso et al., 2010). Operational
Taxonomic Units (OTUs) were assigned according to the RDP database (RDP Classifier
2.2). An OTU Network was generated in which nodes were assigned their respective highest level of classification up to the taxonomic level of genera using Cytoscape 3.0
(Smoot et al., 2011). Nodes were assigned putative functions in cases where that node reached the taxonomic level of family to organisms facilitating those processes (Table
2.1). Taxonomic data was reduced to include only phylogenetic lineages associated with those organisms.
47
Kingdom Phylum Class Order Family Reference Nitrification or Anammox Bacteria Nitrospirae Nitrospira Nitrospirales Nitrospiraceae (Altmann et al., 2003) Bacteria Proteobacteria β-proteobacteria Nitrosomonadales Nitrosomonadaceae (Purkhold et al., 2000) δ-proteobacteria Desulfobacterales Nitrospinaceae (Ionescu et al., 2012) Planctomycetes Planctomycetia (none present) (none present) (Arrigo, 2005) Archaea Crenarchaeota Thaumarchaeota Nitrososphaerales Nitrososphaeraceae (Tourna et al., 2011) Denitrification Bacteria Proteobacteria α-proteobacteria Rhizobiales Rhizobiaceae (Zehnder, 2002) Bradyrhizobiaceae (Prieme et al., 2002)
48 Rhodobacterales Rhodobacteraceae (Prieme et al., 2002)
Proteobacteria β -proteobacteria Burkholderiales Alcaligenaceae (Prieme et al., 2002) Hydrogenophilales Hydrogenophilaceae (Zehnder, 1988) Methylophilae Methylophilaceae (Kalyuhznaya et al., 2009)
γ-proteobacteria Pseudomonadales Pseudomonadaceae (Prieme et al., 2002) Methanotrophy Bacteria Proteobacteria α-proteobacteria Rhizobiales Methylocystaceae (Dedyesh et al.,2003) γ-proteobacteria Methylococcales Methylococcaceae (Dedyesh et al.,2003) Bacteria Verrucomicrobia Methylacidiphilae Methylacidiphilales LD19 (Op den Camp et al., 2009)
Denitrification + Methanotrophy Bacteria NC10 41632 JH-WHS47 JH-WHS47 (Ettwig et al., 2010) Table 2.1 Putative functional assignment of taxonomic units detected in 454-sequences obtained from the ORWRP
Continued 48
Table 2.1 continued Kingdom Phylum Class Order Family Reference
Methanogenesis Archaea Euryarchaeota Methanobacteria Methanobacteriales Methanobacteriaceae (Ferry, 1993) WSA2 (Ferry, 1993) Euryarchaeota Methanomicrobia Methanomicrobiales Methanomicrobiales (Ferry, 1993) Methanomicrobiaceae (Ferry, 1993) Methanoregulaceae (Ferry, 1993) Methanospirillaceae (Ferry, 1993) Euryarchaeota Methanomicrobia Methanosarcinales ANME-2D (Ferry, 1993) Methanosaetaceae (Ferry, 1993) Methanosarcinaceae (Ferry, 1993)
49 Acetogenesis or Syntrophy Bacteria Firmicutes Clostridia Clostridiales Clostridiaceae (Schink et al., 1997) Lachnospiraceae (Schink et al., 1997) Peptococcaceae (Schink et al., 1997) Peptostreptococcaceae (Ma et al.,1997) Syntrophomonadaceae (Zhao et al., 1993) Veillonellaceae (Ng, 1971) Proteobacteria α-proteobacteria Rhodospirillales Acetobacteraceae (Kuesel et al., 1999) Proteobacteria δ-proteobacteria Syntrophobacterales Syntrophaceae (Gray et al., 2011) Syntrophobacteraceae (Schink et al., 1997) Syntrophorhabdaceae (Schink et al., 1997)
49
2.4 Results
2.4.1 Factors Affecting Potential Carbon Fluxes
-1 -2 Average flux potentials were 5.1 (± 5.9 std) mg C-CH4 hr m methane and 6.6
-1 -2 (± 2.9 std) mg C-CO2 hr m (Figure 2.2a-b, respectively). Treatment flux potentials had
large variability between experimental factors. The full-factorial mixed-effects model
determined that temperature and biome sampled had only marginal independent effects
on methane flux potential with no significant interaction between factors (Table 2.2). The
open-water (OW) samples average methane flux rate was greater than the other two sites.
Overall, average methane fluxes for bottles held at 30ºC were about three times fluxes
those held at 20ºC. Temperature and biome experimental factors did not have any
significant effects on mean carbon dioxide fluxes. We observed a positive linear
relationship between carbon dioxide and methane fluxes (r2 = 0.28, p = 0.025, Regression
test).
Average methane flux potentials in growing-season treatments were higher than
-1 -2 those measured during dormant-season at 8.6 (± 8.9 std) mg C-CH4 hr m methane, but
-1 -2 average carbon dioxide flux potentials was lower at 5.4 (± 3.7 std) mg C-CO2 hr m
(Figure 2.3a-b, respectively). Variability within treatment sets was much lower for this
experiment. Biome sampled, temperature, and depth had no significant independent
effects on mean methane flux potential. However, the interaction between depth and
temperature was found to significantly explain differences in mean methane fluxes (Table
2). This indicated that temperature has an effect but only on sediments from the shallow
50
zones. Depth, despite what appeared to be significant differences based on the results of
the post-hoc Tukey’s test, was not identified as the factor affecting this result (p = 0.22).
Consistent with the dormant-season experiment, none of our experimental factors
explained differences in mean carbon dioxide flux potential. Mean flux potential at 30ºC
were approximately double those at 20ºC for both methane and carbon dioxide. The
shallow sediments, in particular, averaged 9-fold higher methane flux and about 50% more carbon dioxide flux than deep sediments. In growing-season treatments, we observed an even stronger pairwise correlation between carbon dioxide and methane
fluxes (r2 = 0.57, p < 0.0001, Regression test).
51
Figure 2.2 Carbon flux potentials for dormant season experiment. Potential methane (A) and carbon dioxide (B) flux rates estimated in units of mg C m-2 hr-1 under the two
incubation temperatures. Error bars represent the standard deviation between replicates
(n=3). Letters above the bar represent levels of average means, based on a turkey-HSD test (no differences were detected in carbon dioxide flux).
52
Figure 2.3 Carbon flux potentials for growing season experiment. Potential methane (A) and carbon dioxide (B) flux rates estimated in units of mg C m-2 hr-1 under the two
incubation temperatures. Error bars represent the standard deviation between replicates
(n=5 for non-amended, n=3 for amended). Letters above the bar represent levels of
average means, based on a turkey-HSD test. Greek letters above the bar represent levels
of average means, based on a turkey-HSD test, compared to the OW-DE samples. OW-
DE samples were at the lowest level for both fluxes in the amendment study (not shown).
Sodium acetate amendments to deep, open-water sediment significantly increased
production of methane and carbon dioxide compared to ambient samples (Figure 2.3a-b,
respectively). The full-factorial model testing only open-water deep cores (OW-DE)
control and the amended samples found that amendments and the interaction of
amendments with temperatures were significant factors. Specifically, acetate addition
promoted methanogenesis while temperature was found to marginally impact those
samples. The effect of amendments on carbon dioxide flux was marginally significant, 53
with this resulting from increased flux within the CO2 amendment treatment. There was a significant interaction between the amendment and temperature effects on carbon dioxide fluxes, and this too was an effect of temperature increasing carbon dioxide flux from the acetate amended samples.
54
Model Methane Carbon Dioxide df F-ratio P-value df F-raito P-value Dormant-season Overall model 5 3.45 0.037 5 1.45 0.277 Biome 2 16.01 0.059 2 2.34 0.300 Temperature 1 15.66 0.058 1 0.31 0.635 Biome × Temperature 2 0.35 0.714 2 1.04 0.384
Growing-season Overall model 6 35.45 <.0001 6 9.32 <.0001 Biome 1 12.85 0.856 1 - - Depth 1 7.34 0.226 1 82.13 0.859 Temperature 1 1.40 0.456 1 438.70 0.938 Biome × Depth 1 0.98 0.328 1 0.33 0.569 Biome × Temperature 1 0.23 0.633 1 0.26 0.612 Depth × Temperature 1 21.55 <.0001 1 0.84 0.367
Amendments Overall Model 5 23.22 <.0001 5 59.87 <.0001 Amendment 2 20.25 0.047 2 12.66 0.073 Temperature 1 0.82 0.458 1 6.61 0.123 Amendment × Temperature 2 2.70 0.098 2 9.03 0.002 Table 2.2 Full-factorial model statistical analysis. Results are from models run for each experiment with the amendment study showing comparisons between amended and non- amended OW-DE microcosms.
2.4.2 Biogeochemical Trends
Dissolved organic carbon (DOC) trends followed a similar pattern in both open- water and vegetated sediments for both shallow and deep sections (Figure 2.4).
55
Concentrations initially increased to 55.1 mg L-1 due to biological growth in the first 168
hours, then fluctuated between about to 28 to 7.6 mg L-1 for the remainder of the
experiment. Acetate was initially detected in both shallow and deep treatments, was
rapidly utilized within 500 hours, and remained around the detection limit (~10 µg L-1)
for the remainder of the experiment. Combined nitrate and nitrite concentrations showed
negligible differences throughout the experiment, being depleted to the detection limit
(~20 µg L-1) within 168 hours (Figure 2.5a). Sulfate was depleted within 500 hours of the experiment in shallow sediment samples, but did not begin to decline for deep sediments until sometime after 840 hours (Figure 2.5b). The depletion of sulfate in the
shallow sediments corresponded to the period where methane production stabilized and
linear rates were detected. Data for the 1500 hour sampling was lost due to mechanical
failure.
56
Figure 2.4 Dissolved carbon of the shallow and deep treatments from the growing season experiment. Dissolved organic carbon (triangles) and acetate (circles) concentrations in mg C L-1 analyzed from IC and TOC analyses, error bars represent standard deviation
(n=12).
57
Figure 2.5 Dissolved anion concentrations in shallow and deep treatments from the
growing season experiment. Dissolve sulfate (a) in mg S L-1 and nitrogen, in the form of
nitrate and nitrite, (b) in mg N L-1 measured by IC analysis of water quality microcosms, error bars represent standard deviation (n=12).
2.4.3 Microbial Community Dynamics
The β-proteobacteria were the most prevalent clade present at the ORWRP site with the lowest abundance found in the deep vegetated sediments (10%) while all other sites averaged 15% (Figure 6). Open-water sediments averaged 12% relative abundance of Euryarchaeota compared to 5% in the vegetated sediments. Since methanogens belong
solely to two classes within this phyla, determinations were made which found that while
the open-water sediments averaged 4% of methanogens and 1.5% in the shallow,
vegetated site, methanogens composed less than 0.1% of the deep, vegetated microbial
community. Firmicutes were found to be most prevalent in the deep, open-water
58
sediments at around 2.5% but were only present at 1% in the other three samples. A high abundance of δ-proteobacteria (acetogens/syntrophs, nitrifiers) were found in the open- water deep sediments and vegetated shallow sediments (15.8%, 14.8%, respectively), but were less prevalent at the open-water shallow sediments and vegetated deep sediments
(12.7%, 11.3%, respectively). The α-proteobacteria averaged 1.9% across the ORWRP with the lowest abundance in the OW-DE sediments and highest in the VEG-SH. The γ- proteobacteria were most abundant in the open-water sediments (7%), but were less evenly distributed across the deep (1.5%) and shallow (5%) vegetated sediments. The lowest relative abundance of Verrucomicrobia was also found in the deep, vegetated zone
(2%) while the average of the other three sites was around 4.5%. Relative abundance of
Nitrospira was highest at the vegetated site (7.5%) compared to the open-water sediments
(3%). Planctomycetes were most prevalent in deep sediments (3.5%) than the shallow
(2%), and were highest in both vegetated sediments compared to open-water at the same depth. The Crenarchaeota composed slightly more of the microbial community at the vegetated biome (1.5%) than the open-water biome (1.2%). The NC10 phylum was found in all sediments albeit at very low abundance ratios across the ORWRP (0.2-0.4%).
59
Figure 2.6 Relative abundances of taxonomic units from 454-sequencing analysis of sediments collected in the growing season experiment. Distribution is of the phyla, but classes of Proteobacteria included in Table 2.1 were separated into individual units. All phyla from Table 2.1 are displayed, while any other organism contributing less than one percent average abundance at the ORWRP were combined into the “All Others” group.
In general, the OTU network showed that there was a higher density of putative families associated with nitrogen cycling in the vegetated sediments than open-water
(Figure 7). Putative methanotrophs were associated with shallow sediments, methanogens with the open-water sites, and acetogen/syntrophs were relatively ubiquitous. Putative methanotrophs from the Methanococcaceae (family) and
Methylosinus (genus) were aligned with many methanogenic groups and were shared
60
between the open-water samples and vegetated, shallow sediments. Also situated near these groups was the putative denitrifying Alcaligenaceae (family). The
Pseudomonadaceae (genus) were, remarkably, only present in the vegetated, shallow sediments. The putative acetogenic Acetobacteraceae (family) and its member genus,
Roseomonas, were only present in the shallow sediments from both biomes sampled.
61
Figure 2.7 OTU network of genera from 454-sequencing analysis of sediments collected in the growing season experiment. Samples are indicated for VEG-SH ( ), VEG-DE (
), OW-SH ( ), and OW-DE ( ). Taxonomies that have been associated to putative functions (Table 1) for nitrification or anammox ( ), denitrification ( ), syntrophy and/or acetogenesis ( ), methanogenesis ( ), methanotrophy ( ), and denitrification linked to methane oxidation ( ) are highlighted with all other OTUs ( ). Highest classification level names are displayed to the left of each marker. 62
2.5 Discussion
2.5.1 Methane and Carbon Dioxide Flux Potentials
The combined results from the experiments indicate that temperature is an
important factor on microbial respiration of wetland ecosystems. This matches similar
conclusions drawn from methane flux studies across field studies (Altor and Mitsch,
2008b; Conrad, 2002; Liu et al., 2011; Nahlik and Mitsch, 2010). Deeper sediment
samples were unaffected by temperature, however, the fact that methane production
increased with the addition of acetate showed that the lack of effect was due to the lack of
acetate production. In nature, these depths would be in communication and acetate
infiltration from vertical water column movement would likely introduce acetate to these
sediments in the absence of production. The acetate amended deep, open-water sediments
produced methane and carbon dioxide at comparable mean rates to the non-amended
open-water shallow samples. The dormant-season experiment was only marginally affected by temperature and biome sampled, the latter of which disappeared from significance in the growing-season experiments. The inclusion of upland samples
combined with a low water table during the dormant-season sampling might explain the
disagreement of our two experiments. Water was present only to the surface of the
vegetated site and may have allowed oxygen intrusion and disrupted methanogenic
activity (Jarrell, 1985). This study found average rates that were nearly identical between
63
the exposed vegetated sediments and upland sites. It is documented that upland sites
carry innate methanogenic ability across many land types (Angel et al., 2012). Here we
found that the methanogenic potential of these soils is comparable to hydric samples, especially the regions characterized by a fluctuating water table.
Methane flux rates from both studies were compared to chamber-measured in situ maximum diffusive flux rates from previous studies conducted at the ORWRP. Average flux rates from the dormant-season experiment were similar to average methane flux rates calculated from chamber measurements while flux rates from growing-season experiments were higher than chamber measurements (Table 2.4) (Altor and Mitsch,
2008b; Nahlik and Mitsch, 2010). However, a previous study had found flux rates higher than 140 mg C hr-1 m-2, although these appeared to be outliers. The rates we observed
were also compared to eddy flux covariance results from 2011, and showed that they
closely matched measurements near 20°C (Figure 2.8). Soil temperatures have not
reached 30°C at the ORWRP.
64
20°C 30°C VEG OW VEG OW
Dormant-season 1.38 (1.1) 6.36 (7.1) 4.77 (5.1) 13.76 (5.2) Growing-season 9.56 (1.2) 12.09 (4.6) 20.04 (6.7) 24.62 (4.6)
Altor and 2004 1.88 (0.4) 5.58 (3.1) 6.61 (0.7) 7.14 (1.2) Mitsch (2008b) 2005 1.26 (0.2) 6.53 (2.3) 7.18 (1.2) 18.50 (4.0)
Table 2.3 Methane flux potentials or chamber measured methane flux. Results are in
-2 -1 units, mg C-CH4 m hr . Standard deviations are shown in parentheses. Chamber measurements are from a pulsing hydrology study with fluctuating water tables (2004) or a steady-flow (2005). Average temperature from the spring was 19°C, and from the summer was 26°C. Growing-season flux potentials were determined by adding deep and shallow microcosms counterparts and averaging the group (n=5) while dormant seasons were calculated from each treatment set (n=3).
65
Figure 2.8 Comparison of microcosm methane flux potentials against eddy-flux
covariance measurements. Rates are shown in mg C-CH4 hr-1 m-2. Eddy flux covariance data was from the year 2011, and shows half-hour interval data with the preceding seven day average temperature rounded to the nearest degree.
The comparisons with field measurements suggest that the microcosms were successful in predicting maximum amount of anaerobic microbial respiration and methane flux, but multiple environmental factors could affect the actual fluxes. For example, the anaerobic microcosms were designed to restrict aerobic methanotrophy
(Hanson and Hanson, 1996). Methanotrophic activity has been shown to linearly correlate with that of methanogenesis in a functional gene study (Freitag et al., 2010).
66
Here we found particular methanotroph taxonomic units which shared similar habitats to methanogens indicating a similar relationship at this site. Since methanotrophy would also likely increase due to temperature, it is assumed that based on our study, greenhouse gases are most likely to increase with temperature, although the ratio of methane to carbon dioxide is uncertain (Hanson and Hanson, 1996).
2.5.2 Factors Affecting Microbial Activity
Dissolved organic carbon fluctuated within this experiment and initially showed a large increase due to bacterial growth. Coinciding with this was the depletion of nitrate which occurred between 48-168 hours. Potentially, there was some conversion of particulate organic carbon released to its dissolved state by the microbial activity (Worral et al., 2006). DOC began to decrease and reached its lowest concentration around 500 hours of the experiment, which was also when sulfate levels were depleted in the shallow sediments. Yet, DOC experienced a similar pattern in the deep sediment despite no sulfate reduction. It was upon the depletion of sulfate that methanogenesis stabilized in the shallow sediments. However, methane production was measured prior to this point and concurs with other studies which have found that sulfate only reduces methane flux in freshwater systems (Lovley and Klug, 1983). The lack of reduction in deep sediments until very late in the experiment do not provide any evidence to suggest that sulfate reduction coupled to methane oxidation had a role (Pester et al., 2013). Therefore, another element must be missing from the methanogenic degradation pathway.
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Similar to our findings, results from northern-peatland incubations have
demonstrated a lack of methane production from the deeper sediments (Williams and
Crawford, 1984). These results were attributed to the lack of carbon – perhaps a
particular form - for methane production. Carbon stocks in the deep and shallow regions
of the ORWRP are not vastly different, with deep sediments having approximately 20%
less than shallow sediments (Bernal and Mitsch, 2013). DOC concentrations averaged
slightly less in the deep, but the lack of acetate is possibly the missing element,
specifically as the acetate amendment to the open-water deep sediments caused
methanogenesis to occur.
There is the potential that the Acetobacteraceae, which were only found in
shallow sediments, are the reason for lack of acetate production. Although there were
several ubiquitous, putative acetogen/syntrophs, it may even be caused by these
organisms’ specific abilities, such as possibly oxidizing ethanol to acetate, which is
critical to methanogenesis in the microcosms (Dworkin and Falkow, 2006). Ethanol is unlikely to have been the missing carbon species in our experiment due the absence of ethanol-oxidizing organisms. Nonetheless, this pathway deserves further consideration.
Future studies are needed to understand the cause of restricted methanogenesis.
Due to low initial concentrations of oxidized nitrogenous compounds and rapid utilization, no rates were calculated in this study for possible denitrification. Summations of putative denitrifiers – the α-proteobacteria, β-proteobacteria, and γ-proteobacteria – were compared from pyrosequencing. Open-water sites had the greatest abundances of
68
these three groups, which did not follow the pattern showing they followed the vegetated
shallow areas in terms of denitrification potential (Hernandez and Mitsch 2007a).
However, the Pseudomonadaceae family, which related the γ-proteobacteria to denitrification, was present only at the vegetation biome. If they were subsequently removed from the total abundance, the pattern of highest to lowest abundance reflected the order of rate potentials.
The fact that greater density of organisms is related to at least the family hierarchal level illustrates the probability that these abundances are indicative of function.
It is possible that investigations of community composition can be used to reasonably predict potential microbial activity rates in wetland samples. However, relatively high abundance of methanogens at the open-water deep site, which lacked methane production, alludes to the fact that there should be cautions in this sort of analysis.
Notably, pyrosequencing-based taxonomy inferences suffers from two assumptions, the foremost being primer bias (Kumar et al., 2011). No primer bias was indicated by these samples and the clone libraries, specifically as the dominant β- proteobacteria were found in similar abundances as the pyrosequencing results (Figure
2.9). Another assumption was made in assigning the putative functions to taxonomies, which would overestimate the number of organisms’ active in those roles. Further studies must be performed to link these taxonomies to their functions (Urich et al., 2008).
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Figure 2.9 Clone library relative abundances of Sanger-sequencing analysis on sediments collected in the dormant season experiment. The relative abundance of taxonomic orders for Archaea (a) and Bacteria (b) 16s rRNA. Results are from clones for the UP (n=5,
32), VEG (n=35, 32), and OW (n=52, 32) biomes.
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2.6 Conclusions
The results of this study combined with previous field results from the ORWRP site suggest that vegetated areas support nitrogen-cycling organisms while open-water areas harbor more methanogens. This would lead to the suggestion that, for the purpose of improving the ecosystem services of nutrient removal from surface water while minimizing greenhouse-gas emissions, vegetated areas are preferable to open-water areas. This claim, however, requires further investigation of the role of vegetation: whether in providing methanogenic substrate through root exudates, supplying oxygen to the rhizosphere for methanotrophs, or acting as conduits for rapid methane flux to the atmosphere (Laanbroek, 2010; Tanner et al., 1997; Potter, 1997). Investigations need to ascertain how these functional rates relate to those that are occurring in the field.
Here we demonstrated that the microcosm approach is an accurate method for predicting maximum methane flux. Under controlled laboratory conditions we confirmed that temperature has an important effect on the production rates of GHGs by microorganisms in wetlands. The biomes at which the microbial communities were sampled explain some of the variability of flux rates among samples, but cannot be determined as the definitive factor across all incubation temperatures and depths. This study provided evidence that the taxonomic distribution varies across a wetland site, depending upon biomes, and may help predict the functional capabilities of the ecosystem.
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Chapter 3: Isolation of Wetland Microorganisms Related to Nitrogen and Methane
Cycling
3.1. Abstract
The isolation of microorganisms remains a viable research technique despite
technological advances in genomic and proteomic analyses. There remain gaps in our
scientific understanding of many organisms existent in nature. Organisms isolated from
wetlands may be able to retrieve yet unidentified organisms useful to applications of
“omic”-studies and provide useful model organisms in determination of biogeochemical fluxes from these systems. A uniform, defined minimal mineral media was designed with
alternate substrates targeting environmentally important metabolic functions. Functions
desired included acetate generation, methylotrophic methanogenesis, denitrification, and
anammox. Seven organisms have been successfully grown in pure cultures falling into
five previously described species. Four viable organisms have been isolated in what are
presumed to be pure cultures, but remain unidentified. The continued attempts to identify
these organisms will be used to prove they are indeed members of the selected
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biogeochemical functions and be used in future laboratory gene quantification assays or
microcosm experiments.
3.2. Introduction
The isolation and cultivation of microorganisms remains a much needed research
method that is necessary despite advances in genomic and proteomic capabilities
(Giovannoni, 2007). To the present date organism of great importance are still being isolated and identified to reveal novel metabolic pathways (Ettwig et al., 2010; Tourna M et al., 2011). Entire bacterial phyla, such as the groups OD1 and TM7, have never successfully been cultured, and the activity of these groups remains largely unknown
(Hugenholtz et al., 2001; Peura et al., 2012). The great diversity of microbial ecosystems leaves a large number of organisms to be identified and is needed to advance the application of “omic”-analyses.
Further, the use of these organisms in providing controls for genomic analyses is also valuable. The use of the genomic materials of known cultures is used in the quantification of functional and ribosomal genes (Freitag et al., 2010; García-Lledó et al.,
2011). Pure cultures are useful in serving as models for functional groups, and
combinations can also be used to model ecosystem interactions between groups
(Giovannoni, 2007; Pengerud,et al., 1987). These types of studies can shed light on
highly diverse microbiomes such as in wetlands.
Wetland ecosystems are host to a very diverse microbial community (Cleary et al., 2012). Organisms in these systems span many metabolic pathways such as
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methanogenesis and denitrification – two critical biogeochemical processes with impacts to the environment (Bowden, 1987; Schink, 1997). While denitrifiers generally consist of facultative Bacteria, methanogens are strictly anaerobic Archaea which are reliant on other organisms. Ancillary to both these functions are other metabolisms which can provide them with substrate. This include acetogenesis for methanogens or nitrifiers for denitrification (Bowden, 1987; Schink, 1997).
Denitrifiers utilize the substrates of nitrate or nitrite with carbon sources which are primarily derived from primary productivity of vegetation. Either nitrogen or carbon can be limiting to their growth depending upon the concentration of the other and may vary across different sites (Ullah and Zinati, 2006; Warneke et al., 2011). In the absence of allochthonous nitrogen input, wetlands can experience the process of nitrification which converts ammonia stored in the sediments to nitrate (Bowden, 1987). This process helps ensure that denitrification can still occur despite limited quantities obtained from water inputs by creating the supply within the sediments.
Nitrification is an aerobic process, but there is also the ability of some organisms to oxidize ammonia under anaerobic conditions in a process known as anammox. The result of this can lead to nitrite/nitrate formation or potentially N2 gas with archael members accomplishing the former and bacterial members the latter (Purkhold et al.,
2000; Tourna et al., 2011). Denitrification is typically considered the transformation of nitrate to N2 gas, however, the proper terminology is the dissimilatory reduction of nitrate, and this is shared with another group which ultimately produces ammonia
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(DNRA) (Bowden, 1987). These organisms compete with denitrifiers for the same
substrate and can diminish an ecosystem’s ability to remove nitrogen to its gaseous forms
– with very opposite environmental effects. Ammonia in water is considered potentially
hazardous while removing nitrogen to the atmosphere accomplishes the goal of water
purification ideal to treatment wetlands (Bruland et al., 2009).
Methanogens themselves are classified to two orders of a single phylum, the
Euryarchaeota (Ferry, 1993). These organisms follow two metabolic pathways:
methylotrophy and chemolithotrophy using acetate or carbon dioxide and hydrogen,
respectively. However, methanogenesis is a system which is reliant on a series of poorly
understood decomposition pathways (Conrad, 2002). Methanogens are limited, relative to denitrifiers, to only several possible substrates (Schink, 1997). In many cases, they are
mutually benefitted by forming relationships with other bacteria (syntrophs) to complete
the terminal decomposition process. Syntrophs are classified to many of the same
microbial clades as acetogens (generate acetate), since acetate supports methanogenic
activity, whereas syntrophs most often oxidize fatty acids to provide methanogens with
hydrogen (Drake, 1994; Schink, 1997). Competition for methanogens comes from a
other organisms’ ability to use more entropic electron acceptors in the decomposition of
carbon.
Denitrification and methanogens form the terminal ends of the anaerobic
decomposition pathway in wetlands with denitrification being the first anaerobic
respiration process. Yet, little is known about the direct relationship shared by these
75
organisms where studies have yielded conclusions from limited temporal studies using
substrate additions (Lovley and Klug, 1983; Stadmark and Leonardson, 2007). For
instance, Stadmark and Leonardson (2007) found that nitrate additions repressed
methane production, but the study did not investigate residual effects. For example, the
denitrification activity may have produced other substrates that eventually stimulated
methanogenesis. More direct links between functional relationships should help identify
the organisms which have currently been labeled as “primary and secondary
decomposers”.
There are no experiments to the present date that have used model organisms to
replicate the true interaction between organisms rather than the effect of nutrient
additions. Unless the temporal scale sufficiently covers the depletion of the amendment
and lag of the methanogenic process, no true effect can be made. Modeling experiments
may benefit our understanding of how organisms affect greenhouse gas production from
a tertiary or further perspective. For example, there is the potential that denitrifiers or
even DNRA organisms are essentially the primary decomposers and lead to
methanogenesis, but this is yet unknown.
Isolation attempts were performed at a constructed wetland site to gather organisms in preparation of functional gene studies and potential laboratory-based studies
on the interaction of these groups. Denitrifiers and methanogens were the most critical
isolates that were cultured, foremost to derive conclusions of these interactions of great
environmental importance. Isolations were also attempted for acetogens and both
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Bacteria and Archaea performing anammox, as these are also important to the former
processes. A uniform basal media was used to isolate organisms from wetland sediments
suited to grow under similar environmental conditions. Multiple serial dilutions as well
as three-phase streak plates were used to pick individual colonies resulting in the
isolation of several pure cultures. Ongoing efforts continued to isolate organisms
performing denitrification, anammox, and acetogenesis while DNRA and methanogenic
organisms have been isolated and identified.
3.3 Materials and Methods
3.3.1 Preparation of Media
A basal mineral media was derived using DSM825 media (DSMZ.de) as a
guideline. Two stock solutions were prepared and autoclaved separately to prevent precipitation (Table 3.1). Solution 1 was made by adding potassium phosphate monobasic (2.5 g) and potassium carbonate (1.25 g) to distilled water (0.9 L). The pH of the solution was adjusted to ~7.0 using hydrochloric acid and the final solution volume was adjusted to 1 L. Solution 2 was made by adding ammonium chloride (2 g), sodium chloride (5 g), magnesium chloride heptahydrate (1.5 g), and yeast extract (5 g) to distilled water (0.9 L). The pH was adjusted to ~6.8 using hydrochloric acid or potassium hydroxide and the final volume was adjusted to 1 L. To prepare solid-phase media, agar (15 g L-1, final media volume) was added to Solution 1. Solutions 1 and 2 were autoclaved at 121°C for 30 minutes, allowed to cool and stored at 4°C. Vitamin, mineral, and titanium citrate stock solutions were prepared as previously described and
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were all filter-sterilized (Wolfe, 2011; Zehnder and Wuhrmann, 1976). Vitamins and
minerals were stored at 4°C while titanium citrate was stored under a N2:CO2 atmosphere
and left at room temperature. An antibiotic solution was prepared fresh by dissolving
ampicillin (100 mg) and kanamycin (20 mg) to distilled water (4 ml) and was filter-
sterilized.
Methanogenic substrate solution was prepared by the addition of sodium acetate trihydrate (1.5 g) to distilled water (23 ml). Denitrification substrate solution was prepared by the addition of potassium nitrite (1.8 g), and glucose (1.8 g) to distilled water
(30ml). Methylene blue (<1 mg) was added as a selective agent to obtain only gram negative organisms. Anammox (Archaea) substrate solution was prepared by the addition of ferrous sulfate (0.9 g) and ammonium hydroxide (1.5 ml) to distilled water
(27.5 ml). Anammox (Bacteria) substrate solution was prepared with the addition of potassium nitrate (0.3 g), potassium nitrite (0.3 g), sodium propionate (0.9 g), and ammonium hydroxide (1.5 ml) was added to distilled water (28.5 ml). Acetogenic substrate solution was prepared with calcium carbonate (~0.2 g) added to distilled water
(19 ml). The pH of each solution was adjusted using potassium hydroxide or hydrochloric acid to ~6.8, except the acetogenic substrate solution which was altered to pH 5.6. Stock substrate solutions were autoclaved at 121°C for 30 min and allowed to cool. Ethanol (11 ml) was filter-sterilized and added to the acetate stock. Antibiotic solution (1 ml) was added to both the anammox (Archaea) and methanogenic solutions.
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Vitamin (1.7 ml), mineral (1.7 ml), and titanium citrate (3.3 ml) were added to the methanogenic solution.
79
Stock solutions Solution 1 Final conc. (g/L) Solution conc. (g/L)
KH2PO4 1 2.5
K2CO3 0.5 1.25 Solution 2
NH4Cl 0.8 2 NaCl 2 5
MgCl2*6H2O 0.6 1.5 Yeast extract 2 5
Methanogens Vitamin 10ml 50ml Mineral 10ml 50ml TiC6H5O7 20ml 100ml
Na OOCCH3*3H 2O 9 45
amp + Antibiotics 150µg/50µg kan
Denitrifiers
KNO2 10 50 C6H12O6 10 50
Anammox – Archaea
NH4OH 9ml 45ml
FeSO4 5 25
amp + Antibiotics 150µg/50µg kan
Anammox - Bacteria
NH4OH 9ml 45ml
KNO3 2 10
KNO2 2 10 NaOOCCH2CH 3 5 25
Acetogen CH3CH2OH 70ml 350ml
CaCO3 0.5 2.5 Table 3.1 Media used for isolation of pure cultures
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Glass vials (Bellco Glass, Inc., Vineland, NJ), rubber septa, and aluminum crimps were
autoclaved at 121°C for 30 min and used for all broth media. Aerobic/facultative media
(denitrification, acetogenic) was prepared under a laboratory atmosphere while anaerobic
media (anammox, methanogenic) was prepared in a glove-box under an atmosphere of
N2:CO2:H2 (85:10:5) with a palladium catalyst to remove oxygen. Solution 1 and 2 (4 ml, each) were added to each vial. Vials were then treated to one of the substrate solutions (2 ml), sealed, and crimped. The same ratio of mixture was used in the preparation of solid-phase media, which was poured into petri dishes and stored either at
4°C for aerobic/facultative media and in the glove-box at room temperature for anaerobic media. Sterilized glycol (2 ml) was added to some vials which were then inoculated with a culture, grown, and aliquoted into cryotubes for long term preservation at -80°C.
3.3.2 Sample Collection and Organism Isolation
Samples were gathered from the Olentangy River Wetland Research Park (Figure
3.1). Sites were selected along a gradient from edge zones toward the center of the wetland. Aerobic/facultative processes were gathered nearest to the edge while anaerobic samples were gathered closes to the continuously inundated zones. A sterile syringe and needle inserted into the sediment and used to obtain ~0.5 ml of sediment. This sediment was applied to the various media created and transported to the laboratory where they were incubated at 28.5°C. Dilutions were made while continuously growing the cultures, and where applicable, solid media was used with three-phase streaking techniques to
81
isolate individual colonies. Sampling was repeated by returning to the approximate site if
no growth was observed, or if cultures were lost.
Figure 3.1 Site sampling locations to obtain functional cultures. Sampling locations varied slightly, depending upon hydrological conditions.
Samples were grown until growth was observed via turbidity. All samples were
attempted to be cultured as a lawn of organisms on solid-phase media. If growth
occurred on this media, individual colonies were streaked to attempt to isolate a single
organism. After repeated isolation, colonies were mixed into a phosphate-buffered saline
solution and added to broth media. Organisms that were incapable of growth on solid-
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phase media were subjected to serial dilutions with the lowest dilution displaying growth
used as the pure culture.
3.3.3 DNA Extraction and Culture Identification
Cultures were transferred into 15-ml tubes and centrifuged for 15 min at 8,000
rpm. Broth was poured off the culture and the remainder was vortexed to re-suspend the organism (~1 ml). Nucleic acids were extracted using the PowerSoil DNA Isolation kit according to the manufacturer’s instructions (MoBio, Carlsbad, CA). Amplification was performed using PCR with the primer sets Archaea Ar109F /Ar912R or Bacteria
8F/907R (Frank et al., 2009; Lueders and Friedrich, 2000). PCR mixtures (50 µL) contained ~30 ng of template DNA, 5 µL of 10x polymerase buffer, 3mM MgCl2, 1 µL
of bovine serum albumin (0.5 mg/ml), 200 µM deoxyribonucleoside triphosphates, 25
pmol of forward and reverse primers, and 1.25 U of Taq polymerase. Reactions were
carried out in a S1000 Thermal Cycler (Bio-Rad Laboratories, Foster City, CA)
beginning with a 5-min denaturation at 95ºC followed by 27-30 cycles of 95ºC (1 min),
52ºC (1 min), 72ºC (1.5 min) and a final elongation step at 72ºC (7.5 min).
Sequencing was performed at the Plant-Microbe Genomics Facility at The Ohio
State University with an ABI Prism cycle sequencing kit (BigDye terminator cycle sequencing kit) using an ABI 3700 instrument. Sequences were compared to the NCBI database using the blastn method, optimized for high dissimilarity. A cutoff value of
97% similarity was used to declare organisms a known or novel species.
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3.4 Results
Although multiple cultures were successfully obtained through the various media used in the experiment, many were found to be the same species. A total of seven unique organisms have thus far been grown in pure culture (Table 3.1). Of the organisms discovered, five were achieved from the denitrification (DN) media used in the experiment. These five organisms were classified into only three unique species
(DNRA1,2,3) within 97% similarity to known organisms in the NCBI data base. All members belong to the gram negative, γ-proteobacteria within the Enterobacteriaceae family, except for Aeromonas of the Aeromondales family.
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% Confirmed Culture ID NCBI Identity Similarity Species Denitrification DNRA1 Citrobacter freudenii 99 Yes DNRA2 Cronobacter dublinesis 97 Yes DNRA3 Aeromonas media 98 Yes DN1 N/A N/A N/A
Methanogenesis Methanobacterium MTH1* palustre 98 Yes Clostridia butyricum 98 Yes MTH2 N/A N/A N/A
Anammox - Bacteria AOB1 N/A N/A N/A
Anammox - Archaea AOA1 N/A N/A N/A
Acetogenesis ACE1 N/A N/A N/A Table 3.2 Isolated cultures and their identity confirmed through the NCBI database
Attempts to separate the co-culture of a Clostridium and Methanobacterium proved unsuccessful. Despite a large dosage of antibiotics, the Clostridiaceae component was able to grow with the methanogen. Methanogenic growth was observed by the formation of “flocs” in the broth which were producing bubbles. Methanobacterium palustre is currently in preservation at -80°C, but has demonstrated the ability to grow upon re-inoculation in the media.
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Presumed to be isolated pure cultures, the ACE1, AOA1, AOB1, DN1, and
MTH2 organisms has been accomplished, but remain to be confirmed. Isolations have
been accomplished for each of these individual cultures by selection of individual
colonies three-phase-streaked out on plates (except MTH2) (Figure 3.2). ACE1 was
distinguished by growing on the ethanol-lime media and selecting for acid-producing
bacteria by the conversion of the media from translucent to transparent. AOB1, AOA1,
and DN1 were undifferentiated by the media, although selection occurred through various
media additives. MTH2 shares the appearance of a methanogenic culture as MTH1, with flocs producing bubbles during growth.
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Figure 3.2 Cultures isolated but yet to be identified through genetic analysis. MTH2 (a) was unable to grow on solid-phase media and is shown growing in broth with bubbles produced from the cultured indicated by arrows (). AOB1 (b), AOA1 (c), and ACE1
(d) are shown growing as colonies on solid-phase media.
3.5 Discussion
All confirmed organisms isolated have yielded previously described species. The isolation attempts for a denitrifier have captured only those group members performing
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dissimilatory reduction of nitrate to ammonia (DNRA) (Chai et al., 2012; Park et al.,
2010; Rehr, 1989). Identification and confirmation of these organisms functional ability will require measurements of ammonia production. However, literature provides that the three DNRA(1-3) cultures are capable of using fatty-acid substrates including acetate as their substrates (Chai et al., 2012; Park et al., 2010; Rehr, 1989). This suggests a competition between this function with methylotrophic methanogens. Although DN1 is yet to be confirmed as a true denitrifier, a culture of Pseudomonas aeruginosa has been provided by another facility on campus and may be substituted for a wetland isolate if remains unsuccessful.
Our isolates indicate that the dissimilatory reduction of nitrate to ammonia
(DNRA) is capable of out-competing “true” denitrification in our current media.
Attempts to use of magnesium chloride combined with UV light as a differentiating method have not successfully found a Pseudomonas isolate (Lovrekovich et al., 1972). It is possible that Pseudomonads are not present in large enough proportions of the microbial community at the ORWRP (Appendix 2). However, no Enterobacteriaceae were discovered within the 454-sequencing data, yet they have come to dominate the isolated cultures. Redesigning the media may be necessary to retrieve the desired organism. Typically, Pseudomonads are the dominant denitrifiers obtain from cultivation of agricultural soils, but wetland soils may not be as hospitable to this group (Tiedje et al., 1982).
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Methanogenic media provided a mixed-culture of what is predicted to be a syntroph (Clostridium butyricum) with a chemolithotrophic methanogen
(Methanobacterium palustre). Members of Clostridia are prominent members of these syntrophic relationships and more intensive genomic analysis is needed to confirm whether this organism is a known or novel species (Chauhan et al., 2004). This co-
culture provides an acceptable candidate to use in laboratory experiments designed to
capture the interaction of the microbial ecosystem. However, a pure cultured
methanogen is still desirable for those experiments or as standard in gene quantification studies. Therefore, the MTH2 culture was commenced to obtain a pure culture.
No determination has yet been made for the identity of cultures AOB1, AOA1, or
ACE1 (Figure 3.2b-d). Growth has been observed and isolated colonies are now
currently growing in what are assumed to be pure cultures. Media for these organisms
were selected from other literature in hopes of guaranteeing isolation of targeted
organisms involved in these processes. The ACE1 culture was grown on calcium
carbonate solution as a way to indicate acid production following a well-established
method (Asai et al., 1964). The protocol follows methods of isolating Acetobacteraceae
organisms, which were identified in Chapter 2 as organisms that may be responsible for
limited methane production in deep samples. The addition of these organisms to deep
sediment microcosms could reveal whether it is this organism or another element which
is critical to methane production.
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Ammonia oxidizing organisms represent both competition and provisional support depending on whether they are Bacteria or Archaea. Thus they were desired to help further their understanding. AOB1 was grown on media derived from a marine isolate, albeit with lower salt concentrations (Kartal et al., 2007). In the case that this culture is not identified to the Planctomycetes, other isolations will be performed using ampicillin as is often used in isolation of these organisms (Wang et al., 2002). The use of kanamycin with ampicillin was used as described by other methods to capture the AOA1 isolate in the hopes that it would provide only Archaea (Tourna et al., 2011). However, growth was immediate for this culture, whereas literature describes a long lag-phase of up to thirty days (Tourna et al., 2011).
3.6 Conclusions
Using selective agents has proven to be useful in the isolation of organisms from the ORWRP. The lack of novel cultures so far may have to be overcome in the future with the use of more advanced culturing techniques (Giovannoni, 2007). Using media with lower, more natural organic matter concentrations as is present at the site may enrich only fastidious organisms which have yet to be identified. Regardless, the present organisms would function well as model organisms in mixed culture studies and provide genomic and proteomic standard. For example, functional gene expressions may offer to predict nutrient flux rates at the site as related to measurements from future microcosm projects.
90
Basic microbial techniques (gram staining, substrate utilization) must be performed on these cultures to properly identify them. Growth curves will also need to be performed to identify the exponential stage of growth if these organisms are to be included in any experiments. Work will continue to identify the organisms which have not yet been confirmed. Upon a literature review, an experiment will be designed to elucidate ecosystem interactions occurring in sediments in the hope of providing useful applications to wetland ecosystem development.
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Chapter 4: Conclusions
Methane production potential at the ORWRP was most influenced by temperature but only on sediments collected from the shallow regions, with a 10°C increase causing a doubling of methane production. There were only minor differences in the concentration of nutrients between sites with the greatest differences found between sediments collected from 0-15cm and 15-30 cm. Despite depth not being a significant factor, it noticeably produced minimal methane unless stimulated with acetate. There is the potential that an Acetobacteraceae was missing from those microcosms and restricted methane production. There were many differences noticed between wetland communities with the greatest association of based upon site (open-water vs. vegetation). The effect of biome was only found to significantly impact microbial respiration when using a full 30 cm core, and if sediments were collected when exposed to the atmosphere. Bacterial isolations have failed to identify any novel organisms from this site. However, there are a variety of purposes that these cultures can serve in future experiments.
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Komagata, K. (1964). The flagellation and taxonomy of genera Gluconobacter and
Acetobacter with reference to the existence of intermediate. Journal of General and
Applied Microbiology, 10(2), 95-126.
112
Appendix A: Gas Flux Linear Rates and Data Tables
113
Biome CH4 Flux Potential CO2 Flux Potential -1 -2 -1 -2 Sampled (mg C-CH4 hr m ) (mg C-CO2 hr m ) Dormant season 20°C 30°C 20°C 30°C UP A -0.01 3.15 6.30 7.04 B 0.22 8.59 4.39 5.29 C 0.23 0.75 4.89 4.51 VEG A 1.15 2.09 3.32 2.80 B 2.60 10.65 3.43 7.68 C 0.38 1.56 6.41 12.12 OW A 4.13 8.37 8.22 6.84 B 14.33 14.23 13.82 9.16 C 0.61 18.69 6.00 7.90 Growing season VEG-SH A 10.37 22.27 4.47 7.99 B 8.28 15.85 4.18 6.04 C 7.91 13.86 3.66 5.69 D 10.77 15.71 4.61 6.82 E 9.80 21.04 3.19 7.22 VEG-DE A 0.00 0.06 2.83 2.45 B 0.40 0.45 3.73 3.35 C 0.01 1.02 0.26 4.31 D 0.02 0.15 0.86 3.13 E 0.25 9.78 1.42 10.70 OW-SH A 12.04 27.91 2.62 9.52 B 5.55 23.45 1.94 6.53 C 11.58 20.93 2.55 6.41 D 7.72 19.21 2.78 5.54 E 12.48 23.37 3.14 6.81 OW-DE A 0.05 0.14 1.66 5.26 B 0.01 7.04 1.11 4.36 C 0.03 0.71 1.88 4.21 D 10.82 0.08 3.99 3.39 E 0.17 0.28 1.83 4.91 Amendment ACETATE A 18.59 22.13 3.67 10.32 B 11.46 17.98 1.89 9.31 C 14.95 27.19 2.07 10.10 CO2 A 0.19 0.05 10.83 15.51 B 0.01 0.07 10.62 15.25 C 0.01 0.06 12.96 11.33 Table A.1 Individual microcosm’s (A-C/F) methane and carbon dioxide flux potential temporal rates. Rates calculated for individual microcosms. Red indicates a weak
(r2<0.5); blue intermediate (0.5
114
Site Microcosm Time of Sampling (hr)
UP 20°C 0 6 24 96 168 336 504 672 840 1680 1848
A 2.0 2.1 3.8 40.8 3.9 11.0 10.4 14.5 22.9 7.2 7.2
B 9.7 7.9 15.9 41.4 13.9 21.6 18.0 34.1 27.1 202.5 427.9
C 8.2 10.8 10.9 41.6 13.8 18.9 19.3 27.4 32.7 169.4 473.9
Avg. 6.6 6.9 10.2 41.3 10.6 17.2 15.9 25.3 27.5 126.4 303.0
Std. 4.1 4.4 6.1 0.4 5.7 5.5 4.8 9.9 4.9 104.5 257.2
30°C 0 6 24 96 168 336 504 672 840 1680 1848
A 0.0 15.7 14.1 47.3 10.0 24.8 29.2 45.1 107.2 8635.2 1307.0
B 15.5 1.3 4.8 28.2 7.0 4.3 48.9 110.0 258.0 N/A 13189.2
C 0.0 12.3 1.0 39.1 4.2 4.2 24.8 22.2 16.7 144.3 1803.6
Avg. 5.2 9.8 6.6 38.2 7.1 11.1 34.3 59.1 127.3 4389.7 5433.2
Std. 8.9 7.6 6.7 9.6 2.9 11.9 12.8 45.5 121.9 6003.9 6721.4
VEG 20°C 0 6 24 96 168 336 504 672 840 1680 1848
A 2.3 12.3 16.0 36.6 7.2 17.9 37.9 68.4 63.2 452.3 3038.9
B 0.8 3.4 4.4 34.6 96.9 99.7 716.5 1270.8 1706.0 5184.4 5395.2
C 0.0 11.5 18.6 26.6 11.8 126.2 215.0 357.2 383.0 1721.2 275.1
Avg. 1.0 9.1 13.0 32.6 38.6 81.3 323.1 565.5 717.4 2452.6 2903.1
Std. 1.2 4.9 7.6 5.3 50.5 56.4 352.0 627.7 871.0 2449.4 2562.7
30°C 0 6 24 96 168 336 504 672 840 1680 1848
A 11.3 14.8 8.5 39.5 18.8 18.9 356.4 820.5 1602.8 893.6 6636.0
B 10.1 7.5 11.2 48.7 17.7 248.4 933.4 2357.8 5117.1 5512.2 30194.6
C 0.9 11.2 12.5 44.3 5.5 12.6 34.4 100.1 122.2 1919.9 3100.5
Avg. 7.4 11.1 10.8 44.1 14.0 93.3 441.4 1092.8 2280.7 2775.2 13310.4
Std. 5.7 3.7 2.1 4.6 7.4 134.3 455.5 1153.2 2565.5 2425.2 14728.6 Table A.2 Methane concentrations of headspace samples corrected for dilutions in ppm
CH4 from dormant season experiment
Continued
115
Table A.2 Continued
Site Microcosm Time of Sampling (hr)
OW 20°C 0 6 24 96 168 336 504 672 840 1680 1848
A 21.8 22.8 25.2 57.7 30.2 31.9 297.3 253.5 354.4 1047.8 12271.0
B 17.9 19.3 28.2 54.9 48.6 26.5 144.9 431.8 724.7 4113.4 40985.6
C 52.9 76.6 43.3 95.2 34.0 33.7 190.9 327.5 626.9 1735.3 1090.0
Avg. 30.9 39.6 32.2 69.3 37.6 30.7 211.0 337.6 568.7 2298.8 18115.5
Std. 19.2 32.1 9.7 22.5 9.7 3.7 78.2 89.6 191.9 1608.6 20579.9
30°C 0 6 24 96 168 336 504 672 840 1680 1848
A 18.2 25.3 39.5 70.5 38.8 37.5 328.5 1845.9 1839.4 9152.0 20369.7
B 42.0 85.1 102.0 160.1 169.5 138.9 454.4 2733.6 7230.4 32965.5 22350.1
C 40.0 72.6 68.4 48.1 26.2 93.0 394.5 2036.4 3127.3 33176.4 33774.5
Avg. 33.4 61.0 70.0 92.9 78.2 89.8 392.5 2205.3 4065.7 25098.0 25498.1
Std. 13.2 31.5 31.3 59.3 79.4 50.8 62.9 467.3 2815.3 13810.0 7235.6
116
Microco Site sm Time of Sampling (hr)
UP 20°C 0 6 24 96 168 336 504 672 840 1680 1848
A 4133.4 4047.0 5175.1 5506.9 6355.8 6537.9 8592.8 10657.9 10480.6 17758.2 19142.9
B 3985.6 3642.3 5239.1 4956.8 6032.6 6478.0 9336.6 9947.5 12758.5 16270.0 18190.7
C 4088.8 4464.6 5313.1 5730.0 6423.4 6573.7 7870.6 10818.7 13893.4 16328.7 19347.2
Avg. 4069.3 4051.3 5242.4 5397.9 6270.6 6529.9 8600.0 10474.7 12377.5 16785.7 18893.6
Std. 75.8 411.2 69.1 398.0 208.9 48.3 733.0 463.6 1738.0 842.8 617.2
30°C 0 6 24 96 168 336 504 672 840 1680 1848
A 3599.8 5133.0 5937.1 6205.7 7708.1 7604.0 16322.7 14337.7 18747.1 34744.3 22057.0
B 3985.6 4517.1 6246.4 9593.1 7936.7 11902.5 17814.8 23722.6 25225.6 N/A 30133.6
C 0.0 4652.5 5233.5 7104.1 8096.5 9034.8 11768.6 15621.2 9392.8 16600.7 22425.4
Avg. 2528.5 4767.6 5805.7 7634.3 7913.8 9513.7 15302.0 17893.8 17788.5 25672.5 24872.0
Std. 2198.2 323.6 519.1 1754.8 195.2 2188.9 3149.7 5088.5 7959.8 12829.5 4560.4
VEG 20°C 0 6 24 96 168 336 504 672 840 1680 1848
A 4989.3 5709.8 6398.1 6427.6 8141.4 6966.1 10178.9 19234.5 15315.0 16830.8 23173.6
B 5602.2 7174.1 7541.0 6482.2 8665.9 8170.7 12322.1 15230.5 17024.1 19974.9 21101.1
C 5383.3 6139.1 6356.9 8676.1 10631.1 9937.3 12349.3 14669.5 15764.4 22083.0 28510.4
Avg. 5324.9 6341.0 6765.4 7195.3 9146.1 8358.0 11616.8 16378.2 16034.5 19629.6 24261.7
Std. 310.6 752.8 672.0 1282.7 1312.5 1494.4 1245.3 2489.5 886.0 2643.1 3822.6
30°C 0 6 24 96 168 336 504 672 840 1680 1848
A 5386.7 5759.2 7974.8 7932.9 7868.1 8064.3 15085.3 18293.6 22949.2 17634.3 27072.4
B 5328.7 6289.5 7709.3 8944.6 7513.2 12538.4 16828.7 19355.1 23492.8 22661.0 41475.3
C 5546.0 5996.1 7259.4 10338.5 13305.3 9975.6 11825.5 17770.9 17870.0 32012.8 41777.4
Avg. 5420.5 6014.9 7647.8 9072.0 9562.2 10192.8 14579.9 18473.2 21437.3 24102.7 36775.0
Std. 112.5 265.7 361.6 1207.8 3246.5 2244.9 2539.6 807.2 3101.3 7296.9 8404.1 Table A.3 Carbon dioxide concentrations of headspace samples corrected for dilutions in ppm CO2 from dormant season experiment.
Continued
117
Table A.3 Continued
Site Microcosm Time of Sampling (hr)
OW 20°C 0 6 24 96 168 336 504 672 840 1680 1848
A 3936.5 5299.0 5437.8 5741.4 6446.5 6705.0 9882.6 9710.1 10752.4 15926.5 34127.8
B 4875.1 5027.0 10920.6 6170.2 6684.3 7004.1 8360.5 10588.9 12193.5 18473.9 51535.0
C 3670.3 4785.7 5235.7 5466.1 6233.2 6381.3 7977.6 9383.7 12661.4 22280.1 20424.5
Avg. 4160.6 5037.2 7198.0 5792.6 6454.7 6696.8 8740.2 9894.2 11869.1 18893.5 35362.4
Std. 632.9 256.8 3225.4 354.8 225.6 311.5 1007.7 623.3 995.0 3197.5 15591.9
30°C 0 6 24 96 168 336 504 672 840 1680 1848
A 4776.5 5384.4 6439.9 6957.2 7426.3 7291.4 10743.2 19380.1 19932.0 24115.8 32739.4
B 4411.5 4928.1 5902.2 7219.8 8880.4 8891.9 10571.5 14372.3 18005.5 27187.2 34686.4
C 4359.0 5388.3 6107.8 6130.5 6577.7 8623.3 11827.4 18190.0 16540.3 29742.5 31050.2
Avg. 4515.7 5233.6 6150.0 6769.2 7628.1 8268.9 11047.4 17314.2 18159.3 27015.2 32825.3
Std. 227.4 264.6 271.3 568.5 1164.6 857.1 681.0 2616.3 1701.1 2817.3 1819.6
118
Site Microcosm Time of Sampling (hr) VEG -SH 20°C 0 48 168 504 840 1344 1848 A 465.4 638.5 1123.4 3169.0 18615.0 40102.7 54360.0 B 115.2 113.5 692.9 756.5 5707.9 26739.9 39636.5 C 321.4 851.1 349.7 539.8 5785.7 23980.5 38276.6 D 230.8 521.5 545.0 798.3 12147.2 30395.6 54621.1 E 485.8 265.5 789.7 3807.4 18872.5 34587.5 53604.0 Avg. 323.7 478.0 700.1 1814.2 12225.7 31161.3 48099.6 Std. 156.9 293.6 289.0 1547.8 6499.3 6391.1 8368.7 30°C 0 48 168 504 840 1344 1848 A 342.1 642.9 1858.6 33147.9 72868.6 104269.5 148216.7 B 135.9 127.2 239.8 7594.5 25386.8 56611.4 85629.4 C 121.0 142.5 263.5 1996.7 17982.6 42604.5 71094.2 D 93.8 83.2 124.3 4359.9 28821.9 51159.4 85077.6 E 199.4 141.4 671.1 9451.7 34993.7 86077.1 110431.6 Avg. 178.4 227.4 631.5 11310.1 36010.7 68144.4 100089.9 Std. 99.4 233.5 716.5 12541.9 21500.6 25982.6 30407.1 VEG -DE 20°C 0 48 168 504 840 1344 1848 A 203.8 86.1 145.1 99.9 97.6 98.0 112.7 B 73.1 92.7 133.3 181.2 211.4 402.4 1772.0 C 70.2 87.8 110.8 87.7 73.4 86.9 119.0 D 74.2 103.3 91.7 71.8 72.7 80.3 136.1 E 72.5 95.5 114.9 106.9 131.6 400.7 1062.4 Avg. 98.7 93.1 119.1 109.5 117.3 213.7 640.5 Std. 58.7 6.8 20.7 42.2 57.8 171.6 752.2 30°C 0 48 168 504 840 1344 1848 A 63.7 85.0 105.5 74.4 97.9 137.2 325.2 B 74.6 90.1 108.4 91.2 151.6 840.7 1779.2 C 73.8 79.2 100.2 100.0 874.4 2539.1 3934.6 D 69.0 70.9 110.8 88.3 77.5 153.5 670.9 E 85.5 124.4 500.5 3732.4 14641.7 27864.0 41648.6 Avg. 73.3 89.9 185.1 817.3 3168.6 6306.9 9671.7 Std. 8.1 20.6 176.4 1629.7 6422.3 12090.4 17931.1 Table A.4 Methane concentrations of headspace samples corrected for dilutions in ppm
CH4 from growing season experiment.
Continued
119
Table A.4 Continued
Site Microcosm Time of Sampling (hr)
OW -SH 20°C 0 48 168 504 840 1344 1848 A 428.1 596.6 1095.5 10665.9 27369.6 52531.8 71628.6 B 172.6 256.5 310.3 1565.1 4194.6 16698.8 28791.6 C 606.6 1205.5 2287.7 14542.4 30003.6 51534.1 73924.6 D 288.3 334.8 2213.7 3601.9 22511.6 41630.5 42799.5 E 554.0 840.9 2781.2 12431.3 30384.8 55528.6 75989.4 Avg. 409.9 646.9 1737.7 8561.3 22892.8 43584.8 58626.7 Std. 180.9 387.9 1008.5 5672.8 10914.5 15910.5 21477.7
30°C 0 48 168 504 840 1344 1848 A 1923.8 2074.7 4291.7 13860.6 79521.9 120062.2 163636.7 B 1301.1 1988.9 1801.5 47240.8 83982.1 122607.7 169561.8 C 655.8 780.5 3276.4 57485.1 95951.2 70619.6 184945.4 D 336.3 498.6 1477.1 18841.5 49991.2 85265.0 118128.6 E 667.3 2282.6 2309.5 47545.1 85538.3 137080.4 165776.5 Avg. 976.9 1525.1 2631.2 36994.6 78996.9 107127.0 160409.8 Std. 634.6 821.4 1150.8 19370.6 17296.6 27905.7 25063.2
OW -DE 20°C 0 48 168 504 840 1344 1848 A 10079.2 927.2 954.0 990.9 1060.7 1102.2 1185.9 B 84.7 70.2 103.1 105.5 123.3 102.7 136.7 C 95.5 165.2 248.9 336.1 367.4 401.6 465.7 D 73.4 83.8 138.9 7106.1 8751.0 8097.7 51048.1 E 114.8 138.8 168.0 425.2 458.0 514.9 760.8 Avg. 2089.5 277.1 322.6 1792.8 2152.1 2043.8 10719.4 Std. 4466.4 365.5 357.0 2988.1 3705.0 3403.6 22547.7
30°C 0 48 168 504 840 1344 1848 A 82.0 107.6 186.8 628.9 813.5 904.6 1179.3 B 89.9 125.4 152.6 327.2 503.2 9171.1 26107.5 C 97.2 101.7 121.4 249.4 402.3 690.2 3051.6 D 61.9 78.6 105.3 102.0 139.6 255.6 410.4 E 76.8 96.5 114.8 231.3 376.5 645.2 1283.3 Avg. 81.5 102.0 136.2 307.8 447.0 2333.3 6406.4 Std. 13.5 17.0 33.4 196.9 244.4 3829.6 11055.7
Continued 120
Table A.4 Continued
Site Microcosm Time of Sampling (hr)
OW -DE 20°C 0 48 168 504 840 1344 1848 + A 531.6 839.1 8647.0 24753.4 28120.9 28280.4 28596.6 ACETATE B 176.6 1070.9 11724.1 17416.6 25551.2 25977.4 25812.8 C 132.4 1904.2 5481.4 20403.3 27718.1 33052.0 33465.1 Avg. 280.2 1271.4 8617.5 20857.8 27130.1 29103.3 29291.5 Std. 218.8 560.2 3121.4 3689.4 1382.1 3608.4 3873.2
30°C 0 48 168 504 840 1344 1848 A 320.4 1694.4 23569.8 33603.0 30149.0 29472.7 40083.4 B 239.2 903.3 10836.5 24689.1 29198.4 37230.8 55369.0 C 241.2 1125.4 9706.0 35341.6 38774.8 43963.5 45469.9 Avg. 266.9 1241.0 14704.1 31211.2 32707.4 36889.0 46974.1 Std. 46.4 408.0 7698.7 5714.8 5276.0 7251.4 7753.0
OW -DE 20°C 0 48 168 504 840 1344 1848 + A 73.3 142.2 73.0 180.4 822.0 1005.2 951.8 CO2 B 81.0 123.6 88.4 114.2 105.2 132.6 151.1 C 82.6 109.6 102.0 130.6 128.8 157.9 175.7 Avg. 79.0 125.2 87.8 141.7 352.0 431.9 426.2 Std. 5.0 16.4 14.5 34.5 407.2 496.6 455.4
30°C 0 48 168 504 840 1344 1848 A 87.8 91.9 86.9 524.1 581.0 622.8 706.8 B 80.2 107.7 123.8 344.4 395.2 465.7 611.6 C 76.8 99.4 106.8 215.1 222.5 316.0 425.0 Avg. 81.6 99.7 105.8 361.2 399.6 468.2 581.1 Std. 5.6 7.9 18.5 155.2 179.3 153.4 143.3
121
Site Microcosm Time of Sampling (hr) VEG -SH 20°C 0 48 168 504 840 1344 1848 A 5994.8 8987.5 11753.1 9787.0 22077.5 28321.1 33394.8 B 4539.7 5496.0 11932.7 14262.2 25360.8 33678.6 38957.2 C 7656.6 10096.3 8584.5 13692.0 22287.0 29507.5 35203.8 D 5823.6 11315.6 11473.1 14155.6 24980.9 29921.2 42448.2 E 8024.0 7060.0 12738.2 17936.8 24312.2 29770.7 36795.4 Avg. 6407.7 8591.1 11296.3 13966.7 23803.7 30239.8 37359.9 Std. 1429.5 2332.9 1587.3 2890.9 1528.8 2022.7 3504.7 30°C 0 48 168 504 840 1344 1848 A 8664.9 12994.2 21173.4 36514.1 52625.3 62383.8 84940.9 B 8364.3 8469.2 18468.8 32289.2 37008.8 50578.7 66177.9 C 8467.6 10826.5 18849.9 30376.0 38398.8 48691.2 63469.1 D 11882.0 9033.9 10677.2 26237.6 42035.4 50857.1 67638.6 E 9869.5 7950.2 22503.8 25725.4 38566.9 51774.3 67939.4 Avg. 9449.7 9854.8 18334.6 30228.5 41727.0 52857.0 70033.2 Std. 1487.5 2062.8 4592.4 4471.6 6367.8 5442.4 8519.2 VEG -DE 20°C 0 48 168 504 840 1344 1848 A 6691.0 15861.6 10775.4 22441.8 25217.6 30262.7 34775.4 B 8197.3 13439.7 16136.2 16799.5 18794.3 22000.8 33877.0 C 11560.5 9429.9 14764.9 23966.8 23120.3 23714.7 24913.6 D 7216.1 9585.8 14217.3 16350.1 17680.8 19377.3 20109.1 E 8139.9 11388.0 13452.0 18091.6 19029.4 22573.0 23929.3 Avg. 8361.0 11941.0 13869.2 19530.0 20768.5 23585.7 27520.9 Std. 1898.2 2728.4 1988.0 3456.9 3231.9 4057.3 6474.2 30°C 0 48 168 504 840 1344 1848 A 14170.1 15476.5 24891.8 26053.0 30121.9 32925.6 37371.5 B 9392.5 7744.6 18026.6 20757.6 23383.3 29926.8 35108.3 C 10258.9 16784.2 24160.7 25817.7 27286.3 34673.2 44353.4 D 13553.2 23389.8 32066.7 40942.3 41913.9 44516.3 55228.3 E 14509.4 24023.5 40149.4 34109.7 53325.3 67495.4 83223.8 Avg. 12376.8 17483.7 27859.1 29536.0 35206.1 41907.5 51057.1 Std. 2373.8 6652.4 8484.0 7969.3 12264.3 15314.2 19613.1 OW -SH 20°C 0 48 168 504 840 1344 1848 A 5585.6 5719.7 14598.8 20465.1 23237.8 29741.7 35526.8 B 5724.4 6574.6 5797.3 15153.4 18721.0 23101.6 26590.9 C 6531.3 8346.2 11028.8 18073.5 21852.6 26360.0 33393.0 D 4835.1 5336.9 8515.0 5155.9 18586.8 25510.9 22372.3 E 6223.4 7976.2 11698.0 14437.5 20264.8 26772.8 33140.7 Avg. 5780.0 6790.7 10327.6 14657.1 20532.6 9193.9 12000.8 Std. 650.9 1335.3 3332.5 5830.4 2012.5 2415.3 4676.0 Table A.5 Carbon dioxide concentrations of headspace samples corrected for dilutions in ppm CO2 from growing season experiment.
Continued
122
Table A.5 Continued
Site Microcosm Time of Sampling (hr) OW-SH 30°C 0 48 168 504 840 1344 1848 A 13290.2 19740.6 22470.1 17954.4 45753.6 59066.3 77702.1 B 9344.5 12377.4 10149.3 31775.3 41322.6 53988.5 70594.3 C 7995.8 7791.5 12566.1 33513.8 41725.7 NA 74739.3 D 8144.1 9031.3 15485.2 27514.5 38857.9 48517.1 61163.4 E 7195.2 11063.2 10830.1 31692.3 42708.1 59075.7 71405.3 Avg. 9193.9 12000.8 14300.2 28490.1 42073.6 55161.9 71120.9 Std. 2415.3 4676.0 5010.8 6289.6 2498.5 5036.3 6243.3 OW -DE 20°C 0 48 168 504 840 1344 1848 A 5508.4 9283.2 7433.8 9819.7 11430.8 13368.3 17103.7 B 6524.0 10163.0 9444.3 10223.8 11336.8 12900.4 15032.9 C 4728.6 7641.2 7200.4 9464.3 11039.3 14611.4 17299.2 D 5704.6 5963.4 7638.5 10837.7 13688.5 15677.9 29079.0 E 5810.0 6690.3 5756.0 9523.8 11082.2 13828.1 17389.6 Avg. 5655.1 7948.2 7494.6 9973.9 11715.5 14077.2 19180.9 Std. 644.5 1753.8 1316.7 568.9 1115.3 1095.1 5617.9 30°C 0 48 168 504 840 1344 1848 A 6470.8 8956.0 14096.8 14953.0 21021.3 27881.8 38010.3 B 6519.8 8437.1 13066.8 21182.1 25482.4 33503.1 39514.9 C 8770.4 10918.4 15127.0 19540.7 22626.2 30032.1 37216.7 D 12238.0 12008.1 15079.7 13457.9 20722.5 23896.8 29094.2 E 11321.5 11363.1 14968.9 18521.4 22514.1 30015.3 39510.4 Avg. 9064.1 10336.6 14467.8 17531.0 22473.3 29065.8 36669.3 Std. 2667.1 1557.2 888.9 3224.4 1887.7 3523.0 4348.8 OW -DE 20°C 0 48 168 504 840 1344 1848 + A 4637.8 6374.8 6271.0 11297.9 14014.0 16776.7 19121.9 ACETATE B 5452.0 8076.0 8059.9 10857.3 15004.9 17694.9 20079.6 C 4621.1 11068.6 7036.4 12522.5 16289.9 19774.8 5106.8 Avg. 4903.6 8506.4 7122.4 11559.2 15103.0 18082.1 14769.4 Std. 475.0 2376.3 897.5 862.8 1141.1 1536.1 8381.8 30°C 0 48 168 504 840 1344 1848 A 10662.8 11590.2 15710.0 26632.6 29927.8 37809.4 48500.4 B 10679.5 10156.3 11307.2 23035.7 27922.2 34164.7 39446.3 C 10129.4 10763.0 11136.2 24490.6 29494.4 32719.6 37583.3 Avg. 10490.6 10836.5 12717.8 24719.6 29114.8 34897.9 41843.3 Std. 312.9 719.8 2592.7 1809.3 1055.3 2622.9 5839.9 OW -DE 20°C 0 48 168 504 840 1344 1848 + A 165195.2 136070.3 154392.1 139201.5 149863.6 186452.3 179847.1 CO2 B 136351.8 135910.6 132937.8 128880.7 149966.4 168527.8 185879.4 C 101340.3 147756.3 147833.6 141212.8 157112.3 177046.8 186392.6 Avg. 134295.8 139912.4 145054.5 136431.7 152314.1 177342.3 184039.7 Std. 31977.1 6793.5 10993.8 6616.2 4155.7 8965.9 3640.0 30°C 0 48 168 504 840 1344 1848 A 87834.0 190154.3 174404.7 169908.1 183870.9 219621.1 232837.3 B 72990.6 178023.8 191544.4 185237.6 204800.1 227940.1 251335.1 C 58239.0 172406.1 140967.9 182553.8 178488.7 211933.0 224674.9 Avg. 73021.2 180194.7 168972.3 179233.2 189053.3 219831.4 236282.5 Std. 14797.5 9071.1 25722.1 8186.5 13900.2 8005.6 13659.9
123
Appendix B: Taxonomic Data from 454-sequencing and Diversity Indices
124
Figure B.1Visualization of taxonomic distribution for four wetland sediments. Legend does not appear, but order follows the sequential order of table B.1.
125
OTU ID OW-DE OW-SH VEG-DE VEG-SH Unclassified (k.) 0.48% 0.72% 0.18% 0.41% Archaea (k.) 0.72% 1.05% 0.09% 0.13% Crenarchaeota (p.) 0.06% 0.00% 0.05% 0.00% MBGB (c.) 0.06% 0.06% 0.00% 0.00% MCG (c.) 0.30% 0.00% 0.09% 0.06% pGrfC26 (o.) 0.72% 1.16% 0.60% 1.24% Candidatus Nitrososphaera (g.) 0.00% 0.00% 0.83% 0.06% Euryarchaeota (p.) 0.72% 0.88% 0.51% 0.25% DSEG (c.) 0.00% 0.00% 0.00% 0.10% DHVE3 (o.) 0.00% 0.00% 0.00% 0.06% Methanobacteriaceae (f.) 0.12% 0.00% 0.00% 0.13% Methanobacterium (g.) 0.36% 0.06% 0.00% 0.22% WSA2 (f.) 0.00% 0.06% 0.00% 0.00% Methanomicrobia (c.) 0.06% 0.06% 0.00% 0.00% Methanomicrobiales (o.) 0.60% 0.72% 0.00% 0.06% Methanomicrobiaceae (f.) 0.00% 0.11% 0.00% 0.00% Methanoregulaceae (f.) 0.00% 0.11% 0.05% 0.03% Candidatus Methanoregula (g.) 1.75% 1.82% 0.00% 0.35% Methanolinea (g.) 0.60% 0.55% 0.00% 0.19% Methanospirillaceae (f.) 0.00% 0.00% 0.00% 0.03% Methanospirillum (g.) 0.06% 0.00% 0.05% 0.00% ANME-2D (f.) 0.12% 0.00% 0.00% 0.03% Methanosaeta (g.) 1.03% 0.66% 0.00% 0.32% Methanosarcina (g.) 0.06% 0.39% 0.00% 0.19% E2 (o.) 0.18% 0.11% 0.09% 0.03% E2 (o.) 0.00% 0.00% 0.05% 0.00% CCA47 (f.) 0.00% 0.06% 0.05% 0.06% Table B.1 Relative abundance up to level 6 of each taxonomy identified from 454- sequencing analysis. Organism name of highest classification is shown and classification level is indicated for kingdom (k.), phylum (p.), class (c.), order (o.), family (f.), or genus
(g.).
Continued 126
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH DHVEG-1 (f.) 1.51% 5.35% 0.09% 0.00% TMEG (f.) 0.00% 0.11% 0.00% 0.00% WCHD3-02 (f.) 1.39% 1.60% 0.28% 0.22% Micrarchaeles (o.) 0.00% 0.22% 0.55% 0.25% WCHD3-30 (o.) 1.75% 1.43% 2.81% 1.20% YLA114 (o.) 0.72% 0.66% 0.88% 0.89% Bacteria (k.) 5.19% 7.72% 8.43% 6.08% HDBW-WB69 (c.) 0.00% 0.11% 0.00% 0.16% SHA-114 (c.) 0.12% 0.39% 0.05% 0.03% TA06 (c.) 0.06% 0.00% 0.00% 0.03% AD3 (p.) 0.00% 0.00% 0.09% 0.00% Acidobacteria (p.) 0.00% 0.00% 0.05% 0.06% Acidobacteria (p.) 0.12% 0.00% 0.18% 0.03% AT-s54 (c.) 0.00% 0.00% 0.00% 0.03% Acidobacteriales (o.) 0.00% 0.00% 0.05% 0.03% Acidobacteriaceae (f.) 0.00% 0.00% 0.00% 0.03% Candidatus Koribacter (g.) 0.00% 0.00% 0.05% 0.10% Acidobacteria-2 (c.) 0.00% 0.00% 0.23% 0.06% Acidobacteria-6 (c.) 0.06% 0.17% 0.05% 0.03% CCU21 (o.) 0.24% 0.00% 0.09% 0.22% iii1-15 (o.) 0.00% 0.00% 0.00% 0.03% RB40 (f.) 0.00% 0.06% 0.00% 0.00% BPC102 (c.) 0.00% 0.00% 0.00% 0.06% Chloracidobacteria (c.) 0.12% 0.00% 0.00% 0.06% Holophagales (o.) 0.00% 0.00% 0.05% 0.10% Geothrix (g.) 0.00% 0.00% 0.00% 0.13% MVS-40 (c.) 0.42% 0.28% 0.55% 0.57% OS-K (c.) 0.00% 0.06% 0.14% 0.13% PAUC37f (c.) 0.00% 0.00% 0.05% 0.10% RB25 (c.) 0.42% 0.17% 0.05% 0.22% Candidatus Solibacter (g.) 0.85% 0.55% 3.18% 1.55% Sva0725 (c.) 0.06% 0.00% 0.05% 0.03% Sva0725 (o.) 0.00% 0.00% 0.18% 0.16% Continued
127
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH TM1 (c.) 0.18% 0.00% 0.14% 0.06% 32-20 (o.) 0.00% 0.00% 0.05% 0.00% DS-18 (o.) 0.00% 0.00% 0.14% 0.00% SJA-36 (o.) 0.00% 0.00% 0.05% 0.06% Acidimicrobiales (o.) 0.00% 0.00% 0.00% 0.03% Acidimicrobiales (o.) 0.00% 0.06% 0.00% 0.10% AKIW874 (f.) 0.00% 0.00% 0.09% 0.00% C111 (f.) 0.18% 0.06% 0.09% 0.10% EB1017 (f.) 0.00% 0.00% 0.05% 0.00% koll13 (f.) 0.06% 0.11% 0.28% 0.06% Actinobacteria (c.) 0.00% 0.00% 0.05% 0.00% Actinomycetales (o.) 0.18% 0.22% 0.55% 0.19% Actinomycetales (o.) 0.00% 0.00% 0.05% 0.10% ACK-M1 (f.) 0.06% 0.00% 0.00% 0.00% Kineosporiaceae (f.) 0.00% 0.00% 0.00% 0.38% Microbacteriaceae (f.) 0.00% 0.00% 0.00% 0.03% Micrococcaceae (f.) 0.18% 0.00% 0.00% 0.06% Micromonosporaceae (f.) 0.24% 0.00% 0.05% 0.03% Kribbella (g.) 0.06% 0.00% 0.00% 0.00% Streptomyces (g.) 0.12% 0.00% 0.05% 0.00% Micrococcales (o.) 0.00% 0.00% 0.37% 0.03% At425_EubF1 (f.) 0.18% 0.00% 0.18% 0.03% MB-A2-108 (c.) 0.00% 0.00% 0.09% 0.00% 0319-7L14 (o.) 0.30% 0.00% 3.92% 0.32% OPB41 (c.) 0.12% 0.00% 0.05% 0.00% Thermoleophilia (c.) 0.00% 0.00% 0.05% 0.00% Gaiellales (o.) 0.48% 0.39% 0.09% 0.22% Gaiellaceae (f.) 0.12% 0.00% 0.46% 0.25% Solirubrobacterales (o.) 0.00% 0.00% 0.05% 0.03% Solirubrobacterales (o.) 0.06% 0.06% 0.28% 0.06% Solirubrobacteraceae (f.) 0.00% 0.00% 0.05% 0.03% SHA-37 (c.) 0.00% 0.00% 0.05% 0.00% SJA-176 (c.) 0.36% 0.06% 0.32% 0.16% Fimbriimonadaceae (f.) 0.00% 0.06% 0.00% 0.03% Continued 128
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH NPL-UPA2 (c.) 0.00% 0.00% 0.00% 0.03% PRR-11 (c.) 0.00% 0.06% 0.00% 0.00% Bacteria (p.) 0.06% 0.06% 0.23% 0.03% Bacteroidetes (p.) 0.79% 1.27% 0.74% 1.14% Bacteroidales (o.) 0.30% 0.11% 0.09% 0.22% Bacteroidales (o.) 4.11% 5.13% 7.56% 6.37% Bacteroidaceae (f.) 0.00% 0.00% 0.00% 0.06% BF311 (g.) 0.00% 0.00% 0.05% 0.00% Bacteroides (g.) 0.00% 0.00% 0.05% 0.10% Marinilabiaceae (f.) 0.00% 0.00% 0.00% 0.03% Prevotella (g.) 0.00% 0.00% 0.00% 0.03% Flavobacteriia (c.) 0.00% 0.00% 0.05% 0.00% Flavobacteriia (c.) 0.00% 0.00% 0.00% 0.03% Cryomorphaceae (f.) 0.00% 0.00% 0.00% 0.03% Flavobacteriaceae (f.) 0.00% 0.06% 0.00% 0.03% Flavobacterium (g.) 0.00% 0.17% 0.00% 0.03% Sphingobacteriales (o.) 0.00% 0.06% 0.00% 0.06% Sphingobacteriales (o.) 0.00% 0.00% 0.05% 0.22% Chitinophagaceae (f.) 0.06% 0.11% 0.09% 0.06% Chitinophagaceae (f.) 0.72% 0.72% 0.09% 0.29% Cyclobacteriaceae (f.) 0.00% 0.00% 0.00% 0.16% Ekhidnaceae (f.) 0.00% 0.00% 0.14% 0.03% A4 (g.) 0.30% 1.10% 0.05% 0.48% Flexibacteraceae (f.) 0.00% 0.00% 0.05% 0.00% Saprospiraceae (f.) 0.24% 0.39% 0.05% 0.60% Haliscomenobacter (g.) 0.00% 0.00% 0.00% 0.03% Sphingobacteriaceae (f.) 0.00% 0.00% 0.23% 0.00% WCHB1-02 (o.) 0.00% 0.06% 0.00% 0.06% BA059 (f.) 0.06% 0.00% 0.00% 0.00% LCP-26 (g.) 0.00% 0.22% 0.00% 0.00% Chlamydiales (o.) 0.18% 0.06% 0.32% 0.10% Parachlamydiaceae (f.) 0.00% 0.00% 0.00% 0.06% Parachlamydiaceae (f.) 0.00% 0.00% 0.05% 0.00% Candidatus Protochlamydia (g.) 0.00% 0.00% 0.00% 0.06% Continued 129
Table B.1 Continued
Chlorobi (p.) 0.00% 0.00% 0.05% 0.03% A89 (o.) 0.00% 0.00% 0.00% 0.10% C20 (o.) 0.18% 0.11% 0.65% 0.63% PK329 (o.) 0.18% 0.88% 0.32% 0.22% VC38 (o.) 0.00% 0.00% 0.09% 0.03% Ignavibacteriales (o.) 0.00% 0.06% 0.00% 0.00% Ignavibacteriales (o.) 0.06% 0.00% 0.00% 0.19% Ignavibacteriaceae (f.) 0.00% 0.06% 0.00% 0.03% Ignavibacteriaceae (f.) 0.60% 0.99% 0.23% 0.44% OPB56 (c.) 0.00% 0.00% 0.05% 0.10% SJA-28 (c.) 0.00% 0.00% 1.29% 0.48% Chloroflexi (p.) 0.06% 0.11% 0.32% 0.13% Chloroflexi (p.) 0.00% 0.00% 0.00% 0.03% Anaerolineae (c.) 0.60% 0.66% 0.32% 0.73% Anaerolineae (c.) 0.06% 0.00% 0.05% 0.00% A31 (o.) 0.06% 0.06% 0.60% 0.19% S47 (f.) 0.00% 0.00% 0.05% 0.00% Anaerolineales (o.) 0.00% 0.06% 0.00% 0.00% Anaerolinaceae (f.) 0.00% 0.06% 0.14% 0.06% Anaerolinaceae (f.) 0.54% 0.17% 0.09% 0.10% Anaerolinea (g.) 0.00% 0.22% 0.05% 0.13% C1_B004 (g.) 0.00% 0.00% 0.05% 0.00% SHD-14 (g.) 0.06% 0.00% 0.00% 0.00% WCHB1-05 (g.) 0.18% 0.00% 0.00% 0.00% CFB-26 (o.) 0.00% 0.00% 0.09% 0.00% Caldilineaceae (f.) 0.18% 0.06% 0.00% 0.03% Caldilineaceae (f.) 0.00% 0.00% 0.00% 0.03% Caldilinea (g.) 0.00% 0.06% 0.00% 0.06% GCA004 (o.) 0.48% 0.72% 0.55% 1.30% OPB11 (o.) 0.24% 0.00% 0.00% 0.00% S0208 (o.) 0.12% 0.00% 0.09% 0.16% SB-34 (o.) 0.00% 0.11% 0.18% 0.03% SBR1031 (o.) 0.00% 0.00% 0.00% 0.10% A4b (f.) 0.00% 0.00% 0.00% 0.06% Continued
130
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH SHA-31 (f.) 0.00% 0.00% 0.05% 0.00% oc28 (f.) 0.00% 0.00% 0.00% 0.06% SHA-20 (o.) 0.12% 0.06% 0.09% 0.06% SJA-15 (o.) 0.72% 0.39% 0.18% 0.35% WCHB1-50 (o.) 0.00% 0.00% 0.05% 0.00% envOPS12 (o.) 0.30% 0.06% 0.69% 0.35% pLW-97 (o.) 0.06% 0.00% 0.00% 0.00% B07_WMSP1 (o.) 0.00% 0.00% 0.05% 0.00% Kouleothrixaceae (f.) 0.00% 0.00% 0.05% 0.00% Kouleothrixaceae (f.) 0.00% 0.00% 0.05% 0.00% Dehalococcoidetes (c.) 0.42% 0.17% 0.09% 0.10% Dehalococcoidetes (c.) 0.54% 0.11% 0.14% 0.06% Dehalococcoidaceae (f.) 0.12% 0.00% 0.00% 0.03% Dehalococcoidaceae (f.) 0.18% 0.00% 0.00% 0.00% FS117-23B-02 (o.) 0.18% 0.28% 0.00% 0.00% GIF9 (o.) 0.24% 0.06% 0.00% 0.10% Ellin6529 (c.) 0.06% 0.22% 0.83% 0.25% JG30-KF-AS9 (o.) 0.00% 0.00% 0.00% 0.03% S085 (c.) 0.00% 0.00% 0.14% 0.00% S085 (o.) 0.00% 0.00% 0.14% 0.00% mle1-48 (o.) 0.00% 0.00% 0.05% 0.06% Cyanobacteria (p.) 0.00% 0.00% 0.00% 0.13% MLE1-12 (o.) 0.00% 0.00% 0.00% 0.06% Chloroplast (c.) 0.00% 0.00% 0.00% 0.03% Chlamydomonadaceae (f.) 0.00% 0.11% 0.00% 0.10% Stramenopiles (o.) 0.12% 0.55% 0.05% 0.48% Streptophyta (o.) 0.00% 0.06% 0.00% 0.00% Oscillatoriophycideae (c.) 0.00% 0.06% 0.00% 0.00% Chroococcales (o.) 0.00% 0.00% 0.00% 0.06% Prochlorococcus (g.) 0.00% 0.06% 0.00% 0.00% Elusimicrobia (p.) 0.00% 0.11% 0.05% 0.03% Elusimicrobia (c.) 0.00% 0.00% 0.00% 0.03% Elusimicrobiales (o.) 0.12% 0.00% 0.00% 0.06% FAC88 (o.) 0.12% 0.06% 0.05% 0.19% Continued 131
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH IIb (o.) 0.18% 0.17% 0.18% 0.25% MVP-88 (o.) 0.12% 0.17% 0.00% 0.10% Endomicrobia (c.) 0.06% 0.06% 0.32% 0.29% OP2 (c.) 0.06% 0.00% 0.00% 0.00% FCPU426 (p.) 0.06% 0.00% 0.00% 0.00% B5-096 (c.) 0.06% 0.00% 0.00% 0.06% SBZC_2415 (c.) 0.00% 0.00% 0.05% 0.03% TG3 (c.) 0.00% 0.00% 0.00% 0.03% TG3-1 (o.) 0.00% 0.06% 0.00% 0.03% TG3-1 (o.) 0.00% 0.00% 0.09% 0.00% TSCOR003-O20 (f.) 0.00% 0.00% 0.14% 0.22% Firmicutes (p.) 0.00% 0.06% 0.00% 0.00% Bacilli (c.) 0.12% 0.06% 0.09% 0.00% Bacillales (o.) 0.06% 0.00% 0.00% 0.00% Bacillaceae (f.) 0.00% 0.00% 0.05% 0.00% Bacillus (g.) 0.00% 0.00% 0.05% 0.00% Ammoniphilus (g.) 0.06% 0.00% 0.00% 0.00% Paenibacillus (g.) 0.06% 0.00% 0.05% 0.00% Sporosarcina (g.) 0.06% 0.00% 0.05% 0.00% Exiguobacterales (o.) 0.36% 0.00% 0.05% 0.00% Clostridia (c.) 0.00% 0.06% 0.00% 0.10% Clostridiales (o.) 0.06% 0.11% 0.14% 0.29% Clostridiaceae (f.) 0.00% 0.00% 0.00% 0.06% Clostridiaceae (f.) 0.00% 0.11% 0.00% 0.00% Clostridium (g.) 0.48% 0.33% 0.41% 0.41% Fusibacter (g.) 0.00% 0.00% 0.00% 0.06% Lachnospiraceae (f.) 0.00% 0.00% 0.00% 0.03% Butyrivibrio (g.) 0.00% 0.00% 0.05% 0.03% Peptococcaceae (f.) 0.12% 0.00% 0.00% 0.00% Pelotomaculum (g.) 0.06% 0.00% 0.00% 0.00% Peptostreptococcaceae (f.) 0.06% 0.00% 0.00% 0.00% Peptostreptococcaceae (f.) 0.00% 0.00% 0.00% 0.03% Ruminococcaceae (f.) 0.00% 0.00% 0.00% 0.10% Syntrophomonas (g.) 0.00% 0.06% 0.00% 0.00% Continued 132
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH Veillonellaceae (f.) 0.00% 0.00% 0.05% 0.00% Veillonellaceae (f.) 0.30% 0.06% 0.09% 0.06% vadinHB04 (g.) 0.30% 0.00% 0.00% 0.00% Coriobacteriaceae (f.) 0.24% 0.06% 0.14% 0.16% OPB54 (o.) 0.12% 0.00% 0.00% 0.00% PSB-M-3 (g.) 0.00% 0.06% 0.00% 0.00% GN02 (p.) 0.00% 0.11% 0.14% 0.06% 3BR-5F (c.) 0.00% 0.00% 0.05% 0.00% BB34 (c.) 0.00% 0.00% 0.05% 0.06% GN04 (p.) 0.12% 0.06% 0.18% 0.00% GN04 (p.) 0.06% 0.06% 0.41% 0.25% 5bav_B12 (c.) 0.00% 0.00% 0.05% 0.00% GN15 (c.) 0.18% 0.06% 0.05% 0.25% GOUTA4 (p.) 0.12% 0.00% 0.32% 0.06% Gemmatimonadetes (p.) 0.06% 0.00% 0.00% 0.00% Gemm-1 (c.) 0.18% 0.33% 0.32% 0.32% Gemm-2 (c.) 0.12% 0.11% 0.14% 0.03% Gemm-5 (c.) 0.00% 0.00% 0.05% 0.06% Gemmatimonadetes (c.) 0.00% 0.00% 0.32% 0.00% Gemmatimonadetes (c.) 0.06% 0.00% 0.00% 0.03% Gemmatimonadales (o.) 0.00% 0.06% 0.00% 0.00% KD8-87 (o.) 0.00% 0.00% 0.00% 0.06% JL-ETNP-Z39 (c.) 0.00% 0.00% 0.23% 0.00% H-178 (p.) 0.00% 0.00% 0.00% 0.06% KSB3 (p.) 0.00% 0.00% 0.05% 0.06% KSB3 (p.) 0.12% 0.11% 0.00% 0.00% LCP-89 (p.) 0.00% 0.06% 0.05% 0.00% SAW1_B44 (c.) 0.12% 0.22% 0.00% 0.00% Victivallales (o.) 0.00% 0.00% 0.05% 0.03% Z20 (o.) 0.12% 0.11% 0.00% 0.10% MAT-CR-M4-B07 (p.) 0.00% 0.00% 0.05% 0.00% MVS-104 (p.) 0.00% 0.00% 0.14% 0.06% NC10 (p.) 0.00% 0.00% 0.05% 0.00% JH-WHS47 (o.) 0.42% 0.22% 0.32% 0.35% Continued 133
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH NKB19 (p.) 0.00% 0.06% 0.05% 0.00% Nitrospirales (o.) 0.00% 0.00% 0.23% 0.06% 41393 (g.) 0.85% 0.44% 4.61% 3.17% Nitrospira (g.) 0.06% 0.17% 0.05% 0.13% Thermodesulfovibrionaceae (f.) 0.00% 0.11% 0.60% 0.19% Thermodesulfovibrionaceae (f.) 0.06% 0.00% 0.05% 0.32% BD2-6 (g.) 0.00% 0.00% 0.65% 0.38% GOUTA19 (g.) 0.54% 0.39% 0.65% 1.74% HB118 (g.) 0.30% 0.06% 0.00% 0.00% LCP-6 (g.) 1.81% 1.82% 1.43% 1.27% OC31 (p.) 0.00% 0.00% 0.05% 0.00% OD1 (p.) 0.30% 0.22% 0.18% 0.03% OD1 (p.) 0.48% 0.22% 0.92% 0.22% ABY1 (c.) 0.24% 0.17% 0.32% 0.38% Mb-NB09 (c.) 0.24% 0.00% 0.97% 0.06% SM2F11 (c.) 0.00% 0.00% 0.09% 0.03% ZB2 (c.) 0.06% 0.00% 0.32% 0.13% MSBL6 (c.) 0.18% 0.17% 0.00% 0.00% OPB14 (c.) 0.06% 0.00% 0.00% 0.00% OP11 (p.) 0.00% 0.00% 0.14% 0.00% OP11-2 (c.) 0.00% 0.11% 0.00% 0.00% OP11-2 (c.) 0.00% 0.00% 0.00% 0.03% OP11-3 (c.) 0.00% 0.00% 0.32% 0.10% OP11-4 (c.) 0.06% 0.00% 0.18% 0.00% K2-4-19 (o.) 0.00% 0.00% 0.14% 0.00% OP3 (p.) 0.18% 0.33% 0.09% 0.03% BD4-9 (c.) 1.33% 0.61% 0.83% 0.38% PBS-25 (c.) 0.00% 0.33% 0.05% 0.10% koll11 (c.) 0.00% 0.11% 0.05% 0.00% koll11 (c.) 0.06% 0.00% 0.09% 0.03% GIF10 (o.) 0.60% 0.61% 1.20% 0.76% kpj58rc (f.) 0.00% 0.06% 0.00% 0.22% OP8_1 (c.) 0.06% 0.11% 0.00% 0.03% OP8_1 (c.) 0.00% 0.22% 0.28% 0.00% Continued 134
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH SHA-124 (o.) 0.06% 0.00% 0.05% 0.03% OP8_2 (c.) 0.48% 0.28% 0.09% 0.16% SB-45 (o.) 0.12% 0.00% 0.00% 0.00% PAUC34f (p.) 0.00% 0.00% 0.05% 0.00% Planctomycetes (p.) 0.12% 0.11% 0.23% 0.16% Planctomycetes (p.) 0.06% 0.00% 0.05% 0.03% BD7-11 (c.) 0.12% 0.17% 0.00% 0.00% MVS-107 (o.) 0.12% 0.06% 0.05% 0.00% Ucm1571 (o.) 0.00% 0.00% 0.09% 0.00% d113 (o.) 0.00% 0.00% 0.05% 0.00% T8-B82 (o.) 0.06% 0.00% 0.14% 0.00% CL500-15 (o.) 0.00% 0.06% 0.00% 0.00% agg27 (o.) 0.00% 0.00% 0.00% 0.10% Phycisphaerae (c.) 0.00% 0.00% 0.05% 0.00% Phycisphaerae (c.) 1.27% 0.99% 2.40% 1.24% Phycisphaerales (o.) 0.00% 0.00% 0.00% 0.06% Phycisphaerales (o.) 0.06% 0.06% 0.00% 0.10% Phycisphaeraceae (f.) 0.00% 0.06% 0.09% 0.03% Pla3 (c.) 0.18% 0.06% 0.00% 0.03% Pla4 (c.) 0.00% 0.06% 0.05% 0.00% Gemmataceae (f.) 0.00% 0.00% 0.00% 0.13% Pirellulaceae (f.) 0.00% 0.00% 0.05% 0.06% Pirellulaceae (f.) 0.54% 0.28% 0.60% 0.41% Planctomyces (g.) 0.00% 0.00% 0.00% 0.03% p04_C01 (o.) 0.06% 0.00% 0.09% 0.03% Poribacteria (p.) 0.00% 0.00% 0.05% 0.00% Proteobacteria (p.) 0.42% 1.27% 0.41% 1.05% α-proteobacteria (c.) 0.00% 0.17% 0.00% 0.19% Caulobacterales (o.) 0.00% 0.06% 0.00% 0.00% Caulobacteraceae (f.) 0.00% 0.00% 0.00% 0.06% Phenylobacterium (g.) 0.00% 0.00% 0.00% 0.03% Ellin329 (o.) 0.00% 0.00% 0.00% 0.03% Rhizobiales (o.) 0.85% 0.33% 0.09% 0.22% Bradyrhizobiaceae (f.) 0.00% 0.00% 0.05% 0.06% Continued 135
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH Balneimonas (g.) 0.00% 0.00% 0.05% 0.00% Bradyrhizobium (g.) 0.00% 0.06% 0.05% 0.06% Hyphomicrobiaceae (f.) 0.00% 0.00% 0.00% 0.06% Hyphomicrobiaceae (f.) 0.00% 0.00% 0.05% 0.00% Devosia (g.) 0.00% 0.00% 0.09% 0.03% Hyphomicrobium (g.) 0.18% 0.22% 0.83% 0.25% Pedomicrobium (g.) 0.00% 0.00% 0.05% 0.00% Rhodoplanes (g.) 0.00% 0.00% 0.46% 0.19% Methylosinus (g.) 0.06% 0.17% 0.00% 0.10% Rhizobiaceae (f.) 0.00% 0.00% 0.05% 0.00% Rhodobiaceae (f.) 0.00% 0.00% 0.00% 0.03% Hyphomonadaceae (f.) 0.00% 0.06% 0.00% 0.06% Hyphomonas (g.) 0.00% 0.00% 0.00% 0.03% Jannaschia (g.) 0.00% 0.11% 0.00% 0.00% Rhodobacter (g.) 0.12% 0.00% 0.05% 0.16% Rhodospirillales (o.) 0.06% 0.28% 0.00% 0.03% Rhodospirillales (o.) 0.00% 0.00% 0.00% 0.10% Acetobacteraceae (f.) 0.00% 0.17% 0.00% 0.00% Acetobacteraceae (f.) 0.00% 0.00% 0.00% 0.06% Roseomonas (g.) 0.00% 0.11% 0.00% 0.06% Rhodospirillaceae (f.) 0.00% 0.00% 0.00% 0.16% Rhodospirillaceae (f.) 0.06% 0.00% 0.09% 0.00% Rickettsiales (o.) 0.00% 0.00% 0.05% 0.00% Rickettsiaceae (f.) 0.00% 0.06% 0.00% 0.00% mitochondria (f.) 0.00% 0.00% 0.05% 0.16% Erythrobacteraceae (f.) 0.00% 0.11% 0.00% 0.00% Erythromicrobium (g.) 0.00% 0.00% 0.00% 0.03% Sphingomonadaceae (f.) 0.00% 0.00% 0.00% 0.10% Kaistobacter (g.) 0.00% 0.00% 0.00% 0.03% Novosphingobium (g.) 0.00% 0.06% 0.00% 0.10% β-proteobacteria (c.) 1.27% 3.09% 3.13% 2.85% β-proteobacteria (c.) 0.18% 0.22% 0.18% 0.54% UD5 (f.) 0.00% 0.00% 0.05% 0.06% Burkholderiales (o.) 0.12% 0.11% 0.00% 0.10% Continued 136
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH Alcaligenaceae (f.) 2.11% 1.10% 0.05% 0.19% Comamonadaceae (f.) 1.57% 1.21% 1.94% 2.12% Hydrogenophaga (g.) 0.00% 0.00% 0.05% 0.13% Polaromonas (g.) 0.36% 0.00% 0.65% 0.22% Rhodoferax (g.) 0.06% 0.00% 0.00% 0.03% Rubrivivax (g.) 0.12% 0.17% 0.05% 0.29% Oxalobacteraceae (f.) 0.06% 0.00% 0.00% 0.03% Herminiimonas (g.) 0.00% 0.00% 0.00% 0.03% Ellin6067 (o.) 0.36% 0.39% 0.65% 0.63% Gallionellales (o.) 0.48% 0.11% 0.09% 0.79% Gallionella (g.) 0.12% 0.06% 0.37% 0.29% Hydrogenophilaceae (f.) 0.12% 0.06% 0.05% 0.10% Thiobacillus (g.) 5.01% 4.85% 0.51% 3.52% MND1 (o.) 0.00% 0.00% 0.00% 0.13% Methylophilales (o.) 0.06% 0.00% 0.00% 0.00% Methylotenera (g.) 0.00% 0.28% 0.00% 0.10% Nitrosomonadaceae (f.) 0.00% 0.06% 0.05% 0.00% Rhodocyclaceae (f.) 1.63% 2.37% 0.51% 1.33% Azospira (g.) 0.00% 0.06% 0.00% 0.00% Azovibrio (g.) 0.00% 0.06% 0.00% 0.03% Candidatus Accumulibacter (g.) 0.00% 0.00% 0.14% 0.00% Dechloromonas (g.) 0.00% 0.00% 0.09% 0.00% Dok59 (g.) 0.00% 0.11% 0.09% 0.03% Propionivibrio (g.) 0.00% 0.06% 0.14% 0.03% Sulfuritalea (g.) 0.36% 0.11% 0.09% 0.22% Zoogloea (g.) 0.00% 0.00% 0.00% 0.03% SBla14 (o.) 0.18% 0.17% 1.20% 0.86% SC-I-84 (o.) 0.72% 0.72% 0.09% 0.57% Thiobacterales (o.) 0.18% 0.00% 0.00% 0.06% δ-proteobacteria (c.) 2.36% 2.92% 1.43% 2.06% δ-proteobacteria (c.) 0.48% 0.28% 0.05% 0.22% BPC076 (o.) 2.05% 0.50% 1.38% 1.39% Bdellovibrionales (o.) 0.00% 0.00% 0.00% 0.03% Bacteriovoracaceae (f.) 0.06% 0.00% 0.00% 0.03% Continued 137
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH Bdellovibrio (g.) 0.00% 0.06% 0.05% 0.06% Desulfobulbaceae (f.) 0.30% 0.00% 0.09% 0.22% Desulfobulbaceae (f.) 0.24% 0.17% 0.05% 0.32% Desulfobulbus (g.) 0.12% 0.06% 0.00% 0.10% Desulfocapsa (g.) 0.12% 0.00% 0.05% 0.00% Nitrospinaceae (f.) 0.12% 0.00% 0.05% 0.03% Desulfovibrio (g.) 0.00% 0.00% 0.05% 0.06% Desulfuromonadales (o.) 0.00% 0.22% 0.05% 0.06% Desulfuromonadales (o.) 0.60% 0.83% 0.00% 0.06% Geobacteraceae (f.) 0.06% 0.00% 0.18% 0.35% Geobacter (g.) 0.18% 0.06% 0.00% 0.35% Pelobacteraceae (f.) 0.00% 0.00% 0.09% 0.03% GW-28 (o.) 0.12% 0.11% 0.00% 0.00% MBNT15 (o.) 0.42% 0.00% 1.38% 0.76% MIZ46 (o.) 0.00% 0.00% 0.00% 0.06% Myxococcales (o.) 0.24% 0.22% 0.78% 0.63% Myxococcales (o.) 0.48% 0.17% 0.23% 0.57% Cystobacterineae (f.) 0.06% 0.06% 0.05% 0.10% Haliangiaceae (f.) 0.06% 0.00% 0.00% 0.06% Anaeromyxobacter (g.) 0.30% 0.33% 0.41% 0.19% Nannocystis (g.) 0.06% 0.00% 0.00% 0.00% OM27 (f.) 0.00% 0.00% 0.05% 0.00% Polyangiaceae (f.) 0.06% 0.00% 0.09% 0.03% NB1-j (o.) 0.06% 0.11% 0.00% 0.03% JTB38 (f.) 0.00% 0.00% 0.00% 0.03% NKB15 (o.) 0.12% 0.11% 0.00% 0.00% Spirobacillales (o.) 0.00% 0.00% 0.00% 0.16% Sva0485 (o.) 0.06% 0.06% 0.00% 0.00% Syntrophobacterales (o.) 0.54% 0.50% 0.28% 0.19% Desulfobacteraceae (f.) 0.00% 0.06% 0.05% 0.54% Desulfobacteraceae (f.) 0.00% 0.17% 0.05% 0.13% Desulfobacterium (g.) 2.54% 2.76% 0.60% 1.58% Desulfococcus (g.) 0.06% 0.39% 0.09% 0.35% Syntrophaceae (f.) 0.36% 0.39% 0.14% 0.10% Continued 138
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH Syntrophaceae (f.) 1.03% 0.83% 0.88% 0.38% Desulfobacca (g.) 0.06% 0.11% 0.97% 0.67% Desulfomonile (g.) 0.36% 0.11% 0.69% 0.79% Syntrophus (g.) 0.18% 0.11% 0.00% 0.00% Syntrophobacteraceae (f.) 0.24% 0.22% 0.09% 0.16% Syntrophobacteraceae (f.) 0.85% 0.28% 0.69% 0.86% Syntrophobacter (g.) 0.36% 0.33% 0.23% 0.32% Syntrophorhabdaceae (f.) 0.42% 0.28% 0.09% 0.73% Entotheonellales (o.) 0.00% 0.00% 0.00% 0.03% Sulfuricurvum (g.) 0.42% 0.06% 0.05% 0.51% γ-proteobacteria (c.) 0.72% 1.43% 0.32% 1.05% γ-proteobacteria (c.) 0.00% 0.06% 0.00% 0.03% 125ds10 (f.) 0.06% 0.11% 0.00% 0.00% OM60 (f.) 0.00% 0.06% 0.00% 0.00% OM60 (f.) 0.00% 0.06% 0.00% 0.13% Congregibacter (g.) 0.06% 0.17% 0.00% 0.03% Chromatiales (o.) 0.60% 0.00% 0.28% 0.13% Chromatiaceae (f.) 0.00% 0.00% 0.00% 0.16% Chromatiaceae (f.) 0.12% 0.28% 0.05% 0.51% Ectothiorhodospiraceae (f.) 0.54% 0.17% 0.14% 0.70% Thiovirga (g.) 0.00% 0.00% 0.00% 0.03% FCPT525 (f.) 0.00% 0.00% 0.00% 0.03% HTCC2089 (f.) 0.60% 0.06% 0.05% 0.00% Legionellales (o.) 0.00% 0.00% 0.14% 0.00% Coxiellaceae (f.) 0.06% 0.00% 0.00% 0.19% Methylococcales (o.) 0.18% 0.06% 0.09% 0.19% Methylococcales (o.) 0.12% 0.06% 0.00% 0.00% Crenothrix (g.) 1.03% 1.60% 0.14% 0.86% Methylococcaceae (f.) 0.12% 0.28% 0.00% 0.13% Methylocaldum (g.) 0.00% 0.00% 0.00% 0.03% Methylomicrobium (g.) 0.00% 0.06% 0.00% 0.03% Methylomonas (g.) 0.06% 0.00% 0.00% 0.00% Pseudomonadaceae (f.) 0.00% 0.00% 0.00% 0.10% Xanthomonadales (o.) 0.18% 0.22% 0.00% 0.03% Continued 139
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH Xanthomonadales (o.) 0.06% 0.00% 0.05% 0.19% Sinobacteraceae (f.) 1.51% 1.49% 0.05% 0.38% Sinobacteraceae (f.) 1.21% 0.61% 0.00% 0.29% Xanthomonadaceae (f.) 0.12% 0.00% 0.00% 0.00% Xanthomonadaceae (f.) 0.06% 0.00% 0.00% 0.06% Dyella (g.) 0.00% 0.00% 0.00% 0.03% Lysobacter (g.) 0.00% 0.11% 0.00% 0.00% Thermomonas (g.) 0.00% 0.06% 0.00% 0.03% Xanthomonas (g.) 0.00% 0.00% 0.00% 0.03% SC4 (p.) 0.00% 0.00% 0.14% 0.03% SR1 (p.) 0.00% 0.06% 0.00% 0.00% Spirochaetes (p.) 0.06% 0.00% 0.00% 0.00% Spirochaetes (c.) 0.06% 0.00% 0.05% 0.00% M2PT2-76 (o.) 0.06% 0.06% 0.00% 0.29% Spirochaetaceae (f.) 0.48% 0.11% 0.41% 0.16% Spirochaetaceae (f.) 0.00% 0.00% 0.05% 0.00% Spirochaeta (g.) 0.06% 0.00% 0.92% 0.19% Treponema (g.) 0.00% 0.06% 0.09% 0.44% SHA-116 (f.) 0.06% 0.00% 0.00% 0.00% A0-023 (f.) 0.06% 0.06% 0.00% 0.00% Brevinematales (o.) 0.06% 0.00% 0.00% 0.13% Leptospirales (o.) 0.00% 0.00% 0.00% 0.03% Leptospiraceae (f.) 0.00% 0.00% 0.18% 0.00% Sediment-4 (f.) 0.12% 0.00% 0.00% 0.16% Sediment-4 (f.) 0.00% 0.17% 0.00% 0.00% SJA-88 (g.) 0.00% 0.00% 0.00% 0.13% SBRH58 (c.) 0.24% 0.22% 0.09% 0.03% SJA-4 (c.) 0.24% 0.00% 0.28% 0.22% Verrucomicrobia (p.) 0.00% 0.06% 0.00% 0.00% Opitutales (o.) 0.00% 0.00% 0.00% 0.06% Opitutaceae (f.) 0.00% 0.00% 0.00% 0.03% Opitutus (g.) 0.00% 0.11% 0.00% 0.00% Verruco-5 (c.) 0.00% 0.17% 0.05% 0.06% LD1-PB3 (o.) 0.24% 0.11% 0.09% 0.00% Continued 140
Table B.1 Continued
OTU ID OW-DE OW-SH VEG-DE VEG-SH LD1-PB3 (o.) 0.24% 0.00% 0.05% 0.13% WCHB1-41 (o.) 0.00% 0.00% 0.00% 0.10% WCHB1-41 (o.) 0.24% 0.17% 0.14% 0.13% RFP12 (f.) 0.00% 0.00% 0.00% 0.03% Verrucomicrobiaceae (f.) 0.00% 0.00% 0.00% 0.10% Luteolibacter (g.) 0.18% 0.11% 0.05% 0.29% Prosthecobacter (g.) 0.00% 0.00% 0.00% 0.03% Verrucomicrobium (g.) 0.00% 0.00% 0.05% 0.00% Methylacidiphilae (c.) 0.12% 0.00% 0.00% 0.00% LD19 (f.) 0.00% 0.00% 0.00% 0.29% S-BQ2-57 (o.) 0.00% 0.17% 0.18% 0.10% Pedosphaerae (c.) 0.12% 0.11% 0.00% 0.10% Pedosphaerales (o.) 0.60% 1.43% 0.46% 0.63% Pedosphaerales (o.) 0.24% 0.39% 0.51% 0.67% Ellin515 (f.) 1.81% 1.38% 0.46% 1.11% R4-41B (f.) 0.00% 0.17% 0.00% 0.03% auto67_4W (f.) 0.24% 0.22% 0.14% 0.22% Chthoniobacteraceae (f.) 0.00% 0.06% 0.00% 0.00% Candidatus Xiphinematobacter (g.) 0.00% 0.06% 0.05% 0.00% WS1 (p.) 0.06% 0.00% 0.05% 0.00% SHA-109 (c.) 0.00% 0.00% 0.05% 0.00% PRR-12 (c.) 0.00% 0.00% 0.00% 0.03% PRR-12 (c.) 0.12% 0.06% 0.00% 0.13% LD1-PA13 (o.) 0.00% 0.00% 0.00% 0.13% PBS-III-9 (o.) 0.00% 0.06% 0.05% 0.03% SSS58A (o.) 0.24% 0.11% 0.00% 0.00% Sediment-1 (o.) 0.00% 0.17% 0.37% 0.38% MSB-4E2 (f.) 0.18% 0.06% 0.00% 0.00% PRR-10 (f.) 0.18% 0.00% 0.09% 0.00% WS4 (p.) 0.00% 0.00% 0.00% 0.03% ZB3 (p.) 0.00% 0.00% 0.00% 0.03%
141
Figure B.2 Rarefaction curves of α-diversity indices of the four wetland sites. Green is
the shallow, vegetated; blue is shallow, open-water; orange is deep, open-water; and red is deep, vegetated sediments.
142
Figure B.3 Rarefaction values calculated by all metrics of the α-diversity of each sample
143
Figure B.4 PCoA chart separating β-diversity by depth for deep (red) and shallow (blue) samples.
Figure B.5 PCoA chart separating β-diversity by site for open-water (red) and vegetated
(blue) samples.
144
Figure B.6 Jackknifed β-diversity tree of hierarchael clustering. Samples are grouped by microbial assemblage similarity. There was 75-100% support for this hierarchal clustering (indicated by the red line) signifying these samples show high similarities.
145
Appendix C: Water Chemistry Analysis Data
146
Site Microcosm Time of Sampling (hr) VEG - SH 20°C 0 48 168 504 840 1848 A 0.10 1.78 0.08 0.03 0.10 0.14 B 0.03 1.70 0.03 0.03 0.03 0.03 C 0.03 0.06 0.05 0.03 0.03 0.03 Avg. 0.05 1.18 0.05 0.03 0.05 0.07 Std. 0.04 0.97 0.02 0.00 0.04 0.07
30°C 0 48 168 504 840 1848 A 0.07 0.03 0.17 0.08 0.08 0.11 B 0.07 0.03 0.03 0.03 0.03 0.03 C 0.06 0.06 0.03 0.03 0.03 0.03 Avg. 0.07 0.04 0.08 0.04 0.05 0.06 Std. 0.01 0.02 0.08 0.03 0.03 0.05 VEG - DE 20°C 0 48 168 504 840 1848 A 0.07 0.03 0.20 0.08 0.03 0.08 B 0.03 0.03 0.03 0.03 0.03 0.03 C 0.03 0.05 0.03 0.03 0.03 0.03 Avg. 0.04 0.04 0.09 0.05 0.03 0.05 Std. 0.03 0.01 0.10 0.03 0.00 0.03
30°C 0 48 168 504 840 1848 A 0.24 0.03 0.03 0.13 0.34 0.08 B 0.03 0.03 0.03 0.06 0.06 0.10 C 0.63 0.05 0.15 0.16 0.03 0.03 Avg. 0.30 0.04 0.07 0.12 0.15 0.07 Std. 0.30 0.01 0.07 0.05 0.17 0.03 Table C.1 Combined nitrate and nitrite measurements from water quality microcosms.
Units are in mg N L-1.
Continued 147
Table C.1 Continued
Site Microcosm Time of Sampling (hr) OW - SH 20°C 0 48 168 504 840 1848 A 0.10 0.04 0.15 0.14 0.03 0.10 B 0.03 2.93 0.06 0.08 0.03 0.04 C 0.03 0.06 0.03 0.03 0.03 0.03 Avg. 0.05 1.01 0.08 0.09 0.03 0.06 Std. 0.04 1.66 0.06 0.06 0.00 0.04
30°C 0 48 168 504 840 1848 A 0.07 0.03 0.04 0.07 0.03 0.17 B 0.07 0.03 0.03 0.03 0.03 0.03 C 0.06 0.07 0.03 0.03 0.03 0.03 Avg. 0.07 0.05 0.03 0.04 0.03 0.08 Std. 0.01 0.02 0.01 0.02 0.00 0.08 OW - DE 20°C 0 48 168 504 840 1848 A 0.07 0.03 0.19 0.08 0.04 0.12 B 0.03 0.03 0.03 0.03 0.03 0.04 C 0.03 0.08 0.05 0.03 0.03 0.03 Avg. 0.04 0.05 0.09 0.05 0.03 0.06 Std. 0.03 0.03 0.08 0.03 0.01 0.05
30°C 0 48 168 504 840 1848 A 0.24 0.05 0.03 0.17 0.32 0.09 B 0.03 0.03 0.08 0.09 0.11 0.03 C 0.63 0.06 0.05 0.03 0.03 0.03 Avg. 0.30 0.05 0.05 0.10 0.15 0.05 Std. 0.30 0.02 0.02 0.07 0.15 0.04
148
Site Microcosm Time of Sampling (hr) VEG - SH 20°C 0 48 168 504 840 1848 A 105.18 104.21 79.20 2.73 3.75 1.00 B 116.16 102.66 69.89 2.92 43.23 1.00 C 98.95 102.68 88.93 2.90 13.44 1.00 Avg. 106.76 103.18 79.34 2.85 20.14 1.00 Std. 8.71 0.89 9.52 0.11 20.57 0.00
30°C 0 48 168 504 840 1848 A 94.92 97.33 118.75 146.12 119.52 147.37 B 105.36 90.79 131.47 92.49 142.28 118.99 C 90.34 101.26 116.33 128.93 116.07 58.95 Avg. 96.87 96.46 122.18 122.51 125.96 108.44 Std. 7.70 5.29 8.14 27.39 14.24 45.14
VEG - DE 20°C 0 48 168 504 840 1848 A 36.25 183.34 16.15 2.49 4.62 1.00 B 67.43 211.52 16.43 2.71 6.43 1.00 C 93.15 32.53 15.59 2.02 4.61 1.00 Avg. 65.61 142.46 16.06 2.41 5.22 1.00 Std. 28.49 96.24 0.43 0.35 1.05 0.00
30°C 0 48 168 504 840 1848 A 86.90 32.69 102.18 61.84 98.82 99.78 B 1.00 37.43 94.85 81.09 103.92 182.14 C 88.01 96.19 80.76 88.75 94.26 1.00 Avg. 58.64 55.44 92.60 77.22 99.00 94.31 Std. 49.92 35.37 10.89 13.86 4.84 90.69 Table C.2 Sulfate measurements from water quality microcosms. Units are in mg S L-1.
Continued 149
Table C.2 Continued
Site Microcosm Time of Sampling (hr) OW - SH 20°C 0 48 168 504 840 1848 A 105.18 90.37 98.07 4.06 5.71 1.00 B 116.16 86.17 33.17 2.50 4.23 1.00 C 98.95 115.44 34.08 2.76 5.41 1.00 Avg. 106.76 97.33 55.11 3.11 5.12 1.00 Std. 8.71 15.83 37.21 0.84 0.78 0.00
30°C 0 48 168 504 840 1848 A 94.92 99.78 119.02 82.54 97.79 1.00 B 105.36 101.00 112.32 169.05 103.30 106.63 C 90.34 110.82 189.14 83.78 100.85 1.00 Avg. 96.87 103.86 140.16 111.79 100.65 36.21 Std. 7.70 6.05 42.55 49.59 2.76 60.99
OW - DE 20°C 0 48 168 504 840 1848 A 36.25 131.12 97.76 2.85 5.08 1.00 B 67.43 98.89 90.35 4.21 6.87 1.00 C 93.15 91.45 46.96 3.06 6.94 1.00 Avg. 65.61 107.15 78.36 3.37 6.30 1.00 Std. 28.49 21.09 27.44 0.73 1.05 0.00
30°C 0 48 168 504 840 1848 A 86.90 85.89 45.41 92.89 93.37 132.91 B 87.21 78.16 77.08 89.94 92.69 15.76 C 88.01 95.96 94.77 90.84 103.00 91.09 Avg. 87.37 86.67 72.42 91.22 96.35 79.92 Std. 0.58 8.92 25.00 1.51 5.77 59.36
150
Site Microcosm Time of Sampling (hr) VEG -SH 20°C 0 48 168 504 840 1848 A 1.52 25.36 1.24 0.33 0.64 1.43 B 0.78 31.01 0.98 0.51 0.38 0.01 C 0.54 1.27 0.01 0.01 0.38 0.01 Avg. 0.95 19.21 0.74 0.28 0.47 0.48 Std. 0.51 15.80 0.65 0.25 0.15 0.82
30°C 0 48 168 504 840 1848 A 3.09 0.31 1.08 0.72 0.93 1.21 B 0.78 0.79 0.01 0.35 0.29 0.53 C 0.24 1.31 0.01 0.01 0.01 0.01 Avg. 1.37 0.80 0.37 0.36 0.41 0.58 Std. 1.51 0.50 0.62 0.36 0.47 0.60
VEG -DE 20°C 0 48 168 504 840 1848 A 40.66 0.35 0.86 0.01 0.30 1.25 B 17.54 0.01 0.75 0.24 0.01 0.91 C 1.37 1.01 1.00 0.01 0.01 0.98 Avg. 19.86 0.46 0.87 0.09 0.11 1.05 Std. 19.74 0.51 0.13 0.13 0.17 0.18
30°C 0 48 168 504 840 1848 A 0.89 4.30 0.01 0.01 0.01 1.04 B 1.10 2.59 0.37 0.01 0.71 1.37 C 0.45 0.40 0.62 0.46 0.01 0.01 Avg. 0.81 2.43 0.33 0.16 0.24 0.81 Std. 0.33 1.96 0.31 0.26 0.40 0.71 Table C.3 Acetate measurements from water quality microcosms. Units are in mg C L-1.
Continued 151
Table C.3 Continued
Site Microcosm Time of Sampling (hr) OW-SH 20°C 0 48 168 504 840 1848 A 1.52 0.23 1.36 0.01 0.90 0.92 B 0.78 0.34 0.25 0.42 0.01 0.85 C 0.54 1.22 0.51 0.01 0.01 0.74 Avg. 0.95 0.60 0.71 0.15 0.31 0.84 Std. 0.51 0.54 0.58 0.23 0.51 0.09
30°C 0 48 168 504 840 1848 A 3.09 0.48 0.80 0.01 0.01 1.17 B 0.78 0.01 0.42 0.01 0.01 0.65 C 0.24 1.41 0.23 0.01 0.01 0.01 Avg. 1.37 0.63 0.49 0.01 0.01 0.61 Std. 1.51 0.71 0.29 0.00 0.00 0.58
OW -DE 20°C 0 48 168 504 840 1848 A 40.66 0.01 0.87 0.01 0.42 1.11 B 17.54 0.41 0.01 0.01 0.01 0.34 C 1.37 1.20 0.01 0.28 0.33 0.01 Avg. 19.86 0.54 0.30 0.10 0.25 0.49 Std. 19.74 0.60 0.50 0.15 0.22 0.57
30°C 0 48 168 504 840 1848 A 0.89 0.49 14.60 0.26 0.74 0.55 B 1.10 0.72 7.81 0.32 0.01 0.01 C 0.45 0.85 0.18 0.04 0.01 0.01 Avg. 0.81 0.69 7.53 0.21 0.25 0.19 Std. 0.33 0.18 7.21 0.15 0.42 0.31
152
Site Microcosm Time of Sampling (hr) VEG -SH 20°C 0 48 168 504 840 1848 A 0.29 0.19 0.88 0.53 0.96 0.09 B 1.14 0.08 0.53 0.74 0.43 0.02 C 0.02 0.75 0.02 0.69 0.72 0.02 Avg. 0.48 0.34 0.48 0.65 0.70 0.04 Std. 0.58 0.36 0.43 0.11 0.27 0.04
30°C 0 48 168 504 840 1848 A 0.42 0.39 1.36 1.96 2.12 0.19 B 0.04 0.22 0.59 1.54 1.54 0.12 C 0.06 1.03 0.02 1.15 1.20 0.10 Avg. 0.17 0.55 0.66 1.55 1.62 0.14 Std. 0.21 0.43 0.67 0.40 0.46 0.05
VEG -DE 20°C 0 48 168 504 840 1848 A 1.29 0.02 1.83 2.15 0.02 0.07 B 1.03 1.24 2.09 0.06 0.02 0.17 C 0.29 1.19 2.39 3.72 0.02 0.17 Avg. 0.87 0.82 2.10 1.98 0.02 0.14 Std. 0.52 0.69 0.28 1.84 0.00 0.06
30°C 0 48 168 504 840 1848 A 0.02 3.09 0.02 0.14 0.02 0.02 B 0.02 1.39 0.02 0.02 0.02 0.02 C 0.02 0.02 0.23 0.04 0.02 0.02 Avg. 0.02 1.50 0.09 0.07 0.02 0.02 Std. 0.00 1.54 0.12 0.06 0.00 0.00 Table C.4 Phosphate measurements from water quality microcosms . Units are in mg P
L-1.
Continued
153
Table C.4 Continued
Site Microcosm Time of Sampling (hr) OW-SH 20°C 0 48 168 504 840 1848 A 0.29 0.02 0.02 1.15 1.16 0.09 B 1.14 0.02 0.21 0.33 0.89 0.02 C 0.02 0.50 0.02 0.30 0.02 0.02 Avg. 0.48 0.18 0.08 0.59 0.69 0.04 Std. 0.58 0.28 0.11 0.48 0.59 0.04
30°C 0 48 168 504 840 1848 A 0.42 0.20 1.61 1.17 0.02 0.31 B 0.04 0.02 1.12 0.02 0.92 0.15 C 0.06 1.63 0.98 3.77 0.02 0.02 Avg. 0.17 0.61 1.24 1.65 0.32 0.16 Std. 0.21 0.88 0.33 1.92 0.52 0.15
OW -DE 20°C 0 48 168 504 840 1848 A 1.29 0.36 2.52 0.29 4.00 0.14 B 1.03 0.51 1.93 1.23 0.02 0.16 C 0.29 1.24 4.08 1.90 1.57 0.02 Avg. 0.87 0.70 2.84 1.14 1.86 0.11 Std. 0.52 0.47 1.11 0.81 2.01 0.08
30°C 0 48 168 504 840 1848 A 0.02 0.98 0.36 0.02 0.02 0.02 B 0.02 0.47 0.02 0.02 0.02 0.02 C 0.02 0.02 0.02 0.02 0.02 0.02 Avg. 0.02 0.49 0.13 0.02 0.02 0.02 Std. 0.00 0.48 0.20 0.00 0.00 0.00
154
Site Microcosm Time of Sampling (hr) VEG -SH 20°C 0 48 168 504 840 1512 1848 A 10.55 N/A 61.40 19.24 10.75 15.92 15.94 B 22.37 11.27 56.09 15.92 9.18 7.09 14.97 C 12.86 14.23 46.05 11.84 10.54 6.88 N/A Avg. 15.26 12.75 54.51 15.66 10.16 9.96 15.46 Std. 6.26 2.09 7.80 3.71 0.85 5.16 0.69
30°C 0 48 168 504 840 1512 1848 A 10.55 11.06 50.15 22.42 11.98 23.34 18.52 B 22.37 48.44 56.57 14.17 10.71 47.71 19.25 C 12.86 45.63 53.20 4.12 8.11 14.80 21.53 Avg. 15.26 35.05 53.31 13.57 10.27 28.62 19.77 Std. 6.26 20.82 3.21 9.16 1.97 17.08 1.57
VEG -DE 20°C 0 48 168 504 840 1512 1848 A 9.20 7.79 51.26 6.80 5.19 15.88 29.95 B 17.54 7.27 16.70 5.47 3.79 N/A 16.38 C 18.90 10.15 19.43 11.31 7.47 30.08 11.16 Avg. 15.22 8.40 29.13 7.86 5.49 22.98 19.16 Std. 5.25 1.53 19.21 3.06 1.86 10.04 9.70
30°C 0 48 168 504 840 1512 1848 A 9.20 8.36 39.33 12.75 9.91 73.98 31.78 B 17.54 17.25 19.72 5.79 6.29 11.85 17.66 C 18.90 9.20 22.49 8.85 6.94 9.04 N/A Avg. 15.22 11.60 27.18 9.13 7.71 31.62 24.72 Std. 5.25 4.91 10.62 3.49 1.93 36.71 9.98 Table C.5 Total dissolved, non-purgeable organic carbon measurements . Units are in mg
C L-1.
Continued 155
Table C.5 Continued
Site Microcosm Time of Sampling (hr) OW-SH 20°C 0 48 168 504 840 1512 1848 A 50.46 9.98 46.39 21.29 11.65 7.92 50.16 B 28.94 9.97 47.64 32.45 10.24 35.99 14.76 C 17.17 N/A 58.46 8.15 7.03 39.39 N/A Avg. 32.19 9.98 50.83 20.63 9.64 27.77 32.46 Std. 16.88 0.00 6.64 12.16 2.37 17.27 25.03
30°C 0 48 168 504 840 1512 1848 A 50.46 8.93 63.52 23.41 0.69 57.24 20.40 B 28.94 14.29 60.04 10.55 10.47 18.05 20.13 C 17.17 34.78 63.52 23.47 9.29 N/A 17.33 Avg. 32.19 19.33 62.36 19.14 6.82 37.65 19.29 Std. 16.88 13.65 2.01 7.44 5.34 27.71 1.70
OW -DE 20°C 0 48 168 504 840 1512 1848 A 9.25 9.68 35.39 13.92 11.09 18.05 14.54 B 10.39 11.18 35.07 34.05 7.10 25.12 12.86 C 8.43 12.18 34.91 2.60 6.41 10.19 11.75 Avg. 9.36 11.01 35.12 16.86 8.20 17.79 13.05 Std. 0.98 1.26 0.24 15.93 2.52 7.47 1.40
30°C 0 48 168 504 840 1512 1848 A 9.25 43.19 41.23 8.93 11.36 33.84 44.82 B 10.39 25.11 39.10 10.85 7.64 35.52 12.92 C 8.43 13.57 53.24 11.85 7.60 43.28 N/A Avg. 9.36 27.29 44.52 10.54 8.87 37.55 28.87 Std. 0.98 14.93 7.62 1.48 2.16 5.04 22.56
156
Appendix D: Sediment Density
157
Dormant season Site g mL -1 Δml Soil mass (g) UP A 2.00 5 9.98 B 1.86 10 18.58 Avg. 1.93 - -
VEG A 1.75 7.5 13.15 B 1.75 10 17.46 Avg. 1.75 - -
OW A 1.74 7 12.15 B 1.53 10 15.28 Avg. 1.63 - - Growing season Site g mL -1 Δml Soil mass (g) VEG-SH A 1.28 10 12.84 B 1.33 10 13.27 C 1.35 10 13.52 Avg. 1.32 - - Std. 0.03 - -
VEG-DE A 1.63 10 16.33 B 1.66 11 18.3 C 1.84 10 18.39 Avg. 1.71 - - Std. 0.11 - -
OW-SH A 1.32 10 13.23 B 1.34 10 13.41 C 1.19 9.5 11.53 Avg. 1.29 - - Std. 0.08 - - Table D.1 Estimated sediment bulk density (wet weight) measured by addition of sediments to DI water in a volumetric instrument. Not enough sediment remained from the dormant season experiment to provide three readings.
Continued 158
Table D.1 Continued
Growing season Site g mL -1 Δml Soil mass (g) OW-DE A 1.82 10 18.24 B 1.73 10 17.3 C 1.74 10 17.36 Avg. 1.76 - - Std. 0.05 - -
159