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Inorganic Nitrogen Transformations and Microbial Assemblage Compositions in the Santa Fe River Tributary Sediments

Permanent Link: http://ufdc.ufl.edu/UFE0042395/00001

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Title: Inorganic Nitrogen Transformations and Microbial Assemblage Compositions in the Santa Fe River Tributary Sediments
Physical Description: 1 online resource (184 p.)
Language: english
Creator: Kim, Ha
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: Soil and Water Science -- Dissertations, Academic -- UF
Genre: Soil and Water Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Inorganic nitrogen transformations in tributary systems have broad implications with respect to water quality, and carbon sequestration. Previous research has focused on nitrogen removal from small streams because of their higher efficiencies of nitrate removal compared to large rivers. I investigated relative importance of select inorganic nitrogen transformations, including nitrification, anammox, denitrification, and DNRA, and the microbial assemblages affecting nitrogen transformations in tributary sediments. Results demonstrated that nitrification and denitrification mainly accounted for removal of ammonium and nitrate from tributary sediments, while Anammox and DNRA were insignificant to nitrogen cycling in tributary sediments. Potential nitrification, denitrification, and DNRA rates were influenced by pH, TC:TN ratios, and labile carbon contents in tributary sediments. Furthermore, the diversity of nirS (representing denitrifiers) was related to potential denitrification rates, and this relationship was regulated by organic carbon contents, emphasizing the importance of relationships between microbial structure, functions, and biogeochemical properties. Unlike denitrifiers, the diversity of Archaeal amoA (representing nitrifiers) was not related to potential nitrification rates. However, the distribution of Archaeal amoA was correlated with pH of the systems, implying the importance of the relationship between microbial structure and biogeochemical properties.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ha Kim.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Ogram, Andrew V.
Local: Co-adviser: Reddy, Konda R.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042395:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042395/00001

Material Information

Title: Inorganic Nitrogen Transformations and Microbial Assemblage Compositions in the Santa Fe River Tributary Sediments
Physical Description: 1 online resource (184 p.)
Language: english
Creator: Kim, Ha
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: Soil and Water Science -- Dissertations, Academic -- UF
Genre: Soil and Water Science thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Inorganic nitrogen transformations in tributary systems have broad implications with respect to water quality, and carbon sequestration. Previous research has focused on nitrogen removal from small streams because of their higher efficiencies of nitrate removal compared to large rivers. I investigated relative importance of select inorganic nitrogen transformations, including nitrification, anammox, denitrification, and DNRA, and the microbial assemblages affecting nitrogen transformations in tributary sediments. Results demonstrated that nitrification and denitrification mainly accounted for removal of ammonium and nitrate from tributary sediments, while Anammox and DNRA were insignificant to nitrogen cycling in tributary sediments. Potential nitrification, denitrification, and DNRA rates were influenced by pH, TC:TN ratios, and labile carbon contents in tributary sediments. Furthermore, the diversity of nirS (representing denitrifiers) was related to potential denitrification rates, and this relationship was regulated by organic carbon contents, emphasizing the importance of relationships between microbial structure, functions, and biogeochemical properties. Unlike denitrifiers, the diversity of Archaeal amoA (representing nitrifiers) was not related to potential nitrification rates. However, the distribution of Archaeal amoA was correlated with pH of the systems, implying the importance of the relationship between microbial structure and biogeochemical properties.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ha Kim.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Ogram, Andrew V.
Local: Co-adviser: Reddy, Konda R.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042395:00001


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1 INORG A NIC NITROGEN TRANSFORMATIONS AND MICROBIAL ASSEMBL A GE COMPOSITIONS IN SANTA FE RIVER TRIBUTARY SEDIMENTS By HARYUN KIM A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Haryun Kim

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3 To my husband, Jaemyeong and my parents dedicated in their support of my studies

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4 ACKNOWLEDGMENTS My deepest gratitude is to my advisor Dr. Ogram and Dr. Reddy ., who introduced me to microbial ecology and biogeochemistry Their expertise and ability to find my independent thinker provided priceless guidance and instruction throughout my graduate education that were crucial to this dissertation I would like to thank the members of my committee Dr. Clark, Dr. Inglett and Dr. Cohe n who were like a mentor for me during my graduate studies and for their continued help, encouragement and scientific assistance. Their input provided insightful comments and contributions to my work. I would like to express my gratitude to Dr. Kanika S. I nglett for her scientific insights and technical assistance. Dr. Rongzhong Ye i s acknowledged for their assistance in statistical analysis. My thanks to Dr. Abid al Agely and Ms. Yu Wang for their laboratory assistance and to Gavin Wilson for his assistan ce troubleshooting equipment malfunctions. I also like to send my love and appreciation to my friends in the lab, Hiral Gohil, Mosik, Taegoo, Joungsung, Dr. Hee Sung Bae Dr. Haeyoung Nam and Greg Most importantly, none of this would have been possible wi thout the love and support of my immediate family. My family, to whom this dissertation is dedicated to, has been a constant source of love, support and strength during my life and helped me keep my faith and remember who I am during these hectic times. I would like them to know they are always in my heart.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INORGANIC NITROGEN TRANSFORMATIONS AND ASSOCIATED MICROBIAL ASSEMBLAGE COMPOSITIONS IN TRIBUTARY SEDIMENTS ...... 14 Site Description ................................ ................................ ................................ ....... 18 Objectives ................................ ................................ ................................ ............... 20 Dissertation Format ................................ ................................ ................................ 20 2 LITERATURE REVIEW ................................ ................................ .......................... 22 Nitrification ................................ ................................ ................................ .............. 22 Terrestrial Ecosys tems ................................ ................................ ..................... 23 Ocean Ecosystems ................................ ................................ .......................... 24 Coastal Wetlands ................................ ................................ ............................. 25 Streams ................................ ................................ ................................ ............ 25 Wastewater Treatment Plants ................................ ................................ .......... 26 Fact ors Affecting Nitrification Rates ................................ ................................ 27 Ammonium ................................ ................................ ................................ 27 pH ................................ ................................ ................................ .............. 27 Oxygen ................................ ................................ ................................ ...... 28 Temperature ................................ ................................ .............................. 29 Organic carbon ................................ ................................ .......................... 29 Biochemistry of Nitrifiers ................................ ................................ ................... 30 Microbial Diversity of Bacterial and Archaeal Nitrifiers ................................ ..... 30 Measurement of Nitrification Rates ................................ ................................ .. 31 Anaerobic Am monium Oxidation (Anammox) ................................ ......................... 32 Ocean Ecosystems ................................ ................................ .......................... 33 Estuarine Sedime nts ................................ ................................ ........................ 33 Wastewater Treatment Plants ................................ ................................ .......... 34 Freshwater Systems ................................ ................................ ......................... 34 Factors Affecting Anammox Rates ................................ ................................ ... 35 Nitrite and ammonium ................................ ................................ ................ 35 Organic carb on ................................ ................................ .......................... 35 Oxygen ................................ ................................ ................................ ...... 36

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6 Biochemistry of Anammox Bacteria ................................ ................................ .. 36 Microbial Diversity of Anammox Bacteria ................................ ......................... 37 Measurement of Anammox Rates ................................ ................................ .... 37 Isotope pairing method ................................ ................................ .............. 37 Intermediate analysis ................................ ................................ ................. 38 Denitrification ................................ ................................ ................................ .......... 38 Forest Ecosystems ................................ ................................ ........................... 38 Agroecosystems ................................ ................................ ............................... 39 Coastal Ecosystems ................................ ................................ ......................... 39 Freshwater and Riparian Ecosystems ................................ .............................. 40 Factors Affecting Denitrification Rates ................................ ............................. 41 Nitrate ................................ ................................ ................................ ........ 41 Organic carbon ................................ ................................ .......................... 41 Temperature ................................ ................................ .............................. 42 Plants ................................ ................................ ................................ ......... 42 Biochemistry of Denitrification ................................ ................................ .......... 42 Microbial Community of Denitrifiers ................................ ................................ .. 42 Measurement of Denitrification Rates ................................ .............................. 43 Acetylene blocking method and measurement of nitrate consumption rates ................................ ................................ ................................ ........ 43 Measurement of the ratio of change relative to a conservative property .... 43 Isotope method ................................ ................................ .......................... 44 Dissimilatory Nitrate Reduction to Ammonium (DNRA) ................................ .......... 44 Agricultural Soils ................................ ................................ ............................... 45 Freshwater and Riparian Ecosystems ................................ .............................. 45 Estuarine and Coastal Ecosystems ................................ ................................ .. 46 Lakes ................................ ................................ ................................ ................ 46 Factors Affecting DNRA Rat es ................................ ................................ ......... 46 Nitrate ................................ ................................ ................................ ........ 46 Organic carbon ................................ ................................ .......................... 47 Sulfate ................................ ................................ ................................ ........ 47 Plants ................................ ................................ ................................ ......... 47 Biochemistry of DNRA ................................ ................................ ...................... 48 Microbial Diversity of DNRA Bacteria ................................ ............................... 48 Measurement of DNRA Rates ................................ ................................ .......... 49 Summar y ................................ ................................ ................................ ................ 49 3 INORGANIC NITROGEN TRANSFORMATIONS IN TRIBUTARY SEDIMENTS ... 50 Materials and Methods ................................ ................................ ............................ 53 Site Descr iption ................................ ................................ ................................ 53 Sampling ................................ ................................ ................................ .......... 54 Analyses of Biogeochemical Properties ................................ ........................... 54 Potential Nitrogen Transformations Rates ................................ ........................ 55 Statistical Analysis ................................ ................................ ............................ 58 Results ................................ ................................ ................................ .................... 58 Biogeochemical Properties ................................ ................................ ............... 58

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7 Inorganic Nitrogen Transformations Rates ................................ ....................... 59 Relationships between Inorganic Nitrogen Transformation Ra tes and Biogeochemical Properties ................................ ................................ ............ 60 Comparisons of Inorganic Nitrogen Transformation Rates between Stream and Riparian Sediments ................................ ................................ ................ 60 Discussion ................................ ................................ ................................ .............. 61 Inorganic Nitrogen Transformations ................................ ................................ 61 Relationship between Nitrogen Transformations and Biogeochemical Properties ................................ ................................ ................................ ...... 63 Comparison of Inorganic Nitrogen Transformation Rates between Stream and Riparian Sediments ................................ ................................ ................ 66 Summary ................................ ................................ ................................ ................ 67 4 RELATIONSHIP BETWEEN EXTRACELLULAR ENZYME ACTIVITY AND DENITRIFICATION RATES IN TRIBUTARY SEDIMENTS ................................ .... 79 Materials and Methods ................................ ................................ ............................ 81 Site Description ................................ ................................ ................................ 81 Samp ling ................................ ................................ ................................ .......... 81 Analyses of Biogeochemical Properties ................................ ........................... 82 Analyses of Extracellular Enzyme Activities ................................ ..................... 82 Statistical Analysis ................................ ................................ ............................ 84 Results ................................ ................................ ................................ .................... 84 Extracellular Enzyme Activities ................................ ................................ ......... 84 Correlation between Extracellular Enzyme and Potential Denitrification Rates ................................ ................................ ................................ ............. 85 Discussion ................................ ................................ ................................ .............. 86 Extracellular Enzyme Activities ................................ ................................ ......... 86 Relationships between Extracellular Enzyme and Potential Denitrification Rates ................................ ................................ ................................ ............. 87 Summary ................................ ................................ ................................ ................ 89 5 RELATIONSHIPS BETWEEN BIOGEOCHEMICAL PROPERTIES, DENITRIFICATION, AND ASSOCIATED MICROBIAL ASSEMBLAGE COMPOSITIONS IN TRIBUTARY SEDIMENTS ................................ .................... 95 Materials an d Methods ................................ ................................ ............................ 96 Site Description ................................ ................................ ................................ 96 Sampling ................................ ................................ ................................ .......... 97 Nucleic Acid Extraction, PCR Amplification, Cloning, and Sequencing ............ 97 Construction of Phylogenetic Tree and Diversity Analysis ................................ 99 Unifrac and Mantel Test ................................ ................................ ................. 100 Results ................................ ................................ ................................ .................. 100 nirS Phylogenetic Tree ................................ ................................ ................... 100 Diversity Indices for nirS Assemblage Composition ................................ ....... 101 Relationship between Microbial Assemblages of the nirS and Biogeochemical Properties ................................ ................................ .......... 102

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8 Discussion ................................ ................................ ................................ ............ 103 nirK Denitrifiers ................................ ................................ ............................... 103 Relationships between Microbial Diversity and Richness of nirS and Biogeochemical Properties ................................ ................................ .......... 104 Relationships between Microbial Assemblages of the nirS and Biogeochemical Properties ................................ ................................ .......... 106 Summary ................................ ................................ ................................ .............. 107 6 RELATIONSHIPS AMONG BIOGEOCHEMICAL PROPERTIES, NITRIFICATION, AN D ASSOCIATED MICROBIAL ASSEMBLAGE COMPOSITION OF TRIBUTARY SEDIMENTS ................................ ................... 117 Materials and Methods ................................ ................................ .......................... 118 Site Description ................................ ................................ .............................. 118 Sampling ................................ ................................ ................................ ........ 118 Nucleic Acid Extraction, PCR Amplification, Cloning and Sequencing ........... 119 Construction of Phylogenetic Tree and Diversity Analysis .............................. 121 Unifrac PCA and Mantel Test ................................ ................................ ......... 121 Results ................................ ................................ ................................ .................. 122 AOA Phylogenetic Tree ................................ ................................ .................. 122 Diversity Indices for AOA Assemblages ................................ ......................... 124 Relationship between the AOA Assemblage Compositions and Bio geochemical Properties ................................ ................................ .......... 124 Discussion ................................ ................................ ................................ ............ 125 Bacterial amoA ................................ ................................ ............................... 125 Relationships between Microbial Diversity and Richness of AOA and Biogeochemical Properties ................................ ................................ .......... 125 Relationships between Microbial Assemblages of AOA and Biogeochemical Properties ................................ ................................ ................................ .... 128 Summary ................................ ................................ ................................ .............. 129 7 CONCLUSION S AND SYNTHESIS ................................ ................................ ...... 140 Inorganic Nitrogen Transformations and Biogeochemical Properties ................... 141 Extracellular Enzyme Activities and Denitrification Rates ................................ ..... 142 Microbial Assemblage Compositions fo r Denitrifiers and Nitrifiers, Biogeochemical Properties, and Rates ................................ .............................. 142 Synthesis ................................ ................................ ................................ .............. 143 Future Studies ................................ ................................ ................................ ...... 1 44 APPENDIX ; CACULATION OF ANAMMOX RATES ................................ ................... 146 LIST OF REFERENCES ................................ ................................ ............................. 148 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 184

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9 LIST OF TABLES Table page 3 1 Summary of biogeochemical properties of tributary sediments. ......................... 69 3 2 Summary of TN, TC, ratio of TC:TN, and ratio of MBC:MBN of tributary sediments. ................................ ................................ ................................ .......... 70 3 3 Summary of inorganic nitrogen transformation rates in tributary sediments. ...... 71 3 4 Summary of the relative percentage of nitrogen retained in each inorganic nitrogen transformations in tributary sediments. ................................ ................. 72 3 5 Literature reviews for ammonium oxidation rates in various ecosystems. .......... 73 3 6 Literature reviews for nitrate reduction rates in various ecosystems. ................. 74 4 1 Extracellular enzyme activities based on TC in tributary sediments. .................. 90 5 1 nirS diversity and richness in tributary sediments. ................................ ............ 108 5 2 Resutl from the Mantel test for nirS ................................ ................................ 109 6 1 Relative abundances of PCR products of A rchaeal and B acterial amoA in tributary sediments. ................................ ................................ .......................... 131 6 2 Archaeal amoA diversity and richness in tributary sediments. .......................... 132 6 3 Result from the Mantel test for AOA ................................ ................................ 133

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10 LIST OF FIGURES Figure page 1 1 The Santa Fe River watershed. The asterisk mark on the map is the research place. ................................ ................................ ................................ .................. 21 3 1 Tributary T 1 T2 U and T2 D in the Boston Farm Santa Fe Ranch Beef Unit Research Center of the Santa Fe River Watershed, northern Alachua County, FL. ................................ ................................ ................................ ......... 75 3 2 Relationships between pH and potential nitrification rates in stream sediments and riparian sediments. ................................ ................................ ..... 76 3 3 Relationships between TC:TN ratio and potential denitrification rates and Ext. Org C concentrations and potential DNRA rates in riparian sediments. ............. 77 3 4 Relationships between ammonium concentrations and potential ammonification rates in riparian sediments. ................................ ....................... 78 4 1 Extracellular enzyme activities in stream and riparian sediments, and litter. ...... 91 4 2 Relationships between potential denitrification rates and cellobiohydrolase enzyme activities of tributary sediments. ................................ ............................ 93 4 3 Canonical plot of extracellular enzyme activities and potential denitrification rates of riparian sediments and litter. ................................ ................................ .. 94 5 1 Neighbor joining tree of nirS sequences obtained from stream sediments of tributaries. ................................ ................................ ................................ ......... 110 5 2 Neighbor joining tree of nirS sequences obtained from riparian sediments of tributaries. ................................ ................................ ................................ ......... 110 5 3 Rarefaction curves for nirS in tributary sediments ................................ ........... 111 5 4 Relationships between Ext. Org C and the Shannon index, and MBC and the Shannon and Simpson indices for nirS in riparian sediments. .......................... 113 5 5 Relationship between p otential denitrification rates and Chao 1 ind ex in tributary sediments. ................................ ................................ .......................... 115 5 6 Principal component analysis for nirS using Unifrac in tributar y sediments. ..... 116 6 1 Maximum parsimony tree of Archaeal amoA sequences obtained from stream sediments. ................................ ................................ ............................ 134 6 2 Maximum parsimony tree of Archaeal amoA sequences obtained from riparian sediments.. ................................ ................................ .......................... 135

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11 6 3 Rarefaction curves for archaeal amoA in tributary sediments. ......................... 136 6 4 Relationships between pH and the Simpson index for AOA in stream sediments, and Ext. Org C and the Shannon index for AOA in riparian sediments ................................ ................................ ................................ ....... 138 6 5 Principal component analysis for archaeal amoA in tributary sediments. ......... 139 7 1 Potential rates of inorganic nitrogen transformations in tributary sediments. .... 145

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12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy INORGANIC NITROGEN TRANSFORMATIONS AND MICROBIAL ASSEMBLAGE COMPOSITIONS IN SANTA FE RIVER TRIBUTARY SEDIMENTS By Haryun Kim December 2010 Chair: A Ogram Cochair: K. R Reddy Major: Soil and Water Science Inorganic nitrogen transformations in tributar y systems have broad implications with respect to water quality, and carbon sequestration. Previous r esearch has focused on nitrogen removal from small streams because of their higher efficienc ies of nitrate removal compared to large rivers. I investigated relative importance of select inorganic nitrogen transformations, including nitrification, anammox, denitrification, and DNRA, and the microbial assemblage s affecting nitrogen transformations in tributary sediments. R esults demonstrated that nitrification and denitrification mainly accounted for removal of ammonium and nitrate from tributary sediments, while Anammox and DNRA were insignificant to nitrogen cycling in tributary sediments Potential n itrification, denitrification and DNRA rates were influenced by pH, TC: TN ratio s and labile carbon contents in tributary sediments Furthermore, the diversity of nirS (representing denitrifiers) was related to potential denitrification rates, and this relationship was regulated by organic carbon contents, emphasizing the importance of relationship s between microbial structure, f unctions and biogeochemical properties. Unlike denitrifiers, the diversity of A rchaeal amoA (representing nitrifiers) was not related to

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13 potential nitrification rates. How ever, the distribution of A rchaeal amoA w as correlated with pH of the systems, implying the importance of the relationship between microbial structure and biogeochemical properties.

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14 CHAPTER 1 INORGANIC NITROGEN TRANSFORMATIONS AND ASSOCIATED MICROBIAL ASSEMBLAGE COMPOSITION S IN TRIBUTARY SEDIMENTS Inorganic nitrogen stored in ecosystems includes ammonium and nitrate which are produced by biological nitrogen fixation, mineralization and nitrif ication processes. Mineralization is the biological transformation of organic nitrogen to ammonium In addition, inorganic nitrogen is added from external sources such as nitrogen fertilizer and agricultural activities The ammonium produced by mineralization can be consumed by plants for growth (assimilation of nitrogen) or incorporated into microbial biomass (immobilization). A mmonium may also accumulate in anaerobic system s such as may be present in riparian or wetl and ecosystems. When oxygen is present, ammonium is oxidize d to nitrate by aerobic Bacterial and Archaeal nitrifiers (Reddy and De L aune, 200 8 ). The nitrate produced by nitrifiers is reduced to nitrite, nitric oxide, nitrous oxide, and nitrogen gas by denit rification, such that nitrous oxide and nitrogen gas are re leased to the atmosphere Thus, d enitrification i s considered to be a major process of nitrate removal in anaerobic environments. I t was recently observed that ammonium can be anaerobically oxidized to nitrogen gas in some marine and estuarine sediments (Thamdrup, 2002; Engstr m 2005; Trimmer, 2003) AN aerobic A MM oni um o X idation (anammox) account ed for up to 40% of nitrogen loss in the Black Sea (Kuypers, 2003) and between 19% and 35% in th e Golfo Dulce coastal bay in Costa Rica ( Dalsgaard 2003). However, research on anammox in anoxic ecosystems other than marine ecosystems remains limited. Therefore, research on anammox in various ecosystems including stream, riparian, and freshwater ecosy stems is needed to fully understand the role in removal of ammonium from anaerobic eco systems.

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15 Unlike the removal process of nitrate by denitrification nitrate can be reduced to ammonium by D issimilatory N itrate R eduction to A mmoni um (DNRA) under highly reduced conditions (Michael, 2003). This process can enhance the accumulation of ammonium in systems rather than promoting removal of nitrate (King, 1985). It has been reported that DNRA rates can be as high as denitrification rates in shallow estuarine and tidal systems (Kaspar, 1983; Rysgaard, 1996; Tobias, 2001). In addition, the pasture ecosystem impacted by livestock manure can be a hot spot for DNRA since the microbe performing DNRA was found in rumen and fecal materials (Maier et al., 20 00 ) Inorganic nitrogen transformation rates are regulated by biogeochemical properties such as pH and availabilit y of electron acceptors and donors (Kaspar, 1983, Tobias et al., 2001; Tate, 2000). For example, lower pH caused by nitrogen fertilization, surrounding vegetation, or organic acids can reduce the availability of ammonia, which can affect the function of nitrifiers. Also, high organic matter content stimulate s denitrification and DNRA rates via a n increased supply of electron do nor s for denitrifiers and DNRA bacteria ( Kaspar 1983 ; Tobias et al. 2001 ; Yin et al. 2002) Nitrate concentrations can control denitrification and DNRA rates, and the concentrations of nitrite can control anammox rates via supply of electron acceptors In addition, the change in TC:TN ratio due to nitrogen supplies from a live stock manure or nitrogen fertilization could shift decomposition rates, affecting the level of available organic carbon to denitrification and DNRA. Previous research demonstrated that stream and riparian sediments are hotspots for inorganic nitrogen transformation s in tributary ecosystems because these places

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16 receive nitrogen and organic matter from uplands and stream water (Martin et al., 1999 ; Steinhart et al., 1998; Chatarpaul et al., 1980; Hill, 1983; Swank and Caskey, 1982 ; Holmes et al. 1996 ; Mulholland and Hill 1997 ; Bowden et al. 1992 ; Pinay et al. 1993, 1995 ; Freeze and Cherry 1979 ; Hill 199 6; Hill and Waddington 1993 ; Likens et al. 1977 ) In addition, fluctuat ing water tables in riparian sediments creates anaerobic conditions, favoring denitrification ( Cooper 1990 ; Bowden et al. 1992 ; Lawrence 1992 ; Schipper et al. 1993 ; Hanson et al. 1994 ; Pinay et al. 1995 ; Bowden et al. 1992 ; Pinay et al. 199 3 and 1995 ). Thus, stream and riparian sediments can function as effective sinks for nitrogen in tributary ecosystems (Jones and Holmes, 1996). However, previous research has mainly focused on denitrification and nitrification rates in tributary sediments (Peterson et al., 2001; Lowrance et al., 1997; Hill, 1996; Likens et al., 1977). There is limited research on the potential roles of DNRA and anammox in removal of nitrate and ammonium from tributary sediments. Also, the biogeochemical factors affecting anammox and DNRA rates have not been well studied in tributary sediments. Organic carbon availability is one of the most important factors regulating denitrification rates in tributary sediment s Carbon availability can vary depending o n the vegetation types and quality of organic matter present in the systems (Martin et al., 1999). For example, the organic matter inputs to tributary systems surrounded by woody vegetation may consist of litter with higher lignin and lower cellulose conte nts than systems dominated by herbaceous vegetation. The low quality of litter and organic matter exhibit s a slower rate of decomposition and is therefore a poor source of carbon for heterotrophic microbes (Chapin et al., 2002). Previous research demonstra ted that grass y sites exhibited greater denitrification rates than woody sites (Schnabel et al.

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17 1997; Groffman et al. 1991) Also, d enitrification rates significantly correlated with respiration rate s in planted sites, indicating that the decomposition of organic matter is a main factor regulating denitrification rates via a supply of available carbon to denitrifiers (Walton and Jiannino, 2005). However, most studies on denitrification have focused on the impact of vegetation and soil organic matter content s on denitrification rates (Knoepp, 1998; Gurlevik et al., 2004; Ebrecht and Schmidt, 2003 ; Garten and Van Miegroet, 1994) rather than on the quality of organic carbon Therefore, research on the effects of organic matter decomposition rates associated wi th carbon quality is needed to better understand regulator for denitrification rates. Nitrification and denitrification are main processes regulating nitrogen concentrations in tributary system even though anammox and DNRA can be observed. Also, r elative rates of denitrification were reported to be higher than those of anammox and DNRA in various ecosystems (Rich et al., 2008; Koop Jakobsen and Gibli 200 9 ; Omnes et al., 1996; Revsbech et al., 2005). Therefore, many studies have focused on nitrification an d denitrification processes related to nitrogen cyclings in tributary eco systems. However, most studies have focused on how human activities, land use practice s and vegetation type s influence nitrification and denitrification rates rather than the microb ial structure s responsible for these processes. Also, little work has been done to determine how the microbial communit y structure might be related to nitrification and denitrification rates and how shifts in communit y structure may be linked to biogeochemical properties For example, l ow pH can affect the microbia l assemblage compositions of nitrifiers, through c hang e in the diversity of nitrifier assemblage compositions In case of denitrification, carbon and nitrogen concentrations affect

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18 diver sity of denitrifiers influencing their rates. Therefore, research on relationsh ips between biogeochemical factors, microbial assemblage compositions of nitrifiers and denitrifiers, and potential rate s of these processes in tributary sediments is needed S ite D escription The Santa Fe River Watershed (SFRW) (3574 km 2 ) spreads across eight counties in N orth E ast Florida (Figure 1 1 ) and comprises the southeastern part of the Suwannee River Basin that drains into the Gulf of Mexico. Research has shown an increase in nitrate nitrogen concentration in the Suwannee River Basin covering southern Georgia and north central Florida (Ham and Hatzell, 1996) Also, the increased nitrate nitrogen has been observed in spring, surface and ground water s in the Suwannee River Basin area ( Hornsby et al., 2001 ). Despite that the Santa Fe River Watershed comprises only 13% of the Suwannee River Basin area, the Santa Fe River Watershed is responsible for 22% of the total nitrogen input to the Suwannee River Basin ( Suwannee River Water Management District 200 3 ). Therefore, research on nitrogen cycling in the S anta Fe River Watershed needs to be investigated in order to establish the management for nitrogen controls. For land use, the pine plantation ( 23% ) and agric ultural land use (including crop and improved pasture 37%) occup ied over the 60% of lands use, following the wetlands (1 8 %), upland forest ( 11 %) and urban ( 6%) (Sabesan, 2004). Comparing with the land use patter n in 1990, the forest ed area has shown to de crease while the agricultural area increased (Sabesan, 2004) Therefore, nitrogen fertilizer from agricultural and timber production, and livestock manure from ranch activity are concerns in the Santa Fe R iver W atersheds. The soil type is classified as to Ultisols (37%), Spodosols (26%)

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19 and Entisols (15%), and the soil texture is predominately composed of sandy loamy and organic soils ( Lamsal et al., 2006). The site for this research is tributary sediments at the Boston Farm Santa Fe Ranch Beef Unit Research Center (SFBRU) in the Santa Fe River W atershed, northern Alachua County. Land uses on this site include a low intensity cattle operation with approximately 300 heifers on 1,600 acres and a nursery operation using nitr ogen fertilizer (Holly F actory Nursery) (Frisbee, 2007) One of two tributaries in my research sites T ributary 1 (T1) is located along CR 241roadway, flows into pond and finally into the Santa Fe River. T1 is surrounded by a improved pasture and affected by ranch activity. The sediment adjacent to the stream water (riparian sediments) contains relatively high organic matter washed from the upland soils containing livestock manure Also, riparian sediments receive their water from groundwater and seepage w ater via a subsurface flow rather than stream water However, the sediment in the stream water (stream sediments) receives water from stream, and contains sandy soil and relatively low carbon contents The vegetation type is mixture of herbaceous and woody plants including Cary a sp., Pinus sp., Quercus sp., Magnolia grandiflora Saururus cernuus J u ncus sp., Cephalanthus occidentalis Hydrocotle umbellata and Polygonum sp. The U p stream region of T ributary 2 (T2 U) is affect ed by nitrogen fertilization from a nursery operation and surrounded with hardwood plants including Carya sp., Quercus sp., and Magnolia grandiflora and soft wood including Pinus sp. The D ownstream region of T ributary 2 (T2 D) is influenced by a improved pasture and nitrogen fertili zation from head water, and covered with grass including Saururus cernuus Juncus sp. and

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20 Polygonum sp., deciduous shrub plants including Cephalanthus occidentalis and aquatic plants including Hydrocotle umbellata (Frisbee, 2007). Objectives The overall o bjective of this research was to investigate the potential inorganic nitrogen transformation rates, biogeochemical factors regulating these rates, and associated microbial assemblage compositions in tributary sediments of the Santa Fe R iver. Speci fic objectives of this research were to: Investigate the relationship between potential inorganic nitrogen transformation rates and biogeochemical properties in tributary sediments. I nvestigate the relationship between extracellular enzyme activities associated with organic matter decomposition and potential denitrification rates in tributary sediments. I nvestigate the relationships between the biogeochemical factors affecting denitrification and nitrification and their microbial assemblage composition s in tributary sediments Dissertation Format This dissertation is composed of four main part s. The first part is the literature review (Ch apter 2) The second part presents research on the biogeochemical properties and potential inorganic nitrogen transf ormation rates (Ch apter 3 ) and research on the relationship between extracellular enzyme activities in tributary sediments and litter, and potential denitrification rates (Ch apter 4 ). The third part of this dissertation describes r esearch on relationship s between biogeochemical properties and the microbial assemblage compositions associated with denitrification (Ch apter 5 ) and nitrification (Ch apter 6 )

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21 Figure 1 1 The Santa Fe R iver W atershed The asterisk mark on the map is the research place.

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22 C HAPTER 2 L ITERATURE REVIEW Increased nitrogen created by fertilization and improper disposal from ranch activity has been considerable concern s in agriculture ecosystem s The excessive input of nitrogen promote s eutrophication in aquatic ecosystem s which can cause a decrease in oxygen and sunlight available to other organisms. Also, nitrate is toxic to human health when consumed in drinking water. However, riparian ecosystem s can reduce nitrate through denitrification, plant uptake and microbial immobilization. In particular since denitrification is the permanent removal mechanism of nitrate this process is considered to be a desirable way of nitrate removal in watershed ecosystems. However, anaerobic ammonium oxidation (anammox) can also remove ammonium and nitrite from riparian ecosystems and nitrification can increase denitrification rates via a supply of nitrate to denitrifiers. In addition, dissimilatory nitrate reduction to ammonium (DNRA) can accumulat e ammonium in riparian ecosystems. Thus, to describe function s of riparian ecosystems for nitrogen removal, the comprehensive understanding of nitrification, anammox, denitrification and DNRA is needed. This chapter reviewed previous research on importance of nitrification, an ammox, denitrification and DNRA in various ecosystems bio geochemical factors affecting these rates physiology of microbes associated with these processes and analysis methods measuring each rate. Thus, t his literature review will help to understand ino rganic nitrogen transformations in riparian ecosystems. Nitrification Nitrification is the biological oxidation of ammoni um to nitrate using oxygen as an electron acceptor. Nitrification is an important step in the nitrogen cycle in soil s because

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23 nitrate produced by nitrification can be used as electron acceptors for denitrification. There are two types of nitrifiers: (1) a utotro phic nitrifiers using ammonium as an energy source to fix carbon dioxide used in growth and maintenance and (2) heterotrophic nit rifiers using organic nitrogen as an energy source instead of ammonium (Chapin et al., 2002). This review mainly focuse s on autotrophic nitrifiers because heterotrophic nitrifiers are not well studied in various ecosystems (Tate, 2000 ). Terrestrial E cosys tems Rapid rates of ammonification and nitrification are observed in tropical forest ecosystems because of relatively high concentrations of foliar a nd litter fall nitrogen (Vitousek and Matson, 1988). T emperate forest ecosystems can be expected to exhibi t lower nitrification rates due to lower amounts of ammonium via a plant uptake relative to fertilized grasslands and freshwater ecosystems. It has been also suggested that increased nitrogen deposition due to fossil fuel burning and fertilizer u tilization enhance d mineralization rate via a decrease in the TC:TN ratio of litter and forest soils, possibly increasing nitrification rates (Aber et al., 1998). The increase d nitrification rate could lead to an increase of nitrate leaching from northern temperate forest ecosystems to adjacent watersheds (Aber et al., 1988). However, results from NITREX (Nitrogen Saturation experiment) demonstrated that the nitrogen retention efficiency was relatively high in NITREX sites even after observing a linear relationship between forest floor nitrogen concentration and nitrification rate (Aber et al., 199 5 ). Disturbances in forest ecosystems such as logging (Matson and Vitousek, 1984; Vitousek and Andariese, 1986), fires ( Polygala et al., 1986; White, 1986; Weston and Att iwill, 1990), and addition of fertilizer (Adams and Attiwill, 1983) can affect mineralization rates which in turn influence nitrification rates in forest soils. Additionally,

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24 some research has observed heterotrophic nitrification and fungi using organic n itrogen as an electron donor instead of ammonium in acid ic forest soils (Nishio et al., 1998 ; Robertson 1982). These microbes were less sensitive to pH, implying that heterotrophic nitrifiers can play an important role in nitrification of acidic forest so ils. However, t heir contribution to nitrification in forest ecosystems has not been well documented because of the difficulty in distinguishing rates between autotrophic and heterotrophic nitrifiers. Ocean E cosystems The majority of ammonium released by mineralization is consumed by phytoplankton (Harrison et al., 1992 and 1996) or is oxidized to nitrate at surface water (Yool et al., 2007). Also, t he high flux of organic nitrogen supplied from sediments and upwelling deep ocean water is oxidiz ed to ammo nium in subsurface oxygen minimum zones (OMZ) The ammonium is oxidized to nitrate in OMZ (Ward, 2002) lead ing to denitrification (Codispoti et al., 1985; Ward and Zafiriou, 1988; Lipschultz, 1990; Naqvi and Noronha, 1991) and anammox (Lam et al., 2006; K uypers et al., 2005 and 2006; Hamersley et al., 2007; Thamdrup et al., 2006). Th us nitrification is indirectly responsible for the loss of nitrogen in ocean ecosystems. The recent discovery of archaeal nitrifiers has made it possible to explain a broader distribution of nitrifiers in ocean ecosystems where nitrifiers have not been detected using molecular analysis. Archaeal nitrifiers were found to be much more abundant than bacterial nitrifiers in marine systems (Wchter et al., 2006), implying that the versatile physiology and metabolism of archaeal nitrifiers could play a pivotal role in ocean nitrification.

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25 Coastal W etland s Coastal wetland s are one of the most productive ecosystems due to ti d al flooding frequency and import of nutrient s from terrestri al and ocean ecosystems. Due to the limitation of oxygen by tidal flooding, most of the inorganic nitrogen is ammonium. Heterotrophic microbes, phytoplankton and marsh vegetation consume this ammonium for their growth s (Henriksen and Kemp, 1988). As a res ult, the nitrification process can be limited by ammonium and oxygen concentrations in coastal wetland ecosystems Salinity can also influence nitrification in costal wetland ecosystems. Although a marine Nitrosomonas sp. was reported to survive in estuarine sediments at 15% salinity ( Henriksen and Kemp, 1988), nitrifying bacteria exhibited lower rates at higher salinities (Rysgaard et al., 1999). Nitrification processes can occur in the surficial oxidized zone of sedi ments ranging from depths of 1 to 1.6 mm (Seitzinger, 1988; Kemp et al., 1990), as well as in plant root zones due to the release of oxygen via their roots, thereby stimulating coupled nitrification and denitrification processes (Rysgaard et al., 1996) Ph ysical perturbation and bioturbation by macrofauna could create zones where oxygen can penetrate, supplying oxygen to nitrifiers (Rysgaard et al., 1996; Kemp et al., 1990). Stream s Human activities can supply excess nitrogen to ecosystems, changing the ba lance between supply and demand for available nitrogen to plants, and leading to nitrogen saturation in terrestrial and aquatic ecosystems (Aber et al., 1998; Vitousek et al., 1994 ). Increased nitrogen deposition and agricultural activities can also increa se nitrogen input to streams (David and Gentry, 2000), ultimately adding to the nutrient loads of large rivers (Seitzinger et al., 2002) and estuarine systems (Rabalais et al., 2002), and

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26 causing water quality problems and eutrophication (Justic et al., 19 93; Nixon, 1995). In addition, recent studies have shown that small streams can be important sites for nitrogen transformations and retention of nutrients. D espite their relatively small proportion of watershed surface area s mall streams account for 85% o f total stream length within a watershed and receive draining water and dissolved nutrients from adjacent terrestrial ecosystems (Fisher et al., 1998; Alexander et al., 2000 ) Therefore, nitrogen cycling in small streams is an important factor regulating n itrogen input to other systems. Nitrification in small streams has been a common subject of research because nitrate is a mobile chemical species (Starry and Valett, 2005). Previous research has suggested that nitrification rates account for 50% of the var iability observed in stream nitrate concentrations, underscoring the importance of nitrification in river ecosystems (Peterson et al., 2001; Bohlen et al., 2001). Also, Peterson et al. (2001) h ave reported that 12 headwater streams, as part of the Lotic Intersite Nitrogen eXperiment (LINX), exhibited a high nitrification rate despite low ammonium concentration indicating that small streams are potentially important sources of nitrate to other ecosystems. Nitrification in stream ecosy stems can be limited by a variety of factors including : (1) physical and chemical prop erties (e.g., dissolved oxygen concentration and pH) (Strauss et al., 2002); ( 2 ) agricultural activities adjacent to watersheds ( Omernik 1977); and ( 3 ) in stream nitrog en adsorption or transformation s (Triska et al., 199 0 ; Jones and Holmes, 1996; F isher et al., 1998). Waste w ater T reatment P lants Each person contributes 8 to 12 pounds of nitrogen per year to wastewater treatment systems Sixty percentage of the nitrogen in waste material is bound in the complex organic matter and the remaining nitrogen form is ammoni um (Tchobanolgous

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27 and Burton, 1991 ). To remove inorganic nitrogen in wastewater plants, ammonium must be oxidized to nitrate before being reduc ed to nitrogen gas by denitrifiers. Nitrifiers tend to have long lives, reproduce much more slowly, and be limited by oxygen levels (Hammer and Mackichan 1981). Wastewater plants are thus designed to have a longer average residence time to extend the cont act time to biofilters and the aeration basin containing nitrifiers and dissolved oxygen (Weber, 1972). Because nitrifiers are sensitive to temperature, pH, and oxygen calcium carbonate is applied to maintain the optimal pH (pH 7 to 7.5), oxygen is produced by rotating the biofilter, and warmer temperatures are maintained (Hammer and Mackichan 19 81 ). Fa ctors A ffecting N itrification Rates Ammonium The availability of ammonium could regulate nitrification (Triska e t al., 1990), and thus the ammonification process, the biological conversion of organic nitrogen to ammonium, can influence nitrification rates (Jones et al. 199 5 ). Previous research has found a positive relationship between ammonium concentration s and ni trification rate s in stream s ( Kemp and Dodds, 2002; Mulholland et al., 2000; Triska et al., 1990), lakes (Hall, 1986), ground water (Strauss and Dodds, 1997) and soil s (Davidson and Hackler, 1994) However, plant roots and microbes also consume ammonium and thus nitrifiers compete for ammonium in tundra ecosystems due to the lack of ammonium (Chapin et al., 2002). pH In a laboratory culture experiment the optimal pH for nitrification rate ranges from pH 6.6 to 8.0 with negligible nitrification below pH 4.5 (Tate, 2000 ). The inhibitory effect of acidic pH to nitrification is due to the decreasing the ratio of amm o nia to ammonium

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28 at low pH; however the exact mechanism of pH inhibition to nitrification is not yet com pletely understood (Bothe et al, 2007). Even though pH inhibition to nitrifiers in culture s has been observed, many acidic ecosystems have shown relatively high nitrification rates (Stark and Hart, 1997). Explanations for the increased nitrification rates include : (a) microsite variation s in soil pH; (2) the presence of acidophilic autotrophic nitrifiers in acid ic soils; (3) the presence of heterotrophic nitrifiers and fungi capable of the oxidation of organic nitrogen in acid ic soils (Doxtander and Alexand er, 1966; Eylar and Schmidt, 1959); and (4) adaption of nitrifiers to acidic conditions ( Hankinson and Schmidt 1988). Oxygen Nitrification rate is limited by oxygen because nitrifiers use oxygen as an electron acceptor for oxidation of ammonium to nitra te. The minimum concentration of oxygen for nitrifier survival ranges from 1 to 6 Decreased nitrification in coastal ecosystems has been observed due to overgrowth of phytoplankton, which depletes oxygen availability for nitrifiers (Jenkins and Kemp, 1984; Hansen et al., 1981). Some research ers have reported the presence of the nitrification process under lower oxygen concentrations For example, Nitrosomonas europaea survived at oxic anoxic interface condition s (Voytek and Ward, 1995) ; the growth rate of Nitrosomonas marina was the highest with 5% oxygen content (Gore au et al., 1980) ; and nitrification occurred with oxygen concentration s as low as 0.3 l 1 (Tate, 2000 ). In addition, nitrification can take place near the plant rhizosphere (Sand Jensen et al., 1982; Wium Andersen and Andersen, 1972), in oxygenated water of flooded soils at the anoxic oxic interface or within soil microsite s in sediments.

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29 Temperature The temperature for ammonium oxidation to nitrite i n pure culture ranges from approximately 0 C to 65C, and nitrite oxidation to nitrate occurs at a pproximately 0 C to 40C (Tate, 2000 ). The optimal temperature for nitrification in laboratory settings ranges from 25 C to 30 C (Kadlec and Reddy, 2001) However, nitrification occurs from tropical to tundra soils at temperatures exceeding optimal ranges for ammonium oxidation. T emperature can affect other variables, including mineralization rates and oxygen concentrations, which in turn influence nitrifi cation rate (Richardson 1985 ; Sheibley et al. 2003). Organic c arbon Previous research has shown mineralization to have a positive relationship with nitrification because ammonium produced by mineralization can be used as an energy source for nitrifiers (St arry and Valett, 2005; Duff and Triska 2000; Bianchi et al., 1999). Organic matter content could enhance nitrification rates via an increased mineralization rate H owever, organic carbon content could limit nitrification rate s because organic carbon could enhance heterotrophic microbes requiring ammonium for their growth (immobilization) in stream water (Bernhardt and Likens, 2002; Strauss et al., 2002), agricultural soils (Venterea and Rolston, 2000), forest soils (Montagnini et al, 1989; Ollinger et al., 2002) and wastewater treatment plants (Richardson, 1985). In addition, the response of nitrification to organic carbon could depend on the TC:TN ratio of organic matter because the TC:TN ratio could determine the mineralization and immobilization rate s Thus, organic matter with a low TC:TN ratio could stimulate ammonification, increasing nitrification rate via the supply of ammonium. Organic matter with a high TC:TN ratio could enhance immobilization rates resulting in competition

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30 between heterotrophic microbes and nitrifiers for ammonium (Strauss and Lamberti, 2000; Verhagen and Laanbroek, 1991). Biochemistry of N itrifiers Nitrification has two steps : ( 2 1) the oxidation of ammonium to nitrite mediated by an ammoni um oxidizer ; and ( 2 2) the oxidation of nitrite to nitrate mediated by a nitrite oxidizer (Schmidt, 1982) 2 NH 4 + + 3 O 2 2 NO 2 + 4 H + + 2 H 2 O ; G= 272kJ mol 1 ( 2 1) The second reaction of nitrite to nitrate is; 2NO 2 + O 2 3 ; G= 75kJ mol 1 ( 2 2) Rec ently, ammon ia oxidizing archaea (AOA) were discovered in soils, oceans and estuarine sediments, with population sizes reported to be larger than th ose of ammoni a oxidizing bacteria (AOB). S tudies of the Nitrosopumilus maritimus one of the archaeal amoA nitrifiers, genome revealed that archaeal amoA had electron transport different from other ammonia oxidizing bacteria. N. m ari timus has also been shown to have a limited capacity for organic carbon assimilation, meaning that they can grow not only autotrophically but also mixtrophical ly and thus they can influence nitrogen and carbon cycl e s in ocean ecosystems (Walker et al., 2010). M icrobial D iversity of B acteria l and A rchaea l N itrifiers One environmental factor affecting the composition of nitrifiers in terrestrial ecosystems is pH because ammonium which is unsuitable as a substrate for ammonia monooxygenase is formed under acidic conditions (Kowalchuk and Stephen, 2001). However, p revious research has shown that Nitrosopira sp can tolerate acidic conditions ( d e B oer et al., 1995). Culture dependent techniques (M a cDonald, 1986) and molecular surveys such as 16S r R NA gene sequenc ing or Polymerase C hain R eaction

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31 Denaturing Gradient Gel Electrophoresis ( PCR DGGE ) have demonstrated a dominance of Nitrosopira in acidi c forest soils (Laverman et al., 2000) and acidic stream (Kowalchuk et al., 2000; Stienstra et al., 1994). However, t he mechanisms for nitrification in acidic soils are not fully understood Recent research has shown that Crenarchaeota was widely distribut ed and contributed significantly to nitrification rate in hot thermal springs (Hatzenpichler et al., 2008), marine ocean ecosystems (Francis et al., 2007), estuarine systems (Santoro et al., 2008), soils (Leininger et al., 2006) and wastewater treatment p lants (Park et al., 2006). B ased on the sequences retrieved from previous studies a rchaeal nitrifiers are divided into cluster s representing soil and marine group s (Francis et al., 2005; Park et al., 2006). Soil groups are likely to be less sensitive to organic matter while the marine group has been shown to be uninhibited by acidic pH (He et al., 2007). However, t here is not yet enough information on which factors can influence the division between the marine and soil groups. Archaeal nitrifiers seem to be better adapted to environmental stress compared to bacterial nitrifiers because they have low permeability membranes and several metabolic pathways capable of u sing dissolved organic carbon as a carbon source (Valentine, 2007). Measurement of Nitrification Rate s The easiest way to measure the nitrifi cation rate is to analyze concentrations of ammonium, nitrite, and nitrate in laboratory and environmental samples (Ferguson et al., 2007). Another method includes specific nitrification inhibitors ( n itrapyrin and allylthiourea ) which ha ve been used to measure nitrification rates in coastal sediments (Macfarlane and Herbert 1984; Hansen et al., 1981). However, i t remains unclear if

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32 these components inhibit archaeal ammonia monooxygenase, and if they also inhibit mineralization, which would result in un derestimat ion of nitrification rate s N itrification rate s are calculated b y the difference in ammonium concentrations or nitrate accumulation rate s in the presence and absence of the inhibitor. The 15 N nitrate isotope dilution technique is also widely used to measure nitrification rate s Following addition of 15 N ammonium and in cubation the concentration of 15 N nitrate oxidized from 15 N ammonium is analyzed (Koike and Hattori, 1978). However, added 15 N ammonium could possibly stimulate nitrification rates th ereby resulting in overestimat ion of nitrification rates relative to in situ rates As the molecular approach quantitative Polymerase C hain R eaction ( q PCR ) ha s been used to estimate quantification (as absolute number of copies ) of both bacterial and archaeal amoA genes in marine waters soils and waste treatment systems ( Harms et al., 2003; Geets et al., 2007 ; Kim et al., 2001 and 2004; W chter et al., 2006 ) Anaerobic Ammonium Oxidation ( Anammox ) In 2002 anaerobic ammonium oxidation ( anammox ) process was observed under anoxic conditions in marine and estuarine sediments in nature, playing an important role in nitrite and ammonium removal s (Thamdrup and Dalsgaard 2002; Engstrm et al., 2005; Trimmer et al., 2003). Most anammox research tended to focus on ocean and estuarine ecosystems because the removal and accumulation of ammonium and nitrite w ere related to anoxic condition s in the deep water column However, recently the identification of anammox has been extended to freshwater and arctic ecosystems (Schubert, 2006; Jetten et al., 2003; Rysgaard, 2004)

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33 Ocean Ecosystems Kuypers et al., (2003) discovered that anammox in the Black Sea removed inorganic nitrogen accounting for up to 40% of total nitrogen gas production in anoxic regions Specia lly, at a 200m dep th in the anoxic waters of Golfo Dulce, 19 % to 35% of total nitrogen gas production was performed by the anammox process (Dalsgaard et al., 2003) and at Benguela OMZ s anammox produced 4.2 g N day 1 (Kuypers et al., 2005). The water che mistry of these oceanic ecosystems is very similar to that of oxygen depleted zones in the oceans where 30 % to 50% of the global nitrogen removal is expected to occur (Dalsgaard et al., 2003). I nterestingly anammox was detected in the Arctic Oce a n al though the contribution was low relative to overall nitrogen gas production (less than 5%) (Rysgaard et al., 2004). Therefore, anammox may be a globally important removal process for oceanic nitrogen cycling. Estuarine Sediments The contribution of anamm ox to nitrogen production varies among ecosystems. In the estuarine ecosystems of the continental shelf sediments in the Skagerrak of the Baltic North Sea, anammox produces total 2 per day, accounting for 24 % to 67% of total nitrogen gas pr oduction (Thamdrup et al., 2002). In the Aarhus Bay, the anammox production of nitrogen gas reaches 83 of N 2 per day. However, the contribution of anammox to total nitrogen gas production was insignificant relative to denitrification in a eutrophic coastal bay because high concentrations of organic matter enhanced activities of denitrifiers ( Thamdrup et al., 2002) In sediments with high organic matter, anammox bacteria created 0. 54 g N g soil 1 day 1 at Thames Estuary, UK and 2 .8 g N g soil 1 day 1 at Chesapeake Bay, but the relative contribution of

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34 anammox to total nitrogen gas production was very small (8%) ( Engstrm et al., 2005; Rich et al., 2008). Wastewater T reatment Plant s Most anammox research in freshwater ecosystems is performed in man made systems, such as wastewater treatment plants (Sliekers et al., 2002; Strous et al., 1998; Fux et al., 2002; Third et al., 2005). Since bioreactors using anammox bacteria consume ammoni um as an energy source and carbon dioxide as a carbon source (E gli et al., 2003), these systems can reduce the operational costs up to 90%, while still being environmental ly friendly (Sliekers et al., 2003). Also, it has been reported that anammox bacteria were not affected by the chemical composition of waste water, surviving various types of waste water sludge (Pilcher et al., 2005). However, enrichment of anammox bacteria takes a relatively long time ( 11 to 30 days ) (Strous et al., 1998; Van de Graff et al., 1996). Freshwater S ystems In freshwater ecosystems, anam mox has been identified in Lake Tanganyika, the second largest lake in the world. The lake produces 3.4 g N per day (13%) (Schubert et al., 2006). U sing 16S rRNA gene analysis Penton (2006) found evidence for the widespread distribution of anammox, inclu ding at the Kellogg biological station (freshwater sediment), Wintergree n organic wetland) and the Everglades WCA 2A (Water Conservation Area 2A subtropical wetlands). However, studies on the anammox p rocess in freshwater, wetland ecosystems or floodplains similar to anoxic oceanic conditions have not been well investigated.

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35 Factors Affecting A nammox Rates Nitrite and a mmonium Because anammox has been detected in natural ecosystems and proven to be important to nitrogen cycling, many researchers have begun to investigate factors controlling the anammox process. Dalsgaard (2002) observed that in anoxic incubation, anammox accounted for 65% of nitrogen gas formation. In addition, nitrite production fr om nitrate was faster than nitrite consumption, and therefore did not strain the rate of anammox. Engstr m (2005) suggested that competition between anammox and denitrification for nitrite is a significant determinant of absolute and relative anammox rates in coastal marine sediments. Trimmer (2005) also reported that anammox can be regulated by availabilit y of nitrite, as well as the relative size s or activit ies of anammox population s The ammonium supplied by mineralization may be consumed by anammox bact eria as an electron donor ( Kartal et al., 200 7 ) However, there is little research on the effect of ammonium concentration on anammox rate Therefore, research on the general regulators and controlling factors for anammox in broader ecosystems is needed. Organic carbon Anammox bacteria are known to be autotrophic and use carbon dioxide as a carbon source T hus it was expected that the supply of organic carbon could not control anammox rates. (Strous et al., 1998). However, bioavailability of organic carbo n enhances denitrification rates Therefore, it can be assumed that higher amounts of organic carbon will stimulate denitrification. The enhanced denitrification rate may hinder the anammox process through competition for nitrite However, previous studies have shown controversial results about the relationship between anammox rate and

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36 organic carbon concentrations. In sediment from the Baltic North Sea transition, a negative correlation was observed between the relative importance of anammox and organic matter (Thamdrup and Dalsgaard, 2002), while in the Thames estuary, the anammox process was positively correlated with sediment organic content (Trimmer et al., 2003). Therefore, more research on relationship between anammox rate and organic carbon content is needed in various ecosystems Oxygen The anammox process in a waste treatment plant was found to be inhibited by 1.1 oxygen and occurred under anaerobic conditions (Strous et al., 1998). Alt hough the anammox process exposed to oxygen, the removal of oxygen can revive the process (Jetten et al., 1998; Third et al., 2005). However, t here are no reports on the effects of oxygen on ana mmox in natural systems. Presumably, anammox is also inhibited by oxygen in natural system s because most naturally occurring anammox rate has been detected in anoxic conditions. Biochemistry of A nammox B acteria Based on 15 N n itrogen experiments, the foll owing anammox mechanism has been postulated: A nammox bacteria are chemolithoautotrophs and consume ammonium and nitrite in a ratio of 1 to 1. They reduce nitrite (NO 2 ) to hydroxylamine (NH 2 OH) by a nitrite reducing enzyme (NiR). Next, hydroxylamine (NH 2 OH) and ammonium (NH 4 + ) are condensed to hydrazine (N 2 H 4 ) and water by a hydrazine hydrolase (HH). Finally, a hydroxylamine oxidoreductase (HAO) oxidizes hydrazine (N 2 H 4 ) to dinitrogen (N 2 ) (Jetten et al., 1998 ; 2003). Overall process of anammox is followi ng equation. NO 2 + NH 4 + 2 + 2H 2 O G0= 358 kJmol 1 ( 2 3 )

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37 M icrobial D iversity of A nammox B acteria The first anammox bacteria, Brocadia anammoxidans were detected using 16S rRNA gene sequence analysis and FISH with specific oligonucleotide probes i n biofilm. Brocadia anammoxidans was found to be phylogenetically related to Planctomycetales (Strous et al., 1999 ; 2000). Using anammox specific 16S rRNA gene primers (mainly using Pla 46F primer) and anammox specific oligonucleotide probes, researches ha ve reported the presence of at least three other anammox bacteria. These genera are Brocade (Kartal et al., 2004) Kuenenia ( Schmidt et al., 2000) and Scalindua (Kuypers et al., 2003 and Schmidt et al., 2003). Recently, the Candidatus Anammoxoglobus propi onicus species was found in a bioreactor (Kartal et al. 200 7 ). Penton et al., (2007) suggested a new primer that was 100% specific in the recovery of 700bp 16S rRNA gene sequence with 96% homology to the Scalindua group of anammox bacteria. This new prim er detected anammox bacteria in 11 geographically and biogeochemical ly diverse freshwater and marine sediments (Penton et al., 2007). Measurement of A nammox Rate s Isotope pairing method The isotope pairing method involves additions of 15 N ammonium and 14 N nitrite to samples. After incubation, the ratio of 14 N 15 N to 14 N 14 N is analyzed using a gas chromatography isotope ratio mass spectrometry and can be expressed as 14 N 15 N values following equation ( Kuypers et al., 2003). 14 N 15 N = [( 14 N 15 N: 14 N 14 N) sample ]: [( 14 N 15 N: 14 N 14 N) standard ] 1 (A ir can be used as the standard)

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38 Intermediate analysis As mentioned above, hydrazine and hydroxylamine are intermediate products in the anammox process Therefore, the intermediate production rate is related to the overall r ate of anammox. Ammonium, hydrazine and hydroxylamine are measured colorimetrically at time intervals after anoxic incubation of anammox bacteria with media (Jetten et al., 2005) Denitrification D enitrifiers are heterotrophs and use nitrate and nitrite as electron acceptors and organic matter as an energy source Most denitrifiers are facultative anaerobes and prefer to use oxygen when oxygen is available in the system (Chapin et al., 2002). The sequence of nitrate reduction is nitrate nitrite nitric oxide nitrous oxide nitrogen gas The regulators for denitrification are organic matter availability, nitrate concentrations, anaerobic conditions, pH and temperature in nature ecosystems (Tate, 199 9 ). Forest E cosystem s Forest ecosystems are usually limited by nitrogen so that this system can have a substantial capacity to store excess nitrogen. The NITREX (Nitrogen saturation experiment) research in Europe reported that most nitrate amended to a forest ecosystem was retained in soils (Dise, 199 5 ). Also, the rate of denitrification tends to be relatively low due to well drained soil condition s (Nadelfohher, 2001). However, chronic nitrogen addition s can saturate forest ecosystem s with nitrogen, which can enhance the loss of retained nitrogen to the a tmosphere by denitrification At Hogwald Forest, Germany, significant amount s of nitrogen gas originated from nitrogen deposited o n spruce and beech experiment al plot s (Butterbach et al., 2002). In forest s having high

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39 nitrate contents, the gaseous losses o f nitrate w ere stimulated (Aber et al., 1995). Also, it was observed that tropical forest s exhibited a higher rate of denitrification due to high moisture and nitrate content s Therefore, increasing nitrogen deposition from the atmosphere and nitrogen fertilizer will stimulate denitrification rate s in forest ecosystem s, despite the overall nitrogen limit ation Agroecosystems Agroecosystems receive over 75% of their nitrogen from human activities. However, because this system has a low retention capacity for nitrogen, most nitrogen flows to other systems t hrough denitrification or leaching (Galloway et al., 2004). Denitrification is a major process in nitrogen removal from agroecosystems ; however, removal rates of nitrate by denitrification can vary subst antially (Mosier et al., 2002). For example, the fraction of nitrogen removal via denitrification varied from 3% to 56% in flooded soils (Galbally et al., 1987; Freney et al., 1990) I n an irrigated wheat field, 50% of the added nitrogen fertilizer was den itrified (Freney et al., 1992). Other research reported that only 1% to 4% of added nitrogen fertilizer was removed by denitrification (Mosier et al., 1986). Coastal E cosystem s Nitrogen input to coastal ecosystem s has increased due to human activities. Th is increased nitrogen can influence the estuaries and coastal ecosystems stimulating the growth of phytoplankton. Increased phytoplankton growth can suppress the growth of plants and benthic organisms due to reduced light availability and depleted oxygen. Thus, denitrification is an important mechanism for nutrient removal in coastal ecosystems (Canfield et al., 2005). Denitrification rates in coastal ecosystems are

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40 regulated by organic matter loading, macrofauna, aquatic grasses, anoxi c conditions, and ni trate ( Cornwell et al., 1999) Freshwater and R iparian E cosystem s Frequent flooding, nitrogen inflow from terrestrial ecosystems, and the presence of vegetation in riparian ecosystems can affect denitrification rates Green et al. (2004) estimated that globally, 50% of the nitrogen entering watershed s including streams, is denitrified. Thus, denitrification is considered to be the most desirable ways of nitrate removal from watershed ecosystems. In riparian ecosystems, seasonal variation in denitrifica tion rates is important because the most concentrated nitrate inflow occurs during winter, coinciding with low microbial activity and plant uptake of nitrogen (Martin et al 1999). However, there is no typical relationship between denitrification rate and s easonality. In Chesapeake Bay, and the southwest of France, the denitrification rate tended to increase during winter (Weller et al 1994 and Pinay et al 1993). Other research found that denitrification rate was enhanced during spring and fall, and highest during summer in Kingston, Rhode Island (Groffman et al. 1996). This variability in denitrification rate s is due to varying amounts of organic matter, the presence of vegetation, flooding times and oxygen levels (Reddy and Delaune, 200 8 ). In addition, b eca use denitrification rate is regulated by carbon availability, nitrification activity for nitrate supply, microbial activity, and optimal pH and temperature, it is expected that the lowest activity will be observed in deeper sediments (Martin et al., 1999). However, some research has found significant denitrification rates in deeper soils in riparian ecosystems ( Sotomayor et al., 1996; Francis et al., 1989; Lind et al., 1989).

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41 Factors Affecting Denitrification Rate s Nitrate Forest ecosystems typically are n ot limited by oxygen and thus microbes prefer to use oxygen rather than an alternative electron acceptor such as nitrate. In addition, plant and microbial growth typically suffer s from nitrogen limitation in forest ecosystems, except for fertilized soils ( Robertson and Tiedje 1984 ; Davidson and Swank, 1987 ) However, i n wetland ecosystems, nitrate is used more actively as an alternative electron acceptor due to absence of oxygen and thus the nitrate availability can regulate denitrification rate s in wet land soils (Reddy and DeLaune, 2008) Nitrate availability is also affected by the movement of nitrate into anaerobic sites from aerobic zones with nitrification, even though an external source of nitrate is present such as waste water, runoff, or precipit ation. Organic ca rbon The addition of carbon enhances denitrification rates because d enitrifiers are heterotrophs, using organic carbon as an energy source ( de Catanzaro and Beauchamp, 1985; Paul and Beauchamp, 1989 ). Research has shown that the denitrif ication rate is correlated with organic carbon concentrations (Reddy and De L aune, 200 8 ). However, carbon does not regulate denitrification rate in mineral soils with high organic matter, such as sludge amended soil due to carbon saturation (Maier et al., 2000). Therefore, in carbon limited condition s the addition of a carbon source m ay enhance denitrification while under high carbon condition s the impact of nitrate addition on denitrification can be more apparent.

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42 Temperature Denitrification increases with temperature according to the Q 10 value. The optimal temperature ranges from 30C to 40C, although temperatures often fall outside this range (Tate, 2000). Plants The presence of plants can not only increase denitrification but also compete with deni trification. Plant uptake of nitrate can decrease the supply of electron acceptors for denitrifi ers and inhibit the process However, root exudates by plants can provide an energy source for denitrification, and oxygen released from plants can stimulate ni trification, enhancing the supply of nitrate to denitrifiers (Hopfensperger et al., 2009). Biochemistry of D enitrifi cation Denitrification is the reduction of nitrate to nitrogen gas. Enzymes involved in the process include nitrate reductase, nitrite reductase, nitric oxide reductase and nitrous oxide reductase. The common nitrate reduction process can be summarized as (Reddy and De L aune, 200 8 ) : NO 3 2 2 2 (gas) ( 2 4 ) Denitrifying bacteria are found in many divisions of the dom ain Bacteria. The majority of denitrifiers are heterotrophs and t he overall process is following (Reddy and De L aune, 200 8 ) : 5 C 6 H 12 O 6 (glucose) + 24 NO 3 + 24 H + = 30 CO 2 + 42 H 2 O +12N 2 G0 = 14562.09 kJ mole 1 ( 2 5 ) Microbial Community of D enitrifiers Denitrifiers are classified into three groups based on their energy source: o rganotrophs, which use organic matter, lithrotrophs which use inorganic matter (e.g.

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43 hydrogen or reduced sulfur compounds), and phototrophs, which use light as their energy sourc e s The most common denitrifiers are organotr o p hs (Tate, 2000 ). T here are numerous bacterial genera that include strains capable of denitrification. All molecular research on the denitrifying bacterial community use s functional gene coding for nitrate reductase such as nirS and nirK and coding for nitrous oxide reductase such as no sZ (Bothe, 2000). Measurement of Denitrification Rate s Acetylene blocking method and measurement of nitrate consumption rates D irect measurement of nitrogen gas as the end product of denitrification is very difficult because the atmosphere contains about 80% of nitrogen gas Therefore indirect measurement methods are used. As the indirect method, a cetylene blocking is the most extensively used technique of denitrifi cation rate measurement because of its low cost, simplicity, and high sensitivity. The principle of this method is to measure the amount of nitrous oxide produced by denitrification in the headspace using gas chromatography after acetylene inhibits the red uction of nitrous oxide to nitrogen gas (Tiedje et al., 198 8 ). However, acetylene inhibits nitrification and thus will underestimate denitrification rate s when nitrate is low (Seitzinger et al., 1993). Another method is to measure the consumption rate s of nitrate for a certain time inter v al However, nitrate can be lost by immobilization, resulting in low sensitivity (Alef and Nannipieri, 1995). Measurement of the ratio of change relative to a conservative property This method is based on measuring th e change in ratio of a conservative molecule (e.g. chloride or argon ) to nitrate or nitrogen gas When using chloride, the method assumes that nitrate and chloride have the same mobility A fter the nitrate is consumed by denitrification the r atio of nitra te to chloride will be decreased (Alef and Nannipieri,

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44 1995). The measurement of the change in relative ratio of nitrogen to argon gas in the sample is premised on the fact that nitrogen gas production b y denitrification can increase the ratio of nitrogen to argon gas This method is not as sensitive compared to the methods mentioned above but can give a represent ative measure of denitrification in large scale natural settings (Groffman et al., 2006). Isotope method I sotope method can improve the sensitivi ty and specificity of detection of denitrification. This method assumes a random association and homogenous distribution of 15 N nitrogen in the denitrification zone. After 15 N labeled nitrate is added to the sample and incubated, t he isotopic composition of nitrogen gas is analyzed using a isot o pe ratio mass spectrometry following separation by gas chromatography ( Groffman et al., 2006 ). The ratio of 15 N nitrogen gas ( 14 N 15 N to 14 N 14 N ) is calculated as excess above their natural abu ndance (Thamdrup et al., 2000). However, enrichment of 1 5 N labeled nitrate can stimulate denitrification rates and it is difficult to mix 1 5 N labeled nitrate homogeneously in to soils (Groffman et al., 2006). Dissimilatory Nitrate Reduction to Ammonium ( DNR A ) Nitrite and nitrate are reduced to nitrogen gas in soils by anammox and denitrification respectively T hus thes e processes play important role s in remov al of nitrogen from soils. However, n itrate can be also reduced to ammonium by dissimilatory nitrate reduction to ammonium (DNRA) (Fewson et al., 1961). DNRA is performed by obligate anaerobes that live in highly reduced conditions such as lake sediment s or permanently waterlogged wetlands (Reddy and DeLaune, 2008)

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45 Agricultural Soils DNRA is desirable for agricultural soil s because conserved nitrogen can be used by plants in a nitrogen limited environment (King et al., 1985). DNRA can also be a beneficial process in non eutrophic environments as a supply of nutrient to organisms. Most research regarding DNRA in agroecosystem s has focused on identifying favorable conditions for DNRA in order to retain nitrogen in systems. For example, the addition of glucose and highly reduced conditions enhance d DNRA up to 5 times that of nitrate reduction in Chinese and Australian paddy soil s (Yin et al., 2002). Also, in a cultivated field soil in France, the addition of carbon source with an increas ed TC :TN ratio was found to stimulate DNRA (Fazzolari et al., 1998). However, the contribution of DNRA to nitrate removal tends to be smaller than other processes ( such as immobilization or denitrification ) in agroecosyste ms Freshwater and R iparian E cosystems Freshwater sediments and riparian wetlands have good conditions for DNRA due to the absence of oxygen and accumulation of organic matter (Matheson et al., 2002). For example, in unplanted riparian wetland soil, DNRA can be the principal mechanism of nitrate removal (49%), but with the presence of plants, denitrification was found to be the primar y mechanism of nitrate removal (61 % to 63%) due to increased nitrification via a supply of oxygen from root zone (Matheson et al., 2002). Typically, the overall proportion of nitrate removal due to DNRA is not larger (1 % to 9%) than denitrification in fres hwater and riparian ecosystems, except for in unplanted soils and sediment s with high amounts of organic matter (Matheson et al., 2002).

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46 Estuar ine and C oastal E cosystems Estuar ine and coastal ecosystems have a mixture of freshwater and salt water A s a res ult they may have relatively high amounts of organic matter flowing from the land Denitrification in these ecosystems plays an important role in the removal of nitrate, preventing eutrophication of the ocean ecosystem. However, DNRA can adversely accelerate eutrophication through the accumulation of ammonium in coastal ecosystems. Estu ar ine and coastal ecosystems have higher amounts of organic matter and nutrients and lower oxygen concentrations than terrestrial ecosystems, leading to favorable conditions for DNRA. Laguna Madre and Baffin and Concepcion Bay s showed high rates of DNRA ( 0.656 to 32.94 mM Nm 2 day 1 ), resulting in eutrophication (An et al., 2002) Lak es Organic matter accumulation through eutrophication in lakes leads to an increase in electron donor availability and a decrease in oxygen release, which can be favorable to DNRA. However, the presence of plants can increase the oxygen released into the rhizosphere and this can stimulate denitrification via a supply of nitrate from nitrification rather than DNRA. Also, when nitrate is limited and the carbon is high, fermen tative bacteria responsible for DNRA can be dominant due to the increased carbon content s However, o nce nitrate was added, denitrifying bacteria became dominant due to the supply of an electron acceptor (Angelo et al., 1993). Factors A ffecting DNRA Rate s Nitrate Under nitrate limited conditions, the DNRA process is more efficient than denitrification because DNRA consumes 8e per mole of nitrate reduced to ammonium

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47 versus 5e for denitrification (Tiedje, 1988). In highly reduced and nitrate limited conditions, DNRA activity increased up to 20% (Ambus et al., 1992). However, i n extremely nitrate limited conditions such as a forest ecosystem, most nitrate was consumed by immobilization and less than 5% of nitrate was reduced via dissimilatory pathway s such as DNRA (Bengtsson and Bergwall, 200 0 ) Organic carbon T he addition or increase of a carbon source such as glucose, DOC, or organic matter increase s DNRA rates (Fazzolari et al., 1998; Tobias et al., 2001; Kelso et al., 1999 ; Ambus et al., 1992) Hi gh DNRA rates can be expected in saturated and carbon rich conditions such as stagnant water, sewage sludge, high organic matter sediments and rumen s (Maier et al., 2000) Sulfate R educed sulfur compounds ( hydrog e n sulfide, iron sulfide, and thiosulfate ) can be used as electron donor s for DNRA instead of organic matter. Thus, the addition of sulfide has been found to increase the oxidation of sulfide while simultaneously reducing nitrate to ammonium (Brunet et al., 1996). In Laguna Madre and Baffin Bay, Texas, sulfide induced DNRA contributed to the accumulation of ammonium (An et al., 2002). Plants Plants can provide oxygen to the soil profile via root respiration s However, DNRA microbes are fermentative and favor highly reduced conditions T hus the presence of plants can decrease DNRA rates It has been reported that the presence of plants inhibit s DNRA through the supply of oxygen in riparian and lake ecosystems (Matheson et al., 2002).

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48 Biochemistry of DNRA As mentioned above, t he DNRA p rocess consist s of two steps: nitrate reduction to nitrite and nitrite reduction to ammonium. Nitrate reduction to nitrite produces energy in most organisms (Betlach et al. 1982 ) However, t he second step reduction of nitrite to ammonium, does not create additional energy, but regenerates reducing equivalents through the reoxidation of NADH 2 to NAD + Theses reducing equivalents are then used to oxid ize carbon substrates. Overall reaction is following (Tiedje, 1988): C 6 H 12 O 6 + 3 NO 3 + 3 H + = 6CO 2 + 3 NH 3 + 3H 2 O ( 2 6 ) == 144 kcal (Tiedje, 1988). M icrobial D iversity of DNRA B acteria U sing a culture medium method b acteria found to be capable of DNRA were Clostridium and Bacillus sp. (Caskey et al. 1979 and 1980). The ammonium producing isolates were Clostridium KDHS2, Clostridium KDHS3 and Bacillus KFHS6. Recent research has found that low nitrate concentrations resulted in a DNRA community composed of Bacillus strains in the rhizosphere of Glyceria ma xima (Nijburg 1997). It has also been reported that Bacillus jeotgali related strains and two newly identified strains of GD0705 and GD0706 capable of DNRA are isolated in cultures from ammonium enriched soils (Fan et al., 2006). However, at present rese arch on microbial communities capable of DNRA ha s not been performed based on 16S rRNA or FISH ( Fluorescent I n S itu H ybridization ) Therefore, the primer and probes used to detect DNRA bacteria need to be developed and used to investigate DNRA communities i n natural ecosystems with DNRA rate s

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49 Measurement of DNRA Rate s When 15 N labeled nitrate is added to sediments, DNRA bacteria reduce the 15 N labeled nitrate to 15 N ammonium The 15 N ammonium content s are measured using a i sotope r atio m ass s pectrophotometer equipped with an e lemental a nalyzer Summary A review of the literature indicated that inorganic nitrogen transformation rates vary depending on ecosystem types (e.g. agriculture soils, forest soils, estuarine sediments, stream, or ocean water), plants, and biogeochemical properties (e.g. concentrations of nitrite, nitrate, ammonium, and organic carbon, and oxygen, pH, level of reduced conditions ) Also, higher nitrification rates can lead to increased denitrif ication rates while higher denitrification rates can inhibit anammox and DNRA rates due to competition for electron acceptors However, most previous studies were performed in terrestrial soils, stream water, estuarine sediments, or ocean water Research on inorganic nitrogen transformation rates in tributary sediments is limited even though tributary sediments can function as s ink s for nitrogen in watershed ecosystems Also, all inorganic nitrogen transformation rates including nitrification, anammox, de nitrification and DNRA have not been thoroughly investigated Furthermore information on the relationship s between microbial f un ction and structure associated with these processes is limited. Thus comprehensive research on inorganic nitrogen transformat ion rates and microbial assemblage structure s associated with these processes is needed in tributary sediments

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50 CHAPTER 3 INORGANIC N ITROGEN TRANSFORMATIONS IN TRIBUTARY SEDIMENTS A n excessive input of nitrogen to aquatic ecosystems can result in eutrophication, impairment of water quality and human health problem s such as a blue baby syndrome (Prasad and Power, 1995; Ward and Elliot 1995; Sotomayor and Rice, 1996) and cancer s (Prasad and Power, 1995; Starr and Gillham, 1993). I t has been report ed that riparian zones and tributaries in a watersheds can attenuate nitrogen through plant uptake, microbial immobilization, denitrification and soil storage (Lowrance et al., 1997). Specifically, small streams have a higher efficiency of NO 3 removal th an larger rivers because they have a smaller volume to surface ratio, increas ing the possibility for benthic denitrification (Alexander et al., 2000). R esults from twelve headwater streams as parts of the Lotic Intersite Nitrogen eXperiment (LINX) demonstrated that NH 4 + entering small streams was oxidized to NO 3 by nitrification, enhancing denitrification rates via a supply of NO 3 to denitrifiers (Peter son et al., 2001). Therefore, previous research by many scientists ha s focused on denitrification rates in small streams (Ceya et al., 1999; Mulholland et al., 2001 and 2004 ; Steinhart and Groffman, 1998 ). Several studies have documented that denitrificati on i s a major contributor to nitrogen removal in tributary ecosystems ( Steinhart and Groffman, 1998; Clausen et al., 2000; Lowrance et al., 1997; Korom, 1992). However, denitrification rates can be affected by other nitrogen transformations. For example, nitrification provides NO 3 to denitrifiers. A mmonification suppl ies NH 4 + to nitrifi ers, increasing nitrification rates ANaerobic AMMonium OXidation (Anammox) can anaerobically oxidize NH 4 + to N 2 ( Dalsgaard et al., 2005), potentially decreasi ng NH 4 + conc entrations. Dissimilatory Nitrate Reduction to Ammonium (DNRA) potentially leads

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51 to accumulat ion of NH 4 + (An et al., 2002) Therefore to understand the function of small streams with respect to N removal, further understanding is needed on relative rates of ammonification, nitrification, anammox denitrification, and DNRA processes in tributary ecosystems. Inorganic nitrogen transformation rates can be controlled by soil moisture content ( Klemedtsson et al ., 1988) hydraulic conductivity ( Gilliam, 1994 ) temperature (William et al., 1997), different agricultural activit ies (Martin et al., 1999), vegetation types ( Haycock and Pinay 1993) geomorphological properties ( Opdyke et al. 2006), and seasonal variation (Rysgaard et al. 2005 ; Christensen et al., 1990 ). Many of these factors regulate the levels of electron donors and acceptors that can have profound influence on the relative rates of inorganic nitrogen transformation rates (Bu r ford and Bremner, 1975; Reddy et al., 1982). For example, organic matter can increase denitrification and DNRA rates because these processes use organic matter as energy sources ( Kaspar 1983 ; Tobias et al. 2001 ; Yin et al. 2002) The concentrations of NO 3 and NO 2 can also control denitrification and anammox rates via supply of electron acceptors respectively The critical zones for inorganic nitrogen transformation s in tributarie s are benthic sediment s (Steinhart et al., 1998; Chatarpaul and Robinson, 1979; Hill, 1983; Swank and Caskey, 1982 ; Holmes et al. 1996, Mulholland and Hill 1997 ) and riparian sediments ( Bowden et al. 1992 ; Pinay et al. 1993 and 1995 ) Even though microbes in the water column of streams can participate in nitrogen cycling, t he faster velocit ies of water flow and l ower amounts of organic matter in stream water s can limit the removal of nitrate However, stream sediments with high microbial activities can function as

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52 effective sinks for nitrogen (Jones and Holmes, 1996). In addition riparian sediments ( the transitional area between upland and streams) can receive nitrogen and organic matter runoff from upland s ( Freeze and Cherry 1979 ; Hill 199 6; Hill and Waddington 1993 ; Likens et al. 1977 ) F luctuatin g water table s in r iparian sediments create s alternative aerobic and anaerobic conditions that favor denitrification ( Bowden et al. 1992 ; Pinay et al. 1993, 1995 ) The Santa Fe River tributar ies in the Boston Farm Santa Fe Ranch Beef Unit Research Center (SFBRU) receive n itrogen from nursery operation s and carbon from dairy manure and surrounding vegetation s I t was reported that a tributary with marsh and open water adjacent to the Santa Fe River had the buffer capacity for nitrogen removal through denitrification (Frisbe e 2007). Even though tributary systems have higher potential denitrification rates their rates can be affected by other nitrogen transformations such as ammonification ( Seitzinge r 1994; Gardner et al., 1987 ), nitrification ( Jenkins and Kemp, 1984; Kemp and Dodds, 2002), anammox ( Rysgaard et al., 2004; Rich et al., 2008) and DNRA ( Rysgaard et al., 1996; Bonin et al., 1998; Michotey and Bonin, 1997) Thus, to understand nitrogen removal in tributaries of the Santa Fe River, I have developed th e following objective s to determine relative potential rates of select ed inorganic nitrogen transformation s in tributary sediments. The overall objective of this research was to investigate how biogeochemical properties influence potential inorganic nitrogen transformation rates in tributary sediments. The specific objectives were to: Determine potential rates of select inorganic nitrogen transformation s in tributary sediments

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53 Investigate the relationships between potential rates of inorganic nitrogen transformation s and biogeochemical properties in tributary sediments Compare potential rates of inorganic nitrogen transformation s between stream and riparian sediments. Materials and Methods Site D escription The site for this research is tributary sedime nts (stream sediments and riparian sediments) at the Boston Farm Santa Fe Ranch Beef Unit Research Center (SFBRU) of the Santa Fe River Watershed, northern Alachua County FL (Figure 3 1) Land uses on this site include a low intensity cattle operation with approximately 300 heifers on 1,600 acres and the nursery operation using nitrogen fertilizer (Holly Factory Nursery) (Frisbee, 2007) One of t wo tributaries, T ributary 1 (T1) is located along the CR 241roadway a and flow s into a pond before entering in to the Santa Fe River (Figure 3 1) The T1 system is surrounded by improved pasture s and is impacted by ranch activity. The riparian sediment contains relatively high organic matter contents and receives water from groundwat er and seepage water (Figure 3 1 (b)) T he stream sediments receive water from stream and ha ve relatively low organic matter contents (Figure 3 1 (b)) The vegetation type adjacent to the tributary sediments is a mixture of herbaceous and woody plants including Carya sp., Pinus sp., Quercus sp., Magnolia grandiflora Saururus cernuus Juncus sp., Cephalanthus occidentalis H ydrocotle umbellata and P olygonum sp. Tributary 2 (T2) is affec ted by a nursery operation u sing nitrogen fertilizer (NH 4 NO 3 and urea) at the headwater stream, drains cattle pasture on the SFBRU and flows in to the Santa Fe River (Figure 3 1) The u p stream region of T ributary 2 (T2 U) is affec ted

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54 by nitrogen fertilization and is surrounded with hardwood plants including Carya sp., Quercus sp., and Magnolia g randiflora sp. and soft wood including of Pinus sp. The downstream region of T ributary 2 (T2 D) is influenced by improved pasture s and nitrogen fertilization from the head water (Figu re 3 1) The land adjacent to T2 D is surrounded with grass es including Saururus cernuus sp., Juncus sp. and P olygonum sp., deciduous shrub plants including Cephalanthus occidentalis sp., and aquatic plants including H ydrocotle umbellata sp.(Frisbee, 2007). Sampling Samples were collected to a depth of 3 cm with a PVC core (diameter 7.5cm) at T1 T2 U and T2 D. S urface sediments (3 cm dept of sediments) were collected because surface sediments likely have much higher microbial activitie s compared to deep er sediments. Three samples from three locations (total of 9 samples) were collected in each tributary sediment in October 2007, January, April and July 2008. Repeated sediment sampling (4 times) was considered as replicates for each sed iment type. T he sediments were transported to the lab oratory on ice and stored at 4C until analysis Nine samples from each site were mixed to make a single composite sample. After mixing, triplicate sediments were distributed and prepared for the analysis. All roots and litter material s were removed from the sediment prior to analysis. Analyses of Biogeochemical Properties Sediment pH ( 1:1 ratio of sediments to water ) was measured using a Fisher AR5 0 pH meter (Thomas, 1996). A fter sediment samples were extracted with 0.5 M K 2 SO 4 (Bundy and Meisinger, 1994), extractable NH 4 + N concentration was measured using a Seal AQ2+ automated Discrete Analyzer (EPA Method 350.1) E xtractable NO 3 N concentrations was analyzed using a n Alpkem Rapid Flow Analyzer 300 Series (EPA

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55 Method 353.2). The K 2 SO 4 extract was digested for total extractable organic nitrogen ( Ext. Org N) via Kjeldahl block digestion T he concentration of Ext. Org N was analyzed using a Seal AQ2+ Automated Discrete Analyzer (EPA Method 351.1). Total extractable organic carbon concentration ( Ext. Org C) was analyzed from the extract using a Shimadzu TOC 5050A Total Organic Carbon Analyzer equipped with a ASI 5000A auto sampler. M icrobial biomass ca rbon (MBC) and microbial biomass nitrogen ( MBN) were extracted from sediments using a chloroform fumigation extraction method (Brookes et al., 1985). Both the chloroform fumigated and non fumigated samples were extracted with 0.5 M K 2 SO 4 for analyses of MB C and MBN. To measure MBC, each extracted sample was analyzed for extractable organic carbon (Ext. Org C ) concentrations using a Shimadzu TOC 5050A Total Organic Carbon Analyzer equipped with a n ASI 5000A auto sampler. To measure MBN, each extracted sample was digested for extractable organic nitrogen (Ext. Org N ) via K jeldahl digestion (Brookes et al., 1985) T he concentration s of MBN w ere measured using a Seal AQ2+ Automated Discrete Analyzer (EPA Method 351.1). MBC and MBN concentrations were calculated by the difference between the fumigated and non fumigated samples. The subsamples of sediments were dried at 7 0 C for 3 days and then ground using a ball grinder for total nitrogen (TN) and carbon (TC) analys e s. T N and TC concentrations wer e measured on dry sediments using a Thermo Electron Corp. Flash EA 1112 Series NC Soil Analyzer (Nelson and Sommers, 1996; Bremmer, 1996). P otential N itrogen T ransformations Rates Potential rates were determined on triplicate sediments For analysis of p otential ammonification rates 5 g of wet sediment and 5 ml of d istilled d e ionized (DDI) water were added to 60 ml serum bottles The sediments were purged with pure N 2 to create

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56 anaerobic condition s and incubated for seven days at 24C. After incubation, s ediment s were extracted using 0.5 M K 2 SO 4 solution and extractable NH 4 + N concentrations w ere measured using a Seal AQ2+ Automated Discrete Analyzer (War n ing and Brem n er 1964) (EPA Method 350.1). For analysis of potential nitrification rates 10 g of wet sediment w ere added to an 16 0 ml open bottle Sediments were amended with 1400 g of N H 4 + N ( as NH 4 Cl) followed by 30 ml DDI water. All samples w ere incubated in a Model 25 incubator shaker at 1 3 0 rpm (New Brunswick Scien ti fic Co) for seven days at 24C (Berg and Rosswall 1995). After incubation, sediments were extracted using 0.5 M K 2 SO 4 solution and extractable NO 3 N concentration was measured using an Alpkem Rapid Flow Analyzer 300 Series (EPA Method 353.2). Potential anammox rates were determined using 15 N label ed NH 4 + N (99 at om % 15 N as 15 NH 4 Cl) and 14 NO 2 N (as NaNO 2 ) amended to sediments (Kuypers et al., 2003). T en grams of wet sediment s w ere added to a 160 ml serum bottle and purged with N 2 for 15 minutes to create anaerobic condition s Sediments spiked with 1500 g of 15 N labeled 15 NH 4 + N (99 at% 15 N as 15 NH 4 Cl ) and 1400 g of 1 4 NO 2 N (as NaNO 2 ) using a syringe All bottles (with sediments) were incubated in a Model 25 shaker incubator (New Brunswick Scientific Co.) at 1 3 0 rpm for seven days at 24C. After incubation, the gas in the headspace was extracted into a 2 m l serum vacuum bottle The ratio s of 2 8 N 2 to 2 9 N 2 w ere measured using a isotope mass spec Costech Instrument Elemental Analyzer (Kuypers et al., 2003; Dalsgaard et al., 2005) The potential rates of anammox w ere calculated by the difference in the ratio s of 28 N 2 to 29 N 2 between natural abundance and samples. The ratio of 15 N labeled N 2 ( 28 N 2 and 29 N 2 ) was calculated as

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57 excess above their natural abundance using the equation suggested by Hau c k et al. ( 1958 ) Potential denitrification rates were determined by an acetylene blocking method ( Tiedje 1999) T en g rams of wet sediment s w ere added to a 160 ml serum bottle and purged with N 2 for 15 minutes to create anaerobic condition s Each bottle was amended with 1400 g of N O 3 N (as KNO 3 ) per 10 g of wet sediment s and 20 ml of acetylene gas (12.5% of 1 6 0 ml serum bottle ). N 2 O was measured at pre determined time intervals up to 4.5 hours using a Shimadzu Gas Chromatograph 14 A (GC) (Tiedje 1999). Potential denitrification enzyme activity (DEA) was also measured as above. However, 1.44 mg of C 6 H 12 O 6 C (glucose) and 280 g of NO 3 N (as KNO 3 ) were added to 10 g of wet sediments for supply of sufficient carbon to denitrifiers (Tiedje, 1999). For analysis of potential DNRA rates 10 g of wet sediments w ere added to a 160 ml serum bottle and purged with N 2 for 15 minutes to create anaerobic condition s Using a syringe, 1500 g of 15 N labeled NO 3 N (99 at% 15 N as K 15 NO 3 ) per 10 g of wet sediment s w ere added to each bottle and mixed with sediments All bottles were placed in a Model 25 incubator shaker at 1 3 0 rpm (New Brunswick Scie nti fic Co. INC) for seven days at 24C. After incubation, s ediment s w ere extracted with 2 M KCl The 15 NH 4 + N in the extracted solution was converted to 15 NH 3 on an acid disk using a diffusion technique (Stark and Hart 1996). The ratio s of 15 NH 4 + N to 14 NH 4 + N on the acid disk w ere analyzed with a Thermo Finnigan MAT Delta Plus XL Mass Spectrophotometer equipped with a Costech Instrument Elemental Analyzer for flash combustion of solid material for N analysis ( Rysgaard Petersen and Rysgaard 1993).

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58 Statistical Analysis Statistical analysis was conducted using JMP version 8.0 (SAS 2007). One way analysis of variance test (ANOVA) was performed to investigate difference in biogeochemical properties and potential inorganic nitrogen transformation rates between sites. Least significant difference at the 5% confidence level was used for comparisons. All post comparisons of means w ere accomplished using a Tukey Kramer HSD test which adjusted for the overall error rates. Regression analysis was performed to determine if any relationships existed among t hese parameters using a Standard Least Square s model. Results Biogeochemical Properties The low est pH of stream sediments was observed in T2 U with an average pH of 5. 0 T he highest NO 3 N concentration was observed in stream sediments of T2 U and T2 D (p<0.05) However, there were no significant differences in NH 4 + N Ext. Org N MBN and TN concentrations among the sites (Table s 3 1 and 3 2 ). No d ifferences in Ext. Org C, MBC and TC concentrations among the sites were found in stream sediments (Tables 3 1 and 3 2 ). No significant differences between sites were observed for TC:TN and MBC:MBN ratios in stream sediments (Table 3 2 ). In riparian sediments, t he pH in T2 U (pH 4.6) was lower than that of T2 D (pH 6.2) (p<0.05) T2 U had higher NO 3 N Ext. Org C and MBC concentration s than those of T2 D (p<0.05) However, NO 3 N Ext. Org C and MBC concentrations of T2 U were not significantly different from those of T1. Ammonium and Ext. Org N concentrations were not different between sites (Tables 3 1). T2 D had the lowest concentrations of TN and TC in riparian sediments (p<0.05) (Table 3 2 ).

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59 Inorganic Nitrogen Transformations Rates To understand inorganic nitrogen transformation rates, potential ammonification, ammonium oxidation (nitrification and anammox) and nitrate reduction rates (denitrification and DNRA) were measured. The results of potential inorganic nitrogen transformation rates are sum marized in Table 3 3 When comparing potential inorganic nitrogen transformation rates between sites, potential nitrification rates in T2 U were lower than in T2 D of stream sediments (p<0.05) (Table 3 3). For riparian sediments, T2 U exhibited the lowest potential nitrification rates compared to T1 and T2 D (p<0.05). Potential denitrification rates were highest in T2 D compared to T1 and T2 U in riparian sediments (p<0.05). No differences in potential ammonification, anammox and DNRA rates were observed between s i tes (Table 3 3 ). When considering the relative importance of potential inorganic nitrogen transformation rate s in stream sediments, 5%, 1% and 11% of added NH 4 + N were aerobically oxidized to NO 3 N by nitrification in T1, T2 U and T2 D respectively Of the NH 4 + N added, 0. 07 % to 0. 08 % was anaerobically oxidized to N 2 by anammox in T1, T2 U and T2 D. Approximately 2 8 % of added NO 3 N was reduced to N 2 by denitrification in stream sediments. Of the added NO 3 N, 0. 54 % 0.23%, and 0.1 1 % wa s reduced to NH 4 + N by DNRA in stream sediments of T1, T2 U and T2 D respectively (Table 3 4). Thus, most of added NO 3 N was reduced to N 2 rather than to NH 4 + N in stream sediments. For riparian sediments, 2 5 % to 28% of added NH 4 + N was aerobically oxidi zed to NO 3 N by nitrification in T1 and T2 D. However, only 7% of added NH 4 + N was aerobically oxidized to NO 3 N by nitrification in T2 U. Approximately 0. 09 % of added NH 4 + N was anaerobically oxidized to N 2 by anammox in riparian sediments. Thus, most

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60 of added NH 4 + N was aerobically oxidized to NO 3 N rather than to N 2 in riparian sediments. In case of nitrate reduction, 6 8 % and 6 3 % of added NO 3 N was reduced to N 2 by denitrification in T1 and T2 U respectively ; however, 95 % of NO 3 N added to T2 D wa s reduced to N 2 by denitrification in riparian sediments. Between 0.5 4 % to 1 .11 % of added NO 3 N was reduced to NH 4 + N by DNRA in all sites (Table 3 4). Thus, most of added NO 3 N was reduced to N 2 rather than to NH 4 + N in riparian sediments. Relationships between Inorganic Nitrogen Transformation Rates and Biogeochemical Properties Potential nitrification rates were positively correlated with pH in stream sediments (R 2 =0. 8 3, p=0.00 01 Figure 3 2 (a)). Riparian sediments also exhibited a weak positive relationship with potential nitrification rate s and pH (R 2 =0.4 6 p= 0.0 15 Figure 3 2 (b)). Potential d enitrification rates exhibited an exponentially negative correlation with TC : T N ratio of riparian sediments (R 2 =0. 48 p=0.0 13 Figure 3 3 (a)). Ext. Org C was weakly positively correlated with potential DNRA rates in riparian sediments (R 2 =0.4, p= 0.0 3 58 Figure 3 3 (b)). Potential ammonification rates were linearly correlated with NH 4 + N concentrations in riparian sediments (R 2 =0. 58 p =0.0 065 Figure 3 4 ). Comparisons of I norganic N itrogen T ransformation Rates between S tream and R iparian S ediments In order to investigate how the location of tributary sediments affects potential inorganic nitrogen transformation rates, the rates of stream and riparian sediments were compared. The stream sediments are located in the stream water and the interface between sediments and stream water. The stream sediments contain an average of 0. 4 % of total carbon in T1, T2 U and T2 D. The riparian s ediments are located at the transitional zones between stream water and upland soils and thus receive the organic material from uplands The total carbon was 4.1 %, 4.8 % and 2 % in T1, T2 U and T2 D,

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61 respectively. Therefore, the stream and riparian sediment s can be categorized as low and high organic matter systems, respectively. R iparian sediments exhibited higher potential ammonification, nitrification, denitrification anammox and DNRA rates than those of stream sediments (p<0.05) (Tables 3 3) Discussio n Inorganic N itrogen T ransformations Ammonium added to stream and riparian sediments was primarily oxidized to NO 3 by nitrification. Also, denitrification was the dominant process involved in removal of added NO 3 N as compared to DNRA in stream and riparian sediments. Thus, nitrogen cycling in tributary sediments was mainly regulated by nitrification and denitrification rather than anammox and DNRA. Potential nitrification rates in tributary sediments were lower in T2 U than in T2 D due to low pH. Other studies also reported the inhibition of pH to nitrification rates in various ecosystems ( Srn a and Baggaley, 1975, Ward 1987; Engel et al., 1958; Wild et al., 1971; McHarness and McCarty 197 3 ; Ste Marie and Pare 199 9; Kyveryga et al., 2004). Nitrification rates in my study ranged from 0.2 to 8.8 mg N kg dry sediment 1 day 1 and were higher than reported for stream sediments in Indiana, USA ( ranging from 0.1 to 0.5 mg N kg soil 1 day 1 ) (Strauss and Lamberti, 2002) (Table 3 5) Added NH 4 + N removal through anammox was insignificant compared to nitrification in tributary sediments. Potential anammox rates in my study ranged from 0. 07 to 0. 12 mg N kg dry sediment 1 day 1 accounting for less than 0. 1 % removal from added NH 4 + N Rates in my study was lower than those reported for a tidal marsh (average of 0.36 mg N kg soil 1 day 1 ), which were account ed for less than 1% of total nitrogen production (Table 3 5) and lower than those reported for estuarine sediments

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62 of Chesapeake Bay (ranging from 2.9 mg N kg soil 1 day 1 ), accounting for 0 to 22% of total nitrogen production (Rich et al., 2008; Koop Jakobsen and Gibli 2009) (Table 3 5 ). N itrogen removal rates through anammox were significant in ocean water compared to my results; anammox accounted for 45 % to 50% of total nitrogen productions in ocean ecosystems ( Kuypers et al., 2003 and 2005). In addition, 15 N values ( 15 N=[(R sample /R air ) 1]*1000) of my samples range from 0.41 to 10 and this range could belong to minimum detection value of measurement. Thus, anammox rates of my samples can be overestimated because of low values and it needs to be caution to interpret the anammox rates in my study. Potential denitrification rates were similar to those reported for riparian wetlands, constructed wetlands, and plant associated sediments (Cl ment et al., 2002; Hill et al., 2004; Sir i ved h in and Gray, 2006; Schaller et al., 2004) (Table 3 6 ). Approximately, 26 % to 95 % of added NO 3 N was converted to N 2 suggesting the denitrification was the dominant process in tributary sediments. Potential DNRA rates in my study were lower than those reported for estuarine sediments (ranging from 3.4 to 1 7mg N kg soil 1 day 1 ) ( J rgensen, 1989) DNRA accounted for less than 1. 2 % of from added NO 3 N in my research; however, other studies exhibited higher percentage of total nitrogen production by DNRA ranging from 4 % to 75% in estuarine sediments, ocean water and river (Omnes et al., 1996; J rgensen, 1989; An and Gardner, 2002) (Table 3 6 ). Additionally, NO 3 removal through DNRA was less significant compared to denitrification in tributary sediments. Thus, denitrification plays a major role in reducing NO 3 compared to DNRA process in my research sites.

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63 Relationship between N itrogen T ransformations and Bioge ochemical Properties Potential n itrification rates exhibited a positive relationship with increasing pH in stream sediments (R 2 =0. 8 3 p=0.00 01 Figure 3 2 (a) ) Generally as pH decreases, the rate of nitrification also declines (Sharmmas, 1986) Since NH 3 (ammonia) rather than NH 4 + (ammonium) serves as an electron donor for the ammonia mono oxygenase enzyme, the ratio of NH 3 to NH 4 + can be a critical factor for determining the nitrification rate (Ward et al., 198 7 ; Suzuki et al., 1974). The ratio of NH 3 to NH 4 + is drastically reduced with decreasing pH (Ford et al., 1980) T hus at a lower pH the substrate may not be sufficient for the ammonia mono oxygenase enzyme to function (Huesemann et al., 2002). Previous studies have confirmed the inhibition of nitrification at low pH values in marine ecosystems (Srn a and Baggaley 197 3 ; Wickins 1983; Ward et al., 1987; Engel et al., 1958; Wild et al., 1971; McHarness et al., 1972) and in forests and agricultural soils (Ste Marie et al., 1999; Kyveryga et al., 2004). I n thirty six streams in the Midwestern United States, a positive relationship between nitrification rate and pH was observed, suggesting that nitrification may be inhibited at low pH (Strauss et al., 2002). In addition it was reported that nitrific ation began to slow in the pH range of 4 to 6 (Alexander 1965; Schmidt 1982) T he lowest limits for autotrophic nitrifiers have been reported to range from pH 4.0 and 4.7 in forest soils (Sahrawat 1982; De Boer et al., 1989; Persson and Wiren 1995). Thus, at pH from 4 to 6 in stream sediments, nitrification rates was strongly inhibited by pH, while after pH 6, nitrification rate could be increased. As a result, the potential nitrification rates could increase sharply after pH 6 in stream sediments. F or riparian sediments, potential nitrification rates exhibited a weaker correlation with pH than stream sediments (R 2 =0.46, p = 0.0 15 Figure 3 2 (b)) Riparian sediments contain higher NH 4 + concentrations than stream sediments ( p<0.05,

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64 Table 3 1 ). Thus, the nitrifiers in riparian sediments could have higher opportunit ies to use NH 4 + than those in stream sediments. As a result, the effect of pH inhibition on nitrification rates was less in riparian sediments than stream sediments. The factors regulating denitrification are organic carbon, NO 3 O 2 temperature and vegetation type s (Tate, 2000) Of these factors, organic carbon can be a critical factor for determining denitrification rate because organic carbon is the major sources of electron donors for denitrifiers. Previous researchers have exhibited a positive relationship between organic matter availability and denitrification rates in agricultural and forest soils (Burford and Bremner 1975; Bijay Singh et al., 1988; Boyer and Groffman 1996), riparian buffer zones (Hill et. al., 2004; Rotkin Ellman et al., 2004) and created and rest or ed wetlands (Poe et al., 2003; Teiter and Mander, 2005; S i rivedhin et al., 2006). However, the quality of organic carbon is also important for determining denitrification rates in tributary ecosystems ( Beauchamp et al., 1989 ; Hernadez and Mitsch 2007; Bachand and Horne, 2000 ) In my study, extractable organic carbon contents w ere not higher in T2 D even though it exhibited the highest denitrifi cation rate. However, the T C: T N ratio s of riparian sediments w ere negatively cor related with denitrification rates This means that the quality of organic matter could influence the amounts of carbon available to denitrifiers because T C: T N ratio is relate d to the degradation rate of organic matter. For example, research on the decomposition of crop residue has shown that plant material with a lower T C: T N ratio became an available carbon source to denitrifiers increasing denitrification rates (Beauchamp et al., 1989). However plant material with high lignin and low cellulose contents like higher T C: T N ratio s, tended to supply less available organic carbon to denitrifiers because lignin i s

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65 more resistant to decomposition than cellulose. Studies in constr ucted wetlands indicated that plant species with high cellulose contents such as Typha or mixed vegetation of macrophytes and grasses released more available carbon to denitrifi ers than did bulrush plants such as Schoenoplectus tabernaemontani or Scirpus species with relatively high lignin and low cellulose contents leading to higher rates of denitrification (Hernadez and Mitsch 2007; Bachand and Horne, 2000). Schipper et al. (1994) also reported that the addition of watercress and fresh pine needles led to an increase in denitrification rate of up to five times higher than the addition of senescent pine needles in riparian soils, emphasizing the importance of organic carbon quality rather than carbon quantity. Bremner and Shaw (1958) suggested that T C: T N ratio of 3:1 for labile organic carbon sources optimized denitrification whereas T C: T N ratio of 70:1 was required for more ligneous organic carbon sources. A negative relationship between T C: T N ratio and denitrification rates can be also explained by increased immobilization. Previous research using 15 NH 4 + N and 15 NO 3 N exhibited that microbes immobilized both NO 3 and NH 4 + even though NH 4 + uptake was greater (Vitousek and Matson, 1988; Fenn et al., 1998). Under high T C: T N ratio conditions, micro bes prefer to immobilize inorganic nitrogen rather than mineralize organic nitrogen to NH 4 + (Chapin et al, 2002). Thus, high T C: T N ratio in tributary sediments could lead to decrease in denitrification by removing NO 3 through immobilization. Previous rese arch also reported that higher T C: T N ratio of substrates and additions of carbon sources enhanced immobilization of NO 3 resulting in decreased denitrification ( Craswell 1978; Rivera Monroy and Twilley 1996; G k and Ottow, 1988).

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66 DNRA regenerates reducing equivalents through the reoxidation of NADH 2 to NAD + simultaneously with oxidation of carbon substrates. Thus, it has been demonstrated that DNRA was favored by rich carbon conditions (Tiedje et al., 1988; Fazzolari et al., 1998 ; Sylvia et al., 2005 ). My research also indicated that Ext. Org C was weakly positively correlated with potential DNRA rates, implying that organic carbon content could be one of factors for determining DNRA rates in riparian sediments. However, other factors can also affect potential DNRA rates. For example, added NO 3 can be consumed by denitrification and immobilization by microbes. Thus, these other processes might affect DNRA rates, resulting in a weak correlation between potential DNRA rates and organic carbon concentrations in riparian sediments. Previous research on the DNRA process has examined coastal and estuarine sediments, demonstrating a 4% to 75 % of NO 3 reduction by DNRA ( Christensen et al., 2000; J rgensen 198 9; An and Gardner, 2002 ) In tropical forest soils, the DNRA rate (0.6 mg N kg soil 1 day 1 ) was three times greater than denitrification rate and resulted in a reduction of NO 3 availability to denitrifiers (Silver et al., 2001). I n the hyporheic zone of streams (Storey and Williams, 2004), DNRA occurred and outcompeted denitrification for consuming NO 3 as well as in the paddy soils in China (Yin et. al., 2002), a riparian wetland in New Zealand (Matheson et al., 2002) and river sediments in the UK (Takeuchi 2006). Comparison of Inorganic N itrogen T ransformation Rates between S tream and R iparian S ediments All sites demonstrated that riparian sediments exhibited higher ammonification, nitrification, denitrification anammox and DNRA rates than those of stream sediments. Riparian sediments are characterized by high input of terrestrially derived water and

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67 ground and seepage water accumulation of dissolved NO 3 and NH 4 + supply of carbon from upland soils and long hydraulic r esidence times (Hedin et al., 1998; Peterjohn 1984; Lowrance et al., 1985; Cooke and Cooper 1988; Cooper 1990; Hill et al., 1990; Mul h olland et al., 1992). Thus, riparian sediments are the hotspot for nitrogen cycling in tributary ecosystems due to larger supply of electron donors and acceptors compared to stream sediments. In contrast, for the stream sediments, the rapid flow of stream water prevents organic matter from being accumulated and shortens t he residence time of nutrients in the sediments (Jones and Holmes, 1996; Alan, 1995), resulting in lower rates of inorganic nitrogen transformation rates (Hill et al., 1996). Even though low denitrification nitrification and DNRA rates in stream sediments compared to riparian sediments previous research has suggested that hyporheic zone functioned as a inorganic nitrogen sink in stream ecosystems (Stanley and Jones, 2000) M ixing of downwelling surface water and upwelling groundwater in hyporheic zones cr eates the diffusions of inorganic nitrogen and organic carbon concentrations between stream sediments and stream water, which leads to increased denitrification rates H yporheic zones in N rich agricultural stream were reported to remove inorganic nitrogen from the stream water (Jones and Holmes, 1996; Hill et al., 1996). Thus, unlike stream sediments, hyporheic zone needs to be re considered a hot spot for inorganic nitrogen cyclings, linking to hydraulic co nductivity between groundwater and surface water. Summary In the study tributar ies NO 3 and NH 4 + concentrations in stream water (ranging 2 to 7 mg l 1 for NO 3 N and 0.2 to 0.4 mg l 1 for NH 4 + N inT1, T2 U, and T2 D ; Data not shown ) exceeded the national background level (0.6 mg l 1 for NO 3 N and 0.1 mg l 1 for NH 4 + N ) However, tributary sediments can function as potential sinks for NO 3 through

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68 denitrification These r esults indicated that the relative rates of NO 3 removal through denitrification were higher than those of DNRA in these tributary sediments. Also, the NH 4 + removal rates through nitrification were significant compared to anammox. Thus, removal rate of inorganic nitrogen in tributary sediments were main ly regulated by nitrification and denitrification rather than anammox and DNRA. Potential n itrification rates in tributary sediments were lower w ith lower pH likely due to the decreased ratio of NH 3 to NH 4 + under acidic conditions. Potential d enitrification rate s w ere lower with higher TC:TN ratios in riparian sediments implying that the quality of organic carbon can be one of the major factors controlling denitrification rates in tributary sediments. Also, sediments with higher T C: T N ratios could enhance immobilization of NO 3 resulting in decreased denitrification. In addition, potential DNRA rates were regulated by organic carbon contents in riparian sediments, implying that high organic carbon contents could enhance DNRA rates Thus, when we add organic carbon to soils for removing NO 3 through denitrification, we have to consider the in fluence of organic carbon on immobilization carbon quality and DNRA rates in the systems.

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69 Table 3 1 Summary of the pH, nitrate N (NO 3 N), ammonium N (NH 4 + N), extractable organic nitrogen ( Ext. Org N) m icrobial biomass nitrogen (MBN), extractable org anic carbon ( Ext. Org C), and m icrobial biomass carbon (MBC) of tributary sediments (n= 4, p<0.05 Characters not labeled by same l e tter are significantly different at 95% confidence level ). pH NO 3 N NH 4 + N Ext. Org N MBN Ext. Org C MBC (pH) (mgkg dry sediment 1 ) Stream sediments T1 5.4 ( 0. 4 ) 0.7 ( 0. 3 ) b 5.9 ( 0. 7 ) 8 ( 2 ) 1 9 ( 7 ) 58 ( 9 ) 308 ( 91 ) T2 U 5. 0 ( 0. 1 ) 1.6 ( 0. 3 ) a 5.2 ( 0. 7 ) 6 ( 3 ) 1 3 ( 7 ) 63 ( 28 ) 329 ( 88 ) T2 D 5.8 ( 0. 3 ) 1.6 ( 0. 2 ) a 5.3 ( 0. 9 ) 7 ( 1 ) 2 4 ( 7 ) 51 ( 5 ) 334 ( 2 9 ) Riparian sediments T1 5. 4 ( 0. 4 ) a b 0.9 ( 0. 4 ) a b 15 ( 4 ) 1 3 ( 7 ) 28 ( 9 ) b 95 ( 24 ) ab 812 ( 325 ) ab T2 U 4.6 ( 0. 4 ) b 1.4 ( 0. 5 ) a 13 ( 3 ) 2 5 ( 12 ) 9 1 ( 14 ) a 133 ( 14 ) a 1184( 184 ) a T2 D 6.2 ( 0. 1 ) a 0.5 ( 0. 1 ) b 19 ( 5 ) 7 ( 2 ) 3 8 ( 20 ) a b 84 ( 24 ) b 472 ( 64 ) b

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70 Table 3 2 Summary of the total nitrogen (TN), total carbon (TC), ratio of carbon to nitrogen (TC:TN), and ratio of microbial biomass carbon to microbial biomass nitrogen ( MBC:MBN) of tributary sediments (n= 4 for each site Characters not labeled by same l e tter are significantly different at 95% confidence level ). TN(gkg 1 ) TC(gkg 1 ) TC:TN MBC:MBN Stream sediments Riparian sediments Stream sediments Riparian sediments Stream sediment Riparian sediments Stream sediments Riparian sediments T1 0. 3 ( 0. 2 ) 2.0 ( 0. 4 ) a 3 ( 1.2 ) 41 ( 7 ) a 14 ( 2 ) 20 ( 1 ) 3 5 ( 19 ) 2 8 ( 18 ) T2 U 0.4 ( 0. 2 ) 2. 6 ( 0. 5 ) a 4 ( 1.4 ) 48 ( 10 ) a 12 ( 2 ) 1 8 ( 1 ) 23 ( 7 ) 1 4 ( 2 ) T2 D 0.4 ( 0. 2 ) 1.2 ( 0. 3 ) b 5 ( 1.5 ) 20 ( 3 ) b 1 7 ( 5 ) 1 7 ( 2 ) 21 ( 9 ) 1 9 ( 4 )

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71 Table 3 3 Summary of the potential ammonification (PA), nitrification (PN), anammox (PAn), denitrification (PD), denitrification enzyme activity (DEA), and DNRA (DNRA) rates of the tributar y sediments (n= 4 for each site, is the not detected value Characters not labeled by same l e tter are significantly different at 95% confidence level ). Sediments Sites PA PN P An P D DEA DNRA (mg N kg dry sediment 1 day 1 ) Stream sediments T1 0.02 ( 0.0 1 ) 1. 2 ( 0. 4 ) ab 0. 08 ( 0. 0 1 ) 1. 5 ( 0. 2 ) 10 ( 8 ) 0. 1 3 ( 0. 1 ) T2 U 0.2 ( 0.0 4 ) b 0. 07 ( 0. 02 ) 1 ( 0. 3 ) 3 ( 2 ) 0. 06 ( 0. 04 ) T2 D 0. 1 ( 0. 1 ) 2.6 ( 0. 7 ) a 0. 09 ( 0) 5.7 ( 4 ) 1 6 ( 11 ) 0. 03 ( 0. 03 ) Riparian sediments T1 0.9 ( 0. 4 ) 7.5 ( 1 ) a 0. 11 ( 0. 03 ) 1 5 ( 2 ) b 68 ( 49 ) 0.3 3 ( 0. 17 ) T2 U 1. 1 (0. 4 ) 2 .4 ( 0. 9 ) b 0. 12 ( 0. 0 1 ) 1 4 ( 1 ) b 96 ( 8 2 ) 0.2 2 ( 0. 09 ) T2 D 3. 7 ( 1.7 ) 8.8 ( 1.2 ) a 0. 1 ( 0. 03 ) 39 ( 12 ) a 65 ( 2 4 ) 0.2 8 ( 0. 09 )

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72 Table 3 4 Summary of the relative percentage of nitrogen retained in each inorganic nitrogen transformations from the NH 4 + N or NO 3 N applied in tributary sediments of the T1, T2 U and T2 D systems (Additions of 1400 g of NH 4 + N to 10g wet sediments for nitrification, 1500 g of NH 4 + N to 10g wet sediments for anammox, and 1500 g of NO 3 N to 10g wet sediments for denitrification and DNRA) Sediments Sites Nitrification Anammox Denitrification DNRA (% of added NH 4 + N) (% of added NO 3 N) Stream sediments T1 5 0. 08 2 6 0.5 4 T2 U 1 0. 07 2 7 0. 23 T2 D 11 0. 08 30 0.1 1 Riparian sediments T1 25 0. 09 68 1.1 1 T2 U 7 0. 08 63 0. 5 4 T2 D 2 8 0. 1 95 0. 89

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73 Table 3 5 Literature review for ammonium oxidation rates in various ecosystems. Type Location Rates (mg N kg soil 1 day 1 ) % of total N production Reference Nitrification Lake sediments Aarhus, Denmark 0.001 0.73 (g N m 2 day 1 ) J ensen et al., 1993 River Colorado USA 0.01 0.29 (mg N L 1 day 1 ) S jodin et al., 1997 Lake sediments New York, USA 0.37 (g N m 2 day 1 ) Pauer & Auer, 2000 Stream sediments Ontario Canada 0 180 (mg N m 2 day 1 ) W a yer, 1988 Stream sediments Indiana, USA 0.1 0.5 Strauss & Lamberti, 2002 Forest soils Vermont, USA 8 Kaur et al., 2010 Tributary sediments Santa Fe River, FL 0.2 8.8 1 2 8 % My study Anammox Marine sediments Skagerrak, Norw ay Long Island Sound, USA 0.18 1.2 (mg N L 1 day 1 ) 4 79% Engstrm et al., 2005 Estuarine sediments Chesapeake Bay, USA 2.9 0 22% Rich et al., 2008 Marine sediments Greenland, Denmark 0.01 1.3 (mg N m 2 day 1 ) 1 35% Rysgaard et al., 2004 Lake Tanganyika 3.36 ( g N m 2 day 1 ) 13 % Schubert et al., 2006 Tidal marsh New England, USA 0.36 < 1 % Koop Jakobsen & Gibli 2009 Tributary sediments Santa Fe River, FL 0.0 7 0. 12 < 0. 1 % My study

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74 Table 3 6 Literature review for nitrate reduction rates in various ecosystems. Type Location Rates (mg N kg soil 1 day 1 ) % of total N production Reference Denitrification Riparian sediments River Morand, Swiss 0.1 0.9 Cosandey et al., 200 3 Constructed wetland Illinois, USA 1 22 Sirivedhin & Gray, 2004 Riparian sediments Toronto Canada 2 30 Hill et al., 200 2 Riparian wetlands Brittany, France 1 8.6 Cl ment et al., 2002 Forest wetland Northeast, Spain 1 3 Bernal et al., 2007 Plant associated sediments Illinois, USA 29 Schaller et al., 2004 R iparian wetland Maryland, USA 0.96 1.2 Vidon 2004 Tidal marsh New England, USA 3 10 Koop Jakobsen & Gibli 2009 Tributary sediments Santa Fe River, FL 1 39 2 6 95 % My result DNRA River France 9.8 35 ( g N L 1 day 1 ) 12 33% Omnes et al., 1996 Riparian sediments Denmark 0.008 0.12 ( g N m 2 day 1 ) Revsbech et al., 2005 Estuarine sediments Denmark 105 (mg N m 2 day 1 ) Christensen et al., 2000 Estuarine sediments Denmark 3.4 17 4 21% Jrgensen 1989 Sea water Texas, USA 18 25 (mg N m 2 day 1 ) 15 75% An & Gardner, 2002 Tropical forest soils P uerto Rica, USA 0.6 75% Silver et al., 2001 Tributary sediments Santa Fe River, FL 0. 03 0. 33 < 1 .2 % My result

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75 (a) (b) Figure 3 1. Tributary 1 (T1) and T ributary 2 (T2 U and T2 D) in the Boston Farm Santa Fe Ranch Beef Unit Research Center (SFRBU) (a) and stream and riparian sediments (b) of the Santa Fe River W atershed, northern Alachua County, FL.

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76 (a) (b) Figure 3 2 Relationships between pH and potential nitrification rates in stream sediments (a) and riparian sediments (b) (n=12).

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77 (a) (b) Figure 3 3 Relationships between TC:TN ratio and potential denitrification rates (a) and extractable organic carbon (Ext. Org C) concentrations and potential DNRA rates (b) in riparian sediments (n=12).

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78 Figure 3 4 Relationships between ammonium concentrations and potential ammonification rates i n riparian sediments (n=12).

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79 CHAPTER 4 RELATIONSHIP BETWEEN EXTRACELLULAR ENZYME ACTIVITY AND DENITRIFICATION RATES IN TRIBUTARY SEDIMENTS Organic carbon availability is one of the most important factors regulating the denitrification rate in sediments. For example, a low quality of organic matter typically may be high in lignin and low in cellulose contents, exhibits a low rate of organic m atter decomposition, supplying a poor source of carbon for denitrifiers. However, previous research mainly focused on the effect of organic carbon additions on denitrification rates ( DeLaune et al. 1996; Davidson and Stahl 2000 ; Kozub and Liehr 1999 ; Ge rsberg et al., 1984 ; Ingersoll and Baker 1998 ; Ragab et al. 1994) Organic matter decomposition is associated with the activities of various extracellular enzyme produced by microbes (Weiss et al., 1991) Of various regulators affecting extracellular enzyme activities, including temperature, pH, moisture content, and availability of nutrient s (King 1986; Chamier and Dixon, 1982; Dilly a and Munch, 1996; Sinsabaugh et al., 1992 and 1993) the quality of org anic material is among the most important (Nannipieri et al., 2002) For example, phenol oxidase is associated with decomposition of lignin and is produced by microbes and fungi and c ellobiohydrolase and glycosidase are associated with degradation of cell ulose (Nannipieri et al., 2002) Therefore, phenol oxidase and cellobiohydrolase enzyme activities can represent decomposition rates of lignin and cellulose components in soil organic matter and litter (Sinsabaugh 1993 and 2002 ). Soil organic matter is comprised of plant derived complex polymers such as cellulose and lignin (Benner et al. 1984) Plant litter decomposition (expressed as mass loss from decaying litter ) was found to be highly correlated with the a ctivities of extracellul ar enzymes involved in lignocellulose degradation (Sinsabaugh et al., 1991

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80 and 1993) In case of lignin degradation, a negative correlation between phenol oxidase and lignin content s was observed in w etland soils and river ine sediments (Freeman et al., 2004 ; Sinsabaugh and Linkins, 1989). Decreased phenol oxidase activity due to a high lignin content leads to the accumulation of phenolic compounds in soils (McLatchey and Reddy, 1998) This accumulation of phenolic compounds inhibit other extracellular e nzyme activities such as glycosidase, chitinase, phosphatase and sul f atase, resulting in the retardation of organic matter decomposition and the accumulation of recalcitrant organic matter in soils (Freeman et al., 2004; Wetzel 1992; Appel 1993). Ther efore, phenol oxidase activities are linked with decomposition rate s and used for estimating carbon availability for heterotrophic microbes in soil s (Carreiro et al., 2000). E xcessive input of nitrogen increase s urease, acid phosphatase, glycosidase and N acetyl D gl u cosaminase activities; however, high nitrogen input decreases phenol oxidase activities in organic soil s (Saiya Cork et al., 2002). It was observed that addition of nitrogen stimulated c ellobiohydrolase activit ies in litters of dogwood and red maple However, phenol oxidase activities substantially declined up on nitrogen addition to oak litter with high lignin content (Carreiro et al., 2000). Therefore, these variations of extracellular enzyme activities can determine the rate s of decomposition of complex polymers In turn, this will influence the amount of labile carbon sources available to heterotrophic microbes. The tributaries of the Santa Fe R iver Florida receive litter and organic matter from surrounding vegetation and t he agricultural activities in the watershed. Thus, it can be expected that the different extracellular enzyme activities associated with litter and sediments would affect the amount of labile organic carbon This will a ffect

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81 denitrification rates in the Sa nta Fe R iver tributary sediments via a supply of available carbon to denitrifiers Therefore, I have conducted a study to determine the relationship between extracellular enzyme activities and denitrification rates in tributary sediments. Specific objecti ves were to: Investigate the relationship between extracellular enzyme activities and biogeochemical properties in tributary sediments. Investigate the relationship between extracellular enzyme activities and potential denitrification rates in tributary s ediments. Materials and Methods Site Description The site for this research is tributary sediments (stream sediments and riparian sediments) at Boston Farm Santa Fe Ranch Beef Unit Research Center (SFBRU) in the Santa Fe River Watershed, northern Alachua County FL The Tributary 1 ( T1 ) is bordered with a pasture ecosystem that is vegetated with grass es and forest The up stream region of T ributary 2 (T2 U ) is affec ted by N fertilization and is sur rounded by hard wood species including Carya sp., Quercus sp., and Magnolia g randiflora sp. and soft wood including Pinus sp. The downstream region of Tributary 2 ( T2 D ) is affected by N fertilization from the headwater and bordered with a pasture that is covered with grass including Saururus cernuus sp., and Juncus sp., and deciduous shrub plants including Cephalanthus occidentalis sp. (Frisbee 2007). Sampling Samples were collected to a depth of 3 cm with a PVC core (diameter 7.5cm) at T1 T2 U and T2 D Surface sediments (3 cm dept of sediments) were collected because surface sediments likely have much higher microbial activities compared to deeper sediments. Three samples from three locations (total of 9 samples) were

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82 collected in each tributary sedime nt in October 2007, January, April and July 2008. Repeated sediment sampling (4 times) was considered as replicates for each sediment type. T he sediments were transported to the lab oratory on ice and stored at 4C until analysis Nine samples from each si te were mixed to make a single composite sample. After mixing, triplicate sediments were distributed and prepared for the analysis. All roots and litter material s were removed from the sediment prior to analysis. Litter from three locations at each site was also collected by hand. The litter w as transported to the lab oratory on ice and stored at 4C until analysis All soils were removed from the litter by hands. Litter was fragmented to an approximately 1cm quadrangle of samples with scissors for enzyme analysis. Analyses of B iogeochemical P roperties S ubsamples of sediments and litter were dried at 7 0 C for 3 days and ground using a ball grinder for total nitrogen (TN) and carbon (TC) analys e s. Total N (TN) and C (TC) from the dry sediment and litter sam ples were measured using a Thermo Electron Corp. Flash EA 1112 Series NC Soil Analyzer. Potential denitrification rates were analyzed by the acetylene blocking method ( Tiedje, 1999 ) T en g rams of wet sediment w ere added to a 160 ml serum bottle and purged with N 2 for 15 minutes to create anaerobic condition s Each bottle was amended with 1400 g of N O 3 N (as the form of KNO 3 ) per 10 g of wet sediment and 20 ml of acetylene gas (12.5% of 160 ml serum bottle ). N 2 O was measured at pre deter mined time intervals of up to 4.5 hours using a Shimadzu Gas Chromatography 14 A (GC) Analyses of E xtracellular E nzyme A ctivities D glucosida se a ctivity, 500 4 Methylumbelliferyl (MUF) D glucoside was used as the substrate model. One gram of wet sediment was diluted with

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83 9 ml d istilled d e ionized (DDI) water and mixed. For the litter, 1 g of chopped litter was mixed with 9 ml DDI water and mixed using a vortex mixer Two hundred l of suspension from the mixture w ere transferred in to 96 well microplates and 50 substrate w ere added to each well. The 96 well microplate was incubated in the dark at room temperature for three hours. A Bio Tek FL600 fluorometric plate reader measured the fluorescence of samples every 30 min utes fo r three hours using an excitation wavelength of 360 nm and an emission wavelength of 460 nm Enzyme activities are expresse d as m M substrate k g dry sediment 1 hr 1 and m M substrate k g litter 1 hr 1 (Hoppe 1993). For assay of cellobio h ydrolase activities 4 n itrophenyl D cellobioside was used as the model substrate. One g ram of wet sediment was diluted with 9 ml DDI water and mixed with a vortex mixer For litter, 1 g of chopped litter was mixed with 9 ml DDI water and mixed. One ml of homogenate was transferred to 2 ml amber centrifuge tube s, and 1 ml of substrate was added t o samples. After the sample was incubated for one day at 24C, the tubes were centrifuged for 1 min ute at 10,000 rpm. Five hundred l of suspendants were transferred to a new tube Fifty l of 1N NaOH and 2 ml of DDI water were added to samples to terminate the reaction After mixing samples using a vortex mixer the absorbance was measured at a wavelength of 410 nm using a Shimadzu UV160 Visible Recoding Spectrophotometer. Enzyme activities were expresse d as m M substrate k g dry sediment 1 hr 1 for sediment samples and m M substrate k g litter 1 hr 1 for litter samples ( Linkins et al. 1990) For analysis of phenol oxidase activity, the model substrate L dihydroxy phenylalanine (L DOPA) was used. One gram of wet sediment or one gram of chopped

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84 litter was added t o 9 ml of 10 mM L DOPA ( S igma) solution. S ample s w ere incubated for 15 min ute at 24C After incubation, samples were centrifuged for 10 min ute at 6000 rpm and supernatants w ere filtered through #45 W hatman paper and trans ferr ed to new tube s The absorbance was measured at 460 nm using a Shimadzu UV160 Visible Recoding Spectrophoto meter. Enzyme activities were expresse d as M diqc ( 2 carboxy 2, 3 DI hydroindole 5,6 Q uinone Compound from the enzym at ic oxidation of L DOPA) produced k g dry sediment 1 day 1 and M diqc produced k g l itter 1 day 1 (Pind et al. 1994). Statistical Analysis Statistical analys i s w as conducted using JMP version 8.0 (SAS 2007). One way analysis of variance (ANOVA) was determin ed to investigate difference s in extracellular enzyme and denitrification activit ies between sites. Least significant difference at the 5% confidence level was used for comparisons. All post comparisons of means was accomplished using a Tukey Kramer HSD test that protects the overall error rates. Regression analysis was performed to determine if any relationships existed among these parame ters using a Standard Least Square model. Canonical analysis was used to investigate the interrelationship among sets of extracellular enzyme and denitrification activities across the sites. Results Extracellular E nzyme A ctivities Cellobiohydrolase (CBH) activities in litter were highest in T2 D as compared to T1 and T2 U. CBH activities in riparian sediments were higher in T2 D than in T1 (Figure 4 1(a), p<0.05). However, CBH activities in stream sediments were not different from each site (Figure 4 1(a)). D glucosida se activities (Glu) were not significantly different

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85 between sites (Figure 4 1(b)). Phenolic oxidase activities (PO) in stream and riparian sediments were highest in T1 compared to T2 U ( Figure 4 1(c), p<0.05). However, no significant differences in PO activities were observed in litter between sites (Figure 4 1(c)). Extracellular enzyme activities ( EEA ) including the C BH Glu and PO activit ies w ere significantly higher in litter than stream and riparian sediments (p<0.05, Figure 4 1) Across all sites, EEA were found to be higher in riparian sediments than in stream sediments (p<0.05, Figure 4 1) The EEA based on the amount of total carbon ( T C) were calculated to test the carbon efficiency for EEA (Table 4 1) T2 D exhibited the high est C BH activit ies in riparian sediments and litter (p<0.05, Table 4 1) However, there w ere no difference s in Glu and PO activities between T1, T2 U, and T2 D sites in stream and riparian sediments and litter (Table 4 1). Correlation between E xtracellular E nzyme and Potential Denitrification Rates Potential denitrification rates of tributary sediments were positively correlated with CBH activities of tributary sediments (R 2 =0.86, p=0.00 8 3 Figure 4 2 (a)) In addition, the TC:TN ratio of litter was weakly negatively correlated with CBH activity of litter (R 2 =0.4 p=0.0 32 Figure 4 2 (b)). I performed a canonical analysis with EEA of litter and riparian sediments, and potential denitrification rates of ripar ian sediments Canonical correlation analysis is a multivariate statistical model that uses the interrelationships among sets of multiple dependent variables and multiple independent variables. Whereas regression analysis predicts a single dependent variab le from a s ingle independent variable, canonical correlation can predict multiple dependent variables from multiple independent variables (Hair 1998). Therefore, this analysis can present how the relationship between EEA

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86 and potential denitrification rate was linked with site variations. R esult s showed that T2 D was separated from T2 U and T1. This separation was determined by the interrelationships based on EEA and potential denitrification rates (Figure 4 3 ). In addition, T2 U showed a weak positive relationship between Glu and CBH activities in riparian sediments while T2 D exhibited a strong relationship between potential denitrification rates in riparian sediments and CBH activities of litter. Discussion E xtracellular E nzyme A ctivities To describe the relationship between EEA and potential denitrification rates in tributary sediments, CBH Glu and PO enzyme activities were measured in the stream and riparian sediments and litter. My results demonstrated that T2 D exhibited higher CBH activities in litter than those of T1 and T2 U. Also, CBH activities in riparian sediments of T2 D w ere higher than those of T1. C ellulose degradation is a major process in the supply of available carbon to heterotrophic microbes (Sinsabaugh 1 991). Cellobiohydrolase (CBH) is an enzyme that breaks down cellulose into cellobi o se glycosidase (Glu) is an enzyme that hydrolyses cellobi o se into glucose ( L a w r e nce 2000) CBH activity can be affected by substrate quality. For example, CBH is neg atively correlated with the lignin content of litter (Keeler et al., 2009). Linkins et al. (1990) observed that heavily lignified litter such as oak exhibited a lower CBH activity than the less lignified litter such as dog wood. In addition, Hidaka (1984) showed that CBH activity of the fungus Trichoderma viride was significantly reduced by lignin extracted from soft and hard wood s. In general, litter with a low TC:TN ratio tends to have less lignified materials, even though the TC:TN ratio of litter does n ot precisely represent the amount of lignin ( Fogel and Cromack 1977 ;

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87 Herman et al. 1977 ; Meentemeyer 1978 ; Melillo et al. 1982). In my study, the litter of T2 D exhibited a lower TC:TN ratio (29) t han th ose of T1 (31) and T2 U ( 39 ) and a weak negative relationship between CBH activities and TC:TN ratio in litter was observed. Therefore, a lower TC:TN ratio of litter could support increased CBH activities in litter of T2 D In addition, CBH activities based on total carbon of litter were highest in T2 D. This implies that T2 D had a higher efficiency of enzyme activities for decomposition of litter. However, other factors including moisture contents, litter chemistry (i.e. ratio of lignin to nitrogen contents), and microbial community associated with litter decomposition, could also affect degradation rates of litter (Sinsabaugh et al., 1991, 1992 and 1993) Thus, these factors could result in a weak correlation between TC:TN ratio and CBH activities in litter. Relationships between E xtracellular E nzyme and Potential Denitrification Rates EEA of soils and sediments is commonly used as an index of organic matter decomposition rate s (Sinsabaugh et al., 2005; Asmar et al., 1994) The EEA is well correlated with the loss of litter and the amount of soil nutrients such C, N, and P ( Sinsabaugh et al., 19 91 1992 and 1993 ) Therefore, systems exhibiting high CBH activities are favorable to microbes using organic carbon as an energy source Even though T2 D contains less organic matter, the sediments and litter of this system showed high CBH and high carbon use efficiency for denitrification (Chapter 3) Therefore, it can be expected that the differ ing CBH activities might drive the rate s o f d ecomposition of litter and sediments which might influence the level of labile carbon available for denitrification To investigate the relationship between EEA and denitrification rates I performed regression and canonical analys e s using EEA of litter and sediments to predict potential

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88 denitrification rate of sediments. The results indicated that potential denitrification rates of sediments w ere positively related with CBH activities of tributary sediments. Also, it was found that T 2 D was associated with potential denitrification rates and CBH activities of litter. Thus, the litter of the T2 D system might be more easily decomposed due to the higher CBH activity of litter This will result in an increased quality of carbon availabl e to d enitrifiers, enhancing their rates Previous research has shown that vegetation type with high quality substrate s such as meadows (Rich et al. 2003), submerged plant ( E. canadensis sp.) (Bastviken et al. 2004), grassland s (Lowrance et al. 1995), or emergent macrophyte s (Hernadez and Mitsch 2007) had higher soil denitrification rate s than areas affect ed by plant species with a high TC:TN ratio such as woody forests. Volokita (1996) and Nakajima Kambe (2005) demonstrated that the addition of cellulose increased denitrification rates with isolated denitrifying microorganisms utilizing the cellulose as an energy source. In addition, Rich and colle agues (2003) reported that meadow soils with a lower TC:TN ratio harbored various groups of denitrifier assemblages and exhibited higher denitrification rates than f orest soils with higher TC:TN ratio s They concluded that the carbon quality of soils infl uenced the biogeochemical properties of soil, such as the degradation rate and level of available organic carbon. In turn, these changes can affect the denitrifier community structure and their rates (Rich et al., 2003) Therefore, in my research sites, it can be expected that the difference in carbon quality of litter and organic matter in sediments could change the labile carbon availability in the system via differences in EEA which can influence potential denitrification rates in tributary sediments

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89 Summary EEA can be used as an index of the decomposition rate because t he EEA is well correlated with the loss of litter and organic matter, and the amount of soil nutrients such C, N, and P Also, since decay rates of litter and organic matter are associated with the level of available carbon ( Melillo et al., 1989; Howard and Howard, 1974; Swift et al., 1979) the analys e s of EEA in the sediments and litter can explain the carbon availability to denitrifiers Therefore, this research investigated th e relationship between EEA and potential denitrification rates in tributary sediments. My results showed that tributary sediments with higher CBH activities in litter exhibited higher potential denitrification rates. A positive relationship between CBH act ivities and potential denitrification rates was observed in tributary sediments (R 2 =0.86, p=0.008 3 ) Also, CBH activities of litter were weakly negatively correlated with TC:TN ratios (R 2 =0.4, p=0.0 32 ) Thus, the substrate quality representing the TC:TN ratio could affect CBH activities in litter This will determine the availability of carbon source to denitrifiers, influencing their rates Additionally, these results suggested that carbon quality affecting extracellular enzyme activities can be one of factors for determining potential denitrification rates in tributary sediments.

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90 Table 4 1 Extracellular enzyme activities based on total carbon contents (TC) in tributary sediments (CBH is cellobiohydrolase ; D glucosidase ; and PO is phenol oxidase activities ). Characters not labeled by same l e tter are significantly different at 95% confidence level (n=4). CBH Glu PO ( M substrate k g T C 1 hr 1 ) ( M diqc produced k g T C 1 day 1 ) Types Sites Ave Ave Ave Stream sediments T1 2 20 ( 1 38 ) 0. 36 ( 0. 21 ) 649 ( 305 ) T2 U 77 ( 3 4 ) 0.22 ( 0.1 3 ) 42 ( 11) T2 D 1 39 ( 8 5 ) 0.3 4 ( 0.2) 4 5 ( 1 6 ) Riparian sediments T1 1 09 ( 25) b 1. 65 ( 0. 25 ) 5 2 ( 2 5 ) T2 U 134 ( 26) b 1.77 ( 0. 39 ) 14 ( 2) T2 D 4 12 ( 8 8 ) a 2.11 ( 0. 66 ) 7 9 ( 2 7 ) Litter T1 1 2 ( 4 ) b 0.62 ( 0. 18 ) 13 ( 1 ) T2 U 50 ( 23 ) b 1.71 ( 0. 82 ) 1 8 ( 4) T2 D 146 ( 4 1 ) a 1.77 ( 0.66 ) 36 ( 1 9 )

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91 (a) (b) Figure 4 D glucosidase (Glu) (b), phenol oxidase (PO) (c) activities in stream and riparian sediments and litter (Characters not labeled by same letter are significantly different at 5% confidence level n= 4 ) The unit of CBH and Glu is m M substrate k g dry sediment 1 hr 1 and m M substrate k g litter 1 hr 1 The unit of PO is M diqc produced k g dry sediment 1 day 1 and M diqc produced k g litter 1 day 1

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92 (c) Figure 4 1 Continued

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93 (a) (b) Figure 4 2. R elationships between potential denitrification rates (PD) and cellobiohydrolase enzyme (CBH) activit ies of tributary sediments (Open circle=rates in stream sediments; Dark circle= rate in riparian sediments, Each data point is the average of 4 samples, n=24) (a) and TC:TN ratio and cellobiohydrolase enzyme (CBH) activity of litter (b) (n=12).

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94 Figure 4 3 C anonical plot of extracellular enzyme activities and potential denitrification rates of riparian sediments and litter CBH_litte r PO_litter

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95 CHAPTER 5 RELATIONSHIPS BETWEEN BIOGEOCHEMICAL PROPERTIES, DENITRIFICATION, AND ASSOCIATED MICROBIAL AS S EMBLAGE COMPOSITIONS IN TRIBUTARY SEDIMENTS Human activities such as fertilizer application and ranch activities can alter the physical and chemical properties of ecosystems in numerous ways, including chang ing pH ( Kowalenko et al., 1978) and the availabilities of carbon and nitrogen which i n turn can regulate biogeochemical process es ( Burkart, and James, 1999; Kohl et al., 1971; Lefebvre et al., 2007 ; Sanchez, 2001 and 2004 ; Christensen, 1992; Angers and N 1991; Gerzabeck et al., 2001 ) These changes to biogeochemical properties can also change the structures and functions of microbial communities ( Polymenakou et al., 2005; Torsvik and vres 2002; Balser and Firestone 2005) For example, the amount of availabl e carbon and nitrogen may affect the composition of the denitrifying assemblage in soils Therefore, the organic matter and nitrogen content s affect not only the pathway of inorganic nitrogen transformations but also the structures of microbial communitie s responsible for these processes ( Henderson et al., 2010; Cao et al., 2008; Mills et al., 2008; Hunter et al., 2006; Santoro et al., 2006; Rich et al., 2003; Priem et al., 2002) M ost previous studies have focused on biogeochemical processes; however, much attention is currently being paid to the structures of microbial communit ies impacted by human activity. L ittle is known about the relationship between microbial function and structure responsible for nitrogen cycles. Research on how microbial communit y structure relate s to inorganic nitrogen transformations is needed to better understand the impact of human activities on ecosystem function. Most molecular ecological studies on denitrif ier assemblages have focused on functional ge nes such as those that encode either nitrite reductase ( nirK and nirS ) or

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96 nitr ous oxide reductase ( nosZ ). Nitrite reductase converts nitrite to nitric oxide and may be encoded by two distinct metalloenzymes : nirK with a copper center ; and nirS with a heme based cytrochrome cd (Bothe et al., 2000). nirK and nirS are functional ly equivalent ; however, nir S is more widely distributed within A rchaea and B acteria than nir K (Bothe et al., 2000). nirK has been found to dominate in costal and marsh ecos ystems, while nirS dominates in a broad range of environments ( Braker et al., 2000). nirS clone libraries have been shown to exhibit higher diversities than th ose of nirK clone libraries from wetland soils ( Priem et al., 2002), ground waters (Yan et al., 2001; Santoro et al., 2006), and ocean sediments (Liu et al., 2003). Also it was reported that nirS denitrifier communities responded differently than nirK denitrifier communities to environmental gradients, including ammonium and nitrate concentrations, and salinity in various ecosystems ( Jones and Hallin, 2010). Wolsing and Priem (2004) showed that sites treated with mineral fertilizers or dairy manure h arbored different assemblages defined by nirK and nirS clone libraries. Thus, this study investigated how nirS and nirK genotypes are distributed according to various biogeochemical properties and explored the relationships between denitrification rates and diversity of nirS and nirK clone libraries in tributary sediment s of the Santa Fe R iver. Materials and M ethods Site D escription The site for this research is tributary sediments (stream sediments and riparian sediments) at the Boston Farm Santa Fe Ranch Beef Unit Research Center (SFBRU) in the Santa Fe River Watershed, northern Alachua County FL Land uses on this site include a low intensity cattle operation with about 300 heifers on 1,600 acres and the nursery operation using nitrogen fertilizer (Holly F actory Nursery) (Frisbe e, 2007)

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97 Tributary 1 system (T1) is affec ted by a pasture ecosystem vegetated with grass and trees, while upstream region of Tributary 2 ( T2 U ) is affected by N fertilization, and both hard wood ( Carya sp., Quercus sp., and Magnolia g randiflora sp) and soft wood including Pinus sp. The d ownstream region of T ributary 2 ( T2 D ) is affected by N fertilization from the headwater and a pasture ecosystem, and is covered with grass including Saururus cernuus sp., and Juncus sp., and deciduous shrub plants in cluding Cephalanthus occidentalis sp. (Frisbee 2007). Sampling Samples were collected to a depth of 3 cm with a PVC core (diameter 7.5cm) from three sites Surface sediments (3 cm dept of sediments) were collected because surface sediments likely have mu ch higher microbial activities compared to deeper sediments. Three samples from three places (total 9 samples) were collected from each tributary for January and July 200 8 T he s amples were transported to the lab oratory on ice. Nine samples from each site were mixed to make a single composite sample. After mixing, triplicate samples were taken and prepared for analysis. All roots and litter materials were removed from sediments prior to analysis. The samples were stored at 8 0C until analysis. Nucleic Acid Extraction, PCR Amplification, Cloning and Sequencing N ucleic acids were extracted from 0.25 g of sediments with the Power Soil DNA Isolation kit (MoBio, Carlsbad, CA, USA) following the instruction DNA extra cts were used as a template in Polymerase C hain R eaction (PCR) using primer sets designed by Yan et al. (200 3 ), consisting of primers TCA TGG TGC TGC CGC CKG ACG GAA CTT GCC GGT KGC CCA GAC amplify an approximately 326 b p region of nirK TCA CAC CCC GAG CCG CGC

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98 GT AGK CGT TGA ACT TKC CGG TCG G which amplify an approximately 774 bp region of nirS The a mpli fi cation reaction mixture was composed l GoTaq Green Master Mix (Promega, Madison WI), 1 l of each primer (100 pmol l 1 ), 13 l of d istilled d e ionized (DDI) water and 10 l of diluted DNA solution. An iCycler thermal cycler (BIORAD, Hercules, CA) was used for PCR amplification with the foll owing conditions: initial enzyme activation and DNA denaturation of 15 min at 95 C followed by 30 seconds at 94 C, 30 seconds at 60 C, 60 seconds extension at 72 C for 30 cycles, and a final extension of 72 C for 7min. The PCR products were analyzed by el ectrophoresis through 1.5% TAE agarose gels. For cloning, a pGEM T and pGEM T Easy Vector Systems (Promega WI) was used : the total volume of ligation reaction mixture was 1 0 l and ligation mixture contained 5 l of 2X Rapid Ligation Buffer 3 l of fresh PCR amplicons and 2 l DDI water The r eaction mix ture was ligated into pCRII TOPO cloning vector and transformed into chemically competent XL10 Gold Ultracompetent Cells ( Stratagene CA) following the protocol. Inserts within white c olonies were confirmed by PCR amplification with the same primer set and PCR protocol described earlier, and their size was confirmed by electrophoresis on 1.5% TAE agarose gels. The i nsert bearing clones were transferred to 96 well plates containing 200 l of Luria Bert a ni broth and 8% (v/v) glycerol and ml 1 ). Plates were incubated overnight at 37 C covered with gas permeable membranes (Breath easy, Diversified Biotech, USA) and total 326 clones were sent t o the University of Florida Gen ome Sequencing Service Laboratory for sequencing.

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99 Construction of Phylogenetic Tree and Diversity Analysis The number of clones sequenced from each library is presented in Table 5 1. BLAST queries (Gen Bank, http://www.ncbi.nlm.nih.gov ) were used to compare all DNA sequence s of nirS with previous studies, as well as to check sequence relevance to the target genes. ClustalX2 was used to align the sequences with related sequences and one out group species, and to check for appropriate alignment (Larkin et al., 2007) Phylogenetic trees were con structed using MEGA version 4 (Tamura et al., 2007) with a neighbor joining analysis using a Jukes Cantor method for distance estimation Bootstrap analysis (1000 re sampling) was used to estimate reproducibility o f phylogenic trees. The selected out group for phylogenetic analysis was nirN from Pseudomonas aeruginosa (D84475) ( Kawasaki et al., 1997) For community analys i s, DOTUR generated operational taxonomic units (OTUs) using a furthest neighbor algorithm I used a cutoff of 10% difference in nucleic acid sequences because most of studies designated 5 or 10% as cut off and 10% is appropriate point for comparing diversity between species ( Oakley et al, 200 6 Priem et al., 2002; Philippot et al., 2009; Kandel er et al., 2006) Based on rarefaction analysis using the numbers of clones per OTU, three indices (Shannon, Simpson, and Chao 1) were calculated for analyses of diversity (Shannon and Simpson) and richness (Chao 1) of nirS sequences (Schloss and Handelsm an, 2005 ). Among diversity indices, Chao1 index considers the richness of species (number of the species present in the system) ( Hughes et al., 2001), while the Shannon and Simpson indices take account of both richness and evenness ( a measure of the relati ve abundance of the different species making up the richness of the system) of species present in the system ( Schloss and Handelsman 200 5 ) Relative to the Shannon index, the Simpson index is more

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100 sensitive to the abundance of the most common species in t he system ( Schloss and Handelsman 200 5 ) PHYLIP was used to calculate pair wise distances between sequences (Felsenstein et al., 2004) Unifrac and Mantel Test In order to determine if denitrifier assemblages are significantly different from each other depending on environmental factors or location, Unifrac PCA (Principal component analysis) analysis was used ( Lozupone and Knight, 2005) P hylogenetic tree was constructed by Mega version 4 using a Jukes Cantor and maximum parsim ony. T o investigate the relationships between microbial assemblages and environmental properties Mantel test w as used (Mantel et al., 1967; Mantel and Valand 1970). Mantel test was performed in R Vegan package (R Development Core Team 2008 ; Oksanen et al., 20 05 ) and is based on a nonparametric general regression model using distance matrices of phylogenetic trees and environmental factors (Dutilleul et al., 2000). Results nirS Phylogen et ic Tree Attempts to amplify nirK from sediment samples failed, likely due to lower abundance of these genes. However, nirS was amplified in all samples. nirS sequences were grouped into t wo distinct phylogenetic clusters for stream sediments (A and B) (Figure 5 1) and into three distinct phylogenetic clusters (A, B and C) for riparian sediments (Figure 5 2). Genes from all clusters shared 70% to 98% similarity with sequences from previously obtained environmental clones in the Gen Bank. Cluster A was composed of three sub clusters (A 1, A 2, and A 3). Cluster A 1 consists of sequences cloned from all site samples. Clones in A 1 cluster with the Beta

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101 and Gammaproteobacteria However, Cluster A 2 is comprised only of clones from T2 D and clusters with Alpha and Betaproteobateria Cluster A 3 consists of clones from T1 and T2 U and clusters with the Gammaproteobacteria Cluster B consists of sequences from all site samples and clusters with to the Alpha and Betaproteobateria (Figure 5 1). nirS sequences from riparian sediments grouped within three clusters (A, B and C). Sub cluster A 1 was comprised of clones from all site samples, and is associated with the Beta and Gammaproteobacteria However, Cluster A 2 is only composed sequence s from T2 D Sequences of Cluster A 2 belong to the Alpha, Beta and Gammaproteobacteria Cluster B contains two sub clusters (B 1 and B 2). Sub cluster B 1 is comprised of clones from T2 U while Sub Cluster B 2 is composed of clones from T1 and T2 D, and cluster wit h the Gammaproteobacteria Cluster C consists of sequences from T2 D (Figure 5 2). Diversity Indices for nirS Assemblage Composition In my study, OTUs were defined by DOTUR using a 90% DNA sequence similarity cutoff, assuming that sequence similarities g reater than 90% represented the same species. The clone libraries from T1 and T2 U riparian sediments represented nearly all the nirS diversity when using 90% similarity of sequences as a cutoff point. However, rarefaction curves of stream sediments from a ll sites and T2 D riparian sediments did not reach a plateau at 10% cutoff. These rarefaction c urves were steeply sloped at the 10% cut off points for sequences obtained from stream sediments and T2 D riparian sediments When comparing rarefaction curves between stream and riparian sediments, libraries from stream sediments exhibited steeper nirS rarefaction curves than libraries from riparian sediments for all sites (Figure 5 3).

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102 Results of diversity analyses indicated that Shannon and Simpson indices of stream sediments showed no difference in nirS libraries between sites. However, the Chao1 index was highest in T2 D, followed by T2 U, and T1 for stream sediments. For riparian sediments, the Shannon and Chao1 were highe st in T2 D, followed by T1, and T2 U (Table 5 1). To investigate the biogeochemical factors controlling microbial diversity and richness of nirS Shannon, Simpson and Chao1 indices were correlated with biogeochemical properties. The Shannon index was negat ively correlated with extractable organic carbon (Ext. Org C) contents of tributary sediments (R 2 =0.8, p=0.017, Figure 5 4 (a)). The concentrations of microbial biomass carbon (MBC) of tributary sediments were also negatively correlated with the Shannon (R 2 = 0.99, p<0.0001, Figure 5 4 (b)) and Simpson (R 2 =0.79, p = 0.018, Figure 5 4(c)) indices In addition, a positive relationship between potential denitrification rates and richness index was observed in riparian sediments ( R 2 =0. 99 p = 0.0 06 4 Figure 5 5); however this relationship was not detected in stream sediments. Relationship between Microbial Assemblages of the nirS and Biogeochemical Properties R esults of the Unifrac PCA analysis indicated that the composition s for nirS in T2 D w ere significantly di fferent from those of T1 and T2 U in stream sediments, explaining 21.34 % of variation in assemblage composition (Figure 5 6) For riparian sediments, the microbial assemblage composition of the nirS in T 2 D was separated from th ose of T1 and T 2 U explaining 26.82 % of variation in assemblage compositions (Figure 5 6) R esults of the Mantel test demonstrated that extractable organic carbon ( Ext. Org C) (Mantel r=0. 7 p=0.0 4 7 ) and organic nitrogen (Ext. Org N) (Mantel r=0.89, p=0.009)

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103 ( Table 5 2 ) an d microbial biomass carbon (MBC) (Mantel r=0. 79 p=0.0 2 ) ( Table 5 2 ) were the main factors affecting microbial assemblage compositions for nirS i n tributary sediments. Thus the greater the difference in Ext. Org C Ext. Org N and MBC concentration s between sites, the greater difference in microbial assemblage composition for nirS Discussion nirK Denitrifiers nirK was not amplified from sediments at my study sites, while ni rS w as amplified in all site samples It is difficult to explain why nirK was not detected in my research sites because functions of nirS and nirK for nitrite reductase are equivalent and biogeochemical properties are significantly correlated with each other ( Philippot et al., 2009). However, the absence of nirK in my site sugg ests that environmental conditions might select nirS rather than nirK Previous research demonstrated that the levels of pH, nitrate, and soil moisture determined the nirS to nirK distribution ratio in grassland pasture ( Philippot et al., 2009). Also, org anic carbon concentrations affected the copy numbers of nirK while carbon, nitrate and pH did not affect the copy number of nirS in soils, indicating nirS may adapt to variations in environmental properties ( Kandeler et al., 2006). Thus, various biogeochemical properties in study sites might more exclude the nirK Furthermore, I used nirK primers designed for denitrifier in groundwater and Everglades soils ( Yan et al. 200 3; Smith, 2006) Thus, my primer set may not have detected nirK genes in tributary sediments, indicating that the nirK genes in my sites could be different from those of groundwater and Everglades soils

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104 Relationships between Microbial Diversity and Richness of nirS and B iogeochemical Properties Rarefaction curves of stream sediments from all sites and riparian sediments did not reach a plateau at the 10% cutoff. This implies that the number of clones sequenced from clone libraries did not represent the entire nirS diversity Thus, more clones sho uld be sequenced for these samples for covering the diversity of denitrifiers in my research sites. However, the number of clones sequenced in my research (from 29 to 69) was not lower than those of other research (30, 18, 50 to 75, 30 to 40) (Priem et al ., 2002; Braker et al., 2000; Santoro et al., 2006). In addition, previous research showed a steeper rarefaction curve at 5 % to 10% cutoff (Braker et al., 2000; Santoro et al., 2006; Priem et al., 2002). Thus, nirS assemblage composition might be very di verse so that numerous numbers of clones need to be sequenced for presenting diversity. However, expensive cost in analyzing sequences is also limitation for covering the diversity. Despite this limitation, rarefaction curves of riparian sediments were low er than those of stream sediments. It may not be surprising that the riparian sediments ha d a low er nirS diversity because th ey contain higher carbon contents compared to stream sediments Thus, high carbon content may likely lead to the dominant growth of a few species, resulting in less diverse group in riparian sediments compared to stream sediments. R esults from diversity analysis indicated that T2 D with highest denitrification rates also had the highest values of richness index scores in riparian sediments. Also, a negative relationship between the Shannon index and extractable organic carbon (Ext. Org C ) content s was observed This implies that higher carbon content s in tributary sediments may allow one specie s to outcompete oth ers which in turn decreases denitrifier richness and denitrification rates in the system. In contrast, more diverse

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105 groups of denitrifiers such as that of T2 D seems to be related to higher denitrification rates, even though T2 D contained lower carbon content s than to T2 U. There are two non mutually exclusive explanations linking diversity and productivity in ecology : the c omplementarity effect and sampling effect C omplementarity effect refers to the process by which species richness and diver sity enhance productivity due to niche differentiation or positive interaction s between species, resulting in a higher efficiency of resource utilization (Venail et al., 2008; Loreau et al., 2001 ). The sampling effect occurs when more diverse communities c ontain species with a higher average productivity than communities with lower diversity. In both situations, ecosystems with higher diversity have more opportunities to select prominent species, increasing productivity of the system, and increasing benefic ial interaction for all species (Venail et al., 2008; Loreau et al., 2001 ). Thus, diverse group of denitrifiers in T2 D in riparian sediments could exhibit higher denitrification rates according to complementarity and sampling effects. Sequences in T2 D ri parian sediment cluster with Pseudomonas stutzeri, known to be a widely distributed denitrifier in natural environments. P. stutzeri is characterized as having high genetic diversity and occup ies numerous ecological niches ( Lalucat et al., 2006). P. stutzeri shows great metabolic versatility, capable of growing on a wide range of organic substrates utilized by few other P seudomona s sp. (e.g., starch, maltose, and ethylene glycol) Therefore, the various metabolic capacities of these denitrifier spe cies in T2 D riparian sediments could enhance microbial diversity and increase denitrification rates despite low carbon contents compared to T2 U riparian sediments. Higher diversity indices in stream sediments compared to riparian sediments did not lead t o increased denitrification rates. This can be explained in that relatively lower

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106 carbon availability for denitrifiers could not support denitrification rates, despite of higher diversity. Relationships between Microbial Assemblages of the nirS and Biogeo chemical Properties From the PCA, nirS of T2 U was different from T2 D for riparian sediments. Mantel tests indicated that extractable organic carbon content was a significant factor determining nirS assemblage compositions. Thus, the larger difference in carbon contents between sites, the greater the difference in compositions of nirS Thus, the difference of organic carbon contents between T2 U and T2 D could be linked to the difference in denitrifier assemblage compositions, which in turn will affect de nitrification rates at each site. Microbial biomass carbon (MBC) was also found to be a main factor separating microbial assemblage compositions for nirS in my study. Higher MBC contents could imply a larger population of microbes. Thus, this may imply that the difference in population size also could affect distribution of microbial assemblage compositions for nirS in my sites. Environmental factors known to af fect denitrifier assemblage compositions are the level of salinity for nirS (Jones et al., 2010; Santoro et al., 2006), pH for nosZ (Enwall et al., 2005) and nirS (Priem et al., 2002), and the amount of organic matter for nirK and nosZ (Kandeler et al., 2 006). However, there has been no previous research on influence of carbon content on the microbial assemblage composition of nirS even though carbon is one of the main regulators for denitrification rates. This study suggests the importance of organic car bon content in distributing the nirS in tributary sediments.

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107 Summary The objective of this research was to investigate how denitrifiers coded by nirS functional genes were distributed according to various biogeochemical properties and to observe r elations hips between biogeochemical factors affecting denitrification rates and denitrifier assemblage compositions in tributary sediment s of the Santa Fe R iver. Results indicated that T2 D showed the highest diversity index of nirS clone libraries and denitrification rates in riparian sediments. In addition, the level of extractable organic carbon content was found to be one of the main regulators for diversity of nirS T2 U had the highest organic carbon contents but the lowest denitrification rate s, while T2 D had the lowest organic carbon contents but the highest denitrification rates. Generally, denitrification rates increase with available organic carbon contents in the system; however, my results do not follow this pattern Therefore, diverse assemblage compositions of nirS could influence denitrification rates via the selection of denitrifiers capable of using carbon source more efficiently In addition, my research showed that the difference in nirS assemblage compositions among sites was det ermined by the difference in organic carbon contents among sites. Therefore, the larger the difference in organic carbon levels between sites, the larger the difference of nirS assemblage composition. Additionally the higher the content of organic carbon in the system, the lower the diversity of the nirS assemblage compositions, corresponding to a decrease in denitrification rate. These results imply that the microbial assemblage compositions of nirS can affect the microbial function, and these relationshi ps can be regulated by the level of organic carbon in the systems.

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108 Table 5 1. nirS d iversity and richness in tributary sediments, estimated by the Shannon and Simpson indices, and the Chao 1 index. Sites No. of clones sequenced No. of OTUs Shannon index Simpson index Chao 1 index S tream sediments T1 60 42 3.6 (3.4, 3.8) 0.99 79 (57,136) T2 U 62 43 3.6 (3.4, 3.8) 0.98 105 (67,201) T2 D 66 45 3.6 (3.3, 3.8) 0.98 135 (72,250) ALL 188 Riparian sediments T1 40 24 3.0 (2.8, 3.3) 0.97 33 (26,56) T2 U 29 19 2.8 (2.6, 3.1) 0.97 28 (21,55) T2 D 69 43 3.5 (3.3, 3.8) 0.98 121 (81,271) ALL 138 Estimates of OTUs, the Shannon, Simpson and Chao1 indices are all based on difference of 10% or less in nucleic acid sequence alignments; values in parentheses are upper and lower bounds of 9 5 % confidence intervals as calculated by DOTUR.

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109 Table 5 2. Regression analysis of transformed data taken from distribution matri ces ; y axis represent nirS assemblages and x axis represents biogeochemical properties g enerated by the Mant e l test pH NO 3 N Ext. Org N MBN Ext. Org C MBC Mantel r 0. 39 0. 22 0.89 0. 71 0. 7 0. 79 p value 0.1 6 0. 89 0. 009 0. 18 0.0 4 7 0.0 2

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110 Figure 5 1. Neighbor joining tree of nirS sequences obtained from stream sediments of tributaries (Dark circles indicate values over 90, empty circles indicate values from 70 to 90, and empty triangle s indicate values from 50 to 70. Nodes are bootstrap scores based on percent occurrence of 1000 re samplings). Figure 5 2. Neighbor joining tree of nirS sequences obtained from riparian sediments of tributaries (Dark circles indicate values over 90, emp ty circles indicate values from 70 to 90, and empty triangle s indicate values from 50 to 70. Nodes are bootstrap scores based on percent occurrence of 1000 re samplings).

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111 (a) (b) Figure 5 3. Rarefaction curves for nirS from DOTUR analysis using furthest neighbor assignment algorithm in tributary sediments in T1 (a), T2 U (b), and T2 D (c). Similarity cut off was 90%.

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112 (c) Figure 5 3 Continued

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1 13 (a) (b) Figure 5 4. Relationships between concentrations of extractable organic carbon (Ext. Org C) and the Shannon index (a), and microbial biomass carbon (MBC) and the Shannon (b) and Simpson (c) indices for nirS in tributary sediments ( Open circle= stream sediments, Dark circle=riparian sediments, Dash ed line s are the 95% confidence intervals of the regression line )

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114 (c) Figure 5 4 Continued

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115 Figure 5 5. Relationship between p otential denitrification rates and Chao 1 ind ex in riparian sediments and stream sediments. (Open circle=stream sediments Dark circle=riparian sediments )

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116 Figure 5 6. Principal component analysis for nirS assemblage composition using Unifrac in all tributaries sediment s

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117 CHAPTER 6 RELATIONSHIPS AMONG BIOGEOCHEMICAL PROPERTIES, N ITRIFICATION AND ASSOCIATED MICROBIAL ASSEMBLAGE COMPOSITION OF T RIBUTARY SEDIMENT S Nitrification is an important process in nitrogen transformations because n itrate produced from nitrification can be used as an electron acceptor by denitrifiers ( Vanerborght a nd Bille n 1975; Vanerborght et al. 1977 ). Nitrification is a two step process: in the first step, ammonia oxidizing bacteria (AOB) oxidize ammonium to nitrite ; and in the second step, nitrite oxidizing ba cteria convert nitrite to nitrate ( Tate 2000 ). It was accepted that AOB were responsible for most nitrification in various ecosystems including acidic soils, terrestrial, and marine ecosystems ( Pedersen et al., 1999; Boer and Kowalchuk 2001; Kuai and Verstraete 1998; Barraclough and Puri 1995; Killham 19 86; Pennington and Ellis 1993; Stroo et al., 1986 ) However, the presence of Archaeal ammonia oxidizers (AOA) was recently detected in oceans (Francis et al., 2005), estuarine sediments (Be r man et al., 2006), marine sponges (Steger et al., 2008), terrestrial soils (Leininger et al., 2006) and hot springs (Zhang et al., 2 008) AOA copy numbers exceed those of AOB in these systems ( Leininger 2006; Wchter 2006; Mincer 2007; Martens Habbena 2009) suggesting that AOA account for most nitrification in a wide range of ecosystems. Thus, considerable research has focused on the role of Archaea in nitrogen cycling s in various ecosystems. One reason why AOA attract attention is their physiological characteristics. Archaea was considered to occupy niches fro m wh ich Bacteria are excluded, such as low pH and high salinities ( Weidler et al., 2007 ; Zhang et al., 2 008 ; Pearson et al., 2004 ). The discovery of AOA in diverse ecosystems suggests that nitrification may be significant in a wide range of ecosystem types than previously recognized (Francis et al., 2007; Karl 2007; Nicol and Schleper

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118 2006). However, little is known about the relationships among microbial diversity of AOA, nitrification rates, and environmental characteristic s in tributary ecosystems, despite observations of their presence in various soils. Thus, this research investigated if AOB and AOA contribute to the nitrification rates in tributary sediments, and the relationship between nitrifier diversity, nitrificatio n rates, and biogeochemical properties in tributary sediments. I hypothesized that v ariations in biogeochemical properties influence the microbial structure and assemblage compositions of nitrifiers. Materials and Methods Site D escription The site for this research is tributary sediments (stream sediments and riparian sediments) at the Boston Farm Santa Fe Ranch Beef Unit Research Center (SFBRU) in the Santa Fe River Watershed, northern Alachua County FL Land uses on this site include a low intensity catt le operation with about 300 heifers on 1,600 acres and a nursery operation using nitrogen fertilizer (Holly F actory Nursery) (Frisbee, 2007) Tributary 1 (T1) is affec ted by a pasture ecosystem vegetated with grass and trees, while upstream region of T ributary 2 ( T2 U ) is affected by N fertilization and both hard wood ( Carya sp., Quercus sp., and Magnolia g randiflora sp).and soft wood including Pinus sp. The d ownstream of T ributary 2 ( T2 D ) is affected by N fertilization from the headwater and a pasture ecosystem, and is covered with grass including Saururus cernuus sp., and Juncus sp., and deciduous shrub plants including Cephalanthus occidentalis sp. (Frisbee 2007). Sampling Samples (stream and riparian sediments) were collected to a depth of 3 cm with a PVC core (diameter 7.5cm) from three sites Surface sediments (3 cm dept of

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119 sediments) were collected because surface sediments likely have much higher microbial activities compared to deeper sediments. Three samples from three sites (total of 9 samples) were collected in each tributary sediment for December 2006, March, May, July, August and October 2007, January, April and July 2008. T he sediments were transported to the laboratory on ice. Nine samples from each site were mixed to make a sin gle composite sample. After mixing, triplicate samples were taken and prepared for analysis. All roots and litter material s were removed from sediment s prior to analysis. The samples were stored at 8 0C until analysis. Nucleic Acid Extraction, PCR Amplif ication, Cloning and Sequencing For all samples nucleic acids were extracted from 0.25 g of sediments with the Power Soil DNA Isolation kit (MoBio, Carlsbad, CA, USA) following the instruction A n approximately 491bp fragment of AOB in T1 a nd T2 U for December 2006, March, May, July and August 2007 samples was amplified using primer set amoA 1 F (5' GGGGTTTCTACTGGTGGT 3') and amoA 2 R (5' CCCCTCKGSAAAGCCTTCTTC 3') developed by Rotthauwe et al. (1997). The P olymerase C hain R eaction ( PCR ) mixture was composed of 25 l GoTaq Green Master Mix (Promega, Madison WI), 1 l of each primer (100 pmol l 1 ), 13 l of d istilled d e ionized (DDI) water and 10 l of diluted DNA solution. A n iCycler thermal cycler (BIORAD, Hercules, CA) was used for PCR amplification with the following conditions : i nitial enzyme activation and denaturation at 95 C for 15 min, followed by 35 cycles of 95 C for 30 s econds 55 C for 45 seconds and 72 C for 45 seconds with a final extension step at 72 C for 7 min. PCR p roducts were analyzed by electrophoresis through 1.5% TAE agarose gels

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120 Amplification of AOA was conducted using primer sets designed by Leininger (2006), consisting of primers A 19 F (5' ATG GTC TGG CT(AT) AGA CG 3') and 643 R (5' TCC CAC TT(AT) GAC CA(AG) G CG GCC ATC CA 3') which amplify a n approximately 624bp region of AOA from sample s collect ed from December 2006, March, May, July, August and October 2007, January, April and July 2008 The mixture for the PCR amplification was composed of 25 l GoTaq Green Master Mix (Promega, Madison WI), 1 l of each primer (100 pmol l 1 ), 13 l of DDI water, and 10 l of diluted DNA solution. A n iCycler thermal cycler (BIORAD, Hercules, CA) was used for PCR amplification with following conditions: i nitial en zyme activation and DNA denaturation of 15 min at 95 C followed by 30 seconds at 95 C, 45 seconds at 55 C, and 45 seconds extension at 72 C for 30 cycles, and a final extension of 72 C for 7min. PCR products were analyzed by electrophoresis through 1.5% TAE agarose gels. For cloning, pGEM T and pGEM T Easy Vector Systems (Promega WI) were used. The total volume of ligation reaction mixtures was 1 0 l and contained 5 l of 2X Rapid Ligation Buffer 3 l of fresh PCR amplicons and 2 l DDI water The r eaction mix ture was ligated into the pCRII TOPO cloning vector and transformed into chemically competent XL10 Gold Ultracompetent Cells ( Stratagene CA) according to manufacture r's protocol. I nserts within white colonies were confirmed by PCR amplifi cation with same primer set and agarose gel electrophoresis analysis I nsert bearing clones were transferred to 96 well plates containing 200 a ni broth and 8% (v/v) glycerol and kanamycin (50 ml 1 ). P lates were incubated overnight at 37 C covered w ith gas permeable membranes (Breath easy, Diversified

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121 Biotech, USA) and sent t o the University of Florida Genome Sequencing Service Laboratory for sequencing Construction of Phylogenetic Tree and Diversity Analysis The number of clones sequenc ed from each library is presented in Table 5 2. All DNA sequence s of AOA w ere compared to other sequences from previous research using BLAST ( Gen Bank, http://www.ncbi.nlm.nih.gov) and to check for relevance to AOA genes S equences were aligned with relat ed sequences and one out group sequence using ClustalX2 and the alignment was manually edited (Larkin et al., 2007). The o ut group for amoA selected was amoA from Nitrosomonas ( AY958704 ) ( Park and Noguera 2007) Phylogenetic trees were conducted using MEGA version 4 with a maximum parsimony analysis using a Jukes Cantor method for distance estimation (Tamura et al., 2007) Bootstrap analysis (100 re sampling) was used to estimate reproducibility of phylogen et ic tr ees. For analysis of microbial assemblage composition diversity DOTUR generated operational taxonomic units (OTUs) using a furthest neighbor algorithm with a cutoff of 10% difference in nucleic acid sequences. The reason why I used a 10% cutoff in my rese arch is that most of the studies designate 5% or 10% as cut off, and 10% is an appropriate point for comparing diversity between species ( Francis et al., 2005; Leininger et al., 2006; Zhang et al., 2008 ) R ichness and diversity indices including the Shannon Simpson and Chao1 ind ices were calculated using DOTUR (Schloss and Handelsman, 2005). PHYLIP was used to calculate pair wise distance between sequences (Felsenstein et al., 2004) Unifrac PCA and Mantel Test To determine if clone libraries were significantly different from each other and related to environmental factors or location, Unifrac PCA (Principal Component Analysis)

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122 analysis was conducted ( Lozupone and Knight, 2005) The p hylogenetic tree for Unifrac PCA analysis was constructed using Me ga 4 and Jukes Cantor and distance method with a maximum parsimony method for tree constructions. Also, to investigate the relationship between microbial assemblage compositions and environmental properties the Mantel test was performed in R Vegan (R Dev elopment Core Team 2008 ; Oksanen et al., 2010 ; Mantel 1967; Mantel and Valand 1970) Results AOA Phylogenetic Tree AOA PCR bands were consistently more intense than those of AOB in T1 and T2 U samples for December 2006, March, May, July, and August 2007 (Table 6 1) Bands intensity is not truly a quantitative comparison but is frequently used as a semi quantitative tool for comparisons. PCR bands from AOB w ere weak for October 2 007, January, April, and July 2008 samples However, PCR bands from AOA were intense for all samples and AOA was amplified from all samples and sequenced. Phylogenetic trees were constructed for AOA genes with October 2007, January, April, and July 2008 s amples. AOB was not studied further due to low abundance of AOB PCR bands in most samples. AOA sequences were grouped into four distinct phylogenetic clusters for stream sediments. Cluster A was divided into two subgroups (A 1and A 2). Cluster A 1 was comp osed of clones sequenced from all sites and was similar to clones obtained from N fertilized soils (EF207208 and EF207209) (He et al., 2007), sandy soils (DQ534885, DQ534870, DQ148878 and DQ 6 534864) (Leininger et al., 2006), bioreactors (DQ278581) (Park et al., 2006), ocean water (DQ148587) (Francis et al., 2006), and hot spring (EU281321) (Z h ang et al., 2008). Cluster A 2 was comprised of clones

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123 sequenced from T2 D and contained sequences obtained from freshwater (EU309878 and EU309880) ( Herrmann 200 9 ) and ocean ecosystems (AACY01575171) ( Venter et al., 2004).This cluster also included Nitrosopumilus maritimus cultured from the marine systems at Seattle (EU239959) (K nneke, 2005). Unlike Cluster A, Cluster B consisted of clones from T2 U and included cl ones sequenced from terrestrial soils impacted by N fertilization (EF207215) (He et al., 2007). Cluster C consisted of clones sequenced from T1 and T2 U, and include d clones obtained from ocean sediments (DQ148789) (Francis et al., 2005). Cluster D include d clones from all sites. In summary, sequences from stream sediments of T1 and T2 U clustered with sequences from soils, while sequences from stream sediments of T2 D clustered with sequences from both marine water and soils (Figure 6 1). AOA sequences we re grouped into three distinct phylogenetic clusters for riparian sediments. Cluster A included sequences from T1 and T2 U, and grouped with sequences from uncultured clones obtained from terrestrial soils receiving N fertilizer (EF207215) (He et al., 2007 ), and Candidatus Nitrosocaldus yellowstonii cultured from a hot spring sediment (EU239961) ( de la Torre et al., 2008). Cluster C consisted of sequences from T2 D and sequences obtained from a hot spring (EU553449) (Z h ang et al., 2008), ocean (DQ148587) ( Francis et al., 2005), freshwater systems (EU309880) (Herrmann et al., 2008), and sandy soils (DQ534854) (Leininger et al., 2006). Also, this cluster included Nitrosopumilus maritimus (EU239959) cultured from marine ecosystems (K nneke, 2005) and Candidatu s Nitrososphaera gargensis cultured from a hot spring (EU281321) (Hatzenpichler, 2008). Cluster B included sequences from riparian sediments from all sites and were similar to uncultured clones sequenced from

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124 ocean water, sediments (DQ148789) (Francis et a l., 2005), and N fertilized soils (EF207221) (He et al., 2007). Therefore, sequences from T1 and T2 U were similar to sequences obtained from hot spring sediments or N fertilized soils, and sequences from T2 D tended to cluster with sequence from ocean and soil systems (Figure 6 2). Diversity Indices for AOA Assemblages Rarefaction curves reached a plateau at 10% cut off, implying that the sequences represented all the AOA diversity in clone libraries when using 90% similarity of sequences as a cutoff point. When c ompari ng rarefaction curves between stream and riparian sediments, libraries from stream sediments exhibited steeper AOA rarefaction curves than libraries from riparian sediments for all site s (Figure 6 3). T2 D amoA sequences exhibited the higher Shannon and Simpson indices in T2 D than those of T1 and T2 U in stream and riparian sediments; however, Chao1 richness index was highest in T1 stream sediments and T2 U riparian sediments (Table 6 2 ). I correlated the Shannon, Simpson and Chao1 indices with biogeochemical properties t o investigate biogeochemical factors controlling microbial diversity and richness of AOA pH was weakly positively correlated with the Simpson index in stream sediments (R 2 =0.42, p=0.0229, Figure 6 4 (a)). Extractable organic carbon concentrations were weakly negatively correlated with the Shannon index in riparian sediments (R 2 =0.41, p=0.0259, Figure 6 4 (b)). Relationship between the AOA Assemblage Compositions and Biogeochemical Properties Unifrac PCA analysis indicated that assemblage composition s for AOA in T2 D w ere separated from th ose of T1 and T 2 U in tributary sediments explaining 50.45% of variation (Figure 6 5 ). The Mantel test demonstrated that pH was a main factor

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125 controlling the differences between microbial assemblage composition s in tributary sediments for all sites ( Table 6 3, Mantel r=0. 44 p=0.0 4 ). Thus, a greater difference in pH between sites is associated with greater difference in microbial ass emblage composition for AOA Discussion Bacterial amoA Bacterial amoA (AOB) was not well amplified in my samples. This lack of amplification may be due to either low abundance or may indicate a methodological problem Many previous studies reported that AOA were found in various ecosystems including ocean water, estuarine sediments, fertilized soils, hot springs, the guts of animals, and plant leaves ( Moissl et al., 2008; Schleper, 2010). The copy numbers of AOA are h igher than AOB in most environments (Schleper, 2010). In addition, Nitrosomonas growth a AOB genus, is inhibited below pH 6.5 and bacterial n itrification rates are inhibited at pH 6.0 or l ower ( Tate, 1999; Bothe et al., 2007 ) Furthermore, I used AOB prim ers designed for sequences obtained from rice roots, activated sludge, a freshwater and Everglades soils ( Rotthauwe et al. 1 997 ; Smith, 2006) Thus, my primer set may not have detected AOB genes in tributary sediments, indicating that the AOB genes in my samples could be different from those of rice roots, activated sludge, a freshwater and Everglades soils. Relationships between Microbial Diversity and Richness of AOA and Biogeochemical Properties Rarefaction curves of stream and riparian sediments fro m all sites were near plateau at 10% cutoff. Thus, the number of clones sequenced accounted for diversity of amoA in my libraries Diversity ind ices showed a weakly positively correlation with pH in

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126 stream sediments; however this relationship was not observed in riparian sediments. Soil pH is one of major factors influencing nutrient chemical forms, concentration s availability of substrate, cell growth, and transformation rates (Kemmitt et al., 2006). A strong linear correlation between pH and microbi al diversity has been observed in terrestrial (Fierer et al., 2006) and aquatic ecosystems ( Lindstrm et al., 2005). In particular, nitrification rates are known to be low under acidic conditions (Boer and Kowalchuk, 2001 ; Tate, 2000 ). This phenomenon can be explained by reduction in NH 3 availability with decreasing pH through ionization to NH 4 + (Frijlink et al., 1992). Thus, the availability of NH 3 under acidic conditions could limit nitrification rates and nitrifier diversity. However, most previous resea rch focused on the effect of pH on AOA gene abundances rather than on diversity of AOA, and the results are also very controversial. Some research demonstrated a positive relationship between pH and abundances of AOA genes in lake sediments (pH 6.8 8.0), a cidic soils (pH 3.7 5.8), and river s, (Wu et al., 2010; He et al, 2007; Liu et al., 2010). Other studies showed either a negative relationship between AOA abundances and pH in acidic soils (pH4.5 7.0) (Nicol et al., 2008) and fresh sediments (pH 4.8 7.7) ( Herrmann et al, 2009), or no correlation between them in estuarine sediments (pH 6.7 7.3) (Mosier and Francis, 2008) and fertilized soils (pH 8.3 8.7) (Shen et al., 2008). Thus, it is difficult to describe the relationship between AOA diversity and pH in m y research Despite this limitation, o ne possible explanation can be the level of ammonium concentrations. Stream sediments had a relatively higher pH, lower ammonium concentrations, lower nitrification and ammonification rates R iparian sediments showed a relatively lower pH, higher ammonium concentrations, and higher nitrification and ammonification rates compared

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127 to stream sediments (Chapter 3). Thus, under ammonium limited conditions such as stream sediments, the effect of pH on NH 3 availability could be significant for influencing AOA diversity, resulting in a weak positive relationship between pH and AOA diversity. However, under relatively high ammonium conditions such as riparian sediments, NH 3 could less limit the AOA diversity because the continu ous supply of ammonium to systems via ammonification or external supply from organic matter may maintain NH 3 availability to AOA despite lower pH conditions. Also, AOA in riparian sediments might be more well adapt to acidic conditions for long term than that of stream sediments. Thus, a greater supply of ammonium and adaptation of AOA to acidic conditions could lead to the absence of a relationship between pH and AOA diversity in riparian sediments. Furthermore, o xygen concentrations and immobilization of ammonium could affect AOA assemblage compositions. Thus, these variations may result in the absence of a correlations between diversity index and pH in riparian sediments, and weak correlations between them in stream sediments. Results indicated that hig h organic carbon contents led to a weakly decreased diversity of AOA in riparian sediments. Previous research demonstrated that t he Archaea Nitrosopumilus maritimus initially cultured from ocean water, gr ew chemoautotrophically via conversion of ammoni a to nitrite using bicarbonate as a carbon source However, the addition of organic carbon inhibited its growth rate et al., 2005) and reduced the number of amoA gene copies (Hallin et al., 2009) Also, W u et al., (2010) also demonstrated that AOA abundance s were negatively correlated with accumulation of organic matter in lake sediments This implies that inhibition by organic matter could prevent the AOA from becoming a more diverse group

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128 in systems with higher amounts of organic matter. Therefore higher carbon content in T1 and T2 U could restrict growth rates of AOA, resulting in a less diverse group. However, until now the mechanism linking the inhibition of organic matter to AOA remained unclear. Thus, correlations between Ext. Org C and diver sity index might be regulated by other factors such as oxygen content, immobilization of ammonium, or ammonification rates capable of supplying ammonium to nitrifiers, resulting in a weak correlation between them. I examined if diversity index of AOA corr elates with nitrification rates in tributary sediments. However, there were no correlations between them, indicating that diversity of AOA was not related to potential nitrification rates in my samples. Therefore, unlike results from denitrification, nitri fication rates could be more related to biogeochemical factors rather than microbial assemblage compositions Relationships between Microbial Assemblages of AOA and Biogeochemical Properties The AOA assemblage of T2 D was significantly different from those of T1 and T2 U. Also, the Mantel test showed that pH was a primary factor controlling microbial assemblage compositions for AOA in tributary sediments. Previous studies have described the ef fect of pH on the abundance of amoA gene and nitrification rates ( He et al., 2007; Hallin et al., 2009 ); however, there is little study investigating the role of pH in the distribution of the AOA assemblage compositions. According to my results, the larger the difference of pH between sites, the larger the difference between AOA groups. Other factors reported to determine the distribution of AOA microbial assemblage in a subtropical macro tidal estuary are salinity and the T C: T N ratio (Abell et al., 2010). In hot springs, geography had been found to be a main factor determining microbial

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129 assemblages of AOA (Z h ang et al., 2008). Therefore, more research is needed on distribution s of AOA, comparing environmental factors across the globe, and mechanisms linking pH with Archaeal amoA gene distribution s Summary Tributary s ediments are transitional gradients between terrestrial and aquatic ecosystems, and thus may exert environmental controls over microbial community structure and function. E nvironmental characteristics can control microbial assemblage compositions in tributary ecosystems, which in turn could affect microbial function s in these systems. My research investigated the relationships between biogeochemical properties, nitrification rates and Archaeal amoA (AOA) assemblage compositions in tributary sediments. Results indicated that a weak positive relationship exhibits between diversity ind ex for AOA (Simpson index ) and pH ( R 2 =0.42, p =0.0229 ) in stream sediments possibly due to inc reased competition in the low pH For riparian sediments, a weak negative correlations between diversity index for AOA (Shannon index) and organic carbon contents was observed (R 2 =0.41, p =0.0259). However, until now, mechanism linking the organic matter co ntents and diversity of AOA remains unclear. Also, diversity index did not influence potential nitrification rates, implying that nitrification rates may be regulated by biogeochemical factors rather than microbial assemblage compositions in tributary sedi ments. The Mantel tests showed that pH was a significant factor affecting microbial assemblage compositions for AOA in tributary sediments (Mantel r=0. 44 p=0.0 4 ). Thus, the larger the difference in pH between sites, the larger the difference of AOA assem blage composition s in tributary sediments. Even though this research did not observe a s i gnificant relationship between diversity index (microbial structure) and nitrification rates (microbial function), the results

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130 suggested that diversity index might be linked with pH and organic matter contents in tributary sediments. Also, my research suggested that the distribution of AOA was regulated by pH in tributary sediments.

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131 Table 6 1 Relative abundances of PCR product s of AOA ( Archaeal amoA ) and AOB ( Bacterial amoA ) in tributary sediments of T1 and T2 U for December 2006, March, May, July and August 2007 samples Comparisons were determined by relative intensities of EtBr ( Ethidium bromide ) stain. Seasons T1 Stream sediments T1 Riparian sediments T2 U Stream sediments T2 U Riparian sediments AOA Dec 20 0 6 ++++ ++++ +++ Mar 20 07 + + + + May 20 07 + + + ++ Jul 20 07 + + +++ ++ Aug 20 07 + + + AOB Dec 20 0 6 ++ Mar 20 07 + May 20 07 + + + Jul 20 07 + Aug 20 07 + +

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132 Table 6 2. Archaeal amoA diversity and richness in tributary sediments, estimated by the Shannon, Simpson, and Chao 1 indices. Sites No. of clones sequenced No. of OTUs a Shannon index Simpson index Chao1 Index Stream sediments T1 174 24 2.4 ( 2.2 2.5 ) 0.86 28 ( 25 46 ) T2 U 138 26 2.5 ( 2.2 2.7 ) 0.85 19 ( 27 42 ) T2 D 132 23 2.7 ( 2. 5 2.8 ) 0.91 26 ( 33 40 ) A ll 444 Riparian sediments T1 121 14 1. 8 ( 1.6 2 ) 0.75 19 ( 14 46 ) T2 U 151 16 1. 8 ( 1 .6 2 ) 0.75 21 ( 16 44 ) T2 D 69 13 2.36 ( 2 .2 .25 ) 0.91 14 ( 13 21 ) A ll 341 a Estimate d average of OTUs, diversity and richness are all based on 10% or less differences in nucleic acid sequence alignments; upper and lower bounds of 9 5 % confidence intervals as calculated by DOTUR.

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133 Table 6 3. Regression analysis of transformed data taken from distribution matri ces ; y axis represent A rchaeal amoA assemblages and x axis represents biogeochemical properties, g enerated by the Mant e l test pH NH 4 + N Ext. Org N MBN Ext. Org C MBC Mantel r 0.44 0.3 0.12 0.06 0.03 0.002 p value 0.04 0.93 0.61 0.58 0.36 0.52

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134 Figure 6 1. Maximum parsimony tree of A rchaeal amoA sequences obtained from stream sediments. The dark circle s (values over 90), empty circle s (values from 70 to 90) and empty triangle s (values from 50 to 70) on nodes are bootstrap scores based on percent occurrence of 100 re samplings.

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135 Figure 6 2. Maximum parsimony tree of A rchaeal amoA sequences obtained from riparian sediments. The dark cir cle s (values over 90), empty circle s (values from 70 to 90) and empty triangle s (values from 50 to 70) on nodes are bootstrap scores based on percent occurrence of 100 re samplings.

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136 (a) (b) Figure 6 3. Rarefaction curves for A rchaeal amoA from DOTUR analysis using a furthest neighbor assignment algorithm in T1 (a), T2 U (b) and T2 D (c) tributary sediments. The similarity cutoff was 90%.

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137 (c) Figure 6 3 Continued.

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138 (a) (b) Figure 6 4. Relationships between pH and the Simpson index for A rchaeal amoA in stream sediments (a), and extractable organic carbon concentrations (Ext. Org C) and the Shannon index for Archaeal amoA in riparian sediments (b) ( Dash ed line are the 95% confidence intervals of the regression line n=12).

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139 Figure 6 5. Principal component analysis for A rchaeal amoA assemblage composition using the Unifrac in tributar y sediment s

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140 CHAPTER 7 CONCLUSIONS AND SYNTHES I S Human activities have doubled the rate of reactive nitrogen input to the global nitrogen cycles and the amount of nitrate input has been continuing to increase (Vitousek et al., 1997) This anthropogenic increase in nitrogen caused by fertilizer utilization is estimated to be 80Tgyr 1 (FAO 1993; Schlesinger et al. 1992; Matthews 1994). I n pasture ecosyst ems dairy manure is also considered to be a key source of excessive input of nitrogen ( Adriano et al., 1971; Smith et al., 1980; Cooper et al., 1984 ; Burkart and James, 1999) Of the total nitrogen produced by human activities including N fertilizer, agr icultural activities, and N depositions, rivers are estimated to receive 20 to 25% of the N added to ecosystems (Galloway et al., 2004; Green et al., 2004 ) However, an estimated half of the nitrogen entering river s i s denitrified in buffer systems such as riparian, tributaries and floodplains in watershed s ( van Breemen et al., 2002; Galloway et al., 2004). In particular, s mall streams have a significantly higher efficiency of nitrate removal due to increased benthic denitrification rates (Alexander et al. 2000) However, denitrification rates in small streams can be affected by other inorganic nitrogen transformations such as nitrification, anammox, and DNRA. Nitrification can supply nitrate to denitrifiers, and DNRA and anammox can compete with denitrifi cation for gaining nitrate and nitrite respectively. T o better understand the function of tributary ecosystems for nitrogen removal, I investigated the potential inorganic nitrogen transformation rates including nitrification, anammox, denitrification an d DNRA, and biogeochemical factors affecting these rates. I investigated how extracellular enzyme activities in litter and tributary sediments influenced potential denitrification rates, one of main process es for nitrate removal in

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141 tributary ecosystems. Al so, since the diversity of microbial community composition can affect microbial functions, I investigated if microbial assemblage compositions for nitrifier and denitrifier were related to their process rates, and explored the biogeochemical properties affecting microbial assemblage compositions. Inorganic Nitrogen Transformations and Biogeochemical Properties Relative rate s of nitrate removal through denitrification w ere higher than th ose of DNRA in tri butary sediments. Ammonium removal rate s through nitrification are significant compared to anammox. Thus, removal of nitrate and ammonium in tributary sediments were mainly regulated by denitrification and nitrification rather than DNRA and anammox (Figure 7 1) In addition, r iparian sediments exhibited higher potential rates of inorganic nitrogen transformation s than those of stream sediments due to higher labile organic carbon contents Thus, riparian sediments can be more important for inorganic nitrogen cycling in tributary ecosystems than stream sediments (Figure 7 1) In order to explain the effec t of biogeochemical properties on potential inorganic nitrogen transformation rates in tributary sediments I conducted regression analyses with their rates a nd biogeochemical properties. R esults showed a weak positive relationship between pH and potential nitrification rates in tributary sediments, implying that the decreased p H might suppress nitrification rates via a decreased ratio of NH 3 to NH 4 + under acidic conditions. For denitrification, potential rates were negatively correlated with the TC:TN ratio in riparian sediments implying that carbon quality may regulate denitrification rates. However, increased TC:TN ratio could enhance immobilizati on of nitrate, which in turn decrease nitrate availability to denitrifiers. In case of DNRA, potential rates increased with organic carbon contents in riparian sediments,

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142 implying that high contents of organic carbon could enhance DNRA rates. Thus, when we add organic carbon to soils for removing nitrate through denitrification, we have to consider the influence of organic carbon on immobilization and carbon quality in the systems. Additionally, increased ammonification rates were associated with increased ammonium concentrations in riparian sediments. Extracellular Enzyme Activities and Denitrification Rates To explain the effec t of carbon quality on the supply of organic carbon to denitrifiers, cellulase D glucosidase, and phenolic oxidase enzyme activities were measured as an index of decomposition rate in tributary sediments and litter material s R esults indicated that the tributary s ystem with lower cellulase activities in litter had lower denitrification rates than the tributary system with higher cellulase activities in litter Cellulase activities in tributary sediments were positively correlated with potential denitrification rates in tributary sediments. Also, a weak negative relationship between cellulase activi ties and TC:TN ratio of litter was observed. Thus, the substrate quality representing the TC:TN ratio could affect cellulase activities in litter, affecting availability of organic carbon to denitrifiers and their rates. Microbial Assemblage Compositions for Denitrifiers and Nitrifiers, Biogeochemical Properties, and Rates The riparian sediments with the highest organic carbon contents exhibited the lowest potential denitrification rates, while riparian sediment s with the lowest organic carbon contents showed the highest potential denitrification rates. Generally, many studies have shown that denitrification rate increases with available organic carbon contents; however, results did not follow this pattern Theref ore, the diverse microbial assemblage composition of nirS (representing denitrifiers) could influence denitrification

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143 rates via the selection of denitrifiers with genes capable of using the carbon source more efficiently The diversity index for nirS was n egatively correlated with organic carbon contents, and the systems with highest diversity index exhibited the highest denitrification rates. Thus, assemblage compositions for nirS may be linked with their microbial functions, and these relationships can be regulated by the level of organic carbon in the systems In case of nitrifiers, a weak positive relationship between diversity ind ex for a rchaeal amoA (AOA) and pH was observed in stream sediments likely due to increased competition in the low pH system. In addition, the distribution of AOA was regulated by pH in tributary sediments. However, t he system with the more diverse AOA did not exhibit in creased potential nitrification rates in tributary sediments. Thus, unlike results from denitrifiers, microbial assemblage composition for AOA was not directly related to the process Synthesis T ributary system s with relatively low pH and low quality of carbon such as forest stream s or wetlands will exhibi t low nitrification and denitrification rates. Acidic conditions may decrease the availability of NH 3 resulting in decreased nitrification rates. This reduction of nitrification rate could decrease denitrification rate via a lower supply of nitrate to den itrifiers. Accumulation of low quality carbon such as recalcitrant organic matter or lignin litter could retard the decomposition rates, resulting in lower suppl ies of labile organic carbon to denitrifiers. Thus, decreased nitrification and denitrification rates may lead to accumulations of ammonium and nitrate in the systems. T ributary system s with relatively high pH and relatively high quality of carbon such as grassland buffer s trips will exhibit high nitrification and denitrification rates.

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144 Nitrification will not be inhibited by pH at this site, as it is at the other sites due to their low pH, resulting in an increase of nitrification rates with relative high pH In addition, hi gh quality of carbon substrate such as low lignin and high cellulase contents, will supply more labile carbon to denitrifiers via higher extracellular enzyme activities associated with decomposition rates of litter and organic matter. This will enhance de nitrification rates, possibly resulting in the reduction of nitrate concentrations. Thus, to understand the buffer capacity of tributary ecosystem s for nitrate removal, the relationships between overall inorganic nitrogen transformations and carbon quality should be considered Future Studies Future studies should focus on measuring in situ rates of nitrogen transformations to clarify the relationship s between biogeochemical properties and inorganic nitrogen transfo rmations at landscape scales. We need to investigate if these relationships in various tributary ecosystems are affected by human activities in order to establish appropriate management plans for nitrogen. Also, to maximize denitrification rates in tributary ecosystems, we should clarify the effe ct of carbon quality on availability of labile carbon using analyses of in situ litter decomposition rates and litter chemistry such as lignin, hemicellulose and cellulose contents. Furthermore, to better understand how microbial structure affects microbi al function, the research on enumeration of nitrifier s and denitrifier s is needed, as are studies on the diversity of anammox and DNRA microbes.

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145 (a) (b) Figure 7 1 Potential rates of ino r ganic nitrogen transformations in (a) stream sediments and (b) riparian sediments.

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146 APPENDIX CACULATION OF ANAMMOX RATES The potential rates of anammox are calculated by the difference in the ratio s of 14 N 15 N to ( 14 N 14 N + 14 N 15 N+ 15 N 15 N) between natural abundance and samples. Assuming the p is the atom fraction of 14 N and q is the atom fraction of 15 N, p=atom fraction of 14 N, q=atom fraction of 15 N so we can say p 2 = 14 N 14 N, 2pq= 14 N 15 N, q 2 = 15 N 15 N We can a ssum e that From above equation, With atm% of 15 N, we can get the "r" value from above equation We know the "r" value from the above equation so that we can get the r'. The mole fraction of the various isotopic species in N 2 gas is (a+2b+c=1), where a= 14 N 14 N to ( 14 N 14 N+ 14 N 15 N+ 15 N 15 N) 2b= 14 N 15 N to ( 14 N 14 N+ 14 N 15 N+ 15 N 15 N), and c= 15 N 15 N to ( 14 N 14 N+ 14 N 15 N+ 15 N 15 N) can be expressed in terms of the measured rations, r and r', as follows (Hauk 1958) :

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147 With r and r', we can calculate a, 2b, and c. After total amount of N 2 in the sample is calculated, this amount is multiplied by 2b and divided by incubation days.

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148 LIST OF REFERENCES Abell, G.C., A.T. Revill, C. Smith, A.P. Bissett, J.K. Volkman, and S.S. Robert. 2010. Archaeal ammonia oxidizers and nirS type denitrifiers dominate sediment nitrifying and denitrifying populati ons in a subtropical macrotidal estuary. The ISME. 4:286 300. Aber, J. D. and J.M. Melillo 2001. Terrestrial Ecosystems, 2nd Ed. Academin Press, San Diego, CA. 231 pages Aber J.D., A. Magill, S.G. McNulty, R.D. Boone, K. J. Nadelfoffer, M. Downs and R. Hallett. 1995. Forest biogeochemistry and primary production altered by nitrogen saturation. Wat Air Soil Pollut. 85: 1665 1670. Adams, M.A. and P.M. Attiwill. 1983. Patterns of nitrogen mineralization in 23 year old pine forest following nitrogen fer tilization. For. Ecol. Manage. 7:241 248. Allan, J.D. 1995. Stream Ecology. In J.D. Allan (ed.). Chapman and Hall. London, UK. Alexander, M. 1965. Nitrification. p. 307 343. In W.V. Bartholomew et al. (ed.) Soil nitrogen Agron. Monogr 10 ASA Madison, WI USA. Alexander R B ., R.A. Smith and G.E. Schwarz. 2000. Effect of stream channel size on the delivery of nitrogen to the Gulf of Mexico. Nature 403: 758 61. Ambus P, A. Mosier, and S. Christensen 1992 Nitrogen turnover rates in a riparian fen de termined by 15 N dilution. Bio Fert Soils 14: 230 236 An S. and W.S. Gardner 2002. Dissimilatory nitrate reduction to ammonium as a nitrogen link, versus denitrification as a sink in a shallow estuary (Languna Madre/Baffin Bay, Texas). Mari Ecol Prog Ser. 237: 41 50. Angelo E.M. and K.R. Reddy. 1993. Ammonium oxidation and nitrate reduction in sediments of a hypereutrophic lake. Soil Sci Soc Am J 57: 1156 1163. Angers, D.A. and nitr ogen and carbohydrate contents of a silt loam and its particle size fractions. Biol. Fertil. Soils 11 : 79 82. Appel, H.M. 1993. Phenolics in ecological interactions: the importance of oxidation. J. Chem. Ecol. 19: 1521 1552. Asmar, F., F. Eiland, and N. E. Nielsen. 1994. Effect of extracellular enzyme activities on solubilization rate of soil organic nitrogen. Biol. Fert. Soils. 17: 32 38. Bachand, P A.M and A.J. Horne. 2000. Denitrification in constructed free water surface wetlands: Effects of vegetatio n and temperature. Ecol Eng 14: 17 32.

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184 B IOGRAPHICAL SKETCH Haryun Kim was born in Seoul Korea Sh e received h er bachelor 's degree in environmental education in 200 0 from the Korea National University of Education Korea. Sh e studied at the Department of Environmental Science and Engineering in Ewha Womans University in Seoul, Korea which had an emphasis on ecology and gave h er an opportunity to research nitrogen cycling During h er master's studies, Dr. H o j eo ng Kang was fascinated with the studies and re search conducted in the lab. At first, s he assisted the research on nitrogen cycling of constructed wetlands The following semester, s he received h er own project about nitrogen cycling in forest ecosystems as a master's thesis T his research was funded by Korea Environment Ministry. Since s he enjoyed working with Professor Dr. Hojeong Kang and loved conducting research with biogeochemistry and micro biology s he was looking to expand the scope of research to the entire system in which they operate. Biogeochemistry and microbial ecology attracted her because it enabled h er to see the entire picture from the smallest details (e.g., genes) up to the biggest ones (the entire system). On 200 5 s he joined the Ph. D program at UF in the Soil and Water Science Department with Dr. Andrew Ogram and Dr. Reddy as her advisor. Sh e wanted to learn more about biogeochemistry and how to apply this knowledge to the management and recovery of soil and river ecosystems