Molecular Exploration of Bioavailable Dissolved Organic Matter Across Aquatic Ecosystems Yuan Shen University of South Carolina

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Theses and Dissertations

2017 Molecular Exploration of Bioavailable Dissolved Organic Matter Across Aquatic Ecosystems Yuan Shen University of South Carolina

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This Open Access Dissertation is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. MOLECULAR EXPLORATION OF BIOAVAILABLE DISSOLVED ORGANIC MATTER ACROSS AQUATIC ECOSYSTEMS

Yuan Shen

Bachelor of Science Xiamen University, 2009

Master of Science University of South Carolina, 2011

Submitted in Partial Fulfillment of the Requirements

For the Degree of Doctor of Philosophy in

Marine Science

College of Arts and Sciences

University of South Carolina

2017

Accepted by:

Ronald Benner, Major Professor

James Pinckney, Committee Member

Lori Ziolkowski, Committee Member

Karl Kaiser, Committee Member

ii DEDICATION

I would like to dedicate this dissertation to my wife Jiani Zheng and my parents for all their love and support.

iii ACKNOWLEDGEMENTS

I would like to express my deepest appreciation to my advisor, Professor Ronald

Benner, for his guidance, patience, encouragement, and support. His enthusiasm, vision, and creative thinking have been a great influence on me throughout my graduate studies.

He made every project that we worked together so much enjoyable. One could not ask for a better advisor. I thank James Pinckney, Lori Ziolkowski, and Karl Kaiser for being on my committee and providing helpful comments and suggestions. I am very much thankful to Karl Kaiser and Cédric Fichot for teaching me the amino acid analysis and the

DOC and CDOM analyses, respectively. I also thank my other lab mates and colleagues,

Mike Philben, Oliver Lechtenfeld, Shengkang Liang, Qinghui Huang, Dandan Duan,

Tae-Hoon Kim, Doug Bell, etc. for their companionship and the fun chatting on science and life. Special thanks go to my friends at the Gamecock Badminton Club (Richard

Cheng, Xinyu Huang, Xinfeng Liu, Jo Evaristo, Xiaolong Liu, etc.), I appreciate their friendship and the good times that we spent together making my journey more joyful and memorable. Finally, I thank my collaborators for their contributions to this work and the

US National Science Foundation for funding my research.

iv ABSTRACT

Dissolved organic matter (DOM) in aquatic ecosystems is a large reservoir of reduced carbon that is mostly resistant to degradation. A small fraction of DOM cycles relatively quickly and is biologically utilized on timescales of days to months. This bioavailable DOM (BDOM) supports aquatic food webs, drives major elemental cycles, and is coupled to atmospheric CO2. Despite wide-ranging importance, bioavailability of

DOM and its linkages to ecosystem properties (e.g., primary production, nutrients) are poorly characterized, particularly at the ecosystem level. Bioassay experiments are commonly used to determine BDOM, but this approach alters conditions and has limited spatial and temporal coverage. In this dissertation, biochemical indicators of DOM bioavailability were developed and implemented in a wide range of ecosystems to reveal large-scale distributions of BDOM in the Arctic (Chapter 1) and Antarctic Oceans

(Chapter 2), locate seasonal biological hotspots in a subtropical ocean margin (Chapter

3), and to trace transport and fate of BDOM from surface to ground waters (Chapter 4).

Measurements of amino acids, a major bioactive component of BDOM, were compared between the high (Chukchi Sea) and low (Beaufort Sea) productivity regions of the western Arctic Ocean. Bulk concentrations of dissolved organic carbon (DOC) were similar in the two systems despite their contrasting productivity, but DOM bioavailability as indicated by amino acid yields was much higher in the more productive Chukchi Sea.

Seasonal trends of amino acids revealed elevated production and rapid off-shelf transport of BDOM in the Chukchi Sea during a season with reduced sea-ice cover.

v The use of amino acids as BDOM indicator was further tested in the Southern

Ocean during austral winter when primary production is light-limited and minimal. The sampling encompassed ice-covered and ice-free waters along a latitudinal gradient in the region of the Antarctic Peninsula, one of the fastest warming regions on Earth. Unlike the

DOC that varied irregularly, amino acid-based indices illustrated a significant northward increase in DOM bioavailability from ice-covered to open waters. Overall, the observations in the polar oceans indicate a correspondence between DOM bioavailability and nutrient- and light-driven changes in ecosystem productivity.

A novel approach using two biochemical indicators (amino acids and carbohydrates) of BDOM was developed during a large-scale seasonal survey of the river-influenced Louisiana margin. These indicators revealed patchy distributions of compositionally distinct types of BDOM hotspots that varied with phytoplankton biomass and nutrient levels, and they further indicated a diel variability in sources of BDOM, with zooplankton grazing at night and phytoplankton extracellular release during daytime under nutrient limitation.

Biochemical indicators were extended to surface and groundwater in South

Carolina. Amino acids, lignin phenols, and chromophoric DOM were monitored monthly over two years. Linking groundwater DOM with surface water DOM and precipitation revealed differential transport of hydrophilic and hydrophobic molecules through soils and depletion of BDOM in groundwater. These observations guided a development of the

Regional Chromatography Model to illustrate processes regulating the composition and bioavailability of DOM during transport through the soil column to the saturated zone.

vi TABLE OF CONTENTS

DEDICATION ...... iii

ACKNOWLEDGEMENTS ...... iv

ABSTRACT ...... v

LIST OF TABLES ...... viii

LIST OF FIGURES ...... ix

LIST OF ABBREVIATIONS ...... xi

CHAPTER 1: DISSOLVED ORGANIC MATTER COMPOSITION AND BIOAVAILABILITY REFLECT ECOSYSTEM PRODUCTIVITY IN THE WESTERN ARCTIC OCEAN ...... 1

CHAPTER 2 BIOAVAILABLE DISSOLVED ORGANIC MATTER AND BIOLOGICAL HOT SPOTS DURING AUSTRAL WINTER IN ANTARCTIC WATERS ...... 29

CHAPTER 3 BIOLOGICAL HOT SPOTS AND THE ACCUMULATION OF MARINE DISSOLVED ORGANIC MATTER IN A HIGHLY PRODUCTIVE OCEAN MARGIN ...... 54

CHAPTER 4 ORIGINS AND BIOAVAILABILITY OF DISSOLVED ORGANIC MATTER IN GROUNDWATER ...... 86

CHAPTER 5 OVERVIEW AND SYNTHESIS ...... 120

REFERENCES ...... 123

APPENDIX A – SUPPLEMENTARY TABLES ...... 143

APPENDIX B – SUPPLEMENTARY FIGURES ...... 144

APPENDIX C – PERMISSION TO REPRINT ...... 146

vii LIST OF TABLES

Table 1.1 Physicochemical characteristics in the Chukchi and Beaufort Seas ...... 19

Table 1.2 Comparisons of concentrations of DOC and TDAA, and TDAA yields ...... 20

Table 1.3 Comparisons of concentrations of DOC and TDAA, and TDAA yields ...... 21

Table 2.1 Summary of oceanographic parameters at the sampling stations ...... 46

Table 2.2 Physicochemical properties of water masses and biological hotspots ...... 47

Table 3.1 Average values and ranges for DOC, TDAA, and TDNS in the mixed layer during the five cruises on the Louisiana margin ...... 75

Table 3.2 Concentrations and compositions of DOM during the shipboard bioassays .....76

Table 3.3 Surface mixed layer reservoirs of DOC on the Louisiana shelf ...... 77

Table 4.1 DOC and amino acids in ground and surface waters and freshly-produced bacterial DOM ...... 106

Table 4.2 Optical properties of DOM in ground and surface waters ...... 108

Table 4.3 Correlation matrix between precipitation and DOM in groundwater ...... 109

Table 4.4 Lignin phenols in ground and surface waters ...... 110

Table 4.5 Yields of D-amino acids in freshly-produced bacterial DOM ...... 111

viii LIST OF FIGURES

Figure 1.1 Locations of sampling stations in the western Arctic Ocean ...... 22

Figure 1.2 Mixing patterns of DOM in the Mackenzie River plume ...... 23

Figure 1.3 Concentrations of DOC in the Chukchi Sea and Beaufort Sea ...... 24

Figure 1.4 Concentrations of TDAA in the Chukchi Sea and Beaufort Sea ...... 25

Figure 1.5 DOC-normalized yields of TDAA in the Chukchi Sea and Beaufort Sea ...... 26

Figure 1.6 Average concentrations of DOC and TDAA, and TDAA yields in the Chukchi and Beaufort Seas ...... 27

Figure 1.7 Average concentrations of DOC and TDAA, and TDAA yields in the Chukchi Sea between 2002 and 2004 ...... 28

Figure 2.1 Sampling sites off the South Shetland Islands (Antarctica) in August 2012 ...48

Figure 2.2 Distributions of temperature and salinity ...... 49

Figure 2.3 Latitudinal distributions of chl-a and POC in different water masses ...... 50

Figure 2.4 Latitudinal distributions of DOC and TDAA in different water masses ...... 51

Figure 2.5 Profiles of chl-a, DOC, TDAA and POC at ice-free and ice-covered stations 52

Figure 2.6 Depth profiles of TDAA yields, glycine, nonprotein amino acids, and D-amino acids at four ice-free stations ...... 53

Figure 3.1 Study area and sampling sites on the Louisiana margin ...... 78

Figure 3.2 Seasonal and spatial distributions of DOC, TDAA, and TDNS ...... 79

Figure 3.3 Seasonal distributions of TDAA and TDNS yields ...... 80

Figure 3.4 Hot spots of labile DOM on the Louisiana margin ...... 81

ix Figure 3.5 Conceptual illustration showing estimation of accumulated marine DOC .....82

Figure 3.6 Seasonal and spatial distributions of marine DOC (total and accumulated) ....83

Figure 3.7 Surface concentrations of accumulated marine DOC on the margin ...... 84

Figure 3.8 Average concentrations of DOC and accumulated marine DOC ...... 85

Figure 4.1 Temporal variations of DOC, a254, TDAA, and D-AA in groundwater ...... 112

Figure 4.2 Concentrations of DOC and TDAA during bioassay experiments ...... 113

Figure 4.3 Yields of TDAA during the unamended experiments, and relationship between bioavailable DOC and TDAA yields ...... 114

Figure 4.4 Seasonal variations of bioavailable DOC in groundwater and precipitation .115

Figure 4.5 Percentages of glycine, γ-Aba, and D-amino acids during the unamended experiments and in the field samples ...... 116

Figure 4.6 Seasonal variations in percentages of glycine, γ-Aba, and D-amino acids ...... 117

Figure 4.7 Seasonal variations in DOC, SUVA254, and bacterial DOC ...... 118

Figure 4.8 Regional Chromatography Model ...... 119

x LIST OF ABBREVIATIONS

Ala ...... Alanine

AMLR ...... Antarctic Marine Living Resources

AR ...... Atchafalaya River

Arg ...... Arginine

Asx ...... Asparagine + Aspartic Acid

β-Ala ...... beta-Alanine

CDOM...... Chromophoric Dissolved Organic Matter

CDW ...... Circumpolar Deep Water

Chl-a ...... Chlorophyll-a

CO2 ...... Carbon Dioxide

CTD...... Conductivity–Temperature–Depth

CuO ...... Cupric Oxide

DOC ...... Dissolved Organic Carbon

DOM ...... Dissolved Organic Matter

γ-Aba ...... gamma-Aminobutyric Acid

GF/F ...... Glass Fiber Filter

Gly...... Glycine

Glx...... Glutamine + Glutamic Acid

HCl ...... Hydrochloric Acid

HPLC ...... High Precision Liquid Chromatography

xi His ...... Histidine

Ile ...... Isoleucine

Leu ...... Leucine

Lys...... Lysine

M-ARS ...... Mississippi-Atchafalaya River System mDOC ...... Marine Dissolved Organic Carbon

MR ...... Mississippi River

NGoM ...... Northern Gulf of Mexico

PAL ...... p-hydroxybenzaldehyde

PAD...... p-hydroxybenzoic acid

Phe...... Phenylalanine

PON...... p-hydroxyacetophenone

S275-295 ...... Spectral Slope Coefficient in the 275-295 nm range

S350-400 ...... Spectral Slope Coefficient in the 350-400 nm range

SAL ...... Syringylaldehyde

SAD...... Syringic Acid

Ser ...... Serine

SON...... Acetosyringone

SR ...... Ratio of S275-295 to S350-400

SSI ...... South Shetland Islands

SUVA254 ...... Specific UV Absorbance at 254 nm

TBW ...... Transitional Bellingshausen Water

TDAA ...... Total Dissolved Amino Acids

xii TDLP...... Total Dissolved Lignin Phenols tDOC ...... Terrigenous Dissolved Organic Carbon

Thr ...... Threonine

Tyr ...... Tyrosine

TWW...... Transitional Weddell Water

USGS ...... U.S. Geological Survey

UV ...... Ultraviolet

Val ...... Valine

VAL ...... Vanillin

VAD ...... Vanillic Acid

VON ...... Acetovanillone

WW ...... Winter Water

xiii CHAPTER 1

DISSOLVED ORGANIC MATTER COMPOSITION AND BIOAVAILABILITY REFLECT ECOSYSTEM PRODUCTIVITY IN THE WESTERN ARCTIC OCEAN1

1.1 INTRODUCTION

Two contrasting systems, the Chukchi and Beaufort Seas, occur adjacent to each other in the western Arctic Ocean. The Chukchi Sea is a large (620×103 km2) and shallow

(~80 m avg.) inflow shelf area that receives nutrient-rich Pacific waters via Bering Strait, which support a very productive ecosystem (Jakobsson et al. 2004; Sakshaug 2004;

Grebmeier et al. 2006). In comparison, the Beaufort Sea is a narrow and small (178×103 km2) river-influenced interior shelf that is relatively deep (~124 m avg.) (Jakobsson et al.

2004). The major nutrient sources to the Beaufort shelf are the Mackenzie River and upwelling (Macdonald et al. 1987). Primary productivity is limited in regions of the

Beaufort shelf due to the high water turbidity and stratification caused by coastal erosion and river runoff (Carmack and Wassmann 2006).

The distinct shelf typology and nutrient supply lead to the contrasting productivity between the Chukchi and Beaufort Seas. Primary productivity in the Chukchi Sea parallels nutrient concentrations and increases from 30–90 g C m-2 yr-1 in the northeast to

1 Shen, Y., C. G. Fichot, and R. Benner. 2012. Dissolved organic matter composition and bioavailability reflect ecosystem productivity in the Western Arctic Ocean. Biogeosciences 9: 4993-5005. Copyright (2012) by Author(s). CC Attribution 3.0 License.

1 720 g C m-2 yr-1 in the southwest (Walsh et al. 1989; Springer and Mcroy 1993; Cota et al. 1996; Hill and Cota 2005). Areas associated with seasonal upwelling, such as Barrow

Canyon, are typically more productive (e.g., 8 g C m-2 d-1) than adjacent waters (Hill and

Cota 2005). In contrast, primary productivity in the Beaufort Sea is relatively low (10–70 g C m-2 yr-1) due to lower nutrient availability and reflects the strong influence from the

Mackenzie River (Carmack et al. 2004; Sakshaug 2004; Lavoie et al. 2009). Shelf waters are less productive within the river plume (e.g., < 10 g C m-2 yr-1) as a result of limited light penetration and become more productive outside the plume (30–70 g C m-2 yr-1)

(Sakshaug 2004; Carmack and Wassmann 2006). Relatively high primary production is observed in the eastern Beaufort Sea where the Cape Bathurst polynya forms during

May–June at the entrance to the Amundsen Gulf. These open waters extend the phytoplankton growth season, resulting in elevated primary production (Arrigo and Van

Dijken 2004; Brugel et al. 2009).

Off-shelf transport of organic matter from productive shelf waters is thought to be an important carbon source for heterotrophic metabolism in the interior Arctic basins

(Walsh et al. 1989; Davis and Benner 2005; Davis and Benner 2007; Mathis et al.

2007b). The disparity between a large metabolic demand for carbon in the basins and low concentrations of particulate organic carbon (POC) suggests the shelf-basin connection likely relies on dissolved organic carbon (DOC) (Wheeler et al. 1997). The extent of this reliance, however, is largely determined by the concentrations and bioavailability of dissolved organic matter (DOM), which are likely to vary spatially and temporally along with ecosystem productivity. Exploring the role of DOM in the shelf-basin connection

2 therefore requires the assessment of DOM bioavailability under varying productivity regimes.

DOM is often categorized into three pools of reactivity, labile, semi-labile and refractory, which have broadly defined turnover times of hours to weeks, months to years and centuries to millennia, respectively (Kirchman et al. 1993; Carlson and Ducklow

1995). Labile DOM is often operationally defined using bioassay experiments, but this approach has limited utility for defining semi-labile and refractory DOM (Ogura 1975;

Søndergaard and Middelboe 1995; Del Giorgio and Davis 2002; Benner 2003). The inherent biochemical properties of DOM shape its bioavailability, which in combination with environmental conditions and microbial community composition, determine the turnover time of labile and semi-labile DOM. Herein, we use amino acids as molecular indicators of the bioavailability of DOM (Benner 2003). Amino acids are the building blocks of peptides and proteins, and they are abundant in plankton and plankton-derived

DOM (Lee et al. 2004; Davis and Benner 2007). They are bioreactive components of labile and semi-labile DOM, making them good indicators of the bioavailability of DOM in aquatic systems (Amon et al. 2001; Davis and Benner 2007; Davis et al. 2009).

High concentrations of bioavailable DOM, as indicated by high concentrations and DOC-normalized yields of total dissolved amino acids (TDAA), are observed in the

Chukchi Sea (Davis and Benner 2005; Davis and Benner 2007). It is speculated that bioavailable DOM produced in the Chukchi shelf is entrained into the halocline of the

Canadian Basin and fuels oxygen utilization there (Walsh et al. 1997; Davis and Benner

2007). It is unclear whether a similar process is active in the Beaufort Sea due to a paucity of data for this region. In addition, despite the fact that the Chukchi Sea is more

3 productive than the Beaufort Sea, differences in surface-water concentrations of DOC are not apparent (Davis and Benner 2005; Guéguen et al. 2005; Mathis et al. 2005). In the present study, the concentrations of DOC and TDAA in the Chukchi and Beaufort Seas were compared to investigate the composition and bioavailability of DOM in these adjacent but quite different systems. Our results reveal that the contrasting productivity between the Chukchi and Beaufort Seas is reflected in DOM bioavailability, as indicated by the concentrations and yields of TDAA.

1.2 METHODS

The Chukchi and Beaufort Seas were surveyed during four summer cruises of three different Arctic projects (Figure 1.1). In 2002 (17 July–21 August) and 2004 (18

July–26 August), water samples from the Chukchi Sea and the adjacent Canada Basin were collected aboard the research vessel USCGC Healy, as part of the Western Arctic

Shelf-Basin Interactions (SBI) project (http://www.eol.ucar.edu/ projects/sbi/). In 2008

(19 July–29 July), water samples were collected from the Mackenzie River plume,

Beaufort Sea, and Amundsen Gulf on the CCGS Amundsen, as part of the Circumpolar

Flaw Lead (CFL) program (http://web.mac.com/barber1818/iWeb/IPY-CFL/). In 2009

(27 July–27 August), waters in the Mackenzie River plume, Beaufort Sea, and Canada

Basin were sampled on the CCGS Amundsen as part of the Malina (MAL) program

(http://malina.obs-vlfr.fr/). Water samples from the four cruises were collected at various depths using Niskin bottles mounted on a rosette with a conductivity-temperature-depth

(CTD) sensor. Samples were filtered through combusted (450˚C, 4 h) GF/F glass fiber filters and stored frozen (-20˚C) in 60 mL high-density polyethylene (HDPE) screw-cap

4 bottles until analyses of DOC, total dissolved nitrogen (TDN), and TDAA were performed in the home laboratory. The HDPE bottles were soaked in 0.5 mol L-1 hydrochloric acid (HCl) for 24 h and rinsed with Milli-Q UV-Plus water before cruises.

The broad spatial scale of sampling sites in this study covers a wide range of environments that vary in primary productivity. The SBI 2002 and 2004 cruises covered relatively productive waters of the Chukchi Sea, which receives nutrient-rich water from the Pacific Ocean, whereas the CFL 2008 and MAL 2009 cruises covered the less productive southern Beaufort Sea, which is influenced by runoff from the Mackenzie

River. In this study, sampling regions were separated into shelf (bottom depth ≤ 100 m, salinity ≥ 27.0), slope (100 m < bottom depth ≤ 1000 m), and basin (bottom depth > 1000 m) areas. In the slope and basin regions, surface water was defined as 0–80 m depth, which includes the chlorophyll maximum layer. The upper halocline in the slope and basin was delimited by depth (80–180 m) and salinity (32.0 ≤ salinity ≤ 33.9; Table 1.1).

Aliquots of filtered (Whatman GF/F; 0.7-μm nominal pore-size) water samples were acidified to pH ≈ 2 with 2 mol L-1 HCl for DOC and TDN analyses. DOC and TDN were measured using high temperature combustion and a Shimadzu TOC-V analyzer equipped with an inline chemiluminescence nitrogen detector (Shimadzu TN-1) (Davis and Benner 2005). Milli-Q UV-Plus water (blank) and reference standards (deep

Sargasso Sea water obtained from the University of Miami) were injected every 6th sample to check the accuracy of the measurements (Benner and Strom 1993). Blanks were negligible and values for reference standards were within 5% of reported values.

Aliquots of filtered water samples were hydrolyzed for analysis of TDAA using an Agilent High Performance Liquid Chromatography (HPLC) system equipped with a

5 fluorescence detector (Excitation: 330 nm; Emission: 450 nm). Water samples were dried with pure nitrogen gas and hydrolyzed using a vapor phase method with 6 mol L-1 HCl at

150˚C for 32.5 min. After neutralization, TDAA were measured as o-phthaldialdehyde

(OPA) derivatives following the method of Kaiser and Benner (2005). The separation of compounds was performed on a Licrosphere RP18 (4.6×150 mm, 5 μm particles) or a

Zorbax SB-C18 (4.6×150 mm, 3.5 μm particles) column. Eighteen amino acids were included in the analysis: asparagine + aspartic acid (Asx), glutamine + glutamic acid

DOC-normalized yields of TDAA (%DOC) were calculated as the percentage of

DOC measured as amino acids. The degradation index (DI) is a diagenetic indicator derived from a principal component analysis of protein amino acid compositions (Dauwe and Middelburg 1998). In this study, the DI was calculated following the method of

Dauwe et al. (1999), as modified by Kaiser and Benner (2009) for application to DOM.

In general, positive DI values indicate recently produced DOM and declining values indicate more diagenetically altered DOM (Davis et al. 2009).

Three categories of DOM biological lability (labile, semi-labile, and refractory) were defined by Davis and Benner (2007) based on DOC-normalized yields of TDAA.

Refractory DOM refers to deep-water DOM (> 1000 m) that has an average TDAA yield of 0.70%DOC in the Arctic Ocean and is resistant to biological utilization over long timescales (decades to millennia) (Davis and Benner 2007). In this study, DOM with

6 TDAA yields > 0.70%DOC is considered bioavailable, and increasing yields of TDAA reflect increasing concentrations of bioavailable DOM.

Statistical analyses were performed with SPSS 20.0 (IBM Statistical Package for the Social Sciences Inc.). The significance of correlations between variables was determined using the Spearman’s rho test (two-tailed, α = 0.05) because the data were not normally distributed. Statistical differences were assessed using the Mann-Whitney U test

(two-tailed, α = 0.05) because of unequal group sizes and non-normal distribution of the data.

1.3 RESULTS

Small boat surveys of surface waters in the Mackenzie River plume (salinity:

0.15–29.90) were conducted during the CFL 2008 and MAL 2009 cruises (Figure 1.1).

Concentrations of DOC ranged from 106 to 458 μmol L-1, with the highest value occurring in the Mackenzie River (salinity: 0.15). The concentrations of DOC across the salinity gradient followed a similar conservative mixing trend during the two cruises (R2

= 0.9260, p < 0.001, n = 19; Figure 1.2a). Non-conservative mixing across the salinity gradient was observed in concentrations of TDAA, and sources of TDAA were evident at mid salinities (6.5–15.5; Figure 1.2b). DOC-normalized yields of TDAA were minimal in the river (0.42 %DOC) and progressively increased with salinity (Figure 1.2c). The two highest yields (1.20 and 1.32 %DOC) were observed at mid-salinity locations with elevated TDAA concentrations, indicating a plankton source. Yields of TDAA were generally higher at mid salinities (6.5–15.5) during CFL 2008 than MAL 2009 (Figure

1.2c).

7 DOC concentrations in Chukchi and Beaufort shelf waters ranged from 59 to 146

μmol L-1 (avg.: 81 μmol L-1) and showed considerable variability at all depths (Figure

1.3a). In comparison, DOC concentrations were lower in slope and basin waters (41–201

μmol L-1; avg.: 67 μmol L-1; Figure 1.3a–c; Table 1.1). Concentration ranges and depth trends of DOC were similar among cruises, with elevated concentrations occurring in near surface waters and decreasing concentrations with depth (Figure 1.3b–c).

TDAA concentrations in shelf, slope, and basin waters were more variable than

DOC concentrations and ranged from 70 to 983 nmol L-1 (avg.: 311 nmol L-1; Figure

1.4a– c). TDAA concentrations were substantially higher in the Chukchi Sea (SBI 2002,

2004; avg.: 323 nmol L-1) than in the Beaufort Sea (CFL 2008, MAL 2009; avg.: 186 nmol L-1). Peak concentrations of TDAA in shelf waters were typically found at 10–30 m

(Figure 1.4a). Concentrations of TDAA generally declined from shelf waters to slope and basin waters (Figure 1.4a–c; Table 1.1). Elevated concentrations of TDAA were found at greater depths (~200 m) in slope and basin waters during SBI 2002 and 2004 (Figure

1.4b–c). Concentrations of TDAA in slope waters during CFL 2008 (avg.: 185 nmol L-1) were significantly higher than those at similar depths during MAL 2009 (avg.: 161 nmol

L-1; p < 0.05; Figure 1.4b).

DOC-normalized yields of TDAA were much higher in the Chukchi Sea (0.39–

4.23 %DOC, avg.: 1.41%DOC) compared with the Beaufort Sea (0.47–3.29 %DOC, avg.: 0.84 %DOC) (Figure 1.5a–c). Maximal TDAA yields in shelf waters were found at

10–30m (Figure 1.5a). Yields of TDAA in slope and basin waters were particularly high in the upper 200 m during SBI 2004 and sometimes exceeded those in shelf waters

(Figure 1.5b–c). Yields of TDAA in slope waters during CFL 2008 (avg.: 0.88 %DOC)

8 were significantly higher than those at similar depths during MAL 2009 (avg.: 0.76

%DOC; p < 0.001; Figure 1.5b).

Average amino acid degradation index (DI) values for the four cruises ranged from 1.58 to 1.08 (Table 1.1). During SBI 2002, 2004, and MAL 2009, DI values decreased from shelf waters to slope-basin surface waters, and with depth (Table 1.1). An opposite trend was observed during CFL 2008. In general, DI values were lower in the

Chukchi Sea (SBI 2002, 2004) than in the Beaufort Sea (CFL 2008, MAL 2009) (Table

1.1). The variable DI values reflect amino acid compositional heterogeneity, which is influenced by source as well as diagenetic alterations. It appears source plays an important role in shaping DI values in these margin waters, which can have high and variable contributions of riverine DOM. The DI values for Mackenzie River DOM in

2008 and 2009, 1.34 and 0.37, respectively, were very different indicating large variability in TDAA composition in riverine DOM. In contrast, the TDAA yields for

Mackenzie River DOM in 2008 and 2009, 0.44 and 0.38 %DOC, respectively, indicating minimal variability in TDAA yields in riverine DOM. Correlations between DI values and TDAA yields were quite variable among cruises (SBI 2002: r = 0.9064, p < 0.001;

SBI 2004: r = 0.4694, p < 0.001; CFL 2008: r = 0.2537, p = 0.2945; MAL 2009: r =

0.6838, p < 0.001), and it appears the influence of riverine DOM on DI values contributes to the weak correlation between DI and TDAA yield during the CFL cruise in 2008.

Based on these observations, TDAA yields were considered better indicators of DOM bioavailability than DI values. In addition, bioassay experiments with a variety of substrates in waters from the Chukchi Sea concluded DI values are not reliable indicators of labile DOM (Davis et al. 2009).

9 The SBI 2002 and 2004 data were combined to represent the Chukchi Sea region, and the CFL 2008 and MAL 2009 data were combined to represent the Beaufort Sea region. In both regions, average concentrations of DOC and TDAA, and TDAA yields generally decreased from shelf waters to slope-basin surface waters, with a greater gradient occurring in the Beaufort Sea (Figure 1.6a–c). One exception was that yields of

TDAA in Chukchi shelf and slope-basin surface waters were quite similar (~1.6 %DOC;

Figure 1.6c). Significant differences in DOC concentrations and TDAA yields between shelf waters and slope-basin surface waters were found in the Beaufort Sea (p < 0.01) but not in the Chukchi Sea (p > 0.1; Table 1.2; Figure 1.6a, c). Differences in TDAA concentrations were highly significant among shelf waters, slope-basin surface waters, and slope-basin upper halocline waters (p < 0.01; Table 1.2; Figure 1.6b). DOC and

TDAA concentrations and TDAA yields in the upper halocline of both regions were substantially lower than those in shelf waters and slope-basin surface waters (Table 1.2;

Figure 1.6a–c).

Interannual variation of DOM concentrations and composition in the Chukchi Sea was examined by comparing data from SBI 2002 with data from SBI 2004. In shelf waters, none of the three parameters were significantly different between 2002 and 2004

(p > 0.2; Figure 1.7a–c). Differences were significant in slope-basin surface waters, where DOC concentrations were significantly higher in 2002 (p < 0.05; Figure 1.7a) and concentrations and yields of TDAA were substantially higher in 2004 (concentration: p =

0.0585, yield: p < 0.0001; Figure 1.7b–c). In contrast to DOC (p = 0.3028; Figure 1.7a),

TDAA concentrations and yields in the upper halocline were significantly higher in 2004 than in 2002 (p < 0.01; Figure 1.7b–c). In 2002, average DOC and TDAA concentrations

10 and TDAA yields decreased from shelf to slope-basin waters (Figure 1.7a–c). In 2004, however, elevated concentrations and yields of TDAA were observed in both slope-basin surface and upper halocline waters (Figure 1.7b–c), and average yields of TDAA were even higher in slope-basin surface and upper halocline waters than in shelf waters (Figure

1.7c).

Average DOC concentrations in both shelf waters and slope-basin surface waters were slightly higher in the Beaufort Sea than in the Chukchi Sea (Figure 1.6a), but not significantly different (shelf waters: p > 0.4; slope-basin surface waters: p > 0.05; Table

1.3). Significantly higher DOC concentrations were observed in upper halocline waters in the Chukchi compared with the Beaufort (p < 0.0001; Table 1.3; Figure 1.6a).

Concentrations and yields of TDAA in shelf waters were significantly higher

(~1.5-fold) in the Chukchi than in the Beaufort (p < 0.0001; Table 1.3; Figure 1.6b–c).

The differences in concentrations and yields of TDAA between the two regions were more pronounced in slope-basin surface and upper halocline waters, with the values in the Chukchi Sea almost double those in the Beaufort Sea (p < 0.0001; Table 1.3; Figure

1.6b–c).

1.4 DISCUSSION

Surface concentrations of DOC and TDAA in the Chukchi Sea were spatially variable and generally corresponded with chlorophyll-a concentrations and primary productivity (Davis and Benner 2005; Hill and Cota 2005; Kirchman et al. 2009a).

Concentrations of DOC and TDAA decreased by ~10% from shelf to slope-basin surface waters, and primary production also decreased from shelf to basin waters (Kirchman et

11 al. 2009a). DOC-normalized yields of TDAA displayed minor spatial variations in shelf and slope-basin surface waters. The yields (~1.6 %DOC) were more than 2-fold greater than those in refractory DOM (0.70 %DOC) (Davis and Benner 2007), indicating a substantial supply of bioavailable DOM in surface waters of the Chukchi Sea. Maximal concentrations and yields of TDAA were observed at depths of 10–30 m where chlorophyll concentrations and primary production were also maximal in summer (Cota et al. 1996; Hill and Cota 2005). This subsurface maximum was not observed for DOC concentrations. In comparison, concentrations of DOC and TDAA and yields of TDAA were significantly lower in the upper halocline (p < 0.01), but TDAA yields (1.38

%DOC) were greater than those of semi-labile and refractory DOM (1.1 and 0.70

%DOC, respectively; Davis and Benner 2007). This indicates that although there was a substantial drawdown of bioavailable DOM below the euphotic zone, concentrations of bioavailable DOM remained relatively high in these waters.

The seasonal variability of bioavailable DOM in the Chukchi Sea was previously described by Davis and Benner (2005, 2007). Here, we further discuss the interannual variations of bioavailable DOM in different Chukchi regions. Concentrations of DOC and

TDAA and yields of TDAA in shelf waters were high in the summers of 2002 and 2004 and were not significantly different between the two years (p > 0.2), indicating bioavailable DOM in shelf waters was relatively abundant and displayed small interannual variations. Concentrations and yields of TDAA decreased by over 30% from shelf to slope-basin surface waters in 2002. In contrast, in 2004 concentrations of TDAA in slope-basin surface waters remained high and the TDAA yields were even higher than those in shelf waters (1.84 vs. 1.58 %DOC). Summer primary productivity in 2004 (0.62

12 g C m-2 d-1) was ~2.5 times higher than that in 2002 (0.24 g C m-2 d-1), but the highest production in 2004 occurred in shelf waters rather than in slope-basin waters (Kirchman et al. 2009a). The observations of higher primary production in shelf waters and greater

DOM bioavailability in slope-basin surface waters suggest a rapid off-shelf transport of bioavailable DOM in 2004. Concentrations and yields of TDAA in upper halocline waters were significantly higher in 2004 than in 2002 (p < 0.0001). In 2004, the average yield of TDAA in upper halocline waters (1.64 %DOC) was very similar to that of shelf waters (1.58 %DOC), thereby suggesting a source of shelf-produced labile DOM (Davis and Benner 2007). The off-shelf transport of bioavailable DOM, however, was not apparent in 2002. These comparisons exhibit irregular interannual variability of bioavailable DOM in different Chukchi regions, as controlled by both biological and physical processes.

Concentrations of DOC in the Mackenzie River plume ranged from 458 μmol L-1 in the river to 106 μmol L-1 at a salinity of 29.9 and exhibited fairly conservative mixing across the salinity gradient in 2008 and 2009, as observed previously (Emmerton et al.

2008) and in other Arctic river plumes (Cauwet and Sidorov 1996; Kattner et al. 1999;

Amon 2004). Desorption from sediments and plankton productivity can be major sources of DOM in river plumes (Macdonald et al. 1998; Benner and Opsahl 2001; Dagg et al.

2004), whereas flocculation, bio- and photo-degradation can be important sinks of DOM

(Chin-Leo and Benner 1992; Uher et al. 2001; Bélanger et al. 2006; Garneau et al. 2006).

Concentrations and yields of TDAA in the Mackenzie River plume were more variable across the salinity gradient, with elevated concentrations and yields of TDAA at mid salinities indicating a plankton source of bioavailable DOM. However, compared to river

13 plumes at lower latitudes (e.g., the Mississippi River plume; Dagg et al. 2004), primary production, production of bioavailable DOM, and microbial processes in the Mackenzie

River plume are relatively low (Retamal et al. 2007; Emmerton et al. 2008; Retamal et al.

2008).

Relatively high concentrations of DOC were observed in regions of the Beaufort shelf and reflected the influence of the Mackenzie River. Riverine input of nutrients supported patches of elevated primary production (Raimbault et al. unpublished data) and elevated concentrations and yields of TDAA in shelf waters, but generally low nutrient concentrations together with stratification of shelf waters during the summer typically resulted in the formation of a chlorophyll and productivity maximum at ~30 m (Carmack et al. 2004; Lavoie et al. 2009; Raimbault et al. unpublished data). This pattern is reflected in elevated concentrations and yields of TDAA in subsurface waters. Bacterial production was strongly correlated with TDAA concentrations in the Beaufort Sea

(Ortega-Retuerta et al. 2012), demonstrating amino acids are reliable indicators of bioavailable DOM. DOC and TDAA concentrations and yields of TDAA decreased rapidly (by 9%, 31%, and 27%, respectively) from shelf to slope-basin surface waters, as conditions became more oligotrophic due to the extensive sea ice cover in the summer of

2009. The low levels of primary production were reflected in low concentrations (200 nmol L-1) and yields (0.85 %DOC) of TDAA during this study. Concentrations of DOC and TDAA were lowest in upper halocline waters, and the off-shelf subsidy of bioavailable DOM was not apparent. Yields of TDAA in the upper halocline (0.78

%DOC) were comparable to values in refractory DOM (0.70 %DOC), indicating DOM in these waters is resistant to biodegradation.

14 Heterogeneous distributions of bioavailable DOM in the Beaufort Sea were apparent from comparisons of the Amundsen Gulf (CFL 2008) and the southeastern

Beaufort Sea (MAL 2009). Concentrations (p < 0.05) and yields (p < 0.001) of TDAA were significantly higher in the Amundsen Gulf than in the southeastern Beaufort Sea, but there was no significant difference (p = 0.4294) in DOC concentrations between the two regions. These higher TDAA concentrations are likely attributable to higher primary productivity (0.28 g C m-2 d-1) in the Amundsen Gulf than in the southeastern Beaufort region (0.07 g C m-2 d-1) at the time of sampling (Raimbault et al. unpublished data)

(Sallon et al. 2011). Although the observed variability of DOM and productivity can also be due to differences in sampling years (2008 vs. 2009), higher primary production in the

Cape Bathurst polynya (52–175 g C m-2 yr-1) than in the rest of the Beaufort Sea

(including the Mackenzie shelf) is a well recognized feature in the Amundsen Gulf

(Arrigo and Van Dijken 2004; Brugel et al. 2009; Forest et al. 2011). In addition to the release of bioavailable DOM from plankton, bacterial degradation of particulate organic matter and zooplankton activities provide additional sources of bioavailable DOM in the

Amundsen Gulf (Juul-Pedersen et al. 2010; Forest et al. 2011; Kellogg et al. 2011). The elevated concentrations and yields of TDAA in the Amundsen Gulf are consistent with the higher productivity in this region.

Interannual variability of DOM in the Beaufort Sea is difficult to address given our limited data, but areas covering similar salinity ranges in the Mackenzie River plume and some shelf and slope waters were sampled during CFL 2008 and MAL 2009.

Comparisons among these regions indicated higher TDAA concentrations and yields in

2008 than in 2009, with negligible differences in DOC concentrations. Strong seasonal

15 forcing governs biological productivity in the Beaufort Sea such that interannual variability in DOM is not unexpected, but we anticipate less temporal variability in the

Beaufort than in the Chukchi. Previous field and remote sensing analyses indicate less pronounced interannual variations in phytoplankton biomass and production in the

Beaufort Sea as compared with the Chukchi Sea (Arrigo and Van Dijken 2004; Brugel et al. 2009).

It is important to explore how the contrasting productivity between the Chukchi and Beaufort Seas influences the concentrations and bioavailability of DOM in the two systems. Primary productivity in the Chukchi Sea (e.g., 30–720 g C m-2 yr-1; Springer and

McRoy 1993; Cota et al. 1996; Kirchman et al. 2009a) is typically much higher than that in the Beaufort Sea (e.g., 12–28 g C m-2 yr-1; Carmack et al. 2004; Brugel et al. 2009;

Lavoie et al. 2009). Given the higher primary productivity, higher rates of DOM production and consumption through the microbial loop are expected in the Chukchi Sea.

However, DOC concentrations in shelf and slope-basin surface waters were not significantly different between the Chukchi and Beaufort Seas. Slightly higher concentrations of DOC were often observed in the Beaufort Sea, apparently due to the influence of the Mackenzie River. The difference in primary productivity between the two regions was not reflected in the concentrations of bulk DOC.

In contrast to the similarities in DOC concentrations, TDAA concentrations in

Chukchi shelf and slope-basin surface waters were 50–90% higher (p < 0.0001) than those in the Beaufort Sea. The bioavailability of DOM, as indicated by yields of TDAA, was also significantly (p < 0.0001) higher (by 90%) in surface waters of the Chukchi Sea compared with those of the Beaufort Sea. Primary productivity in the Chukchi Sea during

16 the sampling periods ranged from 0.24 to 0.62 g C m-2 d-1 and resulted in the production of DOM that is rich in amino acids and is of high bioavailability (Davis and Benner 2005;

Davis and Benner 2007; Kirchman et al. 2009a). In comparison, primary productivity in nutrient-poor waters of the Beaufort Sea was low (0.03–0.45 g C m-2 d-1; Sallon et al.

2011; Raimbault et al. unpublished data) and led to lower concentrations of bioavailable

DOM. The contrasting productivity of the Chukchi and Beaufort Seas appears to be reflected in the abundance and distribution of TDAA.

Significantly higher DOC and TDAA concentrations and yields of TDAA were evident in the upper halocline of the Chukchi region in comparison to the Beaufort. High concentrations and yields of TDAA were observed to depths of 200m in the Chukchi region and appear to be derived from shelf and slope waters in the region (Davis and

Benner 2007). A variety of physical processes likely contribute to the transport of bioavailable DOM into upper halocline waters, including the injection of dense Pacific

Winter Water and mesoscale eddies that form along the shelf break (Manley and Hunkins

1985; Mathis et al. 2007b; Spall et al. 2008). Additional sources of bioavailable DOM in halocline waters include the direct release from plankton, grazing, viral lysis, and release from sinking particles and sediments (Strom et al. 1997; Cooper et al. 2005; Azam and

Malfatti 2007). In contrast, these indicators of bioavailable DOM were not observed in the upper halocline of the Beaufort Sea. Bioavailable DOM is important for sustaining the heterotrophic community in the upper halocline (Wallace et al. 1987; Cota et al.

1996). Concentrations of bioavailable DOM in the upper halocline of the Chukchi region were higher than those in the Beaufort, thereby revealing the important role of the

17 Chukchi Sea in providing bioavailable DOM to low-productivity basins of the Arctic

Ocean. This strong shelf-basin interaction was not apparent in the Beaufort Sea.

The observed net accumulation of bioavailable DOM during the summer in the

Chukchi Sea and adjacent slope-basin waters indicates an uncoupling between the biological production and utilization of DOM. The direct cause(s) of this uncoupling is unknown, but it suggests DOM remineralization in the microbial loop is depressed.

Temperature, availability of labile substrates and nutrient concentrations play important roles in regulating bacterial growth and the functioning of the microbial loop (Pomeroy and Deibel 1986; Thingstad et al. 1997; Kirchman et al. 2009b; Ortega-Retuerta et al.

2012). Additions of relatively high concentrations of bioavailable substrates to water collected from the Chukchi Sea can result in delayed responses from the microbial community that can persist for days to weeks before utilization occurs (Davis et al. 2009).

This slow response to bioavailable substrates could also indicate deficiencies in the metabolic diversity of the microbial community. In any case, the net accumulation of bioavailable DOM in the Chukchi Sea and other productive shelves, such as the Barents

Sea, could be critical for sustaining heterotrophic microbial communities and microbial diversity in the highly oligotrophic waters of the central Arctic basins.

Table 1.1 Physicochemical characteristics in shelf, slope and basin waters of the Chukchi and Beaufort Seas*

Depth Temperature Salinity DOC TDN TDAA TDAA Cruise DI n (m) (˚C) (psu) (µmol L-1) (µmol L-1) (nmol L-1) (%DOC) Shelf (0-80 m) SBI 2002 24±11 1.29±3.26 31.17±1.41 77±12 8.0±4.5 462±177 1.70±0.66 -0.20±0.80 22 SBI 2004 26±16 4.53±3.37 31.40±0.89 83±17 7.9±4.4 406±107 1.58±0.43 -0.50±0.54 19 CFL 2008 nd nd nd nd nd nd nd nd nd MAL 2009 19±16 1.56±2.48 29.95±1.67 85±20 6.1±1.9 289±138 1.17±0.57 0.45±0.90 18 Slope-basin (surface: 0-80 m) SBI 2002 39±16 -1.32±0.41 31.40±1.17 76±6 10.1±5.8 319±61 1.12±0.21 -0.90±0.61 24 SBI 2004 35±17 -0.10±1.77 31.24±1.12 73±5 7.7±4.0 402±165 1.84±0.79 -1.17±1.09 44 CFL 2008 5±1 6.20±1.87 29.01±1.25 74±5 5.1±0.6 225±14 1.00±0.07 0.49±1.13 12

MAL 2009 37±26 0.09±2.17 29.69±3.51 78±25 5.8±2.0 192±47 0.80±0.14 -0.44±0.41 37 Slope-basin (upper halocline: 80-180 m, 32.0-33.9 psu) SBI 2002 131±29 -1.53±0.17 33.22±0.41 69±3 18.6±2.1 243±41 0.92±0.17 -1.58±0.42 23 SBI 2004 129±23 -1.50±0.15 33.09±0.35 70±5 17.2±1.4 344±119 1.64±0.66 -1.35±1.43 20 CFL 2008 112±30 -1.39±0.12 33.20±0.33 66±4 16.9±2.8 177±19 0.84±0.06 1.08±1.15 9 MAL 2009 136±26 -1.38±0.06 32.93±0.39 64±3 16.2±1.6 150±18 0.75±0.11 -0.63±0.46 13

*Samples were collected during four summer cruises (July–August). Definitions: Shelf (bottom depth ≤ 100 m, salinity ≥ 27.0); Slope (bottom depth 100–1000 m); Basin (bottom depth > 1000 m). Surface water in the slope and basin was defined as 0–80 m depth. Upper halocline water in the slope and basin was defined based on depth and salinity (80–180 m, 32.0 ≤ salinity ≤ 33.9). Data are reported as averages ± standard deviations. nd: not determined.

Table 1.2 Statistical comparisons of the concentrations of DOC and TDAA, and TDAA yields in the Chukchi and Beaufort Seas (Mann-Whitney U test).

DOC TDAA TDAA Chukchi Sea (µmol L-1) (nmol L-1) (%DOC) Shelf vs. Slope-basin surface p = 0.1408 p < 0.01 p = 0.3678 Shelf vs. Slope-basin upper halocline p < 0.001 p < 0.0001 p < 0.01 Slope-basin (surface vs. upper halocline) p < 0.0001 p < 0.01 p < 0.05

Beaufort Sea Shelf vs. Slope-basin surface p < 0.01 p < 0.0001 p < 0.001 Shelf vs. Slope-basin upper halocline p < 0.0001 p < 0.0001 p < 0.0001 Slope-basin (surface vs. upper halocline) p < 0.0001 p < 0.0001 p = 0.0842

Table 1.3 Comparisons of the concentrations of DOC and TDAA, and TDAA yields in the Chukchi and Beaufort Seas* (Mann-Whitney U test).

DOC (µmol L-1) TDAA (nmol L-1) TDAA (%DOC) Chukchi Beaufort Chukchi Beaufort Chukchi Beaufort 80±15 85±20 428±140 289±138 1.62±0.53 1.17±0.57 Shelf (p = 0.4338) (p < 0.0001) (p < 0.0001)

Slope-basin 74±5 77±22 378±147 200±44 1.63±0.74 0.85±0.15 (surface) (p = 0.0570) (p < 0.0001) (p < 0.0001)

Slope-basin 70±4 65±4 307±109 161±22 1.38±0.64 0.78±0.10 (upper halocline) (p < 0.0001) (p < 0.0001) (p < 0.0001) *SBI 2002 and 2004 were combined to represent the Chukchi Sea region, whereas CFL 2008 and MAL 2009 were combined to represent the Beaufort Sea region. Data are reported as the average ± standard deviation, with the p-value in parentheses.

Figure 1.1 Locations of sampling stations in the western Arctic Ocean. Four cruises were conducted during the summer and sampled the shelf, slope, and basin environments of the Chukchi and Beaufort Seas. Blue circles – SBI 2002; red circles – SBI 2004; brown inverse triangles – CFL 2008; black triangles – MAL 2009. The Mackenzie River plume was surveyed during CFL 2008 (CFL-east) and MAL 2009 (MAL-west and MAL-east). The 100 and 1000 m isobaths are shown.

500 y = -11.70x + 459.45 a 2

) R = 0.9260, p < 0.001

1 - 400

m 300

(

O 200 MAL-west D MAL-east 100 CFL-east

1300 1200 b

)

- 700

o 600

( 500

D 400

T 300

1.4 c

)

C 1.2

% 1.0

(

A 0.8

T 0.6 0.4

0 5 10 15 20 25 30 Salinity

Figure 1.2 Mixing patterns of DOM in the Mackenzie River plume. Distributions of (a) dissolved organic carbon (DOC), (b) total dissolved amino acids (TDAA), and (c) yields of TDAA (%DOC) across the salinity gradient.

-1 -1 -1 DOC (µmol L ) DOC (µmol L ) DOC (µmol L ) 50 75 100 125 150 40 80 120 160 200 50 60 70 80 90

0 0

100 20

50 60 70 80 90 ) 200

(

t 40

5 D 300

0 60 1

400 0

1 (b)

(a) Shelf 0 (c) Basin 80 500 2 Slope

SBI 2002 SBI 2004 CFL 2008 MAL 2009 Figure 1.3 Concentrations of dissolved organic carbon (DOC) in (a) shelf, (b) slope, and (c) basin waters of the Chukchi Sea (SBI 2002, SBI 2004) and Beaufort Sea (CFL 2008, MAL 2009).

-1 -1 -1 TDAA (nmol L ) TDAA (nmol L ) TDAA (nmol L ) 200 400 600 800 1000 200 400 600 800 1000 200 400 600 800

0 0

100 20

200 400 600

) 200

(

t 40

5 D 300

0 60 1 400

(a) Shelf 0 (b) Slope (c) Basin 80 500 2

SBI 2002 SBI 2004 CFL 2008 MAL 2009 Figure 1.4 Concentrations of total dissolved amino acids (TDAA) in (a) shelf, (b) slope, and (c) basin waters of the Chukchi Sea (SBI 2002, SBI 2004) and Beaufort Sea (CFL 2008, MAL 2009).

TDAA (%DOC) TDAA (%DOC) TDAA (%DOC)

0.5 1.0 1.5 2.0 2.5 3.0 3.5 1.0 2.0 3.0 4.0 1.0 2.0 3.0 4.0

0 0

100 20

0.8 1.2 1.6 2.0

) 200

(

t 40

5 D 300

0 60 1 400

(a) Shelf 0 (b) Slope (c) Basin 80 500 2

SBI 2002 SBI 2004 CFL 2008 MAL 2009 Figure 1.5 DOC-normalized yields of total dissolved amino acids in (a) shelf, (b) slope, and (c) basin waters of the Chukchi Sea (SBI 2002, SBI 2004) and Beaufort Sea (CFL 2008, MAL 2009).

100 500 2.0 a Chukchi b c Beaufort

90 400 1.5

)

l 80 300

n ( 1.0

(

70 A 200

T 0.5 60 100

50 0 0.0 in ) in ) in ) lf s n e lf s n e lf s n e e a ) i n e a ) i n e a ) i n h b e s i h b e s i h b e s i - c a l - c a l - c a l S e a -b c S e a -b c S e a -b c p f e lo p f e lo p f e lo o r p a o r p a o r p a l u o h l u o h l u o h S (s l r S (s l r S (s l r S e S e S e p p p p p p (u (u (u Figure 1.6 Spatial variability of average concentrations of (a) dissolved organic carbon (DOC) and (b) total dissolved amino acids (TDAA), and (c) yields of TDAA in shelf, slope, and basin waters of the Chukchi and Beaufort Seas. Error bars represent two times the standard error.

SBI 2002 a 2 b 2.5 * c

5 600

6 100 SBI 2004 5

500 =

2.0 =

90 .

)

400

. 1.5

80 0

(

300

O 1.0 70 D

D T 200

60 0.5 100

50 0 0.0 in ) in ) in ) lf s n e lf s n e lf s n e e a ) i n e a ) i n e a ) i n h b e s i h b e s i h b e s i - c a l - c a l - c a l S e a -b c S e a -b c S e a -b c p f e lo p f e lo p f e lo o r p a o r p a o r p a l u o h l u o h l u o h S (s l r S (s l r S (s l r S e S e S e p p p p p p (u (u (u Figure 1.7 Temporal comparisons of average concentrations of (a) dissolved organic carbon (DOC) and (b) total dissolved amino acids (TDAA), and (c) yields of TDAA in shelf, slope, and basin waters between SBI 2002 and 2004. Error bars represent two times the standard error. Significant (p < 0.05) and highly significant (p < 0.01) differences are marked with one and two asterisks, respectively.

CHAPTER 2

BIOAVAILABLE DISSOLVED ORGANIC MATTER AND BIOLOGICAL HOT SPOTS DURING AUSTRAL WINTER IN ANTARCTIC WATERS2

2.1 INTRODUCTION

Ocean waters surrounding the Antarctic continent are dynamic and heterogeneous environments characterized by pronounced spatial and temporal variability in light, ice cover, water masses, and micronutrient conditions (Ducklow et al. 2007). Primary production in these areas is limited to varying extents by light and iron, with high rates occurring during the austral summer in iron-rich shelf waters (>1 g C m-2 d-1) and low or negligible rates prevailing during the winter (Moore and Abbott 2000). Plankton activity, including direct release from phytoplankton and release during protozoan grazing and viral lysis, produces bioavailable dissolved organic matter (DOM) that supports the microbial loop in which heterotrophic bacteria remineralize DOM (Pomeroy 1974; Azam et al. 1983) and also produce refractory DOM (Ogawa et al. 2001; Lechtenfeld et al.

2015). The later process contributes to carbon sequestration and was recently conceptualized as the “microbial carbon pump” (Jiao et al. 2010).

Previous studies of Antarctic marine ecosystems indicate a relatively small

2Shen, Y., R. Benner, A. E. Murray, C. Gimpel, B. G. Mitchell, E. L. Weiss, and C. Reiss. 2017. Bioavailable dissolved organic matter and biological hot spots during austral winter in Antarctic waters. J. Geophys. Res.-Oceans 122: 508–520. Copyright (2017) American Geophysical Union.

fraction (<10%) of primary production is utilized by heterotrophic bacteria compared to that in low-latitude oceans (Kirchman et al. 1995; Bird and Karl 1999; Duarte et al.

2005). The low bacterial activity in Antarctic waters has been attributed to low temperature, high grazing pressure, and low bioavailability of DOM (Bird and Karl 1999;

Pomeroy and Wiebe 2001; Duarte et al. 2005). However, several studies have suggested a minor effect of temperature on bacterial growth (Carlson et al. 1998; Ducklow and Yager

2007; Kirchman et al. 2009b), and also observed minor grazing on bacteria during phytoplankton blooms (Anderson and Rivkin 2001; Ducklow and Yager 2007). Bacterial production is often found to be significantly correlated with chlorophyll-a concentrations and primary production, suggesting a close coupling between bacteria and phytoplankton

(Morán et al. 2001; Ortega-Retuerta et al. 2008; Murray et al. 2011; Ducklow et al. 2012;

Teira et al. 2012). Bioassay experiments and field measurements have shown rapid bacterial responses to the supply of bioavailable DOM (Carlson et al. 1998; Obernosterer et al. 2008; Ducklow et al. 2011). Kirchman et al. (2009b) reported a positive correlation between bacterial growth rates and concentrations of semi-labile dissolved organic carbon (DOC) in the Ross Sea. These observations suggest that heterotrophic bacterial activity in Antarctic waters is strongly influenced by the bioavailability of DOM.

Previous research on DOM in the Southern Ocean has mostly focused on measuring concentrations of organic carbon and nitrogen (Kähler et al. 1997; Wiebinga and De Baar 1998; Ogawa et al. 1999; Carlson et al. 2000; Doval et al. 2002; Clarke et al.

2008), and few studies have addressed the composition and bioavailability of DOM

(Kähler et al. 1997; Carlson et al. 2000; Rosenstock et al. 2005; Tremblay et al. 2015).

Logistic challenges have limited most Antarctic expeditions to austral summer months, so

little is known about microbial processes in the austral winter. DOM is generally considered to be of limited bioavailability during the winter (Scott et al. 2000; Pearce et al. 2007). However, active bacterial growth and production have been observed in various dimly lit regions (55˚S–65˚S) during winter periods (Hanson et al. 1983;

Kottmeier and Sullivan 1987; Mordy et al. 1995; Manganelli et al. 2009). This suggests the sporadic occurrence of bioavailable DOM, likely derived from production by phytoplankton, ice algae, and chemoautotrophs (Kottmeier and Sullivan 1987; Cota et al.

1992; Manganelli et al. 2009). Patches of bioavailable DOM form hot spots that can enhance biogeochemical processes relative to surrounding areas (Mcclain et al. 2003;

Azam and Malfatti 2007; Shen et al. 2016a; Shen et al. 2016b). The presence of reactive

DOM is not readily reflected in DOC concentrations but is detectable in the chemical composition and bioavailability of DOM (Kirchman et al. 2001; Davis and Benner 2005;

Shen et al. 2012a).

The bioavailability of DOM is commonly determined using bioassay incubations that measure biological consumption of DOM over time periods of weeks to months

(Søndergaard and Middelboe 1995; Lønborg et al. 2009). This approach provides insights about the rates of DOM utilization as well as its relative bioavailability, but it is impractical for large-scale oceanographic surveys of bioavailable DOM. An alternative approach is based on the chemical analysis of specific molecular indicators of bioavailable DOM that have been identified using bioassay incubations (Amon et al.

2001; Benner 2003; Davis et al. 2009; Goldberg et al. 2010). Molecular indicators of bioavailable DOM can be directly measured in seawater and can provide insights about the occurrence of bioavailable DOM without the need for lengthy incubations. Amino

acids have been used as qualitative and quantitative indicators of bioavailable DOM over broad spatial and temporal scales in various aquatic systems (Davis and Benner 2007;

Shen et al. 2015; Shen et al. 2016b). Certain amino acids, such as glycine and nonprotein amino acids (β-alanine and γ-aminobutyric acid), increase in relative abundance during

DOM degradation, thereby providing additional insights into the extent of DOM alteration (Kaiser and Benner 2009; Shen et al. 2015). Furthermore, D-enantiomers of amino acids are derived from bacteria and are useful biomarkers for tracing the bacterial origin of DOM in the ocean (Mccarthy et al. 1998; Kaiser and Benner 2008).

The objective of this study is to investigate the concentration, chemical composition, and bioavailability of DOM during winter in Antarctic waters and to understand how these features vary with ecosystem productivity and hydrography.

Distributions of chlorophyll-a, particulate organic carbon (POC), DOC, and amino acids are examined in different water masses in sea ice-free and ice-covered regions around the

South Shetland Islands off the northwestern Antarctic Peninsula during August 2012.

Various biochemical indicators are applied to identify specific locations (referred to as biological hot spots) exhibiting elevated concentrations of bioavailable DOM relative to surrounding waters. The western Antarctic Peninsula is among the fastest warming regions on the planet and the most pronounced warming occurs during austral winter, with the mean surface air temperature having risen 5–6˚C since 1950 (Vaughan et al.

2003; Ducklow et al. 2007). Such dramatic winter warming has reduced extent and duration of sea ice, altered phytoplankton communities and production, and affected ecosystem food web (Ducklow et al. 2007; Montes-Hugo et al. 2009), highlighting the importance of conducting studies in this region, particularly in winter.

2.2 METHODS

Sampling was conducted as part of the Antarctic Marine Living Resources

(AMLR) Program aboard the RVIB Nathaniel B. Palmer off the Antarctic Peninsula and

South Shetland Islands (SSI) during August 2012 (Figure 2.1). The sampling areas are characterized by the confluence of different water masses originating from the Antarctic

Circumpolar Current, Bellingshausen Sea, and Weddell Sea. A total of 110 seawater samples were collected from discrete depths (5, 10, 15, 50, 75, 100, 200, 750 m) at 25 stations within the historic AMLR research area, extending from the southern Drake

Passage to the Bransfield Strait (Figure 2.1). A rosette sampler system with 24, 12 L bottles and a Seabird Conductivity-Temperature-Depth (CTD) instrument was used for water collections. Water samples for chlorophyll-a (chl-a) were filtered (GF/F; 0.7 mm pore size; Whatman) immediately following collection. Samples for POC measurements were collected mostly at 5 and 200 m, filtered (GF/F; Whatman; precombusted at 450˚C for 5 h), and stored frozen until analysis. Water samples for analyses of DOC and amino acids were stored frozen (280˚C) in 60 mL high-density polyethylene screw-cap bottles immediately after collection. Hydrographic data were obtained from the CTD sensors and were used to determine the depth of upper mixed layer and to identify water masses

(Table 2.1).

The filters for chl-a determinations were extracted in 7 mL of methanol for 24 h, centrifuged, and measured for fluorescence using an acidification module in a Turner

Trilogy fluorometer. Readings were calibrated with a 5-point calibration curve using a chl-a standard obtained from Sigma, the concentration of which was determined using a

Lambda-18 spectrophotometer. Samples (GF/F filters) for POC analysis were treated

with 10% v/v hydrochloric acid (HCl), dried at 60˚C, and analyzed using an Exeter

Analytical CEC 440HA elemental analyzer.

Water samples for DOC and amino acid measurements were filtered through 0.2 mm pore size membranes (SuporVR -200, Life Sciences). The Supor membranes were cleaned with methanol and then rinsed thoroughly with Milli-Q UV-Plus water before use. The DOC samples were acidified to pH 2–3 with 2 mol L-1 HCl. Concentrations of

DOC were determined by high-temperature combustion using a Shimadzu total organic carbon TOC-V analyzer equipped with an autosampler. Milli-Q UV-Plus water and seawater reference standards were injected every sixth sample (Benner and Strom 1993).

Blanks (Milli-Q water) were negligible and the measured concentrations of reference standards were within the reported range (41–44 µmol L-1). The coefficient of variation among four injections of a given DOC sample was typically ±1.1%.

The D-enantiomer and L-enantiomer of amino acids were analyzed using an

Agilent 1260 ultrahigh performance liquid chromatography (UPLC) system equipped with a fluorescence detector (excitation: 330 nm; emission: 450 nm) (Shen et al. 2015).

Amino acids were determined in all samples that were filtered through Supor membranes

(0.2 µm pore-size) and in a subset of unfiltered samples as total dissolved amino acids

(TDAA) and total particulate amino acids (TPAA), respectively. Hydrolysis and derivatization followed the procedures described by Kaiser and Benner (2005). Briefly, water samples (100 mL) were dried and hydrolyzed using a vapor-phase technique with 6 mol L-1 HCl at 150˚C for 32.5 min. Amino acid enantiomers were derivatized with o- phthaldialdehyde and N-isobutyryl-L-cysteine and were separated on a Poroshell 120 EC-

C18 (4.6×100 mm, 2.7 µm particles) column. A linear binary gradient was used starting

-1 with 100% potassium di-hydrogen phosphate (KH2PO4; 48 mmol L , pH = 6.25) to 61%

KH2PO4 and 39% methanol:acetonitrile (13:1, v/v) at 13.3 min, 46% KH2PO4 at 19.2 min, 40% KH2PO4 at 21.3 min, and 20% KH2PO4 at 22 min. Eighteen amino acids were included in the analysis: asparagine + aspartic acid (Asx), glutamine + glutamic acid

(Glx), serine (Ser), histidine (His), glycine (Gly), threonine (Thr), β-alanine (β-Ala), arginine (Arg), alanine (Ala), γ-aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine (Ile), leucine (Leu), and lysine (Lys). Acid-catalyzed racemization was corrected according to Kaiser and Benner (2005). This method has a limit of quantification of 0.5 nmol L-1 for individual amino acids. D-enantiomers of Asx,

Glx, Ser, and Ala are reported in this study.

Concentrations of TDAA and TPAA were determined as the total concentrations of the eighteen dissolved and particulate amino acids, respectively. DOC-normalized yields of TDAA were calculated as the percentage contributions of amino acid carbon to the total DOC, using equation (1) and being reported in units of %DOC:

[TDAA - C] TDAA(%DOC) = ´100 (1) [DOC]

, where [DOC] and [TDAA-C] are the concentrations of bulk DOC and carbon measured in the total dissolved amino acids, respectively. This calculation excluded the two nonprotein amino acids (β-Ala and γ-Aba) that are thought to be byproducts of decomposition (Cowie and Hedges 1994).

The significance of least squares linear regression analyses between variables was determined using the enter approach in SPSS 20.0 (IBM Statistical Package for the Social

Sciences Inc.). Normality of residuals was tested using a Kolmogorov-Smirnov test (two- tailed, α = 0.05). Statistical differences between variables were analyzed using the

nonparametric Mann-Whitney U test (two-tailed, α = 0.05), which makes no assumptions of equal group size and normality of data distribution.

2.3 RESULTS

Areas around the South Shetland Islands (SSI) are influenced by various water masses during austral winter, including Winter Water (WW), Transitional Bellingshausen

Water (TBW), Circumpolar Deep Water (CDW), and Transitional Weddell Water

(TWW). These water masses were identified at the sampling stations (5–750 m; Table

2.1; Figure 2.2a) according to their temperature and salinity characteristics as described in previous studies (Sangrà et al. 2011; Teira et al. 2012). Cold (21.9 to 20.5˚C) and less saline (33.5–34.3) WW was located in the upper 100 m within the surface mixed layer

(Figure 2.2b). Below the WW intrusions of relatively warm and dense waters from adjacent seas resulted in elevated temperatures and salinities that were characteristic of different water masses in the mesopelagic zone (200–750 m) in the region of the SSI

(Figure 2.2b). In the Drake Passage, the Antarctic Circumpolar Current brought warm (0–

2˚C) and salty (34.4–34.7) CDW that dominated the mesopelagic waters north of the SSI

(60.0–60.25˚S). By contrast, areas south of the SSI (SSI shelf and the Bransfield Strait;

60.5–62.5˚S) were influenced by inflows from the TBW and Weddell Sea (Figure 2.1).

The TBW was not well sampled in this study (n = 3) and is therefore not further discussed. The relatively cold (21.8 to 20.5˚C) and saline (34.3–34.6) TWW entered

Bransfield Strait from the Weddell Sea and was a major component of mesopelagic waters in the Bransfield Strait.

The sampling areas encompassed both sea ice-free and sea ice-covered waters

(Figure 2.1 and Table 2.1). Surface waters in the north of the SSI were largely ice-free, whereas waters south of the SSI were mostly ice-covered. Sea ice affects light reflection and penetration in the water column, and the varying sea ice extent could strongly affect the spatial variability in primary production during this low-light season.

Concentrations of chl-a and POC in the surface WW were quite variable and decreased significantly toward the south from 0.22 to 0.01 µg L-1 (r2 = 0.53, p < 0.01) and from 3.2 to 0.8 µmol L-1 (r2 = 0.29, p < 0.01), respectively (Figure 2.3a,c; Table 2.2).

In comparison, concentrations of chl-a and POC in the CDW and TWW were significantly lower (Mann-Whitney U test, p < 0.01), with most values below 0.04 µg L-1 and 2 µmol L-1 (Figure 2.3b,d; Table 2.2). No significant difference was observed in concentrations of chl-a (Mann-Whitney U test, p > 0.05) and POC (Mann-Whitney U test, p > 0.5) between the CDW and TWW.

Concentrations of DOC around the SSI ranged from 35 to 58 µmol L-1 (average:

42±4 µmol L-1) and showed no latitudinal gradients in the surface waters (r2 = 0.00, p >

0.5) or vertical gradients between surface and mesopelagic layers (Mann-Whitney U test, p > 0.1) (Figure 2.4a,b; Table 2.2). In comparison, concentrations and DOC-normalized yields of TDAA were more variable, ranging 3-fold from 84 to 257 nmol L-1 and from

0.6% to 1.7% in the surface WW and 2-fold from 83 to 144 nmol L-1 and from 0.5% to

1.1% in the mesopelagic waters, respectively (Figure 2.4c–f; Table 2.2). Concentrations and yields of TDAA in the surface WW displayed weak but significant latitudinal trends

(r2 = 0.11–0.13, p < 0.05), while in the mesopelagic waters these values exhibited significant differences between the CDW and TWW (p < 0.05).

Depth profiles of chl-a, DOC, and amino acids at seven sea ice-free and six sea ice-covered stations are presented in Figure 5. At the ice-free stations, concentrations of chl-a were elevated near the surface and decreased with increasing depth (Figure 2.5a).

Concentrations of DOC were generally low (37–45 µmol L-1) and showed no apparent depth trends (Figure 2.5b). Maximal concentrations of DOC (40–57 µmol L-1) were observed at a depth of 75 m at four stations (W0701, W0202, W0804, and W0407).

Surface concentrations of chl-a and POC at these four stations were among the highest values measured in all profiles. TDAA concentrations (60–257 nmol L-1) were more variable than DOC concentrations and the maximal values (>160 nmol L-1) also occurred at 75 m at the four stations (Figure 2.5c). TPAA were measured in a subset of unfiltered samples and showed markedly high concentrations at 75 m (40–110 nmol L-1; Figure

2.5d), coinciding with the depth of maximal DOC and TDAA concentrations.

Concentrations of chl-a, DOC, TDAA, and TPAA at the ice-covered stations

(Figure 2.5e–h) were much lower and less variable than those in the open waters. Chl-a concentrations were below 0.1 µg L-1 near the surface and gradually decreased with increasing depth (Figure 2.5e). Concentrations of DOC and TDAA were distributed relatively uniformly throughout the water column and ranged mostly from 40 to 45 µmol

L-1 and from 85 to 140 nmol L-1, respectively (Figure 2.5f,g). Concentrations of TPAA displayed considerable variability and ranged from 6 to 95 nmol L-1 (Figure 2.5h). None of these parameters showed a notable subsurface maximum at the sea ice-covered stations.

DOM bioavailability at the ice-free and ice-covered stations was investigated using four independent amino acid-based indicators (DOC-normalized yields of TDAA

and mole percentages of glycine, nonprotein amino acids and D-amino acids)

(Supplementary Figure B.1). DOM of high bioavailability is typically enriched in total amino acids with a low relative abundance of glycine, β-Ala and γ-Aba, and D-amino acids. As microbial alteration proceeds, TDAA yields decrease due to preferential removal of most amino acids relative to bulk DOC, whereas the mole fractions of glycine, β-Ala and γ-Aba, and D-amino acids increase as a result of selective preservation, decarboxylation of glutamine and asparagine, and release of bacterial D-amino acids, respectively (Cowie and Hedges 1994; Nguyen and Harvey 1997; Kaiser and Benner

2008; Kaiser and Benner 2009). An outstanding feature was found at the four ice-free stations with subsurface maxima of DOC and amino acid concentrations (Figure 2.6).

Yields of TDAA at these four stations showed maximal values (1.3–1.7 %DOC) at 75 m

(Figure 2.6a). Remarkable declines in mole percentages of glycine (20–30%), nonprotein amino acids (β-Ala + γ-Aba: 6–9%), and D-enantiomers of amino acids (20–30%) was observed also at 75 m (Figure 2.6b–d). These molecular indicators were consistent in showing elevated bioavailability of DOM in subsurface waters. This feature was not evident at other sea ice-free or ice-covered stations (Supplementary Figure B.1).

2.4 DISCUSSION

There is a paucity of data on concentrations of organic carbon in Antarctic waters during austral winter due to difficult sampling conditions. Concentrations of POC (1–3

µmol L-1) and DOC (35–58 µmol L-1) measured in this study were comparable to the few existing winter values (POC: 0–5 µmol L-1; DOC: 35–45 µmol L-1) in the region of

Antarctic Peninsula (Cota et al. 1992; Manganelli et al. 2009). Much higher and more

variable winter concentrations of DOC were reported in coastal waters near the Davis

Station (90–170 µmol L-1) (Pearce et al. 2007) and in the northern Marguerite Bay and western Antarctic Peninsula (40–100 µmol L-1) (Clarke et al. 2008). Other field surveys of organic carbon in the Southern Ocean were mostly conducted during spring-summer

(October to January), and they covered regions of Antarctic Peninsula (Doval et al. 2002;

Ducklow et al. 2007; Clarke et al. 2008; Ortega-Retuerta et al. 2010), Weddell Sea

(Wedborg et al. 1998), Ross Sea (Carlson et al. 2000; Gardner et al. 2000), and northern areas (40–60˚S) of the Southern Ocean (Kähler et al. 1997; Wiebinga and De Baar 1998;

Ogawa et al. 1999; Tremblay et al. 2015). With a few exceptions, surface concentrations of DOC measured in these studies (mostly 45–60 µmol L-1) are comparable to our winter results but are lower than most values found in other major ocean basins (60–100 µmol L-

1) (Kaiser and Benner 2009; Hansell 2013; Shen et al. 2016a).

Significant latitudinal changes in DOM composition were observed in surface waters around the SSI and appeared to reflect variability in ecosystem productivity.

Winter primary production in this dimly lit environment is light-limited and the surface distributions of chl-a and POC revealed a significant decrease in phytoplankton biomass and potential production from open waters (north of the SSI) to sea ice-covered regions

(south of the SSI). This pattern appeared to parallel the trends in the surface concentrations and DOC-normalized yields of dissolved amino acids, which decreased significantly from the Drake Passage to Bransfield Strait and indicated a southward decline in DOM bioavailability. The latitudinal change in amino acid yields (from 1.7% to 0.7%) in these surface waters approximated vertical gradients occurring in other ocean basins (Kaiser and Benner 2009), indicating substantial spatial variability in DOM

bioavailability in this region. The coincident pattern of ecosystem productivity and DOM bioavailability also appears to occur during the austral summer, but in a reverse latitudinal trend. An increasing north-south trend in protein-like fluorescence of DOM has been measured around the SSI in summer and it corresponds to a southward increase in primary production (Teira et al. 2012). Unlike the wintertime scenario, summer primary production is generally lower in the Drake Passage, where phytoplankton growth is limited by iron, and is elevated toward the south in the Bransfield Strait, where the mixing of iron-replete waters from the adjacent Weddell Sea supports high phytoplankton biomass and production (Holm-Hansen and Hewes 2004; Ardelan et al. 2010; Teira et al.

2012; García-Muñoz et al. 2014).

DOM composition in the mesopelagic layer displayed a different pattern that was related to hydrographical conditions. Concentrations and yields of amino acids between

200 and 750 m were lower in the Drake Passage than those in the Bransfield Strait, and this appeared to be associated with varying sources and utilization of bioavailable DOM in the different water masses. CDW and TWW dominate the mesopelagic layers of the

Drake Passage and the Bransfield Strait, respectively. CDW is a thick layer (200–3000 m) formed by mixing deep waters from other ocean basins (Rintoul et al. 2001) and it receives a very limited supply of bioavailable DOM (Kaiser and Benner 2009). In addition, heterotrophic activity may decompose bioavailable substrates during transport in the CDW. The low oxygen level typically found in the upper CDW (oxygen <180–200

µmol kg-1; Rintoul et al. 2001) is consistent with the extremely low amino acid yields

(0.5–0.8 %DOC), indicating DOM in the CDW has undergone extensive microbial alteration and is of limited bioavailability. By contrast, higher amino acid yields (mostly

0.8–1.2 %DOC) were observed in the TWW, indicating elevated concentrations of bioavailable DOM. Intermediate and deep waters in the eastern and southern regions of the Bransfield Strait originate primarily from the Weddell Sea and have a chlorofluorocarbon (CFC)-derived age younger than 8 years (CFC-11: 3–5 pmol kg-1)

(Gordon et al. 2000; Sangrà et al. 2011). The relatively rapid ventilation allows some bioavailable DOM (e.g. semi-labile DOM with a turnover time of months-decades) produced on the Weddell shelf to be transported to the Bransfield Strait.

The present study corroborated previous findings that common biochemicals, such as amino acids and carbohydrates, can reveal insights about DOM bioavailability that are not apparent from DOC concentrations. The dynamics of ecosystem productivity and ocean circulation around the SSI were reflected in the composition and bioavailability of DOM but not in DOC concentrations. Similar features have been noted in other polar waters. Kirchman et al. (2001) observed a 10-fold increase in concentrations of dissolved neutral sugars during a summertime phytoplankton bloom in the Ross Sea, while concentrations of DOC increased by only 20%. Likewise, Shen et al.

(2012) measured similar concentrations of DOC in two Arctic systems with contrasting productivity (Chukchi and Beaufort Seas) but observed substantial accumulations of amino acid-rich DOM over the more productive Chukchi shelf. Recent observations in a highly productive ocean margin also indicated pronounced temporal and spatial variability in plankton-derived DOC in surface waters, in sharp contrast to the low variability of DOC concentrations (Shen et al. 2016b). Plankton production and heterotrophic microbial processes rapidly cycle amino acids and carbohydrates. These biomolecules generally comprise a relatively small fraction of seawater DOM (<15

%DOC) (Benner 2002; Kaiser and Benner 2009), and their production and consumption are often not apparent from changes in DOC concentrations.

Biochemical indicators revealed the occurrence of biological hot spots around the

SSI during winter. Maximal concentrations and yields of amino acids were observed near a depth of 75 m at most sea ice-free stations and the values were twice those in surrounding waters. The maximal amino acid yields (1.3–1.7 %DOC) were comparable to those (1–2 %DOC) in coastal and open ocean surface waters (Kaiser and Benner 2009;

Shen et al. 2016b), indicating the occurrence of bioavailable DOM in these polar waters.

Low mole percentages of glycine, nonprotein amino acids (β-Ala and γ-Aba) and D- amino acids further indicated the fresh biochemical nature of the DOM and minimal diagenetic alterations. The factors regulating the microbial utilization of the bioavailable

DOM in these waters are not known, but the occurrence of hot spots of bioavailable

DOM indicates the potential to support heterotrophic microbial activity during winter’s months when the abundance of labile substrates is low.

The observation of hot spots during austral winter is surprising and intriguing given the low water temperatures and primary productivity. The effects of temperature on bacterial metabolism are complex (Pomeroy and Wiebe 2001; Kirchman et al. 2005;

Ducklow et al. 2012), but there is evidence that bacteria in the polar oceans are adapted to low temperature (Carlson et al. 1998; Kirchman et al. 2009b). Field observations in the

Ross Sea (~1.7˚C) and off the Kerguelen Island (2–4˚C) showed remarkable increases in bacterial biomass and production with the supply of bioavailable substrates produced during phytoplankton blooms (Carlson et al. 1998; Obernosterer et al. 2008).

The sources of bioavailable DOM to the hot spots are difficult to determine but are interesting to consider. The deep chl-a maximum, a common feature at 50–100 m in the AMLR sampling region during austral summer (Holm-Hansen and Hewes 2004), was not observed during the winter (Manganelli et al. 2009; this study). All hot spots were characterized by the absence of sea ice cover during sampling, and the concentrations of chl-a and POC at these stations were among the highest in the present study. This implies surface phytoplankton production could be a potential source of bioavailable DOM in the subsurface waters. The relatively low mole percentages of D-amino acids in the hot spots were consistent with an algal source of the bioavailable DOM. Depth distributions of particulate amino acids indicated the occurrence of amino acid-rich particles in the hot spots. It is likely that biogenic particles produced locally or elsewhere release bioavailable DOM into the subsurface waters via dissolution, grazing, and viral lysis

(Strom et al. 1997; Carlson 2002; Azam and Malfatti 2007). Another possible source is chemoautotrophic production, which has been found to be substantial (0.1–40 ng C L-1 d-

1, 10% of the total prokaryotic carbon production) during austral winter around the

Antarctic Peninsula (Manganelli et al. 2009; Grzymski et al. 2012).

The observations of biological hot spots reveal spatial heterogeneity in Antarctic waters and highlight a potential role of heterotrophic bacteria in carbon cycling during the winter. There is growing evidence that bacterial metabolism and processes can transform labile DOM into refractory DOM and thereby contribute to the long-term storage of carbon in the ocean (Brophy and Carlson 1989; Ogawa et al. 2001;

Lechtenfeld et al. 2015), a process recently conceptualized as the “microbial carbon pump” (Benner 2010; Jiao et al. 2010). The biological hot spots likely represent

important sites of bacterial activity and the potential for sequestration of carbon through the microbial carbon pump in a low primary production regime. It has already been noticed in various Antarctic oceanic regions that bacteria are abundant and active during the austral winter (Kottmeier and Sullivan 1987; Mordy et al. 1995; Manganelli et al.

2009), suggesting bacterial transformation of bioavailable carbon to more bioresistant forms occurs during periods of diminished primary productivity in high-latitude environments.

Table 2.1 Summary of oceanographic parameters at the sampling stationsa

Latitude Longitude Sea ice SST Z b Sampling Major water Station SSS UML n (˚S) (˚W) coverage (˚C) (m) Depth (m) Massc W07-01 -60.001 -56.501 Ice-free 33.96 -1.26 75 5 – 750 WW, CDW 7 W08-02 -60.252 -57.000 Ice-free 33.96 -1.38 97 5 – 750 WW, CDW 7 W02-02 -60.244 -54.008 Ice-free 34.09 -1.54 100 5 – 750 WW, CDW 7 W08-04 -60.747 -56.998 Ice-free 33.93 -0.78 120 5 – 750 WW, CDW 7 W08-05 -60.998 -56.994 Ice-free 33.91 -0.94 92 5, 200 WW, CDW 2 W07-05 -60.993 -56.496 Ice-free 33.97 -1.47 115 5, 200 WW, CDW 2 W055-05 -61.000 -55.753 Ice-free 34.14 -1.57 nd 5 - 100 WW 5 W08-06 -61.248 -56.995 Ice-free 34.00 -1.35 112 5, 200 WW, CDW 2 W08-07 -61.507 -57.001 Ice-free 34.06 -1.83 177 5 – 450 WW, TWW 7 W04-07 -61.489 -55.010 Ice-free 34.06 -1.53 77 15 – 750 WW, TWW 6 W07-07 -61.500 -56.506 Ice-covered 34.06 -1.82 172 5, 200 WW 2 W02-07 -61.496 -54.026 Ice-covered 34.10 -1.80 98 5 – 750 WW, TWW 7 W08-08 -61.690 -57.122 Ice-covered 34.07 -1.84 154 5, 200 WW 2 W07-08 -61.757 -56.498 Ice-covered 34.12 -1.84 108 5, 200 WW, TWW 2 W055-08 -61.744 -55.768 Ice-covered 34.14 -1.84 145 5, 200 WW, TWW 2 W08-09 -62.000 -56.998 Ice-covered 34.09 -1.86 126 5, 200 WW, TWW 2 W07-09 -62.003 -56.540 Ice-covered 34.11 -1.83 111 5 – 750 WW, TWW 7 W055-09 -61.998 -55.768 Ice-covered 34.12 -1.83 127 5 – 750 WW, TWW 7 W04-09 -61.982 -55.024 Ice-covered 34.07 -1.83 155 15 – 750 WW, TWW 6 W03-09 -62.003 -54.515 Ice-covered 34.10 -1.79 72 5, 200 WW, TWW 2 W10-10 -62.256 -58.001 Ice-covered 34.02 -1.83 54 15 – 750 WW, TWW 6 W08-10 -62.240 -57.008 Ice-covered 34.10 -1.85 99 5 – 750 WW, TWW 7 W07-10 -62.250 -56.516 Ice-covered 34.12 -1.86 91 5, 200 WW, TWW 2 W09-11 -62.503 -57.501 Ice-covered 34.16 -1.86 101 5, 200 WW, TWW 2 W07-11 -62.489 -56.536 Ice-covered 34.21 -1.87 44 5, 200 WW, TWW 2 aSSS, sea surface salinity; SST, sea surface temperature; nd, not determined; and n, number of samples. bZUML: Depth of the upper mixed layer, determined as the depth in which potential density is 0.05 kg m23 greater than the value at 5 m (Hewes et al. 2008). Similar results were obtained by using a temperature-based criterion (i.e., SST+0.2˚C). cThree major water masses were identified in this study: Winter Water (WW), Circumpolar Deep Water (CDW), and Transitional Wed- dell Water (TWW).

Table 2.2 Physical and chemical properties of major water masses and the biological hot spotsa

Latitude Temp. Depth Chl-a POC DOC TDAA TDAA Salinity (˚S) (˚C) (m) (µg L-1) (µmol L-1) (µmol L-1) (nmol L-1) (%DOC) WW (n=76) 60.00-62.50 34.05±0.15 -1.52±0.45 5-100 0.09±0.05 1.9±0.5 42±4 122±32 0.9±0.3

CDW (n=10) 60.00-61.50 34.61±0.09 1.48±0.78 200-750 0.01±0.01 1.3±0.3 41±3 88±16 0.7±0.1

TWW (n=21) 60.25-62.50 34.42±0.10 -0.95±0.38 200-750 0.02±0.01 1.3±0.3 41±3 104±16 0.8±0.2

Hot spots (n=4) 60.00-61.50 34.04±0.08 -1.24±0.42 75 0.12±0.02 nd 47±7 205±43 1.5±0.2

All 60.00-62.50 34.17±0.24 -1.14±0.98 5-750 0.07±0.05 1.7±0.5 42±4 116±30 0.9±0.2

47 a

Data are reported as average ± standard deviation. bWW, Winter Water; CDW, Circumpolar Deep Water; TWW, Transitional Weddell Water; and nd, not determined.

Figure 2.1 Sampling sites off the South Shetland Islands during the Antarctic Marine Living Resources (AMLR) cruise in August 2012. The sampling regions include various water masses from the Antarctic Circumpolar Current (ACC), Bellingshausen Sea, and Weddell Sea. The sea ice-free (n = 11) and sea ice-covered (n = 14) stations are denoted by red and blue solid circles, respectively. Stations with 5–7 sampling depths (5–750 m) are labeled with station names (n = 13) and represent the vertical profiles in Figures 2.5 and 2.6. The dashed rectangles include the stations plotted in Figure 2.2b. Map was generated using Ocean Data View 4.7.4 (Schlitzer, R., Ocean Data View, http://odv. awi.de, 2015).

Figure 2.2 (a) Temperature versus salinity of all water samples (n = 110) collected in the present study. WW, Winter Water; CDW, Circumpolar Deep Water; TBW, Transitional Bellingshausen Water; TWW, Transitional Weddell Water. TBW was not well sampled in this study and were excluded from the discussion. (b) Latitudinal distributions of temperature and salinity in the upper 750 m along the two transects extending from the Drake Passage to the Bransfield Strait (see Figure 2.1).

Figure 2.3 Latitudinal distributions of (a, b) chlorophyll-a (chl-a) concentrations and (c, d) particulate organic carbon (POC) concentrations in Winter Water (WW, 5–100 m), Circumpolar Deep Water (CDW, 200–750 m), and Transitional Weddell Water (TWW, 200–750 m).

Figure 2.4 Latitudinal distributions of (a, b) dissolved organic carbon (DOC) concentrations, (c, d) total dissolved amino acid (TDAA) concentrations, and (e, f) DOC- normalized yields of TDAA in Winter Water (WW, 5–100 m), Circumpolar Deep Water (CDW, 200–750 m), and Transitional Weddell Water (TWW, 200–750 m).

Figure 2.5 Depth profiles of (a, e) chlorophyll-a (chl-a) concentrations, (b, f) dissolved organic carbon (DOC) concentrations, (c, g) total dissolved amino acid (TDAA) concentrations, and (d, h) total particulate amino acid (TPAA) concentrations at (upper plots) seven sea ice-free stations and (lower plots) six ice-covered stations. Note that the ice-free and ice-covered stations are labeled separately as in plots a and e, respectively.

Figure 2.6 Depth profiles of (a) DOC-normalized yields of total dissolved amino acids (TDAA), (b) mole percentages of glycine, (c) mole percentages of two non-protein amino acids: β-alanine (β-Ala) and γ-aminobutyric acid (γ-Aba), and (d) percentages of D-enantiomers of amino acids at the four sea ice-free stations with subsurface maximal DOC and TDAA. Percentages of D-amino [D - AA ] acids were calculated as: D - amino acids (%) = 4 ´100, where [D-AA4] and [L-AA4] [D - AA4 ]+[L - AA4 ] were concentrations of the four D- and L-amino acids (Asx, Glx, Ser, and Ala).

CHAPTER 3

BIOLOGICAL HOT SPOTS AND THE ACCUMULATION OF MARINE DISSOLVED ORGANIC MATTER IN A HIGHLY PRODUCTIVE OCEAN MARGIN3

3.1 INTRODUCTION

Ocean margins account for < 10% of global ocean surface area but play a disproportionally large role in biological productivity, respiration, and carbon burial

(Gattuso et al. 1998). Autotrophic and heterotrophic processes interact in a dynamic manner and are superimposed on strong physical forcing, promoting rapid and diverse biogeochemical processes. This topic has attracted considerable attention in the past two decades (Smith and Hollibaugh 1993; Bauer et al. 2013). The Louisiana margin in the northern Gulf of Mexico is a very dynamic system, with large inputs of nutrients and organic matter from the Mississippi–Atchafalaya River system that render this region among the world’s most productive ocean margins (> 300 gC m-2 yr-1) (Goolsby et al.

2001; Heileman and Rabalais 2008; Shen et al. 2012b). Heterotrophic bacteria respire a large amount of organic matter and exert a pronounced influence on air–sea CO2 exchange and the development of hypoxic conditions in stratified bottom waters (Amon and Benner 1998; Rabalais et al. 2002; Green et al. 2006).

3Shen, Y., C. G. Fichot, S.-K. Liang, and R. Benner. 2016. Biological hot spots and the accumulation of marine dissolved organic matter in a highly productive ocean margin. Limnol. Oceanogr. 61: 1287-1300. Copyright (2016) The Authors Limnology and Oceanography published by Wiley Periodicals, Inc. on behalf of Association for the Sciences of Limnology and Oceanography. This article is published with open access.

Studies of plankton activity on the Louisiana margin reveal spatial linkages among primary production, bacterial production, and remineralization processes in surface waters (Chin-Leo and Benner 1992; Gardner et al. 1994; Murrell et al. 2013).

This is largely attributed to plankton production of labile dissolved organic matter

(DOM) that fuels bacterial growth and activity (Amon and Benner 1998; Benner and

Opsahl 2001). The rapid response of microorganisms to patches of labile DOM results in specific locations and time periods of enhanced biogeochemical processes, thereby creating hot spots and hot moments, respectively (Mcclain et al. 2003; Azam and Malfatti

2007; Stocker et al. 2008). The occurrence of hot spots and hot moments reveals the spatial and temporal heterogeneity of the system and is closely linked to the production of labile DOM.

Despite the well-documented high primary productivity, spatial and temporal distributions of labile DOM on the Louisiana margin have not been well characterized.

Bioassay experiments with filtered water samples and associated microorganisms can be used to estimate the labile fraction of DOM (Søndergaard and Middelboe 1995). These authors defined labile DOM as the amount of dissolved organic carbon (DOC) decomposed within 1–2 weeks in bioassay experiments, and in a review of the literature they found that 19±12% of the DOC in a variety of marine environments was labile. A study using shorter-term (2–5 d) bioassay experiments found similar variability (11±7%) in the labile fraction of DOC in surface waters of the Louisiana margin (Gardner et al.

1994). Measurements of the biochemical components of DOM that can be rapidly utilized, such as amino acids and carbohydrates, can also be used to estimate the labile fraction of DOC (Benner 2003; Davis and Benner 2007). Benner and Opsahl (2001) used

neutral sugars as biochemical indicators of plankton-derived DOM and found elevated production and consumption of carbohydrate-rich DOM at mid-salinity locations in plume waters.

Plankton production of labile DOM is strongly influenced by the highly variable nutrient and light conditions across the margin (Smith and Hitchcock 1994; Lohrenz et al.

1999; Turner and Rabalais 2013). Under nutrient limitation, phytoplankton growth is inhibited while photosynthesis still proceeds, resulting in preferential production of carbohydrates over proteins (Myklestad and Haug 1972; Jiang et al. 2012). Labile DOM is released into the environments through various processes, including extracellular release from phytoplankton and grazing-mediated release (Carlson and Hansell 2015).

The former is active at high irradiance (Cherrier et al. 2015), whereas the latter can be active at night when zooplankton migrate to the surface to feed (Dagg 1995; Dagg et al.

1998). As a result, labile DOM produced under varying nutrient and light regimes can be compositionally different and have varying effects on bacterial activity and carbon cycling (Azam and Malfatti 2007; Buchan et al. 2014). The chemical composition of labile DOM can provide valuable insights about the interplay between primary and secondary production, but remains poorly characterized on the Louisiana margin.

Furthermore, although previous studies have measured high rates of primary production and community respiration, the net balance of these processes remains uncertain. Seasonal accumulation of DOC in surface waters has been reported in a wide range of aquatic environments and attributed to relatively low bioavailability of DOM and mineral nutrient limitation (Copin-Montégut and Avril 1993; Carlson et al. 1994;

Williams 1995; Zweifel et al. 1995; Thingstad et al. 1997; Søndergaard et al. 2000).

Bioavailable DOC that persists for periods of months to years is characterized as semi- labile DOM (Carlson and Hansell 2015), and it is likely that plankton-derived DOM on the Louisiana shelf includes semi-labile components that accumulate in surface waters.

The highly variable nutrient conditions in the region can also promote the accumulation of semi-labile DOM. The estimation of marine DOC accumulation in river-influenced systems is challenging due to the confounding presence of terrigenous DOC (tDOC).

Concentrations of tDOC were measured during this project (Fichot and Benner 2012;

Fichot and Benner 2014), which facilitated the calculation of marine DOC concentrations in this ocean margin.

In this study, we identify hot spots of labile DOM on the Louisiana margin over an annual cycle using biochemical indicators (amino acids and carbohydrates) and examine how their spatial and temporal distributions reflect patterns of plankton production and nutrient limitation. The accumulation of marine DOC is estimated for each season, and the mechanisms controlling DOC accumulation are inferred from biochemical indicators and contemporaneous measurements of nutrients (Chakraborty and Lohrenz 2015). Furthermore, the mixed layer reservoirs of total and accumulated marine DOC on the Louisiana shelf are estimated and the implications are discussed.

3.2 METHODS

Surface water samples were collected during five cruises to the Louisiana margin in the northern Gulf of Mexico on the R/V Cape Hatteras and the R/V Hugh Sharp in

January, April, July, October–November 2009, and March 2010 as part of the

GulfCarbon project. The study region is strongly influenced by the Mississippi and

Atchafalaya River system (Figure 3.1), which discharged 16,000–32,000 m3 s-1 of freshwater into the margin during these sampling months (low in summer and high in spring; Shen et al. 2012b). About 50 stations were surveyed during each cruise (Figure

3.1), except in January when 24 stations were sampled. A total of 222 surface water samples were collected from upper 3 m of water column across a salinity range of 0–37 with 10-L Niskin bottles mounted on a rosette with a conductivity–temperature–depth instrument. About 60–80% of the samples were collected during daylight hours.

Immediately following collection, samples were gravity filtered through precleaned

(450˚C, 4 h) glass fiber (GF/F) filters (0.7-µm pore-size) and stored frozen (220˚C) in precleaned (450˚C, 4 h) clear glass vials until analyses of DOC, amino acids, and neutral sugars were conducted in the home laboratory. Samples for measurements of neutral sugars were not collected during the April cruise. This study reports results within the salinity range of 20–37 (n = 199), which covers the vast majority of the ocean margin and excludes low salinity plumes dominated by river water.

Shipboard bioassay experiments were conducted during the March 2010 cruise and used to determine the labile fraction of DOM, validate the use of amino acids and neutral sugars as biochemical indicators of labile DOM, and to examine the microbial utilization of amended labile substrates. Surface waters (salinity 23.3 and 27.8) from two locations were gravity filtered through precleaned GF/F filters. Filtered waters were divided into control treatments and treatments with additions (23–24 µmol DOC L-1) of labile, plankton-derived DOM (Fichot and Benner 2012). The plankton-derived DOM was collected previously from a coastal diatom bloom, filtered (GF/F) and stored frozen

(-20˚C). The plankton-derived DOM was enriched in free and combined forms of amino

acids and neutral sugars (~40% of the DOC), and it was added to the water samples at a

1:500 dilution (v/v). The addition of inorganic nutrients from the plankton DOM amendment was minor. Each treatment was conducted in triplicate in precleaned (450˚C,

4 h) 125-mL Kimax glass bottles. One set of replicates (n = 3) was immediately frozen at

-20˚C on day 0 (initial time point), and the other set (n = 3) was incubated onboard at ambient seawater temperatures (15–20˚C) in the dark for 10–12 d. Water samples were frozen (-20˚C) after incubation. All replicates were analyzed for concentrations of DOC, amino acids, and neutral sugars.

Concentrations of DOC were measured using high-temperature combustion and a

Shimadzu total organic carbon (TOC) and total nitrogen (TN) analyzer equipped with an autosampler. Blank water (Milli-Q UV-Plus) and seawater reference standards were injected every 6th sample (Benner and Strom 1993). Blanks were negligible and the measured concentrations of reference standards were within the range of reported values

(41–44 µmol L-1). The coefficient of variation among four injections of a given DOC sample was typically ±0.6%.

Amino acids were analyzed following the method of Kaiser and Benner (2005).

Briefly, nitrogen-dried samples were hydrolyzed with 6 mol L-1 hydrochloric acid at

150˚C for 32.5 min in a CEM Mars 5000 microwave and measured as o-phthaldialdehyde derivatives using an Agilent 1100 high performance liquid chromatography system equipped with a fluorescence detector (Excitation: 330 nm; Emission: 450 nm). A

LiChrosphere RP18 column (4.6×150 mm, 5 µm) was used to separate the following 16 amino acids: aspartic acid + asparagine (Asx), glutamic acid + glutamine (Glx), serine

(Ser), glycine (Gly), threonine (Thr), β-alanine (β-Ala), arginine (Arg), alanine (Ala), γ-

aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine

(Ile), and lysine (Lys). Neutral sugars, the most abundant carbohydrates, were determined using a Dionex 500 high performance liquid chromatography system with a PA 1 column coupled with a pulsed amperometric detector (Skoog and Benner 1997; Kaiser and

Benner 2009). Samples were hydrolyzed with 1.2 mol L-1 sulfuric acid, neutralized with a self-absorbed ion retardation resin, and desalted using a mixture of cation and anion exchange resins. Seven neutral sugars were quantified in the analysis: fucose (Fuc), rhamnose (Rha), arabinose (Ara), galactose (Gal), glucose (Glc), mannose (Man), and xylose (Xyl).

The concentration of each amino acid and neutral sugar was quantified using an external calibration curve generated with five concentrations of standards that bracketed the entire range of values observed in the samples. The final concentrations of total dissolved amino acids (TDAA) and neutral sugars (TDNS) were calculated as the sum of the sixteen amino acids and the seven neutral sugars, respectively. DOC-normalized yields of TDAA and TDNS were calculated as the percentages of DOC measured in

TDAA and TDNS, respectively, as in Eq. 2 and Eq. 3:

[TDAA - C] TDAA(%DOC) = ´100 (2) [DOC]

[TDNS - C] TDNS(%DOC) = ´100 (3) [DOC]

, where [DOC], [TDAA-C], and [TDNS-C] are concentrations of bulk DOC, and carbon measured in TDAA and TDNS, respectively. The two nonprotein amino acids (β-Ala and

γ-Aba) are thought to be products of diagenetic alteration (Cowie and Hedges 1994), and were not included in the calculation of amino acid yields.

Statistical differences between variables were determined using the Mann–

Whitney U-test (two-tailed, α = 0.05) with SPSS software (version 20.0; IBM SPSS).

3.3 RESULTS

Concentrations of DOC varied 4.6-fold (63–290 µmol L-1) over a salinity gradient of 20–37, with higher values occurring in mid-salinity (22–30) waters (Figure 3.2a; Table

3.1). DOC concentrations were highly variable at mid-salinities and were more conservative at salinities > 30 (Figure 3.2a). Average concentrations of DOC ranged from

114 µmol L-1 in January to 132 µmol L-1 in July (Table 3.1). In comparison, concentrations of amino acids and neutral sugars varied 12-fold (173–2080 nmol L-1) and

8.5-fold (273–2319 nmol L-1), respectively (Figure 3.2b,c; Table 3.1). Concentrations of amino acids and neutral sugars were greatly elevated at salinities of 22–30 and decreased rapidly with increasing salinity. Remarkably high concentrations of neutral sugars were observed in July, when the values almost doubled those at similar salinities during other seasons (Figure 3.2c).

DOC-normalized yields of amino acids and neutral sugars were quite variable and displayed different distributions (Figure 3.3). Yields of amino acids ranged over a factor of 5 from 0.7% to 3.5% of the DOC (avg.: 1.3±0.4 %DOC), but average values differed minimally among seasons (avg.: 1.2–1.4 %DOC; Mann–Whitney U-test, p > 0.01; Table

3.1). Elevated yields of amino acids (> 2 %DOC) occurred largely in mid-salinity waters

(Figure 3.3a). Most yields were below 2% of the DOC and were scattered across the entire salinity range. In comparison, yields of neutral sugars ranged from 2.1% to 7.8% of the DOC (avg.: 4.2±1.0 %DOC) and showed strong spatial and temporal variability

(Figure 3.3b). The highest neutral sugar yields were observed in July across the 20–37 salinity range. In October–November, yields of neutral sugars remained low (e.g., < 3

%DOC) in mid-salinity waters and were elevated above 4 %DOC at salinities > 34.

Yields of neutral sugars in March varied between 3% and 5%DOC and showed no clear spatial gradient (Figure 3.3b). The spatial distributions of elevated yields varied between amino acids and neutral sugars, with most elevated amino acid yields at salinities < 29 and most elevated neutral sugar yields at salinities > 27.

Two short-term (10–12 d) bioassay experiments were conducted with mid-salinity surface waters to determine the labile fraction of DOC and to measure the yields of amino acids and neutral sugars in labile DOM. The unamended bioassay experiments showed that concentrations of labile DOC ranged from 2 µmol L-1 to 10 µmol L-1, accounting for a small fraction (1–6%) of the total DOC (Table 3.2). In the amended treatments, the addition of plankton-derived DOM corresponded to a 14–17% increase

(23–24 µmol L-1) in DOC concentrations. After 10–12 d of incubation, the DOC concentrations decreased rapidly to values similar to those in the controls (Table 3.2), suggesting most or all of the added DOC was readily consumed and therefore labile. The utilization of labile DOC included the preferential removal of amino acids and neutral sugars, as revealed by the decreases in DOC-normalized yields of amino acids and neutral sugars (Table 3.2). These results corroborate previous findings showing that labile

DOM is enriched in amino acids and neutral sugars (Amon et al. 2001; Davis and Benner

2007; Goldberg et al. 2009). The DOM with the lowest concentrations of labile DOC (~ 2

µmol L-1) had an amino acid yield of 2.0% and a neutral sugar yield of 3.7%. All of the

DOM experiments with higher initial yield values contained higher concentrations of labile DOC.

In the amended experiments, the addition of labile plankton DOM resulted in a two to threefold increase in concentrations and yields of amino acids and neutral sugars

(Table 3.2). After 10–12 d of incubation, concentrations and yields of neutral sugars decreased rapidly to levels comparable to those in the controls, whereas values of amino acids remained slightly elevated in the amended treatments (Table 3.2). These results indicate the microbial community was capable of rapidly utilizing labile DOM and probably produced some metabolites (e.g., D-amino acids) that were resistant to decomposition (Kawasaki and Benner 2006; Lechtenfeld et al. 2015).

Based on the bioassay results, an amino acid yield higher than 2.0 %DOC and a neutral sugar yield higher than 4.0 %DOC were indicative of a significant amount (> 2

µmol L-1) of labile DOM (Mann–Whitney U-test, p < 0.05). These yields were used as cutoffs for indicating the presence of labile DOM in the field samples. Labile DOM can be enriched in amino acids, neutral sugars or both. In this study, areas showing the occurrence of labile DOM were considered biological hot spots. Hot spots with labile

DOM enriched in amino acids were identified in a relatively small number of stations (n

= 12) that were largely located at mid-salinities (Figure 3.3a). It is interesting to note that most amino acid hot spots (n = 9) were identified in water samples collected between dusk and dawn. The DOM in amino acid hot spots was significantly enriched in Glx, basic amino acids (Arg and Lys), and certain hydrophobic amino acids (Val and Phe), but depleted in Asx, Ser, Gly, Thr, and nonprotein amino acids (β-Ala and γ-Aba)

(Supplementary Figure B.2a). In comparison, hot spots with labile DOM enriched in

neutral sugars were prevalent across a broad salinity gradient of 23– 37, accounting for more than half of the samples (n = 81; Figure 3.3b). Note that neutral sugars were not measured during the April cruise. The neutral sugar hot spots were identified in waters collected both during the day and at night. Less pronounced differences in compositions of neutral sugars were observed between neutral sugar hot spots and other areas

(Supplementary Figure B.2b). Glc was the dominant neutral sugar and was slightly enriched in hot spots, whereas Xyl, Man, and Ara were depleted in hot spots.

These compositionally different types of hot spots exhibited very different distributions in margin surface waters (Figure 3.4). Amino acid hot spots occurred sporadically during most seasons and they were largely confined to nearshore regions.

More amino acid hot spots were observed in March, with a few appearing over the shelf edge (Figure 3.4a). In comparison, neutral sugar hot spots were prevalent over much larger areas of the Louisiana margin, and their seasonal recurrence was evident at most locations (Figure 3.4b). The hot spots were most prominent in July, with very high yields of neutral sugars occurring in nearly all shelf and slope waters sampled in this study. In

October–November, neutral sugar hot spots shifted offshore and were present on the outer shelf and continental slope. Hot spots in March were largely absent in slope waters and were mostly located on the shelf (Figure 3.4b). These results revealed large spatial and temporal variability of hot spots on the Louisiana margin and indicated the dynamic nature and heterogeneous composition of labile DOM, which was predominantly enriched in carbohydrates.

The accumulation of marine-derived DOC during each season was investigated by accounting for the inputs of tDOC from rivers. The concentration of tDOC (Fichot and

Benner 2012; Fichot and Benner 2014) was subtracted from the total DOC concentration to yield a calculated concentration of marine DOC (mDOCtotal) as in Eq. 4, mDOCtotal = DOC – tDOC (4)

A conservative mixing line of mDOCtotal across the salinity gradient from the river to marine end-members was used to estimate concentrations of accumulated marine

DOC (mDOCacc) (Figure 3.5). Positive deviations from conservative mixing indicate an accumulation of marine DOC. The concentration of mDOCtotal is zero at the river endmember and the average concentration of mDOCtotal at salinity > 36 was used as the marine endmember value for each cruise (Supporting Information Table A.1). The concentration of mDOCacc at a given salinity was calculated by subtracting the value on the conservative mixing line (mDOCconservative) from the observed concentration of mDOCtotal as in Eq. 5, mDOCacc = mDOCtotal – mDOCconservative (5)

The small variability in mDOCtotal at the marine endmember (1–7%;

Supplementary Table A.1) had a minor impact on the calculated concentrations of mDOCacc.

The accumulation of marine DOC was observed during all five cruises and at 174 of the 199 stations spanning the 20–37 salinity range (Table 3.1; Figure 3.6).

-1 -1 Concentrations of mDOCacc ranged from 1 µmol L to 125 µmol L with an average value of 31±24 µmol L-1, accounting for as much as 52% of the total DOC (avg.:

22±13%; Table 3.1). The concentrations of mDOCacc varied substantially among seasons, with lowest concentrations in January (18±16 µmol L-1), increasing concentrations in

April (27±21 µmol L-1), highest concentrations in July (48±26 µmol L-1), and declining concentrations in October–November (32±26 µmol L-1) and March (23±15 µmol L-1).

Concentrations and percentages of mDOCacc were elevated at intermediate salinities (26–

-1 32) during all seasons. There was >30 µmol L of mDOCacc at these mid-salinities and the mDOCacc accounted for over 20% of the total DOC, with a few exceptions in January and March (Figure 3.6). The observed patterns of mDOCacc generally followed those of primary production (Redalje et al. 1994; Lohrenz et al. 1999).

Spatial distributions of surface mDOCacc varied seasonally on the Louisiana margin (Figure 3.7). In April and October–November, mDOCacc was largely confined to the shelf and higher concentrations of mDOCacc were observed in nearshore waters of intermediate salinity. In July, a strikingly large area of surface waters was enriched in mDOCacc. The mDOCacc-rich waters spread much further to the east and south. They crossed the shelf break and were present over much of the continental slope, in stark contrast to the confined distributions in other months. Concentrations of mDOCacc in these waters were typically higher than 60 µmol L-1 and closely paralleled the distribution of salinity (Mann–Whitney U-test, p < 0.001). In March, waters with elevated

-1 concentrations of mDOCacc (e.g., > 30 µmol L ) mostly resided on the shelf, but they showed a broader distribution across the Louisiana–Texas shelf compared with those in spring and fall (Figure 3.7).

Concentrations of bulk DOC and mDOCacc revealed contrasting seasonal variability (Figure 3.8). Average concentrations of DOC varied by less than 10% and were not significantly different among the five cruises (Mann–Whitney U-test, p > 0.05).

In comparison, average concentrations of mDOCacc varied two to threefold among seasons. Compared with the average values for all cruise data, mDOCacc concentrations were 45% and 20% lower in January and April, respectively, and were 67% higher in

July (Figure 3.8). The elevated production of marine DOC in summer was offset by the concurrently enhanced removal of tDOC (Fichot and Benner 2014), making the changes in total DOC concentrations less discernible during productive seasons. These results are consistent with previous studies in other ocean margins (Davis and Benner 2005; Mathis et al. 2007a; Shen et al. 2012a), demonstrating that ecosystem productivity is reflected in the composition and bioavailability of DOM but not in bulk DOC concentrations.

The seasonal reservoirs of DOC, marine DOC, and accumulated marine DOC were quantified in the surfaced mixed layer of the Louisiana shelf following the approach used in Fichot and Benner (2014) (Table 3.3). The reservoir of DOC varied from 0.70 Tg

C to 1.51 Tg C, and was dominated by marine DOC (0.50–1.32 Tg C). The reservoir of accumulated marine DOC was substantial (0.11–0.23 Tg C) and accounted for 13–31% of marine DOC on the Louisiana shelf, with the highest fraction occurring during the summer.

3.4 DISCUSSION

The Louisiana margin receives large nutrient loads from the Mississippi–

Atchafalaya River system and is among the world’s most productive ocean margins

(Goolsby et al. 2001; Heileman and Rabalais 2008). This margin is characterized by highly variable environmental conditions (e.g. river discharge, solar irradiance, nutrients, and currents) that drive large spatial and temporal gradients in plankton community structure and productivity (Lohrenz et al. 1999; Chakraborty and Lohrenz 2015; Cherrier et al. 2015). Maximal phytoplankton biomass and productivity commonly develop at mid-salinities due to lower chromophoric DOM and suspended sediment loads and

elevated nutrients, and they can exceed 30 mg chlorophyll m-3 and 10 gC m-2 d-1, respectively, during spring and summer (Lohrenz et al. 1999; Lehrter et al. 2009;

Chakraborty and Lohrenz 2015). Despite the high nutrient inputs, phytoplankton growth on the margin is often limited by nitrogen (N), phosphate (P), or co-limitated by N and P depending on season and location (Smith and Hitchcock 1994; Turner and Rabalais

2013). Phytoplankton biomass remains low (< 1 mg chlorophyll m-3) in nutrient-depleted offshore waters, where primary production (0.5–1.0 gC m-2 d-1) is dominated by small species, such as cyanobacteria (Lehrter et al. 2009; Chakraborty and Lohrenz 2015).

Grazers, such as copepods and protozoa, are widespread on the margin, and they can release a large fraction of phytoplankton production as labile DOM (Strom et al. 1997;

Liu and Dagg 2003). In this study, DOM directly released from phytoplankton as well as

DOM released during grazing and viral lysis is collectively referred to as plankton DOM.

Bacterial respiration and production are tightly linked to plankton DOM, and maximal rates are often observed at mid-salinities during summer in waters with elevated concentrations of carbohydrates (Chin-Leo and Benner 1992; Benner and Opsahl 2001).

Nutrient enrichment experiments also indicate the potential for N or P limitation of bacterial growth in margin surface waters (Chin-Leo and Benner 1992; Pomeroy et al.

1995).

The observed distributions of DOC, amino acids and neutral sugars were consistent with previous investigations demonstrating major production of plankton

DOM at intermediate salinities on the Louisiana shelf (Benner and Opsahl 2001; Wang et al. 2004). High rates of primary production and zooplankton grazing are characteristic at mid-salinities (Redalje et al. 1994; Liu and Dagg 2003), and both processes can release

DOM that is enriched in proteins, peptides, and carbohydrates (Myklestad and Haug

1972; Strom et al. 1997). Concentrations of amino acids and neutral sugars, the most abundant carbohydrates, were greatly elevated at mid-salinities and the values were much higher than those in adjacent marine waters. High concentrations of these biochemicals provide clear molecular evidence of plankton-derived DOM in mid-salinity waters, which was most prominent during the summer when concentrations of carbohydrates were highest.

Plankton DOM includes labile components that can rapidly trigger the formation of hot spots with high rates of microbial growth, respiration, and nutrient regeneration

(Pakulski et al. 1995; Azam and Malfatti 2007; Stocker et al. 2008). Bioassay experiments are commonly used to determine the labile fraction of DOM (Søndergaard and Middelboe 1995; Lønborg et al. 2009), but this approach alters environmental conditions and is limited in spatial and temporal coverage. Amino acids and carbohydrates are common components of labile DOM and their relative abundance can reflect the inherent bioavailability of DOM. These biochemicals have been used as indicators of the presence of labile DOM across broad spatial and temporal scales (Amon et al. 2001; Benner 2003; Davis and Benner 2007; Goldberg et al. 2009). Bioassay experiments quantify the abundance of labile DOM and measure its rate of biological utilization under specific environmental conditions. Biochemical indicators can also quantify the abundance of labile DOM, but they do not provide information about the rate of DOM biological utilization, which is dependent on physicochemical conditions and microbial community composition (Carlson and Hansell 2015). As used in this study, measurements of biochemical indicators identify the presence of labile DOM that can be

utilized within 2 weeks under favorable environmental conditions. The bioassay experiments in this study demonstrated that labile DOM was present in water samples when amino acids or carbohydrates comprised a minimum of 2% or 4% of the DOC, respectively. These values are similar to those observed after labile DOM was consumed in other bioassay experiments (Amon et al. 2001; Davis and Benner 2007), and they were used in this study as baseline values for the detection of labile DOM. Labile DOM can be enriched in amino acids, carbohydrates, or both, and the locations where labile DOM was detected were designated as biological hot spots.

A large number of hot spots were identified in margin surface waters and they exhibited substantial spatial and temporal variability, reflecting the productive and dynamic nature of this ecosystem. Hot spots frequently occurred at mid-salinities (26–29) on the inner shelf, where high phytoplankton biomass and production are often observed during most seasons (Redalje et al. 1994; Chakraborty and Lohrenz 2015). Interestingly, unlike neutral sugar hot spots, amino acid-rich hot spots were mostly identified in waters collected during low-and no-light time periods, suggesting diel variability in the source of labile DOM. Extracellular release from phytoplankton is active at high solar irradiance

(Cherrier et al. 2015), preferentially releasing carbohydrates (Myklestad et al. 1989). In comparison, grazing releases a potpourri of cell contents that include amino acids and other biomolecules (Carlson and Hansell 2015), and can vary with the diel vertical migration of zooplankton. Previous diel measurements of gut-pigments in copepods in surface waters of the Louisiana shelf showed elevated values at night (Dagg 1995). It appears that grazing in surface waters at night is an important source of amino acid-rich labile DOM.

Hot spots with labile DOM enriched in amino acids showed distinct spatial distributions from those with carbohydrate-rich labile DOM, indicating compositional variability in labile plankton DOM. Amino acid-rich hot spots were found at relatively few locations, and they mostly occurred on the inner shelf where concentrations of nutrients and chlorophyll were high (Chakraborty and Lohrenz 2015). A few amino acid hot spots appeared further south near the shelf-edge in March, coinciding with a widespread nutrient-rich plume that resulted in elevated concentrations of chlorophyll across outer shelf and slope waters (Huang et al. 2013; Chakraborty and Lohrenz 2015).

Amino acid hot spots appeared to reflect areas of high phytoplankton biomass. A similar pattern was observed in productive waters of the Chukchi Sea where concentrations of dissolved and particulate amino acids were strongly correlated with chlorophyll concentrations (Davis and Benner 2005).

In contrast, carbohydrate-rich hot spots were prevalent over a large area of the

Louisiana margin and appeared to reflect widespread nutrient limitation of phytoplankton growth (Smith and Hitchcock 1994; Turner and Rabalais 2013). Under nutrient limitation, phytoplankton growth is inhibited while light-driven photosynthesis still proceeds, resulting in enhanced release of extracellular carbohydrates, particularly when nitrate: phosphate ratios are high (Myklestad and Haug 1972). Low concentrations of phosphate (< 0.2 µmol L-1) and chlorophyll (< 2 mg m-3), and high ratios of dissolved inorganic nitrogen to soluble reactive phosphate (DIN: SRP > 22) were measured in shelf and slope waters in July (Chakraborty and Lohrenz 2015). Carbohydrate hot spots were most pronounced during July, providing strong evidence for plankton production of carbon-rich labile DOM that was produced under conditions of nutrient limitation.

Additional evidence is gained from observations in October–November when carbohydrate hot spots largely shifted to the continental slope and corresponded with low concentrations of DIN (0.4±0.1 µmol L-1), SRP (0.05±0.02 µmol L-1) and chlorophyll

(0.11±0.08 mg m-3) (Chakraborty and Lohrenz 2015).

The widespread occurrence of hot spots during the summer cruise is consistent with general observations of elevated rates of plankton production during the summer. In comparison, plankton production is much lower during winter, resulting in a very limited number of hot spots in January. Overall, the amino acid and carbohydrate biochemical indicators highlighted areas of high biological production and activity in a spatially and temporally dynamic ocean margin.

Large accumulations of marine DOC (0.11–0.23 Tg C) were observed in the shelf mixed layer, and most of the accumulated marine DOC appeared to be of semi-labile reactivity despite the widespread occurrence of labile DOM hot spots. The average yields of amino acids (1.3±0.4 %DOC) and carbohydrates (4.2±1.0 %DOC) on the Louisiana margin were comparable to those in oceanic surface waters and were indicative of semi- labile DOM (Davis and Benner 2007; Goldberg et al. 2009; Kaiser and Benner 2009). In addition, the average amino acid and neutral sugar composition of the DOM was indicative of degraded and altered material of a semi-labile nature. Most of the accumulated DOM on the Louisiana margin is composed of semi-labile molecules that have turnover times of months to years and accumulate in surface waters, which have a 2- to 3-month residence time on the Louisiana shelf (Dinnel and Wiseman 1986; Fichot and

Benner 2014). The most prominent accumulation of marine DOC occurred at mid- salinities during the summer, corresponding to the spatial and temporal patterns of

plankton production and heterotrophic activity (Benner and Opsahl 2001; Dagg et al.

2007). Most plankton DOM has undergone substantial microbial alteration, which reduces its bioavailability and contributes to its accumulation.

It appears that nutrient limitation promotes the accumulation of marine DOM on the Louisiana margin as it enhances phytoplankton production of carbohydrates and limits bacterial utilization of C-rich DOM (Myklestad and Haug 1972; Chin-Leo and

Benner 1992; Thingstad et al. 1997; Skoog et al. 2002). There were relatively low concentrations of nutrients on the shelf and slope during summer (Chakraborty and

Lohrenz 2015), when large accumulations of carbohydrate-rich DOM were observed.

Concentrations of nutrients were elevated on the outer shelf and slope during March

(Chakraborty and Lohrenz 2015), and this could have resulted in the relatively low concentrations of accumulated DOC at mid-to high-salinities at this time. Our results indicate there are multiple controls on the accumulation of DOC on the Louisiana margin, which is consistent with observations in other coastal ecosystems (Zweifel et al. 1995).

Marine DOM produced on the shelf is an important source of energy and key bioelements for microbial food webs (Chin-Leo and Benner 1992; Amon and Benner

1998; Green et al. 2006). The reservoir size of accumulated marine DOC (0.11–0.23 Tg

C; 0.16±0.05 Tg C) was similar to but lower than the seasonal estimates of net community production in the Mississippi River plume (0.11–0.51 Tg C) (Green et al.

2006; Guo et al. 2012). The semi-labile nature of mDOCacc facilitates its transport by eddies and wind-driven currents. Easterly winds are usually prevalent in all seasons but summer, when wind direction shifts to the south and triggers Ekman transport of shelf waters eastward toward the shelf break, promoting cross-shelf transport of shelf waters

(Morey et al. 2003; Fichot et al. 2014). An impressive cross-shelf export of mDOCacc occurred during summer 2009. This event represents a major subsidy of shelf-derived marine DOM (> 60 µmol DOC L-1) to offshore waters, which doubles the typical DOC concentrations in surface waters on the continental slope (60–70 µmol L-1) (Benner and

Opsahl 2001; Fichot and Benner 2014). Overall, these physical processes can deliver shelf-produced DOM and associated nutrients rapidly to offshore waters, thereby supporting microbial food webs in the open Gulf of Mexico.

Table 3.1 Average values and ranges for dissolved organic carbon (DOC), total dissolved amino acids (TDAA), and total dissolved neutral sugars (TDNS) in the mixed layer during the five cruises on the Louisiana margin

Temp DOC TDAA TDNS tDOC mDOCtotal mDOCacc mDOCacc nacc/ntotal Cruise Salinity ˚C µmol L-1 nmol L-1 %DOC nmol L-1 %DOC µmol L-1 %DOC 33.1±3.8 19±3 114±43 473±415 1.3±0.6 708±293 3.5±0.7 34±42 81±15 18±16 13±10 Jan 2009 19/22 (21.2-36.4) (14-23) (75-244) (189-2080) (0.8-3.5) (319-1392) (2.2-5.0) (4-186) (58-115) (1-56) (1-34) 32.2±4.7 23±1 123±46 453±280 1.2±0.4 32±36 91±14 27±21 18±10 Apr 2009 nd nd 35/44 (22.3-37.0) (21-25) (75-238) (173-1387) (0.7-2.3) (2-125) (72-142) (1-86) (1-36) 32.4±2.7 30±1 132±33 513±229 1.2±0.4 1176±426 5.1±0.9 15±15 117±21 48±26 34±13 Jul 2009 42/44 (27.2-36.8) (27-31) (79-213) (199-1207) (0.7-2.3) (467-2319) (2.7-7.8) (2-64.9) (77-148) (1-87) (1-52) Oct-Nov 32.0±4.5 24±2 126±50 467±310 1.2±0.5 782±172 3.8±0.8 30±39 96±21 32±26 22±13 37/43 2009 (20.9-36.6) (20-27) (78-290) (178-1697) (0.7-3.4) (578-1318) (2.1-5.1) (3-142) (66-180) (1-125) (1-43) 30.0±5.2 17±1 125±46 526±273 1.4±0.4 838±339 3.9±0.7 48±46 76±13 23±15 17±8.9 Mar 2010 41/46 (20.6-36.5) (15-20) (63-225) (180-1319) (0.8-2.9) (273-1527) (2.2-5.2) (3-166) (43-112) (2-71) (1-32)

All 31.8±4.4 23±5 125±44 488±291 1.3±0.4 903±369 4.2±1.0 32±38 93±22 31±24 22±13 174/199 cruises (20.6-37.0) (14-31) (63-290) (173-2080) (0.7-3.5) (273-2319) (2.1-7.8) (2-186) (43-180) (1-125) (1-52)

Temp, temperature; tDOC, terrigenous DOC; mDOCtotal, marine DOC; mDOCacc, accumulated marine DOC; nd, not determined. Data are reported as the average6standard deviation; the range of values is presented in the parenthesis. nacc/ntotal, number of stations where accumulation of marine DOC was observed, relative to the total number of stations sampled.

Table 3.2 Concentrations and compositions of dissolved organic matter (DOM) during the shipboard bioassay experiments.*

Control Amended t = 0 t = final t = 0 t = final Exp 1 (salinity = 23.3) DOC (µmol L-1) 168±1 158±1 191±1 160±1 TDAA (nmol L-1) 897±11 643±14 2331±18 949±11 TDNS (nmol L-1) 1364±38 730±76 2524±180 703±20 TDAA (%DOC) 2.0±0.0 1.6±0.0 5.0±0.0 2.4±0.0 TDNS (%DOC) 4.7±0.1 2.7±0.3 7.8±0.6 2.5±0.1 Exp 2 (salinity = 27.8) DOC (µmol L-1) 134±1 132±1 157±1 130±1 TDAA (nmol L-1) 731±35 670±35 2142±50 859±11 TDNS (nmol L-1) 852±12 713±71 2067±224 629±30 TDAA (%DOC) 2.0±0.1 1.9±0.1 5.5±0.1 2.5±0.0 TDNS (%DOC) 3.7±0.1 3.1±0.3 7.7±0.8 2.8±0.1

*Plankton-derived DOM was added to the amended treatments. The incubation time was 12 d for experiment 1 (Exp 1) and 10 d for experiment 2 (Exp 2). Data are reported as the average ± standard deviation (n = 3). DOC, dissolved organic carbon; TDAA, total dissolved amino acids; TDNS, total dissolved neutral sugars.

Table 3.3 Surface mixed layer reservoirs of dissolved organic carbon on the Louisiana shelf.*

Mixed layer volume DOC mDOC mDOC Cruise total acc (km3) (Tg C) (Tg C) (Tg C) Apr 2009 920 1.13 0.95 0.13 Jul 2009 586 0.82 0.75 0.23 Oct-Nov 2009 1285 1.51 1.32 0.17 Mar 2010 562 0.70 0.50 0.11 AVG±SD 838±340 1.04±0.36 0.88±0.34 0.16±0.05

*Ordinary kriging was used to interpolate discrete field measurements over the shelf (62,068 km2; Fichot and Benner 2014). Calculations were not performed for the January 2009 cruise due to insufficient data. mDOCtotal: marine dissolved organic carbon; 12 mDOCacc: accumulated marine dissolved organic carbon. 1 Tg = 1 × 10 g. AVG±SD: average ± standard deviation.

Figure 3.1 Study area and sampling sites on the Louisiana margin in the northern Gulf of Mexico. Surface water samples were collected from 24 stations in January 2009 and from 50 stations in April, July, October–November 2009, and March 2010. The sampling regions included the continental shelf (bottom depth 200 m) and slope (200 m < bottom depth ≤ 2000 m). The 100-, 200-, 1000-, and 2000-m isobaths are shown as thin grey lines.

Figure 3.2 Seasonal distributions and concentrations of (a) dissolved organic carbon (DOC), (b) total dissolved amino acids (TDAA), and (c) total dissolved neutral sugars (TDNS) across the salinity gradient (20– 37). Note that concentrations of TDNS were not determined for the April 2009 cruise.

Figure 3.3 Seasonal distributions of DOC-normalized yields of (a) total dissolved amino acids (TDAA, %DOC) and (b) total dissolved neutral sugars (TDNS, %DOC). The dashed line represents cutoff values used to detect the presence of labile DOM (TDAA: ≥ 2.0%; TDNS: ≥ 4.0%). Note that yields of TDNS were not determined for the April 2009 cruise.

Figure 3.4 Hot spots of labile DOM on the Louisiana margin. (a) Amino acid hot spots and (b) neutral sugars hot spots were regions where amino acid- rich labile DOM and neutral sugar-rich labile DOM were detected, respectively. The five cruises were denoted by numbers (1: January 2009; 2: April 2009; 3: July 2009; 4: October–November 2009; 5: March 2010), and as in Figs. 3.2, 3.3. Note that neutral sugar hot spots were not determined for the April 2009 cruise.

Figure 3.5 Conceptual illustration showing the estimation of accumulated marine dissolved organic carbon (mDOCacc). A conservative mixing line was drawn for concentrations of marine DOC (mDOCtotal) between the river and marine endmembers. The mDOCtotal values lying above the mixing line indicate an accumulation of marine DOC. At a given salinity, the concentration of mDOCacc was calculated as the difference in concentration between mDOCtotal and the corresponding mDOCconservative value on the mixing line.

Figure 3.6 Seasonal distributions and concentrations of (a) marine DOC (mDOCtotal) and -1 (b, c) accumulated marine DOC (mDOCacc) in units of µmol L and %DOC across the salinity gradient (20–37).

Figure 3.7 Surface concentrations of accumulated marine DOC (mDOCacc) on the Louisiana margin in April, July, October–November 2009, and March 2010. Solid dots represent sampling stations and solid lines depict the contour lines of salinity. The 200- and 2000-m isobaths are shown as dashed lines.

Figure 3.8 Relative changes in average concentrations of dissolved organic carbon (DOC) and accumulated marine DOC (mDOCacc) during each cruise. The relative change (%) is calculated as (Xeach – Xall) / Xall × 100, where Xeach and Xall are the average concentrations of DOC (or mDOCacc) during each cruise and all five cruises, respectively. Results are reported as the average ± standard error.

CHAPTER 4

ORIGINS AND BIOAVAILABILITY OF DISSOLVED ORGANIC MATTER IN GROUNDWATER4

4.1 INTRODUCTION

Groundwater stores the majority of liquid fresh water on Earth and is a major source of water for human consumption and agriculture. Groundwater quality is a key environmental issue and is often assessed by monitoring dissolved organic matter (DOM) through measurements of dissolved organic carbon (DOC) concentrations (Leenheer et al. 1974; Barcelona 1984). Concentrations of DOC can reflect the likelihood of contamination by synthetic organic compounds (Barcelona 1984). Humic and fulvic acids in DOM affect the solubility of organic pollutants in groundwater and can contribute to the long-range transport of harmful chemicals (Chiou et al. 1986). High DOC loading in groundwater can also lead to production of carcinogenic disinfection byproducts during drinking water treatment (Singer 1994; Chomycia et al. 2008). Groundwater DOM also serves as a source of carbon and energy for heterotrophic metabolism and drives the bioremediation of many contaminants (Hendriksen et al. 1992; Mccarty 1997; Baker et al. 2000).

Early studies of groundwater emphasized measuring the concentrations of DOC,

4Shen, Y., F. H. Chapelle, E. W. Strom, and R. Benner. 2015. Origins and bioavailability of dissolved organic matter in groundwater. Biogeochemistry 122: 61-78. Copyright (2014) The Author(s). This article is published with open access at Springerlink.com

so little is known about the origin, composition, and bioreactivity of groundwater DOM

(Leenheer et al. 1974; Thurman 1985). More recent studies indicate surface plant litter and soil are important sources of DOM in groundwater (Baker et al. 2000; Kalbitz et al.

2000). Surface precipitation drives the transport of DOM through the soil column to the saturated zone. During infiltration, selective interactions between DOM and soil minerals occur and result in differential retention of certain DOM constituents within the soils.

This concept has been described in the regional chromatography model in river systems

(Hedges et al. 1986; Hedges et al. 1994) and is also applicable to groundwater environments (Jardine et al. 1989; Qualls and Haines 1992; Kaiser et al. 2004). The chemical composition and bioavailability of DOM, which are key factors affecting water quality and diagenetic processes in groundwater, are altered during its transport through the soil column. The bioavailability of DOM is tightly linked to its origins and its utilization by microorganisms and is not readily reflected in the concentrations of DOC

(Benner 2003; Chapelle et al. 2009).

Bioavailability of DOM is commonly measured using bioassay experiments (e.g.,

Sondergaard and Middelboe 1995), which are time consuming to conduct and therefore limited in spatial and temporal coverage. Molecular analyses of specific biochemical components of DOM can provide qualitative and quantitative insights about the bioavailability and diagenetic alteration of DOM without conducting bioassay experiments (Benner 2003; Davis and Benner 2007; Shen et al. 2012a). Amino acids are bioreactive components of DOM and have been used as molecular indicators of bioavailable DOM in marine environments (Benner 2003; Davis et al. 2009) and have been applied recently to groundwater (Chapelle et al. 2009). The composition of

hydrolysable amino acids in DOM can be informative for revealing the extent of diagenetic processing of DOM. For instance, mole fractions of glycine, non-protein amino acids (β-alanine and γ-aminobutyric acid), and D-enantiomers of amino acids (D-

AA) increase with advancing diagenetic alteration of DOM (Jørgensen et al. 1999; Davis and Benner 2005; Kaiser and Benner 2009). Likewise, some biomolecules have specific sources and can be excellent tracers of the origins of DOM. Lignin is a structural biopolymer unique to vascular plants, making lignin phenols biomarkers of plant-derived

DOM (Meyers-Schulte and Hedges 1986; Opsahl and Benner 1997; Benner et al. 2005).

The combined forms of D-aspartic and glutamic acids, D-serine, and D-alanine occur in a variety of macromolecules that are only found in bacteria, thereby serving as useful biomarkers for tracing bacterial-derived DOM (Mccarthy et al. 1998; Kaiser and Benner

2008).

Several studies have examined the bioavailability of DOM in groundwater using amino acids and other bioreactive compounds (e.g., carbohydrates) (Routh et al. 2001;

Chapelle et al. 2009; Peter et al. 2012). These studies have provided a snapshot of the relatively low bioavailability of DOM in groundwater. Nevertheless, the temporal variability of DOM bioavailability and its connection with surface DOM are poorly understood. Microbes, especially heterotrophic bacteria, play an important role in transforming bioavailable DOM to refractory DOM (Ogawa et al. 2001). The bacterial contributions to groundwater DOM, however, have not been investigated.

In this study, long-term (2-year) variations of DOC concentration, origin, composition and bioavailability were monitored monthly in a fractured-rock aquifer in the Carolina Slate Belt overlain by weathered bedrock located in South Carolina. The

Carolina Slate Belt consists of sedimentary rocks of Cambrian age (~550 million years) that have been subjected to several cycles of high- to low-temperature metamorphosis

(Hibbard et al. 2002). These metamorphic events transformed the sediments into lithified phyllites, slates, mica schists, and quartzites intruded with metaigneous rocks.

Importantly, these metamorphic events effectively coalified the organic carbon originally buried with the sediments, presumably rendering it largely non-biodegradable. Thus,

DOM present in the groundwater in this region is predominantly derived from surface litter and soil rather than from the fractured-rock aquifer matrix.

4.2 METHODS

Groundwater samples were collected from a U.S. Geological Survey (USGS) well

(site number: 340837081173800-RIC-748; 34°080 3700 N, 81°170 3800 W) located in a forested area of Richland County, South Carolina. The well is part of the USGS climate response network because it reflects climatic variability (e.g., seasonal changes in recharge and subsequent water levels), and not human (Cunningham et al. 2007). The well was drilled to a depth of 76 m through a soil horizon, weathered bedrock, (0–12 m) and fractured rock (12–76 m), with an open hole below the casing depth of 12 m. The average water temperature in the well was 17±1˚C and the water level varied from 2.3 to

6.7 m below the land surface during the study period from July 2010 to August 2012. The well (domestic) was shut off at least 8 h prior to sampling. Water samples were pumped from a depth of 9 m with a peristaltic pump at a flow rate of about 500 mL min-1. Water pressure between the fractures recorded at 33, 51, and 66 m was likely different due to differences in depth, and convection currents due to temperature differentials would

occur without recent pumping (Vroblesky et al. 2006), resulting in water mixing within the well. The collected samples are therefore thought to integrate the optical and biochemical characteristics of water and DOM throughout the saturated zone.

Groundwater samples were collected approximately at monthly intervals (n = 24).

Surface water samples were collected from small creeks in the vicinity (~1 km) of the well (n = 4). Water samples were collected in clean amber glass bottles, stored in the dark at 4˚C, and processed in the laboratory within 72 h of collection. Samples were filtered through pre-cleaned 0.2-µm pore-size membrane filters (Supor®-200, Life Sciences) prior to optical and chemical analyses.

Two different types of bioassay experiments were used to measure (1) the microbial utilization and bioavailability of DOM, and (2) the microbial production of

DOM. In the DOM bioavailability experiments, ground and surface waters were filtered through pre-combusted filters (0.7 µm pore-size GF/F, Whatman) to remove most particles and eukaryotes while leaving the prokaryotic community largely intact. Filtered water samples were incubated in duplicates (500 mL) in glass bottles and kept in the dark at room temperature (23–26˚C). Concentrations of DOC and amino acids were monitored on days 0, 7, 14, 21, 28, 35, and 42. In the bacterial DOM production experiments, a groundwater inoculum was prepared by filtering a water sample (0.7 µm pore-size GF/F), and a 1:50 dilution of the filtered water was added as an inoculum to a mineral media with an adjusted pH (~6.9) similar to that in the groundwater inoculum (Porcella and

Others 1980). Glucose (250 µmol C L-1) was added as the sole C and energy source, and sodium nitrate (53 µmol L-1) and monopotassium phosphate (2.6 µmol L-1) were added as

N and P sources. Three replicates (500 mL each) were incubated in the dark at room

temperature (23–26˚C). Subsamples for DOC and amino acids were collected on days 0,

2, 4, 9, 14, 21, and 28. In both experiments, water samples were filtered through precleaned 0.2-µm pore-size membrane filters (Supor®-200, Life Sciences) before analysis.

Concentrations of DOC were measured using high-temperature combustion via a

Shimadzu TOC-V analyzer (Benner and Strom 1993). Milli-Q UV-Plus water was injected every 6th sample as a blank and the blanks were negligible. Absorbance spectra

(250–800 nm) of water samples were determined using a dual-beam Shimadzu 1601 spectrophotometer and 10-cm quartz cuvettes. For highly absorbing surface water samples, 1-cm quartz cuvettes were used. Absorbances corrected for blank (the average absorbance between 690 and 700 nm) were converted to Napierian absorption

-1 coefficients: aλ (m ) = 2.303 Aλ/r, where Aλ is the absorbance measured across pathlength r at a wavelength of λ. Spectral slope coefficients, S, were calculated using a linear fit of log- linearized aλ: aλ = aλ0 exp [-S (λ- λ0)], where aλ and aλ0 are absorption coefficients at wavelengths

λ and λ0 (λ > λ0). In this study, S in the 275–295 and 350–400 nm spectral ranges were

-1 determined and are reported as S275–295 and S350–400 with units of nm . The slope ratio,

SR, was calculated as the ratio of S275–295 to S350–400. Specific UV absorbance at 254 nm

(SUVA254) was determined by dividing the UV absorbance at 254 nm by the DOC concentration and is reported in units of L mgC-1 m-1 (Weishaar et al. 2003).

The D- and L-enantiomers of amino acids were analyzed using an Agilent 1200 high-performance liquid chromatography system equipped with a fluorescence detector.

Water samples were dried under nitrogen gas and subjected to vapor-phase hydrolysis with 6 mol L-1 hydrochloric acid at 150˚C for 32.5 min in a CEM Mars 5000 microwave

(Kaiser and Benner 2005). Free amino acids were analyzed in a subset of samples that

were not hydrolyzed. Amino-acid enantiomers were derivatized with o-phthaldialdehyde and N-isobutyryl-L-cysteine and were separated on a Poroshell 120 EC-C18 (4.6×100 mm, 2.7 µm particles) column. Acid-catalyzed racemization of enantiomers during hydrolysis was corrected according to Kaiser and Benner (2005). Eighteen amino acids were included in the analysis: asparagine + aspartic acid (Asx), glutamine + glutamic acid (Glx), serine (Ser), histidine (His), glycine (Gly), threonine (Thr), β-alanine (β-Ala), arginine (Arg), alanine (Ala), γ-aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine (Ile), leucine (Leu), and lysine (Lys). D-enantiomers of

Asx, Glx, Ser, and Ala were reported in this study. DOC-normalized yields of total

C in TDAA dissolved amino acids (TDAA) were calculated as: TDAA (%DOC) = ´100 , DOC where the denominator and numerator represent concentrations of total DOC and the

DOC comprised by TDAA, respectively. The two non-protein amino acids (β-Ala and γ-

Aba) are thought to be by-products of diagenesis (Cowie and Hedges 1994) and were not included in the yield calculation.

Lignin phenols were analyzed in four surface and groundwater samples to trace plant-derived molecules in the DOM. Water samples (1–4 L) were filtered through

Nuclepore filters (0.2-µm pore size) and were acidified to pH 2.5 with 5 mol L-1 sulfuric acid. Acidified samples were extracted using C-18 cartridges (Varian MegaBond Elut) at a flow rate of 50 mL min-1 (Louchouarn et al. 2000). Dissolved lignin was eluted in 30 mL of methanol and analyzed using the CuO oxidation method as described by Kaiser and Benner (2012). Lignin oxidation products were determined as trimethylsilyl derivatives and quantified using an Agilent 7890 gas chromatograph with a DB5-MS capillary column and an Agilent 5975 mass selective detector. Eight lignin phenols were

measured: vanillin (VAL), acetovanillone (VON), vanillic acids (VAD), syringaldehyde

(SAL), acetosyringone (SON), syringic acid (SAD), p-coumaric acid (CAD), and ferulic acid (FAD). The sum of six lignin phenols (VAL, VON, VAD, SAL, SON, and SAD) are reported in this study as total dissolved lignin phenols (TDLP6). DOC-normalized yields of TDLP6 were calculated as the percentage of DOC measured in the six lignin phenols.

Bacterial contributions to groundwater DOC were estimated using D-amino acids as tracers of bacterial carbon (Kaiser and Benner 2008). The fraction of bacterially-

D - AA derived DOC was calculated as: Bacterial DOC(%) = DOM , where D-AADOM D - AAbacterial DOM and D-AAbacterial DOM are the DOC-normalized yields of individual D-amino acids (Asx,

Glx, Ser, Ala) in groundwater DOM and freshly-produced (4–28 days) bacterial DOM, respectively.

The significance of correlations between variables was determined using a

Pearson Correlation (two-tailed, α = 0.05). For variables that were not normally distributed, the Spearman’s rho test was used (two-tailed, α = 0.05). Normality of variables was tested using a Kolmogorov–Smimov (K–S) test (two-tailed, α = 0.05).

Statistical differences were assessed using the Mann–Whitney U test (two-tailed, α =

0.05) because this test does not require assumptions of normality and equal group size.

All the statistical analyses were performed using SPSS 20.0 (IBM Statistical Package for the Social Sciences Inc.).

4.3 RESULTS

The concentrations of DOC in groundwater displayed moderate temporal variations and ranged from 69 to 99 µmol L-1, which were ~10 times lower than those

(322–1,538 µmol L-1) in surface water (Figure 4.1a; Table 4.1). The absorption

-1 coefficient, a254, varied from 3.01 to 4.82 m in groundwater, and had a maximal value in

March 2012 when the highest concentrations of DOC were measured (Figure 4.1b; Table

4.2). Temporal patterns of a254 and DOC concentrations were similar, and a significant

2 relationship was found between these variables (a254 = 0.058 × DOC-1.24, R = 0.785, p

< 0.001, n = 24). Concentrations of TDAA in groundwater ranged from 111 to 234 nmol

L-1, which were ~25-fold lower than those (1,668–5,880 nmol L-1) in surface waters

(Table 4.1). Compared with DOC concentrations, TDAA concentrations were more variable and displayed a different temporal pattern (Figure 4.1a,c). Combined forms of D- amino acids (D-AA) were measured in groundwater and the total concentrations of D-AA ranged from 8.0 to 19 nmol L-1 (Figure 4.1d; Table 4.1).

Concentrations of DOC decreased from 95 to 81 µmol L-1 in groundwater and from 608 to 429 µmol L-1 in surface water during 35-days of incubation (Figure 4.2) in the unamended bioassay experiments. No change and a minor decline (~2%) in DOC concentrations were observed after 35-days in ground and surface waters, respectively.

Bioavailable DOC (BDOC) accounted for 14 and 29% of the DOC in ground and surface waters, respectively. A larger decrease in TDAA concentrations was observed, with 54 and 68% utilized during 35-days in ground and surface waters, respectively (Figure 4.2).

The average DOC-normalized yield of TDAA was significantly lower in groundwater (0.68±0.17 %DOC, n = 24) than in surface water (2.25±1.57 %DOC, n = 4)

(p < 0.002; Table 4.1). During 35-days of incubation, TDAA yields declined from 1.02 to

0.43% of DOC in groundwater (Figure 4.3a). In surface water, TDAA yields followed a

similar pattern (from 2.13 to 0.81 %DOC) but were higher at the end of the experiment

(Figure 4.3a).

The BDOC at each time point (ti) was calculated as the difference in DOC concentrations between ti and day 35 in the groundwater experiment. A significant positive relationship was observed between percentages of BDOC and TDAA yields (R2

= 0.975, p < 0.001; Figure 4.3b). Based on these experimental results, we assumed that all BDOC was utilized when TDAA yields were below 0.43 %DOC. TDAA yields varied from 1.02 to 0.43 %DOC during the experiments, and the yields in all but one of the field samples fell within this range (Table 4.1).

A 2-year time series of BDOC in groundwater samples was derived from TDAA yields. The calculated percentages of BDOC exhibited large seasonal variations and ranged from 1 to 17% (avg. 8±4%; Figure 4.4). The influence of precipitation on the relative abundance of BDOC in groundwater was investigated by correlating the integrated surface precipitation during the previous 1–60 days with the percentage of

BDOC in groundwater. The most significant correlation was observed between the percentage of BDOC and the previous 10-days of precipitation (r = 0.735, p < 0.001;

Table 4.3; Figure 4.4). The correlation was weaker when longer preceding time periods of precipitation were considered (e.g., 27-days; r = 0.419, p < 0.05; Table 4.3). In general, high percentages of BDOC occurred immediately after precipitation events and low percentages of BDOC occurred during periods lacking precipitation (Figure 4.4).

One exception occurred in August 2012, when precipitation was very high (20.45 cm; data not shown) and BDOC did not increase proportionally. Transpiration by vegetation is very high during the summer, thereby reducing groundwater recharge. Thus, high

percentages of BDOC appear to correlate with active recharge to the aquifer, whereas low percentages of BDOC correlate with a lack of recharge.

Three amino acid-based indicators of DOM diagenesis were developed from the unamended incubation experiments (Figure 4.5). Mole percentages of glycine increased gradually from 22 to 33% throughout the incubation period in surface water (Figure

4.5a). In comparison, mole percentages of glycine in groundwater started with a lower initial value (16%) but increased rapidly and were similar to values (30–31%) in surface water (Figure 4.5a). Mole percentages of γ-aminobutyric acid (γ-Aba) were similar in the initial ground and surface waters and increased dramatically from 1.2 to 5.5 % in groundwater while only a minor increase (from 0.84 to 1.4%) was observed in surface water (Figure 4.5b). Mole percentages of D-AA in groundwater increased threefold after

14-days and reached a plateau (23%) after 28-days (Figure 4.5c). In surface water, D-AA increased to 19% on day 14 and then remained constant (Figure 4.5c). Average mole percentages of glycine, γ-Aba, and D-AA were all significantly higher in groundwater than in surface water (p < 0.005; Figure 4.5d, e, f). Differences were most pronounced in

γ-Aba, which was six-fold higher in groundwater, followed by D-AA and glycine (Figure

4.5d, e, f).

Likewise, mole percentages of γ-Aba showed the most pronounced temporal variations in groundwater, followed by D-AA and glycine (Figure 4.6). Significant negative correlations were observed between the previous 10-days of precipitation and mole fractions of glycine, γ-Aba, and D-AA (Figure 4.6; Table 4.3). A large decline in the mole percentage of γ-Aba during August 2012 corresponded to a very high precipitation

event. Declines were less substantial in D-AA and were not observed in the mole percentage of glycine (Figure 4.6).

Lignin phenols (TDLP6) were measured in two groundwater samples and two surface water samples, with much lower concentrations occurring in groundwater (Table

4.4). The acid to aldehyde ratios of vanillyl and syringyl phenols were not significantly different between surface water DOM and groundwater DOM (p < 0.1, Table 4.4). A

2 significant relationship between TDLP6 concentrations and a254 (R = 0.882, p = 0.061, n

= 4) indicated a254 is a useful proxy for lignin phenols in groundwater. SUVA254 values in groundwater ranged from 1.42 to 1.77 L mgC-1 m-1 and were about half of those (2.33–

3.19 L mgC-1 m-1) in surface water (Table 4.2). Examining the correlation between

SUVA254 in groundwater and various periods (1–60 days) of integrated precipitation revealed the best relationship when using the previous 27-days of precipitation (r =

0.600, p < 0.005; Figure 4.7b). Likewise, concentrations of DOC were found to correlate best with the previous 27-days of precipitation (Table 4.3; Figure 4.7a).

The yields of D-AA in freshly-produced bacterial DOM were used to calculate the bacterial contribution to DOC in groundwater (Table 4.5; Figure 4.7c). Bacterial DOC in groundwater varied from 15 to 34% of the total DOC, with an average value of 20 ± 4%

(Figure 4.7c). Percentages of bacterial DOC were not significantly correlated with precipitation (Figure 4.7c; Table 4.3).

4.4 DISCUSSION

Surface water, soil, and groundwater are hydrologically linked as precipitation and other forms of surface water percolate through soils to the saturated zone

(Sophocleous 2002). Selective interactions among water, organic matter, soil minerals, and microorganisms result in varying retention times of dissolved molecules during migration through the soil column, a phenomenon that has been described as regional chromatography (Hedges et al. 1986; Hedges et al. 1994). Aluminosilicate clay minerals and metal oxides/hydroxides in soil selectively retain specific functional groups of organic molecules. Hydrophobic constituents are more strongly sorbed and more slowly transported through soils than hydrophilic molecules (Jardine et al. 1989; Gu et al. 1995;

Kaiser et al. 2004). Molecular hydrophobicity is primarily controlled by molecular size, polarity, charge, and bioavailability (Jardine et al. 1989; Gu et al. 1995; Arnarson and

Keil 2000; Theng 2012). Soil organic matter is subjected to degradation and remineralization by microbiota (Baker et al. 2000), which also release metabolites

(Kawasaki and Benner 2006; Kaiser and Kalbitz 2012; Hobara et al. 2014). Hydrology drives DOM transport through the soil column, and physicochemical and biological processes control retention time and shape the concentrations and compositions of colloidal and dissolved molecules during passage through the soil column (Fig. 4.8).

The long-term monitoring of precipitation and groundwater DOM demonstrated variable temporal patterns in the hydrological connectivity between surface and ground waters in the present study. The concentrations of DOC in groundwater were significantly correlated with the previous 27-days of precipitation. This feature was observed throughout the 2-year sampling period, revealing a tight DOM connection between surface and ground waters. The measurements of plant-derived lignins in groundwater provide corroborated evidence for this statement. The continuity and strength of this hydrological connection can vary greatly with surface DOM

concentrations, unsaturated zone sorption capacity, and the timing and intensity of precipitation (Datry et al. 2006). One example from this study is the uncoupling between

DOC concentrations and precipitation during the last two sampling periods. A major precipitation event occurred at this time and it appeared to overwhelm the leaching capacity of soils resulting in a decrease in DOC concentrations in the groundwater. Baker et al. (2000) observed a similar variation in groundwater DOC concentration in response to the snowmelt.

The hydrological connections between surface and ground waters drives chromatographic migration of organic molecules through the soil columns described in the regional chromatography model. The concentrations of DOC were ~10-fold lower in groundwater than in surface water, apparently due to biotic and abiotic removal processes. Surface soils are important sources of DOC in groundwater (Baker et al. 2000;

Aravena et al. 2004). Assuming DOC concentrations in surface waters are representative of those in surface soils, about 90% of surface-derived DOC is removed prior to reaching the saturated zone. The unamended bioassay experiments indicated ~29% of surface water DOC was bioavailable, suggesting a large fraction of DOC was removed by abiotic processes (e.g., sorption) during infiltration (Kalbitz et al. 2000). The unsaturated zone in the present study contains a weathered clay layer (saprolite) where strong DOC sorption usually occurs (Jardine et al. 1989; Kalbitz et al. 2000). The relatively low concentrations of DOC in groundwater are consistent with previous studies showing decreasing concentrations of DOC with soil depth (Pabich et al. 2001; Goldscheider et al. 2006;

Inamdar et al. 2011).

Linking groundwater DOM with surface precipitation revealed differential transport of DOM constituents through soils. The bioavailable fraction of DOC and other amino acid parameters were most strongly correlated with the previous 10-days of precipitation, whereas DOC and the fraction of plant-derived DOM, as indicated by

SUVA254, were correlated with a longer precipitation period (27-days). These patterns are indicative of regional chromatography, which describes the selective removal and differential retention of DOM constituents during transport from surface to ground waters. Molecular properties (e.g., size, hydrophobicity, and charge) affect sorption processes, with hydrophobic macromolecules, such as lignins, being preferentially sorbed onto minerals and retained for longer periods of time during passage through the soil column (Kaiser et al. 2004; Inamdar et al. 2012). Some bioavailable components of

DOM, such as amino acids and carbohydrates, are highly mobile and eluted rapidly to the saturated zone (Kaiser et al. 2004). Fungi and bacteria are abundant in soils, exposing

DOM to a diverse array of enzymes that selectively alter and remineralize components during their percolation through the soil (Kalbitz et al. 2000). Low concentrations of lignin-derived phenols were measured in groundwater samples, indicating substantial removal of these plant-derived molecules during transport through the soil. The DOC- normalized absorbance at 254 nm, SUVA254, has been used to trace the lignin component of DOM in rivers (Spencer et al. 2010) and is used in this study for tracing the fraction of plant-derived DOM in groundwater. Significantly lower SUVA254 values in groundwater than in surface water corroborate the relatively low concentrations of plant-derived chromophoric DOM in groundwater. The SUVA254 values reported in this study fall within the range of values measured in groundwater and in rivers dominated by

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groundwater inputs (Spencer et al. 2008; Chapelle et al. 2011; Inamdar 2011; Spencer et al. 2012). The S275–295 values in groundwater were significantly higher than those in surface waters (Shen et al. 2012b; this study), indicating the average molecular weight of

DOM in groundwater is lower than that in surface water (Helms et al. 2008). Microbial degradation and sorption onto minerals in the unsaturated zone are likely responsible for the removal of lignin and other components of chromophoric DOM (Kaiser et al. 2004;

Inamdar et al. 2011; Ward et al. 2013).

The bioavailability of DOC is commonly determined using bioassay experiments, and in the present study this approach was used to develop a molecular indicator of

BDOC that could be measured in water samples without conducting the time consuming bioassay experiments during each collection of groundwater samples (Benner 2003;

Davis and Benner 2007). The fraction of BDOC in groundwater was estimated using

TDAA yields based on the robust relationship between these variables during bioassay experiments. We assumed that no DOC was bioavailable when TDAA yields were below

0.43 %DOC, the lowest value observed during the 42-days bioassay experiments. This threshold value is at the lower end of the range of values (0.4–0.8 %DOC) observed in deep ocean water, where BDOC is in extremely low concentrations (Davis and Benner

2005; Kaiser and Benner 2009). In addition, the range of TDAA yields observed in groundwater fell within the range (23 out of 24; 96%) observed during the bioassay experiments, further validating the quantitative approach used in this study.

An average of 8±4% of the DOC in groundwater was bioavailable in this study, which is similar to estimates in other studies using bioassay measurements (Grøn et al.

1992; Romaní et al. 2006; Chomycia et al. 2008). The TDAA yields measured in 47

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groundwater samples from California, Georgia, South Carolina, and New Jersey were mostly below 1 %DOC, indicating similarly low BDOC in groundwater from a wide range of systems (Chapelle et al. 2009; Chapelle et al. 2011). In comparison, substantially higher percentages of BDOC were measured in surface water (29±1%; this study), which is consistent when compared to average literature values for streams (11–27%), lakes

(14%), rivers (19%), and seawater (19%) (Qualls and Haines 1992; Søndergaard and

Middelboe 1995; Volk et al. 1997; Stutter et al. 2013). The percentages of BDOC reported in coastal estuaries were quite variable (8–29%) but mostly higher than those in groundwater (Moran and Hodson 1999; Raymond and Bauer 2000; Lønborg et al. 2009;

Lønborg and Søndergaard 2009). Differences among reported estimates could be due to variable incubation times and estimation methods (Søndergaard and Middelboe 1995).

Nevertheless, BDOC in groundwater is consistently low in comparison with values for other aquatic systems.

The litter layer is an important source of DOM, and it appears leachates from the litter layer contribute to the BDOC in groundwater. The correlation between BDOC and recharge to the aquifer, as indicated by the previous 10-days of precipitation, indicates the rapid transport of BDOC through the soil. Substantial remineralization of BDOC occurs in the upper soil horizons, resulting in the relatively low concentrations of BDOC in groundwater. This interpretation is consistent with previous observations in sand and gravel aquifers that show concentrations of DOC decrease rapidly with increasing thickness of the unsaturated zone (Pabich et al. 2001).

Amino acid-based indicators revealed that DOM in groundwater is diagenetically altered. Glycine, γ-aminobutyric acid, and D-AA have been used as diagenetic indicators

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of sedimentary organic matter, estuarine and marine DOM (Cowie and Hedges 1994;

Jørgensen et al. 1999; Kaiser and Benner 2009). Incubation experiments in this study clearly demonstrated an enrichment of the three indicators during biodegradation, and the mole percentages of these molecular indicators were significantly higher in groundwater than those in surface water. These high values are consistent with the low BDOC, low

TDAA yields, low dissolved oxygen concentrations (avg.: 44±19 µmol L-1), and low molecular weight of DOM, indicating DOM in groundwater has undergone extensive microbial alteration. Values of the three indicators were similar to those found in deep

Pacific Ocean DOM, which is highly altered and resistant to biodegradation (Kaiser and

Benner 2008; Kaiser and Benner 2009).

The low bioreactivity of DOM in groundwater appears to be an integrated result of biotic and abiotic processes in soils and groundwater. The DOM collected from surface and ground waters showed distinct compositions during the bioassay incubations, suggesting microbial degradation of surface DOM in situ is not the only process shaping the composition of groundwater DOM. Other alteration processes must be active as surface DOM infiltrates into the groundwater. Hydrologic mixing can influence DOC concentrations in groundwater (Foulquier et al. 2010), but mixing does not appear to be the major factor controlling the composition of DOM in groundwater. Sorption alters the composition of DOM by preferentially removing hydrophobic macromolecules, but it has less influence on bioavailable components (e.g., amino acids and carbohydrates) (Jardine et al. 1989; Kaiser et al. 2004; Inamdar et al. 2012). The extremely low TDAA yields in groundwater indicate extensive microbial alteration of DOM in groundwater

(Goldscheider et al. 2006). Biodegradation experiments revealed consistently lower

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TDAA yields and higher mole percentages of γ-Aba in groundwater DOM than in surface water DOM, implying microbial community structure could also play a role in shaping the composition and bioreactivity of DOM.

Several studies have demonstrated the presence of biomarkers derived from bacteria and fungi in soil organic matter (Guggenberger et al. 1999; Amelung et al. 2006;

Hobara et al. 2014), but the contributions of these microbes to DOM in groundwater have not been investigated. In the present study, the yields of D-AA indicated 15–34% of the

DOC in groundwater (avg.: 20±6%) was of bacterial origin. These relatively high values are consistent with the low bioreactivity of DOM in groundwater, revealing the important role of soil bacteria in producing, as well as consuming, DOM (Benner 2010). The average bacterial contribution to DOC in groundwater is comparable to that in the ocean

(~25%) (Kaiser and Benner 2008) and is somewhat lower than that estimated in streams

(32±10%; this study) and lake water (30–50%DOC; Kawasaki et al. (2013). The weak correlation between precipitation and DOC of bacterial origin indicates other factors, such as bioavailable substrates and groundwater residence time, regulate the contributions of bacterial to DOC in groundwater. The bacterial DOC in groundwater could be derived from surface water, soils in the unsaturated zone, and in situ production in groundwater.

Overall, DOM in groundwater is a mixture of components derived from various sources and altered by multiple processes, making it difficult to predict the composition and bioreactivity of DOM solely from bulk DOM analyses (Chapelle et al. 2009; Shen et al. 2012a). The regional chromatography model is presented as a conceptual framework for understanding the sources and processes influencing the properties of DOM during its

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transport from the surface litter layer to groundwater. Additional studies of water transit times and pathways through soils would provide further insights about the processes shaping the concentration, composition, and bioavailability of DOM in groundwater. A fundamental understanding of these processes is needed for wise management of groundwater resources and the implementation of bioremediation strategies for contaminated groundwater.

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Table 4.1 DOC and amino acids in ground and surface waters and freshly-produced bacterial DOM

DOC TDAA TDAA L-Asx D-Asx L-Glx D-Glx L-Ser D-Ser L-Ala D-Ala Gly γ-Aba D-AA* (µmol L-1) (nmol L-1) (%DOC) (nmol L-1) Groundwater 7/31/10 69 130 0.72 9.0 1.8 9.9 1.0 9.8 0.8 11 4.5 37 8.3 8.0 9/5/10 82 111 0.51 7.2 2.0 6.6 1.2 6.9 0.9 10 5.1 32 8.8 9.2 10/2/10 85 183 0.82 11 2.6 35 1.1 13 1.2 14 8.0 46 10 13 10/30/10 81 150 0.66 11 2.0 9.2 1.3 16 1.8 12 5.5 46 4.0 11 12/4/10 76 133 0.65 7.5 1.4 5.8 1.1 14 0.8 14 5.3 40 8.3 8.6 1/9/11 77 131 0.64 10 1.6 9.9 1.2 12 0.6 13 4.7 36 5.5 8.0 2/6/11 81 118 0.56 9.3 1.7 7.2 1.3 8.0 0.8 12 5.2 32 4.2 9.0 3/6/11 84 144 0.67 11 2.1 8.8 1.7 9.0 0.7 14 6.5 39 6.0 11 4/2/11 90 234 0.98 17 2.0 34 0.9 27 1.1 21 6.6 59 6.2 11 5/7/11 83 127 0.58 8.7 1.7 7.6 1.5 7.1 0.6 13 6.2 37 6.9 10 6/11/11 74 207 1.10 14 2.0 15 1.9 15 0.9 24 14 53 5.4 19 7/9/11 85 205 0.99 18 2.3 16 2.0 13 0.9 19 8.2 47 5.2 13 7/30/11 78 118 0.56 7.9 1.9 6.9 1.7 6.0 0.8 12 6.0 35 6.9 10

106 9/3/11 80 112 0.53 7.1 1.8 6.5 1.6 5.3 0.7 12 6.3 31 6.0 10 10/10/11 83 156 0.69 10 2.1 13 1.8 12 0.9 17 8.6 43 4.4 13

11/6/11 82 113 0.51 6.4 1.2 11 0.9 7.3 0.9 12 5.1 34 3.8 8.1 12/10/11 82 122 0.56 7.3 1.6 11 1.2 10 0.7 13 4.4 34 4.5 8.0 1/8/12 81 139 0.64 8.7 1.4 13 1.0 12 0.9 15 6.0 38 7.0 9.3 2/5/12 90 114 0.47 6.2 1.5 6.6 1.6 6.1 0.9 12 5.6 32 9.0 9.5 3/17/12 99 142 0.53 9.3 2.6 8.6 1.8 6.9 1.0 16 6.6 42 7.7 12 5/19/12 90 153 0.65 11 2.0 16 1.0 14 0.9 16 5.6 38 8.0 9.6 7/15/12 85 125 0.55 11 1.3 11 1.4 9.4 0.6 13 4.9 32 5.6 8.2 7/21/12 85 180 0.82 17 1.5 15 1.7 12 0.8 18 5.8 42 11 9.8 8/25/12 84 187 0.87 16 1.6 21 1.3 15 0.6 18 5.0 50 3.8 8.5 AVG 83 147 0.68 10 1.8 13 1.4 11 0.9 15 6.2 40 6.5 10 SD 6.0 35 0.17 3.4 0.4 7.8 0.3 4.8 0.2 3.5 2.0 7.4 2.0 2.5

Surface water 4/7/11 322 3782 4.49 368 12 675 0 746 10 339 31 757 25 53.4 5/19/12 688 1668 0.92 199 30 107 15 129 11 190 37 387 17 92.5 7/20/12 1538 5880 1.47 794 37 408 38 525 26 641 77 1350 33 179 6/17/13 608 3219 2.13 359 37 236 25 243 16 343 71 694 29 149 AVG 789 3637 2.25 430 29 357 20 411 16 378 54 797 26 118 SD 524 1742 1.57 254 12 246 16 279 8 189 23 403 7 56.1

Bacterial AVG 24.6 535 11 41 1.4 37 3.9 191 1.8 39 6.7 60 4.1 14 DOMSD 16.6 181 5.3 16 0.5 18 1.4 70 1.6 15 2.0 30 3.1 4.6

* D-AA = D-Asx + D-Glx + D-Ser + D-Ala. AVG: Average, SD: standard deviation.

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Table 4.2 Optical properties of DOM in ground and surface waters

a a a SUVA S S 254 280 350 254 275-295 350-400 S (m-1) (L mgC-1 m-1) (nm-1) R Groundwater 7/31/10 3.10 1.97 0.415 1.62 0.0188 0.0258 0.731 9/5/10 3.53 2.19 0.445 1.56 0.0199 0.0255 0.781 10/2/10 3.36 2.18 0.402 1.43 0.0202 0.0257 0.787 10/30/10 3.18 2.01 0.398 1.42 0.0203 0.0264 0.771 12/4/10 3.01 1.89 0.365 1.44 0.0199 0.0272 0.731 1/9/11 3.01 1.89 0.356 1.42 0.0204 0.0277 0.738 2/6/11 3.26 2.02 0.380 1.46 0.0207 0.0265 0.778 3/6/11 3.61 2.27 0.431 1.55 0.0205 0.0276 0.743 4/2/11 3.89 2.46 0.471 1.55 0.0201 0.0270 0.744 5/7/11 3.49 2.17 0.417 1.53 0.0206 0.0274 0.753 6/11/11 3.26 2.03 0.388 1.60 0.0209 0.0285 0.732 7/9/11 3.49 2.18 0.430 1.49 0.0205 0.0267 0.767 7/30/11 3.29 2.03 0.389 1.53 0.0209 0.0268 0.777 9/3/11 3.33 2.06 0.382 1.50 0.0217 0.0277 0.783 10/10/11 3.52 2.11 0.396 1.53 0.0223 0.0268 0.832 11/6/11 3.44 2.11 0.395 1.52 0.0220 0.0265 0.833 12/10/11 3.44 2.12 0.397 1.52 0.0220 0.0259 0.849 1/8/12 3.54 2.19 0.404 1.58 0.0224 0.0278 0.806 2/5/12 4.02 2.46 0.448 1.61 0.0226 0.0279 0.808 3/17/12 4.82 2.99 0.564 1.77 0.0217 0.0262 0.827 5/19/12 3.80 2.35 0.434 1.53 0.0219 0.0269 0.816 7/15/12 3.71 2.25 0.424 1.57 0.0226 0.0260 0.869 7/21/12 3.72 2.27 0.402 1.57 0.0227 0.0270 0.843 8/25/12 3.95 2.41 0.438 1.71 0.0227 0.0275 0.825 AVG 3.53 2.19 0.415 1.54 0.0212 0.0268 0.788 SD 0.39 0.23 0.042 0.09 0.0011 0.0008 0.042 Surface water 4/7/11 20.7 14.9 4.42 2.33 0.0163 0.0200 0.817 5/19/12 60.8 43.0 12.5 3.19 0.0160 0.0204 0.783 7/20/12 129 97.1 32.4 3.05 0.0141 0.0186 0.754 6/17/13 49.9 37.3 12.0 2.97 0.0150 0.0196 0.763 AVG 65.2 48.1 15.3 2.88 0.0153 0.0197 0.779 SD 46.0 34.9 11.9 0.38 0.0010 0.0008 0.028 AVG: Average, SD: standard deviation.

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Table 4.3 Correlation matrix between precipitation (previous 10-days and 27-days) and DOM parameters in groundwater

Bacterial DOC a a SUVA BDOC Gly γ-Aba D-AA* (n = 23) 254 350 254 DOC (µmol L-1) (m-1) (L mg-1 m-1) (%DOC) (mol %) (%) (%DOC) r -0.098 -0.089 -0.092 - 0.003 0.735 -0.614 -0.406 -0.648 0.268 10-d p > 0.6 > 0.6 > 0.6 > 0.9 < 0.001 < 0.002 < 0.05 < 0.001 > 0.2

r 0.415 0.570 0.568 0.600 0.419 -0.481 -0.230 -0.181 0.233 27-d p < 0.05 < 0.05 < 0.05 < 0.005 < 0.05 < 0.05 > 0.2 > 0.4 > 0.2

*D-AA (%) = ([D-Asx] + [D-Glx] + [D-Ser] + [D-Ala]) / ([L-Asx] + [D-Asx] + [L -Glx] + [D-Glx] + [L-Ser] + [D-Ser] + [L-Ala] + [D-Ala]) × 100

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Table 4.4 Lignin phenols in ground and surface waters*

V S C TDLP6 Date S/V C/V (Ad/Al)S (Ad/Al)V (nmol L-1) (nmol L-1) (%DOC) Ground 5/19/12 1.12 0.51 0.17 0.46 0.15 0.92 0.85 1.6 0.015 water 7/21/12 1.74 0.63 0.17 0.36 0.10 0.69 0.66 2.4 0.023

Surface 5/19/12 102 40 10 0.38 0.10 0.71 0.83 141 0.17 water 7/20/12 732 438 62 0.60 0.08 0.75 0.95 1170 0.65

*V = VAL + VON + VAD, S = SAL + SON + SAD, C = CAD + FAD. S/V = molar ratio of S to V; C/V = molar ratio of C to V. (Ad/Al)S = molar ratio of SAD to SAL; (Ad/Al)V = molar ratio of VAD to VAL. Abbreviations were provided in the Methods.

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Table 4.5 Yields of D-amino acids in freshly-produced bacterial DOM

D-Asx D-Glx D-Ser D-Ala (nmol mgC-1) Bacterial DOM 5.8±3.3 15±7.8 6.6±3.8 24±11

Data are reported as average ± standard deviations.

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Figure 4.1 Temporal variations in (a) concentrations of dissolved organic carbon (DOC), (b) CDOM absorption coefficients at 254 nm (a254), (c) concentrations of total dissolved amino acids (TDAA), and (d) concentrations of D-enantiomers of amino acids (D-AA) in groundwater.

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Figure 4.2 Concentrations of dissolved organic carbon(DOC)and total dissolved amino acids (TDAA) during bioassay experiments with (a) ground and (b) surface waters. The two y-axes in each plot are scaled to be proportional. Error bars represent the standard deviations of three replicate incubations.

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Figure 4.3 (a) DOC-normalized yields of total dissolved amino acids (TDAA) during the unamended experiments with ground and surface waters. Error bars are the standard deviations. The purple and brown dashed lines represent the average values in the ground (0.68 %DOC, n = 24) and surface water samples (2.25 %DOC, n = 4), respectively. (b) Relationship between percentages of bioavailable DOC and TDAA yields.

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Figure 4.4 Seasonal variations in percentages of bioavailable dissolved organic carbon (BDOC) and previous 10-days precipitation in groundwater.

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Figure 4.5 Mole percentages of (a, d) glycine, (b, e) γ-aminobutyric acid (γ-Aba), and (c, f) D-enantiomers of amino acids (D-AA) during the unamended experiments (left panel) and in the field samples (right panel).

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Figure 4.6 Seasonal variations in mole percentages of (a) glycine, (b) γ-aminobutyric acid (γ-Aba), and (c) D-enantiomers of amino acids (D-AA). Blue dashed lines represent previous 10-d precipitation.

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Figure 4.7 Seasonal variations in (a) concentrations of dissolved organic carbon (DOC), (b) specific UV absorbance at 254 nm (SUVA254), and (c) percentages of bacterial DOC. Blue dashed lines represent precipitation of previous 27-d (a, b) and 10-d (c).

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Figure 4.8 Regional Chromatography Model—precipitation and surface water leaches dissolved organic matter (DOM) from vegetation and plant litter and percolates through the soil column to the saturated zone. The concentration, composition, and bioavailability of DOM are altered during transport through the soil column by various physicochemical and biological processes, including sorption, desorption, biodegradation and biosynthesis. Hydrophobic molecules are preferentially partitioned onto soil minerals and have a longer retention time in soils than hydrophilic molecules. The hydrophobicity and retention time of colloids and dissolved molecules in soils are controlled by their size, polarity, charge, and bioavailability. Bioavailable DOM is subjected to microbial decomposition, resulting in a reduction in size and molecular weight. Novel molecules are synthesized by soil microbes, and some of these metabolites enter the DOM reservoir in groundwater.

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CHAPTER 5

OVERVIEW AND SYNTHESIS

Amino acids and carbohydrates are recognizable biochemical components of

DOM, and they are valuable molecular informants that carry a wealth of information about their origin, history, and potential fate. Observations from this dissertation demonstrate that the “reading” of biomolecular indicators can broaden our understanding of not only the chemical characteristics of DOM and but also their intimate linkages with biological and physicochemical properties of ecosystems. The major findings and potential implications of this work are summarized below.

1) demonstrate the broad-scale utility of amino acids and carbohydrates as molecular indicators of bioavailable DOM in aquatic environments

Our understanding of DOM bioavailability in aquatic systems has been fragmented because the classical bioassay experiments used to determine bioavailable

DOM are limited in spatial and temporal coverage and they provide condition-dependent results that are difficult to compare among studies. The use of amino acid- and carbohydrate-based indicators bypasses the need for bioassay experiments and exploits information from the inherent chemical constitution of DOM that reflects its potential bioavailability. Paired-analysis of these biomolecules and bulk DOC is relatively simple and fast, and thereby facilitates high-resolution (e.g., Chapters 1, 2, 3) and long-term

(e.g., Chapters 3, 4) monitoring of bioavailable DOM. While our work was conducted in

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subtropical and polar regions, this molecular approach should also be applicable to other aquatic ecosystems. The broad-scale application of biochemical indicators has the potential to greatly advance our understanding of factors that regulate the production and degradation of DOM and the associated major elemental cycles. Furthermore, the novel capability to measure bioavailable DOM in groundwater can be very valuable for the implementation of bioremediation in contaminated groundwater systems.

2) establish DOM bioavailability as an ecosystem property that reflects productivity

The molecular methods facilitate large-scale surveys that can dramatically expand our knowledge of the spatial and temporal distributions of bioavailable DOM and their linkages to physicochemical and biological parameters at the ecosystem level. Our results reveal a positive response of DOM bioavailability to nutrient- and light-driven change in phytoplankton biomass and productivity (e.g., Chapters 1, 2, 3). These observations provide a snapshot illustrating some of the biological consequences of changing physiochemical conditions in the ecosystem. Climate variability over the past century appears to have altered hydrographic stability and light availability in many oceanic regions and have induced pronounced interannual and decadal shifts in primary production (Boyce et al. 2010). Large fluctuations in marine productivity are projected to continue in a complex manner in the future (Steinacher et al. 2010). Application of molecular indicators of DOM bioavailability will help elucidate broad-scale dynamics of ecosystem productivity and production of bioavailable DOM that shapes and sustains the structure and functioning of microbial food webs.

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3) reveal patchy occurrence of biological hot spots in the ecosystem

The ocean is a patchy environment where microbial activities occur at the microscale. The high-resolution implementation of molecular indicators assists identification of specific locations of unusual biochemical signals. We have observed the patchy occurrence of hot spots with elevated concentrations of bioavailable DOM during most surveys (e.g., Chapters 1, 2, 3). These locations are likely to host enhanced biogeochemical processes given the rapid response of microorganisms. However, it is important to point out that the biochemical indicators provide information about the stock of bioavailable DOM, but they do not indicate the rate of DOM biological utilization, which can be determined using bioassay experiments. The observations of patchy hot spots demonstrate spatial heterogeneity in the biological production and utilization of

DOM, and environmental conditions (e.g., nutrient and light). The latter can affect mechanisms of DOM production, resulting in release of compositionally distinct bioavailable DOM that undergoes different microbial transformations and has different fates. Overall our results suggest the DOM cycle is far more dynamic and intricate than previously recognized, and they highlight the immense potential of biochemical tools to help decipher the complex interplay between molecules and microbes in a wide variety of aquatic ecosystems.

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APPENDIX A – SUPPLEMENTARY TABLES

Table A.1 Average (± standard deviation) concentrations of dissolved organic carbon (DOC), terrigenous DOC, and marine DOC (mDOCtotal) in the mixed layer of the marine end member (salinity > 36) during each cruise.

DOC tDOC mDOC Cruise Salinity total n (µmol L-1) Jan 2009 36.3±0.1 77±1 5±1 72±1 5 Apr 2009 36.4±0.2 82±5 3±1 79±5 13 Jul 2009 36.7±0.1 82±2 2±0 80±2 5 Oct-Nov 2009 36.4±0.2 81±4 3±0 78±3 9 Mar 2010 36.4±0.1 72±6 4±1 68±5 9 n: the number of stations.

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APPENDIX B – SUPPLEMENTARY FIGURES

Figure B.1 Depth profiles of (a, e) DOC-normalized yields of total dissolved amino acids (TDAA), (b, f) mole percentages of glycine, (c, g) mole percentages of two non-protein amino acids: β-alanine (β-Ala) and γ-aminobutyric acid (γ-Aba), and (d, h) percentages of D-enantiomers of amino acids at seven sea ice-free stations (upper panels) and six ice- covered stations (lower panels). Note that the ice-free and ice-covered stations are labeled separately as in panels a and e, respectively. Percentages of D-amino acids were [D - AA ] calculated as: D - amino acids (%) = 4 ´100, where [D-AA4] and [L-AA4] [D - AA4 ]+[L - AA4 ] were concentrations of the four D- and L-amino acids (Asx, Glx, Ser, and Ala).

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30 a ** Hot spots 25 Other areas

m 15 **

a *

l 10

o *

M * ** 5 ** ** ** ** ** ** 0 r r a a r l e e s sx lx e ly h l rg la b y a h l y S T A A T V I L A G G - A -A P b g 25 b

r 20

a 15 **

r *

e *

n 10

M 5

0 Fuc Rha Ara Gal Glc Man Xyl

Figure B.2 Compositional differences in (a) amino acids and (b) neutral sugars between hot spots and other areas. Error bars are two times standard error (Amino acid hot spots: n = 12; Neutral sugar hot spots: n = 81). Significant (p < 0.05) and highly significant (p < 0.01) differences are indicated by one and two asterisks, respectively.

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