The Pennsylvania State University the Graduate School Department

The Pennsylvania State University

The Graduate School

Department of Geosciences

MOLECULAR AND ISOTOPIC INVESTIGATIONS OF THE BIOGEOCHEMISTRY

OF ARCHAEAL ETHER LIPIDS

A Thesis in

Geosciences

Courtney Hanna Turich

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

December 2006

ii The thesis of Courtney Hanna Turich was reviewed and approved* by the following:

Katherine H. Freeman Professor of Geosciences Graduate Program Chair Thesis Advisor Chair of Committee

Mary Ann Bruns Associate Professor of Crop and Soil Sciences

Michael A. Arthur Professor of Geosciences

Christopher House Associate Professor of Geosciences

A. Daniel Jones Professor of Chemistry

*Signatures are on file in the Graduate School

iii ABSTRACT

Once thought to inhabit only extreme environments, the Archaea are now known to occur globally in oceans, marshes, lakes, sediments, and soils. Archaea abundance and metabolic diversity link these microbes to important biogeochemical transformations.

Archaea also generate abundant and diagnostic membrane lipids that are widespread in modern environments as well as the sedimentary record. These lipid compounds offer important evidence for past Archaea distribution and activity, and are a key means for understanding archaeal contributions to biogeochemical cycles, especially carbon and nitrogen, over Earth’s history.

To link records of naturally occurring lipid to their biological and metabolic origins, I studied archaeal lipid distributions from a global set of modern waters in order to test the hypothesis that specific lipid assemblages will correspond to specific genotypic groups. Cluster analysis showed that marine lipid distribution patterns fell into two groups: the epipelagic zone and mesopelagic/upwelling zones. Multivariate analysis with environmental data showed that nitrate concentrations largely explained the variation observed within these two marine groups, thereby linking metabolism to the archaeal lipid distributions ultimately in preserved sediments. In the more extreme gradient from marine to hypersaline environments, differences in lipid assemblages form the basis of a novel paleosalinity indicator, the “archaeaol/caldarchaeol ecometric” (ACE). The robust linear relationship between ACE and salinity in modern environments can be applied to ancient sedimentary organic matter deposited in the Mediterranean prior to the Messinian

Salinity Crisis (5.7 Ma). ACE-based salinity values are consistent with mineral and iv faunal evidence of salinity increases, and complement bulk organic and isotopic geochemical records of differences in productivity and carbon cycling in the lead up to the Messinian Salinity Crisis. Finally, targeting the hydrogen (for hydrological reconstruction) and carbon (for metabolic reconstruction) isotope compositions of archaeal lipids requires a method capable of measuring isotope ratios in these large and non-volatile lipid compounds. Thus far, sample sizes have been reduced by several orders of magnitude. This novel technique, Nano-EA-irMS, will allow studies of the isotopic properties of compounds which could not previously be analyzed.

v TABLE OF CONTENTS

LIST OF FIGURES ...... viii

LIST OF TABLES...... xii

ACKNOWLEDGEMENTS...... xiv

Chapter 1 Introduction ...... 1

Archaea in the Environment ...... 1 Archaeal lipids...... 3 TEX86 ...... 7 Compound-specific isotope analysis ...... 10 Thesis objectives and outline...... 11 Patterns and provenance of archaeal membrane lipid assemblages in the water column ...... 11 Archaeal lipids record transitions from marine to hypersaline environments ...... 12 Productivity changes in marine to hypersaline transitions, Late Miocene, Tripoli Formation, Sicily...... 13 Element-analysis isotope-ratio mass spectrometry of nanomolar quantities of individual compounds ...... 13

Chapter 2 Marine archaeal lipids: patterns and provenance in the water-column...... 16

Abstract...... 16 Introduction...... 17 Materials and Methods ...... 21 Sample collection and lipid extraction ...... 21 Lipid analysis...... 24 Statistical Analysis ...... 27 Results...... 29 Epipelagic water-column lipid abundances...... 29 Mesopelagic water-column lipid abundances ...... 32 Correlation between lipid abundances and hydrographic properties ...... 34 Cluster analysis: water-column lipid distributions...... 35 Ordination analysis: water chemistry and lipid assemblage patterns...... 38 Surface sediment lipid distribution: comparison with the water-column...... 41 Discussion...... 43 Lipid distribution patterns and archaeal communities...... 43 Lipid distribution and archaeal metabolisms...... 45 Archaeol, Caldarchaeol, and Salinity ...... 46 TEX86...... 47 Conclusions...... 52 vi Acknowledgements...... 53

Chapter 3 Archaeal lipids record transitions from marine to hypersaline environments...... 55

Abstract...... 55 Introduction...... 56 Materials and Methods ...... 57 Archaeol, caldarchaeol, and salinity...... 60 Paleosalinity at the onset of the Messinian Salinity Crisis...... 63 Origin of lipid-salinity relationship ...... 67 Conclusions...... 69 Acknowledgements...... 69

Chapter 4 Organic carbon records of the transition from marine to hypersaline conditions in the Late Miocene (Tripoli Formation, Sicily)...... 71

Abstract...... 71 Introduction...... 72 Geologic Setting ...... 73 Experimental Approach...... 75 Sample Preparation and Instrument Analysis...... 76 Results...... 77 Torrente Vaccarizzo ...... 77 Serra Pirciata ...... 82 Discussion...... 85 Organic matter type and maturity...... 85 Isolation and productivity changes across the Caltanissetta Basin ...... 86 Hydrogen isotopic changes in Messinian waters?...... 90 Conclusions...... 92 Acknowledgements...... 93

Chapter 5 Development of the Nano-EA-irMS: A novel method for analyzing the isotopic composition of large non-volatile compounds...... 94

Abstract...... 94 Introduction...... 95 Experimental approach ...... 99 Trap design ...... 99 Samples...... 101 Isotopic Analysis ...... 101 Results and Discussion ...... 102 Hydrogen ...... 102 Trapping and chromatography ...... 102 Isotopic measurements and blank correction ...... 104 Precision and accuracy...... 105 vii Nitrogen and Carbon ...... 110 Trapping and chromatography ...... 110 Measurements and blank correction...... 111 Precision and accuracy...... 114 Conclusions...... 116

Chapter 6 Conclusions and Future Work...... 118

Future Directions ...... 120 Biogeochemical controls on biomarker production and preservation...... 120 Ancient biomarker records at critical intervals ...... 121

References...... 122

Appendix A Considerations for hydrogen isotope measurement and interpretation..152

Appendix B Water chemistry and isotope measurements ...... 157

Appendix C Data from Torrente Vaccarizzo and Serra Pirciata...... 163

Appendix D Simultaneous analysis of archaeol and glycerol dibiphytanyl glycerol tetraethers by HPLC-APCI-MS...... 164

Introduction...... 164 Experimental...... 165 Results and Discussion ...... 167 Identification of archaeol...... 167 DGD and GDGT response factors...... 167 Conclusions...... 170

viii

LIST OF FIGURES

Figure 1-1: Environmental distribution of Archaea...... 2

Figure 1-2: Examples of diphytanyl glycerol diether (DGD) lipids synthesized by Archaea...... 4

Figure 1-3: Glycerol dibiphytanyl glycerol tetraether (GDGT) lipids of Archaea. Crenarchaeol is assumed the dominant lipid in ocean dwelling Group I Crenarchaeaota. Caldarchaeol, GDGT I, II, and III occur in methanogens and probably the marine Euryarchaeota. GDGT I, II, and III are used in the TEX86 paleotemperature proxy, along with an isomer of crenarchaeol...... 6

Figure 1-4: Pathways of organic matter production and preservation (Killops & Killops, 1993)...... 9

Figure 2-1: Locations of the sample sites. Inset shows the locations of sampling sites for the fresh to marine transected collected in Chincoteague Bay...... 21

Figure 2-2: a) Liquid-chromatography atmospheric pressure chemical ionization mass spectrometer (LC-APCI-MS) chromatogram of archaeal lipids SPM from the Bermuda Time Series site (210 m). From 0-10 minutes, the extracted ion chromatogram (653.5-655.4 m/z) shows archaeol, and from 10- 25 minutes, a total ion chromatogram shows the glycerol dibiphytanyl glycerol tetraethers (GDGTs), b) Mass spectra of archaeol isomer peaks and MS-MS spectra of archaeol showing characteristic fragmentation pattern. c) Mass spectra of GDGTs...... 26

Figure 2-3: Lipid abundances normalized to volume in a) the Black Sea, b) Mediterranean, c) equatorial Pacific Survey 1 and d) equatorial Pacific Survey 2. The left panel shows tetraethers (circles: caldarchaeol, diamonds: crenarchaeol, squares: all other cyclic GDGTs combined. The right panel shows archaeol (triangles). Note the variable scales on the x-axes...... 30

Figure 2-4: Lipid abundances normalized to water volume in a) Bermuda Time Series, b) Arabian Sea station MS-1, c) Arabian Sea station MS-4, and d) Arabian Sea station MS-3. The left panel shows tetraethers (circles: caldarchaeol, diamonds: crenarchaeol, squares: all other cyclic GDGTs combined. The right panel shows archaeol (triangles). Note the variable scales on the x-axes...... 34

Figure 2-5: Cluster analysis of archaeal lipids from suspended particulate matter collected in ocean, estuarine and terrestrial environments. Numbers ix correspond to samples listed in Table 2-1.* exceptions to the cluster generalizations...... 36

Figure 2-6: Non-metric multidimensional scaling of relative archaeal lipid abundances in all samples (Bray-Curtis distance measure, Stress 3.84)...... 38

Figure 2-7: Epipelagic zone and mesopelagic zone/upwelling lipid distribution ordination using non-metric multidimensional scaling of relative distribution of archaeal lipids (Bray-Curtis distance measure, Stress 3.86)...... 40

Figure 2-8: Relative distribution of archaeal lipids in surface sediments...... 41

Figure 2-9: Box plots of relative average amounts of dominant compounds in lipid grouping a) fresh/estuarine and hypersaline environments. b) epipelagic and mesopelagic/upwelling zone waters, and surface sediments...... 43

Figure 2-10: Temperature residuals (Calculated TEX86 temperatures – in situ temperature) from this study and from Wuchter et al. (2005) plotted against depth. Sites include the Arabian Sea, the Equatorial Pacific, and the Bermuda Time Series site and the Bermuda-Atlantic Time Series site...... 49

Figure 2-11: Temperature residuals (Calculated TEX86 temperatures – in situ temperature) from this study and from Wuchter et al. (2005) plotted against nitrate. Sites include the Arabian Sea, the Equatorial Pacific, and the Bermuda Time Series site and the Bermuda-Atlantic Time Series site...... 50

Figure 3-1: Extracted ion chromatograms of ether lipids from modern suspended particulate matter (SPM) in A) normal marine salinity (Equatorial Pacific 110 m) and B) hypersaline salterns (Sicily coast) and archaeal lipid structures I) archaeol and II) caldarchaeol...... 61

Figure 3-2: The relationship between salinity and the relative abundance of archaeol to caldarchaeol from modern marine suspended particulate matter in waters >260 m. The numbers correspond to Table 2-1...... 63

Figure 3-3: Extracted ion chromatograms of ether lipids from Serra Pirciata Messinian section at A) 17.4 m and B) 19.7 m, at onset of hypersalinity...... 64

Figure 3-4: Stratigraphy and raw A/A+C *100 (ACE) values and calculated salinity in Torrente Vaccarizzo (A) and Serra Pirciata (B). Stratigraphy reproduced from Bellanca et al. 2001...... 65

Figure 4-1: Outcrop locations and stratigraphy of Torrente Vaccarizzo and Serra Pirciata (from Bellanca et al. 2001)...... 74 x Figure 4-2: Elemental analysis of whole rock powders and kerogen in sediments in Torrente Vaccarizzo. Stratigraphy from Bellanca et al. 2001...... 78

Figure 4-3: δ13C values of carbonates, decarbonated sediments, and δ18O values 13 18 of carbonates in Torrente Vaccarizzo. Stratigraphy, δ C, and  δ O of carbonates from Bellanca et al. 2001...... 81

Figure 4-4: Comparison of δ13C values of decarbonated samples and isolated kerogen in both Torrente Vaccarizzo and Serra Pirciata...... 81

Figure 4-5: Modified van Krevalan diagram of HI v. OI in bulk sediments from Torrente Vaccarizzo (triangles) and Serra Pirciata (squares)...... 82

Figure 4-6: Elemental analysis of whole rock powders and kerogen in Serra Pirciata. Stratigraphy from Bellanca et al. 2001...... 83

Figure 4-7: δ13C of carbonates, decarbonated sediments, and δ18O of carbonates in Serra Pirciata. Stratigraphy, δ13C and δ18O of carbonates from Bellanca et al. 2001...... 84

Figure 4-8: Synthesis of proposed changes in the depositional system leading to organic and inorganic carbon isotope compositions in Torrente Vaccarizzo and Serra Pirciata...... 90

Figure 4-9: δD of kerogen in Serra Pirciata with insolation and eccentricity curves (from Rouchy and Caruso, personal communication) ...... 92

Figure 5-1: The two states of the Nano-EA-irMS configuration with a single trap. ...100

Figure 5-2: Chromatogram of Nano-TC-EA-irMS of hydrogen standards. The first three peaks are hydrogen reference gas...... 103

Figure 5-3: Hydrogen isotope data from Nano-TC-EA-irMS sample run series, showing changes in the background isotopic composition over time...... 106

Figure 5-4: Nano-TC-EA-irMS analysis of hydrogen standards. The y-intercept of 1/area plots should reflect the true isotopic composition of the standards if there is two-component mixing...... 108

Figure 5-5: Nano-TC-EA-irMS of hydrogen standards with expected v. uncorrected measured isotope compositions ...... 109

Figure 5-6: Chromatograms of Nano-EA-irMS of nitrogen and carbon standards. ....111

Figure 5-7: Nano-EA-irMS runs of nitrogen and carbon showing the changes in background isotopic composition over time...... 113 xi Figure 5-8: Nano-EA-irMS of nitrogen and carbon standards. 1/area plot illustrates the mixing lines from sample blank to the y-intercept “true” isotopic composition of the standards...... 115

Figure 5-9: Nano-EA-irMS measured isotope compositions v. expected values of nitrogen standards...... 116

Figure A-1: Isoprenoid biosynthesis pathway ...... 154

Figure D-1: Diphytanyl glycerol diether (archaeol) and glycerol dibiphytanyl glycerol tetraethers synthesized by Archaea...... 165

Figure D-2: a) HPLC-APCI-MS chromatogram of archaeol standard, b)HPLC- APCI spectrum of archaeol standard, c) daughter scan of archaeol standard...... 167

Figure D-3: Changes in the response factors for diphytanyl glycerol diether (archaeol) and glycerol dibiphytanyl glycerol tetraether (caldarchaeol) over time...... 168

Figure D-4: Chromatogram of sediment (Bear Meadows bog, Centre Country Pennsylvania) showing relative and absolute concentrations of archaeol and caldarchaeol in one HPLC-MS sample injection...... 169

xii

LIST OF TABLES

Table 2-1: Hydrographic data and sample numbers for SPM and sediments...... 22

Table 2-2: Concentration of lipids normalized to liters...... 31

Table 3-1: Ionic compositions of seawater, Infersa salt pans, and other hypersaline sites...... 58

Table 4-1: Rock-Eval analysis of whole rock powders and kerogen from Torrente Vaccarizzo and Serra Pirciata sections of the Tripoli Formation...... 79

Table 5-1: Existing methods and detection limits for elemental analysis isotope- ratio mass spectrometry ...... 97

Table 5-2: Standards used in Nano-EA-irMS method development...... 99

Table 5-3: Peak areas for different cup cleaning protocols ...... 101

Table 5-4: δD measured and blank corrected isotope values for 106 s trapping time runs of blanks and standards. All δD values are v. SMOW...... 104

Table 5-5: Average δD values (v. SMOW) for 42 and 106 s trapping times...... 107

Table 5-6: Nano-EA-irMS isotope measurements of nitrogen standards...... 112

Table 5-7: Nano-EA- irMS of a caffeine carbon standard...... 114

Table B-1: Major ion compositions in Bear Meadows, the Black Sea, Chincoteague Bay, and the Mediterranean...... 157

Table B-2: Major ion compositions at the Bermuda Time Series site...... 158

Table B-3: Major ion compositions in Infersa Saline...... 159

Table B-4: δD (‰ v. VSMOW) of waters in Black Sea, Bear Meadows, and Chincoteague Bay...... 160

Table B-5: δD (‰ v. VSMOW) of waters in Infersa Saline ...... 161

Table B-6: δD (‰ v. VSMOW) of PSU standard water with KCl added at various concentrations...... 162 xiii Table C-1: Elemental Analysis of whole rock powders and kerogens...... 163

xiv

ACKNOWLEDGEMENTS

I am grateful to my committee, Katherine H. Freeman, Mary Ann Bruns, Michael

Arthur, Christopher House, and Daniel Jones for their advice and support. I always

looked forward to my committee meetings, even the difficult exams, because every committee member brought wise and distinct insights into my work. I also appreciate their excellent editorial skills. I especially thank my advisor Kate, for her patience, encouragement, and collegiality throughout my graduate training.

Members of the Freeman Lab, past and present, spent hours teaching me organic geochemical and isotope techniques and in particular I thank Jennifer Eigenbrode,

Nikolai Pedentchouck, Pratigya Polissar, and Francesca Smith. Technician Christopher

Lernihan was also instrumental in the field where he made sure I could start the outboard

motor on my own. Jay Geagan was also a terrific field assistant. Jesse Thornburg ably

assisted in the lab as an undergraduate research assistant. I owe Denny Walizer never-

ending gratitude for his technical expertise and good-humor. I am convinced that learning

to run and fix instruments with Denny is the best training anywhere.

Many many long-time friends, new dear friends, and my family helped create a

normal life during this endeavor. I am lucky to have you all in my life, and am grateful to

you all. Extra special thanks to Mom and Dad who encouraged me early on to always go

for it.

Chapter 1

Introduction

Archaea in the Environment

Once thought to inhabit only extreme and anoxic environments, the Archaea are

now well-documented virtually everywhere, including oceans, salt marshes, freshwater

lakes, soils, the deep subsurface (Figure 1-1) (Beja et al., 2002; Delong et al., 1993;

DeLong et al., 1998; Hershberger et al., 1996; Hoefs et al., 1997; Karner et al., 2001;

Munson et al., 1997). The Archaea are one of the three domains of life, and contain two

major groups: the Euryarchaeota and the Crenarchaeota. The Euryarchaeota are the well-

studied methanogens and halophiles, as well as uncultivated marine mesophiles.

Crenarchaeota include many hyperthermophiles and abundant marine mesophiles, such as

the recently cultivated Nitrosopumilus maritimus. Crenarchaeota may make up as much

23% of all marine prokaryotes (Karner et al., 2001; Konneke et al., 2005). They are

prevalent throughout the water column, and generally increase with depth from the surface to the mesopelagic zone (Karner et al., 2001), although in cold waters, such as the

Drake Passage, they inhabit the upper water column only (Beja et al., 2002; Massana et

al., 2000). Crenarchaeota are absent at very high salinities (Ochsenreiter et al., 2002), and

2 are metabolically active in sapropels and subsurface sulfate methane transitions zones

(Biddle et al., 2006; Coolen et al., 2002).

Figure 1-1: Environmental distribution of Archaea.

Decoding the environmental significance of the marine Archaea is an active and emerging field, fueled by an arsenal of innovative molecular techniques such as compound-specific radiocarbon isotope analysis and genomic labeling (e.g. fluorescent in situ hybridization (Alonso & Pernthaler, 2006; Biddle et al., 2006; Ingalls et al., 2006;

Ouverney & Fuhrman, 2000; Pearson et al., 2001b; Smittenberg et al., 2004a). The sheer abundance of Archaea links their metabolisms to important chemical transformations in the environment. The estimated 1.3x1028 archaeal cells in the oceans (Karner et al., 2001) generate 0.6-0.8 Gt C/yr at a rate of 0.014 fmol C/Archaea -1 day -1 through mainly

autotrophic pathways (Herndl et al., 2005; Ingalls et al., 2006).

Archaeal contributions to the nitrogen cycle may be profound. Marine

Crenarchaeaota are nitrifiers, and archaeal ammonia oxidation genes outnumber bacterial

3 ammonia oxidation genes. Archaea could produce more than 1.3 Gt/ yr nitrite from the

+ oxidation of NH4 , equivalent to the amount of N that is exported from surface water

primary production to the deep ocean (Francis et al., 2005; Hallam et al., 2006; Konneke et al., 2005; Wuchter et al., 2006). In contrast, the biogeochemistry of marine

Euryarchaeota in the ocean is still unknown, although these microbes compete with

heterotrophic bacteria for amino acids in surface waters, suggesting a potentially

significant heterotrophic role as well (Alonso & Pernthaler, 2006).

Archaeal lipids

Archaea generate abundant diagnostic cell membrane lipids that are widespread in

modern environments and the sedimentary record (DeLong et al., 1998; Kuypers et al.,

2001; Menzel et al., 2006; Schouten et al., 2001b). These compounds offer important

evidence for past Archaea distribution and activity, and are a key means for

understanding archaeal contributions to both carbon and nitrogen cycles over Earth’s

history.

The lipids of Archaea are unique 43- and 86-carbon isoprenoid hydrocarbons

ether-linked to glycerol. Structural diversity and functional variations derive from the

addition of rings and polar head groups (Kates, 1997). The head groups are useful

taxonomic markers, and intact polar lipids are important compounds for examining active

Archaea (Biddle et al., 2006; Litchfield et al., 2000; Shimada et al., 2002; Sturt et al.,

2004). The more-readily preserved core lipids also provide useful taxonomic and

environmental information.

Two major forms of C43 diphytanyl glycerol diethers (DGDs), archaeol and

hydroxyarchaeol, are chemotaxonomic markers of Euryarchaeota although Crenarchaeota

are also known to synthesize at least archaeol (Koga et al., 1993; Nishihara & Koga,

1991; Sprott et al., 1993) (Figure 1-2). Macrocyclic DGDs containing cyclopentane rings have been attributed to methanogens in the Black Sea (Stadnitskaia et al., 2002). Carbon isotope analysis of DGDs confirms that in specific cases, Archaea are associated with the anaerobic oxidation of methane (Hinrichs et al., 2000; Nauhaus et al., 2002; Orphan et al., 2002; Schouten et al., 2001c). Halophilic Euryarchaeota are known to produce only

DGDs (Kamekura, 1993; Kamekura & Kates, 1999; Kates, 1997); and DGD structures document contributions from halophiles in ancient evaporite deposits (Grice et al., 1998a;

Teixidor et al., 1993).

archaeol sn-3-hydroxyarchaeol

C20-C25 diphytanyl glycerol Macrocyclic diphytanyl glycerol diether

Figure 1-2: Examples of diphytanyl glycerol diether (DGD) lipids synthesized by Archaea

The C86 glycerol dibiphytanyl glycerol tetraethers (GDGT) are structurally

diverse and can have 0 to 8 cyclic rings (Figure 1-3). Some Euryarchaeota methanogens

and thermophiles synthesize acyclic caldarchaeol, as well as GDGTS I, II, and III

(Deluca et al., 1986; Gattinger et al., 2002; Gonthier et al., 2001; Madigan et al., 2000).

Cyclic GDGTs create a densely packed membrane structure to protect thermophilic

Crenarchaeota cells at high temperatures (40-102°C). Mesophilic Crenarchaeota apparently evolved a six-member ring to create a “kink” that ultimately increases membrane fluidity at lower temperatures (Sinninghe Damsté et al., 2002b). This GDGT

is termed “crenarchaeol” because it appears to be the dominant membrane lipid of pelagic

Crenarchaeota. However, crenarchaeol is also associated with terrestrial Archaea, and the

exact provenance of the compound has not been shown conclusively. Nonetheless,

Damsté et al. (2002) suggest that there could be as much as 6.5 Mt of crenarchaeol in the

oceans today.

crenarchaeol

caldarchaeol

GDGT-I

GDGT-II

GDGT-III

TEX86

Cyclic GDGTs may optimize membrane fluidity in response to temperature changes in typical marine settings. The temperature proxy, TEX86 (TetraEthers indeX), is

2 based on a linear relationship (TEX86= 0.015T °C + 0.28, r =0.92) observed between the

relative concentration of cyclic GDGT structures and sea surface temperature (SST)

(Schouten et al., 2002). TEX86 is defined as:

(GDGT-II + GDGT-III + Crenarchaeol isomer)

(GDGT-I + GDGT-II + GDGT-III + Crenarchaeol isomer)

TEX86 is calibrated from 5-28°C (Schouten et al., 2003a; Schouten et al., 2002),

k’ and does not correlate well with U 37 (a temperature-sensitive lipid ratio based on

alkenones from haptophyte algae) reflecting differences in seasonal growth patterns

(Huguet et al., 2006; Schouten et al., 2003a; Wuchter et al., 2004; Wuchter et al., 2005).

Nonetheless, TEX86 has strong potential to provide paleotemperature information, and

has been used in conjunction with other proxies to fine-tune ocean reconstructions as well as terrestrial climate records (Jenkyns et al., 2004; Powers et al., 2004).

Importantly, archaeal lipids can provide information where other organic proxies

k’ are absent. For example, U 37 can only be used in relatively young sediments because

alkenone-producing algae evolved 6 Ma while, to date, the geological range of the

Crenarchaeota extends at least to the Cretaceous (Kuypers et al., 2001; Kuypers et al.,

2002). Archaeal lipid preservation may be enhanced in environments where other

8 chemical proxies are ineffective, such as basins inimical to growth and preservation of calcareous algae.

TEX86 does appear to record SST in many cases, providing evidence that archaeal

lipids used in the TEX86 proxy are transported from surface waters, through the water

column, and then preserved in sediments. Sinking particles in the ocean provide both

ballast and mineral protection to organic matter (Hedges et al., 2001; Wakeham et al.,

1997b). Decapod fecal matter provides at least one vector for archaeal lipid incorporation

into particle ballast (Huguet et al., 2005). Alteration and condensation of organic matter

in sediments eventually forms long-term organic carbon reservoirs in sediments

representing <1% of original production (Figure 1-4). However, the exact ecological and

biological origins of the relationship between TEX86 and sea surface temperature have

not been conclusively described.

Interpreting TEX86 in the sedimentary record depends partially on preservation

conditions. Hydrous pyrolysis experiments show decrease in TEX86 at temperatures

>210°C, as the cyclic GDGTs are degraded (Schouten et al., 2004). Therefore, TEX86

should only be used in relatively immature sediments. On the other hand, ether lipids do

not appear to bind to sulfur macromolecules, TEX86 can also be used in sulfur-rich

sediments (Koopmans et al., 1996; Schouten et al., 2004).

Figure 1-4: Pathways of organic matter production and preservation (Killops & Killops, 1993).

Compound-specific isotope analysis

The isotopic properties of lipids and other cellular products (amino acids, carbohydrates) in modern and ancient materials provide an archive of metabolic and environmental processes (Hayes, 2001b; Hayes et al., 1990). Specifically, the carbon isotope compositions of archaeal lipids have substantiated important metabolic discoveries. For instance, δ13C and Δ14C values consistent with dissolved inorganic carbon sources led to the discovery that mesophilic Crenarchaeota are chemoautotrophic

(Ingalls et al., 2006; Pancost & Sinninghe Damsté, 2003; Pearson et al., 2001b;

Smittenberg et al., 2004a).

There is growing interest in compound-specific hydrogen isotope analyses as a tool for measuring deuterium abundance of ancient waters and reconstructing past hydrology. Terrestrial plant and algal compounds record water isotope compositions and terrestrial temperatures (Sauer et al., 2001; Sternberg, 1988; Yapp & Epstein, 1982).

Grass n-alkane lipids may serve as a paleoaridity index, because transpirational enrichment (controlled by anatomy and growth form) influences hydrogen isotope composition (Sachse et al., 2006; Smith & Freeman, 2006). In the marine realm, the hydrogen isotope composition of alkenones, long-chain alkanes synthesized by haptophyte algae, appears to be related to salinity and growth rate, providing a possible tool for paleosalinity reconstructions (Schouten et al., 2006). Although hydrogen isotopic

11 analysis of archaeal lipids has not yet been achieved, these lipids offer an exciting potential for reconstructing water isotope compositions in a wide variety of environments.

Thesis objectives and outline

The main objective of this dissertation is to relate archaeal lipid assemblage patterns to biogeochemical characteristics in environments ranging from fresh, marine, and hypersaline in modern and ancient ecosystems. This dissertation provides a foundation for my long-term research goals of understanding controls on archaeal lipid synthesis and using these biomarkers to interpret paleoenvironmental properties (chapters

2 and 3). An organic geochemical study contextualizes the interpretation of archaeal lipids in 5.7 Ma sediments (chapter 4). I also describe the development of a new method of compound-specific analysis of large, non-volatile lipids (chapter 5). This “Nano-EA” will provide carbon and hydrogen isotope data for archaeal lipids, connecting archaeal lipids to metabolic processes and paleoenvironmental parameters.

Patterns and provenance of archaeal membrane lipid assemblages in the water column

(in review in Geochimica et Cosmochimica Acta)

12 This study examines the relationships between archaeal lipids and biogeochemical factors in a range of contemporary water samples. Understanding the controls on lipid synthesis in the water column is key to interpreting the biomarker record in ancient sediments. In this study, I test the hypothesis that heterogeneity in Archaea communities in freshwater, epipelagic and mesopelagic marine environments, and hypersaline waters would also be reflected in the distribution of ether lipid moieties. The patterns we describe provide a link between the known ecological and metabolic distributions of archaeal groups and the lipid biomarkers generating an important geochemical archive.

The co-authors of this manuscript will be Katherine H. Freeman (doctoral advisor) and in alphabetical order: Mary Ann Bruns (doctoral co-advisor, microbial ecologist), Maureen

Conte (Marine Biological Labs, chemical oceanographer and chief scientist on Bermuda research cruise), A. Daniel Jones (committee member, chemist), and Stuart G. Wakeham

(Skidaway Institute of Oceanography, provided archived lipid samples).

Archaeal lipids record transitions from marine to hypersaline environments

(for re-submission to Geology)

Based on the distribution of archaeal communities and lipids along the salinity gradients described in the previous section, we tested the utility of archaeal lipids as indicators of past salinity. We describe a relationship between the ratio of archaeol/caldarchaeol and salinity in modern environments and in sedimentary organic

13 matter deposited during the Messinian Salinity Crisis, a period of drastic salinity change.

(Katherine H. Freeman, doctoral advisor, is the co-author of this manuscript).

Productivity changes in marine to hypersaline transitions, Late Miocene, Tripoli

Formation, Sicily

(for submission to Chemical Geology in conjunction with additional compound-specific data)

Understanding the origin and alteration of sedimentary organic matter aids interpretation of biomarkers and paleoenvironment. The properties of organic matter in the Messinian (5.7 Ma) Tripoli Formation reflects changes in productivity and paleoenvironment prior to the onset of the Messinian Salinity Crisis, elucidating depositional and post-depositional processes affecting organic biomarkers and isotopes, and the Archaea synthesizing the lipids analyzed in the previous section. (Manuscript co- authors will be Katherine H. Freeman (doctoral advisor) and Antonio Caruso (University of Palermo, provided samples).

Element-analysis isotope-ratio mass spectrometry of nanomolar quantities of individual compounds

(partially for submittal to Analytical Chemistry)

14 Carbon and hydrogen isotope1 abundances in lipid biomarkers provide powerful

insights to carbon and water sources within the environment of synthesis. However, compound-specific analysis of intact core ether lipids is hampered by size of the molecules; they are too large and non-volatile for routine analysis by gas chromatography. Derivitization that breaks the molecule into two C40 chains creates

acidic conditions and H-isotope exchange, and mixes lipid chains from different source

microbes. A more complete and interpretable lipid isotope record could be achieved by

analyzing the whole core lipid, which requires reducing the minimum sample size for

compound-specific isotope analysis of non-volatile compounds.

The work described in chapter 5 will ultimately enable us to study the apparent

isotopic effect of waters and archaeal lipids over a broad range in salinities and water

isotopic compositions. We can then assess the usefulness of archaeal lipids as

hydrological proxies. Measuring the carbon isotopic composition of intact lipids from

cultivated and environmental Archaea will also provide better insights into carbon

sources, metabolic processes, and interpretation of ancient lipids records (e.g. Pearson et

al. 2004; Wakeham et al. 2003; Jahnke et al. 2001; Hinrichs et al., 2000).

Method development was a major process in my doctoral training, and as of this

writing, other workers in the laboratory have built on my efforts to develop nitrogen and

carbon isotope analysis. We have learned more about chemical processes affecting the

development of the Nano-EA for hydrogen isotope analysis. Appendix I describes in

1 Carbon isotope ratios are reported relative to standard PeeDee Belemnite (PDB) in delta notation as δ13C= 13 12 Rsample/Rstandard -1] * 1000, where R= C/ C. Hydrogen isotope ratios are reported relative to Standard Mean Ocean Water (SMOW).

15 more detail the interpretive strategy for future work focused on tetraether δD and δ13C

analyses. We are currently working to submit a paper to Analytical Chemistry that will describe the Nano-EA method, with co-authors Katherine H. Freeman (doctoral advisor),

Pratigya Polissar (post-doctoral scientist), Jamey Fulton, and Christopher Junium (fellow graduate students).

Chapter 2

Marine archaeal lipids: patterns and provenance in the water-column

Abstract

We measured archaeal lipids from globally distributed samples of freshwater, marine, and hypersaline suspended particulate matter. Cluster analysis of relative lipid distributions identified four environmentally distinct groups, including: 1) marine epipelagic waters, 2) marine mesopelagic/upwelling waters, 3) freshwater/estuarine waters, and 4) hypersaline waters. A pronounced difference in lipid composition patterns is the near absence of ring-containing glycerol dibiphytanyl glycerol tetraethers (GDGTs) at high salinity. Different archaeal communities populate marine (mesophilic

Crenarchaeota and Euryarchaeota), and hypersaline environments (halophilic

Euryarchaeota) and community shifts must regulate differences in lipid patterns between marine and hypersaline waters. We propose that community changes within meosphilic marine Archaea also regulate the lipid patterns distinguishing epipelagic and mesopelagic/upwelling zones. Changes in the relative amounts of crenarchaeol and caldarchaeol and low relative abundances of ringed structures in surface waters differentiate lipids from the epipelagic and mesopelagic/upwelling waters. Patterns of lipids in mesopelagic (and upwelling) waters are similar to those expected of the nitrifying Group I Crenarchaeota, with predominance of crenarchaeol and abundant cyclic GDGTs; non-metric multidimensional analysis (NMDS) shows this pattern is

17 associated with high nitrate concentrations (likely tracking nitrite). In contrast, limited culture evidence indicates marine Group II Euryarchaeota produce mainly caldarchaeol and some, but not all, of the ringed GDGTs and we suggest that these organisms contribute significantly to lipids in epipelagic marine waters. Calculated TEX86

temperatures in mesopelagic samples (reported here and in published data sets) are

always much warmer than measured in situ temperatures. We propose lipids used in the

temperature proxy likely derive from both Euryarchaeaota and Crenarchaeota, and

observed values of TEX86 are subject to changes in their ecology as influenced by

nutrient fluctuations or other perturbations. In ancient applications, reported extreme temperatures shifts may indicate the TEX86 lipids are not recording temperature alone, but

equally interesting changes in nutrient concentrations, oceanographic conditions, and archaeal ecology.

Introduction

Two main archaeal groups, Crenarchaeota and Euryarchaeota, are common and widespread in the world’s oceans, accounting for 23-84% of total marine prokaryotes

(Fuhrman et al., 1992; Herndl et al., 2005; Karner et al., 2001; Massana et al., 2000;

Murray et al., 1998a). Group I Crenarchaeota are the prevalent microbe in mesopelagic

waters (i.e., below the photic zone) in much of the marine realm and are also seasonally

abundant in surface waters (Herndl et al., 2005; Wuchter et al., 2005). Three distinct

groups of Euryarchaeota are present in surface, mesopelagic, and deep waters, and are

generally reported to have lower abundances than Crenarchaeota (Bano et al., 2004;

18 Lopez-Garcia et al., 2001; Massana et al., 1998; Moreira et al., 2004). Notably, enhanced membrane permeation techniques yield Euryarchaeota abundances that double previous estimates and suggest this group may be more abundant than previously thought (Herndl et al., 2005).

The distribution and ubiquity of Archaea signifies an important role in global carbon and nitrogen cycling (Ingalls et al., 2006; Leininger et al., 2006; Wuchter et al.,

2006). Studies using molecular techniques have documented marine Archaea mixotrophy; lipid isotope compositions (δ13C and Δ14C) are consistent with a dissolved

inorganic carbon source (Ingalls et al., 2006; Pancost & Sinninghe Damsté, 2003;

Pearson et al., 2001b; Smittenberg et al., 2004a) but mesocosm and isotope-labeling experiments also show they can take up bicarbonate, amino acids, and glucose (Alonso &

Pernthaler, 2005; Alonso & Pernthaler, 2006; Herndl et al., 2005; Ouverney & Fuhrman,

2000; Wuchter et al., 2003). Metagenomic analysis of Crenarchaeota DNA from the

environment and Cenarchaeum symbiosium (a symbiont in Axinella mexicana

demosponges (Schleper et al., 1998)) confirmed the presence and relatedness of genes for

both autotrophic (modified 3-hydroxypropionate cycle) and heterotrophic (tricarboxylic

acid cycle) carbon assimilation (Hallam et al., 2006).

Ecological associations of the lipid crenarchaeol with nitrite maxima suggested a

role in the nitrogen cycle (Massana et al., 1997; Murray et al., 1999; Sinninghe Damsté et

al., 2002a). The presence of ammonia oxidation genes (amo) from environmental

Crenarchaeota genomes co-varied with archaeal lipid abundance, and indicated a

potential for nitrification (Francis et al., 2005; Hallam et al., 2006). A nitrifying role was

confirmed with the cultivation of a free-living marine crenarchaeon, the putative

19 Nitrosopumilus maritimis, which converts ammonia to nitrite, and grows solely on bicarbonate (Konneke et al., 2005). Archaeal ammonia monooxygenase (amoA) genes outnumber bacterial amoB genes by as much as 3 orders of magnitude (Wuchter et al.,

- 2006). Ingalls et al. (2006) estimate that Crenarchaeota can produce >1.2 Gt/yr NO2 , equivalent to the 10-20% of primary production N which is exported below the photic zone.

Comparatively less is known about the marine Euryarchaeota because they remain uncultivated. To date, two metagenomic surveys of Euryarchaeota in surface and deep waters have revealed conserved archaeal genes, as well as previously undocumented open reading frames which suggest novel metabolic functions (Beja et al., 2000; DeLong,

2006; Moreira et al., 2004).

Archaeal lipids are unique biomarkers, and form a bilayer composed of C43 diphytanyl glycerol diethers (DGDs) such as archaeol, or a monolayer composed of C86

glycerol dibiphytanyl glycerol tetraethers (GDGTs) containing 0-5 pentacyclic rings in

their two biphytane moieties. The most common ether lipids in the environment are

caldarchaeol (GDGT-0), and crenarchaeol (with 1 hexacyclic ring and 3 pentacyclic

rings). It remains unclear whether the marine Group II Euryarchaeota can synthesize the

full suite of archaeal lipids (DeLong, 2006; Gattinger et al., 2002; Hallam et al., 2006).

Cultivated Euryarchaeota include only extremophiles, such as the halophiles, which synthesize only diethers, and the methanogens, which synthesize diethers, caldarchaeol and GDGTs with 1-3 pentacyclic rings (Gattinger et al., 2002). Marine Euryarchaeota are also closely related to the Thermoplasmatales, and have membranes containing >70% tetraethers (DeLong, 2006).

20 Archaeal lipids are widespread in sediments, and document archaeal presence and activity in the past (Grice et al., 1998b; Schouten et al., 2000; Schouten et al., 2001c;

Sinninghe Damsté & Schouten, 1997). On geologic timescales, ether-linked membrane lipids provide a record of ancient Archaea (Delong, 1992; Hoefs et al., 1997; Schouten et al., 2000; Wakeham et al., 1997a). A paleotemperature proxy, TEX86, based on the relative distribution of archaeal lipid moieties was recently proposed (Schouten et al., 2002) and evaluated with lipid data from a global set of suspended particulates and core-top sediments

(Schouten et al., 2000; Wuchter et al., 2004; Wuchter et al., 2005). Researchers have determined TEX86 ratios for ancient sediments and have used these to interpret

paleotemperatures in Cretaceous to near-Recent oceans and lakes (Dumitrescu et al., 2006;

Huguet et al., 2006; Jenkyns et al., 2004; Menzel et al., 2006, Powers et al., 2005a; Powers et

al., 2005b; Sluijs et al., 2006).

Decoding the geochemical significance of Archaea is stimulating interdisciplinary

studies that will ultimately shape our understanding of the paleoenvironmental record of

archaeal biomarkers. In this study, our goal is to understand the distribution of a broad

suite of core archaeal lipids in the environment, and provide a template for understanding

the provenance of these ubiquitous molecules in the water-column and sediments. We

examined lipid distributions within suspended particulate matter and sediments collected

from hypersaline, freshwater, and marine waters across diverse aquatic and

oceanographic provinces. We use multivariate statistical tools to discern pronounced

patterns in lipid distributions that relate to environmental and ecological factors.

21 Materials and Methods

Sample collection and lipid extraction

We analyzed suspended particulate matter (SPM) from sites with salinities

ranging from fresh to hypersaline, and a variety of nutrient regimes, from upwelling

zones to oligotrophic sites, and distances offshore, from coastal to open ocean (Figure 2-

1, Table 2-1).

Figure 2-1: Locations of the sample sites. Inset shows the locations of sampling sites for the fresh to marine transected collected in Chincoteague Bay.

Table 2-1: Hydrographic data and sample numbers for SPM and sediments (* indicates data is from location or date different from sample collection).

23 These include archived lipid extracts from previously collected and extracted

SPM from: the Black Sea (station 2 of R/V Knorr cruise 172/8; http://www.ocean.washington.edu/cruises/Knorr2003/); the Arabian Sea (sediment trap mooring sites MS1, MS3, and MS-4 of R/V Thomas G. Thompson cruise TTN047, http://www1.whoi.edu/arabian.html); Santa Monica Basin (Bidigare et al., 1997); Peru upwelling margin (Pancost et al., 1997); the Equatorial Pacific (Thompson survey cruises

TT007 (Survey 1, El Niño conditions), TT011 (Survey 2, non-El Niño conditions), http://www1.whoi.edu/datasys/mrg/eqpac_proc_study.html and Wakeham et al. 1997); the northeastern Mediterranean (R/V Tethys II MedFlux cruise; http://www.msrc.sunysb.edu/MedFlux/); and Mono Lake, CA. USA (Scholten et al.,

2005). Samples collected specifically for this study include SPM from the Bermuda Time

Series (BTS) collected aboard the R/V Weatherbird II from December 5-10, 2004 (Conte et al., 2001a; Conte et al., 2001b), hypersaline samples from Infersa Saline (private salt production ponds, Marsala, Italy), Chincoteague Bay (Accomack County, VA, USA) and

Bear Meadows bog (Centre County, PA, USA). Hydrographic data (Table 2-1) were collected as part of routine oceanographic analysis. In some cases, the data were from a nearby location and/or were collected on cruises close in time to the sampling expedition.

In all cases, we matched season, location, and depth as closely as possible to SPM collection conditions. In the Arabian Sea, SPM were collected during the spring intermonsoon cruise TTN-047 of R/V Thomas G. Thomson and hydrographic data were collected during April 1996 (TTN-045) at nearby stations. The Bermuda Time Series nutrient data were collected during a November 2003 cruise (http://bats.bbsr.edu). In

Chincoteague Bay, we used Virginia Department of Environmental Quality data

24 (https://www.deq.state.va.us/webapp/wqm_station.get_ana_storet_parm?p_sta_id=7-

SGT002.46).

SPM at the Bermuda Time Series site was collected using on 143 mm Whatman

GF/F filters (nominal pore size 0.7 μm) with in-situ pumps (WTS, McLane Labs,

Falmouth MA). SPM collection methods for all ocean sites generally follow large- volume in-situ filtration methods in Wakeham et al. (2003), using 0.7 μm pore-size glass fiber filters. At Infersa Saline and Chincotegue Bay, we collected SPM (3 L in turbid waters near shore and up to 60 L in the middle of the bay) on 1-μm pore-size glass fiber filters (Pall GF A/E) (with 3−μM glass fiber prefilter in Chincoteague Bay). Samples were stored cold (on ice or in a freezer) until they reached laboratory storage at -80°C.

Archaeal cells are typically smaller then the pore sizes used and the actual lipid abundances are likely higher than those observed because of this sampling bias

(Sinninghe Damsté et al., 2002a).

Lipids were extracted from filters using 9:1 DCM:MeOH in a Soxhlet apparatus for 24 hours. For filters collected from high salinity sites, the organic phase was rinsed with hexane-extracted DI water to remove salts, and then dried over extracted NaSO4.

Lipid analysis

Analysis of ether lipids in all samples generally followed methods developed in

Hopmans et al. (2001), using a Micromass Quattro-II API-III LC/MS/MS. Liquid chromatographic separation of archaeol and GDGTs was achieved on a 2.1 x 150 mm 3-

μm Prevail Cyano column (Alltech, Deerfield, IL, USA) maintained at 30°C. The mobile

25 phase was an isocratic solution of 100% hexane for five minutes, followed by a 45- minute linear gradient to 98.2% hexane: 1.8% propanol with a flow rate of 0.2 ml/L. In between samples, we flushed the column for 10 min with 75% hexane: 25% propanol followed by a 10 minute equilibration with the starting mobile phase.

Standards and extracted samples were analyzed in selective ion monitoring (SIM) mode using selected ion scans restricted to specific retention time windows (0–10 min at m/z 600-700, 10-20 min at m/z 1280-1350, and 20-30 min at m/z 1000-1100). Instrument conditions include: corona voltage = 5.5 kV, cone voltage = 15 V, source temperature =

130°C, APCI probe temperature = 550°C. Peak areas were calculated using Masslynx 3.5 software (Micromass, London UK) based on M+H+ ions. Figure 2-2 shows typical

chromatogram, mass spectra, and lipid structures.

Figure 2-2: a) Liquid-chromatography atmospheric pressure chemical ionization mass spectrometer (LC-APCI-MS) chromatogram of archaeal lipids SPM from the Bermuda Time Series site (210 m). From 0-10 minutes, the extracted ion chromatogram (653.5- 655.4 m/z) shows archaeol, and from 10-25 minutes, a total ion chromatogram shows the glycerol dibiphytanyl glycerol tetraethers (GDGTs), b) Mass spectra of archaeol isomer peaks and MS-MS spectra of archaeol showing characteristic fragmentation pattern. c) Mass spectra of GDGTs. 27 Archaeol, which eluted prior to the GDGTs, was identified based on comparison with the structurally identical 1,2-di-O-phytanyl-sn-glycerol standard (Avanti Lipids,

Alabaster, AL, USA). Two archaeol peaks are apparent in many sample chromatograms, each with identical mass spectra indicating the possibility of a partially resolved stereoisomer (Figure 2-2b). To determine the concentrations of diether and tetraether lipids, calibration curves were constructed using the purchased archaeol standard mentioned above, and caldarchaeol which we purified from freshwater sediment extracts.

A dilution series of diether standards was run daily; because of limited availability of

GDGT standards, dilution series of GDGTs were constructed weekly. Response factors for diether standards range from 7000-78000 area/ng; GDGT response factors were typically several of orders of magnitude less, from 90-300 area/ng. Minimum detection limits for DGD was 0.1 ng and GDGT was 7.5 ng. Reproducibility for duplicate analysis, when possible, was within 30%. We apply this error to all samples and consider it a conservative maximum error estimate.

Statistical Analysis

Relationships between lipid distributions and environmental factors were analyzed using cluster analysis and ordination on relative lipid abundances established using response factors as described above. All statistical analyses were performed in R which is a language and environment for statistical computing (freeware available at http://cran.r-project.org/ (Maindonald & Braun, 2003). We used the vegan package

(Oksanen, 2005) to transform lipid distributions to fractional abundances (function

28 decostand) and to calculate the dissimilarity matrix using Bray-Curtis distance measure

(function vegdist) (McCune & Grace, 2002). In the cluster package (Maechler, 2005), we performed hierarchical agglomerative clustering using flexible beta method (function agnes).

We also used ordination analysis to extract dominant patterns in a complex dataset (Legendre & Legendre, 1998). We experimented with correspondence analysis

(CA), principal components analysis (PCA), and non-metric multidimensional scaling

(NMDS). All methods gave very similar ordination diagrams. We present NMDS results.

NMDS is a robust unconstrained non-parametric ordination method that plots similar objects close together in ordination space, typically on 1-3 axes (Legendre & Legendre,

1998; Minchin, 1987). Through iterative comparisons, NMDS maximizes rank-order correlations between the distance measure and distance in ordination space. With each iteration, points representing site-specific lipid distributions are moved to minimize

"stress,” which is the difference between the distance in the original dissimilarity matrix and the distance in the ordination space. Stress <10 is considered to yield reliable results

(McCune & Grace, 2002). NMDS also provides a good method for overlaying environmental data.

We used metaMDS in R (vegan package), based on Kruskal’s MDS (Oksanen,

2005). We used the same transformation and distance measure as the cluster analysis

(standardized by row totals, Bray-Curtis distance measure). The function postMDS moves the origin of the ordination map to the axes averages, and rotates the configuration to maximize variance along the first axis using principal components analysis. The resulting ordination plot displays both sites (as site numbers) as well as the average position of

29 each lipid. We also used the function envfit (vegan package) to overlay environmental factors and determine the relationship between salinity, temperature, depth, and nitrate concentrations and the ordination of lipid distributions and sample sites.

Results

Epipelagic water-column lipid abundances

Lipid concentrations in seawater were determined for samples from the Black

Sea, the Mediterranean Sea, the Equatorial Pacific, the Bermuda Time Series site, and the

Arabian Sea. The highest total archaeal lipid concentrations (>300 ng/L) were observed in the Black Sea (station 2) samples (13-15) (Figure 2-3 , Table 2-2). In all other marine sites, total archaeal lipids were an order of magnitude less abundant than in the Black

Sea, although tetraether lipid abundances were usually highest at depth.

Table 2-2: Concentration of lipids normalized to liters

In the Black Sea (KN-172/8 station 2, 25 km east of the Bosphorus straits in the western basin of the Black Sea), GDGT abundances increased from the oxic 30 to the suboxic 70 m sample. The amounts of caldarchaeol and archaeol increased with depth between the suboxic (70 m; sample 14) and anoxic (100 m; sample 15) samples, whereas

GDGT-1 concentrations did not change, and crenarchaeol and the cyclic GDGT concentrations decreased. Concentrations were lower at an earlier occupation of station

BSK-2 in the central Black Sea but in both this and the previous study, maximum GDGT concentrations were found near the suboxic-anoxic transition zone (57-130 m) (Wakeham et al., 2004).

32 In the Mediterranean MedFlux site (32-35; Figure 2-3), archaeal lipids were not detected in surface waters (2 m; 32). GDGTs and archaeol were present at 20, 50, and 75 m (33,34,35), with maximum concentrations (~55 ng/L) at 50 m. Crenarchaeol and caldarchaeol were in nearly equal abundances in these shallow waters.

In the equatorial Pacific, survey cruises in 1992 from February (Survey 1; TTN-

007) and August (Survey 2; TTN-011) showed variations in archaeal lipid abundances

(26-31; Table 1, Figure 2-3). Although maximum total archaeal lipids were 10-11 ng/L at

110 m in both surveys (28,31), the lipid distributions were different. During Survey 1, which sampled an El Niño period, caldarchaeol and crenarchaeol concentrations were equal, and most abundant, at 39 m (27). During the normal (non-El Niño) Survey 2, tetraether concentrations increased with depth, with crenarchaeol concentration always greater than the other GDGTs. Additionally, at 15 m, lipid concentrations were extremely low during the February survey (26), and higher during the August survey (29).

Mesopelagic water-column lipid abundances

In basins where we analyzed SPM collected in deeper waters (~1500 m, Bermuda

Time Series and Arabian Sea), archaeal lipids were generally rare in the shallowest waters, and reached maximum concentration in mesopelagic zones. In the Bermuda Time

Series (BTS) site depth profile (17-23; 10-1467 m), neither archaeol nor GDGTS were detected in the surface waters (Table 2-2, Figure 2-4 ). Archaeal lipid concentrations increased with depth to 650 m. from 2.7 ng/L at 210 m (17) to 4.3 ng/L at 650 m (18), and reached highest concentrations (22 ng/L) in the deepest sample (23, 1467 m).

33 Crenarchaeol was the most abundant GDGT lipid in all BTS samples. Archaeal lipids in the Arabian Sea collected during the spring intermonsoon (May 1995) from stations MS-

1 and MS-4 (1-3, 8-11) had GDGT concentration profiles similar to those observed at

Bermuda, and in previous studies in the Arabian Sea (Sinninghe Damsté et al., 2002a). In surface waters, total detected lipid concentrations were 3-6 ng/L (1,4,8). The highest concentrations were at 500 m (26 ng/L at station 1 (2) and 33 ng/L at station 4 (9), Table

1, Fig. 3). In contrast, at station MS-3, highest archaeal lipid concentrations were in the surface waters (4; 13 ng/L), with equal abundances of caldarchaeol and crenarchaeol. At

500 m (5), anomalously high concentrations of archaeol (2.4 ng/L) were nearly equal to caldarchaeol concentrations, and no other ether lipids were detected. At 1000 m (6), archaeol was not detected, and crenarchaeol was twice as abundant as caldarchaeol.

Figure 2-4: Lipid abundances normalized to water volume in a) Bermuda Time Series, b) Arabian Sea station MS-1, c) Arabian Sea station MS-4, and d) Arabian Sea station MS- 3. The left panel shows tetraethers (circles: caldarchaeol, diamonds: crenarchaeol, squares: all other cyclic GDGTs combined. The right panel shows archaeol (triangles). Note the variable scales on the x-axes.

Correlation between lipid abundances and hydrographic properties

Abundances of individual archaeal lipids often increased with depth, and therefore tended to correlate with hydrographic properties that also change with depth.

For instance, in the equatorial Pacific samples during Survey 1, there were strong correlations between cyclic GDGTs and salinity (crenarchaeol: r2 = 0.95; GDGT-1: r2 =

35 0.91, GDGT-2: r2 = 0.97), temperature (crenarchaeol: r2 = 0.64; GDGT-1: r2 = 0.73,

GDGT-2: r2 = 0.98), oxygen (crenarchaeol: r2 = 0.71; GDGT-1: 0.62; GDGT-2 0.98), and

nitrate (crenarchaeol: r2 = 0.71; GDGT-1 r2 = 0.62; GDGT-2: r2 = 0.98). In contrast,

during Survey 2, only archaeol and caldarchaeol increased with depth and therefore had

positive correlations with salinity, nitrate, and oxygen (archaeol: r2 =0.91), but negatively

correlated with nitrate, especially archaeol (r2 = 0.99).

Correlations between lipid abundances and hydrographic properties were particularly remarkable in the deeper profiles, where changes in oxygen and nitrate

concentrations do not strongly correlate with depth. For instance, at the Bermuda Time

Series site, the abundance of GDGTs did not vary with any measured environmental

variable. In contrast, archaeol was strongly negatively correlated to nitrate concentrations

(r2 = 0.99 from 210-1467 m). In the Arabian Sea, caldarchaeol was negatively correlated with nitrate, phosphate, silicate and positively correlated with temperature (r2 = 0.70) and

salinity (r2 = 0.69), although other GDGTs and archaeol did not correlate with

hydrographic or nutrient properties.

Cluster analysis: water-column lipid distributions

Cluster analysis identified four distinct environments based exclusively on

relative lipid distributions. These lipid-based groupings include: 1) marine epipelagic

zone, 2) marine mesopelagic and upwelling zones, 3) freshwater/estuarine waters, and 4)

hypersaline waters (Figure 2-5).

Figure 2-5: Cluster analysis of archaeal lipids from suspended particulate matter collected in ocean, estuarine and terrestrial environments. Numbers correspond to samples listed in Table 2-1.* exceptions to the cluster generalizations.

The fresh/estuarine group includes the sample from Cockle Point (CJF) at the outlet of Swangut creek (44) (see Figure 2-1) and from Bear Meadows, which is a small peaty bog with standing, acidic surface waters. The dominance of caldarchaeol was the main characteristic of this lipid group, and the distribution of all other lipids was heterogeneous.

37 The epipelagic zone groups SPM from marine surface waters (>100 m) only except for BTS 1050 m. Samples in this cluster come from diverse oceanographic settings including the Arabian Sea (mooring stations 1, 3, and 4 at 4 m), the Santa

Monica Basin (stations 4, 5, 6 at 40 m), the Mediterranean (MedFlux station, 20, 50 and

75 m), surface waters from two sites in Chincoteague Bay (May and July), the Black Sea oxic (30 m) and suboxic (70, 100 m) samples below the chemocline and one equatorial

Pacific sample (Survey 1, 39 m). Archaeal lipid distributions in these samples were dominated by nearly equal amounts of caldarcheol and crenarchaeol, but with varying amounts of cyclic GDGTs and archaeol. Surface samples tended to have very low and possibly undetectable concentrations of lipids, which could potentially change the relative distribution shown here. However, the Black Sea samples had the highest concentrations but nested within the epipelagic zone group, indicating that lipid distribution pattern, not overall low lipid concentrations, controls this clustering.

The mesopelagic zone/upwelling lipid group contains SPM samples collected from below the photic zone (≥ 200 m), as well as from nearly all of the upwelling sites including equatorial Pacific samples and one Peru Margin sample (40 m). The mesopelagic samples include the depth profiles from the three stations in the Arabian Sea and the Bermuda Time Series site. The pattern of lipid distribution in these samples is generally ~ 50% crenarchaeol, 25% cyclic GDGTs, and 25% caldarchaeol, with minor amounts of archaeol. Two outliers formed a branch from this main cluster (16, 26); very low lipid abundances in these samples may have influenced observed lipid patterns.

The hypersaline group includes samples from surface waters of the salt pans from the coast of Sicily. Mono Lake samples contained only archaeol and cluster separately

38 from all other samples. In the salt pans, archaeol and caldarchaeol were the dominant lipids, with the proportion of archaeol increasing with increasing salinity. Pond 38 (40) also contained a small amount of crenarchaeol. One sample from the Arabian Sea at 500 m (5) anomalously clustered within this hypersaline group because it contained a comparably high proportion of archaeol.

Ordination analysis: water chemistry and lipid assemblage patterns

Indirect ordination analysis using non-metric multidimensional analysis (NMDS) reproduced the four groupings (epipelagic zone, mesopelagic and upwelling zones, terrestrial/estuarine, and hypersaline) observed in the cluster analysis (final stress=

3.8415, Figure 2-6).

Figure 2-6: Non-metric multidimensional scaling of relative archaeal lipid abundances in all samples (Bray-Curtis distance measure, Stress 3.84).

39 Vectors superimposed onto the ordination using available environmental variables

(temperature, salinity, and depth) showed the direction of the most rapid change in lipid patterns relative to the environmental variables as well as the maximal correlation with the ordination; the length of these vectors correspond to their correlation with the ordination axes. Goodness of fit statistics and p-values for permutation tests (n=1000) showed salinity (r2=0.35, p-value= <0.001), though not the only environmental factor,

was the most robust environmental variable explaining the ordination axis. The relative

amount of archaeol increased with increasing salinity, while caldarchaeol is associated

with the epipelagic zone cluster, and the ringed GDGTs are associated with the

mesopelagic zone/upwelling cluster.

The distribution of lipids in the ordination of the two marine clusters is

emphasized in NMDS analysis of these two groups alone. In this ordination, the groups

maintained their relative separation in ordination space (final stress = 3.869, Figure 2-7 ).

Vectors from available environmental variables (temperature, depth, salinity, nitrate)

showed nitrate (r2=0.42, p-value= <0.001) to be the most significant variable correlated to

axis 2. The ringed GDGTs are closely associated with the increase in depth and nitrate.

Total organic carbon played a minor role in lipid distribution and archaeal lipid abundance. In the subset of samples in the equatorial Pacific and the Arabian Sea with available total organic carbon (TOC) data (Table 1), TOC was not strongly correlated with lipid distributions (r2= 0.31, p-value= 0.15). Archaeal lipid abundances had similar

depth profiles whether normalized to water volume or TOC, suggesting that total organic

carbon (i.e. primary photosynthate) and archaeal lipid concentrations may be decoupled

(Wakeham et al., 2004). This is further evidence that despite mixotrophic capabilities of

Archaea, autotrophic archaeal lipid production is probably more prevalent than

heterotrophic production (Ingalls et al., 2006; Pearson et al., 2001a).

41 Surface sediment lipid distribution: comparison with the water-column

Figure 2-8 shows the lipid distribution patterns in four surface sediment samples: the Arabian Sea (50), the Black Sea (51), Chincoteague Bay (52), and the equatorial

Pacific (54). Crenarchaeol ranged from 48-54% of total archaeal lipids, and caldarchaeol ranged from 24-46%. The proportion of cyclic GDGTs increased in the Arabian Sea and

Chincoteague Bay surface sediments because of elevated concentrations of GDGT-1 and

GDGT-2. We looked for but did not detect cyclic GDGTs in Santa Monica Basin sediments in this study, although they have been detected in Santa Monica Basin sediments previously (Pearson et al., 2001b). The relative amounts of crenarchaeol and caldarchaeol in the sediments we investigated were intermediate between the amounts characteristic of epipelagic and mesopelagic samples from the overlying waters.

Figure 2-8: Relative distribution of archaeal lipids in surface sediments.

Archaeal lipid distributions in Black Sea sediments were remarkably similar to water-column distributions at 100 m and 70 m. Only the equatorial Pacific sediments had a significant amount of archaeol (3%). The pattern of GDGT distribution best matched

SPM from the 39 m sample of Survey 1, which clustered in the epipelagic group B. The

42 Arabian sea sediment distribution contained relative amounts of caldarchaeol comparable to the Arabian Sea surface waters but also contained cyclic GDGTs only found in the deeper Arabian Sea water-column. Chincoteague Bay (CJF) sediment contained GDGTs-

1, 2, and 3 which, remarkably, were not detected in any water-column sample from

Chincoteague Bay. Box plots illustrate the basic differences in the percent abundance of dominant lipids in each group (Figure 2-9). Caldarchaeol dominates the terrestrial/estuarine group. Archaeol and caldarchaeol were in nearly equal abundance in hypersaline sites; the ratio of these lipids was proportional to salinity and leads to the skewed distribution of caldarchaeol (Figure 2-9). The relative distribution of crenarchaeol and caldarchaeol in surface sediments closely resembles that of the overlying epipelagic waters, but also contain greater relative ringed structures and crenarchaeol as in the mesopelagic zone/upwelling samples.

Discussion

Lipid distribution patterns and archaeal communities

The relative distribution of crenarchaeol and caldarchaeol as well as the abundance of ringed GDGTs distinguish the epipelagic lipid group from the mesopelagic/upwelling lipid group. Since the mesopelagice/upwelling group includes

SPM from both deep waters and a shallow upwelling zone, the distinct lipid groupings can not be attributed simply to depth.

44 Multivariate statistical analysis shows that the crenarchaeol-dominated mesopelagic zone/upwelling samples have a particularly strong relationship with nitrate concentrations. Crenarchaeol was initially proposed as a biomarker for marine Group I

Crenarchaeota, which dominate the mesopelagic zone and are now known to be potentially significant ammonia oxidizers (Francis et al., 2005; Konneke et al., 2005).

We suggest that the mesopelagic/upwelling group lipid distribution pattern is characteristic of a comparatively homogenous community of Group I Crenarchaeota because of the abundance of crenarchaeol as well as the prevalence of the ringed GDGTs.

The association of this lipid grouping with high nitrate concentrations supports our interpretation. Nitrite is produced partially from the oxidation of ammonia by

Crenarchaeota, which is then oxidized to nitrate via biotic and abiotic processes

(Sarmiento & Gruber, 2006). Nitrite and ammonia were either not measured or detected at all sites. The attribution of mesopelagic/upwelling group lipid distribution pattern to

Group I Crenarchaeota also accounts for the grouping of shallow equatorial Pacific lipid distributions with the mesopelagic samples; the upwelled supply of nitrogen (e.g. nitrate converted to ammonia) could possibly stimulate ammonia-oxidixing Group I

Crenarchaeota.

Archaeal lipids in SPM from surface waters (non-upwelling) are generally characterized by equal abundances of caldarchaeol and crenarchaeol. We propose that this lipid distribution pattern reflects a mixed contribution from Crenarchaeota and

Euryarchaeota. Euryarchaeota may be more abundant in the ocean than previously estimated, and their abundance may be under-emphasized in the literature. We surveyed published crenarchaeotal and euryarchaeotal abundance estimates from DNA-based

45 studies in a variety of sites, across a spectrum of seasons and depth, and found that

Euryarchaeota can account for one-third of marine Archaea (Bano et al., 2004; DeLong et al., 1998; Karner et al., 2001; Massana et al., 2000; Massana et al., 1997; Murray et al., 1998b). Numerous authors argue that surface water archaeal lipids are preferentially incorporated into sedimentary organic matter (Ingalls et al., 2006; Smittenberg et al.,

2004a; Wuchter et al., 2005), and, if so, a mixed community of surface-dwelling

Euryarchaeota and Crenarchaeota contributes to the sedimentary record.

Lipid distribution and archaeal metabolisms

Changes in the Δ14C in archaeal lipids with water depth in the North Pacific

suggest changes in the dominant metabolism of their source organisms (Ingalls et al.,

2006). In surface waters, GDGTs Δ14C values reflect incorporation of young carbon

(surface inorganic and organic carbon have similar 14C signatures and therefore cannot be

differentiated). At 670 m, Ingalls et al (2006) estimated that 83% of GDGT lipid carbon

derives from coexisting DIC. Labeling experiments have examined community

affiliations of autotrophic and heterotrophic metabolisms. Herndl et al. (2005) showed that euryarchaeotal and crenarchaeotal uptake of leucine and DIC varies throughout the water-column, with the fraction of crenarchaeotal DIC uptake increasing with depth, while euryarchaeotal DIC uptake decreases with depth. In the North Sea, Euryarchaeota, which dominate surface waters during phytoplankton blooms (Spring), are facultative mixotrophs, taking up free amino acids at twice the rate of bacteria. They also incorporate

DIC (Alonso & Pernthaler, 2005; Alonso & Pernthaler, 2006). Catabolic processes may

46 also vary with depth; for example, the nucleotide sequences of ammonia monooxygenase

(amo) genes cluster according to depth (Francis et al., 2005; Hallam et al., 2006; Wuchter et al., 2006).

Available reports offer no evidence for changes in archaeal core lipid synthesis under different metabolisms. Hyperthermophilic Crenarchaeota grown heterotrophically or autotrophically produce the same relative distributions of core lipids, but different polar headgroups (Langworthy, 1977). Variations in growth rate and cell division associated with different carbon uptake pathways or ammonia-oxidation by different amo enzymes may have some impact on lipid synthesis and therefore on lipid distributions.

Distinct lipid clusters could result from metabolic controls on lipid production, or a combination of metabolic and community changes. Studies of isolated mesophilic

Archaea are required to determine whether metabolic changes associated within one taxonomic group or shifts in production between two groups could produce changes in observed lipid distributions.

Archaeol, Caldarchaeol, and Salinity

The ratio of archaeol to caldarchaeol increased with increasing salinity through brackish, freshwater, marine and hypersaline sites, and clearly distinguished hypersaline from marine samples. In the hypersaline environments, cyclic GDGTs were detected in only one sample, caldarchaeol was present in low abundances, and archaeol was always abundant. In Mono Lake, our lipid results suggest only Euryarchaeota inhabited the surface waters since only archaeol was present. This is similar to the hypersaline

47 meromictic Solar Lake, Egypt in which Euryarchaeota groups are stratified with depth and no Crenarchaeota were detected (Cytryn et al., 2000).

The multiple linear regressions in the NMDS analysis showed that small variations in salinity in the marine to brackish samples did not directly affect lipid distributions, and we infer that salinity does not influence lipid synthesis in this range, as previously observed (Schouten et al., 2002). However, archaeal community shifts from marine to hypersaline sites is a dramatic example of community control on lipid distribution, as we have suggested for epipelagic and mesopelagic lipid distributions.

TEX86

To date, temperature is the most significant parameter reported to produce

phenotypic changes in the number of rings in the core lipids of mesophilic Archaea including those in the water-column (Schouten et al., 2003b; Wuchter et al., 2004;

Wuchter et al., 2005). The abundance of GDGT-2 and -3 relative to GDGT-1 in archaeal

lipids from surface waters and core-top/surface sediments has a robust correlation with average sea surface temperatures and form the basis of the TEX86 paleotemperature proxy

(Schouten et al., 2002; Wuchter et al., 2004).

However, in deeper waters, calculated TEX86 temperatures were higher than in

situ temperatures (Ingalls et al., 2006; Wuchter et al., 2005). We also found TEX86-

derived temperatures below 500 m were consistently higher than in situ temperatures. At the Bermuda Time–Series site, TEX86 values in surface waters averaged 0.7, yielding a

temperature of 25°C using the initial calibration in Schouten et al. 2002. Calculated

TEX86 temperatures then decreased with depth just as in situ temperatures did, although

in all cases, TEX 86 temperatures were 10-15°C warmer than actual temperatures (e.g.

33°C (210 m) to 31°C (950 m) to 20°C (1467 m). Interestingly, in the equatorial Pacific, the maximum TEX86 temperature of all the samples (31°C) occurred in the deepest

sample (110 m) during an El Niño event (Survey 1) which produced a 2-3°C temperature

anomaly. In samples from Survey 2 (collected August 1992, six months after the

maximum El Niño temperatures), TEX86 temperatures also increased with depth, perhaps

reflecting slow sinking of surface water-derived GDGTs.

These data, combined with data from Wuchter et al. (2005), show that the residual

temperatures (calculated TEX86 temperature – in situ temperature) increase with

increasing depth to 500 m (Figure 2-10).

From surface waters to 500 m, residual temperature increases from 0 to 20°C.

Below 500 m, the offset between TEX86 calculated temperature and in situ temperature

remains constant at about ~20°C). The higher TEX86 values at depth strongly suggest that

the production of ringed GDGT lipids by archaeal communities from deeper waters has a

different sensitivity to temperature than for epipelagic communities. As discussed above,

lipids in deeper waters may be solely or more completely derived from Group I

Crenarchaeota, while lipids in surface waters reflect a mixed Euryarchaeota and

Crenarchaeota community. Because nitrate concentrations increase with depth, it is

50 possible that changes in nutrient status and resulting lipid changes (as shown here from modern oceans) are reflected in ancient organic matter (Figure 2-11). Increased production and preservation of Crenarchaeota lipids derived from deep-water communities, either through shoaling or enhanced production and preservation during nutrient-rich episodes would produce elevated TEX86 ratios and significantly warmer

reconstructed temperatures if based on surface-water calibrations. Wuchter et al. (2005) also suggest nutrient enrichment caused increased GDGT production by marine

Crenarchaeota in the late winter/early spring in the Sargasso Sea (Wuchter et al., 2005).

51 Recent studies have reported very warm paleo-SSTs during the Creteceous

(Dumitrescu et al., 2006; Jenkyns et al., 2004; Schouten et al., 2003a) and the Paleocene-

Eocene Thermal Maximum (Zachos et al., 2006). In most cases, there is matching evidence for nutrient enrichment and stratification, such as increases in TOC and nanofossil evidence for increased productivity (Gibbs et al., 2006). This may also apply to terrestrial records; a warm interval is also reported from 5 ka in Lake Malawi sediment core (Powers et al., 2005a) corresponding to increased abundances of benthic diatoms, in the sediment record possibly indicating increase in productivity and nutrient enrichment

(Johnson et al., 2002).

In contrast, Menzel et al. (2006) reported cooling of 10-15°C in Pliocene

Mediterranean sapropels. The lipid distributions in Menzel et al. (2006) show that the warmer temperatures are derived from samples with the mesopelagic/upwelling lipid pattern we observe here, while lipid distributions generated from sapropel organic matter resemble the epipelagic lipid distribution pattern we describe. If these groupings represent different ecological niches for the source Archaea as we have proposed, then cooler SST-calculated temperatures can be explained by changes in GDGT production as a function of ecology. Although the correlation between SST and TEX86 values derived

from sedimentary and water-column archaeal lipids has been used as evidence that

sedimentary lipids are derived primarily from surface water archaeal production (e.g.,

Wuchter et al., 2005), radiocarbon evidence suggests archaeal lipids in the sediment originate in both surface waters (Smittenberg et al., 2004a) and deeper waters (Pearson et al., 2001a). Also, surface sediment distribution patterns reported in this study of all

GDGTs and archaeol are intermediate between surface water and deep water patterns

52 (Figure 9). Therefore, we suggest a mixed surface and deep-water origin for archaeal lipids at the sediment-water interface, such that changing ecology throughout the water- column has the potential to affect TEX86–based temperatures profoundly. Non-cultivation

and cultivation techniques focusing on the Euryarchaeota will be necessary to test our

hypothesis that a mixed Archaea community generates lipids in marine surface waters, to determine the effect of temperature on Euryarchaeota lipid synthesis and to discern the

influence of nutrient supply on lipid patterns for both marine Euryarchaeota and

Crenarchaeota.

Examining the full suite of intact core archaeal lipids, particularly the relative

amounts of caldarchaeol and crenarchaeol, may help explain the source of archaeal lipids,

and possibly provide a correction to temperature overestimates. There is also a need to develop more temperature calibrations as more Archaea are cultured, and lipid synthesis controls are constrained in many oceanic provinces.

Conclusions

Cluster analysis based on relative archaeal lipid distributions revealed four distinct groups for suspended particulate matter: 1) epipelagic waters, 2) mesopelagic/upwelling waters, 3) fresh/estuarine waters, and 4) hypersaline waters.

Indirect ordination analysis using non-metric multidimensional analysis (NMDS) reproduced these groupings and showed group affiliations with salinity and nitrate

concentrations. We suggest that the crenarchaeol-dominated mesopelagic/upwelling

53 group is characteristic of a homogenous community of Group I Crenarchaeota because of the dominance of crenarchaeol, and the association with mesopelagic waters and high nitrate concentrations. Archaeal lipid distributions in epipelagic group samples, characterized by equal proportions of caldarchaeol and crenarchaeol, likely represent a mixed community of Crenarchaeota and Euryarchaeota. Distinct lipid clusters could also result from metabolic controls on lipid production or a combination of metabolic and community changes. These distribution patterns suggest that both Crenarchaeota and

Euryarchaeota contribute to surface-water lipid production. Production and preservation of lipids from in deeper-dwelling communities, because of community shoaling or changes in grazing and packaging, can produce warm TEX86 temperatures. The patterns

we describe here can be used to compare the provenance of archaeal lipids in other

waters, and in sediments through time.

Acknowledgements

E.C. Hopmans, S. Schouten, and J.S.S. Sinninghe Damsté were generous with

initial HPLC-MS training at NIOZ. Infersa Saline (Marsala, Italy) and the A. D’Ali Staiti

family kindly provided access to salt pan sites where R.L. Folk served as translator and

titrator. The Marine Science Consortium (Wallops Island, VA) provided access to

Chincoteague Bay sites. We thank the crew aboard the R/V Weatherbird II, and all other

sample collecting assistants (Jay Geagan and Chris Lernihan). Mark Patzkowsky assisted

with statistical analysis. NSF OCE-327377, NSF-IGERT DGE-9972759, and the NASA

Astrobiology Institute (NCC2-1057) funded this research (KHF). Funding also includes

54 NSF OCE-0113687 and OCE 0117824 (to Stuart Wakeham who provided samples) and

NSF OCE-0097288 (Maureen Conte, chief scientist aboard the R/V Weatherbird II).

Chapter 3

Archaeal lipids record transitions from marine to hypersaline environments

Abstract

We present an ecologically based biomarker method to estimate past salinity changes in marine to hypersaline paleoenvironments. In modern settings, the relative amounts of acyclic diether and tetraether lipids (Archaeol and Caldarchaeol Ecometric;

ACE = 100 * A/A+C) synthesized by Archaea correlates with salinity over the range of

30-250 psu. We test the utility and preservation of the ACE-salinity relationship in sedimentary organic matter using samples from two sequences deposited just prior to the

Messinian Salinity Crisis. Archaeal lipids and ACE in particular have potential to characterize salinity variations within desiccating basins in climatically sensitive seas

(e.g. Dead Sea, Permian Delaware Basin) (Horita et al., 2002; Kirkland et al., 2000;

Klein-BenDavid et al., 2004). This novel lipid biomarker approach to salinity reconstruction offers several important advantages: the lipids are synthesized and preserved over a range of extreme conditions where isotopic proxies are absent or altered, and they are also sensitive over a salinity range that complements and refines mineralogical assemblage data. The archaeol caldarchaeol ecometric along with analyses of the other GDGTs promises novel and important insights to past ocean biogeochemistry.

56 Introduction

Archaea are ubiquitous microbes in the environment (Beja et al., 2002; Delong,

1992; Fuhrman, 2002; Herndl et al., 2005; Hershberger et al., 1996; Karner et al., 2001;

Ochsenreiter et al., 2002; Sinninghe Damsté et al., 2002a) and their unique lipid biomarkers provide a new approach for estimating past salinity in marine to hypersaline environments. The core isoprenoid lipids of Archaea are, like all lipids, relatively recalcitrant in sedimentary organic matter, and they are preserved at least from the

Cretaceous (Kuypers et al., 2002; Schouten et al., 2003a). Structures include 43-carbon diphytanyl glycerol diethers (DGD) and 86-carbon glycerol dibiphytanyl glycerol tetraethers (GDGT) (see Figure 1-2 and Figure 1-3 for structures). Halophiles appear to produce only DGDs (Grice et al., 1998a; Teixidor et al., 1993), while marine

Euryarchaeota produce DGDs including “archaeol” (I) and an acyclic GDGT called

“caldarchaeol” (II), and perhaps other structures. Crenarchaeota lipids include archaeol and caldarchaeol, plus other GDGT structures containing pentacyclic and hexacyclic rings (Gulik et al., 1986; Sinninghe Damsté et al., 2002b). Ether lipid-based proxies have recently been proposed and applied to paleotemperature reconstruction (Huguet et al.,

2006; Jenkyns et al., 2004; Powers et al., 2004; Schouten et al., 2003a; Schouten et al.,

2002) and to quantify terrestrial input to marine sediments (Hopmans et al., 2004).

Distinct differences in the lipid structures and salinity tolerances of halophiles and marine mesophiles led us to propose and test the potential of archaeal lipids as a new salinity proxy. We evaluated correlations between GDGTs, DGDs, and salinity in

57 suspended particulate matter (SPM) collected from hypersaline, marine, estuarine and fresh waters. We also analyzed sedimentary organic matter deposited in two marginal basins in the late Miocene (ca. 5.8 Ma), just prior to a period of severe evaporation in the

Mediterranean, to establish geologic preservation of lipid-salinity signatures.

This new method to assess empirically the salinity in the marine to hypersaline range (30-250 practical salinity units or psu) has broad utility to validate models of dessication and refilling of hypersaline basins (Meijer & Krijgsman, 2005), track salinity changes in climatically sensitive seas (e.g. Dead Sea, Permian Delaware Basin), and to test hypotheses of increased global salinity in the early Phanerozoic (Hay et al., ; Hay et al., 2001; Horita et al., 2002; Kirkland et al., 2000; Klein-BenDavid et al., 2004; Meijer et al., 2004; Slingerland et al., 1996).

Materials and Methods

Brackish and marine sites represent a variety of nutrient regimes (from upwelling zones to oligotrophic sites) and distances offshore (from coastal to open ocean), and include both previously extracted SPM as well as new samples collected for this study

(following methods in Wakeham et al., 2003). Hypersaline samples were collected at

Infersa Saline, a previously undescribed industrial saltern. Ionic compositions of salterns waters were similar to other Mediterranean coastal (salt pan, Spain) and Dead Sea waters

(Eder et al., 2001; Madigan et al., 2000; Ochsenreiter et al., 2002) (Table 3-1).

Table 3-1: Ionic compositions of seawater, Infersa salt pans, and other hypersaline sites.

Messinian sediments were collected from outcrops of the Tripoli Formation (6.96-

5.93 Ma) in central Sicily, which provide high-resolution and continuous records spanning the transition from marine to hypersaline conditions that mark the onset of the

Messinian Salinity Crisis (MSC) (Hilgen & Krijgsman, 1999). We analyzed samples from two sub-basins, the northern Torrente Vaccarizzo (TV) and the southern Serra

Pirciata (SP) (Butler et al., 1999), both of which are correlated to the most complete and deepest Falconara section based on 21 ky precessional cycles (Bellanca et al., 2001). The

TV section spans from 6.35-6.02 Ma and incorporates cycles 34-49 (Blanc-Valleron et al., 2002). Laminar gypsum (1.4 m) caps cycle 49. SP spans from 6.07-5.93 Ma, and includes cycles 47-52 (6 m), which underlie the Calcare di Base, a massive carbonate bed with replaced gypsum and halite signifying the MSC.

Lipid extraction and analysis generally follows Schouten et al. 2002, except for the novel HPLC-MS analysis of archaeol. Archaeol eluted prior to the GDGTs and was

59 identified based on an archaeol standard (Avanti Lipids, Alabaster, AL USA). Response factors were determined using a dilution series of this archaeol standard and a caldarchaeol standard purified from sediment. GDGT response factors were typically several of orders of magnitude less than DGD response factors, indicating that sensitivity is greater for diethers than for tetraethers. Minimum detection limits for DGD is 0.1 ng and GDGT is 7.5 ng. Archaeol appears to have a co-eluting peak of unknown structure.

Mass spectra indicate is it also archaeaol, and may be a co-eluting stereoisomer. Peak areas were measured using only masses m/z 653-654 (archaeol plus first isotope peak).

We included only SPM collected above 260 m water depth in our modern samples. Organic matter from shallow waters is more likely to reach the sediment-water interface, and therefore become incorporated into the sedimentary organic matter

(McCave, 1984; Wakeham & Beier, 1991). The TEX86 proxy (Schouten et al., 2002) and

compound-specific radiocarbon ages (Smittenberg et al., 2004b) validate that archaeal lipids specifically are transported from shallow waters to sediments.

Suspended particulate matter (SPM) from the oxygenated surface waters of the

Black Sea (Wakeham & Beier, 1991) define the low end of the salinity range (20 psu).

Marine sites include the Arabian Sea (Lee et al., 1998), Santa Monica Basin (Bidigare et

al., 1997), Peru upwelling margin (Pancost et al., 1997), and the Equatorial Pacific

(Wakeham et al., 1997a), and the Bermuda time series site aboard the R/V Weatherbird II

from December 5-10, 2004 (Conte et al., 2001b). We collected marine samples from

Chincoteague Bay, VA (USA) aboard a Marine Science Consortium Inc. (Wallops Island,

VA) vessel in 2003, and hypersaline samples in 2002 along a salinity gradient (100-250

psu) within Infersa Saline, a small industrial salt collecting operation 7 km south of

60 Marsala, Sicily (37º51.41 N x 12º28.67 E). Salinity was determined with brine hydrometer in the salt pans, and Sonde multi-parameter probe (YSI Inc. Yellow Spring,

OH USA) in Chincoteague Bay. In both these sites, 3-20 liters of water were filtered onto

142-mm, ashed glass-fiber filters with nominal pore size of 1 μM. Samples were stored on ice until they reached the lab and then stored at -80°C.

Lipid extraction from filters and powdered sedimentary surface-cleaned rocks using 9:1 DCM:MeOH in a soxhlet apparatus for 24 hours. For filters collected from high salinity sites, the organic phase was rinsed with hexane-extracted DI water to remove salts, and then dried over extracted NaSO4. The total solvent extract from the Messinian

sediments was separated into asphaltene and maltene fractions with iso-octane; we

analyzed only the maltene fraction. Archaeol eluted in the same fraction as the GDGTs

during column chromatography. Analysis of ether lipids in all samples followed

(Hopmans et al., 2000), using a Micromass Quattro-II API-III LC/MS/MS. Archaeol

eluted prior to the GDGTs and was identified based on an archaeol standard (Avanti

Lipids, Alabaster, AL, USA). Peak areas were calculated using Masslynx 3.5

(Micromass, London UK) based on M+H+ ions. TOC of bulk ground Messinian

sediments was determined by Rock-Eval (Humble Geochemical Inc.)

Archaeol, caldarchaeol, and salinity

Differences in the relative abundance of archaeol and caldarchaeol in marine and

hypersaline environments are shown in Figure 3-1. Marine sites typically contained little

archaeol and an abundance of GDGTs, while hypersaline sites contained predominantly

61 archaeol with smaller amounts of caldarchaeol. Evaluating both DGDs and GDGTs within the same sample injection, allowed comparison of relative abundances of archaeol and the ring-less GDGT caldarchaeol in each sample chromatogram.

In freshwater, brackish, and marine SPM, there is significant variability in the relative amounts of acyclic diether and tetraether lipids, where A/A+C * 100 ranges from

0.02-19. The lowest A/A+C * 100 values (0.02-1) were found in the Black Sea, the

Equatorial Pacific, Santa Monica Basin, and the Peru Margin. Intermediate A/A+C * 100 values (1.5-3.3) included SPM from Bear Meadows, shallow Equatorial Pacific, and

Chincoteague Bay (Watt’s Bay). Much greater A/A+C * 100 values (2 to 10 times) were found in a tidally flushed stream (Swansgut creek) as well as within the Sargasso Sea chlorophyll max (6.7; 210 m), the Arabian Sea (6.3 and 15.3; 4 m Stations 1 and 4) and

Chincoteague Bay (15.9, 19).

Even the highest A/A+C * 100 values values from estuarine and marine SPM were much lower than those measured in hypersaline salt ponds. A/A+C * 100 values

62 ranged from 46-72, and increase linearly by nearly 2:1 from 103-236 psu. However, in

Mono Lake, an alkaline hypersaline lake, caldarchaeol was not detected.

Over the range from 20 to 250 psu of the entire sample suite, there is a strong linear correlation between salinity and the relative abundances of archaeol to caldarchaeol with A/A+C *100 = 0.36 (Salinity) – 6.04; r2 = 0.86 (Figure 3-2). We term the relationship between archaeol, caldarchaeol and salinity ACE, the Archaeol (A) and

Caldarchaeol (C) Ecometric; ACE = 100 * A/A+C). The relationship between salinity and ACE in the hypersaline range is largely responsible for the excellent correlation,

though the average marine value and brackish site ACE values provide a low anchor

point for this correlation. Sampling temperatures range from 14ºC (Santa Monica Basin)

to 30ºC (salt ponds); there was no correspondence between temperature and ACE values.

This is true over the entire suite of samples, as well as within hypersaline sites.

Paleosalinity at the onset of the Messinian Salinity Crisis

In order to determine if the relationship between salinity and ACE observed in modern SPM is preserved in ancient materials, we analyzed lipids from sedimentary organic matter deposited during a period of known salinity changes in the geological record. Mineralogical, faunal, and isotopic evidence as indicative of a diachronous onset of hypersalinity in the shallow, more proximal Torrente Vaccarizzo (TV) section and the distal and deeper Serra Pirciata (SP) (Bellanca et al., 2001). Evaporite minerals

64 pseudomorphs (gypsum and celestite) are intermittently present throughout the sequence, foraminifera and calcareous nannoplankton gradually disappear, and diatom diversity decreases sharply toward the top of the sections (Bellanca et al. 2001). HPLC-MS chromatograms from Messinian samples reveal lipid distributions for normal marine and hypersaline environments that are strikingly similar to those observed in modern environments (Figure 3-3).

Figure 3-3: Extracted ion chromatograms of ether lipids from Serra Pirciata Messinian section at A) 17.4 m and B) 19.7 m, at onset of hypersalinity.

In the TV section, ACE values ranged from 1.4 to 6.1 throughout the lower section (1.8-11.8 m). However, at 13.9 m, prior to the deposition of laminar gypsum at

15.2 m, it increased to 47.5. In the SP section, ACE was generally higher and showed larger fluctuations. At the lowest point of the section (14.1 m), the ACE values were 3.7, and increased to 20.5 at 17.6 m. Above 17.6 m, ACE decreased to 11.4 at 18.4 m and to

9.7 at 18.95 m. At 19.55 m and 19.77 m, ACE values increased significantly by 5x, to

51.4 and 57.2.

We used the linear salinity-ACE relationship determined from modern samples to reconstruct salinity leading up to the MSC from ACE values in ancient sedimentary

65 organic matter (Figure 3-4). In the lower TV section, the estimated salinity is consistently a brackish ~20 psu. Within 2 m of the onset of hypersalinity (deposition of laminar gypsum), our lipid based salinity estimate is ~140 psu, more than double normal marine values and well within the salinity at which gypsum precipitates (110 psu).

Figure 3-4: Stratigraphy and raw A/A+C *100 (ACE) values and calculated salinity in Torrente Vaccarizzo (A) and Serra Pirciata (B). Stratigraphy reproduced from Bellanca et al. 2001.

In the lower section of SP, the estimated salinity was also in the brackish range, although higher than in TV (25-28 psu). The salinity increases to 60 psu at 17.6 m, which is 1.6 times higher than average Mediterranean salinity today (38 psu). Upsection from this increase, the estimated salinity stabilizes at normal marine values (32-36 psu).

Finally, at 19.55 m, just below the onset of the MSC, there is a dramatic increase in calculated salinity ranging from 150 psu to a briny 165 psu.

Calculated salinity estimates are consistent with expected values for a transition from marine to hypersaline conditions and track periods of evaporation and dilution

66 recorded in the stable isotope composition of minerals (Bellanca et al. 2001). In the TV section, several negative excursions in δ18O throughout the section are evidence for

freshwater input (Bellanca et al. 2001), and δ18O values vary between +5.1 and +8.1‰

prior to deposition of laminar gypsum indicate an enriched, evaporative brine. The rise in

calculated salinity to 80 psu at 13.9 m follows, within a few meters, this period of δ18O enrichment. In the SP section, the first evidence of increased estimated salinity from archaeal lipids occurs as the δ18O carbonate values increase from -2‰ to +5‰. However,

the δ18O and our lipid-based salinity estimates diverge in the top-most portion of the

section. The δ18O values decrease from +4‰ to -4.7‰, which Bellanca et al. (2001) interpret as representing an influx of continental water while our calculated salinity data show that overall hypersaline conditions prevailed, consistent with the presence of lenticular gypsum and evolution of basin waters towards hypersalinity.

Our lipid-based salinity estimates assume planktic Archaea lipids are producing the bulk of the ether lipid record. This implies subsurface Archaea inputs, both during and after deposition, do not influence relative archaeol and caldarchaeol abundances.

Metabolically active post-depositional subsurface Archaea have been found in sapropels

(Coolen et al., 2002). However, there is no correlation between increases in ACE and total organic carbon (TOC) where TOC data are available. More importantly, metabolic rates in subsurface environments are extremely slow and extant archaeal biomass is low despite its dominance in sulfate-methane transition zones in the sediments off the Peru margin (Biddle et al., 2006; D'Hondt et al., 2002; Schippers et al., 2005).

67 Anoxia in the TV and SP sub-basins has been suggested (Bellanca et al. 2001), and anaerobic biomass in the water column could potentially contribute to the sedimentary record. However, ether lipids produced in the anoxic water column in the

Black Sea, a basin larger but comparable to the marginal sub-basins, are not effectively transported into underlying sediment (Wakeham et al., 2003). It is also possible that methanogenic halophiles are present and active, but produce a similar salinity-lipid response as the microbes in the modern surface water calibration. Therefore, contributions from deeper waters or the subsurface sediments are either negligible or at least did not alter the relationships between lipid distribution and salinity.

Origin of lipid-salinity relationship

The relationship between archaeal lipid distributions and salinity is likely the result of a community shift from a mixed population of marine Euryarchaeota and

Crenarchaeota, to one dominated by Euryarchaeota halophiles under hypersaline conditions. Osmoregulation in halophiles partially depends on lipid polar head groups

(Roessler & Muller, 2001) but the core lipids used in this study are not involved in osmoadaptation. Furthermore, Crenarchaeota are absent in hypersaline environments, while the halophilic Euryarchaeota are diverse and active (Ovreas et al., 2003), tolerating salinities up to NaCl saturation (Oren, 2002).

The freshwater, brackish, and marine sites with ACE values from 1-20 are associated with increased nutrients derived from coastal inputs (Swansgut, Watt’s Bay,

Arabian Sea), upwelling (Peru margin, Equatorial Pacific), or seasonal mixed layer

68 shoaling (Sargasso). Therefore, high ACE values may be associated with nutrient inputs.

The two highest ACE values were during the summer in the estuarine Chincoteague Bay

(15.9-19), which receives excessive nutrient input, especially in the lower portion of the watershed (Primrose, 2004) where these samples were collected. This supports a relationship between nutrient input and ACE, and shows a possible anthropogenic control on the abundance of archaeol and caldarchaeol in agricultural coastal environments.

The presence of caldarchaeol in hypersaline waters is unexpected. Published reports to date find Euryarchaeota halophiles grown in culture synthesize only archaeol, although genomic studies suggest they may be capable of GDGT synthesis (Boucher et al., 2004). Wind or local run-off may also deposit Crenarchaeota-produced caldarchaeol, although such contributions are probably not significant based on the absence or barely detectable amounts of cyclic GDGTs. Halophilic methanogens produce both archaeol and caldarchaeol but are unlikely to survive in the well oxygenated surface waters.

Methanogens in anoxic sediments are beneath a thick crust (~1 cm) at the sediment-water interface that provides a physical barrier and restricts mixing of sedimentary materials into overlying waters. The lack of caldarchaeol in Mono Lake may indicate that the as yet undescribed enzymatic pathway for synthesizing tetraethers from diethers (Lange et al.,

2000) may be present in non-alkaliphilic halophiles. Experiments using enrichment cultures (Wuchter et al., 2004) and the newly isolate mesophilic Crenarchaetoa cultures

(Konneke et al., 2005) are the obvious next steps to further evaluate this potential salinity indicator.

69 Conclusions

The relative abundance of archaeol to caldarchaeol correlates with salinity across a wide range and therefore provides a robust potential prozy for reconstructing salinity in transitions from marine to hypersaline conditions. Changes in the amounts of archaeol and caldarchaeol reflect ecological patterns, apparently independent of hydrological factors directly responsible for salinity that can affect other proxies, such as mineral δ18O values This archaeal lipid salinity indicator also has the important advantage of preferential preservation under conditions where other proxies are absent or altered. Few macrofauna are available to indicate high salinity, and Archaea and their lipid biomarkers tend to endure in basins when other biological indicators of salinity are absent such as during periods of anoxia or calcium carbonate dissolution (Schouten et al. 2002). As

HPLC-MS analysis becomes routine in organic geochemistry, the archaeol caldarchaeol ecometric along with analyses of the other GDGTs promises novel and important insights to ocean chemistry through time. Future investigations of archaeal lipid distributions in marine waters will increase our understanding the effect of nutrient and physical conditions on lipid distributions and aid interpretation of ACE in the narrow but important normal marine range.

Acknowledgements

A.D. Jones (Michigan State) assisted with HPLC-MS analysis at Penn State, and

E.C. Hopmans, S. Schouten, and J.S.S. Damsté were generous with initial HPLC-MS

70 training at NIOZ. Infersa Saline, Marsala, Italy and the A. D’Ali Staiti family kindly provided access to salt pan sites where R.L. Folk (University of Texas) served as translator and alkalinity titrator. The Marine Science Consortium (Wallops Island, VA) provided access to Chincoteague Bay sites. S. Wakeham provided many marine SPM samples and M. Conte allowed us to join the scientific party aboard the R/V Weatherbird

II. We also thank the R/V Weatherbird II crew. A. Caruso (University of Pisa) helpfully provided Messinian samples and directions to sites. M. Arthur, two anonymous reviewers and R. Summons gave insightful criticisms which facilitated considerable improvements.

An AAPG Grant-in-Aid, NSF-IGERT DGE-9972759 and NSF OCE-327377 funded this research.

Chapter 4

Organic carbon records of the transition from marine to hypersaline conditions in

the Late Miocene (Tripoli Formation, Sicily)

Abstract

Comparative geochemistry of sedimentary organic matter in two marginal sub- basins reflects changes in productivity and paleoenvironment prior to the onset of the

Messinian Salinity Crisis. We show that the organic matter in these sections is thermally immature and sulfur-rich typical of type II-S kerogen. The Torrente Vaccarizzo section contains low amounts of organic matter, and carbon isotopic compositions reflect production from primarily C3 photosynthetic pathways. In contrast, the Serra Pirciata

section reflects periods of higher productivity prior to hypersalinity. Additionally,

changes in TOC and δ13C provide evidence for euxinia, carbon limitation, and remineralization of OM. These changes herald salinity increases as the basin waters

stratify, and support mineralogical and petrographic evidence for enhanced salinity

stratification. Large variations in the δD of kerogen may also reflect hydrological

fluctuations well before the onset of hypersalinity.

Introduction

During a 640 kyr interval of the latest Miocene termed the “Messinian Salinity

Crisis” (MSC), tectonic events led to isolation and desiccation of the Mediterranean, resulting in deposition of >1000 m of evaporites throughout the region (Hsu et al., 1973;

Krijgsman et al., 2002; Krijgsman et al., 1999b; McKenzie, 1985; Rouchy & Caruso,

2006; Soria et al., 2005). The transition to hypersalinity is well-preserved and exposed in outcrops around and within the present day Mediterranean (Manzi et al., 2005; Soria et al., 2005; Krijgsman et al., 2002; Wrobel & Michalzik, 1999). Detailed bio- and cyclostratigraphy in the Messinian Tripoli Formation is recorded in two sections, the northern Torrente Vaccarizzo (TV) and the southern Sierra Pirciata (SP). These sections are correlated to the transition from marine to hypersaline conditions across central

Sicily, and provide a robust stratigraphic framework for studying the influence of this transition on organic matter production and preservation. Reconstructing changes in productivity and carbon cycling provide new and complementary evidence for progressive and dramatic environmental changes across the Caltanissetta Basin prior to the Messinian Salinity Crisis.

73 Geologic Setting

The Tripoli Formation (6.96-5.93 Ma) provides a high-resolution and continuous record spanning the transition from marine to hypersaline conditions marking the onset of the Messinian Salinity Crisis (MSC) (Blanc-Valleron 2002, Bellanca et al. 2001, Butler et al. 1999, Hilgen and Krijsmen 1999). The formation consists of 52 cycles of homogenous white marls, laminated red marls, and diatomites deposited from 7.0 Ma to 5.93 Ma, and is overlain by the Calcare di Base, a carbonate unit containing halite and gypsum pseudomorphs. In Central Sicily, several synclinal basins, related to the Magherebian thrust and associated Appenine orogeny, record the diachroneous onset of hypersaline conditions (Butler et al., 1995; Rouchy & Caruso, 2006).

We focused on the organic geochemistry of two sections exposed as outcrops, the southern Serra Pirciata (SP) and the northern Torrente Vaccarizzo (TV), which correlate to the Falconara reference section in southern Sicily, based on depositional sequences related to 21 ky precessional periodicity (Figure 4-1) (Bellanca et al., 2001; Hilgen &

Krijgsman, 1999; Krijgsman et al., 1999a).

Figure 4-1: Outcrop locations and stratigraphy of Torrente Vaccarizzo and Serra Pirciata (from Bellanca et al. 2001).

The TV section is 50 m thick, and includes cycles 34-52. It consists of diatomitic silty marls and laminated carbonates, and lacks diatomites. At cycle 50 (19 m), a cap of

1.4 m of laminar gypsum indicates onset of hypersaline conditions locally. The rest of the section (cycles 51-52 to 21 m) corresponds to the Calcare di Base, and contains a series of marls and autobrecciated limestones with halite moulds. SP is only 19 m in thickness with 25 sedimentary cycles, but it has the well-defined cyclicity of the Tripoli Formation.

Successions of marls and diatomites contain intermittent evidence (i.e. gypsum) of incipient hypersalinity. The onset of continuous hypersalinity culminates at cycle 52

(6.57 Ma) with the deposition of the Calcare di Base (Bellanca et al. 2001). Faunal, isotopic, and mineralogical changes consistent with onset of restricted conditions were diachronous in these basins, at 6.15 Ma in TV (cycle 42) and 6.32 Ma in SP (cycle 34)

75 (Bellanca et al., 2001) consistent with the interpretation that SP is deeper than TV (Butler et al. 1999).

Experimental Approach

In order to estimate the origin and thermal maturity of organic matter, we analyzed decarbonated and bulk sediment, and isolated kerogen using Rock-Eval, elemental analysis, and stable isotopic techniques. Kerogen is an insoluble fraction of sedimentary organic matter which forms from deposited organic matter through a series of biological, chemical, and physical reactions. The resulting kerogen has complex macromolecular structure; “cracking” of kerogen refers to the subsequent thermally driven breakage of carbon-carbon bonds, which generates free hydrocarbons as bitumen and petroleum. The chemical properties of kerogen record both biological sources and subsequent maturation processes.

Rock-Eval pyrolysis was used to determine TOC, Tmax, and hydrogen and oxygen

indices to estimate the type and maturity of the organic matter. During Rock-Eval

pyrolysis, a sample is heated, and hydrocarbons are released and measured. The material

released in the first fraction (S1) are the free hydrocarbons. The second fraction (S2) is composed of hydrocarbons generated through cracking of nonvolatile organic matter, measured in units mg HC/g. Tmax is the temperature of maximum hydrocarbon release,

and is an indication of the stage of maturation of the organic matter. Typically, Tmax =

400°- 430°C represents immature organic matter, Tmax = 435°-450°C represents mature

or oil-generating hydrocarbons, and Tmax > 450°C are in the over-mature zone. The third

76 fraction (S3) released during Rock-Eval pyrolysis represents the remaining CO2 (in mg

CO2/g), and represents the amount of oxygen in the kerogen.

Kerogen atomic H/C ratios are also indicators of thermal maturity; typical immature type I kerogens have H/C ratios of 1.35-1.50 and type II kerogens have H/C ratios of 1.20-1.35 (Baskin, 1997; Baskin & Peters, 1992; Tissot & Welte, 1984).

-1 -1 The ratio of The HI (HI = (100 x S2)/TOC in mg HC g TOC ) and OI (OI =

-1 -1 (100 x S3)/mg CO2 g TOC ) indices correspond to organic matter origin. HI ranges

from 100-600 are associated with lipid and protein origins. In contrast, OI increases with

increasing contributions from carbohydrate-rich plant compounds.

Sample Preparation and Instrument Analysis

Bulk rock samples were mechanically cleaned to expose fresh surfaces before

grinding in a ball mill. Powders were decarbonated using 2N HCl, followed by 6N HCl with heating for samples containing dolomite. Humble Geochemical Services, Inc. performed the Rock-Eval analysis and kerogen isolation. Methods for kerogen extraction follow (Durand, 1980; Mukhopadhyay, 1992; Mukhopadhyay et al., 1985). Heavy liquid mineral separation was not performed. XRD analysis from both Bellanca et al. (2001)

and this study (data not shown) did not detect pyrite in SP bulk sediments although

jarosite (KFe3(SO4)2(OH)6, potassium iron sulfate hydroxide) is present at 14.1 m, 17 m,

and 18.95 m. In TV, Bellanca et al. (2001) report pyrite at the bottom of cycle 35 only (2

m).

77 Elemental analysis of C, S, N, and H contents were determined on: (i) the untreated whole-rock powdered sample; (ii) the residual powder after treatment with 2N

HCl (and 6N HCl with heating for samples containing dolomite), and (iii) the isolated kerogen. Instrument detection limit was 0.01 wt.% for all elements, and the

reproducibility was 0.1 wt.% for C and N and 0.2 wt.% for S and H.

Carbon isotope abundances were determined using a Thermo Finnigan XP mass

spectrometer coupled to Costech elemental analyzer for powders (i),(ii), and (iii) as noted

above. The δ13C values are presented in δ-notation relative VPDB; the reproducibility is

better than ±0.1‰. δD values were determined on freeze-dried kerogen with Thermo

Finnigan XP mass spectrometer coupled to to a Thermo-Finnigan thermal conversion

elemental analyzer. The δD values are presented in δ-notation relative to VSMOW; the

reproducibility is better than ±10‰. δD values reflect both exchangeable and non-

exchangeable H within the kerogen, which can vary between 5-20% (Schimmelmann et

al., 2006).

Results

Torrente Vaccarizzo

Throughout the 15 m Torrente Vaccarizzo (TV) section, H, S, and N in whole

bulk rock powders were all <1 wt. % regardless of lithology (Figure 4-2). The amount of

total carbon varied from 4-12 wt. % in bulk sediment. High carbon values (9.3 wt. %)

78 occurred at the bottom of the section, lower values (3-6 wt. %) occurred up-section, and the highest values occurred at the top (8.7-11.1 wt. %). Conversely, TOC (determined from Rock-Eval analysis of whole rock powders) ranged from 0.2-2.3 wt. % (Table 4-1).

Maximum TOC values were in the middle of the section (7-11 m) and lowest values were at the bottom and at the top of the section. Neither wt. % C or TOC correlated with lithology or cycle boundaries. However, the correlation coefficient between wt. % C and

S was r2= 0.6.

Figure 4-2: Elemental analysis of whole rock powders and kerogen in sediments in Torrente Vaccarizzo. Stratigraphy from Bellanca et al. 2001.

Table 4-1: Rock-Eval analysis of whole rock powders and kerogen from Torrente Vaccarizzo and Serra Pirciata sections of the Tripoli Formation

Isolated kerogen had below 5 wt. % N, and >10 wt. % S (Figure 4-2). The highest amounts of S (22 wt. %) were in kerogens from the bottom of the section; S then decreased and averaged 10 wt. % through the rest of the section. Maximum wt. % carbon in kerogen was at 8 m (Figure 4-2 ). Below 8 m, carbon in kerogen was between 13-18 wt. %; above 8 m, it decreased to 3-4 wt. %. C/N ratios in kerogen were constant through the TV section, generally between 10 and 12. Atomic H/C ratios in kerogen ranged from

2-4, and the S/C ratios ranged from 0.58-6.5.

80 Organic-carbon isotope compositions in decarbonated sediments ranged from -

13 20.4‰ to -24.9‰ (Figure 4-3). The δ Ckerogen is nearly equivalent, indicating a similar

source for both bitumen and kerogen). The most enriched δ13C (-20.4‰) at 13.9 m

corresponded to the highest wt. % carbon in decarbonated sediments. In general, there is

not a linear correlation between total carbon in decarbonated sediments and δ13C values

(r2 = 0.27), indicating that the carbon isotope compositions are not biased by

allochthonous carbon inputs. Tmax for whole rock powders for the whole section were

between 414-433°C. Hydrogen index (HI) values were between 84-436 mg CO2/g TOC in whole rock powders. Oxygen index (OI) values were between 47-439 mg CO2/g TOC

in whole rock powders. A Rock-Eval based van Krevelen-type diagram shows that

organic matter appears to be predominantly Type II algal material. Incorporation of

organic detrital material possibly gives rise to those samples that fall between Types II

and III (Figure 4-5). High sulfur content further categorizes the kerogen as Type-IIS

(Tissot & Welte, 1984)

Figure 4-3: δ13C values of carbonates, decarbonated sediments, and δ18O values of 13 18 carbonates in Torrente Vaccarizzo. Stratigraphy, δ C, and  δ O of carbonates from Bellanca et al. 2001.

Figure 4-4: Comparison of δ13C values of decarbonated samples and isolated kerogen in both Torrente Vaccarizzo and Serra Pirciata.

Figure 4-5: Modified van Krevalan diagram of HI v. OI in bulk sediments from Torrente Vaccarizzo (triangles) and Serra Pirciata (squares).

δD of three kerogen from TV increase upsection, from -114.6‰ at 5.5 m,

-111.4‰ at 7.1 m, and increasing to -72.7‰ at 13.9 m.

Serra Pirciata

In Serra Pirciata, bulk chemical characteristics were more variable than in

Torrente Vaccarizzo. Wt. % N was always <1%, but the amount of H fluctuated between

0.2 to 2 wt. %, and S varied from 0.2 to 5 wt. % (Figure 4-6). Total wt % C in bulk sediments had a negative correlation with sulfur content (r2=0.83, excluding the 14.4 m

83 sample, which had anomalously low carbon and sulfur and corresponded to the bottom of a marl-dolostone transition). TOC values in bulk sediments, determined by Rock-Eval analysis, varied between 2.0 to 12.0 wt. %; high TOC correlated with marls and dolomitic limestones (Table 4-1). Total S also co-varied with TOC (r2=0.73) and the total carbon in

kerogen.

Figure 4-6: Elemental analysis of whole rock powders and kerogen in Serra Pirciata. Stratigraphy from Bellanca et al. 2001.

In isolated kerogen, wt. % N was generally <2% and wt. % H ranged from 2-8%,

and sulfur was generally between 5-10%. Total wt. % C in kerogen ranged from 5.6 to

49%, and averaged 31.2%. C, S, and H all trended toward highest values at the top of the

section, where nitrogen decreased to its lowest value. The atomic H/C ratio in kerogen

ranged from 1.9-4, and averaged 2.16. Atomic S/C ratios varied from 0.4 to 0.8.

13 13 Values for δ Corg varied from -17.4‰ to -23‰ (Figure 4-7). C enriched carbon

occurred in the diatomites and marls, usually at precessional cycle boundaries except at

84 the top of the section. Isotopically depleted carbon occurred in the red marls and marly

13 13 limestones. Overall, the δ Corg values increased upsection. As in the TV section, δ Corg

13 values and δ Ckerogen values were very similar.

Figure 4-7: δ13C of carbonates, decarbonated sediments, and δ18O of carbonates in Serra Pirciata. Stratigraphy, δ13C and δ18O of carbonates from Bellanca et al. 2001.

Tmax of whole rock powders ranged from 360-400°C. Hydrogen index (HI) values

were between 196 to 842 mg CO2/g TOC in whole rock powders. Oxygen index (OI)

values were between 21-252 mg CO2/g TOC in whole rock powders. A van Krevelen-

type diagram based on HI and OI shows that organic matter appears to be predominantly

Type II algal material (Figure 4-5). In addition, the high sulfur content is indicative of

Type-IIS kerogen.

δDkerogen values ranged from -71‰ to -138‰. The highest δDkerogen value at 15 m

correlated with the highest sulfur wt. % in whole rock powders. However, the other

positive excursions in δDkerogen were not correlated with lithology or other chemical

properties.

85 Discussion

Organic matter type and maturity

All indicators suggest both TV and the SP sections contain Type II-S organic matter. Tmax ranges for both sections are below the 435°C, and the organic matters is herefore considered immature (Tissot & Welte, 1984). The high kerogen H/C values (2-

4) are also an indication of thermally immature organic matter because dehydrogenation

reactions reduceH/C ratios. The HI v. OI van Krevelan diagram (Figure 4-5) place TV

sediments well within the Type II keorgen, although data from SP are more variable.

Incorporation of detrital terrestrial material could possibly give rise to those samples that

fall between Types II and III; the increase in oxygen arising from the relative oxygen-rich

plant compounds (polysaccharides, lignin). Oxygen isotope and microfaunal assemblage

data suggest inputs of freshwater and terrestrially derived nutrients which would also

bring in detrital plant material (Bellanca et al., 2001).

Kerogens also have high S content (6-21 wt. %) and high S/C ratios (0.05-0.7).

Sulfur-rich kerogens have abundant and weak C-S and S-S bonds promote early, low- temperature oil generation, and can lead to bitumen expulsion at lower temperatures

(Baskin & Peters, 1992). The correlation between δ13C values of kerogen and

decarbonated TOC indicates kerogen and extractable organic matter in these sediments have the same origin.

High sulfur content also affects the preservation of organic compounds. High S

content and a correlation (r2=0.5) between S and C wt. % in decarbonated powders

86 indicate the possible presence of organic-sulfur compounds. Organic-sulfur compounds have been reported in similar Messinian material from the Lorca Basin (Spain) (Russell et al., 1997) and the Vena del Gesso (N. Italy) (Sinninghe Damsté et al., 1995). Organic- bound sulfur macromolecules form during early diagenesis in the presence of reduced sulfur species, and therefore indicate syndepositional anoxic conditions (Brassell et al.,

1986; Deleeuw & Damsté, 1990; Kohnen et al., 1991; Sinninghe Damsté et al., 1988).

Isolation and productivity changes across the Caltanissetta Basin

Organic geochemical and isotopic records support lithological evidence for changes in productivity and development of euxinia in the shallow TV and deep SP sections at the onset of hypersalinity.

Bellanca et al. (2001) suggested that diatomite formation in SP was evidence for nutrient enrichment and high productivity, while the lack of well-developed diatomites in the northern TV is indicative of lower productivity. Throughout both sections, the organic carbon content confirms this contrast; TV TOC was low (0.88 to >4 wt. %) compared to

SP (2 to >15 wt. %).

13 The δ Corg values also record differences in productivity between the two basins.

13 In TV, δ Corg values were relatively constant (-20 ‰ to -23‰) and considered a normal

13 range for marine algae (Preuss et al., 1989), but in SP, the δ Corg values shifted up -19 to

-14 ‰ in diatomites.

87 13C enrichment organic matter can reflect changes in the carbon isotopic composition of terrestrial organic matter transported to the basins, or to productivity changes within the basins. During the late Miocene, there was a global expansion in C4

13 13 plants, which produce terrestrial organic matter more enriched in C (δ Corg = -12‰)

13 than C3 plants (δ Corg = -28‰) (Cerling et al., 1997; Smith & Epstein, 1971). If this expansion also occurred in the Mediterranean region, deposition of organic matter

13 produced from a C4 photosynthetic pathway would lead to an increase in δ Corg (Kump

& Arthur, 1999). In Sicilian basins prior to evaporite deposition, pollen data evidence of

expansion of C4 plants are equivocal. The relative amount of grass pollen (Poaceae)

increases prior to evaporite deposition (Fauquette et al., 1999), but grass taxa today are

equally divided into C3 and C4 metabolisms, and it is impossible to discern the

occurrence of these pathways from pollen data alone. Importantly, carbon-isotope

compositions of mammalian teeth show that the C4 plants did not contribute significantly

to plant biomass in western Europe or the Mediterranean region over the last 20 Ma

(Cerling et al., 1997). Moreover, there are no major changes in terrestrial vegetation

during this period, despite major oceanographic changes within the Mediterranean Sea

(Bertini, 2006; Fauquette et al., 2006). One notable floral change is the development of

mangrove ecosytems, which lead to 13C enrichment (Fauquette et al., 2006). Although mangroves use the C3 photosynthetic pathway, mangrove species living inland tend to be

enriched by 1-2‰ in 13C relative to coastal species (Muzuka & Shunula, 2006). However, this is unlikely to produce the degree of enrichment we see. Therefore, it is unlikely that delivery of 13C enriched plant biomass increased during this interval.

13 The higher δ Corg values may also reflect a change in production within the

basin, such as a shift from calcareous algae to diatom-based productivity in SP, which is also reflected in the micropaleotological record. Fractionation of carbon isotopes between biomass and CO2 (εp) in calcareous algae (e.g. haptophyte) is consistent, governed by

CO2 concentration, growth rates, and phosphate concentration (Bidigare et al., 1997;

Popp et al., 1998). In diatoms, cell size and geometry are also important although active

transport of DIC (or CO2) and tendency to grow rapidly in nutrient-rich waters leads to

smaller fractionation factors (Pancost et al., 1997; Popp et al., 1998). High demand for

CO2 carbon during periods of increased productivity may also led to carbon limitation,

further driving pronounced shifts in δ13C values (Rau et al., 1987; Schouten et al.,

2001a).

Hypersalinity may also lead to increasing δ13C values of organic matter. As

increasing salinity reduces CO2 solubility, carbon limitation, and decreasing isotopic

discrimination may drive isotopic shifts (Andersen et al., 2001). The isotopic effects of

carbon limitation in hypersaline environments have been observed in the enriched carbon

isotope compositions in organic matter from hypersaline settings in Dead Sea sediments

(Grice et al., 1998a), Red Sea brines (Pierret et al., 2001), and hypersaline microbial mats

(Schidlowski, 2001). We have also observed 13C enriched particulate organic carbon in

modern brines surface waters (Appendix C).

If a combination of changes in εp from diatom productivity and CO2 limitation

created isotopically enriched organic matter, then what is driving the 13C depletion in the

inorganic carbonates observed in both sections?

89 Previous workers attributed 13C depleted carbonates in these and other Messinian

sections to rapid and localized remineralization of organic matter creating a pool of

- isotopically depleted HCO3 . Sulfate reduction would also enhance the precipitation of

carbonates (principally dolomite at the top of both sections) from the isotopically depleted bicarbonate retained in anoxic waters (Bellanca et al., 2001; Blanc-Valleron et al., 2002; Horita et al., 1991; Rouchy et al., 2001). Salinity-driven starification of the

water column and subsequent shut down of the biological pump decoupled the organic

and inorganic carbon systems in SP. The resulting transport and isolation of organic

matter below a pynocline or chemocline can explain both 13C enrichment in surface water produced organic matter, and progressive depletion of carbonates precipitated at the sediment-water interface during syndepositional diagenesis. Oxygen isotope evidence in carbonates for freshwater pulses amid the development of high salinity provide a mechanism for enhanced development of density stratification (Bellanca et al., 2001).

In contrast, the more shallow TV section had relatively invariant carbon isotope compositions, suggesting that this basin remained relatively well-mixed. The depletion in

13 δ Cdolomite which occurred prior to the deposition of laminar gypsum, reflects

remineralization of organic matter, and active sulfate reduction.

Increased sulfur content in both sections is also indicative of sulfate-reduction and subsequent precipitation of sulfides reacting with organic matter (Peters 2005). The model for production of organic matter in SP and TV are summarized in Figure 4-8 .

Figure 4-8: Synthesis of proposed changes in the depositional system leading to organic and inorganic carbon isotope compositions in Torrente Vaccarizzo and Serra Pirciata

Hydrogen isotopic changes in Messinian waters?

The D/H of sedimentary organic matter reflects the isotopic composition of water during biomass synthesis, as well as diagenetic exchange reactions with waters in the sedimentary environment and, in the case of very labile H bonds, any subsequent water the sedimentary organic matter (SOM) contacts (Schimmelmann et al., 2006).

Thermal maturity can alter the D/H composition of the kerogen, especially in thermally mature organic matter (Pedentchouk et al., 2006; Schimmelmann et al., 2006).

In Type-IIS kerogens in the Micoene Monterrey Formation, thermally mature kerogens

91 are 40‰ more enriched in D than immature kerogens (Schimmelmann et al., 2006).

However, in the Creteceous Congo basin, the δD of kerogen (and n-alkanes) were not significantly affected by molecular rearrangement and isomerization which take place at the beginning of oil generation (Pedentchouk et al., 2006).

Although a percentage of the total kerogen must reflect some portion (typically 5-

20%) of exchangeable H, could the overall δD variation for kerogen reflect the trends in

Messinian water δD values at the onset of hypersalinity? Andersen et al. (2001) showed within marls deposited during the Miocene (Lower Evaporites), the δD values of cholestane (derived from Eukaryotes) increased dramatically (~100‰) over time. They inferred that this shift reflected H isotopic changes in marine waters of a similar magnitude because of evaporation. This is consistent with the +67‰ range in δD we observe.

We propose the observed δD shifts reflect astronomical changes in insolation and eccentricity derived from Laskar 93 solution (Hilgen & Krijgsman, 1999; Laskar et al.,

1993; Rouchy & Caruso, 2006) (Figure 4-9). Poor temporal resolution of our samples prevents effective spectral analysis. However, increases in δD of kerogen appear to correspond to insolation maxima, possibly recording δD enrichment in basin waters. At the top of the section, the δD of kerogen decreases at maximum insolation. This period corresponds to peak maximum eccentricity when increased seasonality may produce attenuating dilution and deuterium depletion.

Figure 4-9: δD of kerogen in Serra Pirciata with insolation and eccentricity curves (from Rouchy and Caruso, personal communication)

A higher resolution dataset, and compound-specific isotope analysis will reveal whether insolation signals are reflected in well-preserved and unaltered hydrogen isotope compositions of plant and diatom lipids

Conclusions

The sedimentary organic matter deposited in two marginal sub-basins prior to the onset of the Messinian Salinity Crisis is thermally immature and contains type II-S kerogens. The Torrente Vaccarizzo section has low organic matter content and carbon isotopic compositions reflect primarily C3 production. In contrast, the Serra Pirciata

section reflects periods of higher productivity prior to hypersalinity, as well as density-

stratified waters, euxinia, and carbon limitation. The differences in organic matter

93 production in the two basins provide evidence of the diachronous onset of major productivity changes associated with the isolation of the Mediterranean, as well as the gradual onset of euxinia and carbon limitation in SP. δD of kerogen may reflect hydrological fluctuations well before the onset of hypersalinity as well, and even freshening prior evaporite precipitation.

Acknowledgements

We thank Dan Jarvie and Humble Geochemical, Inc. for Rock-Eval analysis of bulk sediments gratis. Michael Arthur provided excellent editorial assistance. This manuscript also benefited from discussions with Francesca Smith. We gratefully acknowledge several funding sources: AAPG Grant-in-Aid for kerogen isolation and

Rock-Eval of kerogen; NASA Astrobiology Institute (NCC2-1057), NSF-IGERT DGE-

9972759 and NSF OCE-327377.

Chapter 5

Development of the Nano-EA-irMS: A novel method for analyzing the isotopic composition of large non-volatile compounds

Abstract

Compound-specific stable isotope analysis (CSIA) is a powerful tool for tracing metabolic, ecological, and environmental processes. Existing methods for the introduction of individual compounds to an isotope-ratio mass spectrometer include gas

(GC) and liquid chromatographic (LC) interfaces. While these methods are flexible and robust, there are limitations on the size and polarity of analyzable compounds. In order to measure the isotopic composition of a broader range of compounds (especially intact tetraether membrane lipids and pigments), we developed a system to reduce the minimum sample size required for elemental analysis mass spectrometry (EA-irMS). EA-irMS techniques enable a nearly universal range of materials to be analyzed, although sample size requirements are artificially large because high carrier gas flow rates dilute the samples.

To take advantage of the universal applicability of both combustive and reductive

EA-irMS, we have designed a system that traps sample gas (CO2, N2, H, and CO) from

the high-flow effluent. The trapped gas is then introduced into a low-flow helium stream

(1 cm3/min), thus reducing the carrier gas:sample gas ratio. Sensitivity is thus improved

by at least 2 orders of magnitude in the hydrogen system, and 3 orders of magnitude for

carbon and nitrogen.

95 We tested this system with a suite of standards for nitrogen, carbon, and hydrogen isotopes. In each case, instrument background and/or memory effects led to both systematic and non-systematic variations in the measured isotope values. In the nitrogen system, background N2 from the elemental analyzer produced peaks of variable size, but

consistent isotopic composition. Possible sources for this background include small leaks

or impurities the carrier and/or oxidation gas. In the hydrogen system, the isotopic

composition of the H2 background is variable although consistently small. We

hypothesize that H2 present in the carrier gas charges a reservoir of exchangeable H2

within the glassy carbon furnace, creating significant isotopic fractionation. The pool of

exchangeable H imparts a memory effect and fractionation to both background and

sample gases. This effect has been observed in conventional TC-EA systems, but the

effect is much more pronounced in the Nano-TC-EA systems. Reducing the volume of

carbon in the reservoir may reduce the fractionation and overall size of the background.

Introduction

Compound-specific stable isotope analysis (CSIA) is a powerful tool for tracing

metabolic, ecological, and environmental processes preserved on geological time scales

in sedimentary organic matter (Desmarais et al., 1980; Freeman et al., 1990; Hayes et al.,

1990; Rieley et al., 1991). Carbon isotopes yield information about metabolic starting

materials (Hayes, 2001a; House et al., 2003), carbon cycling (Joachimski et al., 2001;

Kump & Arthur, 1999; Kurtz et al., 2003), and serve as proxies for CO2 concentrations

(Bidigare et al., 1997; Laws et al., 2001; Mix et al., 2000; Pagani et al., 2005). Hydrogen

96 isotopes provide insights into the past hydrological cycles and perhaps salinity (Andersen et al., 2001; Pedentchouk et al., 2006; Sauer et al., 2001; Schouten et al., 2006). Nitrogen isotopes of pigments may reveal nitrogen cycle systematics in ancient basins (Chikaraishi et al., 2005).

The two common methods for the introduction of individual compounds into an isotope-ratio mass spectrometer: off-line preparation and continuous-flow (CF).Table 5-1 lists the sample size requirements and precision for a number of existing methods. More recently, methods for the isotopic analysis of large and non-volatile compounds separated using liquid chromatography have been developed. In one system, water soluble compounds are separated in a carbon-free aqueous mobile phase and then chemically oxidized to CO2 (Krummen et al., 2004). A second design, the LC-moving wire-irMS,

evaporates the mobile phase on an inert moving wire before the analyte is oxidized and

shunted to the mass spectrometer (Brand & Dobberstein, 1996; Caimi & Brenna, 1993;

Sessions et al., 2005). This method is very promising for analysis of carbon isotopes in

large, non-volatile compounds, but has not been developed for other elements.

Table 5-1: Existing methods and detection limits for elemental analysis isotope-ratio mass spectrometry

To analyze hydrogen, carbon, and nitrogen isotopes of large non-volatile compounds, we have explored an alternative approach. We adapted conventional elemental analyzer (EA) continuous-flow inlets for analysis of compounds isolated by independent (i.e., “off line”) liquid chromatographic systems. Under normal operating conditions, continuous flow EA sample requirements are artificially high because elemental analyzers must employ high helium flow rates (50 to 150 mL/minute) to achieve complete sample combustion, yet less than 1% of this stream (~0.2 mL/minute) can be transferred via capillary flow to the ion source of the mass spectrometer. As a result, sample size in continuous-flow instruments is a function of both the minimum acceptable He flow in the EA and the maximum flow to ion source. Fry et al. (1992) published the first EA system for analyzing small quantities of C, N and S. Their

“trapping box” first removed CO2, N2 and SO2 from the EA effluent, then released the

gases through a crimped capillary to a dual inlet MS.

Our system adapts the ideas used in Fry et al. (1992) to make continuous-flow measurements of cryogenically trapped N2 and CO2 gas, and extends it to the analysis to

H2 using thermal conversion EA-irMS. We have designed a system capable of trapping

sample gas (CO2, N2, H2, and CO) from a high-flow EA effluent and releasing it into a low-flow helium stream. The concentrated sample gases are separated on a capillary column, and directed through an open split to the mass spectrometer.

Our primary motivation for the development of the Nano-EA is to facilitate isotopic analyses of trace quantities of large compounds extracted from natural samples such as intact membrane lipids and pigment-derived tetrapyrrole compounds. Improved

LC/MS ionization methods (ESI and APCI) over the past decade has supplied novel

methods for analyzing diverse natural products, enabling better separation of compounds

and “softer” ionizing conditions, and allowing large and complex molecular structures to

be identified and quantified (Hopmans et al., 2000; Hopmans et al., 2005; Sturt et al.,

2004). This technique is designed to measure the isotopic composition of these

compounds will enable a greater understanding of precursor biology and utility as

paleoenvironmental archives.

99 Experimental approach

Trap design

We designed a system to trap sample gases (CO2, N2, H2) from a high-flow EA

effluent (Figure 5-1). In the first system we designed (not shown), gases were trapped on

1/8” column packed with silica gel (CO2, N2) or molecular sieve (H2) and submerged in

liquid nitrogen. After trapping the entire peak, the column is heated and the sample

introduced into a low-flow helium stream (1-2 mL/min). This low-flow carrier stream is

also cryogenically trapped, on 0.32 mm i.d. PLOT (CO2, N2)/ 5Å MolSieve (H2) column

in order to focus the peak. Upon release, the gases are separated by the same capillary

column and 0.2 mL/min of this flow directed to the mass spectrometer. In the EA system,

N2 and CO2 are separated in the carbon PLOT capillary column, and N2 elutes first.

Standards were typically diluted in organic solvents, and injected into sample boats using thoroughly cleaned gas tight syringes. See Table 5-2 for list of standards used.

Table 5-2: Standards used in Nano-EA-irMS method development.

To reduce the blank, we eliminated the second trap for the hydrogen system

(Figure 5-1, results discussed below). In this system, sample gases are trapped on a

100 custom 0.75 ID silicosteel trap packed with 5Å MolSieve (Restek, Bellefonte, PA). The sample is then released into a low-flow helium stream, and the gases are separated, but not focused on the carbon PLOT capillary column. The trap is wrapped with heating tape, and maintained at 120°C except during trapping. This has greatly reduced the amount of water trapped in the molecular sieve. Other lab members are currently testing this one- trap system for nitrogen and carbon. This system significantly reduces the minimum sample size required for EA analysis by both increasing the height/width ratio of the analyte peak (taller peaks for an equivalent sample size) and decreasing the EA-irMS split ratio from between 250:1-750:1 to ~5:1. The lower split ratio increases the peak area by 50-150x while the improved peak shape increases peak height by ~2x. The net effect is a 100 to 300-fold decrease in sample size. Trapping times were determined by observing flow rates through 1) the elemental analyzer to vent, 2) elemental analyzer to the first trap, and 3) first trap to the second trap and through the capillary column.

Figure 5-1: The two states of the Nano-EA-irMS configuration with a single trap. a) The EA effluent (100 mL/min He) vents or enters the mass spectrometer while a slow (1-2 mL/min) helium stream flows through the trap and GC column before entering the mass spectrometer. b) During trapping, the EA effluent enters the trap and the sample gas is trapped at liquid nitrogen temperatures. The trap is then heated to release the gas into the low flow He stream.

101 Samples

We tested several sizes, manufacturers, and treatments of tin and silver boats. We found the 3.5 x 5 mm silver cups (CE Instruments) and 4 x 6 mm silver cups (Costech) ashed at 500° C gave the lowest background in all three isotope systems tested (Table 5-

3).

Table 5-3: Peak areas for different cup cleaning protocols

Isotopic Analysis

Isotope ratios were measured on a Thermo-Finnigan Delta Plus XP with a

Thermo Finnigan thermal conversion element analyzer (TC-EA) at 1450° C for hydrogen analysis and a Costech elemental analyzer (furnace temperatures: 1050° C (combustive) and 650° C (reductive)). Isotope ratios were calculated relative to a gas reference standard calibrated using conventional EA and TC-EA interfaces. For hydrogen isotope

+ analysis, the average H3 factor was measured daily. All values are reported v. VSMOW.

The relative abundance of stable nuclide isotopes is expressed in delta notation:

δ sam = [(D / H sam − D / H SMOW ) / D / H SMOW ]*1000

102

Results and Discussion

Hydrogen

Trapping and chromatography

The flow rate from the TC-EA to the mass spectrometer was 100 mL/min; the flow was reduced by 60% when channeled through the heated column (100°C). In high- flow mode, the minimum detectable peak width was 40 s through the column. Using this as a minimum trapping time, we experimented with a range of trapping times to account for changes in pressure and flow rate when the column is cooled to liquid nitrogen temperatures and as the graphite crucible fills.

Typical chromatography with samples and blanks is shown in Figure 5-2. The first sample peak is a blank, with a small peak amplitude. The next three peaks are H2 gas generated from NBS-22.

103

Figure 5-2: Chromatogram of Nano-TC-EA-irMS of hydrogen standards. The first three peaks are hydrogen reference gas.

The size and isotopic composition of the background changes as a function of the trapping time. Blank peak areas increased with increasing trapping time, from 3-5 V*s

(56 s) to 12 V*s (106 s). The 106 s extended trapping time was conservative and used in order to rule out any effects of incomplete trapping. The δD values of the blank also became D depleted with increasing trapping time. Three blank samples with very low δD

(-250‰ to -300‰) were measured following the installation of a repacked glassy carbon furnace, and the isotopic composition of the background became increasingly D enriched over the lifetime of the furnace.

104 Isotopic measurements and blank correction

Table 5-4 shows measured and blank corrected isotope values for 106 s trapping time runs of blanks and standards.

Table 5-4: δD measured and blank corrected isotope values for 106 s trapping time runs of blanks and standards. All δD values are v. SMOW.

The blank correction uses the mass balance equation:

(1) ATδT = A sδ s + A bδ b

in which A refers to the quantity of the sample (measured in any unit that is

proportional to the abundance, in this case, the peak area) and T refers to the total,

measured gas, s refers to the sample and b refers to the blank, can be rearranged

(2) δ s = (A T δT - A bδ b )/A s

105 to solve for the isotopic composition of the sample.

The δD of C41 n-alkane (in-house standard, -205‰) measured with 42 s trapping time was -118.2‰ for C41 dissolved in solvent and -145.1‰ for C41 introduced as a solid. Blank correction yielded δD values of -153.3‰ and -151‰ respectively, but with

blank corrected samples also had larger standard deviations (36‰ and 15‰). When trapped for 106 s, C41solid the average δD was -113.7‰ (measured) and -108.1‰ (blank

corrected). Because C41 introduced in solvent typically does not dissolve effectively, we

began to run only C41solid.

Measured isotopic values of NBS-22 (NIST standard oil, -118.5‰) trapped for

42 s averaged -81.7‰. With blank correction, the values decreased slightly to -78.4‰.

The average NBS-22 δD trapped for 106 s was -72.1‰, blank corrected to -68.4‰.

Androstane (in-house standard, -256‰) was only run with the 106 s trapping time. The average measured δD was -144.3‰, and the blank corrected dD was -152‰. In both cases, the standard deviation was low (1.6‰ and 3.5‰ respectively).

Precision and accuracy

The precision of sample measurements varied from 1.7-16‰, and the longer trapping time yields better precision (Table 5-5). Blank δD measurements had the poorest precision. In part, this is because the isotopic composition of the blank is sensitive to the composition of the H2 gas from the previous sample. Figure 5-3 shows the

effects of sample composition on background isotopic composition, which was +85‰

before samples are run, and decreased when NBS 22 (-118.5‰) was introduced into the

106 system. This effect was even more pronounced with more depleted compounds; the isotopic composition of the background plummeted to -130‰ with the introduction of androstane (-256‰). The background composition tends to retain the memory effect of the previous sample for ~45 minutes, or until the introduction of another sample.

Figure 5-3: Hydrogen isotope data from Nano-TC-EA-irMS sample run series, showing changes in the background isotopic composition over time.

107

Table 5-5: Average δD values (v. SMOW) for 42 and 106 s trapping times.

Because the blank corrections are not consistent, we also used a modified Keeling plot (Mortazavi & Chanton, 2004) to examine the isotopic composition of the sample gas entering the mass spectrometer, as a function of changing background composition, and possibly other factors such as fractionation or mixing during trapping. Using the mass balance equation, and using notation from Sessions and Hayes (Sessions & Hayes, 2005), substituting As=AT-Ab and rearranging yields:

δT = δ s − Ab (δ s − δb ) / AT (3)

an equation in the form y = b + mx. In this equation, 1/AT is x, δT is y, and the

slope (m) is the -Ab*(δs- δb), and the y-intercept (b) is the blank-corrected sample isotope

value. Therefore, plotting 1/AT against δT for multiple samples yields a y-intercept that

should represent the accurate isotopic value of the sample. The y-intercept represents the

true isotopic composition of the sample. This approach assumes a homogenous and constant background.

108 Figure 5-4 shows the offset between expected and measured values as a function of 1/area. With 42 s trapping time, the y-intercept of NBS-22 was -92.4‰ and C41 was -

152.4‰. When trapped for 106 s, the y-intercept of NBS-22 was -61‰, C41 was -120‰, and androstane was -140‰.

Figure 5-4: Nano-TC-EA-irMS analysis of hydrogen standards. The y-intercept of 1/area plots should reflect the true isotopic composition of the standards if there is two- component mixing.

As with the blank-corrected values, these are not accurate isotopic compositions, and plotting the measured value against the expected isotope value also shows that the effects of the variable blank size and composition (Figure 5-5). Plotting the expected values of these standards versus both the y-intercept values and the measured samples demonstrates the strong correlation between the measured and expected values, but suggests that there is large fractionation effect within the furnace.

109

Figure 5-5: Nano-TC-EA-irMS of hydrogen standards with expected v. uncorrected measured isotope compositions

We conclude that two factors are affecting the hydrogen isotope composition of standards in the Nano-TC-EA system. First, background H2 is trapped and mixed with the

sample gas. However, as the 1/area plot shows, the background does not conform to a

simple mixing line with the standards. Therefore, there must be a second process

affecting the isotopic composition of both the background and the sample gas. We

hypothesize that a pool of hydrogen gas exchanges with the sample gas. Every sample

therefore resets the reservoir of exchangeable H, which changes the composition of the

background which, in turn, affects the isotope composition of the sample gas. This

memory effect is also observed in conventional TC-EA analysis, but is much less

significant.. Describing and reducing this fractionation effect will produce adequate

110 precision and accuracy for the analysis of non-GC amenable compounds using the Nano-

EA-irMS.

It is clear that there is both mixing and isotope fractionation in this system.

Because of mass differences between deuterium and protium, the fractionation of

hydrogen isotopes is frequently large. As a result, the approximation δ p = αδ s + ε where

p= product and s = source does not yield accurate results, and R values, rather than δ values, must be used to determine fractionation factors in this system (Sessions & Hayes,

2005, Appendix A).

Nitrogen and Carbon

Trapping and chromatography

Trapping times of 106 s were also used for the nitrogen and carbon system based on flow rates and travel times from EA to the MS. Typical chromatograms for nitrogen and carbon analysis are shown in Figure 5-6. The first peak is the nitrogen gas, and the second peak is CO2. The small peak in between is an artifact formed during the mass

spectrometry jump from N2 to CO2.

111

Figure 5-6: Chromatograms of Nano-EA-irMS of nitrogen and carbon standards.

Measurements and blank correction

Typical N2 results for different standards and one unknown (chlorophyll) are

shown in Table 5-6. The δ15N of blank cups had an average value of -3.73‰. The

average measured values for standard N1 (NIST value, - 0.4‰) were -2.20‰. Nano-EA

measure values for N2 (NIST value, +20.3‰) averaged +3.67‰. We also measured two in-house lab standards, ABS-1 and Peru Mud, both homogenized and decarbonated bulk sediments. The average value of ABS-1 measured -3.61 (lab value, +1.7‰), and the Peru

Mud measured -2.49 (lab value +7.0‰). Blank corrections yield widely varying isotope values which are strongly dependent on the calculated sample area. Figure 5-7 shows changes in blank composition and size during over the course of several runs.

112

Table 5-6: Nano-EA-irMS isotope measurements of nitrogen standards

113

Figure 5-7: Nano-EA-irMS runs of nitrogen and carbon showing the changes in background isotopic composition over time.

In general, the carbon system is less well-developed. Thus far, we ran only blank cups and a caffeine standard (Table 5-7). The blank cups were less variable, with δ13C = -

27.8‰. Both the blank δ13C and caffeine standards become lighter over the course of the run (Figure 5-7).

114

Table 5-7: Nano-EA- irMS of a caffeine carbon standard

Precision and accuracy

Precision, the standard deviation (1σ) for multiple δ15N measurements of empty

cups (blanks), is 0.84‰. The bulk sediment powders ABS-1 and Peru Mud have the

lowest standard deviation of all the standards (0.10‰ and 0.91‰), indicating that they

are homogenous. As with the blank corrections, the precision in measured isotope values

depends strongly on the size of the sample peak.

The modified Keeling (1/area) plots for the nitrogen system shows the variable blank size of the current Nano-EA system. This results in a “mixing triangle” between the

blank 1/area line and the true isotopic composition of the sample. More than one variable

is likely contributing to the nitrogen background (Figure 5-8).

115

Figure 5-8: Nano-EA-irMS of nitrogen and carbon standards. 1/area plot illustrates the mixing lines from sample blank to the y-intercept “true” isotopic composition of the standards.

Unlike nitrogen, carbon 1/area plots large reflect scatter in δ13C values of both blank cups and caffeine (Figure 5-8), suggesting the caffeine powder may be too heterogeneous or combusts inconsistently. Other compounds should be used for future

Nano-EA development.

Plotting measured isotope values against the expected isotope values also illustrates the effects of the variable blank size (Figure 5-9). The samples are not

“homogenized” with a consistent sized blank. Therefore, the measured isotope values of the standards do not consistently co-vary with the expected isotope values. Possible sources for this background include small leaks or impurities the oxidation gas. A correction could be applied once we reduce the variability in the background size. Other members of the lab groups are currently testing the one-trap EA system for carbon and nitrogen. This system dramatically reduced the variability in the background of the hydrogen system, so we predict that the loss of packing volume and fittings (no second

116 traps) will also reduce the N2 background. Other standards (NIST sucrose, NBS-22)

should be used in future attempts to develop the Nano-EA for carbon isotope analysis. As

with nitrogen, we will also test the one-trap design.

Figure 5-9: Nano-EA-irMS measured isotope compositions v. expected values of nitrogen standards.

Conclusions

We have designed a system that traps sample gas (CO2, N2, H, and CO) from a

high-flow EA effluent; the trapped gas is then introduced into a low-flow helium stream

(1 cm3/min) thus reducing the carrier gas:sample gas ratio, and increasing sensitivity of

isotope analysis by as much as 3 orders of magnitude. For nitrogen, carbon, and hydrogen

117 isotopes, instrument background and/or memory effects led to both systematic and non- systematic variations in the measured isotope values.

118

Chapter 6

Conclusions and Future Work

The goals of my research are to link molecular fossil and organic carbon records to their biological and metabolic origins in order to enhance our interpretation of past environments and ecologies. Specifically, I study the unique and recalcitrant lipid biomarkers of Archaea. Archaea survive and thrive in a tremendous range of environments including hot springs, ice, deep see hydrothermal vents, oceans, lakes, soils and sediments. (Benlloch et al., 1995; Biddle et al., 2006; Cavicchioli, 2006; Delong,

1992; Fuhrman et al., 1992; Herndl et al., 2005; Karner et al., 2001; Schrenk et al., 2003;

Thiel et al., 1999; Trincone et al., 1992). If specific biomarker patterns and properties can be linked to specific biological and metabolic origins, then archaeal biomarkers have great potential to provide biological and environmental information across extreme transitions in space and time.

In chapter 2, I described archaeal lipid distributions from a globally distributed set of modern waters in order to test the hypothesis that specific lipid assemblages will correspond to specific environments and reflect different genotypes. Cluster analysis showed that marine lipid distribution patterns fell into two distinct groups, and freshwater and hypersaline lipid assemblages also formed two unique groups. Multivariate analysis with environmental data showed that nitrate concentration explained the variation observed within these two marine groups. We hypothesize that these lipid distribution patterns provide a link to specific archaeal subgroups with different metabolisms, namely

119 the nitrifying chemoautotrophic Group I Crenarchaeota, and the Group II Euryarchaeota

(metabolism unknown) thus connecting archaeal biomarkers to their biological source.

Determining the biological origin of marine archaeal lipids is important to interpreting

TEX86 proxy temperatures in paleoenvironments.

In chapter 3, the difference in lipid assemblages in marine and hypersaline

environments was used to develop the “archaeol/caldarchaeol ecometric” (ACE) based on

the distribution of two lipids. We also examine ACE in ancient sedimentary organic

matter deposited in the Mediterranean Sea region prior to the Messinian Salinity Crisis

(5.7 Ma). This novel approach to salinity reconstruction offers several important

advantages. First, synthesis and preservation of archaeal lipids occurs under conditions

where isotopic proxies (i.e. biogenic carbonates) are absent or altered. In addition,

overlapping Archaea niches provide sensitive biomarker records across an extreme and

broad salinity range.

In order to understand the depositional system in which I interpret these ACE-

based salinity estimates, chapter 4 describes the bulk organic geochemical record of the

two subbasins, revealing difference in productivity and carbon cycling prior to the

Messinian Salinity Crisis.

Finally, chapter 5 describes efforts to develop the Nano-EA-irMS. This project

offers many opportunities to study the isotopic properties of compounds that were previously inaccessible to isotopic analysis, including intact archaeal lipids, pigments, and any other large and non-volatile compounds. Thus far, we have achieved a 3-fold reduction in sample size and determined the sources of error in both the reductive and combustive furnaces. I also designed the one-trap system which has been implemented by

120 other users (J. Fulton, C. Junium) to sufficiently reduce the background in the carbon and

nitrogen system to make Nano-EA analysis routine.

Future Directions

Biogeochemical controls on biomarker production and preservation

Both phenotypic and genotypic variations lead to the archaeal lipid distribution

changes we observe with depth and salinity. Recent studies have shown archaeal

communities and metabolisms also change across depth and salinity gradients (Hallam et

al., 2006; Ingalls et al., 2006; Ochsenreiter et al., 2002). In the future, I will explicitly test

relationships between lipid synthesis and environmental factors such as temperature, salinity, pH, nitrogen and phosphorus concentrations using controlled mesocosm experiments from a suite of freshwater and hypersaline sites. Coupled geochemical and genomic analysis test the hypothesis that different metabolic groups are responsible for chemical transformations and lipid distributions. Experiments with mesocosms and cultured marine Archaea can be used to determine isotope fractionation between known sources of cellular carbon and archaeal lipids, to both elucidate metabolic pathways and generate carbon isotope fractionation data directly comparable to experimental work on hyperthemophilic Archaea (House et al., 2003).

As we learn more about the ecological and physical controls on the genotypic distribution of Archaea, we will become more skilled at interpreting their well-preserved molecular fossil record in ancient environments. An important next step for research on

121 ACE is to use archaeal lipids to calculate salinity changes in other ancient basins, as well as further testing in modern environments in with cultures as they become available.

Archaeal lipid records coupled with compound-specific hydrogen isotope records, in other Messinian basins, salt lake sediments, and ancient epeiric basins, which experience extreme yet variable salinity ranges, may provide clues of timing of closure of basins and evaporation: precipitation ratios over time.

Ancient biomarker records at critical intervals

The record of intact archaeal lipids currently extends to the Cretaceous (Kuypers et al., 2001) and fragments of these lipids have been identified in much older rocks

(Logan et al., 1997; Summons et al., 1988). Geochemical evidence shows that archaeal methanogenesis, in the form of light carbon isotope compositions in kerogen, dates back to the late Archaean and early Proterozoic 2.8 Ga (Logan et al., 1997; Strauss, 2000;

Teske, 2005). However, an important task is simply to establish the ancient record of all branches of the Archaea, which is critical to understanding the timing of and triggers for the expansion of Archaea from extreme to non-extreme environments currently hypothesized to have occurred 100 Ma (Kuypers et al., 2001)

122

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152

Appendix A

Considerations for hydrogen isotope measurement and interpretation

Interpretation of D/H data of archaeal lipids will use the approach outlined by

Sessions and Hayes (2005) for other lipids. It is useful first to consider a simplified system, when only one hydrogen source (i.e. water in photosynthesis) is present. In this system, the reactant δD and product δD are linearly related.

(1) δp= α∗δs + ε

where p is product, s is source. Since ε=α-1, these fractionations should be equal.

Empirically, however, in hydrogen isotope systems the slope and intercept are frequently

not equivalent, indicating other processes complicate the resulting isotope composition of

lipids. The ε and α are ‘‘apparent’’ enrichment factors, and the net fractionation they

represent can be the result of biochemical processes such as growth stage, and physical

process such as evaporative or transpiration effects (Sessions, 2006; Sessions & Hayes,

2005; Smith & Freeman, 2006).

A study of δD values for lipids from a methane-oxidizing bacterium

Methylococcus capsulatus provides a template for developing and interpreting

fractionation associated with more than one biosynthetic H source (Sessions & Hayes,

2005; Sessions et al., 2002).

In systems with two hydrogen sources, one or both components may be

fractionated relative to the analyte. One component fractionation found, for instance, in a

153 sample containing both an isotopically equilibrated exchangeable H pool and an unknown non-exchangeable H pool, can be described by:

(2) δT = fe (αe/wδw+ εe/w) + (1-fc)δn

where fe is the fraction of exchangeable H, and the subscripts (e, n and w) refer to exhangable, non-exchangeable, and water hydrogen (Sessions, 2006). The situation

becomes more complicated when both components experience fractionation:

(3) δT = X1(α1δ1+ ε1) + (1-X2)(α2δ2+ ε2)

In this system, the relationship between the source δD and product δD is

nonlinear, and there are multiple solutions for X1, α1, and α2. To resolve this issue,

Sessions and Hayes (2005) combine X and α.

(4) RT=p1R1+p2R2

where RT = the total measured isotope ratio (D/H), R1 and R2= D/H of the two

components, p1= X1α1 and p2= (1-X2)α2. These terms provide practical constraints on X1,

α1, and α2 values. X1 must be between 0-1, and fractionation (α1 and α2) has a limited

range (e.g. 0.650-0.900 between organics and water), and must be >0 (Estep & Hoering,

1980; Sessions et al., 1999; Sessions & Hayes, 2005).

154 This multiple source scenario is most appropriate for the system I will be analyzing. In general, the biochemical starting material for lipid synthesis is acetyl-CoA.

Genomic evidence strongly suggests Archaea exclusively use the mevalonic pathway

(Figure A-1)(Lange et al., 2000).

Figure A-1: Isoprenoid biosynthesis pathway

155 In this pathway, acetyl-CoA ultimately condenses into isoprenoids. Hydrogen for acetyl-CoA and the condensation reactions comes ultimately from water, organic substrates (via metabolic precursors), and NADPH. The main sources of cellular NADPH pools include: 1) the oxidation of the aldehyde group of aldoses coupled to the cyclic oxidative pentose phosphate (OPP) cycle, 2) the reduction of 3-hydroxy acids to 2-oxo compounds in the tricarboxylic acid (TCA) cycles, and 3) oxidation of ferredoxin

(Lengeler et al., 1999). Hydrogenation during biosynthesis contributes H to lipids, and may have a significant effect on the isotopic composition of organic matter (Hayes,

2001a).

Dual-Property Salinity Proxy?

I am also motivated to develop tools for analyzinf and interpreting H isotopes compositons of archael lipids because of the potential to develop a dual property salinity proxy based on the distribution and hydrogen isotope composition of archaeal lipids.

Salinity plays an essential role in the circulation of ocean water masses, but geochemical proxies for past ocean salinity remain problematic. Residual variations in foraminifera shell δ18O values are attributed to salinity, and these properties correlate within individual

water masses. However, changes in temperature and in the sea-ice and freshwater budget

perturb this relationship, and such calculations, compounded with error propagation,

severely limit the δ18O proxy (Bauch & Weinelt, 1997; Henderson, 2002; Rohling, 2000;

Rohling & Bigg, 1998). The δD composition of the water would provide constraints to

the indirect oxygen isotope residuals approach, and also independently help to define

water mass salinity.

156 Hydrogen isotopic compositions of environmental water can be preserved in organic matter (Schimmelmann et al., 1999). However, variability in the source of organic matter and selective preservation of bulk organic materials can obscure δD signatures derived from source water composition (Hassan & Spalding, 2001; Sauer et al., 2001). The analysis of individual compounds enables δD reconstruction for specific

regions in the water column as in the case of photic zone values from algal lipids (Sauer

et al., 2001).

To compare the δD (and δ18O) values of water with both δD signatures and

molecular structure distributions of ether lipids, I simultaneously collected water and

suspended particulate matter in study sites were selected from a broad range of marine

environments and salinities. In preparation for comparison of lipid δD and water δD, I

analyzed the δD of water in most of these sites (Table B-1).

My approach will be to determine lipid structure distribution patterns and

isotopic signatures, providing two relatively independent sources of data to evaluate in

the context of environmental conditions. If I am able to establish robust relationships

between water properties and other environmental conditions with lipid signatures, the

combined information may be invaluable to refining and constraining interpretations in

ancient sediments.

157

Appendix B

Water chemistry and isotope measurements

Table B-1: Major ion compositions in Bear Meadows, the Black Sea, Chincoteague Bay, and the Mediterranean.

158

Table B-2

Table B-2: Major ion compositions at the Bermuda Time Series site

Table B-3 z

159

Table B-3: Major ion compositions in Infersa Saline.

160

Table B-4: δD (‰ v. VSMOW) of waters in Black Sea, Bear Meadows, and Chincoteague Bay

161

Table B-5: δD (‰ v. VSMOW) of waters in Infersa Saline

Table B-6

162

Table B-6: δD (‰ v. VSMOW) of PSU standard water with KCl added at various concentrations

163

Appendix C

Data from Torrente Vaccarizzo and Serra Pirciata

Table C-1

Table C-1: Elemental Analysis of whole rock powders and kerogens.

164

Appendix D

Simultaneous analysis of archaeol and glycerol dibiphytanyl glycerol tetraethers by HPLC-APCI-MS

Introduction

The two main core archaeal lipids, diphytanyl glycerol diethers (DGDs) and glycerol dibiphytanyl glycerol tetraethers (GDGTs), differ significantly in polarity, size, and volatility (Figure D-1). Typically, intact DGDs are analyzed using gas chromatography (GC) following derivitization (methylation or trimethylsilyation) of the glycerol hydroxyl group, while the larger intact core GDGTs are analyzed by liquid chromatography atmospheric pressure chemical ionization mass spectrometry (LC-APCI-

MS). We have adapted existing LC-MS techniques to identify and quantify both the

GDGTs and the most abundant DGD, archaeol, without derivatization and in the same sample run. This is a promising method for generating a more complete representation of the relative and absolute abundances of archaeal core lipids in the environment, cultures, and ancient materials.

165

Figure D-1: Diphytanyl glycerol diether (archaeol) and glycerol dibiphytanyl glycerol tetraethers synthesized by Archaea.

Experimental

To identify the retention time and response factor of archaeol, we used a dilution series of an authentic standard 1,2-di-O-phytanyl-sn-glycerol standard which is structutally identical to the naturally occurring archaeol (Avanti Lipids, Alabaster, AL,

USA). On-column injections ranged from 0.6 to 750 ng. To compare the DGD and

GDGT response factors, we isolated 5 μg of caldarchaeol from sediment of Bear

Meadows bog (40.73 N, 77.76 W, Centre County, Pennsylvania, USA) using preparative liquid chromatography. We confirmed the purity of the isolated compound, and thus the reliability of the standard weight, by scanning 100-1400 m/z in APCI mode.

To test elution of archaeol in natural samples, ~ 5 g of sediment from Bear

Meadows was soxhlet extracted in 9:1 (v/v) dichloromethane: methanol for 24 h. The

166 lipid extract was separated into three fractions on aluminum oxide (activated for 2 h at

150°C) in 9:1 hexane: dichloromethane (apolar fraction) and 1:1 dichloromethane:methanol (polar fraction) and finally 100% methanol. Archaeol eluted in the polar fraction.

We characterized and quantified lipids following methods adapted from Hopmans et al. (2001), using a Micromass Quattro-II API-III LC/MS/MS. Liquid chromatographic separation of archaeol and GDGTs was achieved on a 2.1 x 150 mm with 3-μm particles

Prevail Cyano column (Alltech, Deerfield, IL, USA) maintained at 30°C. The mobile

phase was an isocratic solution of 100% hexane for five minutes, followed by a 45-

minute linear gradient to 98.2% hexane: 1.8% propanol with a flow rate of 0.2 ml/L. In

between samples, the column was flushed for 10 min with 75% hexane: 25% propanol

followed by 10 minute equilibration with 100% hexane.

Standards and extracted samples were analyzed in selective ion monitoring (SIM)

mode that targeted DGDs (m/z 600-700 from 0–10 min) and GDGTs (m/z 1280-1350

from 10-26 min). Instrument conditions include: corona voltage = 5.5 kV, cone voltage =

15 V, source temperature = 130°C, APCI probe temperature = 550°C. Peak areas were

calculated using Masslynx 3.5 software (Micromass, London UK) based on M+H+ ions

(653.5-655.4 for archaeol and 1301.5-1303.4 for caldarchaeol).

167 Results and Discussion

Identification of archaeol

The retention time of archaeol standard ranged between 4.26-4.58 (Figure D-2a).

The APCI mass spectrum of archaeol (653 m/z) shows the main protonated molecule

M+H+(Figure D-2b). Daughter ions generated from MS-MS scans of the 654 Da

molecule (collision energy 20 eV, scan range 50-700 m/z) show the parent ion (653 Da),

and fragments from the loss of the glycerol (597 Da), cleavage of ether bonds (373 Da),

and the fragmentation of the isoprenoid chains (e.g. 155 Da, Figure D-2c).

Figure D-2: a) HPLC-APCI-MS chromatogram of archaeol standard, b)HPLC-APCI spectrum of archaeol standard, c) daughter scan of archaeol standard.

DGD and GDGT response factors

Response factors for diether standards range from 7000-78000 peak area (in

arbitrary units) per ng; GDGT response factors were typically several of orders of

magnitude less, from 90-450 peak area (in arbitrary units) per ng. Sensitivity is far greater

for diethers than for tetraethers, probably because the molecules are smaller, more

168 volatile and therefore more efficiently ionized via APCI; the minimum detection limits for DGD was 0.1 ng and GDGT was 7.5 ng. The relative response factor between DGDs and GDGTs also varied over time (Figure D-3), and it will therefore always be necessary to create standard curves for both DGDs and GDGTs during each run cycle. The standard deviation calculated from duplicate analysis of standards over the course of all run cycles was typically 30%, and we apply this as a mean error to all samples. Response factor instability likely reflects changes in background material in the source, instrument tuning, and general run conditions such as room temperature and humidty.

Figure D-3: Changes in the response factors for diphytanyl glycerol diether (archaeol) and glycerol dibiphytanyl glycerol tetraether (caldarchaeol) over time.

169 3.3 Archaeol in sediment sample

To illustrate the utility of this method in natural samples, we also analyzed the retention time and spectra of archaeol and caldarchaeol in sediment from a freshwater bog in central Pennsylvania. The retention time of both archaeol and caldarchaeol was identical to that of the standards. This demonstrates that archaeol can be separated and identified in complex mixtures of natural compounds without matrix effects preventing detection. Using the response factors calculated from standard calibration curves, the injected amount of archaeol was ~100 ng, and caldarchaeol was ~500 ng (Figure D-4).

Figure D-4: Chromatogram of sediment (Bear Meadows bog, Centre Country Pennsylvania) showing relative and absolute concentrations of archaeol and caldarchaeol in one HPLC-MS sample injection.

170 Conclusions

We have presented a method for analysis of both diphytanyl glycerol diethers

(DGDs) and glycerol dibiphytanyl glycerol tetraethers (GDGTs) without derivitization, using high performance liquid chromatography atmospheric pressure chemical ionization mass spectrometry (HPLC-APCI-MS). DGDs and GDGTs differ significantly in polarity, size, and volatility and the resulting differences in the response factor can be determined using both DGD and GDGT standards. As HPLC-MS techniques in organic geochemistry become routine, this method offers a tool for reconstructing a more complete picture of

Archaea diversity and abundance in the environment.

171

Vita Courtney Hanna Turich

Education Pennsylvania State University, Ph.D. candidate in Geosciences Advisor: K.H. Freeman

The University of Texas/Austin, M.S. Geological Sciences, 2000 Advisor: B.L. Kirkland (now at Mississippi State University) Thesis: Paleoecology and Diagenesis of the lower Capitan Reef, Guadalupe Mountains

Oberlin College, B.A. Biology, 1995

Experience 2003 – present Pennsylvania State University, Dept. of Geosciences, Research Assistant 2001 - 2002 Pennsylvania State University, Dept. of Geosciences, Teaching Assistant 1999 - 2000 The University of Texas, Dept. of Geological Sciences, Research Assistant 1998 - 1999 Texas Memorial Museum, Exhibit Consultant and Research Assistant 1997 - 1998 The University of Texas, Dept. of Geological Sciences, Teaching Assistant 1996 - 1997 AmeriCorps for Math and Literacy, Charles A. Dana Center, Austin, TX 1995 - 1996 The University of Texas, Dept. of Zoology, Laboratory Assistant

Honors and Awards Penn State University University of Texas at Austin Tait and Krynine Scholarships, American Texaco Fellowship, Ronald K. DeFord Field Association of Petroleum Geologists Grant-in- Scholarship, Martin B. Lagoe Student Research Aid, Biogeochemical Research Initiatives for Fund for Micropaleontology, Albert W. and Alice Education NSF- IGERT fellowship, 1st place M. Weeks Fund, Dorothy Ogden Carsey Oral Presentation Graduate Student Colloquium Memorial Scholarship, Harlan Tod Sutherland and Environmental Chemistry Symposium Memorial Scholarship Activities • Co-Chair, “Archaea in the Environment” session, Goldschmidt Conference, May 2005 • Chair, Penn State Dept. of Geosciences Colloquium series, 2002-2003 • Co-Chair, Penn State Environmental Chemistry Symposium, 2001-2002 • Co-taught and designed course at Oberlin College, “Gender Issues in Science” 1995. • Community volunteer (Penn State Space Day, Women in Science and Engineering, Austin Children’s Museum, AISD, Austin Science Fun Day) • DJ, WKPS State College, 90.7 FM