Copyright by Veronica Jacqueline Anderson 2015

The Dissertation Committee for Veronica Jacqueline Anderson Certifies that this is the approved version of the following dissertation:

UPLIFT AND EXHUMATION OF THE EASTERN CORDILLERA OF COLOMBIA AND ITS INTERACTIONS WITH CLIMATE

Committee:

Brian K. Horton, Supervisor

Richard A. Ketcham

Daniel F. Stockli

Timothy M. Shanahan

Carmala N. Garzione UPLIFT AND EXHUMATION OF THE EASTERN CORDILLERA OF COLOMBIA AND ITS INTERACTIONS WITH CLIMATE

by

Veronica Jacqueline Anderson, B.S., Geophysics

Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

The University of Texas at Austin May 2015

Acknowledgements

I would like to thank a number of people for their time, advice, and insight during the course of this work. First, I would like to thank my advisor, Dr. Brian Horton, for his consistent and level-headed guidance throughout the course of my Ph.D, and for being an excellent role model as a scientist. I would also like to thank each of my committee members – Richard Ketcham, Daniel Stockli, Timothy Shanahan, and Carmala Garzione – for being available for spirited and productive scientific discussions that greatly improved this work. Although he was not an official member of the committee, I would also like to thank Joel Saylor for his major contributions to this work as an involved and insightful collaborator and coauthor. In addition, I would like to thank Andrés Mora and his colleagues at Ecopetrol for their continued support of our research, and their invaluable assistance in planning and executing several successful field seasons, as well as funding a significant portion of this reasearch. Juliana Barrientos Mesa and Miguel Corcione in particular provided critital assistance to us in the field that allowed the 2011 field season to be exceptionally productive and enjoyable. Sean Sylva and Konrad Hughen at WHOI generously committed their time and resources to assisting us with leaf wax hydrogen isotopic analyses that played a key role in Chapter 3. Dan Breecker and Toti Larsen provided essential guidance and scientific knowledge for the soil carbonate analysis in Chapter 4. Finally, I would like to thank my friends and family for their support and companionship over the last 5 years, and for making this time fly by as fast as it did. In particular, I’d like to thank my husband Jeff, who has happily put up with my worries, frustrations, and complaints as a Ph.D student, and who consistently makes all of this work seem worthwhile.

iv UPLIFT AND EXHUMATION OF THE EASTERN CORDILLERA OF COLOMBIA AND ITS INTERACTIONS WITH CLIMATE

Veronica Jacqueline Anderson, Ph.D The University of Texas at Austin, 2015

Supervisor: Brian K. Horton

Recent breakthroughs in assessing past elevation using stable isotopes of sedimentary materials have provided important constraints on the timing and geodynamics of surface uplift in various orogenic systems. These advances in paleoaltimetry have enabled discrimination between competing models of topographic development in the Tibetan plateau, have provided constraints on the longevity of the Sierra Nevada as a major topographic feature in western North America, and have highlighted the possible role of lower lithospheric delamination in the central Andes of South America. However, there remains considerable debate over the geodynamic mechanisms involved in Andean uplift, as most available estimates on the timing and pace of past elevation gain show an irregular spatial and temporal distribution. In particular, uncertainty persists over the timing of surface uplift of the Eastern Cordillera in the tropical northern Andes of Colombia. Although changes in sediment accumulation, provenance, and thermochronometric estimates of bedrock exhumation suggest Andean shortening in the Eastern Cordillera since late Eocene-Oligocene time, the rise of the ~2600-m-high Bogotá plateau (Sabana de Bogotá), a intermontane hinterland basin appears to have significantly lagged the onset of shortening in the fold- thrust belt. In addition, there is dramatic variation in structural style along strike within the Eastern Cordillera, making it unclear whether a major basement-involved topographic high (the Garzón Massif) at the southern end of the range was contemporaneous with the rest of the Eastern Cordillera. Studies of pollen assemblages in clastic sedimentary fill of v the Bogotá plateau suggest that it may have risen rapidly from ~6-3 Ma and has maintained the same elevation thereafter. However, this scenario of rapid latest Miocene- Pliocene uplift followed by post-3 Ma stasis appears inconsistent with the structural geologic record, as more than half of the total shortening along the eastern Andean flank has occurred since ~3 Ma. We investigate the elevation history of the Bogotá plateau using novel lipid biomarker proxies for past surface temperature and isotopic composition of precipitation, and update the geochronologic framework of this basin using a refined magnetic polarity stratigraphy. We also utilize a multidisciplinary approach to determine the timing of uplift-induced exhumation of the Garzón Massif, employing U-Pb detrital zircon geochronological and sandstone petrographic results as tracers of sedimentary provenance, apatite fission track (AFT) thermochronometry to constrain exhumation, and the isotopic composition and elemental composition of paleosols and carbonate nodules to track climatic shifts associated with the uplift of the Garzón Massif. These approaches indicate that (1) the Bogotá Plateau had likely been partialy elevated prior to the late Miocene (~7.5 Ma) and has been uplifting continuously since then, (2) and that while the timing onset of exhumation of the Garzón Massif is similar to other parts of the Eastern Cordillera, it did not begin to build substantial topography until ~ 6 Ma. These results imply that the Eastern Cordillera did not become a contiguous topographic barrier, until late Miocene-Pliocene time, providing new constraints on the establishment of the Magdalena River, a northward-draining system that contributes an enormous sediment load to the Caribbean Sea, as a discrete system fully separated from the Amazon basin.

vi Table of Contents

List of Tables ...... x

List of Figures ...... xi

Chapter 1: Introduction ...... 1

Chapter 2: Sources of local and regional variability in the MBT’/CBT paleotemperature proxy: Insights from a modern elevation transect across the Eastern Cordillera of Colombia ...... 3 Abstract ...... 3 2.1 Introduction ...... 5 2.2 Materials and Methods ...... 9 2.2.1 Field Area and Sampling ...... 9 2.2.2 Sample Preparation ...... 11 2.2.3 HPLC Analyses ...... 12 2.2.4 Calculation of the BIT Index ...... 13 2.3 Results and Discussion ...... 15 2.3.1 Colombia Dataset ...... 15 2.3.2 Evaluation of interpolation schemes for MAT data ...... 17 2.3.3 Local Variability Within the Colombia Dataset ...... 21 2.3.4 Comparison to the Global Soil Dataset ...... 25 2.3.5 Heterogeneity in brGDGT Patterns Across Five Transects ...... 29 4. Conclusions ...... 33

Chapter 3: Paleoelevation records from lipid biomarkers: Application to the tropical Andes ...... 35 Abstract ...... 35 3.1 Introduction ...... 37 3.2 Paleoaltimetry and organic geochemical proxies ...... 41 3.2.1 Isotopic constraints on paleoelevation ...... 41 3.2.2 The MBT’/CBT proxy ...... 42 3.2.3 Hydrogen isotopes in leaf waxes ...... 44 vii 3.2.4 Biomarker-based paleoaltimetry ...... 45 3.3 Chronostratigrapic Framework ...... 47 3.3.1 Field localities ...... 47 3.3.2 Paleomagnetic chronology ...... 50 3.4 Geochemical methods ...... 53 3.4.1 Biomarker sampling and sample processing...... 53 3.4.2 GDGT measurement and quantification ...... 53 3.4.3 Leaf wax δD measurements ...... 55 3.5 Geochemical results ...... 58 3.5.1 GDGT-based paleoelevation estimates ...... 58 3.5.2 δD of Leaf Waxes ...... 63 3.6 Discussion ...... 64 3.6.1 Paleotemperature data and implications for surface uplift ...... 64 3.6.2 Isotopic constraints on surface uplift ...... 65 3.6.4 Geodynamic implications ...... 67 3.7 Conclusions ...... 69

Chapter 4: The uplift of the Garzón massif: implications for the development of the Magdalena and Orinoco River systems ...... 70 Abstract ...... 70 4.1 Introduction ...... 72 4.2 Geologic Setting...... 73 4.3 Stratigraphy ...... 77 4.3.1 Northern Site: La Venta ...... 77 4.3.2 Southern Site: Gigante ...... 79 4.4 Sedimentary Provenance ...... 82 4.4.1 Sandstone Petrography...... 82 4.4.2 Detrital Zircon Geochronology ...... 86 4.4.2.1 Source Areas ...... 87 4.4.2.2 U-Pb Results ...... 88 4.5 Apatite Fission-Track Thermochronometry ...... 90

viii 4.5.1 Methods...... 91 4.5.2 Results ...... 94 4.6 Paleosol data ...... 96 4.6.1 Methods...... 98 4.6.2 Results and Interpretation ...... 102 4.7 Discussion ...... 103 4.8 Conclusions ...... 109

Appendix A: Supplemental Data for Chapter 2 ...... 111

Appendix B: Supplemental Data for Chapter 3 ...... 112 B.1 Details of magnetostratigraphic Analyses ...... 112 B.1.1 Representative Demagnetization Plots ...... 112 B.1.2 Method for classifying paleomagnetic samples ...... 115 B.2 Details of the Hydrogen Isotopic Analyses ...... 119 B.3 Results of Bootstrapping Analysis ...... 122

Appendix C: Supplemental Data for Chapter 4 ...... 123 C.1. Table of sample locations for Chapter 4 ...... 123 C.2 Summary of measured paleocurrents ...... 126 C.3. Primary classifications used in sandstone petrographic point counts 127 C.4. U-Pb measurement data for individual detrital zircon samples ...... 128 C.5 Isotopic measurements on individual carbonate nodules ...... 158 C.6 Details of Paleosol XRF Measurements ...... 161

References ...... 162

ix List of Tables

Table 2.1: Measured MBT' and CBT indices and sampling locations for modern

soils...... 14

Table 2.2: Fitted coefficients from comparison between instrumental temperature

interpolation methods...... 20

Table 2.3: Fitted coefficients for each of the plots presented in Figure 2.7...... 27

Table 3.1: Calculated MBT’ and CBT indices for samples used in this study, with

reconstructed paleotemperatures ...... 56

Table 3.2: Measured δD for each odd-chain n-alkane in the analyzed samples 62

Table 4.1: Results of point counts from petrographic thin sections of sandstones

collected from the Honda Group and Gigante Formation...... 84

Table 4.2: Maximum depositional ages constrained by the young detrital zircon

populations...... 86

Table 4.3: Summary of AFT data from the six samples analyzed ...... 92

Table 4.4: Summary of isotopic measurements of soil carbonate nodules ...... 100

Table 4.5: Summary of XRF data measured from bulk paleosol samples ...... 101

x List of Figures

Figure 2.1: Structures of the brGDGT’s referred to in the text...... 5

Figure 2.2: Overview map showing the field area and sampling locations for modern

soils in the Eastern Cordillera...... 8

Figure 2.3: Summary of annual soil temperature data recorded by in-situ data

loggers...... 10

Figure 2.4: Comparison of interpolation methods for temperature data...... 17

Figure 2.5: Comparison of replicate measurements to instrumental data...... 21

Figure 2.6: MBT'/CBT-derived paleotemperatures plotted against instrumental

temperature for global datasets...... 24

Figure 2.7: The MBT’ and CBT indices plotted against MAT and pH in order to illustrate regional differences in the empirical relationships between the

branched GDGT parameters and environmental variables...... 26

Figure 2.8: Plot of the recalibrated dataset including only the Colombia transect, the ECT data of Dirghangi et al. (2013), and sites from the global calibration of Peterse et al. (2012) that received >1000 mm/yr of precipitation

annually...... 28

Figure 3.1: Overview map showing major Andean ranges in Colombia ansd an inset

of the field area in the the Bogotá plateau (Sabana de Bogotá)...... 40

Figure 3.2: Chemical structural formulas of the nine known brGDGTs (branched

Glycerol Dialkyl Glycerol Tetraethers)...... 42

Figure 3.3: Measured stratigraphic columns for the three sampled sections

(Tequendama, Guasca, and Subachoque)...... 48

xi Figure 3.4: Measured paleomagnetic directions for Subachoque, Guasca, and

Tequendama, with the interpreted chronology...... 52

Figure 3.5: Plot of measured MBT’/CBT-based paleotemperatures versus

depositional age ...... 58

Figure 3.6: Plot of measured δD of the C29 and C31 n-alkane versus depositional

age for all three sampled sections ...... 61

Figure 4.1: Overview map showing major tectonic provinces within Colombia, including drainage divides between Orinoco, Amazon, and Magdalena

River systems ...... 75

Figure 4.2: Geologic map of the Garzón Massif and Neiva Basin. Adapted from Geologic map of the Garzón Massif, Neiva Basin, and detailed geologic

maps of the two field areas with sampling transects shown...... 76

Figure 4.3: Chronostratigraphy of the Honda Group and Gigante Formation, adapted

from Guerrero et al. (1997)...... 81

Figure 4.4: Ternary diagrams of the composition of sandstones measured in the two

field locations...... 83

Figure 4.5 Results of sedimentary provenance analyses, including detrital zircon

data and sandstone petrographic compositions ...... 85

Figure 4.6. Results of the apatite fission track analyses, with HeFTy modeling

results ...... 93

Figure 4.7: Carbonate nodule and paleosol measurements, plotted against the

stratigraphic section for the La Venta area ...... 99

Figure 4.8: Schematic illustration of the interpreted tectonic history for the Neiva

Basin and Garzón Massif ...... 108

xii Chapter 1: Introduction

Recent breakthroughs in assessing past elevation using stable isotopes of sedimentary materials have provided important constraints on the timing and geodynamics of surface uplift in various orogenic systems. These advances in paleoaltimetry have enabled discrimination between competing models of topographic development in the Tibetan plateau, have provided constraints on the longevity of the

Sierra Nevada as a major topographic feature in western North America, and have highlighted the possible role of lower lithospheric delamination in the central Andes of South America. However, there remains considerable debate over the geodynamic mechanisms involved in Andean uplift, as most available estimates on the timing and pace of past elevation gain show an irregular spatial and temporal distribution. This dissertation utilizes an integrated approach to a series of active debates about the uplift history of the Eastern Cordillera of Colombia. In particular, although changes in sediment accumulation, provenance, and thermochronometric estimates of bedrock exhumation suggest Andean shortening in the Eastern Cordillera since late Eocene- Oligocene time, the rise of the ~2600-m-high Bogotá plateau (Sabana de Bogotá), a intermontane hinterland basin appears to have significantly lagged the onset of shortening in the fold-thrust belt. In addition, there is dramatic variation in structural style along strike within the Eastern Cordillera, making it unclear whether a major basement- involved topographic high (the Garzón Massif) at the southern end of the range was contemporaneous with the rest of the Eastern Cordillera. Studies of pollen assemblages in clastic sedimentary fill of the Bogotá plateau suggest that it may have risen rapidly from ~6-3 Ma and has maintained the same elevation thereafter. However, this scenario of rapid latest Miocene-Pliocene uplift followed by post-3 Ma stasis appears inconsistent 1 with the structural geologic record, as more than half of the total shortening along the eastern Andean flank has occurred since ~3 Ma. Chapter 2 details an investigation of the environmental controls on a novel, biomarker-based proxy for temperature, the MBT’/CBT index. In this chapter, the optimal conditions for use of this paleotemperature proxy are explored via a thorough investigation of modern soils in which the MBT’/CBT proxy can be measured and directly compared to instrumental data. These findings are utilized in Chapter 3, in which the MBT’/CBT index and the hydrogen isotopic composition of leaf waxes are used to reconstruct the surface uplift history of the Bogotá Plateau by constraining paleoclimatic changes related to surface uplift. Chapter 4 focuses on the poorly understood southernmost segment of the Eastern Cordillera (the Garzón Massif) and uses sedimentary provenance data, thermochronometry, and paleoclimatic data to determine the timing of uplift in the range and to isolate effects of climatic as well as provide insight on tectonic events that may have led to its uplift. This work has implications for the separation and independent development of the Magdalena, Orinoco, and Amazon River systems, which were linked in the Northern Andes prior to the uplift of the Eastern Cordillera. In addition, this data is used to constrain several geodynamic hypotheses for the driving mechanisms behind the rapid uplift of the Eastern Cordillera. Finally, the utility of biomarker-based paleoclimate proxies is investigated for use in reconstructing paleoelevations.

2 Chapter 2: Sources of local and regional variability in the MBT’/CBT paleotemperature proxy: Insights from a modern elevation transect across the Eastern Cordillera of Colombia1

ABSTRACT

Distributions of brGDGTs (branched glycerol dialkyl glycerol tetraethers) in soils have been utilized as a proxy for temperature and pH via the MBT’ and CBT indices (Methylation of Branched Tetraethers and Cyclisation of Branched Tetraethers, respectively). However, there is substantial scatter in the empirical relationships between the global MBT’/CBT index and modern temperature, resulting in uncertainties as large as ±5.0°C in reconstructed paleotemperatures. In this study, we seek to determine the magnitude of several sources of calibration error using a new set of samples spanning a large gradient in elevation (~3000 m) and temperature (~16°C) across an Andean transect in the Eastern Cordillera of Colombia. Using in-situ temperature monitoring, this field study of an equatorial montane location provides an opportunity to investigate several potential confounding factors affecting the MBT’/CBT paleothermometer, including the mismatch between air temperature and actual soil temperature, and the effects of soil moisture on brGDGT production. In the well-constrained environment of our study, we determine the RMSE (root mean squared-error) for a local calibration of the MBT’/CBT proxy to be 3.0°C, in contrast with the 5.0°C value reported in the most recent global calibration. A comparison between in-situ soil temperature measurements at our soil sampling locations and interpolated weather-station temperature data indicates that errors in interpolation schemes are not likely to be a significant contributor to the observed scatter in the MBT’/CBT- temperature relationship. Rather, we conclude that the majority of the 3°C of scatter in this calibration for the northern Andes arises from

1 The full text of this chapter was published in Organic Geochemistry in 2014. (Vol 69, pp 42-51) 3 transient, site-specific variations in factors such as soil moisture and nutrient availability, with several cases showing a difference of up to 7.8°C between closely spaced sites (< 600 m in distance, <50 m in elevation apart). In addition, we compare our dataset to five published regional soil elevation transects, and find that that the MBT’ index is strongly correlated with temperature in sites with high precipitation, such as Colombia (R2 = 0.67, 0.47), but is only weakly correlated with temperature in arid sites (R2 = 0.01, 0.04).

Instead, the arid sites show a strong relationship among MBT’, CBT and pH that is absent in the higher-precipitation transects. This bolsters previous findings that the relationship between brGDGT distributions and temperature may change at sites with low soil moisture. This heterogeneous response in brGDGT distributions to temperature and pH may be largely responsible for the significant scatter in the global calibration dataset. We therefore conclude that the RMSE of 3°C found in our local calibration may represent a lower bound on the error on MBT’/CBT-based temperature reconstructions in moist regions, but further work is needed to reliably identify suitable conditions for the application of this proxy in ancient samples.

4 2.1 INTRODUCTION

Lipid-based paleoclimate proxies provide a powerful means of reconstructing past climate. GDGTs (glycerol dialkyl glycerol tetraethers) are a class of biomarkers showing promise as a paleothermometer for diverse sedimentary environments such as marine sediments (Hopmans et al., 2000; Wuchter, 2004; Schouten et al., 2007; Kim et al., 2008), soils, lakes, peat bogs, and caves (Weijers et al., 2007; Sinninghe Damsté et al., 2008; Tierney, Russell, et al., 2010; Blaga et al., 2010; Sun et al., 2011; Pearson et al.,

2011; Loomis et al., 2012; Weijers, Schouten, Spaargaren, et al., 2006; Yang et al., 2011; Blyth and Schouten, 2013). Two classes of GDGT’s with different chemical structures have been identified: the isoprenoid (isoGDGTs) and branched GDGT’s (brGDGTs). Although both classes of GDGTs are microbial membrane lipids, brGDGTs are produced by bacteria in soils, whereas isoGDGTs are produced by archea in aquatic settings (Schouten et al., 2013; Weijers, Schouten, Hopmans, et al., 2006). Nine individual brGDGT compounds have been identified; the relative abundance of each of these nine brGDGTs in soils varies with mean annual temperature (MAT) and soil pH, likely as an adaptation to maintain membrane fluidity (Weijers, Schouten, Spaargaren, et al., 2006).

Figure 2.1: Structures of the brGDGT’s referred to in the text.

5 Using modern samples, empirical relationships between brGDGT distributions and temperature have been determined for soil and peat (Weijers et al., 2006; Weijers et al., 2007; Peterse et al., 2012), lake sediments (Tierney et al., 2010; Pearson et al., 2011; Loomis et al., 2012), and speleothem and flowstone deposits (Blyth and Schouten, 2013). Each of these studies measured the abundances of brGDGTs in modern samples across a wide variety of climate conditions, and then determined a numerical relationship between instrumental mean annual air temperature (MAAT) and The MBT’ and CBT indices are defined as follows:

MBT’ = (Ia + Ib + Ic)/(Ia + Ib + Ic + IIa + IIb + IIc + IIIa) (Eq. 2.1) CBT = -log((Ib + IIb)/(Ia +IIa)) (Eq. 2.2)

These two parameters were calibrated against instrumental mean annual air temperature (MAAT) to yield the following equation (Peterse et al. 2012):

MAAT = 0.81 – 5.67 × CBT + 31.0 × MBT’ (r2 =0.59, RMSE = 5.0°C, p < 0.00001) (Eq. 2.3)

In this most recent calibration the root mean square error (RMSE) is 5.0°C, which indicates that although there is a strong linear correlation, the MBT’-CBT–derived temperature differs from the instrumental temperature by more than 5.0°C at 32% of the measured sites. There is little agreement on the best way to report errors when using the MBT’-CBT proxy. Although the RMSE on the calibration is the standard choice, it likely represents an over-estimate of the true random error. The scatter could potentially be reduced by isolating other factors that influence the distribution of branched GDGTs. 6 One likely source of uncontrolled variability in the calibration is the effect of seasonal variations and biases on branched GDGT distributions (Peterse et al., 2012; Schouten et al., 2013). If the bacteria that produce GDGTs grow at a faster rate during one season, the mean annual temperature derived from their membrane lipids would be skewed towards the growth season temperature rather than the annual mean. Studies of this phenomenon have produced contradictory results; in a study that monitored the distributions of branched GDGTs at several mid-latitude sites for an entire year, Weijers et al. (2011) found no significant differences in GDGT distributions from month to month, even though the soils at some sites were covered in snow for part of the year. However, estimates of the turnover time for branched GDGTs in soils suggest that it takes approximately 20 years (Weijers et al., 2010) for the pool of branched GDGTs to be completely replaced; in that case, one year of branched GDGT production would represent only 5% of the total GDGTs sampled, and would therefore make monthly variations nearly undetectable. In addition, the mean annual air temperature reported at weather stations versus the subsurface temperature in soils may differ by as much as 5°C, depending on the depth of the sample and local conditions (Smerdon et al., 2006, 2004). Because the mismatch between temperature data used in regression models versus observed soil temperatures is not systematic (Smerdon et al., 2004), it could be responsible for some of the scatter in the calibration. In this study, we address several possible sources of variability in the empirical relationship between brGDGTs and temperature by investigating brGDGT distributions and climatic parameters within a well-constrained field area. In order to reduce the effect of seasonal variability, we selected a field site in Colombia, which is located only 5 degrees north of the equator and therefore experiences a very small range of seasonal temperature variations. Samples span wide temperature and elevation gradients (16°C 7 over ~3000 m) along the Eastern Cordillera and have small variations in soil pH (µ = 4.7, σ = 0.46). In-situ temperature loggers were deployed for a year at each site in order to provide accurate constraints on seasonal soil temperature variations at each sampling site. The in-situ temperature loggers also provide an opportunity to assess the effectiveness of different interpolation methods for estimating local soil temperatures from distant weather station data when in-situ temperature data is unavailable. Although this study does not eliminate all of the possible sources of variability, It offers tighter controls on some of the confounding variables and thereby provides for a more accurate representation of the actual random error that should be reported when using brGDGT- derived paleotemperature estimates.

Figure 2.2: (A) Map showing the geographic setting in northwestern South America. The field area occupies a region of high precipitation rates due to orographic lifting of easterly moisture-bearing air-masses. (B) Detail of the field area in Colombia, with sampling locations (large circles) superimposed on a map of MAAT values calculated from the IDEAM weather station sites (small circles). 8 2.2 MATERIALS AND METHODS

2.2.1 Field Area and Sampling

Soil samples were collected from 31 sites along three elevation transects in the Eastern Cordillera of Colombia (approximately 5°N, 73°W; see Figure 2.2), ranging from 363 m above sea level in the Llanos Basin up to 3300 m a.s.l. in the high mountain passes (transects 1, 2, and 3 of Saylor et al. (2009)). The sampling locations include a wide range of environments and vegetation zones, from warm tropical rainforest in the lower foothills to the montane Páramo shrubland (Hooghiemstra et al., 2006). Sampling sites were selected at regular elevation intervals along several highways, and were located in undisturbed soil as far from the road as possible (typically > 50 m away). Temperature loggers (HOBO Pendant® Temperature/Light Data Logger 8K - UA-002-08) were buried at regular depths at each site, and soil samples were taken from the same depths where the temperature loggers were buried. The temperature loggers recorded the soil temperature every 20 minutes from their burial in June 2010 to their retrieval in July 2011. Not all of the deployed temperature loggers were retrieved in 2011, so only soils corresponding to the 24 sites where the loggers were recovered have been included in this dataset. Mean annual temperatures recorded at these locations range from 26.5°C to 10.7°C, but the annual range of temperatures was extremely small due to the equatorial location 4-6° N). On average, the standard deviation of all temperature measurements throughout the year at each site was 1.35°C. Figure 2.3 summarizes the recorded temperatures throughout the year at 5 cm depth at each site. Only four sites have a standard deviation greater than 1.5°C; at most sites, the temperature recorded falls within 3 degrees of the mean annual temperature for 95% of the year.

9

Figure 2.3: Summary of soil temperature data recorded at each site throughout the year, represented as a box and whisker diagram. The width of the box represents two standard deviations of the temperatures recorded at each site; whiskers indicate the minimum and maximum temperatures recorded throughout the year. The centerline in each box represents the mean annual temperature calculated for the site.

To estimate mean annual air temperatures (MAAT) and mean annual precipitation (MAP) for each of the study sites, climate data were obtained from the Colombian national weather service (IDEAM: Instituto de Hidrología, Meteorología y Estudios Ambientales de Colombia). These data included monthly and annual averages of temperature and precipitation at 458 monitoring stations across Colombia, averaged over the period from 1971-2000 (IDEAM, 2012). Annual precipitation varied across the transect from 944 to 2398 mm/year; total precipitation values tended to decrease with 10 increasing elevation. Global climatic data used in the comparison with the global dataset was obtained from the National Climatic Data Center (NCDC) Global Historical Climatology Network (GHCN) monthly temperature and precipitation dataset; the measurements for each monitoring site were averaged over the time period spanning from January 1990 to January 2013.

2.2.2 Sample Preparation

Soil samples were freeze-dried and homogenized using a mortar and pestle; roots and other woody material were removed using solvent-cleaned forceps. A 5g aliquot of each sample was mixed with 10 mL of ultrapure water for pH measurement with a Fisher Scientific Accumet™ AP110 handheld pH meter (Tierney and Russell, 2009; Loomis et al., 2011). 20 g of each sample was extracted in a solution of 9:1 dichloromethane:methanol (v/v) using a microwave extraction system (MARS 5 Xpress,

CEM Corporation; 10 minutes at 100°C). Total lipid extracts were filtered through sodium sulfate to remove any particulates and remaining water, and then separated into polar and nonpolar fractions using silica gel column chromatography. Nonpolar compounds eluted in 8 mL of hexane, and the polar compounds, including GDGTs, eluted in 10 mL of hexane:methyl acetate (4:1 v/v). This chromatographic method has been tested using an extracted laboratory standard and has been found to result in no significant losses of brGDGT’s during chromatoraphy. Polar fractions were dried under nitrogen and re-dissolved in a solution of 1% isopropanol in hexane prior to analysis via high performance liquid chromatography (HPLC). Throughout the preparation procedure, all glassware was cleaned by combustion for at least 6 hours at 450°C, and all

11 metal and ceramic tools were cleaned by triple rinsing with hexane, dichloromethane, and methanol.

2.2.3 HPLC Analyses

Branched GDGTs were measured using high-performance liquid chromatography/atmospheric pressure chemical ionization mass spectrometry

(HPLC/APCI-MS) following Schouten et al. (2007). We used an Agilent 1200 series HPLC with a Prevail Cyano column (2.1 x 150 mm, 3mm, Alltech) maintained at 30°C. The initial solvent composition (1% isopropanol in hexane) was maintained at 0.2 mL/min for 5 minutes, then ramped to 1.5% isopropanol in hexane over 28.1 minutes; this is the same gradient used by Schouten et al. (2007), but the ramp is terminated slightly sooner to reduce run times. After each run, the flow rate was increased to 0.25 mL/min for 10 minutes and the composition changed to 10% isopropanol in hexane to remove polar residues; then the flow in the column was reversed for 10 minutes before being returned to the initial conditions (1% isopropanol in hexane, 0.2 mL/min) for 5 minutes before the next run. APCI source parameters were identical to those used by Schouten et al. (2007). The mass spectrometer was operated in single-ion-monitoring (SIM) mode, monitoring for the following ions: m/z = 1018, 1020, 1022, 1032, 1034, 1036, 1046, 1048, 1050, 1292, 1296, 1298, 1300, 1302. These ions corresponded to branched GDGTs Ic, Ib, Ia, IIc, IIb, IIa, IIIc, IIIb, IIIa, and isoGDGTs 0-4. An in-house extracted laboratory sediment standard containing isoGDGTs was analyzed repeatedly at several dilutions in order to constrain the analytical error on repeated measurements; the analytical error on reconstructed TEX86 temperatures from 12 measurements spanning an order of magnitude in concentration was 0.15°C.

12 2.2.4 Calculation of the BIT Index

It has been shown that the overall production of brGDGT compounds is substantially reduced in arid regions, where isoGDGTs tend to dominate the GDGT distributions (Dirghangi et al., 2013; Wang et al., 2013). Two indices describing the ratio of isoGDGTs to brGDGTs may be used to quantify this effect. The first, the branched and isoprenoid tetraether index (BIT index), was originally proposed as a way of quantifying terrigenous input into aquatic settings (Hopmans et al., 2004), but also demonstrates a strong relationship with soil moisture (Xie et al., 2012; Wang et al., 2013). The BIT index is formulated as follows:

BIT = (Ia + IIa + IIIa)/(Ia + IIa + IIIa + 4), (Eq. 2.4)

where Ia, IIa, and IIIa are the three most common brGDGTs and 4 is the isoGDGT crenarcheol. Alternatively, soil moisture is also strongly correlated to the ratio of the abundances of all five isoGDGTs to all 9 brGDGTs; this index is referred to as

Ri/Rb (Xie et al., 2012).

13 Sample Logger Depth MAT Soil MAP BIT Latitude Longitude MBT' CBT Name Site (cm) (°C) pH (mm/yr) Index 010610-06 TS02 5 4.58702 -74.0262 10.6 4.71 1044 0.3621 1.4047 0.989 010610-10 TS03 5 4.56463 -74.0085 11.4 4.38 1060 0.5885 1.485 0.999 010610-14 TS04 5 4.56329 -73.9876 11.1 4.52 1060 0.5901 1.1438 0.979 010610-23 TS06 5 4.55966 -73.9619 11.4 5.62 1060 0.4642 1.1189 0.998 010610-28 TS07 5 4.5409 -73.9283 17.6 5.53 1135 0.6011 0.95 0.993 010610-36 TS09 5 4.49843 -73.9141 19.7 6.19 1139 0.7183 -0.0377 0.993 030610-15 TS10 5 4.75366 -73.0176 26.3 4.19 2398 0.9699 2.1078 0.987 030610-16 TS10 25 4.75366 -73.0176 26.3 4.33 2398 0.9568 1.9747 0.998 100109-05 TS10 5 4.75518 -73.0224 26.3 3.83 2398 0.8251 1.653 1 030610-21 TS11 5 4.81039 -73.0584 26.5 4.39 2083 0.9386 2.0642 0.998 030610-22 TS11 25 4.81039 -73.0584 26.5 4.55 2083 0.9492 2.2392 0.931 030610-23 TS11 50 4.81039 -73.0584 26.5 4.47 2083 0.9161 1.7553 0.999 030610-28 TS12 5 4.84458 -73.2191 23.7 5.78 1752 0.8626 1.0403 0.995 040610-03 TS16 5 4.94012 -72.6689 27.4 4.54 1789 0.9234 2.2276 0.988 040610-13 TS17 5 5.2324 -72.651 23.9 4.5 1543 0.5335 1.4805 0.997 110109-03 TS17 5 5.23611 -72.6475 23.9 4.28 1543 0.9299 1.7456 0.992 040610-21 TS18 5 5.23797 -72.6802 22.7 4.92 1543 0.8783 1.5752 0.981 040610-33 TS20 5 5.36178 -72.6848 21.9 4.98 1544 0.8201 1.7739 0.999 040610-36 TS21 5 5.39068 -72.7047 20.6 4.6 1544 0.8467 1.1992 1 040610-37 TS21 25 5.39068 -72.7047 20.7 5.36 1544 0.8518 1.2694 0.993 040610-39 TS22 5 5.42208 -72.7259 20.7 4.79 1566 0.828 1.5649 1 110109-07 TS22 5 5.42032 -72.7233 20.7 4.6 1566 0.5209 1.1612 0.996 040610-43 TS23 5 5.43934 -72.7248 18.9 5.02 1566 0.711 1.3745 0.998 110109-09 TS23 5 5.43885 -72.7256 18.9 4.52 1566 0.8193 1.4117 1 040610-49 TS24 25 5.45065 -72.7222 17.5 4.58 1450 0.6762 1.2364 0.999 050610-33 TS25 5 5.47785 -72.7165 16.2 3.9 1450 0.8041 1.6647 0.988 110109-12 TS25 5 5.4739 -72.7139 16.2 4.17 1450 0.6453 1.3881 0.991 050610-36 TS27 5 5.54843 -72.7967 14.6 4.65 1251 0.532 1.7129 1 050610-39 TS28 5 5.59559 -72.827 12.5 4.55 1251 0.3721 1.7638 0.994 060610-02 TS31 5 5.7767 -73.1333 16.6 4.43 1030 0.6883 1.539 0.995 060610-06 TS32 5 5.49251 -73.4025 15.3 5.04 944 0.5208 1.2646 0.989

Table 2.1: Soil samples from the Colombia transect.

14 2.3 RESULTS AND DISCUSSION

2.3.1 Colombia Dataset

Paragraphs GDGTs Ia, IIa, and IIIa make up the overwhelming majority of the branched GDGTs in the 31 samples measured, comprising 88% or more of the total branched GDGTs in all except one sample (010610-36). These brGDGT distributions are consistent with the low pH measured in all samples (pH = 4.7 ±0.5) except 010610-36, which had a pH of 6.2. GDGTs IIIb and IIIc were only detected in 6 of our samples, so we use the abbreviated MBT’ index (Peterse et al., 2012) in this study, rather than the full MBT index (Weijers et al., 2007). The MBT’ index ranged from 0.362 at the lowest temperature site to 0.970 at one of the warmest sites (Table 2.1; Appendix A). Isoprenoid GDGTs were relatively rare in these samples, with the BIT index (Hopmans et al., 2004) greater than 0.93 in all samples. High BIT values for the Colombian soils, which all come from locations with MAP >944 mm/yr, are in agreement with previous studies suggesting that crenarcheol is scarce in areas that receive greater than 700-800 mm/year of precipitation (Dirghangi et al., 2013; Wang et al., 2013). Mean annual temperature as measured by the in-situ temperature loggers is well- predicted by the fractional abundances of the branched GDGTs in the Colombia dataset. We examined two model forms and found that both fit the data well with relatively high correlation coefficients. Using MBT’ and CBT as predictors of MAT, the regression equation and associated errors are as follows:

MAT = 1.2 + 22.3 × MBT’ + 1.5 × CBT (R2 = 0.69, RMSE = 3.05°C, p < 0.00001) (Eq. 2.5) While this represents some improvement over the fit of the global calibration, this is not the best-fitting model for the Colombia dataset; in this model, the CBT coefficient 15 is not significant (p = 0.29), probably because our dataset does not cover a wide range of pH variability. We also tried a few additional calibrations in order to determine, in a general sense, how much of the variability in MAT can be explained by variations in brGDGT abundances in the Colombia data. The best fitting model included the relative abundances (in percent) of all seven of the more common brGDGTs, with the cyclic compounds (Ib, Ic, IIb, IIc) log-transformed so that they would vary linearly with pH.

Compounds whose concentration was below the detection limit were assigned a fractional abundance of 10-6% before being log-transformed. In this case, the resulting equation was:

MAT = 29.1– 0.017×(Ia) – 0.61×log(Ib) – 3.34×log(Ic) – 0.34×(IIa) – 0.11×log(IIb) + 0.44×log(IIc) – 0.067× (IIIa) (R2 = 0.77, RMSE = 2.90°C, p < 0.00001) (Eq. 2.6)

This result suggests that brGDGT abundances can explain up to 77% of the variability in MAT, with a RMSE on reconstructed temperatures of 2.9°C. This is significantly smaller than the 5°C RMSE of the global calibration, suggesting that seasonal variations, variable pH values, and/or mismatches between in-situ soil temperatures and soil temperatures interpolated from nearby weather station records may be the source of the additional scatter in the global dataset. In Colombia we have constraints on all of these variables. Therefore, in the following sections, we explore how these possible sources of this scatter affect our dataset, and compare this to the global calibration data.

16

Figure 2.4: In-situ MAT derived from temperature loggers, plotted against the estimated temperatures at each site determined via the three interpolation techniques (Linear interpolation, kriging, nearest neighbor).

2.3.2 Evaluation of interpolation schemes for MAT data

One potential source of scatter in the current global calibration is the discrepancy between actual soil temperature at a sample site and the air temperature measured at nearby climate monitoring stations. In Colombia, our in-situ temperature data combined with decadally averaged mean annual air temperature from local weather stations allow

17 direct testing of whether this mismatch contributes significantly to the calibration error. Using ESRI ArcGIS, we employed three different interpolation schemes to determine the temperature at each of our sample sites from the weather station data. We alternately used the nearest temperature station, unweighted linear interpolation, and Kriging, and then compared the interpolated estimates at each site to the in-situ temperatures. To determine the magnitude of the possible effect of interpolation error on the GDGT- temperature calibration, we regressed MBT’ and CBT against the temperature data provided by each interpolation scheme, then compared the results to equation 2.5.

The interpolation methods used are 2D functions and do not take into account any additional parameters, such as elevation. They were selected because they require minimal user input or manual calculations to implement, and would therefore be most applicable for a large global dataset. Linear interpolation would be most analogous to the site correction technique that was used previously in the global calibration – at any site that was at a significantly different elevation from the nearest weather station, a temperature lapse rate was applied to correct the temperature in the calibration study (Weijers et al., 2007; Peterse et al., 2012). While it is unlikely that the temperature at the nearest station differs significantly from the sampling location at the majority of sites used in the global dataset, the field setting in Colombia represents a worst-case scenario for this approach due to the mountainous, high-relief terrain. Therefore, the results of the analysis presented here likely reflect the greatest possible error related to mismatch between weather station data and in-situ monitoring. Of the three interpolation methods used, temperatures derived from linear interpolation and Kriging both had strong correlations with the in-situ MAT, with R2 = 0.83 and 0.86, respectively (Figure 2.4). As expected, the nearest station tended to match the in-situ temperature poorly (R2 = 0.49) because of the large changes in elevation over 18 small distances in our study locality. While the linearly interpolated values produced a lapse rate of -4.3°C/km, close to the actual lapse rate of -4.9°C/km, the lapse rate calculated from the Kriging-interpolated data was only -2.6°C/km, which is approximately half of the actual lapse rate. This may be due to the fact that Kriging is essentially an adaptation of least-squares multiple linear regression, which attempts to find a best-fit surface through a set of points and therefore does not necessarily adopt the exact measurement value at each point (Cressie, 1990). Because this method tends to smooth out extreme values in the dataset, it is not recommended as a means of accurately interpolating station MAAT data. Notably, the linearly interpolated MAAT data not only reproduced the lapse rate well, but it also matched soil MAT with no significant offset (Figure 2.4). This is likely due to the lack of large seasonal variations in temperature and abundant vegetation and cloud cover blocking the soils from direct insolation. Because the two datasets provide essentially the same temperature information, we will continue to use the soil MAT in association with the Colombia soil GDGT data. However, we should note that the global MBT’/CBT calibration was performed against MAAT, so the use of soil MAT would not be an appropriate choice had the two sources of temperature data been significantly offset, as they often are in other regions.

To further test the effect of the slight differences between interpolated and in-situ temperature on the modeled temperature reconstructions, we regressed MBT’ and CBT against each set of temperature data and noted the changes in the fitted coefficients for each term. The regression models produced by Kriging and linear interpolation had correlation coefficients that were very similar to the regression against in-situ temperatures, with R2 = 0.71 and 0.64, respectively (compared to R2 = 0.69 for the in-situ data). Again, the nearest neighboring station data produced a poor fit with MBT’ and 19 CBT, with R2 = 0.23. The estimated coefficients for each term in the regression were most similar to the in-situ calibration for linear interpolation, and were increasingly different for Kriging and the nearest neighbor data (Table 2.2). Because linear interpolation most closely reproduces the fit coefficients from the in-situ temperature data, it seems to be the most appropriate method to use for automatically correcting station data to a sampling location. Site mismatch does not appear to have a significant

impact on the quality of the temperature reconstruction; the difference between the RMSE when the model is generated using in-situ temperatures compared to the linearly

interpolated temperatures is only 0.29°C.

MBT' CBT Intercept R2 RMSE p-value Coefficient Coefficient Global Calibration 31.0 ± 3.94 -5.69 ± 1.55 0.83 ± 1.82 0.59 5.00 < 0.00001 In-situ temperatures 22.37 ± 6.69 1.47 ± 2.73 1.21 ± 5.08 0.69 3.06 < 0.00001 Linear interpolation 15.93 ± 7.35 5.79 ± 3.0 -1.93 ± 5.58 0.64 3.35 < 0.00001 Kriging 10.35 ± 10.44 3.43 ± 8.49 2.89 ± 3.84 0.71 1.81 < 0.00001 Nearest Neighbor 7.72 ± 10.72 3.85 ± 4.27 5.19 ± 8.14 0.23 4.89 0.024

Table 2.2: Fitted coefficients for comparison between interpolation methods. All coefficients calculated for a model of the form MAT = a ∙ MBT' + b ∙ CBT + c, with the MAT values taken from each of the interpolated datasets. Reported errors on estimated coefficients represent 95% confidence intervals.

20

Figure 2.5: Reconstructed temperatures for each of the sites where replicate samples were taken. Temperatures plotted for the soil samples (open and filled circles) were calculated using the local MBT’/CBT calibration (Eq. 2.5). The open circles represent samples that were not taken from the exact location of the temperature logger and were sampled 5 months earlier. The closed circles were taken from the exact site of the temperature logger at the time of installation. Triangles represent the MAT recorded by the soil temperature loggers.

2.3.3 Local Variability Within the Colombia Dataset

One possible explanation for the 3.0°C of scatter remaining in the local calibration may be the presence of local effects such as differences in the amount of exposure to sunlight, vegetation cover, nutrient availability or soil moisture that could influence the growth of brGDGT –producing bacteria. At seven of the temperature logger sites, 1 or 2 additional samples were taken in order to obtain an estimate of the

21 degree of variability in the branched GDGT compositions within a localized area with the same assumed MAT and MAP. At five of these sites, the additional samples were collected 5 months prior to deployment of the temperature loggers during a scouting trip for suitable sites; at the other two sites, the replicate samples are sampled from the exact location of the temperature logger at the same time as the remaining samples were collected. The five samples that predate the temperature loggers are located within 600 m of the final location for the temperature logger, and differ in elevation by no more than 50 m. Branched GDGT temperatures were computed for each of the soil samples using the local calibration (Eq. 2.6) The smallest temperature ranges were observed at the two sites where the replicate samples were all taken from the exact site of the temperature logger, with a range of 0.3 and 1.9°C at sites 21 and 11, respectively. At sites where replicates were not taken at the exact location of the temperature logger, the range of reconstructed temperatures between the replicate samples was higher; a range of 2.1, 3.5, 3.7, 6.4, and 7.8°C was observed in the reconstructed temperatures at these sites (Figure 2.5). The sites with differences in reconstructed temperature of 6.4 and 7.8°C are particularly difficult to explain, as these ranges are approximately as large as the 5°C annual temperature range at these sites. A similar phenomenon has been noted in a set of samples collected around the catchment of a small lake in Switzerland, in which two “irregular soils” were identified because their reconstructed temperatures were 8.2°C higher than the other seven samples analyzed (Niemann et al., 2012). In both our study and in the study of Niemann et al. (2012), these anomalous soil samples were taken from within 600 m of other samples from similar sedimentary environments, yet the brGDGT abundances were dramatically different. This suggests that general soil characteristics may be insufficient to explain some variation in brGDGT abundances; there may be 22 localized differences in available nutrients, soil moisture, or in the preservation of brGDGTs that are influencing reconstructed temperature values. At the sites where the variability between replicate samples is smaller, it is notable that the ranges of values observed are approximately equal to the RMSE for the local MBT’/CBT calibration. This suggests that these unconstrained local variations are a significant source of the uncertainties in the brGDGT temperature calibrations; we have already shown that mismatch between weather station data and in-situ soil temperatures likely contributes a negligible amount of scatter. It remains unclear, however, whether these variations are likely to be systematic within a single site, as some have suggested (Tierney et al., 2010; Peterse et al., 2011). While this issue cannot be directly assessed using these data, we note that if the variations that we observe over small distances are due to subtle variations in microenvironment, these factors could vary temporally as well as spatially. Consequently, temporal changes in local microenvironmental conditions at a single site could potentially produce variations in brGDGT distributions in the absence of climatic forcing. Thus, while the 3-5 °C in error estimated from local and global calibrations is likely a good estimate of the error, MBT’/CBT-derived temperatures should be interpreted with the understanding that larger errors are indeed possible, and not uncommon.

23

Figure 2.6: (A) Temperatures calculated from the global MBT’/CBT calibration (Eq. 2.7) plotted against the measured instrumental temperature. Solid line is a 1:1 Line. (B) The residuals for the fit in (A) plotted against temperature to illustrate the bias in the current calibration. Diamonds: global dataset (Peterse et al., 2012), triangles: elevation transects from France (Peterse et al. 2012), circles: elevation transect from China (Peterse et al., 2009), square: elevation transect from Colombia (this study).

24 2.3.4 Comparison to the Global Soil Dataset

In order to determine whether the Colombian transect differs significantly from the samples used in the global calibration, we added our 31 soils to the global dataset and produced a new MBT’-CBT regression against MAT. In-situ soil MAT was used as the temperature source for the Colombia data, due to its good correspondence with MAAT, although temperature data from other publications is derived from air temperatures; we refer to all of these measurements as MAT in this section to include both soil MAT from the Colombia dataset and MAAT from the global datasets. We highlight two other regional transects included in the global dataset, from the French Pyrénées, and Mount Gongga, China (Peterse et al., 2009) which represent moderately arid regions, ranging from 500–1000 mm of precipitation annually. The new calibration equation from the expanded dataset is similar to the calibration published by Peterse et al. (2012):

MAT = -0.033 + 31.5 × MBT’ – 4.45 × CBT

(R2 = 0.61, RMSE = 5.07°C, p < 0.00001) (Eq. 2.7)

The similarity of this calibration to the existing one suggests that the Colombian data are largely consistent with the global dataset. However, even with the addition of our dataset, the Colombian data are generally under-estimated by this calibration, with most of the Colombian sites plotting below a 1:1 line (Figure 2.6A). To further explore the causes for this bias, we also plotted the residuals on the calibration (reconstructed

MAT – instrumental MAT) against temperature. If a regression model fits the data well, and there are no variables missing from the calibration, a plot of residuals against each parameter is expected to show a random distribution of points centered about zero. However, we observe a significant linear trend (R2 = 0.39 and p < 0.00001) between the 25 residuals and temperature (Fig. 6B), indicating that MBT’ and CBT do not explain the full extent of the variation in MAT in the global dataset. This bias is not due to the unsuitability of MBT’ or CBT as predictor variables; similar plots of the residuals against MBT’ and CBT show no relationship whatsoever (Peterse et al., 2012).

Figure 2.7: The MBT’ and CBT indices plotted against MAT and pH in order to illustrate regional differences in the empirical relationships between the branched GDGT parameters and environmental variables. Left: the MBT’ index plotted against (A) instrumental temperature and (C) soil pH. Right: the CBT index plotted against (B) instrumental temperature and (D)soil pH. Symbols are the same as in Figure 2.6. Lines reflect linear regressions through each individual dataset. Regression coefficients, R2 values, and p- values for each of these plots are listed in Table 2.3.

26

Colombia ECT China France WT

MAT = m ∙ MBT' + R2 0.67 0.47 0.01 0.04 0 b (Figure 8A) slope 23.81 ± 6.16 20.41 ± 3.82 -2.91 ± 12.58 -10.16 ± 20.26 -0.81 ± 7.46 Intercept 2.37 ± 4.62 -1.67 ± 5.76 7.25 ± 4.98 13.93 ± 7.04 6.63 ± 1.42

p-value < 0.0001 < 0.0001 0.65 0.33 0.83

MAT = m ∙ CBT + R2 0.19 0 0.6 0.44 0.06 b (Figure 8B) slope 5.11 ± 3.96 0.62 ± 3.24 -5.25 ± 1.68 -6.04 ± 2.66 1.25 ± 2.24 Intercept 12.14 ± 6.16 12.52 ± 5.34 10.35 ± 1.58 15.26 ± 2.44 5.55 ± 1.76

p-value 0.015 0.7 < 0.0001 0.0001 0.28

pH = m ∙ MBT' + b R2 0.03 0.02 0.44 0.19 0.03 (Figure 8C) slope -0.54 ± 1.08 0.65 ± 1.58 -7.06 ± 3.14 -6.7 ± 5.40 0.03 ± 0.08 Intercept 5.1 ± 0.80 4.98 ± 1.18 9.19 ± 1.24 8.66 ± 1.88 -0.06 ± 0.58

p-value 0.32 0.41 0.0001 0.02 0.51

pH = m ∙ CBT + b R2 0.48 0.05 0.8 0.67 0 (Figure 8D) slope -0.82 ± 0.32 0.31 ± 0.48 -2.00 ± 0.40 -2.17 ± 0.60 -0.06 ± 0.74 Intercept 5.94 ± 0.50 4.97 ± 0.78 8.10 ± 0.38 8.1 ± 0.54 7.29 ± 0.58

p-value 0.0002 0.21 < 0.0001 < 0.0001 0.88

Table 2.3: Fitted coefficients for each of the plots presented in Figure 2.7. Reported errors on estimated coefficients represent 95% confidence intervals.

27

Figure 2.8: Plot of the recalibrated dataset including only the Colombia transect, the ECT data of Dirghangi et al. (2013), and sites from the global calibration of Peterse et al. (2012) that received greater than 1000 mm/yr of precipitation annually. The instrumental MAT is plotted against the reconstructed MAT in (A), and the residuals are plotted against MAT in (B). Symbols are the same as in Figure 2.6 and 2.7.

28 2.3.5 Heterogeneity in brGDGT Patterns Across Five Transects

In order to better understand some of the heterogeneities in the global dataset, we compared the temperature dependence of the branched GDGTs in the Colombia dataset against four published soil transects. (1) The first of the transects in the Peterse et al. (2012) dataset was taken from the flanking slopes of Mount Gongga, China, with samples spanning from 1180 to 3819 m a.s.l. (2) The second transect was from the French Pyrénées, and spans elevations from near sea level to approximately 2200 m a.s.l. These two transects are both from areas of moderate precipitation; samples in the Pyrénées receive 500-1000 mm of precipitation annually (Peterse et al., 2012), and our estimates from GHCN precipitation data suggest that Mount Gongga receives approximately 750 mm of precipitation annually. The other two transects from Dirghangi et al. (2012) were taken along (3) the east coast of the United States (ECT) and represent a moist, temperate climate with annual precipitation ranging between 1000 and 1600 mm/yr, and a transect across (4) the western mountain states of Utah, Colorado, Wyoming and Idaho (WT), where annual precipitation was substantially lower – 171 to 580 mm/yr (Dirghangi et al., 2013). For brevity, we will refer to the transects as China, France, ECT (east coast U.S. transect), WT (western U.S transect) and Colombia. These five transects span a wide range of climate regimes, with WT representing a hyper-arid climate, China and France representing areas of moderate precipitation, and Colombia and ECT representing extremely moist regions. Plots of the MBT’ and CBT indices against instrumental temperature and pH for each of these separate datasets vary significantly among the transects (Figure 2.7, Table 2.3). In particular, the relationship between MBT’ and MAT (Figure 2.7A) is weak in all of the low precipitation sites, such as France (R2 = 0.04, p = 0.33), China (R2= 0.01, p= 0.65), and WT (R2 = 0, p = 0.83) (Table 2.3). In contrast, Colombia and the ECT display 29 strong linear trends (R2 = 0.67, p < 0.0001; R2 = 0.47, p < 0.0001; table 2.3) and have similar slopes (23.8 and 20.4, respectively). This pattern is reversed when examining the relationship between MBT’ and pH, with a significant correlation between MBT’ and pH for France and China (R2 = 0.19, p = 0.02; R2 = 0.44, p = 0.0001; table 2.3), but not for Colombia (R2 = 0.03, p = 0.32; table 2.3) and the ECT (R2 = 0.02, p = 0.41; table 2.3). Although it has been noted that pH explains more of the variation in brGDGT distributions across the global dataset than either MAT or MAP (Peterse et al., 2012), it is surprising that the correlation among MBT’, pH, and MAT differs so dramatically among these transects. This suggests that in areas of moderate to low precipitation, changes in MBT’ may be more strongly influenced by pH than by temperature. This may explain why the correlation between MBT’ and MAT alone is so poor in the global dataset; at sites with moderate to low precipitation, the MBT’ index may actually reflect pH changes, requiring the inclusion of the CBT index in order to account for this effect.

The relationship between CBT and pH, while more consistent among sites than MBT’ and pH, also varies significantly among transects. While France and China plot along lines with similar slopes and absolute values, the regression lines for Colombia and ECT data have much shallower slopes, such that small variations in pH seem to produce large variations in the CBT index (figure 2.7D). Again, WT shows little correlation at all, suggesting that neither MBT’ or CBT is responding strongly to climate in this hyper- arid regime. Finally, the correlation between CBT and MAT is generally poor across all transects, confirming previous observations of the lack of relationship between these two variables alone (Weijers et al., 2007; Peterse et al., 2012). As a whole, these data suggest essentially three regimes in which the MBT’ and CBT indices respond differently to environmental parameters. (1) Below approximately 500 mm/yr, brGDGT production is extremely low, and neither the MBT’ or CBT indices 30 appear to respond to climate forcing. (2) In areas of moderate precipitation (~500–1000 mm/yr), it seems that the MBT’ and CBT indices would work well as a soil pH proxy, but not as a paleotemperature proxy. (3) In regions receiving more than 1000 mm/yr, the correlation between both indices and pH weakens, and MBT’ seems to be predominantly controlled by MAT. Thus, a significant portion of the scatter in the global dataset can be attributed to the inclusion of samples from areas that show no correlation with MAT. To determine if this actually the case, we performed another calibration including the Colombia transect, the ECT, and sites in the global dataset that receive greater than 1000 mm/yr:

MAT = -1.48 + 31.2 × MBT’ – 3.50 × CBT (R2 = 0.61, RMSE = 4.4°C, p < 0.00001) (Eq. 2.8)

The coefficients in this fit are nearly identical to the previous values, suggesting that the trend may have been largely determined by these wetter sites in the global dataset (i.e., the correlation between MAT and MBT’/CBT at low precipitation sites was not sufficiently systematic to significantly alter the coefficients). The RMSE in this calibration is improved from 5.07 to 4.4°C, but the correlation coefficient remains the same. This is similar to the results of Peterse et al. (2012) in which they removed all of the sites with < 500 mm/yr in precipitation yet found no improvement in the correlation.

In addition, the systematic trend between the residuals and instrumental temperature remains (figure 2.8B). Thus, the inclusion of data from arid regions was not the cause of this bias; rather, it indicates that there is still a parameter missing from the model to fully explain the variation in MAT.

31 The lack of change in the calibration using only moist sites also highlights the fact that the delineation between these precipitation regimes is quite arbitrary and doesn’t significantly improve our understanding on the controls on GDGT’ production. To take advantage of the increased precision of reconstructed MAT from high precipitation sites, one would have to determine a priori which precipitation regime is appropriate when using the MBT’ index to reconstruct temperatures from ancient samples. However, recent work on the BIT index and the ratio of isoprenoid to branched lipids (Ri/Rb) highlights a possible solution to this problem in soils (Xie et al., 2012; Dirghangi et al.,

2013; Liu et al., 2013). Samples from extremely moist regions appear to have BIT indices close to 1 and Ri/Rb very close to zero, whereas BIT values are low and Ri/Rb is high in areas of low soil moisture. These two parameters have been proposed as proxies for soil moisture (Xie et al., 2012; Wang et al., 2013), but could also be included in the calibration as a means of correcting for the effects of soil moisture. Or, they could provide a more reliable way of identifying samples that are more likely to record MAT, and are therefore more suitable for paleoclimate reconstructions. In the transects examined in this work, all of the Colombia and ECT samples had BIT indices greater than 0.93, and only two of the WT soils had BIT indices greater than 0.55; BIT indices for France and China were not reported. However, this approach would be ineffective in lakes and other aquatic settings where isoprenoid GDGTs are produced in the water column and the BIT index reflects terriginous input rather than soil moisture. In these cases, particularly in lakes where a large influx of soil-derived brGDGTs is suspected, further work is needed to identify whether brGDGTs will still be effective recorders of temperature in arid environments.

32 4. CONCLUSIONS

We show that in a well-constrained field region (~3000 m elevation range) with limited seasonal variability, minimal pH variability, and in-situ temperature constraints, the error on the local calibration between MBT’, CBT and temperature is significantly lower than the error in the global calibration. Depending on the form of the calibration, the RMSE ranges between 2.8 and 3.0°C, which likely represents a lower bound on the error on temperatures reconstructed using the MBT’-CBT calibration. Mismatches between interpolated weather station data and in-situ measurements are considered a negligible source of error in the global calibration, provided that the linear interpolation or lapse rate corrections are used. Instead, transient site-specific variations among closely spaced samples appear to be the largest contributors to the remaining scatter in our dataset, accounting for 2-3°C of variability in reconstructed values from samples taken within 600 m of a temperature logger. This research highlights a need for replicate sampling in studies of past climate in order to account for such small-scale spatial variations. The local variations, possibly related to temporally and spatially transient microclimates, were observed in two of seven sites with replicate sampling, and can result in differences between close neighbors of roughly 2 to 8°C. In the global calibration, the temperatures at our sites were consistently under- estimated, and a significant linear dependence between the residuals and measured temperature was identified, but could not be easily explained by either MAP or seasonal variations. A comparison of the controls on branched GDGT distributions among several regional transects showed significant differences in the response of MBT’ and CBT to MAT and pH. While extremely arid regions show no relationship between brGDGT distributions and climatic parameters, in areas receiving moderate amounts of precipitation, both MBT’ and CBT show strong correlations with pH, but not MAT. The 33 two transects from extremely wet regions both demonstrated a strong correlation between MBT’ and temperature, but little correlation among MBT’, CBT and pH. This suggests that the bacteria in each of these regimes are responding differently to climatic forcing, depending on precipitation or soil moisture. We therefore advocate the use of the BIT index or the Ri/Rb in future studies in order to screen samples for suitability, and to make the delineation of each of these regimes tied to a more relevant parameter, such as soil moisture (rather than MAP), via these indices.

34

Chapter 3: Paleoelevation records from lipid biomarkers: Application to the tropical Andes2

ABSTRACT

New results from two novel lipid biomarker-based proxies help constrain the late Cenozoic surface elevation history of the Eastern Cordillera in the tropical northern Andes of Colombia. Previous well-known studies have suggested rapid latest Miocene-

Pliocene (6-3 Ma) uplift on the basis of an abrupt shift in pollen species assemblages within sedimentary basin fill of the elevated Bogotá plateau. From resampling of these original study localities, we provide a revised chronology based on magnetic polarity stratigraphy, and we evaluate paleotemperatures using the MBT’/CBT indices (Methylation of Branched Tetraethers/Cyclisation of Branched Tetraethers) and hydrogen isotopic composition (δD) of leaf waxes as two independent proxies of past surface elevation. Reconstructed paleotemperatures from the MBT’/CBT proxy show a more gradual cooling trend from ca. 7.6 Ma to present, consistent with less than 1000 m of elevation gain since latest Miocene-Pliocene time and in agreement with geologic evidence for accelerated shortening and exhumation at this time. The leaf wax isotopic data, on the other hand, lack a systematic trend, potentially due to fractionation changes associated with uplift-induced turnover in floral populations. Such changes could obscure isotopic variations in meteoric water, suggesting that leaf-wax isotopic compositions may not provide a direct proxy for elevation in this particular situation involving uplift in a heterogeneous tropical environment. More promising in this case is the MBT’/CBT proxy, which may offer a suitable alternative to carbonate-based

2 The full text of this chapter has been submitted to GSA Bulletin and has undergone two rounds of reviews. 35 paleoelevation proxies in tropical regions where year-round high precipitation rates prevent formation of soil carbonates.

36 3.1 INTRODUCTION

Uncertainty persists over the timing of surface uplift in major mountain belts, including the Eastern Cordillera fold-thrust belt in the tropical northern Andes of Colombia. Although changes in sediment accumulation, provenance, and thermochronometric estimates of bedrock exhumation suggest Andean shortening in the Eastern Cordillera since late Eocene-Oligocene time, the rise of the ~2600-m-high Bogotá plateau (Sabana de Bogotá), an intermontane hinterland basin (figure 3.1), appears to have lagged this early onset of shortening (Mora et al., 2006; Horton et al., 2010; Saylor et al., 2011; Saylor et al., 2012). Studies of pollen assemblages in clastic sedimentary fill of the Bogotá plateau suggest that it may not have undergone significant uplift until late Miocene-Pliocene time (van der Hammen et al., 1973; Gregory-Wodzicki, 2000; Hooghiemstra et al., 2006a). Specifically, the combination of lowland pollen taxa identified in deposits as young as 6 Ma along with periodic cycling of high Andean forest and alpine Páramo taxa from 3 Ma onward suggest that the plateau abruptly attained modern elevations by 3 Ma and remained high enough to experience incursions of alpine taxa during glacial cycles (Hooghiemstra et al., 2006a). However, structural analysis indicates that more than half of the total shortening along the eastern flank (15.9 out of 30 km) has occurred since ca. 3 Ma (Mora et al., 2008; Bayona et al., 2008). A scenario that harmonizes the apparently inconsistent structural and paleofloral uplift histories requires increased erosion due to the intensification of orographic precipitation following uplift of the Eastern Cordillera to balance all subsequent rock uplift despite a rapid (~5mm/yr) shortening rate. Alternately, surface elevation gain may have been restricted by coeval growth of a negatively buoyant crustal root (e.g., Garzione et al., 2006; DeCelles et al., 2009), enhanced erosion (Mora et al., 2008), or a change in plate kinematics or plate coupling (Meade and Conrad, 2008; Iaffaldano and Bunge, 2008; Quinteros and Sobolev, 37 2012). In any case, quantitative estimates of the elevation history provide key information on the magnitudes and rates of the processes restricting or promoting topographic growth. Recent breakthroughs in assessing past elevation using stable isotopes of sedimentary materials have provided important records of the timing of surface uplift and constraint geodynamic models of surface uplift in various orogens. These advances in paleoaltimetry have enabled discrimination between competing models of topographic development in the Tibetan plateau (e.g., Garzione et al., 2000; Quade et al., 2011;

Rowley and Currie, 2006), have provided constraints on the longevity of the Sierra Nevada as a major topographic feature in western North America (e.g., Cassel et al., 2009; Molnar, 2010; Mulch et al., 2008), and have highlighted the possible role of lower lithospheric delamination in the central Andes (e.g., Garzione et al., 2006, 2008b). However, there remains considerable debate over the geodynamic mechanisms involved in Andean uplift, as most available estimates on the timing and pace of paleoelevation show an irregular spatial and temporal distribution of surface uplift (Garzione et al., 2006, 2008, 2014; Bershaw et al., 2010; Leier et al., 2013; Baker et al., 2014; Canavan et al., 2014; Carrapa et al., 2014; Saylor and Horton, 2014). For most paleoaltimetry techniques, the isotopic composition of precipitation can be estimated from authigenic minerals such as soil carbonate nodules, which preserve the isotopic composition of meteoric waters at the time of their formation (Chamberlain and

Poage, 2000). A few studies employing soil carbonate-based paleoaltimeters have also used the Δ47 clumped-isotope paleothermometer to provide additional constraints on past changes in elevation (Ghosh et al., 2006; Garzione et al., 2008; Huntington et al., 2010; Snell et al., 2012; Lechler et al., 2013). However, such studies rely on unaltered soil carbonate, which is potentially complicated by seasonal carbonate deposition (Breecker 38 et al., 2009; Hough et al., 2014; Passey et al., 2010; Snell et al., 2012; Quade et al., 2011; Huntington et al., 2015) and may have limited applicability in high-rainfall regions where soil carbonate preservation is low. An alternative approach involves techniques utilizing the molecular and stable isotope composition of organic matter preserved in soils (e.g., Hren et al., 2010; Polissar et al., 2009), including glycerol dialkyl glycerol tetraether (GDGT)-based soil paleothermometers (Weijers et al., 2007; Peterse et al., 2012) and hydrogen isotope compositions of source-specific biomarkers such as leaf waxes (Sachse et al., 2012).

In this study, we aim to independently constrain paleoclimatic variations linked to surface uplift of the Bogotá plateau in the Eastern Cordillera of the northern Andes of Colombia. In such tropical regions, however, the climate conditions are too humid for sustained preservation of soil carbonates. Therefore, we employ two ubiquitous lipid biomarker-based proxies—the methylation of branched tetraethers/cyclization of branched tetraethers (MBT’/CBT) index and the hydrogen isotopic composition (δD) of leaf waxes—to assess past elevation.

39

Figure 3.1: Generalized shaded-relief map of Colombia and adjacent regions, showing the major Andean ranges. The Bogotá plateau (Sabana de Bogotá) is outlined in the inset figure, which shows the locations of the three sampled sections.

40 3.2 PALEOALTIMETRY AND ORGANIC GEOCHEMICAL PROXIES

3.2.1 Isotopic constraints on paleoelevation

Because tectonic uplift substantially impacts atmospheric circulation and local climate, most methods of estimating past elevation rely on climatic parameters such as paleotemperature and the isotopic composition of precipitation to infer past elevation changes (Rowley and Garzione, 2007). Isotopic methods take advantage of Rayleigh distillation, the process by which a vapor mass rising over a mountain range becomes increasingly depleted in heavier isotopes (18O, 2H) that are preferentially condensed into precipitation. In modern water samples from small streams across the Eastern Cordillera of Colombia, Rayleigh distillation provides the dominant process controlling isotopic compositions of precipitation (Saylor et al., 2009), consistent with thermodynamic models and global empirical observations (Blisniuk and Stern, 2005; Molnar, 2010; Rowley et al., 2001). Although seasonal variations in the amount of precipitation drive isotopic compositional variations of approximately 80‰ within an individual year, this effect is not visible in sampled stream waters, which are representative of average precipitation values over multiple years (Saylor et al., 2009). The modern hydrogen isotopic lapse rate for stream waters is calculated to be:

Z = -68.63 × δD -1767 (Eq. 3.1)

where Z is elevation in meters, and the 2σ uncertainty is determined via bootstrapping to be 405 m.

41

Figure 3.2: Chemical structural formulas of the nine known brGDGTs (branched Glycerol Dialkyl Glycerol Tetraethers).

3.2.2 The MBT’/CBT proxy

The MBT’/CBT index (Methylation of Branched Tetraethers/ Cyclisation of Branched Tetraethers) measures variations in the relative abundances of a particular class of bacterial membrane lipids, branched Glycerol Dialkyl Glycerol Tetraethers (brGDGTs), in order to reconstruct modern and past soil temperatures. These brGDGTs are primarily found in soils and other moist sediments and appear to be produced by bacteria of the phylum Acidobacteria (Weijers, Schouten, Hopmans, et al., 2006). Nine structural variants of these brGDGTs (figure 3.2) have been identified that contain different combinations of cyclopentyl groups and methyl branches (Weijers, Schouten, Hopmans, et al., 2006). The MBT’ index is a measure of the relative abundance of brGDGTs with additional methyl groups, and the CBT index is a measure of the relative abundance of brGDGTs with cyclopentyl groups (Weijers, Schouten, Spaargaren, et al.,

2006). The relative abundances of these nine variants are strongly correlated with pH and mean annual soil temperature, apparently the result of adaptations to maintain membrane fluidity during changing temperature and pH conditions (Weijers, Schouten, Spaargaren, et al., 2006; Weijers et al., 2007; Peterse et al., 2012; Schouten et al., 2013).

42 MBT’ and CBT indices have been measured in over 200 modern soil samples where the mean annual air temperature (MAAT) is known, and from these data the following empirical calibration between the MBT’ and CBT indices and temperature has been developed (Weijers et al., 2007; Peterse et al., 2012).

MBT’ = (Ia + Ib + Ic)/(Ia + Ib + Ic + IIa + IIb + IIc + IIIa) (Eq. 3.2)

CBT = -log((Ib + IIb)/(Ia +IIa)) (Eq. 3.3)

MAAT = 0.81 – 5.67 × CBT + 31.0 × MBT’ (r2 =0.59, RMSE = 5.0°C, p < 0.00001) (Eq. 3.4) Although this calibration has been shown to produce paleotemperature estimates consistent with other proxies, a high degree of scatter suggests that additional factors besides MAT and soil pH may influence the MBT’ and CBT indices of soils. Recent studies have shown that these indices may be less effective recorders of temperature in arid soils (Dirghangi et al., 2013; Yang et al., 2013; Xie et al., 2012; Anderson et al., 2014; Yang et al., 2014). A significant factor appears to be soil moisture, whose effect can be assessed to some extent using the BIT (Branched to Isoprenoid Tetraether) index, the most commonly used indicator of soil aridity. The BIT index is expressed as the abundance of GDGT 4, a particular aquatic isoprenoid GDGT or isoGDGT (defined as a structurally similar group of compounds produced by Archea in aquatic settings but found in soils at consistent low concentrations; (Dirghangi et al., 2013; Yang et al., 2013), relative to the most common brGDGTs (Ia, IIa, IIIa) (Hopmans et al., 2004).

43 BIT = (Ia + IIa + IIIa)/(Ia + IIa + IIIa + 4) (Eq. 3.5)

Because background levels of isoGDGTs in soils appear uniform across a range of soil moisture values, the BIT index essentially expresses the normalized rate of production of brGDGTs. Humid climates typically have BIT indices very close to 1, indicating mostly branched rather than isoprenoid GDGTs. Although the BIT index has not been employed as a strict criteria for screening unsuitable samples, it is a useful means of highlighting samples that may pose potential problems.

3.2.3 Hydrogen isotopes in leaf waxes

In terrestrial environments, long-chain n-alkanes with 25 or more carbon atoms are produced exclusively by plants as a component of the waxy epicuticular coating on the outer surface of leaves (Eglinton et al., 1967). These n-alkanes are synthesized within plant leaves, using leaf water as the hydrogen source. The isotopic composition of leaf wax tracks the composition of input leaf water, but is offset by an isotopic enrichment (ε), which is a function of the biosynthetic fractionation factor (α):

εa/b = 1000∙(αa/b – 1) = 1000∙((δa + 1000)/(δb + 1000) – 1) (Eq. 3.6)

where “a” is typically the final measured phase (i.e., leaf wax n-alkanes), and “b” is typically the input being fractionated (i.e., leaf water or meteoric water). Although the biosynthetic fractionation between leaf water and leaf waxes (εwax/lw) appears to be constant for n-alkanes (Sessions et al., 1999; Sachse et al., 2012; Chikaraishi et al., 2004), leaf water may be enriched in δD relative to meteoric water due to evapotranspiration

44 (Sachse et al., 2010; Feakins and Sessions, 2010a; Kahmen et al., 2013). Thus, depending on the plant’s growth habit, photosynthetic pathway, and moisture management strategies, the apparent fractionation between meteoric water and leaf waxes ranges from -50‰ to -180‰ (Sachse et al., 2012; Polissar and Freeman, 2010; Feakins, 2013; Nelson et al., 2013). However, in aggregate sedimentary records that do not span substantial changes in aridity or species assemblages, the isotopic fractionation is confined to a much smaller range, potentially making leaf wax n-alkanes suitable for reconstructing shifts in the isotopic composition of meteoric water (e.g., Aichner et al.,

2010; Feakins and Sessions, 2010a; Hou et al., 2008; Pu and Weiguo, 2011). These sedimentary n-alkanes show a strong relationship with the mean annual isotopic composition of precipitation, and therefore are considered to record long-term average values for precipitation (Nelson et al., 2013; Feakins and Sessions, 2010a; Kahmen et al., 2013).

3.2.4 Biomarker-based paleoaltimetry

The isotopic composition of n-alkanes and the MBT’/CBT paleothermometer have been evaluated as paleoaltimeters in studies using measurements from modern soil elevation transects (Ernst et al., 2013; Peterse et al., 2009; Sinninghe Damsté et al., 2008; Jia et al., 2008; Luo et al., 2011; Liu et al., 2013; Pu and Weiguo, 2011; Bai et al., 2012; Anderson et al., 2014). While most studies have found that MBT’/CBT-derived temperatures correlate well with instrumental lapse rates (Ernst et al., 2013; Peterse et al., 2009; Sinninghe Damsté et al., 2008), the results have been somewhat mixed with respect to the δD of leaf waxes. Of the eight leaf wax δD transects presented in these recent studies, five showed strong linear trends with elevation (Jia et al., 2008; Ernst et al.,

45 2013; Luo et al., 2011; Pu and Weiguo, 2011). However, no strong correlation was found between leaf wax δD and elevation along the slopes of Mt. Kilimanjaro (Peterse et al., 2009), and a reversal of the isotope-elevation trend was observed in the northern Tibetan Plateau and in the Tianshan mountains bordering the Tarim basin in China (Luo et al., 2011; Bai et al., 2012). Each of these cases was explained by the influence of other climatic processes: large variations in rainfall amount along the slopes of Mount

Kilimanjaro, and mixing of different moisture sources in the both of the Tibetan studies. In the Colombian Andes, controls on the isotopic composition of rainfall should be less complicated; moisture is derived exclusively from the east and Rayleigh distillation is the primary long-term driver of changing water compositions with elevation (Saylor et al., 2009). Two studies have successfully used the δD values of leaf waxes to reconstruct paleoelevations in the Sierra Nevada of western North America and the Hoh-Xil basin of the Tibetan plateau (Polissar et al., 2009; Hren et al., 2010). In both cases, the reconstructed paleoelevations were consistent with paleoelevation estimates from fossil leaf margin analyses. In the Sierra Nevada, the MBT/CBT proxy also reproduces independent paleotemperature estimates from authigenic mineral-based proxies (Hren et al., 2010). These studies suggest that biomarker proxies can be effective for reconstructing paleoelevations, although their application to long-term paleoclimate reconstructions is still relatively recent and needs further evaluation.

46 3.3 CHRONOSTRATIGRAPIC FRAMEWORK

3.3.1 Field localities

To estimate paleoelevations in the Colombian Andes, we sampled fine-grained sediments from three Neogene successions in the Sabana de Bogotá (figure 3.1), an externally drained hinterland basin currently at ~2600 m elevation in the Eastern Cordillera. Strata were deposited unconformably on deformed Cretaceous–Paleogene rocks. During the Miocene, the basin was dominated by fluvial and alluvial-fan deposits sourced from local topography (Helmens, 1990; Wijninga and Kuhry, 1990). From ca. 3 Ma until the Holocene, the Sabana de Bogotá was filled by a large glacially mediated lake, with alluvial and fluvial deposition continuing along the margins (Hooghiemstra, 1988; Helmens and van der Hammen, 1994; Torres et al., 2005). The oldest strata from the Neogene succession are the Lower Tilatá Formation; the type locality is located on the erosional scarp where the Bogotá river cascades over the Salto de Tequendama waterfall (4° 34’ 25” N, 74° 17’ 44” W). At this Tequendama locality, the Lower Tilatá disconformably overlies the Cretaceous Guaduas Formation (Helmens, 1990). This section (figure 3.3) consists of a basal 3 m of poorly organized conglomerate overlain by 45 m of fine-to medium-grained, massive sandstone, with interbedded granule-pebble conglomerate lenses containing wood fragments. Only minor changes in grain size occur throughout the section, with trough and ripple cross- stratification occasionally visible; this formation has been interpreted to be representative of a long period of fluvial deposition (Helmens, 1990). The Lower Tilatá Formation has been considered to predate the modern topography, because the depositional patterns do not correspond with modern depocenters, and the pollen species assemblages appear consistent with low-elevation taxa (Helmens, 1990; Wijninga, 1996a). The upper age limit of the formation is constrained by a zircon fission track (ZFT) age of 5.3 ± 1.0 Ma 47 from an ash bed found in a nearby section, correlated to the upper Tequendama section via palynological ties (Río Frío; Wijninga, 1996b). The age of the basal Tilatá Formation was calculated as 16 Ma by using pollen concentrations and sediment thickness assuming a constant pollen flux (Wijninga, 1996a, 1996b; Helmens, 1990; Andriessen et al., 1994). We reassess these age assumptions below on the basis of new magnetostratigraphic results.

Figure 3.3: Measured stratigraphic columns for the three sampled sections. Note the significant presence of bioturbated paleosol facies in the Subachoque and Guasca sections, but limited presence in the coarser-grained Tequendama section.

48 The other two sections in this study include the Guasca (4° 52’ 36” N, 73° 51’ 51” W), and Subachoque (4° 54’ 32” N, 74° 12’ 51” W) localities, which contain the Upper Tilatá Formation and Subachoque Formation. The Guasca section (figure 3.3) is composed of mottled, orange to purple claystones with a few interbedded peaty lenses at the base. The section coarsens upward with increasing abundance of cross-bedded sandstones and pebble conglomerates in the upper 20 m of the section. The claystones, typically mottled and lacking distinct beds or sedimentary structures, are interpreted as paleosols. The trough cross stratification and imbricated pebbles of the upper 20 m are consistent with fluvial deposition. This section likely represents a period of increasing sediment supply from the margins of the Sabana de Bogotá (Helmens, 1990; Wijninga and Kuhry, 1993). The 50 m thick Subachoque section (figure 3.3) also displays an upward coarsening trend. Grey siltstones lacking prominent bedding or other sedimentary structures dominate the lower third of the section, and are periodically cut by 10cm to 1m thick lenticular pebble conglomerates and cross-bedded sandstones with erosive bases. Planar laminated, m-scale sandstone beds with erosive bases dominate the middle third of the section; peaty lenses with leaf remains are found in several beds. After an 8m interval of massive mudstones, 0.5-2m thick pebble conglomerate beds with erosive bases dominate the uppermost 10 m. Similar to the Guasca section, this succession is attributed to a channelized fluvial system on the margins of the Sabana de Bogotá, with a gradual increase in coarse sediment supply (Helmens, 1990; Wijninga and Kuhry, 1990). ZFT ages for small tephra beds suggest the Guasca section spans from approximately 3.2 to 1.1 Ma and the Subachoque section from 3.1 to 0.6 Ma (Helmens et al., 1997).

49 3.3.2 Paleomagnetic chronology

Age constraints for each succession were refined using new paleomagnetic results that integrate ZFT dates as tie points, allowing accurate correlation of the observed magnetic polarity stratigraphy to the geomagnetic polarity timescale (Andriessen et al., 1994; Helmens et al., 1997). In the field, each section was sampled at ~1 m intervals, with 3 samples taken at each stratigraphic level. Samples from the Guasca and Subachoque sections were demagnetized using alternating field (AF) demagnetization, while thermal demagnetization was employed for samples from the Tequendama section. In the sections where AF demagnetization was used, samples were collected by inserting oriented plastic sampling cups into poorly consolidated outcrops after surface material had been scraped away. At Tequendama, where AF demagnetization was ineffective due to the presence of weakly magnetized minerals with a low Curie temperature, poorly consolidated samples were collected using 1-in-diameter (2.54 cm) borosilicate glass tubes inserted into the outcrop and later sealed with sodium silicate glue.

Magnetization directions for each sample were measured using a Superconducting Magnetometer (2G Technologies) equipped with an automated mechanical sample changing system in the University of Texas at Austin Paleomagnetics Laboratory (Horton et al., 2015), a member of the RAPID (Rock And Paleomagnetism Instrument Development) consortium. Representative demagnetization plots for each of the three sections are included. For each stratigraphic level, the reported magnetization direction represents the arithmetic average of individual measurements yielding a maximum angular deviation (MAD) < 20°. The magnetization directions for each stratigraphic level are classified by relative quality: “Good” samples reflect stratigraphic levels where the magnetization directions for all core samples were within 90° of each other; “Fair” samples are from stratigraphic levels where the majority of the measured magnetizations 50 were within 90°; and “Poor” samples are from stratigraphic levels where measured magnetization directions were inconsistent, or only one measurement had a MAD < 20° (figure 3.4). A detailed discussion of this classification scheme, and the Python code used to determine the appropriate classification for each sample is provided in the data repository. When assigning polarities to each stratigraphic section, “Good” samples were considered to be clear indicators of geomagnetic polarity, while a reversal of a single

“Fair” or “Poor” sample was not considered robust unless an adjacent sample showed the same polarity. Preferred correlations with the geomagnetic polarity timescale (Gradstein et al., 2012) were determined using ZFT age constraints and minimizing variations in sedimentation rate for each section. The updated Guasca and Subachoque chronologic framework is consistent with previous age estimates, with Guasca spanning 3.7-0.7 Ma, and Subachoque spanning 2.8- 0.4 Ma (Andriessen et al., 1994; Helmens et al., 1997). However, our interpretation for

Tequendama differs from previous inferences of a ca. 16 Ma age for the base of the section, as the small number of paleomagnetic reversals makes it unlikely. First, such an interpretation would suggest >15 major magnetic reversals, in conflict with the observed reversals. Second, the interpretation would require an exceptionally thin stratigraphic panel representing a roughly 10 Myr period. Therefore, based on ties to the 5.3 ± 1.0 Ma

ZFT age constraint for the top of the section, the new paleomagnetic data are most consistent with a correlation spanning from 7.6 to 6.1 Ma (figure 3.4).

51

Figure 3.4: (a), (b), (c): Measured paleomagnetic directions for Subachoque, Guasca, and Tequendama, respectively. Directions are indicated by shaded circles; the shade of the circle (black, grey, or white) is indicative of the quality of the sample data (classifications ”good”, “fair”, and “poor” as described in the text, and in detail in the Appendix). The total width of the error bars is the average angular difference between measurements at each stratigraphic interval. Interpreted reversal patterns are indicated to the right of each set of paleomagnetic measurements. (d): Correlation of reversal patterns to the geomagnetic polarity timescale.

52 3.4 GEOCHEMICAL METHODS

3.4.1 Biomarker sampling and sample processing

Samples were taken for biomarker analyses every 1-3 m in all three sections, preferentially sampling the finest horizons available. Whereas paleosols were collected at Guasca and Subachoque, the absence of paleosols at Tequendama required sampling of silty to fine sandy intervals with coal fragments. In each sampling location, 0.6-1.0 kg of sediment was collected after removing ~15 cm of the near-vertical outcrop face to avoid surface material. At the University of Texas at Austin organic geochemistry laboratory, samples were freeze-dried, then homogenized using a mortar and pestle, with a total lipid extract obtained via a microwave extraction system (MARS 5 Xpress, CEM Corporation; 10 min at 100°C). For each sample, 200-700 g of material was extracted in 30 g increments. Total lipid extracts were evaporated under nitrogen and separated into nonpolar and polar fractions using silica gel column chromatography. Nonpolar compounds, including n-alkanes, eluted in 8 mL of Hexane, and polar compounds, including brGDGTs, eluted in 10 mL of hexane:methyl acetate (4:1 v/v). Polar fractions were dried under nitrogen and re-dissolved in a solution of 1% isopropanol in hexane prior to analysis via high performance liquid chromatography (HPLC). Throughout all steps in the sample preparation, glassware was cleaned by combustion for at least 6 hours at 450°C, and all metal and ceramic tools were cleaned by triple rinsing with hexane, dichloromethane, and methanol.

3.4.2 GDGT measurement and quantification

BrGDGTs were measured using high-performance liquid chromatography/atmospheric pressure chemical ionization mass spectrometry

53 (HPLC/APCI-MS) following Schouten et al. (2007). We used an Agilent 1200 series HPLC with a Prevail Cyano column (2.1 x 150 mm, 3mm, Alltech) maintained at 30°C. The initial solvent composition (1% isopropanol in hexane) was maintained at 0.2 mL/min for 5 min, then ramped to 1.5% isopropanol in hexane over 28.1 min; this is the same gradient used by Schouten et al. (2007), but the ramp is terminated slightly earlier to reduce run times. After each run, the flow rate was increased to 0.25 mL/min for 10 min and the composition changed to 10% isopropanol in hexane to remove polar residues; then the flow in the column was reversed for 10 min before being returned to the initial conditions (1% isopropanol in hexane, 0.2 mL/min) for 5 min before the next run. APCI source parameters were identical to those used by Schouten et al. (2007). The mass spectrometer was operated in single-ion-monitoring (SIM) mode, monitoring for the following ions: m/z = 1018, 1020, 1022, 1032, 1034, 1036, 1046, 1048, 1050, 1292, 1296, 1298, 1300, 1302. These ions corresponded to branched GDGTs Ic, Ib, Ia, IIc, IIb,

IIa, IIIc, IIIb, IIIa, and isoGDGTs 0-4. 13 samples were measured using a different method where only brGDGT concentrations are recorded, so isoGDGTs 0-4 are not reported for these samples. An in-house extracted laboratory sediment standard containing isoGDGTs was analyzed repeatedly at several dilutions in order to constrain the analytical error on repeated measurements; the analytical error on reconstructed

TEX86 temperatures from 12 measurements spanning an order of magnitude in concentration was 0.15°C. Paleotemperatures were reconstructed by calculating the

MBT’ and CBT indices using the most recent global soil calibration (Eq. 3.4).

54 3.4.3 Leaf wax δD measurements

n-Alkanes were isolated from branched and cyclic hydrocarbons in the non-polar fraction by urea adduction. A small aliquot of each sample was analyzed using an Agilent 7890 GC-FID (Gas Chromatograph with Flame Ionization Detection), and each n-alkane was identified and quantified by comparison to known standards. After quantification, the carbon preference index (CPI) for each sample was calculated in order to screen for major post-depositional alteration.

CPI = 0.5*[(C25+C27+C29+C31+C33)/(C26+C28+C30+C32+C34) + (C25+C27+C29+C31+C33)/(C24+ C26+C28+C30+C32)] (Eq. 3.7)

Alkanes derived from vascular plants tend to contain significantly higher concentrations of odd-numbered n-alkanes than even ones; the carbon preference index expresses the ratio of odd-to-even alkanes. A lack of preference for odd alkanes may indicate that the sample has been thermally or microbially altered in such a way as to remove the original signature (Waples, 1980). Hydrogen isotopic ratios of extracted n-alkanes were measured at the Woods Hole Oceanographic Institution marine chemistry and geochemistry laboratories on a Finnigan Delta+XL isotope ratio mass spectrometer (IRMS) coupled to an Agilent 6890 GC via a pyrolysis furnace held at 1440°C. Propane peaks were injected at the beginning and end of each run; one peak at each end was used to correct for instrumental drift. An external standard containing fatty acid methyl esters (FAMEs) of known isotopic compositions (F8 standard, Indiana University biogeochemical laboratories) was analyzed between sets of 5 samples in order to constrain longer-term instrument performance and drift. All 55 samples were hand injected due to low sample concentrations; samples that had sufficient material for several injections were used to characterize measurement error; the average error on repeated measurements across the C27-C33 n-alkanes was 1.8 ‰, with a maximum of 8.6 ‰. Individual peaks with amplitudes < 2000 mV or > 10,000 mV were rejected due to poor reproducibility and a lack of linearity for laboratory standards run outside of these ranges.

Sample Strat Age Section Name Level (m) (Ma) MBT' CBT BIT MAT Gusaca 2GUA 0.5 0.5 3.79 0.692596 1.016597 - 16.51639 0.5 3.79 0.672552 1.28127 - 14.3943 0.5 3.79 0.68929 1.009282 0.99865 16.45535 Gusaca 1GUA 1 1 3.77 0.635299 0.53292 - 17.4826 1 3.77 0.638461 0.565747 - 17.39451 1 3.77 0.64167 0.563906 0.997337 17.50442 Gusaca 081211 -02 4 3.59 0.710606 0.09876 0.988712 22.27882 Gusaca 1GUA 4.1 4.1 3.59 0.783989 1.507036 - 16.56877 4.1 3.59 0.778708 1.505358 - 16.41456 4.1 3.59 0.795872 1.473318 0.992898 17.12832 Gusaca 081211 -03 4.5 3.56 0.660424 0.842327 0.974363 16.50715 Gusaca 1GUA-8.1 8.1 3.35 0.48429 0.582584 0.814542 12.51973 Gusaca 2GUA 11.1 11.1 3.18 0.627896 1.036077 - 14.4002 Gusaca 1GUA 15.1 15.1 2.86 0.666778 1.491767 - 13.02181 Gusaca 081211-08 17 2.64 0.71727 1.466053 0.986388 14.73286 Gusaca 081211-09 18 2.54 0.619047 0.724288 0.937843 15.89375 Gusaca 081211-10 22 2.20 0.699275 0.660949 0.971875 18.73995 Gusaca 081211-11 23.5 2.08 0.599358 0.665286 0.981861 15.61791 Gusaca 081211-12 26 1.86 0.644446 0.733483 0.96921 16.62897 Subachoque 081311-01 0 2.79 0.60752 1.463301 0.972386 11.34619 Subachoque 1SUB 1 1 2.58 0.757446 0.97238 - 18.77744 1 2.58 0.750955 1.021481 - 18.2978 Subachoque 2SUB 1.1 1.1 2.56 0.774847 1.086313 - 18.67086

Table 3.1: Calculated MBT’ and CBT indices, and reconstructed temperatures for all samples used in the study. BIT indices are listed for samples where isoGDGT measurements were recorded. 56 Sample Strat Age Section Name Level (m) (Ma) MBT' CBT BIT MAT Subachoque 081311-02 3.5 2.06 0.677456 1.019753 0.97751 16.02915 Subachoque 081311-03 5.5 1.76 0.598122 0.480023 0.988844 16.63005 Subachoque 2SUB 14.25 14.25 1.49 0.61085 0.960141 - 14.30233 Subachoque 2SUB 15.5 15.5 1.46 0.53009 1.088246 - 11.07242 Subachoque 2SUB 50 50 0.42 0.645822 1.253109 - 13.72535 50 0.42 0.641906 1.281904 - 13.4407 Tequendama 1TEQ 3.95 3.95 7.56 0.857527 1.33844 - 19.80439 Tequendama 1TEQ 4.3 4.3 7.55 0.914152 1.546444 - 20.38038 Tequendama 1TEQ 4.4 4.4 7.54 0.864102 1.272192 - 20.38382 4.4 7.54 0.857851 1.264496 - 20.2337 4.4 7.54 0.867557 1.267308 - 20.51862 Tequendama 1TEQ 4.9 4.9 7.52 0.879714 1.309192 - 20.658 4.9 7.52 0.88244 1.318308 - 20.69082 4.9 7.52 0.874059 1.308156 - 20.48859 Tequendama 1TEQ 7.1 7.1 7.41 0.845851 1.54737 - 18.25779 7.1 7.41 0.846283 1.544243 - 18.28891 7.1 7.41 0.836227 1.441389 - 18.56036 Tequendama 1TEQ 8.1 8.1 7.36 0.911386 1.463812 - 20.76314 Tequendama TEQ12-01 11 7.22 0.804164 1.390625 0.968239 17.85424 Tequendama TEQ12-04 14 7.13 0.949592 2.177165 0.99766 17.90284 Tequendama TEQ12-05 15 7.11 0.863885 1.390495 0.992619 19.70633 Tequendama TEQ12-06 16 7.09 0.616724 1.207565 0.979675 13.08155 Tequendama TEQ12-07 17 7.07 0.7212 1.414272 0.959449 15.14827 Tequendama TEQ12-08 18 7.05 0.728639 1.371457 0.948604 15.62166 Tequendama TEQ12-10 20 7.01 0.815446 1.542408 0.997213 17.34338 Tequendama TEQ12-11 21 6.99 0.908316 1.672836 0.98928 19.48281 Tequendama TEQ12-13 23 6.95 0.952352 1.563922 0.995512 21.46547 Tequendama TEQ12-14 24 6.94 0.321281 0.495152 0.913803 7.96221 Tequendama TEQ12-15 25 6.90 0.941325 1.563059 0.998409 21.12853 Tequendama TEQ12-19 29 6.74 0.675274 0.805906 0.979841 17.17402 Tequendama TEQ12-22 32 6.63 0.940668 1.412016 0.994283 21.96457 Tequendama TEQ12-27 37 6.43 0.558665 0.977154 0.98048 12.58814 Tequendama TEQ12-33 43 6.20 0.912182 1.762065 0.995885 19.09672

Table 3.1, Continued.

57 `

Figure 3.5: Plot of MBT’/CBT-based paleotemperatures versus depositional age. Temperatures are calculated from Equation 8; the shaded area represents the standard deviation of each section through time and the line follows the average trend. For comparison, pollen-based paleoelevation estimates from Hooghiemstra et al. (2006) are plotted as dashed rectangles.

3.5 GEOCHEMICAL RESULTS

3.5.1 GDGT-based paleoelevation estimates

Estimated temperatures for 39 samples from the three sections ranged from

11.1°C at Subachoque to 22.3°C at Tequendama. The average temperatures were 18.4°C (σ=3.2°C) for the Tequendama section, 16.3°C (σ=2.1°C) for the Guasca section, and 15.2°C (σ=2.9°C) for the Subachoque section. On average, this represents 3.2°C of cooling between the oldest section at Tequendama to the youngest section at Subachoque

58 (figure 3.5 and table 3.1). Despite the substantial scatter, the cooling trend is statistically significant between measured temperatures at Tequendama and both Guasca and Subachoque (p=0.012 between Guasca and Tequendama and p=0.010 between Subachoque and Tequendama, using a 2-tailed Student’s t-test assuming unequal variance). There is no statistically significant difference between temperatures measured at Guasca and Subachoque (p=0.308). The MBT’/CBT data from these locations appears suitable for temperature reconstruction due to high soil moisture in tropical regions. Of the 29 samples where the BIT index was measured (equation 3.5), all except one had values greater than 0.9, suggesting moist soils and minimal input of aquatic isoGDGTs (table 3.1). However, given the scatter in these data, a larger or smaller degree of cooling is also possible. In order to quantify the likelihood that differential cooling is supported by our data, we used a bootstrapping technique that randomly resampled the data from each section 10,000 times with replacement and recorded the difference in average temperature between sections in each resampled dataset. Over these 10,000 runs, the difference in average temperature between Tequendama and Guasca averaged 2.1 ± 0.8°C (1), and the difference in temperature between Tequendama and Subachoque averaged 3.2 ± 1.1°C (1) (see appendix). Therefore, our data support 0.6–3.6°C of cooling between the Tequendama and Gusaca sections, and a cooling of 1.0–5.3°C (2) between Subachoque and Tequendama.

However, for several reasons, the unweighted average of reconstructed temperatures at Tequendama may underestimate the true paleotemperature. First, because these samples are fine-grained sediments of a fluvial floodplain, it is possible that some lipids were transported from cooler, higher elevation regions. Recent work has shown that the brGDGTs in river sediments are primarily derived from nearby soils and 59 tend to produce reconstructed temperatures similar to local soils (Kim et al., 2012; Zell et al., 2014; Yang et al., 2013). Although it appears that the lipids are not transported great distances, the source effect could bias measured temperatures towards cooler values, particularly for fluvial sediments sourced from steep terrain (Yang et al., 2013; De Jonge et al., 2014). Second, some brGDGTs are produced in the water column of rivers and other terrestrial water bodies, potentially resulting in an underestimation of true temperature by the MBT’/CBT proxy of 2-3°C (Yang et al., 2013; Sun et al., 2011; Pearson et al., 2011; Tierney and Russell, 2009; Zell et al., 2013). However, such in-situ production in the water column is typically associated with an increased concentration of aquatic isoGDGTs, and is therefore often reflected in a lower BIT index (Yang et al., 2013; Kim et al., 2012). For this study, because the BIT index is close to 1 and isoGDGT concentrations were negligible in all except one measured sample, we consider this effect as less likely, although it remains a possibility for samples in which isoGDGT concentrations were not measured. These two characteristics of brGDGTs in fluvial systems suggest that the wide range of values measured for fine-grained floodplain sediments from the Tequendama section could be the result of variable input of transported lipids. In this case, the actual average paleotemperature at Tequendama could be 2-3°C warmer than the overall average (potentially closer to 20°C than the unweighted average of 18.4°C), which would result in an overall magnitude of cooling closer to 5°C since 7.6 Ma. In contrast, because paleosol samples from the Guasca and Subachoque sections likely represent in-situ values unaffected by transported lipids, we take the average value of these measurements at face value.

60

Figure 3.6: Plot of measured δD of the C29 and C31 n-alkane versus depositional age for all three sampled sections. Vertical δD error bars (vertical) represent the average error on multiple injections; age error bars (horizontal) represent the time spanned by combining stratigraphically adjacent samples.

61

Avg. Strat Age Age amt of δD C27 δD C29 δD C31 δD C33 Sample Name Level Range CPI (Ma) analyte (‰VSMOW) (‰VSMOW) (‰VSMOW) (‰VSMOW) (m) (Ma) (ng) 081311-09 47 0.51 441.9 2.78 - - - - 081311-01 0 2.79 992.25 3.68 - - - - 081211-15+16 37.5 0.85 0.18 715.79 2.95 -175.9 -148.8 -150.1 - 081211-13 31.5 1.38 1550.05 3.18 -177.8 -177.5 -177 -165

081211-07+08+09 17 2.64 0.11 907.27 2.87 -140.2 -148.7 -133.4 -131.3 081211-03+04+05 13 3.07 0.29 597.07 2.23 -170.2 -168.4 -162.4 - 081211-01+02 3.5 3.62 0.03 741.69 2.17 -163.3 -165.9 -161.2 -146.1 TEQ12-33 43 6.2 617.67 2.33 -170.3 -171.8 -164.4 -153.6

TEQ12-32 42 6.24 1840.55 2.52 -162.3 -151.3 -148.1 -142.6

TEQ12- 19 7.03 0.02 907.27 2.85 - -175.7 -163.9 - 08+09B+10 TEQ12-07 17 7.07 4530.82 6.7 -185.3 - -179.7 -197.7

-182.4 - -188.3 -192.7

TEQ12-03 13 7.15 1021.43 5.2 - -171.8 - -175.1

TEQ12-01 11 7.22 1379.13 3.96 -150.6 -156.1 -158.8 -151 - -156.4 -159.6 -153 081211- 23.5 2.08 0.17 927.46 1.94 -164.8 -165 -169.6 - 10+11+12* TEQ12-17+18* 27.5 6.8 0.02 553.96 1.43 -127.6 -128.1 -139.6 -133.2 TEQ12-02* 12 7.17 5642.29 0.85 -119.6 -122.3 -137 -130.7

-121.6 -122.4 -139.9 -

Table 3.2: Measured δD for each odd-chain n-alkane in the analyzed samples. Average amount of analyte (ng) is calculated as the average of the total amount of C27, C29, C31, and C33 remaining in the sample prior to isotopic measurements by GC-IRMS. Dashes indicate measurements where the peak amplitude was < 2000 mV or > 10000 mV; these measurements are not shown due to poor reproducibility outside of these ranges. * Indicates that the carbon preference index of these samples is low, consistent with post-depositional alteration. These samples were excluded from Figure 6.

62 3.5.2 δD of Leaf Waxes

Of the 48 samples extracted and purified, 43 samples had measurable quantities of long-chain n-alkanes. Due to low concentrations, several samples with similar n-alkane distributions from adjacent stratigraphic levels were combined to increase the total amount of analyte available for isotopic analysis. Ultimately, δD values are reported for 16 samples, including 7 composite values representing combinations of 2 or 3 original samples (table 3.2). Carbon preference indices for measured samples range from 0.85 to

6.70, with 13 of the 16 samples having a CPI greater than 2. CPI values less than two are suggestive of post-depositional thermal or microbial alteration; the 3 samples with low CPI values were therefore excluded from subsequent analyses (see Appendix). Measured δD values for the four reported n-alkanes (C27, C29, C31, C33) in each sample fell within a relatively narrow range (within 25‰) for all but one sample (081211-15+16). Because the C29 and C31 n-alkanes were the most abundant long-chain alkanes in most samples, we focus on temporal variations in the δD of these compounds. These values range from -188.3‰ to -122.3‰, with an average value of -158.4‰. There is no systematic difference between the average δD values measured for C29 (-157.4‰) and C31 (-159.4‰) n-alkanes; the average δD is -157.4‰. In contrast to the progressive temperature decrease inferred from the MBT’/CBT data, there is no clear relationship between δD and age (figure 3.6). In fact, there is no statistically significant difference between measured values at Tequendama and the two younger sections for either C29 or C31 n-alkanes (p=0.38 for C29 alkanes, p=0.88 for C31 akanes).

63 3.6 DISCUSSION

3.6.1 Paleotemperature data and implications for surface uplift

The new MBT’/CBT paleotemperature data suggest gradual cooling (1.0 – 5.3°C since 7.5 Ma) consistent with surface uplift. Below, we explore the implications of purely uplift-driven cooling, and then discuss other factors that may influence the interpretation of paleoelevations. To quantify the maximum amount of surface uplift that could have driven the cooling trend observed in our data, we employ the modern lapse rate derived from soil temperature loggers deployed across the Eastern Cordillera (Anderson et al., 2014).

MAT (°C) = -4.9 × elevation (km) + 28.0 (R2 = 0.97, RMSE = 1.0°C, p < 0.0001) (Eq. 3.8)

At face value, the net cooling of 3.2±1.1°C supported by our data would represent 650±220 m of total surface uplift from 7.6 Ma to present, with 430±160 m occurring by 2 Ma (the uppermost Guasca section). This suggests the Eastern Cordillera would have already attained an elevation of roughly 2000 m by the latest Miocene. Recognizing the possibility of transported lipids biasing the measured temperatures toward cooler values at Tequendama (7.6–6.1 Ma), we suggest that the data do not rule out a larger degree of surface uplift. In this case, as discussed above, there may have been an additional 2-3°C of cooling and corresponding uplift, which would yield closer to 1000 m of surface uplift. Well-documental global temperature shifts may slightly reduce the degree to which the observed cooling trend can be explained by elevation change. Modeling studies and global paleoclimate records agree that northern South America was likely 2- 3°C warmer during the late Miocene (Jeffery et al., 2012; Poulsen and Jeffery, 2011; 64 Zachos et al., 2001). However, there are few constraints on land surface temperatures in neotropical South America, with records from the Last Glacial Maximum suggesting a complex and spatially heterogeneous response to global cooling, making it difficult to prescribe an exact temperature correction (Pinot et al., 1999; Farrera et al., 1999; Thompson et al., 2003, 1995; Stute et al., 1995; Cruz et al., 2006). Even without an explicit correction, evidence for some degree of cooling independent of surface uplift suggests that our results constitute an upper bound on the amount of cooling that can be explained by elevation gain. This supports the interpretation that the ~2600-m-high

Bogotá plateau had already attained significant elevation (1600-2000 m) by ca. 7.6 Ma (the basal Tequendama section), which is near the upper limits of pollen-based inferences (700 ± 500m) of paleoelevation for these lowermost levels of the basin-fill succession (Hooghiemstra et al., 2006b).

3.6.2 Isotopic constraints on surface uplift

Although modern meteoric waters show a robust isotopic lapse rate (Saylor et al., 2009), our results show minimal change in the δD of leaf waxes since the late Miocene. Using the modern lapse rate for the δD of stream waters (equation 3.1), we predict a <14±6‰ decrease in stream water δD and leaf wax δD associated with up to 1000 m of surface uplift over this period. This change is relatively small and we contend that it could be obscured by scatter introduced in the preservation of meteoric δD in leaf waxes.

Recall that the use of leaf wax δD assumes a fairly narrow range of apparent fractionation

(εwax/MW) between meteoric waters and leaf waxes. Many studies have shown that plants with differing morphologies (i.e., trees, shrubs, forbs, and grasses) and photosynthetic mechanisms experience nonuniform fractionation factors as a result of isotopic

65 enrichment of leaf water via evapotranspiration or soil water evaporation (e.g., Sachse et al., 2006; Smith and Freeman, 2006; Kahmen et al., 2008; Pu and Weiguo, 2011; Sachse et al., 2012; Kahmen et al., 2013). For example, the fractionation factor εwax/MW for grasses can be 50‰ lower than trees (Krull et al., 2006; Pu and Weiguo, 2011); the fractionation factors of C3 grasses can differ from C4 grasses by 20‰ and from CAM (Crassulacean Acid Metabolism) plants by as much as 30‰ (Feakins and Sessions,

2010b; Smith and Freeman, 2006). Global vegetation shifts since ca. 7.6 Ma involved changes in species, plant morphology, and photosynthetic mechanisms (van der Hammen and Hooghiemstra, 2000). However, even with constraints on palynological changes associated with uplift, it is difficult to predict the change in fractionation factor when vegetation changes from a low-elevation tropical rainforest to a high-elevation neotropical alpine (páramo) landscape (Nelson et al., 2013; Tipple and Pagani, 2013; Feakins, 2013). Obstacles to the use of pollen data to correct sedimentary n-alkane δD results include the low pollen production rate of tropical trees that would result in underestimation of their abundance and the inability to distinguish C3 from C4 grass pollen without carbon isotopic constraints (Feakins, 2013; Tipple and Pagani, 2013; Kahmen et al., 2013; Nelson et al., 2013). However, the sum of many species-related changes in fractionation can be observed in aggregate, and has been strongly linked to changes in aridity (Hou et al., 2008; Douglas et al., 2012; Polissar and Freeman, 2010). In a transect across central

America, significant variations in δD of leaf waxes despite minimal variations in meteoric water δD (Douglas et al., 2012) can be explained by the aridity index (AI), defined as the mean annual precipitation (MAP) divided by mean potential evapotranspiration (PET). In Colombia, data from the CGIAR-CSI (Global Aridity Index) Climate Database show an aridity index from roughly AI=2 to AI=1 from the 66 modern foothills to Bogotá plateau. Over a comparable AI change in Central America, a

22‰ average decrease in the fractionation factor εwax/MW is observed (Douglas et al.,

2012). If the fractionation factor εwax/MW decreases with uplift, the measured composition of leaf waxes will be less depleted in deuterium relative to meteoric water, progressively enriching the leaf waxes relative to their typical offset from meteoric water. This relatively small change in aridity could sufficiently dampen the 14±6‰ decrease in δD values expected for leaf waxes at this time. In this framework, the results are within the range of error inherent in the n-alkane δD proxy, and are consistent with our paleotemperature data in suggesting less than 1000 m of surface uplift since 7.6 Ma. These considerations suggest that leaf wax δD for paleoaltimetry may be most effective in zones of large-magnitude uplift, as 1000 m of elevation change would only result in a 15‰ change in δD, which could be overprinted by εwax/MW variations associated with changes in aridity and plant species assemblages. In addition, a better understanding is needed of the relationship between changes in species assemblages and fractionation of meteoric water by plants. The tropical Andes in particular may represent an end-member for vegetation-driven changes in fractionation, due to the remarkable modern biodiversity and the dramatically different biozones revealed by pollen studies. Therefore, it appears these methods may exhibit greater sensitivity in temperate environments and the δD of leaf waxes should be combined with other proxies wherever possible.

3.6.4 Geodynamic implications

Results presented here inform the debate over proposed phases of rapid, high- magnitude uplift and their attendant geodynamic mechanisms. In the northern Andes,

67 most questions have centered on earliest uplift and possible latest Cenozoic shifts in orogenic dynamics. In terms of assessing the onset of uplift, our new paleotemperature and isotopic constraints are in agreement with regional geologic evidence for late Eocene-middle Miocene shortening and persistent, long-term exhumation of the Eastern Cordillera (Bande et al., 2011; Mora et al., 2010; Parra et al., 2010; Saylor et al., 2011; Saylor et al., 2012). However, potential <2-5 Ma shifts in northern Andean geodynamics include a proposed change from shallowly detached to basement-involved deformation (Dengo and Covey, 1993), a reorientation of stress directions related to shifts in Nazca-

Carribean-South America plate kinematics (Taboada et al., 2000), and increased strike- slip deformation and orogen-parallel tectonic extrusion (Trenkamp et al., 2002; Egbue and Kellogg, 2010). Even more critical is an accurate reconstruction of the magnitude and timing of crustal shortening. A synthesis of Eastern Cordillera deformation (Mora et al., 2008) shows ~30 km of total Cenozoic shortening on the eastern flank, with half of that accommodated by accelerated shortening since ca. 3 Ma. Thermochronometric data further suggest a comparable several-fold increase in erosional exhumation at ca. 3 Ma (Mora et al., 2008; Parra et al., 2009). Our new paleoelevation estimates suggest that past proposals for abrupt 6-3 Ma uplift (Gregory-Wodzicki, 2000; Hooghiemstra et al., 2006a) can be revised to a more gradual uplift history from 7.6 Ma to present. Given the extreme latest Cenozoic deformation, we consider upper-crustal shortening to be sufficient in driving the rate and magnitude of post-7.6 Ma surface uplift, although we do not rule out potential further contributions related to plate kinematics or other lithospheric processes. Continuous topographic development of the Eastern Cordillera since 7.6 Ma would be expected to drive progressively enhanced precipitation and erosion on the eastern flank of the Andes, potentially reaching threshold conditions in which further rock uplift may be balanced by 68 intense erosion (e.g., Mora et al., 2008). This is consistent with the presence of extremely thick, coarse deposits of latest Miocene-Pliocene age that are found in the Llanos Foreland (e.g., Parra et al., 2010)

3.7 CONCLUSIONS

Paleotemperature estimates from the MBT’/CBT proxy suggest 3.2 ± 1.1°C of cooling since 7.6 Ma, supporting the broad conclusions of previous pollen-based paleoelevation estimates for Andean uplift of the Bogotá plateau in the Eastern Cordillera of Colombia. However, although the temperature shift is statistically significant, it suggests less than ~1000 m of uplift since 7.6 Ma, especially in light of the well- documented 2-3°C of global cooling since the late Miocene.. The lack of a comparable shift in the δD values of leaf waxes may reflect changes in fractionation between leaf waxes and meteoric water and/or the potential product of dramatic temporal and spatial changes in aridity and vegetation type associated with uplift. Based on our paleotemperature constraints, we propose that the Eastern Cordillera was partially elevated prior to 7.6 Ma, consistent with geologic evidence for pre-late Miocene shortening. In addition, the results suggest that intensification of erosion associated with orographic precipitation played an important role in mitigating the topographic development of the Eastern Cordillera; it is likely that that the less than 1000 m of elevation gain was attained since the late Miocene during a phase of extremely rapid shortening.

69 Chapter 4: The uplift of the Garzón massif: implications for the development of the Magdalena and Orinoco River systems

ABSTRACT

The Garzón massif defines a major topographic barrier (2500 m), forms a large climatic rain shadow, and represents the largest exposure of Precambrian basement in the northern Andes. The massif and its corresponding foreland uplift define the headwaters and drainage divides of the Amazon, Orinoco, and Magdalena Rivers. A detailed understanding of the topographic development of the Garzón massif is critical to the evolution of these major river systems draining northern South America. However, the Garzón history of uplift-induced exhumation and its relationship to the structural evolution of the broader northern Andes remain unclear. The region has undergone major shortening since the Oligocene, with significant fold-thrust deformation and topographic development in the Eastern Cordillera occurring during the late Miocene. On the basis of extensive, coarse-grained deposits of late Miocene-Pliocene age, previous studies have inferred uplift of the Garzón massif during the late Miocene, coincident with a rapid rise in topography elsewhere in the Eastern Cordillera. We take an integrated, multi-proxy approach to more precisely constrain topographic growth and distinguish between exhumation and surface uplift of the massif. We present new U-Pb detrital zircon provenance data, sandstone petrographic data, and paleo-precipitation data from upper Miocene clastic fill in the adjacent Upper Magdalena Valley, the modern hinterland basin. In addition, six new apatite fission-track (AFT) ages from the massif directly constrain its recent exhumation history. These data indicate that while exhumation began at approximately 12.5 Ma, a substantial topographic barrier was not established until at least 6-7 Ma, when over 3 km of material was exhumed over a 2-3 Myr period. Thermal history modeling of the fission-track data suggest diminished

70 exhumation from 3-4 Ma until the present, likely due to oblique Nazca-South America convergence. These results are consistent with biological data suggesting divergence of the three river systems by 10 Ma, with subsequent transcontinental drainage of the Amazon River.

71 4.1 INTRODUCTION

The northern Andes form the major topographic barrier separating the modern Orinoco, Amazon, and Magdalena river watersheds. Although these rivers have collectively drained the northern half of South America, governed much of Andean exhumation, and influenced Caribbean and Atlantic ocean chemistry, their genesis remains highly debated (e.g., Figueiredo et al., 2009; Hoorn et al., 2010; Sacek, 2014). During Paleogene time, most of northern South America drained northward into the

Caribbean Sea, forming a large delta in the Maracaibo Basin of Venezuela (Díaz de Gamero, 1996). Today, the Orinoco River empties into the equatorial Atlantic >1000 km farther east and the Magdalena River of Colombia is now the largest single contributor of sediment to the Caribbean Sea. The disruption of the original northward drainage configuration and the establishment of independent Orinoco, Amazon, and Magdalena systems is critically tied to the uplift of the Eastern Cordillera in Colombia. Paleocurrents, palynological assemblages, and fossil data suggest that Amazon capture of the southern Orinoco drainage and isolation from the Magdalena system was primarily driven by Neogene uplift of the Eastern Cordillera and Mérida Andes (Hoorn et al., 1995; Hoorn, 1994; Díaz de Gamero, 1996). In contrast, some reconstructions of the Maracaibo basin and Caribbean margin suggest that an independent Magdalena River was already established by ~30 Ma (Mann et al., 2006; Castillo and Mann, 2006; Escalona and Mann, 2006; Lugo and Mann, 1995). Although uplift of the Mérida Andes was long considered the driving mechanism behind the delineation of the Orinoco and

Amazon systems, more recent work has recognized the fundamental influence of sediment accumulation and foreland basement arches on drainage evolution (Mora et al., 2011).

72 Understanding the evolving configuration of the Orinoco, Amazon, and Magdalena systems has fueled a vigorous debate about the formation of the modern transcontinental Amazon River, with estimates ranging from a middle Miocene to Pleistocene onset of transcontinental drainage (e.g., Potter, 1997; Campbell et al., 2006; Campbell, 2010; Latrubesse et al., 2010). New constraints on the precise timing, mechanics, and geomorphology of the transition from early Cenozoic to modern drainage configurations are critical to accurate reconstructions of past rivers and their implications for the geological and biological dynamics of South America (e.g., Ribas et al., 2012;

Baker et al., 2014). In this study, we utilize a multiproxy approach in order to more precisely constrain the timing of separation of the Magdalena River system. We focus our study on the Garzón Massif, a large exposure of Proterozoic basement that bounds the Andean headwaters of the Magdalena watershed, and whose foreland counterpart (the Vaupes arch) forms the Amazon-Orinoco drainage divide. We constrain the exhumation age of the massif by studying Neogene clastic deposits of the adjacent Upper Magdalena Valley basin, using sediment provenance techniques to track the influx of basement-derived material, and apatite fission-track (AFT) thermochronometry to constrain the timing and pace of exhumation. In addition, we use two paleosol-based paleoclimatic proxies to place bounds on initial development of the modern topographic barrier and its corresponding orographic rain shadow.

4.2 GEOLOGIC SETTING

The northern Andes of Colombia contain the record ofCenozoic advance of fold- thrust systems in the Eastern and Central Cordillera. The Central Cordillera formed the

73 Late Cretaceous-Paleogene thrust front, while the Eastern Cordillera developed later as a bivergent contractional belt reactivating a Mesozoic rift system (Dengo and Covey, 1993; Mora et al., 2006). These north-trending range systems are separated by the Magdalena Valley, which narrows towards its southern headwaters, where the Eastern and Central Cordilleras converge (Figure 4.1). The Upper Magdalena Valley is bounded in the west by the Chusma Fault system, an Eocene-Oligocene thrust system at the eastern front of the Central Cordillera (van der Wiel et al., 1992; Sarmiento and Rangel, 2004; Mojica and Franco, 1990; Butler and Schamel, 1988). The eastern boundary is defined by the

Algeciras fault system, a transpressional right-lateral fault system (Velandia et al., 2005; Chorowicz et al., 1996; Montes et al., 2005) bounding the Garzón Massif at the southern tip of the Eastern Cordillera. This complex fault zone marks the southern expression of major transpression in the northern Andes, and has been active as a strike-slip fault since at least 2 Ma (Chorowicz et al., 1996; Velandia et al., 2005). However, the pronounced structural relief across the >150 km-long fault system requires dip-slip displacement for a significant portion of its history (e.g., Bakioglu, 2014). This transition is thought to have occurred at the beginning of the Pliocene, but the precise timing remains unclear. The long-term burial and exhumation history of the Garzón Massif is poorly constrained. Eocene strata have been mapped in nonconformable contact with the

Proterozoic basement on the eastern flanks, suggesting that it was exposed at the surface as recently as the middle to late Eocene prior to Neogene burial (Rodríguez et al., 2003;

Bakioglu, 2014). The massif was exhumed during the late Miocene-Pliocene, although precise constraints on the timing of uplift remain elusive. Two apatite fission-track ages have been reported to date to 13.9±2.3 and 9.2±2.0 Ma (van der Wiel, 1991), while other estimates based on structural and stratigraphic constraints range from 12.9 Ma (Guerrero, 1997, 1993) to 6.4 Ma (van der Wiel et al., 1992; Butler and Schamel, 1988). This broad 74 spread of the estimated uplift age is reflective of the diverse approaches taken by the limited previous efforts; we aim to refine these estimates by integrating new data capable of providing a more complete picture of the massif’s topographic history.

Figure 4.1: Map showing major tectonic provinces within Colombia, including drainage divides between Orinoco, Amazon, and Magdalena River systems. Geologic map of inset area is shown in Figure 4.2A

75

Figure 4.2: (A) Geologic map of the Garzón Massif and Neiva Basin. Adapted from a national geologic map of Colombia (Gómez et al., 2007). (B) Geologic map of the La Venta Area. Sampling transects spanning the Honda group are indicated in red; the transect numbers are indicated for each sample discussed. (C) Geologic map of the Gigante Field area. The sampling transect through the Gigante Formation is shown in red (numbered 6), and the locations of each of the 6 AFT samples is indicated in red circles. Both (B) and (C) are adapted from a geologic map of the Huila Department in Colombia (Marquínez and Velandia, 2001).

76 4.3 STRATIGRAPHY

We studied two field localities in the Neiva Basin and obtained samples spanning 13.8 to 6.4 Ma. This stratigraphic interval is exposed in two main areas, with the older Honda Group exposed in the northern basin (La Venta site; Figure 2B) and the younger Gigante Formation exposed at the southernmost basin limit (Gigante site; Figure 2C). Due to variable exposure and accessibility, all Honda and Gigante samples were taken from these field localities. There have been numerous efforts to establish a stratigraphic framework for Neogene clastic fill in the Upper Magdalena Valley, resulting in a variety of naming schemes for the same stratigraphic sections (Wellman, 1970; van Houten, 1976; Guerrero, 1997). Here we use the stratigraphic and geochronological framework for the Miocene Honda Group detailed in La Venta paleontological studies in the northern Neiva basin (Flynn et al., 1997; Guerrero, 1997, 1993). For the upper Miocene- Pliocene Gigante Formation, exposed primarily in the southern Neiva Basin, we use the nomenclature of van der Wiel et al. (1992), although their Honda divisions differ slightly from those of Guerrero et al. (1997).

4.3.1 Northern Site: La Venta

At La Venta, the Honda Group is divided into the La Victoria and Villavieja formations. The units are separated by a 5-10 m clast-supported pebble conglomerate, the Cerbatana Conglomerate, which is observed across the northern Neiva basin

(Guerrero, 1997). The older La Victoria Formation is mainly composed of bioturbated mudstones, with decimeter-scale bands of grey, purple and green. Many intervals contain carbonate nodules, with well-developed rhizoliths near the top of the section. These claystones and siltstones are periodically interrupted by meter-scale, trough cross- bedded sandstones with erosive bases. While most pinch out over 20-30 m, a few thicker 77 sandstone intervals are continuous across the 2-4 km basin width and form important marker beds (Guerrero, 1997). The La Victoria type section is ~500m thick, but it increases southward to ~1000 m near Gigante. The formation has been interpreted as a meandering river system, with significant soil development on fine-grained overbanks (Guerrero, 1997; Wellman, 1970). The Villavieja Formation, 580 m thick in its type section, conformably caps the

Cerbatana Conglomerate. While the base contains fossiliferous green and grey mudstones and paleosols similar to the La Victoria Formation, the upper 350 m is characterized by brilliant red paleosols and mudstones referred to as the Polonia Red Beds. Soil carbonate nodules are less well-developed and less common in the red beds, but still found in discrete horizons. Trough cross-bedded sandstones occur more frequently in this formation, with their abundance relative to overbank deposits increasing upsection. In contrast to the multistory channel belts of the La Victoria

Formation, these sandstones occur as restricted 1-5 m-thick channelized units that extend no further than 10-20 m along strike. The contact between the Villavieja and overlying Gigante Formation (Huila Group of Guerrero, 1997) is marked by a shallow angular unconformity at La Venta, but is conformable to the south near Gigante (van der Wiel et al., 1992; Guerrero, 1997). In addition, the Villavieja Formation is recognized near

Gigante, but is thinner (~250 m) and has a higher proportion of fluvial channel to overbank facies than La Venta (van der Wiel and van den Bergh, 1992).

The chronology of the Honda Group has been established through magnetostratigraphy and 40Ar/39Ar geochronology of 31 samples (hornblende and plagioclase) of interbedded tuffs. The Honda Group spans from 13.8 to 11.6 Ma, with the La Victoria-Villavieja contact estimated at 12.5 Ma (Flynn et al., 1997; Guerrero, 1993) (Figure 4.3). Based on these age constraints,it appears that the sedimentation rate 78 for the La Victoria Formation was slower than the overlying Villavieja Formation (380 m/Myr vs 530 m/Myr). This finding is consistent with the observed change in sedimentary architecture, in which the La Victoria Formation is characterized by broad, amalgamated channel belts, and narrow, isolated channel bodies are much more common in the Villavieja Formation (e.g., Leeder, 1977; Mackey and Bridge, 1995; Heller and Paola, 1996)

In addition, paleocurrents were measured on trough cross-bedded sandstones throughout the Honda Group using measurements of at least 10 right and left limbs at each site (DeCelles and Schwartz, 1983). The lower two intervals in the La Victoria Formation display predominantly east-directed flow, while the middle three, including the Cerbatana Conglomerate, appear to alternate between east- and west-directed paleoflow. These are capped by an interval of northward flow observed in the upper two samples of the Villavieja Formation (Figure 4.5). The combination of the paleocurrent shift with the changes in sedimentation rate and channel architecture is considered indicative of the transition from an unconfined, east-directed meandering drainage system to an axial drainage system confined by the development of topography to the east of the La Venta Area (e.g., Guerrero, 1997).

4.3.2 Southern Site: Gigante

In the southern Neiva Basin, near Gigante, the type section of the Gigante

Formation (Neiva, Los Altares, and Garzón members) is exposed at Quebrada Guandinosita (van der Wiel, 1991). The ~150 m thick Neiva Member conformably caps the upper Honda Group and represents a significant shift in depositional environments, with the lower 50 m predominantly composed of pebble conglomerates and coarse,

79 trough cross-bedded sandstones with lenticular channel fill features. The intermediate 50 m is a poorly exposed, mudstone-dominated interval capped in turn by 50 m of multistory clast-supported conglomerates with clasts as large as 30 cm (van der Wiel, 1991). The upper 50 m is interpreted to represent a north-flowing braided fluvial system resulting from a post-Honda increase in sediment supply (van der Wiel et al., 1992). The transition to the Los Altares Member is marked by a decrease in grain size, with the lower 70 m characterized by alternating mudstones and sandy channel deposits. The most significant change in this member is the appearance of pumice-rich volcaniclastic debris flow deposits separated by thin sandstone intervals with erosive bases (van der Wiel, 1991). Upsection, planar-laminated volcaniclastic sandstones dominate. Clasts from the Garzón Member are predominantly metamorphic, suggesting a shift in sediment source from the Central Cordillera magmatic arc to the Garzón Massif. The age of the Gigante Formation is established by biotite and hornblende K-Ar ages of volcaniclastic rock. These dates constrain the Los Altares Member to 8.0 Ma at the base to 6.4 Ma at the upper transition to the Garzón Member (van der Wiel et al., 1992; van der Wiel, 1991). Further constraints are provided by new maximum depositional ages from the youngest U-Pb populations in two of our detrital zircon samples: 8.1 ± 0.4 Ma from the upper Neiva Member, and 7.5 ± 0.4 Ma from Los Altares volcaniclastic debris flows (Samples Z9 and Z10, Table 4.2). The U-Pb age constraints are consistent with existing K-Ar results, suggesting that the previous work provides robust estimates for the age of the Gigante Formation.

80

Figure 4.3: Chronostratigraphy of the Honda Group and Gigante Formation, adapted from Guerrero et al. (1997). Sampling transects shown in Figure 4.2B and 4.2C are plotted against the stratigraphy.

81 4.4 SEDIMENTARY PROVENANCE

4.4.1 Sandstone Petrography

To determine the provenance of the Miocene Honda Group and Gigante Formation, petrographic thin sections were prepared for 15 sandstones from the northern (La Venta) and southern (Gigante) segments of the Neiva basin for modal compositional analyses. Thin sections were stained for calcium and potassium feldspars to aid in grain identification. For each sample, at least 300 point counts of mineral grains greater than 62.5 µm were recorded according to the Gazzi-Dickinson method (Ingersoll et al., 1984). Primary classifications are listed in Appendix C.3. The counts were aggregated into composite metrics of quartz, feldspar, and various lithic fractions for plotting in standard ternary diagrams (Dickinson et al., 1983). Enigmatic microcrystalline grains with iron oxide staining added some uncertainty in distinguishing components of the lithic fraction, particularly in terms of chert versus microcrystalline metamorphic fragments. Therefore, we focus on Q-F-Lf and Qm-F-Lt rather than Qt-F-L ternary diagrams, in order to exclude chert from the quartz fraction. Samples throughout all three formations exhibit heterogeneous compositions relatively evenly distributed among quartz, feldspar and lithic fractions. Most samples are categorized as lithic arkose or feldspathic litharenites (Figure 4.4B), with the La Victoria Formation slightly more quartzose than the overlying Villavieja and Gigante formations. An exception is uppermost Gigante sample S15, which has the largest quartz

(Q) fraction at 50.2% (Table 4.1). Total feldspar (F) is relatively constant throughout the La Victoria and Villavieja formations, with the exception of sample S5, which is significantly richer in plagioclase and non-volcanic lithics than the rest of the Honda Group. This sample, from immediately below the Cerbatana Conglomerate, may highlight a slight provenance change linked to the transient shift in depositional 82 conditions. The Gigante Formation samples (S11-S15) tend to have much higher proportions of volcanic lithics and feldspars than samples from the Honda Group (Table 1; Figure 4.5), likely due to greater proximity to magmatic arc rocks of the Central Cordillera.

Figure 4.4: Ternary diagrams of the composition of sandstones measured in the two field locations. Sample numbers are listed inside the circles, and are color coded by formation: white circles for the La Victoria Fm., grey circles for the Villavieja Fm., and black circles for the Gigante formation.

83 Figure 4.5 Figure 4.4A Figure 4.4B

Transect Accessory Formation Sample Number Total Q F Lv Ls + Lm Qm F Lt Q F Lf Number Minerals

La Victoria S1 1 306 125 49 59 40 33 88 49 136 125 49 98 La Victoria S2 1 302 127 56 41 48 30 96 56 120 127 56 76 La Victoria S3 1 312 129 51 86 42 4 98 51 159 129 51 111 La Victoria S4 2 306 100 56 107 6 37 74 56 139 100 56 113 La Victoria S5 2 304 81 106 40 57 20 67 106 111 81 106 82 Villavieja S6 3 312 108 71 94 30 9 71 71 161 108 71 124 Villavieja S7 4 300 89 43 92 72 4 71 43 182 89 43 139 Villavieja S8 4 313 107 31 92 72 11 73 31 198 107 31 164 Villavieja S9 5 309 98 57 97 56 1 81 57 170 98 57 136 Villavieja S10 6 302 107 36 77 77 5 72 36 189 107 36 154 Gigante S11 6 305 93 43 89 53 27 71 43 164 93 43 123 Gigante S12 6 300 52 82 133 7 26 44 82 148 52 82 140 Gigante S13 6 304 63 116 91 2 32 61 116 95 63 116 93 Gigante S14 6 302 64 139 70 7 22 60 139 81 64 139 70 Gigante S15 6 300 139 75 62 1 23 135 75 67 139 75 63

Table 4.1: Results of point counts from petrographic thin sections of sandstones collected from the Honda Group and Gigante Formation. Pooled categories listed are consistent with the conventions outlined by (Dickinson et al., 1983; Ingersoll et al., 1984); the original point counts are included in Appendix C.3. Transect number listed refers to the sampling transects mapped on Figure 4.2B and 4.2C; precise sample locations are included in Appendix C.1.

84

Figure 4.5 Results of sedimentary provenance analyses. Relative abundances of the major constituents of each sandstone sample are shown in the center column; the data used to generate this plot is shown in Table 4.1. Histograms of the ages of measured zircon grains are shown at the right with probability density functions superimposed for each sample.

85

Average Age of Number Sample Youngest Grains ± of Number (Ma) Grains Z2 13.32 0.44 12 Z3 13 1 8 Z5 13.25 0.48 7 Z9 8.13 0.44 13 Z10 7.51 0.39 16

Table 4.2: Maximum depositional ages constrained by the young detrital zircon populations. Ages were determined as the average of 7 or more youngest grains in a coherent population; all samples containing young zircons except Z5 appear to closely match depositional ages determined by 40Ar/39Ar dating and magnetostratigraphy (Guerrero, 1993; Flynn et al., 1997; van der Wiel et al., 1992).

4.4.2 Detrital Zircon Geochronology

We analyzed 11 sandstone samples (7 La Venta, 4 Gigante) with detrital zircon

U-Pb geochronology. Samples were crushed and zircon grains separated using standard techniques, including a water table, heavy liquids, and magnetic separation. A random selection of ~120 zircons in each sample were measured via laser ablation-inductively coupled plasma mass spectrometry (LA-ICPMS) at the University of Arizona LaserChron center, following standard analytical procedures (Gehrels, 2010; Gehrels et al., 2008). A known standard of Sri Lanka zircon was measured between every 5 unknown grains to correct for transient variations in inter- and intra-element fractionation over the course of the run. The 206Pb/207Pb age is reported for all grains with a 206Pb/238U age greater than 900 Ma; due to the relatively low abundance of 207Pb in younger samples, the 206Pb/238U age was reported for these grains. Individual measurements with errors greater than 10% in 206Pb/238U and 206Pb/207Pb ratios were discarded. For grains where the 206Pb/207Pb age 86 was reported, measurements exhibiting greater than 30% discordance or greater than 5% reverse discordance were also discarded. Samples with significant populations of young zircons were used to estimate a maximum depositional age by calculating the average age for 7 or more grains with tightly clustered ages (Table 4.2).

4.4.2.1 Source Areas

Age populations in our sample set are consistent with potential sediment source regions surrounding the Neiva Basin. In stratigraphic order, these sources include: (1) the Garzón Massif metamorphic basement, (2) Jurassic plutonic rocks, (3) recycled cratonic zircons from Cretaceous sedimentary rocks, and (4) Cenozoic volcanic rocks from the Central Cordillera magmatic arc. Below, we describe the age and distribution of these different sources. 1. Garzón metamorphic basement: The Garzón Massif is composed of several distinct metamorphic units with both igneous and sedimentary protoliths. Grains derived from the massif display a continuous range of ages spanning from the formation age of some protolith zircons at 1450 Ma to metamorphic ages at 990 Ma (Ibañez-Mejía et al., 2011; Cordani et al., 2005). Because metamorphic basement is not exposed west of the Garzón Massif, identification of these grains in the Neiva Basin would represent a robust indication of an eastern sediment source. 2. Jurassic plutonic rocks: The most significant volumes of intrusive igneous rocks in Colombia were emplaced during several phases spanning the Late Triassic through Jurassic, with typical ages ranging from 150 to 170 Ma and from 190 to 201 Ma in the southern part of the Eastern Cordillera (Aspden et al., 1987; Bustamante et al.,

87 2010; Villagómez et al., 2011). Plutons intruded into Precambrian basement are situated on both the eastern and western sides of the Neiva Basin (Figure 4.2A). 3. Recycled cratonic zircons: Cretaceous sedimentary rocks in Colombia display great variation in their detrital zircon age spectra. Lower Cretaceous stratigraphic units are often heterogeneous, containing significant populations of Cambrian-Ordovician age (400-500 Ma), Sunsas-Grenville age (~1000 Ma), and Proterozic grains ranging from

1300 to 2060 Ma (Horton et al., 2010) . The abundance of younger grains diminishes upsection, such that uppermost Cretaceous units are dominated by ages ranging from

1300 to 2060 Ma, likely due to gradual burial of Andean sources and dominance by cratonic sources (Horton et al., 2010). Cretaceous strata were deposited disconformably over Jurassic sedimentary and plutonic rocks, and are abundant on both flanks of the Neiva Basin. 4. Cenozoic volcanic rocks: The Central Cordillera has been part of the active magmatic arc in Colombia since the Late Cretaceous (Montes, Cardona, et al., 2012; Montes, Bayona, et al., 2012; Bayona et al., 2012). Although minor volcanic provinces exist elsewhere, there are no mapped Cenozoic volcanic units at the southern end of the Eastern Cordillera (Pindell and Tabbutt, 1995; Cooper et al., 1995; Farris et al., 2011). Therefore, Cenozoic-age zircon grains in the Neiva basin can only be derived from a western source in the Central Cordillera.

4.4.2.2 U-Pb Results

Samples Z1-Z3 from the La Victoria Formation contain predominantly Cenozoic and Jurassic age zircon grains, suggesting a consistent Central Cordillera source during deposition. The abundance of young zircon grains allows calculation of maximum

88 depositional ages for two samples: 13.3 ±0.4 Ma (N=12) for Z2 and 13.0 ± 1.0 Ma (N=8) for Z3 (Table 4.2). These ages agree well with the existing paleomagnetic chronology, which places the La Victoria Formation between 13.8 Ma and 12.5 Ma (Flynn et al., 1997; Guerrero, 1993). Sample Z4, from the Cerbatana Conglomerate at the La Victoria-Villavieja transition, differs dramatically from the three La Victoria samples. Two new populations centered at 1450 and 1750 Ma occur in this sample, in addition to the Cenozoic and Jurassic populations observed in Z1-Z3. Although the population centered at 1450 Ma overlaps slightly with the ages of potential Garzón Massif zircons, the presence of the older population and lack of ~990 Ma grains suggests Cretaceous sedimentary rocks as the most probable source. While Cretaceous rocks are found on both sides of the basin, mapped relationships do not support significant Miocene fault motion in the Central Cordillera (Butler and Schamel, 1988; Mojica and Franco, 1990); these exhumed rocks are therefore more likely to have originated from the east side of the Neiva Basin. While sample Z5 returns to populations observed in Z1-Z3, sample Z6 marks an abrupt dominance of Jurassic grains, with 79 of 87 total ages between 170 and 200 Ma. Then, the Jurassic population is abruptly replaced by a heterogeneous mix of 1000-1650 Ma zircons in sample Z7. While the oldest zircons suggest some recycling of Cretaceous sources, the grains ranging from 1000 to 1450 Ma could have been sourced from the Garzón Massif. Notably, both samples Z6 and Z7 lack significant Cenozoic populations, suggesting no major western sediment source at this time. Detrital zircon results from the Gigante section are very similar to the Neiva section. The lower three samples (Z8, Z9, Z10) are all predominantly composed of Cenozoic and Jurassic grains, implying a western source similar to the first three samples in the Honda Group. The uppermost sample (Z11), originating from the Garzón Member, 89 contains a large population of grains spanning from 950 to 1500 Ma, and a minor population of Jurassic zircons, but is lacking any Cenozoic zircons. The age spectrum observed in sample Z11 is nearly identical to samples taken directly from the Garzón Massif (Ibañez-Mejía et al., 2011), and suggests a shift in provenance to the east. The alternating sediment sources observed in these samples after 12.5 Ma likely reflects either (A) the combined contribution of sediment from both the eastern and western flanks of the basin, or (B) competing alluvial fan systems originating from each side. Given the disappearance of evidence for western sources in sample Z7, and the disappearance of eastern-derived sources in samples Z8-Z10, (B) is most consistent with the patterns of sediment sourcing. We propose that the Neiva Basin was filled by competing distributary systems sourced from both sides of the basin from 12.5 to 6.4 Ma, with some evidence for mixing of the two sediment sources occurring in the earliest stages of development of the eastern topographic barrier.

4.5 APATITE FISSION-TRACK THERMOCHRONOMETRY

Apatite fission-track (AFT) analysis was used in order to precisely constrain the age of recent exhumation of the Garzón Massif structural domain. The abundance, length, and distribution of tracks resulting from the spontaneous fission of 238U provides constraints on the thermal history of a sample below apatite’s 100-120°C closure temperature (e.g., Donelick et al., 2005; Ehlers, 2005). We collected 10 samples along a short elevation transect across the Garzón Massif, crossing the strike-slip Algeciras fault along the western margin (Figure 4.2C). The 6 lower-elevation samples were found to have adequate apatite material for AFT analysis and their results are reported below.

90 4.5.1 Methods

Samples were crushed and milled, and the apatite grains were separated using a water table, heavy liquids, and magnetic separation. AFT analyses were performed by Apatite to Zircon Inc. (Viola, Idaho, USA), following the procedures outlined by Donelick et al. (2005). Apatite grains were mounted, polished, and etched with 5.5N

HNO3 to expose fission-tracks. Kinetic parameters were measured for each grain, and spontaneous tracks were counted to determine the AFT age. Uranium concentrations were then determined via laser ablation-inductively coupled plasma mass spectrometry (LA-ICPMS). Finally, the samples were irradiated with a 252Cf source, and re-etched to expose more spontaneous fission-tracks for measurement of track lengths. Chlorine concentrations for each grain (weight %) were measured using an electron microprobe. In order to distinguish multiple apatite populations having different kinetic parameters, track lengths, AFT ages, and U-Pb ages calculated for each grain were plotted against the multiple kinetic parameter rmr0 (Ketcham et al., 1999; Carlson et al., 1999) and examined for potential dependencies via the 2 test. Thermal histories for each sample were inverse-modeled using the HeFTy (version 1.8.3; June 2014), with track lengths projected onto the c-axis to account for track angle (Ketcham, 2005). Each modeling run was performed with the surface temperature fixed at 20°C and with the condition that the massif be buried below 140°C at some point between 40 and 10 Ma, a loose constraint placed by geologic evidence that the massif was buried from the Eocene until its eventual exhumation (Rodríguez et al., 2003).

91 AFT Age calculation AFT track length calculation AvgTrack Elev # Avg D Avg D Pooled # # of Avg Std Dev. Sample Latitude Longitude N ρ par per Χ2 prob 95 % CI Length (m) grains s s (μm) (μm) Age (Ma) grains tracks r (µm) mr0 (µm)

GM01 2.57525 -75.37080 676.9 27 27 6.5E-05 1.72 0.33 0.5576 3.59 (2.36, 5.47) 10 11 0.84 11.87 2.32 GM02 2.54568 -75.35829 789.4 21 18 3.8E-05 1.61 0.31 0.7736 3.91 (2.44, 6.28) 6 7 0.85 12.64 1.71 GM03 2.53648 -75.34039 879.5 40 66 0.00012 1.55 0.31 0.8923 4.6 (3.59, 5.9) 44 72 0.85 13.48 1.71 GM04 2.56324 -75.27018 1179.3 40 53 7.6E-05 1.55 0.34 0.0215 5.76 (4.37, 7.6) 11 19 0.83 14.32 0.28 GM05 2.57521 -75.25468 1282.2 32 57 8.8E-05 1.79 0.38 0.2443 5.37 (4.11, 7.01) 21 32 0.84 14.72 1.23 GM06 2.59105 -75.23712 1405 40 31 5.1E-05 1.6 0.34 0.9992 5.04 (3.51, 7.23) 21 28 0.83 14.41 1.28

Table 4.3: Summary of AFT data from the six samples analyzed. Ns = number of spontaneous fission-tracks; ρs = density (by area) of spontaneous fission-tracks

92

Figure 4.6. Results of the apatite fission-track analyses. HeFTy model results for each sample are shown in the left column; histograms of track lengths are shown in the right column. Magenta lines represent “Best Fit” paths; green lines represent “Acceptable Fit” modeled paths according to HeFTy modeling critera (Ketcham, 2005; Ketcham et al., 2009). AFT ages are indicated on each plot, with 95% confidence intervals indicated by the shaded grey rectangle. Typical ranges of closure temperatures for apatite fission-tracks are indicated by the dashed red lines.

93 4.5.2 Results

Apatite fission-track results for the six analyzed samples show consistent, elevation-correlated values, with pooled AFT ages ranging from 3.6 to 5.8 Ma (Figure 4.6; Table 4.3). All were completely reset, displaying unimodal distributions of track lengths with average values close to typical young, un-annealed track lengths of 12-16 m. Average track lengths for the three samples west of the Algeciras Fault were significantly shorter than those east of the fault, with average lengths of 12.7 µm to the west and 14.9 µm to the east (Table 3). Most samples were dominated by a single kinetic population, and in all but one case (GM05), fewer than three anomalous tracks were removed to obtain a single population for modeling. Our HeFTy modeling revealed that this uniform resetting requires that all samples were originally located below the zone of partial annealing (Ehlers, 2005; Ketcham et al., 1999), suggesting removal of a roughly 3-4 km overburden. Therefore, these calculated AFT ages are representative of the minimum age of the onset of exhumation, suggesting that fault motion initiated prior to the AFT age. The three samples from the west side of the fault (GM01, GM02, GM03) displayed steady, continuous exhumation since their cooling age, with an average exhumation rate of roughly 25°C/Myr since 3-4 Ma. Intriguingly, the samples on the east side of the Algeciras fault consistently showed convex exhumation paths with rapid exhumation from approximately 5.5-3 Ma followed by a protracted period of slow, near-surface cooling. These thermal histories are the product of the extremely long tracks preserved in these samples, as the only thermal models producing acceptable results require that the samples were first exhumed extremely rapidly and then remained near the surface where annealing rates were diminished (Figure 4.6).

94 The AFT ages broadly agree with sedimentary provenance data suggesting that major exhumation of the Garzón Massif is unlikely to have begun earlier than 10 Ma, with at least 3-4 km of exhumation since 6 Ma. However, the disparity in thermal histories on eastern versus western flanks of the Algeciras fault suggests some additional complexity. The convex exhumation paths displayed by the samples on the east side reflect a significant reduction in exhumation rate since 3-4 Ma, which could either be indicative of the transition to transpression, or to the propagation of the thrust front to the west. Although current geodetic measurements and satellite data show that the Algeciras

Fault is currently a right-lateral strike slip fault, there is at least a kilometer of structural relief across the fault (with basement rocks juxtaposed against Plio-Quaternary basin fill), suggesting an earlier history as a dip-slip fault (Chorowicz et al., 1996; Velandia et al., 2005). The AFT exhumational cooling paths should record this temporal transition. In this scenario, the eastern (hanging-wall) samples were rapidly exhumed along the southeast-dipping Algeciras fault prior to the early Pliocene, and then slowly approached the surface by erosion after transverse motion began to dominate along the fault. This is consistent with reconstructions of plate motion, which show that Nazca-South America plate convergence has become increasingly oblique since the Pliocene, as the northern Andean block began to rigidly escape to the northeast (Tibaldi and Leon, 2000;

Trenkamp et al., 2002; Norabuena et al., 1999). Samples on the western (footwall) side of the fault (GM01, GM02, GM03) show more continuous cooling paths since 4.6-3.6

Ma, consistent with propagation of the thrust front to the west, possibly due to strain partitioning as the Algeciras fault began to accommodate transverse motion. These data suggest that the most rapid exhumation occurred in the latest Miocene-Pliocene (5-6 Ma), and that overall exhumation likely slowed, but continued on different faults as a result of strain partitioning after 3-4 Ma. 95

4.6 PALEOSOL DATA

The previous results help constrain the timing of exhumation and rock uplift for the Garzón Massif, but do not provide direct constraints on surface uplift. To detect climatic changes that could be driven by the development of topography, we use two paleoclimatic proxies that could potentially help define the onset of the semi-arid conditions now characterizing the Upper Magdalena Valley (Tatacoa Desert) along the western flank of the Garzón Massif. Today, the 4-km-high massif forms an effective orographic barrier, intercepting moisture derived from easterly air masses of the intertropical convergence zone (ITCZ) (Rozanski and Araguás, 1995; Poveda and Mesa, 1997). The modern climate of the Tatacoa Desert is characterized by an intensely evaporative environment; although it receives 1300 mm/yr of precipitation, the potential evapotranspiration is estimated to be over 1600 mm/yr, resulting in some of the most arid conditions within Colombia (IDEAM, 2012). We utilize two independent paleoclimatic proxies to differentiate between a few possible scenarios in the uplift history of the range. Typically in paleoelevation studies, the oxygen isotopic composition of soil carbonates is assumed to be primarily controlled by Rayleigh distillation, and meteoric water is expected to become increasingly isotopically depleted at higher elevations (e.g., Quade et al., 2007; Hoke et al., 2009; Garzione et al., 2006). While this effect is also observed in low-elevation areas in the rain shadow of a mountain range, other effects may also influence the isotopic composition. In particular, an increase in aridity can also cause meteoric waters to be enriched due to increased evaporative fractionation. This scenario is precisely the one that we would expect to see in the Neiva basin; while meteoric waters should be depleted after their transit over the Garzón Massif, they should

96 also be increasingly subject to evaporative enrichment as the basin effect of the rain shadow intensifies and blocks moisture from entering the basin. Because it is unclear which of these effects should predominate and at what threshold each would take effect, we use the weathering index of paired paleosol samples to constrain the effect of increasing aridity associated with uplift. The weathering indices use well-known solubility relationships between major cations in order to estimate the degree of chemical weathering that has occurred since the original deposition of the soil (Maynard, 1992; Nordt and Driese, 2010; Sheldon et al.,

2002; Sheldon and Tabor, 2009). In order to standardize the measurement of weathering across a variety of soils, the abundance of soluble oxides are calculated in reference to oxides resistant to weathering, such as aluminum and silicon oxides. These are collectively referred to as weathering indices, and have been shown to correlate well with mean annual precipitation (MAP) (Nordt et al., 2014; Nordt and Driese, 2010; Sheldon et al., 2002). Although a number of these indices have been developed, we utilized two of them: the CIA-K index, which is calculated as Al2O3/(Al2O3 +CaO + Na2O)×100 and the

CALMAG index, defined as Al2O3/(Al2O3 +CaO + MgO)×100. We utilize the following empirical calibrations in order to reconstruct paleo-precipitation:

MAP = 18.64 · CIA-K – 350.4 (Eq. 4.1) MAP = 22.69 · CALMAG – 435.8 (Eq. 4.2)

The CIA-K calibration had a standard error of ± 146 mm/yr, and the CALMAG calibration had a standard error of ± 108 mm/yr in the calibration dataset. We selected these two calibrations in particular because their soil datasets included vertisols and inceptisols, similar to those observed in the La Venta Area (Guerrero, 1997). 97

4.6.1 Methods

We collected paired soil carbonate and bulk paleosol samples from horizons where soil carbonate nodules were present. 3-4 individual nodules were selected from each horizon, after being sawed in half and inspected for signs of alteration. The nodules were drilled with an abrasive microtool, and 150-550 µg of powder from each nodule was weighed out for analysis and placed into gas-tight vials. The vials were flushed with helium and reacted with 1 mL of phosphoric acid at 60°C for 24h. Carbon dioxide gas in the headspace of each vial was then measured using continuous flow- isotope ratio mass spectrometry in the stable isotope lab at the University of Texas, Austin. A laboratory standard of powdered marble was run between every 7 unknowns to correct for instrument drift, and an official NBS-18 and NBS-19 standard was run at the beginning and end of each session to tie the in-house standard to VSMOW values.

Bulk paleosol samples were crushed with a hammer and homogenized into a fine powder using a mortar and pestle; all tools were cleaned thoroughly with de-ionized water between samples. A few grams of powder from each sample were fused into a glassy disc using Li2B4O7, and were analyzed for their bulk elemental abundance using wavelength dispersive X-ray fluorescence spectrometry (WDXRF) at the Michigan State University XRF Lab. Measurement accuracy was determined by comparison to two laboratory standards, and is reported in Appendix C.6.

98

Figure 4.7: Carbonate nodule and paleosol measurements, plotted against the stratigraphic section for the La Venta area. Stratigraphic location is indicated by black bars next to the stratigraphic column. (A) The measured isotopic composition of soil carbonate nodules is shown; black circles indicate δ18O values, grey triangles indicate δ13C values. Each point represents the average of 3-4 nodules measured at each stratigraphic interval; the width of the shaded region represents the standard deviation of these measurements. (B) MAP reconstructed from the CIA-K (grey diamonds) and CALMAG (white squares) indices. Error envelope represents the calibration error for each proxy reported in the global calibration studies (Nordt and Driese, 2010; Sheldon et al., 2002).

99 Strat Transect Sample # of δ13C StdDev δ18O StdDev Level Number number nodules (‰VPDB) δ13C (‰VPDB) δ18O

1 1 LV1-17 4 -10.31 0.084 -9.08 0.210 2 1 060811-01 4 -9.71 0.173 -9.60 0.375 3 1 LV1-03A 2 -11.12 0.110 -8.89 0.071 4 1 LV1-20B 4 -10.74 0.360 -8.43 0.337 5 1 LV2-03A 4 -10.19 0.112 -9.44 0.097 6 1 LV2-5B 4 -9.67 0.794 -9.68 0.080 7 1 LV2-06A 4 -9.31 0.066 -9.15 0.039 8 1 060811-03A 4 -10.03 0.139 -9.52 0.138 9 1 060811-08A 4 -9.57 0.153 -8.81 0.106 10 1 060811-07A 2 -10.90 0.677 -8.85 0.571 11 1 060811-06A 4 -8.79 3.751 -8.91 0.664 12 3 LV3-12A 4 -12.76 1.281 -9.66 0.086 13 3 LVGB-10 4 -10.01 0.241 -8.42 0.153 14 3 LVGB-16B 1 -10.55 -9.36 15 3 LVGB-17B 4 -10.27 0.076 -9.18 0.276 16 3 LVGB-20 4 -10.83 0.485 -8.50 0.213 17 3 LVGB-21 4 -11.79 0.426 -8.75 0.349 18 3 LVGB-24A 4 -10.56 0.301 -8.88 0.228 19 3 ECGB-05 4 -16.86 1.615 -8.52 0.229 20 3 ECRB-10A 4 -12.14 0.467 -7.15 0.776 21 3 ECRB-09A 4 -13.06 1.934 -8.07 0.403 22 4 PRB-28A 4 -3.27 3.964 -9.37 0.443 23 4 PRB-31A 4 -9.03 1.165 -8.39 0.451 24 4 PRB-33A 4 -4.45 1.269 -9.14 0.446 25 4 PRB-62B 4 -14.40 0.765 -8.31 0.849 26 4 PRB-66B 4 -13.32 1.317 -8.28 0.230 27 5 PRB-18A 2 -11.62 0.767 -7.63 0.757 28 5 PRB-03A 4 -9.57 0.660 -7.09 1.344

Table 4.4: Summary of isotopic measurements of soil carbonate nodules. Values reported here are the average of 2-4 nodules measured from each stratigraphic interval. Individual nodule measurements are included in Appendix C.5.

100

Elemental Abundances Weathering Indices Mean Annual Precipitation (mm/yr) Strat Transect Al2O3 MgO CaO Na2O CIA-K CALMAG Level Number Sample (%) (%) (%) (%) CIA-K CALMAG Calibration Calibration 1 3 LV1-03 18.37 3.14 2.4 1.58 82.2 76.8 1181.7 1307.5 1 4 LV1-20A 15.04 1.89 1.57 1.75 81.9 81.3 1176.5 1408.8 1 5 LV2-3B 17.09 1.96 1.67 0.98 86.6 82.5 1263.4 1435.7 1 6 LV2-5A 17.45 1.96 1.53 0.72 88.6 83.3 1300.7 1455.0 1 7 LV2-6B 18.42 3.48 2.82 0.79 83.6 74.5 1208.2 1254.9 1 8 060811-3B 18.42 3.25 1.7 0.77 88.2 78.8 1293.2 1352.6 1 9 060811-8B 16.64 3.1 5.91 0.81 71.2 64.9 977.4 1036.2 1 10 060811-7B 14.74 2.89 8.64 1 60.5 56.1 776.6 837.3 1 11 060811-6B 15.62 2.78 2.18 1.74 79.9 75.9 1139.7 1286.3 3 12 LV3-12B 15.34 2.35 2.26 1.29 81.2 76.9 1163.3 1308.9 3 13 LVGB-11 18.33 3.69 1.58 0.79 88.6 77.7 1300.2 1326.5 3 14 LVGB-16A 17.86 3.37 2.2 0.86 85.4 76.2 1240.9 1293.8 3 15 LVGB-17A 18.42 2.22 1.15 0.89 90.0 84.5 1327.7 1482.3 3 17 LVGB-22 21.66 1.06 0.62 0.37 95.6 92.8 1432.1 1669.9 3 18 LVGB-24B 18.5 3.43 4.2 0.79 78.8 70.8 1117.6 1170.6 3 19 ECGB-3 18.06 3.32 1.7 0.83 87.7 78.2 1284.6 1339.7 3 20 ECGB-9B 15.16 0.98 1.37 0.72 87.9 86.6 1287.8 1528.7 3 21 ECGB-10B 18.16 1.28 0.92 0.33 93.6 89.2 1393.6 1588.0 4 22 PRB-28B 15.18 1.17 0.5 0.74 92.4 90.1 1372.8 1608.3 4 23 PRB-30A 15.95 1.06 0.39 0.32 95.7 91.7 1434.2 1644.1 4 24 PRB-32 18.83 1.34 0.88 0.19 94.6 89.5 1413.4 1593.9 4 26 PRB-66A 13.39 1.11 1.57 0.07 89.1 83.3 1310.2 1454.8 5 27 PRB-18B 12.27 1.07 2.89 0.13 80.2 75.6 1145.4 1279.6 5 28 PRB-3A 15.45 1.92 0.9 0.73 90.5 84.6 1335.7 1483.0 5 28 PRB-3A 19.96 1.49 0.81 0 96.1 89.7 1440.9 1598.8

Table 4.5: Summary of XRF data measured from bulk paleosol samples, including calculated indices and reconstructed mean annual precipitation (MAP).

101 4.6.2 Results and Interpretation

The oxygen and carbon isotopic compositions for 28 sets of carbonate nodules were measured; bulk elemental abundances of paleosol samples from the same interval were measured for 24 of those samples. Average 18O compositions for the carbonate nodules were remarkably consistent upsection, with an average value of -8.75 ‰ (VPDB) and a standard deviation of 0.69 ‰. The minimum measured isotopic composition was - 9.68‰ near the base of the La Victoria Formation, and the maximum value was -7.09‰

(Figure 4.7; Table 4.4). Carbon isotopic compositions were also relatively consistent, on average (µ= -10.5‰ VPDB), although the variability in carbon isotopic data increased dramatically in the Villavieja Formation, with 13C values ranging from -3.27‰ to - 16.86‰ near the earliest appearance of the red beds. The variation in carbon isotopic composition is consistent with changing saturation levels of the soils, with more enriched

(more positive) compositions sourced from atmospheric CO2 during periods when the soil was well-drained, and more depleted values representing CO2 derived from the breakdown of soil organic matter during times when the soil was saturated (e.g., Cerling, 1984). In this framework, we suggest that the soil carbonate nodules in the La Victoria Formation were formed in saturated soils, whereas the nodules in the Villavieja formation were formed in more variable conditions in which the soils were periodically well- drained and exposed to the atmosphere. CIA-K and CALMAG indices were calculated from the measured elemental abundances in the bulk paleosol samples, and the mean annual precipitation (MAP) was calculated using empirical calibrations that have been developed from reference datasets (Equations 1 and 2). The CIA-K and CALMAG reconstructions follow the same trend, although the CALMAG estimate tends to show 100-200 mm/yr less precipitation than the CIA-K estimate Figure 4.7; Table 4.5). Both show a dramatic excursion towards drier 102 values in intermediate levels of the La Victoria Formation, with a gradual increase in precipitation upsection through the Villavieja Formation. The lack of significant change in the isotopic composition of the soil carbonate nodules combined with the overall increase in precipitation reconstructed by the bulk elemental abundances in the paleosols suggests that the modern rain shadow was not present during deposition of the Honda Group. The lack of a major orographic barrier is consistent with provenance data from the La Venta area in suggesting that earliest faulting did not occur until the La Victoria-Villavieja transition. Therefore, initial exhumation and probable surface uplift of the Garzón Massif was only just underway during deposition of the uppermost Villavieja Formation, at roughly 11-12 Ma.

4.7 DISCUSSION

Our new results on sediment provenance and exhumational cooling provide an important context for the growth of major Andean barriers and the evolution of the Magdalena, Orinoco, and Amazon River systems. Topographic growth of major basement blocks in the northern Andes and adjacent foreland has diverted the Orinoco River eastward, revised the northern limit of the Amazon drainage, and funneled the Magdalena River along the narrow intermontane segment between the Central and Eastern Cordilleras of Colombia. Uplift of the Garzón basement massif (which defines the southernmost Eastern Cordillera) forms a critical element of this history, as it divided the Andean foreland basin from the intermontane Magdalena Valley and separated the Magdalena River from the Orinoco and Amazon Rivers. This topographic development also induced an orographic rain shadow, which is expressed in the modern semi-arid Tatacoa Desert of the Upper Magdalena Valley. The expected signatures of this uplift

103 event include (1) rapid exhumation of crystalline basement rocks composing the Garzón Massif and, in the Upper Magdalena Valley, (2) shifts in depositional pathways related to topographic diversion of sedimentary systems, (3) a stratigraphic appearance of distinctive erosional products from the Garzón Massif, and (4) post-depositional processes reflective of increased aridity. The apatite fission-track data provide the most direct window into the most recent exhumation history of the Garzón Massif. Our data suggest that basement exhumation was well underway by 5.4 Ma, and may also record the transition from predominantly compressional to transpressional tectonics. This shift is suggested by a significant decrease in modeled exhumation rates after 3-4 Ma, as observed in all three samples on the eastern side of the Algeciras Fault; strike slip motion along this fault had previously only been traced back to ca. 2 Ma. This suggests that the exhumation of the massif may have been extremely rapid during a transitional phase in which plate motions were realigning, and much slower when transpressional motion became well-established after 3-4 Ma. The more moderate, continuous exhumation since 4.5 Ma modeled on the western side of the fault may be representative of exhumation along a more westward thrust front due to strain partitioning. Sedimentary provenance data provide insight into the earlier exhumation of the massif prior to the Plio-Quaternary history illuminated by the AFT data. The earliest evidence for possible fault motion and exhumation east of the Neiva Basin is observed in the influx of Cretaceous-derived zircon grains and non-volcanic lithic fragments at 12.5 Ma. While this source disappears shortly thereafter, it may represent earliest motion on faults along the eastern flank of the Neiva basin and associated establishment of a drainage barrier related to initial uplift of the Garzón Massif. A more pronounced provenance shift occurs in the uppermost Honda Group (11.6 Ma), when possible 104 Garzón-derived zircons first appear, and Cenozoic grains indicative of a western source disappear. A similar trend is observed in the overlying Gigante Formation, in which a significant fraction of the material appears to be derived from the active arc to the west until the uppermost sample (~6.4 Ma), which is overwhelmingly dominated by Garzón- derived material. This apparent lag between the age of first appearance of material derived from the Garzón Massif may either be due to the Gigante site’s closer proximity to the Central Cordillera, or due to a time-transgressive exhumation pattern proceeding from north to south. In either case, this suggests a paleogeography in which substantial exhumation of material from the Garzón Massif was unlikely to have occurred until ~6.4 Ma. However, barriers to the eastern transport of sediment did clearly exist from 12.5 Ma onwards, as evidenced by a gradual transition from east-directed to north-directed paleocurrents, an increased sedimentation rate, and a change in sedimentary architecture in addition to the periodic appearance of eastern-derived material.

Paleoclimatic data spanning the deposition of the Honda group suggest minimal surface uplift from 13.8-11.6 Ma; oxygen isotopic compositions of soil carbonates display little change, and the Neiva basin become wetter, rather than more arid, as would be expected with the development of an orographic rain shadow. While the sedimentary provenance data do suggest that some barriers to drainage were forming during this time interval, it is unlikely, given the paleoclimatic data, that these constituted significant topographic features.

These findings are in agreement with biological data, which suggest that Magdalena flora and fauna were becoming distinct from Amazon by 10 Ma (Ochoa et al., 2012; Hoorn et al., 2010; Aguilera et al., 2013). The divergence of the Magdalena River system from the paleo-Orinoco/Amazon systems by 10 Ma lends support the hypothesis that the modern South American drainage network developed over a protracted period 105 beginning in the Late Miocene (Figueiredo et al., 2009; Hoorn et al., 2010; Shephard et al., 2010). While the impact of the Garzón Massif uplift on the separation of the Orinoco and Amazon watersheds is not immediately apparent, it provides a possible constraint on the timing of activation of related basement-involved structures in the foreland that form the modern drainage divide between the Orinoco and Amazon systems (Mora, Baby, et al., 2010; Wesselingh et al., 2006)

Based on these data, we interpret a 3-stage uplift history for the Garzón Massif. During the earliest stage, spanning from 12.5 to 6.4 Ma, punctuated fault motion was occurring on the east side of the Neiva basin, intermittently blocking eastward drainage, but not forming a substantial topographic barrier. While the precise timing of the end of this stage remains unclear, it likely occurred at the base of the Garzón Member of the Gigante Formation, with the arrival of coarse conglomerates bearing metamorphic clasts, and zircons with ages consistent with a Garzón Massif source. In the middle stage, spanning from at least 6.4 Ma until 3-4 Ma, extremely rapid exhumation occurred as shortening was accommodated via thrusting. In the final stage, from 3-4 Ma until the present, the rate of exhumation slowed dramatically, as the onset of significant transpressional stresses induced strike-slip fault motion along the Algeciras Fault, and the dip slip component was accommodated along shallower, east-dipping faults. The timing of major surface uplift in the Garzón massif is similar to other studies of the Bogotá plateau, which suggest that the most rapid phase of uplift occurred between 3 and 6 Ma

(Hooghiemstra et al., 2006; Anderson et al., 2015). The synchroneity of uplift for these two study localities over a distance of >250 km along strike within the Eastern Cordillera is suggestive of a shared geodymanic driver. Our results strongly suggest that basement uplift of the Garzón Massif may be indicative of significant geodynamic shifts during the Pliocene. In particular, 106 convergence between the Nazca and South American plates is currently oblique along the Colombian margin, due to tectonic escape of the entire northern Andean block to the northeast. A variety of mechanisms for the initiation of tectonic escape have been proposed. These include the collision of the Panama Arc (Farris et al., 2011; Montes, Bayona, et al., 2012; Montes et al., 2005), the onset of subduction of the buoyant Carnegie Ridge (Vargas and Mann, 2013; Gutscher et al., 1999, 2000; Egbue and

Kellogg, 2010), and a shift to more oblique convergence along Nazca-South American plate margin (Trenkamp et al., 2002; Kellogg and Vega, 1995; Freymueller et al., 1993).

Our interpretation of a Pliocene onset of strike-slip motion along the Algeciras Fault is most consistent with the estimated timing of subduction of the Carnegie Ridge, hinting at a possible link between the two. In addition, due to the shared basement configuration and close proximity of the Garzón Massif to the foreland structures forming the Vaupes Swell, we speculate that their uplift may share a driving geodynamic mechanism, consistent with previous work suggesting a similar timing based on structural and stratigraphic constraints (Mora et al., 2011; Wesselingh et al., 2006). These basement arches form the barrier between the modern-day Orinoco and Amazon River systems, and may play a key role in the initiation of transcontinental Amazon drainage. This underscores potential linkages among plate boundary processes, topographic uplift, and regional drainage, greatly impacting the biological dynamics of South America.

107

Figure 4.8: Schematic illustration of the interpreted tectonic history for the Neiva Basin and Garzón Massif. The pre-uplift configuration is shown in (A), with a Central Cordillera sediment source and rivers draining to the east. During the initial stages of uplift (B), Cretaceous and Jurassic units were exposed by thrusting, and a relatively low topographic barrier began to force a reorientation to north-directed axial drainage. From 6 to 3 Ma (C), exhumation and uplift intensified, and the Garzón Massif basement was exposed. Finally, after 3 Ma (D), exhumation slowed as the thrust front propagated westward, and strike slip motion initiated along the Algeciras Fault due to strain partitioning. 108 4.8 CONCLUSIONS

We characterize Neogene topographic development of the Garzón Massif by analyzing the sedimentary provenance of basin fill, apatite fission-track thermochronometry of basement rocks, and stable-isotopic signatures of climatic conditions in the Neiva basin. We find that (Figure 4.8):

(1) The earliest possible exhumation of structures on the east side of the Neiva

Basin is recorded by the 12.5 Ma influx of recycled cratonic zircons and non-volcanic lithic fragments indicative of a Cretaceous sediment source. Sedimentary sources alternate between western-derived and likely eastern sources until 6.4 Ma. (2) Minimal paleoclimatic changes that can be attributed to topographic uplift occur during the interval from 13.8 to 11.6 Ma. Evidence for the formation of an orographically induced rain shadow is lacking, in that reconstructed precipitation is shown to increase, rather than decrease over this time interval. In combination with

the shifting sediment sources inferred from the provenance analysis, these data suggest that there was a relatively low topographic barrier from 12.5 Ma until 6.4 Ma during the early stages of uplift. (3) Inverse modeling of fission-track data suggests profoundly different exhumation histories on either side of the strike-slip Algeciras fault. This may be due to the transition from predominant dip-slip motion to strike-slip motion along the Algeciras Fault during the Pliocene. Based on the mimimum exhumational ages

provided by the fission-track data, as well as the thermal history modeling results, we suggest that rapid exhumation occurred from 6.4 Ma until approximately 3-4 Ma. The Algeciras Fault has likely been a strike-slip fault since 3-4 Ma, consistent with

109 estimates that reconstruct right-lateral motion for at least 2 million years (Chorowicz et al., 1996; Velandia et al., 2005; Egbue and Kellogg, 2010; Egbue et al., 2014)

This sequence of events is consistent with the timing of major uplift and exhumation elsewhere in the Eastern Cordillera, suggesting a shared geodynamic driving mechanism. Our data also lend support to the hypothesis that the northern Andean foreland basin became overfilled in the Pliocene due to rapid widespread exhumation and delivery of material from the Eastern Cordillera.

110

Appendix A: Supplemental Data for Chapter 2

The supplemental data included in the publication of Chapter 2 (Organic Geochemistry, 2014. Vol. 69, pp 42-51) is available online at: http://dx.doi.org/10.1016/j.orggeochem. 2014.01.022.

111 Appendix B: Supplemental Data for Chapter 3

B.1 DETAILS OF MAGNETOSTRATIGRAPHIC ANALYSES

B.1.1 Representative Demagnetization Plots

In the following pages we show two samples from each studied stratigraphic section in order to show the quality of the demagnetization plots, and behavior of these samples under the chosen demagnetization schemes.

Samples from Subachoque are shown below in N-S orthographic view. AF steps 50-800 shown. Tickmark scale on axes is 10-5 Gauss. On the left is sample 2SUB7C, shown as an example of a well-behaved sample with an MAD = 4.2°; on the right is sample 2SUB19B, shown as an example of a more problematic sample, with MAD = 19.4°. Subachoque samples generally were the most strongly magnetized, and behaved well under AF demagnetization.

2SUB7 2SUB1 C 9B

112 Guasca samples are shown in N-S orthographic view. AF steps 50-800 shown. Tickmark scale on axes is 10-6 Gauss. On the left is sample 2GUA9C, shown as an example of a well-behaved sample with an MAD = 14.6°; on the right is sample 2GUA6C, shown as an example of a more problematic sample, with MAD = 15.6°. Guasca samples tended to be significantly more weakly magnetized than at Subachoque, and as a result the measured directions were a bit more scattered, but still generally demagnetized cleanly with AF.

2GUA6 C

2GUA9 C

113 Tequendama samples are shown below in N-S orthographic view. Thermal steps NRM-375 °C shown. Tickmark scale on axes is 10-7 Gauss. On the left is sample 1TEQT18C, with thermal steps NRM-375 °C shown, and MAD = 9.5°. On the right is sample 1TEQT25D, shown as an example of a more problematic sample, with thermal steps NRM-225 °C shown and MAD = 18.2°. The samples in the Tequendama section were generally characterized by extremely weak magnetization, and low unblocking temperatures, as is shown in these two samples.

1TEQT25 D 1TEQT18 C

114 B.1.2 Method for classifying paleomagnetic samples

At each stratigraphic level, 3 or 4 cores were taken; paleomagnetic measurements were taken on each core. In order to classify the quality of the data at each site, we determined the angular distance between each pair of points using the dot product. Each paleomagnetic direction (declination = φ, inclination = θ) was converted into Cartesian unit vectors using the following formulas:

x = sin(φ) cos(θ) y = cos(φ) cos(θ) z = sin(θ)

Then, the angle α between measured paleomagnetic directions was given by

α = cos-1[(v1 ∙ v2)/(∣v1∣∣v2∣)] = cos-1(v1 ∙ v2)

We computed the angular difference between all possible pairs of points, and classified them according to the scheme diagrammed below. “Good” samples were designated in cases where all points lay within 90 degrees of each other, “Fair” samples were designated in cases when the majority of points lay within 90 degrees, and “Poor” samples were designated when there was no consensus among measured paleomagnetic directions. In cases where measurements were disqualified due to having an MAD > 20 degrees, if only 2 samples remained, the highest classification that they could obtain was a “Fair” if they were within 90 degrees of one another. If only one sample remained, it was automatically classified as a “Poor” sample.

115

The final measured direction for each stratigraphic interval was computed by taking the average declination and inclination across all measurements. The error on these measurements (as shown in the error bars in Figure 4) is calculated as the average of the angular distances between all pairs of points. Below is the Python (v3.1.2) code used to perform this analysis.

116 import math as m import itertools def find_angle(v1, v2): # inputs: [dec, inc] in degrees for two vectors # Convert vectors to radians

v1r = [m.radians(x) for x in v1] v2r = [m.radians(x) for x in v2]

# Convert to cartesian coordinates x1 = m.sin(v1r[0])*m.cos(v1r[1]) x2 = m.sin(v2r[0])*m.cos(v2r[1]) y1 = m.cos(v1r[0])*m.cos(v1r[1]) y2 = m.cos(v2r[0])*m.cos(v2r[1]) z1 = m.sin(v1r[1]) z2 = m.sin(v2r[1])

# angle = acos(v1 dot v2) dot = x1*x2 + y1*y2 + z1*z2 angle = m.acos(dot)

return m.degrees(angle)

fin = open('PMag-all.csv') all_data = [] header = fin.readline().strip().split('\t')

# Read in the data. Text file is in tab-delimited format with # columns (indices listed in parentheses): # (0) Section Name , (1) Sample #, (2) Strat level (m), # (3) Declination (degrees), (4) Inclination (degrees), (5) MAD (degrees) for line in fin: temp = line.strip().split('\t') templine = [] for i in range(len(temp)): try: templine.append(float(temp[i])) except ValueError: templine.append(temp[i]) all_data.append(templine) fin.close()

# Initialize temporary storage vectors = [all_data[0][3:5]] cur_strat = all_data[0][2] summary = []

for i in range(1, len(all_data)): if all_data[i][-1] < 20.0: if all_data[i][2] == cur_strat: vectors.append(all_data[i][3:5]) else: # Find all unique pairs of points combos = itertools.combinations(range(len(vectors)), 2) angles = [] # Determine the angle between each measurement point for c in combos: angles.append(find_angle(vectors[c[0]], vectors[c[1]]))

117 # Simplify the angular differences: code 1 for difference < 90 , 0 for > 90 class_vec = [int(a < 90.0) for a in angles]

# Classify each sample based on angular differences. # A = all measurements agree (are within 90 degrees of each other # B = majority of measurements agree (or, only 2 points remain and both agree) # C = 50-50 split, or all measurements scattered.

if len(class_vec) > 3: if sum(class_vec) == 6: sam_class = 'Good'

elif sum(class_vec) >= 3: sam_class = 'Fair'

else: sam_class = 'Poor'

else if sum(class_vec) == 3: sam_class = 'Good' elif sum(class_vec) > 0: sam_class = 'Fair' else: sam_class = 'Poor'

try: avg_dec = sum([v[0] for v in vectors])/float(len(vectors)) avg_inc = sum([v[1] for v in vectors])/float(len(vectors)) avg_angle = sum(angles)/float(len(angles))

except ZeroDivisionError: avg_dec = vectors[0][0] avg_inc = vectors[0][1] avg_angle = -1.0

summary.append([all_data[i-1][0], cur_strat, sam_class, \\ avg_dec, avg_inc, avg_angle])

# Reset the sample list for the next entry cur_strat = all_data[i][2] vectors = [all_data[i][3:5]]

fout = open('pmag_summary.txt', 'w') fout.write('Section\tStrat Level\tClass\tAvg Dec\tAvg Inc\tAvg Angle\n') for i in range(len(summary)): fout.write(summary[i][0]) for j in range(1, len(summary[i])): fout.write('\t' + str(summary[i][j])) fout.write('\n') fout.close()

118 B.2 DETAILS OF THE HYDROGEN ISOTOPIC ANALYSES

Below are selected examples of chromatograms from samples used in this study. These chromatograms are from the GC-FID analyses that were used to screen the samples prior to isotopic analysis on the GC-IRMS.

Best quality. High abundances, good peak separation, strong odd/even preference:

119 Medium quality. Moderate to low abundances, fairly strong odd/even preference:

C29

C31 C27

C33

Poor quality. Low abundances, strong odd/even preference, but contains closely eluting peaks that were not removed during urea adduction. Typically these samples were combined with similar-looking samples from adjacent stratigraphic intervals:

120

Altered sample – discarded from dataset. High abundances of shorter-chain alkanes, low CPI (0.85):

C27 C29

C31

C33

121 B.3 RESULTS OF BOOTSTRAPPING ANALYSIS

In order to estimate the range of possible cooling supported by our dataset, we used a bootstrapping method, randomly resampling the data (with replacement) from each section, calculating the average for each section and recording the difference between sections (10,000x). Below is a histogram of the results, with the differences between sections on the horizontal axis, and the frequency on the vertical axis.

122 Appendix C: Supplemental Data for Chapter 4

C.1. TABLE OF SAMPLE LOCATIONS FOR CHAPTER 4

Page 1 of 3 Sample Sampling Analysis Name Latitude Longitude Code Transect Paleocurrent 1 LV1-1 3.28214 -75.15238 Paleocurrent 1 LV1-2 3.29847 -75.14776 Paleocurrent 2 Ccgl-1 3.26275 -75.18510 Paleocurrent 2 Ccgl-2 3.22607 -75.13447 Paleocurrent 2 TSS-2 3.27179 -75.11971 Paleocurrent 2 TSS-1 3.27223 -75.14490 Paleocurrent 3 SFSS-1 3.22265 -75.18974 Paleocurrent 3 SFSS-2 3.22338 -75.17980 Paleocurrent 4 PRB-4 3.21867 -75.21109 Paleocurrent 5 PRB-2 3.19745 -75.20819 Paleocurrent 1 LV1-1 3.28214 -75.15238 Sandstone Petrography S1 1 LV1-15 3.30757 -75.14614 Sandstone Petrography S2 1 060811-11 3.29989 -75.14721 Sandstone Petrography S3 1 LV1-21 3.30082 -75.14268 Sandstone Petrography S4 2 TSS-01 3.27168 -75.14435 Sandstone Petrography S5 2 LV3-09 3.25961 -75.16226 Sandstone Petrography S6 3 LVGB-13 3.22781 -75.14096 Sandstone Petrography S7 4 PRB-43 3.20406 -75.20350 Sandstone Petrography S8 4 PRB-55 3.19543 -75.20638 Sandstone Petrography S9 5 PRB-07 3.15350 -75.16889 Sandstone Petrography S10 5 PRB-05 3.15265 -75.17200 Sandstone Petrography S11 6 080811-09 2.44282 -75.53212 Sandstone Petrography S12 6 UGM11-05 2.20464 -75.62299 Sandstone Petrography S13 6 080811-12 2.43907 -75.52762 Sandstone Petrography S14 6 N01-04 2.44956 -75.53299 Sandstone Petrography S15 6 070811-03 2.45040 -75.53513 Soil Carbonate Nodules C1 1 LV1-17 3.30540 -75.14497 Soil Carbonate Nodules C2 1 060811-01 3.30324 -75.14944 Soil Carbonate Nodules C3 1 LV2-03A 3.30205 -75.14269 Soil Carbonate Nodules C4 1 LV1-20B 3.30203 -75.14326 Soil Carbonate Nodules C5 1 LV1-03A 3.30345 -75.14450 Soil Carbonate Nodules C6 1 LV2-5B 3.29940 -75.14071 Soil Carbonate Nodules C7 1 LV2-06A 2.29838 -74.13995 123 Table of sample locations used in Chapter 4, Page 2 of 3 Sample Sampling Analysis Name Latitude Longitude Code Transect Soil Carbonate Nodules C8 1 060811-03A 3.29012 -75.15206 Soil Carbonate Nodules C9 1 060811-08A 3.30324 -75.14944 Soil Carbonate Nodules C10 1 060811-07A 3.29012 -75.15206 Soil Carbonate Nodules C11 1 060811-06A 3.30324 -75.14944 Soil Carbonate Nodules C12 3 LV3-12A 3.22724 -75.13416 Soil Carbonate Nodules C13 3 LVGB-10 3.22783 -75.14052 Soil Carbonate Nodules C14 3 LVGB-16B 3.22735 -75.14338 Soil Carbonate Nodules C15 3 LVGB-17B 3.22737 -75.14346 Soil Carbonate Nodules C16 3 LVGB-20 3.22722 -75.14360 Soil Carbonate Nodules C17 3 LVGB-21 3.22714 -75.14367 Soil Carbonate Nodules C18 3 LVGB-24A 3.22534 -75.14756 Soil Carbonate Nodules C19 3 ECGB-05 3.29012 -75.15206 Soil Carbonate Nodules C20 3 ECRB-10A 3.29012 -75.15206 Soil Carbonate Nodules C21 3 ECRB-09A 3.30324 -75.14944 Soil Carbonate Nodules C22 4 PRB-28A 3.21867 -75.21109 Soil Carbonate Nodules C23 4 PRB-31A 3.21639 -75.20860 Soil Carbonate Nodules C24 4 PRB-33A 3.21428 -75.20533 Soil Carbonate Nodules C25 4 PRB-62B 3.19030 -75.20790 Soil Carbonate Nodules C26 4 PRB-66B 3.18725 -75.20910 Soil Carbonate Nodules C27 5 PRB-18A 3.16198 -75.15603 Soil Carbonate Nodules C28 5 PRB-03A 3.15236 -75.17205 Detrital Zircon U-Pb Z1 1 010811-01 3.31905 -75.14398 Detrital Zircon U-Pb Z2 1 060811-04 3.28214 -75.15238 Detrital Zircon U-Pb Z3 2 310711-03 3.27157 -75.14490 Detrital Zircon U-Pb Z4 2 310711-04 3.25796 -75.17433 Detrital Zircon U-Pb Z5 3 300711-07 3.22541 -75.17612 Detrital Zircon U-Pb Z6 4 030811-01 3.18425 -75.20134 Detrital Zircon U-Pb Z7 5 020811-01 3.15236 -75.17205 Detrital Zircon U-Pb Z8 6 070811-03 2.45040 -75.53513 Detrital Zircon U-Pb Z9 6 070811-04 2.45095 -75.53305 Detrital Zircon U-Pb Z10 6 080811-07 2.44468 -75.53226 Detrital Zircon U-Pb Z11 6 UGM11-02 2.20793 -75.62129 Paleosol XRF data PS3 1 LV1-03 3.30345 -75.14450 Paleosol XRF data PS4 1 LV1-20A 3.30203 -75.14326 Paleosol XRF data PS5 1 LV2-3B 3.30205 -75.14269 Paleosol XRF data PS6 1 LV2-5A 3.29940 -75.14071 Paleosol XRF data PS7 1 LV2-6B 3.29838 -75.13995 124 Table of sample locations used in Chapter 4, Page 3 of 3 Sample Sampling Analysis Name Latitude Longitude Code Transect Paleosol XRF data PS8 1 060811-3B 3.29012 -75.15206 Paleosol XRF data PS9 1 060811-8B 3.28861 -75.15057 Paleosol XRF data PS10 1 060811-7B 3.28726 -75.15102 Paleosol XRF data PS11 1 060811-6B 3.28630 -75.15116 Paleosol XRF data PS12 3 LV3-12B 3.22724 -75.13416 Paleosol XRF data PS13 3 LVGB-11 3.22795 -75.14072 Paleosol XRF data PS14 3 LVGB-16A 3.22735 -75.14338 Paleosol XRF data PS15 3 LVGB-17A 3.22737 -75.14346 Paleosol XRF data PS17 3 LVGB-22 3.22714 -75.14367 Paleosol XRF data PS18 3 LVGB-24B 3.22534 -75.14756 Paleosol XRF data PS19 3 ECGB-3 3.23375 -75.15510 Paleosol XRF data PS20 3 ECGB-9B 3.22795 -75.18238 Paleosol XRF data PS21 3 ECGB-10B 3.22806 -75.18202 Paleosol XRF data PS22 4 PRB-28B 3.22139 -75.21172 Paleosol XRF data PS23 4 PRB-30A 3.21639 -75.20860 Paleosol XRF data PS24 4 PRB-32 3.21428 -75.20533 Paleosol XRF data PS26 4 PRB-66A 3.18725 -75.20910 Paleosol XRF data PS27 5 PRB-18B 3.16198 -75.15603 Paleosol XRF data PS28 5 PRB-3A 3.15236 -75.17205 Apatite Fission Track GM01 GM01 2.57525 -75.37080 Apatite Fission Track GM02 GM02 2.54568 -75.35829 Apatite Fission Track GM03 GM03 2.53648 -75.34039 Apatite Fission Track GM04 GM04 2.56324 -75.27018 Apatite Fission Track GM05 GM05 2.57521 -75.25468 Apatite Fission Track GM06 GM06 2.59105 -75.23712

125 C.2 SUMMARY OF MEASURED PALEOCURRENTS Uncorrected Bedding-Corrected Name Strike Dip Direction Lat Lon Trend Plunge Trend Plunge PRB-2 82 5 S 3.19745 -75.2082 39 8 40 12 PRB-4 94 13 S 3.21867 -75.2111 7 19 14 62 SFSS-1 131 5 SW 3.22265 -75.1897 299 4 300 4 SFSS-2 94 6 S 3.22338 -75.1798 125 1 304 4 Ccgl-1 105 8 S 3.26275 -75.1851 69 11 70 16 Ccgl-2 115 10 S 3.22607 -75.1345 270 1 91 4 TSS-2 84 4 S 3.27179 -75.1197 117 8 118 6 TSS-1 40 10 S 3.27223 -75.1449 134 12 314 3 LV1-1 43 15 S 3.28214 -75.1524 92 21 95 9 LV1-2 115 8 S 3.29847 -75.1478 36 7 46 25

126 C.3. PRIMARY CLASSIFICATIONS USED IN SANDSTONE PETROGRAPHIC POINT COUNTS

Neiva Area Gigante Area Sample Number S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 Sample Name LV1-15 060811-11 LV1-21 TSS-01 LV3-09 LVGB-13 PRB-43 PRB-55 PRB-07 PRB-05 070811-03 N01-04 080811-12 UGM11-05 080811-09 Total 306 302 312 306 304 312 300 313 309 302 305 300 304 302 300 Qms - mono 74 72 65 54 55 56 62 66 75 65 24 25 35 39 93 Qmu - mono, undulose 14 24 33 20 12 15 9 7 6 7 47 19 26 21 42 Qps - poly, straight 16 12 13 8 6 18 8 22 1 21 3 4 2 3 3 Qpu - poly, undulose 21 19 18 18 8 19 10 12 16 14 19 4 1 1

K - Potassium 6 4 7 7 5 9 4 3 17 18 13 17 P - Plagioclase 38 28 27 48 72 48 32 28 33 18 32 66 102 118 75 Altered 5 24 17 1 29 14 7 7 11 16 1 4

C- Chert 22 3 2 3 12 5 44 67 26 72 6 3 1

S - Siltstone 2 2 4

Lsh - Mudstone/Shale 5 5 3 3 6 19

Lc - Carbonate 4 1 2 1 7

Lvm - Mafic Volcanic 16 16 40 38 19 16 18 19 37 9 47 83 74 46 33 Lvf - Felsic Volcanic 28 12 32 6 53 22 31 29 34 14 8 2 5 Lvl - Lathwork volcanic 1 4 1 28 2

Lvp - Pyroclastic/glassy 14 21 33 37 15 25 52 42 31 34 14 36 7 22 24 Lph - Phyllite 4 9 12 14 4 3 1 12 11

Lsm - Schist 2 16 8 8 1 8 4 1 1

Lg - Gneissic 2 2 1 2

Lmm - Microcrystalline 1 13 17 15 25 17 19 7

Hornblende 9 10 1 11 1 14 6 23 6 23

Biotite 10 5 3 15 4 1 1 1 3

Muscovite 7 9 1 2 5 1 13 19 6 16

Hydrated Rusty Quartz 5 9 7 4

Hematite 6 1 4 11 4

Coal 1 6

127 C.4. U-PB MEASUREMENT DATA FOR INDIVIDUAL DETRITAL ZIRCON SAMPLES

Sample Z1 – 010811-01. Page 1 of 2 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 010811-01-001 164 35.6 1.9 25.8 21.0 -809.4 2867.6 35.6 1.9 010811-01-002 134 274.2 2.8 266.7 12.6 200.9 121.9 274.2 2.8 010811-01-003 233 14.7 1.3 31.5 78.7 1634.6 659.7 14.7 1.3 010811-01-005 206 970.2 4.5 974.6 9.2 984.4 28.0 984.4 28.0 010811-01-006 44 243.2 15.8 223.0 39.4 14.1 448.3 243.2 15.8 010811-01-006 37 1141.6 26.4 1156.1 33.5 1183.3 81.9 1183.3 81.9 010811-01-008 150 182.9 3.8 164.6 19.5 -90.7 311.7 182.9 3.8 010811-01-009 296 238.9 3.1 236.0 10.0 207.7 105.8 238.9 3.1 010811-01-010 159 181.6 5.2 180.5 11.8 166.2 152.8 181.6 5.2 010811-01-014 264 35.4 2.2 38.8 7.9 257.8 456.7 35.4 2.2 010811-01-015 67 77.9 4.6 99.0 31.1 643.0 715.1 77.9 4.6 010811-01-017 511 81.0 1.4 87.0 6.8 253.2 184.4 81.0 1.4 010811-01-018 134 41.8 3.2 64.3 57.5 1010.8 243.4 41.8 3.2 010811-01-019 39 1118.1 12.0 1124.7 24.9 1137.4 69.1 1137.4 69.1 010811-01-021 138 186.6 4.0 193.1 12.8 272.3 159.0 186.6 4.0 010811-01-023 144 39.8 3.1 39.7 23.8 30.3 1600.2 39.8 3.1 010811-01-024 177 273.7 5.3 269.9 11.8 237.0 105.6 273.7 5.3 010811-01-025 70 82.0 5.2 86.7 31.5 220.0 892.5 82.0 5.2 010811-01-026 42 561.7 16.8 505.5 64.6 259.0 367.5 561.7 16.8 010811-01-027 87 565.4 8.3 562.5 23.8 551.2 115.3 565.4 8.3 010811-01-028 300 183.4 3.6 178.5 9.3 113.8 125.1 183.4 3.6 010811-01-029 167 268.6 4.6 275.9 12.0 338.1 104.9 268.6 4.6 010811-01-030 151 187.2 2.6 175.1 19.9 13.9 296.8 187.2 2.6 010811-01-032 180 233.0 3.9 225.2 14.3 144.6 160.9 233.0 3.9 010811-01-033 143 34.9 2.6 46.2 35.5 683.7 2048.0 34.9 2.6 010811-01-034 52 116.5 7.3 121.2 46.3 214.5 960.6 116.5 7.3 010811-01-035 115 39.8 3.9 43.9 19.8 274.2 1082.4 39.8 3.9 010811-01-039 96 184.0 4.1 189.5 28.5 258.5 377.2 184.0 4.1 010811-01-041 140 37.5 2.5 24.3 12.7 -1149.9 1703.8 37.5 2.5 010811-01-042 135 81.3 2.6 78.6 20.7 -1.5 667.0 81.3 2.6 010811-01-044 135 40.7 3.8 37.0 42.2 -199.1 1354.8 40.7 3.8 010811-01-045 158 188.3 3.3 176.5 19.0 20.9 278.9 188.3 3.3 010811-01-046 315 36.4 1.3 36.7 8.0 59.6 530.5 36.4 1.3 010811-01-047 131 85.0 3.4 83.3 19.4 35.5 579.3 85.0 3.4 010811-01-049 249 288.3 2.7 290.8 6.2 310.6 51.0 288.3 2.7 010811-01-050 71 288.1 6.3 295.8 56.4 357.0 498.7 288.1 6.3 010811-01-051 413 59.9 1.9 56.0 5.2 -110.2 220.9 59.9 1.9

128 Sample Z1 – 010811-01. Page 2 of 2 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 010811-01-052 230 231.4 5.0 228.2 10.0 195.2 101.7 231.4 5.0 010811-01-053 449 44.9 1.4 45.1 6.8 55.4 361.8 44.9 1.4 010811-01-055 491 38.0 2.2 42.9 4.8 324.2 227.6 38.0 2.2 010811-01-056 119 339.2 5.8 339.1 16.8 338.3 125.8 339.2 5.8 010811-01-059 172 39.7 2.4 36.3 11.9 -182.7 841.3 39.7 2.4 010811-01-060 90 409.5 7.7 396.2 23.4 319.4 156.5 409.5 7.7 010811-01-061 234 1216.4 32.5 1212.1 21.7 1204.5 17.8 1204.5 17.8 010811-01-062 186 1200.5 25.0 1206.1 17.0 1216.2 15.1 1216.2 15.1 010811-01-064 45 144.7 7.6 173.7 69.5 589.3 980.1 144.7 7.6 010811-01-065 114 171.5 4.8 166.3 21.0 92.3 319.1 171.5 4.8 010811-01-068 214 257.5 4.1 264.5 9.0 326.8 79.4 257.5 4.1 010811-01-069 110 40.6 3.3 46.1 16.9 342.0 855.7 40.6 3.3 010811-01-070 168 580.3 6.2 578.7 9.3 572.6 39.1 580.3 6.2 010811-01-074 103 94.2 3.0 95.0 31.5 116.2 837.8 94.2 3.0 010811-01-075 136 84.2 6.6 66.1 24.7 -548.6 1046.7 84.2 6.6 010811-01-076 85 164.8 5.1 150.0 27.2 -78.4 474.2 164.8 5.1 010811-01-079 281 246.1 3.8 240.5 10.2 186.0 104.7 246.1 3.8 010811-01-080 216 45.9 2.1 44.7 14.1 -20.2 788.1 45.9 2.1 010811-01-082 109 1889.5 20.3 1892.9 11.8 1896.7 11.0 1896.7 11.0 010811-01-084 78 186.0 4.6 188.4 30.4 218.8 406.9 186.0 4.6 010811-01-085 93 586.6 8.7 602.0 17.9 660.4 77.5 586.6 8.7 010811-01-087 158 40.6 2.9 56.0 17.7 778.0 682.0 40.6 2.9 010811-01-089 161 183.6 4.0 182.7 20.6 171.6 283.4 183.6 4.0 010811-01-090 133 38.7 3.0 35.5 15.8 -181.5 1165.1 38.7 3.0 010811-01-091 93 76.5 5.6 73.3 27.1 -31.8 939.5 76.5 5.6 010811-01-093 354 43.0 1.3 41.9 11.3 -23.9 671.6 43.0 1.3 010811-01-097 98 85.4 5.8 91.5 35.4 254.2 952.6 85.4 5.8 010811-01-099 139 272.7 4.4 260.6 23.1 152.7 232.3 272.7 4.4 010811-01-100 101 75.6 3.6 69.1 20.3 -150.5 759.2 75.6 3.6 010811-01-102 154 68.8 4.1 63.3 13.3 -138.4 518.2 68.8 4.1 010811-01-103 160 466.7 7.2 471.4 10.1 494.2 47.7 466.7 7.2 010811-01-104 78 73.3 5.5 53.2 35.1 -781.3 2129.7 73.3 5.5 010811-01-105 67 159.0 8.8 150.9 36.4 25.5 616.6 159.0 8.8 010811-01-114 166 40.5 3.2 30.3 12.6 -717.6 1195.3 40.5 3.2 010811-01-117 96 1057.1 12.6 1069.5 17.2 1095.0 45.0 1095.0 45.0 010811-01-118 194 38.2 2.1 40.9 15.9 201.6 946.1 38.2 2.1 010811-01-119 132 185.8 3.8 190.9 16.3 254.9 209.7 185.8 3.8

129 Sample Z2 – 060811-04. Page 1 of 3 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 060811-04 001 263 14.0 1.5 20.4 11.7 850.3 1296.5 14.0 1.5 060811-04 002 263 40.8 2.3 41.9 6.7 105.9 365.1 40.8 2.3 060811-04 003 46 894.7 12.1 893.8 29.3 891.6 97.5 894.7 12.1 060811-04 005 283 15.1 1.3 28.8 57.4 1404.4 606.2 15.1 1.3 060811-04 006 319 14.2 1.6 29.6 29.5 1571.7 62.3 14.2 1.6 060811-04 007 468 13.8 1.2 16.7 4.2 467.5 528.3 13.8 1.2 060811-04 008 171 253.5 6.2 251.3 14.7 231.3 141.3 253.5 6.2 060811-04 009 65 75.4 5.6 58.6 23.6 -582.7 1142.5 75.4 5.6 060811-04 010 170 315.3 11.0 341.0 19.0 520.5 120.7 315.3 11.0 060811-04 013 470 41.7 1.7 38.7 5.5 -138.9 347.4 41.7 1.7 060811-04 014 384 13.1 2.1 16.0 14.9 473.3 609.7 13.1 2.1 060811-04 016 259 604.8 16.5 610.8 13.7 633.1 18.2 604.8 16.5 060811-04 017 278 191.0 4.1 187.9 12.7 149.6 165.6 191.0 4.1 060811-04 018 124 41.0 5.0 28.2 43.0 -965.1 0.0 41.0 5.0 060811-04 019 98 249.7 8.5 251.3 32.4 266.1 325.8 249.7 8.5 060811-04 020 84 37.8 4.9 46.0 64.2 497.3 981.6 37.8 4.9 060811-04 021 156 93.6 2.8 68.4 22.9 -746.6 990.8 93.6 2.8 060811-04 022 192 17.7 1.4 40.0 85.3 1731.5 418.0 17.7 1.4 060811-04 023 185 13.6 2.6 15.5 8.5 331.7 1252.1 13.6 2.6 060811-04 026 601 13.7 1.0 15.2 6.7 267.2 1054.5 13.7 1.0 060811-04 027 151 1464.2 16.6 1499.7 10.8 1550.2 9.9 1550.2 9.9 060811-04 029 85 1894.1 22.7 1885.9 12.4 1876.9 7.6 1876.9 7.6 060811-04 030 91 23.3 3.2 63.2 75.7 2080.8 261.8 23.3 3.2 060811-04 031 286 13.3 1.4 12.7 7.9 -95.7 1678.9 13.3 1.4 060811-04 032 396 13.8 1.8 14.5 4.0 125.8 588.5 13.8 1.8 060811-04 033 623 13.8 1.9 16.5 4.8 434.8 573.1 13.8 1.9 060811-04 034 148 184.8 8.4 193.7 20.3 303.0 241.5 184.8 8.4 060811-04 035 109 578.7 71.9 588.6 60.7 627.0 84.7 578.7 71.9 060811-04 038 88 38.9 5.0 33.1 21.0 -373.4 1811.6 38.9 5.0 060811-04 039 221 13.9 1.2 29.6 10.4 1607.5 665.8 13.9 1.2 060811-04 040 153 74.7 2.7 75.5 7.6 100.5 232.3 74.7 2.7 060811-04 042 739 37.9 1.4 35.9 4.3 -97.8 288.0 37.9 1.4 060811-04 043 155 855.3 34.3 890.8 27.2 979.9 33.1 979.9 33.1 060811-04 044 278 14.7 2.6 19.0 14.0 598.0 1839.7 14.7 2.6 060811-04 045 71 188.9 8.6 166.6 20.4 -140.7 309.3 188.9 8.6 060811-04 046 437 13.1 1.0 6.1 5.8 NA NA 13.1 1.0 060811-04 047 235 13.8 2.3 -25.5 -123.6 NA NA 13.8 2.3 060811-04 047 167 186.2 4.0 191.0 12.8 250.6 162.3 186.2 4.0 060811-04 049 221 14.0 2.3 178.2 NaN NA NA 14.0 2.3 060811-04 050 219 185.8 5.2 189.3 8.8 233.7 97.9 185.8 5.2 060811-04 051 263 14.0 1.6 22.3 39.4 1035.1 778.4 14.0 1.6

130 Sample Z2 – 060811-04. Page 2 of 3 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 060811-04 052 79 1218.9 22.1 1227.4 17.3 1242.4 27.3 1242.4 27.3 060811-04 054 307 631.8 49.2 647.3 40.0 702.0 36.1 631.8 49.2 060811-04 055 251 286.2 4.8 287.9 10.2 301.1 84.3 286.2 4.8 060811-04 056 407 14.0 0.9 15.0 3.7 190.5 571.7 14.0 0.9 060811-04 057 252 189.1 5.6 196.0 9.9 279.6 107.5 189.1 5.6 060811-04 058 186 78.4 4.1 89.2 15.5 388.1 393.1 78.4 4.1 060811-04 059 403 14.0 1.2 14.8 10.1 141.2 1829.2 14.0 1.2 060811-04 060 140 39.2 3.3 -304.6 NaN NA NA 39.2 3.3 060811-04 062 330 13.9 1.4 34.3 63.3 1883.7 171.7 13.9 1.4 060811-04 063 155 188.0 3.8 185.3 14.4 151.6 193.3 188.0 3.8 060811-04 064 355 13.4 2.1 18.7 7.2 779.9 771.1 13.4 2.1 060811-04 065 121 2994.3 83.5 3064.2 38.8 3110.3 31.7 3110.3 31.7 060811-04 066 237 13.6 1.6 17.2 22.8 557.6 864.4 13.6 1.6 060811-04 067 133 1223.3 26.9 1234.8 17.9 1254.9 12.6 1254.9 12.6 060811-04 068 120 1035.7 27.1 1038.7 22.2 1045.0 38.5 1045.0 38.5 060811-04 070 397 13.8 1.3 13.7 6.2 -10.0 1129.1 13.8 1.3 060811-04 072 143 255.2 18.0 270.6 20.7 406.0 110.8 255.2 18.0 060811-04 074 421 13.7 1.1 12.9 12.2 -137.9 1083.3 13.7 1.1 060811-04 075 165 71.3 4.6 64.5 11.6 -181.7 437.8 71.3 4.6 060811-04-076 255 13.6 1.4 46.6 68.6 2464.6 376.0 13.6 1.4 060811-04-077 201 13.6 1.9 15.8 8.4 357.1 1250.2 13.6 1.9 060811-04-078 176 1732.4 27.1 1745.1 15.2 1760.3 6.5 1760.3 6.5 060811-04-080 192 81.3 1.7 74.2 12.9 -148.1 448.7 81.3 1.7 060811-04-082 281 12.7 1.9 14.2 6.7 287.9 1072.6 12.7 1.9 060811-04-083 641 13.8 0.8 13.9 2.9 30.2 494.3 13.8 0.8 060811-04-085 401 13.7 0.9 10.3 4.8 -738.9 1355.8 13.7 0.9 060811-04-086 162 40.2 2.7 33.2 14.5 -449.9 1209.9 40.2 2.7 060811-04-087 235 162.5 4.0 169.6 10.0 268.9 136.2 162.5 4.0 060811-04-088 214 30.9 2.9 30.3 11.8 -15.5 960.0 30.9 2.9 060811-04-089 69 580.7 8.8 584.3 37.9 598.3 181.7 580.7 8.8 060811-04-090 506 596.5 17.9 609.6 15.0 658.4 20.2 596.5 17.9 060811-04-091 278 13.6 1.8 -28.6 -165.1 NA NA 13.6 1.8 060811-04-092 386 13.7 1.7 -2833.3 NaN NA NA 13.7 1.7 060811-04-093 396 35.7 1.3 35.6 4.9 29.6 322.8 35.7 1.3 060811-04-094 186 13.3 2.1 19.7 8.6 896.9 880.1 13.3 2.1 060811-04-095 41 74.9 10.5 107.3 142.3 903.7 629.9 74.9 10.5 060811-04-096 1019 32.5 0.8 33.2 3.3 86.2 231.5 32.5 0.8 060811-04-097 94 154.2 6.2 136.1 37.7 -169.0 743.9 154.2 6.2 060811-04-098 561 13.8 0.6 10.2 6.7 -779.5 2083.8 13.8 0.6 060811-04-099 533 187.0 2.3 188.1 4.0 201.8 46.7 187.0 2.3 060811-04-100 252 13.3 1.1 81.7 590.6 3442.0 247.2 13.3 1.1 060811-04-101 178 41.2 3.0 37.3 16.4 -205.9 1159.6 41.2 3.0 131 Sample Z2 – 060811-04. Page 3 of 3 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 060811-04-103 297 14.0 0.9 54.5 190.3 2686.6 161.0 14.0 0.9 060811-04-104 457 14.1 0.9 11.2 6.8 -572.6 1783.4 14.1 0.9 060811-04-105 78 26.3 4.5 718.8 NaN NA NA 26.3 4.5

132 Sample Z3 – 310711-03. Page 1 of 2 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 310711-03-001 77 14.3 2.8 58.1 73.7 2,768.5 651.0 14.3 2.8 310711-03-001 43 78.1 7.7 72.2 66.7 -119.5 1,077.2 78.1 7.7 310711-03-003 140 84.0 2.0 82.1 10.5 27.2 314.6 84.0 2.0 310711-03-005 666 14.1 0.7 15.4 2.8 221.0 406.4 14.1 0.7 310711-03-006 111 190.8 5.8 184.2 18.8 100.5 254.8 190.8 5.8 310711-03-007 115 186.2 6.1 188.1 13.7 212.2 169.0 186.2 6.1 310711-03-008 80 12.2 4.4 299.4 NaN NA NA 12.2 4.4 310711-03-009 73 474.8 8.0 469.0 22.4 440.6 127.0 474.8 8.0 310711-03-010 304 44.4 1.3 34.7 6.8 -590.0 538.4 44.4 1.3 310711-03-011 190 15.9 2.7 27.4 29.5 1,202.8 221.4 15.9 2.7 310711-03-012 93 13.0 5.0 58.7 44.1 2,931.8 1,033.4 13.0 5.0 310711-03-013 192 13.4 2.3 14.5 13.4 202.5 796.1 13.4 2.3 310711-03-014 176 23.7 1.9 18.6 8.8 -607.1 1,345.2 23.7 1.9 310711-03-015 186 1,013.4 47.5 1,016.2 32.8 1,022.3 13.1 1,022.3 13.1 310711-03-016 212 776.5 29.5 780.2 22.7 790.7 22.3 776.5 29.5 310711-03-017 121 13.5 3.4 87.6 437.5 3,530.2 159.8 13.5 3.4 310711-03-020 141 35.2 4.2 33.0 21.7 -126.8 1,819.1 35.2 4.2 310711-03-021 178 18.6 3.4 36.7 48.9 1,480.9 189.0 18.6 3.4 310711-03-022 177 41.6 3.2 39.2 14.2 -109.4 916.4 41.6 3.2 310711-03-023 74 43.9 4.3 29.6 17.5 -1,025.7 1,914.1 43.9 4.3 310711-03-025 197 14.3 2.4 -23.0 17.5 NA NA 14.3 2.4 310711-03-026 89 81.9 5.6 71.5 17.5 -262.0 644.8 81.9 5.6 310711-03-027 86 84.0 3.5 68.5 17.5 -443.1 807.7 84.0 3.5 310711-03-028 1543 34.9 1.1 35.5 17.5 81.8 104.5 34.9 1.1 310711-03-029 111 279.5 7.9 284.3 17.5 323.9 128.3 279.5 7.9 310711-03-030 111 525.5 13.7 527.9 17.5 538.4 56.4 525.5 13.7 310711-03-032 121 12.7 3.1 17.3 17.5 717.8 429.6 12.7 3.1 310711-03-033 308 346.1 23.7 353.8 17.5 404.6 136.2 346.1 23.7 310711-03-034 280 15.1 2.2 19.4 17.5 590.9 608.6 15.1 2.2 310711-03-035 123 43.2 2.3 52.7 17.5 508.2 975.7 43.2 2.3 310711-03-037 205 286.2 19.9 285.9 17.5 283.5 64.9 286.2 19.9 310711-03-038 164 186.6 4.3 185.1 17.5 166.1 166.1 186.6 4.3 310711-03-039 273 685.2 6.3 681.9 17.5 671.0 10.6 685.2 6.3 310711-03-040 200 86.3 1.3 89.7 17.5 180.7 146.9 86.3 1.3 310711-03-042 95 283.7 6.0 282.7 17.5 274.1 124.5 283.7 6.0 310711-03-043 294 430.7 33.5 437.9 17.5 476.5 25.3 430.7 33.5 310711-03-044 487 234.1 2.2 233.3 17.5 225.2 49.8 234.1 2.2 310711-03-045 182 39.3 2.9 30.3 17.5 -632.3 1,031.4 39.3 2.9 310711-03-046 120 40.8 6.3 44.3 17.5 237.5 999.8 40.8 6.3 310711-03-047 145 189.1 3.3 193.3 17.5 244.4 188.9 189.1 3.3 310711-03-048 140 187.7 2.0 194.6 17.5 279.5 137.7 187.7 2.0

133 Sample Z3 – 310711-03. Page 1 of 2 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 310711-03-049 120 41.6 4.5 55.0 17.5 688.6 1,102.0 41.6 4.5 310711-03-050 145 17.0 2.2 24.7 17.5 855.0 2,349.7 17.0 2.2 310711-03-051 180 188.4 3.5 192.5 17.5 242.6 114.6 188.4 3.5 310711-03-052 82 95.4 3.7 74.7 17.5 -545.1 980.6 95.4 3.7 310711-03-053 134 35.9 3.1 35.4 17.5 0.4 1,661.1 35.9 3.1 310711-03-054 137 78.1 2.5 71.3 17.5 -151.5 804.5 78.1 2.5 310711-03-055 143 16.7 2.8 12.5 17.5 -753.9 0.0 16.7 2.8 310711-03-057 159 325.9 2.8 323.2 17.5 304.0 80.0 325.9 2.8 310711-03-058 45 78.6 8.8 55.5 17.5 -857.9 1,378.7 78.6 8.8 310711-03-059 52 923.5 27.0 935.8 17.5 964.9 70.9 964.9 70.9 310711-03-060 53 591.0 20.3 595.3 17.5 612.0 87.2 591.0 20.3 310711-03-062 74 47.8 4.9 74.7 17.5 1,051.7 964.9 47.8 4.9 310711-03-063 130 82.5 2.9 74.5 17.5 -173.3 339.5 82.5 2.9 310711-03-064 97 35.7 3.6 40.9 17.5 359.7 956.3 35.7 3.6 310711-03-065 134 13.3 3.1 23.5 17.5 1,247.9 400.4 13.3 3.1 310711-03-066 472 86.5 3.3 91.0 17.5 210.3 140.4 86.5 3.3 310711-03-067 296 35.6 1.7 37.9 17.5 184.9 392.1 35.6 1.7 310711-03-068 373 15.5 0.6 30.2 17.5 1,447.6 868.7 15.5 0.6 310711-03-069 57 16.4 5.1 92.8 17.5 3,322.9 595.0 16.4 5.1 310711-03-070 272 35.0 2.3 27.0 17.5 -640.6 1,141.3 35.0 2.3 310711-03-071 66 1,069.7 19.8 1,077.6 17.5 1,093.6 36.9 1,093.6 36.9 310711-03-072 107 13.7 2.9 51.4 17.5 2,625.4 468.4 13.7 2.9 310711-03-073 110 12.2 3.7 -113.2 17.5 NA NA 12.2 3.7 310711-03-074 382 277.0 5.6 276.9 17.5 275.3 30.1 277.0 5.6 310711-03-075 146 166.7 2.7 172.7 17.5 256.3 106.8 166.7 2.7 310711-03-076 261 13.8 1.2 13.8 17.5 17.7 1,322.3 13.8 1.2 310711-03-077 77 185.6 3.2 188.4 17.5 224.1 293.1 185.6 3.2 310711-03-078 192 78.3 2.3 78.3 17.5 78.3 353.2 78.3 2.3 310711-03-079 103 187.3 4.3 176.5 17.5 33.9 210.3 187.3 4.3 310711-03-081 193 13.8 1.1 80.2 17.5 3,356.0 352.9 13.8 1.1 310711-03-082 118 13.4 2.3 45.2 17.5 2,441.5 267.8 13.4 2.3 310711-03-083 32 2,623.1 23.8 2,626.2 17.5 2,628.6 8.6 2,628.6 8.6 310711-03-084 125 12.5 2.3 69.8 17.5 3,277.3 83.4 12.5 2.3 310711-03-085 489 78.8 2.8 79.0 17.5 83.0 114.8 78.8 2.8

134 Sample Z4 – 310711-04. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) CP42122-001 38 519.0 7.6 527.4 52.8 563.9 278.3 519.0 7.6 CP42122-002 332 35.9 2.3 33.2 8.5 -160.1 639.1 35.9 2.3 CP42122-003 597 23.0 0.7 23.9 3.7 108.9 361.3 23.0 0.7 CP42122-004 138 1355.4 10.7 1357.6 9.9 1360.9 18.9 1360.9 18.9 CP42122-005 121 214.7 4.7 210.6 12.4 165.6 142.9 214.7 4.7 CP42122-006 32 1118.4 22.5 1113.4 21.1 1103.8 44.5 1103.8 44.5 CP42122-007 319 23.8 2.4 21.0 7.5 -283.4 907.1 23.8 2.4 CP42122-008 259 1196.1 14.7 1198.9 10.6 1203.8 13.2 1203.8 13.2 CP42122-010B 298 25.6 2.2 21.0 8.6 -481.3 1112.5 25.6 2.2 CP42122-011 20 1713.2 19.3 1714.5 23.7 1716.1 47.0 1716.1 47.0 CP42122-013 108 594.3 10.3 581.0 17.6 529.4 77.5 594.3 10.3 CP42122-014 96 1602.6 23.0 1599.5 15.4 1595.3 18.9 1595.3 18.9 CP42122-015 49 1405.4 16.5 1395.4 16.9 1380.1 34.6 1380.1 34.6 CP42122-016 114 24.7 3.5 -355.3 NaN NA NA 24.7 3.5 CP42122-017 51 1708.7 46.3 1695.9 26.9 1680.1 19.7 1680.1 19.7 CP42122-018 63 68.1 9.4 4582.9 NaN NA NA 68.1 9.4 CP42122-019 37 1405.2 19.1 1388.7 29.7 1363.5 69.7 1363.5 69.7 CP42122-020 138 1491.3 20.3 1489.8 12.8 1487.5 11.3 1487.5 11.3 CP42122-021 96 1433.9 11.4 1434.5 10.5 1435.4 19.9 1435.4 19.9 CP42122-022 40 1430.2 24.4 1433.3 19.6 1438.0 32.7 1438.0 32.7 CP42122-023 103 1720.9 32.7 1732.4 23.2 1746.2 32.4 1746.2 32.4 CP42122-025 58 1029.8 17.8 1032.2 21.2 1037.3 54.3 1037.3 54.3 CP42122-026 109 72.3 5.5 67.2 24.8 -109.9 949.5 72.3 5.5 CP42122-027 693 178.2 1.0 178.4 5.1 179.7 71.2 178.2 1.0 CP42122-028 56 1440.3 15.8 1451.2 15.2 1467.0 29.2 1467.0 29.2 CP42122-029 539 26.0 1.2 26.7 5.5 95.6 488.2 26.0 1.2 CP42122-031 69 2711.5 15.4 2703.5 6.9 2697.6 3.6 2697.6 3.6 CP42122-032 64 66.9 5.5 69.7 53.6 165.3 2257.1 66.9 5.5 CP42122-033 656 23.3 1.1 18.7 6.6 -538.8 976.4 23.3 1.1 CP42122-034 46 165.9 5.9 207.6 18.7 713.0 197.3 165.9 5.9 CP42122-035 30 572.0 24.4 608.2 51.6 745.3 219.4 572.0 24.4 CP42122-036 32 968.9 26.7 973.3 28.3 983.1 69.2 983.1 69.2 CP42122-038 218 26.5 2.4 22.7 15.9 -369.1 2074.5 26.5 2.4 CP42122-039 71 1687.5 21.7 1688.1 14.9 1688.7 19.9 1688.7 19.9 CP42122-040 31 1672.4 34.7 1671.2 27.1 1669.6 42.7 1669.6 42.7 CP42122-041 32 1401.4 13.4 1385.5 24.3 1361.0 58.3 1361.0 58.3 CP42122-042 88 1711.1 14.7 1698.8 9.6 1683.7 11.8 1683.7 11.8 CP42122-043 96 1513.0 6.1 1510.0 9.3 1505.8 20.7 1505.8 20.7 CP42122-045 190 168.4 3.4 159.8 13.8 34.7 218.8 168.4 3.4 CP42122-046 77 1456.4 22.2 1450.9 16.6 1442.9 25.1 1442.9 25.1 CP42122-047 24 1183.0 47.8 1164.1 45.1 1129.1 95.2 1129.1 95.2 CP42122-048 18 1428.3 25.8 1429.3 33.5 1430.7 74.0 1430.7 74.0 135 Sample Z4 – 310711-04. Page 2 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) CP42122-050 86 29.5 4.4 136.9 288.0 3039.5 488.2 29.5 4.4 CP42122-052 171 1425.2 30.1 1419.6 18.4 1411.2 9.4 1411.2 9.4 CP42122-053 201 94.4 3.9 78.3 17.3 -390.2 593.3 94.4 3.9 CP42122-054 199 1289.5 20.7 1331.2 13.7 1399.1 9.7 1399.1 9.7 CP42122-057 638 1607.2 44.0 1638.1 25.2 1677.9 3.9 1677.9 3.9 CP42122-058 57 1445.7 21.0 1433.9 17.6 1416.3 30.8 1416.3 30.8 CP42122-059 34 1462.2 16.8 1455.4 25.1 1445.5 56.7 1445.5 56.7 CP42122-060 123 1211.6 11.0 1212.9 9.3 1215.0 16.7 1215.0 16.7 CP42122-062 74 25.4 5.7 168.2 693.3 3623.0 432.5 25.4 5.7 CP42122-063 39 1444.6 13.6 1439.0 15.2 1430.8 31.8 1430.8 31.8 CP42122-064 76 1443.3 39.9 1460.8 30.3 1486.4 45.7 1486.4 45.7 CP42122-065 114 29.5 9.1 68.1 69.2 1793.0 200.2 29.5 9.1 CP42122-066 555 247.1 2.9 249.0 5.2 266.3 46.9 247.1 2.9 CP42122-067 261 1709.6 17.4 1710.7 9.7 1712.0 3.5 1712.0 3.5 CP42122-068 191 1418.4 18.6 1424.7 12.8 1434.0 15.5 1434.0 15.5 CP42122-069 129 1730.1 26.3 1712.0 15.1 1689.9 10.9 1689.9 10.9 CP42122-070 385 1472.0 30.2 1472.1 19.5 1472.2 19.5 1472.2 19.5 CP42122-071 28 1677.4 26.3 1690.7 36.8 1707.3 75.3 1707.3 75.3 CP42122-072 138 1477.1 31.3 1460.9 19.2 1437.4 13.6 1437.4 13.6 CP42122-073 30 1850.4 16.7 1844.1 18.9 1837.1 35.6 1837.1 35.6 CP42122-074 135 168.1 2.8 174.3 29.8 260.4 429.3 168.1 2.8 CP42122-075 73 173.6 12.4 178.4 39.2 242.3 532.2 173.6 12.4 CP42122-076 472 1417.1 154.8 1513.4 96.3 1650.9 18.8 1650.9 18.8 CP42122-077 177 1706.8 17.6 1705.0 10.6 1702.9 9.6 1702.9 9.6 CP42122-078 27 1439.3 19.0 1423.9 25.0 1400.8 56.0 1400.8 56.0 CP42122-079 191 26.7 2.6 24.0 14.1 -235.4 1611.8 26.7 2.6 CP42122-080 59 1441.9 25.2 1437.6 18.0 1431.1 24.6 1431.1 24.6 CP42122-081 114 1467.9 27.8 1455.5 17.7 1437.4 16.4 1437.4 16.4 CP42122-082 539 191.7 5.9 192.5 7.5 203.2 67.2 191.7 5.9 CP42122-083 151 195.2 7.7 197.2 20.1 220.8 243.7 195.2 7.7 CP42122-084 19 1180.8 24.8 1124.5 40.8 1017.2 112.5 1017.2 112.5 CP42122-085 32 1412.3 24.9 1408.2 29.1 1402.1 62.7 1402.1 62.7 CP42122-086 188 534.1 11.3 532.1 12.2 523.3 42.8 534.1 11.3 CP42122-087 43 166.3 11.2 117.6 50.3 -783.9 1320.6 166.3 11.2 CP42122-088 281 26.9 4.2 39.6 31.7 897.1 2082.3 26.9 4.2 CP42122-089 217 26.5 1.8 24.4 13.8 -172.2 1530.0 26.5 1.8 CP42122-090 295 26.4 1.6 25.3 9.4 -72.4 932.8 26.4 1.6 CP42122-092 92 1696.7 28.6 1706.1 18.7 1717.7 22.2 1717.7 22.2 CP42122-093 60 22.0 5.4 -391.5 NaN NA NA 22.0 5.4 CP42122-094 55 1673.8 18.6 1668.7 14.3 1662.3 22.4 1662.3 22.4 CP42122-095 414 23.5 1.1 32.1 9.9 733.4 670.1 23.5 1.1 CP42122-097 90 1664.4 24.1 1675.2 14.0 1688.7 8.9 1688.7 8.9 136 Sample Z4 – 310711-04. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) CP42122-098 109 217.5 7.2 208.9 28.5 112.9 348.4 217.5 7.2 CP42122-099 112 1430.0 10.5 1432.7 9.2 1436.7 16.5 1436.7 16.5 CP42122-100 156 1427.8 17.0 1431.2 11.8 1436.3 15.0 1436.3 15.0

137 Sample Z5 – 300711-07. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 300711-07 001 271 38.9 2.4 42.4 5.9 245.7 297.0 38.9 2.4 300711-07 002 44 77.5 7.4 36.7 21.4 NA NA 77.5 7.4 300711-07 003 110 283.9 6.0 280.5 20.8 252.3 188.7 283.9 6.0 300711-07 004 59 19.9 5.1 46.4 49.4 1793.2 170.2 19.9 5.1 300711-07 005 108 40.8 3.4 31.1 21.5 -663.8 2169.9 40.8 3.4 300711-07 006 125 39.6 3.1 41.5 21.5 150.4 1308.1 39.6 3.1 300711-07 007 159 39.7 2.5 40.5 8.4 87.9 485.7 39.7 2.5 300711-07 008 123 13.5 2.5 7.1 5.8 -1899.7 982.6 13.5 2.5 300711-07 009 50 141.6 5.1 141.9 35.6 145.6 634.4 141.6 5.1 300711-07 010 186 1178.6 20.6 1178.0 14.0 1176.9 12.0 1176.9 12.0 300711-07 011 178 13.7 3.2 11.8 13.3 -368.9 1462.9 13.7 3.2 300711-07 012 78 80.3 8.0 87.5 29.1 287.1 779.1 80.3 8.0 300711-07 014 99 39.3 3.0 41.0 22.1 141.2 1374.0 39.3 3.0 300711-07 015 421 13.3 1.0 12.6 4.4 -119.2 856.7 13.3 1.0 300711-07 016 31 77.4 11.3 63.1 63.2 -448.9 1426.3 77.4 11.3 300711-07 017 142 84.1 3.2 70.3 14.1 -377.8 532.9 84.1 3.2 300711-07 018 132 189.0 2.6 185.8 9.6 145.4 128.6 189.0 2.6 300711-07 019 293 40.1 1.2 35.7 5.7 -253.0 406.9 40.1 1.2 300711-07 020 109 188.0 4.5 186.7 15.2 171.2 200.0 188.0 4.5 300711-07 020 129 188.0 3.8 192.2 15.6 244.8 199.8 188.0 3.8 300711-07 022 447 577.9 4.7 578.5 5.6 580.7 20.2 577.9 4.7 300711-07 023 106 74.6 10.2 62.7 17.6 -372.5 669.5 74.6 10.2 300711-07 024 26 84.5 20.6 123.9 149.9 967.7 497.0 84.5 20.6 300711-07 025 50 1023.5 14.2 1020.9 18.0 1015.4 47.7 1015.4 47.7 300711-07 026 51 1520.4 35.2 1558.3 25.0 1610.2 32.6 1610.2 32.6 300711-07 027 36 235.0 19.1 213.0 33.4 -24.3 371.5 235.0 19.1 300711-07 028 161 231.2 5.8 228.8 13.6 204.3 142.7 231.2 5.8 300711-07 029 235 186.6 1.6 180.8 7.2 106.3 101.0 186.6 1.6 300711-07 030 70 1498.6 10.3 1508.9 9.7 1523.3 18.2 1523.3 18.2 300711-07 031 310 37.7 1.0 35.6 7.2 -106.9 507.1 37.7 1.0 300711-07 032 317 129.5 2.2 131.8 6.7 173.0 119.4 129.5 2.2 300711-07 034 373 68.9 1.9 70.1 5.6 109.0 184.0 68.9 1.9 300711-07 035 191 230.2 2.7 231.5 14.0 244.6 153.0 230.2 2.7 300711-07 036 90 289.5 7.9 292.9 25.4 320.0 218.4 289.5 7.9 300711-07 037 260 185.6 3.1 191.5 10.1 265.5 126.5 185.6 3.1 300711-07 038 61 24.2 5.0 63.0 35.7 2003.8 1063.9 24.2 5.0 300711-07 040 104 37.4 4.2 39.0 12.9 141.3 761.4 37.4 4.2 300711-07 041 122 163.0 3.6 183.7 16.1 458.2 207.1 163.0 3.6 300711-07 042 129 138.0 2.8 143.1 19.9 227.4 342.2 138.0 2.8 300711-07 043 317 81.1 1.3 82.7 6.9 129.5 202.7 81.1 1.3 300711-07 045 56 68.2 5.8 102.2 138.8 992.9 584.1 68.2 5.8

138 Sample Z5 – 300711-07. Page 2 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 300711-07 046 157 185.7 3.5 182.1 10.4 135.7 139.4 185.7 3.5 300711-07 048 126 41.2 3.0 43.0 15.4 145.0 863.6 41.2 3.0 300711-07 048 206 155.4 3.3 158.5 12.0 204.7 183.1 155.4 3.3 300711-07 050 70 187.2 7.0 181.4 19.7 106.7 266.6 187.2 7.0 300711-07 051 58 79.5 6.2 58.6 32.8 -731.3 1719.3 79.5 6.2 300711-07 053 153 1510.8 32.1 1526.7 19.4 1548.8 11.2 1548.8 11.2 300711-07 055 34 1082.8 21.1 1111.0 29.4 1166.7 75.2 1166.7 75.2 300711-07 056 236 188.9 2.2 187.4 7.7 169.0 101.1 188.9 2.2 300711-07 057 144 40.9 2.6 19.2 24.4 NA NA 40.9 2.6 300711-07 059 150 12.6 2.1 388.1 NaN NA NA 12.6 2.1 300711-07 060 78 24.0 4.9 86.9 167.1 2592.2 270.4 24.0 4.9 300711-07 061 152 33.9 2.7 29.0 13.7 -363.1 1282.1 33.9 2.7 300711-07 063 105 785.3 8.4 791.5 11.8 808.9 38.0 785.3 8.4 300711-07 064 414 37.6 0.7 33.9 6.7 -219.3 507.2 37.6 0.7 300711-07 065 74 37.4 4.5 41.5 18.0 287.8 1012.4 37.4 4.5 300711-07 066 61 38.5 8.4 170.9 540.6 2997.1 179.8 38.5 8.4 300711-07 067 220 13.1 1.6 37.3 58.6 2139.6 127.6 13.1 1.6 300711-07 068 92 41.5 5.1 35.3 30.6 -372.4 2975.9 41.5 5.1 300711-07 069 57 78.6 8.3 56.5 33.6 -804.3 1860.1 78.6 8.3 300711-07 070 85 569.0 6.1 562.6 14.2 536.9 67.7 569.0 6.1 300711-07 071 144 188.4 5.9 183.8 13.5 124.8 173.0 188.4 5.9 300711-07 074 160 14.0 2.4 41.9 68.0 2228.5 163.1 14.0 2.4 300711-07 075 69 81.6 6.1 57.3 25.7 -871.1 1368.6 81.6 6.1 300711-07 076 171 74.6 4.0 66.6 12.7 -212.9 480.8 74.6 4.0 300711-07 077 154 44.8 4.9 36.2 16.7 -506.3 1278.7 44.8 4.9 300711-07 079 75 570.9 15.3 571.9 23.8 575.8 101.3 570.9 15.3 300711-07 080 121 42.0 2.9 37.1 17.1 -267.6 1235.4 42.0 2.9 300711-07 081 139 278.4 3.9 272.7 13.9 224.3 130.8 278.4 3.9 300711-07 082 167 37.5 3.0 30.4 9.2 -504.9 809.0 37.5 3.0 300711-07 084 75 75.2 6.3 63.9 23.8 -342.6 996.2 75.2 6.3 300711-07 085 35 77.8 12.2 56.0 96.3 -801.8 0.0 77.8 12.2 300711-07 086 211 190.3 3.9 177.9 11.3 15.7 159.2 190.3 3.9 300711-07 087 129 37.3 2.3 85.0 222.6 1781.7 603.5 37.3 2.3 300711-07 089 200 77.0 1.8 70.8 16.9 -131.1 617.0 77.0 1.8 300711-07 090 528 13.3 0.7 12.6 3.4 -117.1 671.2 13.3 0.7 300711-07 091 290 35.1 2.6 28.4 11.4 -512.7 1110.9 35.1 2.6 300711-07 092 160 43.6 5.3 41.6 11.5 -73.6 632.1 43.6 5.3 300711-07 093 36 90.9 6.6 130.1 73.6 923.1 1359.2 90.9 6.6 300711-07 094 126 23.3 4.1 33.1 9.6 812.1 504.0 23.3 4.1 300711-07 095 97 188.3 6.4 187.2 19.2 173.6 249.8 188.3 6.4 300711-07 096 60 75.0 5.1 35.8 27.8 NA NA 75.0 5.1 300711-07 097 93 183.0 6.9 181.0 17.6 154.3 232.1 183.0 6.9 139 Sample Z5 – 300711-07. Page 3 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 300711-07 098 55 153.2 2.7 144.9 33.1 11.6 595.0 153.2 2.7 300711-07 099 77 1135.3 19.1 1160.4 17.0 1207.6 32.3 1207.6 32.3 300711-07 101 56 80.3 12.6 54.0 40.9 -999.6 2637.7 80.3 12.6 300711-07 102 202 352.9 17.3 421.1 21.2 813.9 74.5 352.9 17.3 300711-07 103 40 81.7 7.1 858.6 NaN NA NA 81.7 7.1 300711-07 104 224 68.1 4.0 64.4 34.5 -69.7 1445.6 68.1 4.0 300711-07 105 263 549.6 14.3 548.0 12.3 541.2 21.8 549.6 14.3 300711-07 106 24 1508.2 30.3 1516.0 26.2 1526.8 46.0 1526.8 46.0 300711-07 107 146 230.5 4.9 219.2 18.5 99.8 216.3 230.5 4.9 300711-07 108 194 187.8 2.2 185.4 10.1 154.6 137.1 187.8 2.2 300711-07 109 129 12.4 3.1 115.6 856.4 4101.7 330.4 12.4 3.1 300711-07 110 118 517.5 10.4 521.4 13.2 538.4 53.9 517.5 10.4

140 Sample Z6 – 030811-01. Page 1 of 3 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 030811-01-001 104 1007.3 7.5 1005.4 11.8 1001.1 33.8 1001.1 33.8 030811-01-002 143 186.7 4.5 200.6 9.2 367.3 99.1 186.7 4.5 030811-01-003 189 184.8 4.6 193.2 10.0 296.9 116.1 184.8 4.6 030811-01-004 166 185.2 3.5 183.3 13.7 157.8 185.8 185.2 3.5 030811-01-005 412 190.9 2.2 189.6 5.5 172.4 69.6 190.9 2.2 030811-01-006 95 189.5 6.9 192.6 19.3 231.6 239.5 189.5 6.9 030811-01-007 108 187.3 3.4 191.0 18.4 236.3 241.2 187.3 3.4 030811-01-008 468 170.0 2.1 171.2 6.1 187.5 85.3 170.0 2.1 030811-01-009 212 189.1 3.4 187.8 7.5 171.6 92.8 189.1 3.4 030811-01-010 123 74.2 3.1 66.1 19.1 -219.4 755.7 74.2 3.1 030811-01-011 189 186.3 2.5 188.9 15.2 221.2 201.7 186.3 2.5 030811-01-012 103 186.7 9.4 189.6 17.9 226.1 207.4 186.7 9.4 030811-01-013 143 187.3 4.2 182.3 11.7 119.0 156.9 187.3 4.2 030811-01-014 391 1521.6 14.2 1516.0 8.5 1508.1 5.4 1508.1 5.4 030811-01-015 133 187.2 2.0 183.4 16.8 135.0 233.6 187.2 2.0 030811-01-016 50 1892.6 17.6 1887.1 13.7 1880.9 21.3 1880.9 21.3 030811-01-017 93 185.6 8.3 190.5 18.6 251.9 222.8 185.6 8.3 030811-01-018 153 1012.8 19.5 1009.0 15.0 1000.7 21.7 1000.7 21.7 030811-01-019 135 185.8 3.8 195.0 28.3 307.5 362.0 185.8 3.8 030811-01-020 181 189.3 2.1 179.0 13.7 44.7 197.9 189.3 2.1 030811-01-021 46 1007.5 17.1 995.7 24.9 970.0 70.6 970.0 70.6 030811-01-022 62 189.5 8.4 180.2 24.1 59.6 332.8 189.5 8.4 030811-01-023 130 185.7 4.0 181.7 16.5 129.9 227.7 185.7 4.0 030811-01-024 94 188.2 4.2 177.1 21.5 30.8 313.6 188.2 4.2 030811-01-025 70 184.5 8.2 155.3 32.2 -271.1 561.6 184.5 8.2 030811-01-026 119 187.8 3.6 176.5 26.6 27.1 393.1 187.8 3.6 030811-01-027 155 188.2 4.1 181.5 16.9 94.8 234.8 188.2 4.1 030811-01-028 44 188.0 9.1 141.6 65.8 -579.7 1413.6 188.0 9.1 030811-01-029 223 188.3 2.7 191.7 6.2 233.5 74.5 188.3 2.7 030811-01-030 81 282.9 8.7 281.7 31.9 271.5 288.8 282.9 8.7 030811-01-031 123 185.1 3.3 183.8 17.1 167.9 234.8 185.1 3.3 030811-01-032 190 186.2 6.2 188.2 8.5 213.9 85.1 186.2 6.2 030811-01-033 107 186.0 4.2 173.5 21.2 6.8 316.3 186.0 4.2 030811-01-034 171 188.3 3.5 192.4 16.5 243.9 212.3 188.3 3.5 030811-01-035 200 184.9 4.4 190.2 9.4 257.5 111.7 184.9 4.4 030811-01-036 227 188.2 3.4 180.5 11.5 81.6 159.7 188.2 3.4 030811-01-037 36 1392.4 33.5 1486.0 32.5 1622.2 59.3 1622.2 59.3 030811-01-038 204 185.5 3.5 191.9 10.5 271.9 130.3 185.5 3.5 030811-01-039 27 273.1 16.8 288.8 48.5 418.2 410.2 273.1 16.8 030811-01-040 110 186.2 4.5 189.8 22.4 234.8 293.9 186.2 4.5 030811-01-041 156 185.5 3.4 187.2 14.0 209.2 185.6 185.5 3.4

141 Sample Z6 – 030811-01. Page 2 of 3 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 030811-01-042 145 188.1 4.1 189.9 19.6 212.2 257.6 188.1 4.1 030811-01-043 338 1174.3 9.8 1171.8 7.1 1167.1 9.1 1167.1 9.1 030811-01-044 141 187.2 2.4 188.5 12.6 204.5 167.2 187.2 2.4 030811-01-045 127 167.8 2.7 168.1 20.4 171.3 305.8 167.8 2.7 030811-01-046 176 169.9 3.7 170.6 13.8 181.2 199.2 169.9 3.7 030811-01-047 155 279.1 6.4 280.7 10.5 294.0 81.8 279.1 6.4 030811-01-048 72 185.8 6.1 192.8 21.5 279.2 270.4 185.8 6.1 030811-01-050 117 191.2 3.8 193.7 20.2 224.6 261.2 191.2 3.8 030811-01-051 126 184.7 3.8 183.2 17.7 163.6 242.7 184.7 3.8 030811-01-052 154 1071.5 20.1 1082.0 15.6 1103.1 23.0 1103.1 23.0 030811-01-053 164 188.2 1.6 186.6 15.3 166.3 209.6 188.2 1.6 030811-01-054 42 1513.5 40.2 1503.0 29.0 1488.2 41.3 1488.2 41.3 030811-01-055 113 186.3 3.3 193.9 19.5 287.2 250.1 186.3 3.3 030811-01-056 213 188.5 3.1 191.3 10.1 225.7 127.9 188.5 3.1 030811-01-057 96 187.2 2.8 190.8 21.8 236.0 287.3 187.2 2.8 030811-01-058 155 186.0 6.3 190.9 21.6 252.7 274.8 186.0 6.3 030811-01-059 39 183.5 13.1 196.4 41.1 355.4 498.4 183.5 13.1 030811-01-060 63 184.9 10.3 176.0 35.0 57.8 502.4 184.9 10.3 030811-01-061 146 183.3 5.6 179.4 17.4 128.3 238.9 183.3 5.6 030811-01-062 161 277.4 4.5 284.6 14.9 344.7 130.4 277.4 4.5 030811-01-063 289 188.1 4.6 190.6 9.3 220.9 109.6 188.1 4.6 030811-01-064 70 187.3 5.0 179.1 26.4 72.6 378.9 187.3 5.0 030811-01-065 111 188.8 5.2 179.4 24.5 56.9 351.1 188.8 5.2 030811-01-066 61 162.5 14.3 172.7 34.7 314.3 457.9 162.5 14.3 030811-01-067 110 191.2 3.8 183.8 18.6 89.0 258.7 191.2 3.8 030811-01-068 76 187.3 6.6 194.5 19.2 282.1 234.4 187.3 6.6 030811-01-069 84 992.5 8.8 990.8 15.7 987.1 46.6 987.1 46.6 030811-01-070 382 192.1 2.3 191.2 8.2 179.7 105.6 192.1 2.3 030811-01-071 120 1028.0 9.4 1025.9 12.1 1021.2 32.1 1021.2 32.1 030811-01-072 91 206.0 8.4 219.9 23.4 371.5 249.8 206.0 8.4 030811-01-073 62 1457.7 20.7 1464.1 15.9 1473.4 24.4 1473.4 24.4 030811-01-074 15 941.4 21.8 943.1 54.5 947.1 174.4 947.1 174.4 030811-01-075 215 188.2 4.5 188.8 8.3 195.8 97.1 188.2 4.5 030811-01-076 137 190.3 3.4 198.1 12.0 292.6 146.9 190.3 3.4 030811-01-077 86 1147.6 15.4 1143.2 15.8 1134.8 35.5 1134.8 35.5 030811-01-078 91 187.7 9.3 189.6 18.1 214.1 213.3 187.7 9.3 030811-01-079 23 1477.4 21.8 1485.7 24.7 1497.6 51.2 1497.6 51.2 030811-01-080 114 189.7 3.4 181.3 18.9 72.9 268.6 189.7 3.4 030811-01-081 266 189.2 1.8 185.3 8.8 134.9 119.6 189.2 1.8 030811-01-082 122 15.0 2.0 44.4 44.8 2216.5 446.8 15.0 2.0 030811-01-083 381 191.3 4.5 191.3 6.8 191.2 71.5 191.3 4.5 030811-01-084 216 185.4 2.9 184.8 10.3 177.7 137.5 185.4 2.9 142 Sample Z6 – 030811-01. Page 3 of 3 Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 030811-01-085 45 91.0 16.9 112.1 80.8 587.3 1894.4 91.0 16.9 030811-01-086 178 192.7 5.0 193.9 13.0 208.5 159.4 192.7 5.0 030811-01-087 206 189.4 2.8 183.3 10.3 105.7 141.5 189.4 2.8 030811-01-088 369 1969.1 16.0 2011.5 8.4 2055.3 3.3 2055.3 3.3 030811-01-089 168 188.3 3.4 189.0 7.2 198.1 87.1 188.3 3.4 030811-01-090 166 188.5 1.8 182.5 10.5 105.7 146.5 188.5 1.8 030811-01-091 98 188.4 6.8 177.3 20.5 32.1 289.0 188.4 6.8 030811-01-092 130 190.0 2.5 183.7 15.5 104.1 215.4 190.0 2.5 030811-01-093 78 186.8 8.5 202.2 27.2 385.5 318.8 186.8 8.5 030811-01-094 137 178.1 3.6 177.2 21.5 164.2 307.2 178.1 3.6 030811-01-095 68 183.6 7.4 178.5 28.7 111.7 404.7 183.6 7.4 030811-01-096 48 190.8 8.9 152.0 44.9 -416.9 840.5 190.8 8.9 030811-01-097 95 149.0 4.5 150.3 23.2 170.1 382.7 149.0 4.5 030811-01-098 63 184.7 5.4 171.5 35.4 -6.9 542.1 184.7 5.4 030811-01-099 137 187.8 4.6 173.5 17.7 -17.4 262.8 187.8 4.6 030811-01-100 104 189.5 7.3 185.3 16.4 133.2 209.0 189.5 7.3 030811-01-101 87 1483.0 22.9 1493.0 15.9 1507.3 20.1 1507.3 20.1 030811-01-102 62 190.7 9.2 196.7 35.7 269.5 447.0 190.7 9.2 030811-01-103 82 189.3 6.2 176.5 33.2 7.7 490.2 189.3 6.2 030811-01-104 95 189.8 6.1 196.2 19.9 272.8 244.5 189.8 6.1 030811-01-105 144 169.5 3.7 158.6 19.3 -0.3 313.2 169.5 3.7

143 Sample Z7 – 020811-01. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 020811-01-001 204 1291.2 35.3 1278.9 22.3 1258.4 9.8 1258.4 9.8 020811-01-002 184 1119.6 18.9 1106.6 14.0 1081.0 19.1 1081.0 19.1 020811-01-004 223 1029.7 11.2 1024.0 12.8 1011.7 32.4 1011.7 32.4 020811-01-005 189 1334.9 20.5 1336.7 13.9 1339.5 15.2 1339.5 15.2 020811-01-006 229 1552.8 21.3 1535.7 12.5 1512.3 6.9 1512.3 6.9 020811-01-007 33 1474.3 18.6 1456.5 20.1 1430.7 41.6 1430.7 41.6 020811-01-008 571 1091.7 13.1 1101.0 9.1 1119.2 8.0 1119.2 8.0 020811-01-009 66 1019.1 24.0 1016.6 22.7 1011.2 49.5 1011.2 49.5 020811-01-010 590 1017.7 11.5 1016.0 8.0 1012.3 4.8 1012.3 4.8 020811-01-011 68 1313.2 20.1 1320.1 16.6 1331.5 28.5 1331.5 28.5 020811-01-012 269 1510.7 37.5 1569.4 22.8 1649.2 11.8 1649.2 11.8 020811-01-013 132 1199.6 49.7 1211.2 34.8 1232.1 36.8 1232.1 36.8 020811-01-014 163 987.1 18.9 1003.6 14.8 1039.8 21.6 1039.8 21.6 020811-01-016 137 1263.1 22.0 1277.5 17.0 1301.9 26.0 1301.9 26.0 020811-01-017 340 1171.3 22.5 1195.2 15.3 1238.8 10.7 1238.8 10.7 020811-01-019 271 1131.8 18.2 1135.9 13.1 1143.6 15.0 1143.6 15.0 020811-01-020 72 1025.2 21.3 1024.5 21.5 1023.1 50.1 1023.1 50.1 020811-01-021 98 985.4 13.7 990.6 15.8 1002.2 40.3 1002.2 40.3 020811-01-022 421 1022.6 13.6 1021.3 10.0 1018.6 11.8 1018.6 11.8 020811-01-023 81 274.4 8.0 262.8 28.5 160.5 279.5 274.4 8.0 020811-01-024 178 1603.7 25.7 1602.0 15.1 1599.7 8.4 1599.7 8.4 020811-01-025 282 1498.9 26.6 1506.0 16.0 1516.0 8.0 1516.0 8.0 020811-01-026 94 1602.3 75.7 1595.6 44.3 1586.7 25.2 1586.7 25.2 020811-01-027 168 1010.7 14.8 1004.5 13.6 991.0 28.8 991.0 28.8 020811-01-028 272 1002.1 20.2 1018.3 16.2 1053.4 25.3 1053.4 25.3 020811-01-029 363 1293.6 28.4 1320.2 18.8 1363.6 14.7 1363.6 14.7 020811-01-030 55 1006.3 21.5 1007.6 28.0 1010.5 75.5 1010.5 75.5 020811-01-031 713 1018.4 11.9 1016.7 8.4 1013.2 7.3 1013.2 7.3 020811-01-032 154 1009.8 6.0 1003.8 7.3 990.7 19.4 990.7 19.4 020811-01-033 264 1030.2 11.0 1026.0 9.8 1016.9 19.7 1016.9 19.7 020811-01-034 162 1325.2 28.4 1319.6 19.2 1310.4 20.5 1310.4 20.5 020811-01-035 281 1407.7 66.1 1449.1 40.7 1510.3 13.4 1510.3 13.4 020811-01-036 151 1031.8 20.7 1021.7 17.2 1000.1 31.3 1000.1 31.3 020811-01-037 50 1022.4 17.0 1008.4 21.8 978.3 58.9 978.3 58.9 020811-01-039 126 1345.6 24.3 1345.1 16.4 1344.5 17.7 1344.5 17.7 020811-01-040 91 1225.1 16.3 1237.7 13.4 1259.6 22.8 1259.6 22.8 020811-01-041 42 1661.7 45.3 1636.5 39.6 1604.3 70.3 1604.3 70.3 020811-01-042 139 1108.8 35.6 1104.3 24.4 1095.5 19.7 1095.5 19.7 020811-01-043 138 1500.6 31.9 1497.9 19.1 1494.2 9.9 1494.2 9.9 020811-01-044 104 955.6 13.6 963.1 19.7 980.3 56.5 980.3 56.5 020811-01-045 103 1359.4 77.2 1374.1 48.7 1397.0 27.3 1397.0 27.3

144 Sample Z7 – 020811-01. Page 2 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 020811-01-046 57 1620.4 27.7 1612.1 17.7 1601.2 19.0 1601.2 19.0 020811-01-047 501 1386.1 51.6 1414.8 32.4 1458.3 18.5 1458.3 18.5 020811-01-048 133 1096.2 31.4 1112.4 24.2 1144.2 35.1 1144.2 35.1 020811-01-049 231 1003.1 12.7 1003.2 11.0 1003.3 21.5 1003.3 21.5 020811-01-050 80 1703.1 52.3 1770.7 31.3 1851.4 23.9 1851.4 23.9 020811-01-051 138 1322.7 10.5 1323.8 8.6 1325.6 14.9 1325.6 14.9 020811-01-052 867 1228.9 31.3 1259.5 20.8 1312.1 13.2 1312.1 13.2 020811-01-054 321 1605.9 34.5 1601.6 19.9 1596.0 8.5 1596.0 8.5 020811-01-055 137 1015.0 13.8 1023.3 14.4 1041.2 34.0 1041.2 34.0 020811-01-056 227 1264.8 51.2 1284.3 32.9 1317.1 12.6 1317.1 12.6 020811-01-058 249 1011.5 26.8 1006.1 19.5 994.4 21.8 994.4 21.8 020811-01-059 528 1071.9 10.4 1104.2 11.9 1168.3 28.2 1168.3 28.2 020811-01-060 87 1545.8 29.0 1526.6 19.1 1499.9 22.4 1499.9 22.4 020811-01-061 120 1539.0 20.8 1549.9 13.4 1564.8 13.9 1564.8 13.9 020811-01-062 84 1066.7 91.8 1106.8 64.8 1186.5 43.7 1186.5 43.7 020811-01-063 146 986.9 16.4 977.6 13.6 956.8 25.2 956.8 25.2 020811-01-064 142 1176.6 10.7 1180.2 10.4 1186.9 22.1 1186.9 22.1 020811-01-065 179 1041.4 11.5 1036.5 10.9 1026.0 23.7 1026.0 23.7 020811-01-066 285 1032.1 25.9 1035.1 19.3 1041.4 24.4 1041.4 24.4 020811-01-067 149 1163.4 28.2 1173.8 20.3 1193.1 24.2 1193.1 24.2 020811-01-068 163 1485.5 18.6 1503.3 11.9 1528.4 11.0 1528.4 11.0 020811-01-069 313 1520.6 64.7 1517.9 37.7 1514.1 4.9 1514.1 4.9 020811-01-070 114 1029.3 30.6 1026.4 24.2 1020.3 39.0 1020.3 39.0 020811-01-071 481 1512.7 27.9 1537.9 18.2 1572.6 19.0 1572.6 19.0 020811-01-072 389 1315.2 18.8 1314.4 12.1 1313.2 8.3 1313.2 8.3 020811-01-073 183 1014.1 15.4 1011.0 13.6 1004.4 27.4 1004.4 27.4 020811-01-074 149 1216.8 14.3 1228.2 12.0 1248.2 21.2 1248.2 21.2 020811-01-075 96 1046.9 19.1 1042.1 22.8 1032.1 58.4 1032.1 58.4 020811-01-076 116 1016.7 15.9 1019.6 16.9 1025.8 40.8 1025.8 40.8 020811-01-077 197 1003.5 10.4 1006.2 9.6 1012.1 20.5 1012.1 20.5 020811-01-078 79 1399.0 75.0 1405.3 46.9 1414.8 29.6 1414.8 29.6 020811-01-079 157 1513.7 25.4 1515.5 16.2 1518.0 15.7 1518.0 15.7 020811-01-080 286 1442.6 28.9 1463.3 17.7 1493.4 9.6 1493.4 9.6 020811-01-081 65 965.1 14.8 985.8 21.9 1032.3 61.7 1032.3 61.7 020811-01-082 162 1292.3 13.2 1296.6 10.4 1303.7 16.8 1303.7 16.8 020811-01-083 70 974.7 11.4 983.1 27.8 1001.9 85.8 1001.9 85.8 020811-01-084 102 1193.9 29.5 1182.4 21.8 1161.3 30.7 1161.3 30.7 020811-01-085 271 1460.2 31.6 1474.6 19.4 1495.3 12.1 1495.3 12.1 020811-01-086 722 1190.5 53.7 1232.2 35.7 1306.0 14.7 1306.0 14.7 020811-01-087 142 1529.2 50.7 1556.2 30.6 1593.0 17.4 1593.0 17.4 020811-01-088 214 1544.1 14.8 1542.8 9.2 1540.9 8.1 1540.9 8.1 020811-01-089 318 1066.1 24.9 1069.6 17.4 1076.8 14.4 1076.8 14.4 145 Sample Z7 – 020811-01. Page 3 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 020811-01-090 747 978.7 26.2 992.8 18.6 1024.1 11.8 1024.1 11.8 020811-01-091 61 1002.5 18.0 998.8 23.6 990.7 64.3 990.7 64.3 020811-01-092 58 1045.6 15.1 1038.4 21.5 1023.1 58.9 1023.1 58.9 020811-01-093 87 1015.0 32.0 1011.6 25.4 1004.1 41.2 1004.1 41.2 020811-01-094 163 1020.4 22.5 1024.0 16.6 1031.6 19.9 1031.6 19.9 020811-01-095 100 1154.5 21.2 1152.2 19.6 1147.9 40.1 1147.9 40.1 020811-01-096 153 1344.8 21.5 1344.1 14.5 1343.1 15.9 1343.1 15.9 020811-01-097 65 1460.7 47.3 1486.7 32.0 1524.1 36.5 1524.1 36.5 020811-01-098 103 1108.8 19.7 1106.4 18.5 1101.5 39.0 1101.5 39.0 020811-01-100 160 1009.8 17.0 1012.3 17.4 1017.8 41.0 1017.8 41.0 020811-01-101 252 1227.8 39.8 1244.1 27.9 1272.4 30.4 1272.4 30.4 020811-01-102 199 1302.5 79.8 1342.1 50.9 1405.8 17.7 1405.8 17.7 020811-01-103 86 1042.7 31.4 1043.5 25.3 1045.3 42.3 1045.3 42.3 020811-01-104 167 1267.8 17.3 1269.3 13.5 1271.8 21.3 1271.8 21.3 020811-01-105 140 1537.0 20.6 1540.1 15.2 1544.4 22.2 1544.4 22.2 020811-01-107 288 1286.2 83.4 1301.7 52.9 1327.3 15.4 1327.3 15.4 020811-01-108 98 1528.2 23.3 1527.9 17.2 1527.5 25.6 1527.5 25.6 020811-01-109 686 1349.7 41.2 1341.9 27.1 1329.3 26.1 1329.3 26.1 020811-01-110 463 994.3 10.7 993.3 8.0 991.1 10.0 991.1 10.0 020811-01-111 247 997.6 8.5 996.8 6.1 995.2 5.0 995.2 5.0 020811-01-112 411 1496.4 30.3 1497.1 17.9 1498.0 5.0 1498.0 5.0 020811-01-113 127 1777.1 28.7 1774.5 17.0 1771.3 15.5 1771.3 15.5 020811-01-114 356 1026.4 10.7 1021.1 8.5 1009.8 14.1 1009.8 14.1 020811-01-115 85 997.0 15.0 1008.6 18.5 1033.8 48.3 1033.8 48.3 020811-01-116 192 1312.4 13.3 1317.8 11.5 1326.6 20.7 1326.6 20.7 020811-01-117 33 1502.0 17.4 1488.7 23.6 1469.8 51.8 1469.8 51.8 020811-01-118 91 1003.0 18.7 999.5 18.3 991.7 41.9 991.7 41.9 020811-01-119 92 988.6 9.8 990.5 19.5 994.6 58.8 994.6 58.8 020811-01-120 299 1197.0 32.2 1244.9 21.9 1328.7 15.6 1328.7 15.6

146 Sample Z8 – 070811-03. Page 1 of 2. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 070811-03-002 106 181.8 7.2 193.8 25.3 341.8 313.1 181.8 7.2 070811-03-003 224 34.8 2.0 28.0 17.1 -518.2 1796.0 34.8 2.0 070811-03-004 94 151.7 4.4 126.1 16.3 -334.2 346.4 151.7 4.4 070811-03-005 57 157.4 10.9 126.2 83.5 -426.8 2083.2 157.4 10.9 070811-03-006 170 35.5 3.3 58.6 90.3 1143.7 570.2 35.5 3.3 070811-03-007 339 36.2 2.0 31.4 7.4 -325.1 603.2 36.2 2.0 070811-03-009 97 80.4 3.8 40.3 32.8 NA NA 80.4 3.8 070811-03-010 1097 189.0 2.2 196.2 4.5 283.9 50.6 189.0 2.2 070811-03-011 172 43.8 3.9 47.1 16.3 220.8 816.2 43.8 3.9 070811-03-012 228 282.4 5.8 287.8 10.2 332.5 78.5 282.4 5.8 070811-03-013 140 38.3 4.4 35.6 17.3 -137.9 1253.4 38.3 4.4 070811-03-014 93 186.5 8.5 186.8 38.9 190.7 524.7 186.5 8.5 070811-03-015 287 8.6 2.1 -26.3 -82.3 NA NA 8.6 2.1 070811-03-016 116 84.0 2.3 59.9 19.6 -823.7 982.2 84.0 2.3 070811-03-017 84 1197.7 27.5 1192.0 20.8 1181.5 31.3 1181.5 31.3 070811-03-018 98 1058.3 14.4 1049.2 18.1 1030.2 47.2 1030.2 47.2 070811-03-019 279 1051.2 12.5 1049.8 9.8 1046.8 15.1 1046.8 15.1 070811-03-020 61 15.5 5.8 -236.8 NaN NA NA 15.5 5.8 070811-03-021 172 33.1 3.4 46.7 21.8 819.6 1026.9 33.1 3.4 070811-03-022 489 1546.4 12.3 1538.6 7.3 1527.8 4.2 1527.8 4.2 070811-03-023 60 2121.2 15.6 2128.3 8.0 2135.2 4.2 2135.2 4.2 070811-03-024 105 36.4 3.3 677.1 NaN NA NA 36.4 3.3 070811-03-025 131 87.9 5.6 68.4 24.1 -570.8 994.8 87.9 5.6 070811-03-026 71 83.2 7.2 54.0 21.7 NA NA 83.2 7.2 070811-03-028 289 521.3 5.1 521.7 8.6 523.4 40.5 521.3 5.1 070811-03-029 235 37.6 1.5 40.3 12.8 203.3 763.3 37.6 1.5 070811-03-030 85 37.9 5.8 507.1 NaN NA NA 37.9 5.8 070811-03-031 154 83.0 3.9 59.5 16.6 -810.2 821.5 83.0 3.9 070811-03-032 76 37.4 5.1 179.5 401.3 3126.8 503.0 37.4 5.1 070811-03-034 71 144.7 7.1 98.4 38.2 -917.6 1217.6 144.7 7.1 070811-03-035 69 139.2 7.9 133.0 30.3 23.9 572.6 139.2 7.9 070811-03-038 254 1084.1 26.1 1076.3 18.1 1060.6 15.6 1060.6 15.6 070811-03-039 81 83.3 4.5 61.2 36.1 -735.4 1842.1 83.3 4.5 070811-03-040 148 276.7 6.4 277.6 15.1 284.7 131.6 276.7 6.4 070811-03-041 74 141.2 9.8 106.7 54.5 -609.4 1549.6 141.2 9.8 070811-03-043 95 134.6 7.7 116.8 38.4 -232.4 886.5 134.6 7.7 070811-03-044 115 88.7 3.1 86.0 26.9 10.9 798.4 88.7 3.1 070811-03-045 139 36.6 4.2 80.7 179.2 1721.7 477.5 36.6 4.2 070811-03-046 116 39.5 4.3 22.0 14.9 NA NA 39.5 4.3 070811-03-047 256 8.6 3.2 42.4 53.6 3065.0 844.8 8.6 3.2 070811-03-048 633 12.3 0.7 13.3 3.0 190.2 519.1 12.3 0.7

147 Sample Z8 – 070811-03. Page 2 of 2. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 070811-03-049 115 1007.9 15.9 1028.4 12.8 1072.2 20.1 1072.2 20.1 070811-03-050 53 182.5 16.2 172.7 124.9 40.5 2243.3 182.5 16.2 070811-03-052 130 190.6 4.2 191.9 23.2 208.3 304.3 190.6 4.2 070811-03-053 320 37.9 1.3 38.2 7.9 58.4 497.7 37.9 1.3 070811-03-054 50 286.5 14.6 220.3 48.3 -439.7 632.9 286.5 14.6 070811-03-055 57 933.0 27.9 951.9 28.2 995.9 65.8 995.9 65.8 070811-03-057 276 166.6 2.6 159.2 11.3 50.3 179.9 166.6 2.6 070811-03-059 82 38.8 4.1 110.0 221.4 2195.3 41.3 38.8 4.1 070811-03-060 196 41.9 1.6 42.7 15.4 89.7 892.9 41.9 1.6 070811-03-062 186 165.5 3.5 164.1 13.7 144.7 206.2 165.5 3.5 070811-03-063 150 163.6 4.8 166.6 19.9 210.1 292.7 163.6 4.8 070811-03-066 91 36.4 3.8 447.4 NaN NA NA 36.4 3.8 070811-03-067 150 32.3 4.4 63.2 31.3 1481.1 1001.4 32.3 4.4 070811-03-068 64 136.2 6.4 108.4 32.7 -466.4 848.8 136.2 6.4 070811-03-069 120 138.5 4.8 129.7 33.5 -27.9 672.5 138.5 4.8 070811-03-070 86 895.0 25.5 902.0 23.8 919.1 52.8 919.1 52.8 070811-03-071 44 1167.3 14.3 1163.6 25.6 1156.7 68.4 1156.7 68.4 070811-03-073 50 137.9 9.3 125.7 44.6 -97.8 937.8 137.9 9.3 070811-03-074 109 81.4 6.7 80.0 15.2 38.9 430.9 81.4 6.7 070811-03-075 46 136.2 6.3 213.7 63.5 1185.6 660.7 136.2 6.3 070811-03-076 33 267.7 13.5 265.9 180.6 250.4 2101.9 267.7 13.5 070811-03-077 154 79.6 2.5 85.7 19.1 258.7 535.5 79.6 2.5 070811-03-079 163 39.4 2.6 45.9 22.9 400.3 1207.5 39.4 2.6 070811-03-083 202 79.2 6.4 68.8 18.8 -279.5 698.5 79.2 6.4 070811-03-084 164 32.6 2.7 31.5 15.9 -50.1 1310.1 32.6 2.7 070811-03-085 144 41.4 2.7 211.7 NaN 3253.0 914.3 41.4 2.7 070811-03-086 107 44.0 3.9 51.9 21.5 433.4 963.8 44.0 3.9 070811-03-088 138 79.9 4.6 83.2 14.5 179.8 402.3 79.9 4.6 070811-03-089 74 142.2 6.8 120.5 32.8 -290.5 737.9 142.2 6.8 070811-03-090 78 81.6 5.5 73.3 18.3 -190.0 633.1 81.6 5.5 070811-03-091 176 40.6 2.1 797.8 NaN NA NA 40.6 2.1 070811-03-092 154 40.9 3.3 35.3 12.7 -331.4 945.0 40.9 3.3 070811-03-094 128 160.1 4.8 152.6 44.8 38.7 769.5 160.1 4.8 070811-03-096 162 79.7 3.2 85.2 14.7 242.9 406.0 79.7 3.2

148 Sample Z9 – 070811-04. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 070811-04-001 262 155.4 3.3 157.9 7.2 195.3 103.0 155.4 3.3 070811-04-002 76 156.2 11.3 156.3 36.4 158.5 569.0 156.2 11.3 070811-04-003 323 224.4 2.9 225.0 7.1 231.3 75.8 224.4 2.9 070811-04-004 177 8.5 2.4 12.8 23.4 912.5 908.6 8.5 2.4 070811-04-005 106 1232.6 17.2 1235.1 14.9 1239.3 27.8 1239.3 27.8 070811-04-007 136 151.9 5.3 167.1 32.6 388.2 472.1 151.9 5.3 070811-04-008 1877 8.9 0.4 9.1 1.4 70.4 345.3 8.9 0.4 070811-04-009 110 142.9 4.2 148.1 21.3 232.5 352.0 142.9 4.2 070811-04-011 79 1347.4 27.6 1354.4 20.9 1365.6 31.2 1365.6 31.2 070811-04-012 125 145.2 8.9 148.6 21.3 204.2 329.0 145.2 8.9 070811-04-013 297 8.3 1.3 7.7 11.8 -179.4 0.0 8.3 1.3 070811-04-014 436 199.6 5.6 218.3 7.0 425.6 48.4 199.6 5.6 070811-04-016 191 150.5 2.7 169.0 9.5 436.0 130.6 150.5 2.7 070811-04-017 537 9.5 1.3 25.5 55.6 2037.2 200.1 9.5 1.3 070811-04-018 307 10.3 1.5 21.2 20.2 1540.9 NA 10.3 1.5 070811-04-019 93 139.9 4.1 161.6 14.0 493.1 196.2 139.9 4.1 070811-04-020 458 9.1 1.2 11.1 7.8 467.1 1794.0 9.1 1.2 070811-04-021 601 51.4 1.1 53.3 6.6 136.7 294.6 51.4 1.1 070811-04-022 159 152.1 3.7 145.6 21.7 42.2 379.8 152.1 3.7 070811-04-023 318 134.1 4.8 135.0 8.9 150.1 142.2 134.1 4.8 070811-04-024 58 137.2 8.0 124.5 39.4 -111.8 835.0 137.2 8.0 070811-04-025 274 10.0 1.5 25.1 50.4 1910.8 220.7 10.0 1.5 070811-04-026 170 7.7 2.8 32.6 50.2 2803.1 576.3 7.7 2.8 070811-04-027 358 114.1 2.5 117.7 6.9 191.6 135.2 114.1 2.5 070811-04-028 203 11.0 2.1 54.0 96.5 3062.2 624.7 11.0 2.1 070811-04-029 55 139.1 11.3 119.4 46.4 -257.2 1060.2 139.1 11.3 070811-04-030 52 161.3 12.1 170.1 46.7 294.7 670.4 161.3 12.1 070811-04-032 291 61.2 3.8 54.5 9.5 -232.6 423.7 61.2 3.8 070811-04-033 493 1017.0 24.4 1023.1 17.0 1036.1 8.7 1036.1 8.7 070811-04-034 240 10.1 2.2 18.1 16.4 1268.9 54.1 10.1 2.2 070811-04-035 154 538.6 15.7 654.4 15.0 1077.0 14.5 538.6 15.7 070811-04-036 47 544.2 21.1 537.1 46.2 507.3 227.1 544.2 21.1 070811-04-037 116 147.3 4.2 142.6 22.9 64.9 406.4 147.3 4.2 070811-04-038 98 9.0 5.4 85.6 399.1 4099.5 614.9 9.0 5.4 070811-04-040 95 294.7 8.7 271.2 24.6 72.6 235.4 294.7 8.7 070811-04-041 808 8.4 0.7 6.0 3.1 -884.0 1574.3 8.4 0.7 070811-04-042 200 7.9 2.8 13.1 14.8 1111.5 285.2 7.9 2.8 070811-04-043 191 148.8 5.3 153.2 13.0 222.2 193.7 148.8 5.3 070811-04-044 100 150.5 2.8 159.0 18.7 287.3 287.6 150.5 2.8 070811-04-045 80 149.9 3.1 146.1 37.3 84.5 658.2 149.9 3.1 070811-04-046 109 82.4 4.7 70.2 36.3 -330.0 1459.6 82.4 4.7

149 Sample Z9 – 070811-04. Page 2 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 070811-04-047 431 1139.9 18.8 1151.0 12.7 1172.0 8.1 1172.0 8.1 070811-04-048 61 144.0 12.2 160.8 51.2 415.9 763.2 144.0 12.2 070811-04-049 153 150.8 3.7 140.5 17.7 -29.7 321.8 150.8 3.7 070811-04-050 47 1496.9 24.4 1495.9 16.8 1494.4 21.3 1494.4 21.3 070811-04-051 272 627.1 7.0 630.4 9.0 642.2 32.5 627.1 7.0 070811-04-052 638 157.9 2.9 160.3 4.7 195.6 59.4 157.9 2.9 070811-04-054 202 7.8 2.6 -53.1 -203.1 NA NA 7.8 2.6 070811-04-055 84 294.7 4.8 297.9 28.4 323.4 247.6 294.7 4.8 070811-04-057 175 8.9 3.5 19.6 25.3 1661.5 13.1 8.9 3.5 070811-04-058 197 7.8 3.6 44.6 88.5 3297.4 718.1 7.8 3.6 070811-04-059 556 157.5 2.0 156.8 6.3 146.2 97.3 157.5 2.0 070811-04-061 520 122.9 11.9 124.8 12.4 161.0 95.9 122.9 11.9 070811-04-062 59 154.3 12.9 149.6 48.2 76.5 817.7 154.3 12.9 070811-04-063 153 150.5 3.5 144.4 17.5 45.3 306.6 150.5 3.5 070811-04-064 159 10.2 3.7 30.6 34.1 2232.7 433.4 10.2 3.7 070811-04-065 92 159.9 15.7 170.5 27.4 319.1 327.3 159.9 15.7 070811-04-066 212 8.1 1.7 -25.2 -40.2 NA NA 8.1 1.7 070811-04-067 124 528.2 8.4 521.5 15.1 492.3 73.3 528.2 8.4 070811-04-068 487 10.0 0.8 13.8 19.5 722.6 800.0 10.0 0.8 070811-04-069 315 126.5 5.0 129.4 10.8 183.8 184.4 126.5 5.0 070811-04-070 82 135.6 9.2 145.7 32.0 313.4 519.6 135.6 9.2 070811-04-071 221 8.2 2.0 -49.8 -197.4 NA NA 8.2 2.0 070811-04-072 122 103.7 8.5 101.7 27.9 55.7 668.2 103.7 8.5 070811-04-073 286 11.1 1.7 13.9 5.2 527.8 780.1 11.1 1.7 070811-04-074 241 10.2 1.9 -196.3 NaN NA NA 10.2 1.9 070811-04-075 151 11.3 3.9 57.8 151.0 3138.2 412.4 11.3 3.9 070811-04-077 399 10.0 1.1 12.4 5.1 516.4 916.4 10.0 1.1 070811-04-078 768 153.8 1.9 153.7 3.1 152.0 42.4 153.8 1.9 070811-04-079 490 153.6 2.3 154.0 4.4 161.4 63.6 153.6 2.3 070811-04-080 215 156.1 4.4 153.5 15.3 113.6 245.2 156.1 4.4 070811-04-081 60 155.6 10.2 159.3 49.2 215.4 775.6 155.6 10.2 070811-04-082 269 146.7 2.6 150.0 11.8 203.6 192.0 146.7 2.6 070811-04-083 85 159.4 5.9 165.3 23.7 250.5 348.1 159.4 5.9 070811-04-084 129 152.3 3.6 143.4 26.5 -1.4 477.4 152.3 3.6 070811-04-086 239 8.8 1.5 48.8 131.7 3263.2 467.3 8.8 1.5 070811-04-087 680 59.9 3.3 62.3 4.6 154.9 123.4 59.9 3.3 070811-04-088 184 8.5 2.1 38.1 115.4 2903.3 134.1 8.5 2.1 070811-04-089 145 7.8 3.0 25.2 23.2 2347.3 611.7 7.8 3.0 070811-04-090 122 159.4 3.6 151.0 26.6 21.0 455.2 159.4 3.6 070811-04-091 36 137.6 11.2 158.4 120.0 481.3 2245.7 137.6 11.2 070811-04-092 100 156.9 5.9 154.9 32.4 125.6 529.0 156.9 5.9 070811-04-093 193 8.1 3.3 30.7 34.1 2613.3 662.7 8.1 3.3 150 Sample Z9 – 070811-04. Page 3 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 070811-04-094 173 156.1 3.6 152.0 15.0 88.8 245.8 156.1 3.6 070811-04-095 66 145.0 7.3 142.5 57.6 100.9 1062.2 145.0 7.3 070811-04-096 242 11.5 0.9 -27.0 -58.2 NA NA 11.5 0.9 070811-04-098 139 138.1 13.2 135.7 19.2 93.9 275.5 138.1 13.2 070811-04-099 638 8.9 0.9 8.8 3.6 -5.3 1003.0 8.9 0.9 070811-04-100 112 150.8 6.9 147.1 23.7 88.5 397.3 150.8 6.9 070811-04-101 405 152.5 5.6 155.2 12.7 196.2 185.5 152.5 5.6 070811-04-102 354 6.5 1.6 22.2 31.7 2445.0 398.8 6.5 1.6 070811-04-103 491 280.7 7.9 287.2 12.1 340.3 88.3 280.7 7.9 070811-04-105 568 152.6 4.6 152.0 6.8 142.1 88.2 152.6 4.6 070811-04-106 294 82.5 6.5 89.1 11.1 268.2 237.7 82.5 6.5 070811-04-108 90 160.3 6.1 148.4 35.0 -37.5 616.2 160.3 6.1 070811-04-109 395 143.3 4.8 145.9 7.3 189.8 97.8 143.3 4.8 070811-04-110 161 151.7 3.5 169.3 32.2 423.4 461.4 151.7 3.5 070811-04-111 203 157.9 3.5 162.4 16.0 228.0 241.0 157.9 3.5 070811-04-112 1431 154.3 7.2 154.4 7.1 155.3 34.2 154.3 7.2 070811-04-113 80 148.3 6.9 132.7 30.0 -138.1 591.7 148.3 6.9 070811-04-114 64 382.8 25.9 479.7 38.7 974.3 150.2 382.8 25.9 070811-04-115 136 442.9 13.2 450.0 16.4 486.3 72.8 442.9 13.2 070811-04-118 396 10.6 1.2 30.8 81.3 2176.2 278.6 10.6 1.2 070811-04-119 144 155.5 4.4 143.7 10.7 -46.4 181.3 155.5 4.4 070811-04-120 82 159.2 6.0 167.6 27.9 287.5 406.4 159.2 6.0

151 Sample Z10 – 080811-07. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 080811-07-001 210 144.6 3.9 138.5 14.4 34.9 259.8 144.6 3.9 080811-07-002 285 7.1 1.5 167.4 NaN NA NA 7.1 1.5 080811-07-003 67 150.7 7.2 132.1 34.4 -191.1 694.1 150.7 7.2 080811-07-004 147 15.4 5.0 15.0 10.7 -56.5 1749.2 15.4 5.0 080811-07-005 299 145.7 3.9 144.5 9.1 124.3 146.3 145.7 3.9 080811-07-006 73 154.1 12.5 170.7 38.3 406.9 519.9 154.1 12.5 080811-07-007 128 156.8 6.0 162.3 18.8 244.0 276.1 156.8 6.0 080811-07-008 252 7.5 1.9 66.2 325.3 3975.2 476.0 7.5 1.9 080811-07-009 84 150.0 5.6 129.5 46.2 -233.2 983.3 150.0 5.6 080811-07-010 138 140.8 4.8 126.6 16.3 -133.5 329.1 140.8 4.8 080811-07-011 153 135.3 4.1 140.4 15.5 226.7 264.1 135.3 4.1 080811-07-012 142 152.3 6.1 150.7 21.0 124.7 341.2 152.3 6.1 080811-07-013 311 29.6 3.6 25.1 13.4 -393.3 1461.6 29.6 3.6 080811-07-014 86 150.8 3.6 136.8 34.4 -98.9 668.1 150.8 3.6 080811-07-015 171 32.2 3.6 36.7 10.9 341.7 647.8 32.2 3.6 080811-07-017 378 146.5 3.0 155.4 7.4 293.2 107.2 146.5 3.0 080811-07-018 521 8.2 1.4 20.9 42.5 1946.0 201.9 8.2 1.4 080811-07-019 182 12.1 2.6 8.4 7.2 -937.9 2984.9 12.1 2.6 080811-07-020 116 133.9 5.7 123.9 38.1 -63.4 807.1 133.9 5.7 080811-07-021 294 7.6 1.9 -55.2 -301.0 NA NA 7.6 1.9 080811-07-022 460 7.6 2.0 -59.5 -362.5 NA NA 7.6 2.0 080811-07-023 124 141.5 6.0 130.1 20.1 -73.7 391.4 141.5 6.0 080811-07-024 155 152.2 4.7 144.0 22.1 10.5 390.6 152.2 4.7 080811-07-025 320 8.1 1.5 23.8 23.8 2175.9 NA 8.1 1.5 080811-07-026 155 657.6 25.3 694.4 27.8 815.4 78.6 657.6 25.3 080811-07-027 355 101.1 1.5 99.5 6.5 60.6 159.9 101.1 1.5 080811-07-028 589 35.7 4.7 37.4 7.9 147.9 404.2 35.7 4.7 080811-07-029 437 57.4 1.4 64.9 6.5 350.9 227.0 57.4 1.4 080811-07-030 150 17.6 3.9 -185.8 NaN NA NA 17.6 3.9 080811-07-031 296 8.1 1.9 25.8 30.4 2321.5 436.8 8.1 1.9 080811-07-032 91 110.1 10.7 88.6 37.4 -456.6 1179.8 110.1 10.7 080811-07-034 170 9.1 4.0 -42.4 -258.8 NA NA 9.1 4.0 080811-07-035 303 8.2 1.7 16.5 16.0 1507.0 NA 8.2 1.7 080811-07-036 93 148.4 5.1 138.7 16.9 -24.9 305.0 148.4 5.1 080811-07-037 769 137.5 6.8 138.7 7.1 160.3 53.2 137.5 6.8 080811-07-038 163 100.7 3.7 102.2 14.8 137.8 347.8 100.7 3.7 080811-07-039 281 7.3 2.1 33.2 39.2 2919.0 791.2 7.3 2.1 080811-07-040 91 153.8 5.4 160.2 21.2 255.4 320.3 153.8 5.4 080811-07-041 171 60.2 2.8 45.7 15.1 -663.2 942.1 60.2 2.8 080811-07-042 66 147.8 5.4 116.8 34.8 -475.5 846.5 147.8 5.4 080811-07-043 440 42.5 0.9 47.9 7.9 330.2 379.8 42.5 0.9

152 Sample Z10 – 080811-07. Page 2 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 080811-07-044 78 152.4 9.5 146.2 22.2 46.8 360.7 152.4 9.5 080811-07-046 110 153.5 9.0 152.2 28.0 132.0 448.5 153.5 9.0 080811-07-047 336 839.1 21.3 867.7 16.9 941.5 21.2 839.1 21.3 080811-07-048 349 155.9 3.7 152.6 7.4 102.5 110.3 155.9 3.7 080811-07-049 244 149.1 2.8 150.2 13.1 168.3 215.3 149.1 2.8 080811-07-050 470 12.9 1.7 14.0 6.8 202.3 1149.2 12.9 1.7 080811-07-051 127 148.3 6.5 141.3 23.1 25.1 408.0 148.3 6.5 080811-07-052 79 146.7 7.7 98.0 38.8 -972.2 1256.4 146.7 7.7 080811-07-053 434 7.9 2.1 54.9 248.5 3606.6 284.7 7.9 2.1 080811-07-056 185 6.4 2.9 75.9 429.8 NA NA 6.4 2.9 080811-07-057 385 7.3 1.1 44.7 169.7 3405.7 291.3 7.3 1.1 080811-07-058 67 143.4 9.6 169.4 41.9 550.2 576.2 143.4 9.6 080811-07-059 116 153.2 6.2 159.1 21.0 247.7 316.3 153.2 6.2 080811-07-060 156 128.9 7.4 129.8 12.9 145.0 208.9 128.9 7.4 080811-07-061 124 143.7 5.6 139.8 23.1 73.6 412.7 143.7 5.6 080811-07-062 483 139.4 5.8 142.7 10.1 198.2 147.3 139.4 5.8 080811-07-063 583 7.5 1.0 10.6 10.4 777.1 NA 7.5 1.0 080811-07-064 240 54.1 2.5 50.3 14.0 -131.2 706.0 54.1 2.5 080811-07-065 88 119.1 7.0 115.1 18.2 32.8 377.4 119.1 7.0 080811-07-066 90 150.8 6.5 107.6 51.6 -768.9 1497.8 150.8 6.5 080811-07-068 180 7.5 2.9 16.8 34.5 1682.7 394.2 7.5 2.9 080811-07-069 490 155.0 1.7 156.7 6.2 182.4 96.3 155.0 1.7 080811-07-071 100 147.6 10.6 127.6 22.0 -231.7 427.1 147.6 10.6 080811-07-072 82 154.6 8.1 133.0 42.9 -236.5 878.9 154.6 8.1 080811-07-073 196 150.4 5.9 157.4 15.2 263.7 221.5 150.4 5.9 080811-07-074 175 7.7 2.5 25.8 14.7 2422.5 862.2 7.7 2.5 080811-07-076 62 152.2 7.2 136.8 43.8 -122.7 857.3 152.2 7.2 080811-07-077 215 120.4 4.5 118.9 13.1 88.0 262.4 120.4 4.5 080811-07-078 127 149.8 6.3 158.7 25.2 294.5 381.5 149.8 6.3 080811-07-079 298 7.6 1.5 174.0 NaN NA NA 7.6 1.5 080811-07-080 250 8.0 2.0 58.4 328.8 3691.5 155.1 8.0 2.0 080811-07-081 266 31.2 2.9 33.6 12.6 207.8 889.8 31.2 2.9 080811-07-082 260 12.8 1.7 179.0 NaN NA NA 12.8 1.7 080811-07-084 261 7.9 2.4 36.0 72.3 2929.8 474.6 7.9 2.4 080811-07-085 84 148.8 5.0 160.0 25.3 329.1 381.8 148.8 5.0 080811-07-086 154 152.8 6.9 151.8 27.2 135.6 443.3 152.8 6.9 080811-07-087 361 8.2 3.3 48.9 151.5 3368.7 437.0 8.2 3.3 080811-07-088 125 150.8 6.8 153.9 35.1 201.6 566.9 150.8 6.8 080811-07-089 767 130.3 3.4 129.7 4.2 119.9 50.9 130.3 3.4 080811-07-090 127 151.5 4.4 155.6 18.3 219.3 286.4 151.5 4.4 080811-07-091 70 161.5 7.7 159.4 35.6 128.3 562.4 161.5 7.7 080811-07-092 88 148.0 3.6 128.4 28.0 -220.9 587.1 148.0 3.6 153 Sample Z10 – 080811-07. Page 3 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) 080811-07-095 552 7.6 1.0 9.9 17.2 614.6 1120.7 7.6 1.0 080811-07-096 101 108.7 7.8 99.7 33.9 -110.5 883.2 108.7 7.8 080811-07-097 104 152.3 5.3 147.8 28.8 76.7 494.8 152.3 5.3 080811-07-098 70 156.2 9.9 137.0 42.3 -183.6 827.2 156.2 9.9 080811-07-099 244 28.6 2.9 33.1 15.4 376.6 1090.9 28.6 2.9 080811-07-100 74 146.3 6.9 138.2 34.4 1.0 640.4 146.3 6.9 080811-07-101 153 151.7 3.6 142.6 12.2 -5.7 213.3 151.7 3.6 080811-07-102 363 96.1 2.5 92.3 6.3 -5.7 162.0 96.1 2.5 080811-07-104 59 153.6 9.9 193.1 39.1 708.0 456.1 153.6 9.9 080811-07-105 174 152.7 2.9 151.8 20.9 139.1 345.8 152.7 2.9 080811-07-106 120 57.2 4.1 46.0 18.1 -506.4 1094.4 57.2 4.1 080811-07-107 235 7.8 2.2 99.7 480.7 NA NA 7.8 2.2 080811-07-108 190 149.2 4.5 147.5 14.0 119.7 230.3 149.2 4.5 080811-07-109 325 16.0 2.1 13.9 14.9 -322.3 1370.1 16.0 2.1 080811-07-110 33 139.0 12.1 16.1 36.4 NA NA 139.0 12.1 080811-07-111 66 141.6 7.8 127.6 38.3 -125.4 792.3 141.6 7.8 080811-07-112 910 281.6 6.5 284.4 7.8 308.2 48.2 281.6 6.5 080811-07-113 80 152.0 6.5 128.3 32.3 -291.6 684.2 152.0 6.5 080811-07-114 321 8.0 1.4 -134.5 NaN NA NA 8.0 1.4 080811-07-115 336 7.7 1.4 251.6 NaN NA NA 7.7 1.4 080811-07-116 67 131.6 11.4 131.7 58.9 132.0 1160.1 131.6 11.4 080811-07-117 145 88.9 6.3 93.3 22.2 208.3 559.1 88.9 6.3 080811-07-119 93 154.4 9.6 139.9 34.4 -99.7 636.7 154.4 9.6 080811-07-120 141 149.9 4.6 141.7 21.0 7.3 376.9 149.9 4.6

154 Sample Z11 – UGM11-02. Page 1 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) UGM11-02-001 90 1088.9 30.3 1126.6 36.1 1200.1 85.6 1200.1 85.6 UGM11-02-002 469 1301.9 27.7 1357.6 17.9 1446.4 8.5 1446.4 8.5 UGM11-02-003 58 1073.5 12.2 1085.4 21.7 1109.4 60.2 1109.4 60.2 UGM11-02-004 194 996.1 14.8 998.8 12.3 1004.7 22.2 1004.7 22.2 UGM11-02-005 117 1017.5 11.2 1008.3 12.0 988.5 29.5 988.5 29.5 UGM11-02-006 88 997.9 12.5 1017.8 14.5 1061.0 36.4 1061.0 36.4 UGM11-02-007 56 1048.3 18.2 1047.7 22.4 1046.6 57.6 1046.6 57.6 UGM11-02-008 226 1137.9 13.3 1133.9 11.9 1126.4 23.6 1126.4 23.6 UGM11-02-009 73 1086.1 16.9 1089.5 16.5 1096.2 36.2 1096.2 36.2 UGM11-02-010 285 1082.0 12.5 1093.9 8.8 1117.6 8.3 1117.6 8.3 UGM11-02-011 218 1167.9 18.9 1163.3 13.5 1154.8 16.6 1154.8 16.6 UGM11-02-012 195 1134.7 36.4 1141.9 25.8 1155.7 27.1 1155.7 27.1 UGM11-02-013 99 1104.3 22.4 1118.0 19.6 1144.8 37.1 1144.8 37.1 UGM11-02-014 65 1114.4 11.9 1131.7 14.2 1165.0 34.4 1165.0 34.4 UGM11-02-015 176 1520.3 40.0 1526.9 24.1 1536.0 14.7 1536.0 14.7 UGM11-02-016 41 133.4 9.3 142.5 99.9 298.3 2015.4 133.4 9.3 UGM11-02-017 184 1049.9 20.7 1064.7 16.2 1095.1 24.3 1095.1 24.3 UGM11-02-018 108 1047.7 21.9 1069.8 20.2 1115.0 41.2 1115.0 41.2 UGM11-02-019 219 172.0 2.9 165.1 8.7 67.6 129.1 172.0 2.9 UGM11-02-020 137 1012.7 9.5 1027.5 10.0 1059.0 23.5 1059.0 23.5 UGM11-02-021 23 1033.6 37.0 1048.2 50.2 1078.7 132.9 1078.7 132.9 UGM11-02-022 183 1111.4 14.8 1120.0 11.2 1136.5 15.9 1136.5 15.9 UGM11-02-023 138 171.9 4.1 181.0 13.4 301.6 176.1 171.9 4.1 UGM11-02-024 126 1008.2 15.6 1020.4 15.1 1046.8 33.4 1046.8 33.4 UGM11-02-025 254 1007.6 20.4 1012.3 14.8 1022.4 15.1 1022.4 15.1 UGM11-02-026 137 1019.7 11.4 1029.4 12.2 1050.1 29.4 1050.1 29.4 UGM11-02-027 617 997.4 9.0 1008.4 7.4 1032.4 12.4 1032.4 12.4 UGM11-02-028 45 975.8 13.4 980.3 22.2 990.6 65.2 990.6 65.2 UGM11-02-029 84 1093.2 14.8 1121.5 17.5 1176.6 41.7 1176.6 41.7 UGM11-02-030 113 1024.8 42.3 1057.7 36.1 1126.3 63.7 1126.3 63.7 UGM11-02-031 131 986.5 14.9 997.8 15.4 1022.9 36.3 1022.9 36.3 UGM11-02-032 106 985.7 10.7 984.5 15.9 981.8 45.4 981.8 45.4 UGM11-02-033 138 1106.9 20.2 1152.9 19.4 1240.5 39.0 1240.5 39.0 UGM11-02-034 301 170.5 3.7 166.9 13.5 115.3 201.1 170.5 3.7 UGM11-02-035 148 1149.6 14.1 1158.2 12.8 1174.3 25.2 1174.3 25.2 UGM11-02-036 165 1043.1 15.5 1052.4 13.8 1071.7 27.3 1071.7 27.3 UGM11-02-037 114 1033.0 8.5 1044.7 17.7 1069.2 51.5 1069.2 51.5 UGM11-02-039 102 1144.0 46.8 1137.5 33.5 1125.1 40.0 1125.1 40.0 UGM11-02-040 134 1037.2 21.4 1037.3 17.5 1037.5 30.1 1037.5 30.1 UGM11-02-041 151 1013.1 14.9 1014.6 12.5 1017.9 22.7 1017.9 22.7 UGM11-02-042 120 177.4 4.4 172.2 27.3 101.8 405.8 177.4 4.4

155 Sample Z11 – UGM11-02. Page 2 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) UGM11-02-043 237 1007.2 7.6 1011.7 7.9 1021.7 18.8 1021.7 18.8 UGM11-02-044 261 1087.0 37.6 1092.9 25.5 1104.5 11.4 1104.5 11.4 UGM11-02-045 88 987.1 11.1 981.0 15.2 967.4 42.7 967.4 42.7 UGM11-02-046 183 1028.6 15.4 1033.0 13.4 1042.4 25.9 1042.4 25.9 UGM11-02-047 50 1078.6 17.3 1093.2 21.0 1122.3 51.9 1122.3 51.9 UGM11-02-048 67 1091.4 11.0 1109.0 19.2 1143.6 52.1 1143.6 52.1 UGM11-02-049 50 1137.2 25.0 1131.4 21.3 1120.2 39.8 1120.2 39.8 UGM11-02-050 115 1040.3 18.4 1042.3 16.4 1046.6 32.8 1046.6 32.8 UGM11-02-051 64 1128.0 14.3 1131.2 18.6 1137.3 46.8 1137.3 46.8 UGM11-02-054 52 1500.0 21.9 1496.3 19.4 1491.0 35.2 1491.0 35.2 UGM11-02-055 82 1121.0 25.9 1125.5 21.8 1134.1 39.4 1134.1 39.4 UGM11-02-056 223 986.7 11.5 990.2 12.7 997.9 32.0 997.9 32.0 UGM11-02-057 68 1095.9 15.6 1110.7 15.3 1139.8 32.8 1139.8 32.8 UGM11-02-058 77 1141.9 15.2 1131.0 14.3 1110.3 30.1 1110.3 30.1 UGM11-02-059 228 971.1 12.4 979.9 10.9 999.7 21.6 999.7 21.6 UGM11-02-060 66 1086.1 14.3 1087.1 18.3 1089.2 46.8 1089.2 46.8 UGM11-02-061 935 1188.7 11.8 1219.1 8.8 1273.3 11.5 1273.3 11.5 UGM11-02-062 111 1006.0 11.6 1007.2 17.7 1010.0 50.2 1010.0 50.2 UGM11-02-064 141 998.1 16.4 998.6 15.6 999.9 34.2 999.9 34.2 UGM11-02-065 75 1081.5 14.0 1082.3 17.6 1083.8 44.7 1083.8 44.7 UGM11-02-066 73 1034.2 13.0 1042.6 20.4 1060.3 56.8 1060.3 56.8 UGM11-02-067 119 984.3 6.3 980.8 12.2 973.2 36.9 973.2 36.9 UGM11-02-068 82 1007.4 14.7 997.2 20.2 974.7 56.2 974.7 56.2 UGM11-02-069 243 1017.9 10.4 1030.8 9.4 1058.5 18.8 1058.5 18.8 UGM11-02-070 45 170.3 11.0 99.3 71.2 NA NA 170.3 11.0 UGM11-02-071 145 1060.9 11.5 1065.3 10.4 1074.3 21.2 1074.3 21.2 UGM11-02-072 56 1136.2 25.4 1145.8 26.5 1163.9 59.4 1163.9 59.4 UGM11-02-073 286 1095.3 7.7 1103.1 6.4 1118.6 11.0 1118.6 11.0 UGM11-02-074 46 1021.4 16.8 1044.2 27.7 1092.1 77.4 1092.1 77.4 UGM11-02-075 54 1077.9 16.5 1076.5 40.4 1073.8 117.6 1073.8 117.6 UGM11-02-076 96 1057.5 13.6 1065.5 15.0 1082.0 36.2 1082.0 36.2 UGM11-02-077 78 1117.9 23.4 1119.8 17.5 1123.6 23.7 1123.6 23.7 UGM11-02-078 219 1082.1 20.4 1088.1 14.6 1100.1 14.8 1100.1 14.8 UGM11-02-079 138 1009.2 10.6 1013.0 11.8 1021.5 29.2 1021.5 29.2 UGM11-02-081 160 1011.5 14.4 1027.6 11.7 1062.1 19.2 1062.1 19.2 UGM11-02-082 316 1060.8 10.4 1066.8 8.6 1078.9 15.1 1078.9 15.1 UGM11-02-083 158 1362.6 95.0 1375.4 62.1 1395.1 54.4 1395.1 54.4 UGM11-02-085 62 1053.0 23.6 1067.1 21.2 1096.1 42.1 1096.1 42.1 UGM11-02-086 95 1019.4 23.1 1021.9 25.8 1027.3 64.2 1027.3 64.2 UGM11-02-087 118 1068.1 17.4 1071.9 14.2 1079.6 24.1 1079.6 24.1 UGM11-02-088 99 1163.7 22.0 1155.6 16.2 1140.5 21.8 1140.5 21.8 UGM11-02-089 44 1151.1 15.7 1151.7 27.5 1153.0 73.6 1153.0 73.6 156 Sample Z11 – UGM11-02. Page 3 of 3. Apparent ages (Ma) Analysis U conc 206Pb* ± 207Pb* ± 206Pb* ± Best age ± (ppm) 238U* (Ma) 235U (Ma) 207Pb* (Ma) (Ma) (Ma) UGM11-02-090 131 1114.2 11.3 1121.9 9.7 1136.8 17.9 1136.8 17.9 UGM11-02-091 158 188.1 5.4 182.5 15.0 110.2 199.9 188.1 5.4 UGM11-02-092 118 997.0 11.3 1001.1 9.4 1010.0 16.4 1010.0 16.4 UGM11-02-093 202 990.3 8.8 993.8 9.4 1001.4 23.0 1001.4 23.0 UGM11-02-094 109 1098.5 27.9 1100.3 21.9 1104.0 34.4 1104.0 34.4 UGM11-02-095 206 1030.0 9.5 1037.4 7.8 1053.2 13.3 1053.2 13.3 UGM11-02-097 92 1118.1 16.0 1124.5 15.3 1136.9 32.3 1136.9 32.3 UGM11-02-098 278 1386.6 18.0 1408.8 11.9 1442.5 11.5 1442.5 11.5 UGM11-02-099 108 1058.6 22.3 1064.7 17.0 1077.2 23.6 1077.2 23.6 UGM11-02-100 84 1117.6 15.3 1125.9 15.1 1141.9 32.9 1141.9 32.9 UGM11-02-101 119 1011.8 15.9 1002.2 18.9 981.2 49.7 981.2 49.7 UGM11-02-102 106 1004.3 10.4 1009.0 20.8 1019.1 61.8 1019.1 61.8 UGM11-02-103 192 1090.8 19.0 1098.2 14.4 1112.8 20.4 1112.8 20.4 UGM11-02-104 110 1013.5 7.6 1018.5 17.6 1029.2 53.0 1029.2 53.0 UGM11-02-105 92 1129.3 16.6 1130.3 12.7 1132.2 18.5 1132.2 18.5 UGM11-02-106 65 1023.8 9.4 1014.2 11.9 993.6 31.9 993.6 31.9 UGM11-02-107 205 1338.0 17.5 1339.1 12.4 1340.7 16.1 1340.7 16.1 UGM11-02-108 122 1060.5 27.2 1062.8 23.8 1067.6 46.1 1067.6 46.1 UGM11-02-109 70 978.3 12.2 981.4 17.2 988.5 48.5 988.5 48.5 UGM11-02-110 100 1130.0 14.5 1135.8 12.5 1146.7 23.7 1146.7 23.7 UGM11-02-111 98 1006.0 20.9 1002.0 16.7 993.3 27.3 993.3 27.3 UGM11-02-112 159 999.4 11.6 1000.8 9.1 1003.9 13.9 1003.9 13.9 UGM11-02-113 62 1110.5 21.0 1106.5 19.7 1098.7 41.5 1098.7 41.5 UGM11-02-114 375 1065.8 12.3 1077.8 8.8 1102.2 9.1 1102.2 9.1 UGM11-02-115 99 961.9 78.6 998.7 61.2 1080.5 78.2 1080.5 78.2 UGM11-02-116 30 1003.2 18.3 996.8 22.6 982.7 60.4 982.7 60.4 UGM11-02-117 240 1006.2 10.1 1019.9 8.8 1049.6 16.9 1049.6 16.9 UGM11-02-118 102 1066.0 16.3 1062.1 15.8 1054.2 35.0 1054.2 35.0 UGM11-02-119 157 1099.3 31.6 1100.9 21.9 1104.0 18.5 1104.0 18.5 UGM11-02-120 191 1084.2 7.8 1096.0 9.5 1119.6 23.5 1119.6 23.5

157 C.5 ISOTOPIC MEASUREMENTS ON INDIVIDUAL CARBONATE NODULES

Measurements highlighted in red were not included in the averaged value due to high variability during measurement; these samples showed a standard deviation greater than 0.1 ‰ for either the carbon or oxygen measurement.

Isotopic measurements of individual carbonate nodules. Page 1 of 3.

Strat Nodule Ampl Area δ13C StdDev δ18O δ18O StdDev Name Level # 44 (mV) 44 (VPDB) δ13C (VSMOW) (VPDB) δ18O 1 LV1-17 1 4955 22.621 -10.37 0.011 21.5 -9.13 0.031 2 5632 25.699 -10.2 0.013 20.99 -9.62 0.021 3 4741 21.598 -10.31 0.024 21.38 -9.25 0.029 4 4152 18.994 -10.38 0.014 21.25 -9.37 0.023 2 060811-01 1 5788 26.311 -9.54 0.011 20.57 -10.03 0.03 2 6289 28.592 -9.59 0.013 20.84 -9.76 0.013 3 4781 21.652 -9.87 0.011 21.43 -9.19 0.026 4 7656 34.841 -9.84 0.019 21.22 -9.4 0.022 3 LV1-03A 1 4070 20.958 -11.22 0.214 23.13 -7.55 0.065 2 1866 9.655 -11.2 0.013 21.7 -8.94 0.031 3 4365 22.371 -11.05 0.012 21.8 -8.84 0.022 4 LV1-20B 1 5459 24.886 -11.13 0.016 22.49 -8.17 0.011 2 6390 29.216 -10.95 0.026 22.55 -8.11 0.012 3 4381 19.998 -10.38 0.012 21.94 -8.7 0.024 4 4356 19.788 -10.5 0.018 21.9 -8.74 0.023 5 LV2-03A 1 3407 15.404 -10.32 0.008 21.32 -9.3 0.041 2 2530 11.354 -10.2 0.047 21.18 -9.44 0.032 3 2239 10.12 -10.2 0.021 21.11 -9.5 0.033 4 2317 10.437 -10.05 0.029 21.11 -9.51 0.024 6 LV2-5B 1 3363 15.319 -10.86 0.023 21.01 -9.61 0.025 2 3760 17.073 -9.28 0.019 20.87 -9.74 0.023 3 4925 22.554 -9.29 0.02 20.99 -9.63 0.016 4 6118 27.96 -9.26 0.023 20.84 -9.77 0.023 7 LV2-06A 1 4613 20.906 -9.28 0.031 21.41 -9.21 0.045 2 6215 28.195 -9.41 0.012 21.5 -9.13 0.025 3 5753 25.991 -9.28 0.007 21.48 -9.15 0.018 4 6352 28.912 -9.29 0.011 21.5 -9.13 0.03 8 060811-03A 1 4004 18.186 -9.86 0.017 21.3 -9.32 0.044 2 7289 33.315 -10.17 0.018 20.97 -9.64 0.017 3 5247 24.01 -10.11 0.018 21.03 -9.58 0.029 4 5323 24.252 -9.98 0.024 21.06 -9.55 0.027 158

Isotopic measurements of individual carbonate nodules. Page 2 of 3.

Strat Nodule Ampl Area δ13C StdDev δ18O δ18O StdDev Name Level # 44 (mV) 44 (VPDB) δ13C (VSMOW) (VPDB) δ18O 9 060811-08A 1 4080 18.565 -9.43 0.025 21.82 -8.82 0.039 2 4457 20.364 -9.77 0.02 21.96 -8.68 0.013 3 5171 23.628 -9.46 0.011 21.82 -8.82 0.018 4 3489 15.804 -9.6 0.021 21.7 -8.94 0.01 10 060811-07A 1 1308 6.692 -10.42 0.034 21.37 -9.26 0.053 2 891 4.623 -11.38 0.062 22.2 -8.45 0.072 3 2169 11.153 -8 3.014 22.55 -8.11 1.522 11 060811-06A 1 1614 7.243 -11.34 0.039 21.44 -9.19 0.041 2 1469 6.552 -12.03 0.043 20.97 -9.64 0.066 3 3503 15.871 -7.95 0.014 21.93 -8.71 0.018 4 4324 19.441 -3.84 0.024 22.57 -8.09 0.026 12 LV3-12A 1 6155 27.897 -12.32 0.018 20.96 -9.65 0.011 2 6345 28.944 -14.11 0.017 20.83 -9.77 0.019 3 2770 12.448 -13.41 0.027 20.97 -9.64 0.029 4 4705 21.168 -11.19 0.013 21.05 -9.57 0.035 13 LVGB-10 1 4165 18.92 -10 0.015 22.2 -8.45 0.056 2 3731 17.01 -10.15 0.011 22.3 -8.36 0.018 3 1689 7.623 -10.22 0.042 22.41 -8.25 0.064 4 3937 17.869 -9.68 0.02 22.03 -8.61 0.03 14 LVGB-16B 1 5907 30.732 -10.55 0.006 21.26 -9.361 0.031 2 3925 20.086 -10.67 0.177 21.45 -9.175 0.197 3 2712 14.076 -10.14 0.642 22.74 -7.926 0.194 15 LVGB-17B 1 6757 30.868 -10.31 0.009 21.75 -8.89 0.026 2 4234 19.076 -10.36 0.011 21.61 -9.02 0.038 3 5666 25.761 -10.23 0.019 21.12 -9.5 0.026 4 5686 25.875 -10.19 0.013 21.32 -9.3 0.025 16 LVGB-20 1 4199 18.943 -10.56 0.028 22 -8.65 0.039 2 3684 16.663 -10.63 0.025 21.93 -8.71 0.027 3 4892 22.179 -10.57 0.029 22.39 -8.27 0.014 4 4853 21.918 -11.56 0.009 22.27 -8.38 0.012 17 LVGB-21 1 2789 12.575 -12.21 0.025 21.96 -8.68 0.06 2 3892 17.663 -11.2 0.018 22.01 -8.64 0.029 3 3547 16.026 -11.87 0.019 22.22 -8.43 0.049 4 7790 35.84 -11.88 0.02 21.38 -9.24 0.017 18 LVGB-24A 1 2795 12.799 -10.23 0.025 21.62 -9.01 0.043 2 3738 17.049 -10.83 0.017 21.93 -8.71 0.04 3 5231 23.853 -10.8 0.02 21.97 -8.67 0.024 4 5403 24.818 -10.38 0.018 21.49 -9.14 0.013

159

Isotopic measurements of individual carbonate nodules. Page 3 of 3.

Strat Nodule Ampl Area δ13C StdDev δ18O δ18O StdDev Name Level # 44 (mV) 44 (VPDB) δ13C (VSMOW) (VPDB) δ18O 19 ECGB-05 1 5979 27.247 -17.01 0.023 22.14 -8.5 0.036 2 1213 5.487 -18.87 0.038 22.43 -8.23 0.082 3 7474 34.236 -16.62 0.014 22.1 -8.55 0.026 4 5470 25.041 -14.94 0.018 21.85 -8.78 0.019 20 ECRB-10A 1 2849 14.64 -11.63 0.022 24.32 -6.39 0.032 2 3619 18.754 -12.55 0.023 22.72 -7.94 0.062 3 3834 19.797 -12.22 0.065 23.58 -7.11 0.02 21 ECRB-09A 1 3366 15.3 -12.08 0.03 22.73 -7.93 0.028 2 2470 11.146 -12.08 0.012 22.8 -7.86 0.027 3 4216 19.205 -12.13 0.025 22.85 -7.81 0.029 4 1599 7.187 -15.96 0.036 21.97 -8.67 0.068 22 PRB-28A 1 2991 13.571 -1.25 0.042 21.73 -8.91 0.03 2 4927 22.378 -1.56 0.006 21.46 -9.17 0.037 3 4502 20.588 -1.06 0.021 21.16 -9.46 0.011 4 1652 7.462 -9.21 0.032 20.66 -9.94 0.037 23 PRB-31A 1 4837 22.139 -7.92 0.018 21.74 -8.9 0.043 2 3889 17.653 -10.02 0.023 22.5 -8.16 0.024 3 3502 15.887 -8.14 0.009 22.04 -8.61 0.04 4 3392 15.429 -10.06 0.013 22.78 -7.89 0.03 24 PRB-33A 1 7671 35.054 -4.82 0.014 21.27 -9.35 0.014 2 6907 31.765 -2.61 0.018 22.18 -8.47 0.044 3 4841 22.006 -4.8 0.03 21.32 -9.3 0.021 4 5158 23.581 -5.55 0.025 21.19 -9.43 0.034 25 PRB-62B 1 4051 18.205 -14.7 0.013 21.9 -8.74 0.035 2 4911 22.225 -15.34 0.021 21.73 -8.9 0.018 3 4213 18.934 -13.82 0.017 22.1 -8.55 0.035 4 4601 20.841 -13.74 0.022 23.64 -7.06 0.021 26 PRB-66B 1 2659 11.919 -12 0.064 22.02 -8.62 0.049 2 6552 29.332 -12.39 0.022 22.5 -8.16 0.042 3 2392 9.794 -14.27 0.752 22.44 -8.21 0.046 4 4591 20.68 -14.62 0.02 22.53 -8.13 0.045 27 PRB-18A 1 3906 20.329 -11.08 0.021 22.49 -8.16 0.029 2 3728 19.094 -10.47 0.682 23.28 -7.4 0.159 3 3264 17.044 -12.17 0.023 23.6 -7.09 0.031 28 PRB-03A 1 4761 21.729 -9.83 0.012 25.28 -5.46 0.031 2 5438 24.774 -9.78 0.019 24.04 -6.66 0.024 3 3891 17.764 -9.93 0.023 23.02 -7.66 0.027 4 6117 28.022 -8.72 0.013 22.05 -8.59 0.025

160 C.6 DETAILS OF PALEOSOL XRF MEASUREMENTS Strat SiO2 TiO2 Al2O3 Fe2O3 MnO MgO CaO Na2O K2O P2O5 Sum LOI Sample Level (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 3 LV1-03 54.8 0.83 18.37 9.1 0.12 3.14 2.4 1.58 1.62 0.26 92.22 7.65 4 LV1-20A 62.45 0.74 15.04 6.24 0.04 1.89 1.57 1.75 1.37 0.06 91.15 8.73 5 LV2-3B 61.19 0.74 17.09 7.29 0.04 1.96 1.67 0.98 1.53 0.08 92.57 7.29 6 LV2-5A 60.18 0.83 17.45 8.08 0.05 1.96 1.53 0.72 1.33 0.05 92.18 7.71 7 LV2-6B 53.93 0.75 18.42 8.69 0.12 3.48 2.82 0.79 1.55 0.17 90.72 9.09 8 060811-3B 55.03 0.87 18.42 8.61 0.11 3.25 1.7 0.77 1.65 0.08 90.49 9.36 9 060811-8B 52.17 0.78 16.64 8.2 0.17 3.1 5.91 0.81 1.54 0.16 89.48 10.39 10 060811-7B 51.11 0.74 14.74 7.07 0.19 2.89 8.64 1 1.33 0.13 87.84 12.03 11 060811-6B 61.63 0.76 15.62 6.3 0.08 2.78 2.18 1.74 1.44 0.15 92.68 7.17 12 LV3-12B 62.7 0.68 15.34 7.07 0.03 2.35 2.26 1.29 1.57 0.06 93.35 6.54 13 LVGB-11 54.57 0.78 18.33 8.95 0.06 3.69 1.58 0.79 1.92 0.19 90.86 9 14 LVGB-16A 55.02 0.88 17.86 8.59 0.19 3.37 2.2 0.86 1.66 0.16 90.79 9.06 15 LVGB-17A 55.42 0.77 18.42 7.53 0.03 2.22 1.15 0.89 1.67 0.1 88.2 11.65 17 LVGB-22 60.93 0.89 21.66 4.13 0.01 1.06 0.62 0.37 1.65 0.1 91.42 8.4 18 LVGB-24B 51.58 0.8 18.5 7.7 0.15 3.43 4.2 0.79 1.67 0.12 88.94 10.92 19 ECGB-3 54.93 0.94 18.06 9.18 0.1 3.32 1.7 0.83 1.41 0.17 90.64 9.23 20 ECGB-9B 67.4 0.72 15.16 4.93 0.08 0.98 1.37 0.72 1.73 0.12 93.21 6.63 21 ECGB-10B 62.48 0.81 18.16 5.89 0.06 1.28 0.92 0.33 1.66 0.11 91.7 8.15 22 PRB-28B 67.89 0.75 15.18 5.17 0.1 1.17 0.5 0.74 1.88 0.15 93.53 6.33 23 PRB-30A 66.65 0.75 15.95 5.93 0.05 1.06 0.39 0.32 1.64 0.11 92.85 6.98 24 PRB-32 61.08 0.82 18.83 6.76 0.06 1.34 0.88 0.19 1.64 0.17 91.77 8.1 26 PRB-66A 67.84 0.74 13.39 5.16 0.13 1.11 1.57 0.07 1.53 0.16 91.7 8.16 27 PRB-18B 68.81 0.68 12.27 4.19 0.54 1.07 2.89 0.13 1.54 0.12 92.24 7.53 28 PRB-3A 62.97 0.86 15.45 7.68 0.07 1.92 0.9 0.73 1.77 0.13 92.48 7.38 28 PRB-3A 57.84 0.82 19.96 7.75 0.06 1.49 0.81 0 1.79 0.12 90.64 9.31 Standard RGM-1 74.23 0.27 13.66 1.89 0.04 0.26 0.99 4.13 4.32 0.06 99.85 RGM-1 GIVEN 73.45 0.27 13.72 1.86 0.04 0.27 1.15 4.07 4.30 0.05 99.17 % Error 1.06 0.00 0.44 1.61 11.11 3.70 13.91 1.47 0.47 25.00 0.68 Standard BHVO-1 50.23 2.73 13.65 12.33 0.17 7.32 11.35 2.30 0.52 0.27 100.87 BHVO-1 GIVEN 49.94 2.71 13.80 12.23 0.17 7.23 11.40 2.26 0.52 0.27 100.53 % Error 0.58 0.74 1.09 0.82 1.19 1.24 0.44 1.77 0.00 1.10 0.34

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