Bioavailable Strontium from Plants and Diagenesis of Dental Tissues at Olduvai Gorge, Tanzania

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2018-07-11 Bioavailable Strontium from Plants and Diagenesis of Dental Tissues at Olduvai Gorge, Tanzania

Tucker, Laura Lillian

Tucker, L. L. (2018). Bioavailable Strontium from Plants and Diagenesis of Dental Tissues at Olduvai Gorge, Tanzania (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/32357 http://hdl.handle.net/1880/107135 master thesis

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Bioavailable Strontium from Plants and Diagenesis of Dental Tissues at Olduvai Gorge,

Tanzania

Laura Lillian Tucker

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF ARTS

GRADUATE PROGRAM IN ARCHAEOLOGY

CALGARY, ALBERTA

JULY, 2018

Abstract Stable strontium isotope analysis is used to assess the migration and mobility of past populations of people and animals. This study aimed to determine the feasibility of conducting future studies using this method at Olduvai Gorge, Tanzania by determining the variability in biologically-available strontium (87Sr/86Sr) throughout the region from areas with metamorphic and volcanic bedrock, as well as recent unconsolidated lacustrine sediments. This was done by analysing modern plants collected from 33 different localities. As well, the degree to which archaeological animal teeth from Juma’s Korongo, a ~1- million-year-old site at Olduvai Gorge, have been affected by diagenetic alteration was assessed. To do this, the dentine and enamel of the teeth were analysed with and without pre-treatment with weak acetic acid: a protocol used for removing diagenetic strontium from dental specimens. There was no difference in 87Sr/86Sr values of volcanic (n=19) and metamorphic (n=9) sampling localities, but the lacustrine localities (n=5) had significantly higher values. 87Sr/86Sr values tended to decrease moving northeast towards the active volcano Oldoinyo Lengai, a major source of soil constituents for the area. Also, localities where trees were sampled had significantly higher 87Sr/86Sr values than those without them. Despite the homogeneous 87Sr/86Sr values described between metamorphic and volcanic localities, much higher values have been found in the northern extent of Serengeti National Park (Copeland et al., 2012), suggesting that animals who have immigrated into the area from long distances away can be identified as non-local. The animal teeth (n=7), which include zebras, crocodiles, and a hippopotamus, were all from local animals. There was a significant difference between enamel and dentine values after acid washing, suggesting that biogenic 87Sr/86Sr values are preserved in the enamel. These values were consistently higher than the modern bioavailable strontium values, possibly due to environmental differences between the past and present. The results of this study suggest that Olduvai Gorge is a suitable area for future studies using stable strontium isotope analysis, though more work is required to fully understand the inconsistencies between ancient and modern bioavailable strontium.

Acknowledgements

There are many people and organizations without whom the success of this project would not have been possible.

Firstly, I would like to thank my supervisor, Dr. Julio Mercader for his constant support, feedback, and inspiration over the past couple of years, both in Calgary and in the field. In addition, his generous financial support has covered my laboratory costs, and many of the impressive costs associated with travelling. As well, I would like to thank the rest of the Stone Tools, Diet, and Sociality team: Matthew Abtosway, Tope Akeju, Robert Bird,

Mariam Bundala, Siobhan Clarke, Julien Favreau, Jamie Inwood, David Isilebo, Makarius

Itambu, Fergus Larter, Patrick Lee, Aloyce Mwambwiga, Robert Patalano, Maria Soto

Quesada, and Lisa Tillotson. Their unconditional friendship is invaluable to me, and I cannot thank them enough for all of their support.

I would also like to thank the Department of Anthropology and Archaeology at the

University of Calgary and Alberta Student Aid for funding me through my degree.

Specifically, my work was made possible by the Queen Elizabeth II Graduate Scholarship in both 2016-2017 and 2017-2018, and by the Alberta Graduate Student Scholarship. As well, I also thank my examination committee, Dr. Gerry Oetelaar of the Department of

Anthropology and Archaeology, and Dr. Michael Wieser of the Department of Physics and

Astronomy.

I thank Dr. Neduvoto Mollel for her assistance in collecting and identifying the plant samples discussed herein. Her expertise was invaluable to me throughout the sampling procedure. I also thank the staff at the Tropical Pesticides Research Institute in

Arusha, Tanzania for their further assistance in identifying my plant samples as well. In

iii addition, I thank Dr. Pastory Bushozi of the University of Dar es Salaam for his help in

Tanzania.

As well, I thank Kerri Miller of the Isotope Science Laboratory. Her expertise regarding stable strontium isotope analysis helped me immensely, as she walked me through the ion extraction protocol, processed my samples, and helped me with the technical aspects of this thesis. As well, I thank Tracy Wyman for her help with ArcGIS and statistics. Without her assistance I would have been completely lost fumbling through the program and endless information on statistical tests. I would also like to thank Warren

Fitch of the Department of Biological Sciences for his assistance in identifying the hippopotamus specimen.

I would also like to thank my friends, both at the university and outside (you all know who you are!), without whom I could not have possibly come this far. Special thanks go out to my officemates Shalcey Dowkes, Tatyanna Ewald, Alyssa Haggard, Madisen

Hvidberg, David Milley, Kelsey Pennanen, Christina Robinson, and Megan Sampson for their encouragement and companionship. As well, I would like to thank Catherine Butts for the great discussions we have had about our isotope work.

Last, but not least, I want to thank my family and our family friends: Mom, Dad,

Cathy, Nanny, Brenda, and Rob. Without their never-ending support and faith in me I would not be where I am today. I would especially like to thank my dad for proofreading my thesis and catching all of the grammatical and syntax errors I missed – thank you so much for all the time you have dedicated to helping me improve my project, Dad!

Table of Contents Abstract…………………………………………………………………………..….…….ii Acknowledgements…………………………………………………………….….…...... iii Table of Contents…………………………………………………………….……..……..v List of Tables……………………………………………………………………...………ix List of Figures and Illustrations…………………………………………………………....x List of Abbreviations Used………………………………………………...………...... xii List of Equations………………………………………………………………………...xiii

CHAPTER 1: INTRODUCTION………………………………………………...……….1 1.1 Research Problems………………………………………………….………...……1 1.2 Background………………………………………………………………………...2 1.2.1 Landscape use and ranging study shortcomings……………..………………2 1.2.2 Olduvai Gorge………………………………………………………...……..3 1.3 Organisation of this Thesis…………………………………………………………5

CHAPTER 2: HISTORY OF ISOTOPE RESEARCH IN ARCHAEOLOGY……………9 2.1 Introduction……………………………………………………………….………..9 2.2 Isotope Overview……………………………………………………………..…..10 2.3 Radioactive Isotopes………………………………………………...……………10 2.4 Stable Isotopes…………………………………………………………………….11 2.4.1 Introduction to stable isotopes………………………………..…………….11 2.4.2 Stable isotope fractionation…………………………………...……………13 2.4.2.1 Mass-dependent isotope effects…………………………………….…14 2.4.2.2 Mass-independent isotope effects……………………….………….…17 2.5 Isotope Research in Archaeology…………………………………………………18 2.5.1 Radioactive isotopes in archaeology………………………….………….…18 2.5.2 Stable isotopes in archaeology………………………………..………….…19 2.5.2.1 Stable carbon isotope analysis…………………………..………….…21 2.5.2.2 Stable nitrogen isotope analysis…………………………………….…25 2.5.2.3 Stable hydrogen and oxygen isotope analysis…………..……………..29 2.5.2.4 Stable strontium isotope analysis……………………….……………..33 2.5.2.5 Multi-isotope approaches……………………………….…………….37 2.6 Conclusion………………………………………………………….……………..40

CHAPTER 3: STRONTIUM ISOTOPE ANALYSIS IN PALAEOSCIENCE 3.1 Introduction………………………………………………………….……………42 3.2 Strontium in the Palaeosciences…………………………………….…..…………42 3.2.1 Palaeoceanography…………………………………………………………42 3.2.2 Palaeontology………………………………………………………………44 3.3 Isotopes in Human Origins……………………………….…………….…………45 3.3.1 Carbon isotope analysis……………………………….……………………45 3.3.2 Nitrogen isotope analysis…………………………………..…….…………47 3.3.3 Multi-isotope approaches…………………………………….….…………48 3.4 Strontium in Palaeoanthropology…………………………………………………51 3.4.1 Asia…………………………………………………………..…….………52

3.4.2 Europe………………………………………………………..……….……53 3.4.3 South Africa……………………..…………………………………………55 3.4.4 East Africa………………………...……………………………..…………58 3.5 Conclusion………………………………..…………………………….…………63

CHAPTER 4: METHODOLOGICAL OVERVIEW 4.1 Introduction……………………………………………………...………………..65 4.2 Isotopes of Strontium……………………...………………………………………65 4.3 Strontium and Rubidium in Rocks………………………………………………...66 4.4 Sources of Strontium in the Environment…………………………………………68 4.4.1 Overview of the strontium cycle……………………………………………68 4.4.2 Rivers………………………………………...…………………………….69 4.4.3 Sea spray……………………………………………………………………69 4.4.4 Precipitation…………………………………………...…………………...71 4.4.5 Dry fall……………………………………………...……………………...72 4.4.6 Fertilisers………………………………………………...…………………72 4.5 Determining Local Biologically-Available Strontium Values…………………….74 4.5.1 Introduction to biologically-available strontium……………...……………74 4.5.2 Measuring bioavailable strontium………………………………………….76 4.5.2.1 Plants………………………………………………………………….76 4.5.2.2 Animal bones and teeth…………….………………………………….76 4.5.2.3 Land snail shells………………………………………………………77 4.5.2.4 Surface waters………………………………………………………...78 4.6 Strontium Isotopes in the Skeleton………………………………………………..79 4.6.1 Formation and composition of teeth………………….…………………….79 4.6.2 Bone formation and composition……………………….…………………..80 4.6.3 Sampling bones and teeth………………………………….……………….81 4.7 Diagenesis of Fossil Remains……………………………………….…………….83 4.7.1 Diagenesis of skeletal material………………………………….………….83 4.7.2 Diagenetic strontium……………….………………………………………85 4.7.3 Removal of diagenetic strontium……………………………………….…..87 4.8 Laboratory Techniques……………………………………………………………89 4.8.1 Ion extraction……………………………………………………………….89 4.8.2 Mass spectrometry…………………………….……………………………90 4.8.2.1 Thermal ionisation mass spectrometry…….………………………….91 4.8.2.1 Multiple collector inductively coupled plasma mass spectrometry……91 4.9 Conclusion……………………………………………………………………….. 92

CHAPTER 5: TECHNIQUES APPLIED 5.1 Field Methods……………………..…………………..…………………………..95 5.1.1 Plant collection……………………..…………………..…………………..95 5.1.2 Teeth and their collection……...……………..…………………..………..101 5.2 Laboratory Methods…………………....…………………..……………………102 5.2.1 Plant sample preparation……….…………..…………………..…………102 5.2.2 Ultrasonication experiment..…….…………..……………………………103 5.2.3 Tooth sample preparation………..…………..……………………………104

5.2.4 Isotope analysis methods.....………………………………………………106 5.3 Data Analysis and Representation………..…………..………………………….108

CHAPTER 6: THE STUDY AREA 6.1 Introduction……………………………………………………………….……..109 6.2 Olduvai Gorge Region…………………..…………………..…………………...109 6.2.1 The Ngorongoro Conservation Area..…………………..…………………109 6.2.2 Geology…………………..………….………..…………………………..111 6.2.3 Ecology…………………..………….………..…………………………..114 6.2.3.1 People and livestock……………..……..…………………..………..114 6.2.3.2 Wildlife…………………..……….…………..……………………..116 6.2.3.3 Plants…………………..…………..………..……………………….117 6.2.3.4 Soil…………………..……………..……..…………………………119 6.2.3.5 Topography…………………..……...……………..………………..121 6.2.3.6 Surface waters…………………..……...……………..……………..122 6.2.4 Climate…………………..………………………………………………..123 6.3 Olduvai Gorge…………………..…………………….…………………………124 6.3.1 The Beds …………………..….……………..……………………………124 6.3.1.1 Bed I…………………..…………………..…...…………………….127 6.3.1.2 Bed II…………………..…………………..……………………..….128 6.3.1.3 Beds III and IV…………………..………………….………………..129 6.3.1.4 The Masek Beds…………………..…………………………………131 6.3.1.5 The Ndutu and Naisiusiu Beds…………………..……….…………..131 6.3.2 Palaeoenvironment and palaeoclimate……………………………………132 6.4 Juma’s Korongo…………………..…………………..…………………...…….134 6.4.1 Stratigraphy and archaeology of JK…………………..……………….…..135 6.4.2 Palaeoenvironment of JK…………………..……………………………...140 6.5 Sampling for this Study………………….…..…………………………………..141 6.5.1 Plant sampling…………………..……………………..………………….141 6.5.2 Excavations at JK…………………..……………………………………..142 6.6 Conclusions……………………………………………………………………...146

CHAPTER 7: RESULTS 7.1 Geographical Variation in Bioavailable Strontium……………………….……...148 7.1.1 Geological differences…………………..…………………………….…..148 7.1.2 Elevation…………………..…………………..………………………….148 7.1.3 Latitude…………………..…………………..………………...…………150 7.1.4 Longitude…………………..…………………..………………...……….150 7.2 Plant Types Sampled…………………..…………………..……………...……..153 7.3 Ultrasonication Control Experiment…………………..…………………………157 7.4 Teeth…………………..…………………..…………………..……………...….158 7.4.1 Acid washing experiment…………………..………………….....……….158 7.4.2 Differences between taxa…………………..……………………...………166

CHAPTER 8: DISCUSSION 8.1 Variation in Bioavailable Strontium………………………………………..……167

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8.1.1 Hydrological sources of variation……………………………………..…..167 8.1.2 Geographical sources of variation……………………………………..….168 8.1.3 Variation between plant types…………………………………………..…171 8.2 Bedrock vs. Atmospheric Input………………………………………………….172 8.2.1 Ultrasonication control experiment...……………………………………..172 8.2.2 Bedrock strontium…………………...……………………………………173 8.2.3 Atmospheric strontium………………...………………………………….174 8.3 Dental Tissues…………………………………...………………………………177 8.3.1 Diagenesis…………………………………...……………………………177 8.3.2 Animal teeth by taxon……………………………………………………..179 8.4 Modern Versus Ancient Bioavailable Strontium………………………..……….183 8.5 Conclusion……………………………………………………………………….188

CHAPTER 9: CONCLUSIONS 9.1 Introduction……………………………………………………...………………189 9.2 Variation in Bioavailable Strontium……………………………………………..190 9.3 Dental Tissues……………………………………………………...……………193 9.4 Conclusion……………………………………………………………………….194

CHAPTER 10: FUTURE OUTLOOKS 10.1 Introduction…………………………………………………………………….196 10.2 Future Work on Bioavailable Strontium……………………………………….196 10.2.1 Expanding the isoscape………………………………………………….196 10.2.2 Defining sources of strontium……………………………………………197 10.3 Archaeological Work……………… …………………………………………..199 10.3.1 Quantifying diagenesis………………………………..…………………199 10.3.2 Reconstructing ancient bioavailable strontium………...………………...199 10.3.3 Incremental sampling…………………………………...……………….200 10.3.4 Palaeoecology of animals and hominins…………………………………201 10.4 Modern Enamel Values………………………………………………...………202 10.5 Conclusion………………………………………………………………...……203

LITERATURE CITED…………………………………………………………………205

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List of Tables Table 2.5.1: Stable isotopes commonly used in archaeology with their abundances and uses…………………………………………………………………………..22

Table 4.2.1: Relative abundances and sources of stable Sr isotopes……………...……..66

Table 4.3.1: Average concentrations of rubidium, potassium, strontium, and calcium in sedimentary and igneous rock types, as well as deep sea sediments………….67

Table 5.1.1: Plants collected at each locality to calculate bioavailable strontium…...…..96

Table 5.2.1: A list of teeth sampled, their provenance, and their measurements………110

Table 6.3.1: Estimates of C4 biomass coverage and mean annual temperature from Cerling and Hay (1986)………………………………………………………...132

Table 6.5.1: Counts of teeth, bones, and stone tools recovered from JK in 2017……...144

Table 7.1.1: 87Sr/86Sr results for each locality, the geological area, and the type of plants collected………………………………………………………………….152

Table 7.1.2: Summary statistics for 87Sr/86Sr values of each geological area…………..153

Table 7.1.3: Spearman’s correlation coefficients describing the relationship between 87Sr/86Sr values and geographic variables……………………………………....154

Table 7.2.1: Descriptive statistics and Mann-Whitney U Test results for localities with certain types of plants collected…………...………………………………160

Table 7.2.2: Descriptive statistics and Mann-Whitney U Test results for localities with certain types of plants collected…………..……………………………….161

Table 7.3.1: Unrounded results of the ultrasonication control experiment……………..162

Table 7.4.1: Summary statistics for 87Sr/86Sr results of dental tissues………………….163

Table 7.4.2: 87Sr/86Sr results for the acid washing experiment………………………....164

Table 7.4.3: Wilcoxon Signed Rank Test results from the acid washing experiment….164

Table 7.4.4: Descriptive statistics for acid-washed enamel 87Sr/86Sr results by family…………………………………………………………………………...166

List of Figures and Illustrations

Figure 2.4.1: Depictions of a) equilibrium and b) kinetic fractionation…………………15

Figure 2.5.1: δ13C and δ15N value ranges for terrestrial and marine food webs, including values for air, dissolved N2, and bicarbonate CO2…………………….38

Figure 3.4.1: Reproduction of Figure 1 from Copeland et al. (2012) approximating the geological zones in the larger region…………………..………………….…59

Figure 5.1.1: A map of sampling localities and the geological domains they fall on…....96

Figure 6.2.1: Maps showing the location of the Ngorongoro Conservation Area within Tanzania, and Tanzania within Africa…………………………………..110

Figure 6.2.2: A map of the Olduvai Gorge region which labels some of the major features………………………………………………………………………….112

Figure 6.2.3: Various plant landscapes near Olduvai Gorge…………………………...120

Figure 6.2.4: A digital elevation model of the Olduvai Gorge region………………….122

Figure 6.4.1: Satellite images showing the location of JK within Olduvai Gorge……..137

Figure 6.4.2: Reproduction of Figure 14 from Kleindienst (1973) showing the locations of her trenches A, B, and 9 in JK2 West and Trenches 1-8 and 10 in JK2 East……………………………………………………………………...138

Figure 6.5.1: A topographic map created with total station points by the SDS team in 2017……………………………………………………………..………………143

Figure 6.5.2: A view of the eastern portion of JK (facing southwest) showing the SDS trenches A, B, and D……………………………………………………………144

Figure 7.1.1: 87Sr/86Sr results for each locality by bedrock type……………………….149

Figure 7.1.2: Isoscape showing approximate distribution of 87Sr/86Sr values in the area sampled…………………………………….………………………………151

Figure 7.1.3: 87Sr/86Sr values for each of the various locality types plotted against the labelled geographical variable………………..…………………………….154

Figure 7.2.1: 87Sr/86Sr values for samples from all localities including specific plant types versus samples not containing them………………………….…………..159

Figure 7.2.2: 87Sr/86Sr values for samples from metamorphic and volcanic localities

including specific plant types versus samples not containing them……………159

Figure 7.4.1: 87Sr/86Sr values of dental tissues and plant values from the various areas…………………………………………………………………………….162

Figure 7.4.2: Tooth enamel 87Sr/86Sr values with and without treatment with 0.1 M acetic acid for 30 minutes………………………………………………………165

Figure 7.4.3: Dentine 87Sr/86Sr values with and without treatment with 0.1 M acetic acid for 30 minutes……………………………………………………………..165

Figure 8.1.1: Olbalbal flooded vs. Olbalbal dry…………………………………..……170

Figure 8.2.1: An example of a dust storm at Olduvai Gorge…………………….……..176

Figure 8.4.1: Volcanoes of the Ngorongoro Volcanic Highland…………………….…185

Figure 8.4.2: Approximate 87Sr/86Sr value ranges for the NVH volcanoes……….……186

List of Abbreviations Used

Ar: Argon C: Carbon Ca: Calcium CAM: Crassulacean Acid Metabolism D: Deuterium GMWL: Global Meteoric Water Line H: Hydrogen ICP-MS: Inductively-Coupled Plasma Mass Spectrometer JK: Juma’s Korongo K: Potassium ka: Thousand years ago LMWL: Local Meteoric Water Line Ma: Million years old MC-ICP-MS: Multiple collector Inductively-Coupled Plasma Mass Spectrometer NCA: Ngorongoro Conservation Area NIST: National Institute of Standards and Technology NVH: Ngorongoro Volcanic Highland O: Oxygen PDB: Pee-Dee Belemnite Rb: Rubidium SIA: Stable Isotope Analysis SRM: Standard Reference Materials SMOW: Standard Mean Ocean Water Sr: Strontium TIMS: Thermal Ionisation Mass Spectrometer TPRI: Tropical Pesticides Research Institute

xii

List of Equations

Equation 2.1: t = (1/ λ)loge{1+(D/P)}

3 Equation 2.2: δX= ([Rsample/Rstandard]-1) • 10

Equation 2.3: α = (Rreactant)/(Rproduct)

Equation 2.4: ΔX-Y = δX - δY

3 Equation 2.5: εX-Y = (α – 1) • 10

Equation 2.6: δD = 8(δ18O) + 10

Equation 2.7: δD = 7.68(δ18O) – 0.21

xiii

Chapter 1: Introduction

1.1 Research Problems

The goal of this thesis is to set the foundation for and evaluate the potential of future studies of migration and mobility in hominins and animals in the Olduvai Gorge region of

Northern Tanzania. This entails comparing the chemistry, specifically the ratio of stable strontium isotopes, of fossilised teeth from Olduvai Gorge to that of various locations on the landscape to estimate where the individual originated. Stable isotope analysis of many elements is a method commonly employed in archaeology to reconstruct diet and mobility in past populations. It has continually been applied and improved since Vogel and van der

Merwe (1977) first used stable carbon isotopes in human teeth to detect corn consumption in New York state. Mobility has been studied using strontium isotopes for many archaeological populations of modern humans (e.g. Ericson, 1985; Knudson et al., 2005;

Conlee et al., 2009; Nystrom et al., 2011; Slovak et al., 2011; Frei and Price, 2012; Wright,

2012; Buzon and Simonetti, 2013; Perry et al., 2017), but is relatively unexplored for hominins (e.g. Sillen et al., 1995; 1998; Copeland et al., 2011; Balter et al., 2012; Lugli et al., 2017). There have been no studies to date that have directly analysed strontium isotopes of East African hominin remains. A short pilot study by Copeland et al. (2012) looked at some variation in strontium values in a few locations over a very large area around Olduvai

(larger than the scope of this study) and analysed some animal teeth from the lowermost bed in the gorge. Another study by Joordens et al. (2011) looked at the variation in strontium values in a palaeolake over time in the Turkana Basin. As such, there is a lack of direct evidence for landscape use for purposes such as resource tracking and dispersal by ancient hominins, particularly in East Africa.

There are two research problems that will be addressed to accomplish this goal. The first problem is assessing regional spatial variability in biologically available strontium in

Northern Tanzania near Olduvai Gorge. This will be done by looking at the ratios of stable strontium isotopes in plants from across the region from Cenozoic volcanic and metamorphic geological domains and a seasonal floodplain, which theoretically should all yield different 87Sr/86Sr ratios (Bentley, 2006; Capo et al., 1998). The theory behind this and a detailed discussion of bioavailable strontium can be found in Chapter 4.

The second problem is determining the degree to which diagenetic alteration has occurred in animal teeth from Juma’s Korongo, a site at Olduvai Gorge that is dated to approximately 1.3 to 1.0 million years old (Ma) (Hay, 1976; Tamrat et al., 1995; McHenry et al., 2007). To do this, strontium values in enamel and dentine from teeth excavated in summer of 2017 will be assessed. Dentine is more susceptible to diagenetic contamination than enamel due to structural differences, so there should be differences between these parts of the same tooth (Hillson, 2005). This will determine the feasibility of using teeth for reconstructing past mobility. In conjunction, the answers to these two research problems will determine the merit of conducting future studies examining migration of hominins and animals using archaeological remains from Olduvai Gorge.

1.2 Background

1.2.1 Landscape use and ranging study shortcomings

While the distribution of archaeological sites and stone tools may be indicators of habitat preference for a species, they do not reveal a full picture of hominin behaviours

(Thompson, 2009). For example, they do not reveal any individualistic information about

2 movement that could be used to infer dispersal and land use at the species level. This kind of data can shed light on aspects of hominin sociality, such as sex differences in dispersal patterns (e.g. Copeland et al., 2011), which otherwise are invisible through only indirect evidence of movement such as tool assemblages and morphological features of fossils. For example, stable carbon isotope analysis of Paranthropus boisei teeth has been used to debunk assumptions of a predominantly hard food diet based on craniodental morphology by showing that the species consumed very large quantities of grasses (Cerling et al., 2011).

This example will be further discussed in Chapter 3.

In addition, residency and mobility in species can also be indicative of the quality of food and water resources in the area, where animals may stay in an area year-round if there are substantial high-quality food resources present (Estes, 2014). Typically, Oldowan assemblages are found around lake margins, whereas Acheulean assemblages, while typically found in association with permanent water sources, are found in a wider variety of environmental contexts (Thompson, 2009). The association of archaeological sites with particular environments has implications for ecological preferences of hominins, but these are very unlikely to be the only locations that hominins ever go on the landscape (Drinkall,

2014). Since there appears to be a habitat preference change between hominins of the

Oldowan and Acheulean, there may also have been a land-use change over time or between species, possibly based on resource availability or other reasons. Notably, Homo erectus dispersed from Africa approximately 1.8 Ma, thus being subjected to a wide variety of new climates and environments as they moved to inhabit Europe and Asia (e.g. Antón and

Swisher, 2004; Rightmire et al., 2006; Moncel and Schreve, 2016). Ranging and nesting behaviours can be inferred by using extant non-human primate models (Hernandez-

Aguilar, 2009), but unless hominin remains are tested and their palaeoenvironmental contexts are well-understood we cannot have direct evidence of their movement patterns or intra- and interspecific variation in mobility.

1.2.2 Olduvai Gorge

Olduvai Gorge is a famous palaeoanthropological and archaeological research destination in Northern Tanzania, as it contains a continuous record of human evolution over the past two million years. The present-day region is very hot and dry with little shade

(Anderson and Talbot, 1965; Hay, 1976). However, in the past the area was much wetter and featured a large saline lake during Bed I and Bed II times (~2.1-1.3 Ma), and smaller perennial water sources until Masek Bed times (~1.0-0.4 Ma), which saw the beginnings of a semi-arid climate much like that of today (Hay, 1976). Many Oldowan and Acheulean archaeological sites are located near the palaeolake margins and streams, and remains of

Paranthropus boisei, Homo habilis, Homo erectus, and Homo sapiens have all been found in the gorge (Hay, 1976). Archaeological hominin remains at Olduvai, therefore, are excellent candidates for studying mobility within and between species over time.

Using strontium isotopes, migration can be traced over different kinds of geological basements. Within the Olduvai Gorge region there is predominantly young Cenozoic volcanic bedrock which originated in the Ngorongoro Volcanic Highland to the south and southeast (Hay, 1976). As well, there are a few small metamorphic areas to the north (Hay,

1976). Moreover, there is a seasonal lacustrine area, Olbalbal, immediately to the east of the gorge, which may have a slightly elevated strontium isotope ratio. These ratios should not change much over time, and thus present-day landscape results should be indicative of

4 the landscape in the past (Bentley, 2006). Archaeological teeth at Olduvai Gorge can then be compared to these regional values to determine whether or not they are local.

1.3 Organisation of this Thesis

Chapter 2 of this thesis will cover general theories regarding unstable and stable isotopes. This includes the decay of radioactive isotopes and fractionation of stable isotopes in systems and reactions. As well, it will discuss how they are used in various disciplines.

The focus of the chapter will be on the isotopes commonly analysed in archaeological studies and how they have influenced our understanding of past populations over the years.

These isotopes used in archaeology include both stable and unstable varieties of different elements. Within archaeology, unstable isotopes are used for dating materials and sites.

Stable isotope analysis is becoming increasingly popular and being used for reconstructing diet, environment, and movement of past populations. The stable isotopes of hydrogen, carbon, nitrogen, oxygen, and strontium will be discussed. The chapter concludes with a few examples of how isotopes of multiple isotopes can be used in conjunction to answer complicated research problems and interpret results that would otherwise be ambiguous.

Chapter 3 will focus specifically on stable strontium isotope analysis and how it is used in the palaeosciences, including palaeoanthropology and archaeology. There will also be a brief discussion of other stable isotopes used in studies of human evolution, including carbon, nitrogen, and oxygen. This then leads into a discussion of how, within human origin studies, strontium isotope analysis is underutilised, especially in East Africa. There will be a review of all studies that could be found that have used stable strontium isotope

5 analysis to look at human evolution in the Old World. The chapter will end with a discussion of the two previous studies that were conducted in East Africa.

The next chapter will discuss the theory regarding stable strontium isotopes and analytical methods used to quantify them with a focus on archaeology. The chapter begins by describing how strontium isotopes are formed, their relative abundances, and how they come to be in rocks on earth. Different kinds of rock contain different proportions of strontium isotopes, which leads to variability in strontium isotope values across the landscape. Next, the various parts of the strontium cycle will be discussed, including geological, hydrological, atmospheric, and anthropological strontium. This leads into a discussion of bioavailable strontium, labile strontium in soil that is available for use by plants, and various ways in which it can be quantified. Next, skeletal tissues and their compositions are discussed, and how strontium ions can substitute in the place of calcium therein. After burial, the chemical composition of bones and teeth may be altered through a process called diagenesis, which can lead to the individual’s biogenic strontium values being overwritten. Methods of removing diagenetic strontium are then discussed. Finally, the chapter concludes with a discussion of analytical methods used for quantifying stable strontium isotope ratios.

Following this, Chapter 5 will discuss the methods used in this study. This includes field and laboratory methods. To begin, methods of plant sampling around the Olduvai

Gorge region and the identification of plants are discussed. Next is a brief explanation of excavation and recovery of teeth from Juma’s Korongo, Olduvai Gorge. Then, there is a description of the laboratory techniques applied to the plants and teeth at the University of

Calgary. As well, data analysis methods are reviewed.

Chapter 6 will introduce the study area: the Olduvai Gorge region. The first part explains where Olduvai Gorge is situated, and describes the ecology, topography, and climate of the area. Included in this is an explanation of the different vegetation around the region, and some of the animals commonly seen. Next is a description of Olduvai Gorge itself and the different stratigraphic layers therein, including a breakdown of the different beds. JK, the site where the teeth analysed in this study were found, is described in terms of previous work that has been conducted on its geology and archaeology. The chapter ends with details of the plant sampling sites chosen, and details of the excavations at JK.

Next, results are presented in Chapter 7. These include bioavailable strontium results, which are presented in their raw form, as well as averages and medians for each of the geological areas and statistical tests to determine if there are differences between them.

As well, correlations between strontium values and geographical variables are presented.

Next come the results for enamel and dentine, which are presented per tooth and by taxa.

Chapter 8 is a discussion of the results and possible explanations for the trends observed. This features a discussion of possible sources of atmospheric strontium and its relative contribution to the local bioavailable strontium reservoir, differences in strontium values by geographic location, and differences between the lacustrine area and other localities sampled. Furthermore, the levels of diagenesis for the site are explained, and the residency of the animals sampled is determined. Finally, there is a discussion of how modern bioavailable strontium values differ from those in Bed III times, approximately one million years ago.

The next chapter, Chapter 9, is a conclusion of all the work done in this study. It recaps the main goals of the study and discusses how they were addressed. As well, it sums

7 up the conclusions drawn in the discussion chapter and reiterates them all again briefly. It then transitions into Chapter 10, which is the final installment of this thesis.

Chapter 10 discusses how this study was successful, yet there is much more work to be done. It proposes ways in which future studies may elaborate upon the work done herein. This includes future work to expand knowledge of bioavailable strontium variation in the region and its sources, and ways in which archaeological remains may be studied to better understand movement of animals and hominins, and thus make inferences about their palaeoecology and behaviour.

Chapter 2: History of Isotope Research in Archaeology

2.1 Introduction

Isotopes of many different elements can be analysed to answer a wide variety of research questions in many fields. These applications range from studies in theoretical physics to forensics to geology to archaeology. In this chapter, both stable and radioactive isotopes are introduced, as are some general ways they can be used in various research fields. Furthermore, the increasing popularity of isotope research in archaeology will be discussed together with how it can be used to better understand archaeological material.

To do so, there will be a focus on the isotopes of four elements that are commonly used in archaeology: carbon (δ13C), nitrogen (δ15N), oxygen (δ18O), and strontium (87Sr/86Sr). As well, isotopes of hydrogen (δD), which are closely linked to δ18O and are sometimes studied in archaeology, will also be discussed.

2.2 Isotope Overview

The word “isotopes” means “same place” in Greek (Faure, 1986). They are atoms of an element that have the same atomic number, but different numbers of neutrons and therefore mass numbers (Sulzman, 2007). The number in superscript preceding the element symbol is the mass number, which is the number of protons plus the number of neutrons.

Atomic nuclei most commonly have even numbers of both protons and neutrons, though some have even numbers of one and odd of the other. It is rare for atomic nuclei to have odd numbers of both protons and neutrons (Hoefs, 2010).

Isotopes can either be stable, where they do not break down, or radioactive, where they decay into a stable form over time by emitting radiant energy and nuclear particles

(Faure and Mensing, 2005). Approximately 1,200 unstable and 300 stable isotopes have been discovered thus far (Hoefs, 2010). Just 21 elements have only one stable isotope; the remainder have at least two (Hoefs, 2010). Some stable isotopes are predicted to actually be radioactive, but with incredibly long half-lives. This is because some known radioactive isotopes have half-lives longer than the age of the universe. The longest known half-life belongs to tellurium-128 (128Te) at 160 x 1012 times longer than the age of the universe

(Dorado et al., 2012).

Isotope analysis can be used to test theoretical assumptions or assess real-world situations. Within physics it is used to understand things like quantum effects, molecular structure, and chemical kinetics (Bigeleisen, 1965). As well, by analysing isotopes of various elements within natural materials, we can enhance our understanding of biological organisms and the environment (Faure and Mensing, 2005). There are 75 elements whose stable and unstable isotopes are used to assess various geochemical processes, such as dating materials, rate information, fingerprinting chemical processes, and others (Porcelli and Baskaran, 2011). To interpret isotopic ratios, the element’s incorporation into geological, hydrological, biological, and atmospheric cycles on earth, as well as other places in the solar system must be well understood (Faure and Mensing, 2005). Some specific isotopes and their applications will be discussed in detail later in this section.

2.3 Radioactive Isotopes

Radioactivity has been studied since its discovery in 1896 by Henri Becquerel

(York and Farquhar, 1972). Radioactive isotopes of various elements have different decay rates, and therefore different uses. A common application for them is geochronometric

10 dating, wherein the ratio of an unstable isotope is compared to its stable daughter within a substrate (Faure, 1986; Faure and Mensing, 2005). The equation by which isotopic geological age is determined is as follows:

Equation 2.1: t = (1/ λ)loge{1+(D/P)} where t is the elapsed time in years, λ is a disintegration constant for the element being used, D = number of daughter atoms created from the original parent, and P is the number of parent atoms remaining at t (York and Farquhar, 1972). Two dating techniques commonly used in archaeology, radiocarbon and potassium-argon dating, will be discussed later in this chapter.

Radioactive isotope analysis also has biological and medical applications. These include measuring quantities of radioactive isotopes in relation to non-radioactive ones during bodily functions to diagnose medical issues. These applications incude looking at iron metabolism in blood using 59Fe, detecting the presence of pernicious anaemia by

58 labeling vitamin B12 with Co and measuring how much radioactive B12 is excreted through urine, and using 131I to measure thyroid functionality (van der Werff, 1966).

2.4 Stable Isotopes

2.4.1 Introduction to stable isotopes

Knowledge of stable isotopes and their principles goes back to the 1930s. In 1931

Harold C. Urey discovered deuterium (2H or D), a heavy isotope of hydrogen that received its name because it has a mass almost double that of 1H, after Birge and Menzel proposed that there are naturally-occurring isotopes of the element (Faure, 1986). He did this by spectroscopically examining the residual gas left behind after evaporation of liquid

11 hydrogen (Faure, 1986). After discovering deuterium, he proposed that there were more natural-occurring isotopes and that they could be used for understanding natural processes.

Specifically, he suggested that isotopes of oxygen would be useful proxies of palaeotemperature in the ocean because temperature affects fractionation (Faure, 1986).

This will be discussed in more detail later in this chapter.

Analyses of stable isotope ratios are used in many disciplines, including but not limited to: anthropology and archaeology, ecology, environmental sciences, and geology.

Some of the ways they are used include identification of sources (e.g. pollutants such as fertilisers in bodies of water), inference of processes (e.g. nitrification of bacteria in soils and water), estimation of rates (e.g. turnover of carbon in soils), determining proportional input (e.g. amount of certain kinds of plants in one’s diet), and tracing mobility of people and animals (Sulzman, 2007).

Stable isotope ratios are typically presented as lower-case delta (δ) values in parts per thousand (‰). This value is represented by the following equation:

3 Equation 2.2: δX= ([Rsample/Rstandard]-1) • 10

Here, X represents the heavy isotope (2H, 13C, etc.), and R is the ratio of the heavy isotope to the light isotope for the element in question (2H/1H, 13C/12C, etc.) (Peterson and Fry,

1987). Negative delta numbers indicate that the sample has a lower heavy to light isotope ratio than the standard, positive numbers indicate they have a higher ratio, and 0 indicates that it is equal to the standard. The standard for each element has a set abundance ratio to which mass spectrometers are calibrated to promote consistency between labs. Standards are circulated by the National Board of Standards (NBS) and the International Atomic

Energy Agency (IAEA) in Vienna (Katzenberg 2008). Some original standards have been

12 used up, and therefore laboratory-manufactured controls of a similar isotopic ratio as the standard are used instead. These are represented by a V (for Vienna) in the standard’s name, such as V-SMOW and V-PBD (Sulzman, 2007). In the case of V-PDB, there is a slightly different absolute abundance from PDB (13C/12C = 0.0111797 instead of 0.0112372), and therefore is not universally accepted among researchers (Sulzman, 2007). Because international standards are in limited supply, labs typically also use internal standards

(Katzenberg, 2008). For example, the Isotope Science Lab at the University of Calgary

(Earth Sciences #513) managed by Stephen Taylor uses a sample of snowfall from Calgary of known isotopic value for this purpose when analysing stable oxygen and hydrogen isotopes via laser spectroscopy. This normalisation standard is run between every three samples to ensure analytical accuracy (Taylor, personal communication).

2.4.2 Stable isotope fractionation

Although they have the same chemical properties, isotopes of an element behave physically in different ways due to what are known as isotope effects (Dorado et al., 2012).

There are two kinds of isotope effects: mass-dependent, and mass-independent. The different physical properties due to differences in atomic mass are known as mass- dependent isotope effects (Hoefs, 2010). Mass-independent isotope effects, as the name suggests, are unrelated to the mass of atoms (Sulzman, 2007). Both kinds of isotope effects impact the ratio of the numbers of heavy to light isotopes concentrated in substances or phases of compounds. The partitioning of heavy and light isotopes between two phases of a substance or two different substances is known as fractionation (Hoefs, 2010).

Temperature affects the energy of atoms, thus isotope effects are a function of temperature

(Craig, 1953). Typically, there is an inverse relationship with temperature. As temperature increases, there are less marked isotope effects (Hoefs, 2010). Isotopic fractionation can be expressed in three different ways. The first is as a fractionation factor α value, and is the ratio of the reactant over the product:

Equation 2.3: α = (Rreactant)/(Rproduct)

e.g. αDwater-vapour = (D/Hwater)/(D/Hvapour)

Two other useful factors are enrichment (ε) and separation (Δ). These two values both represent the isotopic difference between two compounds in a reaction and are both expressed in ‰ notation. The two are approximately equal, and can be calculated using the following equations:

Equation 2.4: ΔX-Y = δX - δY

3 Equation 2.5: εX-Y = (α – 1) • 10

Typically, α values are used to calculate fractionation for theoretical reactions, while ε and

Δ are used for observed, real-world reactions (Clark and Fritz, 1997).

2.4.2.1 Mass-dependent isotope effects

Molecules with heavy atoms vibrate more slowly than lighter ones, and therefore have less energy and form stronger bonds with other atoms (Sulzman, 2007; Hoefs, 2010).

This molecular energy includes electronic, rotational, translational, and vibrational energy

(Bigeleisen, 1965). As a result, bonds formed with light isotopes will react slightly quicker than those with heavy isotopes (Sulzman, 2007; Hoefs, 2010). Elements with larger mass differences between heavy and light isotopes (e.g. hydrogen) experience stronger isotope

Figure 2.4.1: Depictions of a) equilibrium and b) kinetic fractionation. Both examples use liquid and vapour water to show differences in δ18O values between product and reactant in the two situations.

effects than elements whose isotopes are closer in mass (e.g. strontium) (Bentley, 2006).

Isotopes do not operate under classical theories where differences between physical and chemical processes of systems at equilibrium do not exist (Bigeleisen, 1965). There are two main types of mass-dependent fractionation: equilibrium and kinetic.

Equilibrium fractionation reactions occur in closed, well-mixed systems where products and reactants can continually react (Sulzman, 2007; see Figure 2.4.1a). In these reactions, the product and reactant are the same chemically, but have different masses

15 because the light isotopes react more readily (Faure 1986). In general, heavy isotopes become concentrated in the densest phase (e.g. concentrated in liquid water over water vapour), the highest molecular mass (e.g. CaCO3 over CO2), or in the highest oxidation

state (e.g. concentrated in CO2 over CO) because this is where the bonds are strongest

(Craig, 1953; Bigeleisen, 1965). Sulzman (2007) provides the example of carbon dioxide reacting with water in a closed jar where the carbon dioxide will eventually hold the majority of 18O ions and the water holds most of the 16O ions because the bond with the heavy oxygen is stronger with the carbon. This example shows that the substance with the higher oxidation state preferentially sequesters heavy isotopes. Equilibrium reactions are seldom maintained in nature, though these reactions can still be seen in situations where systems are close to being in equilibrium. For example, rain and snow form in isotopic equilibrium with vapour, but for there to be precipitation, the in-cloud temperature must drop below the dewpoint and therefore that of thermodynamic equilibrium (Clark and Fritz,

1997).

Kinetic (non-equilibrium) isotope effects occur in open systems where reverse reactions are not possible, and thus are the result of unidirectional reactions (Sulzman,

2007; see Figure 2.4.1b). They include processes such as evaporation, diffusion, biological reactions, and dissociation reactions, and are primarily dependent on the reaction rates of isotopic molecules (Hoefs, 2010). Light isotopes move at higher velocities than heavy ones, and their bonds with other atoms are broken faster. As a result, in these unidirectional processes there is always an enrichment of light isotopes in the product (Hoefs, 2010;

Porcelli and Baskaran, 2011). For example, there is kinetic fractionation of carbon isotopes

13 12 ( C/ C) during carbon fixation in photosynthesis (ΔCO2-CH2O ≈ -17‰ for C3 plants) (Clark

16 and Fritz, 1997). Further elaboration of fractionation during photosynthesis will be presented later in this chapter.

Kinetic and equilibrium isotope effects are important in understanding fractionation processes, but they can be difficult to distinguish outside the laboratory (Porcelli and

Baskaran, 2011). This is because it is difficult to tell if there is true equilibrium in the natural environment, and because some processes, such as microbial reduction, result in kinetic fractionation that mimics equilibrium fractionation (Porcelli and Baskaran, 2011).

As well, equilibrium may exist for short periods of time, but is not maintained. For example, in Rayleigh distillation processes, small amounts of a phase are formed in equilibrium, but is then removed almost immediately from the reaction, as is the case with crystals precipitating from a solution that do not continue reacting with the solution after their creation (Porcelli and Baskaran, 2011). Atmospheric precipitation follows the same

Rayleigh distillation pattern when at liquid-vapour equilibrium (100% humidity), but is governed by kinetic processes associated with evaporation, temperature, and condensation at other times (Craig, 1961).

2.4.2.2 Mass-independent isotope effects

Mass-independent isotope effects do not follow the mass-dependent rules described above. The exact mechanisms for these effects are unknown (Hoefs, 2010), but they may be caused by differing nuclear structure between isotopes that lead to differences in nuclear spin (Sulzman, 2007). It is unclear how important mass-independent fractionation is in most circumstances, but it has been demonstrated in a variety of studies (Sulzman, 2007).

An example of mass-independent isotope effects in practice is in oxygen isotopes in the stratosphere and troposphere. The ratio of δ17O to δ18O found in ozone is higher than expected for mass-dependent fractionation, which is attributed to asymmetrical molecules with different rotational energies and geometries (Gao and Marcus, 2001; Faure and

Mensing, 2005). As well, mass-independent effects have been seen in sulfur isotopes (δ33S,

δ34S, and δ36S) in Precambrian rocks, suggesting that the sulfur cycle 2.4 billion years ago was influenced by atmospheric chemical reactions, and that effects of microbial oxidation and reduction and oxidative weathering were minimal at that time (Farquhar et al., 2000).

2.5 Isotope Research in Archaeology

2.5.1 Radioactive isotopes in archaeology

Radioactive isotopes are used for dating sites and materials in archaeology.

Obviously this is an important application, as without a date, the materials recovered mean very little. Two common radioactive isotope dating methods include radiocarbon dating and potassium-argon dating. They are limited in their usefulness based on decay rates and the material needed for analysis, but they both provide useful age estimations.

Radiocarbon dating uses the ratio of carbon-14 to carbon-12 (14C/12C) to determine the age of organic material in the archaeological record up to about 50,000 years old, as anything older does not have a detectable amount of 14C (Dorado et al., 2012). 14C, which decays into 14N, has a half-life of 5,730 years (Katzenberg, 2008; Dorado et al., 2012). In

1960, Willard Frank Libby received the Nobel Prize for his work in developing radiocarbon dating for use in archaeology (Clark and Fritz, 1997). Libby (1955) postulated that radiocarbon dating is possible because all biological beings have radioactive carbon in their

18 tissues that originated in uniformly-distributed atmospheric carbon dioxide. Since then, much work has been done to further refine radiocarbon dating methods, which includes application to more materials. For example, bone collagen can be radiocarbon dated, as it contains 95% of the carbon in bones and is more resistant to diagenesis than inorganic bioapatite (Zazzo and Saliège, 2011). However, in recent years many experimental studies have been dedicated to refining methods to accurately date bioapatite, as collagen is not preserved well in arid and semi-arid environments (Zazzo and Saliège, 2011).

Another method of dating in archaeology uses potassium-40 (40K) and its daughter isotope, argon-40 (40Ar). 40K has a long 1,250 million-year half life (Aitken, 1990), making potassium-argon dating suitable for much older material than radiocarbon dating. Unlike radiocarbon dating, this method dates geological features and hard rock to establish geochronology (Geyh and Schleicher, 1990). For example, it can be used to date lava flows above and below archaeological materials to determine the upper and lower age limits of the site, as lava retains argon in its potassium-bearing minerals after it cools (Aitken, 1990).

This type of dating has been used on tuffs at hominin sites such as those in Bed I at Olduvai

Gorge in Tanzania and other places in East Africa with ages of up to several million years old (Aitken, 1990). Potassium-argon dating is useful for geologically young rocks (up to only a few thousand years old) as only a very small amount of radiogenic argon must be detected (Geyh and Schleicher, 1990).

2.5.2 Stable isotope research in archaeology

Compared to other fields, archaeologists are late to incorporate studies of stable isotope analysis (SIA) into their research (Katzenberg, 2008). However, over the last few

19 decades it has become increasingly popular (particularly in the last ten years; see Roberts et al., 2018) and refined to the point where it is now well-established and routinely used to study mobility and diet of ancient people and animals (Makarewicz and Sealy, 2015). The first archaeological application was Vogel and van der Merwe’s (1977) study using δ13C values to understand maize consumption in New York. Inspiration for this type of work came from a study van der Merwe did in South Africa where a man who looked to be of

Khoisan descent was buried within an Iron Age settlement. When radiocarbon dating the skeleton, a sample of collagen was analysed to reveal that his diet was like the Bantu agriculturalists, thus raising questions about hunter-gatherer/farmer interactions in this area

(Loftus et al., 2016). Stable carbon isotope analysis therefore followed directly from archaeologists’ familiarity with radiocarbon dating (Katzenberg, 2008).

Isotopes within bones and teeth of individuals can yield valuable archaeological information. Their chemical makeup provides direct evidence of diet, and the environment in which the individual lived (Katzenberg, 2008). Studying the mobility of people and the animals they used is key to understanding resource acquisition, trade of objects and food, and social relationships (Makarewicz and Sealy, 2015). See Table 2.5.1 for a few stable isotopes commonly analysed in archaeology, their standards, and their applications.

It is important to note that there can be significant isotopic differences between tissues within the same individual. This is due to different secondary fractionation effects, synthesis from different parts of the diet, varying turnover rates, and differences in tissue composition (Lee-Thorp et al., 1989). Also, there are many poorly-understood confounding factors involved in tissue formation that limit the validity of claims made from SIA. This issue can be resolved through further interdisciplinary collaboration as many of these

20 limitations stem from fields in which archaeologists are non-experts, rather than technological advances (Sillen et al., 1989). As well, there is a need for standardised terminology, methods, sampling, and data representation in archaeological studies using

SIA to allow for researchers to access and compile data from previous studies easily and reliably (Roberts et al., 2018). Despite these issues, SIA is commonly applied successfully to studies of archaeological materials. The remainder of this chapter will discuss some common applications of SIA and their limitations in archaeology.

2.5.2.1 Stable carbon isotope analysis

Stable carbon isotope analysis was the first kind of SIA utilised in archaeology and now has multiple applications. The 13C/12C ratio in a substance is presented in relation to the standard, V-PDB, as a δ13C value. PDB stands for Pee Dee Belemnite, and is from a marine fossil sample collected from the Cretaceous Pee Dee Formation in South Carolina,

USA (Dorado et al., 2012). The original standard has been used up, so laboratories now use a newly synthesised standard known as V-PDB, which has been calibrated to closely match that of the original PDB (Sulzman, 2007; Dorado et al., 2012).

Stable carbon isotopes are used to reconstruct diet. The δ13C value of a tissue can reveal the amount of dietary protein obtained from eating plants using the C3 and C4 photosynthetic pathways, which utilise atmospheric CO2 in different ways (Lee-Thorp et al., 1989; Katzenberg, 2008). In bone, the preferred tissue for this kind of analysis is collagen as it is resistant to alteration and its biogenic carbon can be easily isolated, whereas inorganic carbon in hydroxyapatite of bone is easily diagenetically contaminated over time

Element Isotopes Relative mass International Absolute Abundance Use in Archaeology difference (%) Standard of Standard Hydrogen 1H 100 Vienna Standard Mean 2H/1H = 0.0111797 Palaeoclimate reconstruction 2H Ocean Water (V- SMOW)

12 13 12 Carbon C 8.3 Vienna Pee Dee C/ C = 0.0112372 Diet: C3 vs. C4 plants, marine vs. 13C Belemnite (V-PBD) terrestrial sources; palaeoenvironmental reconstruction

Nitrogen 14N 7.1 AIR (atmospheric 15N/14N = 0.0036765 Diet: trophic level, marine vs. 15N nitrogen) terrestrial resources, weaning

Oxygen 16O 12.5 V-SMOW (water) 18O/16O (V-SMOW) = Migration; seasonality; weaning; 18O 0.0020052 palaeoclimate reconstruction

18 16 V-PDB (CO2 or O/ O (V-PDB) = carbonate) 0.0020672

Strontium 86Sr 1.1 NIST SRM987* 87Sr/86Sr = 0.710240+ Tracing migration; distinguishing 87Sr (strontium carbonate) locals vs. non-locals; dietary reconstruction (terrestrial vs. marine); palaeoclimate reconstruction Table 2.5.1: Stable isotopes commonly used in archaeology with their abundances and uses (modified from Sulzman, 2007). * Other materials, such as seawater (e.g. Capo et al., 1998) and US Geological Survey Tridacna are also used (Sulzman, 2007). + From Britton et al. (2009)

(Lee-Thorp et al., 1989). Due to advancements in methods and understanding of carbon isotope behaviour in the body, many recent studies present both collagen and apatite δ13C values (Katzenberg, 2008). Tooth enamel can also be used for analysis of carbon isotope ratios (Cerling et al., 1997; Cerling et al., 2011) and is particularly useful in studies of fossils because it is resistant to diagenesis (Keenan, 2016). There is fractionation that occurs between dietary carbon and deposition into their tissues, and fractionation factors for each tissue therefore must be understood to interpret results. For example, secondary fractionation occurs during amelogenesis that leaves tooth enamel enriched by about

+14.3‰ (Cerling et al., 1997). Fractionation in bone collagen formation is much lower, at about +3-6‰ (Lee-Thorp et al., 1989; Katzenberg, 2008). In apatite it is estimated to be between +9.6‰ (DeNiro and Epstein, 1978) and +13‰ (Sullivan and Kreuger, 1981).

Plants that use the C3 (Calvin cycle) photosynthetic pathway discriminate more

13 against C than plants that use the C4 (Hatch-Slack cycle) pathway, and therefore have

13 lower δ C ratios than C4 plants (Chesson et al., 2011). These processes differ in that C3 plants only have one CO2 fixation step during photosynthesis, whereas C4 plants have two

(Dorado et al., 2012). C3 plants dominate earth’s plant biomass (~95%), including most trees, shrubs, fruits, vegetables, and temperate grasses (including wheat, barley, rye, and rice), and have δ13C values typically ranging from -30 to -22‰, with a mean of about -

27‰ (Lee-Thorp et al., 1989; Cerling et al., 1997; Koch, 2007; Chesson et al., 2011). C4 plants include grasses, maize, and tropical grains such as millet and teff and have δ13C values of about -9 to -19‰, and a mean of -13‰ (Cerling et al., 1997; Koch, 2007; Chesson et al., 2011). Because maize has different δ13C values from other plants growing in temperate climates, as seen in Vogel and van der Merwe’s (1977) study mentioned

23 previously, it can be easily detected in predominantly C3 environments. A very small number of dry-adapted plants, mainly succulents, use an uncommon third photosynthetic pathway, known as Crassulacean acid metabolism (CAM) (Koch, 2007). These plants

13 alternate between C3 photosynthesis during the day and C4 at night and have δ C values between those of C3 and C4 plants (-10 to -20‰) (Clark and Fritz, 1997; Koch, 2007). As well, some plants such as Portulacaria afra and Portulaca oleracea can switch between their regular pathways (C3 and C4, respectively) and CAM during droughts to conserve

13 water (Dorado et al., 2012). The δ C values of soil CO2 represent the kinds of plants growing in it due to decomposition of dead organic matter (Clark and Fritz, 1997).

As well, δ13C can be used to distinguish between marine and terrestrial food sources

(Katzenberg, 2008). Freshwater fish have values that resemble C3 plants, whereas marine fish resemble C4 plants (Dorado et al., 2012). This is due to differences between atmospheric carbon values (-7‰ in pre-Industrial times, though this number is steadily increasing due to burning of fossil fuels) and dissolved ocean bicarbonate values (0‰)

(Hobson and Collier, 1984; Clark and Fritz, 1997; Katzenberg, 2008). Oceanic δ13C values are maintained through various trophic levels (with enrichment of about +1‰ per trophic level) (DeNiro and Epstein, 1978; Hobson and Collier, 1984), though fractionation of carbon isotopes between ocean water and marine plankton can vary significantly based on temperature changes (Fontugne and Duplessy, 1981). However, local differences in fractionation due to temperature are relatively small and would likely not impact interpretations of marine vs. terrestrial diet (Hobson and Collier, 1984).

For example, Hobson and Collier (1984) used stable carbon isotope analysis on the remains of Australian Aborigines from a burial site at Broadbeach in Southern Queensland

24 to better understand their diet. This site was used for approximately 1,000 years, up until the 19th century (Hobson and Collier, 1984). Here, remains of marine mammals, fish, and shellfish were found, as well as some terrestrial animals. In Australia, most C4 plants grow in the hot interior of the continent as opposed to coastal regions, so they believe that higher

13 δ C values are likely the result of marine food resource exploitation as opposed to C4 resources (Hobson and Collier, 1984). They found that the population had an average δ13C value of -16.6‰, suggesting a maximum dietary intake of 51% marine resources (Hobson and Collier, 1984). They compared these data to remains from Swanport, a site in Southern

Australia on the Murray River and approximately 60 km from the sea. At this site, remains of terrestrial animals and freshwater fish were recovered. The Swanport people δ13C values averaged -20.1‰, a significantly different value leaning more toward terrestrial foods

(maximum of 85% terrestrial), suggesting that there was little movement between their settlement on the river and the coast (Hobson and Collier, 1984).

2.5.2.2 Stable nitrogen isotope analysis

Following the success of stable carbon isotope analysis, interest in how other elements could be applied to archaeology was heightened (Katzenberg, 2008). Stable nitrogen isotope analysis (15N/14N) is another tool for reconstructing past diets and was the second element to be incorporated into archaeological studies, beginning with studies of trophic level differences in isotopic ratios (Katzenberg, 2008; see DeNiro and Epstein,

1981). The largest reservoir of nitrogen is the atmosphere, with over 99% of the earth’s supply (3.9 x 1021 g), whereas terrestrial biomass and soil contain smaller fractions (3.5 x

1015 g and ~100 x 1015 g, respectively) (Schlesinger, 1997). The analytical standard for stable nitrogen analysis is AIR (atmospheric nitrogen) (Sulzman, 2007).

Nitrogen in the body is a by-product of protein metabolism, some of which is stored in tissues and some is excreted through urea (Hedges and Reynard, 2007) and, to a lesser extent, feces (Sponheimer et al., 2003). 14N is preferentially excreted in urea, leaving body tissues enriched in 15N (Hedges and Reynard, 2007). Feces are enriched in 15N, but typically more nitrogen is expelled through urea (Sponheimer, 2003). Therefore, the δ15N value of an individual’s tissues is slightly elevated compared to dietary proteins due to fractionation. Carbohydrates and lipids do not contain nitrogen (Ambrose, 1993). δ15N is an indicator of trophic level within a food web, as with each subsequent step up the food chain there is an enrichment of heavy isotopes by about 3.2‰ (Peterson and Fry, 1987).

Carnivores, therefore, will have higher δ15N values than herbivores, and herbivores will be higher than the plants they eat (Katzenberg, 2008). As well, marine animals will typically be enriched in 15N compared to terrestrial animals because there are usually more steps in marine food webs (Drucker and Bocherens, 2004). δ15N is assessed by analysing organic material in bone collagen and dentine, limiting this kind of analysis to specimens in which organics are preserved.

The enrichment factor of 3.2‰ per trophic level in humans is under scrutiny because there can be variation depending on the amount of nitrogen consumed through protein, the quality of the nitrogen sources, and what foods were eaten in combination with one another (Hedges and Reynard, 2007; Robbins et al., 2010). Factors such as thermal stress, as seen in East African mammals (Ambrose and DeNiro, 1986), and nutritional stress, as seen in birds (Hobson et al., 1993), may also influence nitrogen enrichment

26 between trophic levels. However, the mechanisms for these differences in δ15N values are poorly understood and require further study (Sillen et al., 1989; Sponheimer et al., 2003;

Coutu et al., 2016). Ideally, when conducting a study using nitrogen isotopes, researchers analyse values of plants and animals in the area and assess human values in relation to those (Ambrose, 1993; Katzenberg, 2008), particularly because δ15N values vary between ecosystems and are elevated in arid regions (Ambrose, 1991).

Most plants cannot directly fix nitrogen from the atmosphere and have an elevated

δ15N value compared to air (0‰) due to utilisation of decomposed organic nitrogen in the soil in the form of NH3 and NO3 (Katzenberg, 2008). Legumes, however, do fix nitrogen directly, so there is little enrichment in 15N at this stage (Delwiche et al., 1979). Therefore, when legumes comprise a significant portion of a diet, they may result in a low overall

δ15N value. For example, Ambrose and DeNiro (1986) found that, in a study of 43 mammal species from East Africa, baboons had the lowest δ15N values. They attributed this to the high proportion of legumes in their diet relative to other animals (Ambrose and DeNiro,

1986). However, leguminous species may still be drawing on nitrogen sources other than the air, so this assumption should be used with caution (Handley et al., 1994). Handley et al. (1994) found that it was impossible to distinguish N2-fixing acacias in Kenya from non- fixers in the same area, and that there was wide variation between all plants in the area sampled. This could partially be explained by the decomposition of dead leguminous plants resulting in 15N-depleted soil, and potential seasonal differences in nitrogen utilisation

(Delwiche et al., 1979). Soil δ15N values cannot be analysed to understand plant values, as most nitrogen in soil is highly recalcitrant and unavailable to plants (Marshall et al., 2007).

Although studies of stable nitrogen isotope analysis have been useful in a variety of contexts, they have been especially effective in studies of Neanderthal diet, human weaning practices, and distinguishing marine vs. terrestrial dietary input (Sponheimer et al., 2003). Neanderthal dietary reconstruction will be discussed in Chapter 3. Stable nitrogen isotopes can be used to understand weaning practices because there is a visible change in δ15N values before and after weaning (Katzenberg et al., 1996). At birth, babies have δ15N values identical to their mothers, but when they nurse their only source of protein is their mothers’ milk and therefore they will have elevated δ15N values in relation to their mothers (Katzenberg et al., 1996; Schurr, 1998). Once children begin eating other foods, their δ15N value will gradually decrease to represent the foods that they are now consuming

(Schurr, 1998). For example, Pfeiffer et al. (2017) found that in 14th to 17th century Huron-

Wendat infants from Southern Ontario weaning began between 8 and 18 months of age and was complete by 3.5 years of age in all cases. Because the Huron-Wendat people practiced maize agriculture at this time, the results of this study which show that there was a late weaning age refute Bocquet-Appel’s (2011) theory that agricultural groups have shorter interbirth intervals due to high-calorie foods (cereal grains, maize, etc.) and conserved energy because they do not have to carry children around as much.

Nitrogen isotopes are also useful in distinguishing between marine and terrestrial dietary resources, as marine vertebrates have higher δ15N values than terrestrial vertebrates at similar trophic levels (Schoeninger et al., 1983), although marine invertebrates tend to have lower δ15N values (Slovak and Paytan, 2011). Slovak and Paytan (2011) used nitrogen isotopes to assess the diet of Middle Horizon (550 AD to 1,000 AD) people from Ancón,

Peru. During that time, there were fewer marine shell and fish remains at the site in relation

28 to the times before and after, which implies that marine resources were not being utilised as much, potentially the result of trade (Slovak and Paytan, 2011). However, they found that all individuals had δ15N values of between +13 and +15‰, indicating that marine resources were still a significant source of dietary protein (Slovak and Paytan, 2011).

2.5.2.3 Stable hydrogen and oxygen isotope analysis

Following the popularity of nitrogen isotope analysis, archaeologists looked to the viability of studying other elements as well. Stable oxygen (18O/16O) and hydrogen (2H/1H or D/H) isotopes are useful as tracers of migration based on climate and water source differences (Katzenberg, 2008; Makarewicz and Sealy, 2015). The oxygen in hydroxyapatite and collagen comes mainly from body water, which is derived from local meteoric water (Schwarcz et al., 2010). Hydrogen isotopes come from the same sources, but approximately 20% of hydrogen in bone collagen is exchanged with atmospheric sources (Cormie et al., 1994). This process is not yet fully understood, whereas the behaviour of oxygen isotopes is, making them the more popular choice for analysis

(Makarewicz and Sealy, 2015). Nonetheless, studies have used δD values from archaeological materials alone or in conjunction with other elements (e.g. Leyden et al.,

2006; von Holstein et al., 2016; Wang et al., 2017).

As previously mentioned, the isotopic values of precipitation are affected by factors such as evaporation, temperature, and condensation, but they are also subject to latitude, continental, amount, and altitude effects (Hobson, 2007). These effects are largely related to temperature, as with increasing latitude, continentality, and altitude, as well as seasonal differences, there are typically decreases in temperature (Hobson, 2007), and, as previously

29 mentioned, isotope fractionation increases with decreasing temperature. Craig (1961) found that there was a linear relationship between oxygen and hydrogen isotopes in precipitation that is consistent across the globe. The equation for this relationship, the

Global Meteoric Water Line (GMWL) is:

Equation 2.6: δD = 8(δ18O) + 10

Places also have a Local Meteoric Water Line (LMWL), a linear relationship describing precipitation within a specific area which sometimes can differ quite markedly from the

GMWL. For example, based on a 9-year study of precipitation, Peng et al. (2004) defined the LMWL for the area around Calgary, Canada as:

Equation 2.7: δD = 7.68(δ18O) – 0.21

They argued that this equation differs from the GMWL primarily because of altitude and continental effects (Peng et al., 2004). The standard by which δ18O and δD values are compared is V-SMOW (Sulzman, 2007).

Because people and animals consume local water, the oxygen and hydrogen isotope ratios in their bones and teeth should reflect the local water values (Leyden et al., 2006;

Schwarcz et al., 2010; Makarewicz and Sealy, 2015). Therefore, locals vs. non-locals can be distinguished in archaeological assemblages by establishing their δ18O and δD values and comparing them to those for the region around the site (Schwarcz et al., 2010;

Makarewicz and Sealy, 2015). This method of tracing migration is limited to more recent archaeological sites due to changing climate and rainfall patterns over time. Oxygen isotopes in body water fractionate consistently from ingested local water within a species, and the δ18O of body water varies linearly with local meteoric water (Longinelli, 1984).

Non-obligate drinker species display differences in their δ18O values compared to obligate

30 drinkers, as they receive their water from the plants they consume. As a result, their δ18O values reflect relative humidity, not precipitation (Loftus et al., 2016).

Longinelli (1984) proposed that δ18O values of bone phosphate of fossil specimens can be used to research palaeoclimate and palaeohydrology of areas rather than mobility, so long as the fractionation factor between body water and ingested water is well understood for the species being analysed. In tissues with low turnover rates, such as bone collagen, isotopic records of climate are averaged over a long period of time, thus blending records of seasonal and interannual differences in temperature (Leyden et al., 2006).

Because δD and δ18O are closely linked to one another, δD is applicable to this kind of study as well. For example, Leyden et al. (2006) compared δD values of 7 modern and 52 archaeological bison bone collagen samples from various localities and time periods

(~10,300 to 1,050 years before present) in Southern Saskatchewan, Canada. Bison have diets almost entirely restricted to grass and sedges (Peden, 1976), which do not display wide ranges of δD values (Leyden et al., 2006). They found significant differences between bone collagen δD over the years (ranging from about -132‰ to -120‰, where lower δD values imply lower temperatures) which almost certainly represents changes in average temperature, and that the implied fluctuations compare favourably to studies using other proxies for palaeoclimate reconstruction (Leyden et al., 2006).

Like nitrogen isotopes, stable oxygen isotopes can also be used in archaeology to estimate the age of weaning. When water is consumed, 16O is preferentially released as water vapour, whereas 18O accumulates in body tissues, including tooth enamel and breast milk (Katzenberg, 2008). While breast-feeding, infants consume the oxygen isotopes in the water within the milk, which is already enriched in heavy isotopes from the mother (Britton

31 et al., 2015). Thus, the infant’s body becomes enriched further in 18O in relation to the mother. After weaning, infants no longer consume this heavy isotope-enriched food source, thus their δ18O value will decrease (Katzenberg, 2008). Britton et al. (2015) found this to be the case in Wharram Percy, a well-studied Medieval village in Northeastern England.

δ18O values in bone phosphate of perinatal and neonatal individuals were elevated, whereas around 2-3 years of age this value began to decrease to levels consistent with those of adults. They argued that stable oxygen isotope analysis is a valuable tool for assessing weaning practices, though stated that it should be used in conjunction with δ15N values to provide two lines of evidence (Britton et al., 2015).

Stable oxygen isotope analysis can also be used to understand seasonal occupation of coastal sites. This is done by analysing mollusk shell midden at archaeological sites

(Killingley, 1981; Bailey et al., 1983; Jew et al., 2014). In theory, the shell should have the highest δ18O values in the coldest month and lowest values in the hottest month (Bailey et al., 1983). To understand when a mollusk was killed, the shell must be sequentially analysed to determine seasonal variations in temperature within that individual’s lifetime and estimate the season of death based on the edge δ18O values for that animal (Bailey et al., 1983). Therefore, since archaeological mollusks likely came from different years, it is not necessary to try and recreate the exact temperature of the water due to annual variation in marine water temperatures. As well, they argue that modern samples of the selected mollusk species should be collected from the same location to understand variability in seasonal δ18O values in that environment today (Bailey et al., 1983).

A study by Jew et al. (2014) used this methodology to assess seasonal occupation at an 8,200-year-old site on Santa Rosa Island in California (CA-SRI-666). The site,

32 located approximately 45 m from the coast, contained a variety of marine shellfish midden and scattered stone tools and debitage (Jew et al., 2014). Almost all faunal remains (99% by weight) were California mussel shells, suggesting a heavy reliance on rocky intertidal habitats for food (Jew et al., 2014). By analysing the terminal growth band of 41 mussel shells, they found that there were seasonal differences in collection of mussels, but all seasons were represented in the assemblage. The site, therefore, was likely a residential base for relatively sedentary people (Jew et al., 2014).

2.5.2.4 Stable strontium isotope analysis

Strontium isotope analysis has been used in archaeology since Ericson’s (1985) pilot study on its applicability to marriage and residency research in California. The ratio of strontium-87 to strontium-86 (87Sr/86Sr)1 in skeletal and dental material can be used to map the migrations of past individuals (Bentley, 2006; Capo et al., 1998; see Britton et al.,

2009; Copeland et al., 2011; Copeland et al., 2016; Hodell et al., 2004 for examples).

Specific examples of 87Sr/86Sr analysis in palaeoanthropology, as well as other palaeoscientific disciplines, will be discussed in detail in Chapter 3. Strontium isotopes can also be used for dietary reconstruction by differentiating between marine and terrestrial resources, so long as the area studied is a coastal zone with a different 87Sr/86Sr value than the ocean (Sealy et al., 1991; Katzenberg, 2008).

In humans and animals, strontium is incorporated into the hydroxyapatite of bone and, when the individual is young, tooth enamel, in place of calcium (Montgomery, 2010).

This occurs because strontium has an ionic radius of 1.18 Å (1.18 x 10-10 m), which is very

1 87 87 3 87 86 87 86 This ratio can also be expressed as a delta value, δ Sr, where δ Sr = 10 [( Sr/ Srsample)/( Sr/ Srseawater) - 1] (Capo et al. 1998). This notation is less common than 87Sr/86Sr, so it will not be used here. 33 close to that of calcium (1.00 Å) (Faure and Powell, 1972; Capo et al., 1998). The mobility of strontium in bone is high, so tooth enamel is preferred for sampling (Loftus et al., 2016).

There is no conclusive evidence to suggest that strontium serves an essential metabolic function, so it is likely that the body processes it as though it were calcium (Underwood,

1977; Burton and Wright, 1995).

Strontium isotopes mainly come from the erosion of minerals in bedrock, and therefore areas of differing geological composition have different 87Sr/86Sr ratios (Bentley,

2006). 87Sr/86Sr ratios effectively identify mobility of past populations because the geological 87Sr/86Sr values of bedrock is essentially constant over time. This will be clarified further in Chapter 4. Biologically available (bioavailable) strontium is strontium from local groundwater available for use by plants, which are subsequently consumed by animals (Sillen et al., 1998). People and animals therefore acquire local 87Sr/86Sr values by eating and drinking, making it possible to estimate where an individual originated on the landscape (Bentley, 2006). 87Sr/86Sr fractionation does not occur at the temperatures found in biological systems, and therefore directly reflects the system from which it came rather than exhibiting a trophic-level effect (Capo et al., 1998; Bentley, 2006). Bioavailable strontium comes from the underlying rocks, but it has other sources as well, such as precipitation, sea spray, dry fall, and anthropogenic sources like fertiliser (Bentley, 2006).

As well, processes involved in soil formation such as erosion, weathering, and mixing can reduce variability in bioavailable strontium in relation to that of the basement geology

(Loftus et al., 2016). To interpret archaeological 87Sr/86Sr values, the bioavailable strontium values for the area must first be known (Hartman and Richards, 2014). These values can be determined by sampling biological material such as plants, invertebrates, or rodent

34 bones from across a region (Hartman and Richards, 2014). Bioavailable strontium will be discussed in detail in Chapter 4. The international standard NIST SRM987 (NIST=National

Institute of Standards and Technology; SRM=standard reference materials) is analysed with every set of samples to normalise the data (Britton et al., 2009). Unlike the other elements previously discussed, strontium isotope data are not typically presented as δ values in relation to the standard, but as 87Sr/86Sr ratios of the specimen itself. The standard,

NIST SRM987, is instead used to normalise the values

A study by Knudson et al. (2005) looked at strontium isotope values of individuals to determine whether or not they were local to an area to better understand their burials and the grave goods associated with them. They determined the 87Sr/86Sr ratios for remains of three male natural mummies from the Middle Horizon (500-1100 AD) from Juch’uypampa cave in southern Bolivia to see if they were local people or migrants from Tiwanaku near

Lake Titicaca or the related San Pedro de Atacama site in Chile, where some of the grave goods seemed to have originated. These three areas are geologically and isotopically distinct, which allowed Knudson et al. (2005) to determine the origin of the mummies using enamel from two and bone from one individual. They found that two of the individuals likely spent their childhoods in the area they were interred, and the third spent the last twenty or so years of his life there. The 87Sr/86Sr values of the individuals ranged from

0.712358 to 0.713994, which were closest to the area around Juch’uypampa (~0.7132) as opposed to Lake Titicaca (0.7083-0.7112) and San Pedro de Atacama (0.704-7079)

(Knudson et al., 2005). These findings led the authors to wonder about the influence the

Tiwanaku polity had over southern Bolivia, prompting future work.

Strontium isotopes of archaeological materials besides skeletal remains can be analysed to understand their provenance too. Frei et al. (2009) studied the potential of conducting strontium isotope analysis on archaeological woollen textiles by analysing modern sheep hair and some archaeological textile fibres that were decontaminated by freeing the samples of dust and lipids via ultrasonic cleaning with 20% hydrofluoric acid.

The modern sheep wool came from animals raised ecologically in New Zealand, Norway,

Denmark, Sweden, the Faroe Islands, and the Shetland Islands, plus they collected a topsoil sample from each feeding pasture. Moreover, small amounts (~30 mg) of two Iron Age archaeological textile samples from Danish sites and one from Sweden were studied. They found that the decontaminated values they obtained correlated strongly with the soluble bioavailable strontium values of the pastures, and the differences between the 87Sr/86Sr values of the archaeological samples from the different sites (Frei et al., 2009).

Strontium isotope analysis can also be used to quantify reliance on marine vs. terrestrial foods of coastal people so long as the terrestrial 87Sr/86Sr ratio is different from that of the ocean (~0.7092) (Sealy et al., 1991). Sealy et al. (1991) studied the 87Sr/86Sr values of archaeological human skeletons from various sites near Cape Town, South Africa to estimate the importance of marine and terrestrial foods in their diets. Some skeletons analysed were from the coastal plains, and others originated further inland in areas of shale and sandstone bedrock. They found that coastal skeletons all had values close to that of the ocean (0.70920–0.71009), whereas the skeletons from further inland had higher 87Sr/86Sr values (0.71382–0.71898). This implies that people living near the coast relied much more on marine resources than inland people, who likely lived off local terrestrial resources.

2.5.2.5 Multiple-isotope approaches

Many archaeological studies look at isotopes of multiple elements to answer their research questions (e.g. Richards et al., 2000; Salazar-García et al., 2014; Coutu et al.,

2016; Pickard et al., 2016; among many others). Because different elements have different sources, each one yields a unique finding, thus providing multiple lines of evidence to support conclusions drawn. For example, carbon and nitrogen isotopes can both be used to

13 infer diet in past populations. δ C can be used to infer reliance on C3 vs. C4 plants and marine vs. terrestrial resources, whereas δ15N is used to infer trophic level within a food web. Because marine food webs have more trophic levels and therefore higher δ15N values, nitrogen isotopes can be analysed to clarify whether high δ13C values are due to marine or

C4 foods (see Figure 2.5.1). As well, different isotopes can be used in conjunction to determine the provenance of people and artifacts because they are all indicative of different environments (marine vs. coastal, C3 vs. C4, bedrock type, precipitation pattern, etc.).

Salazar-García et al. (2014) used carbon and nitrogen isotope analysis on human

(n=9) and faunal (n=66) remains from three Mesolithic sites in the Iberian Mediterranean region near Valencia to try and understand marine vs. terrestrial resource use in different populations. These sites are Coves de Santa Maira, Penya del Comptador, and Cingle del

Mas Nou. There were no known edible C4 plants during the Mesolithic in these areas, so the authors believed high δ13C values were indicative of marine resource consumption.

They determined that the people at all three sites had primarily terrestrial C3 diets, as the

δ13C values of people were much lower than any marine fauna they sampled. Also, the human δ15N values were close to those of marine fish and mammals, indicating that they were at a similar trophic level (Salazar-García et al., 2014). They concluded that residents

Figure 2.5.1: δ13C and δ15N ranges for terrestrial and marine food webs, including values for air, dissolved N2, and bicarbonate CO2 (reproduced from Ambrose, 1993).

of the northern inland site Cingle del Mas Nou had some marine resources in their diet, which indicated a connection with the coast; inhabitants of Santa Maira, which was about

30 km from the coast, consumed low amounts of marine food, and those from Penya del

Comptador showed no isotopic evidence of marine resource consumption (Salazar-García et al., 2014).

Another example is Coutu et al.’s (2016) study of 7-10th century ivory artifacts from three sites (KwaGandaganda, Wosi, and Ndondondwane) in the KwaZulu-Natal province of South Africa. Because ivory was abundant at these sites, they wanted to determine what species it came from, and whether it was sourced from local populations or from further abroad. Zooarchaeology by mass spectrometry (ZooMS) results showed that all artifacts came from elephants, despite the presence of hippopotamus remains at the sites. To estimate the provenance of the ivory, they determined the artifacts’ δ13C, δ15N, and

87Sr/86Sr values. The authors found that the elephants from Ndondondwane and

13 KwaGandaganda had low δ C values and therefore ate almost entirely C3-based diet, suggesting that they came from a densely-forested area. The Wosi elephants had higher

δ13C values, suggesting more grass in their diet. The δ15N values from Ndondondwane and

KwaGandaganda were not significantly different from one another, but both were significantly different and lower than those from Wosi. This suggests that the Wosi elephants ate vegetation in a drier savanna-like environment, which concurs with the carbon isotope findings. Based on these findings, the ivory from Wosi was not local, as the local environment is a moist riverine area. The 87Sr/86Sr results showed overlap between

Ndondondwane and Wosi samples which were both distinct from the higher values at

KwaGandaganda, also implying that the elephants came from different areas, though there is very high variability in strontium isotope values throughout the region (Coutu et al.,

2016). They believe that the ivory recovered could have been destined for widespread trade, as it was abundant and some of it was coming from far away (Coutu et al., 2016).

2.6 Conclusion

Analysis of radioactive and stable isotopes have many applications in a variety of disciplines and have been studied for many decades. Radioactive isotopes are useful for geochronology and dating archaeological materials as they decay at known, predictable rates. The ratio of the daughter isotope to the parent can be used to estimate the age of materials. Unlike radioactive isotopes, stable isotopes do not decay over time. Instead, differences in ratios of heavy and light isotopes can be used to understand a variety of processes including rates of accumulation and turnover of elements, and proportional inputs of various sources into systems such as diets of people and animals.

Stable isotopes undergo fractionation processes that can be mass-dependent or mass-independent. Mass-dependent isotope effects occur due to mass differences resulting from differing numbers of neutrons in atoms and include kinetic and equilibrium effects.

Kinetic isotope effects are the result of one-way reactions where the lighter isotope reacts faster, leaving the product enriched in light isotopes. Equilibrium isotope effects occur in systems where continuous reactions can occur. In these reactions, the product and reactant are the same chemically, but have different physical properties due to differences in mass between isotopes. Typically, in mass-dependent fractionation heavy isotopes are concentrated in the densest phase of a compound, in the compound of the highest oxidation state, or in the heaviest compound. These isotope effects are most marked in elements with the largest relative mass difference between isotopes (e.g. there are stronger effects between 1H and 2H than 14N and 15N). Mass-independent isotope effects are poorly understood, though likely are caused by differences in the nuclear spin of atoms. They have been identified in sulfur isotopes in rocks and oxygen isotopes in the ozone.

Within archaeology, SIA is still a relatively new technique, though it has been rapidly growing in popularity and becoming more refined with regards to understanding fractionation within human and animal tissues in different environments. Stable carbon isotope analysis was the first to be introduced into the discipline. It can be used to estimate dietary input from C3 vs. C4 plants and terrestrial vs. marine food resources. Stable nitrogen isotope analysis came about second and is popular, though there are more uncertainties regarding fractionation within the body and in different environments. Nonetheless, it has proven to be an effective tool for identifying the trophic level of individuals, distinguishing between marine and terrestrial food use, and determining weaning age. Hydrogen and oxygen isotopes are closely related and are used to trace migration based on precipitation.

Oxygen isotopes can also be used to determine seasonality of sites that have accumulations of mollusk shells, and for determining weaning age. Stable strontium isotope analysis is most commonly used to trace migration in individuals over areas of varying geological bedrock, though it can also be used to estimate terrestrial vs. marine dietary input. SIA in archaeology is most reliable when using multiple elements as lines of evidence, and when it is used in conjunction with other kinds of data, such as zooarchaeological remains.

However, for it to be effective, referential isotopic information for the study area must be well understood. The following two chapters will focus on studies using stable strontium isotope analysis. Chapter 3 will look at strontium in the palaeosciences specifically, and

Chapter 4 will explore methodological considerations for conducting strontium isotope analysis.

Chapter 3: Strontium Isotope Analysis in Palaeoscience

3.1 Introduction

Strontium isotope analysis is used throughout the palaeosciences for a variety of purposes, including understanding how the earth changed over time, plus looking at movement together with habitat use and preference of various extinct or ancient species.

This chapter will discuss how SIA influences our understanding of various processes in multiple disciplines within the palaeosciences including palaeoceanography, palaeontology, and palaeoanthropology. The focus will be on strontium isotopes in human evolution and discuss case studies where they have been used in the Old World.

3.2 Strontium in the Palaeosciences

3.2.1 Palaeoceanography and the hydrological cycle

One application of stable strontium isotope analysis is using 87Sr/86Sr values to understand palaeoceanographic and lacustrine evolution by interpreting changes through time (e.g. McArthur et al., 2007; Kocsis et al., 2013; Reghizzi et al., 2017). Due to changing climate and tectonic activity, the average 87Sr/86Sr of the ocean has experienced short- and long-term fluctuations (Richter et al., 1992; Capo et al., 1998). This will be further elaborated in Chapter 4.

For example, Reghizzi et al. (2017) analysed the 87Sr/86Sr values of foraminifera, mollusks, and evaporites in sediments from the Sorbas basin in Spain to understand how the composition of the Mediterranean Sea changed approximately 6 Ma leading into the

Messinian salinity crisis, which resulted in the devastation of aquatic ecosystems within the sea. As well, they determined the δ18O and δ13C values for 25 foraminifera specimens

42 that had enough material for multiple analyses. Their samples came from approximately

6.70 to 5.70 Ma. Strontium isotopes in this context are useful in detecting non-oceanic contributors (such as riverine inputs) in semi-enclosed basins, as average global values for the oceans over time are well-understood. Reghizzi et al. (2017) found that prior to and during the first stage of the salinity crisis, most 87Sr/86Sr values were consistent with global oceanic values. A few samples were much more radiogenic than ocean values, suggesting significant terrigenous inputs into the basin over a short time, but not enough to support that the composition of the basin was majorly different from that of the open ocean during the whole Messinian salinity crisis. However, they do show evidence of short- and long- term oscillations in 87Sr/86Sr values, indicating that precession and eccentric Milankovich cycles could play a role in determining the geochemistry of water basins. δ13C and δ18O values both show a linear negative trend through time and do not correlate with the strontium values but are like patterns seen in the Atlantic Ocean for the Late Neogene, though they are hesitant to draw conclusions from these data.

Strontium isotopes can also be used to the understand how Milankovitch forcings have affected lacustrine environments through time (e.g. Joordens et al., 2011; Baddouh et al., 2016). Milankovitch theory is an orbital theory of the palaeoclimate (OTP), which explains changes in global climate through time (Bol’shakov, 2017). Milankovitch theory essentially postulates that changes in the earth’s climate are linked to the eccentricity of the earth’s orbit, the tilt of earth’s axis in relation to its orbital plane, and the climatic precession pertaining to the longitude of the perihelion (the point in orbit where the earth is closest to the sun) in relation to the point of the vernal equinox (Bol’shakov, 2017).

Specifics of the theory are ambiguously defined and therefore is interpreted differently by

43 various researchers, which hinders the development of a strong, unified OTP (Bol’shakov,

2017). Nonetheless, it is commonly used to explain changes seen regularly in climate (such as glacial and inter-glacial events). A study that looked at effects of astronomic events on lacustrine water composition in East Africa will be discussed later in this chapter.

3.2.2 Palaeontology

Strontium isotopes have also been used by palaeontologists to look at land usage and habitat preference of animals that lived many millions of years ago (e.g. Mateus et al.,

2009; Martin et al., 2016). This is done in a similar manner to the archaeological examples given in the previous chapter, as skeletal 87Sr/86Sr ratios are being compared to various terrestrial and marine values. Bones and teeth from the Jurassic period can maintain the biogenic 87Sr/86Sr values of the animal, though the quality of preservation of the different tissues varies between sites. The composition of bones and teeth and the susceptibility to diagenesis of different tissues will be further discussed in Chapter 4.

Martin et al. (2016) used 87Sr/86Sr values to determine habitat preference for fossil marine crocodiles (thalattosuchians) from the Phu Noi fossil site in Thailand approximately dating to the Late Jurassic period (~163–45 Ma). They compared 87Sr/86Sr values of crocodile teeth to scales and teeth of other vertebrates from the site (turtles, sauropods, and theropods) and found that the crocodiles had 87Sr/86Sr values consistent with the other terrestrial animals (~0.711). These values were more radiogenic and therefore unlike that of Late Jurassic seawater (~0.707). This implies that the crocodiles had a strong preference for freshwater, despite typically being marine-dwelling animals. Of all species they sampled, only two turtle bones showed strong evidence of diagenetic alteration, as they

44 had more radiogenic 87Sr/86Sr ratios than the other animals sampled (approximately 0.719 and 0.722). This suggests that remains many millions of years old are appropriate for strontium isotope analysis in some burial contexts.

3.3 Isotopes in Human Origins Studies

When applied to palaeoanthropological contexts in the Old World, stable strontium isotope analysis can be a useful tool for understanding landscape use and behaviour of hominins and extinct fauna. However, it has been underutilised in studies on human evolution, particularly in Africa. Nonetheless, isotopes of other elements have been quantified for many African hominin specimens. These studies look at the chemical composition of hominin teeth to directly infer diet, as well as teeth of other animals to reconstruct the environment that hominins inhabited. The advent of stable isotope analysis allowed palaeoanthropologists to move past using strictly morphological observations and indirect evidence such as stone tools and butchered animal bones to infer the diets of human ancestors (Ungar and Sponheimer, 2011; Lee-Thorp and Sponheimer, 2013). This section will briefly review isotope work that has been done in studies of human evolution, leading into a discussion on stable strontium isotope analysis in human origins.

3.3.1 Carbon isotope analysis

Carbon isotopes have been widely used to better understand hominin diets and have been studied in over 75 hominin specimens of numerous species from East and South

Africa, ranging in age from approximately 4.4 to 0.8 Ma (Ungar and Sponheimer, 2011; e.g. Lee-Thorp et al., 1994, 2000; Sponheimer and Lee-Thorp, 1999; van der Merwe et al.,

2003; van der Merwe et al., 2008; White et al., 2009; Cerling et al., 2011). In general, data show that early hominins had diets unlike those of modern apes (Ardipithecus ramidus is

13 an exception, with δ C values closer to the C3-rich diets of savanna chimpanzees [Ungar and Sponheimer, 2011; White et al., 2009]) though there is a great deal of variation within and between species (Ungar and Sponheimer, 2011). Most species have δ13C values that imply mixed C3/C4 diets with no clear leaning towards either endmember.

One exception to this is Paranthropus boisei. Cerling et al. (2011) analysed enamel of P. boisei teeth representing over half a million years from the Turkana, Natron, Baringo, and Olduvai regions to test hypotheses that this species used its massive, thickly-enameled dentition and huge masticatory muscles to break open hard food or break down tough food.

This followed up a study of antemortem microwear on seven P. boisei teeth that indicated that none of the individuals ate hard or tough foods in the days before they died, and that if they did eat hard fallback foods then it was less frequently than P. robustus in South

Africa (Ungar et al., 2008). Cerling et al. (2011) found that the 24 P. boisei teeth ranged in value from -3.4‰ to +0.7‰, suggesting that about 77 ± 7% of their diet was C4 plant- derived. The value that they found was indistinguishable from coeval grass-consumers including equids, suids, and hippopotamids, and was very different from C3-browsing giraffes (Cerling et al., 2011). This suggests that the species relied heavily on grasses and sedges for food as opposed to things like nuts and hard fruits, as would have been expected based on their robust craniodental morphology.

In another study, van der Merwe et al. (2003) analysed tooth enamel carbonates of

10 Australopithecus africanus individuals from Member 4 at Sterkfontein in South Africa

(~2.0 to 2.5 Ma) to determine their δ13C values to reconstruct their diets. They found that

46 the δ13C values of A. africanus ranged from about -8.8 to -4.4‰ with a mean of about

-7‰, implying a much more mixed diet than P. boisei in the previous example. These values do not overlap with any of the associated faunal remains, of which Antidorcas recki,

Parapapio sp., and Tragelaphus strepsiceros were identified as browsers and Damaliscus sp., Hippotragus equinus, Antidorcas bondi, and Connochaetes sp. had values more consistent with grazers (van der Merwe et al., 2003). Theropithecus oswaldi was sampled as well and yielded a δ13C value higher than the upper limit of A. africanus but lower than the grazers. A. recki and Damaliscus sp. had the most extreme δ13C values, suggesting that the former was strictly a browser and the latter strictly a grazer. They noted that three other potential A. africanus teeth were sampled but not presented with their results as they could not be positively identified and may have been Theropithecus oswaldi. Van der Merwe et al. (2003) estimate that on average the A. africanus diet is comprised of approximately 60%

C3 plants and 40% C4 plants, though the isotopic range for the species at Sterkfontein is very large for a single species. When results for A. africanus from another study at the nearby Makapansgat site (Sponheimer and Lee-Thorp, 1999) are added, the range for the species becomes -10.9 to -4.4‰. This suggests that the species had a variable diet and consumed a wide variety of plant and animal food, as carnivores take on the isotopic values of their prey (van der Merwe et al., 2003).

3.3.2 Nitrogen isotope analysis

Although stable nitrogen isotope analysis is restricted to samples in which collagen is preserved, it has proven to be very useful for reconstructing Neanderthal diet. Homo neanderthalensis is the oldest known hominin species to which stable nitrogen isotope

47 analysis can be applied, as they can be found in cold, dry deposits that aid in collagen preservation (van der Merwe et al., 2003). Analyses of δ15N values in Neanderthal bone collagen have shown that meat was their primary source of dietary protein (Richards et al.,

2000; Richards and Trinkhaus, 2009; Bocherens et al., 1991; 2005). For example,

Bocherens et al. (2005) found that a Neanderthal from Saint-Cesaire had a higher δ15N value than cave hyaenas, a top predator from the same area. This indicates that this individual obtained an enormous amount of dietary protein by eating animals, to the extent that any plant material they may have consumed was essentially invisible in their δ15N values. Neanderthal diet will be further discussed in the next section, as many of these studies incorporate δ13C values into their analyses as well as δ15N.

3.3.3 Multi-isotope approaches

As mentioned in the previous chapter, studies that incorporate isotopes of multiple elements are often the strongest and most robust. One such study in human evolution is

Kingston and Harrison’s (2007) analysis of δ13C and δ18O to reconstruct the palaeoenvironment of Laetoli, a palaeoanthropological site in Northern Tanzania just south of Olduvai Gorge. Reconstructing past environments is not only useful for understanding diet, but also for inferring evolutionary pressures that led hominins to evolve unique morphological features and behaviours such as bipedalism. To reconstruct the environment at Laetoli, Kingston and Harrison (2007) sampled animal teeth from 21 herbivorous species from 8 families from the Upper and Lower Laetolil Beds (3.8-3.5 Ma and 4.3-3.8 Ma, respectively) and Upper Ndolanya Beds (2.58-2.66 Ma). By determining the dietary isotopic values of these animals, it is possible to estimate the proportions of different kinds

13 of vegetation that was in the area due to differences in δ C values of C3 and C4 plants and differences in δ18O values of obligate vs. non-obligate drinkers, and therefore infer the environment in which hominins such as Australopithecus afarensis (Upper Laetolil) and

Paranthropus aethiopicus (Upper Ndolanya) thrived (Kingston and Harrison 2007). As well, they compared the fossilised animal teeth to modern ones from the area to try and account for differences between the past and present. Previous palaeoecological studies at

Laetoli used flora, fauna, and lithology and determined that the Laetoli area was likely arid to semi-arid grassland with some scattered tree and bush cover, much like the modern-day

Serengeti Plains (Hay, 1987).

They found that there was wide variation in δ13C both between and within species

(~-20 to +5‰), indicating many different diets. This indicates that the environment was heterogeneous and ecologically stable both locally and likely regionally through time, and included grasslands, open and closed woodlands, and possibly forest (Kingston and

Harrison, 2007). They found that δ18O values were quite constant through time too, though variable within and between species, and were consistent with modern herbivore populations and other fossil assemblages. In comparison to modern species, they found that all taxa except purely-browsing giraffids had eclectic mixed diets. This indicates that the fossil species had more generalised diets than modern ones, and that the present environment is not a good analogue for Laetoli at this time. They believe that in the past,

Laetoli had a patchy mosaic ecology and was not the open savanna it was previously believed to be, which fits with interpretations of other Pliocene hominin sites deemed to be mosaic areas. This suggests that hominins preferred woodland or woodland-forest habitats over open areas (Kingston and Harrison, 2007).

Roche et al. (2013) conducted a very similar study in Kenya’s Tugen Hills on fauna from the Lukeino Formation (6.8-5.7 Ma) and the Mabaget Formation (5.3-4.5 Ma) to reconstruct the environment at the time the earliest hominins arose in the area. They sampled 181 teeth of species representing 9 families and three orders. Their δ13C and δ18O results suggest that these hominins, including Orrorin tugenensis and Ardipithecus ramidus, inhabited an area that likely was composed of wooded grassland with patches of woodland but with no continuous canopy, much like Laetoli in the previously mentioned study. They believe that there were moister conditions by the Early Pliocene (~5.7 Ma) than earlier, and that there was an increase in C3 biomass by that time. This increase in trees is consistent with palaeontological remains, as many species are recovered in these formations that are associated with forested areas, such as fruit bats, colobus monkeys, and lorisids (Roche et al., 2013).

Carbon and nitrogen isotopes are also useful in conjunction, for instance when studying Neanderthal diet. A study by Richards et al. (2000) used these two lines of evidence to infer the diet of two late Neanderthal specimens (radiocarbon dated to ~30,000 years before present) and a few associated faunal remains from Vindija Cave in Northern

Croatia. The faunal remains include a Bos/Bison individual, a cervid, and two Ursus spelaeus (cave bear) individuals. They could not extract collagen from more species, including carnivores, so they analysed a few more animals from later sites. They found that the Vindija cave bears had the lowest δ15N values, which is interesting from a palaeobiological standpoint because it indicates that they relied heavily on plant foods rather than meat. The Neanderthals, on the other hand, had the highest δ15N values (10.1 and 10.8‰), thus indicating that most of their dietary protein was derived from animals.

They also had δ13C values of -19.5 and -20.5‰, suggesting that they preyed upon open- ranging herbivores. These δ13C and δ15N values were similar to later wolves and arctic foxes, suggesting that they had a similar diet. However, because many faunal samples associated with the Neanderthals did not have well-preserved collagen it was impossible for the authors to determine which species were actually consumed. This study by Richards et al. (2000) had similar findings to studies of older Neanderthal collagen δ13C and δ15N values done by Bocherens et al. (1999), who studied one Neanderthal individual from

Scladina Cave in Belgium dating to approximately 80,000 ybp. They found that the individual had a δ13C value of -19.9‰ and a δ15N value of 10.9‰. A study by Fizet et al.

(1995) also showed similar results. They found that two Neanderthals from Marillac,

France dating to between 40,000 and 45,000 ybp had δ13C values of -20.2 and -19.1‰ and

δ15N values of 9.3 and 11.6‰, respectively. This implies that Neanderthals may have had very similar diets through time and space.

3.4 Strontium in Palaeoanthropology

As mentioned, strontium isotopes are relatively under-utilised in human evolution research, despite the large amount of work done with other isotopes. Despite this, they have been used to assess land use, as well as reconstruct the environment. One study looked at marine environment use by Homo erectus at the 1.5 Ma site of Trinil in Indonesia (Joordens et al. 2009), but no other palaeoanthropological studies in Asia that used stable strontium isotope analysis could be found. Some work has been done in Europe with Neanderthals

(e.g. Richards et al., 2008; Britton et al., 2011; Willmes et al., 2016), and one study looked at mobility of a Middle Pleistocene Homo sapiens woman (Lugli et al., 2017). As well,

51 there have been a few studies done recently on Australopithecus and Paranthropus material from South Africa (e.g. Sillen et al., 1998; Copeland et al., 2011). No studies have been done directly on East African hominin remains, but one small pilot study by Copeland et al. (2012) assessed variation in bioavailable strontium across a very large area in Northern

Tanzania and analysed a few teeth from various Bed I sites at Olduvai Gorge. As well, a study by Joordens et al. (2011) analysed 87Sr/86Sr values of lacustrine fish fossils from hominin-bearing layers in the Turkana Basin to reconstruct the environment from approximately 2 to 1.85 Ma. This section will briefly describe each of these studies using strontium isotope analysis and discuss how they influence our understanding of human evolution.

3.4.1 Asia

Joordens et al. (2009) conducted the only study that could be found using stable strontium isotope analysis for human origins research in Asia. They strove to understand how research questions pertaining to marine resource usage in hominins can be addressed by analysing marine fauna recovered from the 1.5 Ma Trinil site in Indonesia, which was riverine and situated relatively near other large bodies of water such as lagoonal lakes, the

Mojokerto Delta, and the Java Sea. They determined the 87Sr/86Sr ratios for historically excavated marine faunal remains including fish and shells from marine and non-marine mollusks from the Homo erectus site to determine their water provenance and assess the availability of aquatic food resources to the hominins. They found that all mollusks had

87Sr/86Sr values lower than that of Pleistocene seawater, indicating that they were all from freshwater environments, but the fish had slightly higher strontium ratios, suggesting that

52 some marine water mixed with the river of their origin. Therefore, the marine faunal remains originated at two different sources. They believe that four fish species recovered at Trinil (climbing perch, walking catfish, forest walking catfish, and Asian redhead catfish) could have been caught by Homo erectus without fishing technology, as these large-bodied fish move into shallow water in flooded forests to breed, and have auxiliary breathing organs that permit them to come onto land for short periods of time. As well, 11 of the 32 of the mollusk species are edible by humans and live in shallow water where they can be easily caught. Although there is no direct evidence on the aquatic remains that they were exploited by hominins, the authors believe that it is plausible.

3.4.2 Europe

Much of the stable strontium isotope analysis work done in the context of human evolution in Europe has focused on Neanderthals, largely because there is a dispute over the degree to which Neanderthals were mobile. This mainly stems from the lack of widely- sourced lithic material at sites coupled with confounding evidence suggesting tracking of gregarious herbivores to hunt (Richards et al., 2008). Richards et al. (2008) sought to understand Neanderthal mobility in Greece approximately 40 thousand years ago (ka) using the first direct evidence of mobility for the species, 87Sr/86Sr values of tooth enamel from an individual at the Lakonis site. They used laser ablation to analyse a lower third molar incrementally to observe changes in 87Sr/86Sr values. They believe that the tooth began forming around six years of age and completed when the individual was about eleven. They found that the enamel values were not indicative of seawater or the local coastal limestone bedrock but are rather suggestive of somewhere with more radiogenic

(likely volcanic) values. They speculate that the individual spent time in a volcanic area about 20 km away, or further away in Northern Greece. Regardless, their results show that the individual was not local, and suggests that Neanderthals do move around on the landscape through their lives.

Studies of faunal remains can also shed light upon behaviours of those who hunted them. Britton et al. (2011) used sequential stable strontium isotope analysis of three Middle

Palaeolithic reindeer (Rangifer tarandus) teeth and one bison (Bison cf. priscus) from

Quina Mousterian deposits at the Jonzac site in France to indirectly understand hunting practices of Neanderthals. These are important prey species, and they wanted to understand how these species move on the landscape to predict Neanderthal behaviour. Because climate and population changes have occurred since that time period, modern observations of Rangifer behaviour is not sufficient to understand what they did in the past. They found that the reindeer teeth had higher 87Sr/86Sr values and more intra-tooth variability than the bison, suggesting that the bison was a local, non-migratory animal that would have been available to Neanderthals there year-round. The reindeer teeth were quite homogenous in relation to one another, suggesting that the animals likely shared the same migration route.

Britton et al. (2008) suggest that the Neanderthals returned to this site to hunt a seasonally- available resource (reindeer) as their migration route passed nearby.

A study by Lugli et al. (2017) examined 87Sr/86Sr values in a deciduous archaic

Homo sapiens incisor from Isernia la Pineta in Southern Italy to understand the mobility of the mother and, by doing so, infer the mobility of pregnant women there at that time.

The tooth is from the Middle Pleistocene (~570 ka), and formed while the child was still in utero, thus reflecting the mother’s 87Sr/86Sr values. They analysed the tooth in five places

54 via laser ablation and found that there was a range of values between 0.7089 and 0.7094.

They also analysed teeth from rodents, bison, and rhinos and some local plants for comparison. They found no statistical difference between the human tooth and the rodents, but there were significant differences between the rhinos and bison and local bioavailable strontium values, thus they were deemed to be non-local. They determined that the woman was very sedentary, as the 87Sr/86Sr ratio of the tooth was closest to plants within 15 km of the site and nearby rodents. Despite this being a sample-size of one and limited understanding of deciduous tooth strontium values, Lugli et al. (2017) propose that the restricted home range of the mother provides tentative evidence that by the Middle

Pleistocene there was already a strong division of labour between men and women where men went on long trips and women stayed at camp with the children, though they make this claim without having analysed any adult male teeth from the settlement.

3.4.3 South Africa

Most strontium isotope analysis studies in human evolution in Africa have been conducted in South Africa. Few have been done to date, though the ones that have primarily focus on multiple species of hominin. Sillen et al. (1995) conducted a strontium isotope

(87Sr/86Sr) study on P. robustus and Homo sp. from Swartkrans Member I (~1.8 Ma) in the

Sterkfontein Valley of South Africa. As well, they looked at the ratio of strontium to calcium (Sr/Ca) in addition to 87Sr/86Sr for the hominins. The Sr/Ca ratio decreases with increasing trophic level as plants are more concentrated in strontium than the animals that eat them. As well, there is intraindividual variation in plant Sr/Ca values in different tissues, with leaves having lower Sr/Ca values than other parts of the same plant (Sillen et al.,

1995). They aimed to define the dietary niches occupied by each species and determine if different amounts of alkaline earth elements in bedrock have any effect on Sr/Ca ratios by comparing them to 87Sr/86Sr values. As well, 87Sr/86Sr values would potentially show species differences in land use. They analysed bone samples from eight P. robustus individuals plus two Homo sp. individuals, and found that there was a big difference in

Sr/Ca values for the two Homo individuals sampled, yet both show 87Sr/86Sr values consistent with being local to the site. They therefore interpret the difference as dietary and not geological in nature. One Homo specimen’s (SK27) Sr/Ca ratio was close to that of the robust australopithecines, whereas the other was higher. P. robustus and SK27 therefore appear to have been at a higher trophic level than the other Homo sp. individual (SK847).

Only one specimen, P. robustus SK876, had a 87Sr/86Sr value indicating that it was non- local.

In a later study, Sillen et al. (1998) revisited the hominin 87Sr/86Sr results from

Sillen et al. (1995) considering new bioavailable strontium results for the area from plants, surface water, and soil samples. They found that SK847 had results consistent with riparian habitats they sampled, though it is unlikely that this individual ate all their food from one specific area. Rather, they still believe the individual to be non-local. To test this further, they took an enamel sample from the individual’s second molar and compared this 87Sr/86Sr value to the previous bone result. They found that it yielded a lower value than the bone and was six standard deviations lower than the average value for the other hominins presented in Sillen et al. (1995). They concluded that this individual came from somewhere far away, and that the elevated bone 87Sr/86Sr value was due to a mixture of strontium from its original location and the Sterkfontein Valley.

Copeland et al. (2011) conducted another study on specimens from the Sterkfontein

Valley, though they looked at P. robustus and Australopithecus africanus rather than Homo sp. They used laser ablation MC-ICP-MS to analyse tooth enamel from 11 P. robustus

(~1.8 Ma from Swartkrans) and 9 A. africanus (~2.2 Ma from Sterkfontein) individuals from the Sterkfontein Valley to trace their movement across the landscape. They compared them to contemporaneous animals that had small home ranges (thus representing the local

87Sr/86Sr values), and to plants collected from within a 50 km radius of the site covering 11 different bedrocks. They found that 32% of the hominins were non-local, and there was not a significant difference between the proportion of non-local P. robustus and A. africanus. However, they found that small-bodied hominins were more likely to be non- local than the large-bodied ones. They interpreted these to be sex differences in landscape use due to marked sexual dimorphism in these species, suggesting that females move away from their residential groups, which is the same as in some modern human groups, Gorilla, and Pan.

Balter et al. (2012) present another study on diet and landscape use in South African hominins by analysing the barium-calcium (Ba/Ca) and Sr/Ca ratios, as well as the 87Sr/86Sr ratios, of hominin teeth. Ba/Ca is like Sr/Ca in that the ratio is inversely related to trophic level due to calcium biopurification (Balter et al., 2012). They analysed enamel from broken teeth of four A. africanus, seven P. robustus, and three early Homo individuals from

Swartkrans, Sterkfontein, and Kromdraai B in South Africa for each of these ratios using laser ablation. Many of the teeth were ablated in multiple locations to assess intra-tooth variability. They also analysed a few bovid teeth and presented values from studies of other faunal remains for comparison. They claim that the results found by Copeland et al. (2011)

57 which suggest that the smallest teeth had the highest inter-tooth variability were the result of analytical bias, as they analysed whole teeth and therefore did not have a flat sampling surface. They found that the three hominin species had indistinguishable 87Sr/86Sr values and therefore had home ranges of similar size and geological substrate. This is in direct contrast with the findings of Copeland et al. (2011), who found that there were sex differences in 87Sr/86Sr values. As well, the Sr/Ca ratio of A. africanus is significantly higher and Ba/Ca ratio of early Homo is significantly lower than the other taxa. For P. robustus, both ratios are intermediate between the other species. The Sr/Ca and Ba/Ca values for P. robustus and early Homo are indistinguishable from the sampled browsers and carnivores, respectively, whereas A. africanus is more complex and likely consumed a diet of two distinct sources, possibly woody plant material and meat. They believe P. robustus likely ate mainly woody plants, and Homo consumed considerably more meat.

3.4.4 East Africa

Strontium isotope ratios in East Africa are largely unexplored in palaeoanthropology and archaeology. Unlike South African studies, no study could be found that directly analysed East African hominin teeth. One 87Sr/86Sr study was done in

Northern Tanzania and was a conference poster presentation done by Copeland et al.

(2012). This was a pilot study on bioavailable strontium variability in the greater region surrounding Olduvai Gorge and Laetoli using rodent bones collected from owl roosts from six localities: four in Serengeti National Park, one on the shore of Lake Manyara, and one in Tarangire National Park (Figure 3.4.1). As well, they looked at 87Sr/86Sr values for several animal teeth (they do not specify the number and their chart is too compact to count)

Figure 3.4.1: Reproduction of Figure 1 from Copeland et al. (2012) approximating the geological zones in the larger region. The red dots show where the owl roosts they sampled were located.

from various Bed I sites at Olduvai Gorge. They found that strontium ratios do vary across the region with bedrock type and are the highest in the western and northern parts of the

Serengeti. However, they did not analyse rodent bones for bioavailable strontium from localities directly around Olduvai nor Laetoli, nor did they present their findings for the samples from Tarangire on the poster.

According to their map, Laetoli and Olduvai are both located predominantly in a young pyroclastic geological area (the eastern end of Olduvai near the Olbalbal depression

59 extends into a lacustrine zone) (Copeland et al., 2012). Three of their points, Seronera,

Northern Extension, and Barafu, come from the young pyroclastic zone, but much further north and west of the two paleoanthropological sites. Barufu and Seronera yield slightly different, non-overlapping 87Sr/86Sr ratios, despite coming from the same geological bedrock type (averaging ~0.7045 and 0.7050, respectively). The Seronera point appears to be very close to the Tanzania Craton, an Archean metamorphic geological domain that is billions of years older than the Cenozoic pyroclastic rocks, but does not appear to be within it. The owl may have hunted from within that geological domain, however. The owl roost in the northern extension of Serengeti National Park yielded much more radiogenic (and more variable) 87Sr/86Sr values than anywhere else that they sampled. The range for this area was approximately 0.7130 to 0.7190, likely because the samples came from or near areas underlain by Archean bedrock, not young Cenozoic volcanics. The fossilised animal teeth sampled represent an elephant, antelopes, hippopotamus, crocodiles, and pigs. All of them come from sites dated to 1.8 Ma, including HWKE, VEK, and somewhere from the western gorge. The Olduvai fauna have “local” 87Sr/86Sr values close to Seronera, Barufu, and Manyara (~0.7050), though these areas are quite far away (the Barufu sampling site is the closest and appears to be approximately 60 km from the western edge of the Olduvai

Gorge). One elephant tooth had a much higher ratio than the rest of the animals (~0.7065) and as such was interpreted to be the only non-local sampled.

The methods section on the poster is very short, and only specifies that the teeth were all analysed using solution multiple collector inductively-coupled plasma mass spectrometry (MC-ICP-MS) at the Max Planck Institute for Evolutionary Anthropology and at the University of Cape Town. They do not specify whether or not the teeth were pre-

60 treated or swabbed with weak acid to leach diagenetic strontium or not, nor do they specify which teeth were analysed, nor their preservation condition. One of the unexpected results they found was that the Antilopini and Alcelophini teeth they analysed all yielded local values, which is unusual considering that modern species in these tribes are migratory

(Copeland et al., 2012). It is possible (though by no means certain) that the strontium content in these teeth was diagenetically altered, at least in part, by strontium in the sediment. This is unlikely, however, as the proboscidean sample yielded a much higher

87Sr/86Sr value. Strontium isotope methodology and the rationality behind it will be discussed in detail in the next chapter.

This study can be extrapolated by expanding the number of localities sampled around the Olduvai Gorge and Laetoli area. This will help to understand if there is any variation on the local geological substrates. The geological map they provide is roughly approximated and does not feature the smaller geological areas, such as spots north and west of Olduvai with outcrops of the underlying metamorphic basement (Stollhofen and

Stanistreet, 2012). As well, teeth from other sites at Olduvai should be examined to determine whether faunal remains from younger sites can be analysed successfully.

Another study that used 87Sr/86Sr ratios in the context of human evolution, though in a different manner than the others, was done by Joordens et al. (2011). They analysed lacustrine fish bones from the Turkana Basin at hominin-bearing sites dating to 2 to 1.85

Ma to reconstruct the climate. They aimed to develop a methodology to address changes in climate and environment over short periods of time (<20 kyr) to assess the importance of the rapid and extreme climatic and environmental fluctuations associated with precession Milankovich timescales (~19-23 kyr) on human evolution and dispersal. The

Turkana Basin is connected via rivers to the Ethiopian Highlands, and thus offers an opportunity to see the effects of orbitally-forced monsoon rains inland (Joordens et al.,

2011). The water chemistry changes associated with these monsoons are predicted to be the driving force behind chemical changes in the recurring palaeolakes in the Turkana

Basin. Since 87Sr/86Sr ratios of lakes are indicative of water provenance, they can be used to track the chemical changes associated with the monsoons (Joordens et al., 2011).

Joordens et al. (2011) analysed fish fossils from the Upper Burgi Member of the

Koobi Fora Formation in the Turkana Basin. This palaeolake, Lake Lorenyang, was fed by the palaeo-Omo river from the north, and smaller rivers from the southwest as minor sources. These rivers have different chemical compositions; the Omo River in the past and present carries basalts with relatively unradiogenic 87Sr/86Sr values (~0.7040), and the southwestern rivers carry metamorphic rocks that are much more radiogenic (>0.7200).

Thus, during wet periods in Ethiopia the lake water would be expected to have a lower

87Sr/86Sr value than the Omo River, and in dry periods the value would be higher like those of the southwestern rivers. They suspected that the constantly-wet lake may have been a refuge for water-dependent animals (including hominins) during dry times.

Fish fossils were sampled every metre of the sequence, which was dated using radiometric dating on the KBS tuff present at the top and palaeomagnetism every 50cm.

They found that the fossils had lower 87Sr/86Sr ratios (~0.7048) near the base of the sequence (also the base of the Olduvai chron) and slightly higher ratios near the top

(~0.7052), with long-term cyclic patterning throughout. Because fish were present through the whole sequence, it indicated that the lake never dried up during dry periods. Using predictions of sedimentation rates, they were able to determine the approximate dates of

62 these cycles and their position in the sequence. They then placed 12 previously discovered hominin fossils within this astronomically-tuned timescale and found that the hominins were present at the lake during wet and dry periods in the precession cycle, implying that the area could have been a refuge for them and other animals during dry periods (Joordens et al., 2011).

3.5 Conclusion

Stable strontium isotope analysis is a valuable tool in the palaeosciences for a variety of reasons. It can be used to reconstruct palaeotemperatures and palaeoenvironments from many millions of years ago. As well, it can be used to understand land use by ancient animals including dinosaurs, as well as more recent species such as hominins. Relatively little stable strontium isotope analysis has been done in studies of human evolution, though it has great potential to answer questions about mobility in hominins between and within species. Isotopes of other elements, especially carbon, have been utilised extensively within palaeoanthropology and have been used to address questions regarding diet in hominin species and, in conjunction with oxygen isotopes, to reconstruct the environment. Nitrogen isotopes have also been used in many studies of

Neanderthal diet, as bone collagen can preserve well in the cold, dry environments of

Europe.

Within palaeoanthropology, South Africa has been the focus of most stable strontium isotope analysis work, though there have been a few studies done in Europe,

Asia, and East Africa as well. Relatively little work has been done in East Africa with strontium isotopes, though a preliminary study by Copeland et al. (2012) in Northern

Tanzania has shown that the area is promising as it has large variation in 87Sr/86Sr values between geological substrates. As well, Copeland et al. (2012) found that 1.8 Ma teeth from

Olduvai Gorge can be successfully analysed to assess mobility in faunal remains. Another

East African study was done by Joordens et al. (2011), which showed that strontium isotopes of lacustrine deposits can be used to identify wet and dry periods associated with

Milankovich cycling and infer hominin behaviour therein, provided that the samples can be accurately dated.

Chapter 4: Methodological Overview

4.1 Introduction

Strontium isotope analysis is a valuable tool used by archaeologists to trace mobility and migration of past populations together with domesticated and wild animals.

Before it can be used, modern variation in 87Sr/86Sr values in the study region must be well understood. This chapter will discuss the specifics of strontium isotopes and where these ions come from. As well, it will present an overview of the strontium cycle and how various stages contribute strontium ions to be used by plants and animals. Then, it will show how strontium available to organic life is quantified as reference material, and how archaeological material is chosen, prepared, and sampled for strontium isotope analysis.

4.2 Strontium Isotopes

There are four naturally-occurring isotopes of strontium, the most abundant of which is 88Sr (Table 4.2.1). 87Sr is the only radiogenic isotope of strontium, formed from the beta minus decay of a naturally-occurring radioactive isotope of rubidium, rubidium-

87 (87Rb) (Faure and Powell, 1972). β-decay is a process by which radioactive isotopes decrease their number of neutrons to become stable (Faure and Mensing, 2005). 87Rb has a half-life of about 4.88 x 1010 years (Bentley, 2006). The other stable strontium isotopes were created through nucleosynthesis (Bentley 2006), a process by which nuclear reactions occur in the interior of stars to form elements by the fusion of lighter elements to produce heavier ones (Faure and Mensing, 2005; Fletcher, 2011). Because 87Sr is radiogenic, substances that contain 87Rb will very gradually increase in 87Sr as the 87Rb decays, whereas the amounts of other strontium isotopes will remain constant.

Stable strontium isotope Relative abundance Source 84Sr 0.56% Non-radiogenic 86Sr 9.87% Non-radiogenic 87Sr 7.04% Radiogenic 88Sr 82.53% Non-radiogenic Table 4.2.1: Relative abundances and sources of stable Sr isotopes (after Bentley, 2006).

4.3 Strontium and Rubidium in Rocks

Different rock types yield varying amounts of strontium and rubidium due to their mineralogical composition. For example, ultramafic and basaltic rocks are very depleted in both these elements, whereas high calcium granites are relatively enriched in Sr and Rb

(Bentley, 2006; Turekian and Wedepohl, 1965). Rubidium (Rb+) occurs in rocks that have a high potassium (K+) content, as its ionic radius (1.52 Å) is close to K+ (1.38 Å), thus it can substitute in the place of K+ (Capo et al. 1998). Rocks that have high Rb+ content include mica, K-feldspar, and clay minerals, whereas Sr2+ occurs most in apatite, carbonates, and plagioclase-feldspar (Faure and Powell, 1972; Faure, 1986). Sr2+ can substitute for calcium (Ca2+) in calcium-bearing minerals and can also substitute for K+ ions in K-Feldspar (Faure and Powell, 1972). When Sr2+ substitutes for K+, there must also be a substitution of silicon (Si4+) for aluminium (Al3+) to maintain electrical neutrality

(Faure, 1986). The ratio of 87Sr/86Sr, then, is a function of the original amount of 86Sr and

87Rb in the rock, as well as its age (Faure, 1986; Capo et al., 1998; Bentley, 2006). See

Table 4.3.1 for concentrations of strontium, rubidium, calcium, and potassium in common rock types. The 87Rb/87Sr and 87Sr/86Sr ratios can be used in conjunction to determine the age of igneous rocks, so long as the rocks remained closed to outside sources of rubidium and strontium (Faure and Powell, 1972; Faure, 1986).

Rock Type Rb (ppm) K (ppm) Sr (ppm) Ca (ppm) Basaltic 30 8,300 465 76,000 Carbonate 3 2,700 610 302,400 Deep sea carbonate 10 2,900 2,000 312,400 Deep sea clay 110 25,000 180 29,000 Low Ca granitic 170 42,000 100 5,100 High Ca granitic 110 25,200 440 25,300 Sandstone 60 10,700 20 39,100 Shale 140 26,600 300 22,100 Syenite 110 48,000 200 18,000 Ultramafic 0.2 40 1 25,000 Table 4.3.1: Average concentration of rubidium, potassium, strontium, and calcium in sedimentary and igneous rock types, as well as deep sea sediments (from Turekian and Wedepohl, 1961).

Based on analyses of moon rocks and stony meteorites, the primordial 87Sr/86Sr ratio of earth is estimated to be about 0.699, so this value for all rocks since has been slowly increasing (and will continue to do so) due to 87Rb decay (Faure, 1986). 87Sr/86Sr values are highest in the oldest rocks with the highest original Rb/Sr ratios, as opposed to young rocks with low original Rb/Sr ratios which yield the lowest 87Sr/86Sr ratios (Sealy et al.,

1995; Price et al., 2002). For example, young Cenozoic volcanic rocks (such as those near

Olduvai Gorge, see Chapter 6) tend to have values less than 0.70600 (Price et al., 2002).

This is not always the case, however. Some magmas can be contaminated by terrigenous sediments or by radiogenic 87Sr from old sialic rocks and their derivatives, leading to increased 87Sr/86Sr values (Faure, 1986). Continental volcanic rocks can have values as high as 0.714 or as low as 0.702, though values between 0.703 and 0.708 are most common

(Faure, 1986). 87Sr/86Sr ratios in rocks can be considered fixed due to the long half-life of

87Rb, although technically it is always increasing at a very slow rate (Sealy et al., 1995).

Oceanic 87Sr/86Sr values have fluctuated through time, but the modern value is

67 approximately 0.7092 (DePaolo and Ingram, 1985). Modern day rock values typically vary between 0.700 and 0.750, depending on their age and composition (Price et al., 2002).

These differences between rock types, while seemingly small, are vastly different from an instrumental standpoint and fall well outside of an instrument’s range of analytical uncertainty, which is often in the sixth decimal place (Price et al., 2002). Some bedrock is extremely variable in its 87Sr/86Sr due to heterogeneity in composition, so multiple locations should be sampled to understand the range of values expected therefrom (Sealy et al., 1991). Variation in 87Sr/86Sr values across a region are often presented as isoscapes, being maps visually depicting isotopic differences across a landscape (for examples where isoscapes were successfully created and applied, see Copeland et al., 2016; Evans et al.,

2010; Harman and Richards, 2014; Kootker et al., 2016).

4.4 Sources of Strontium in the Environment

4.4.1 Overview of the strontium cycle

Although weathering of local bedrock is a major source of strontium in the environment, it is not the only one. Environmental strontium can best be described using a mixing model accounting for geological, hydrological, atmospheric, and anthropogenic inputs and outputs (Bentley, 2006). Atmospheric strontium on the ground decreases with increasing soil depth, while strontium deriving from bedrock increases with soil depth

(Whipkey et al., 2000). Eventually terrestrial strontium gets to the ocean, typically by river transport. The isotopic composition of rivers reflects that of exposed, weathered crust

(Capo et al., 1998). To leave the ocean, strontium is usually deposited in marine carbonates

(Capo et al., 1998). The amount and isotopic composition for each stage of the cycle vary

68 with tectonic activity and climate change, as these affect the amount of weathering occurring and the kinds of exposed rocks (Capo et al., 1998).

4.4.2 Rivers

Rivers typically have high 87Sr/86Sr ratios, approximately 0.7110 to 0.7120

(Wadleigh et al., 1985; Palmer and Edmond, 1989). Whereas strontium in groundwater and lakes comes mainly from bedrock weathering and atmospheric deposition, rivers are influenced by continental erosion and weathering, which vary with changing sea level and climate (Palmer and Edmond, 1989). For example, Sharma et al. (2017) analysed water samples from the Indus River in India over approximately 350 km and found that river values were consistently between 0.7102 and 0.7120, except one locality near Leh City that was lower (0.7072). Because they are the product of widescale mixing, rivers are assumed to be representative of the global average 87Sr/86Sr ratio (Wadleigh et al., 1985).

Despite this, there can still be considerable variation within a single river. For example, Brennan et al. (2016) analysed water samples and used predictive modelling to discover that the Nushagak River in Alaska had a great deal of variation in 87Sr/86Sr values, with higher values (0.7131-0.7190) concentrating in the northern, more elevated portion, and lower values to the south and east (as low as 0.7038). They also found that the concentration of strontium ions was higher in the areas with elevated 87Sr/86Sr values

(Brennan et al., 2016).

4.4.3 The ocean and sea spray

The oceans are a large reservoir for strontium, and sea spray contributes strontium

69 ions to terrestrial systems. Currently, the 87Sr/86Sr value of ocean waters is 0.7092 (Capo et al., 1998). Strontium is homogeneously distributed throughout seawater because it has a longer residency period (4 x 106 years) than the mixing period of the ocean, which is less than 1,000 years (Wadleigh et al., 1985). The ratio of strontium has changed in the oceans over time, which is demonstrated by the isotopic values of well-preserved calcareous marine fossils, brachiopods, phosphatic fish debris, and conodonts, which represent oceanic strontium values at the time that the fossils formed (Capo et al., 1998). Rubidium is in a very low concentration in seawater and marine phosphates and carbonates, so diagenetic 87Sr is negligible (Faure, 1986). Fluctuation of seawater values over long periods of time (in the order of millions of years) is due to major tectonic events, such as up uplifting of the Himalayas (Richter et al., 1992), whereas fluctuations over shorter periods of time (a million years or less) is likely due to climate change such as glaciation events

(Capo et al., 1998). Through time ocean 87Sr/86Sr values have fluctuated between approximately 0.707 and 0.709 (Capo et al., 1998). There was a general downward trend in oceanwater values from about 550 to 175 million years ago, and a general upward trend from 175 million years ago to present (Burke et al., 1982).

Sea spray is droplets of ocean water or evaporated particulates carried by the wind over land that can introduce marine-derived strontium into coastal soils and onto vegetation

(Art et al., 1974; Whipkey et al., 2000). For example, aerosols from sea spray can be deposited on foliage and into the subsurface with subsequent rainfalls, therefore being incorporated into the terrestrial ecosystem. In a study of Scots pine trees in Sweden,

Franzen (1980) found that the pine needles had less salt on them with increasing distance from the coast up to 300 km away. As well, he found that the relationships between ions in

70 the pine needles were like those of common cations and anions found in seawater, including strontium (Franzen, 1980). This implies that in coastal areas, the strontium ratios of terrestrial systems can be influenced by marine strontium values. This is the case in Britain, where Evans et al. (2010) built a strontium isoscape. They determined that sea spray and rainwater off the ocean contribute strontium to the biosphere, particularly along the western seaboard (Evans et al., 2010). In areas near Anglesey they found that bioavailable strontium values were close to that of seawater, despite underlying Neoproterozoic rocks which should have yielded a higher ratio (Evans et al., 2010).

4.4.4 Precipitation

Near ocean basins, precipitation typically has 87Sr/86Sr values close to ocean water

(0.7092), but the concentration of strontium is less than ocean water by several orders of magnitude, particularly with increasing distance from the coastline (Andersson et al., 1990;

Capo et al., 1998). As well, with increasing distance from the ocean, the percentage of seawater contribution in precipitation 87Sr/86Sr values decreases (Andersson et al., 1990).

In a study across Scandinavia, Andersson et al. (1990) found that seawater contributed about 90% of strontium to precipitation near the coast, but 300 km inland this changed to about 10-30%, with the rest of the input being from pollution, possibly wood burning. With other, lighter isotopes such as δ18O and δD, rainwater becomes increasingly depleted in heavy isotopes with subsequent rainouts moving further inland due to mass-dependent isotope effects (Peng et al., 2004). However, since strontium does not fractionate in this manner, this rainout effect does not occur.

4.4.5 Dry fall

Dry fall is the deposition of solid particulates from the wind, including dust, occluded gases, and other components onto the earth’s surface (Whitehead and Feth, 1964), and can be a significant contributor of strontium ions to an area (Gosz et al., 1983;

Graustein and Armstrong, 1983), though this is not always the case (Green et al., 2004).

Aeolian dust is important for soil fertility and pedogenesis, especially in continental arid environments (Capo and Chadwick, 1999). These wind-blown particles can originate far from where they are found. For example, the Sahara Desert is the world’s largest source of aeolian dust, which can be found thousands of kilometers from its source, including over the North Atlantic Ocean and as far as the southeastern United States (Prospero, 1999;

Nakamae and Shiotani, 2013). This indicates that non-local strontium can be introduced into an environment and incorporated into the soils to be used by plants and animals. The degree to which aeolian dust is a significant contributor of strontium is different for every location, and can change over time (Coble et al., 2015). It must, therefore, be considered as a potential source of strontium ions in all studies of this sort conducted.

4.4.6 Fertilisers

Use of fertilisers can introduce non-local, typically radiogenic strontium into an area (Frei and Price, 2012). Fertilisers contain strontium, and can be made up of dolomite, phosphate, nitrogen, and potash materials, which can influence the isotopic values of soils and, subsequently, groundwaters (Böhlke and Horan, 2000). Dolomites in particular are rich in strontium ions (Böhlke, 2002). Böhlke and Horan (2000) analysed surface waters and groundwaters in Locust Grove, Maryland that had been anthropogenically

72 contaminated by fertiliser in at least 90% of the sample area for more than half a century and found that streams had elevated 87Sr/86Sr ratios, and some sampling sites had elevated strontium concentrations. They predicted that contaminated groundwater can also impact the 87Sr/86Sr values and strontium concentration of other localities due to recharge and discharge processes, as well as riverine Sr concentrations, watershed lithologies, weathering rates, and Sr/Ca ratios (Böhlke and Horan, 2000). Hosono et al. (2007) believe that approximately 25% of dissolved strontium in the Lake Biwa area in Japan is agricultural in origin, and that agriculture has a substantial impact on environmental strontium values. In their study they witnessed a decrease in average river water 87Sr/86Sr values from 0.71163 to 0.71127 between the pre-irrigation season (April) and the post- cultivation season (June). The high river values in April were elevated toward the value of

Lake Biwa irrigation water (0.7123) and nearby rice paddy surface water (0.7113-0.7117), whereas by June they were lower than these agricultural sources (Hosono et al., 2007).

Essentially, river values became elevated during times of intensified agricultural practices and decreased after crops were harvested.

The pH of soil can also be altered through use of chemical fertilisers. With use of

+ + nitrates, microbial oxidation of NH4 ions releases H ions into the soil, rendering it more acidic (Böhlke, 2002). Although in some fertilisers dolomites are added to buffer against increasing acidity, Böhlke (2002) found that the pH of soils in Maryland decreased to about

5 regardless of dolomites present. Soil with increased acidity can lead to increasing weathering rates and solubilities of minerals, and to increased release rates of trace elements in the soil (Böhlke, 2002). Böhlke and Horan (2000) believe that increased strontium in soil at Locust Grove could be the result of reactions between the soil and

73 agricultural contaminants, increasing the release of strontium ions. They do not rule out, however, that it could also be due to use of historical fertilisers that included a higher strontium content than modern ones (Böhlke and Horan, 2000). Regardless of what causes changes to soil composition, modern anthropogenic contamination is of concern when constructing bioavailable isoscapes as the present 87Sr/86Sr values will be unlike those of the past (Frei and Frei, 2011).

4.5 Determining Local Biologically-Available Strontium Values

4.5.1 Introduction to biologically-available strontium

There are differences between the 87Sr/86Sr values of geological substrates and the strontium available for use by plants and animals (Price et al., 2002). Therefore, 87Sr/86Sr values from rocks cannot be analysed directly to understand an individual’s movement across the landscape. This is because the strontium used by living organisms comes from both geological and, as previously discussed, atmospheric and anthropogenic sources in varying amounts, though typically bedrock weathering contributes most strontium ions

(Beard and Johnson, 2000; Price et al., 2002). As well, plants are only able to use strontium in the soil that is easily exchangeable which is known as bioavailable strontium (Sillen et al., 1998; Beard and Johnson, 2000). Bioavailable strontium can be quantified by analysing a soil sample that has been leached with weak HCl or ammonia acetate, though the composition of soil is ever changing due to weathering, atmospheric, and anthropogenic effects (Åberg, 1995; Beard and Johnson, 2000). Better ways to accurately quantify bioavailable strontium that are more representative of the whole area do exist and will be discussed later in this section.

Many studies have looked at the contribution of various sources of strontium in the environment. For example, Graustein and Armstrong (1983) found that in the Santa Fe range of the Sangre de Cristo Mountains of New Mexico 70% of the strontium content of vegetation came from atmospheric sources, including aeolian dust blown from elsewhere with a 87Sr/86Sr ratio which did not match the ratios of the sampling area’s underlying geology. Gosz et al. (1983) looked at atmospheric sources of strontium as well and found that in coniferous forests of New Mexico the strontium contribution from dry fall was an order of magnitude larger than that from precipitation, though in deciduous forests precipitation contributed more strontium. In a study using 87Sr/86Sr as a tracer for calcium in water, Clow et al. (1997) found that atmospheric deposition accounted for 20-33% of strontium input to streams in the Loch Vale area in Colorado. As well, the authors found that the various rock types were weathering at different rates, thus unevenly contributing strontium ions to the streams.

Due to these mixed sources, it is more reliable to sample biological material (plants, land snail shells, and rodent bones and teeth) or water sources for bioavailable strontium rather than soil or rocks themselves. Strong correlations exist between the Sr values of plants and the invertebrates and small herbivores that consume them (Hartman and

Richards, 2014). This is because there is no fractionation of strontium within biological systems (Bentley, 2006). An experimental study done by Flockhart et al. (2015) showed this as they found no measurable fractionation of strontium between soil, plants, and monarch butterflies in a controlled laboratory setting. Each method has pros and cons, which will be discussed below.

4.5.2 Measuring bioavailable strontium

4.5.2.1 Plants

Analysing plants is a reliable way to measure local bioavailable strontium

(Copeland et al., 2016). Flockhart et al. (2015), however, note that there are possible differences in 87Sr/86Sr values for different plants due to species-differences in nutrient uptake including differing root depth and nutrient cycling processes (see also Poszwa et al., 2004; Maurer et al., 2012; Hartman and Richards, 2014). Different root depths are important for hydrological balance in the ecosystem, as well as nutrient and carbon cycling by the plants themselves (Canadell et al., 1996). Canadell et al. (1996) found that on average at the global scale, trees had roots that were 7.0 ± 1.2 m deep, shrubs were 5.0 ±

0.8 m, and herbaceous plants were 2.6 ± 0.1 m deep. Another study by Jackson et al. (1996) found that globally 44% of grass root biomass is in the top 10 cm of soil, and 75% is in the top 30 cm, whereas shrubs had 21% of their root biomass in the top 10 cm and 47% in the top 30 cm. Trees had 78% of their roots within the top 50 cm of soil. Shallow-rooted plants that obtain their water and nutrients from the top part of the soil take in more atmospheric strontium, whereas deeper-rooted plants take in more from the bedrock, though there can be differences between soil and bedrock type (Hartman and Richards, 2014). Therefore, it is best to collect a combination of shallow- and deep-rooted plants from sampling localities to account for variation between them (see Hoppe et al., 1999; Copeland et al., 2016).

4.5.2.2 Animal bones and teeth

The bones and teeth of small animals can also be analysed to determine bioavailable strontium. Small animals such as rodents do not have large enough home ranges to move

76 from one geological domain to another, so they should only reflect the local 87Sr/86Sr values of the area in which they are found (Hartman and Richards, 2014; Kootker et al., 2016).

However, sampling modern animals could be problematic if they consume exotic or anthropogenic strontium through fertiliser or airborne sources (Bentley, 2006).

Archaeological remains of domesticated animals and rodents can be used as well

(Bentley et al., 2004; Kootker et al., 2016). This eliminates the risk of contamination from modern anthropogenic sources and provides an averaged local 87Sr/86Sr value (Bentley,

2006). Cows, goats, and sheep are not good contenders for this, as they are usually grazed over a large area. However, pigs and dogs are typically kept at one site, and thus should carry the local 87Sr/86Sr values, presuming they are not fed extremely restricted diets that are non-representative of the local strontium values, and that they are not diagenetically altered (Bentley et al., 2004). However, there are a few sites where non-local pigs have been found (Madgwick et al., 2012). Bentley et al. (2004) believe that bioavailable strontium for the site using archaeological animals should be presented with two standard deviations about the mean to account for some variation among individuals.

4.5.2.3 Land snail shells

Land snail shells are also useful for creating bioavailable strontium isoscapes because they have very small home ranges and can be easily found in some places. As well, they have strontium-rich shells that can be easily sampled (Evans et al., 2010). A study by

Blum et al. (2000) found that snail shells, caterpillars, eggshells of resident birds, and leaves from two forests in the northeastern USA all had similar 87Sr/86Sr values to each

77 other for each forest, suggesting that they are all good representatives of local bioavailable strontium values.

However, some studies have found issues with snail shell strontium values. For example, Maurer et al. (2012) found that snail shells were biased toward soil carbonate values. As well, Evans et al. (2010) found that land snail shells in Britain were biased toward rainwater and did not have the same value as local plants (Evans et al., 2010).

Another study by Evans et al. (2009) found a snail shell in Scotland that was outside of possible mixing values for the basalt bedrock and seawater at the site, so they believe it may be an exotic specimen carried to the site by a bird. Due to these complications, snail shell sampling may not always be reliable.

4.5.2.4 Surface waters

Surface waters such as rivers and lakes can also be used as a proxy for bioavailable strontium to track the movement of large animals (Frei and Frei, 2011). This is because large bodies of fresh water are major sources of strontium intake for large-bodied obligate drinkers such as humans and large carnivores. Bodies of water mix and therefore reduce variation in local 87Sr/86Sr values, thus representing the average for the area (Frei and Frei,

2011). Due to strontium’s heavy atomic mass, the 87Sr/86Sr ratio of standing surface water is not affected by reservoir effects where the water becomes enriched in heavy isotopes due to preferential evaporation of light ones. Hoppe et al. (1999) found that surface water

87Sr/86Sr values from Florida were similar to plant samples collected from the same areas.

Surface water sampling can be challenging in certain areas and at certain times of year if the region is exceptionally dry or experiences periods of aridity. Nonetheless, it has proven

78 to be an effective measure of bioavailable strontium for Florida (Hoppe et al., 1999) and

Denmark (Frei and Frei, 2011; 2013).

4.6 Strontium Isotopes in the Skeleton

As a person or animal eats and drinks, strontium ions from whatever they consume are incorporated into their tissues, including bones and teeth. In archaeological contexts enamel, dentine, and bone can be analysed to understand the individual’s movement on the landscape. Bones and teeth form and turnover differently, and thus yield information about different periods of an individual’s life. This section will discuss the formation and turnover rates of these tissues, and the times that their compositions represent.

4.6.1 Formation and composition of teeth

Teeth form predictably from the apex to the cervix of the crown (Balasse, 2003).

Calcified tissues in living beings consist of 69% to 99% inorganic material, mostly calcium phosphate in the form of hydroxyapatite, Ca10(PO4)6(OH)2 (Hillson, 2005). During amelogenesis, enamel is laid down in two stages. In the first, only about 10-20% of the enamel matrix is inorganic, whereas in the second stage it is 80-90% inorganic (Balasse,

2003). Mature enamel in mammals is nearly entirely inorganic and is acellular; it is comprised of 96% inorganic materials, 1% organic materials, and 3% water (Hillson,

2005). The organic material in enamel is mostly non-collagenous proteins (Koch, 2007).

During mineralisation, fluoride ions can substitute for hydroxyl in hydroxyapatite and form intermediate apatites called fluorhydroxyapatites (Hillson, 2005). Other common substitutions include sodium and strontium for calcium; orthophosphate, carbonate, or

79 hydrogen carbonate for phosphate; and chloride or carbonate for hydroxyl (Hillson, 2005).

In dental tissues, these very small apatite crystals are known as crystallites and are long, narrow, and hexagonal in shape (Hillson, 2005).

Once a tooth is fully mineralised there is no analytical evidence showing that in vivo 87Sr/86Sr values can be altered in the enamel or at the enamel-dentine junction, thus the strontium in the tooth is representative of the time when it formed (Budd et al., 2000;

Hillson, 2005). Also, enamel and dentine of a tooth have similar 87Sr/86Sr values while the individual is alive (Budd et al., 2000). Upon burial, apatites survive remarkably well in many archaeological and geological contexts and are often visually indistinguishable from modern specimens, though some may be lost or altered over time (Wyckoff, 1972; Hillson,

2005). Diagenetic alteration is discussed below.

4.6.2 Bone formation and composition

Bone is comprised of a mixture of organic and inorganic components. The organic portion is approximately 35% of the bone and is made up of cells including osteoblasts, osteoclasts, bone-lining cells, and osteocytes, as well as the osteoid, which consists of ground substance and collagenous fibres (Marieb and Hoehn, 2013). The remaining 65% of bone is inorganic and is hydroxyapatites, giving bone its hardness (Marieb and Hoehn,

2013). During development, bones are created via endochondral and intramembranous ossification, and until maturity they continue to grow in length (Marieb and Hoehn, 2013).

Unlike teeth, bone is continuously remodelled and repaired over an individual’s life. Bone is resorbed at different rates by osteoclasts depending on the part of the bone and which bone it is in the body, allowing for the constituents (mainly calcium) to be released

80 into the bloodstream to be used by the body as needed. To counter resorption, osteoblasts deposit new bone matrix, thus removing its constituents from the blood. These processes are governed by a negative feedback loop that maintains homeostasis of Ca2+ in the bloodstream, as well as mechanical and gravitational forces putting stress on bones, which lead to an increase in bone mass to offset them (Marieb and Hoehn, 2013). Typically, in healthy young adults, bone resorption and deposition are in equilibrium. However, as people age there is more bone loss than deposition (Roberts and Manchester, 2005).

Because bone tissue turns over and teeth do not, adult bone 87Sr/86Sr ratios can be compared to those of their teeth to understand an individual’s movement over their lifetime (Sealy et al., 1995; Price et al., 2002).

4.6.3 Sampling bones and teeth

Small amounts of bones and teeth can be removed to analyse their 87Sr/86Sr ratios.

Because SIA is destructive, it is preferential to remove as small a sample as possible so as to preserve the specimen (Owen, 2002). Sampling of bones and teeth is typically done with either a drill (e.g. Owen, 2002; Maurer et al., 2012) or a Dremel tool (e.g. Frei and Price,

2012; Pfeiffer et al., 2017) equipped with a hard, narrow bit. This allows for small amounts of powdered sample to be collected from a small area. The outside of the tooth or bone should be free of any adhering sediment or weathering rinds, and, when enamel is being sampled, free of any adhering dentine (Kingston and Harrison, 2007). Sometimes specimens are analysed directly with laser ablation, which is less destructive than drilling.

This will be discussed later in this chapter.

Cortical bone is usually sampled instead of trabecular bone because it is harder and lacks large spaces, meaning the bone is less likely to be destroyed (Owen, 2002). Also, cortical bone forms in layers, which represent discrete periods of an individual’s life

(Turner Tomaszewicz et al., 2015). Sampling across the whole bone can yield an averaged

87Sr/86Sr value over many years or the entire lifetime of the individual, whereas sampling small portions of one layer from a cross-section represents a discrete time when the layer formed (Turner Tomaszewicz et al., 2015). It is important to note that different parts of bones turn over at different rates. For example, in humans the distal femur completely turns over every 5-6 months, whereas the shaft has a much slower turn over rate (Marieb and

Hoehn, 2013).

Since teeth are resistant to alteration and reflect strontium values during stages of childhood development, they are often analysed to understand movement between childhood and adulthood. If the tooth analysed does not have a 87Sr/86Sr value that matches the local environment, then it is likely that the individual migrated to the area after the tooth formed. The tooth chosen for sampling and the part of the tooth used are both important to consider. Teeth form at different ages within and between species, so depending on the tooth used, different stages of the individual’s life are being reconstructed. When possible, the third molar should be sampled to avoid an isotopic value that reflects weaning, as this tooth develops latest (Copeland et al., 2016).

Sampling different places on a single tooth can reveal detailed information about migration and life history of an individual up until the time the tooth finished forming and distinguish between migratory and sedentary animals (Britton et al., 2009). However, this is complicated because horizontal sequential sampling is not guaranteed to represent a

82 discrete amount of time due to the length, pattern, and direction of mineralisation. A sample that is drilled through an entire enamel layer represents discontinuous time periods

(Balasse, 2003). Pathological features, such as linear enamel hypoplasia, can be helpful in determining the ideal sampling angle as they indicate the direction of matrix secretion

(Balasse, 2003). Despite these limitations, intra-tooth sampling has successfully been used to demonstrate migration routes in modern and fossil ungulate species (e.g. Copeland et al., 2016; Britton et al., 2009).

4.7 Diagenesis

4.7.1 Diagenesis of skeletal material

Diagenesis is the transformation of an original compound due to chemical, physical, and biological interactions. This includes both early and late processes involved in fossilisation, such as degradation of soft tissues and changes in the mineralised portion of the skeletal material, including reworking or total replacement of the original bioapatite

(Keenan, 2016). Post-mortem alterations to the elemental composition of bones and teeth overriding the in vivo isotopic values are major concerns to isotopic studies (Sandford,

1997). Apatite is easily susceptible to substitutions of elements such as strontium, magnesium, iron, and manganese in the place of calcium (Keenan, 2016). Diagenesis is dependent on the composition of the bone and many environmental parameters, such as the composition of groundwater and host sediment (Keenan, 2016). In general, diagenetic alteration is more marked near the surface of bone than in its interior, as there is more direct contact with sediment and groundwater (Sandford, 1997).

Bones are more easily susceptible to diagenesis than teeth due to their higher organic content and their structure (Keenan, 2016). As well, children’s teeth are also more easily altered than adults’ because they are not yet fully mineralised (Bentley et al., 2004).

Dentine is less susceptible to diagenesis than bones, though more so than enamel (Budd et al., 2000). These differences in susceptibility are due to differences in composition. Bones and dentine are both comprised of about 20% organic collagen, and contain some proteins and lipids known collectively as ground substance, whereas fully-formed enamel does not contain them (Hillson, 2005). Within apatites, carbonates are typically more susceptible to diagenesis than are the phosphates, so it is better to use apatite phosphates for strontium isotope analysis (Hillson, 2005). Although enamel is far more resistant to diagenesis than bone, bone is still useful for isotopic studies as it yields information about the later portion of the individual’s life (Sponheimer and Lee-Thorp, 2003).

There are case studies that exemplify the resilience of enamel to diagenesis. For example, Lee-Thorp and Sponheimer (2003) studied bone, enamel, and tusk from a

130,000-year-old elephant skeleton recovered from aeolianite surrounded by seawater at the Reunion Rocks site in South Africa. They found that only the enamel reflected the expected δ18O, δ13C, and 87Sr/86Sr values for the individual in life. The 87Sr/86Sr values of the bone and tusk, for example, clustered around 0.7092, the value of modern ocean water, whereas the enamel was just above 0.7905 (Lee-Thorp and Sponheimer, 2003). However, skeletal elements other than enamel have been found to maintain their biogenic 87Sr/86Sr values in much older fossils. For example, Martin et al. (2016) found evidence of biogenic strontium in bones and teeth in sauropod, theropod, and crocodile fossils dating to the Late

Jurassic period. Diagenetic alteration is likely to be site specific, and probably also depends heavily on the state of preservation of the tissues.

Bone may be diagenetically altered in one way, but not in others. Lee-Thorp and

Sponheimer (2003) suggest that even though a bone may test positive to indicators of diagenesis, the biogenic isotope values of it may not be affected. Their claim is based on a study by White et al. (1998) where they found the infrared splitting factor (IRSF) of bone phosphate peaks to be significantly increased for samples from the Valley of Mexico and the Valley of Oaxaca, though the δ18O values of the bone phosphates were not outside of predicted ranges. The correlation between IRSF and isotopic values of bone may be different for every site, so it is difficult to make generalisations about its usefulness as a proxy for isotopic diagenesis (Lee-Thorp and Sponheimer, 2003). Lee-Thorp and

Sponheimer (2003) suggest that IRSF alteration may actually be a prerequisite for bone apatite to preserve biogenic isotopic ratios.

4.7.2 Diagenetic strontium

There can be post-mortem contamination of skeletal material post-burial by strontium in the soil as groundwater permeates into the bone or via adsorption, bringing its

87Sr/86Sr ratio to equilibrium with that of the soil and obliterating the in vivo values (Sillen,

1986; Bentley et al., 2004; Coutu et al., 2016). The degree to which dental remains reflect the in vivo 87Sr/86Sr values of the individual are dependent upon the quality of preservation of the tooth, as poor preservation results in increased alteration (Budd et al., 2000).

However, it is difficult to determine which factors specifically cause diagenesis, as real-

85 time experiments are expensive and time consuming, and laboratory experiments may not accurately simulate depositional environments and time (Madgwick et al., 2012).

The 87Sr/86Sr values of dentine compared to enamel for a single tooth can be used to assess diagenesis because the enamel should reflect the biogenic values, whereas dentine is more likely to be altered (Budd et al., 2000). In vivo, dentine and enamel have similar strontium concentrations (Underwood, 1977), and similar 87Sr/86Sr values because they form at essentially the same time (Budd et al., 2000). Like 87Sr/86Sr values, concentration of strontium in bones and teeth are largely dependent upon diet and water sources

(Underwood, 1977). Archaeological dentine often has higher concentrations of strontium ions than enamel, which suggests the addition of diagenetic strontium (e.g. Budd et al.,

2000; Magwick et al., 2012). Another method for assessing diagenesis is to compare fossilised rodent teeth with modern rodent teeth in the same location and determine whether there is a difference between them (Copeland et al., 2010). Since modern and archaeological rodents likely both fed in the same restricted area, they should have very similar 87Sr/86Sr ratios.

Most research done on strontium diagenesis of dental tissues in archaeology has been done on human teeth (e.g. Sealy et al., 1995; Sillen and Sealy, 1995; Budd et al.,

2000). This is problematic because there may be differences in the crystallinity of enamel between species (Popowics et al., 2004), thus leading to differences in susceptibility to diagenesis. However, a study by Madgwick et al. (2012) demonstrated that archaeological cow and pig tooth enamel are also resistant to diagenetic alteration, and thus can be used to trace trade of domestic animals. No experimental studies done on other species could be found.

In addition, small differences in 87Sr/86Sr values of single skeletal elements should be regarded with caution as this slight variability is not well understood. As a result, intraindividual differences may not be the result of diagenesis. For example, Sealy et al.

(1995) found that two samples from the same human femur had a relatively large difference in 87Sr/86Sr values, 0.7191 and 0.7194. The difference between enamel and bone of another individual was approximately the same as this, suggesting that it may be normal for there to be wide variability in 87Sr/86Sr values of a person (Sealy 1995). Another adult individual in the same study showed approximately the same amount of variation between their femur and tooth enamel (Sealy et al., 1995), making it difficult to discern whether the differences are due to mobility, diagenesis, or variability within the tested tissues.

4.7.3 Removal of diagenetic strontium

Diagenesis can sometimes be reversed by washing the specimen with weak acetic acid (Bentley et al., 2004). This process removes secondary carbonates and soluble apatites from bones and teeth, which purifies them of diagenetic contaminants (Lee-Thorp and

Sponheimer, 2003). Diagenetic strontium from carbonates that replace phosphates in apatite is more soluble than biogenic strontium, and therefore is dissolved first in the acid

(Sillen and Sealy, 1995; Coutu et al., 2016). Fluoride ions that replace hydroxyl ions are less soluble than biological apatite (Sillen and Sealy, 1995), though they are less commonly an issue than carbonates (Sealy et al., 1991). In the aforementioned example with the elephant skeleton, acetic acid washed samples had a slightly higher 87Sr/86Sr ratio and slightly lower δ13C values than samples that were not treated, including enamel samples,

87 though this treatment was not sufficient on the bone and tusk to return the values to biogenic levels (Lee-Thorp and Sponheimer, 2003).

However, there are issues with acid washing specimens. One problem is that there are doubts as to how this impacts the biogenic isotopic ratios in the tooth. Weak acid washes are done under the assumption that diagenetic strontium is purely additive, and not directly replacing the insoluble biogenic fraction (Budd et al., 2000). As well, acetic acid washes can dissolve enamel of fossils at a rapid rate and should therefore be used briefly and cautiously (Lee-Thorp et al., 1997). Skeletal samples should not be ashed prior to leaching procedures, as this leads to re-crystallisation of apatite, alters the crystalline structure of bone, and diminishes the solubility of bone apatite, making it impossible to properly remove diagenetic strontium (Sillen and Sealy, 1995).

Solubility profiles of bone and tooth samples can be used to assess diagenesis because they look at differences between the solubility of diagenetic and biogenic apatite

(e.g. Sealy et al., 1991; Sillen and Sealy, 1995). This entails repeatedly washing samples in weak acid for a short period of time and observing differences in solubility of strontium and calcium between washes (Sealy et al., 1991). These washes may be repeated as many as 25 times (e.g. Sealy et al., 1991). In solubility profiles there is typically a high solubility in the first few washes and then a drastic decrease and leveling off in subsequent ones, indicating that soluble diagenetic strontium has all been removed. Unlike fossils and archaeological teeth, fresh bone solubility remains constant for all washes (e.g. Sealy et al.,

1991; Sillen and Sealy, 1995).

Despite issues with diagenesis, enamel from fossilised teeth that are millions of years old have been successfully shown to retain the individual’s in vivo 87Sr/86Sr ratio (e.g.

Copeland et al., 2010; 2011; Martin et al., 2016). A study looking at diagenesis of various trace elements (strontium, barium, lead, and zinc) in fossil teeth from South African sites found that only the Zn/Ca ratio of the teeth was altered (Sponheimer and Lee-Thorp, 2006).

This suggests that reliable reconstruction of mobility is possible using very ancient fossilised teeth, though the effects of diagenesis are site-specific due to differences in preservation and carbonate and fluoride concentrations in soil and groundwater (Sillen and

Sealy, 1995).

4.8 Laboratory Techniques

The general laboratory steps in SIA are ion extraction and analysis via mass spectrometry. Gas preparation is required when ions cannot be quantified from a solid material, such as in analyses of light elements. When a solid material can be analysed, the solution formed in ion extraction is dried and analysed in that state. The following section will discuss laboratory methods used in stable strontium isotope analysis both during preparatory stages and ion quantification via mass spectrometry.

4.8.1 Ion extraction

The separation of relevant ions from unwanted ones is essential for most forms of

SIA, as it purifies the desired element from the others in the sample (Dickin, 2005).

Depending on the type of material being analysed for strontium ratios there are different means of extracting ions. First, a subset of the sample must be dissolved in acid. This allows for the different isotopes to thoroughly mix within the solution (Faure and Powell, 1972).

For strontium isotopes in biological material, digestion is done in either hydrochloric or

89 nitric acid, whereas rocks are digested first in hydrofluoric acid and then subjected to hydrochloric or nitric acid leaching (Dicken, 2005). Ion exchange columns are loaded with a strongly acidic strontium-specific resin that holds onto strontium ions preferentially over other elements (Pin and Bassin, 1992). The resin is pre-cleaned with deionised or Milli-Q water and preconditioned with acid, then the dissolved sample passes through it (Dicken,

2005). The resin is rinsed with more acid, and then the strontium ions are eluted with a rinse of water and are collected in a sample tube and dried to be analysed (Dicken, 2005).

4.8.2 Mass spectrometry

Mass spectrometry is a type of analysis used to measure the numbers if isotopes.

Modern mass spectrometers, typically of Nier-type design, quantify isotope ratios based on their atomic masses as they pass through electrical or magnetic fields and are subsequently caught in collector cups (Faure, 1986). Samples are introduced to these devices either as a gas to be ionised with electrons, or as a solid salt mounted onto a filament and subsequently heated in a vacuum until its ions are volatised. They are then passed through an electromagnetic field which separates heavy and light ions based on how much they deflect after they converge. The ions then pass down an analyser tube and are caught in a Faraday cup, a metal collector placed in such a way to collect deflected ions of a specific mass while other unwanted ions are neutralised by hitting the walls of the tube.

Desired ions hit the collector at different rates depending on their relative amount, making it possible to quantify the total number of each isotope from the sample. The beams entering the cups are neutralised and measured on a strip-chart recorder that displays peaks and valleys representing the mass spectrum of the analysed element (Faure, 1986).

Two kinds of mass spectrometers will be discussed: Thermal Ionisation Mass

Spectrometers (TIMS), and Multiple Collector Inductively Coupled Plasma Mass

Spectrometers (MC-ICP-MS) (Hoefs, 2010). The remainder of this section will discuss the pros and cons of using both forms of mass spectrometry regarding strontium isotope analysis. Both mass spectrometers are commonly used to analyse archaeological material.

4.8.2.1 Thermal Ionisation Mass Spectrometry

In TIMS, samples that have been purified via ion exchange and dried into a salt are loaded onto a metal filament and heated up in a vacuum to ionise them, then ions of different masses are collected and quantified in Faraday cups (Dickin, 2005; Hoefs, 2010).

TIMS results in smaller fractionation effects than MC-ICP-MS, although it uses up light isotopes on the filament slightly faster than heavy isotopes, resulting in a slight reservoir effect (Dickin, 2005). Internal normalisation is possible using two non-radiogenic isotopes of strontium (86Sr/88Sr), which are constant throughout the earth at a ratio of 8.57321

(Dickin, 2005; Bentley, 2006). This ratio can be used to correct for instrumental mass bias resulting from fractionation. Using these normalisation standards, the precision of TIMS can be improved from about 1% to 0.01% (Dickin, 2005). TIMS measurements on tooth enamel are usually precise to the sixth decimal place (Copeland et al., 2010).

4.8.2.2 Multiple Collector Inductively Coupled Plasma Mass Spectrometry

Inductively-coupled plasma mass spectrometers can potentially eliminate the need for chemical extraction of ions (Dickin, 2005). This kind of machine uses gas plasma to ionise the sample. MC-ICP-MS has a high ionisation ability as the gas plasma is heated to

5,000 oC, and high sixth-decimal precision like TIMS, though fractionation is more than an order of magnitude greater than TIMS (Dicken, 2005; Hoefs, 2010). MC-ICP-MS can detect ion ratios in concentrations as low as parts-per-trillion (Dicken, 2005; Hoefs, 2010).

There are two kinds of MC-ICP-MS analysis: solution, and laser ablation. Laser ablation MC-ICP-MS allows for the chemical composition of materials to be analysed in situ without requiring extraction of ions, so long as there is a relatively high concentration of ions present (Dickin, 2005). This method is less destructive and faster than traditional solution analysis as it requires less material, and it is cheaper to run analyses (Copeland et al., 2008). Laser ablation MC-ICP-MS uses a sample that is only about 250 by 750 μm ablated directly from the object itself, which is hardly visible macroscopically (Copeland et al., 2008). However, this technique typically yields less precise measurements than solution MC-ICP-MS and TIMS, often with errors up to the third or fourth decimal place in teeth (Copeland et al., 2008; 2010). When the concentration is low, the sample must still be pre-treated to concentrate the ions in a solution (Dicken, 2005). This situation is common in rocks, but typically enamel can be analysed via laser ablation (Copeland et al.,

2008). Solution MC-ICP-MS is more precise than laser ablation, but requires more sample and is more costly to run. Although it has a higher precision than laser ablation, solution

MC-ICP-MS often has a margin of error in the fifth decimal place (Copeland et al., 2010).

4.9 Conclusion

Stable strontium isotope analysis is a valuable tool for reconstructing migration and movement of people and animals from the past and present. Strontium from the environment is taken up by plants, and animals subsequently consume those plants and

92 drink local water. The local 87Sr/86Sr values are then recorded in the animals’ skeletal tissues. The strontium cycle is complicated, and the amounts of strontium in each stage are variable depending on global processes, though typically most strontium in an area derives from weathered bedrock. Nonetheless, regions, particularly those with different bedrock types, vary in their local 87Sr/86Sr values. The bioavailable strontium in these areas can be characterised by analysing biological material such as plants, small animals, archaeological material, or snail shells, as well as inorganic material such as surface water.

Bones represent later, more averaged 87Sr/86Sr values than teeth, which reflect strontium intake from when the teeth. There is also differential preservation of these materials in general, as bones and dentine are more susceptible to diagenesis than enamel.

Overall preservation quality of the tissues also has an impact on the preservation of biogenic 87Sr/86Sr values. Fossil bone and dentine are unlikely to preserve in vivo 87Sr/86Sr values in most cases, though enamel tends to be resilient to alteration even over long periods of time. Diagenesis can often be reversed by washing bones and teeth in a weakly acidic solution, which breaks down soluble diagenetic carbonates. Acid washes should be done with caution on fossils, as they can rapidly break down fossilised enamel.

To analyse 87Sr/86Sr values of modern and archaeological materials, ion extraction must be completed on the samples. For solution mass spectrometry on a TIMS or solution

MC-ICP-MS this is done by digesting the samples in acid, passing the solution through ion exchange columns containing strontium-specific resin, and eluting the ions into a sample tube. For laser ablation MC-ICP-MS, a small portion of the object is vaporised directly using a laser which leaves a very small mark. Laser ablation is much less destructive, though it requires high concentrations of strontium ions and tends to be much less precise

93 than solution mass spectrometry. Despite their differences in accuracy and sample destruction, TIMS and solution and laser ablation MC-ICP-MS are all used commonly for strontium isotope analysis in archaeology.

Chapter 5: Techniques Applied

5.1 Field Methods

5.1.1 Plant collection

Ninety-nine plant samples were collected from 33 localities in the Olduvai Gorge region between July 10th and 13th in 2017 with assistance from Dr. Neduvoto Mollel from the Tropical Pesticides Research Institute (TPRI) in Arusha, Tanzania. See Chapter 6 for a detailed discussion of the ecological and geological setting of Olduvai Gorge and the surrounding area. These plants were collected to assess variation in bioavailable strontium around the region. Sampling localities were chosen based on proximity to varying geological basements (Cenozoic volcanic rocks and Palaeozoic metamorphic outcrops) and changing elevation above sea level to account for variation in bedrock type and altitudinal differences, and to other localities sampled. In total, plants were sampled from 9 metamorphic localities and 19 volcanic localities. As well, five samples were collected from the Olbalbal Depression, a seasonal floodplain and endpoint of the Olduvai River at the easternmost edge of Olduvai Gorge, to see if there was a difference between the lacustrine area and the surrounding dry localities.

The sampling area spanned from approximately 35.228828” to 35.416745” and -

3.04128” to -2.740078” in the Ngorongoro Conservation Area (NCA) in Northern

Tanzania, which covers an area of about 940 km2. The NCA is discussed in detail in

Chapter 6. Elevation of the sampling localities ranged from 1,285 to 1,810 masl. Samples were collected from locations approximately 3-5 km from one another. This allowed for sampling multiple localities on the same bedrock type to understand variation therein. The latitude, longitude, and elevation of the localities were recorded using a Garmin eTrex

Figure 5.1.1: A map of sampling localities and the geological domains they fall on. This map was created using imagery from the Shuttle Radar Topography Mission by NASA. Accessed from https://utility.arcgis.com/usrsvcs/servers/81d8c4b8389f4ccc98a19833a8 c24396/services/WorldElevation/Terrain/ImageServer.

10 Global Positioning System (GPS) unit. See Figure 5.1.1 for a map of the sampling area.

Three plant specimens were collected from each location. The most abundant vegetation types in the area included trees, shrubs, herbs, and grasses. When possible, multiple types of plants, plant species, and plant parts were collected at each locality to account for slight variation in 87Sr/86Sr values between species and parts (Poszwa et al., 2004; Hartman and

Richards, 2014; Copeland et al., 2016). Some localities had very little variation in flora, so at these places the same species or plant type had to be sampled more than once. Only plants that could be identified to at least the genus level (e.g. grasses with flowers) were

Elevation Locality Family Genus Parts Collected (masl) Longitude Latitude 1 Acanthaceae Hypoestes torskaolii stems, flowers, roots 1538.224609 35.264876 -2.943339 1 Fabaceae Acacia tortilis branches, leaves, thorns 1538.89624 35.265089 -2.943348 1 Fabaceae Acacia tortilis branches, leaves, thorns 1538.015259 35.264618 -2.943265 2 Zygophyllaceae Balanites aegyptiaca branches, leaves, thorns, bark 1557.067627 35.230379 -2.938182 2 Burseraceae Commiphora sp. branches, leaves 1556.033325 35.230614 -2.938432 2 Fabaceae Acacia tortilis branches, leaves, thorns 1558.550781 35.230589 -2.938376 3 Poaceae Sporobolus consimilis blades, flowers 1532.922241 35.228828 -2.944456 3 Poaceae Pennisetum mezianum blades, flowers 1533.456543 35.228956 -2.944616 3 Capparaceae Boscia angustifolia branches, leaves 1535.7677 35.229207 -2.944778 4 Asparagaceae Asparagus africanus branches, leaves 1458.819824 35.365573 -2.950064 4 Fabaceae Acacia mellifera branches, leaves 1458.819824 35.365573 -2.950064 4 Amaranthaceae Achyranthes aspera branches, leaves, seeds 1457.46228 35.365691 -2.950151 5 Amaranthaceae Achyranthes aspera branches, leaves 1559.690796 35.378778 -2.914144 5 Asparagaceae Asparagus africanus branches, leaves 1559.690796 35.378778 -2.914144 5 Poaceae Aristidia adoensis blades, flowers 1560.281006 35.378854 -2.914091 6 Acanthaceae Barleria submolis stems, leaves, flowers 1546.79126 35.364292 -2.886326 6 Amaranthaceae Achyranthes aspera branches, leaves, seeds 1544.881592 35.364332 -2.886235 6 Olacaceae Ximenia caffra branches, leaves 1544.573364 35.364364 -2.88619 7 Euphorbiaceae Euphorbia cuneata branches, leaves, fruit 1584.028931 35.3884 -2.880129 7 Asparagaceae Asparagus africanus stems, leaves 1586.366211 35.388325 -2.880068 7 Poaceae Enneapogon cenchroides blades, flowers 1589.675171 35.38842 -2.879972 8 Fabaceae Acacia tortilis branches, leaves, thorns 1593.53064 35.342504 -2.858052 8 Acanthaceae Hypoestes torskaolii stems, leaves, flowers 1592.784302 35.342524 -2.857994 8 Boraginaceae Heliotropium steudneri stems, leaves, flowers 1593.275269 35.342563 -2.85776 9 Acanthaceae Hypoestes torskaolii stems, leaves, flowers 1590.540283 35.320004 -2.834773 9 Poaceae Sporobolus panicoides blades, flowers 1589.259277 35.319976 -2.834772 9 Acanthaceae Hypoestes torskaolii stems, leaves, flowers 1589.765503 35.320129 -2.83505

10 Boraginaceae Cordia monoica branches, leaves 1682.532227 35.330545 -2.803343 10 Fabaceae Acacia xanthophloea branches, thorns, leaves 1682.810547 35.330457 -2.803274 10 Asteraceae Conyza pyrrhopappa branches, leaves 1679.918335 35.330406 -2.803159 11 Boraginaceae Cordia monoica branches, leaves 1810.445312 35.358697 -2.740078 11 Amaranthaceae Achyranthes aspera branches, leaves 1810.149658 35.358734 -2.740143 11 Malvaceae Hibiscus ludwigii branches, leaves, flower, buds 1811.62915 35.358905 -2.740247 12 Fabaceae Acacia drepanolobium branches, leaves, thorns, fruit 1750.945923 35.333732 -2.741435 12 Burseraceae Commiphora habessinica leaves, branches 1753.083618 35.33376 -2.741191 12 Asparagaceae Asparagus africanus stems, leaves 1753.209229 35.333544 -2.741193 13 Burseraceae Commiphora merkeri branches, leaves 1760.194336 35.323153 -2.764971 13 Boraginaceae Cordia monoica branches, leaves 1760.194336 35.323153 -2.764971 13 Asteraceae Conyza pyrrhopappa branches, leaves 1757.081299 35.323057 -2.764687 14 Solanaceae Withania somnifera branches, leaves 1649.061523 35.306921 -2.793945 14 Fabaceae Acacia tortilis branches, leaves, thorns 1650.832153 35.306836 -2.793761 14 Acanthaceae Hypoestes torskaolii stems, leaves, flowers 1650.591431 35.306775 -2.793642 15 Amaranthaceae Achyranthes aspera branches, leaves 1669.861084 35.263021 -2.804032 15 Boraginaceae Heliotropium steudneri branches, leaves 1669.861084 35.263021 -2.804032 15 Asparagaceae Asparagus africanus stems, leaves 1670.119629 35.26301 -2.804043 16 Burseraceae Commiphora sp. branches, leaves 1513.852661 35.286581 -3.015409 16 Fabaceae Acacia tortilis branches, leaves, thorns 1513.918579 35.286655 -3.015361 16 Poaceae Sporobolus panicoides blades, flowers, roots 1513.471313 35.286652 -3.015359 17 Poaceae Sporobolus panicoides blades, flowers, roots 1587.489014 35.266424 -2.857492 17 Poaceae Digitaria macroblephara blades, flowers, roots 1587.489014 35.266424 -2.857492 17 Poaceae Digitaria macroblephara blades, flowers, roots 1587.976074 35.266186 -2.857275 18 Olacaceae Ximenia caffra branches, leaves 1602.297241 35.253132 -2.899225 18 Boraginaceae Heliotropium steudneri branches, leaves 1602.297241 35.253132 -2.899225 18 Fabaceae Acacia mellifera branches, leaves 1602.116089 35.252963 -2.899352 19 Acanthaceae Barleria eranthemoides stems, leaves, flowers 1531.53186 35.278495 -2.925186 19 Acanthaceae Justicia betonica stems, leaves 1531.567627 35.278381 -2.925028

19 Poaceae Sporobolus panicoides blades, flowers 1531.983154 35.278194 -2.925366 20 Olacaceae Ximenia caffra branches, leaves 1452.057983 35.37066 -2.985356 20 Burseraceae Commiphora merkeri branches, leaves 1450.513306 35.370696 -2.98537 20 Fabaceae Acacia mellifera branches, leaves 1452.495117 35.370619 -2.985274 21 Vitaceae Cissus quadrangularis branches 1421.869873 35.396601 -2.980115 21 Asphodelaceae Aloe secundiflora leaves 1423.266602 35.396725 -2.980091 21 Asparagaceae Asparagus africanus branches, leaves 1423.266602 35.396725 -2.980091 22 Zygophyllaceae Balanites aegyptiaca branches, leaves, thorns 1364.428589 35.42993 -2.995884 22 Fabaceae Acacia mellifera branches, leaves 1362.494263 35.430081 -2.995788 22 Olacaceae Ximenia caffra branches, leaves 1362.281372 35.429996 -2.995758 23 Zygophyllaceae Balanites aegyptiaca branches, leaves 1314.102295 35.427352 -3.01027 23 Fabaceae Acacia tortilis branches, leaves, thorns 1315.383301 35.42741 -3.010468 23 Poaceae Sporobolus panicoides blades, flowers 1313.310303 35.427402 -3.010558 24 Zygophyllaceae Balanites aegyptiaca branches, leaves 1301.046631 35.451995 -2.9978 24 Zygophyllaceae Balanites aegyptiaca branches, leaves 1302.591064 35.452326 -2.998078 24 Fabaceae Acacia tortilis branches, leaves 1303.546631 35.452091 -2.998333 25 Fabaceae Acacia mellifera branches, leaves 1292.247192 35.467404 -2.989666 25 Fabaceae Acacia tortilis branches, leaves, thorns 1291.68042 35.467444 -2.989529 25 Poaceae Pennisetum mezianum blades, flowers, roots 1290.83374 35.467764 -2.989522 26 Amaranthaceae Achyranthes aspera branches, leaves 1283.60022 35.474165 -2.96554 26 Fabaceae Acacia xanthophloea branches, leaves 1285.367188 35.474145 -2.965563 26 Zygophyllaceae Balanites aegyptiaca branches, leaves 1285.949219 35.474115 -2.965507 27 Capparaceae Cadaba farinosa branches, leaves 1295.256104 35.459827 -3.022632 27 Fabaceae Acacia tortilis branches, leaves 1292.85144 35.459825 -3.02261 27 Solanaceae Lycium europeum branches, leaves 1292.85144 35.459825 -3.02261 28 Amaranthaceae Achyranthes aspera branches, leaves 1358.419067 35.419005 -2.976165 28 Burseraceae Commiphora sp. branches, leaves 1358.419067 35.419005 -2.976165 28 Zygophyllaceae Balanites aegyptiaca branches, leaves 1358.419067 35.419005 -2.976165 29 Salvadoraceae Salvadora persica branches, leaves 1539.684692 35.275396 -3.04128

29 Asparagaceae Asparagus africanus stems, leaves 1537.335571 35.275407 -3.041281 29 Poaceae Aristidia adoensis blades, roots, flowers 1539.056885 35.275415 -3.041251 30 Asparagaceae Asparagus africanus branches, leaves 1538.319824 35.253204 -3.028353 30 Fabaceae Acacia tortilis branches, leaves, thorns 1538.319824 35.253204 -3.028353 30 Asparagaceae Asparagus africanus branches, leaves, fruit 1538.039185 35.253205 -3.028395 31 Asparagaceae Asparagus africanus branches, leaves 1556.746948 35.232917 -2.998455 31 Fabaceae Acacia tortilis branches, leaves, thorns 1557.235718 35.232916 -2.998424 31 Fabaceae Senna italica leaves, flowers, stems, fruits 1556.709717 35.23295 -2.998339 32 Poaceae Digitaria macroblephara blades, flowers, roots 1519.4823 35.276675 -2.984336 32 Poaceae Sporobolus panicoides blades, flowers, roots 1521.020386 35.27671 -2.984242 32 Convulvulaceae Ipomoea sp. branches, leaves 1521.020386 35.27671 -2.984242 33 Boraginaceae Cordia monoica branches, leaves 1460.593994 35.353044 -2.981804 33 Poaceae Cynodon dactylon blades, roots 1485.998047 35.352388 -2.981115 33 Acanthaceae Barleria eranthemoides branches, leaves 1458.466431 35.352377 -2.981582

Table 5.1.1: Plants collected at each locality to calculate bioavailable strontium.

100 collected. Plants and their localities were all photographed prior to collection, and a description of the locality was recorded. Plant specimens were stored in envelopes and allowed to air dry prior to identification and exportation. All plant specimens were identified by Dr. Mollel and the staff at the TPRI using their extensive reference collection of East African flora. See Table 5.1.1 for a list of all plants collected and their locations on the landscape.

5.1.2 Teeth and Their Collection

All teeth analysed are from JK at Olduvai Gorge, Tanzania. They were all excavated by the Stone Tools, Diet, and Sociality team in July 2017 in various geological trenches at the site, which includes sediment from both Bed III and Bed IV and dates to approximately 1.3 to 0.6 Ma (Hay, 1976; Tamrat et al., 1995; McHenry et al., 2007). The

Olduvai Beds and these excavations are described further in Chapter 6. The teeth belonged to crocodiles (possibly Crocodylus niloticus or Crocodylus anthropophagus; n=3) (Brochu et al., 2010; van der Merwe, 2013), equids (possibly the Olduvai zebra Equus oldowayensis or another species of Equus; n=2), and a hippopotamus (n=1). All teeth are isolated, but the hippopotamus molar is almost completely encased in rock. The crocodile and equid teeth were identified at the University of Calgary in the fall of 2017 using reference books

(Hillson, 2005; Hillson, 1992), online references on Olduvai fauna (Brochu et al., 2010; van der Merwe, 2013). The hippopotamus tooth was identified with the help of Warren

Fitch, the Collections Specialist at the University of Calgary Department of Biological

Sciences Museum of Zoology. To make this identification, cusps, size, and shape of the fossil tooth were compared to a skull of a modern Hippopotamus amphibius, which

101 appeared to be a very close but slightly imperfect match. It was slightly larger than H. amphibius teeth, so the fossil specimen may be H. gorgops, which had larger teeth and persisted in East Africa until less than a million years ago (Boisserie, 2005).

The equid teeth (JK1 and JK6) collected were both molars. Both were very large, and roughly 6-7 cm long. In relation to the other teeth, JK6 was very weathered and in poor condition as it contained numerous cracks and was flaking apart. JK1 was in much better shape overall, though it was cracked in half lengthwise, revealing the inside of the tooth, and was broken on the proximal end. The crocodile teeth (JK2 though JK5) were in varying condition. JK2 was broken in half and included only the top portion of the tooth. JK3 was complete aside from slight breakage at the base and was red and black in colour. JK4 was cracked in half lengthwise, revealing the inside of the tooth. JK5 was broken in half as well and included only the very top portion of the tooth. The hippopotamus tooth, JK7, is black in colour and looks to be complete, though it is difficult to tell with the concretion still encasing the fossil. The cusps are complete, though the distal half is more damaged than the mesial.

5.2 Laboratory Methods

5.2.1 Plant sample preparation

In the Stone Tools, Diet, and Sociality Preparations Laboratory at the University of

Calgary (Earth Sciences #833) approximately 1 g of each plant sample collected (n=99) was cut into small pieces. The three plants collected from the same localities (n=33) were combined in sample bags. Between localities all cutting implements (blades and scissors) were cleaned thoroughly with 99% isopropyl alcohol. The total combined weight for each

102 locality was then recorded using an Acculab Vicon digital scale. Analysing multiple specimens and plant parts from the same location allows for the creation of an average of slightly differing bioavailable strontium values between plant species and parts (e.g.

Copeland et al., 2016). This helps to avoid seeing false variation in 87Sr/86Sr values on the landscape due to sampling bias.

The plants were then ashed in clean, covered crucibles at 400 oC for 48 hours in the

Tropical Archaeology Laboratory (Earth Sciences #811) at the University of Calgary. The crucibles had been sterilised in a Tuttnauer 2340M autoclave for one hour at 120o C and rinsed with 3M HNO3 prior to use. Next, the ashed plant samples were weighed using an

Ohaus Navigator N1D110 Precision Scale on a marble table and the ash was transferred into glass vials for transportation to the Isotope Science Laboratory (Science B #116) at the University of Calgary. The scale was cleaned with 99% isopropyl alcohol between samples, and new autoclaved tools were used for each sample to avoid cross- contamination. 10-15 mg of ash was placed in 1.5 mL centrifuge tubes and was dissolved in 500 μL of 3M HNO3 for two hours. The tubes were then centrifuged at 3000 rpm for 3 minutes to separate the pellet and supernatant. At this time the supernatant was ready for analysis.

5.2.2 Ultrasonication experiment

Five localities (4, 9, 22, 23, and 33) were randomly selected to be used for a control test looking at the effect of ultrasonication on 87Sr/86Sr values. For each of these a duplicate sample was created in the same way as the original, which was ultrasonicated in reverse osmosis de-ionized (RODI) water in a VWR Symphony Ultrasonic Bath (5.7L, 60Hz) for

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5 minutes. After being cleaned, the samples were rinsed and dried for approximately two hours in a VWR 1305U Gravity Convection Oven at 70 oC, re-weighed, and ashed like the other samples. This experiment was conducted because there is no consensus in existing literature regarding the importance of cleaning before analysis and how dirty samples may impact (if at all) the 87Sr/86Sr values of the plants. It is possible that dirty samples may be biased towards atmospheric strontium values if they are coated in aeolian dust.

5.2.3 Tooth sample preparation

In the Preparation Laboratory the teeth were weighed and measured (except the hippopotamus tooth encased in concretion) (n=6 measured) and thoroughly cleaned mechanically with a brush so that all adhering surface sediment was removed from the surface. All measurements and weights are recorded in Table 5.2.1. Weights were obtained with an Acculab Vicon digital scale, and measurements were done with calipers. The outsides of the teeth were then lightly grazed with a Dremel Engraver tool equipped with a 1.2 mm diamond tipped bit on medium speed to remove any remaining contaminants on the outside of the teeth. Between the cleaning stage and collecting samples, and between different samples, the Dremel bit was cleaned with 99% isopropyl alcohol and allowed to air dry to avoid cross-contamination between samples.

The Dremel tool was then used on medium speed to remove approximately 10 mg of enamel from the outside of each tooth, and 10 mg of dentine from inside. Most of the teeth were found broken with the dentine exposed, making dentine collection simple. JK7 was the only complete tooth, and in this case a hole on a cracked edge was enlarged and dentine removed from that space. To ensure no dentine was adhered to enamel samples,

104 care was taken to only remove a thin layer of enamel from the tooth. Samples were collected from as small an area as possible to minimise damage to the teeth and to ensure that the samples are not representing a large time period in the animal’s life and that the teeth were not being destroyed more than necessary, though a larger relative area had to be removed from the small crocodile teeth to ensure that there was enough sample for analysis.

Prior to collecting enamel from JK7, as much loose matrix as possible was removed from the concretion and the tooth itself. During collection for this tooth the Dremel was on low speed to avoid fracturing the adhering concretion and contaminating the samples.

The enamel and dentine samples were then brought into the Tropical Archaeology

Lab and subdivided into two 3-5 mg subsamples on clean weigh paper using an Ohaus

Navigator N1D110 precision scale. Between samples the scale was cleaned with 99% isopropyl alcohol, and clean autoclaved spatulas were used for each new sample to avoid cross-contamination. Each subsample was transferred into a new clean 1.5 mL sample tube.

One set of enamel and dentine from each tooth was soaked in 500 µL of 0.1 M acetic acid for 30 minutes to remove diagenetic strontium. The other half of the samples were not subjected to this step to see if there was a difference in 87Sr/86Sr values with and without acetic acid washing. The sample tubes were then filled with RODI water, centrifuged in a

Corning LSE compact centrifuge at 5000 rpm for 5 minutes, and the supernatant pipetted using a clean pipette tip for each sample and discarded. Prior to use pipette tips were rinsed with RODI water to prevent contamination. This rinsing step was repeated a total of three times to ensure that no acetic acid remained on the enamel and dentine and thus that the samples were back to neutrality. After this the samples were dried overnight under a heat lamp.

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ID Tooth Family Trench Width Height Weight (g) Type (mm) (mm) JK1 Molar Equidae South of E 25 62 60.21 JK2 Unknown Crocodylidae B 14 23 3.93 JK3 Unknown Crocodylidae D 11 30.5 2.98 JK4 Unknown Crocodylidae F 11 25 2.60 JK5 Unknown Crocodylidae B 7.5 13.5 0.65 JK6 Molar Equidae D 32 72 81.04 JK7 Molar Hippopotamidae D ------Table 5.2.1: A list of teeth sampled, their provenance at JK, and their measurements. Measurements are not provided for JK7 because it was encased in concretion and could not be accurately measured or weighed.

Next, all samples were transferred into small sterile Teflon jars with screw-cap lids.

500 µL of 5M HNO3 and 50 µL of H2O2 were then added to each container to dissolve the samples and destroy any organic material that may be on them. The jars were then sealed tightly, and all samples were heated overnight on a hotplate at 60 oC. Once dry, 500 µL of

3M HNO3 was added to each container to leach the strontium from the samples and allowed to react for two hours. Following this they were ready to be eluted.

5.2.4 Isotope analysis methods

The 87Sr/86Sr ratios of all samples were quantified in the Isotope Science Laboratory at the University of Calgary, which is run by Dr. Michael Wieser. Samples were prepared and analysed under the guidance of Kerri Miller. To begin the ion exchange process, 100

μL of Eichrom Sr Resin (50-100 μm) was added to glass ion exchange columns. Prior to usage, the ion exchange columns had been soaking in 0.5 M HNO3 to keep them clean.

The resin was then pre-cleaned with approximately 3 mL of Milli-Q H2O and preconditioned with 500 μL of 3M HNO3. The supernatants from the plant samples and all of

106 the tooth samples, which had previously been dissolved in 3M HNO3, were then placed into their respective ion-exchange column and were dissolved in 500 μL of 3M HNO3. The resin was then rinsed twice with 1 mL of 3M HNO3 to remove matrix while retaining strontium. The strontium was eluted by adding 500 μL of Milli-Q H2O to the column and collected in 1.5 mL centrifuge tubes. Samples were then allowed to dry completely under a heat lamp for 48 hours before being analysed on a Thermo-Fisher Triton thermal ionisation mass spectrometer (TIMS). Samples were processed on four occasions between

October 11th and December 15th 2017.

Several precautions are taken to ensure the TIMS measurements are accurate. For one, gain calibration is done on the amplifiers with every carousel of samples run to calibrate the ion collection cups. To do this, a calibration current is sent into the amplifiers, and a correction factor is calculated from the discrepancy between the measured value and the known value, which is then applied for each signal. Each carousel is also run with the international strontium standard NIST SRM 987 to ensure external precision of the TIMS.

Also, values were corrected for interference from rubidium using measurements of 85Rb.

This is done to correct for isobaric interference in 87Sr counts from 87Rb. Two samples were duplicated for each carousel loaded to test the analytical reproducibility of the machine. The analytical reproducibility of the technique is ±0.000013, so duplicate samples should be within that range of one another. Analytical reproducibility accounts for differences in values due to any treatment prior to analysis, including issues with sample preparation and handling. The internal precision of the TIMS varies for each sample.

Samples that initially came back with a low intensity signal were re-processed to ensure an accurate reading, as these samples had high standard errors. The 87Sr/86Sr ratio for each

107 sample is calculated 150 times, and only measurements that are within two standard deviations of the mean are used to calculate the final value. Finally, all 87Sr/86Sr ratios calculated have been normalised against fractionation caused by instrumental mass bias using the ratio 88Sr/86Sr=8.375209.

5.3 Data Analysis and Representation

Maps of the region sampled were created using Google Earth Pro and ArcGIS

Desktop 10.6, and other original figures were created using Adobe Photoshop Creative

Cloud 2015. Descriptive statistics were determined using the data analysis feature of

Microsoft Excel 2016, and all graphs were also created with this software.

Nonparametric statistical tests were run due to small sample sizes, heterogeneity within data groups, and differences between sizes of groups. All statistical tests were run using PAST 3.19 software (https://folk.uio.no/ohammer/past/). A Kruskal-Wallis test and

Dunn’s post-hoc tests were used to compare 87Sr/86Sr of sampling sites from different geological areas. Wilcoxon Signed Rank Tests were used to compare all paired datasets, including ultrasonicated vs. non-ultrasonicated plant samples, enamel with and without acid treatment, dentine with and without acid treatment, and dentine and enamel of the same tooth both with and without acid washing. Mann-Whitney U Tests were used for all other non-paired datasets, including differences in vegetation sampled between localities, and comparing the enamel and dentine datasets together. Spearman’s rho tests of correlation were used to determine whether or not relationships exist between 87Sr/86Sr and longitude, latitude, and elevation.

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Chapter 6: Study Area

6.1 Introduction

Olduvai Gorge is an important area for palaeoanthropological, archaeological, and geological research. Research in these fields has been extensively conducted since its chance discovery by the German entomologist Wilhelm Kattwinkel, who by chance found fossilised Hipparion teeth while searching the area for butterflies (Leakey, 1978). In this chapter the geology, climate, and ecology of Olduvai Gorge will be discussed with additional focus on its origin, stratigraphic layers, palaeoenvironment, archaeological sites, and research conducted therein. The chapter will conclude with a discussion of the sampling sites chosen around the region for this study, and the site within Juma’s Korongo from which the faunal samples used in this study were found.

6.2 The Olduvai Gorge Region

6.2.1 The Ngorongoro Conservation Area

Olduvai Gorge is in the Arusha region of Northern Tanzania and is more specifically located within the Ngorongoro Conservation Area (NCA). The NCA is contiguous with the southern edge of the Loliondo Game Controlled Area and the eastern edge of Serengeti National Park (Figure 6.2.1). In relation to Olduvai Gorge, the

Ngorongoro Volcanic Highland (NVH) is to the south and east, to the west and northwest are the Serengeti plains, to the north are metamorphic highlands, and directly to the east the gorge gradually slopes downward into the Olbalbal depression, a seasonal floodplain at the foot of the NVH (Hay, 1976; Egeland et al., 2007). See Figure 6.2.2 for a detailed map of the area immediately surrounding Olduvai.

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Figure 6.2.1: Maps showing the location of the Ngorongoro Conservation Area within Tanzania, and Tanzania within Africa. The map is modified from Figure 1 in Boone et al. (2006) to mark the locations of Olduvai Gorge and Laetoli.

The NCA was established in 1959 to preserve wildlife and the environment, and to accommodate local Maasai groups who had previously been excluded from Serengeti

National Park’s larger precursor (Neumann, 1998). The NCA is a UNESCO World

Heritage Site due to its phenomenal wildlife and geological features, and its important paleoanthropological sites, Olduvai Gorge and Laetoli (UNESCO, 2017). The NCA is

2 8,283 km in area and includes a variety of ecosystems, including the mist-forests and

110 montane heath of the Ngorongoro Highlands to the Serengeti Plains surrounding Olduvai

Gorge (Herlocker and Dirschl, 1972). Within the NCA is the world-renowned Ngorongoro

Crater, an inactive volcanic caldera and popular tourist attraction for safaris that features high concentrations of wildlife, with a stable population of 15,000 to 25,000 large mammals, including free-ranging black rhinoceroses (Boone et al., 2006; Melita and

Mendlinger, 2013).

6.2.2 Geology

Olduvai Gorge is on the western flank of the Gregory Rift, part of the eastern branch of the East African Rift System that runs several thousand kilometers across East Africa and is made up of many adjacent rift valleys (Dawson, 1992; Ashley and Hay, 2002;

Chorowicz, 2005). The Olduvai River flows through the main branch of the gorge and is approximately 46 km long, which in the western portion is underlain by metamorphic bedrock (Hay, 1976). The Olduvai Basin’s western border marked by Granite Falls, where the gorge deepens rapidly, and the eastern border by the Olbalbal depression, a seasonal floodplain within a fault graben, and in total is approximately 26 km long (Hay, 1976). A smaller branch, the Side Gorge, meets the main gorge approximately 9 km east of Granite

Falls and originates on the western slope of Lemagrut, a nearby volcano (Hay, 1976). The eastern portion of the Main Gorge has steep sides that are between 46 and 90 m tall and is between 0.5 and 1.5 km across (Hay, 1976).

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Figure 6.2.2: A map of the Olduvai Gorge region which labels some of the major features. The grey box outlines the approximate area sampled for plants in this study. Note the topographical variation, as well as differences in vegetation between the Ngorongoro Highlands and the area directly around Olduvai Gorge. Created with Google Earth Pro using imagery from Landsat/Copernicus.

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The gorge area is underlain by a Palaeozoic metamorphic geological basement that is part of the Mozambique Belt to the west, which has been covered by many layers of volcanic tuff (Anderson and Talbot, 1965; Hay, 1976). The Mozambique Belt is an orogenic geological belt created between about 845 and 715 Ma by tectonothermal events

(Nyamai et al. 2001). Around the gorge are various metamorphic hills and inselbergs such as Naibor Soit and Kelogi, which yield these Palaeozoic metamorphic rocks (Hay, 1976).

The Mozambique Belt is bordered to the west by the Archaean Tanzania Craton (also called the Tanganyika Shield), and is composed of relatively un-metamorphosed quartzites, shales, and sandstones (Hay, 1976). The Tanzania Craton begins approximately 25 km west of Lake Ndutu, and therefore near the headwaters of the Olduvai River (Hay, 1976).

These rocks have been stable for the last 2,500 million years, and include granites and greenstones (Hay, 1976; Manya et al., 2007).

The pyroclastic rocks overlying the Mozambique Belt are from Pleistocene volcanos in the region located south and east of the gorge, which include Lemagrut,

Sadiman, Olmoti, and Ngorongoro (Hay, 1976; Mollel and Swisher, 2012). Another small, isolated volcanic centre just north of the gorge is Engelosin, and to the northeast are

Oldoinyo Lengai, Kerimasi, Loolmalasin, and Embagai (Hay, 1976; Mollel and Swisher,

2012). The contribution of these volcanoes to the stratigraphic layers of Olduvai Gorge will be discussed later in this chapter. The Olduvai sedimentary basin was formed approximately two million years ago due to uplifting in the volcanic highlands, and the two branches of the gorge itself formed over the past 200,000 years from erosion due to the

Ndutu stream emptying into the Olbalbal depression, and stratigraphic layers created by two million years of volcanism in the region are now exposed (Hay, 1976). As well, there

113 has been considerable faulting throughout the Serengeti Plains. These are visible at Olduvai

Gorge. There are five major faults named that intersect Olduvai, and are called the First through Fifth Fault, beginning with the First Fault at Olbalbal and heading westward toward Granite Falls (Hay, 1976).

6.2.3 Ecology

6.2.3.1 People and livestock

Within the NCA there are thousands of human inhabitants, including both Maasai pastoralists and non-Maasai small-scale agriculturalists, though most of the inhabitants are

Maasai (Boone et al., 2006; Melita and Mendlinger, 2013). The impact of agriculture on the environment will be discussed later in this section. The human population has been steadily increasing over the years. In 2013 a census estimated the population inside the

NCA to be 87,851 people (Melita, 2014), which is a large increase from 2007 (64,842) and

2002 (56,856) (Melita and Mendlinger, 2013).

The NCA Maasai own cows, goats, sheep, donkeys, and dogs. Over the years since

1960 the cattle population has fluctuated between about 140,000 and 50,000 with the numbers approximately 131,509 in 2013 (Melita, 2014). The small stock population reached approximately 330,079 as of 2013, a sharp increase from 2007 when the population was estimated to be 193,056 (Melita, 2014). Land use decision-making by local Maasai is determined by proximity to optimal forage-land (predominantly on the plains) and access to water (predominantly in the highlands), though the land available to them is highly restricted due to conservation efforts by the Ngorongoro Conservation Area Authority

(NCAA) (Galvin et al., 2008).

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As previously mentioned, the NCA is also a popular tourist destination. The primary source of income for the local Maasai is tourism, though most of the money generated tends to go to tour guides and not local people (Galvin et al., 2008). As a result, the Maasai prefer to focus on their livestock for their livelihood (Melita and Mendlinger,

2013). Tour groups come through the NCA for safaris and to experience Cultural Bomas, the sector of NCA tourism with the largest Maasai involvement (Melita, 2014; Melita and

Mendlinger, 2013). At these stations tourists can purchase handicrafts from locals, experience what traditional Maasai homes may be like, take photos of people in their traditional attire, and pay to see them dance (Melita and Mendlinger, 2013). Very few full- time tourism jobs are given to local Maasai people because they typically lack necessary skills such as proficiency in English, driving, and primary education (Galvin et al., 2008).

The NCAA limits the activities of NCA inhabitants, greatly restricting agriculture and permanent development (UNESCO, 2017). Agricultural practices in the NCA were formally banned by the government in 1975, although since this greatly damaged Maasai well-being, the government permitted small (1 acre), temporary cultivated plots created exclusively with handheld tools in 1991 (Boone et al., 2006). This allowance helped to alleviate pressures caused by rampant livestock death due to a severe drought and epidemic of disease from contact with wild animals (Melita and Mendlinger, 2013). The total size of the NCA is 8,283 km2, and as of 2000 contained a total of 3,967 ha of cultivated land

(Boone et al., 2006). This equates to <1% of the total area of the NCA and is concentrated on the southeastern border (Boone et al., 2006). Although no sources describing specific cultivation methods could be found, interference to bioavailable strontium values from

115 potential fertiliser strontium is very unlikely to be an issue for the area around Olduvai because so little agriculture is conducted in the region.

6.2.3.2 Wildlife

The Greater Serengeti Ecosystem spans approximately 35,000 km2 from the Rift

Valley to Lake Victoria, encompassing the migratory area used by numerous large mammal species, and includes Olduvai Gorge (Norton-Griffiths et al., 1975). During the wet season there are large populations of animals that graze throughout the NCA, not just in the

Ngorongoro Crater. The most common migratory mammals are Thompson’s gazelles

(Gazella tomsonii), zebra (Equus burchelli), and wildebeest (Connochaetes taurinus), most of which leave the Olduvai Gorge area during the dry season (Anderson and Talbot, 1965).

Giraffes (Giraffe camelopardalis), a variety of browsing antelopes, African buffalo

(Syncerus caffer), and elephants (Loxodonta africana) are also in the NCA (Boone et al.,

2006), of which giraffes and some browsing antelopes such as dik-diks (Madoqua kirkii) stay in the area during the dry season. The migratory animals move to the northwest portion of the Serengeti, where there is more food and higher amounts of rainfall (Sinclair et al.,

2007). In addition to herbivorous mammals, there are also a variety of carnivorous animals, reptiles, and birds in the area.

Large migratory mammal populations are limited by the amount of food resources available to them but are also kept in check by predation (Sinclair et al., 2007). Lions

(Panthera leo) are one such predator that frequents the area but moves to the northwestern plains in the dry season. Other predatory mammals present include (but are not limited to)

116 leopards (Panthera pardus), cheetahs (Acinonyx jubatus), jackals (Canis spp.), and hyaenas (Hyena hyaena, Crocuta crocuta) (Kennedy and Kennedy, 2014).

There are many species of reptiles and birds in the NCA as well. Some reptiles include snakes such as the black mamba (Dendroaspis polylepis) and the black-necked spitting cobra (Naja nigricollis), leopard tortoises (Geochelone pardalis), and agama lizards (Agama spp.) (Kennedy and Kennedy, 2014). Some common large-bodied birds include ostriches (Struthio camelus), secretary birds (Sagittarius serpentarius), and kori bustards (Ardeotis kori), together with a few small-bodied birds including Kittlitz’s plover

(Charadrius pecuarius), southern ground hornbill (Bucorvus leadbeateri), and white- backed vultures (Gyps africanus) (Kennedy, 2014).

6.2.3.3 Plants

There is diverse plant life in the Olduvai Gorge region, and its density is also variable. In general, plants found in this area are dry-adapted due to inconsistent and sporadic rainfall, which will be discussed later in this chapter. Most of the plants are native to the region, though there are some alien species such as Datura stramonium and

Argemone mexicana, which are believed to be transported into the NCA with construction materials from Karatu (Foxcroft et al., 2006). From here on, this section will focus on native vegetation, as it is most abundant.

In their report on vegetation of the NCA, Herlocker and Dirschl (1972) describe five major physiographic units: the western plains, the crater highland massif, the Lake

Eyasi-Kakesio area, the Empakaai Crater, and the Ngorongoro Crater. The western plains of the NCA surrounding Olduvai include Olduvai Gorge itself, the Olbalbal Depression,

117 the Eastern Serengeti Plains, the Sale Plain, the Sand Dunes north of Olduvai, and the

Doinyoogol Hills, and comprise about 50% of the total land area of the NCA (Herlocker and Dirschl, 1972). This section will focus on the vegetation in the western plains. Figure

6.2.3 shows a few of the variable plant landscapes visited around Olduvai Gorge.

The vegetation in the region is variable, as it includes different kinds of both grasslands and woodlands (Herlocker and Dirschl, 1972). The grasslands of the NCA are further classified as long grasslands (dominated by Themeda triandra), intermediate grasslands (dominated by Andropogon greenwayii), and short grasslands (dominated by

Digitaria macroblephara and Michrochloa kunthii) (Herlocker and Dirschl, 1972; Norton-

Griffiths et al., 1975). In the Serengeti Plains there are between 50 and 100 common grass species, depending on the location (Anderson and Talbot, 1965). For the most part the grasslands in the NCA area are sparse, predominantly comprised of short grass species, which contrasts the tall, thick grasses in the western portion of the Serengeti (Anderson and Talbot, 1965). A few factors that could explain the short grasses include high transpiration rates and variable rainfall, a surficial hard pan layer in the soil, and grazing by large concentrations of wild animals in the rainy season and domesticated animals year- round (Herlocker and Dirschl, 1972).

Olduvai Gorge itself can be broken into three distinct physiographic units: the western section, the eastern section, and the south fork tributary (Herlocker and Dirschl,

1972). The western section is quite shallow and relatively homogeneous in flora relative to the rest of the gorge. It features a low woodland of predominantly Commiphora madagascariensis, Acacia mellifera, and Acacia tortilis. There is also a bushy layer comprised mainly of Sansevieria robusta, Cissus quadrangularis, and Cissus cactiformis.

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The most common grasses here are Sporobolus marginatus, Sporobolus consimilis, Chloris gayana, Pennisetum mezianum, Digitaria macroblephara. The eastern section is characterised by steeper canyon walls and has more open tree coverage. This area features many of the species previously listed for the western gorge. A few additional common species include (but are not limited to) Salvadora persica, Lycium spp., and Justicia betonica. The southern fork tributary again includes many of the same species, but also features the trees Acacia drepanolobium and Acacia gerrardi, and the grasses Enneapogon elegans, Themeda triandra, and Aristida adscensionis (Herlocker and Dirschl, 1972). The name “Olduvai” comes from the Maasai word “oldupai”, which translates to “the place of the sisal”, referencing the abundant Sansevieria robusta plants around the gorge (Hay,

1976).

There are major differences in vegetation present in the wet season versus the dry season, with many more species (particularly grasses) present in the former (Mollel personal communication). As well, there are locally large differences in the density of plant species, with some areas being rich in coverage and others having almost no plant life whatsoever (Pearsall 1957). Between Olduvai Gorge and Lemuta Hill to the north there are wide areas of sandy dunes with little to no vegetation coverage, though other areas are stabilised by Commiphora and Acacia (Pearsall, 1957). This is largely due to strong winds and the very hot climate, as will be discussed later (Anderson and Talbot, 1965). Plants in the area are typically fire-resistant, as grass fires in the area are common during the dry season, particularly in years when wildebeest populations are smaller and there is more uneaten grass to burn (Sinclair et al., 2007).

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Figure 6.2.3: Various plant landscapes near Olduvai Gorge; a) an elevated view of Lemagrut (left) and Naibor Soit (right) facing southwest from near the top of Engelosin showing patchy wooded areas (uphill from sampling locality 5); b) open grassland showing sparse covering with short grasses northwest of Naibor Soit (sampling locality 30); c) a patch of Acacia xanthophloea on the outskirts of the Olbalbal depression (sampling locality 26); d) Sansevieria robusta and Commiphora merkeri at Granite Falls, the western limit of Olduvai Gorge (sampling locality 3).

6.2.3.4 Soil

The Serengeti Plains are characterised by deep soils, which around Olduvai Gorge are predominantly volcanic in origin and are very dusty and mobile (Pearsall, 1957). A large portion of the soil is from volcanic ash which originated from Oldoinyo Lengai, an active volcano located just south of Lake Natron approximately 65 km to the northeast of

Olduvai (Anderson and Talbot, 1965). The soils in the area are alkaline, sometimes reaching pH values greater than 9 (Pearsall, 1957). Within the NCA there are three main

120 types of soils: juvenile soils on volcanic ash, calcimorphic soils with hard pans, and vertisols of lithomorphic origin (Anderson and Talbot, 1965). There are not strict boundaries between the types of soil and vegetation growing in them. The soil surrounding

Olduvai Gorge is predominantly nutrient-rich calcimorphic with a hard pan, and results in dwarfed versions of many grasses and herbs growing sparsely therein, including

Sporobolus and Kyllinga (Anderson and Talbot, 1965; Sinclair et al., 2007). To the east near Olbalbal there are juvenile soils on volcanic ash where the dominant plants include

Dactyloctenium and Sporobolus grasses.

6.2.3.5 Topography

As previously mentioned, Olduvai Gorge is bordered to the south and east by the

NVH, which stand at approximately 3000 meters above sea level (masl) (Norton-Griffiths et al., 1975). Though much lower in elevation than these highlands, there is significant topographical variation around the gorge. Around Olduvai Gorge itself the elevation ranges from about 1,350 to 1,550 masl, though to the north there are undulating hills and to the south there is a high plain that rises gently, both of which peak at about 1,850 masl

(Herlocker and Dirschl, 1972). The various volcanoes in the area stand at various heights above the rest of the landscape. Engelosin is the smallest, at approximately 145 m tall, whereas Ngorongoro is between 2,100 and 2,400 m tall, but prior to its collapse, may have been 4,500 to 5,500 m in elevation (Hay, 1976). Olmoti rises to 3,100 m at its highest point,

Lemagrut is about 1,500 m tall, and Sadiman rises to about 450 m (Hay, 1976). See Figure

6.2.4 for a digital elevation model of the area directly surrounding Olduvai Gorge.

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Figure 6.2.4: A digital elevation model of the Olduvai region. Imagery is from the Shuttle Radar Topography Mission by SERVIR AFRICA and NASA. Accessed from https://maps.rcmrd.org/arcgis/services/Tanzania/Tanzania_SRTM30meters/ImageServer

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6.2.3.6 Surface waters

The southern portion of the Eastern Serengeti Plains drains into Olduvai Gorge via the Olduvai River, which moves eastward into the Olbalbal depression (Anderson and

Talbot, 1965; Herlocker and Dirschl, 1972). This water originates at Lake Ndutu and Lake

Masek (Leakey, 1978). The water that passes through the Side Gorge originates on

Lemagrut and joins the Olduvai River at the junction of the two parts of the gorge (Leakey,

1978). In some years there is no water in the river at all, even during the rainy season (Hay,

1976). Any surface water in the area, including the large lakes, is very saline and therefore unfit for consumption (Pearsall, 1957).

6.2.4 Climate

The NCA falls within a rain shadow created by the NVH (Norton-Griffiths et al.,

1975). The main rainy season is approximately April to June, though there can also be a short rainy period in December (Pearsall, 1957). Nonetheless, rainfall during the rainy season is still uncertain (Pearsall, 1957). The Serengeti Plains have a mean annual rainfall of ~800 mm (Norton-Griffiths et al., 1975), but data collected on annual rainfall from the

Leakey camp at Olduvai Gorge over three years show that the Olduvai region only saw

531 mm, 378 mm, and 331 mm in 1965, 1966, and 1967, respectively (Herlocker and

Dirschl, 1972). Another eight-year-long study of rainfall at the gorge found that rainfall varied from 276 to 1052 mm per year with an average of 566 mm (Hay, 1976). This supports Anderson and Talbots’ (1965) claim that the eastern portion of the Serengeti

Plains receive less rain than the western portion.

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Any rain that does fall in the area is usually lost quickly due to run-off. Because of the deep soils, water quickly drains downward and out of reach of plants and animals, except in areas where the underlying bedrock is close to the surface such as near the local inselbergs (Pearsall, 1957). As a result, these inselbergs sometimes feature different or more abundant plant life than other areas on the plains because of the increased access to water (Pearsall, 1957; Anderson and Talbot, 1965).

The temperature at the gorge ranges between approximately 12 °C to 33 °C, with an average annual air temperature of 22.8 °C (Hay, 1976). Typically, January and February are the hottest months and July and August are the coldest (Anderson and Talbot, 1965), though there is little overall seasonal variation (Hay, 1976). The hot temperatures coupled with little shade leads to rapid evaporation of any water in the area. Incomplete evaporation records suggest that there may be an evaporation rate of 10 mm per day,(Hay, 1976). As a result, there is very little surface water available in the area, particularly in the dry season.

The wind in the area can be very strong and mainly comes from the east (Anderson and Talbot, 1965). Mary Leakey recorded the wind speed twice a day (once in the morning and once in the evening) for five months in 1972 and found that the average wind speed was 12.4 km/h (Hay, 1976). High rates of soil erosion resulting in sparse grass cover in the area are probably highest toward the end of the dry season, due to high temperatures and strong winds, coupled with a lack of precipitation (Anderson and Talbot, 1965).

6.3 Olduvai Gorge

6.3.1 The Beds

Olduvai Gorge contains a continuous archaeological record documenting

124 technological and evolutionary change from approximately two million years ago to present which is contained in seven geological formations. The different beds of Olduvai

Gorge were named by Hans Reck in the early 20th century, listing them from Bed I through

Bed V, though in more recent times some alterations were made to his classifications of

Bed IV and Bed V (Hay, 1976, Leakey, 1978). The beds as they are known today include

Bed I (2.0-1.8 Ma), Bed II (1.8 to 1.3 Ma), Bed III (1.3 to 0.7 Ma), Bed IV (0.7 to 0.6 Ma)

Masek Beds (0.6 to 0.4 Ma), and Ndutu and Naisiusiu Beds (0.4 Ma to present) (Hay, 1976;

Tamrat et al., 1995; Ashley and Hay, 2002; McHenry et al., 2007).

It is possible based on magnetostratigraphy that the Masek Beds were deposited about 0.5 Ma earlier than Hay (1976) believed, making the boundary between Bed IV and the Masek Beds approximately 1 Ma. Tamrat et al. (1995) interpreted the reversal to normal polarity within Bed IV to be the Jamarillo (1.07 to 0.99 Ma) or Jamarillo + Cobb Mountain

(1.19 to 0.99 Ma). This, then, would make the age of Beds III-IV together approximately

1.3 to 1 Ma.

The stratigraphy of Olduvai Gorge is also a valuable resource for studying geological phenomena such as rift-related tectonics, volcanism, and sedimentation (Ashley and Hay, 2002). The tuffs in the Olduvai sequence came from a variety of volcanoes within the region, all of which are inactive today except Oldoinyo Lengai. These tuffs have been dated using K-Ar dating and palaeomagnetism (Hay, 1976), and are used to date the archaeological sites throughout the gorge. However, many tuffs have been reworked by things like stream action, wind, and erosion, making them unsuitable for accurate radiometric dating (McHenry et al., 2013).

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The NVH has provided lava and pyroclastic debris to Olduvai Gorge throughout its development. Most of the basement basalt of Bed I at Olduvai Gorge was supplied by

Ngorongoro approximately 2.1 to 2.0 Ma (Hay, 1976). Olmoti provided much of the pyroclastic material and tuffs present in Bed I and Bed II, including Tuff IB (Hay, 1976).

These Olmoti lavas are K-Ar dated to approximately 1.85 to 1.65 Ma. Lemagrut provided clasts to the Side Gorge, and lavas to much of the Laetolil Beds, which are fossiliferous pyroclastic deposits located on the slopes of the mountain, near Kelogi, and further southwest around Laetoli (Hay, 1976). Sadiman is thought to be about the same age of

Ngorongoro and contributed clasts to the lowermost part of Bed I (Hay, 1976). As well,

Oldoinyo Lengai and Kerimasi provided a large amount of volcanic ash to the area in the late Pleistocene (Hay, 1976). The Masek beds are likely associated with Kerimasi, and the

Naisiusiu beds with Oldoinyo Lengai (Hay, 1976). The volcanic source of Bed III pyroclastic material is uncertain, but thought to be Embagai (also known as Elanairobi) or

Loolmalasin (Mollel and Swisher, 2012; Greenwood, 2014).

The archaeology, paleoenvironment, and geology of Beds I and II at Olduvai Gorge have been studied extensively (e.g. Leakey, 1971; Bamford et al., 2006; Copeland, 2007;

Sikes and Ashley, 2007; Stollhofen et al., 2008; Ashley et al., 2010a; 2010b; 2010c;

McHenry, 2012; Diez-Martin, et al. 2015; Domínguez-Rodrigo et al., 2015; 2017;

Organista et al., 2016), whereas the younger beds have not been the subject of as many studies because they do not yield as much archaeological material (e.g. Kleindienst, 1973;

Day and Molleson, 1976; Pante, 2010; 2013; Hlusko et al., 2015; Kearney, 2016). The beds will now be discussed briefly in chronological order.

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6.3.1.1 Bed I

Bed I is composed of sedimentary deposits overlaying lavas (Hay, 1976), and has been extensively studied by researchers in numerous fields. Most famously, it contains the

FLK Zinj site where Mary Leakey discovered the Zinjanthropus (Paranthropus boisei) cranium in 1959. This is the thickest of the beds, reaching 60 m in some parts of the eastern

Main Gorge and 43 m in the west (Hay, 1976). The oldest exposures (~2 Ma) are only found in the west, but all of the known archaeological occurrences are in the layers dated to 1.85 to 1.70 Ma (Hay, 1976).

This bed contains five lithofacies including lake deposits, lava flows, lake-margin terrain, an alluvial plain, and an alluvial fan (Hay, 1976; Stollhofen et al., 2008). The alluvial fan and lava flow deposits are along the eastern margin of the basin, and the alluvial plain deposits are on the western half underlying the lake and lake-margin deposits (Hay,

1976). During that time there was a large saline and alkaline palaeo-lake present which likely fluctuated a great deal in extent and depth, though was shallow and broad most of the time (Hay, 1976; Sikes and Ashley, 2007). The water flooding the southeastern lake- margin appears to have been relatively fresh, and most faunal remains and archaeological sites of Bed I are concentrated in this area (Hay, 1976).

Bed I contains sites with dense concentrations of fossils and stone tools, including the two largest excavated Early Pleistocene archaeological sites, David’s Site (Domínguez-

Rodrigo et al., 2017) and FLK Zinj (Leakey, 1971). These sites are located in the eastern- central portion of the gorge where the oldest and most well-known Bed I archaeological sites are found (Stollhofen et al., 2008). The sites in Bed I contain stone tools only from the Oldowan industry, which persisted from about 2.5 to 1.5 Ma (Hay, 1976; de la Torre et

127 al., 2012; Reti, 2016). Many animal remains (both large and small) found in this bed have cut marks on them, suggesting either hunting or scavenging behaviour by hominins

(Domínguez-Rodrigo, 1997; Fernández-Jalvo et al., 1999; Blumenschine et al., 2012b).

Other fossils, despite being found in association with stone tools, were purely the result of carnivore accumulation and therefore unrelated to hominin activity (Domínguez-Rodrigo et al., 2010).

6.3.1.2 Bed II

Bed II is found over a slightly larger area than Bed I, and is approximately 20-30 m in thickness (Hay, 1976). It contains numerous archaeological sites that represent three tool industries: Oldowan in lower Bed II only, and Developed Oldowan, and Acheulean in mid- and upper-Bed II (Hay, 1976; Blumenschine et al., 2012a; de la Torre et al., 2012), though the oldest known Acheulean handaxe in the area was recently discovered in lower

Bed II at FLK West (Diez-Martin et al., 2015). Because the origins of the Acheulean industry occurred during Bed II times, these layers are studied to better understand the timing of its emergence, as well as the biological and cultural processes which led to its creation (de la Torre et al., 2012). Homo habilis has been found in the lower part of the bed, and Homo erectus in the upper part (Hay, 1976). Paranthropus cf. boisei remains have been found throughout Bed II, including a 1.34 million-year-old partial skeleton with postcranial elements found in situ with diagnostic dental remains (Hay, 1976; Domínguez-

Rodrigo et al., 2013). There is a disconformity in the bed that occurred approximately 1.60

Ma, after which there was an increase in grassland and decrease in lake size, and a change

128 in fauna from swamp-dwelling forms to those favouring open savanna and riverine conditions (Hay, 1976).

There are many lithofacies and environments in Bed II, including lake-margin deposits, lake deposits, alluvial fan deposits, and aeolian deposits from either the east or northeast beneath the disconformity (Hay, 1976; Stanistreet, 2012). This lake, like in Bed

I times, was saline and alkaline and fluctuated in size (Hay, 1976; Blumenschine et al.,

2012a). The lake deposits are bordered by lake-margin deposits, which again contain the majority of faunal remains in lower Bed II. After the disconformity there is a mixture of lake deposits, fluvial-lacustrine deposits, and fluvial deposits. By this time the saline lake was much smaller and continued to shrink until it disappeared before the end of Bed II times (Hay, 1976). Above the disconformity most faunal remains and archaeological materials are found in the fluvial-lacustrine facies (Hay, 1976).

6.3.1.3 Beds III and IV

Beds III and IV contain archaeological material of both the Developed Oldowan and Acheulean industries (Leakey and Roe, 1994), though about 75% of this material is concentrated in the Bed IV sediments (Hay, 1976). The only Bed III site to yield large concentrations of archaeological material is Juma’s Korongo (Pante, 2010; 2013), which will be discussed in detail later in this chapter. Within these beds remains of Homo erectus and Homo sp. have been found (Hay, 1976). These two beds can only be distinguished from one another in the eastern part of the gorge, and in the west they are referred to collectively as “Bed III-IV undivided” (Hay, 1976). Where they are distinguishable Bed

III is approximately 4.5 to 11 m thick, and Bed IV is 2.4 to 10 m thick, depending on the

129 proximity to different faults (Hay, 1976). Bed III is mainly a reddish-brown deposit consisting mainly of volcanic conglomerates and sandstones, and Bed IV contains both metamorphic and volcanic detritus and is mainly grey or brown in colour (Hay, 1976;

Kearney, 2016). Marker beds can be used to subdivide these two beds, though this is only applicable in the eastern part of the gorge and, even there, this is not always successful

(Hay, 1976).

These beds are predominantly composed of claystone sediment, with sandstone being the second most abundant. Both contain resistant and recessive units indicative of fluvial deposition, where the resistant units were likely stream channels and the recessive units may have been floodplain deposits or palaeosols (Kearney, 2016). Beds III and IV are divided into three lithofacies: the eastern fluvial deposits, the western fluvial deposits, and fluvial-lacustrine deposits (Hay, 1976). The eastern fluvial deposits contain volcanic detritus, were deposited on an alluvial surface that sloped gently to the north and west by intermittently-flowing streams and are reddish-brown in colour (Hay, 1976). The western fluvial deposits, containing mainly metamorphic detritus, are grey and brown in colour and were deposited on a southeastern-sloping alluvial plain by meandering streams that flowed most of the year (Hay, 1976). These deposits contain most of the archaeological material found in Beds III and IV, which tend to be concentrated in the main drainageway (Hay,

1976; e.g. Kleindienst, 1973; Leakey and Roe, 1994). The archaeological material at JK is found within this type of deposit. The fluvial-lacustrine facies exhibit both fluvial and lacustrine features and are made of sediments of mixed metamorphic and volcanic detritus which accumulated on a lake margin (Hay, 1976).

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6.3.1.4 The Masek Beds

The Masek Beds were the last to form prior to the erosion of the gorge (Hay, 1976).

They are slightly more widespread than Bed I and have a maximum thickness of 25 m, though this varies in relation to faults (Hay, 1976). These beds are composed of detrital sediment and aeolian tuff and can be subdivided into a lower and upper unit which formed while Olduvai was an alluvial plain (Hay, 1976). The lower unit is made up of a western fluvial facies, eastern fluvial faces, and an aeolian faces (Hay, 1976). For the most part vertebrate remains and stone tools are rare in the Masek beds, and only one archaeological site is known in the beds (Hay, 1976). This is likely because the area was becoming hotter and drier in this time, and is thought to be much like the environment of today (Hay, 1976).

Despite the scarcity of archaeology in the Masek Beds, Homo erectus remains have been recovered therein (Hay, 1976).

6.3.1.5 The Ndutu and Naisiusiu Beds

The Ndutu Beds are subdivided into the lower and upper units (Hay, 1976). The lower unit consists of sandstone, conglomerate, and tuff, whereas the upper unit is made of mainly aeolian tuffs (Hay, 1976). Most volcanic tephra in these beds came from Oldoinyo

Lengai (Hay, 1976). During its formation, Olbalbal became the drainage sump, and remains the lowest part of the Olduvai basin today (Hay, 1976). These beds contain two

Middle Stone Age archaeological sites, one in the upper bed and one in the lower (Hay,

1976).

The Naisiusiu Beds were deposited after the gorge had already eroded to almost its modern level and are made up mainly of aeolian tuff that is found in the sides and bottom

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Period (Ma) C4 Biomass (%) Mean Annual Temperature (°C) 0.2 to 0.00 80-90 20+ 0.40 to 0.20 70-90 20+ 0.60 to 0.40 50-80 20+ 1.17 to 0.60 50-70 15-18 1.34 to 1.17 40-70 15-18 1.62 to 1.34 20-50 15-18 1.67 to 1.62 20-90 22-25 2.2 to 1.67 40-60 13-16

Table 6.3.1: Estimates of C4 biomass coverage and mean annual temperature from Cerling and Hay (1986). Precise temperature estimates for 60,000 years ago onwards were not provided.

of the gorge and all across the plain (Hay, 1976). Oldoinyo Lengai is once again responsible for the tephra of these beds (Hay, 1976). There is one known archaeological site in these beds which contains a microlithic assemblage and dates to about 17,000 BP (Hay, 1976).

6.3.2 Palaeoenvironment and palaeoclimate

As mentioned, a major difference between past- and present-Olduvai is that there was a large saline and alkaline palaeo-lake at Olduvai during Bed I and Bed II times, which was likely similar to the lakes in the region today (Hay, 1976). Lake deposits in Bed I are primarily comprised of claystone and contain authigenic minerals that suggest the high alkalinity and salinity of the lake waters (Hay, 1976). The margins of the lake fluctuated greatly over time.

13 δ C values of paleosol carbonates (CaCO3) from non-lacustrine areas at Olduvai

Gorge suggest that through time C4 vegetation comprised an increasing proportion of total vegetation biomass, thus implying increasing aridity (Cerling and Hay, 1986). Cerling and

Hay (1986) analysed samples from various areas around the gorge (though they did not

132 specify the exact locations) throughout the sequence and could therefore report on changes in climate over approximately two million years. See Table 6.3.1 for their estimates of C4 biomass and temperature based on carbonate isotope results. Aside from a brief period in

Bed II, the Lemuta Member (1.67–1.62 Ma) where the temperature rapidly increased

(though they note that this could also be caused by a decrease in overall precipitation) and

C4 plants may have expanded a great deal, they found that there was a general trend toward increasing C4 biomass and mean annual temperature. From approximately 0.60 Ma onward they do not provide precise temperature estimates, but say that they are above 20 °C and approaching the present day average temperature of about 23 °C. As well, soil carbonates rarely form in areas with annual rainfall exceeding 750-800 mm, and at Olduvai Gorge they are relatively uncommon in Beds I and II, except in the Lemuta Member. This as well implies a drying trend through time (Cerling and Hay, 1986). Later work by Cerling et al.

(1993; 1997) found that grassland expansion was occurring worldwide, not just in East

Africa, for the last seven million years.

Cerling and Hay’s (1986) results for Bed I were consistent with those found by

Sikes and Ashley (2007) for the western gorge. They sampled upper Bed I sediments from

11 trenches over 2.5 km2 on the western side of the gorge between Granite Falls and

Naisiusiu Hill, being the western margin of the former palaeo-lake. Their samples, which contained fluvial and lake deposits, aeolian sediment, palaeosols, and volcanic ash, averaged -4.8‰, suggesting 50% of the vegetation biomass was C4 grass. They found variable δ18O values for Bed I, which they believed is more likely related to changes in amount of precipitation than average temperature (Sikes and Ashley, 2007). Other similar studies have been conducted on carbonates from Beds I and II (e.g. Ashely et al., 2010a;

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Bennett et al., 2012) and other materials such as phytoliths and plant fossils from Bed I

(e.g. Bamford et al., 2006; 2008; Barboni et al., 2010; Albert et al., 2015) for palaeoenvironmental reconstruction, but nothing more has been published for Bed III and higher to date.

6.4 Juma’s Korongo

Juma’s Korongo (JK), also known as JK2 due to Louis Leakey’s uncertainty regarding the exact location of the original JK site which he found in the 1930s, is a Bed

III and IV site at Olduvai Gorge, Tanzania dated to approximately 1.3 to 0.6 Ma based on paleomagnetism and sedimentation rates (Hay, 1976; Manega, 1993; Tamrat et al., 1995;

McHenry et al., 2007; Pante, 2010; 2013), though as previously mentioned it is possible that the boundary between Bed IV and the Masek Beds is actually at approximately 1 Ma

(Tamrat et al., 1995). This would restrict the age of JK to approximately 1.3 to 1.0 Ma. In publications, JK and JK2 refer to the same archaeological locality.

As aforementioned, JK is the only Bed III site known to yield large concentrations of fossils and stone tools (Pante, 2010; 2013). The site was created by fluvial and deltaic depositions (found within the western fluvial deposits of Bed III), and archaeological deposits have been revealed by three branches of a side gulley that join to form a stream which meets the Olduvai River immediately south of the site (Hay, 1976; Pante, 2010;

2013). The site is located 2.2 km east of the junction between the side and main gorges and is on the northern edge of the gorge. It is southeast of Naibor Soit and located in between the Third and Fourth Faults (Hay, 1976). See Figure 6.4.1 for the exact location of JK.

Maxine Kleindienst first excavated at the site from 1961 to 1962 and described the two

134 halves of the site JK2 East and JK2 West (Kleindienst, 1973). Her account of the stratigraphy at the site is the only detailed one to date.

6.4.1 Stratigraphy and archaeology of JK

See Figure 6.4.2 for Kleindienst’s (1973) map of the site and the trenches that she excavated. JK2 East has thicker deposits than JK2 West, and more distinct layers, which represent both Beds III and IV. Kleindienst (1973) reports that at JK2 East there are deposits representing Beds III and IV that are approximately 20 m thick above the contact with Bed II. At JK2 West the deposits are not as thick, approximately 11 m above the Bed

II contact, and represent only Bed III. She found that some sediments previously thought to be diagnostic of Bed IV interfingered sediments of Bed III at this site (Kleindienst, 1973;

Hay, 1976).

Fossil fauna found at JK include hominids, cercopithecids, lagomorphs, rodents, carnivores, Proboscidea, Perissodactyla, and Artiodactyla (Kleindienst, 1973; Day and

Molleson, 1976). Marine fossils are also common; there are scattered fish bones and crocodile teeth through the site (Kleindienst, 1973). The presence of fish, hippopotamus, and crocodiles implies that the area was wet year-round. Many tools and fossils recovered from highly altered areas are extremely weathered, though in other areas they were very well-preserved (Kleindienst, 1973).

Recovered archaeological materials include fossils and Acheulean industry stone tools, including handaxes, cleavers, and other small artifacts (Pante, 2013). Many of the tools found by Kleindienst (1973) in Trench 8 resembled those found at the upper-Bed II sites Thiongo Korongo (TK) and Douglas Korongo (DK) and were thus attributed to the

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“Developed Oldowan B” industry. Kleindienst (1973) dug a series of trenches across all of

JK2. She noted that between JK2 East and JK2 West there is a marked difference in preservation of bone. She found that fossils in JK2 West are black or dark brown in colour and are thin and sometimes abraded. In JK2 East the bones are better preserved and are much lighter in colour, except when the matrix is heavily cemented. Because fossils from both areas have almost identical composition, she believes that the difference in preservation is not of significance (Kleindienst, 1973). The dark colouration and heavy mineralisation is characteristic across many Bed III and Bed IV sites (Day and Molleson,

1976).

Trenches A and B in JK2 West contained many artifacts and fossils of varying condition in sediments over the grey clay bed, though the majority are very well preserved and therefore were likely not transported very far. The locations of Trenches A and B were chosen due to many surface fossil fragments and to Acheulean tools weathering out, respectively. Large amounts of debitage were found in both trenches, indicating that the finds represent human occupation debris. This again suggests an aggrading environment that likely was used continuously by hominins or represents repositioned debris from a nearby living site (Kleindienst, 1973). In Trench A she found artifacts scattered through grey-brown silt, which increased in size and frequency at gradational contact with fine sand and continued to gradational contact with coarse sand (Kleindienst, 1973). In Trench B there was a large concentration of artifacts in the basal part where coarse sand overlies grey silty clay in a disconformity, though otherwise artifacts were scattered throughout

(Kleindienst, 1973). The orientation of the large artifacts in Trench B in three areas implies

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Figure 6.4.1: Satellite images showing the location of JK within Olduvai Gorge. The black outline on the smaller image shows where the large image is situated, and the black outline on the large image shows the site. The black boxes within the outline show the locations of geological trenches excavated by the SDS team in 2017. Created with Google Earth Pro using imagery from Landsat/Copernicus.

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Figure 6.4.2: Reproduction of Figure 14 from Kleindienst (1973) showing the locations of trenches A, B, and 9 in JK2 West and trenches 1-8 and 10 in JK2 East. Palaeogeographic evidence is also shown as follows: bold arrows show predicted major channel axes, small arrows show predicted minor channel axes, and the dashed line is the boundary of the fluctuating clay/sand margin. LAO is the Lower Archaeological Occurrence in Trench 8 in course sand and clay, F.SD is fine sand and C.SD is coarse sand in Trench A, and SD is total sand over grey clay in Trench B. Black rosettes show preferred orientation of elongated objects in the specified layers.

138 that they were transported and deposited by water, and she believes that it is likely that this is true of materials in both Trench A and B (Kleindienst, 1973).

Within Trench 8, which was excavated purely for stratigraphic purposes, there are two layers that yield most of the archaeological material. These are the Upper

Archaeological Occurrence (UAO), which contained scattered fossils and stone tools in coarse sands, and the Lower Archaeological Occurrence (LAO), which contained large quantities of well-preserved lithics and bones found in places overlying the contact between coarse sand and silty clay and within the top of the clay (Kleindienst, 1973). The

LAO of Trench 8 was made up of a sand layer which contained most of the archaeological material and a clay layer and contained much higher concentrations of lithic material than the UAO did (n=365 for UAO and n=3731 for LAO). These high concentrations are in stark contrast with the upper beds of Trench 8 and all of Trenches 5-7 where no artifacts were found, though there were no large edged tools such as handaxes and cleavers found in Trench 8. Kleindienst (1973) believes that the materials in the LAO were deposited in a continuously aggrading environment like a sand bar or stream bank and were not carried over a long distance, as they are in very good condition, except some weathered hippopotamus remains. She proposed that this represented a stream-side butchering site that may have seen multiple occupations (Kleindienst, 1973), though Pante (2010) found no evidence of hominin-induced surface modifications on any faunal remains he analysed from this trench and therefore believes the stone tool and fauna association to be coincidental.

Later, from 1969 to 1971, Mary Leakey and her team excavated at JK West. Their findings were similar to those of Kleindienst, including the huge variation in preservation

139 quality and artifacts that looked to be out of their primary context (Leakey and Roe, 1994).

Leakey excavated her trenches in 10 cm spits but found that the spits could not be correlated between trenches and thus did not try to do so in her analysis (Leakey and Roe, 1994).

Leakey and Roe (1994) describe four stratigraphic layers at JK: coarse grey sands that contain artifacts in the lowest level of the trenches, fine sands that were typically red in colour and sometimes interfingered with the coarse, grey sand, the pink siltstone, and clay above the siltstone which contained artifacts. Most artifacts and faunal remains came from the coarse sands from Trench III, where Leakey found 81 stone tools including many bifaces and choppers and thousands of pieces of debitage (Leakey and Roe, 1994). In the fine-grained, red sand, which was cross-bedded in some areas, they found mainly heavily abraded tools (61 tools in total, plus thousands of debitage flakes) and bones, but some were still in good condition. The pink siltstone yielded 15 artifacts and a series of pits.

These pits, some of which contained bone fragments and stone flakes, ranged from 20-60 cm in diameter and were found over an area approximately 8 m by 12 m (Leakey and Roe,

1994). She had a museum built around the features, which has since been dismantled and the features were destroyed. The foundations of the museum remain at the western edge of the site today. The clay layer above the siltstone in the trial trench contained a few artifacts

(176 in total, 120 of which were flakes) within a small channel, though it was not possible to tell where they came from originally (Leakey and Roe, 1994).

6.4.2 Palaeoenvironment of JK

Geological evidence suggests that most of the deposits at JK were created by stream channels or back channels (Kleindienst, 1973; Leakey and Roe, 1994). These streams likely

140 moved north overall and were probably directed north-westerly in JK2 East and north- easterly in JK2 West (Kleindienst, 1973). Although there is some metamorphic rock in the deposits, most coarse detritus appears to be of volcanic origin, likely from the NVH

(Kleindienst, 1973). The faunal remains suggest that the environment was not wholly unlike that of the present, as many of them have modern relatives that still frequent the area, and pollen samples from grey silty clay in JK2 West indicate that the area was a grassland with proximity to water (Kleindienst, 1973). However, the presence of fish, crocodile, and hippopotamus remains (plus the tilapia nests that Leakey found) implies that the area had a much more permanent, relatively fresh water source than at present. It is possible that there was stronger or perennial stream flow coming from the south leading to a marshland in the area or perennial pools that supported vegetation and attracted animals including hominins (Kleindienst, 1973). This attraction could explain why other Bed III sites show relatively little evidence of hominin activity, though evidence of this at JK is limited to specific, localised areas (Kleindienst, 1973).

6.5 Sampling for this Study

6.5.1 Plant sampling

Plant sampling localities were chosen based on distance from one another, differences in elevation, differences in rock type, and proximity to the seasonal floodplain

Olbalbal. Samples were collected from areas with volcanic soils and bedrock, and from areas directly on or immediately beside metamorphic outcrops and inselbergs to the north

(and Kelogi to the southwest) to see if there was a difference between the two. Olbalbal was of interest because it is where waters from the Olduvai River are deposited, which

141 originates to the west at Lake Masek and Lake Ndutu near very old Tanzania Craton bedrock, and further south from Lemagrut. River water has been shown to have high

87Sr/86Sr values and is believed to be representative of the continental average strontium values (Palmer and Edmond, 1989). It is likely, therefore, that plants that grow using the river water have elevated 87Sr/86Sr values in relation to those which do not.

When possible, sampling localities were spaced out by approximately four to five km. This was done in an attempt to capture small-scale variability over a single geological domain. Spacing between sampling localities was notably smaller between localities 2 and

3 to see if there was a difference between the Granite Falls riverbed and the area just to the north, and at Olbalbal between areas of varying vegetation cover. As well, distance between localities was also sometimes determined by proximity to varying kinds of vegetation. When possible, multiple types of vegetation were collected from each locality to try and avoid biasing results toward bedrock or atmospheric values due to root depth or differences in plant nutrient cycling (Poszwa et al., 2004; Maurer et al., 2012; Hartman and

Richards, 2014; Flockhart et al., 2015). Longer or shorter distances were travelled between sampling localities if there was not sufficient variation in plants to sample, unless there appeared to be no other variation nearby. For example, Photo B in Figure 6.2.3 shows a large area on the landscape with nothing but small, sparse tufts of grass. All samples were collected in the dry season so there was not as much variation in vegetation as in other times of the year. Details about the sampling procedure were discussed in Chapter 5.

6.5.2 Excavations at JK

The Stone Tools, Diet, and Sociality (SDS) research team excavated at JK in

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Figure 6.5.1: A topographic map created with total station points by the SDS team in 2017. The map includes the locations and sizes of the trenches, the total station set-up points, the three heads of the stream that join the Olduvai River, and the location of Mary Leakey’s former museum. This map was created by Robert Patalano for SDS and is used here with his permission

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Figure 6.5.2: A view of the eastern portion of JK (facing southwest) showing Trenches A, B, and D. The locations of Trenches C, E, and F are too low to see from this location.

Trench Teeth Bones Stone Tools A 0 20 23 B 2 16 6 C 0 5 21 D 3 20 3 E/F 1 3 2 Survey/outside trench 1 91 89 Total 7 148 144 Table 6.5.1: Counts of teeth, bones, and stone tools recovered from JK in 2017. Most of the survey work was done near Trench A.

June and July 2017. The team had not worked there previously, so this work was largely exploratory. Excavations were led by Dr. Julio Mercader of the University of Calgary, and the team included PhD students from the University of Calgary (Robert Patalano, Makarius

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Itambu, and Mariam Bundala), Master’s students from the University of Calgary (Patrick

Lee, Julien Favreau, and Laura Tucker), an undergraduate student from the University of

Calgary (Fergus Larter), an employee of the National Museums of Tanzania (Aloyce

Mwambwiga), and 12 local Maasai men (Benja Saruni, Konnei Lesian, Lazaro Tikako,

Sammiel Keuke, Meshack Keuke, Zakayoi Mwaana, Amos Ijackob, Soombei Olekiteyet,

Lekaai Kisota, Joshwa Keuke, Maaseto, and Lengume) who were hired to help excavate for the season.

Six geological trenches (Trench A to F) were dug in the area described by

Kleindienst (1973) as JK2 East to understand the geology of the site. The shapes and sizes of these trenches varied and were located to show as much of the site stratigraphy as possible. See Figure 6.5.1 for a topographic map of JK which shows the locations of the trenches and the datum points used. The topographical survey was done manually by the team over the field season using a Leica total station.

Figure 6.5.2 shows the site and locations of three trenches. Trench A was small and square and served to locate the contact between Beds III and IV in a higher portion of the site. No teeth were found in this trench. See Table 6.5.1 for counts of bones, teeth, and stone tools for each trench and surficial survey finds. Trench B was a long step trench dug into the side of a slope southwest of Trench A. It had three distinct levels, each with a step approximately two metres long and one metre high. Teeth JK2 and JK5 were found in this trench. Trench C was located to the west of Trench B. It was smaller in size and dug along the bottom of the small streambed running through that portion of the site. No teeth were recovered here. Trench D was west of Trench C and much larger, the same size as Trench

B, with no steps built into it. This trench contained teeth JK3, JK6, and JK7. Trench E was

145 small and located west of Trench D near the bottom of the slope, and Trench F was deep and narrow, situated along the streambed and shared its southern wall with the northern wall of Trench E. Tooth JK4 came from Trench F. The tooth JK1 was found in the wall just east of Trench E in a conglomerate level just above the red Bed III sediments and was uncovered by erosion, not excavation. The age of this conglomerate layer is unknown, and potentially much younger than Bed III. Specifics about the teeth were discussed in Chapter

5. In total, 148 bones, 7 teeth, and 144 stone tools were recovered from JK in 2017.

6.6 Conclusion

Olduvai Gorge is in the Ngorongoro Conservation Area in Northern Tanzania, a

UNESCO World Heritage Site which borders Serengeti National Park. This area is underlain by a Palaeozoic metamorphic bedrock, part of the Mozambique Belt, though many layers of volcanic rock have been deposited on the landscape resulting from frequent volcanic activity in the NVH. The metamorphic rocks are present at the surface in the form of inselbergs, mainly clustered north of the gorge. Further to the west, outside of the sampling area but near the headwaters of the Olduvai River, is the Tanzania Craton, an

Archean metamorphic geological domain.

Within the NCA there are thousands of human inhabitants, and their livestock. As well, there is considerable diversity in plants and animals in the area. Many of the animals migrate from the NCA in the dry season to the northwestern portion of the Serengeti where it is wetter and there is more food available. The climate of the Olduvai Gorge region is semi-arid. It is hot year-round with little variation in temperature between the wet and dry seasons, but most of the precipitation falls between April and June, though sometimes there

146 is a short rainy season in December. The region is topographically variable, with the NVH to the south and east, the low drainage sump at Olbalbal, and the metamorphic inselbergs in the north.

Olduvai Gorge itself is an important area for palaeoanthropological, archaeological, and geological research, as it contains a continuous record of the past two million years.

Within the gorge there are seven distinct stratigraphic units referred to as Bed I-IV, the

Masek Beds, the Ndutu Beds, and the Naisiusiu Beds. Of these, the archaeology and geology of Beds I and II are most thoroughly studied. Very little work has been done on the younger layers.

This study focuses on Juma’s Korongo, a Bed III site dated to 1.15 to 0.8 Ma. This site has been studied by Maxine Kleindienst (1973), Mary Leakey (Leakey and Roe, 1994), and Michael Pante (2011; 2013). The site was formed by fluvial or deltaic depositions, and contained perennial streams during Bed III time. A variety of fauna have been found there, including hominins, terrestrial animals, and aquatic animals. As well, stone tools of the

Developed Oldowan and Acheulean have been recovered.

Plants were collected across the region for this study in an area spanning approximately 940 km2. They were collected around the volcanic area, as well as from the metamorphic outcrops and inselbergs to the north (and Kelogi to the south) in such a way to try and account for variability between lacustrine and non-lacustrine localities, varying elevation, and latitudinal and longitudinal differences. Teeth were collected for analysis from JK in summer 2017 from a series of geological trenches excavated to better understand the geology and stratigraphy of the site. In total, seven teeth were found.

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Chapter 7: Results

7.1 Geographical Variation in Bioavailable Strontium

7.1.1 Geological differences

See Table 7.1.1 for a list of 87Sr/86Sr results for each locality, as well as the types of plants collected. The 87Sr/86Sr results for each of the sampled localities and their geological domain can be found in Figure 7.1.1, and the isoscape showing the approximate distribution of 87Sr/86Sr values for the area can be found in Figure 7.1.2. The total range in

87Sr/86Sr values for all localities was 0.70424 to 0.70476. Summary statistics for each geological domain are in Table 7.1.2.

There was a significant difference between 87Sr/86Sr values for localities from different bedrock type (Kruskal-Wallis Test, P <0.01), but only between lacustrine (n=5; median=0.704714) and metamorphic areas (n=9; median=0.70449) (Dunn’s Post-Hoc

Test, P <0.01) and lacustrine and volcanic areas (n=19; median=0.70449) (Dunn’s Post-

Hoc Test, P <0.001). There was no significant difference between metamorphic and volcanic sampling localities (Dunn’s Post-Hoc Test, P=0.8942), which completely overlap one another. There is one metamorphic sampling locality (Locality 14) that is higher than the lowest Olbalbal sample (Locality 23). Otherwise, the Olbalbal locality 87Sr/86Sr values are the highest of all.

7.1.2 Elevation

Graphs of 87Sr/86Sr for each geological area plotted against geographic variables are presented in Figure 7.1.3. See Table 7.1.3 for Spearman’s correlation coefficients describing the relationship between 87Sr/86Sr values of the various geological areas and

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Figure 7.1.1: 87Sr/86Sr results for each locality by bedrock type.

149 geographical variables. These relationships were analysed to determine whether spatial patterning exists in 87Sr/86Sr values within areas of the same bedrock type. R values and P values are both presented in the table, but the R values are the ones of importance. The P values do not indicate the strength of the relationship, only the likelihood of the correlation being the result of chance. There is a moderate negative correlation between 87Sr/86Sr and elevation for all localities (Spearman’s correlation, R=-0.36985). There is no correlation between elevation and metamorphic areas, volcanic areas, nor volcanic and metamorphic combined and elevation. The correlation coefficient for the Olbalbal samples was strongly negative (Spearman’s correlation, R=-0.7), but it is not considered significant due to the small sample size.

7.1.3 Latitude

The correlation between 87Sr/86Sr value and latitude for all localities is significant and moderately negative (Spearman’s correlation, R=-0.45408). There was a moderately negative correlation between latitude and 87Sr/86Sr values of volcanic localities

(Spearman’s correlation, R=-0.33962). There was not a correlation between bioavailable strontium of metamorphic localities and latitude. Lacustrine localities and latitude have a strongly positive correlation (Spearman’s correlation, R=0.8), but again it is not significant due to the small sample size.

7.1.4 Longitude

Longitude appears to be the strongest predictor of 87Sr/86Sr values. There was not a significant correlation between the 87Sr/86Sr values of all localities and longitude, but there

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Figure 7.1.2: Isoscape showing approximate distribution of 87Sr/86Sr values in the area sampled. Olduvai Gorge is represented by the grey lines near the centre of the map. Map created using imagery from the Shuttle Radar Topography Mission by NASA. Accessed from https://utility.arcgis.com/usrsvcs/servers/81d8c4b8389f4ccc98a19 833a8c24396/services/WorldElevation/Terrain/ImageServer.

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Locality 87Sr/86Sr 2 SEa Geological Domain Plant types collectedb 1 0.704539 0.000006 Volcanic TTG 2 0.704513 0.000006 Volcanic TTT 3 0.704523 0.000006 Volcanic SGG 4 0.704324 0.000006 Volcanic SSG 5 0.704241 0.000004 Volcanic SGG 6 0.704443 0.000002 Volcanic SGG 7 0.704381 0.000006 Metamorphic TGG 8 0.704563 0.000006 Metamorphic TGG 9 0.704427 0.000012 Metamorphic GGG 10 0.704402 0.000014 Metamorphic TSS 11 0.704493 0.000004 Metamorphic SSS 12 0.704268 0.000016 Metamorphic SSG 13 0.704485 0.000010 Metamorphic SSS 14 0.704648 0.000004 Metamorphic TSG 15 0.704435 0.000004 Volcanic SGG 16 0.704555 0.000004 Volcanic TSG 17 0.704379 0.000004 Volcanic GGG 18 0.704630 0.000006 Volcanic SSG 19 0.704490 0.000006 Volcanic GGG 20 0.704488 0.000004 Volcanic TSS 21 0.704488 0.000010 Volcanic SSG 22 0.704476 0.000008 Volcanic TSS 23 0.704639 0.000004 Lacustrine TTG 24 0.704738 0.000008 Lacustrine TTT 25 0.704714 0.000008 Lacustrine TSG 26 0.704758 0.000012 Lacustrine TTS 27 0.704684 0.000004 Lacustrine TSS 28 0.704375 0.000004 Volcanic SSS 29 0.704601 0.000004 Metamorphic TGG 30 0.704518 0.000004 Volcanic TGG 31 0.704599 0.000024 Volcanic TGG 32 0.704477 0.000010 Volcanic GGG 33 0.704480 0.000004 Volcanic SGG

Table 7.1.1: 87Sr/86Sr results, the geology, and the type of plants collected for each locality. a 2 SE: Standard error is the internal precision of the TIMS measurements for each sample. b Plant type codes: T = tree, S = shrub, G = grass. Small herbs (such as Asparagus africanus) are included with grasses, and large succulents (such as Aloe secundiflora) are included with shrubs.

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N Mean Median Minimum Maximum SD Volcanic 19 0.704472 0.704488 0.704241 0.704630 0.000009 Metamorphic 9 0.704472 0.704485 0.704268 0.704648 0.000119 Lacustrine 5 0.704707 0.704714 0.704639 0.704758 0.000005 All 33 0.704508 0.70449 0.704241 0.704758 0.000126 Table 7.1.2: Summary statistics for 87Sr86Sr values of each geological area.

was a moderate or stronger correlation between longitude and all other categories. There was a moderately negative correlation between volcanic strontium values and longitude

(Spearman’s correlation, R=-0.54038). As well, there was a moderately negative relationship between longitude and 87Sr/86Sr for only metamorphic localities (Spearman’s correlation, R=-0.51667). When the 87Sr/86Sr value and longitude of metamorphic and volcanic localities are analysed together there is a moderately negative correlation

(Spearman’s correlation, R=-0.48099). These results suggest that there is a decrease in

87Sr/86Sr value for all geological areas moving east. There is a strongly positive correlation between longitude and 87Sr/86Sr values of lacustrine sampling localities (Spearman’s correlation, R=0.7), though these were all collected very close together.

7.2 Plant Types Sampled

The range of 87Sr/86Sr for each type of vegetation at localities where they were collected versus where they were not can be found in Figure 7.2.1. The same information for only metamorphic and volcanic areas can be found in Figure 7.2.2. There was a significant difference in medians of 87Sr/86Sr values between localities where trees were sampled (n=17; median=0.70456) and where they were not (n=16; median=0.70446)

(Mann-Whitney U Test, P <0.01). All localities sampled in the lacustrine area (n=5)

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Elevation Latitude Longitude N Coefficient P Coefficient P Co-efficient P All Localities 33 -0.37595 0.03396 -0.4566 0.00862 -0.065072 0.7235 Volcanic 19 0.14656 0.54937 -0.33962 0.15486 -0.54038 0.01691 Metamorphic 9 -0.15 0.67774 -0.16667 0.64364 -0.51667 0.1618 Lacustrine 5 -0.7 0.23333 0.8 0.08333 0.7 0.23333 Volcanic and 28 0.017791 0.9284 -0.28329 0.14407 -0.53839 0.00957 Metamorphic only Table 7.1.3: Spearman’s correlation coefficients describing the relationship between 87Sr/86Sr values and geographic variables. Significant results are bolded (α=0.05), as are correlations stronger than 0.3.

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155

Figure 7.1.3: 87Sr/86Sr values for each of the various locality types plotted against the labelled geographical variable.

156 included trees and all but one had higher 87Sr/86Sr than any volcanic or metamorphic locality, so the Mann-Whitney U Test was re-run with only metamorphic and volcanic localities, since their 87Sr/86Sr values overlap completely. There was still a significant difference between medians of localities where trees were sampled (n=12; median=0.70453) and where they were not (n=16; median=0.70446) (Mann-Whitney U

Test, P <0.05). There was no significant difference between shrubs and no shrubs or grass and no grass at all localities or only metamorphic and volcanic areas. See Table 7.2.1 for

Mann-Whitney U Test results and summary statistics for other plant types.

There was also a significant difference in median 87Sr/86Sr values between localities of all types with trees sampled (n=9; median=0.70456) and shrubs sampled (n=12; median=0.70446) (Mann-Whitney U Test; P <0.01) (localities where both trees and shrubs were sampled were excluded from this analysis). The result is not significant when only metamorphic and volcanic localities are examined. See Table 7.2.2 for Mann-Whitney U

Test results and summary statistics for other plant types. No other results of this kind were significant, though the difference between trees and grasses for all localities was very close

(Mann-Whitney U Test, P=0.0521).

7.3 Ultrasonication Control Experiment

The results of the ultrasonication control experiment are presented in Table 7.3.1.

There was no significant difference between the ultrasonicated and non-ultrasonicated samples (Wilcoxon Signed Rank Test, P=0.7865), though this is a very small sample size and the statistical test may be inaccurate as a result. Nonetheless, the differences between the treatment conditions were very small. Except for Locality 23, the difference between

157 all paired sets was less than the external error margin of the TIMS on which the samples were analysed (0.000013). This is the only value that shows a difference when rounded to the fourth decimal place.

7.4 Teeth

All 87Sr/86Sr values from the teeth suggest they were very similar and were all higher than any of the bioavailable 87Sr/86Sr results. Raw dental tissue results are presented with their margin of error in Appendix 2. See Figure 7.4.1 for the distribution of acid- washed dental 87Sr/86Sr values in relation to bioavailable strontium values from volcanic, metamorphic, and lacustrine localities. With all the dental values for both enamel and dentine there was only a range of 0.70513 to 0.70547. There was no significant difference between the 87Sr/86Sr values of enamel and dentine of all teeth before acid washing

(Wilcoxon Signed Rank Test, P=0.2367), but after acid washing dentine values were significantly higher than enamel values (Wilcoxon Signed Rank Test, P <0.05). Despite being re-prepped and re-analysed, six out of fourteen samples that did not receive acid pre- treatment still had margins of error that were between 0.000010 and 0.000016. All samples that received acetic acid had errors of 0.000009 or less.

7.4.1 Acid washing experiment

Summary statistics for the acid washing experiment can be found in Table 7.4.1, and the breakdown for each tooth can be found in Table 7.4.2. There was a significant difference between 87Sr/86Sr value medians of acid washed enamel (median=0.70530) and

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Figure 7.2.1: 87Sr/86Sr values for samples from all localities including specific plant types versus samples not containing them. The X represents the mean; the middle line in the box represents the median; the bottom and top of the box are the first and third quartile, respectively; and the vertical line shows the range.

Figure 7.2.2: 87Sr/86Sr values for samples from metamorphic and volcanic localities including specific plant types versus samples not containing them. The X represents the mean; the middle line in the box represents the median; the bottom and top of the box are the first and third quartile, respectively; and the vertical line shows the range.

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All Localities (α=0.05) 87Sr/86Sr Plant Type N Mean Median SD Minimum Maximum Z-score p-value Trees 17 0.704577 0.704563 0.000111 0.704381 0.704758 3.314 0.00092 No Trees 16 0.704435 0.704460 0.000010 0.704241 0.704630 Shrubs 20 0.704496 0.704487 0.000140 0.704379 0.704758 -0.8289 0.40654 No Shrubs 13 0.704528 0.704518 0.000103 0.704241 0.704738 Grasses 23 0.704484 0.704490 0.000122 0.704241 0.704714 -0.7185 0.47248 No Grasses 10 0.704541 0.704491 0.000136 0.704375 0.704758 Metamorphic and Volcanic Only (α=0.05) 87Sr/86Sr Plant Type N Mean Median SD Minimum Maximum Z-score p-value Trees 12 0.704524 0.704529 0.000008 0.704381 0.704648 -2.4144 0.01576 No Trees 16 0.704435 0.704460 0.000010 0.704241 0.704630 Shrubs 17 0.704456 0.70448 0.000111 0.704241 0.704648 -1.1291 0.25885 No Shrubs 11 0.704499 0.704513 0.000008 0.704379 0.704601 Grasses 19 0.704471 0.704484 0.000010 0.704241 0.704630 0.3971 0.74896 No Grasses 7 0.704462 0.704485 0.000005 0.704375 0.704513 Table 7.2.1: Descriptive statistics and Mann-Whitney U Test results for localities with certain types of plants collected. Bolded results are significant (α=0.05).

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All Localities (α=0.05) 87Sr/86Sr Plant Type N Mean Median SD Minimum Maximum Z-score p-value Trees 9 0.704566 0.704563 0.000010 0.704381 0.704738 -2.665 0.007699 Shrubs 12 0.704432 0.704462 0.000003 0.704241 0.704630 Trees 7 0.704580 0.704513 0.000143 0.704402 0.704758 -1.942 0.052115 Grass 13 0.704431 0.704443 0.000107 0.704241 0.704630 Shrubs 8 0.704520 0.704487 0.000133 0.704375 0.704758 -0.289 0.77258 Grass 11 0.704510 0.704518 0.000009 0.704379 0.704639 Metamorphic and Volcanic Only (α=0.05) 87Sr/86Sr Plant Type N Mean Median SD Minimum Maximum Z-score p-value Trees 7 0.704531 0.704539 0.000007 0.704381 0.704601 -2.155 0.031151 Shrubs 12 0.704432 0.704462 0.000003 0.704241 0.704630 Trees 4* 0.704470 0.704482 0.000005 0.704402 0.704513 n.d. n.d. Grass 13 0.704431 0.704443 0.000107 0.704241 0.704630 Shrubs 6 0.704453 0.704481 0.000005 0.704375 0.704493 -1.247 0.21227 Grass 10 0.704497 0.704504 0.000008 0.704379 0.704601 Figure 7.2.2: Descriptive statistics and Mann-Whitney U Test results for localities with certain types of plants collected. No localities with overlapping vegetation types are included here (e.g. when comparing trees and shrubs no localities are included where both trees and shrubs were sampled). Bolded results are significant (α=0.05) * Sample size not large enough to run Mann-Whitney U Test.

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Locality 87Sr/86Sr 87Sr/86Sr |Difference|* Non-ultrasonicated Ultrasonicated 4 0.704324 0.704325 0.000001 9 0.704427 0.704415 0.000012 22 0.704476 0.704475 0.000001 23 0.704639 0.704664 0.000025 33 0.704480 0.704477 0.000003 Table 7.3.1: Unrounded results of the ultrasonication control experiment. *: |Difference| = |unsonicated result – sonicated result|

Figure 7.4.1: 87Sr/86Sr values of dental tissues and plant values from the various areas. The X shows the mean; the middle line in the box is the median; the bottom and top of the box are the first and third quartile, respectively; the vertical line shows the range; and the dot is an outlier point.

acid washed dentine (median=0.70534) (Wilcoxon Signed Rank Test, P <0.05). See Table

7.4.3 for Wilcoxon Signed Rank test results for this experiment. There were no significant differences between any other tissues in this experiment, pre-treated or not pre-treated.

Figures 7.4.2 and 7.4.3 show the results from acid washing enamel and dentine, respectively.

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Aside from JK1, enamel 87Sr/86Sr values hardly changed with the acid treatment.

The next biggest difference between treated and non-treated enamel was for JK2, which increased by only 0.000038. The average absolute difference between treated and non- treated enamel is 0.000054, and when JK1 is excluded it becomes 0.000020. There were larger differences between dentine with and without acid treatment. After JK1, which again experienced the largest change, the next biggest difference was in JK3 at 0.000181. The average absolute difference for dentine values was 0.000094, and when JK1 is excluded it is 0.000070. When JK1 is included, the amount of change in dentine was almost double that of enamel, and when it is excluded the amount of change is more than triple that of enamel. Enamel 87Sr/86Sr values all increased after acid treatment except JK5 and JK7, which only lowered by 0.000010 and 0.000011, respectively. Dentine 87Sr/86Sr values changed more unexpectedly, with four decreases (two of which were greater than 0.00004) and three increases (two of which were greater than 0.00018).

Tissue N Mean Median SD Minimum Maximum Enamel 7 0.705287 0.705296 0.000009 0.705133 0.705433 (Aa) Enamel 7 0.705240 0.705246 0.000004 0.705082 0.705412 (Nb) Dentine 7 0.705335 0.705340 0.000114 0.705138 0.705466 (A) Dentine 7 0.705281 0.705237 0.000009 0.705185 0.705423 (N) Enamel 7 0.705287 0.705296 0.000009 0.705133 0.705433 (A) Table 7.4.1: Summary statistics for 87Sr/86Sr results of dental tissues. a: Acid-washed b: Not acid-washed

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Enamel 87Sr/86Sr Tooth Family Acid No Acid Difference JK1 Equidae 0.705339 0.705082 0.000257 JK2 Crocodylidae 0.705245 0.705207 0.000038 JK3 Crocodylidae 0.705296 0.705264 0.000032 JK4 Crocodylidae 0.705433 0.705412 0.000021 JK5 Crocodylidae 0.705133 0.705143 -0.000010 JK6 Equidae 0.705252 0.705246 0.000006 JK7 Hippopotamidae 0.705313 0.705324 -0.000011 Dentine 87Sr/86Sr Tooth Family Acid No Acid Difference JK1 Equidae 0.705466 0.705226 0.000240 JK2 Crocodylidae 0.705270 0.705329 -0.000059 JK3 Crocodylidae 0.705428 0.705237 0.000191 JK4 Crocodylidae 0.705415 0.705423 -0.000008 JK5 Crocodylidae 0.705138 0.705185 -0.000047 JK6 Equidae 0.705289 0.705200 0.000089 JK7 Hippopotamidae 0.705340 0.705367 -0.000027 Table 7.4.2: 87Sr/86Sr results for the acid washing experiment. The difference column is calculated as difference = acid – no acid

Tissue N Z-score W-value p-value Enamel (A) 7 -1.521 23 0.12819 Enamel (N) 7 Dentine (A) 7 0.6761 18 0.49896 Dentine (N) 7 Enamel (A) 7 2.0284 26 0.04252 Dentine (A) 7 Enamel (N) 7 1.1832 21 0.23672 Dentine (N) 7 Both (A) 14 1.1614 71 0.24549 Both (N) 14 Table 7.4.3: Wilcoxon Signed Rank test results from the acid washing experiment. Significant results are bolded (α=0.05).

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0.705500

0.705450

0.705400

0.705350

Sr 0.705300 86

Sr/ 0.705250 No Acid 87 Acid 0.705200

0.705150

0.705100

0.705050 0 1 2 3 4 5 6 7 8 Tooth Number

Figure 7.4.2: Tooth enamel 87Sr/86Sr values with and without treatment with 0.1 M acetic acid for 30 minutes.

0.705500

0.705450

0.705400

0.705350 Sr

86 0.705300

Sr/ No Acid 87 Acid 0.705250

0.705200

0.705150

0.705100 0 1 2 3 4 5 6 7 8 Tooth Number

Figure 7.4.3: Dentine 87Sr/86Sr values with and without treatment with 0.1 M acetic acid for 30 minutes.

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Enamel 87Sr/86Sr Family N Minimum Maximum Mean SD Equidae 2 0.705252 0.705339 0.705296 0.000006 Crocodylidae 4 0.705143 0.705433 0.705277 0.000124 Hippopotamidae 1 0.705313 0.705313 0.705313 N/A Dentine 87Sr/86Sr Family N Minimum Maximum Mean SD Equidae 2 0.705289 0.705466 0.705378 0.000125 Crocodylidae 4 0.705138 0.705428 0.705313 0.000137 Hippopotamidae 1 0.705340 0.705340 0.705340 N/A Table 7.4.4: Descriptive statistics for acid-washed enamel 87Sr/86Sr results by family.

7.4.2 Differences between taxa

Table 7.4.4 shows enamel 87Sr/86Sr results by family after acid washing. The lowest

(0.70513) and highest (0.70543) 87Sr/86Sr values both belong to crocodiles, and the hippopotamus is in between the range for both crocodile and zebra strontium values. There does not appear to be a difference between aquatic and terrestrial fauna, as the zebra and semi-aquatic hippopotamus values all fit within the range of the crocodiles.

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Chapter 8: Discussion

8.1 Variation in Bioavailable Strontium

Overall the bioavailable strontium values in the Olduvai Gorge region were quite homogeneous. For the entire area sampled, totalling some 940 km2, there was only a range in 87Sr/86Sr values of about 0.70425 to 0.70475. The largest variation was seen in the metamorphic areas, though the 87Sr/86Sr values of the volcanic areas sampled overlap these results completely. The sampling localities from Olbalbal had the highest 87Sr/86Sr values except for one metamorphic locality (Locality 29), which was higher than only the lowest

Olbalbal locality. This section will discuss possible sources of variation between the localities sampled.

8.1.1 Hydrological sources of variation

Olbalbal has the same underlying volcanic rocks as the rest of the surrounding area, though it has higher 87Sr/86Sr values than every other locality but one. This is likely because for a portion of the year it becomes a floodplain, holding water from Lake Ndutu and Lake

Masek and from Lemagrut (Leakey, 1978). Various studies have shown that rivers typically have elevated 87Sr/86Sr values in relation to other features on the land and conclude that they are likely representative of the continental average due to continuous weathering of the continental crust (e.g. Wadleigh et al., 1985; Palmer and Edmond, 1989).

If this is indeed the case with the Olduvai River, then by extrapolation it is carrying with it strontium from these areas and passes through the bottom of Olduvai Gorge along the

Ngorongoro basalt in the river bed (Hay, 1976). The lavas of Ngorongoro have a wide range of 87Sr/86Sr values, from approximately 0.70400 to 0.70800 (Mollel et al., 2008;

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2011). As the river erodes rocks, strontium ions are collected by the river flows and potentially have much more radiogenic 87Sr/86Sr values than the localities sampled.

Volcanic activity in the region will be discussed in further detail later in this chapter. As well, the Tanzania Craton, metamorphic bedrock of Archean age, underlies the area approximately 25 km west of Lake Ndutu (Hay, 1976). These old rocks likely have elevated 87Sr/86Sr values compared to the young Cenozoic volcanic rocks, and it is possible that they contribute strontium to the lake either by groundwater or atmospheric dust.

8.1.2 Geographical sources of variation

Areas of high elevation experience higher rates of weathering than lower elevation, and therefore typically yield 87Sr/86Sr values closer to bedrock than atmospheric values, particularly in streams and rivers (Bentley, 2006; Brennan et al., 2016). Additionally, there are higher concentrations of strontium ions in rivers at higher elevations than lower areas due to the increased erosion (Brennan et al., 2016). In the Olduvai region there was a weak negative correlation between elevation and 87Sr/86Sr values when all sampling localities were included. This is likely biased by the five lacustrine samples, as these were the highest

87Sr/86Sr values with the lowest elevation.

These five samples on their own have a strong negative correlation with elevation.

The correlation between the Olbalbal samples and elevation was not statistically significant, but this is most probably a result of the extremely small sample size. This correlation likely exists because lower parts of Olbalbal hold more water for longer periods of time, thus providing the plants growing there with access to river water with an elevated

87Sr86Sr ratio more often and for longer periods of time. One source of stream water is

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Lemagrut, which is approximately 1,500 m in height (Hay, 1976), thus likely experiencing increased weathering rates relative to lower areas in the region. The 87Sr/86Sr values of lavas from the sides of Lemagrut have been determined to be approximately 0.7047-0.7052

(Mollel et al., 2011). If the stream originating on Lemagrut that joins the Olduvai River carries these 87Sr/86Sr values, then it is likely at least in part responsible for the elevated values seen at Olbalbal. There is very little vegetation growing in the lowest, most frequently flooded area of the depression, though the outer areas can also flood and thereby take on the river’s strontium composition (see Figure 8.1.1). When these five lacustrine samples were excluded from the test the correlation value between volcanic and metamorphic localities (together or combined) became much closer to 0. As a result, there does not appear to be a real relationship between elevation and 87Sr/86Sr in this area.

There is a moderately negative relationship between latitude and 87Sr/86Sr for all localities combined, the volcanic areas, and the metamorphic and volcanic areas combined, suggesting that 87Sr/86Sr tends to decrease with increasing distance north. Metamorphic localities alone have a very weak correlation with latitude, and lacustrine localities have a positive correlation with latitude. Despite these correlations, longitude appears to be the strongest predictor of 87Sr/86Sr for the sampling area. Although there is no correlation between 87Sr/86Sr values for all localities combined and longitude, this is again likely being biased by the five Olbalbal localities, as they exhibit a moderately positive correlation with longitude. The Spearman’s correlation test is only responsive to monotonic relationships, so the positive correlation between 87Sr/86Sr in the lacustrine areas and longitude in conjunction with the negative relationship of metamorphic and volcanic areas to longitude would have cancelled out the other relationship in the test. In Olbalbal, 87Sr/86Sr tends to

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Figure 8.1.1: Olbalbal flooded vs. Olbalbal dry. Photo A shows widespread flooding into the more heavily vegetated area that can be seen on the outskirts of the dry floodplain in Photo B. Photo A was taken in June 2016 and Photo B in July 2017.

increase moving eastward. However, 87Sr/86Sr values of volcanic and metamorphic localities individually and combined tend to decrease moving eastward.

The strontium values of the Olbalbal localities show strong positive correlations with latitude and longitude, and a strong negative correlation with elevation. 87Sr/86Sr, therefore, increases moving north and east, and with decreasing elevation. Due to the relatively small differences between sampling localities at Olbalbal, it is likely that latitude and longitude have less of an effect on 87Sr/86Sr than the relatively large change in elevation

(~30 m). As previously mentioned, the lower portions of Olbalbal likely hold more water from the Olduvai River and therefore it contributes more to their bioavailable strontium reservoir there than the higher areas of the depression.

A possible explanation for the trends in longitude and latitude for metamorphic and volcanic localities is Oldoinyo Lengai, the only active volcano in the region, which lies to the northeast. It contributes volcanic ash to the region via wind deposition and has done so since the late Pleistocene, including multiple eruptions since 1917 (Hay, 1976). Analyses of carbonatites from Oldoinyo Lengai suggest that it extrudes volcanic material with

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87Sr/86Sr values of approximately 0.70437 to 0.70446 for carbonatite lavas, and slightly higher for some silicate rocks it expelled (ranging 0.70412 to 0.70522) (Bell and Simonetti,

1996; Kalt et al., 1997). Of the 23 silicate rocks that Bell and Simonetti (1996) analysed, only 6 had values over 0.70450. The carbonatite values and lower silicate rock values are very close to the modern plant 87Sr/86Sr values from volcanic and metamorphic sampling localities from this study, particularly those concentrated in the northern and eastern portions of the sampling area. It is possible that these places closer to Oldoinyo Lengai contain more strontium from volcanic ash, as opposed to other sources. This will be further discussed later in this chapter.

8.1.3 Variation between plant types

In addition to geographical variables, a likely source of some 87Sr/86Sr variation is in the types of plants collected at each sampling locality. Bioavailable strontium in an area is derived from a mixture of atmospheric strontium from numerous sources, hydrological sources, and from bedrock strontium. For some areas, ligneous plants have been shown to have deeper roots and be more biased toward bedrock strontium values than atmospheric, and vice-versa for shallower-rooted plants such as grasses and shrubs (Maurer et al., 2012;

Hartman and Richards, 2014). This is because atmospheric strontium decreases with increasing soil depth (Whipkey et al., 2000), and factors such as mean annual precipitation can impact the degree to which bedrock weathering occurs and how much meteoric water strontium is contributed to bioavailable strontium reservoirs (Stewart et al., 2001).

Ligneous plants may also have higher concentrations of strontium ions than non- ligneous, which is because soil has higher strontium concentrations than atmospheric

171 sources such as rainfall (Capo et al., 1998; Hartman and Richards, 2014). Different soil types also have different 87Sr/86Sr values and may impact bioavailable strontium values seen in plants (Hartman and Richards, 2014). Soil in the Olbalbal Depression is juvenile soil on volcanic ash, whereas the rest of the sampling localities were from areas of calcimorphic soil with hard pans (Anderson and Talbot, 1965). It is possible that some variation in bioavailable strontium values is due to differing soil types.

Sampling localities where trees were collected had consistently higher 87Sr/86Sr values (tree mean=0.70458, shrub mean=0.70450, grass mean=0.70448), suggesting that bedrock strontium is slightly more radiogenic than atmospheric sources. Sources of bedrock and atmospheric strontium will be discussed later in this chapter. Vegetation in the region is variable overall, though in many places it is homogeneous in the types present.

Efforts were made to collect a grass, shrub, and tree from every sampling locality to avoid variation in 87Sr/86Sr due to vegetation type, but in most cases this was not possible. For example, in some areas there were wide open plains for kilometres with no trees or shrubs to sample, and in other places, since only identifiable grasses were collected, there were no suitable grasses in appropriate quantities to collect.

8.2 Bedrock vs. Atmospheric Input

8.2.1 Ultrasonication Control Experiment

There was no significant difference between the plant samples that had been cleaned via ultrasonication and those which had not. This suggests that the uncleaned samples were not biased toward atmospheric 87Sr/86Sr values any more than the ones that were cleaned. This could mean one of three things. Either there is a very high contribution

172 of atmospheric strontium to the bioavailable reservoir, as has been found in many previous studies (e.g. Gosz et al., 1983; Åberg et al., 1989; Graustein and Armstrong, 1993; Stewart et al., 2001; Poszwa et al., 2004; Clow et al., 2007; Hartman and Richards, 2014); that atmospheric and bedrock strontium values are very similar; or a combination of the two.

The locality that had the highest absolute difference between the ultrasonicated and non-ultrasonicated samples was number 23, from which two trees and one grass were collected. The ultrasonicated sample had a higher 87Sr/86Sr value, which suggests that there was a higher input from bedrock strontium than the non-ultrasonicated sample. As previously mentioned, trees in this area tend to have slightly more radiogenic strontium values than the other types of vegetation. It is possible that the subsample that was ultrasonicated from Locality 23 may have been more radiogenic because it had more tree material and less grass than the other, though an effort was made to contribute material equally from all plants collected, as it does not suggest a higher atmospheric input.

8.2.2 Bedrock strontium

The volcanic area around Olduvai Gorge contains a series of tuffs, rock formed by the consolidation of volcanic ash, that were formed from the ash of numerous volcanoes in the Ngorongoro Volcanic Highland (NVH) which overlay the basement metamorphic rocks (Hay, 1976). The most recent volcanic activity has been from Oldoinyo Lengai, which has been the only active volcano in the region for the last 60,000 or so years (Hay,

1976). Therefore, Oldoinyo Lengai tuff can be considered the bedrock for the area. As previously mentioned, lavas from Oldoinyo Lengai have been found to have 87Sr/86Sr values of close to 0.70440, thus this represents the bedrock 87Sr/86Sr value. The

173 bioavailable strontium values for volcanic localities were between 0.70424 and 0.70463, which is very close to the range of values for Oldoinyo Lengai lavas and silicate rocks.

The samples collected directly from metamorphic inselbergs were statistically indistinguishable from those collected from volcanic areas, ranging from 0.70427 to

0.70465. The metamorphic rocks, therefore, either have very similar 87Sr/86Sr values to the volcanic ones, or there is a very high amount of atmospheric strontium in the region which obscures any difference between them that might exist. This cannot be distinguished with certainty without directly testing the 87Sr/86Sr values of the rocks themselves. However, the metamorphic rocks are part of the Mozambique Belt, which underlies the whole region beneath the layers of volcanic rock. The Mozambique Belt formed approximately ~800

Ma, which is far older and thus likely more radiogenic than the Oldoinyo Lengai tuffs. If this is the case, then atmospheric strontium likely makes up a large portion of the local bioavailable strontium reservoir.

8.2.3 Atmospheric strontium

There can be numerous sources of atmospheric strontium, including meteoric water, sea spray, and dust carried by the wind. The Olduvai region, while very arid in the dry season, does receive varying amounts of rain in the wet season which contributes strontium to the area, although likely at a far lower concentration than places closer to the ocean (Andersson et al., 1990). The 87Sr/86Sr ratios of precipitation are unlikely to directly represent that of the ocean either, as the clouds have probably picked up some strontium from other sources such as anthropogenic pollution (Andersson et al., 1990). The degree to which rainfall strontium interacts with labile strontium in soil is unknown, but Stewart

174 et al. (2001) estimate that in Hawaii only 5-50% of rainwater strontium is exchanged with the labile strontium reservoir in the soil, depending on the amount of mean annual precipitation for the locality sampled. In areas with less than about 150 cm of annual rainfall only some 10% of labile strontium originated from meteoric water (Stewart et al.,

2001). In the Olduvai Gorge area, mean annual rainfall is only about 56 cm (Hay, 1976), so it is very unlikely that rainfall is a significant contributor to the region’s bioavailable strontium. As well, the small amount of water that does fall in the area typically does not stay on the ground for any length of time due to the lack of shade and high temperatures causing rapid evaporation (Hay, 1976). As a result, plants may not have access to much meteoric water and could be much more influenced by other atmospheric sources. As for sea spray, the gorge is approximately 500 km from the Indian Ocean, so it is unlikely that there is a a large direct effect (Franzen, 1980).

Aeolian materials, in this case specifically volcanic dust, can be another significant contributor of atmospheric strontium. Although Hay (1976) presented an average wind speed of 12.4 km/h based on data from Mary Leakey, it can be extremely windy in the area and dust storms are not uncommon. Wind typically comes from the east, carrying with it volcanic ash from Oldoinyo Lengai, as well as the loosely packed dusty soils in the region

(Anderson and Talbot, 1965). See Figure 8.2.1 for an example of the severity of dust storms in the area. The ash being transported from Oldoinyo Lengai likely has a very similar

87Sr/86Sr value to the carbonatitic lavas and silicate rocks analysed by Bell and Simonetti

(1996) and Kalt et al. (1997). Sakhno (2007) found that Paektusan, a volcano on the border between China and North Korea, expelled various kinds of ash and lavas throughout its history. These various materials had 87Sr/86Sr values between 0.7046 and 0.7053, except

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Figure 8.2.1: An example of a dust storm at Olduvai Gorge. These two photos were taken on different days from almost the exact same location at the Aguirre-Mturi Research Camp in 2015. Note the reduced visibility across the gorge, and how Naibor Soit is almost invisible in the second photo.

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only two lava flows which had higher values of approximately 0.70770 and 0.70860

(Sakhno, 2007). No comprehensive study done on the evolution of 87Sr/86Sr values of

Oldoinyo Lengai through time, or an examination of its ash values could be found.

Volcanic ash is likely to be a major source of strontium ions in this area, particularly since it constitutes a large portion of the native soil (Anderson and Talbot, 1965). As a result, atmospheric and bedrock strontium values are likely quite similar.

8.3 Dental Tissues

8.3.1 Diagenesis

The enamel and dentine 87Sr/86Sr values were not significantly different from one another when they were not acid washed. However, dentine 87Sr/86Sr values are significantly higher than enamel values for the same tooth when they were pre-treated with acid. This suggests that diagenetic strontium is more radiogenic than biogenic strontium, and that it was successfully removed with the acetic acid wash. Since all teeth (except possibly JK1) were found in fluvial deposits, it is likely that diagenetic strontium came from the stream water. As previously discussed, stream water is likely to have elevated

87Sr/86Sr values, which could explain the slightly more radiogenic diagenetic strontium.

Most of the values were only slightly different, but both the dentine and enamel for

JK1 increased by more than 0.0002 with the acid. In this specific and unique case, diagenetic strontium appears to be less radiogenic than biogenic strontium. A reason that this tooth was so altered could be that it was cracked lengthwise and in poor condition, which may have allowed diagenetic strontium in water easier access to both tissues. As

177 well, it was found in a conglomerate layer above the red Bed III sediments eroding out of the slope, and therefore is quite possibly younger than the other teeth. As a result, it is difficult to assess the taphonomic processes that this particular tooth underwent. It is possible that the tooth was from Bed III and is taphonomically similar, as diagenetic strontium may not necessarily be predictable or consistent within burials (Budd et al.,

2000).

On average, enamel 87Sr/86Sr values changed less than dentine values. In only two teeth (JK1 and JK4) the enamel changes were higher than the dentine changes, and these were only by 0.000017 and 0.000013, respectively. This suggests that the enamel was less diagenetically altered than the dentine. However, because there were only small changes in the dentine of five teeth, it appears that the effect of diagenesis is not strong at the site.

It is possible that the acid wash used was too short to see more marked differences.

Nonetheless, short acid washes of 30 minutes or less have successfully broken down diagenetic strontium and returned enamel values to biogenic levels in fossilised teeth in numerous cases (e.g. Lee-Thorp et al., 2000; Sponheimer and Lee-Thorp, 2006; van der

Merwe, 2013). Lee-Thorp et al. (1997) found that acetic acid rapidly breaks down fossilised enamel, and that it must be used with caution. The crocodile teeth analysed in this study were small and thus only limited amounts of enamel and dentine could possibly be removed. The risk of prolonged acid exposure, therefore, was high, as there needed to be a sufficient amount of sample for the TIMS to analyse. The dental tissue results of this study were close to those found by Copeland et al. (2012) for many Bed I animal teeth of differing species, which ranged in 87Sr/86Sr from approximately 0.70450 to 0.70550, except one notably different specimen at about 0.70650. Consistency in strontium values over

178 approximately a million years from Bed I to Bed III but with considerable interindividual variation likely indicates that biogenic values are preserved. As well, van der Merwe used a 15-minute 0.1 M acetic acid wash on many animal teeth (including Crocodylus,

Hippopotamus, and Equus) from Beds I and II at Olduvai Gorge for stable carbon and oxygen isotope analysis and found biogenic δ13C and δ18O values to be preserved. This indicates that diagenetic carbonates at the gorge are easily broken down.

8.3.2 Animal teeth by taxon

The enamel and dentine values of the teeth, with or without acid-washing pretreatment, were all much higher than the modern plant samples. The teeth represented crocodiles, zebras, and a hippopotamus. Nile crocodiles (Crocodylus nilotica) in South

Africa have a very small home range, less than 30 km2 (Caverley and Downs, 2015), and thus can be considered local. However, 87Sr/86Sr values of animals reflects their diet, and crocodiles are carnivores. Therefore, if they prey mainly on non-local animals, then they may exhibit non-local 87Sr/86Sr values themselves. Crocodiles do also obtain water through integumentary exchange and drinking in varying amounts, depending on the salinity of the water (Taplin, 1984; Leslie and Spotila, 2000), which will contribute the 87Sr/86Sr values of the local water to their tissues.

Unlike mammals, crocodiles can continuously replace teeth they have lost. Poole

(1961) estimated that Nile crocodiles that are 13 feet long have already replaced their teeth up to 50 times. As well, the replacement rate is higher in young individuals than in old ones, which may have edentulous sockets in some cases (Poole, 1961). As a result, the age

179 of the individual represented by the tooth’s 87Sr/86Sr value cannot be precisely known, though each successive tooth grown tends to be slightly larger than the last (Poole, 1961).

The crocodile teeth analysed in this study showed the largest variation in 87Sr/86Sr values (0.70513-0.70543), though more of them were analysed than other animal teeth.

This wide variation is very unlikely to be the result of interindividual land use differences unless some of the teeth were carried by stream water from far away. Instead, the variation could be due to dietary differences between individuals if they consumed differing proportions of local vs. non-local animals at the times the teeth were forming. This is possible if they formed during a time when many migratory animals were present in the region. As well, the animals could have lived at very different times. Because the stratigraphy of JK has not been precisely dated and the site contains sediment from 1.15 to

0.8 Ma, the crocodiles could have lived anywhere within a 350,000-year window.

Depending on stream flow at the time, there could have been water from slightly different sources which could have marginally altered the 87Sr/86Sr values at JK.

Hippopotamus (Hippopotamus amphibius) are semi-aquatic resident animals that rarely travel more than 2-3 km from water, which is done to feed primarily on herbivorous foods (Bakker et al., 2016). As a result, they should yield the 87Sr/86Sr values of their freshwater aquatic environment and immediate surrounding area. The hippopotamus tooth sampled in this study had a 87Sr/86Sr ratio of 0.70531, which was only lower than one zebra

(JK1) and one crocodile (JK4).

Zebras (Equus quagga) are often migratory animals that change locations between the wet and dry seasons (Lee et al., 2016; Naidoo et al., 2016). They can be extremely mobile, with migration routes up to 500 km spanning multiple countries (Naidoo et al.,

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2016). This zebra migration spanning Namibia and Botswana is the longest of any known species in Africa and lasts for months (Naidoo et al., 2016). The two zebra teeth analysed in this study had 87Sr/86Sr values of 0.70534 and 0.70525. These values are close to the hippopotamus, which likely had a very small home range. Thus, the zebras appear to be local. Modern zebras typically vacate this area and head north to where there is more food and water (Estes, 2014), though a few do stay around the Olduvai area.

It is possible that the landscape had homogeneous 87Sr/86Sr values in Bed III time, which would make migratory animals to appear to have been local. This, however, is unlikely to be the case. The present study found that an area of ~940 km2 had little variation in bioavailable strontium values, but Copeland et al. (2012) found that the north and west areas of the Serengeti have considerably higher 87Sr/86Sr values. However, the values found in this study are close to those found by Copeland et al. (2012) for Barafu in Serengeti

National Park, approximately 30-40 km northwest of Olduvai Gorge. Their results for

Seronera, another 30-40 km or so northwest of Barafu, are slightly higher though overlapping with Barafu. Presuming that the area between Olduvai and Seronera is quite consistent in 87Sr/86Sr values, animals coming from the northwest anywhere in between the two locations would have approximately the same strontium values and therefore be indistinguishable from animals whose movement is restricted to the immediate Olduvai

Gorge area.

Migratory animals including zebras, wildebeest, and Thomson’s gazelles in the

Greater Serengeti Ecosystem today often travel up to 30,000 km2 to track food and water

(Holdo et al., 2009). In their cases, they would certainly move far enough to enter new geological domains (e.g. the Archean Tanzanian Craton to the west) with different 87Sr/86Sr

181 values which were reported by Copeland et al. (2012) to have bioavailable strontium values of up to approximately 0.71900. In their sampling of Bed I fauna, Copeland et al. (2012) found a proboscidean that had a higher 87Sr/86Sr value than all other animals analysed, indicating that there were very different bioavailable strontium values within reasonable travelling distance for animals.

A more likely scenario for the local 87Sr/86Sr values of the zebras is that animals that are highly migratory now were not as mobile in the past. In the NCA today there is a small resident population of wildebeest, which are famous for their long migration routes across the Serengeti, in the Ngorongoro Crater, which receives far more rain and therefore yields more food resources for the animals than the drier area in the rain shadow (Estes,

2014). There are also resident populations in the western Serengeti and in Masai Mara in

Kenya, though most wildebeest follow the typical migration pattern from the southeast in the wet season to the northwest in the dry season (Estes, 2014). The biggest driving force for the migration is access to water and nutritious foods (Estes, 2014; Holdo et al., 2009).

In Bed III times there was a perennial water source at JK, as well as a lacustrine area to the north which served as the main drainage basin, which could suggest that the area was wet enough to keep animals in the area year-round. It is possible, therefore, that the zebras did not have to travel as far to secure access to food and water resources as they typically do today. As well, Bed I times were predicted to have been wetter and cooler than modern times (e.g. Cerling and Hay, 1986). Copeland et al. (2012) found that none of the Bed I

Antilopini and Alcelophini teeth they analysed showed non-local 87Sr/86Sr ratios, which was unexpected due to the mass wildebeest migrations to the western and northern

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Serengeti in modern times. This finding suggests that species which are migratory now may have been resident in the past, potentially due to environmental differences.

While there is not much variation in 87Sr/86Sr values immediately around Olduvai

Gorge, there are different values much further away. As a result, if animals or hominins travelled to Olduvai from very far away, their enamel would have non-local 87Sr/86Sr ratios.

For clearly defining locals vs. non-locals this scenario is beneficial over landscapes that have very high variability in 87Sr/86Sr over short distances. For example, in the Sterkfontein

Valley there are 16 distinct geological zones within an approximate 50 km radius from

Sterkfontein and Swartkrans ranging in 87Sr/86Sr values from 0.721 to 0.774 (Copeland et al., 2011). In the case of Olduvai, the distance threshold of local vs. non-local would be set much further away, but it would likely be easier to determine where non-locals came from on the landscape.

8.4 Modern Versus Ancient Bioavailable Strontium

In general, volcanic areas around the world tend to have 87Sr/86Sr values of approximately 0.70400 due to the relatively unradiogenic mantle of the earth, though some volcanoes have much higher values, such as Roccamonfina in Italy with values up to

0.71000 (Hodell et al., 2004; Conticelli et al., 2009). The modern plant values around

Olduvai Gorge support this, averaging 0.70451 all together. The archaeological teeth, however, have higher 87Sr/86Sr values, averaging 0.70529. The 87Sr/86Sr values of the JK animal teeth do not correspond with modern plants, yet the species represented (except possibly the zebras) should yield 87Sr/86Sr ratios consistent with the local environment. The animal teeth are all much more radiogenic than the modern plants, which is not a result of

183 their age as 87Rb decays very slowly. Rather, it is more likely that an environmental change in the area is responsible.

A study by Maurer et al. (2012) included incremental analysis of tree rings and showed that bioavailable 87Sr/86Sr values in that area changed from 0.71005 to 0.70922 over approximately 40 years. This indicates that it is possible that bioavailable strontium has the potential to change a great deal over time, though in this situation it was likely anthropogenic environmental degradation resulting from mining activity and/or agricultural fertilisers causing the change. Although there is a small amount of agricultural activity permitted in the NCA, none of the samples for this study were collected near any farms. As a result, it is unlikely that contamination from fertilisers is the cause of this change in 87Sr/86Sr values.

One possible explanation for the changes seen in the Olduvai Gorge region is the climate. During Bed III times the Olduvai Gorge region had started to become more arid and hot with expanding C4 grassland (Cerling and Hay, 1986), but in relation to the present it was relatively wet, as evidenced by fossils of marine fauna including hippopotamus, crocodiles, and fish, together with geological evidence of perennial streams (Kleindienst,

1973). If there was more rain in Bed III times, then it is possible that there was a higher contribution of precipitation 87Sr/86Sr values closer to that of oceanwater. As well, sources of meteoric water in this time could have been different and may have included water from the Atlantic Ocean, Indian Ocean, and large lakes in differing proportions from the present

(van der Merwe, 2013), thus potentially having different concentrations of strontium ions and different 87Sr/86Sr ratios than rain today. For example, van der Merwe (2013) postulates

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Figure 8.4.1: Volcanoes of the Ngorongoro Volcanic Highland. Olduvai Gorge and Laetoli are also shown, as well as faults in the area. The sampling area for this study is located within the black box. Modified from Mollel and Swisher, 2012.

that Lake Victoria, which formed about 50 ka, is contributing water to meteoric waters around Olduvai today.

Also, during Bed III times the area around Olduvai Gorge was an alluvial fan, bringing stream water to the area (Hay, 1976). An episode of faulting began approximately

1.15 Ma which resulted in the main drainageway for Bed III shifting about 7 km northeast of its location in the lake in Bed II times (Hay, 1976). This puts it approximately 5 km north of JK, with the main drainageway passing through the site (Hay, 1976). JK, therefore, had streams passing through it from the south and west that were near their final destination

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Figure 8.4.2: Approximate 87Sr/86Sr value ranges for the NVH volcanoes. No data were available for Loolamalasin. Data from Manega, 1993; Bell and Simonetti, 1996; Kalt et al., 1997; Mollel, 2007; Mollel et al., 2008; 2011; Mollel and Swisher, 2012.

in the lacustrine area (Kleindienst, 1973; Hay, 1976). Like the modern streams flowing into

Olbalbal, these most likely originated in the highlands near Lemagrut and Sadiman. It is possible that the streams were carrying with them strontium ions from rocks of those volcanos. Sadiman has 87Sr/86Sr values ranging from approximately 0.70400 to 0.70600

(Mollel et al., 2011), and Lemagrut’s values are approximately 0.70470 to 0.70560

(Manega, 1993; Mollel et al., 2011). These streams likely provided a major water source for animals in the area, and probably contributed to the bioavailable strontium reservoir in the area through groundwater. As a result, flora and fauna at the time likely obtained strontium from these streams, which may have had strontium values similar to those seen in the archaeological teeth in this study.

Another possibility is the volcanism in the area. Over time, the different volcanoes of the NVH (Figure 8.4.1) have erupted and deposited ash, lava, and pyroclastic material

186 on the earth’s surface, which mixes into the soil and can leave tuffs of varying thicknesses and composition across the landscape. There are at least ten volcanic centres in the NVH

(including Engelosin, though it is separate from the others), nine of which are extinct

(Mollel and Swisher, 2012). Dating of lavas has shown that the active volcanic centre of the NVH has shifted from Lemagrut and Sadiman in the southwest gradually up to

Oldoinyo Lengai and Kerimasi in the northeast (Mollel et al., 2011). See Figure 8.4.2 for approximate 87Sr/86Sr values for lavas of some volcanoes in the region. Manega (1993) found that 87Sr/86Sr values decreased for the NVH volcanoes moving northeast towards the rift axis from about 0.706 to 0.704.

There are four numbered tuffs of varying composition in Bed III (Tuff 1 through

4), plus a few others that have not been correlated with the numbered tuffs (Hay, 1976). In some areas of Bed III there is a trachyte tuff separating it from Bed II, but not all (Hay,

1976). Based on mineralogical analyses, these tuffs were likely deposited by Embagai

(Greenwood, 2014), which was active from about 1.2 to 0.6 Ma (Manega, 1993; Mollel and Swisher, 2012). It is also possible that Loolmalasin, for which preliminary dating suggests activity from approximately 1.4 to 1.3 Ma, provided the lowermost Bed III tuffs

(Mollel and Swisher, 2012). The 87Sr/86Sr values of these tuffs have not been determined.

Like Oldoinyo Lengai, the lavas of Embagai have 87Sr/86Sr values of approximately

0.704 (Manega, 1993). The 87Sr/86Sr values of Loolmalasin do not appear to have been studied to date, though it is near Embagai and the older volcanic centre Olmoti (active ~2

Ma), whose lavas have values of approximately 0.7041 to 0.7050 (Mollel, 2007). If the tuffs of Bed III have similar 87Sr/86Sr values to those of Oldoinyo Lengai, then it is unlikely that volcanic activity changing the local tuff bedrock is responsible for the higher

187 radiogenic strontium values of the archaeological teeth. The environmental differences between the past and present, therefore, are more likely to be responsible for the temporal shift in 87Sr/86Sr values.

8.5 Conclusion

A detailed conclusion of the findings of this study can be found in Chapter 9.

Briefly, this chapter broke down the results found for each aspect of this study and provided explanations for them. This included variables explaining variation in bioavailable strontium values, determining that the biogenic strontium was preserved in the fossilised enamel, and concluding that the animals sampled were all local, despite all of them having

87Sr/86Sr that were much more radiogenic than the modern bioavailable strontium values.

As well, this chapter provided explanations as to why modern and ancient bioavailable strontium in the region may have been different. Despite differences between past and present bioavailable strontium values, the feasibility of future studies on mobility using archaeological teeth from Olduvai Gorge looks to be promising.

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Chapter 9: Conclusions

9.1 Introduction

The main goal of this study was to determine the feasibility of conducting future studies on migration at Olduvai Gorge using stable strontium isotope analysis, and to provide some of the necessary referential landscape data. To address this, the first objective was to assess the variation in biologically-available (bioavailable) strontium that exists within the Olduvai Gorge region in Northern Tanzania. One way that bioavailable strontium can be determined is through analysis of plants which have incorporated local bioavailable strontium values into their tissues (Bentley, 2006). As animals consume plant matter, local strontium isotopes become incorporated into their developing tissues

(Bentley, 2006). The animals then move about the landscape, carrying with them the local

87Sr/86Sr values from the area in which they originated. Bedrock is typically the largest contributor of strontium ions to soil, but atmospheric sources such as precipitation and aeolian material also can provide significant proportions of an area’s bioavailable strontium

(e.g. Gosz et al., 1983; Åberg et al., 1989; Graustein and Armstrong, 1993; Stewart et al.,

2001; Poszwa et al., 2004; Clow et al., 2007; Hartman and Richards, 2014).

For this study, 99 plants from 33 localities spanning metamorphic, volcanic, and lacustrine areas were collected and their 87Sr/86Sr ratios quantified. This region was chosen because Olduvai Gorge is an incredibly important area for archaeological research (e.g.

Leakey, 1971; Leakey and Roe, 1994), as it provides a continuous record of technological and morphological change over the last two million years. Nearby to the southwest is

Laetoli, another important palaeoanthropological site where an older record of human evolution is preserved (e.g. Hay, 1987). Understanding the variation in bioavailable

189 strontium in this area is necessary to interpret movement of hominins and animals recovered from both sites.

This study’s second objective was to determine whether diagenetic alteration has occurred in the dental tissues of teeth recovered from Juma’s Korongo (JK), a ~1.3-1.0 or

1.3-0.6 million-year-old Bed III and IV archaeological site at Olduvai Gorge. After an animal dies and its remains are buried, strontium in groundwater or other water sources can overwrite the biogenic values by either adding to or replacing them (Budd et al., 2000).

Enamel is more resistant to diagenesis than both dentine and bone because it has a lower organic component and tight crystalline structure, making it the most reliable tissue to study (Hillson, 2005), particularly when they are this old. Diagenesis can be reversed by washing the sample in weak (usually 0.1 M) acetic acid for a short period of time. It is important to restrict the duration of acid contact, as it rapidly breaks down enamel (Lee-

Thorp et al., 1997). In this study diagenesis was assessed by sampling both enamel and dentine from the same teeth and comparing them to one another, with and without pre- treating them with 0.1 M acetic acid for 30 minutes. The 87Sr/86Sr values of the teeth were then compared to one another and to the bioavailable strontium values as determined by the modern plants to distinguish the local from non-local animals.

9.2 Variation in Bioavailable Strontium

Plants from an area of approximately 940 km2 in Northern Tanzania around

Olduvai Gorge were sampled for bioavailable strontium. Three plants were collected from each locality to try and avoid biasing results toward either bedrock or environmental strontium values (e.g. Hartman and Richards, 2014; Copeland et al., 2016). Depending on

190 the soil and amount of atmospheric strontium contribution in the area, ligneous plants may be biased more toward bedrock values and non-ligneous plants biased toward atmospheric values (Hartman and Richards, 2014). Despite this effort, there were still significant differences between localities where trees were collected and where they were not, though there was no significant difference between localities with grasses and those without, nor ones with shrubs and those without.

Duplicate samples from five sampling localities were created so that one set could be ultrasonicated prior to analysis to remove adhering dust, while the other was processed without this step. There was no significant difference between the ultrasonicated and non- ultrasonicated samples, likely because atmospheric values are very similar to those from bedrock. This is because volcanic ash from Oldoinyo Lengai is likely to be the largest contributor of atmospheric strontium to the region and also provided the local volcanic bedrock.

The results of this study show that there is little variability in labile strontium in the

Olduvai Gorge region, as overall the values were quite homogeneous. They were slightly elevated in the Olbalbal depression compared to the metamorphic and volcanic localities, which was likely due to strontium ions from Lake Masek, Lake Ndutu, and Lemagrut being carried into it via the Olduvai River. River water typically contains higher 87Sr/86Sr values, particularly in areas of high elevation (Brennan et al., 2016). Lemagrut is approximately

1,500 m tall, and therefore probably experiences more rapid weathering of the lava rocks on its slopes than lower areas, and the freed strontium ions are carried down the slope by the stream through Olduvai Gorge and into Olbalbal.

191

Despite the homogeneity through metamorphic and volcanic sampling localities, there are still slight trends. Longitude in particular is a fairly strong predictor of 87Sr/86Sr in these areas. The lowest 87Sr/86Sr values tend to be mainly in the higher portions of the study area in the northeast. Further to the northeast is the only active volcano in the region,

Oldoinyo Lengai, which has been contributing ash to the soils of the region for thousands of years (Anderson and Talbot, 1965; Hay, 1976). The sampling localities closer to

Oldoinyo Lengai may have 87Sr/86Sr values slightly closer to those of volcanic ash extruded from the volcano. No information specifically on the 87Sr/86Sr values of Oldoinyo Lengai ash could be found, but it is likely that the values are similar to those of the lavas, which have ratios of approximately 0.70437 to 0.70522 (Bell and Simonetti, 1996; Kalt et al.,

1997).

Olbalbal has the highest 87Sr/86Sr values in the lowest areas, which are concentrated in the north and east of the depression. This is likely because the lowest parts hold the most river water for the longest periods of time, thus increasing the proportion of strontium in the water that is contributed to the reservoir available for use by plants.

While there is not much variation in 87Sr/86Sr values in the area immediately surrounding Olduvai Gorge, there are slight trends. As well, a previous study by Copeland et al. (2012) found that there is promising variation further north and west in Serengeti

National Park, and to the southeast near Lake Manyara, where bioavailable strontium values are considerably higher (approximately 0.70700 to 0.71900 and 0.70500 to 0.70600, respectively). Annual mammal migrations in this area often cover 30,000 km2 (Estes,

2014), so if these migratory animals move in and out of the Olduvai Gorge region they should show variability in their tissues that can be traced to different geological areas. The

192 much higher 87Sr/86Sr values associated with Serengeti National Park are from sampling sites on the Tanzania Craton, which is composed of metamorphic bedrock of Archean age and therefore is much older than the Cenozoic volcanic rocks around Olduvai Gorge

(Copeland et al., 2012).

9.3 Dental Tissues

The enamel and dentine of seven teeth recovered from JK in summer 2017 were analysed for 87Sr/86Sr. JK formed in a fluvial environment and likely featured perennial water sources, based on the presence of aquatic and semi-aquatic animal remains

(Kleindienst, 1973). Duplicates of each tooth sample were created, so that half could be subjected to a 0.1 M acetic acid pre-treatment to determine if diagenetic strontium was an issue at the site. In the acid washed teeth, dentine was significantly more radiogenic than enamel. This suggests that diagenetic strontium is more radiogenic than biogenic values, possibly the result of being submerged in a freshwater stream channel which had elevated

87Sr/86Sr values.

The teeth analysed are from zebras, crocodiles, and a hippopotamus. Crocodiles and hippopotamus are resident animals with very small home ranges (Caverley and Downs,

2015; Bakker et al., 2016). While hippopotamuses are herbivorous and will exhibit the local 87Sr/86Sr values, crocodiles are carnivorous and obtain strontium ions from their prey.

Therefore, if they consume a lot of non-local animal material they may yield 87Sr/86Sr values that are very different from other resident animals themselves. Zebras are herbivorous and can be highly mobile, with migration routes up to 500 km (Naidoo et al.,

2016). Despite these differences between the animals, there was little variability in 87Sr/86Sr

193 values of the teeth sampled, suggesting that they were all local and fed primarily on local foods. Copeland et al. (2012) analysed numerous teeth from Bed I at Olduvai Gorge and found that all of them, including antelopes, showed local values that were very close to those found in this study, except for one proboscidean which likely immigrated into the area from afar.

The local dental 87Sr/86Sr values did not correspond with modern bioavailable strontium values. This is likely due to environmental differences between the past and present. As mentioned, in Bed III time JK was home to a perennial stream, and therefore was much wetter than anywhere in the region today. It is likely that the water sources passing through the site had high 87Sr/86Sr ratios, which contributed to the local values. As well, the area may have received more rainfall, which could have contributed more radiogenic strontium. Another possibility is differences in volcanism in the past, though the active volcano at this time was Embagai, which had lower 87Sr/86Sr values in its lavas than what was found in the teeth.

9.4 Conclusion

In sum, bioavailable strontium immediately around the Olduvai Gorge region is very homogeneous. However, Copeland et al. (2012) found that areas further away to the west, north, and southeast have much higher 87Sr/86Sr values than those seen in the Olduvai area. As a result, variation in bioavailable strontium on a wider scale than what was specifically analysed in this study appears to be promising to use in archaeological studies looking at mobility of past populations. While the ancient values may not be exactly the

194 same as those today, overall patterns, particularly those seen on the extremely radiogenic

Tanzania Craton rocks, will likely still emerge in fossilised teeth.

Also of interest is that the JK zebras do not appear to be migratory, as both teeth showed values very similar to the hippopotamus and crocodiles. This implies that animals which are now very mobile in the region may have been local in the past, possibly due to increased water and food availability in the region. Copeland et al. (2012) found that antelope teeth from Bed I showed the same pattern: local strontium values in animals that are now known to be migratory.

While further work is needed to be done to expand the bioavailable strontium isoscape of the region and to better understand the input of atmospheric and bedrock sources (Chapter 10), the results found in this study suggest that stable strontium isotope analysis is promising in the area. It could be of value to archaeologists wanting to trace mobility of hominins, and to palaeontologists who want to reconstruct behaviour of animals in the past. The enamel analysed here shows no indication of yielding non-biogenic values, suggesting that fossil remains from Olduvai Gorge may be used successfully to reconstruct the mobility of people and animals in the past.

195

Chapter 10: Future Outlooks

10.1 Introduction

Despite little variation in bioavailable strontium values in the immediate Olduvai

Gorge area, there are trends in the 87Sr/86Sr values determined here and significant variability further away as determined by Copeland et al. (2012). This indicates that strontium isotope analysis will be a valuable tool to understand mobility of archaeological hominins and fauna. There are many ways to expand the work undertaken in this study, which will be discussed in this chapter. This includes expanding the bioavailable strontium isoscape, estimating the proportions of atmospheric and bedrock input, and understanding the hydrological input going into Olbalbal. A great deal of work can be done on archaeological materials as well. More teeth can be analysed to see if more variation exists at JK. This includes fish bones, which can be analysed to determine the 87Sr/86Sr ratio of the stream water of Bed III time. As well, the strontium concentration of enamel and dentine should be assessed with and without acid washing to see if the amount of diagenetic strontium being removed can be quantified. Each of these will be discussed.

10.2 Future Work on Bioavailable Strontium

10.2.1 Expanding the isoscape

As mentioned in the concluding remarks of the previous chapter, there is considerable work to be done with regard to defining bioavailable strontium in the region.

The most obvious is to expand the existing isoscape and fill in the holes between this study and that done by Copeland et al. (2012). It would be valuable to continue sampling around

Olbalbal to determine whether the elevated 87Sr/86Sr values are common in the area east of

196

Olduvai or if they are confined to the depression, and to determine the origin of the radiogenic strontium ions. This entails sampling around Lake Masek and Lake Ndutu, as well as Lemagrut.

Also, the NVH should be sampled as it may have different bioavailable strontium values due to the high elevation (Bentley, 2006), increased mean annual precipitation

(Stewart et al., 2001), and rocks from various volcanoes with different 87Sr/86Sr values (e.g.

Bell and Simonetti, 1996; Kalt et al., 1997; Mollel et al., 2008; 2011). As well, it would be interesting to test the hypothesis posed herein that the lower values seen in the northeastern portion of the sampling area are due to proximity to Oldoinyo Lengai by sampling around that area of the NVH. Finally, since Laetoli is in the same area and researchers there could benefit from the isoscape, it would be useful to sample near it to determine if there are differences between Laetoli and Olduvai. Laetoli is near Tanzania Craton bedrock, which should be very radiogenic (Copeland et al., 2012). As well, it is near Lake Eyasi, which may have elevated lacustrine 87Sr/86Sr values similar to Olbalbal.

10.2.2 Defining sources of strontium

A mixing model could be developed to explain the contribution of various sources of strontium ions in the area. A start to this could be testing rainwater values to determine whether or not meteoric water in the region has 87Sr/86Sr values like ocean water, and by doing so determine if rainfall carries strontium from anthropogenic sources (Andersson et al., 1990; Capo et al., 1998). As well, samples of volcanic ash from Oldoinyo Lengai should be sampled to verify that its chemical composition is similar to its lavas. Also, since

Oldoinyo Lengai may not be the only strontium source of aeolian dust, samples of wind

197 blown material could also be analysed. If there are other significant sources than just the volcano, then the dust 87Sr/86Sr will not likely match the volcanic ash perfectly. Once these values are known, it is possible to determine how much strontium from each source has contributed to the region’s bioavailable strontium reservoir (e.g. Hartman and Richards,

2014).

Metamorphic rocks from the inselbergs around the landscape could also be sampled directly to determine their 87Sr/86Sr content. They are part of the Palaeozoic metamorphic bedrock for the area that has been overlain by volcanic rocks (Hay, 1976). This could be used to explain whether or not their weathering has any impact on the bioavailable strontium reservoir in the region and determine if they have a different 87Sr/86Sr value than the volcanic bedrock.

Soil samples from sampling localities can also be analysed directly to understand the variation in places of differing soil composition, and how different underlying bedrock affects soil values (Hartman and Richards, 2014). Most the sampling localities from this study came from areas of calcimorphic soils with a hard pan, though the Olbalbal samples were collected from an area with juvenile soils on volcanic ash (Anderson and Talbot,

1965). More plant samples should be collected from areas with juvenile soils to determine whether this difference in soil is responsible for the increased 87Sr/86Sr values in Olbalbal.

Finally, individual plants can be sampled to better understand interspecies and intraspecies variation in bioavailable strontium values. Samples of the same species from across the landscape should be compared, as should individual species from the same locality. This can be done to determine whether or not bioavailable strontium values for

198 sampling localities in this study were biased due to plant sampling (e.g. Hartman and

Richards, 2014).

10.3 Archaeological Work

10.3.1 Quantifying diagenesis

Diagenesis can further be understood by determining the concentration of strontium ions in the teeth before and after acid washing. Because soluble strontium is being removed, the concentration of ions should decrease after the acid pre-treatment. As well, dentine that has been diagenetically contaminated will have higher concentrations of strontium ions than enamel (Budd et al., 2000). For large animal teeth with lots of enamel, experimental work can be done to see if differing acid wash protocols (lengths of time and subsequent washes) impact strontium values in different ways than the 30-minute wash used here. This could be accomplished by determining the 87Sr/86Sr value at each step, as well as looking at differences in strontium concentration and how much enamel is destroyed in the acid at different time intervals (Lee-Thorp et al., 1997). If it can be shown that dentine values have been restored to biogenic levels, it is possible that the same process can be used on bone from the same burial context to obtain 87Sr/86Sr values from later in the individual’s life (Budd et al., 2000).

10.3.2 Reconstructing ancient bioavailable strontium

Much more work can be done with archaeological material from Olduvai Gorge.

For instance, more teeth from JK can be sampled to see if there are any animals which show non-local 87Sr/86Sr values, and to determine a broader range of local values. Fish

199 bones from JK, provided that diagenetic strontium can successfully be removed from them, can also be analysed to determine the 87Sr/86Sr of the stream water that passed through in

Bed III time. Analysis of fish bones through time could further provide information about changes in water sources over time (e.g. Joordens et al., 2011; Baddouh et al., 2016), which could then be used to infer small temporal changes in 87Sr/86Sr of local animals.

If any rodent bones are found at Bed III sites they can be used to reconstruct ancient local bioavailable strontium for the area (e.g. Kootker et al., 2016). This pertains to sites of other ages as well. This approach could be useful in understanding how bioavailable strontium has changed through time around Olduvai Gorge, and therefore be useful in distinguishing local/non-local animals from different stratigraphic layers. As well, it would be interesting to see if there are changes in bioavailable strontium toward modern values between Bed III and the younger beds up to the present as the environment continued to become more arid (Hay, 1976; Cerling and Hay, 1986).

The 87Sr/86Sr values of the tuffs at Olduvai Gorge can be analysed to determine the bedrock strontium contribution to bioavailable strontium in the past. Through time the various tuffs were deposited by different volcanoes, which all have unique 87Sr/86Sr values in their lavas and tephra, despite volcanic material typically being depleted in 87Sr due to the earth’s relatively unradiogenic mantle (Bentley, 2006; e.g. Mollel, 2007; Mollel et al.,

2011). If there are large differences in the strontium values of the tuffs, then there are likely to be differences in bioavailable strontium in the region as a result.

10.3.3 Incremental sampling

Incremental sampling could be used on teeth of larger, typically migratory

200 animals. By sampling multiple areas on the same tooth, one can retrace movement over the landscape at various times through the tooth’s development (e.g. Britton et al., 2009;

Copeland et al., 2016). This includes antelope, wildebeest, zebras, and elephants. Remains of these species have been found in the various beds of Olduvai Gorge (e.g. Kleindienst,

1973; Copeland et al., 2012; van der Merwe, 2013). In this study zebras were found to be local, and in Copeland et al. (2012)’s study antelopes which are known to migrate large distances today were also found to be resident. If there are large intra-tooth differences in

87Sr/86Sr values and a mean value that deviates from local values, it indicates that the animal is migratory (Copeland et al., 2016). Incremental sampling could be a way to determine if animals that are migratory today truly were sedentary in the past.

10.3.4 Palaeoecology of animals and hominins

Because animals known to be migratory appear to have been resident in Bed I and possibly Bed III, strontium isotope analysis therefore could be used at Olduvai Gorge to understand changes in migration patterns and determine when these migratory species began travelling away from Olduvai in the dry seasons. By analysing and comparing teeth from Bed I up to modern times, species-level differences in landscape use can be assessed.

Palaeoclimatic and palaeoenvironmental reconstructions should also be elaborated to determine the ecological setting of Olduvai Gorge through the later beds. If animal migrations in the past are associated with wet/dry periods leading to differences in food and water availability in the region as they are today (Estes, 2014), then it is likely that through time as the perennial water sources dried up or became annual, animals would have become more mobile. By Bed IV time the lacustrine area is predicted to have shrunk in

201 size and shifted further toward its modern location in Olbalbal due to faulting, then moved further yet by the Masek Beds time (Hay, 1976). The regional drainage sump moved into its modern location in Olbalbal during Ndutu Bed times and is now only filled with water during the wet season (Hay, 1976).

Identifying ecological differences between the past and present are important to understanding hominin behaviour in the past. Hominin teeth can be analysed incrementally in the same way as animal teeth and can be understood in a like manner. No studies could be found where hominin teeth have been incrementally analysed, which could be due to the destructive nature of isotope analysis. However, as methodology and analytical techniques improve, smaller samples will be required for analysis (e.g. Copeland et al.,

2008), so incremental sampling may become more viable in the future. Nonetheless, hominin teeth from Olduvai Gorge can be analysed in a single place to determine whether the individual was local or non-local at the time of tooth formation. Strontium isotope analysis of hominin teeth could also provide hints as to sex-specific dispersal patterns if they existed (e.g. Copeland et al., 2011), or determine the degree to which hominins move across the landscape to follow resources. Comparisons could then be made between East and South African hominins, for which some strontium isotope data already exist. These data would be especially valuable, as this type of information cannot be interpreted indirectly through spatial variation in archaeological assemblages; nor through variation in material types found within them.

10.4 Modern Enamel Values

It would be useful to compare modern tooth 87Sr/86Sr values of species known to

202 be migratory to those known to be resident and see how they relate to the bioavailable strontium values found in this study and in the study by Copeland et al. (2012). Resident species which are non-obligate drinkers that receive their water almost entirely through their food (such as dik-diks and giraffes) should have 87Sr/86Sr values that are especially close to those found in modern plant values determined in this study. Since many animals migrate from the Olduvai area to the northern extent of the Serengeti, they should have elevated 87Sr/86Sr values compared to those that stay around Olduvai Gorge year-round.

10.5 Conclusion

This study has found that there is relatively little variation in bioavailable strontium values around Olduvai Gorge, but when coupled with previous work done in the larger area it shows that there is promise in using strontium isotope analysis to reconstruct mobility of animals and hominins that move over very long distances. As a result, there are a number of opportunities for many other lines of stable strontium isotope research. The isoscape created herein should be expanded in all directions, particularly toward Laetoli, the NVH to the south and east, and the northern and western areas of the Serengeti, as these areas likely will have 87Sr/86Sr values that are distinct from Olduvai. As well, the various sources of bedrock and atmospheric strontium can be quantified to understand their proportional input into the bioavailable strontium reservoir.

Based on the comparison of modern plant values and local archaeological animals, the bioavailable strontium values appear to be different between the past and present. By analysing archaeological rodent teeth, the ancient bioavailable strontium values of Olduvai can be determined. This should be done for each bed individually, as bioavailable strontium

203 values may have changed more than once through time. A great deal of work can also be done on more archaeological teeth from Bed III, as well as the older and younger beds, to differentiate between local and non-local fauna from various sites. Also, strontium isotope analysis can be used to identify when animals known to migrate long distances in the present first began to exhibit that behaviour.

Strontium isotope analysis has not yet been used on hominin teeth in East Africa.

Teeth at Olduvai Gorge do not appear to be irreversibly contaminated by diagenetic strontium, so this could be a valuable source of behavioural and land-use information.

There is variation in strontium isotope ratios on the landscape farther away from Olduvai than what was assessed in this project, so analyses of hominin teeth could be able to determine whether hominins moved long distances in this region or not, and may improve our understanding of their dispersal patterns. Incremental sampling of teeth could yield valuable information about movement patterns, but thus far it has not been used on hominin teeth, likely due to the destructive nature of this type of analysis.

While there is a great deal of work to be done, this study has revealed that archaeological use of stable strontium isotope analysis shows promise in the Olduvai Gorge region. The volcanic and metamorphic areas around the gorge show little variation in bioavailable strontium values, but there is considerable variation further away. As such, locals and non-locals can be distinguished, provided that the non-locals come from another far-away geological zone. Biogenic 87Sr/86Sr values are preserved in archaeological teeth from JK, thus confirming the potential for reconstructions of past mobility of hominins and animals. This kind of work will be very beneficial to archaeologists seeking to understand landscape use and behaviour of hominins and animals in the past.

204

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