REBOUND HARDNESS RESULTS FOR RAW MATERIAL

LOCATED NEAR PINNACLE POINT, SOUTH AFRICA

AND THE IMPLICATIONS THEREOF

by

CHRISTOPHER M. SHELTON

Presented to the Faculty of the Graduate School of

The University of Texas at Arlington in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF ARTS IN ANTHROPOLOGY

THE UNIVERSITY OF TEXAS AT ARLINGTON

May 2015

ii

Copyright © by Christopher M. Shelton 2015

All Rights Reserved

iii

Acknowledgements

I would first like to thank my advisor, Dr. Naomi Cleghorn, who first inspired and

encouraged my interest in southern African archaeology, and provided my first

opportunity for fieldwork in this region. Without her advising, teaching, encouragement,

and introduction to her professional networks, this study and my career would not have

been possible. For all of the knowledge and experience I have gained under her tutelage,

I will be eternally grateful.

I would especially like to thank Dr. Kyle Brown for providing me with this research

opportunity, and for supplying me with the quartzite and silcrete samples. I would like to

thank Dr. Curtis Marean for providing the opportunity to gain experience at Pinnacle

Point, and for allowing me to use his facilities. I would also like to thank the College of

Liberal Arts, the Department of Sociology and Anthropology, and the Ruch family for

providing the travel grants and scholarships which made this study and ensuing

conference presentations possible. I would like to thank Dr. Shelley Smith and Dr. Karl

Petruso for their time and patience on my thesis board. I would like to acknowledge Eric

Cleveland and Dr. Yu Xinbao for allowing me to use their labs and equipment. I would

also like to thank Dr. Scott Ingram who has always found the time to provide feedback on

my work and has encouraged me along the way

Finally, I would like to thank my family. Without the love, encouragement, and

support from my mother (Susan Shelton), my father (Terry Shelton), my aunt (Marcia

Boswank), and my uncle (Steve Boswank), this thesis would not have been possible.

Most importantly, I would like to express my deepest appreciation and love for Elizabeth

Nelson and Isaac Weston, who provided me with love, encouragement, support, and

immeasurable patience through the entire thesis process.

April 1, 2015

iv

Abstract

REBOUND HARDNESS RESULTS FOR RAW MATERIAL

LOCATED NEAR PINNACLE POINT, SOUTH AFRICA

AND THE IMPLICATIONS THEREOF

Student Christopher M. Shelton, M.A.

The University of Texas at Arlington, 2015

Supervising Professor: Naomi Cleghorn

The focus of this study is to test our ability to glean ancient human behavioral

ecology data through a specific form of raw material mechanical properties testing.

Through the quantitative analysis of raw material in regards to knapping quality (hereafter

referred to as knappability), collection processes and choice patterns of a study group

can be inferred. More precisely, this study serves to test the viability of the use of the

Schmidt hammer as a means of determining knappability, with the area in and around

Pinnacle Point (Western Cape, South Africa) as the focus area and silcrete and quartzite

as the focus lithologies. In the course of this study, it was found that the use of the

Schmidt hammer as a testing device and Young’s modulus of elasticity as a quantitative

measure of knappability should be discounted from future knappability studies. Finally,

this study also demonstrates that the massive silcrete located in the vicinity of Pinnacle

Point occurs in more than one form, which could have had implications for ancient raw

material selection and affects the future use of silcrete source locations as a variable in

agent based modeling and behavioral ecology studies in general.

v

Table of Contents

Acknowledgements .............................................................................................................iii

Abstract .............................................................................................................................. iv

List of Illustrations .............................................................................................................. vi

List of Tables ..................................................................................................................... viii

Chapter 1 Introduction......................................................................................................... 1

Chapter 2 Background ........................................................................................................ 4

Middle Stone Age of southern Africa .............................................................................. 4

Pinnacle Point ............................................................................................................... 11

Raw Materials ............................................................................................................... 14

Quartzite ................................................................................................................... 14

Silcrete ...................................................................................................................... 16

Heat Treatment ............................................................................................................. 19

Rock Fracture Mechanics ............................................................................................. 24

Chapter 3 Methods ............................................................................................................ 30

Chapter 4 Results ............................................................................................................. 38

Chapter 5 Discussion ........................................................................................................ 47

Chapter 6 Conclusion ........................................................................................................ 54

Chapter 7 Future Research .............................................................................................. 57

Appendix A Individual Rebound Hardness Results for Quartzite ..................................... 60

Appendix B Individual Rebound Hardness Results for Untreated Silcrete ....................... 81

Appendix C Individual Rebound Hardness Results for Heat-Treated Silcrete ............... 105

References ...................................................................................................................... 116

Biographical Information ................................................................................................. 135

vi

List of Illustrations

Figure 1 (from Thompson et al., 2010). Typical MSA lithics found at Pinnacle Point

(cave PP13B). ..................................................................................................................... 4

Figure 2 Normalized comparison of the occurrence of quartzite and silcrete in the

Pinnacle Point cave 13B record (162 kya through ~90 kya), broken down by aggregate

(youngest on left). This is based on data from Thompson et al., 2010. .............................. 5

Figure 3 Normalized comparison of the occurrence of quartzite and silcrete in the

Pinnacle Point cave 5/6 record (90 kya through 54 kya), broken down by aggregate

(youngest on left). This is based on data from Brown, 2011. ............................................. 6

Figure 4 (from Brown et al., 2012) Tools found at the top of the figure (DBCS aggregate)

are typical Howieson’s Poort lithics from Pinnacle Point (Cave PP5/6). The Lower tools

(SADBS aggregate) are typical backed blades from the unnamed microlithic industry at

Pinnacle Point. .................................................................................................................... 7

Figure 5 Map of the southern coast of South Africa with Pinnacle Point depicted. .......... 11

Figure 6 (from Brown, 2011). Digital elevation map with silcrete outcrop occurrence

highlighted with red (data from: CGS Mossel Bay and Hartenbos 1:50,000 series geology

maps). ............................................................................................................................... 17

Figure 7 (from Shariati et al., 2011). Depicts the operational system of the Schmidt

Rebound Hammer. ............................................................................................................ 31

Figure 8 The author using the Schmidt Rebound Hammer in Terracon Labs; Fort Worth,

Texas (photo by Zachary Overfield). ................................................................................. 32

Figure 9 Example of a testing grid (silcrete sample- I14-3-3.003). ................................... 34

Figure 10 Box and whisker plot comparison of the quartzite, untreated silcrete, and heat-

treated silcrete results of this study, to the results published by Brown, 2011 ................. 40

vii

Figure 11 C-clamps holding aluminum tension bars, clamping sample to steel base. This

methods was used on three samples during testing in Fort Worth, as discussed in text. 44

Figure 12 Flaked sample exhibiting the color change and luster associated with proper

heat treatment according to Brown and colleagues (2009). ............................................. 47

viii

List of Tables

Table 1 Rebound results for quartzite, silcrete (unheated), and silcrete (heated). ........... 38

Table 2 Descriptive statistics comparing rebound hardness results for all quartzite and

silcrete (unheated) samples. ............................................................................................. 38

Table 3 Levene’s test and student’s t-test comparing rebound hardness results for all

quartzite and silcrete (unheated) samples. ....................................................................... 39

Table 4 Descriptive statistics for all recorded impact readings for three block samples,

comparing Schmidt rebound hammer used in South Africa to the one used in................ 41

Table 5 Levene’s test and student’s t-test comparing all recorded impact readings for

three block samples, comparing Schmidt rebound hammer used in South Africa to the

one used in Fort Worth, Texas. ......................................................................................... 42

Table 6 Comparison of the averages for each round of impacts between the same

silcrete samples tested in Mossel Bay, South Africa and Fort Worth, Texas using different

Schmidt hammers. ............................................................................................................ 42

Table 7 Comparison of unclamped versus clamped heat-treated silcrete samples. ........ 44

Table 8 Descriptive statistics for all recorded impact readings for three block samples,

comparing clamped and unclamped methods. Note that one of the samples failed during

testing. ............................................................................................................................... 45

Table 9 Levene’s test and student’s t-test comparing all recorded impact readings for

three block samples, comparing clamped and unclamped. Note that one of the samples

failed during testing. .......................................................................................................... 45

Table 10 Previously published rebound values for quartzites and unheated silcretes in

Africa ................................................................................................................................. 46

1

Chapter 1

Introduction

The Middle Stone Age record in Southern Africa is a period of increasing

technological variability in stone tool technology and lithic raw material selection (McCall,

2006; Thompson et al., 2010; Wurz, 2002; Minichillo, 2006). The lithic record fluctuates

between low and high quality raw materials and frequent concomitant shifts between

larger flaked tools and small, technologically complex microliths and bifaces (McCall,

2006; Minichillo, 2006). These shifts are both diachronic and geographic. We can

investigate the human decision making processes that resulted in this variability in terms

of a cost-benefit analysis. Specifically, we can model the trade-offs early humans would

have faced in terms of raw material procurement, transport distance, processing effort

(particularly that related to heat treatment), tool use-life and edge durability, and flexibility

of tool design.

To address this problem, mechanical properties testing can be employed to

determine the characteristics of the available raw materials that might have influenced

human choice. These data can then be integrated into a geographic analysis of raw

material sources relative to known Middle Stone Age sites. Recent research

demonstrates that ancient stone tool makers may have chosen raw materials on the

basis of two fundamental mechanical characteristics: the ease and predictability with

which the material can be flaked and the ability of the material to maintain a sharp edge

(Braun et al., 2009; Domanski et al., 1994; Yonekura and Suzuki, 2009). These

mechanical properties can be quantified and integrated with geographic source locations

to model raw material costs. With this research, we can better understand the choice

patterns of some of the first modern humans, and possibly add another line of evidence

which will ultimately allow us to explain and understand the anomalous technological

2

appearances of the Still Bay industry, the Howieson’s Poort industry, and other microlithic

industries which were precocious in comparison to the previous and subsequent stone

tool technologies (Volman, 1981; Lombard, 2005, 2009; Brown et al., 2012).

The Pinnacle Point site on the southern coast of South Africa was chosen as the

focus of this study due to the chronological span of the MSA sediments (occupation

layers ranging from 162kya to 54ky) and the variability of the lithic record (fluctuating

between coarse-grained and fine-grained material) (Marean, 2010a; Brown et al., 2009,

2012; Thompson et al., 2010). The two principal raw material lithologies in the Pinnacle

Point assemblage are quartzite and silcrete (Brown, 2011). Silcrete is non-local relative to

quartzite, and is consistently heat-treated throughout the record to produce a more

knappable material (Brown et al., 2009; Brown, 2011). The heat treatment of silcrete

appears with the earliest human occupation layers at Pinnacle Point(162kya), and is the

earliest known archaeological evidence for this practice. Yet, heat treatment is not a

permanent feature in the record (Brown et al., 2009; 2012). In fact, the lithic record at

Pinnacle Point fluctuates between the local coarse-grained quartzite and the heat-treated

exotic silcrete sporadically until the final occupation layer at 54kya (Brown et al., 2009;

Thompson et al., 2010). Not only does heat-treated silcrete fluctuate in intensity, but

towards the later MSA, technologically advanced microlithic industries made on heat-

treated silcrete appear and disappear in the record as well (Brown et al., 2009, 2012;

Marean, 2010a; Thompson et al., 2010).

It is the seemingly erratic lithic shifts in the record that are the focus of this study.

If the knappability of the available raw materials (both in raw and heat-treated form)

around Pinnacle Point can be quantitatively ranked through mechanical properties

testing, the results can be compared to gathering costs (based on distance from site to

source) in a cost-benefit model. The data from this model can be compared to the shifts

3

in the lithic record, and an investigation for trends can be conducted when assessed with

the less proximate factors (and possible drivers), such as paleoclimate, paleoecology,

sea level and coast proximity, estimated population density, availability of firewood, etc.

To produce such a model, sound methods for quantifying the knappability of raw material

must be employed. The purpose of the following study is to evaluate methods published

by Braun and colleagues (2009) as a first step in creating such a model.

4

Chapter 2

Background

Middle Stone Age of southern Africa

Figure 1 (from Thompson et al., 2010). Typical MSA lithics found at Pinnacle Point

(cave PP13B).

The Middle Stone Age (MSA) was first described by Goodwin and Van Riet Lowe

in 1929, and is characterized by the presence of prepared core tool technology (Figure

1). Many of the cores produced during the MSA are prepared in such a way as to yield

recurrent tools of similar shape and size with little to no platform preparation, such as

blades and points (McBrearty and Brooks, 2000). MSA cores were also prepared in such

a way as to produce one preferential flake (blank) at a time, which is known as Levallois

technology (Klein, 2009). These technologies stand in stark contrast to the Earlier Stone

5

Age (ESA) technology of large bifacial tools, and the Later Stone Age (LSA) technology

characterized by microliths made predominantly on exotic raw material (McBrearty and

Brooks, 2000). The dates for the genesis and termination of the MSA are both highly

debated, with a dearth of well dated sites and no clear shift boundary on either end.

However, for the purpose of this study, the MSA will be defined as 250kya to 40kya

(Klein, 2009)

Figure 2 Normalized comparison of the occurrence of quartzite and silcrete in the

Pinnacle Point cave 13B record (162 kya through ~90 kya), broken down by aggregate

(youngest on left). This is based on data from Thompson et al., 2010.

Early humans on the southern coast of Africa had few choices for flaked stone

raw materials. During most of the Middle Stone Age, they principally exploited locally

abundant quartzite, generally in the form of beach cobbles or proximate outcrops

(Thompson and Marean, 2008; Avery et al., 1997; Thompson et al., 2010; Thackeray,

1989; Minichillo, 2006). However, exotic, fine-grained material (such as silcrete or

chalcedony) is present in some capacity throughout many occupational assemblages,

6

and seems to fluctuate in intensity through the record ( see Figures 2 and 3 for the

Pinnacle Point record) (Brown, 2011; Wurz, 2002; Minichillo, 2006; Brown et al., 2009;

Thompson, 2010).

Figure 3 Normalized comparison of the occurrence of quartzite and silcrete in the

Pinnacle Point cave 5/6 record (90 kya through 54 kya), broken down by aggregate

(youngest on left). This is based on data from Brown, 2011.

Through most of the MSA, the above mentioned prepared core technology

changed very little, save for a gradual trend of the tools becoming slightly smaller

(Volman, 1981). However, this continuity seems to be interrupted near the end of the

MSA with a number of shifts in technology which occur concurrently with dramatic

increases in the use of exotic materials, only to be replaced by the previous, less intricate

technology (Brown, 2011; Minichillo, 2006; Wurz, 2002; Brown et al., 2012).

The first of the major technological shifts away from the general prepared core

technology of the MSA is the Still Bay industry (which is not present at Pinnacle Point)

7

(Henshilwood et al., 2001). Still Bay is characterized by bifacial, foliate shaped points

made on exotic material (Henshilwood et al., 2001; Tribolo et al., 2005; Brown et al.,

2009). This industry is present in several sites in South Africa, but is best represented,

and firmly dated at Blombos cave between 76.8 and 72.7kya (Henshilwood and Dubreuil,

2011; Jacobs et al., 2006; 2013). Recently, Tribolo and colleagues (2013) have argued

for an earlier beginning to the Still Bay at 109kya, however this date is inconsistent with

all other Still Bay occurrences.

Figure 4 (from Brown et al., 2012) Tools found at the top of the figure (DBCS aggregate)

are typical Howieson’s Poort lithics from Pinnacle Point (Cave PP5/6). The Lower tools

(SADBS aggregate) are typical backed blades from the unnamed microlithic industry at

Pinnacle Point.

At Pinnacle Point site 5/6, a microlithic technology appears around 71.3kya

(Figure 4) (Brown et al., 2012). This microlithic technology is quite advanced, as it is

statistically thinner and smaller than the microliths produced in the following Howieson’s

Poort microlithic industry (Brown et al., 2012). As of yet, this technological shift is

unnamed, and has only been found at Pinnacle Point.

8

The most prominent and enigmatic of the shifts in stone technology and material

is the Howieson’s Poort industry. The Howieson’s Poort occurs widely across Southern

Africa, between 64.8 kya and 59.5 kya, and is present at Pinnacle Point (Jacobs et al.,

2008). The industry is characterized by small retouched blades, and is generally made

from exotic, fine-grained stone material (Klein, 2009; Lombard, 2005, 2009; Minichillo,

2006). The sophistication of Howieson’s Poort stone tools seems to anticipate tools that

are found much later in the African LSA (after forty thousand years ago). The Howieson’s

Poort is relatively short lived (only lasting approximately five thousand years), and

subsequent industries return to an emphasis on simpler flaked tools made largely from

quartzite (Klein, 2009; Jacobs et al., 2008; Minichillo, 2006).

Raw material selection appears to be correlated with major shifts in technology.

Some have argued that coarse-grained material, such as beach cobble quartzite,

although plentiful in the region, lacks the fine flaking qualities required for the microlithic

technology of the Howieson`s Poort and the other technological shifts (Brown et al.,

2009; Brown, 2001; Lombard, 2005; Minichillo, 2006). By comparison, quartzite-based

technologies are low cost because materials are locally abundant and do not require

alteration (Mackay et al., 2014; Minichillo, 2006; Brown et al., 2009; Brown, 2011). Exotic

materials, such as silcrete and chalcedony, likely had higher search and transport costs

(Minichillo, 2006). In addition, the use of silcrete for small tool production may have

required heat treatment, a significant time and resource investment (Brown et al., 2009).

This intensive production strategy first appears at Pinnacle Point (Western Cape) around

162 kya, and less than one hundred thousand years later it becomes a regular feature of

the unnamed microlithic industry and later, the Howieson’s Poort industry (Marean, 2010;

Brown et al., 2009; Brown, 2011).

9

Many authors have attempted to explain the dramatic shifts in technology

through behavioral ecology (Lombard, 2005, 2009; Mackay, 2011; Chase, 2010; McCall,

2007; Minichillo, 2006). After all, stone tools would have been quite necessary for

survival, and any change in technology would not necessarily have to be beneficial, but is

unlikely to have been adopted if it was maladaptive (McKay et al., 2014). Due to this,

many have attempted to argue the technological shifts as an adaptation to the

environmental changes associated with Marine Isotope Stage 4 (MIS 4) (Lombard, 2005,

2009; Chase, 2010). However, the adoption of the Howieson’s Poort was nearly

ubiquitous across southern Africa, and across different biomes and rainfall regimes which

would have had different responses to the environmental shifts (Jacobs and Roberts,

2009a,b,c; Jacobs et al., 2008 also see Chase, 2010; Mackay, 2011; McCall 2006, 2007;

Clark and Plug, 2008; Wadley, 2008; Lombard, 2005, 2009).

Another argument for the appearance of technological shifts is an increase in

social interaction between hunter gatherer bands (Jacobs and Roberts, 2009a, b, c).

Some have suggested that the dramatically increased frequency of exotic/non-local

material indicates an expansion and increased reliance on social and/or trade networks

(McCall, 2007; Cochrane, 2008 Lombard, 2005; 2008; 2009; Minichillo, 2006; Ambrose,

2006; Henshilwood and Marean, 2003). These authors claim that the limited occurrence

of the exotic raw materials, coupled with the lithic assemblages that contain a

predominance of these fine-grained materials, would indicate an intricate trade network

(McCall, 2007; Cochrane, 2008; Lombard, 2008, 2009; Henshilwood and Marean, 2003).

McCall (2007) also claims that the rarity of the material along with the increased

complexity of the chaîne opératoire would indicate an increased reliance on social

networks. He continues by arguing that the tools made during this time were exaggerated

in complexity to add value and maintain social ties through exchange, gifts, or trade

10

(McCall, 2007). The rapid expansion of social networks could explain the prevalent and

widespread appearance of the Howieson’s Poort across southern Africa.

More recently, Mackay and colleagues (2014) have argued that the lithic record

exhibits indications of both an intensification due to environmental fluctuations and an

expansion of social interaction during the Howieson’s Poort. Further, the study argues

that slight inter-site variations in the production of Howieson’s Poort tools point to the

origin of the industry being centered in the western portion of southern Africa (Mackay et

al., 2014).

Directly after each of these technological shifts, the lithic record seems to return

to the general MSA production with recurrent and preferential flakes, and raw material

seems to fluctuate in prevalence (Volman, 1981; Minichillo, 2006; Lombard, 2005). At

many sites, the post-Howieson’s Poort lithic record returns to the coarse-grained material

until the introduction of the Later Stone Age (LSA), some 20,000 years later (Volman,

1981; Minichillo, 2006; Lombard, 2005, 2009). Although there seems to be some

relationship between raw material selection and the technology produced, this correlation

does not explain why heat-treated silcrete artifacts appear in the first layers of occupation

at Pinnacle Point (162 kya), and continue to increase and decrease in prevalence

throughout the record until the dramatic increases at the above-mentioned technological

pulses. Further, the correlation does not explain what the raw material and technological

shifts directly meant to the MSA people (i.e. the costs and benefits of the material and

technological shifts). By fully understanding the mechanical properties of the stone tool

materials, the relative advantages of each lithology, and the geographic locations of the

sources, we may better understand the constraints facing the early humans, and the

relative costs and benefits of the raw materials that were being selected.

11

Figure 5 Map of the southern coast of South Africa with Pinnacle Point depicted.

Pinnacle Point

Pinnacle Point locality is a cave complex located on the southern coast of the

Western Cape, South Africa (Figure 5). The most renowned of these caves is PP13B,

which contains intermittent human occupation layers from 162kya to 91kya, and PP5/6,

which contains intermittent occupation layers from 90kya to 53kya (Jacobs, 2010; Bar-

Matthews et al., 2010). When the archaeological records of PP13B and PP5/6 are

combined, they produce a nearly continuous record of human occupation right up until

53kya. The Pinnacle Point caves are unique and important to the archaeological record

for numerous reasons. First, Pinnacle Point is the only coastal site in southern Africa that

contains human occupation layers which date to MIS-6 (Marean, 2010a). MIS-6 occurs

between 195kya and 130kya, and is an extreme glaciation event which interrupted the

African monsoon system and may have caused a mass desertification across most of the

continent (Marean, 2010a). It is thought that this desertification period is the cause of one

12

of our species’ most recent and dramatic bottlenecking events in the genetic record, and

may have been the cause of genetic homogenization of modern humans (Marean,

2010a; Marean and Assefa, 2005; Lahr and Foley, 1998; Fagundes et al., 2007).

Marean and others have claimed that there would have been only a few places

on the continent which could have provided an environment suitable for human survival

through these hostile conditions for such a period of time (Marean, 2008; 2010a; 2011;

Marean and Assefa, 2005; Bassell; 2008). Based on proxy data from the Last Glacial

Maximum, the winter rainfall regime of the cape floral kingdom may have remained stable

during this global cooling (Chase and Meadows, 2007; Marean, 2010a). Additionally, the

intensely diverse and abundant floral biomass in the cape floral kingdom, combined with

the abundance of more protected geophytic plants, would have provided a refugium for

some of our earliest ancestors (Marean, 2010a). Further, Marean (2010a) and others

have hypothesized that the homogeneity of the genetic record would require a small

breeding population in a single area, otherwise, genetic diversity would have been

reestablished and preserved as soon as the survivor groups were large enough to meet

and resume gene flow (see Rogers in Ambrose, 1998; Marean, 2011).

Along with unique geography, Pinnacle Point also includes some very important

additions to the archaeological record. This site contains the first known evidence of

human shellfish acquisition, with evidence for gathering occurring in the earliest

occupation layers at 162kya (Marean et al., 2007). Shellfish provide a unique option for

hunter/gatherers in that return rates are generally high compared to low search costs.

Also, shellfish are high in omega-3 fatty acids, which some have argued would have

provided a nutritional catalyst for cognitive development (Crawford et al., 1999; Marean,

2010a; 2010b). Marean (2010a; 2010b) also argues that the shellfish may be a proxy

measure for increased cognitive ability (Marean et al., 2007). He claims that some

13

shellfish, which occur strictly in the intertidal zone, can only be reached on or near a

strong spring tide. During low sea stands, when the coast was on the very fringes of the

gathering range for those living in the cave, they would have to have had some working

knowledge of moon phases, a way to track them, and a complex language to pass on the

knowledge in order to know when would be the opportune times to abandon inland

patches for coastal foraging (Marean, 2010a; Marean et al., 2007).

Pinnacle Point also contains the earliest known evidence of heat treatment to

improve the flaking qualities of stone (Brown et al., 2009). Evidence for this occurs in the

first layers of occupation with silcrete as the raw material. This discovery has pushed

back the use of heat treatment technology tens of thousands of years, and, due to the

complexity of the heat treatment process, has been argued as evidence for an advance

in human cognitive abilities (Brown et al., 2009; Marean, 2010a; Brown, 2011). Oddly,

heat treatment is not a permanent occurrence in the Pinnacle Point lithic record (Brown et

al., 2009). Instead, the record seems to fluctuate from fine-grained, heat-treated silcrete

to more coarse-grained, local quartzite; and then back again multiple times throughout

the occupations (Brown et al., 2009; Thompson et al., 2010; Brown, 2011). Heat-treated

silcrete appears more regularly ~71kya with the pre-Howieson’s Poort microlithic industry

(Brown et al., 2012).

All of this identifies Pinnacle Point as a very unique and important archaeological

site to southern African archaeology, and paleoanthropology in general. Further, these

archaeological advances and arguments, combined with some of the earliest

occurrences of utilized ochre, places Pinnacle Point firmly in the center of the early

human cognition studies (Marean et al., 2007).

14

Raw Materials

Quartzite

Pinnacle Point, and the surrounding area, is located in the Cape Fold Belt with

the Cape Supergroup forming the bedrock (Booth et al., 2004). The Cape Supergroup is

estimated to be about 8km thick, and was formed during the Early to Late Paleozoic

(Booth and Shone, 2002). The supergroup is comprised of three groups, the Bokkeveld,

the Wittenberg, and the Table Mountain; however, only the Bokkeveld and the Table

Mountain groups can be found as exposed outcrops within the 8km to 12km gathering

range of Pinnacle Point (Booth and Shone, 2002; Shone and Booth, 2005; Brown, 2011;

Binford, 1980, 1982; Kelly, 1995; Marean, 2011). The Bokkeveld group is estimated to be

at least 3km thick and is comprised mainly of argillaceous rock (shale, mudstones,

siltstones, and sandstones containing mudclasts) (Shone and Booth, 2005; Booth et al.,

2004; Tankard et al., 2006). The Table Mountain group is divided into six subgroups, and

is thought to be about 3km thick (Young et al., 2004; Shone and Booth, 2005). It is

comprised mostly of sandstone and quartzite, and has a very high quartz content in

general (Shone and Booth, 2005; Young et al., 2004). The Pinnacle Point caves are

formed within the Table Mountain group, with walls comprised of Skurweberg quartzite

from the Nardouw formation (Brown, 2011; Thamm and Johnson, 2006).

Although the cave was formed in a quartzitic formation, this material has very

poor flaking properties (Brown, 2011). The Skurweberg quartzite has been reported to be

a very friable yellowish gray to light brown lithology, and only accounts for an insignificant

portion of the lithic assemblage (Brown, 2011). In fact, the vast majority of the quartzite in

this area does not consistently fracture conchoidally (Brown, 2011). However, the

Robberg formation quartzites, a dark gray material, tend to be much harder with a more

sound silica cementation (Brown, 2011). The silica cementation in this formation

15

produces a harder, finer grained stone, which lends itself to knapping more readily than

other quartzites found in the Table Mountain group (Brown, 2011).

Quartzite is also available in secondary context as rounded beach cobbles

(Thompson and Marean, 2008; Brown, 2011). In modern context, cobble beaches can be

found directly in front of the Pinnacle Point caves, in several places within the gathering

range along the coastline, and in small patches along the Gouritz river bank (Brown,

2011). The cobble beaches can be comprised of other lithologies (such as chert, silcrete,

or hornsfels); however, rock types other than quartzite are infrequent at best (Brown,

2011). The location of active cobble beach raw material sources can be erratic, as sand

and wave action frequently move or bury cobbles, making gathering at known locations

unpredictable (Brown, 2011). However, with the proximity of the Pinnacle Point caves to

the coast during high sea stands, and the abundance of possible sources, it would seem

that an alternative source would not add much distance to a gathering expedition (Brown,

2011). The infrequency of coastal proximity would not have much effect on gathering

practices as well. As the coast retreats from the mainland in glacial periods, the cobble

beaches are often left behind (Brown, 2011). This is shown within the occupation layers

of Pinnacle Point at times when the coast has been modeled to be 30km or more from

the caves (Brown, 2011; Fisher et al., 2010). During these periods of distance from the

coast, quartzite with beach cobble cortex still dominates as the raw material in the lithic

assemblage (Brown, 2011).

The nearest knappable quartzite outcrop can be found roughly 5km to the east at

the Cape St. Blaize cave, on the Mossel Bay point and on the Cape St. Blaize trail, near

the Mossel Bay Point (Thompson and Marean, 2008). Brown (2011) describes this

outcrop material as being notably more fine-grained than other outcrops and beach

cobbles in the surrounding area. He continues by describing the outcrop material from

16

the point area as a darker gray quartzite, and that the material is so fine-grained that the

individual grains are, or are nearly, invisible without magnification (Brown, 2011). Despite

the knappability of the outcrop quartzite being described as much higher, as well as a

distinct selectivity for the material in the Cape St. Blaize MSA assemblage, cortex studies

from the Pinnacle Point assemblage have shown that the vast majority of the quartzite

used at Pinnacle Point was gathered from a secondary, cobble beach context (Thompson

and Marean, 2008; Thompson et al., 2010; Brown, 2011). This implies that the occupants

of the Pinnacle Point caves rarely traveled more than a few kilometers for quartzite, the

dominant raw material.

Silcrete

The term silcrete was first conceived and defined by Lamplugh (1902) and

describes a lithology formed through the near-surface, low-pressure, and low-heat

precipitation of silica (Summerfield, 1981; Roberts, 2003; Nash and Ullyott, 2007). This

silica cements or replaces, in part or completely, the parent material to become at least

85% silica (averaging over 96% by weight in the Cape Coastal region of Southern Africa)

(Nash and Ullyott, 2007; Summerfield and Goudie, 1980; Summerfield, 1983b). The

potential parent material includes a wide range of geologic formations, from bedrock to

soil (Summerfield, 1983a; 1983b). In the case of the outcrops found near Pinnacle Point,

the parent material is most likely a fine clay/slate, which then produces fine-grained

silcrete (Summerfield, 1981; 1983b, Frankel, 1952, Mountain, 1952). Due to the nature of

silcrete genesis and the variability of the parent materials, its qualities can also be quite

variable geographically, despite chemical homogeneity (Summerfield and Goudie, 1980;

Summerfield, 1986; Roberts, 2003). Most authors agree that the genesis of silcrete can

be used as a paleoclimate proxy; however, there is debate as to the climatic and

environmental conditions that must be present for this genesis to occur (Frankel and

17

Kent, 1937; Summerfield, 1982; 1983c; 1984; 1986; Twidale and Hutton, 1986; Partridge

and Maude, 1987; Ullyott et al., 1998, among others).

Figure 6 (from Brown, 2011). Digital elevation map with silcrete outcrop occurrence

highlighted with red (data from: CGS Mossel Bay and Hartenbos 1:50,000 series geology

maps).

Near Pinnacle Point, silcrete occurs between the northern area of Riversdale and

Albertinia in exposed outcrops, or just under the surface (Figure 6) (Summerfield, 1981).

Modern silcrete quarries are found in the area, as silcrete can be commercially used as

refractory bricks for open-hearth furnaces (Davies, 1952; Frankel, 1952). The nearest

exposed outcrop of the lithology lies a mere 8.5km from the Pinnacle Point site, though

the majority of the outcrops occur well over twice this distance (Brown, 2011). The

elevation of the silcrete in the area occurs between just over 300m and 120m above sea

18

level, with no known outcrops found below the minimum elevation (Figure 6)

(Summerfield, 1981). According to Brown (2011), this lack of occurrence below 140m

above sea level (slightly different than Summerfield’s minimum elevation) makes the

presence of silcrete off the present shoreline unlikely. This would mean the movement of

the shoreline relative to sea level variance would not be a factor in silcrete availability to

the Pinnacle Point cave complex, and the idea of a currently submerged outcrop (which

could have been utilized by Stone Age gatherers during a low sea stand) is improbable

(Brown, 2011). However, Nash and colleagues (2013b) report that although the

Riversdale/Albertinia silcrete outcrops have been uplifted, the discovery of silcrete up to

50m below current sea level in Noordhoek valley (approximately 350km away) should

discourage researchers from discounting inundated outcrops completely (see Rogers,

1980). Further, the peaks in silcrete use at Pinnacle Point seem to correspond with low

sea stands and the subsequent movement of the shoreline away from the caves (Brown

et al., 2009, 2012; Fisher et al., 2010; Nash et al., 2013b). However, this could be the

result of a multitude of factors, such as prey animal and environment change due to the

coast moving outside of a daily gathering range (Brown, 2011).

The Riversdale/Albertinia area is situated within the Bolkeveld shale series, and

the silcrete occurs as flat cappings on top of a fine clay (Summerfield, 1981). These

outcrops are confined by the Langeberg Mountains in the north, and the Table Mountain

Sandstone series to the south (Summerfield, 1981). Silcrete can occur in several different

forms in the Albertinia/Riversdale area; these include glaebular, conglomerate, and

massive (Summerfield, 1981; Frankel, 1952). Glaebular silcrete occurs as small, isolated

nodules found mostly overlying the massive silcrete on the high grounds and, to some

extent, within the clay layers (Summerfield, 1981). Conglomerate silcrete is composed of

angular silcrete and quartz detritous cemented by silcrete (Summerfield, 1981; Frankel,

19

1952). The silcrete detritous in the conglomerate formations is thought to be the eroded

remnants of an older silcrete layer (Marker and McFarlane, 1997; Marker et al., 2002;

Malan and Viljoen, 1990; 2008). Massive silcrete is a homogeneous, fine-grained

material with no detritous and little to no known skeletal grains (Summerfield, 1981). Due

to these characteristics, massive silcrete would be the choice material of the silcrete

forms for stone knapping (Brown, 2011) The massive silcrete near Albertinia, Riversdale,

and Mossel Bay generally occurs as benches eroding out of valley sides or flat cappings

on low hills, and is most usually present at elevations of 205m, 194m, 180m, and 170m

above sea level (Marker and McFarlane, 1997).

It has previously been determined that sourcing silcrete is not possible due to the

variation of trace elemental signatures within single outcrops (Corkill, 1999; Brown,

2011). However, recent research has shown that it is possible to pinpoint the origins of

silcrete samples based on these trace elements, though chemical changes which occur

during the heat treatment processes has thus far rendered the sourcing method null

(Nash et al., 2013a,b). Silcrete can also occur in a secondary fashion as beach or river

cobbles, though these tend to be rare (Brown, 2011). In gathering experiments at local

beaches, Brown (2011) found that silcrete cobbles composed about one percent of the

collected assemblages.

Heat Treatment

The heat treatment of siliceous stone for the purpose of improving knappability

was first recognized experimentally and reported by Crabtree and Butler (1964). Since

this recognition, a whole host of sites containing purposefully heated stone material have

been observed in many places around the world (Domanski and Webb, 2007). The

significance of heat treatment is its recognition as a complex process which greatly

increases the intricacy of the chaîne opératoire and the knappability of the resulting

20

material (Brown et al., 2009; Mourre et al., 2010; Flenniken and White, 1983; Purdy,

1971; Domanski and Webb, 2007). The heat treatment of silcrete has been recognized

archaeologically in Australia for some time, though its occurrence has just recently been

recognized in southern Africa (Brown et al., 2009; Ackerman, 1979; Flenniken and White,

1983; Domanski and Webb, 2007). The recognition of heat treatment at the Pinnacle

Point site by Brown and colleagues (2009) has pushed the earliest known occurrence of

heat treatment to 162kya, with regular occurrence by ~71kya. This discovery has cast a

spotlight on the heat treatment of silcrete on the southern coast of Africa in the debate on

early human cognition (Brown et al., 2009; Mourre et al., 2010; Schmidt et al., 2013).

The significance of the occurrence of early heat-treated silcrete in southern Africa

is its relationship to the variability of stone tool industry during the MSA, and the ramp-up

of technology in the chaîne opératoire (Brown et al., 2009). In experiments by Brown

(2011), it was determined that the process by which the silcrete was heat-treated was

quite intricate, delicate, and resource intensive (Brown et al., 2009; Brown, 2011;

Marean, 2010b). The silcrete stones must first be buried 2-3cm in a sand bath, and a fire

slowly built on top (Brown, 2011). A temperature of ~350°C must be reached and

maintained for about eight hours or more (depending on nodule size) (Brown et al., 2009;

Brown, 2011). The nodules are then allowed to cool slowly before being excavated. If the

material is heated too quickly, or reaches too high a temperature, microfracturing and

crazing will occur and the resulting material will not be suitable for knapping (Brown et al.,

2009, Brown, 2011, Brown, pers. comm.; Schmidt et al., 2013).

This has implications for understanding the South African MSA, in that the

intricacy of the chaîne opératoire is dramatically increased, and has been argued to show

increased cognition and symbolic behavior (Brown et al., 2009; Mourre et al., 2010;

Marean, 2010a, 2010b, 2011; Brown, 2011). Also, it has been argued that a fine-grained

21

material with the flaking properties of heat-treated silcrete is essential to be able to

produce the more technologically advanced industries during the southern African MSA

(such as the Howieson’s Poort, Still Bay, and other unlabeled microlithic industries)

(Brown et al., 2009; Marean, 2010a; Mourre et al., 2010; Brown et al., 2012). It has been

argued that the occurrence and disappearance of predominantly heat-treated material in

the record during the South African MSA is dependent on the availability of fire wood

resources (Brown and Marean, 2010). Experiments show that for every 3kg of raw

silcrete, 20kg of firewood is required, making heat treatment a resource- and energy-rich

investment (Brown, 2011). In short, the ability to improve raw material through heat

treatment not, although an expensive process, only provided MSA hunter/gatherers with

a higher quality stone, but allowed them to dramatically change their lithic technology with

the introduction of microliths (Brown et al., 2009; Marean, 2010b; Brown, 2011).

The physical changes that occur in siliceous stone which improve the

knappability of the material through heat treatment have been poorly understood and

debated since heat treatment was first recognized (Schmidt et al, 2013; Domanski and

Webb, 1992, 2007; Griffiths et al., 1987; Flenniken and Garrison, 1975; Flenniken and

White, 1983; Purdy and Brooks, 1971). Purdy and Brooks (1971) first claimed that chert

has trace impurities in the matrix that bind the microcrystalline quartz grains. They argued

that these impurities reach a melting point at ~350°C, and act as a flux to further bind the

quartz grains and form a more homogeneous material (Purdy and Brooks, 1971; also see

Mandeville, 1973). Flenniken and Garrison (1975) used experiments with novaculite, a

type of chert found in Arkansas, to claim that heat treatment causes internal stress and

resulting microscopic fracturing within and between grains. This microscopic fracturing

reduces the mechanical strength and increases the ease of knappability (Flenniken and

Garrison, 1975). A similar argument is purposed by Griffiths and colleagues (1987), in

22

which they describe the migration of water within the silica lattice as significant enough to

cause microfracturing, thus increasing ease of knappability. Experiments by Domanski

and Webb (1992) support the hypothesis first proposed by Crabtree and Butler in 1964.

In this article, Domanski and Webb claim that heat of around 400°C will cause the

recrystallization of silicic material, in which the crystals are transformed from long,

interlocking fibers to equidimensional quartz crystals (Domanski and Webb, 1992).

However, the authors admit that this transformation can be difficult to see, even with the

aid of a scanning electron microscope (Domanski and Webb, 1992).

More recent research by Schmidt and colleagues (2012 and 2013) has shed

further light on the physical changes of heat-treated siliceous stone at the mineralogical

level. These researchers claim that there is a difference in the way that chalcedony/flint

and silcrete respond to thermal alteration (Schmidt et al., 2013). Chalcedony and flint

contain about 0.7 weight percent silanole (SiOH) within the rock structure (Schmidt et al.,

2012, 2013). Upon a slow ramp-up to between 200°C and 300°C, this silanole converts

to an Si-O-Si bond and H2O (Schmidt et al., 2013). The water is evacuated from the rock

as steam, and the new Si-O-Si bonds repair defects in the crystals and shrink the pores

of the stone, thus creating a harder, more homogeneous material (Schmidt et al., 2013).

Although the authors did not test this in the article, they explain that the increased

hardness of the stone correlates with fracture toughness, a mechanical property

previously recognized by Domanski and colleagues (1994) to be closely associated with

knappability (Schmidt et al., 2013).

Silcrete on the other hand, can contain various amounts of silanole, and in most

cases will contain less than flint (Schmidt et al., 2013). Silcrete, at least the silcrete found

on the western coast by Schmidt and colleagues (2013), contains larger pores than the

tested flint, and therefore has a greater ability to evacuate heated H2O. These authors

23

claim that the same processes occur in silcrete as in flint between 200°C and 300°C, in

which the silanole is converted into Si-O-Si and evacuated water (Schmidt et al., 2013).

However, the amount of silanole is generally too low, and the pores are too large to close

and create a more homogeneous material (Schmidt et al., 2013). These authors claim

(contrary to experiments by Brown and colleagues in 2009) that a second reaction occurs

at ~500°C in which the anatase (TiO2) contained within the rock swells to up to 20% its

original size and closes the pores within the stone to make a harder, more homogeneous

material (Schmidt et al., 2013).

Schmidt and colleagues (2013) also claim that because of the larger pores that

are present in the silcrete (as compared to flint), more water can be evacuated at a

higher rate, and therefore the material can withstand a higher treating temperature and a

more rapid heat ramp during treatment. They also claim that not only are these higher

temperatures possible, they are required for full heat treatment of silcrete (~500°C)

(Schmidt et al., 2013). Although the hypothesis was not tested in this article, the authors

argue that actualistically a small nodule of silcrete could be placed in a small, slightly

cooled bed of coals for just under an hour and be completely heat-treated with a low rate

of failure due to over-heating (Schmidt et al., 2013). An earlier study by Mercieca and

Hiscock (2008) found that this method is possible with very small pebbles and blanks

(smallest sample being 1cm3 and the largest sample being 4cm3). Caution must be

taken when comparing these experiments to the African record, as they were conducted

on silcrete samples from Australia, which will have different chemistry and physical

properties (Mercieca and Hiscock, 2008). These findings are in direct disagreement with

the findings of Brown and colleagues (2009), and others, who claim the ramp-up of the

material must be slow and in a sand bath at lower temperatures for even heating without

crazing or fracturing due to over-heating (Brown, 2011; Marean, 2010b). This debate is

24

significant in that if Schmidt and colleagues (2013) are correct, the heat treatment of

silcrete would be much less of an intricate/delicate process, would require less firewood,

less time, less tending by the individuals, and could probably have been done with other

tasks (such as cooking). This assertion would obviously have an effect on the use of heat

treatment as an example of early human cognition. Scholars on both research teams are

currently conducting further research.

Rock Fracture Mechanics

Understanding rock fracture mechanics and how they relate to knapping and

fashioning stone tools is essential to understanding the entire chaîne opératoire of

ancient hunter/gatherers. The mechanical properties of the stone raw material can

determine what kind of technology can be created, how tools will perform, if the tools can

be retouched, if the tools can be altered (such as heat treatment), and when the tools will

need to be discarded (Yonekura and Suzuki, 2009; Andrefsky, 1994; Braun et al., 2009;

Orton, 2008). Further, these characteristics can define the selectivity of the inhabitants in

regards to raw material (what costs and risks are acceptable in comparison to the

benefits of the selected raw material), and can ultimately help us understand what

determined the choice patterns of these people (Brantingham, 2003; Braun et al., 2009;

Bamforth, 1986; Bamforth and Bleed, 1997; Binford, 1979; Minichillo, 2006; Ugan et al.,

2003).

Historically, the quality of raw materials has been defined qualitatively by

researchers and mo`dern knappers according to their personal experiences and

preferences, and these raw materials are often simply described by the petrological type

(i.e. flint, obsidian, quartzite, etc.) without being divided into any further categories

(Goodman, 1944; Yonekura and Suzuki, 2009; Braun et al., 2009). Understanding and

measuring mechanical properties associated with knapping stone tools allows for a

25

standardized quantitative measurement that can objectively describe the raw material

(Goodman, 1944; Braun et al., 2009). These measurements can then be compared from

site to site to look for trends or dissimilarities in the record (Goodman, 1944; Braun et al.,

2009).

Goodman (1944) was one of the first archaeologists to recognize the importance

of mechanical properties, and was the first to investigate and test specific properties and

their correlation with knappability. The author called on archaeologists to place more

emphasis on understanding all aspects of the environment that produced a specific lithic

industry (without overlooking the influence of individual thought and non-utilitarian cultural

practices) in hopes of better understanding selective choices (Goodman, 1944). More

specifically, she argued that fully understanding the abilities and physical limits of a raw

material were essential in understanding how and why a specific technology was

produced (Goodman, 1944). The properties investigated were density, hardness (tested

by penetration), a form of toughness testing, and rebound hardness using a Shore

Scleroscope (Goodman, 1944). However, she claimed that these were preliminary tests,

and that archaeologists should continue to search for new mechanical properties and

testing procedures that would help us to better understand raw materials in a systematic

way. (Goodman, 1944).

Since Goodman’s introduction of the utility of physical and mechanical properties

to archaeology, several researchers have followed suit (see: Purdy and Brooks, 1971;

Domanski and Webb, 1992; Domanski et al., 1994; Webb and Domanski, 2008; Braun et

al., 2009; Brown et al., 2009; Yonekura and Suzuki, 2009). In 1994, Domanski and

colleagues produced one of the most comprehensive accounts of lithic raw material

mechanical property testing to date. In this article, the researchers test compressive

strength (capacity of a material to withstand an applied compressive load without failure),

26

tensile strength (the maximum stress the material can endure without failing), modulus of

elasticity (resistance to being deformed by a load), and fracture toughness (resistance of

a material to catastrophic fracture propogation), and how these properties relate to

knappability. They conclude that fracture toughness is the most suitable mechanical

property for knappability prediction (though the test is destructive) (Domanski et al., 1994;

see also Domanski and Webb, 1992). Additionally, based on the previous flake

propagation studies by Cotterell and Kaminga (1987), Domanski and colleagues argue

that Young’s modulus of elasticity is notable in predicting the suitability of a material to

produce long blades and flakes (Domanski et al., 1994).

As previously stated, mechanical properties of raw material can be directly

compared and integrated into our knowledge of the archaeological record. Yonekura and

Suzuki (2009) exhibit this use of mechanical and physical properties with their analysis of

the artifacts and raw materials in and around the Ueno-A site, an upper Pleistocene

occupation found in the Yamagata Prefecture, Japan. The majority of the Ueno-A lithic

assemblage is comprised of two main tool types, blades and bifacial points, 99% of which

are some form of shale (Yonekura and Suzuki, 2009). The authors identified thirty-three

types of shale in the gathering range of the site, based on color and in-hand

characteristics (Yonekura and Suzuki, 2009). In this study, the authors use the fact that

mechanical properties have been directly associated with mineral content, grain size,

pore space, and cementing type, to test their hypothesis that the mechanical properties of

a lithology can be non-destructively determined by surface roughness (Yonekura and

Suzuki, 2009). The surface roughness of the collected raw material was tested using a

surface roughness measurement device, and the results were compared to the measured

microhardness and flexural strength (Yonekura and Suzuki, 2009).

27

From these tests, it was found that there are inverse relationships between

surface roughness and microhardness, as well as surface roughness and flexural

strength (Yonekura and Suzuki, 2009). The authors argue that a low surface roughness

reading and subsequently higher microhardness and flexural strength results are

indicative of a more knappable material (Yonekura and Suzuki, 2009). The surface

roughness of a sample of tools in the archaeological assemblage was tested. It was

found that the blades in the assemblage all exhibited a very low surface roughness, and

could be found within a narrow range, while the bifacial points exhibited a very wide

range of surface roughness (Yonekura and Suzuki, 2009). Additionally, the bifacial points

in the assemblage were further divided into two types; type-I bifaces were larger and

thicker, while type-2 were smaller, thinner, and more regularly shaped (Yonekura and

Suzuki, 2009). When the surface roughness results of the two types of bifaces were

compared, it was found that the type-1 bifaces showed a wide range of results, while the

type-2 bifaces exhibited a narrow, lower range of surface roughness (Yonekura and

Suzuki, 2009).

The study by Yonekura and Suzuki (2009) proves that surface roughness can be

associated with the mechanical properties of raw material stone, and gives credence to

the argument that mechanical properties can determine tool type and the extent of

manufacturing that can be successfully accomplished. However, this method cannot yet

be used to quantitatively rank lithologies according to knappability, as rock hardness and

flexural strength have yet to be associated with knapabilty. Moreover, the authors only

test different types of a single petrological type (shale). Although there are strong

correlations between surface roughness and specific mechanical properties, the study

does not take into account the material (chemical) differences between lithologies and

how these differences may affect the results.

28

Recently, Braun and colleagues (2009) have used the properties of rebound

hardness (tested with a Schmidt rebound hammer) and resistance to abrasion (tested

with a Taber abrader) to determine that hominins in the Oldowan industry were making

raw material choices based on mechanical performance of the material. In their study, the

authors found that the hominins in the Kanjera South site in Kenya were regularly

passing up on locally available raw material that could more predictably produce a fine

edge for material with less of a capability for fine edge production, but was much more

durable. It is thought that this is due to the practice of taking and processing very large

game (Braun et al., 2009). Processing large animals with thick hide would have worn the

sharper but less durable flakes down much more quickly, and would have forced the

hominins to spend more time collecting material and knapping (Braun et al., 2009). This

study is a perfect example of how understanding and describing lithic material through

the quantitative methods of mechanical properties testing can help us to better

understand the choice patterns of even our oldest ancestors.

Despite these authors, and their contributions to interpreting the archaeological

record through mechanical properties testing, it is this author’s observation that

mechanical properties remain largely ignored by archaeological researchers. Further, too

often researchers rely on overly simplistic, subjective, and dichotomous relationships

between high- and low- quality materials (Andrefsky, 1994; Braun et al., 2009). Modern

archaeology has failed to fully realize, as Goodman asserted in 1944, the necessity of

wholly understanding the qualities of ancient environments and the raw materials that

were collected in these environments. As Goodman reasoned in 1944, as well as those

who followed in archaeological properties testing, once researchers discover and can

settle on a method and a property or suite of properties that are most closely associated

with and describe the flaking properties of stone, these methods can be applied

29

universally around the world to facilitate comparisons of various lithologies and their

knappability (Goodman, 1944; Domanski and Webb, 1992; Domanski et al., 1994).

30

Chapter 3

Methods

The two most prevalent and prominent lithologies from the Pinnacle Point

assemblage, quartzite and silcrete, are the focus of the mechanical testing for this study,

with modulus of elasticity being the objective mechanical property (associated with the

suitability of the material for producing long flakes) (Domanski et al., 1994; Cotterell and

Kaminga, 1987). The general method for determining modulus of elasticity is by cutting

core cylinders from the sample nodules and preparing them to be placed into a

compressive device (Domanski et al., 1994). The cylinder is then compressed to the point

of failure, while the applied load and length of the cylinder are continuously measured

(Domanski et al., 1994). Although this test is quite accurate, it requires a large amount of

preparation time, specialized equipment, a laboratory setting, and is destructive. To

counter these limitations, all of the mapped and collected samples from both lithiologies

have been tested using a Schmidt rebound hammer to assess the rebound hardness of

the rock. This method is non-destructive, inexpensive, uncomplicated, and does not

require a laboratory setting (Katz et al., 2000; Goudie, 2006).

The Schmidt rebound hammer was originally developed in 1948 as a non-

destructive means of testing concrete hardness, both in situ and in laboratory settings

(Goudie, 2006). By the early 1960s, geologists began using the device to determine the

rebound hardness of stone (Demridag et al., 2009; Aydin and Basu, 2005). These

rebound hardness results were later correlated with both Young’s modulus of elasticity

and uni-axial compression strength (Aydin, 2009, Sachpazis, 1990; Poole and Farmer,

1980).

For these experiments, an N-type Schmidt hammer was used with an impact

energy of 2.207Nm, as this type has been shown to work best on the harder lithologies,

31

and is more closely associated with Young’s modulus of elasticity (Figure 7) (Aydin and

Basu, 2005; Goudie, 2006). The Schmidt hammer works by releasing a spring-loaded

mass against a plunger that is being pressed against the test surface (Katz et al., 2000).

The mass rebounds off the plunger and the maximum height is measured by a sliding

indicator on the side of the device (Katz et al., 2000, Aydin, 2009). The value on the

sliding indicator is registered as a percentage of the initial extension of the spring. (Aydin

and Basu, 2005; Kolaiti and Papadopoulos, 1993). Generally, the lithologies which

produce higher rebound values are more homogeneous, have smaller crystal structures,

and are more resistant to deformation (Braun et al., 2009; Goudie, 2006)

Figure 7 (from Shariati et al., 2011). Depicts the operational system of the Schmidt

Rebound Hammer.

32

Figure 8 The author using the Schmidt Rebound Hammer in Terracon Labs; Fort Worth,

Texas (photo by Zachary Overfield).

The preparation and analysis of the lithologies for rebound hardness tests

followed the procedures outlined by the ISRM (International Society for Rock Mechanics),

the same procedures used by Braun and colleagues (2009) and Brown and colleagues

(2009) (see Aydin, 2009). The methods and procedures (except the heat treatment

procedures) were the same for both the quartzite and the silcrete samples. The samples

were cut using a water-cooled table saw with a diamond impregnated blade or a large

angle grinder. An attempt was made to cut the samples as close to 10cm3 as possible,

which has been recommended as the ideal block size for non in-situ testing (Demeridag

et al., 2009; Viles et al., 2011). However, some of these samples did not lend themselves

33

to these measurements length or width wise, and were either slightly smaller or larger

than the 10cm goal. All of the samples had a test axis (from top to bottom) of at least

10cm. A steel plate with a width and length of 25cm, a thickness of 5cm, and a weight of

25.5kg was used as a base, in conjunction with the methods employed by Braun and

colleagues (2009). The steel base is used to ensure that softer material beneath the

sample does not absorb the impact energy (ISRM, 1978, ASTM, 2001; Aydin and Basu,

2005). The steel base was placed on a flat concrete substrate to further ensure impact

energy was not being absorbed by a softer material (Figure 8) (Aydin, 2009). The

samples were visually examined for rough surfaces and geologic defects which can affect

the results (Aydin and Basu, 2005).

Slippage of the plunger on the testing surface, unseen geologic defects within the

material, and the device being held a few degrees off vertical are errors which are almost

impossible to completely avoid and can have a pronounced impact on the resulting

values (Braun et al., 2009; Brown et al., 2009; Aydin, 2009). In order to compensate for

this, several test points can be taken across the testing surface with multiple hammer

impacts per test point (Braun et al., 2009; Aydin and Basu, 2005). There is, however,

much debate over the number of testing points per testing surface, and the number of

impacts per test point when assessing stone with the Schmidt hammer (Poole and

Farmer, 1980; Aydin and Basu, 2005; Kolaiti and Papadopoulus, 1993; Demirdag et al.,

2009; Fowell and McFeat-Smith, 1976; Hucka, 1965). The debate on the number of

impacts per impact point revolves around the idea of the initial impact being cushioned by

a slightly roughened surface, and the ensuing impacts registering the true (higher) value,

after the roughened surface has been crushed. The differing argument claims that the

initial value is the correct value, while the ensuing impacts crush and compact the rock,

34

which creates an artificially compacted/polished surface resulting in abnormally high

values (Poole and Farmer, 1980; Aydin, 2009; Aydin and Basu, 2005)

Figure 9 Example of a testing grid (silcrete sample- I14-3-3.003).

To allow for these variations in methods, and to allow the data to be adjusted if

better testing methods are recognized at a later date, the testing points were placed in a

grid pattern over the entirety of the testing surface, with each point at least one plunger

diameter (1.5cm) from each other and the edges of the testing surface (Figure 9). The

testing points in the grid were not used if they fell on a geologic defect or an imperfection

in the surface (Williams and Robinson, 1983). A photograph was taken of each testing

surface to indicate the location of each testing point. This photograph can later be

entered into a GIS program with corresponding results for future data analysis methods,

and/or experiment replication on the same samples. At least three impacts were taken at

35

each testing point. The test is non-destructive, and taking several readings from the same

impact point may compensate for low initial numbers due to a roughened surface (Poole

and Farmer, 1980; Aydin and Basu, 2005).

Once the values are collected, the average value across the surface must be

taken. Although there is some evidence that multiple impacts taken at each testing point

can increase consistency (see Poole and Farmer, 1980), a goal of this project is to

provide data for cross-site comparison. Therefore, the methods outlined by the ISRM

were followed (Aydin, 2009). The only modification to these methods was the number of

impacts. Eighteen initial readings, instead of twenty, were used. This is consistent with

the archaeological applications of Braun and colleagues (2009).

To collect the values for data analysis, the most stable surface of the sample was

used as the base. Because the testing points are spread 1.5cm apart over the entirety of

the test surface, the number of testing points regularly numbered more than eighteen. To

reduce the number of testing points to eighteen in accordance with the analysis methods,

any testing points that were determined to be unstable or contain a hidden geologic

defect (as noted during testing) were removed. If the remaining number was still more

than eighteen, the photographed surface was viewed and the testing points closest to the

edge were removed according to proximity until the remaining number of testing points

was equal to eighteen. These eliminations were based on the photograph of the test

surface only, and were blind of the rebound results of each testing point. This was done

in order to eliminate bias for one point over another. The center-most values were used

due to the fact that rebound hardness is negatively affected with proximity to the sample

edge (Aydin and Basu. 2005). The center testing points provide higher, more accurate

results, with more material surrounding the impact site. Any samples that could not

36

provide eighteen testing points with accurate measurements, due to surface size and/or

irregularities, were removed from the analysis.

Although each testing point was tested at least three times, only the initial impact

was used in the analysis in accordance with ISRM guidelines (ISRM, 1978; Aydin, 2009).

The only instances in which this was not followed was when the first impact (sometimes

second impact as well) were obviously incorrect due to an error such as a plunger slip,

small saw marks or ridges that were crushed and absorbed the energy, the device being

held further than five degrees off vertical, etc. In these instances more than three impact

readings were taken until three accurate measurements were acquired, and the first

accurate impact in the series at that testing point was used.

Of the eighteen remaining values, the four highest and the four lowest values

were removed, to account for outliers (Aydin, 2009; Braun et al., 2009). In some

circumstances, the resulting values must be adjusted to compensate for the forces of

gravity. However, for these tests, the device was held in a 90°, vertical position, which

does not need to be corrected (Basu and Aydin, 2004). The ten values were then

averaged and rounded to the nearest whole number to produce the rebound result for the

sample.

Both quartzite and silcrete samples were collected by Dr. Kyle Brown. Nineteen

quartzite samples were collected from both the outcrops near the Mossel Bay point, and

cobbles from the nearby and adjacent beaches. Eleven silcrete outcrops were tested with

paired samples prepared from each selected silcrete nodule. One of each paired silcrete

sample remains unheated, and is curated in Mossel Bay, South Africa as a witness

sample, along with all of the quartzite samples. Both of all paired silcrete samples and all

of the quartzite samples were first tested using the methods described above at the

Mossel Bay Archaeological Project lab located on the grounds of the Diaz Museum in

37

Mossel Bay, South Africa. After the initial testing, one of the samples from each of the

paired samples was selected for heat treatment in the United States.

The heat treatment process was conducted at Arizona State University in the

scientific glassware office, as this department has had previous experience with heat

treatment of silcrete. Methods developed by Brown and colleagues (2009) were used.

Samples were placed in a large electric kiln and slowly heated from 40° Celsius to 350°

Celsius over a period of about four hours. The peak temperature was held for about eight

hours, and the temperature was slowly ramped back down to 40° over another four

hours. Actualistic methods could have been used with an open campfire and a shallow

sand bath (as described in Brown et al., 2009 and Brown, 2011). However, the electric

kiln method has a much narrower margin of error, which was ideal for this study with the

paired samples (Brown, 2011).

A second round of rebound hardness testing was conducted at Terracon Labs in

Fort Worth, Texas. Prior to the heat treatment process, three silcrete samples were

tested and compared to the results of the same samples tested in South Africa. This was

done in order to prove consistency and eliminate any suspicion of bias that may have

occurred due to the introduction of a new device and a different physical setting. Once

continuity was established, and after the heat treatment process was conducted, the

samples were tested again in order to determine any change in knappability. Once again,

in order to reduce any error or potential bias, the grid of test points was shifted so that the

impact points for the second round of testing were not influenced in any way by the

previous tests. Using these methods, I was theoretically able to determine and quantify

the increase in the knappability of silcrete when the heat treatment strategy is utilized

(Brown et al., 2009).

38

Chapter 4

Results

Table 1 Rebound results for quartzite, silcrete (unheated), and silcrete (heated).

Sample

Rebound Hardness

Quartzite

C9-2-62.001 64.9

C9-3-89.001 64.1

C9-3-92.001 63.4

C9-3-95.001 59.0

D11-1-100B5 64.0

D11-1-100B6 67.7

D11-1-78.001 62.3

D11-1-80 62.2

D11-1-81.001 62.6

D11-1-91C1 61.0

D11-1-94e 57.0

D11-1-97D 61.7

D11-1-98E 60.8

D11-2-1.001 63.2

D11-3-1c 65.3

E5-1-14.002 60.4

E7-1-55.002 65.2

E7-1-57.002 61.0

I5-1-83.001 56.9

Average

Rebound

Hardness

62.21

Sample

Rebound Hardness

Silcrete

(Untreated)

Rebound Hardness

Silcrete (Treated)

D9-1-10c 59.0

D9-1-12e 63.0 58.5

E3-1-5C 61.3 59.0

E3-1-5D 59.8

E3-1-6L 61.5 61.5

E3-1-6n 57.3

E4-1-2.001 57.6 53.6

E4-1-2.007 62.0 58.4

E4-1-2.014 45.1

E4-32a.002 68.0

E4-3-2a.003 65.7

E9-5-3c.001 60.7

F10-1-2A.002 65.7 63.0

G11-1-2B 57.9

I14-2-16i 67.2

I14-2-6a 67.2

I14-2-6b 66.0 58.2

I14-3-3.003 62.9

I14-3-3.004 62.4 54.7

I14-3-4.003 60.7

I14-3-5.001 60.7 57.3

I-14-3-5.006 61.3

Average

Rebound

Hardness

61.5 58.24

Table 2 Descriptive statistics comparing rebound hardness results for all quartzite and

silcrete (unheated) samples.

N Mean Std. Deviation Std. Error Mean

Quartzite 19 62.25 2.78 0.64

Silcrete (Untreated) 22 61.50 4.85 1.03

39

Table 3 Levene’s test and student’s t-test comparing rebound hardness results for all

quartzite and silcrete (unheated) samples.

The results of the Schmidt rebound hammer tests show very little change in

rebound hardness between quartzite and raw silcrete (see Table 1). The average

rebound hardness (RH) result for the quartzite pieces (n=19) is 62.2, while the average

for the silcrete samples (n=22) is 61.5. Using a two-tailed independent variables T-test

with an alpha level of 0.05, the null hypothesis cannot be rejected and it must be

assumed that the two means can be equal (see Tables 2 and 3). In fact, the probability of

this is high with a p-value of 0.557. If rebound hardness is used as a proxy for the quality

of knappability, we must assume that the difference between the two lithologies is

minimal, and there would not be a benefit in traveling to distant sources, unless heat

treatment was used.

Eleven unheated silcrete samples were heat-treated in an electronically

controlled kiln located at Arizona State University according to the procedure outlined by

Brown and colleagues (2009). Two of these samples were found to be unfit for testing

after heat treatment due to catastrophic failure during heating, which left a sample size of

nine. These nine samples showed an average rebound hardness of 62.3 in a raw state,

and an average of 58.3 after the heat treatment process. This result is an unexpected

6.4% reduction in rebound hardness.

F Sig. t df Sig. (2-tailed)

Mean

Difference

Std. Error

Differenc Upper Lower

Equal Variances assumed 1.388 0.246 0.592 39.00 0.577 0.747 1.26 -1.81 3.30

Equal Variances not assumed 0.615 34.25 0.543 0.747 1.22 1.72 3.22

Levene's Test

for Equality of

Variances t-test for Equality of Means

95% Confidence

Interval of the

Difference

40

Figure 10 Box and whisker plot comparison of the quartzite, untreated silcrete, and heat-treated silcrete results of this study, to the

results published by Brown, 2011

41

These data were originally suspected to be flawed, as the values are significantly

higher than those previously published on raw and heat-treated material collected from

the same geographic region (Brown et al., 2009; Brown, 2011). Also, the shift in rebound

hardness is counterintuitive, as it is known that the heat treatment process increases the

knappability of silcrete (Brown et al., 2009; Domanski and Webb, 1992; Domanski et al.,

1994; Schmidt et al., 2013). To dispel fears of erroneous data gathering, potential

causes of variation were examined.

The first variable with the greatest potential for error is the calibration of the

Schmidt hammer. This can be tested using a calibration anvil with a known hardness

value. The first rebound hardness tests on quartzite and raw silcrete were conducted in

Mossel Bay, South Africa without access to a calibration anvil to test for accuracy.

However, a sample of three silcrete nodules from the eleven shipped nodules were

tested at Terracon Labs in Fort Worth, Texas to determine consistency before the heat

treatment process was conducted. The Schmidt hammer used at Terracon Labs was

verified to be within tolerance for accuracy with a calibration anvil, and was checked

continuously for accuracy throughout the testing conducted at this location.

Documentation was available for all of the past servicing of the device, and the device

had been professionally serviced recently prior to testing.

Table 4 Descriptive statistics for all recorded impact readings for three block samples,

comparing Schmidt rebound hammer used in South Africa to the one used in

Fort Worth, Texas.

N Mean Std. Deviation Std. Error Mean

South Africa 153 58.58 4.09 0.40

Fort Worth, Texas 153 57.88 4.15 0.34

42

Table 5 Levene’s test and student’s t-test comparing all recorded impact readings for

three block samples, comparing Schmidt rebound hammer used in South Africa to the

one used in Fort Worth, Texas.

The rebound results from the readings taken in South Africa were quite similar to,

or the same as, those taken by the hammer that was known to be in calibration,

suggesting that the initial hammer produced accurate results. To compare the means of

the three samples, the results of all three impact points for each of the three samples

tested in South Africa, were compared to the same samples tested in the United States.

A students t-test, with a confidence interval of 95%, showed that the readings of these

three samples taken in South Africa were not statistically different from those taken in the

United States (p value of 0.175). See Tables 4 and 5 for greater detail.

Table 6 Comparison of the averages for each round of impacts between the same

silcrete samples tested in Mossel Bay, South Africa and Fort Worth, Texas using different

Schmidt hammers.

F Sig. t df Sig. (2-tailed)

Mean

Difference

Std. Error

Differenc Upper Lower

Equal Variances assumed 8.351 0.004 1.358 304.0 0.175 0.706 0.52 -0.317 1.728

Equal Variances not assumed 1.358 295.8 0.175 0.706 0.52 -0.317 1.728

Levene's Test

for Equality of

Variances

95% Confidence

Interval of the

Differencet-test for Equality of Means

Retest Results (United States)

Impact 1 Impact 2 Impact 3 Impact 1 Impact 2 Impact 3

Sample #

D9-1-10D 53.50 54.58 54.33 55.08 55.17 54.75

I14-2-16j 59.63 60.13 59.81 57.06 57.81 57.75

E3-1-5C 60.00 60.00 60.04 59.61 59.52 59.70

Initial Results (South Africa)

43

The results for the comparison between the two hammers can be found in Table

6. In this test, at least two of the samples did not have enough space for eighteen

accurate impacts, and therefore could not be analyzed for the final results. To

compensate for this, the averages for each round of accurate impacts across the surface

were calculated and compared for each of the three samples. Note that the impacts in the

testing conducted in the United States were performed on the same impact sites as those

in South Africa. Repeated impacts in the same location have the potential to slightly

affect the resulting values, as described above.

It is also worth noting that as the Schmidt hammer is used repeatedly, the main

spring becomes worn and lessens the impact against the plunger (McCarroll, 1989). In

most cases, errors produced by a Schmidt hammer that is out of calibration are lower

than those produced by a calibrated device. The results of this study are much higher

than those produced by Brown and colleagues (2009), further indicating that calibration

was not an issue in this study.

44

Figure 11 C-clamps holding aluminum tension bars, clamping sample to steel base. This

methods was used on three samples during testing in Fort Worth, as discussed in text.

Table 7 Comparison of unclamped versus clamped heat-treated silcrete samples.

Impact 1 Impact 2 Impact 3 Impact 1 Impact 2 Impact 3

Sample #

D9-1-10D 51.92 52.00 51.77 52.38 52.62 52.85

I14-2-16j 54.63 54.19 53.88 55.63 55.56 55.69

E3-1-5C 57.33 56.83 57.13

Unclamped Results Clamped Results

Failure During Testing

45

Table 8 Descriptive statistics for all recorded impact readings for three block samples,

comparing clamped and unclamped methods. Note that one of the samples failed during

testing.

Table 9 Levene’s test and student’s t-test comparing all recorded impact readings for

three block samples, comparing clamped and unclamped. Note that one of the samples

failed during testing.

In his 2011 doctoral dissertation, Brown notes that the silcrete samples that were

tested with the Schmidt hammer were clamped to the steel base. This was done to

prevent movement by the rock sample when the weight strikes the plunger, which can

affect the rebound results. To determine if the lack of clamping may have negatively

affected the results of this study, a sample of the heat-treated nodules was retested after

being clamped to the steel base (Figure 11). Results of this comparison can be found in

Table 7, and results follow the same methods as Table 6 (sample E3-1-5C split during

the final round of testing). Two large C-clamps were clamped to each side of the base,

and two galvanized aluminum tension bars were positioned under the arms of the clamps

and over the edges of the nodules. The tension bars held the samples down and

prevented movement during testing. Once again, a student’s t-test comparing all three

impact points for each of the three samples has shown with a 95% confidence interval

N Mean Std. Deviation Std. Error Mean

Unclamped 162 54.81 4.12 0.32

Clamped 99 54.52 3.57 0.36

F Sig. t df Sig. (2-tailed)

Mean

Difference

Std. Error

Differenc Upper Lower

Equal Variances assumed 2.203 0.139 0.599 259.00 0.550 0.3 0.50 -0.685 1.284

Equal Variances not assumed 0.620 230 0.536 0.3 0.48 -0.685 1.251

Levene's Test

for Equality of

Variances t-test for Equality of Means

95% Confidence

Interval of the

Difference

46

that there is not a statistical difference between the clamped and unclamped samples

(see Tables 8 and 9). The lack of a clamp in the study was ruled out as a determining

factor in the disparity of the results.

Table 10 Previously published rebound values for quartzites and unheated silcretes in

Africa

Lastly, the results of this study for both lithologies in their raw form compare well

with results published in other studies for quartzite and silcrete in Africa (Table 10).

Although the lithologies in the other studies were found in different geographic areas and

can be easily argued to have different physical properties, the fact that the results

compare so closely with that of this study adds more evidential support for the accuracy

of the initial device used and these results in general.

Location Rebound Value Author

Quartzite

Boegoeberg, South Africa 64 ±4 Springer et al., 2006

Magaliesberg Series, South Africa 62 Sumner and Nel, 2002

Precambrian quartzite, Botswana 67 Day and Goudie, 1977

Bukoban quartzite, Kenya66.1 (median

value)Braun et al., 2009

Silcrete

Botswana 62.02 Day and Goudie, 1977

Albertinia/Riversdale, South Africa 45.4 Brown et al., 2009

47

Chapter 5

Discussion

Figure 12 Flaked sample exhibiting the color change and luster associated with proper

heat treatment according to Brown and colleagues (2009).

The purpose of this study was to rank the lithologies quantitatively in a non-

destructive fashion and compare these rankings to distance from site to source.

However, the analysis shows that the rebound hardness results taken from the Schmidt

Hammer do not portray knappability as initially suspected. The remaining nine heat-

treated samples were inspected for microfractures and crazing from the heat treatment

process, which would have greatly affected the rebound results. However, no evidence of

poor heat treatment or overheating was detected. Small flakes were removed from the

samples after the final stages of rebound testing. The small flake samples were

48

qualitatively more knappable than pre-treatment, and exhibited the luster indicative of

proper heat treatment (Figure 12) (Brown et al., 2009; Brown, 2011).

In the 2009 study by Brown and colleagues on the heat treatment of silcrete, they

found that there was an average 27.9% increase in rock stiffness between the local

silcrete in its raw form, and after heat treatment modification (based on Young’s modulus

derived from the Schmidt Hammer). The authors attributed this rebound hardness

increase to the increase in flaking quality between the samples (Brown et al., 2009). The

average RH reading for the raw material samples was 45.4 (range: 38.4-54.1), and the

average value for the same samples after heat treatment was 51.3 (range: 47.6-61), a

direct increase in rebound hardness of 13%. The highest percent change of an individual

sample was 26% (sample ALB.A48 RH values 38.4-48.2). Two samples in their study

showed the lowest percent change with a 2% increase.

The current study not only produced a result showing a decrease in RH after

heat treatment, contrasting with the results of Brown and colleagues (2009), but yielded

different initial results than Brown and colleagues as well. The average rebound hardness

value for the raw silcrete was 61.5 (N=22), 35.5% higher than the values published by

Brown and colleagues (2009). The lowest RH value in this sample assemblage was 45,

which directly compares with the other study’s average. Yet, this value is an outlier in the

assemblage. The next lowest RH value is 57, and the remaining samples range from 58

to 68.

The difference in rebound results for both raw and heat-treated silcrete between

this study and that conducted by Brown and colleagues (2009) can best be explained by

Summerfield’s 1981 publication on silcrete in South Africa. In this study, he describes the

silcrete in the Albertinia/Riversdale area (same area sampled for both the Brown and

colleagues (2009) study and the current study) as massive fine-grained material that is

49

creamy-white in color with irregular zones of more dense, gray silcrete (Summerfield,

1981). Further, he tested both colors of silcrete with a Schmidt rebound hammer and

obtained an average reading of 42.9 for the white material, and 55.6 for the gray material

(Summerfield, 1981). Although his sample size was small (total n=10), his average result

for the less dense material is very close to the result for the raw material reported in

Brown and colleagues (2009) of 45.4, and Summerfield’s result for the more dense

material is much closer to the average for the current study (61.5) (Summerfield, 1981).

Brown (2011) acknowledges Summerfield’s finding of two types of silcrete; however,

Brown dismisses Summerfield’s descriptions of the softer, creamy-white silcrete as a

thick cortex or rind to the denser silcrete underneath. Based on the results of this study,

there are in fact at least two types of massive silcrete in the Riversdale/Albertinia area

(possibly more) with different hardness values, and these can be differentiated by color. It

would appear that the majority of the samples tested by Brown and colleagues were of

the softer type, while the majority of those collected for this study were harder.

Nash and colleagues (2013a) were the first to discover a method for

provenancing silcrete sources. In their study, based on the White Paintings Shelter in

northwest Botswana, the researchers found that the MSA hunter/gatherers were traveling

some 220km to collect silcrete for knapping, bypassing not only the local quartz and

quartzite, but the more proximate sources of silcrete as well (Nash et al., 2013a). This is

not an erratic occurrence, but appears regularly throughout the three meters of MSA

deposit at the site (Nash et al., 2013a). The silcrete samples of both the preferred source

and those more proximate that were collected by these researchers, are reported to be

similar in quality, based on simple hand analyses (Nash et al., 2013a). Therefore, a

conclusion has been made that there must be a mechanism for selectivity (cultural or

physical) that has yet to be identified (Nash et al., 2014).

50

Summerfield (1981) claims that the two types of massive silcrete in the

Riversdale/Albertinia area can be differentiated simply by color. If this is correct, then

color could be the mechanism of selectivity for the MSA people. Given the fact that the

massive silcrete can have very different physical properties (based on this study), if color

is the differentiating mechanism, this would have provided ancient gatherers with a rapid

and efficient means of making selective choices and would have improved the entire

collection process in general. Obviously, further testing is required to assess this

hypothesis.

The presence of two types of silcrete where there was previously thought to be

one does not, however, explain the discrepancy in the direction of rebound hardness

change in silcrete between this study and that of Brown and colleagues (2009) and

Brown (2011). What also remains unexplained is why quartzite has a higher RH result

than the heat-treated silcrete, when the coarse-grained quartzite is very obviously more

difficult to knap and cannot produce the same blade and edge qualities. In order to

address these incongruities, Young’s modulus of elasticity as the key mechanical

property and the Schmidt rebound hammer as the principal device in mechanical testing

for the purposes of ranking lithologies according to knappability must be reexamined.

The Schmidt rebound hammer was originally designed to test concrete for civil

engineering. A few impacts with the device are used to determine whether or not a

sample, or in situ concrete, is defective. If the concrete is determined to be defective with

the rebound hammer, a sample is prepared for further, more detailed testing using other

devices and methods (McCarroll, 1989). With this background, the Schmidt hammer was

designed with slight variances in rebound (especially in the higher range) being

meaningless for its purpose (McCarroll, 1989). It was not until later that geologists

adopted the device as a quick method for determining surface hardness. However,

51

despite the fact that both the International Society for Rock Mechanics (ISRM), and the

American Society for Testing and Materials (ASTM) have published methodologies for

testing rocks both in situ and in laboratory settings, these methods vary (ISRM, 1978;

ASTM, 2001; Aydin, 2009; Aydin and Basu, 2005). In fact, a large portion of the peer-

reviewed literature revolving around the Schmidt hammer is published with the purpose

of proposing new methodologies, or supporting/arguing against existing published

methodologies (see Poole and Farmer, 1980; Day and Goudie, 1977; Aydin and Basu,

2005; Aydin, 2009; Kolaiti and Papadopoulus, 1993; ISRM, 1978; ASTM, 2001; Goktan

and Gunes, 2005; Gupta, 2009; Kennedy and Dickson, 2006; Demirdag et al., 2009;

Williams and Robinson, 1983; Fowell and McFeat-Smith, 1976; Hucka, 1965; etc.).

Consensus does not exist on proper sample size, how the sample should be cut, how

many impacts should be taken across a surface, the spacing of the impacts, how many

impacts per test point should be taken, which impact in a series at a specific test point

should be accepted, which data should be added to the analysis and which should be

deleted, how the values should be averaged to produce a result, etc. (McCarroll, 1989).

Moreover, a change in any of these methodologies could change the results completely,

and therefore results are not parallel unless an identical methodology is followed

(Demirdag et al., 2009).

In addition to the variability of the methods and their results, the Schmidt hammer

is also very sensitive to a host of conditions and physical characteristics of the lithology.

Moisture content, unseen geologic defects, micro-fractures, and small surface

irregularities can all have a sizable impact on the resulting values (Goudie, 2006; Viles et

al., 2011). This sensitivity can make the values across the surface of a single sample

extremely variable and difficult to compare with other samples. It is this variability and

52

imprecision of the Schmidt hammer that make the device more practical for preliminary

assessments rather than concluding evaluations (McCarroll, 1989; Yagiz, 2009).

The rebound hammer has been correlated by many authors with Young’s

modulus of elasticity (Goudie, 2006; Yilmaz and Sendir, 2002; Sonmez et al., 2006;

Sachpazis, 1990; Aggistallis et al., 1996; Yilmaz and Yuksek, 2008; Yagiz, 2009; etc.).

However, the formula required to convert rebound hardness to Young’s modulus is not

agreed upon by authors (Dincer et al., 2004; Sonmez et al., 2006; Yaşar and Erdoğan,

2004; Yagiz, 2009). In truth, not only is the formula not agreed upon, but three different

correlation shapes are argued by researchers (Kiliç and Teymen, 2008 see Kidybinski,

1980; Singh et al., 1983; Deere and Miller, 1966). According to Yagiz (2009), although

many of these equations and relationships function correctly in the respective studies,

there is not a specific formula or relationship that can be applied to all rock types and

caution should be taken for any method used.

Finally, we must reevaluate whether or not Young’s modulus of elasticity is the

appropriate mechanical property to determine knappability. As mentioned above,

Cotterell and Kaminga (1987) described Young’s modulus as the property most closely

associated with the ability of a lithology to produce long flakes and blades. This idea was

furthered by Domanski and colleagues in their 1994 study. However, in 2008 Webb and

Domanski published a study which showed that the property had very little variation

between different lithologies, and therefore the researchers would no longer report on

Young’s modulus. This finding is consistent with the results of the present study.

In 1944, Goodman measured the rebound hardness of sample lithologies using a

Shore Scleroscope. The Shore Scleroscope was originally developed for testing metals in

a similar fashion to the Schmidt rebound hammer (Yaşar and Erdoğan, 2004). The device

works by freely dropping a diamond tipped weight onto the testing surface and measuring

53

the resulting rebound of the weight (Yaşar and Erdoğan, 2004). In contrast to the

rebound hammer, the Shore Scleroscope uses a smaller rebounding weight and is not

spring-loaded (Yaşar and Erdoğan, 2004). Given these characteristics, the Sceleroscope

imparts much less energy on the testing surfaces of the samples, possibly reducing the

chance of destroying the sample (Yaşar and Erdoğan, 2004). In her rebound testing,

Goodman (1944) found that there was much more variation and distance among softer

materials (such as limestone) than harder materials (such as flint). Goodman’s (1944)

findings with rebound hardness and Webb and Domanski’s (2008) findings with Young’s

modulus compare well with the findings of this study and implicate a possible hardness

ceiling, above which the differences in Young’s modulus and rebound hardness become

negligible between lithologies. This further indicates that the Schmidt rebound hammer is

not the appropriate device for measuring knappability, as typically the knapped lithologies

will be on the higher range for these measures.

The facts that: 1) there is no agreed upon method for using the Schmidt hammer;

2) there is no agreed upon method for analyzing the results; 3) there are many variables

and conditions that can affect the performance of the Schmidt hammer; 4) and Young’s

modulus of elasticity and rebound hardness seem to vary less among the harder

lithologies, all combine to indicate that the Schmidt hammer should not be the preferred

method of determining knappability. Instead, it is much better suited as a means of

quickly testing in situ lithologies for preliminary results, and should not be the primary

testing method.

54

Chapter 6

Conclusion

This study provides four conclusions that are significant to archaeological

research in the southern African MSA. The first and most important is that there are at

least two different types of massive silcrete in the Albertinia/Riversdale area. In previous

publications, silcrete has been regarded as a single selective option for the MSA

hunter/gatherers (see Brown et al., 2009; Brown 2011; Thompson et al., 2010; Brown et

al., 2012). In reality, agents during this time would have had at least two options of

silcrete available to them, occurring together, in the same geographic area. These two

massive silcrete types have different physical properties (with respect to rebound

hardness), which suggests that they have different knapping qualities, even after heat

treatment. If the knappability of the two silcrete types is substantially different, selection

would have more than likely been affected. This also has implications in raw material

selection for future agent-based modeling and behavioral ecology studies. Without an

understanding of the number of knappable stone types available to the Stone Age

population, proper cost/benefit choice models cannot be constructed.

The second significant finding of this study is that the Schmidt rebound hammer

is an inappropriate device for quantifying knappability of raw material sources. As

discussed above, the Schmidt hammer is too imprecise to compare raw materials for

archaeological purposes. There is no truly agreed upon methodology for using the

device, and there are too many factors to which the device is sensitive that prevent two

lithologies from being directly compared in detail. It is also probable that the lower ranges

of rebound hardness are more descriptive of the material, while different lithologies in the

higher range are less distinguishable, and therefore incomparable (Goodman, 1944).

55

My third significant conclusion is that the N type Schmidt hammer imparts too

much energy onto the sample and is not a truly non-destructive measurement. On

several occasions the device caused flakes (large and small) to detach from the main

sample body. These occurrences were noted during testing, and the impact values on

which they occurred were generally removed from the analysis on suspicion that the

event would cause errors in the value. On one occasion, the sample block was

completely split in two after an impact. According to at least one other researcher, cases

such as these are not rare (Braun pers. comm. 2014). The purpose of the non-destructive

test is to eventually test actual archaeological samples, and to be able to replicate testing

or conduct new methods of testing on the same samples. The Schmidt hammer has

proven to be much too destructive to use on actual archaeological samples, and

therefore is not the appropriate device for this form of testing.

Finally, I conclude that devices that measure rebound hardness with less impact,

such as the L type Schmidt hammer (uses a smaller rebounding weight than the N type)

or the Shore scleroscope, could be used with less likelihood of destroying samples.

These methods may require smaller sample sizes, and the softer impact may be able to

show variability at a finer scale. Ultrasonic p- and s-waves could also be used non-

destructively to directly measure Young’s modulus (although this would require a

laboratory setting) (Chirstaras et al., 1994). However, Goodman (1944) used a Shore

scleroscope in her first study of raw material properties, and found that the rebound

results of those lithologies in the upper range of hardness were much less distinguishable

than the lower range. These findings would suggest that any device used to determine

rebound hardness would encounter the same problems. Additionally, this study serves to

corroborate the finding of Webb and Domanski (2008), in which it was found that there is

little variation of Young’s modulus of elasticity between lithologies. Despite findings by

56

Cotterell and Kamminga (1987), this mechanical property should no longer be used to

describe knappability.

57

Chapter 7

Future Research

The original purpose of this study was to create a cost/benefit model for raw

material procurement near the Pinnacle Point Middle Stone Age site. This was to be done

by first quantitatively ranking the raw materials according to mechanical properties that

have been associated with knappability. Once the raw material outcrops were mapped

and given a numerical ranking of desirability according to quantified knapping qualities, a

GIS model was to be created with least cost distances (based on slope and the

avoidance of large bodies of water) from site to source. From this model, the cost of

collection in either calories or time could be derived for each outcrop and directly

compared to the benefit of knappability (Taliaferro et al., 2010; Howey, 2007; Tobler,

1993). Eventually, these data could be integrated into an agent-based simulation model,

developed at The School of Human Evolution and Social Change, Arizona State

University, to investigate the decision making strategies of Middle Stone Age foragers.

The costs and benefits of raw material acquisitions can be directly compared to the lithic

assemblage at Pinnacle Point, the faunal record, and proxy climatic indicators to better

understand early human choice patterns. These methods can also be employed across a

multitude of MSA sites in South Africa to form a more accurate and detailed analysis of

early Homo sapiens choice patterns and the factors which may have influenced these

patterns. However, based on the results described above, these goals could not be

accomplished in the current study. Below are listed the future steps to be taken in order

to provide the information required for a proper agent based-model for raw material

acquisition.

The first step that must be taken is a more in-depth survey of the silcrete around

Pinnacle Point. Most importantly, it must be determined whether or not the silcrete in the

58

area can consistently be placed in two (possibly more) types based on mechanical

properties. Conversely, the massive silcrete may prove a gradient of hardness

characteristics, and will not lend itself to be placed into specific types. Further, we must

determine if there is a correlation between mechanical properties and the color of the

silcrete. Although Summerfield has conducted many thorough surveys of the silcrete

along the South African southern coast, his focus was not to determine the mechanical

properties of the massive silcrete, nor to identify different types of massive silcrete that

could be used as raw material. To do this, numerous samples must be taken from the

existing outcrops, the color recorded, and a suite of mechanical tests (probably

destructive) must be performed to determine mechanical differences between silcrete

nodules sampled in the area, if this difference is consistent, by what magnitude this

difference exits, and if there is a correlation between differences and color.

Next, we must turn to the archaeological record and determine if the MSA people

were making selection choices based on these mechanical properties. One method

would be to determine if the trace elements could be sourced for the outcrops in the area,

if these trace elements could be associated with physical properties (such as color), and

if trace elements could differentiate mechanical properties as well (Nash et al., 2013a).

However, Nash and colleagues (2013b) have determined that the heat treatment process

changes the chemical composition enough to render their sourcing techniques invalid.

Additionally, the silcrete can change color after heat treatment, which makes the process

of identifying a color choice pattern of the raw material much more difficult (Brown et al.,

2009; Brown, 2011). Experiments will have to be completed in order to see if there is a

pattern from the original color of the raw material to the color after the heat treatment

experiments. In this case, actualistic experiments will need to be performed with

campfires and shallow sand baths, as this method creates environments with a very low

59

amount of oxygen (compared to the electric kiln method), which could affect the color

change process (Brown, 2011).

Ultimately, to understand the nature of the materials the MSA people were

selecting for, a truly non-destructive method which has been proven as a knapapbility

proxy must be employed. As this study has shown, Young’s modulus of elasticity and the

Schmidt rebound hammer are not appropriate for quantifying knappability. Domanski and

colleagues (1994) have shown that the chevron-notched, short-rod fracture toughness

test is the most closely associated test to knappability to date, although it is destructive

and requires a laboratory setting. Surface roughness testing by Yonekura and Suzuki

(2009) also has some promise in determining knappability, though it is destructive and

requires a great amount of laboratory preparation. However, if it can be determined

definitively what types of silcrete are being selected for from the archaeological record,

samples can be collected from the specific outcrops, and directly testing the

archaeological samples will no longer be necessary.

In the future, more emphasis should be directed towards mechanical testing and

physical properties in lithic studies and archaeology in general. It is truly surprising how

this area has been neglected in archaeological analyses. It would seem that the physical

properties of the stone which allow for knappability, and the scientific approach of

quantitatively ranking the raw materials according to their properties would be

fundamental when describing any site archaeologically. Through these investigations, we

can better understand the choices that were presented to Stone Age humans, and

possibly what drove the decisions of these people. As Goodman argued in 1944, to truly

understand an ancient people, all avenues of logic and every possible variable must be

thoroughly and exhaustively examined.

60

Appendix A

Individual Rebound Hardness Results for Quartzite

61

Appendix A shows the individual rebound analyses of each quartzite sample. In the

following table, the accepted ten impact values for each sample are in bold with an asterisk. The

mean of these ten values is the Rebound Hardness value for the sample, and can be found in

bold at the bottom of each sample. The analyses followed the methods defined by Braun and

colleagues (2009), Goudie (2006), and Brown (2011). As described in chapter 3, each point of

impact was struck at least three times. If one or more of the results were flawed due to plunger

slip, geologic defects, surface defects, or some other reason, more impacts were added to

ensure accuracy. Unless flawed, the initial impact at each impact point was used in the analysis.

Eighteen impact values were used for each testing surface. The highest four and the lowest

four values were then removed to account for outliers (this is noted as “high” and “low” in the

“Reason for exclusion” column). Due to the large size of some of the sample surfaces and the

impact points being placed in a grid 1.5cm apart, more than eighteen impact points were

regularly taken. To account for this, the values from impact points closest to the edge were

removed until eighteen remained (explained in greater detail in chapter 3).

62

Sample#

D11-1-97D

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 48 52 53 50 Removed-Close to edge

2 53 53 54 Low

3 53 54 55 Low

4 51 51 51 Low

5 54 56 55 Removed-Close to edge

6 *59 60 59

7 *60 60 59

8 *62 60 58

9 57 57 59 Low

10 *62 64 62

11 *64 63 63

12 65 62 62 High

13 *63 63 62

14 58 58 57 Removed-Close to edge

15 *63 62 62

16 *63 62 64

17 65 66 66 High

18 67 63 63 66 Removed-Close to edge

19 56 60 58 57 Geologic Defect

20 *61 62 61

21 64 63 62 High

22 *60 62 63

23 64 63 62 High

24 52 54 52 Removed-Close to edge

Rebound Hardness= 61.7

63

Sample#

D11-1-81.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *60 61 61

2 56 57 57 Low

3 55 54 54 Low

4 50 50 50 Unstable

5 *66 65 65

6 *60 60 62

7 52 50 52 Unstable

8 *66 67 67

9 68 68 68 High

10 *64 64 63

11 53 56 57 57 Unstable

12 68 67 67 High

13 *63 64 64

14 59 59 59 Low

15 68 70 69 High

16 67 68 67 High

17 *60 61 62

18 57 58 58 Low

19 *64 66 66

20 *63 64 65

21 *60 59 60

22 Unstable

Rebound Hardness= 62.6

64

Sample#

D11-1-100B5

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 58 60 58 Removed-Close to edge

2 58 61 59 Removed-Close to edge

3 60 61 60 Removed-Close to edge

4 63 62 65 Removed-Close to edge

5 63 64 63 Removed-Close to edge

6 59 61 59 Removed-Close to edge

7 60 61 61 Removed-Close to edge

8 *63 65 64

9 *64 66 66

10 *66 65 66

11 *63 65 66 57

12 *62 63 61

13 *62 62 62

14 *67 65 68

15 67 67 70 High

16 69 65 62 High

17 *65 67 62

18 63 63 62 Removed-Close to edge

19 62 58 61 Removed-Close to edge

20 67 66 65 High

21 61 64 64 Low

22 *64 64 65

23 67 69 68 High

24 60 63 64 Low

25 59 60 60 Removed-Close to edge

26 *64 66 66

27 62 62 64 Low

28 56 57 58 Removed-Close to edge

29 62 62 63 Removed-Close to edge

30 58 60 59 Low

Rebound Hardness= 64

65

Sample#

D11-1-80

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 56 59 55 Removed-close to edge

2 55 57 58 Low

3 56 56 56 Low

4 *61 62 60

5 *61 63 61

6 *61 58 61

7 *64 64 63

8 *64 65 67

9 64 64 58 64 Plunger slip-2nd impact-Close to edge

10 66 66 68 High

11 66 68 68 High

12 66 66 66 High

13 *63 64 65

14 64 65 64 High

15 *63 66 66

16 *61 64 64

17 *62 61 61

18 60 61 61 Low

19 *62 62 62

20 59 60 58 Low

Rebound Hardness= 62.2

66

Sample#

D11-1-91C1

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 51 55 51 Removed-close to edge

2 58 56 58 Low

3 *62 61 60

4 *62 62 62

5 *60 58 58

6 58 56 56 Low

7 54 57 60 Removed-close to edge

8 60 63 63 Removed-close to edge

9 64 56 60 Removed-close to edge

10 66 66 66 High

11 *62 63 62

12 58 60 60 Low

13 58 58 58 Low

14 63 63 63 High

15 *62 62 64

16 63 64 64 High

17 *59 59 60

18 *59 60 58

19 58 62 58 Removed-close to edge

20 66 67 66 High

21 58 60 56 Removed-close to edge

22 58 62 62 Removed-close to edge

23 58 62 59 Removed-close to edge

24 *63 61 61

25 *60 60 58

26 *61 62 62

Rebound Hardness= 61

67

Sample#

E5-1-14.002

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 54 56 58 Removed-close to edge

2 61 59 61 Removed-close to edge

3 58 61 62 Removed-close to edge

4 58 58 56 Removed-close to edge

5 51 53 54 Low

6 *59 61 59

7 *63 63 63

8 *61 64 63

9 61 66 66 Removed- Due to variability

10 55 55 55 Low

11 *61 62 61

12 64 67 67 High

13 65 65 65 High

14 63 63 62 High

15 *59 59 58

16 65 64 65 High

17 *62 65 65

18 *62 63 63

19 56 56 55 Low

20 56 58 59 Low

21 *60 60 60

22 *60 61 61

23 *57 58 58

24 55 52 52 Removed-close to edge

Rebound Hardness= 60.4

68

Sample#

E7-1-55.002

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 56 57 57 Removed-close to edge

2 62 61 61 Removed-close to edge

3 62 62 63 Removed-close to edge

4 60 62 62 Removed-close to edge

5 58 58 59 Removed-close to edge

6 54 56 55 Removed-close to edge

7 54 56 55 Removed-close to edge

8 61 61 62 Low

9 63 64 65 Low

10 *64 66 66

11 *65 65 66

12 *64 65 64

13 *65 64 64

14 61 61 58 Removed-close to edge

15 57 60 59 Removed-close to edge

16 *64 65 65

17 68 68 69 High

18 68 67 69 High

19 69 70 70 High

20 *67 67 67

21 *66 67 66

22 63 63 62 Removed-close to edge

23 60 59 60 Removed-close to edge

24 64 64 64 Removed-Surface Defect

25 69 68 68 High

26 *67 68 67

27 62 62 63 Removed-close to edge

28 53 57 54 Removed-close to edge

29 *64 62 62

30 *66 66 66

31 62 64 63 Low

32 65 64 62 Removed-close to edge

33 63 64 63 Removed-Surface Defect

34 62 61 61 Removed-close to edge

35 53 52 54 Removed-close to edge

36 53 54 50 Removed-close to edge

37 52 54 55 Removed-close to edge

38 53 52 55 Removed-close to edge

39 62 64 63 Removed-close to edge

40 60 62 63 Removed-close to edge

41 59 60 58 Removed-close to edge

42 57 57 55 Low

43 55 56 57 Removed-close to edge

Rebound Hardness= 65.2

69

Sample#

D11-1-94e

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 56 60 51 51 Crushed on second impact. Results variable.

2 51 52 50

3 51 52 56

4 *53 56 52 53 Anamolous second impact.

5 50 50 48 Flaked on third impact. Low

6 43 44 44 Low

7 *55 63 56 55 Anamolous second impact.

8 *57 60 58

9 63 63 62 High

10 *60 62 62

11 *58 58 58

12 50 48 46 Flaked on third impact. Low

13 *52 54 53

14 *60 60 61

15 61 64 63 High

16 63 63 63 High

17 *59 58 57

18 52 54 54 Low

19 *56 56 57

20 *60 62 61

21 62 62 62 High

Rebound Hardness= 57

70

Sample#

I5-1-83.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 50 50 49 Removed-close to edge

2 50 50 52 Removed-close to edge

3 53 51 52 Removed-close to edge

4 54 55 56 Removed-close to edge

5 53 54 54 Low

6 *58 60 58

7 *58 60 59

8 *56 58 58

9 *55 55 56

10 59 60 61 High

11 61 63 60 High

12 *58 58 58

13 53 53 54 Low

14 *57 57 58

15 *58 60 59

16 55 *58 59 60 Slippage on first impact.

17 50 52 51 Low

18 *57 57 59

19 59 59 59 High

20 59 59 58 High

21 *54 55 55

22 53 54 55 Low

Rebound Hardness= 56.9

71

Sample#

D11-3-1c

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 60 58 59 Removed-close to edge

2 63 61 61 Removed-close to edge

3 65 65 65 Removed-close to edge

4 62 64 64 Removed-close to edge

5 61 61 61 Removed-close to edge

6 58 58 57 Removed- Unstable

7 58 58 58 Removed- Unstable

8 *64 65 64

9 *65 65 66

10 69 70 70 High

11 *68 68 69

12 *65 65 65

13 62 61 62 Low

14 58 60 59 Removed- Unstable

15 *65 65 64

16 68 68 68 High

17 68 70 70 High

18 68 67 67 High

19 *65 65 65

20 56 57 55 Removed- Unstable

21 *64 63 62

22 64 64 64 Low

23 *66 66 68

24 *66 66 64

25 *65 64 64

26 58 59 58 Low

27 58 58 59 Low

Rebound Hardness= 65.3

72

Sample#

C9-3-89.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 55 54 56 Low

2 61 61 61 Low

3 54 56 56 Removed- Unstable

4 *62 62 64

5 *67 68 67

6 58 57 57 Removed- Unstable

7 *64 65 65

8 *64 68 63 68 63 63 Anamalous 2nd and 4th impacts.

9 70 68 69 High

10 54 55 58 Low

11 *64 62 64

12 69 65 65 High

13 *66 68 68

14 60 60 60 Low

15 *62 64 64

16 67 66 66 High

17 68 68 68 High

18 60 60 58 Removed- Unstable

19 *62 62 60

20 *64 64 64

21 *66 68 66

Rebound Hardness= 64.1

73

Sample#

C9-3-92.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *62 64 62

2 *65 66 65

3 *65 66 65

4 *64 65 65

5 61 61 60 Low

6 *63 62 62

7 *63 64 66

8 66 66 67 High

9 65 66 66 High

10 61 61 61 Low

11 67 67 65 High

12 *65 67 67

13 67 67 67 High

14 *62 62 62

15 *63 63 63

16 *62 62 62

17 60 60 60 Low

18 61 61 61 Low

Rebound Hardness= 63.4

74

Sample#

E7-1-57.002

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 55 *60 57 59

2 65 63 63 High

3 *63 63 63

4 67 68 68 High

5 *58 59 60

6 *58 59 60

7 54 55 53 Removed-On overhang

8 56 59 60 Low

9 58 60 60 Low

10 66 67 67 High

11 55 67 66 68 First hit on surface ridge-High

12 *63 63 63

13 58 60 58 Low

14 54 55 54 Removed-On overhang

15 54 58 58 57 Removed-On overhang

16 57 56 56 Low

17 *64 63 64

18 *60 60 59

19 *63 63 64

20 *61 64 62

21 *60 58 62

22 Removed-Close to edge

Rebound Hardness= 61

75

Sample#

D11-1-100B6

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 55 56 56 Removed-Close to edge

2 60 61 60 Removed-Close to edge

3 60 61 62 Removed-Close to edge

4 64 64 64 Removed-Close to edge

5 64 62 63 Removed-Close to edge

6 63 60 60 Removed-Close to edge

7 60 60 60 Removed-Close to edge

8 *67 67 66

9 66 65 66 Low

10 *68 68 67

11 *67 68 67

12 64 63 64 Low

13 65 64 64 Removed-Close to edge

14 *68 68 68

15 *69 70 69

16 *68 68 67

17 65 66 65 Low

18 63 63 63 Removed-Close to edge

19 64 64 63 Removed-Close to edge

20 69 70 69 High

21 72 73 72 High

22 71 70 70 High

23 67 68 68 Removed-Close to edge

24 63 65 65 Removed-Close to edge

25 *67 67 66

26 *68 68 68

27 63 65 63 Removed-Close to edge

28 61 62 62 Removed-Close to edge

29 *67 68 67

30 *68 69 68

31 70 69 70 High

32 67 69 69 Low

33 64 65 64 Removed-Close to edge

34 61 63 62 Removed-Close to edge

35 61 61 62 Removed-Close to edge

36 65 66 66 Removed-Close to edge

37 64 62 62 Removed-Close to edge

38 60 60 60 Removed-Close to edge

Rebound Hardness= 67.7

76

Sample#

D11-2-1.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *64 64 63

2 *65 63 65

3 *62 64 65

4 *62 60 60

5 55 53 53 53 Low

6 *65 65 65

7 65 66 66 High

8 65 62 63 High

9 60 61 62 Low

10 *63 62 62

11 65 64 65 High

12 68 67 67 High

13 *62 64 65

14 *62 62 61

15 *63 64 63

16 *64 63 61

17 58 57 56 Low

18 60 62 60 Low

Rebound Hardness= 63.2

77

Sample#

D11-1-98E

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 49 48 48 Removed-Close to edge

2 55 55 54 Removed-Close to edge

3 58 56 56 Low

4 *59 59 61

5 *61 60 60

6 63 64 64 Removed-Close to edge

7 53 54 55 Removed-Close to edge

8 *62 60 61

9 *62 64 62

10 *61 63 63

11 *60 62 62

12 63 63 64 Removed-Close to edge

13 55 56 56 Low

14 *60 62 62

15 63 64 63 High

16 64 65 66 High

17 *62 63 63

18 56 58 57 Low

19 52 53 54 Removed-Close to edge

20 *61 60 60

21 64 64 63 High

22 64 65 64 High

23 *60 62 62

24 58 58 58 Low

Rebound Hardness= 60.8

78

Sample#

D11-1-78.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 56 55 56 Removed-Close to edge

2 56 55 56 Low

3 52 54 54 Low

4 54 57 63 62 61 Removed-On a surface ridge

5 *61 62 61

6 *61 62 60

7 *65 66 66

8 65 65 66 High

9 *63 63 64

10 64 64 64 Removed-Close to edge

11 67 67 67 High

12 65 65 66 High

13 *64 63 64

14 *64 63 60 64 Slippage-3rd impact

15 60 *63 62 64 Slippage- 1st impact

16 65 66 66 High

17 *61 61 62

18 59 60 59 Low

19 58 66 61 58 2nd impact is Anomalous, Low

20 *61 60 60

21 *60 60 60

Rebound Hardness= 62.3

79

Sample#

C9-2-62.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 46 47 47 Removed-Close to edge

2 51 52 51 Removed-Close to edge

3 55 55 55 Removed-Close to edge

4 56 55 56 Removed-Close to edge

5 60 61 60 Low

6 59 57 60 Low

7 61 61 63 Low

8 61 67 67 67 Slippage, 1st impact. High

9 *62 62 63

10 53 60 59 61 Slippage, 1st hit, close to edge

11 70 68 67 High

12 *66 66 65

13 *66 66 64

14 Unstable

15 70 69 68 High

16 *65 66 64

17 *66 65 64

18 64 62 63 Removed-Close to edge

19 *65 65 66

20 66 66 66 High

21 *64 64 64

22 57 60 61 62 Slippage, 1st impact. Close to edge

23 *65 66 67

24 *65 65 65

25 *65 63 62

26 61 61 62 Removed-Close to edge

27 61 60 61 Low

28 60 61 58 Removed-Close to edge

29 56 57 60 Removed-Close to edge

Rebound Hardness= 64.9

80

Sample#

C9-3-95.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 unstable

2 48 52 51 52 Slippage-1st impact, Low

3 54 53 53 Low

4 48 52 51 52 Slippage-1st impact, Low

5 Unstable

6 *56 56 58

7 *57 55 57

8 *60 60 61

9 63 63 64 High

10 *61 58 59

11 *60 60 61

12 65 65 65 High

13 *61 62 61

14 65 63 63 High

15 *60 58 60

16 61 61 63 High

17 *59 60 59

18 52 52 52 Low

19 *58 58 60

20 *58 57 60

Rebound Hardness= 59

81

Appendix B

Individual Rebound Hardness Results for Untreated Silcrete

82

Appendix B shows the individual rebound analyses of each unheated silcrete sample. n

the following table, the accepted ten impact values for each sample are in bold with an asterisk.

The mean of these ten values is the Rebound Hardness value for the sample, and can be found

in bold at the bottom of each sample. The analyses followed the methods defined by Braun and

colleagues (2009), Goudie (2006), and Brown (2011). As described in chapter 3, each point of

impact was struck at least three times. If one or more of the results were flawed due to plunger

slip, geologic defects, surface defects, etc. more impacts were added to ensure accuracy.

Unless flawed, the initial impact at each impact point was used in the analysis. Eighteen impact

values were used for each testing surface. The highest four and the lowest four values were

removed to account for outliers (marked in comments as “high” and “low”). Due to the large size

of some of the sample surfaces and the impact points being placed in a grid 1.5cm apart, more

than eighteen impact points were regularly taken. To account for this, the values from impact

points closest to the edge were removed until eighteen remained (explained in greater detail in

chapter 3).

83

Sample#

E4-1-2.014Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *43 40 42

2 *45 41 43

3 *45 46 50

4 53 47 43 High

5 *44 47 43

6 *47 49 49

7 *44 44 46

8 *46 42 43

9 42 41 45 Low

10 *44 44 48

11 49 50 45 High

12 *46 46 48

13 39 41 39 Low

14 38 40 43 Low

15 40 40 42 Low

16 49 43 46 High

17 *47 48 47

18 49 45 49 High

Rebound Hardness= 45.1

84

Sample#

I14-3-5.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 51 56 56 Removed-Close to edge

2 50 54 53 Removed-Close to edge

3 50 54 53 Removed-Close to edge

4 55 55 55 Removed-Close to edge

5 54 57 58 Removed-Close to edge

6 *62 62 62

7 *60 62 59

8 *62 63 62

9 58 60 60 Removed-Close to edge

10 *61 62 62

11 *61 66 65

12 *62 60 63

13 52 56 60 Low

14 59 60 58 Removed-Close to edge

15 57 59 61 Low

16 64 62 64 High

17 68 68 68 High

18 65 68 68 High

19 65 65 65 High

20 *58 56 57

21 57 55 57 Removed-Close to edge

22 54 60 57 Low

23 *58 60 62

24 *63 62 61

25 *60 62 61

26 58 59 58 Low

Rebound Hardness= 60.7

85

Sample#

I-14-3-5.006

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 54 56 56 Removed-Close to edge

2 56 56 57 Removed-Close to edge

3 52 54 55 Removed-Close to edge

4 52 53 51 Removed-Close to edge

5 56 55 55 Removed-Close to edge

6 *63 58 63

7 *60 60 61

8 *59 59 58

9 57 60 59 Removed-Close to edge

10 *63 66 65

11 54 57 60 Low

12 *61 65 63

13 64 63 65 High

14 55 56 57 Removed-Close to edge

15 53 *60 64 64 Slippage=1st impact

16 56 65 60 64 Slippage=1st impact, High

17 66 64 67 High

18 52 61 62 62 Slippage=1st impact

19 56 57 58 Low

20 *62 63 62

21 65 63 63 High

22 *63 64 63

23 54 *61 60 60

24 58 60 58 Removed-Close to edge

25 50 52 52 Removed-Close to edge

26 48 48 52 Low

27 58 60 62 Low

28 60 58 60 Removed-Close to edge

29 *61 61 60

Rebound Hardness= 61.3

86

Sample#

F10-1-2A.002

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 61 60 62 Removed-Close to edge

2 58 62 58 Removed-Close to edge

3 64 65 65 Removed-Close to edge

4 64 64 64 Removed-Close to edge

5 62 63 62 Removed-Close to edge

6 60 62 66 Low

7 *66 66 66

8 *64 66 68

9 *67 67 67

10 *63 64 64

11 *66 67 68

12 69 70 68 High

13 69 68 70 High

14 *68 69 70

15 *67 68 68

16 56 62 60 Removed-Close to edge

17 *66 69 70

18 55 62 69 69 70 Surface ridge, High

19 68 66 67 High

20 *63 61 63

21 Removed-Close to edge

22 58 60 60 Low

23 60 62 62 Low

24 *67 67 68

25 62 63 64 Removed-Close to edge

26 58 56 58 Removed-Close to edge

27 60 61 60 Low

28 66 66 66 Removed-Close to edge

Rebound Hardness= 65.7

87

Sample#

I14-3-4.003

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 55 57 57 Removed-Close to edge

2 56 58 60 Removed-Close to edge

3 53 55 57 Removed-Close to edge

4 60 60 60 Removed-Close to edge

5 55 57 55 Removed-Close to edge

6 53 50 52 Removed-Close to edge

7 56 56 58 Low

8 57 58 60 Low

9 *62 63 62

10 50 52 50 Low

11 *59 62 57

12 *58 59 60

13 *59 62 63

14 *60 62 63

15 65 63 63 High

16 *63 65 63

17 *61 63 65

18 60 55 57 Removed-Close to edge

19 *62 63 63

20 63 60 64 High

21 65 66 64 High

22 65 66 65 High

23 60 61 60 Removed-Close to edge

24 57 60 61 Low

25 *61 60 62

26 *62 58 57

27 50 52 52 Removed-Close to edge

28 62 63 62 Removed-Close to edge

Rebound Hardness= 60.7

88

Sample#

E3-1-6n

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 44 42 38 Flaking on edge

2 43 44 42 Removed-Close to edge

3 44 44 42 Removed-Close to edge

4 50 44 44 Flaking on edge

5 52 54 51 Low

6 40 42 43 Low

7 *53 52 53

8 *55 57 55

9 *58 56 56

10 *56 50 51

11 *59 61 61

12 64 64 63 High

13 63 61 62 High

14 *60 61 61

15 44 45 46 Low

16 50 52 54 Low

17 62 62 60 High

18 65 64 65 High

19 *57 64 63

20 *57 58 57

21 *60 59 57

22 *58 57 57

23 48 46 42 Removed-Close to edge

Rebound Hardness= 57.3

89

Sample#

G11-1-2B

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 53 56 57 Low

2 *56 55 54

3 *57 57 58

4 *57 58 55

5 *58 58 59

6 56 59 61 Unstable

7 63 64 53 High

8 *60 60 61

9 *55 56 57

10 64 65 65 High

11 64 63 63 High

12 *59 56 57

13 *59 59 60

14 *61 63 63

15 *57 57 56

16 Surface defect

17 61 62 62 High

18 48 55 55 Low

19 Surface overhang

20 Surface overhang

21 55 57 56 Low

22 52 52 54 flake removal, Low

23 Surface overhang

24 Surface overhang

Rebound Hardness= 57.9

90

Sample#

D9-1-12e

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 52 57 58 Removed-Close to edge

2 57 56 55 Removed-Close to edge

3 64 65 64 Removed-Close to edge

4 *62 60 63 Flaked on 2nd impact

5 58 60 60 Low

6 56 60 59 Removed-Close to edge

7 59 63 63 Removed-Close to edge

8 *63 63 66

9 57 60 64 64 Slippage-1st impact, Low

10 60 64 65 65 Slippage-1st impact, High

11 *62 64 64

12 59 61 61 Low

13 62 63 62 Removed-Close to edge

14 65 64 64 High

15 60 62 63 Low

16 *62 64 66

17 *62 65 65

18 *64 65 65

19 *64 64 64

20 65 66 66 High

21 *64 67 67

22 *64 67 67

23 *63 64 65

24 65 65 65 High

Rebound Hardness= 63

91

Sample#

I14-3-3.003

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 46 52 43 Removed-Close to edge

2 57 57 57 Removed-Close to edge

3 60 61 60 Removed-Close to edge

4 62 64 64 Removed-Close to edge

5 53 56 58 Surface ridge

6 52 54 53 Removed-Close to edge

7 52 53 53 Removed-Close to edge

8 *61 61 60

9 *66 65 65

10 *65 60 60

11 *60 58 59

12 *60 60 58

13 On edge

14 50 53 54 Removed-Close to edge

15 60 61 61 Low

16 66 66 65 High

17 *65 66 66

18 68 70 69 High

19 60 61 62 Low

20 60 60 60 Removed-Close to edge

21 55 58 58 Removed-Close to edge

22 56 60 66 66 66 Surface ridge, 1st and 2nd impact, high

23 *62 64 63

24 57 63 63 Surface Defect

25 *65 64 64

26 61 62 60 Removed-Close to edge

27 53 54 55 Removed-Close to edge

28 57 57 60 Low

29 *63 67 66

30 66 66 66 High

31 *62 65 65

32 60 62 62 Low

33 59 58 59 Removed-Close to edge

34 52 50 53 Removed-Close to edge

35 58 58 57 Removed-Close to edge

36 61 61 62 Removed-Close to edge

37 58 63 61 Removed-Close to edge

38 58 56 58 Removed-Close to edge

39 56 56 58 Removed-Close to edge

Rebound Hardness= 62.9

92

Sample#

I14-2-16i

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 54 54 54 Flaked on 2nd impact

2 58 60 60 Removed-Close to edge

3 61 63 63 Removed-Close to edge

4 61 60 60 Removed-Close to edge

5 60 61 58 Removed-Close to edge

6 55 60 58 Removed-Close to edge

7 58 59 60 Removed-Close to edge

8 65 64 64 Low

9 *66 66 65

10 *68 68 68

11 *68 67 66

12 62 62 63 Low

13 57 60 61 Removed-Close to edge

14 55 60 61 61 Slippage-1st hit, close to edge

15 64 66 67 Low

16 *67 68 69

17 71 72 70 High

18 69 68 70 High

19 *67 66 67

20 65 64 64 Removed-Close to edge

21 54 55 60 Removed-Close to edge

22 *66 67 65

23 70 70 71 High

24 *68 72 72

25 *68 68 68

26 64 64 64 Removed-Close to edge

27 60 56 60 Removed-Close to edge

28 *66 66 68

29 *68 70 69

30 68 68 70 High

31 64 64 64 Removed-Close to edge

32 58 60 60 Removed-Close to edge

33 64 64 62 Low

34 64 64 64 Removed-Close to edge

Rebound Hardness= 67.2

93

Sample#

I14-2-6a

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 62 63 60 Removed-Close to edge

2 64 65 63 Removed-Close to edge

3 63 62 62 Removed-Close to edge

4 62 62 61 Removed-Close to edge

5 66 66 63 Removed-Close to edge

6 63 66 63 Low

7 *66 67 67

8 62 63 62 Removed-Close to edge

9 *68 67 69

10 *67 66 66

11 *68 70 71

12 66 67 66 Removed-Close to edge

13 *68 66 70

14 *68 68 70

15 66 66 67 Removed-Close to edge

16 69 69 67 High

17 70 71 73 High

18 70 71 70 High

19 *68 66 67

20 65 68 66 Low

21 *66 69 66

22 68 68 69 High

23 60 65 66 Low

24 Removed-Close to edge

25 60 64 64 Low

26 *67 67 67

27 *66 66 66

28 63 62 62 Removed-Close to edge

29 56 56 56 Surface overhang

30 62 60 60 Removed-Close to edge

Rebound Hardness= 67.2

94

Sample#

I14-3-3.004

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 Unstable

2 50 56 57 Removed-Close to edge

3 54 54 53 Removed-Close to edge

4 54 62 62 62 Removed-Close to edge

5 52 58 54 60 Removed-Close to edge

6 59 59 60 Removed-Close to edge

7 50 56 52 50 Removed-Close to edge

8 57 57 55 Removed-Close to edge

9 50 50 50 Removed-Close to edge

10 Unstable

11 57 61 61 Low

12 *62 64 64

13 60 64 64 Low

14 50 67 64 58 64 Surface Defect

15 57 64 62 Surface Defect

16 *61 62 60

17 55 55 55 Removed-Close to edge

18 60 60 61 Removed-Close to edge

19 *65 65 62

20 *62 60 66

21 67 67 68 High

22 68 66 64 High

23 *66 66 66

24 59 60 61 Low

25 57 60 60 Removed-Close to edge

26 62 58 60 Removed-Close to edge

27 66 68 68 High

28 *63 67 68

29 68 68 69 High

30 *61 64 65

31 *60 62 61

32 61 60 58 Removed-Close to edge

33 62 64 64 Removed-Close to edge

34 *60 62 62

35 60 62 64 Low

36 *64 64 64

37 Unstable

38 Unstable

Rebound Hardness= 62.4

95

Sample#

I14-2-6b

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 57 57 56 Removed-Close to edge

2 62 61 62 Removed-Close to edge

3 60 58 61 Removed-Close to edge

4 66 65 66 Removed-Close to edge

5 62 62 63 Removed-Close to edge

6 61 61 60 Removed-Close to edge

7 57 60 60 Removed-Close to edge

8 55 57 55 Removed-Close to edge

9 61 61 61 Low

10 60 65 63 Low

11 69 70 69 High

12 *67 68 66

13 *67 66 65

14 63 63 63 Removed-Close to edge

15 60 61 61 Removed-Close to edge

16 *65 66 66

17 70 69 68 High

18 70 70 70 High

19 65 66 65 Removed-Close to edge

20 61 61 61 Removed-Close to edge

21 *66 67 68

22 *66 64 65

23 70 71 72 High

24 *68 70 70

25 65 66 65 Removed-Close to edge

26 61 62 61 Removed-Close to edge

27 *64 68 66

28 *67 68 68

29 *68 68 68

30 64 64 64 Removed-Close to edge

31 59 59 60 Low

32 62 64 61 Low

33 *62 62 62

Rebound Hardness= 66

96

Sample#

E4-3-2a.003

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *63 63 62

2 *65 65 61

3 60 63 63 Low

4 *68 68 69

5 *68 70 68

6 70 70 69 High

7 *67 70 71

8 70 70 70 High

9 *69 70 69

10 70 69 69 High

11 70 69 69 High

12 *65 67 67

13 62 64 64 Low

14 *65 66 66

15 60 60 60 Flaking on impacts, Low

16 *63 63 63

17 *64 64 64

18 60 61 59 Removed-Close to edge

19 58 60 60 Low

20 59 60 61 Removed-Close to edge

Rebound Hardness= 65.7

97

Sample#

E9-5-3c.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 54 56 56 Low

2 55 58 55 Low

3 *58 58 60

4 50 54 64 55 57 Geologic Defect, Low

5 *57 59 58

6 *60 62 62

7 67 67 68 High

8 67 68 67 High

9 *62 61 61

10 67 67 67 High

11 65 67 65 High

12 *62 63 62

13 *57 59 59

14 *63 64 64

15 *63 65 65

16 *63 64 64

17 *62 62 63

18 56 57 58 Low

Rebound Hardness= 60.7

98

Sample#

E4-1-2.007

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 50 52 51 Low

2 58 58 58 Low

3 58 58 58 Low

4 54 55 55 Removed-Close to edge

5 *58 58 58

6 *64 65 64

7 *63 63 63

8 *61 61 60

9 55 58 58 Removed-Close to edge

10 67 67 67 High

11 67 66 66 High

12 61 61 62 Removed-Close to edge

13 61 61 67 67 67 On ridge- 1st and 2nd impact, High

14 67 67 68 High

15 *65 66 64

16 *62 62 62

17 *62 64 63

18 *64 64 64

19 *61 62 61

20 60 59 59 Removed-Close to edge

21 55 *60 60 59

22 55 51 50 55 54 Slippage on 2nd and 3rd impact. Low

Rebound Hardness= 62

99

Sample#

D9-1-10c

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 45 45 46 Removed-Close to edge

2 51 51 50 Removed-Close to edge

3 51 51 51 Removed-Close to edge

4 53 53 54 Removed-Close to edge

5 45 45 50 Flaking on 1st impact

6 50 50 50 Low

7 55 *60 59 58 Slippage on 1st impact

8 *60 60 59

9 52 *57 57 57 Slippage on 1st impact

10 55 56 55 Low

11 52 53 54 Low

12 *58 60 60

13 64 64 64 High

14 *57 58 58

15 *57 58 59

16 *61 60 58

17 64 64 64 High

18 *60 60 60

19 54 54 54 Low

20 *60 60 60

21 63 63 63 High

22 62 64 62 High

23 *60 60 60

Rebound Hardness= 59

100

Sample#

E4-32a.002

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 60 62 62 Removed-Close to edge

2 59 60 60 Removed-Close to edge

3 57 55 57 Removed-Close to edge

4 67 67 67 Removed-Close to edge

5 *69 69 69

6 *65 65 65

7 63 63 63 Low

8 61 59 60 Low

9 62 63 62 Surface overhang

10 *66 67 66

11 *69 69 71

12 72 73 72 High

13 72 72 71 High

14 *69 70 70

15 65 65 64 Surface overhang

16 60 60 60 Low

17 65 65 65 Low

18 70 70 70 High

19 *70 72 72

20 72 72 71 High

21 *69 70 70

22 *66 66 66

23 64 64 62 Surface overhang

24 *69 68 68

25 *68 69 67

26 70 70 70 Removed-Close to edge

27 60 58 57 Removed-Close to edge

Rebound Hardness= 68

101

Sample#

E3-1-6L

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 44 48 48 Unstable

2 50 48 50 Unstable

3 *60 60 60

4 50 55 55 53 Slippage-1st impact, Low

5 *64 65 64

6 *63 64 65

7 *65 66 67

8 68 68 67 High

9 *62 61 61

10 65 64 65 High

11 *64 64 63

12 65 65 66 High

13 *60 62 63

14 *59 58 60

15 66 65 63 High

16 *58 58 60

17 54 56 54 Low

18 54 54 53 Low

19 *60 61 59

20 58 58 57 Low

Rebound Hardness= 61.5

102

Sample#

E3-1-5D

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 48 49 59 49 52 Geologic Defect

2 *60 58 59

3 62 64 63 High

4 *58 60 61

5 54 53 60 56 60 54 Geologic Defect

6 48 49 50 Removed-Close to edge

7 54 53 54 Removed-Close to edge

8 *58 60 60

9 64 60 65 High

10 60 66 66 60 66 Geologic Defect

11 *62 62 61

12 54 52 51 Low

13 55 58 55 Removed-Close to edge

14 *60 62 61

15 *60 62 62

16 63 66 63 High

17 *60 60 60

18 *60 62 60

19 55 54 52 Low

20 *60 60 60

21 62 62 63 High

22 *60 58 60

23 55 57 58 Low

24 48 56 54 55 Slippage on 1st impact, Low

25 Unstable

26 Unstable

27 Unstable

28 Unstable

29 Unstable

30 Unstable

Rebound Hardness= 59.8

103

Sample#

E3-1-5C

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 55 54 55 Removed-Close to edge

2 57 58 57 Low

3 *60 62 59

4 *60 58 58

5 52 55 55 Low

6 52 53 53 Removed-Close to edge

7 59 60 58 Removed-Close to edge

8 *64 60 62

9 *63 62 64

10 *62 60 61

11 *61 61 62

12 *60 62 60

13 64 62 62 High

14 64 65 65 High

15 65 66 65 High

16 *60 64 64

17 *60 59 60

18 60 59 60 Low

19 *63 63 63

20 65 65 63 High

21 58 60 60 Low

22 57 56 56 Removed-Close to edge

23 59 56 59 Removed-Close to edge

Rebound Hardness= 61.3

104

Sample#

E4-1-2.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 51 52 51 Low

2 *56 60 56 56

3 *58 59 59

4 *56 56 56

5 51 *56 56 56 Slippage-1st impact

6 Unstable

7 52 53 *58 57 55 Surface ridge

8 *57 59 59

9 56 60 60 59 Low

10 62 63 63 High

11 52 52 52 Unstable

12 56 58 58 Low

13 62 64 64 High

14 64 64 65 High

15 *61 62 62

16 57 56 60 Unstable

17 *57 58 57

18 62 62 61 High

19 *60 60 61

20 *57 59 59

21 53 51 52 Low

Rebound Hardness= 61.3

105

Appendix C

Individual Rebound Hardness Results for Heat-Treated Silcrete

106

Appendix C shows the individual rebound analyses of each heat-treated silcrete sample. In the

following table, the accepted ten impact values for each sample are in bold with an asterisk. The

mean of these ten values is the Rebound Hardness value for the sample, and can be found in

bold at the bottom of each sample. The analyses followed the methods defined by Braun and

colleagues (2009), Goudie (2006), and Brown (2011). As described in chapter 3, each point of

impact was struck at least three times. If one or more of the results were flawed due to plunger

slip, geologic defects, surface defects, etc. more impacts were added to ensure accuracy.

Unless flawed, the initial impact at each impact point was used in the analysis. Eighteen impact

values were used for each testing surface. The highest four and the lowest four values were

removed to account for outliers (marked in comments as “high” and “low”). Due to the large size

of some of the sample surfaces and the impact points being placed in a grid 1.5cm apart, more

than eighteen impact points were regularly taken. To account for this, the values from impact

points closest to the edge were removed until eighteen remained (explained in greater detail in

chapter 3).

107

Sample#

E3-1-5C

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 57 53 56 Removed-Close to edge

2 57 58 59 Removed-Close to edge

3 *58 59 59

4 *61 61 60

5 *58 57 55

6 50 49 50 Removed-Close to edge

7 *57 60 60

8 62 63 63 High

9 62 58 60 High

10 62 62 58 High

11 53 54 54 55 Geologic Defect

12 54 52 52 Geologic Defect

13 *61 59 62

14 *60 58 58

15 63 62 58 59 High

16 *58 56 58

17 57 60 56 57 Low

18 54 *58 58 58 Slippage- 1st impact

19 *61 58 57 56 58

20 *58 58 56

21 53 53 54 Low

22 52 54 54 Surface defect

23 51 50 53 Low

24 56 58 57 Low

Rebound Hardness= 59

108

Sample#

I14-2-6b

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 50 48 50 Removed-Close to edge

2 52 54 51 54 Removed-Close to edge

3 54 55 54 Removed-Close to edge

4 51 52 52 Low

5 54 54 54 Removed-Close to edge

6 54 53 55 Low

7 50 50 50 Removed-Close to edge

8 56 56 52 52 Geologic Defect

9 *59 59 58

10 *62 62 62

11 62 60 60 High

12 56 57 56

13 *58 60 61 60

14 64 63 64 High

15 59 59 61

16 63 61 63 High

17 62 60 60 High

18 Geologic Defect

19 46 46 48 Removed-Close to edge

20 *58 58 58

21 *58 58 56

22 *60 61 59

23 *56 58 57

24 55 53 53 Low

25 *57 56 56

26 *56 56 56

27 54 56 56 Low

28 55 55 54 Removed-Close to edge

29 *58 57 57

Rebound Hardness= 58.2

109

Sample#

D9-1-12e

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 57 55 57

2 56 47 Failure with flaking

3 56 *59 59 60

4 *58 59 58

5 *58 57 56

6 53 53 54 Low

7 *59 61 61

8 *60 59 61

9 *60 60 63 60

10 *58 59 56

11 *57 60 57

12 63 61 62 High

13 62 64 65 62 High

14 56 56 56 Low

15 60 62 59 High

16 63 64 64 High

17 62 62 60 Removed-Close to edge

18 57 58 57 Low

19 *58 60 60

20 *58 56 57

21 54 56 55 Low

Rebound Hardness= 58.5

110

Sample#

I14-3-5.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *55 55 56

2 48 *55 54 52

3 53 53 55 Removed-Close to edge

4 50 50 48 Low

5 *58 58 56

6 *57 57 56

7 52 54 56 56 52 Low

8 54 52 54 Low

9 54 53 54 Low

10 *60 58 58

11 48 48 50 52 Geologic Defect

12 62 62 62 High

13 *58 59 58

14 48 48 48 Removed-Close to edge

15 *58 60 60

16 63 61 62 High

17 64 64 62 High

18 Surface defect

19 *57 57 55

20 *58 57 60 60 60

21 60 61 62 High

22 Surface defect

23 55 58 58 58 Slippage-1st impact

24 *57 58 57

25 50 50 52 Removed-Close to edge

Rebound Hardness= 57.3

111

Sample#

E4-1-2.001

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 50 49 46 48 Low

2 50 48 56 Flaked on 2nd impact, Low

3 *56 52 44 53 Flaked on 3rd impact

4 Not enough surface after flaking

5 *51 52 52

6 50 45 42 Flaked on 2nd impact, Low

7 54 60 57 58 Slippage, 1st impactHigh

8 *55 58 56 58

9 59 62 60 High

10 *56 57 56

11 *53 50 51

12 *53 56 54

13 62 60 61 High

14 60 58 59 High

15 *57 55 56

16 50 50 50 Unstable

17 *51 50 52

18 *52 52 52

19 *52 50 52

20 47 46 46 Low

Rebound Hardness= 53.6

112

Sample#

E4-1-2.007

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 50 51 51 Low

2 54 54 55 Removed-Close to edge

3 55 55 56 Removed-Close to edge

4 49 49 50 Removed-Close to edge

5 52 52 52 Low

6 62 60 60 High

7 *60 59 58

8 *57 58 58

9 51 50 52 Removed-Close to edge

10 Surface defect

11 *60 58 60

12 *60 63 62 62

13 60 60 58 Removed-Close to edge

14 53 53 53 Low

15 61 58 61 60 High

16 64 65 63 High

17 *60 62 62

18 61 59 58 High

19 Unstable

20 *58 60 58

21 *58 59 60

22 *59 57 60

23 *57 58 57

24 *55 55 56

25 Flaked-too much surface removal

26 55 55 52 Low

Rebound Hardness= 58.4

113

Sample#

E3-1-6L

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 Surface uneven

2 38 40 44 42 45 Low

3 38 38 38 Cortex

4 55 56 54 Low

5 *58 58 58

6 49 48 51 Low

7 *60 62 62

8 *63 64 63

9 65 63 62 63 High

10 66 65 68 High

11 *63 62 64

12 66 65 66 High

13 64 65 64 High

14 *62 62 60

15 *64 66 66

16 *63 62 62

17 *63 64 66 64 66

18 *59 59 57

19 *60 57 59

20 Flaking-Affected results

21 55 56 55 Low

Rebound Hardness= 61.5

114

Sample#

F10-1-2A

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 *61 60 63

2 *64 62 64

3 *64 63 62

4 58 59 60 Geologic defects

5 60 *63 64 64 Slippage-1st impact

6 *63 63 62

7 66 66 65 High

8 *64 63 63

9 65 63 64 High

10 *63 63 63

11 61 60 60 Low

12 65 64 64 High

13 *64 66 64

14 65 64 65 High

15 61 61 61 Low

16 54 55 51 Unstable

17 60 60 59 Low

18 *62 65 64

19 40 52 50 51 Geologic defects

20 56 58 57 Removed-Close to edge

21 62 58 58 56 Surface defect- Low

22 *62 60 62

Rebound Hardness= 63

115

Sample#

I14-3-3.004

Impact Point # Impact 1 Impact 2 Impact 3 Impact 4 Impact 5 Impact 6 Reason For Exclusion

1 46 48 46 corner Removed-Close to edge

2 51 52 52 Removed-Close to edge

3 54 53 53 Removed-Close to edge

4 50 50 50 Removed-Close to edge

5 48 48 49 Geologic defect

6 50 55 54 56 Low

7 42 40 46 46 46 Slippage- 1st and 2nd impacts, Low

8 48 48 49 Removed-Close to edge

9 52 48 52 48 Geologic defect

10 *54 55 50 56

11 *56 54 57 56

12 52 54 52 Low

13 Too close to edge

14 *54 54 56

15 56 60 60 61 Slippage-1st impact, High

16 *56 60 57 58

17 *55 52 53 53

18 52 56 54 53 Removed-Close to edge

19 *53 51 50 49 50

20 *53 52 54

21 *56 54 55

22 *55 54 56

23 51 54 53 54 Removed-Close to edge

24 51 52 53 Low

25 48 56 57 58 Slippage- 1st impact, High

26 *55 58 55 55

27 58 54 56 58 High

28 48 48 50 corner Removed-Close to edge

29 50 53 50 Removed-Close to edge

30 58 58 56 High

31 59 56 55 55 Removed-Close to edge

32 58 58 57 Removed-Close to edge

Rebound Hardness= 54.7

116

References

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Internationale de Géologie de l’Ingénieur 22 1 (December 1): 3–11.

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Ambrose, S.H., 1998. Late Pleistocene human population bottlenecks, volcanic

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Biographical Information

Since beginning his graduate career at the University of Texas at Arlington,

Christopher Shelton has participated in field projects at three different archaeological

sites (Knysna Eastern Heads Cave 1 (KEH-1), Pinnacle Point 5/6, and Vleesbaai), as

well as archaeological surveys, and lithic laboratory analysis in South Africa. After

completing his M.A., he is scheduled to return to South Africa as the assistant director at

KEH-1 for the 2015 field season. His interests include African Stone Age lithics analysis,

raw material acquisition patterns, the mechanical properties of stone and how they relate

to producing stone tools, and human paleoecology in southern Africa.

Currently, Mr. Shelton is employed as a staff archaeologist at the SWCA cultural

resource management firm in Arlington, Texas. He plans to continue his research in

Africa, while pursuing a PhD. in the near future.