A Model of Lithic Raw Material Procurement

LITHIC TECHNOLOGICAL SYSTEMS AND EVOLUTIONARY THEORYEdited by

NATHAN GOODALEHamilton College

WILLIAM ANDREFSKY, JR. Washington State University

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Library of Congress Cataloging in Publication dataLithic technological systems and evolutionary theory / [edited by] Nathan Goodale (Hamilton College), William Andrefsky, Jr. (Washington State University). pages cm“This volume is an outgrowth of a symposium organized for the 74th Annual Society for American Archaeology meeting in Atlanta, Georgia, titled Evolutionary Approaches to Understanding Stone Technologies as a Byproduct of Human Behavior”–Contents page.Includes bibliographical references and index.ISBN 978-1-107-02646-9 (hardback)1. Stone implements – Analysis – Congresses. 2. Tools, Prehistoric – Analysis. 3. Human evolution – Philosophy. 4. Social archaeology. 5. Human behavior – History. 6. Human ecology – History. I. Goodale, Nathan, 1977– II. Andrefsky, William, 1955– III. Society for American Archaeology. Annual Meeting (74th : 2009 : Atlanta, Ga.)CC79.5.S76L5775 2015930.1–dc23 2014032390

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v

List of Figures and Tables page vii

Contributors xiii

Acknowledgments xvii

Preface xix

PART I LITHIC TECHNOLOGICAL SYSTEMS AND

EVOLUTIONARY THEORY

1 INTERPRETING LITHIC TECHNOLOGY UNDER THE EVOLUTIONARY TENT 3William Andrefsky, Jr., and Nathan Goodale

PART II CULTURE HISTORY AND PHYLOGENETIC EVOLUTION

2 GRAPHING EVOLUTIONARY PATTERN IN STONE TOOLS TO REVEAL EVOLUTIONARY PROCESS 29R. Lee Lyman

3 THEORY IN ARCHAEOLOGY: MORPHOMETRIC APPROACHES TO THE STUDY OF FLUTED POINTS 48Michael Shott

4 INNOVATION AND NATURAL SELECTION IN PALEOINDIAN PROJECTILE POINTS FROM THE AMERICAN SOUTHWEST 61Todd L. VanPool, Michael J. O’Brien, and R. Lee Lyman

PART III APPLICATIONS OF BEHAVIORAL ECOLOGY TO

LITHIC STUDIES

5 A CASE OF EXTINCTION IN PALEOINDIAN ARCHAEOLOGY 83Charlotte Beck and George T. Jones

6 THE NORTH CHINA NANOLITHIC 100Robert L. Bettinger, Christopher Morgan, and Loukas Barton

CONTENTS

CONTENTSvi

7 WHEN TO RETOUCH, HAFT, OR DISCARD? MODELING OPTIMAL USE/MAINTENANCE SCHEDULES IN LITHIC TOOL USE 117Chris Clarkson, Michael Haslam, and Clair Harris

8 PROCUREMENT COSTS AND TOOL PERFORMANCE REQUIREMENTS: DETERMINING CONSTRAINTS ON LITHIC TOOLSTONE SELECTION IN BAJA CALIFORNIA SUR 139Jennifer M. Ferris

9 A MODEL OF LITHIC RAW MATERIAL PROCUREMENT 156Raven Garvey

10 ARTIFACTS AS PATCHES: THE MARGINAL VALUE THEOREM AND STONE TOOL LIFE HISTORIES 172Steven L. Kuhn and D. Shane Miller

11 SIGNALS IN STONE: EXPLORING THE ROLE OF SOCIAL INFORMATION EXCHANGE, CONSPICUOUS CONSUMPTION, AND COSTLY SIGNALING THEORY IN LITHIC ANALYSIS 198Colin P. Quinn

PART IV CULTURAL TRANSMISSION AND MORPHOLOGY

12 AN ANALYSIS OF STYLISTIC VARIABILITY OF STEMMED OBSIDIAN TOOLS (MATA’A) ON RAPA NUI (EASTER ISLAND) 225Carl P. Lipo, Terry L. Hunt, and Brooke Hundtoft

13 CULTURAL TRANSMISSION AND THE PRODUCTION OF MATERIAL GOODS: EVOLUTIONARY PATTERN THROUGH MEASURING MORPHOLOGY 239Nathan Goodale, William Andrefsky, Jr., Curtis Osterhoudt, Lara Cueni, and Ian Kuijt

14 WHAT STEWARD GOT RIGHT: TECHNOLOGY, WORK ORGANIZATION, AND CULTURAL EVOLUTION 253Nathan E. Stevens

15 EVOLUTION OF THE SLATE TOOL INDUSTRY AT BRIDGE RIVER, BRITISH COLUMBIA 267Anna Marie Prentiss, Nathan Goodale, Lucille E. Harris, and Nicole Crossland

Index 293

vii

FIGURES AND TABLES

Figures

2.1. The transformational model and the variational (Darwinian) model of evolution page 31

2.2. Darwin’s (1859) model of evolutionary pattern 322.3. Fred Plog’s (1973) “seriogram” graph of continuous cultural change 332.4. Two illustrations of the relationship between projectile point forms

and the stratigraphy of Mummy Cave 362.5. Percentage stratigraphy graph of 27 projectile point types across 9

stratigraphic units at Mummy Cave 372.6. Clade-diversity graph for the Mummy Cave projectile points 382.7. Measurement values for each of five variables for all individual

points regardless of type per stratigraphic unit at Mummy Cave 392.8. Central-tendency graph of the mean for all points regardless of type

per stratigraphic unit at Mummy Cave 412.9. Central-tendency graph of the mean (vertical line) and one standard

deviation (box) for all points regardless of type per stratigraphic unit at Mummy Cave 42

2.10. Coefficient of variation per attribute for all points regardless of type per stratigraphic unit at Mummy Cave 43

3.1. Regression residual of lnLength upon principal component 1, plotted against reduction measure lnLT in Folsom replicas 57

4.1. Models of stimulated variation resulting from (a) increased interaction among members of two or more previously distinct cultural systems and (b) a rapidly shifting selective environment 63

4.2. The influence of stabilizing selection on variation of a culture trait over time 64

4.3. The influence of directional selection on variation of a culture trait over time 64

4.4. The influence of disruptive selection on variation of a culture trait over time 65

4.5. The influence of a shifting selective environment on variation of a culture trait within a population 67

4.6. Development of “adaptive peaks” resulting from selection operating on increased variation associated with stimulated variation 68

FIGURES AND TABLESviii

4.7. Expectations of the model of initial stimulated variation and subsequent reduction of variation applied to Paleoindian projectile points 70

4.8. Cumulative corrected coefficients of variation for point length and maximum width for Blackwater Draw projectile points 72

4.9. Illustration of the dimensions and attributes recorded for the points in the Eichenberger cast collection 76

4.10. Cumulative corrected coefficients of variation for the eight metric attributes recorded for Paleoindian points represented in the Eichenberger cast collection 77

5.1. Model of proposed movements of Western Stemmed (from west to east) and Clovis (south to west and north) populations 86

5.2. Measurements, attributes, and landmarks of Clovis blades 885.3. Distribution of prismatic blades 895.4. Distribution of Clovis caches 905.5. The relationship between the time spent in the manufacture of a

tool and its utility 935.6. Curve-estimate model for finding time thresholds at which an

optimal forager will switch to a different technological alternative 935.7. Locations of high-quality toolstone sources on the Great Plains 956.1. Relationship between two technologies 1036.2. Relationship between two mutually viable technologies 1046.3. Relationship between manufacturing time and return rate 1056.4. Location of the Dadiwan site in relation to the five early millet

farming complexes of North China 1076.5. Stratigraphic distribution of major Dadiwan technologies by density

per cubic meter 1096.6. Flake-and-shatter quartz technology 1106.7. Microblades 1116.8. Microblade cores showing all specimens recovered from site 1126.9. Height (platform to base) of complete cryptocrystalline microblade

cores 1136.10. Relationships between size and cryptocrystalline fraction of lithic

assemblages 1147.1. Examples of the experimental tools used in the experiments 1207.2. Experimental results showing the asymptotic nature of the declining

gain curve over 10,000 strokes for all three experimental tool types 1227.3. Confidence intervals for gain rate for each tool type over the first

2000 strokes 1237.4. Relative performance declines for each tool type at 200-stroke

intervals 1247.5. Model showing the effect of different manufacturing time (T) on

overall gain rate 1257.6. Model predictions for when to discard each tool type given

different known manufacturing times 1267.7. The effects of maintenance time as well as manufacturing time

on gain rate and overall efficiency as represented by the slope of the tangent 127

FIGURES AND TABLES ix

7.8. Mean cumulative weight of wood removed per 1000 strokes (left y-axis), and mean cumulative weight lost from unretouched flakes per 1000 strokes (right y-axis) 130

7.9. Mean cumulative rate of increase in step terminated scars for the 3 cm used edge (left y-axis) and mean edge rounding rank for the utilized edge (right y-axis) 130

7.10. Average increases in edge angle (in degrees) over the course of the experiment for retouched and unretouched edges 131

7.11. Comparison of edge rounding (upper) and stepped scar formation (lower) on unhafted (broken line) and hafted (solid line) unretouched scrapers 132

8.1. Map of Baja California peninsula 1428.2. Map of Espíritu Santo Island 1438.3. Bar chart displaying percentages of flake types for rhyolite

(type 1) and chert/quartzite (type 2) 1468.4. Line graph of complete flake size grade percentages 1478.5. Line graph of complete flake reduction trajectory 1488.6. Line graph displaying proportions of edge damage patterns for

utilized flake tools by material type 1508.7. Line graph displaying microchip configuration proportions for edge

damage of utilized flake tools by material type 1519.1. Basic model for establishing the critical use time 1639.2. The Atuel River drainage, Mendoza Province, Argentina 164

10.1. The marginal value theorem in graphic form 17510.2. Range of hypothetical artifact utility trajectories 17910.3. Reformulated MVT 18010.4. Optimal number of uses after which an artifact should be

abandoned, as a function of maximum potential yield and artifact cost. (a) Artifact cost = 10. (b) Artifact cost = 25. (c) Artifact cost = 50. Criterion value for abandonment = average potential yield over entire potential lifetime of artifact (20 uses)-cost 182

10.5. Optimal number of uses after which an artifact should be abandoned, as a function of maximum potential yield and artifact cost. (a) Artifact cost = 10. (b) Artifact cost = 25. (c) Artifact cost = 50. Criterion value for abandonment = average potential yield over first 10 uses of artifact-cost. 183

10.6. Plots of length versus body width for complete fluted points from Tennessee 188

11.1. Signaling theory, the fitness continuum, and the relationship between costly and non-costly signals 206

11.2. A general framework for studying costly signaling behavior with material culture 208

12.1. The Pacific Islands, showing Rapa Nui on the remote southeastern edge 226

12.2. Examples of mata’a from Rapa Nui assemblages 22712.3. Location of mata’a assemblages on Rapa Nui used in this analysis 22912.4. Mata’a measurements and class divisions 23012.5. Mata’a class dimensions 231

FIGURES AND TABLESx

12.6. Seriation solution for mata’a classes comprised of stem length/width ratios and shoulder angle measures 232

12.7. Seriation groups for mata’a classes comprised of stem length/width ratios and shoulder angle measures 233

12.8. Seriation solution for classes of mata’a constructed with measures of stem length and width 233

12.9. Seriation groups for classes of mata’a constructed with measures of stem length and width 234

12.10. Seriation solution for qualitative classes of mata’a consisting of stem shape and shoulder shape dimensions 234

12.11. Seriation groups for qualitative classes of mata’a consisting of stem shape and shoulder shape dimensions 235

12.12. Spatial distributions of the mata’a seriation groups on Rapa Nui 23613.1. Dalton point reduction through use, resharpening, and repair 24113.2. Map of the southern Levant and early Neolithic sites 24213.3. An example of an el-Khiam notched point 24313.4. Direct measurements taken for the NPMI 24413.5. Image J software plug-ins for NPMI programming 24513.6. Hierarchical cluster analysis results 24613.7. Several of the statistically significant clusters 24713.8. Projectile points made by Ishi 24814.1. Proposed relationships among behavior, technology, and tradition 25714.2. Locations of California Central Coast archaeological sites 25814.3. Proportions of multifunctional tools in California Central Coast

assemblages 25814.4. Changes in California Central Coast ground stone technology

throughout the Holocene 25915.1. Major archaeological sites in the Middle Fraser Canyon 27215.2. Bridge River site with excavation grid superimposed 27315.3. History of housepit occupations at the Bridge River site 27415.4. Stratigraphic profile of Area 1 in Housepit 54 (Stratum V = roofs,

III = rim, II = floors) 27515.5. Housepit 24 stratigraphic profile (V = roof, III = rim, II = floor) 27515.6. Three (left) and four (right)-sided slate tools from Bridge River 27715.7. Ratio of total slate tools (TST) to excavated volume (V)

(Table 15.2 volume/10,000) 28115.8. Percentages of sawed and chipped tools from BR 2 and 3 contexts

at Bridge River 28115.9. Percentages of ground (G) and not ground (NG) tools during BR

2 and 3 occupations at Bridge River 28115.10. Total sawed and ground slate tools (TSGST) by volume (V)

(Table 15.2 volume/10,000) 28215.11. Number of slate tools (N) per unit of excavated sediment 28215.12. Percentages of sawed and not sawed tools during BR 2 occupations

at Bridge River 28315.13. Ratio of total sawed edge (TSE) to total edge (TE) for all slate

tools in BR 2 occupations 283

FIGURES AND TABLES xi

15.14. Number of slate tools (N) per unit of excavated sediment (V) (Table 15.2 volume/10,000) 283

15.15. Percentage of sawed and not sawed tools from BR 3 occupations at Bridge River 283

15.16. Percentages of ground (G) and not ground (NG) tools from BR 3 occupations 284

15.17. Ratio of sawed and ground slate tools (SGST) to total slate tools (TST) in BR 3 housepits 284

15.18. Ratio of sawed edge length (TSE) to number of tools (N) with sawed margins 285

15.19. Ratio of total sawed edge (TSE) to total edge (TE) for all slate tools in BR 3 housepits 285

15.20. Ratio of total sawed edge (TSE) to total edge of slate tools only (TEST) 285

15.21. Change in percentages of three- and four-sided tools across BR 2 and 3 occupations 286

15.22. Percentages of three- and four-sided tools in BR 3 occupations at Bridge River 287

TABLES

2.1. Frequencies of projectile points used in analyses and age per stratum at Mummy Cave 34

4.1. Summary information for point length and maximum width for Blackwater Draw projectile points 72

4.2. Cultural-historical types and provenience locations for Paleoindian points in the Eichenberger cast collection 73

4.3. Characters and character states used in the paradigmatic classification 74

4.4. Summary information for the metric attributes of Paleoindian points in the Eichenberger cast collection 75

6.1. Dadiwan site components 1107.1. Details of individual specimens used in the experiment 1208.1. Proximal flake cortex frequency 1458.2. Flake type frequency 1468.3. Tool categories included in the richness index 149

10.1. Results of Pearson’s correlations between length and body width for six Paleoindian point types from Tennessee 187

10.2. Descriptive statistics for basic shape measurements for six Paleoindian point types from Tennessee 189

11.1. Variables that archaeologists can study within the generalized framework to identify and explain material culture–based costly signaling behavior in the past 208

15.1. Slate tool data (counts based on manufacture attributes) 28015.2. Slate tool data (summed margin length measurements [cm]) and

excavated volume (cubic cm) 280

xiii

William Andrefsky, Jr.Dean of the Graduate SchoolDepartment of AnthropologyWashington State UniversityPullman, WA

Loukas BartonDepartment of AnthropologyUniversity of PittsburghPittsburgh, PA

Charlotte BeckAnthropology DepartmentHamilton CollegeClinton, NY

Robert L. BettingerDepartment of AnthropologyUniversity of California, DavisDavis, CA

Chris ClarksonSchool of Social ScienceThe University of QueenslandBrisbane, Qld

Nicole CrosslandIndependent ResearcherWenatchee, WA

Lara CueniAnthropology DepartmentHamilton CollegeClinton, NY

Jennifer M. FerrisCardno EntrixSeattle, WA

Raven GarveyDepartment of AnthropologyUniversity of MichiganAnn Arbor, MI

CONTRIBUTORS

CONTRIBUTORSxiv

Nathan GoodaleAnthropology DepartmentHamilton CollegeClinton, NY

Clair HarrisSchool of Social ScienceThe University of QueenslandBrisbane, Qld

Lucille E. HarrisApplied Archaeological Research, Inc.Portland, OR

Michael HaslamResearch Laboratory for Archaeology and the History of ArtUniversity of OxfordOxford, UK

Brooke HundtoftPima County Community College, East CampusDepartment of Humanities, Arts, and FitnessTucson, AZ

Terry L. HuntDean of the Robert D. Clark Honors CollegeUniversity of OregonEugene, OR

George T. JonesAnthropology DepartmentHamilton CollegeClinton, NY

Steven L. KuhnSchool of AnthropologyUniversity of ArizonaTucson, AZ

Ian KuijtDepartment of AnthropologyUniversity of Notre DameNotre Dame, IN

Carl P. LipoDepartment of Anthropology and the Institute for Integrated Research on Materials,

Environments and Society (IIRMES)California State University, Long BeachLong Beach, CA

R. Lee LymanDepartment of AnthropologyUniversity of MissouriColumbia, MO

CONTRIBUTORS xv

D. Shane MillerDepartment of Anthropology and Middle Eastern CulturesMississippi State UniversityMississippi State, MS

Christopher MorganDepartment of AnthropologyUniversity of Nevada, RenoReno, NV

Michael J. O’BrienArts and Science Dean’s OfficeUniversity of MissouriColumbia, MO

Curtis OsterhoudtIndependent ResearcherAnchorage, AK

Anna Marie PrentissDepartment of AnthropologyThe University of MontanaMissoula, MT

Colin P. QuinnMuseum of Anthropological ArchaeologyUniversity of MichiganAnn Arbor, MI

Michael ShottDepartment of Anthropology and Classical StudiesThe University of AkronAkron, OH

Nathan E. StevensFar Western Anthropological Research Group, Inc.Davis, CA

Todd L. VanPoolDepartment of AnthropologyUniversity of MissouriColumbia, MO

xvii

This volume has had a long gestation period. We appreciate all the contribu-tors to this volume for sticking with this effort. We are grateful to the editors, production staff, and copy editor at Cambridge University Press, as well as those at their affiliates who guided this project to publication. Thanks go to three anonymous peer reviewers whose comments greatly improved drafts of the chapters included in this volume.

The editors would like to acknowledge and thank the late George H. Odell, an old friend and inspiration to researchers studying lithic technological sys-tems around the globe.

ACKNOWLEDGMENTS

xix

PREFACE

This volume is an outgrowth of a symposium organized for the 74th Annual Society for American Archaeology meeting in Atlanta, Georgia, titled Evolutionary Approaches to Understanding Stone Technologies as a Bybroduct of Human Behavior. The purpose of the symposium and this volume is to demonstrate the connection between lithic analysis and a body of theory to guide interpretations of past human behavior in studies of lithic technological systems. The hope we had for this volume stemmed from the original sympo-sium and to capture the state of the field of lithic technological organization incorporating a body of theory for guiding interpretation. We view evolution-ary theory very broadly and understand that others may have a much narrower view. With this in mind we invited scholars with diverse perspectives on evo-lutionary thought who also used lithic technological systems as a medium of analysis. Our vision was to begin a conversation about interpreting past human behavior derived from lithic artifacts interpreted through a very wide variety of evolutionary approaches. In doing so we hope that the diverse perspectives on evolutionary thought might be viewed as compatible or complementary rather than exclusionary.

The authors of the various chapters in this volume represent some of the most respected scholars as well as many young contributors to the field of lithic analysis and evolutionary archaeology. We selected this field of scholars in hopes of bringing different perspectives from existing researchers together under one cover and simultaneously adding new opinions on lithics and evo-lution from an up-and-coming generation of archaeologists.

This book contains many of the same papers that were presented in the original symposium. Although we lost a few authors along the way, we also gained new participants during the journey toward publication. We would like to thank all of the participants in that session and especially those who con-tributed their ideas, methodologies, and interpretations to be included in this volume.

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NINE

A MODEL OF LITHIC RAW MATERIAL PROCUREMENT

Raven Garvey

Raw material acquisition is fundamental to any technology. Modern engineers carefully consider the costs and advantages of potential building materials before ground is ever broken, weighing budgetary constraints against structural integrity, for example. People of the prehistoric past were faced with similar decisions both within and apart from formal economies. This chapter consid-ers lithic raw material procurement from an evolutionary perspective, using a model that predicts the amount of tool use necessary to warrant investment in hard to obtain, high-quality raw materials when local but less-good ones are available.

Models that use objective scales of optimal behavior have been applied to archaeological records with appreciable success. A majority of these have focused on aspects of optimal foraging, predicting subsistence behaviors and their attendant patterns of mobility given certain environmental and, to a lesser degree, social or technological parameters (e.g. Basgall 1987; Bettinger and Baumhoff 1982; Bettinger et al. 1997; Broughton 1997; Hildebrandt and McGuire 2002; Jones 2004; Madsen and Schmitt 2003; Waguespack and Surovell 2003). Although economic models have provided some areas of archaeological research with fresh interpretations and falsifiable hypotheses, studies of stone technology have not made extensive use of such models (for important excep-tions, see Beck et al. 2002; Brantingham 2003; Jeske 1992; Surovell 2009). This is not to say that modern lithic analysis lacks reference to economic decisions. Indeed, as Brantingham (2003:504) observes, many lithic studies assume that

A MODEL OF LITHIC RAW MATERIAL PROCUREMENT 157

“optimal foraging strategies must influence and, therefore, be diagnosed by stone raw material procurement patterns.” Research that addresses the relation-ship between technology and subsistence has made important contributions to our understanding of toolkit composition and raw material use. Nonetheless, and perhaps because lithic technology is not easily translated into fitness (cf. Dunnell 1980), there are still few applications of neo-Darwinian principles that assess the direct costs and payoffs associated with stone procurement.

Binford’s (1979) insightful discussion of lithic procurement as an embedded activity changed the way archaeologists think about the economics of stone use. Raw material collection was described as an ad hoc part of subsistence forays because the wise (optimizing) forager would not make “express and exclu-sive” trips to stone sources except under extraordinary circumstances (Binford 1979:259; italics in original). That is, if “everything goes well, there are few or no direct costs accountable for the procurement of raw materials” (Binford 1979:259). Obtaining stone has since been regarded a largely opportunistic endeavor: foragers, finding themselves close to a source and with the free time, energy, and space for carrying it, will collect stone rather than return home empty-handed. Treating lithic procurement as an embedded activity shifts the focus from the getting of stone to the economizing of stone once it is gotten.

Early descriptions of “curated” gear (Binford 1973), which solved the prob-lem of spatial incongruence between stone and food resources, and of embed-ded procurement (Binford 1979), which effectively liberated hunter-gatherers from purposive toolstone excursions, inspired a number of studies designed to interpret the composition of archaeological toolkits. These analyses explore economizing strategies, assessing the degree of reduction and preparation per-formed at stone sources, whether certain materials were used expediently or reserved for formal tools, whether stone tool users were trying to maximize the use-lives of some or all of the tools in a toolkit, and how these decisions were influenced by subsistence resource types and their timing (e.g., Andrefsky 1994; Bamforth 1986, 1990; Bleed 1986; Goodyear 1989; Jeske 1992; Kelly 1988; Kuhn 1991, 1992, 1994; Torrence 1989).

A second major area of lithic research considers the presence of particular stone types in archaeological assemblages. These studies explore variables such as raw material “richness”, “evenness”, and relative size, and distances from sites to sources to gauge a group’s degree of mobility, estimate foraging radii and assess stone conservation (e.g. Bamforth 1990; Basgall 1989; Eerkens et al. 2007; Eerkens et al. 2007; Jones et al. 2003; McGuire 2002; cf. Brantingham 2003). Research of this ilk has been especially fruitful where available raw materi-als have distinct and identifiable chemical signatures, as with obsidians in the western United States.

These two lines of inquiry reflect the influence that “embeddedness” has had on lithic studies in that the behavioral interpretations they afford begin

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post-acquisition. Hunter-gatherers are assumed to have been primarily concerned with subsistence resource procurement and only secondarily with stone procurement; stone was collected when convenient. However, the casu-alness of embedded procurement seems fundamentally at odds with the care-ful planning implied in curation and stone economizing. Further, embedding lithic procurement reduces the opportunity cost of getting stone but is possible only when food and stone are spatially congruent, in which situation the pres-sure to budget time and energy carefully – that is, to embed – is minimal. A third body of lithics literature brings this paradox into sharper focus.

Goodyear (1989) and others (e.g., Funk 1972; Hester and Grady 1977; Kelly and Todd 1988) consider the special case of hypermobile Paleoindian groups, who are noted for their almost exclusive use of high-quality cryptocrystalline silicates. Sources of these materials are often located considerable distances from the sites where manufacturing byproducts and tools were discarded. This phenomenon has been attributed to the premium that Paleoindians placed on mobility; obtaining high-quality stone was worth the effort because its physi-cal properties complemented the mobile lifestyle (Goodyear 1989). Coupled with this is the idea that the paleoenvironment was teeming with high-ranked subsistence resources, unlike later periods when a changed proportion of humans to resources governed mobility. During the Paleoindian period, lithic raw material procurement may have been perfectly embedded in the subsis-tence system and Paleoindians could have relied on high-quality stone because it was, in essence, always “local.” In fact, one might argue that stone was the limiting resource for Paleoindians and that, because food resources were ubiq-uitous, Paleoindians embedded hunting in their pursuit of stone rather than the other way around (cf. Gardner 1977).

Under the current framework, then, choices regarding when and from where to procure stone appear to require at least three distinct explanations. The first treats scenarios in which food resources were ubiquitous, as we pre-sume was true of the Paleoindian period, and high mobility ensured high encounter rates with both game and stone (Kelly 1988). That is, embedding lithic procurement was possible. A second explanation addresses the scenario in which hunter-gatherers were faced with a less abundant resource base and, therefore, potential disjunctions between food and high-quality stone. People experiencing food resource stress may have preferred embedding because it reduced the cost of stone procurement, but resource distributions may have made this strategy untenable. These hunter-gatherers may have “made due” with lower-quality, local raw materials when getting high-quality ones was too costly. A third explanation deals with stone procurement among groups whose resources were tightly time constrained and required a reliable toolkit (Bleed 1986; Torrence 1989), which may have necessitated deliberate, costly treks to high-quality stone sources.


The implication that stone procurement decisions are driven by the relative abundance of food resources is environmentally deterministic (Gould and Saggers 1985). Stone technologies, including the procurement decisions at their base, are seen as adaptations to prevailing environmental conditions. Absent detailed paleoenvironmental data, explanations of the relationships among environment, prey, and technology tend to be circular. Further, if cultures are in homeostasis, perfectly and perpetually adapted to their environments, cul-ture change requires an external catalyst, frequently taken to be a change in the environment (Bettinger 1991). When the environment is seen as the sole cause of cultural change, archaeology ceases to be the study of human behavior and becomes a chronicling of environmental change.

An alternative assessment of raw material procurement, one that avoids envi-ronmental determinism and accounts for all three of the scenarios described previously, uses a simple economic model with a fitness-based explanation of procurement decisions. Because natural selection favors behaviors that maxi-mize somatic maintenance and reproductive success, which are largely invisible archaeologically, proxy measures (e.g., caloric return rates) are used to gauge prehistoric fitness (Bettinger 1991; Kelly 1995; Winterhalder and Smith 1992). The model presented here predicts raw material procurement decisions based on the assumptions that lithic materials are ranked according to their qual-ity and that high-quality materials improve return rates for the activities they are used to perform. Importantly, this model can be combined with others to make and test predictions about complex human behaviors.

The Model

A model of technological intensification, recently described by Bettinger et al. (2006; see also Ugan et al. 2003) predicts the amount of time that must be devoted to a subsistence activity before a tool user will achieve a higher rate of return from a more costly technology relative to a less costly one. Any amount of time less than this critical use time ensures a lower rate of return from the more costly technology because the time and energy required to produce such a tool negates the energetic payoff of the resources procured with it. Given this relationship, “as increased time is devoted to these [particular] sub-sistence activities it pays to invest more in technologies that increase their rate of return” (Bettinger et al. 2006:538).

This model can be used to predict lithic procurement decisions when the currencies and constraints are redefined. Just as technologies differ with respect to manufacturing costs and return rates, stone types differ in the costs associ-ated with their procurement and reduction, as well as the benefits they afford when used. The model parameters necessary to predict when certain materials will be favored include procurement and manufacturing costs, measures of raw

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material quality and rates of return from tools of a given material type, and tool “use time.”

Procurement and Manufacturing Costs

The time or energy required to travel to a stone source, locate, and extract a portion of suitable size and quality from within the source, and manufacture a tool from it are the costs associated with stone tool production. Because stone tool use-lives are understood to be highly variable (Andrefsky 1998), manufacturing costs – the time or energy required to produce usable tools – must be assessed taking a group’s technological system into account. Alternate versions of this cost–benefit calculus could include maintenance (the ease of rejuvenation relative to the frequency with which it is required) or other vari-ables, determined by the materials record and the nature of the inquiry. A more complex equation could be drafted to calculate procurement costs when materials are obtained through trade.

Differences in direct procurement costs will often overwhelm those associ-ated with tool production, which may be minimal once a suitable piece of stone is selected. The total time or energy expended in traveling to the more distant of two stone sources will generally be greater than that expended in producing a tool from the inferior, closer source. However, although differ-ences in manufacturing costs may seem negligible, they ought not be wholly dismissed. The swamping effect may be moderated if procuring the inferior material requires more within-source searching for a suitable piece and involves a higher rate of failed production attempts, which would lessen the difference between the two sources’ procurement costs and accentuate the difference in their manufacturing costs.

Raw Material Quality and Rates of Return

Lithic materials differ in their suitability for tool production. Properties that affect this include isotropism (lack of internal directionality), brittleness (ten-dency to fracture rather than deform under stress) and homogeneity (Andrefsky 1998; Cotterell and Kamminga 1979; Whittaker 1994). High values of these properties permit more efficient tool production and extend use-lives because reduction and rejuvenation are more easily controlled, resulting in less waste and more use-edge per unit weight.

Crystalline structure also influences toolstone workability. Amorphous and cryptocrystalline materials are preferred for their predictability, supe-rior brittleness, and sharp edges (Whittaker 1994). Generally, the larger the crystal structure, the more difficult (because less predictable and controllable) the knapping, the less sharp the flaked edge, and the less easily reworked the


resulting tool. Noncrystalline materials such as obsidian and opal are more easily reduced and maintained than meso- and macrocrystalline materials such as quartzite and basalt.

To reduce subjectivity and allow for comparison between assemblages, Brantingham et al. (2000) describe a means of quantifying the properties described above. Their measures include “percent crystallinity, average and range of crystal size, and abundance of impurities. . .” (Brantingham et al. (2000:257). These variables have clear advantages, but it may not always be possible or practical to incorporate them, and analysts may lack the geological training that would ensure that these measures are consistent across analyses. When circumstances preclude this level of detail, the analyst might refer to Callahan (1979:16), who presents a general classification system for lithic raw materials. Frequently, assemblages will consist of only a low or moderate num-ber of raw material types (Brantingham 2003) and the differences in their qual-ity may be readily distinguishable by more conventional means of assessment.

Tool return rates are directly related to raw material quality for the reasons described earlier, and can be calculated in a variety of ways. For example, return rates might be conceptualized in terms of a tool’s total usable hours or the calories generated by its use. Determining an appropriate return rate will also depend on the material record and the nature of the inquiry.

Use Time

The amount of time that must be devoted to a particular activity before a tool user will achieve a higher rate of return from a more costly item relative to a less costly one is referred to as the critical use time (Bettinger et al. 2006). The critical use time that should trigger a switch from one lithic raw material type to another is determined by the costs and benefits associated with the compet-ing materials, described previously.

In the graphical representation of this model (Figure 9.1a), the dimension “time,” along the x-axis, is divided into time spent obtaining a raw material and crafting a useable tool from it, and time spent using the tool. Time of either kind increases with increased distance from the origin. Return rates lie along the y-axis. The hypothetical relationship in the figure illustrates that material A is characterized by a modest procurement-plus-manufacturing time, perhaps because this material source is located close to the site where it is used, but the return rate is also relatively low, because it has a large crystal structure or numer-ous imperfections. Material B has a higher rate of return given its superior qual-ity, but its procurement-plus-manufacturing cost is also higher, perhaps because the source is located at a considerable or difficult distance from the site. The relationship between these two material types, their respective returns relative to the time it takes to procure each and craft a tool from it, defines the critical

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use time, C, or the amount of time that must be devoted to a task to make higher-quality material B viable in the presence of lower-quality but more accessible material A. As a result, any amount of use time less than the critical use time favors using material A because, at this level of use, the return rate for A is higher than the return rate for B. Conversely, any amount of use time greater than the critical use time favors using material B because the return for B is higher than that for A (Figure 9.1b). This relationship makes it such that, as use time increases, it pays to invest in the material that increases returns.

It is important to note that although this model acknowledges that techno-logical decisions can be guided by subsistence needs, they are not necessarily so guided because increased tool use for any purpose (ceremonial, for instance) influences raw material selection. That is, at a certain level of use, having the most effective tool (i.e., the one with the highest rate of return, however returns are measured) outweighs the cost of procuring hard-to-obtain raw materials.

Model Predictions

Based on the model’s parameters, at least two scenarios could effect a change in the selection of raw materials. The first involves an increase or decrease in tool use time, as described earlier and depicted in Figures 9.1a and 9.1b. Tool use times in excess of the critical use time favor higher-quality raw materials such that resource intensification, for example, should make procuring them “worth it” once the use time threshold is breached. Conversely, decreased time at a task should favor the use of lower quality, local materials.

A second factor that could influence stone selection is a change in the costs associated with procurement or manufacturing. Procurement costs could change if a group’s mobility pattern changes, bringing the group closer to or drawing it farther from a source. Procurement costs might also change as stone sources are used intensively or over long periods, making it more difficult to locate portions of suitable size and quality within a source. Figure 9.1c illustrates the scenario in which changed mobility has brought a group closer to a high-quality and previously hard to obtain source, B. The resultant reduction in the cost of obtaining material B redefines the critical use threshold that would trig-ger a change in procurement. In this case, we would expect a higher frequency of this material at a lower intensity of tool use.

An Application

This model offers a compelling explanation for changed patterns of stone use observed in the archaeological record of the Atuel River drainage in south-ern Mendoza Province, Argentina (Figure 9.2). Local archaeologists report

163

RETURN

A

B

(a)

(b)

(c)

B

A

A

use procurement & manufacturing

TIME

B

C C�

B�

CC�

Cuse B use A

9.1. (a) Graphical depiction of the procurement model showing the critical use time, C, beyond which threshold a tool user will achieve a higher return rate by switching from lower-cost but lower-quality material A to higher-cost, higher-quality material B. The dimension time is divided into procurement and manufacturing time to the right of the origin, and use time to the left of the origin. Return rates lie along the y-axis. (b) Use times in excess of the critical use time will always favor material B. (c) The hypothetical scenario in which changed mobility has brought a group closer to high-quality and previously hard to obtain material B, reducing its procurement and manufacturing costs (from B to B′). The changed procurement and manufacturing costs of material B redefine the critical use time (from C to C′) that would effect a change in material procurement. (Adapted from Bettinger et al. 2006.)

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a dramatic reduction in archaeological sites in parts of southern Mendoza during the middle Holocene, between 8000 and 4000 BP (Gil et al. 2005). Paleoenvironmental data from a number of regions worldwide indicate that this same four thousand year period was a time of variable and generally more arid climatic conditions, during which encounter rates with high-ranked sub-sistence resources would have been reduced (e.g., Antevs 1948; Grayson 1993; Meltzer 1999; Sheehan 1994, 2002). Lithic collections from the few known sites with middle Holocene deposits indicate a dramatic shift in raw mate-rial use from basalts in earlier levels to obsidians in later levels (Garvey 2012; Neme 2007; Neme et al. 2011), and the change appears to track the large-scale climate shifts of the Holocene. Here, data derived from middle Holocene sites in southern Mendoza are compared to the predictions of the stone use model, and these predictions are combined with those of the marginal value

72° W 70° W 68° W

32° S

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36° S

9.2. The Atuel River drainage, Mendoza Province, Argentina.


theorem (Charnov 1976) to tease apart complex behavioral responses to mid-dle Holocene climatic stimuli.

Argentine researchers have obtained 93 radiocarbon dates associated with human activity in southern Mendoza (Gil et al. 2005). Although the middle Holocene interval accounts for 36 percent of the time since initial occupa-tion, only 13 percent (N = 12) of the radiocarbon dates fall between 8000 and 4000 BP. It remains unclear whether the paucity of middle Holocene sites in Mendoza indicates a dramatic population reduction, whether sites of this period are simply not well preserved or visible, or whether people reorganized their settlement patterns to exploit different biotic communities.

In simplest form, the marginal value theorem (Charnov 1976) predicts that, because the total amount of energy available in a given patch diminishes as foragers catch and consume available resources, beyond a critical threshold the optimizing forager should leave the patch or face starvation. The optimal point of departure is determined by the amount of energy available in the envi-ronment as a whole and the distribution of patches on the landscape. When resources are relatively abundant in an environment, optimizing foragers will make correspondingly frequent moves to new resource patches. When, due to environmental or demographic changes, the number of available resource patches is reduced (and holding all other factors constant), optimizing foragers should remain in a given patch longer and extract a greater proportion of its resources before moving to a new one (Bettinger 1991; Charnov 1976).

The behaviors predicted by this model may account for the sparsity of mid-dle Holocene sites in Mendoza Province. Prior to 8000 BP, when population densities appear to have been low, foragers may have been highly mobile, tar-geting only the highest-ranked species, moving frequently in pursuit of them, but not having to move far to find the next resource patch. If patches were centered on water resources (e.g., springs or river margins) as they are likely to have been in this semi-arid environment, and the number of available patches was reduced during middle Holocene droughts, foragers may have responded by moving less often but to more distant patches, exploiting resources around water sources until within-patch foraging returns were low enough to neces-sitate incurring inter-patch travel costs. Thus, the observed decrease in archae-ological sites may not be a product of reduced populations, per se, but of a reduction in the overall number of sites, each occupied for a longer duration.

This scenario can be elaborated to include other parameters that likely con-tributed to differences in settlement patterns before, during, and after the dry-ing trend (i.e., the early, middle, and late Holocene). Biotic zones assume two basic configurations in southern Mendoza. In the Andes and, to a lesser degree, adjacent foothills, resource zones are vertically stratified and change quickly with elevation. In the piedmont and plains east of the Andes, resource zones are arranged horizontally. All of the middle Holocene sites known to date are located between 1500 and 2500 m above sea level, in the upland valleys

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of major rivers including the Atuel and its tributaries. If middle Holocene Mendocinos were bound to water resources as people of other regions appear to have been during this time (e.g., in the Great Basin; Jones et al. 2003), then it is possible that their settlement followed a roughly west-to-east pat-tern, tracking seasonally available resources from the upland river valleys in the west to the rivers’ lower courses to the east. The vertical arrangement of Mendoza’s western biomes may have made foraging nearer obsidian sources in the Andes unnecessary. So, in the early Holocene, when population levels were low and resources abundant, foragers moved often, but not far. In the middle Holocene, when populations were still low (but not necessarily lower than before) and resources scarce, foragers moved less often but longer distances and the moves may have been tied to water resources. In the late Holocene, resources rebounded, but human populations also increased, outstripping local resource availability and forcing people to move into previously unoccupied patches and to intensify subsistence resources.

The predictions of the lithic procurement model can be coupled with this hypothesis for a more complete interpretation of behavioral responses to middle Holocene climatic events. In southern Mendoza, the two most fre-quently occurring raw material types, basalt and obsidian, are distinct in their associated costs and returns. Much of the underlying geology in Mendoza is basaltic (Rodríguez and Ragairaz 1972) and the ubiquity of basalts rela-tive to archaeological sites makes their procurement cost low. However, many Mendozan basalts are grainy, producing serviceable tools but ones that may be relatively hard to craft and maintain, for example. Obsidians, on the other hand, make fine tools that are easily maintained, but their procurement cost is higher because sources are localized, many of them high in the Andes, and often distant from known archaeological sites. The relationship between these two material types, their respective return rates relative to the time it takes to procure them and craft tools from them, defines the critical use time, the amount of time that must be devoted to a task to make obsidian viable in the presence of lower quality but more readily available basalt.

Because there is a measurable difference in the return rates associated with Mendozan basalts and obsidians, their relative abundance in archaeological deposits may serve as a proxy measure for other behaviors. Given the more restricted movement predicted by the marginal value theorem, we should expect local stone to dominate middle Holocene lithic assemblages in the Atuel drainage for two reasons. First, with restricted movement, both the opportu-nity and the absolute costs of resource excursions to the north and south of the Atuel River Valley may have been too high to justify trips to distant obsidian sources. Second, restricted movement implies a widened diet breadth, incor-porating more varied resources including small game, seeds, and plant foods (Jones et al. 2003). Accordingly, procurement was likely too generalized during


the middle Holocene to warrant use of the high-cost, high-return obsidians one suspects were needed for specialized tools (Bettinger et al. 2006).

If populations grew after the environment stabilized in the late Holocene, people would have been forced into previously unoccupied areas and foraging radii may have extended farther into the Andes where high-quality obsid-ian sources are located, thereby decreasing obsidian procurement costs, as in Figure 9.1c. That is, obsidian procurement could have become embedded in food resource forays. A shift in stone use might also reflect a change in sub-sistence. After the middle Holocene, expanded human populations may have outstripped rebounding resources, thereby increasing resource competition, requiring technological intensification and triggering a shift from basalt to obsidian. Thus, changing settlement patterns that reduced obsidian procure-ment costs, intensified food resource procurement that heightened the need for high-quality raw materials, or some combination of these two factors would have favored the use of obsidian after the middle Holocene, but not sooner.

Discussion and Conclusions

Granting that the data available for this analysis are currently few, using an evo-lutionary framework to assess the scarcity of middle Holocene sites in southern Mendoza provides empirically testable predictions and offers an alternative to deterministic accounts of prehistory. The environments in which people lived are sure to have influenced their behaviors, and the present application clearly has an environmental component. Significantly, however, the models applied to the Mendoza scenario are based on the assumption that human action and decision-making are important and powerful counters to environmentally induced resource fluctuations. It also bears repeating that this model of raw material procurement is indifferent to the purpose a tool is put to; increased ceremonial use should have the same influence on raw material selection as increased subsistence use.

Some might argue that viewing stone technologies in terms of evolutionary fitness trades environmental for biological determinism. Evolutionary explana-tions need not be deterministic, however. Evolutionary anthropologists build tractable models as though people are purely rational with the expectation that one’s observations will deviate from the model’s prediction. It is by this method that we hope to learn about behavioral variation and our reductionism is an intentional and appropriate methodology designed to isolate variables that are central to a particular outcome (Friedman 1953:36), reducing the unnumbered complexities of reality to a tractable number of abstractions (Winterhalder and Smith 1992).

Studies of embeddedness and stone economizing behaviors are clearly important for understanding landscape and resource use. Nonetheless, the

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model outlined here offers some potential advantages. First, it eliminates the need for multiple descriptions of stone procurement that require detailed knowledge of subsistence behavior and prevailing environmental conditions. This model of stone procurement can also generate hypotheses regarding the relative amounts of time people were devoting to particular activities, which can be combined with other models’ predictions to interpret complex archae-ological records. Finally, understanding the relationship between use times and returns for particular materials should improve our use of optimal foraging models since resource ranking can change dramatically with changes in tech-nology (Bettinger 1991, 1999).

Truly informative models of behavior are those that are as applicable to hunter-gatherers as to more complex societies, to prehistoric knappers as well as modern engineers. No one model can usefully address all behaviors, but a given model, if it is to successfully articulate empirically observed phenomena with more general theories of human behavior, should address all phenomena of a particular kind. The model presented here is one such attempt to under-stand material procurement decisions because embedding, although perhaps ideal, can happen only under certain circumstances.

Acknowledgments

This paper benefited greatly from the comments of Robert Bettinger, Jelmer Eerkens, Mark Aldenderfer and Bruce Winterhalder. Research abroad was funded by the J. William Fulbright Foundation, the National Science Foundation and the Department of Anthropology, University of California, Davis. Permission to study collections housed at the Museo de Historia Natural, San Rafael was kindly granted by Humberto Lagiglia. Many thanks to Adolfo Gil and Gustavo Neme, who provided invaluable advice and logistical support during my stays in Argentina.

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A Model of Lithic Raw Material Procurement

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