Cultural Landscapes and Long-Term Human Ecology (Interdisciplinary Contributions to Archaeology) 3031496981, 9783031496981

Table of contents :
Acknowledgements
Contents
Contributors
Chapter 1: Cultural Landscapes and Long-Term Human Ecology
1.1 Introduction
1.2 Evolving Diversity of the Ecological Approach in Archaeology
1.3 Scale Dependence of Archaeological Theory, Method, and Data
1.4 The Future of Archaeology
References
Chapter 2: Models, Foragers, Human Beings, and a Hunter-Gatherer Career
2.1 Introduction
2.2 Thinking About Hunters and Gatherers
2.3 Thinking About Optimal Foraging Models
2.3.1 Does Anyone Optimize Ever?
2.3.2 Hunting in the Deeper Past and in Colder Places
2.3.3 We All Have Neighbors and They Have Good Stuff
2.4 A Career Looking Outside of Our Blinders
2.4.1 Archaeology Is Hard: Research Over the Long Haul
2.5 Conclusions
References
Chapter 3: Defining and Modeling the Dimensions of Settlement Choice: An Empirical Approach
3.1 Introduction
3.1.1 Contributing to Theory-Building and Explanation
3.1.2 Considering Variables of the Physical and Social Environments
3.1.3 Getting Rid of “Non-Sites”
3.2 Case Study Settlements and Region
3.2.1 Database and Variables
3.3 Getting at the Dimensions of Settlement Choice: A PCA Tactic
3.3.1 Component Selection
3.3.2 Readjustment of Components by Outlier Removal
3.3.3 Bootstrapping
3.3.4 Restrictive Variation
3.3.5 Visualization of Principal Components
3.4 Putting It All Together: Mahalanobis D2 as a Single-Class Model
3.4.1 Calculation of D2
3.4.2 Conversion to Probabilities
3.5 Model Evaluations
3.5.1 Performance of Transformed Mahalanobis D2
3.5.2 Performance of Logistic Regression Model
3.5.3 Performance of Maximum Entropy Model
3.5.4 Discussion
3.6 Conclusions
References Cited
Chapter 4: Isobiographies and Archaeology Beyond Long-Term Ethnography: Life History Reconstruction Using Stable Isotopes
4.1 Introduction
4.2 Human Skeletal Tissues
4.3 Geographic vs. Dietary Tracers
4.4 Isobiographies
4.4.1 Bone
4.4.2 Teeth
4.4.3 Dental Calculus
4.4.3.1 Hair
4.5 Case Studies from Ancient California
4.5.1 Case 1: Inter-Tooth and Bone Isobiographies
4.5.2 Case 2: Intra-Tooth Isobiographies
4.5.3 Case 3: Short-Term Hair Isobiography
4.6 Conclusions
References
Chapter 5: Caribou Inuit Activity and Settlement Around Yathkyed Lake: A Record of Archaeological Features in an Inland Arctic Landscape, Canada
5.1 Introduction
5.1.1 The Caribou Inuit
5.2 Location, Seasonality and Activity
5.3 Archaeological Data: Correlates of Activity and Season
5.4 Analysis of Sites
5.4.1 Archaeological Evidence for Seasons and Activities
5.5 Conclusions
References
Chapter 6: Resource Acquisition Risk and the Division of Labor: Austral Lessons for Hunter-Gatherer Archaeology
6.1 Introduction
6.2 Theoretical Framework and Empirical Predictions
6.3 Explaining Variation Across Ethnographic Landscapes
6.3.1 When Men’s Hunting Fails, Women’s Work Dominates Production: Martu
6.3.2 When Men’s Hunting Is Reliable, Their Labor Dominates Production: Alyawarre
6.4 Explaining Variation Across Ethnoarchaeological Landscapes
6.5 Explaining Variation Across Archaeological Landscapes
6.6 Summary and Conclusion
References
Chapter 7: Niche Construction and the Ideal Free Distribution: Partners in Characterizing Past Human-Environmental Dynamics
7.1 Introduction
7.2 Niche Construction and the Ideal Free Distribution
7.2.1 Discussion: A Question of Scale? Niches, Habitats, and Suitability
7.2.2 Niche Construction and Transhumance
7.2.3 Transhumance, Niches, and the IFD
7.3 Conclusions
References
Chapter 8: Reconsidering the Amazonian Interfluvial Occupation
8.1 Amazonian Diversity
8.2 The Floodplain and the Interfluve
8.3 The Pardo River, Entering into an Interfluvial Zone
8.4 Excavations
8.5 Patterns
8.6 Discussion
8.7 Is There a Sociopolitical or Environmental Periphery in the Amazon?
8.8 The Interfluve as More Pristine?
8.9 Mobility and Long Distance Travel
8.10 A Vision Moving Forward
References
Chapter 9: Holocene Human Ecology and Adaptation to Millennial- and Centennial-Scale Climate Change: A Case Study from the North Sea Basin
9.1 Introduction
9.2 Coupling the Human System to the Natural System in the Southern North Sea Basin
9.2.1 The Natural System
9.2.2 The Human System
9.3 A Chronological Model for Diachronic Change in the RMS Mesolithic
9.4 A New Hypothesis for Mesolithic Adaptations to Environmental Change in the RMS Region
9.5 Conclusion
References
Chapter 10: Technological Changes in Lithic Reduction as a Chronological Indicator in Surface Artifact Scatters
10.1 Introduction
10.2 Late Palaeolithic
10.3 Early Mesolithic
10.4 Late Mesolithic
10.5 Research History
10.6 Discriminant Analysis
10.6.1 Chronologically Significant Variables
10.6.2 Training Samples
10.6.3 Survey Sites as New Cases to Be Classified
10.6.4 Analysis – Discriminant Analysis
10.7 Conclusion
References
Chapter 11: Neolithic Cultural Landscapes in Southwestern Germany: Exploring Contributions of Regional Survey
11.1 Introduction
11.2 Environmental and Archaeological Context of the Southwest German Neolithic
11.3 Plowzone Surveys: Methods and Survey Coverage
11.4 Geographic Setting and History of Research
11.5 Distribution of Neolithic Archaeological Sites in and around the Study Areas
11.6 Exploring Potential of Plowzone Surveys: A Non-site Approach
11.7 Survey Results
11.7.1 Upper Swabian Study Area: Moraine Lowlands
11.7.2 Swabian Alb Study Area
11.7.3 Identifying Neolithic Materials in Surface Scatters
11.7.4 Low Density of Neolithic Finds in Upper Swabian Surveys
11.7.5 Neolithic Finds from Swabian Alb Surveys
11.8 Exploring Potential Contributions of Regional Survey in Southwest Germany
11.8.1 Neolithic Land Use Patterns in Comparison to Earlier Prehistoric Periods
11.8.2 Distribution of Neolithic Activities around Settlements
11.8.3 Directions for Future Research
References
Chapter 12: Neolithic and Bronze Age Bog Settlements in the Federsee Basin (Baden-Württemberg, Germany)
12.1 Environmental Context and State of Research
12.2 Houses, Settlement Types and Society
12.3 Mobility and Settlement Systems
12.4 Paths of Settlement, Fluctuations of the Lake Levels, Climatic and Economic Changes
12.5 Transformation of the Landscape
12.6 Final Discussion
References
Index

Citation preview

Interdisciplinary Contributions to Archaeology

Erick Robinson Susan K. Harris Brian F. Codding  Editors

Cultural Landscapes and LongTerm Human Ecology

Interdisciplinary Contributions to Archaeology Series Editor Jelmer Eerkens, University of California, Davis, CA, USA Editorial Board Members Canan Çakırlar, University of Groningen, Groningen, The Netherlands Fumie Iizuka, Missouri University Research Reactor, Columbia, MO, USA Krish Seetah, Stanford University, Stanford, CA, USA Nuria Sugranes, Instituto de Evolución, Ecología Histórica y Ambiente, San Rafael, Mendoza, Argentina Shannon Tushingham, California Academy of Sciences, San Francisco, CA, USA Chris Wilson, Flinders University, Bedford Park, Australia

Archaeology stands alone among the sciences in its attempt to enlighten us about the entire record of humankind. To cover such a broad range of time and space, archaeologists must ensure that their findings are integrated into broader spheres of scientific knowledge. The IDCA series aims to highlight the collaborative and interdisciplinary nature of contemporary archaeological research. Topics the series has covered include: • • • • • • •

Paleoecology Archaeological Landscapes Statistical Approaches Laboratory Methods Human Biological and Cultural Evolution Human Nutrition Emergence of Agriculture and Pastoralism

For a copy of the proposal form, please contact Christi Lue (christi.lue@springer. com). Initial proposals can be sent to the Series Editor, Jelmer Eerkens (jweerkens@ ucdavis.edu). Proposals should include: • • • •

A short synopsis of the work or the introduction chapter The proposed Table of Contents The CV of the lead author(s) If available: one sample chapter

We aim to make a first decision within 1 month of submission. In case of a positive first decision the work will be provisionally contracted: the final decision about publication will depend upon the result of the anonymous peer review of the complete manuscript. We aim to have the complete work peer-reviewed within 3 months of submission. This book series is indexed in SCOPUS. For more information, please contact the Series Editor at (jweerkens@ ucdavis.edu).

Erick Robinson  •  Susan K. Harris Brian F. Codding Editors

Cultural Landscapes and Long-Term Human Ecology

Editors Erick Robinson Division of Atmospheric Sciences Desert Research Institute Reno, NV, USA

Susan K. Harris Office of the Registrar University of California Santa Barbara Santa Barbara, CA, USA

Brian F. Codding Department of Anthropology University of Utah Salt Lake City, UT, USA

ISSN 1568-2722     ISSN 2730-6984 (electronic) Interdisciplinary Contributions to Archaeology ISBN 978-3-031-49698-1    ISBN 978-3-031-49699-8 (eBook) https://doi.org/10.1007/978-3-031-49699-8 © Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: The Federsee Lake from the end of the boardwalk through the reeds. Photograph taken by Susan Harris on 8/17/03. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Acknowledgements

This volume has been a practice in resilience. It has experienced death, heart attacks, brain bleeds, career changes, inter-continental moves, retirement, and the first global pandemic in a century. We thank all contributors and Springer Press for their immense patience. We thank Mike Jochim for setting the foundations on which this volume attempts to build. Mike helped show how the archaeological record provides a unique and powerful perspective on the long-term history of human adaptation to myriad social and environmental challenges. We thank Doug Bamforth, Brenda Bowser, Mary Lou Larson, and Andrew Stewart for originally bringing us all together to honor and celebrate Mike’s career. This volume is dedicated to Mike, and one of the ‘council of elders’ that asked us to take on this challenge, Mary Lou Larson.

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Contents

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 Cultural Landscapes and Long-Term Human Ecology������������������������    1 Erick Robinson, Susan K. Harris, and Brian F. Codding

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Models, Foragers, Human Beings, and a Hunter-Gatherer Career��������������������������������������������������������������   19 Douglas B. Bamforth

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Defining and Modeling the Dimensions of Settlement Choice: An Empirical Approach ������������������������������������   41 Kenneth L. Kvamme

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Isobiographies and Archaeology Beyond Long-Term Ethnography: Life History Reconstruction Using Stable Isotopes ������������������������������������������������������������������������������   71 Jelmer W. Eerkens and Eric J. Bartelink

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Caribou Inuit Activity and Settlement Around Yathkyed Lake: A Record of Archaeological Features in an Inland Arctic Landscape, Canada������������������������������������������������   95 Andrew M. Stewart

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 Resource Acquisition Risk and the Division of Labor: Austral Lessons for Hunter-­Gatherer Archaeology������������������������������  129 Brian F. Codding, Rebecca Bliege Bird, David W. Zeanah, and Douglas W. Bird

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Niche Construction and the Ideal Free Distribution: Partners in Characterizing Past Human-Environmental Dynamics����������������������������������������������������������  147 Sarah B. McClure and Douglas J. Kennett

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 Reconsidering the Amazonian Interfluvial Occupation ����������������������  165 Myrtle P. Shock

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Holocene Human Ecology and Adaptation to Millennial- and Centennial-Scale Climate Change: A Case Study from the North Sea Basin������������������������������������������������  185 Erick Robinson and Jacob Freeman

10 Technological  Changes in Lithic Reduction as a Chronological Indicator in Surface Artifact Scatters ������������������  215 Susan K. Harris 11 Neolithic  Cultural Landscapes in Southwestern Germany: Exploring Contributions of Regional Survey����������������������������������������  243 Lynn E. Fisher, Susan K. Harris, Rainer Schreg, and Corina Knipper 12 Neolithic  and Bronze Age Bog Settlements in the Federsee Basin (Baden-­Württemberg, Germany)����������������������  277 Helmut Schlichtherle Index������������������������������������������������������������������������������������������������������������������  295

Contributors

Douglas  B.  Bamforth  Department of Anthropology, University of Colorado Boulder, Boulder, CO, USA Eric J. Bartelink  Department of Anthropology, California State University Chico, Chico, CA, USA Rebecca Bliege Bird  Department of Anthropology, Pennsylvania State University, State College, PA, USA Douglas  W.  Bird  Department of Anthropology, Pennsylvania State University, State College, PA, USA Brian  F.  Codding  Department of Anthropology, University of Utah, Salt Lake City, UT, USA Jelmer W. Eerkens  Department of Anthropology, University of California Davis, Davis, CA, USA Lynn  E.  Fisher  Sociology/Anthropology Department, University of Illinois Springfield, Springfield, IL, USA Jacob Freeman  Anthropology Program and Ecology Center, Utah State University, Logan, UT, USA Susan K. Harris  Office of the Registrar, University of California Santa Barbara, Santa Barbara, CA, USA Douglas J. Kennett  Department of Anthropology, University of California Santa Barbara, Santa Barbara, CA, USA Corina Knipper  Curt Engelhorn Center for Archaeometry, Mannheim, Germany Kenneth  L.  Kvamme  Department of Anthropology, University of Arkansas, Fayetteville, AR, USA Sarah B. McClure  Department of Anthropology, University of California Santa Barbara, Santa Barbara, CA, USA ix

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Contributors

Erick Robinson  Native Environment Solutions LLC, Boise, ID, USA Division of Atmospheric Sciences, Desert Research Institute, Reno, NV, USA School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA Helmut Schlichtherle  Landesamt für Denkmalpflege, Gaienhofen-­Hemmenhofen, Germany Rainer Schreg  Otto-Friedrich University Bamberg, Bamberg, Germany Myrtle P. Shock  Federal University of Western Pará (UFOPA), Pará, Brazil Andrew M. Stewart  Strata Consulting Inc., Toronto, ON, Canada David  W.  Zeanah  Department of Anthropology, California State University Sacramento, Sacramento, CA, USA

Chapter 1

Cultural Landscapes and Long-Term Human Ecology Erick Robinson, Susan K. Harris, and Brian F. Codding

Abstract  In 1976, Michael A.  Jochim published what would become a foundational book for ecological approaches in archaeology. In the decades that followed, his work inspired robust research programs examining long-term cultural change in its environmental context. As illustrated by the chapters in this volume, ecological approaches outlined by Jochim persist as a dominant paradigm in archaeology, and lead to nuanced explanations of behavior across time and space. Here we review these contributions and contextualize them within the intellectual history of ecological archaeology pioneered by Mike Jochim. We end with a brief examination of how this research tradition establishes a foundation for the future of archaeology. Keywords  Human ecology · Cultural landscapes · Hunter-gatherers · Ecological archaeology · Archaeological theory · Michael Jochim

1.1 Introduction The ecological approach thus provides a focus of inquiry, a general framework of logical priority. Consideration of important nonenvironmental factors is not ignored, but must be included within an ecosystem model which derives its primary structure from the relation-

E. Robinson (*) Native Environment Solutions LLC, Boise, ID, USA Division of Atmospheric Sciences, Desert Research Institute, Reno, NV, USA School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA e-mail: [email protected] S. K. Harris Office of the Registrar, University of California Santa Barbara, Santa Barbara, CA, USA e-mail: [email protected] B. F. Codding Department of Anthropology, University of Utah, Salt Lake City, UT, USA e-mail: [email protected] © Springer Nature Switzerland AG 2023 E. Robinson et al. (eds.), Cultural Landscapes and Long-Term Human Ecology, Interdisciplinary Contributions to Archaeology, https://doi.org/10.1007/978-3-031-49699-8_1

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E. Robinson et al. ships of man and the natural environment. It must be remembered, however, that the exploited natural environment is culturally defined, so that the “cognized” environment may differ from that seen by the ecologist. Specifically, the definition of exploitable and desirable resources depends, to a large extent, upon technology and value systems, and this process of definition must be examined. (Jochim, 1976: 9) Archaeological thought is dynamic. It progresses toward new goals, or veers from one to another and, as the pendulum of orthodoxy swings, the archaeologists’ perception of what constitute appropriate foci for research changes with almost every student generation. Given such change and variation, it is small wonder that trends and events of the past grow dim in the discipline’s collective memory; or that current trends are often taken for granted (particularly by students) as being fixed and immutable. (Jennings, 1985: 281)

This volume provides an inspiring contradiction to the second quotation above. It does so by focusing on the resilient contributions of the scholar behind the first quotation, Michael A. Jochim. This first quote comes from Jochim’s (1976) pioneering book Hunter-Gatherer Subsistence and Settlement: A Predictive Model. In his review of this book, Lewis R. Binford wrote: The use of the properties of the gross environment thought relevant for conditioning human adaptations is a powerful strategy. It is one which will receive more attention in the future. In this regard Jochim’s work is a pioneer step in a potentially very productive area of research. (Binford, 1978: 138)

As we write this close to 50 years later, we can state with confidence that Binford was correct in his prediction for the reception of Jochim’s work. But this impact was even more prescient than Binford predicted, and nothing highlights this better than Jennings’ quote above, which was written during the period of the largest fragmentation in our young discipline’s history, when debates raged about the overall ontologies, epistomologies, and aims of archaeology. Most of this debate centered on the role of archaeology as a science and the importance placed on ecological approaches. However, Jochim’s quote above illustrates how proponents of this approach were also considering the non-environmental aspects of past human societies, such as value systems and technology. Most importantly, the ecological approach that Jochim helped pioneer was built around explicit theoretical models that suggested tests for those looking to criticize them. The anti-science and functional ecology critiques did not provide such testable propositions enabling progressive, incremental growth of knowledge, which has led to an amorphous ‘post-truth’ situation (e.g., Hodder, 2018). As a result, after 50 years, the anti-science and ecology critiques have started to “grow dim in the discipline’s collective memory” (Jennings, 1985: 281), while attention to environmental and sociocultural diversity increases each year. This volume illustrates the important contributions of Michael A. Jochim in creating a mature discipline that other fields now look toward as the only long-term perspective on human-environment interaction (e.g. Kohler & Rockman, 2020). Rather than dim, the contributions in this volume highlight how the ecological approach and its focus on the interactions of natural and social environments (cf. Jochim, 1998) shine brighter than ever and continue to provide pathways for the future of the discipline.

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As younger archaeologists, it is important for us to reflect on the history of the field, and how that history provided the foundations of our current work. We believe that future directions are strengthened by a robust knowledge of the theoretical and methodological frameworks and debates that came before us. Archaeology is relatively young compared to most other scientific fields. This means that, typical of all relatively young scientific fields, early disciplinary history will be filled with a series of starts, stops, and dynamic iterations. This can lead to confusion when trying to make sense of the evolution of the discipline. If we had to pick one year that set the theoretical and methodological foundations of the modern profession, it would be 1976. This year saw the publication of Jochim’s book, the publication of Hodder and Orton’s Spatial Analysis in Archaeology, Thomas’ Figuring Anthropology: First Principles of Probability and Statistics, and the publication of Charnov’s “Optimal Foraging, the Marginal Value Theorem”. These four publications established the initial nexus of pathways which developed in their own specific ways and led to the main theory and method foci on which the future of the discipline will continue to grow. Jochim’s book was “one of the first attempts to develop specific expectations of site placement, size, and content (particularly faunal), as a modeled basis for predicting actual site characteristics” (Binford, 1978: 137). Hodder and Orton’s book was one of the first explorations of spatial methods of analysis that highlighted how “archaeologists have been slow to realize the potential for making inferences about prehistoric behavior which use systematic and replicable procedures for describing and interpreting spatial patterning in archaeological remains” (Clark, 1978: 132). Thomas’ book was the first introduction to statistics for anthropology students (Ammerman, 1977). Finally, Charnov’s paper was the first mathematical model for “the use of a ‘patchy habitat’ by an optimal predator” (Charnov, 1976: 129), which set the foundation for many later studies using behavioral ecology in anthropology and archaeology (e.g., Chagnon & Irons, 1979). Each of these four contributions in 1976 set the foundations from which archaeology could start to develop its own unique theories and methods that would guide the discipline forward to the present. Today, modeling, spatial and statistical analyses are more prevalent than ever. But these ever-important approaches to the archaeological record cannot be seen as a blind attempt to turn archaeology into a ‘hard science’ akin to physics or chemistry. As the opening quote in this chapter from Jochim illustrates, already in 1976, as archaeologists were trying to develop theories and methods that would provide better replication of methods and validation of interpretations, the unique challenges of interpreting the fragmented archaeological record and the importance of addressing the non-environmental aspects of that record were coming into increasing focus and clarity. This attention led to the debates that occurred at the time that Jennings talked about the inherent dynamism of the profession. For example, 1976 also saw Thomas publish a paper where he addressed the potential role of ritual and ceremony in the curation of particular artifacts in the past:

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E. Robinson et al. Too many archaeologists mistakenly assume that the hypothesis-testing strategy so lovingly nurtured by ‘new’ archaeologists is relevant only to matters of settlement pattern, technology, and cultural ecology. This is not so. A cultural materialistic framework can be used to explain a very wide range of behavior—including religion and ceremonials… (Thomas 1976: 131)

The next year, Hodder would lay the foundations of the alternative paradigm of ‘post-processualism’ with his study on the distribution of material culture in the Baringo District of Western Kenya: It is shown that many of the usual interpretations of material culture patterning are inadequate because they do not take into account the ability of groups and individuals to use artefacts as a medium for the communication of information about, for example, one’s membership of identity groups and status groups. The importance of the symbolic nature of artefacts for the structuring of material culture distributions is shown at the boundaries between spatial identity groups, and in the distributions of male- and female-associated items. (Hodder, 1977: 239).

After these comments were made by Hodder, the discipline started to splinter into different theoretical camps that were pro-science/ecological or anti-science/symbolic. Much of our early years of education came on the heels of these debates, inspired by our own teachers having focused on one or the other in their early careers. However, as the contributions in this volume show, we might be starting to overcome this unproductive paradigmatic camps approach to the discipline. Here we propose that the ecological and landscape approaches pioneered by Mike Jochim provide a wide foundation for the re-coalescing of different paradigmatic camps in archaeology, and also set the most open framework for the future of archaeology. After 1976 most archaeology professionals started to work in Cultural Resource Management (CRM). In fact, in 1976 one of the creators of CRM made the following statement that carries forward to the present and our current dearth of funding and training relative to other scientific fields: Archaeologists, while convinced themselves of the scientific validity and ultimate importance of their activities, have done little, with a few notable exceptions, to communicate to the public this importance. Nor have they presented in a readable manner the achievements from which the public might discern this importance. Rather they have almost smugly gone about their own affairs content to comment almost exclusively to their colleagues and to the next generation of archaeological students who would then perpetuate the process. (McGimsey III, 1976: 26)

A recent forecast of the CRM market in the US for the next decade highlights how we lack the number of trained archaeologists that will be needed in the future (Altschul & Klein, 2022). The authors call for the following: Finally, we argue that the quantity of CRM archaeology conducted in the upcoming decade has great potential to address many issues but will only reach that potential if academic and applied archaeologists (a) collaborate on the establishment of data standards and the creation of synthetic research tools, (b) join together in pursuit of research goals that benefit the public, (c) shift from project-based to landscape-scale management. (Altschul & Klein, 2022: 2)

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Few archaeologists better prepared us for this shift to landscape-scale management than Mike Jochim. Jochim’s work developed models of human-environment interaction and methods of analysis that integrated broad inter-regional scales with the local scale of the individual archaeological site, at temporal scales spanning millennia. In fact, one could even propose that Jochim helped lay theoretical and methodological groundwork for much of the increasing involvement of the long-term perspectives provided by archaeological data with initiatives such as the IPCC (Kohler & Rockman, 2020) and the Intergovernmental Panel on Biodiversity and Ecosystem Services (Vadrot et al., 2016). Furthermore, in many regions of the world Indigenous populations are beginning to voice their criticisms of the role of excavation in archaeology (Cowie et al., 2019; Schneider & Hayes, 2020). This will therefore require the adaptation of traditional data recovery methods toward the systemic analysis of surface collections in mobile field labs to generate landscape-scale knowledge of long-term Indigenous histories (cf. Harris Chap. 10 and Fisher et  al. Chap. 11). Future archaeologists will have to adapt their methods and associated theories to be more culturally competent and considerate of the Indigenous people and lands in which they work. Mike Jochim’s long history of field surveys and the innovative methods developed by his students and collaborators in this volume must be adopted in other situations around the world. In this chapter, we introduce the 12 different contributions in this volume. We break this introduction down into three sections that we suggest are the principle ‘take-home messages’ from this volume.

1.2 Evolving Diversity of the Ecological Approach in Archaeology The chapters in this volume highlight how the ecological approach in archaeology continues to evolve and incorporate increasingly nuanced approaches to the archaeological record. They build on Jochim’s (1976) original proposal that they include non-environmental approaches to the record that consider how past populations valued different natural resources and made unique adaptations of technology based on past knowledge systems. In Chap. 2 Douglas B. Bamforth criticizes optimal foraging theory and highlights how currencies driving human decision making can change over time and space. He notes “the differences between the geographic scales at which ancient foragers lived and at which modern foraging theorists think” (p. 23). Bamforth references the ‘sunk cost fallacy’, where humans and other species double-down on previous investments rather than base their decision making on future expectations. He cites ethnographic and archaeological examples from different regions (e.g., Inuit and California) that illustrate how prospects of losses versus gains vary depending on geography and the relative availability of key resources. This is an important point that reflects the evolution

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of foraging theory over the past 40  years, many of which were stimulated by Jochim (1983, 1988, 1989): …general theoretical principles alone are not capable of predicting specific behavioral outcomes of individual situations since several technological responses are often equally plausible solutions; furthermore, many aspects of behavior can affect a single lithic attribute. A solution to these limitations is to embed lithic studies within a wider study of culture change in which multiple lines of evidence are examined. (Jochim, 1989: 106).

A key take-home message from this volume is the importance of multiple lines of evidence and multiple spatial and temporal scales of analysis to test hypotheses of cultural and ecological change in the past. However, as Kvamme notes in Chap. 3, “it must be realized that models derived from empirical data cannot be devoid of theory because the process of selecting variables for analysis implies a theoretical orientation.” (p. 44). The importance of being explicit about theoretical orientation is illustrated by Brian F. Codding and colleagues in Chap. 6. They start by negating traditionally misguided criticisms that behavioral ecology theory does not consider social landscapes: Within the framework of behavioral ecology, human decisions are firmly situated on an environmental foundation, with the social landscape emerging from the environmental landscape. This approach is not environmentally deterministic, rather it acknowledges that environmental patterns and ecological interactions will constrain certain behavioral options while allowing or encouraging others. (Codding et al., Chapter 6: p. 130).

Codding and colleagues provide an example of advances in behavioral ecology theory, specifically the consideration of the division of labor. They highlight the novel perspective first introduced by Jochim (1988) that optimal foraging theory was limited by its failure to consider variation in the archaeological record caused by the different patterns in women’s and men’s decision making. They focus on the differentiation of sand monitor lizard hunting by women and kangaroo hunting by men in Martu populations of western Australia. Decision making of women and men in Martu societies are based on different currencies and considerations of resource acquisition risk. Women’s foraging goals are focused on minimizing acquisition risk, whereas men’s goals are focused on riskier foraging where they attempt to maximize energy acquired. They test ethnographic observations from both Martu and Alyawarre populations by examining archaeofaunal assemblages from stratified rockshelter assemblages that provide records of changing resource acquisition through time. They use an abundance index comparing sand monitor to kangaroo bones through time, where increasing abundance of the smaller sand monitor bones relative to kangaroo bones suggest divisions of labor through time. The archaeological data shows a low abundance of kangaroo versus sand monitors through the Late Holocene, therefore confirming a deep history of the division of labor in western Australia that illustrates the rarity of kangaroo and the predominance on women’s labor for caloric returns. This confirmation of ethnographic and ethnoarchaeological observations framed by behavioral ecology theory provides the advances to address variability in the deep time record that Bamforth calls for.

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One criticism of the ecological approach has been the need to consider how past decisions constrain current and future decision making, as Bamforth (Chap. 2) highlights with his reference to the ‘sunk cost fallacy.’ Also, what happens when certain archaeological regions lack (1) ethnographic records of fine-grained resource acquisition decisions, and (2) well-stratified archaeological sites to test ethnoarchaeological observations? Two chapters in this volume propose models that focus on the different feedback relationships involved in long-term human ecology and adaptations to social and environmental changes. Each of these studies occur over broad geographic regions that lack ethnoarchaeological records or well-stratified archaeological sites allowing windows of acquisition risk behaviors in the same location over long spans of time. These situations require flexibility in model formation and more complexity than the simplicity that provides strength to behavioral ecology models. While not ideal for behavioral ecology, they are realistic and do the best they can to conceptualize the myriad diversity presented by the fragmented archaeological record and the still incomplete paleoenvironmental records for many regions. Most importantly, and consistent with the ultimate aims of modeling leading back to Jochim, they present clear hypotheses that can be tested by the available data. The first example of the evolving diversity of the ecological approach comes from Sarah B. McClure and Douglas J. Kennett in Chap. 7. McClure and Kennett combine Niche Construction Theory (NCT) (Odling-Smee et  al., 2003) and the Ideal Free Distribution (IFD) model from population ecology (Fretwell & Lucas, 1969). NCT focuses on the ability of species to modify their environments, which are then inherited by future generations and therefore provide adaptive constraints on those later generations. It is especially well-suited to their case study of the adoption and spread of domesticated animals in the Iberian peninsula, as humans must deforest the environment to bring in stock animals, and through time, those stock animals start to make their own impacts on the environment as their populations grow. This run-on effect of environmental change over time changes the relative suitability of the local environment for human, animal, and plant populations. This is where the IFD model from population ecology comes in. Like NCT, IFD is well suited to studies of colonization processes and the spread of agriculture. IFD predicts species will inhabit the most suitable environment for sustaining their populations first, and as those populations grow, they start to require more resources from that niche, which diminishes the suitability of the habitat. This causes some of the population to move to another less suitable niche, and the process repeats itself. However, an important modification to the IFD model is provided by the ‘Allee affect’, where the increase of populations in the habitat enhances rather than diminishes the suitability. The Allee effect is therefore well-suited to human populations that are able to pool their labor, cooperate, and develop new technologies and other innovations to enhance their local environments. McClure and Kennett propose this combined NCT/IFD model to explain changes in the Iberian Neolithic archaeological record for increasing pastoralism and new agricultural strategies that enhanced local environments, such as manuring. They highlight how their novel model can be used to explain other ‘Neolithisation’ models around the world:

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E. Robinson et al. A multi-scale approach that focuses on niche construction with elements of the Ideal Free Distribution will help us better understand patterns of early food production comparatively, such as the persistence of low-level food production in some parts of the world in comparison with full-blown reliance on agriculture in others, and the development of more intensive forms of agropastoralism such as transhumance and nomadism. (McClure and Kennett, Chapter 7, p.__)

The second example of the evolving diversity of the ecological approach comes from Erick Robinson and Jacob Freeman in Chap. 9. This chapter develops a coupled natural-human systems model for understanding how different kinds of environmental change might have impacted long-observed patterns in the Mesolithic archaeological record. It reflects the tremendous advances in paleoclimate science and paleoecology over the decades since Jochim developed his models for the environmental constraints on human decision making during the Mesolithic. When Jochim developed his models, little was known about the complexity of paleoclimate change and the relationships of these changes to ecosystem changes across Europe. The terminology for different terminal Pleistocene and Holocene environmental changes was derived from other regions that provided high-resolution records for change in different proxies through time (Mangerud et  al., 1974). Knowledge of earth’s climate history was still in its infancy (e.g., CLIMAP Project Members, 1976), and the connections of global scale climate change to ecological changes across Europe have only recently been established (Rasmussen et al., 2014; Walker et al., 2019). Robinson and Freeman develop a model that isolates the various interactions between the climate system, sea-level rise, and ecosystem responses, and the feedback responses they had with the ‘hard’ (e.g., land use and technology) and ‘soft’ (e.g. exchange networks) infrastructures of human systems during the Mesolithic period. They note major uncertainties in determining whether recently recognized abrupt climate change events had causal relationships with changes in Mesolithic land use and stone tool technology. Robinson and Freeman develop a Bayesian chronological model to test whether these changes are coincident with each other. Developing chronological models to test for the contemporaneity of the Early-Middle and Middle-Late Mesolithic transitions with the timing of abrupt climate changes such as the 9.3 and 8.2 cal. BP ‘events’ enables the development of a new hypothesis for Mesolithic land use and technological changes that can be tested by future research. Future research must focus on more quantitative assessments of lithic technology (cf. Harris, Chap. 10) and obtaining high-resolution paleoecological records as close as possible to the archaeological sites of interest. Testing hypotheses for the impacts of paleoclimate change on Holocene hunter-gatherers requires that researchers consider the specific ‘lags’ in the timing of regional ecosystem responses to specific climate change events (cf. Robinson and Riede, 2018). Increasing knowledge of the different and dynamic feedback relationships between humans and their environments over the past decades has broadened the ecological approach in archaeology. As case studies from different regions of the world produced more data each year, the complexity of human-environment interactions came to the fore and started to limit our ability to apply one-size-fits-all deductive models in a wholesale manner. Nuance needed to be added to these

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deductive models. This has highlighted the importance of a more iterative approach that combines deductive and inductive approaches centered around the available archaeological data on hand from a specific region. Nowhere is this need for the interaction between inductive empirical data from a specific region and deductive theory more apparent than in Chap. 3 by Kenneth L.  Kvamme. Kvamme develops a new method for spatial statistical analyses of empirical data to determine the fundamental preferences of settlement niche spaces in the past. This method responds to two important criticisms of archaeological locational modeling: (1) that GIS-based models are not derived from theory, and (2) that they are environmentally determinist and do not consider the social environment. Kvamme’s innovative response to these classic criticisms provides a new foundation for expanding the ecological approach in archaeology. A specific advance of this method is that it minimizes the locational variance of different settlement parameters, rather than providing a confusing mass of variables that have to be selected from. This method therefore winnows down multiple variables collected during decades of field research and enables their efficient analysis. Social networks are an important parameter in defining the settlement niche space of the study, which makes this method responsive to both the social and environmental parameters determining settlement decisions. A key result of this chapter is the role of population density in the relative predictive power of the model. Highlighting the role of population density and social networks in settlement choice harkens back to the comments above made by the combined NCT-IFD model of McClure and Kennett in Chap. 6. Kvamme’s new method points toward the future of the ecological approach where social and demographic variables have as much of a role in the study of past human-environment interactions as the different variables of the natural environment. The importance of the social environment and regional environmental variability in the study of long-term human ecology is further emphasized in Chap. 5 by Andrew M. Stewart. Many of the take-home messages highlighted above concerning the evolving diversity of the ecological approach in archaeology are present in Stewart’s chapter. Harkening back to Jochim’s (1991) earlier criticisms, Stewart highlights the difficulty of applying theories derived from ethnography and ethnoarchaeology to archaeological records from regions that have large amounts of annual environmental, and therefore resource, variability. Stewart emphasizes the massive spatial scales at which current and past societies operated that makes it difficult to apply yearly and locally derived ethnographic models to expectations of patterning in the archaeological record, similar to Bamforth’s criticism in Chap. 2. He focuses on the Caribou Inuit of the forest-tundra transition zone of Nunavut Territory, Canada. In this region, caribou populations are spatially predictable, but temporally unpredictable based on the year-to-year variability of the environment. For this reason, Stewart notes how traditional focus on the predominance of caribou in the social organization decisions of Inuit communities has produced a limited foundation for understanding the variability of the archaeological record. He applies Jochim’s (1991) model relating place, season, and activity to different archaeological sites to better understand the variability of their different assemblages. By

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focusing on the specific sites and their assemblages, Stewart is able to propose the relative strength of relationships between place, season, and activity at each site. This analysis highlights how the role of other resources such as fishing have been underestimated by previous research, and how archaeological site locations cannot be predicted solely on the predominance of the fall caribou hunt. Stewart concludes with an important lesson for the ecological approach in archaeology: This result reinforces the impression that Caribou Inuit occupation of the interior, even in places where it seems most secure and predictable, was not based on resources that could be relied on. Despite a preference for living together in groups of a size that well exceeded the nuclear family, and for traveling to visit and reside with other groups (Birket-Smith 1929: 159–160; Burch 1986; Csonka 1995: 316; Hoffman 1976: 71), this preference was undermined by an unreliable resource base, often forcing people to split up and take different paths in different seasons and years (exhibiting both simultaneous and sequential variability), or stay and suffer together. (Stewart, Chapter 5).

This variability poses important challenges to the ecological approach in archaeology. While simple models for explanation of variability in the archaeological record are ideal, challenges such as those highlighted by Stewart force us to reconsider how we contend with the complexity of the fragmentary archaeological record. The ecological approach guides and makes archaeological inquiry and data selection more efficient. However, case studies such as Stewart’s highlight how growing empirical data on the regional scale must be incorporated to refine the way that we develop models for human-environment interaction and longterm human ecology. In Chap. 8, Myrtle P. Shock reinforces this key take-home message from a very different ecosystem than that of Stewart. Shock focuses on the role of interfluvial occupations in the pre-Columbian history of the Amazon basin. The interfluvial regions have received much less research focus than the larger tributaries of the Amazon, and have therefore been interpreted as a cultural periphery for pre-­ Columbian populations. Shock highlights how this locational bias has also tended to overlook the ecological diversity of different regions within the Amazon. Archaeological excavations from the Pardo River valley are presented that enable a reconsideration of interfluvial regions as mere peripheries. The data show how the interfluvial areas were not empty terrain, but were rather populated with sites every 4 km with evidence for occupations spanning millennia. Shock shows how these sites and their ‘anthropogenic dark earth’ soils were related to larger sites on larger floodplains, and were formed slightly later than the larger sites. However, the evidence she presents suggests the lack of a clearly hierarchical state based organization of core and marginal periphery that was proposed by earlier research. Evidence for anthropogenic dark earths indicate how populations in these regions were every bit as capable of committing their human agency to enhance local environments to make them grow and sustain their populations through time. Shock suggests a similar niche construction ability of human populations as noted by McClure and Kennett with the following comment: If we accept humans as transformative to the vegetation, the interfluvial region should not be seen as an economic periphery in regards to plant food resources. There is less

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availability of riverine resources such as fish, caiman, dolphins, and turtles, due to the nature of the rivers. This is not an insurmountable problem to sedentary life, and there is no indication that game animals would be less plentiful. The interfluvial environment could support people as early as anywhere else in the basin and would not inhibit sedentism. Environmentally the interfluvial region would only be a periphery if perceptions of the necessity of carbohydrate based diets, from annual species exist. (Shock, Chapter 8, p. 179)

Together, both Stewart and Shock provide important lessons for the advancement of the ecological approach in archaeology. This work illustrates how applications of the ecological approach over the past decade have provided new data that necessitates a loosening of the traditional rigidity of assumptions about the key environmental parameters that constrain the development of cultural landscapes and the evolution of long-term human ecologies. The growth of these data and the regional-­ based knowledge they generate can in the future be employed in new method advancements like that produced here by Kvamme to better understand both the social and the environmental parameters behind the millennial-scale development of cultural landscapes in different regions of the world. This ability to link the various aspects of a cultural landscape together to study long-term evolution in human ecology reflects a direct development from the pioneering foundations laid by Mike Jochim.

1.3 Scale Dependence of Archaeological Theory, Method, and Data Many of the advances in the ecological approach highlighted in the previous section center around the take-home message that theory, methods, and data are dependent on the specific scale of analysis undertaken by a project. This is a direct outgrowth of two major advances made by Mike Jochim: (1) his early theory development that tried to understand processes over large spatial and temporal scales from a fragmentary archaeological record (Jochim, 1976, 1981); (2) the realization that the scales of archaeological inquiry are different than those of ethnoarchaeology (Jochim, 1991). The massive increases in archaeological data over the past decades, obtained by projects with vastly different scales of analysis, has enforced the need for clarity in relating theory and method to the specific scales of the data and question(s) of interest. Recognition of the complexity of this mass of data must be properly organized and directly related to the question of interest. The questions we ask must be properly tuned to the realities of the data at hand and the methods we use to measure those data. As archaeology becomes an increasingly quantitative profession in order to make sense of this mass of data, we must not forget the following statement from Jochim, which we believe is possibly more relevant to the present than any of his other prescient contributions: Broadly speaking, although a hierarchy of relationships might often appear to approximate reality, when investigating any particular aspect of any specific group of people, the com-

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E. Robinson et al. plexity of relationships has to be considered. Any aspect of culture—a particular ritual, the selection of marriage partners, or the method of farming—may be related directly to some features of the environment. We should be prepared to observe interactions between any pair of components in our systems, and we should expect that these interactions will be complicated with feedback and multiple implications arising from a single transaction. We must define and subdivide these components into variables that we can observe and measure. The resulting picture of ecological anthropology is rather formidable. One has visions of human societies being reduced to flow diagrams of boxes and arrows and then, as a final insult, of these diagrams becoming illegible in the inky tangle of their ever increasing and crisscrossing arrows. We may rest assured that mathematics and flowcharts have not yet taken over. Humans are still the central focus of study. Reductionism in the name of quantification has not displaced a recognition of the complex fuzziness of human behavior. Any flowcharts must still be designed with a specific focus in mind, a topic of interest to the investigator. As long as questions and ideas must proceed measurement, the central humanity of our science will persist. (Jochim, 1981: 7-8)

Nowhere is the importance of scale dependence between theory, data, and measurement more apparent than it is in Chap. 4 by Jelmer W.  Eerkens and Eric J. Bartelink. Eerkens and Bartelink employ method and instrumental innovations in stable isotope analysis to find a middle ground between the short-term, fineresolution data obtained from ethnography and ethnoarchaeology, and the longterm ethnography provided by archaeological data. They focus on two case studies from Central California to investigate the isotopic biographies of human skeletal remains, which facilitates analyses of the life histories of these populations at both the long scale of the human lifetime and the short scale of the early years of life. The human lifetime scale is assessed by using strontium isotopes (87SR/86SR) in teeth to study migration behaviors of men and women. These analyses allow Eerkens and Bartelink to propose new hypotheses about patrilocal residence patterns interspersed with bride services during the marriage years of life. The short scale of the first decade of life is assessed by incrementally sampling individual teeth of men and women to conduct carbon (δ13C) and nitrogen (δ15N) analyses to study changes in diet over time and different stages of weaning between different populations. These analyses enable the development of a hypothesis for differences in weaning behaviors and parental investment in children between populations. Such fine-scale analyses have the potential to expand archaeological inquiry. They allow for a perspective that provides a direct assessment of human behaviors, as compared to assessing behavior via the indirect proxy of faunal materials found on sites. This builds an extra line of evidence to complement the indirect proxies that formed the foundation of the ecological approach that Mike Jochim helped pioneer. Two important take-home messages implicitly arise from Eerkens and Bartelink’s chapter that are important to the scalar dependence of theory, method, and data in the future advancement of the ecological approach in archaeology. First, the fine scale isobiographic approach to the archaeological record that they present will only be possible in regions where archaeologists are allowed to study human skeletal

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collections in such a manner. In many regions of the world, such as the Americas, Indigenous populations are increasingly standing up against the analysis of burial assemblages. This means that consultation with local populations will be paramount during the earliest phases of research design before such analyses can proceed. The advance of the ecological approach in archaeology will depend on increasing collaboration with local Indigenous populations so we can ensure that our work is being conducted in a respectful and culturally competent manner. Second, such fine-grained, high resolution analyses will only be possible from regions where populations interred their dead and where sedimentary conditions enable the preservation of bone. This second point is especially relevant, as much of the northern hemisphere is comprised of acidic soils that do not permit this kind of preservation. The last four chapters of the volume highlight cultural landscapes where bone preservation is limited, either via past behaviors or taphonomic factors. The final three chapters return us to the actual regions that Mike Jochim worked in throughout his four decade plus career. Scale, theory, methods, and data come to the fore in these final chapters. They are included as the final chapters for these reasons, as well as for the fact that they highlight the enormous long-term contributions that Jochim made to archaeological knowledge in his case study area. Each of these three chapters represent the originality of Jochim’s research program, and how, as suggested at the introduction of this chapter, the theory and methods he developed will become increasingly important as the archaeology profession increasingly shifts toward landscape-scale approaches to managing the archaeological record. All three chapters present landscape-scale approaches to the entire archaeological record for the same region, broken down into multi-millennial time-­ periods comprised of the dominant transitions from hunting-gathering, to farming, to metallurgy. They are a testament to the benefits of considering landscape-scales over long periods of time, inclusive of the often continuous surface scatters that comprise the largest component of the available archaeological record, alongside the best and most well-excavated sites. In Chap. 10, Susan K.  Harris develops an innovative method for determining time from surface scatter assemblages of lithic assemblages. This study focuses on 20 years of data collected by the Southwest German Archaeological Survey Project that was started and led by Mike Jochim in 1992. This project arose on the heels of his “Archaeology as long-term ethnography” paper (Jochim, 1991) that has been cited numerous times throughout this chapter (and volume). Harris’ chapter opens by discussing the original inspiration of the project, which was based in processual archaeology’s interest in human-environment interactions and seasonal settlement patterns that connected humans to different features of their environments, and different sites to each other across large landscape-scales. Most of these landscapes had been previously disturbed through millennia of plowing, which limited the amount of sub-surface assemblages to excavate and provide finer detail to the archaeological record. The sub-surface sites provide the fine chronological details of human occupation of the landscape. However, if they are limited, archaeologists must rely on surface assemblages to determine the long-term history of human

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occupation of the region. Controlling for time from surface assemblages is therefore limited to diagnostic artifacts, in this case microlithic armatures/projectile points. However, most of these types occur in limited frequencies, and therefore provide small sample sizes that could be heavily biased due to a host of different taphonomic and avocational collection histories. Furthermore, most other tool types were used across multiple time periods, and were therefore limited in their ability to control for time. Harris uses discriminant function analysis of assemblages from different well-dated Mesolithic period sites to determine a set of statistically significant technological diagnostics that can then be applied to the dating of surface scatter sites. This method provides an innovative way of overcoming one of the central problems with obtaining useful information from the surface sites that form the bulk of the archaeological record in most regions of the world. In these regions, surface sites provide the most representative sample of long-term human ecology at the landscape scale. Harris’ new method can be exported to other regions of the world; the Great Basin of North America is a good example, where Indigenous populations are increasingly opposed to new excavations, and where the dominant surface record can be assessed in relationship to previously excavated high resolution assemblages that can be re-assessed to provide higher precision Bayesian chronologies and finer-scale information on technological diagnostics. One of the major contributions of the landscape-scale approach that Mike Jochim helped to pioneer was that it focused not only on the long-term ecology of hunter-­ gatherer landscapes, but also how these histories of hunter-gatherer ecology set the stage for the adoption and development of farming. This approach provides the baseline for understanding the feedback relationships that constrained future decisions, similar to those discussed in earlier chapters by Bamforth, McClure and Kennett, and Robinson and Freeman. In Chap. 11, Lynn E. Fisher and colleagues focus on the role of surface scatter surveys in our understanding of the development of Neolithic farming landscapes. Surface scatter sites are important because they fill critical gaps in knowledge of this development. This is because “understandings of Neolithic cultural landscapes in southern Germany have been shaped by ideas about the role and significance of empty space between excavated settlement sites and by temporal discontinuities within clusters of such sites” (Fisher et al., Chap. 11, p.__). Fisher et al. seek to understand how farming moved from the Danube valley into the lake and bog ecologies of the Alpine foreland through the course of the Neolithic. They highlight the important contribution made by the surface archaeological record to assess land use beyond the excavated settlement sites, therefore creating a deeper knowledge of the development of the Neolithic by filling in the gaps between the excavated sites. Like Harris in Chap. 10, their study analyses data obtained over two decades by the Southwest German Archaeological Survey Project developed by Mike Jochim. This survey data is filled in by their own German-American regional landscape archaeology project in the Swabian Alb, which complemented the surface survey records with geomagnetic surveys, systematic soil coring, and test excavations in order to understand how the sub-surface record relates to the surface record. They note how these comparisons of the surface to the sub-surface records can help better

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understand the biases in existing regional databases. The surface records from Upper Swabia and the Swabian Alb are compared. These records show contrasting use of landscapes through the course of the Neolithic. The Upper Swabian sites such as those around Lake Federsee show more abundant finds from earlier periods of the Final Paleolithic-Mesolithic, whereas the Swabian Alb reveals a more abundant Neolithic record compared to earlier time periods. The abundant Swabian Alb records are related to large lithic quarry landscapes, which reveal continuous usage throughout the Neolithic. Results suggest that future research focus on the mapping of individual artifact types across the landscape in order to differentiate different activities zones across the landscape. This is another area where the GIS method proposed by Kvamme in Chap. 3 could be employed to better understand the social and natural parameters determining site use. Likewise, the statistical methods developed by Harris in Chap. 10 to delineate chronological diagnostics from different lithic technology trends can be employed to provide a finer chronology for these different surface sites. On the whole, Fisher et al. conclude with similar results to those of Shock in Chap. 8 in a very different region: if we solely focus on the areas where we expect the most suitable environments for farmers and do not assess the landscape scale that connects different ecosystem contexts together, then we are left with a biased and incomplete knowledge of the development of farming societies. It is the entire landscape scale that is critical to enhancing our knowledge of the evolution of long-term human ecologies in different regions of the world. The importance of the wider landscape scale for understanding the evolution of human ecologies in specific ecosystems is brought to full fruition in the final chapter of the volume by Helmut Schlichtherle. Schlichtherle focuses on the development of Neolithic and Bronze Age settlements in the bog ecologies of the Federsee Basin of Upper Swabia in southwest Germany. This is a region that has had archaeological investigations since 1875. However, Schlichtherle notes knowledge gaps in the region until after the 1980s, when the broader landscape scale was increasingly brought into focus. This research enabled different types of settlements to be identified and studied more intensively. With a broader and more robust knowledge base of the variability of Neolithic and Bronze Age occupations in this region, Schlichtherle focuses on relationships between demographic, social, and economic changes, and their connections to climatic changes and phases of transgressions around Lake Federsee. Due to the rare preservation contexts of waterlogged bog sites, one would think that these are all that needed to be focused on. However, it is the surface finds from the wider landscape that fill the gaps to complete the entire picture. The ability to break down the longterm evolution of human ecology and its relationship to environmental changes displayed in Figs. 12.3, 12.5, and 12.6 provide a model for all future archaeological research to aim for. As Kvamme noted in Chap. 3, earlier criticisms about the lack of consideration for social environments in GIS work failed to consider the fact that knowledge of social environments takes decades of focused research. Schlichtherle’s figures display this perfectly. It took over a century of research that was broad, flexible, and open-minded toward the entire archaeological record, not mere subsets, to understand how human-­environment interactions evolved and developed a unique ecology for this particular region. Similar to Robinson and Freeman in Chap. 9,

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Schlichtherle notes the importance of focusing not on global-scale climate events, but rather how local environments and the specific components of the natural system that were essential for sustaining human populations responded to those climate events. Importantly, Schlichtherle highlights how the evolution of Neolithic and Bronze Age settlements were likely more related to its relative suitability for hunting potential and as a strategic position between different larger population zones, than to the suitability for cereal farming and domestic animals. This is an important lesson for studies on the evolution of farming and metallurgy: just because economies have changed, it does not mean that other components of the wider natural or social environment were not important determinants of these social-ecological systems. Schlichtherle closes with another lesson that can be exported to the study of other archaeological landscapes around the world: “In order to explain the processes of settlement in this wetland area, different scenarios have to be taken into consideration and different models have to be developed to trace back its evolution” (Schlichtherle, Chap. 12, p.__). It is this broad and open-minded, model-based and hypothesis testing approach to long-term patterns in archaeological landscapes and human ecology that we can thank Mike Jochim for.

1.4 The Future of Archaeology As we have tried to highlight throughout this chapter, and as the subsequent chapters testify, the future of archaeology is one that Mike Jochim forecast throughout his career, beginning in 1976. The future is one in which we take the environment seriously, but also acknowledge that it does not determine what past people did, because individuals create their landscape within a social and symbolic context. This sentiment is felt in recent reviews by researchers that span diverse theoretical perspectives, ranging from the ecological (e.g., Codding & Bird, 2015) to the phenomenological (e.g., Thomas, 2015). While actually implementing such a synthetic approach is not straightforward, Jochim again provides a path forward. In his recent paper, “Dots on the Map: Issues in the Archaeological Analysis of Site Locations”, Jochim (2022) notes that something as simple as “[t]he placement of sites can be informative about subsistence activities, economic organization and interaction, social relationships, and political structure, as well as about specific topics such as colonization and economic and political change” (Jochim, 2022). He illustrates how understanding these ecological, social, and political dynamics requires “information (or assumptions) about subsistence needs, access to routes of interaction for trade or other reasons, and landscape features that are socially or religiously meaningful,” which we can leverage in a “decision tree” approach to learn how past people may have viewed the landscape where they made a living, built communities, and created meaning. Approaching the archaeological record within a framework such as this will contribute to prescient questions today. For example, understanding how landscape decisions vary with environmental and social factors will help expand the representation of cultural diversity in studies of climate adaptation (e.g., Burke et al., 2021),

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especially if such work builds mutual partnerships with descendent communities to identify meaningful inputs and culturally appropriate interpretations (e.g., Douglass et al., 2019; Douglass & Cooper, 2020). Throughout his career, Mike Jochim has been ahead of his time. His clear thinking about complex problems continues to guide how many of us approach the archaeological record. As illustrated by this volume, his far reaching impact spans generations of archaeologists.

References Altschul, J.  H., & Klein, T.  H. (2022). Forecast for the US CRM industry and job market, 2022–2031. Advances in Archaeological Practice, 10(4), 1–16. Ammerman, A.  J. (1977). Review of figuring anthropology: First principles of probability and statistics, by David Hurst Thomas. American Antiquity, 79, 456. Binford, L. R. (1978). Review of hunter-gatherer subsistence and settlement: A predictive model, by Michael A. Jochim. American Antiquity, 43(1), 137–138. Burke, A., Peros, M. C., Wren, C. D., Pausata, F. S., Riel-Salvatore, J., Moine, O., et al. (2021). The archaeology of climate change: The case for cultural diversity. Proceedings of the National Academy of Sciences, 118(30), e2108537118. Chagnon, N.  A., & Irons, W.  G. (1979). Evolutionary biology and human social behavior: An anthropological perspective. Duxbury Press. Charnov, E.  L. (1976). Optimal foraging: The marginal value theorem. Theoretical Population Biology, 9, 129–136. Clark, G. A. (1978). Review of spatial analysis in archaeology, by Ian Hodder and Clive Orton. American Antiquity, 43(1), 132–135. CLIMAP Project Members. (1976). The surface of ice-age earth. Science, 191(4232), 1131–1137. Codding, B. F., & Bird, D. W. (2015). Behavioral ecology and the future of archaeological science. Journal of Archaeological Science, 56, 9–20. Cowie, S.  E., Teeman, D.  L., & LeBlanc, C.  C. (2019). Collaborative archaeology at Stewart Indian school. University of Nebraska Press. Douglass, K., & Cooper, J. (2020). Archaeology, environmental justice, and climate change on islands of the Caribbean and southwestern Indian Ocean. Proceedings of the National Academy of Sciences, 117(15), 8254–8262. Douglass, K., Morales, E.  Q., Manahira, G., Fenomanana, F., Samba, R., Lahiniriko, F., et  al. (2019). Toward a just and inclusive environmental archaeology of Southwest Madagascar. Journal of Social Archaeology, 19(3), 307–332. Fretwell, S. D., & Lucas, H. L. (1969). On territorial behavior and other factors influencing habitat distribution in birds. I. Theoretical development. Acta Biotheoretica, 19, 16–36. Hodder, I. (1977). The distribution of material culture items in the Baringo District, Western Kenya. Man, 12(2), 239–269. Hodder, I. (2018). Big history and a post-truth archaeology? The SAA Archaeological Record, 18(5), 43–45. Hodder, I., & Orton, C. (1976). Spatial analysis in archaeology. Cambridge University Press. Jennings, J. D. (1985). River basin surveys: Origins, operations, and results, 1945–1969. American Antiquity, 50(2), 281–296. Jochim, M. A. (1976). Hunter-gatherer subsistence and settlement: A predictive model. Academic. Jochim, M.  A. (1981). Strategies for survival: Cultural behavior in an ecological context. Academic.

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Jochim, M.  A. (1983). Optimization models in context. In J.  A. Moore & A.  S. Keene (Eds.), Archaeological hammers and theories (pp. 157–172). Academic. Jochim, M. A. (1988). Optimal foraging and the division of labor. American Anthropologist, 90(1), 130–136. Jochim, M. A. (1989). Optimization and stone tool studies: Problems and potentials. In R. Torrence (Ed.), Time, energy and stone tools (pp. 106–111). Cambridge University Press. Jochim, M. A. (1991). Archaeology as long-term ethnography. American Anthropologist, 93(2), 308–321. Jochim, M. A. (1998). A hunter-gatherer landscape: Southwest Germany in the late Paleolithic and Mesolithic. Springer. Jochim, M. A. (2022). Dots on the map: Issues in the archaeological analysis of site locations. Journal of Archaeological Method and Theory. https://doi.org/10.1007/s10816-­022-­09580-­8 Kohler, T. A., & Rockman, R. (2020). The IPCC: A primer for archaeologists. American Antiquity, 85(4), 627–651. Mangerud, J., Anderson, S. T., Berglund, B. E., & Donner, J. J. (1974). Quaternary stratigraphy of Norden, a proposal for terminology and classification. Boreas, 3, 109–128. McGimsey, C. R., III. (1976). The past, the present, and the future: Public policy as a dynamic interface. In P. R. Sanday (Ed.), Anthropology and the public interest: Fieldwork and theory (pp. 25–28). Academic. Odling-Smee, F. J., Laland, K. N., & Feldman, M. W. (2003). Niche construction: The neglected process in evolution. Princeton University Press. Rasmussen, S.  O., Bigler, M., Blockley, S.  P., Blunier, R., Buchardt, S.  L., Clausen, H.  B., Cvijanovic, I., Dahl-Jensen, D., Johnsen, S. J., Fischer, H., Gkinis, V., Guillevic, M., Hoek, W. A., Lowe, J. J., Pedro, J. B., Popp, T., Seierstad, I. K., Steffensen, J. P., Svensson, A. M., Vallelonga, P., Vinther, B.  M., Walker, M.  J. C., Wheatley, J.  J., & Winstrup, M. (2014). A stratigraphic framework for abrupt climate changes during the last glacial period based on three synchronized Greenland ice-cores: Refining and extending the INTIMATE event stratigraphy. Quaternary Science Reviews, 106, 14–28. Robinson, E., & Riede, F. (2018). Cultural and paleoenvironmental changes in late glacial to middle Holocene Europe: Gradual or sudden? Quaternary International, 465, 159–161. Schneider, T. D., & Hayes, K. (2020). Epistemic colonialism: Is it possible to decolonize archaeology? The American Indian Quarterly, 44(2), 127–148. Thomas, J. (2015). The future of archaeological theory. Antiquity, 89(348), 1287–1296. Vadrot, A., Jetzkowitz, J., & Stringer, L. C. (2016). IPBES disciplinary gaps still gaping. Nature, 530, 160. Walker, M., Head, M.  J., Lowe, J., Berkelhammer, M., Bjorck, S., Cheng, H., Cwynar, L.  C., Fisher, D., Gkinis, V., Long, A., & Newnham, R. (2019). Subdividing the Holocene series/ epoch: Formalization of stages/ages and subseries/subepochs, and designation of GSSPs, and auxiliary stratotypes. Journal of Quaternary Science, 34(3), 173–186.

Chapter 2

Models, Foragers, Human Beings, and a Hunter-Gatherer Career Douglas B. Bamforth

Abstract  Mike Jochim has been foundational to thinking systematically about the archaeological study of hunters and gatherers. This paper briefly reviews the diversity of approaches to this topic at the time he entered the field and discusses the approach to foraging that has become dominant since the 1980s. I argue that Mike’s work differs from this dominant approach in important ways, anticipates underappreciated issues that are increasingly salient outside of anthropology/archaeology, and points the way our field should move in the future. Keywords  Hunters and gatherers · Optimization theory · Behavioral ecology · Behavioral economics · Michael Jochim

My Approach to Archaeological Landscapes In art, a “landscape” is an image of a part of the natural world depicted from a particular location; many landscapes in this sense depict both the features of the natural world and the human imprint on them. That is, what constitutes an artistic landscape depends on who is looking and when they are looking, on the cultural and historical lens a viewer sees the natural world through. Archaeological landscapes reflect this along with other forces: relevant features of such landscapes differ for hunter-gatherers and farmers, for example. Critically, though, surface-visible architecture and other easily discernable features seduce many archaeologists into neglecting the “archaeological” piece of this: natural processes continuously obscure and destroy evidence of human interaction with the natural world that helps to define what a landscape was in the past.

D. B. Bamforth (*) Department of Anthropology, University of Colorado Boulder, Boulder, CO, USA e-mail: [email protected] © Springer Nature Switzerland AG 2023 E. Robinson et al. (eds.), Cultural Landscapes and Long-Term Human Ecology, Interdisciplinary Contributions to Archaeology, https://doi.org/10.1007/978-3-031-49699-8_2

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2.1 Introduction Mike Jochim has influenced our field in many, many ways, both theoretically and substantively, and this volume celebrates his career. My goal here is to consider how important aspects of his conceptual/theoretical approach to our field anticipate modern critiques of the general theoretical framework that has come to dominate huntergatherer archaeology. I begin by briefly sketching the range of archaeological approaches to hunters and gatherers at the time when Mike began his work and then turn to a more extended discussion of neglected aspects of the now-dominant approach that his work foreshadows. I close by discussing Mike’s work in light of these issues. I do not emphasize the specifics of his important substantive contributions either to the study of hunters and gatherers or to European archaeology, but I acknowledge them here.

2.2 Thinking About Hunters and Gatherers Mike did his graduate work in the 1970s, just after two important events. The first was the publication of Lee and DeVore’s (1968) Man the Hunter volume. Anthropology paid far more attention to Lee’s own Bushman research than to the many other societies that this volume highlighted, but the volume as a whole dramatically increased the visibility of hunter-gatherer research in anthropology in general and Lee’s arguments about the Bushmen offered a revolutionary view of foraging people. The second was the publication of MacArthur and Pianka’s (1966) and Emlen’s (1966) papers in ecology, which were among the best-known work that set the stage for the development of formal models of foraging behavior. Archaeologists interested in hunters and gatherers at that time emphasized the interactions between people and the landscapes they lived on but approached this common interest in a wide variety of ways. Some drew on Lee’s Bushman research, more or less taking many aspects of his work as a description of the basic structure of all hunter-gatherer ways of life (i.e., Hester & Grady, 1977). Others drew on ethnographic information from the specific areas where they worked (Bettinger, 1977; Thomas, 1973). Slightly later, many archaeologists relied heavily on Binford’s (1980) analysis of structural differences in human-environment interactions among ethnographically-known groups. These approaches are diverse and have different strengths and weaknesses. However, they all have in common a basic reliance on observations of the things that real people like the ones highlighted in Man the Hunter really do. Theory in studies like these largely followed from observation. In contrast, a second group of archaeologists approached human-environment interactions among hunter-gatherers on the basis of more abstract generalizations, like those that Emlen and MacArthur and Pianka developed (for example, Wilmsen, 1973 and many authors in Winterhalder & Smith, 1981). These studies began with theory and moved from that to observation.

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Since the 1990s, this latter approach has come to dominate hunter-gatherer research. In archaeology, work like this has focused and continues to focus largely on subsistence (foraging). In contrast, general anthropological work founded in this kind of framework increasingly emphasizes reproduction, under the label of Human Behavioral Ecology (HBE) (Bettinger et al., 2015:117). Archaeological work in this area draws fundamentally on the concepts that MacArthur and Pianka and Emlen pioneered in the late 1960s, building on a variety of formal models developed initially in biology and grouped together under the label of Optimal Foraging Theory (OFT). Winterhalder and Smith (1981) and Smith and Winterhalder (1992) provide early overviews of this approach to thinking about human beings; Bettinger et al. (2015), Bettinger (2009), Codding and Bird (2015), and Winterhalder and Smith (2000) do the same more recently. Kelly (2013) synthesizes the insights HBE offers into hunter-gatherer lives. I focus here on foraging and on aspects of OFT that have received limited attention in the anthropological/archaeological literature and that underscore the distinctiveness of Jochim’s approach to studying hunters and gatherers.

2.3 Thinking About Optimal Foraging Models OFT developed in biology as a way of circumventing the difficulties of studying natural selection, particularly genetic change, in natural populations and of bridging between genotypic and phenotypic approaches to looking at evolutionary process and outcome (Levins, 1966; Lloyd, 1977). It did this by borrowing mathematical models from economics and engineering and using these models to specify ways in which an organism could maximize the value of an output variable given a specified set of constraints (MacArthur & Pianka, 1966; Maynard Smith, 1978). In line with Emlen’s assertion (1966:611) that “the value of a food to an animal is basically determined by its caloric yield per time to that animal”, foraging theorists have overwhelmingly focused on energetic efficiency as their output variable, measured as calories returned from time or calories invested (Bettinger et al., 2015:92). OFT theorists justify this on the basis of the “phenotypic gambit”, “examin[ing] the evolutionary basis of a character as if the very simplest genetic system controlled it” on the grounds that “changes in allele frequencies have made animals good at what they do” (Grafen, 1984:62, 63). The phenotypic gambit allows researchers to focus on behavior (for my purposes here, foraging behavior) without worrying about how that behavior developed and especially without worrying about the genetics underlying that behavior (but see Hadfield et al., 2007, Rittschof & Robinson, 2014, Roff, 2007, and Rubin, 2016 on recent interest in genetics in behavioral ecology). In this sense, optimization theory relies on a kind of evolutionary black box, taking the effects of selection for granted and looking mainly at how organisms respond to the world around them (cf. Kacelnik & Krebs, 1997:22).

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Under the phenotypic gambit, optimization models drive research by generating testable, quantitative expectations that we can compare to data on the things that organisms do: which resources do we take and which do we pass by, or how long do we search for food in a given area? OFT theorists are explicit that their goal is to identify similarities and differences between expectation and evidence in order to provide the basis for generating more realistic and accurate models. In biology, Maynard Smith (1978:52) argued that “the role of optimization theories in biology is not to demonstrate that organisms optimize. Rather, they are an attempt to understand the diversity of life” (also see Kacelnik, 1993). Winterhalder (1986:313) argued similarly that optimization models are “more interesting to stalk than to live by”. As Codding and Bird (2015:10) put it, “HBE models are research tools, not essentialist rules of human behavior or descriptions of observed phenomena” (also see Bettinger et al., 2015:139). Ongoing debate over men’s hunting exemplifies this (Bird, 1999; Gurven & Hill, 2010; Hawkes, 1991; Stibberd-Hawkes, 2018). In a nutshell, this debate wonders whether men hunt to supply their families and communities with food or to attain social status that increases their reproductive opportunities. The heart of this debate turns on whether or not hunting, and especially large-game hunting, is energetically efficient and thus consistent with optimization theory (I discuss the archaeological version of this debate below). However, the way researchers assess similarities and differences between expectation and observation and what they see as possible causes of these similarities and differences have been points of contention from OFT’s earliest days (Lewontin, 1979; Maynard Smith, 1978). Evolutionary biologists have long observed that OFT researchers can be selective about what constitutes a discrepancy between evidence and expectation and which of the assumptions underlying their models they examine in response to such a discrepancy (i.e., Pyke, 1984:525–526). OFT researchers do sometimes tinker with the details of their models in the face of discrepant evidence, often arguing that they need more complex optimization models or that they should look at variables other than calories (perhaps other nutrients). But they almost never critique the underlying framework that supports those models and they often simply dismiss observable deviations between evidence and expectation. Bettinger et  al. (2015):139) make this clear: “historically, where optimal models have been applied to real cases … , the fit between expected and observed behavior has been close enough to satisfy advocates and loose enough to encourage critics”. This is not unique to anthropology. Perry and Pianka (1997:360) observed a similarly ambiguous pattern in the literature on non-human foraging and Giraudeau (2011:911) asserts that “qualitative support, but quantitative failure, has been the hallmark of almost every test of optimal foraging models in behavioral ecology” (Giraudeau’s observation is not new—see Maynard Smith, 1978:52). Much the same is true of optimization theory in economics. In that field, empirical studies that seem to support the assumption that humans optimize often do so because those studies are imprecise in the sense that they examine the direction in which behavior should change rather than the magnitude of that change (Thaler, 2015:8). That is, scholars in a variety of disciplines stress that optimization models are more instrumental than explanatory but in practice do exactly what Bettinger and others

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describe (see Cassini et al., 1993 for a biological example of dismissing partially contrary results; Thaler, 2015, 2016 discusses this in detail in the context of economics, referring to these kinds of arguments as “explainawaytions”). Assessing evidence in this way is neither new nor confined to optimization theorists. It is a textbook example of confirmation bias, or, more generously, of the effects of what Chamberlain (1890; reprinted 1965: 755) referred to as “parental affections” for a “ruling theory”. Such affection cluster[s] about [our] intellectual offspring, and it grows more and more dear to [us], so that, while [we hold] it seemingly tentative, it is still lovingly tentative, and not impartially tentative. So soon as this parental affection takes possession of the mind, there is a rapid passage to the adoption of the theory. There is an unconscious selection and magnifying of the phenomena that fall into harmony with the theory and support it, and an unconscious neglect of those that fail of coincidence.

Deep parental affection often directs the way OFT researchers deal with their evidence in much the same way that blinders direct a horse, by simultaneously guiding its gaze to the road ahead and preventing it from seeing to the side. In the specific context that matters here, the conceptual underpinnings of optimization theory act as blinders limiting the ways in which foraging theorists deal with deviations from expectation. All of us wear disciplinary blinders of one kind or another—OFT blinders take a distinctive form but do not compromise that body of theory any more than analogous blinders compromise any other body of theory. But considering some of the distinctive forms that OFT blinders take and some of the distinctive ways they shape research leads us back to Jochim’s approach to hunters and gatherers. I focus on hunting, the domain of behavior that Jochim emphasized, and I emphasize three issues. These are the assumption that organisms optimize, the general currencies driving the choices hunters make (particularly the likelihood that those currencies have changed over time and vary in space in predictable ways), and the differences between the geographic scales at which ancient foragers lived and those at which modern foraging theorists think.

2.3.1 Does Anyone Optimize Ever? Our goal was to investigate whether consumers indeed follow the maxim of ‘more information is better’ when it comes to food choice. If this were the case, our sample of participants would have engaged in an encompassing search for information, which would then have been integrated into overall evaluations of the available options. We found the opposite (Schulte-Mecklenbeck et al., 2013: 348–349). Let’s be blunt. The model of human behavior based on the premise that people optimize is and has always been highly implausible (Thaler, 2016:3).

The phenotypic gambit as OFT uses it assumes that evolution designed organisms to reason “optimally” or, at least, to act as if we reason that way. Foraging theorists rarely critique this assumption. Did evolution do this?

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Answering this question depends in part on what we mean by “optimal”. In the general sense that we balance multiple and sometimes conflicting costs and benefits when we make choices, it seems a truism to say that we do. But turning to that general meaning of the word to answer this question in the context of OFT is a kind of semantic sleight-of-hand that evades the question at issue: that is not what optimization means in OFT. Thaler (2015:5) refers to the economic definition as “constrained optimization, or “choosing the best from a limited budget”. Economists define this budget in terms of money and foraging theorists define it in terms of calories: optimal solutions to foraging problems are those that “maximize the net rate of energy gain” (Bettinger et  al., 2015:92). In Price and Jones’ (2020:1) words, this view holds “that agents order preferences according to average utilities associated with different choices”. However, detailed research in a number of fields focused specifically on how humans and other creatures actually make decisions about how to balance budgets like these does not offer much support for the argument that we optimize in this specific sense of the word (Baron, 2014; Furnham & Boo, 2011; Gigerenzer & Brighton, 2009; Glockner & Witteman, 2010; Kahneman & Frederick, 2002, 2005; Thaler, 2015, 2016). Empirically, we do not reason the way this narrow view of “optimization” says we should. The outcomes of our reasoning are also inconsistent with the fall-back argument that we reason “as if” we are optimal decision-­makers in this sense. A broader stream of research strives to look outside of the kinds of blinders HBE has held so closely in practice, to see how organisms really make choices and to search for structure in those choices. For example, studies across a number of species including humans identify decisions based on the “sunk cost fallacy” (throwing good money after bad, or continuing to invest in something on the basis of past investments rather than on the basis of expectations for the future; Haller & Schwabe, 2014; Weatherhead, 1979; the discrepant data that Cassini et al., 1993 dismiss are consistent with this). Sweis et al. (2018) document the sunk cost fallacy in rigorous detail in humans, rats, and mice; their results strongly suggest that organisms use very different kinds of deliberative processes under different conditions. That species as diverse as mice, rats, sparrows, guinea pigs, and people act this way (for example, by spending too much time in resource patches while foraging) suggests that this is the kind of evolved trait that optimal reasoning is supposed to be, perhaps linked to intrinsic limits on organisms’ abilities to predict the future or to choices made by weighing factors other than just average return rates (perhaps including travel costs or the risk of predation). Human beings also do not assess the prospect of a loss in the same way that we assess the prospect of a gain, even when the expected outcomes of those losses and gains are exactly the same (Kahneman & Tversky, 1979; Thaler, 2015, 2016). This is a habit with critical implications for foraging behavior. An Inuit hunter on the icepack in winter might perceive the potential escape of a seal through the lens of starvation, seeing a loss; a coastal Californian hunter might perceive such an escape through the lens of a diet that also included deer, acorns, shellfish, and other marine resources and might view seal hunting as offering the possibility of a gain. Indeed, Inuit and Californian harpoon designs show very different degrees of labor

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investment that may be consistent with this (Bamforth & Bleed, 1997). Real human decision-­making appears to rely on simple and intuitive heuristic decision rules that often produce results that are systematically statistically biased relative to the expectations of the standard view of “optimization”. “An example is the ‘availability heuristic’ in which people estimate the frequency of some event by the ease with which they can recall instances of that event” (Thaler, 2016:4). We seem to use more complex kinds of analytical reasoning sparingly and in a very different way (AlosFerrer & Strack, 2013; Baron, 2014; Glockner & Witteman, 2010; Kahneman, 2003). I stress the word “systematically”. These outcomes do not imply that organisms choose irrationally. When researchers look at human decisions carefully, striving to look outside their blinders, they do not find random deviations from idealized optimal solutions. Instead, they find patterned bias relative to those solutions. Perhaps most important, work that explicitly addresses the link between phenotypic (or somatic) success and evolutionary mechanisms instead of treating that link as a black box has emphasized that the need to cope with variable circumstances over the long term requires different choices than strategies that simply and naively maximize short-term energy gain (particularly see Price & Jones, 2020). Work like this tells us not that people do not optimize, but, rather, that the narrow and unrealistic view of “optimization” that dominates anthropology and, especially, archaeology limits our ability to think about how people really grapple with the decisions they have to make. Researchers increasingly explicitly consider real decision-making processes, contemplating issues like variation in external conditions, social mechanisms of information-gathering, limits on information processing abilities, heuristic decision rules, and context-dependent aspects of decision making, all of which alter both the structure of theoretical models and their output (i.e., Davidson & El Hady, 2018; Fawcett et  al., 2012; Frankenhuis et  al., 2019; Hutchinson & Gigerenzer, 2005; Kacelnik & Todd, 1992; Price & Jones, 2020; Todd & Kacelnik, 1993; Vasconcelos et  al., 2015). Ollasson and Ren (2002) show that it is possible to build foraging models that do not incorporate assumptions about optimization and that mimic the outcome of optimization models in some cases and are consistent with empirically-­ observed deviations from optimal expectations in others (see Arrow (1986:S385– S386) for a related discussion in the context of economics). Thinking outside the traditional optimization box to build and test models based on the ways we really make choices is possible and can produce distinct models with predictions that better fit the real world.

2.3.2 Hunting in the Deeper Past and in Colder Places Regardless of the structure of the model we use, we need to specify a currency. Following from their history in studies of food procurement in animal ecology, OFT studies of human beings predict what we do on the basis of the balance between the energy we expend getting food and the energy that food provides us, and in virtually

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no other way. However, at the same time that this generates theoretical expectations that we can subject to empirical test, it fundamentally shapes the ways in which we think about deviations from those expectations. The long-running archaeological debate over the development of hunting in western North America illustrates this. Hildebrandt and McGuire (2002; McGuire and Hildebrandt 2005) argued that a variety of archaeological evidence indicates that hunting, often over long distances, increased significantly and was socially more salient in the American West between 4000 and 1000 years ago and that this violates the optimization principles underlying OFT. Instead of being driven by energetic efficiency, Hildebrandt and McGuire assert that this increase reflects male efforts to achieve higher social status by provisioning their communities and thus increasing their reproductive opportunities; most recently, they have linked this to the theory of “costly signaling” (see Gurven & Hill, 2009, 2010; Hawkes et  al., 2010 for the ethnographic version of this debate). Contributions to this debate have emphasized an array of topics, including the details of costly signaling theory and OFT, ethnographic studies of hunting, the energetic costs and benefits of large game hunting and hunting at a distance, and the practicalities of translating complex theoretical issues into archaeological data (Broughton & Bayham, 2003 Fisher, 2015; Grimstead, 2010; Hockett, 2005; Simms et al., 2014). This is exactly the way that long-term debate fashions strong archaeological inferences (Chapman & Wylie, 2016). However, the crux of the debate so far focuses entirely on the caloric costs and benefits of hunting and the possibility that men may achieve social/reproductive benefits by hunting in ways that are calorically inefficient. Bettinger and others capture this perspective precisely when they note that the “[r]ecognition that hunter-gatherers may be hunting (or gathering) for social reasons rather than strictly for sustenance has led to both innovative research and heated debates” (2015:120, emphasis added). In effect, this debate views hunted animals as direct analogues to packages of meat in modern grocery stores, albeit meat packaged in biodegradeable wrappers (Grimstead’s (2010:74) “protective skin surface” or Winterhalder and Smith’s (2000:57) “low-utility skin” that efficient hunters might prefer not to transport back to camp). This perspective fits the life experiences of modern academics, including foraging theorists, who minimize search and processing costs by procuring meat in grocery stores strictly for sustenance (but who often rely on surprising decision rules for choosing resources of many kinds; Prelec & Simester, 2001; Schulte-­ Mecklenbeck et al., 2013). It also roughly fits the life experiences of many recent hunter-gatherers, including O’Connell and Marshall’s (1989) Alyawarra men who hunt with firearms, travel in motor vehicles, dress in commercially-manufactured clothes, and roast kangaroos without skinning them. It fits much less well for ancient hunters. The hunters who archaeologists study depended on brain-tanned hide for clothing and containers and sometimes for housing and they needed different kinds of hides for these different purposes. Many of them hunted with sinew-strung bows, often made of layers of

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horn and wood or backed with sinew, in either case held together with animal hide glue, and they used bone for essential components of their technology. Hunters like these—including those in the American West between 4000 and 1000 years ago—likely viewed animals as fairly complex bundles of a variety of essential resources rather than as self-propelled and neatly-wrapped packages of calories. Seeing animals as bundles of different kinds of important resources rather than as packets of calories also opens the question of which components of those bundles have mattered in different times and places. On the Great Plains, tipis made from bison hides were essential to life; in tropical forests, winter housing and warm clothing matter less and access to animal skins may accordingly have been less important. Sharp and Sharp (2015: 174–189) stress this point: among recent Subarctic Denésuliné groups, “through the mid-1980s, when demand [for moose hides] began to drop because of changes in Denésuliné life style and the improvement of commercially available substitutes, the skin of a killed moose was valued more than its meat and it would be recovered before the meat was” (Sharp & Sharp, 2015:176–177). Where plant fibers can be used for cordage, sinew may similarly have been less valuable. As the Alyawarra case suggests, the modern hunter-gatherers studied in such meticulous detail by ethnographers are poor models for more ancient hunters because they rely on hunted animals for mainly food and not for other resources, which they get in one way or another from industrial economies. Pre-Colonial Australian hunters depended on kangaroo hides for clothing and presumably skinned the animals they killed before they roasted them (cf. Riley, 2016). To underscore this, all of Winterhalder and Smith’s (2000) illustrations of indigenous people depict individuals dressed either in woven cloth or in commercially manufactured clothing. The existing foraging literature now and then observes issues like these but rarely builds on them. For example, the costly signaling debate notes the importance of obtaining sea otter pelts in some areas of coastal California but relies on this observation more as a source of rhetorical barbs than as a means of advancing substantive analysis (i.e., Hildebrandt et al., 2010; Jones & Codding, 2010). One obvious way to address the complexity of the array of resources that animals offer is by building models that look at them as something more than just calories. There are techniques that make this possible, linear programming in particular (Keene, 1982 used this technique in an early application of foraging models and took account of the importance of hides as well as of a variety of nutrients). Bettinger (2009; also see Bettinger et  al., 2015:141–147) discusses linear programming, but, in keeping with recent foraging theory, he limits his example to food. However, this simply returns us to the previous section of this paper: linear programming is just a more complex kind of traditional optimization model, which is not necessarily a better way to try to understand the choices made by humans and other organisms who demonstrably do not optimize in the way those limited models say they should.

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2.3.3 We All Have Neighbors and They Have Good Stuff Finally, the ways in which archaeologists have contemplated human hunting (and other foraging) lead us to think of people in very local contexts. Archaeology always tempts us to view what my people did at my site as the most important evidence we have for understanding the past. We move easily from this to inferring geographically local answers to the problems we are interested in. This has not always been so, although we have not always thought well at larger geographic scales. Foraging theory came into anthropology and archaeology in the 1970s and 1980s with a more general shift towards locally-focused explanatory perspectives, at about the same time that it became clear that a lot of non-local archaeological thinking was deeply flawed (for example, we learned during the 1970’s that Mycenaean architects did not build Stonehenge (Renfrew, 1973)). However, it also came into our field alongside the Tasaday controversy and the Lee/Wilmsen debate (Headland, 1992; Kurtz, 1994). Whatever we may think of these controversies, they focus us on important processes that extend far beyond the local landscapes people move across from day to day and on the critical importance of interrelationships between local populations and their neighbors. In fact, processes and interrelationships like these were important far beyond the Colonial era and there is no reason why they cannot be considered within the general framework of HBE/foraging theory (optimal or otherwise). However, in practice, foraging theory has been essentially blind to the importance of human connections over large geographic scales and to the possible effects of these kinds of connections on hunter-gatherer lives in general and on foraging behavior in particular. Foraging models focus on the characteristics of resources in particular places on particular landscapes and on the habits and needs of the specific human groups on those landscapes, without regard to the possible importance of resources from the outside. Foraging theorists thus study the resources that specific groups harvest themselves, within whatever area they exploit, and foraging models address the ways in which people choose among those resources. When these theorists see change, they link it to climatically- or anthropogenically-driven changes that altered the abundance or distribution of local resources or to changes in human population sizes that altered the amount of food people had to extract locally (i.e., O'Connell et al., 1982; Weitzel, 2019; Winterhalder, 1986; Zeanah, 2017). But people do not only live locally. To take just one example, hunters-gatherers and horticulturalists over most of the western United States and Canada systematically produced surplus food for a network of exchange that tied communities together from the Pacific coast to the interior Great Plains and beyond (Griswold, 1970). Trade connections between the Columbia River basin and the northern Plains moved enormous quantities—literally tons—of dried fish and fish oil, seal and whale meat, berries, edible roots, bison meat, and agricultural products from community to community. Local communities also produced craft goods for exchange, including processed animal hides and goods manufactured from animal products (bows made from mountain sheep horn, bison hide shields, otter pelts, animal skin tents). Although we know this network

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best from written documents and ethnographic data (Griswold, 1970; Swagerty, 1988), there is no doubt that it existed centuries before European colonization (Bamforth, 2011; Boyd & Surette, 2010; Lints, 2012). Systematic production of surplus for trade has powerful implications for the organization of foraging and systematic trade among communities producing different kinds of surpluses implies that we often cannot understand foraging decisions only on the basis of local access to local resources (cf. Jochim, 1998:25). Expanding our geographic horizons with issues like this in mind is a significant step in grappling with the realities of past hunter-gatherer lives and this means thinking at geographic scales larger than the ones we have often thought at. Until fairly recently, though, hunter-gatherer archaeology rarely thought in terms of processes operating over huge areas. The analytic tools we have developed to make sense of hunter-­ gatherer archaeology, including OFT, have not encouraged us to do that. Hunter-gatherer bison hunting on the northern Great Plains exemplifies many of these issues. Plains hunter-gatherer communities had a use for every single part of a bison (although this does not mean they used every part of every bison that they killed; they manifestly did not). They ate bison meat, did many kinds of work with a variety of bison bones, harvested bison sinew, lived in bison-hide tipis, etc.. They also produced literally tons of pemmican and tanned thousands of bison hides to make into shields, robes, containers, and other implements for their neighbors. They did all of this for centuries. This variety of needs conditioned their hunting. To take only one example, tipis the size of the ones in use during the Colonial era required roughly 12 to 15 hides each and tipis the size of the ones used at some times in the past required as many as 30 (Reilly, 2015). Replacing a worn home or establishing a new household thus required immense amounts of labor, access to large numbers of hides, and likely the efforts of a large part of the community. Many factors, including caloric efficiency, make it best to hunt bison in the Fall, but bison hides are best for tipis in the Spring when they are thinnest: Fall hides are too thick to stitch together (Brink, 2008). Archaeological data leave no doubt that Plains groups made large kills in both seasons (Cooper, 2008). These groups surely did not ignore the meat available from large Spring kills or the hides from Fall kills, but we just as surely cannot address the ways in which they decided how to organize Spring hunts (or, for that matter, any other hunts) or estimate the handling costs or caloric returns of bison carcasses only on the basis of satisfying local nutritional needs as calorically efficiently as possible.

2.4 A Career Looking Outside of Our Blinders Foraging theory and the larger body of thought it grows from thus put easily identifiable blinders on their practitioners, although they do not do this any more problematically than any other body of thought. Our theoretical frameworks always simultaneously direct our attention and prevent us from seeing things; they are useful analytic tools and nothing more. Researchers in a variety of fields are increasingly reshaping

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economic and other theory and practice in light of an increasing understanding of real patterns of human decision-making. As I noted earlier, foraging theorists in biology are doing the same. There is no reason why anthropologists and archaeologists cannot do the same, including devising ways of addressing the importance of obtaining nonfood resources in foraging and of systematic overproduction for trade. If a now-passing generation of OFT researchers has not done this, a future generation will. As I noted, foraging theory’s approach is explicitly meant to guide research into new directions and at some point anthropologists and archaeologists will move their work beyond the intellectual blinders that the theory itself creates. When they do this, they will come face to face with Mike Jochim. Mike’s earliest work (Jochim, 1976:15–24) began with the complexity of real human decision making (he discusses the difference between his approach and that of OFT: Jochim, 1983a, 1998:13–29). As he put it (1998:14), he emphasized “the conscious motives underlying decisions by hunter-gatherers about what to eat, when to move, and where to camp”, which is to say he emphasized the real basis on which real people make real decisions. These motives rest particularly on the availability of food and shelter and he developed ways of quantifying the spatial and seasonal availability of the animal resources found in Mesolithic sites in his study area in southwestern Germany for comparison with the archaeological record with this in mind. Mike recognized the potential importance of energetic efficiency, taking an approach that bridged the two major streams of research I defined earlier. However, in searching the ethnographic literature for the reasons why hunters-gatherers chose resources, he found that, although they wanted to limit their work effort, they emphasized having reliable access to adequate food in the face of variation in resource abundance; efficiency is not all and may not be primary. He also recognized that hunting provides non-food resources like hides, bone, and antler and that people explicitly take this into account. Hunter-gatherers also value the taste and variety of foods in their diet and they link foraging explicitly and consciously to social issues, including prestige, a desire for social aggregations, and gendered differences in activities and status. Importantly, he noted that “reliability seems to be a value guiding the awarding of respect” (Jochim, 1976:17), explicitly linking subsistence and social status together. Jochim (1983a) referred to Chamberlain’s “parental affection”—i.e., confirmation bias—as “anchoring to the model” and he provided an explicit example of how we might assess which of a variety of possible decision rules has the best fit to an archaeological pattern to avoid that anchor. Examining an analysis that assumed at the outset that the micronutrient composition of animals in the diet drove subsistence decisions, he showed that there was no relation at all between modelled expectations and the archaeological data. Instead, ranking those animals by size predicted their relative importance very well, while ranking them by abundance fell in between these two (Jochim, 1983a:169; in passing, size and abundance may be good examples of Kahneman and Frederick’s (2002:53) attribute substitution, in which “when confronted with a difficult question people often answer an easier one instead”, although size may also predict whether or not an animal provides resources like hide or sinew).

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Underscoring the importance of this, Jochim urged archaeologists to compare our data to multiple models that “should differ by more than the addition or subtraction of a few variables … To be useful, competing models should differ in terms of underlying assumptions and mathematical structure as well” (Jochim, 1983a:167). That is, we do not need just another optimization model, whether it is more complex or not, and it is telling that the analysis he critiqued depended on linear programming to derive its results. Focusing on the importance of a gendered division of labor (Jochim, 1988), he further argued that men and women often have different constraints on their ability to allocate time and effort and that these may lead them to take different decision criteria into account. He observed that “no single goal and no single model are likely to be sufficient to explain foraging among all groups” (Jochim, 1988:135) and that “real decisions are usually shortsighted approximations, characterized by restricted knowledge, faulty perceptions, and limited calculating abilities” (Jochim, 1983a:159). All of this focuses on the same issues that have driven fundamental research on human decision-making in other fields (Kahneman, 2003; Thaler, 2015). Furthermore, from his perspective, topics like gender, exchange, or social relations are interesting on their own terms but are not only interesting on their own terms. “These topics are important, not simply because they enable us to ‘flesh out the past’ and humanize past groups, but also because they may have major impacts on those topics that we think we can talk about more easily—subsistence, technology, and settlement.” (Jochim, 1998:27). That is, he understood how essential it is to think beyond calories and resource availability even when the link between diet and the environment is what we want to understand. This is a fundamentally holistic perspective, manifest in Jochim’s emphasis on integrative systemic approaches to human ecology (Jochim, 1979, 1981). Such approaches can be difficult to apply in archaeology because we see the past so imperfectly and incompletely (as he noted—Jochim, 1979:103–105), but they direct our attention to the complexity of the things people do and the reasons we do them. Thinking holistically turns our attention from foragers to people, an important distinction (analogous to Thaler’s (2015, 2016) distinction between Econs and Humans). Our hypothetical foragers (Homo economicus; Thaler, 2015:4–5) may calculate energetic costs, but real people balance multiple concerns simultaneously and imperfectly as they make important decisions. Jochim’s work also focuses explicitly and repeatedly on the importance of thinking about the geographic scale of the regions that affect hunter-gatherer lives and that provide evidence for hunter-gatherer archaeologists studying those lives. His long-term research area (Jochim, 1976, 1998, 2006) covers some 46,000 square kilometers, encompassing parts of two river valleys, the intervening uplands, Bodensee (or Lake Constance), and the adjacent Alpine foothills. Recalling his emphasis on the complexity of hunter-gatherer lives, he has considered the ways in which lithic raw material might inform him about interregional movements and interregional social interaction within this area. He has also attended specifically to the importance of evidence of long-distance connections beyond it (movements of shell over hundreds of kilometers, for example). Durable evidence like

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this—movements of trinkets and exotics—is precisely how the North American archaeological record marks the immense networks that tied the continent together for centuries and that moved huge quantities of perishables; these issues are likely universal (cf. Bennyhoff & Hughes, 1987 on movements of analogous kinds of items over a large part of North America). It is no accident that he argued that he had to look at the European Mesolithic record in general to make sense out of the specific Mesolithic record of southwestern Germany (Jochim, 1998:215–224). The importance of thinking about human processes at immense geographic scales is especially clear in Jochim’s series of papers examining major changes across most of Europe over the span of the Upper Paleolithic (Jochim, 1983b, 1987; Jochim et al., 1998). In a nutshell, he argued that environmental deterioration during the Last Glacial Maximum (LGM) forced Upper Paleolithic populations who had spread throughout Europe into refugia, particularly in southwestern France and northern Spain, suggesting that the spectacular art that is so characteristic of that region marked responses to social pressures that developed as populations in those refugia grew. As climate improved after the LGM, he argued that human groups expanded out of this region, repopulating the landscape in fairly complex ways. This work points out quite explicitly that the patterns he addressed are effectively invisible at the spatial scales at which we traditionally work (especially see Jochim, 1987). That is, he sought causal connections at the geographic scale of most of Europe, connections that traditional approaches to hunters and gatherers could not have seen. But this work also weaves together the holistic concerns so central to his thought, drawing on the kind of faunal information that OFT often uses to assess how intensively ancient hunters used their regions, on stylistic similarities in technology and art across Europe, on estimates of general population densities, and on arguments about the kinds of social processes visible in recent hunter-gatherers.

2.4.1 Archaeology Is Hard: Research Over the Long Haul At the end of the day, archaeology is fundamentally a kind of doing: all of the conceptual sophistication we can muster means next to nothing if we cannot link our questions and insights to archaeological data. This is never easy, if only because the archaeological record is so fragmentary and destruction of organic material can make important resources close to invisible (for example, wild potatoes in the American Southwest; Louderback & Pavlik, 2017). We often think of specific projects answering specific questions, but addressing complex ideas and asking big questions takes time, with the gaps filled during one step of a research program opening questions to be answered in the next step. Our concepts change over time as well, with implications for the evidence we seek and the way we make sense out of what we know. The human history that we seek to understand unfolded over time, but our work on that history unfolds over the course of a career, too.

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We always begin with what our predecessors have done and Jochim’s early work (Jochim, 1976) focused on that. Past work in his study area had serious limitations, though, and the uneven spatial distribution of well-preserved sites hampered his ability to see the regional patterns he hoped to study. However, previous work documented a significant concentration of Mesolithic sites around the Federsee and it was clear that post-glacial shrinking of that lake had left sites preserved in bogs where he could hope to find the kinds of organic remains that European Mesolithic archaeologists so often lack. He devised a way to prospect for sites in the Federsee bogs, discovering and excavating more than one (most spectacularly Henauhof Nordwest; Jochim, 1993, 1998:111–168). A regional approach to hunter-gatherer use of a landscape, though, requires evidence from across the landscape and the kinds of well-preserved sites that so attract the attention of modern archaeologists are not evenly dispersed across the landscape (as he notes: Jochim, 2006:212). Recognizing this, he turned to a program of regional surface survey and limited test excavation, with the intent of either filling in the gaps between the known site concentrations or documenting that these gaps resulted from the behavior of ancient people rather than the excavation preferences of twentieth century scholars (Jochim et al., 1998). Making sense of even the most committed long-term program of archaeological field and lab research, though, inevitably confronts us with the limits on the clarity with which we can see the lives of the ancient people we hope to study. As he described it (Jochim, 1998:ix, x), his work “is an attempt to visit this landscape and hear the whisper of this distant past”, but it is “also a cautionary tale about how limited our abilities are, how soft the whisper really is”. All archaeologists understand the fundamental limitations on our access to the past, particularly those imposed by problems of preservation. But we attend less specifically to equally fundamental effects of the temporal scale that we inevitably work at. Perhaps the most important of these is that the imprecision of our chronologies and the need to aggregate sites for analysis and comparison typically force our analysis to the scale of centuries and sometimes millennia. In contrast, the ethnographic data on which we rely so fundamentally often derive from observations made over a year or two at worst and a few decades at best. These data often tempt us to think in terms of “a” seasonal round, like the idealized rounds that Binford (1980: Fig. 1, Fig. 3) used to illustrate his distinction between foragers and collectors. Even in the very short term, though, these rounds vary in response to external conditions (see, for example, Silberbauer, 1981: Fig. 16). This means that any particular hunter-gatherer community may map itself onto the landscape in many different ways over the course of many years. Aggregate archaeological data combine evidence of these kinds of short-term responses over periods of time that no body of ethnographic observation can match. As Jochim (1991:315) put it: Archaeologists should not expect to follow ethnographies in reconstructing ‘the seasonal round.’ It may not exist. Ethnographies give us brief, individual snapshots, whereas the archeological record represents fragments of the entire family album. Rather, we need to develop approaches that are sensitive to behavioral and environmental variability.

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Our literature grapples constantly with issues of preservation, taphonomy, etc., but it is fair to say that we have yet to really internalize the disjuncture between the temporal scale at which we see human beings in our daily lives and the temporal scale at which we see them in our datasets. Recognizing that these datasets aggregate variable short-term responses to the world does not prevent us from making sense of them, but it does imply that we have to think about them in different ways, at larger geographic and temporal scales than we often do (Jochim, 1991: 315–318; Stewart & Jochim, 1986). If we want a model of how we might do this—and we should want one—we need look no farther than Jochim’s (1998) long-term history of his field area.

2.5 Conclusions If we ask three archaeologists about what happened in the human past there is every possibility that we will get four different answers. Much of what we know is clear, but much of it is imperfect, incomplete, and contested, and we can assume that it will remain that way. When we contest it, though, we do well to remember Jochim’s comment on the excesses of post-processual archaeology: “there is little sense of the need to play with alternative approaches, little humility in the face of our ignorance” (Jochim, 1998: 28). Indeed, humility is in short supply in academia in general and is arguably absent entirely in some schools of thought in our field. There is some humility implicit in recognizing the debt that we all inevitably owe to the scholars who came before us and whose work we build on. This seems particularly important in the case of someone like Mike Jochim, whose work has anticipated so many of the directions that we need to pursue in the future. We cannot do much better in building on what we know than to follow the example that he has set for us. When optimization theorists recognize the significance of social and ritual issues (Zeanah, 2017:13) or women’s foraging (Whelan et al., 2013), they follow in his footsteps. When we recognize the importance of variation in subsistence and settlement from year to year and among people within a single social group (Moss, 1993), we follow in his footsteps. When we contemplate human agency over great distances (Bamforth, 2011), we follow in his footsteps. When we match the temporal scale of our questions to the scale at which archaeological data are most meaningful and informative (Weitzel, 2019), we follow in his footsteps. When we systematically and rigorously design our fieldwork to fill gaps in our evidence and answer questions we did not know we were going to have when we began our careers, we follow in his footsteps. When we begin to explicitly consider the complexity of real human decision-making, we will follow in his footsteps. And if we can do these things with grace, humility, and humanity (and perhaps a cup of coffee), we will follow in his footsteps in the most important way that we can.

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Acknowledgements  Mike Jochim guided me through most of my graduate work at UCSB and shaped me intellectually in ways that I continue to discover to this day. There are no words adequate to express my debt to him. The other members of the Council of Elders (Brenda Bowser, Mary Lou Larson, and Andrew Stewart) worked hard over months to bring together the SAA session that produced this volume; it is difficult to express the sadness of losing Mary Lou before she could see this volume completed. And the Elders of the Future who edited this volume were extraordinarily patient with the time it took me to finish this essay. Really thoughtful comments from several reviewers, including Bob Kelly and Andrew Stewart, improved my prose and my arguments. Don’t blame Bob for anything I say here, though. It’s not his fault. Mike: thank you and I hope you like it.

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McGuire, K., & Hildebrandt, W. (2005). Re-thinking Great Basin foragers: Prestige hunting and costly signaling during the middle archaic period. American Antiquity, 70, 695–712. Moss, M. (1993). Shellfish, gender, and status on the northwest coast: Reconciling archeological, ethnographic, and ethnohistorical records of the Tlingit. American Anthropologist, 95, 631–652. O’Connell, J., & Marshall, B. (1989). Analysis of kangaroo body part transport among the Alyawara of Central Australia. Journal of Archaeological Science, 16, 393–405. O'Connell, J., Jones, K., & Simms, S. (1982). Some thoughts on prehistoric archaeology in the Great Basin. In D. Madsen & J. O’Connell (Eds.), Man and environment in the Great Basin (pp. 227–240). Society for American Archaeology Papers II. Ollasson, J., & Ren, N. (2002). Taking the rough with the smooth: Foraging for particulate food in continuous time. Theoretical Population Biology, 62, 313–327. Perry, G., & Pianka, E. (1997). Animal foraging: Past, present and future. Trends in Ecology and Evolution, 12, 360–364. Prelec, D., & Simester, D. (2001). Always leave home without it: A further investigation of the credit-card effect on willingness to pay. Marketing Letter, 12, 5–12. Price, M., & Jones, J. (2020). Fitness maximizers employ pessimistic probability weighting for decisions under risk. Evolutionary Human Sciences, 2, 1–16. Pyke, G. (1984). Optimal foraging theory: A critical review. Annual Review of Ecology and Systematics, 15, 523–575. Reilly, A. (2015). Women’s work, tools, and expertise: Hide tanning and the archaeological record. MA thesis,. Anthropology Department, University of Alberta. Renfrew, C. (1973). Before civilization: The radiocarbon revolution and prehistoric Europe. Alfred A. Knopf. Riley, L. (2016). Reclaiming tradition and re-affirming cultural identity through creating kangaroo skin cloaks and possum skin cloaks. Journal of Indigenous Well-Being, 1, 5–21. Rittschof, C., & Robinson, G. (2014). Genomics: Moving behavioural ecology beyond the phenotypic gambit. Animal Behavior, 92, 263–270. Roff, D. A. (2007). Contributions of genomics to life-history theory. Nature Reviews Genetics, 8, 116–125. Rubin, H. (2016). The phenotypic gambit: Selective pressures and ESS methodology in evolutionary game theory. Biology and Philosophy, 31, 551–569. Schulte-Mecklenbeck, M., Sohn, M., de Bellis, E., Martin, N., & Hertwig, R. (2013). A lack of appetite for information and computation: Simple heuristics in food choice. Appetite, 71, 242–251. Sharp, K., & Sharp, H. (2015). Hunting Caribou. University of Nebraska Press. Silberbauer, G. (1981). Hunter and habitat in the Central Kalahari Desert. Cambridge University Press. Simms, S., O’Connell, J., & Jones, K. (2014). Some thoughts on evolution, ecology, and archaeology in the Great Basin. In N. Parezo & J. Janetski (Eds.), Archaeology in the Great Basin and southwest: Papers in honor of Don D. Fowler (pp. 177–187). University of Utah Press. Smith, E., & Winterhalder, B. (1992). Evolutionary ecology and human behavior. Aldine de Gruyter. Stewart, A., & Jochim, M. (1986). Changing economic organization in late glacial southwestern Germany. In L. Straus (Ed.), The end of the Paleolithic in the Old World (pp. 47–62). BAR International Series 284.). British Archaeological Reports. Stibberd-Hawkes, D. (2018). Costly signaling and the handicap principal in hunter-gatherer research: A critical review. Evolutionary Anthropology, 28, 144–157. Swagerty, W. (1988). Indian trade in the trans-Mississippi west to 1870. In W. Washburn (Ed.), Handbook of north American Indians, vol. 4: History of Indian-white relations (pp. 351–374). Smithsonian Institution Press. Sweis, B., Abram, S., Schmidt, B., Seeland, K., MacDonald, A., Thomas, M., & Redish, A. (2018). Sensitivity to “sunk costs” in mice, rats, and humans. Science, 361, 178–181.

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Thaler, R. (2015). Misbehaving. W.W. Norton. Thaler, R. (2016, January). Behavioral economics: Past, present and future. Presidential address, American Economic Association. Available at SSRN: https://ssrn.com/abstract=2790606 or https://doi.org/10.2139/ssrn.2790606. Thomas, D. H. (1973). An empirical test for Steward's model of Great Basin settlement patterns. American Antiquity, 38, 155–176. Todd, I., & Kacelnik, A. (1993). Psychological mechanisms and the marginal value theorem: Dynamics of scalar memory for travel time. Animal Behavior, 46, 765–775. Vasconcelos, M., Monteiro, T., & Kacelnik, A. (2015). Irrational choice and the value of information. Nature Scientific Reports, 5, 1–12. Weatherhead, P. (1979). Do Savannah sparrows commit the Concorde fallacy? Behavioral Ecology and Sociobiology, 5, 373–381. Weitzel, E. (2019). Declining foraging efficiency in the middle Tennessee River valley prior to initial domestication. American Antiquity, 84, 191–214. Whelan, C., Whitaker, A., Rosenthal, J., & Wolgemuth, E. (2013). Hunter-gatherer storage, settlement, and the opportunity costs of women's foraging. American Antiquity, 78, 662–678. Wilmsen, E. (1973). Interaction, spacing behavior, and the organization of hunting bands. Journal of Anthropological Research, 29, 1–31. Winterhalder, B. (1986). The analysis of hunter-gatherer diet: Stalking an optimal foraging model. In M. Harris & E. Ross (Eds.), Food and evolution: Toward a theory of human food habits (pp. 311–339). Temple University Press. Winterhalder, B., & Smith, E. (1981). Hunter-Gatherer Foraging Strategies. University of Chicago Press. Winterhalder, B., & Smith, E. (2000). Analyzing adaptive strategies: Human behavioral ecology at twenty-five. Evolutionary Anthropology, 9, 51–72. Zeanah, D. (2017). Foraging models, niche construction, and the eastern agricultural complex. American Antiquity, 82, 3–24.

Chapter 3

Defining and Modeling the Dimensions of Settlement Choice: An Empirical Approach Kenneth L. Kvamme

Abstract  A methodology for the revelation of fundamental dimensions underlying settlement choice is presented. Although variables selected in any analysis typically originate from explicit or implicit theoretical notions, this approach self-selects those variables most relevant to observed settlement distributions. Through a principal components transformation, the variables are linearly combined into distinct independent dimensions that minimize the locational variance of the settlements. The results yield large theoretical implications because the dimensions indicate those elements of the social and physical environments important to settlement choices as revealed by the settlements themselves. A case study of rural farming settlements from 1892 Arkansas is employed where the dimensions of terrain form, hydrology, soils, and the social network of roads define a “settlement space.” The mean vector derived from these dimensions at known settlements defines the most characteristic or “ideal” loci, with places distant from that centroid being less desirable. This perspective is quantitatively enhanced through application of the multivariate Mahalanobis D2 statistic (analogous to a multidimensional z-score) and its associated 0–1 rescaling via a chi-square transformation. Through GIS it permits each location in a region to be rated on a 0–1 scale relative to the settlement ideal and permits graphic appraisal of preferences along each dimension. Moreover, the result offers an alternative approach to archaeological location modeling that is theoretically robust, addressing a common criticism of other modeling methods. It fits nicely within the original definition of niche space advanced in biology that has also been employed as a model for human settlements. Only a site-presence class is required, which avoids the necessity of the “background” or “non-site” classes of other modeling methods. Predictive performance is compared against popular logistic regression and maximum entropy models where a similar level of performance is achieved despite far lower computational costs and data requirements.

K. L. Kvamme (*) Department of Anthropology, University of Arkansas, Fayetteville, AR, USA e-mail: [email protected] © Springer Nature Switzerland AG 2023 E. Robinson et al. (eds.), Cultural Landscapes and Long-Term Human Ecology, Interdisciplinary Contributions to Archaeology, https://doi.org/10.1007/978-3-031-49699-8_3

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Yet, all models illustrate only moderate predictive power when applied to the Arkansas data set. It is argued that in 1892 many locations were equally suitable for settlement, but owing to low population densities and large farms that forced wide separation, few places actually held farmsteads leading to high false positive rates and consequent models of moderate power. Keywords  Locational analysis · Settlement theory · Archaeological location models · Principal components analysis · Mahalanobis distance · Logistic regression · Maximum entropy · Historic Arkansas

My Approach to Archaeological Landscapes I begin with the premise that human behavior is patterned and assume that mechanisms responsible for that pattern can be discovered. Statistical methods are one set of tools that aid this process. On a small-scale map the distribution of human activities represents a point pattern. Looked at another way, past peoples “speak to us” through the patterned distributions of residues left from their activities across a region. The question then becomes “why are those points distributed as they are”? This is the challenge for archaeological analysts. In the past I have followed the common pathway of statistically comparing variables measured at archaeological loci against background or “non-site” places to ascertain the significance of differences and to develop archaeological location models. While insights have been gained, shortcomings remain in this process. Typically, variables are selected based on past results, ethnographic citations, convenience, or ease of availability through GIS. The last is why variables of the physical environment dominate over difficult to quantify social ones in most prior work. In all cases the analyst assumes appropriate variables have been identified—important variables have not been omitted and irrelevant ones have not been included—an assumption not always justified. Moreover, variables may be correlated, representing redundant expressions of similar phenomena. These are some of the issues I address here in an effort to extract underlying dimensions relevant to past location choices based on a suite of variables which may or may not be relevant. In doing so, the most variable aspects of an archaeological distribution are removed, leaving commonalities that define fundamental location requirements and offer a pathway to models of archaeological location.

3.1 Introduction Michael Jochim’s, 1976 book, Hunter-Gatherer Subsistence and Settlement: A Predictive Model, resulted from his doctoral dissertation at the University of Michigan. It was well received at that time, but beyond its original intent and scope it has generated repercussions that echo yet today. At its simplest it offered syntheses of worldwide hunter-gatherer ethnography pertaining to the important archaeological domains of subsistence, settlement, and demography. More importantly, it also offered robust models for each of these areas that were actually tested archaeologically and supported ethnographically. Unfortunately, these models were

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developed in a pre-GIS day that greatly inhibited wide-scale application of prehistoric settlement projections. Nevertheless, these models set an important precedent for the future. They strongly suggested that archaeological distributions could be modeled (a relatively new idea), which I believe got archaeologists thinking along that path. It certainly had that effect on me. More than a decade after its publication, Hunter-Gatherer Subsistence and Settlement saw renewed relevance as regional archaeological location modeling (ALM) developed and grew into a worldwide focus along with GIS technology, which was critical to its application (e.g., see papers in Judge & Sebastian, 1988; Kvamme, 1990; Warren, 1990a). ALM did not follow Jochim’s well-reasoned approach, however. Rather, these methods generally employed samples of known archaeological sites in a region, examined a series of easily obtained GIS-generated variables from the physical environment measured at those sites (e.g., elevation, slope, distance to water), and then assessed statistical differences against the same variables measured at a sample of “non-sites” (places where archaeological sites were known or thought to be absent; see Kvamme, 2020 for a review of these methods). Results yielded evidence of variables related to site presence and absence, and these data could then be ported to simple Boolean operations, discriminant functions, or logistic regressions to generate models for archaeological presence. These models were then applied systematically across a landscape by GIS methods to yield mapped projections of past patterns of settlement in the form of probability or archaeological likelihood surfaces, ideal for management purposes (Warren, 1990b). Initially, ALM was of intense intellectual interest because various statistical methods that could isolate relevant variables coupled with study of many cultural and environmental contexts suggested a means to better realize phenomena relevant to settlement selection and, with study, past location choices (e.g., see Kvamme & Jochim, 1989). In other words, there was, and is, much potential for these methods to contribute to settlement theory and an understanding of settlement distributions across broad landscapes. Yet, inevitably, the pursuit of ALM was overtaken by Cultural Resource Management interests, and for simple reasons. Land managers needed a way to indicate probable locations of archaeological sites for project planning purposes and they had funding. With ALM projects could avoid the archaeological resource base as a means to preserve it and mitigation costs could be reduced, for example, if in planning stages a proposed highway route could be sited through regions of low archaeological likelihood. Numerous and often large modeling projects have been developed worldwide (Brandt et  al., 1992; Duncan & Beckman, 2000; various papers in van Leusen & Kamermans, 2005) and it has been shown that many models offered reasonable power that resulted in considerable cost savings (Hobbs, 2003; however, see Ebert, 2000 and Wheatley, 2004 for opposing views). Unsurprisingly, ALM and related location analyses have faced numerous criticisms almost from their inception (see Wheatley & Gillings, 2002:165–181 for a brief summary of issues; Verhagen & Whitley, 2012 give a more recent update). Three of them are of particular relevance to this paper because each is confronted and addressed by a variety of methodologies rarely used in archaeology.

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3.1.1 Contributing to Theory-Building and Explanation It is not surprising from the foregoing that data-driven modeling approaches have been labeled as “correlative,” because many are indeed based in patterns statistically gleaned from data. Nevertheless, this has been an unfortunate label, originally (and naively) meant to disparage models that did not arise through the “scientific deductions” that once were core aspirations of processual archaeology (e.g., Schiffer, 1988). An early review of ALM by Kohler and Parker (1986) decried the lack of deductive modeling approaches, but failed to illustrate a single non-contrived example. Yet, Jochim’s (1976) pioneering study exemplified exactly the kind of deductions they and later researchers (e.g., Ebert, 2000) called for (Jochim’s settlement location model is derived specifically through mathematical deduction based on a simple gravity model). At the same time, it must be realized that models derived from empirical data cannot be devoid of theory because the process of selecting variables for analysis alone implies a theoretical orientation (whether explicit or implicit; see Wheatley & Gillings, 2002:166). This is exemplified by countless models across the sciences, from biology to psychology, that utilize variables derived from underlying theory and then examine through analyses of data patterns how they interact with each other to gain understanding of causal mechanisms. This is how theory is built in the social and behavioral sciences. Moreover, theory ultimately rests on facts and one would think that analyses and models that reveal solid evidence of variables relevant to settlement choices would be crucial to theory development in archaeology, but few such generalizations have arisen (see Verhagen & Whitley, 2012). In the following pages I illustrate an approach to settlement location analysis and modeling that rests in empirical data, but which has large theoretical implications because it can solidly point to principal dimensions underlying settlement choices (a brief synopsis of this methodology was presented in Kvamme, 2020). Moreover, this approach rests within the ecological idea of “niche space” where I define a “human settlement niche.” In his seminal paper, Hutchinson (1957) originally suggested that the niche of a species could be determined empirically by measuring the locations of individuals of the population along multiple dimensions of environment, with the range defining a niche space in a “hypervolume” defined by multiple axes of measurements. That space can be visualized as containing varying probability densities with certain locations more ideal for a species than others. In fact, it is arguable that the “ideal habitat” of a species can be represented by the mean vector of measurements on each variable, as indicated by the species’ locations. Less desirable habitat is then inferred as any deviation from the mean vector. These ideas were later transferred to human niche spaces and settlement distributions by the geographer Hudson (1969), who defined a settlement niche space. Archaeologists were not far behind in their thinking. Thomas and Bettinger (1976), for example, closely fit several environmental variables measured at archaeological sites in Nevada to normal curves, arguing that their mean values represented “optimal” location choices, but in moving away from their means “the probability of finding

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a campsite diminishes and ultimately approaches zero” (Thomas & Bettinger, 1976:360). These lines of thinking play important roles below. Elsewhere I have argued that the settlement niche space defined by Hudson is a small subset of environment described by a narrow range of variation on a relevant set of variables (Kvamme, 2006:11). The important questions that must be addressed are (1) “what are those variables” and (2) “how do we define their environmental ranges”? In the following pages I address these questions and develop mechanisms for the definition of a settlement niche space.

3.1.2 Considering Variables of the Physical and Social Environments A frequent complaint about analyses of settlement location and ALM is the general focus on variables of the physical environment as opposed to the social environment (Gaffney & van Leusen, 1995; Lock & Harris, 2006; Wheatley, 1993, 2004). Most criticisms come from scholars of European archaeology where built social landscapes have influenced location choices for millennia as well as archaeological perceptions of those landscapes. This orientation is much in contrast to Americanist views that have emphasized the importance of the natural environment in structuring Native American societies, even though built social environments (large villages, mound complexes) clearly exist in some cultural settings. The social environment might include consideration of proximities or adjacencies of settlements to religious, ceremonial, market or political centers, other settlements, or to routes of travel such as road networks. Yet, studies that incorporate such variables tend to be rare (however, see Stančič & Veljanovski, 2000; Arnoldus-Huyzendveld & Citter, 2014; Bala et al., 2014; Verhagen et al., 2016). The reason is partly due to the nature of our GIS databases where information about the physical environment is readily available (e.g., hydrology, soils, elevation, terrain characteristics), albeit the present environment. Nevertheless, researchers generally employ such modern data as a proxy for the past, at least for recent millennia, generally under the argument that terrain, geology, and basic drainage systems change relatively slowly (see Kamermans, 2006 for a discussion of these issues). Details of past social environments tend to be less readily available because the social landscape must be reconstructed by archaeologists, which generally requires a large and long-term effort. For example, to generate an ancient social environment requires knowledge of contemporary settlements and central places that include ceremonial, trade, and political centers, and the routes of travel between them. Once such a reconstruction is made, proximities to nearest ceremonial centers, settlements, or roadways, as well as local settlement densities, may be considered as possible analysis variables. Yet, such information is rarely available except in well-­studied areas (in the USA, Chaco Canyon, Bandelier National Monument, and Colonial Virginia come to mind). Moreover, it may be impossible to obtain similar

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social data in hunter-gatherer contexts because of the difficulty of dating and establishing contemporaneity between camps defined primarily by lithic scatters and because such concepts as political centers, markets, and other aspects of social landscapes may be meaningless. Nevertheless, critiques against models based solely on the physical environment have been severe (Gaffney & van Leusen, 1995; Lock & Harris, 2006; Wheatley, 1993), even devolving to use of the label “environmental determinism” (see Kvamme, 1997 for a response). In a case study below I make specific use of variables reflecting the social environment and show that they can form an important underlying dimension to settlement.

3.1.3 Getting Rid of “Non-Sites” Two-group comparisons have long been employed in archaeological location analysis and modeling (e.g., though t-tests, Kolmogorov-Smirnov tests, logistic regressions) by making use of a site-absent or non-site class to form a contrast against an archaeological presence class (see Kellogg, 1987; Warren, 1990a; Wheatley & Gillings, 2002; Nakoinz & Knitter, 2016; Kvamme, 2020). Revelations of significant differences between the two groups imply that the associated variables are somehow related to archaeological presence. Of course, significant variables could merely represent proxies for other “unknown” variables that were actually the focus for site selections (e.g., perhaps people were not concerned with availability of water, but rather were concerned with a particular plant that grew near water). Whatever the case, the use of non-site locations remains an issue because if one claims archaeological absence at a location (based on field survey evidence) there remains some probability that a site might actually be present, perhaps hidden deeply beneath the surface or simply missed by survey teams due to vegetation cover. This concern also occurs in biological species distribution models where absence data at a particular point in time does not necessarily imply that a species is always absent at a particular location (Browning et al., 2005:33). A related approach to non-site sampling is to take random points from the background environment at large as opposed to “true” field-surveyed non-sites. This strategy is used in regions where archaeological surveys have been rare or poorly distributed such that non-site samples show heavily biased distributions. The random points approach has been justified wherever the a priori probability of an archaeological site is low (the usual case in most regions) because they are unlikely to fall on sites, yielding a distribution similar to “true” non-sites (Duncan & Beckman, 2000; Kvamme, 1988). Approaches examined below for revelation of the dimensions of settlement preferences and ALM do not require non-sites or random background points.

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3.2 Case Study Settlements and Region In order to address some of the foregoing issues, an historical data set is utilized permitting consideration of social and physical environments, with the latter not greatly changed owing to the data set’s proximity to the present. The study includes an 18 x 27 km area of Washington County centered around Fayetteville (locus of the University of Arkansas), in northwestern Arkansas. This region is located in the Boston Mountains, a subset of the Ozarks that offers a varied landscape of hills, plateaus and valleys, with altitudes ranging between 315–577 m. The historic data were obtained from Atlas Map of Washington County, Arkansas (Skelton, 1894), which was compiled from county and state records and field surveys undertaken for a full year in 1892. It illustrates property ownership and every farmstead, cultivated field, orchard, and road that then existed in the county (Fig. 3.1a) with 36 square miles (a township) per large-format page (a scale of two inches per mile, or 1:31,680). The author asserts “I do not claim for the work absolute accuracy in all respects, but I do claim a close approximation” (Skelton, 1894:2). When scans of each atlas page were mosaicked and registered to modern topographic maps (U.S. Geological Survey 7.5 min, 1:24,000 series in the form of digital raster graphics), accuracy was found to be generally remarkable with still-standing residences occurring in the same places, although some deviance occurs between the courses of streams and old dirt roads compared to their modern paved counterparts (Fig. 3.1b). In addition to Fayetteville, the study area includes the southern edge of the town of Springdale and the small hamlet of Prairie Grove (Fig. 3.1c). The 1890 census reports just over 32,000 residents in the county. Within the study area (about 20% of the county) a total of 785 farmsteads or residences are recognized in the Map Atlas of 1892 outside of towns and hamlets, which are the focus of subsequent analyses. Farming was by far the dominant occupation at that time and the agricultural economy revolved around a variety of grain crops (corn, wheat, oats), potatoes, apple and peach orchards, and livestock (cattle, sheep, horse, and particularly swine). Agricultural fields and orchards are prominent in the Map Atlas along with farmsteads (Fig. 3.1a). However, it must be recognized that some of the mapped residences may not have been associated with farming activities, as census records and other accounts relate many other occupations (Shiloh Museum, 1989), including milling, logging, ranching, quarrying, mining, and increased industrial activity near the towns (e.g., a large canning factory existed outside of Springdale). This implies that a mixture of settlement types or location preferences were present in the region. Although the predominant pattern undoubtedly is associated with farming, some “noise” may be introduced by other occupations that may not have followed the typical “farming pattern” when residences were placed. This circumstance may account for one source of error in the analyses and models that follow.

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Fig. 3.1  Images of the study region: (a) segment of 1892 map with quarter-quarter Sections (40 acres or 16.2  ha) forming grid and fields, orchards, roads, farmsteads, streams, and ownership indicated (farmsteads extant today are circled), (b) modern digital raster graphic of corresponding topographic map segment (original scale 1:24,000, U.S. Geological Survey) showing today’s landscape (farmsteads extant in 1892 are circled), (c) full study region on the backdrop of the DEM showing towns, roads and farmsteads extant in 1892

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3.2.1 Database and Variables All data were encoded within GIS in the NAD 83, UTM zone 15 N projection. The farmsteads and 1892 road network were vectorized from the registered and rectified scans of the 1892 Map Atlas (Fig. 3.1c). It is well-understood that testing a model with the same data from which a model was built will yield an inflated view of model performance (Mosteller & Tukey, 1977). Consequently, the 785 farmstead locations were split by random selection, with three-quarters (nmodel = 589) assigned to an analysis and model-building sample and the remainder (ntest = 196) reserved for model testing and evaluation. The test sample will yield a much less biased view of model performance although a small amount of upward bias may still exist owing to similar data values (due to spatial autocorrelation) among farmsteads that are proximate in the two samples, but such bias is on the order of only a few percentage points (Veloz, 2009). A raster digital elevation model (DEM) was downloaded as was the Soil Survey Geographic Database (SSURGO). The latter included 85 distinct soil classifications (a wide variety of silt-loams) distributed in nearly 3000 polygons. All variables in subsequent analyses were derived from these data and adopted the 10 m spatial resolution of the raster DEM including the farmstead locations where a single cell was employed to represent each one. A suite of variables was defined that attempt to quantify characteristics of the physical environment and social landscape (Table  3.1), many of which have been commonly employed by other researchers in regional analyses. Univariate inspection revealed that many variables illustrate mild to large departures from normality. As a later modeling tactic performs best under normality, a variety of transforms were applied to help normalize the data (Table 3.1).

3.3 Getting at the Dimensions of Settlement Choice: A PCA Tactic With a host of defined variables (Table 3.1) the question arises of which were actually important to farmstead choice? There is a large archaeological literature that proposes multiple significance tests to examine this question (e.g., Hodder & Orton, 1976; Kellogg, 1987; Maschner, 1996; Kvamme, 2020). All require comparison of settlements against background or non-site distributions. It was earlier recognized that many variables are utilized out of their convenience of availability in GIS databases (e.g., several in Table 3.1). Moreover, it can be expected that many variables may be partially or highly correlated, representing redundant expressions of similar underlying phenomena. Typically, an analyst investigating settlement preferences assumes that appropriate variables have been identified, usually based on prior knowledge and previous research. In other words, it is generally accepted that important variables have not been omitted and irrelevant variables have not been included. Such assumptions are not justifiable.

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Table 3.1  Definition of variables measured at each farmstead via GIS methods. All data sets possess 10 m spatial resolution Variable (units | transform) AspectNS (Degrees | none)

AspectEW (Degrees | none)

DEM (m | log) Slope (% grade | log) Soil preference (mean density | none)

Soil variety (Count | none)

Stream distance (m | square root)

Stream density (linear distance/ km2 | none) Road distance (m | square root) Road density (linear distance/ km2 | log)

Definition & Comment The azimuth of principal ground facing if on the east half of the compass; otherwise 360 – azimuth. This effectively collapses the east and west compass halves to a linear scale, 0–180°, with 0° = north, 90° = east or west, 180° = south. Comment: With sunshine from the south, certain aspects may have been preferred or avoided during warm summer or cool winter months. Identical to AspectNS, but with the north half of the compass collapsed over the south yielding a linear scale where 0° = east, 90° = north or south and 180° = west. Comment: With prevailing winds and storms approaching from the west, certain aspects may have been preferred or avoided. Elevation above sea level. Comment: With most farming in valleys there may have been elevation preferences. Ground steepness. Comment: With steep slopes along the sides of hills and edges of plateaus, there may have been preferences for specific surface gradients. The density of farmsteads in each of 85 SSURGO soil types was computed as an index of preferences. This variable computes the average density within a 400 m radius of any locus. Comment: This variable avoids use of nominal-level soil categories (unsuited for subsequent analyses) and offers a quantitative scale that reflects preferences. The number of different SSURGO soil types within a 400 m radius. Comment: Soil types may be a proxy for plant communities/environmental variation. Areas of high or low variation may have been preferred or avoided. For a more objective depiction of the hydrologic network a GIS runoff algorithm was applied to the DEM, and a liberal threshold was selected to define a river and stream network. Linear distances to nearest stream were then computed. Comment: Farming depends on soil moisture which relates to river or stream proximity and some areas may have been preferred or avoided. The density of streams as calculated by their total linear distance within a 400 m radius. Comment: Farming depends on soil moisture which relates to river or stream density and some areas may have been preferred or avoided. Linear distance to nearest 1892 road. Comment: Roads enabled connectivity between farmsteads and nearby communities. There may have been preferences for connection to this network. The density of 1892 roads as calculated by their linear distances within a 1609 m radius (1 mile). Comment: Roads enabled connectivity between farmsteads and nearby communities. There may have been preferences for connection to this network.

Different and more robust tactics are required. In the following, all variables (Table  3.1) are simultaneously considered in a multivariate assessment, but in such a way that only the information they carry relevant to settlement placement is utilized, and background or non-site distributions are unnecessary. In so doing

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underlying dimensions pertinent to settlement location in 1892 Arkansas are revealed. The approach utilizes what settlements have in common to define their location requirements and, at the same time, removes the most variable aspects of their distributions. The identification of such a “minimum set” of location requirements for a settlement class facilitates understanding and modeling of settlement occurrences. The methodology rests in an often overlooked characteristic of principal components analysis (PCA; Jolliffe, 2002) that was first realized in species distribution modeling in the biological sciences (Dunn & Duncan, 2000; Browning et al., 2005; Rotenberry et al., 2006). Much of the following is adapted from that approach. PCA is a multivariate method applied to S, an n x k matrix of k variables measured at n settlements. The method returns k uncorrelated principal components (PCs), each representing an independent set of relationships between a settlement distribution and the measured variables. Each PC, ranked 1 through k, is associated with a variance or eigenvalue, λ, that represents a portion of the total variance represented by the k variables in such a way that λ1 > λ2 > … > λk. Each PC is also associated with an eigenvector, k coefficients that when multiplied against the original variables and summed form the linear composites that are the PCs. The absolute sizes of these coefficients indicate the relative importance of each original variable to each component, and thus form the basis for PCA interpretation. In other words, the meaning of a PC is gained by determining which variables are associated with the largest absolute coefficients as they lend most weight to the PC. The data rearrangement that PCA offers through its rotation of orthogonal axes in the k-­ dimensional space often generates unanticipated insights into data relationships and this, indeed, is one reason for its use. Some of the PCs may reveal unforeseen structures in complex multivariate data sets that represent the “true” or latent underlying dimensions of a data set (Jolliffe, 2002). To illustrate some of the foregoing, Fig. 3.2 illustrates a principal components solution in a simple two-variable context where x1 and x2 represent two correlated variables each with variation (reflected by standard deviations, σ1 and σ2) indicated by the spread of the bell-shaped curves in gray. With two variables two principal components result. The first (PC1) defines an axis that maximizes the variance in the data, λ1 (which must be greater than any input σi2). This variation is illustrated by the wider bell-shaped curve in pink with standard deviation √ λ1. As x1 and x2 are correlated each may imperfectly measure some underlying phenomenon; in this case, PC1 represents a new variable that may better characterizes this underlying dimension. PC2, on the other hand, defines an axis that minimizes variation, reflected by the tighter bell-shaped curve in green with small standard deviation, √ λ2. As it lies on an orthogonal axis, PC1 and PC2 are uncorrelated and therefore represent independent dimensions. These dimensions may then be used as variables in subsequent analyses and models. That the lower component minimizes variance is significant in the present context; lower variance reduces variation that must be modeled, suggesting that improved results may be achieved by employing low-variance components. Mathematically, aside from the variance they carry, there is no difference between the highest and lowest components in PCA. It is argued here (following the work

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Fig. 3.2  Illustration of two correlated variables, x1 and x2 with variation σ1 and σ2, and their associate principal components, PC1 and PC2. PC1 is the axis of principal variation, represented by λ1 > max(σ12, σ22) while PC2 is the axis of minimum variance, with λ2 schlichtherle. Schlichtherle, H. (2004). Wagenfunde aus den Seeufersiedlungen im zirkumalpinen Raum. In M. Fansa & S. Burmeister (Eds.), Rad und Wagen. Der Ursprung einer Innovation. Wagen im Vorderen Orient und in Europa (pp. 295–314). Beiheft der Archäologischen Mitteilungen aus Nordwestdeutschland 40. Schlichtherle, H. (2009a). Die archäologische Fundlandschaft des Federseebeckens und die Siedlung Forschner. Siedlungsgeschichte, Forschungsgeschichte und Konzeption der neuen Untersuchungen. In Die früh- und mittelbronzezeitliche « Siedlung Forschner » im Federseemoor. Befunde und Dendrochronologie. Siedlungsarchäologie im Alpenvorland, X (pp.  9–70). Forschungen und Berichte zur Vor- und Frühgeschichte in Baden-Württemberg 113. Stuttgart: Theiss. Schlichtherle, H. (2009b). Eine neue Siedlungskammer im westlichen Federseerseeried und ihre Bedeutung für das Verständnis neolithischer Siedelsysteme. In J. Biel, J. Heiligmann, & D.  Krausse (Eds.), Landesarchäologie (pp.  61–86). Forschungen und Berichte zur Vor- und Frühgeschichte in Baden- Württemberg 100. Stuttgart: Theiss. Schlichtherle, H. (2011). Bemerkungen zum Klima- und Kulturwandel im südwestdeutschen Alpenvorland im 4.-3. Jahrtausend v.Chr. In F. Daim, D. Gronenborn, D., & R. Schreg (Eds.), Strategien zum Überleben. Umweltkrisen und ihre Bewältigung (pp.  155–167). Mainz: Römisch-Germanisches Zentralmuseum, Tagungen 11.

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Schlichtherle, H. (2016a). Jungsteinzeitliche Maske aus Bad Schussenried „Riedschachen“ am südlichen Federsee. Ein sensationeller Fund. Denkmalpflege in Baden-Württemberg. Nachrichtenblatt des Landesdenkmalpflege, 45(1), 28–32. https://doi.org/10.11588/ nbdpfbw.2016.1.28563 Schlichtherle, H. (2016b). Mitten im Leben. Kulthäuser und Ahnenreihen. In 4000 Jahre Pfahlbauten, edited by Archäologisches Landesmuseum Baden-Württemberg and Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart. Schwabenverlag. Schlichtherle, H. (2016c). Älteste Wandmalereien nördlich der Alpen. Zur Rekonstruktion der Bilder für die Präsentation auf der Großen Landesausstellung 2016. Denkmalpflege in Baden-­ Württemberg, 45(1), 11–17. https://doi.org/10.11588/nbdpfbw.2016.1.28560 Schlichtherle, H., Feldtkeller, A., Maier, U., Schmidt, E., & Steppan, K. (2004). Ökonomischer und ökologischer Wandel am vorgeschichtlichen Federsee. Archäologische und naturwissenschaftliche Untersuchungen. Hemmenhofener Skripte 5. Janus-Verlag. Schlichtherle, H., Bleicher, N., Dufraisse A., Kieselbach, P., Maier, U., Schmidt, E., Stephan, E., & Vogt, R. (2010). Bad Buchau -Torwiesen II: Baustrukturen und Siedlungsabfälle als Indizien der Sozialstruktur und Wirtschaftsweise einer endneolithischen Siedlung am Federsee. In E. Classen, T. Doppler, & B. Ramminger (Eds.) Familie – Verwandtschaft – Sozialstrukturen: Sozialarchäologische Forschungen zu neolithischen Befunden (pp.  157–178). Fokus Jungsteinzeit, Berichte der AG Neolithikum 1, Kerpen-Loogh. Schmidt, R. (1930/37). Jungsteinzeit-Siedlungen im Federseemoor. Lieferung I–III. Filser. Schönfeld, G. (1997). Ein jungsteinzeitliches Dorf im Moor bei Unfriedshausen. Landsberger Geschichtsblätter, 95(96), 3–16. Sherratt, A. (2006). La traction animale et la transformation de l’Europe Néolithique. In P. Pétrequin, R.-M. Arbogast, A.-M. Pétrequin, S. Van Willigen, & M. Bailly (Eds.), Premiers chariots, premiers araires. La traction animale en Europe au IVème millénaire av. J.-C (pp. 329–360). CNRS. Steppan, K. (2004). Archäozoologische Untersuchungen in jung- und endneolithischen Moorsiedlungen am Federsee. In Landesamt für Denkmalpflege im Regierungspräsidium Stuttgart (Ed.), Ökonomischer und ökologischer Wandel am vorgeschichtlichen Federsee. Archäologische und naturwissenschaftliche Untersuchungen (pp. 187–231). Hemmenhofener Skripte 5. Janus-Verlag. Strahm, C. (2010). Endneolithische Siedlungsmuster. In I.  Matuschik, C.  Strahm, et  al. (Eds.), Vernetzungen. Aspekte siedlungsarchäologischer Forschung (pp. 317–330). Lavori. Strobel, M. (2000). Die Schussenrieder Siedlung Taubried I (Bad Buchau, Kr. Biberach). Ein Beitrag zu den Siedlungsstrukturen und zur Chronologie des frühen und mittleren Jungneolithikums in Oberschwaben. Theiss. Styring, A., Maier, U., Stephan, E., Schlichtherle, H., & Boogard, A. (2016). Cultivation of choice: New insights into farming practices at Neolithic lakeshore sites. Antiquity, 90(349), 95–110. https://doi.org/10.15184/aqy.2015.192 Torke, W. (2009). Die Ausgrabungen in der Siedlung Forschner. Stratigraphie, Baubefunde und Baustrukturen. In Die früh- und mittelbronzezeitliche « Siedlung Forschner » im Federseemoor. Befunde und Dendrochronologie Siedlungsarchäologie im Alpenvorland, XI (pp.  71–360). Forschungen und Berichte zur Vor- und Frühgeschichte in Baden- Württemberg, 113. Theiss.

Index

A Abrupt climate change, 8, 187–189, 191, 193, 199–202, 204, 205 Acquisition risk, 6, 7, 131–133, 136, 138–140, 142, 143 Activities, 4, 15, 16, 30, 47, 72, 74, 98, 99, 101–110, 116–122, 133–136, 148, 149, 152, 155, 157, 166, 172, 176, 179, 180, 216, 222, 239, 244–246, 253–255, 259, 261–263, 265–269, 286, 289, 290 Adaptation, 5, 16, 123, 136, 150, 158, 159, 166, 178, 186–209 Agriculture, 7, 8, 151, 157, 159, 160, 167, 169, 171, 286 Allee effect, 7, 149, 152–154, 158 Alpine, 14, 31, 245, 249, 251, 279, 281, 287, 288 Alyawarre, 6, 135, 137–140 Amazon, 169 Amazonian Dark Earth, 165 American West, 26, 27 Anthropology, 3, 12, 20, 22, 25, 28 Anticipatory manufacture, 206–208 Archaeological landscape, 96, 140–142, 148, 165, 186, 215, 216, 236, 238, 239, 244, 245, 251, 267, 269, 277 Archaeological locational modeling, 9 Archaeological site, 5, 10, 43, 44, 46, 64, 82, 111, 166, 171–173, 175, 177, 179–181, 193, 248, 252–254 Archaeology, 2–12, 14, 16–17, 20, 21, 25, 28, 29, 31, 32, 34, 43–45, 58, 62, 64, 67, 71–88, 150, 151, 159, 166, 167, 177, 179, 186, 244, 246, 266, 267, 269

Archaic period, 38, 145 Arkansas, 47, 51, 57, 64–67 Atlantic, 191–193, 197, 202, 203, 205, 207, 208, 221, 278 Australia, 6, 130, 132, 134, 135, 137, 140, 142, 149 B Bayesian, 8, 14, 60, 187, 200, 201, 203, 204 Beads, 173, 176 Behavior, 3, 4, 6, 7, 12, 13, 20–24, 28, 33, 72, 80, 81, 86, 88, 98, 99, 117, 131, 132, 134, 142, 148, 149, 151, 152, 159, 167, 186, 216 Behavioral ecology, 3, 6, 7, 21, 22, 130 Behavioral economics, 19, 35, 39 Beuronian, 219, 220 Binford, L.R., 2, 3, 20, 33, 99, 105, 108, 110, 112, 122, 123, 190 Biodiversity, 5, 136, 157, 166, 167 Biology, 21, 22, 30, 44, 52, 58, 66, 148 Bison, 27–29 Blades, 195, 197, 198, 206–208, 220, 221, 224–227, 231, 234–236, 238, 254, 259, 260, 263, 264, 267, 268 Bone repository, 119 Boreal, 100, 192, 193, 197, 219, 221 Bronze Age, 15, 16, 155, 251, 258, 260, 278–291 Burial, 13, 85, 86, 108, 118, 253, 265

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296 C Cache/caching, 103–107, 109, 111, 117, 118, 120, 122 California, 5, 12, 27, 73, 81–86 Canada, 9, 28, 117, 123 Caribou, 9, 10, 96, 98–103, 105–111, 113, 117–123 Caribou Inuit, 9, 10, 96, 97, 100–103, 123 Caves/rockshelters, 246, 252, 253, 269 Central Desert (Australia), 137 Ceramics, 170, 172, 173, 176–178, 249, 250, 253–255, 257, 258, 264, 265, 268, 283 Chronology, 15, 33, 166, 171, 178, 182, 202, 204, 244, 246, 254, 268, 282, 284 Climate change, 8, 186–209 Counteractive perturbation, 150, 155 Counteractive relocation, 150, 155 Coupled natural-human systems, 8, 187, 190 Cultural diversity, 16, 167, 168, 177, 181 Cultural feature, 96, 215 Cultural landscape, 1–17, 54, 130, 166, 168, 177, 181, 186, 187, 189, 244–269, 290 Cultural resource management, 4, 43, 60, 244 Culture, 4, 6, 12, 73, 99, 130, 148, 149, 165–167, 169, 177, 178, 181, 182, 223, 224, 238, 239 D Danube, 14, 222, 245, 248–253, 255, 260, 262, 266, 268, 269, 278, 283, 290 Decision making, 5–8, 25, 30, 31, 34, 123, 266 Deductive model, 8, 9 Demography, 42, 194 Dendrochronology, 251, 253, 288 Dental calculus, 72, 79–80 Dietary tracer, 74–75 Discriminant function analysis, 14 E Early Mesolithic, 96, 195, 197, 198, 200, 202, 206, 208, 216, 218–230, 232–238, 247, 259–262, 264, 266 Early Neolithic, 235, 244, 253, 258, 268, 278, 281 Ecological constructivism, 130 Ecology, 2, 7, 14, 15, 20–22, 25, 131, 137, 142, 149, 152, 155, 166–168, 179, 180, 191, 244 Economic change, 218, 249, 269, 290 Economy, 27, 47, 98, 123, 135, 157, 166, 167, 179, 195, 245, 253, 265, 268, 290 8200 cal. BP event, 189, 191, 200, 203

Index Encounter rate, 133, 137, 141 Energy, 6, 24, 25, 132–134, 137–139, 141, 191, 194, 208 Environment, 2, 7–13, 15, 16, 31, 43–47, 49, 60, 65–67, 74, 80, 98, 123, 130, 133, 137, 138, 148, 150–152, 159, 166–168, 177–180, 186, 206–208, 215, 218, 221, 248, 251, 289, 290 Environmental archaeology, 16, 44, 159, 166, 186, 187, 249 Environmental determinism, 46 Environmental variables, 44, 216 Ethnoarchaeology, 9, 11, 12 Ethnography, 9, 12, 13, 33, 42, 71–88, 98, 131, 218 Europe, 8, 32, 96, 149, 150, 186, 187, 192, 195, 197, 203, 207, 220, 225, 238, 239 Evolutionary ecology, 11, 15 Excavation, 5, 33, 81, 117, 140, 168, 173–177, 215, 229, 233, 236, 237, 244–248, 250–252, 257, 258, 265, 266, 269, 279, 281, 284 Exchange, 8, 28, 31, 96, 194–196, 199 F Farming, 12–16, 47, 50, 53, 54, 66, 153, 155, 157, 159, 218, 244, 245, 249, 252 Fire, 73, 130, 131, 136, 141, 142, 150, 167, 193, 194, 205 Fishing, 10, 101, 103, 107, 110, 111, 122, 181, 244, 245, 252, 253, 267, 268, 279, 289 Flakes, 206, 224, 225, 254, 259, 264, 267 Food preparation, 107, 118 Forests, 27, 100, 123, 152, 166, 167, 169, 173, 179, 193, 194, 197, 205, 208, 220, 221, 249, 267, 284, 290 Forest-tundra transition, 9 Future research, 8, 15, 142, 187, 190, 191, 200, 207, 208, 266 G Geographic Information Systems (GIS), 15, 43, 45, 49, 50, 56, 58, 60, 66, 67, 250, 269 Geographic tracer, 74, 75 Germany, 14, 15, 30, 32, 96, 194, 204, 216, 217, 220–223, 226, 228, 238, 244–269, 278–291 Grasslands, 193, 220 Great Basin, 14 Great Plain, 27–29

Index H Habitat, 3, 7, 44, 58, 96, 136, 141, 149–155, 157–160, 244 Hair isobiography, 86 Heat treatment, 219, 221, 224, 254 Hides, 26–30, 105, 107 History, 2, 3, 5, 6, 8, 10, 12–14, 25, 32, 34, 71–88, 101, 105, 110, 122, 158, 166, 174, 180, 186, 187, 190, 216, 218, 222–223, 235, 248, 251–253 Houses, 72, 86, 101, 110, 117, 194, 249, 281–284, 289 Human behavioral ecology (HBE), 21, 22, 24, 28, 148, 149, 159 Human beings, 20–34 Human ecology, 1–17, 31, 130, 186–209 Human-environment interaction, 2, 5, 8–10, 13, 15, 20, 130, 187, 244 Humanity, 12, 34 Human settlement niche, 44 Human teeth, 72–75, 77–81, 83, 84 Human tissue, 75 Hunter-gatherer, 2, 14, 20–34, 42, 43, 46, 96, 98, 99, 131, 132, 148, 151, 159, 190, 195, 196, 199, 206, 218–220, 249, 252, 253, 267 Hunting, 6, 16, 22–24, 26, 28–30, 96, 98, 99, 101, 102, 105, 106, 118–120, 122, 123, 130, 135–142, 150, 219, 222, 244, 245, 252, 253, 267, 268 Hunting blind, 106, 118–120 Hydrology, 45, 54, 57, 63, 66, 165, 166, 217, 228, 247, 248, 251, 256, 258 Hypothesis, 8, 12, 54, 83, 84, 169, 186–188, 190–192, 199, 200, 204–209, 224, 238, 245, 284, 289 Hypothesis testing, 4, 16 I Iberia, 160, 161, 163 Ideal free distribution (IFD), 7, 8, 148–160 Inceptive perturbation, 150, 154 Inceptive relocation, 150, 154 Indigenous, 5, 13, 14, 27, 166, 169, 180, 244, 249, 253 Inductive model, 9 Infrastructure, 150, 189, 194–196, 208 Interdisciplinary, 244, 245, 251 Interfluvial zone, 168–173, 178, 181 Intra-tooth isobiography, 78, 83–86 Inuit, 5, 9, 24, 96, 98–100, 102, 103, 105, 108, 110, 112, 113, 121–123, 134

297 Isobiography, 71–88 Isoscape, 71, 88 Isotope, 12, 71–88 J Jochim, Michael A./Jochim, Mike, 2–9, 11–14, 16, 17, 20, 29–35, 42, 72, 96, 97, 130, 152, 159, 186, 187, 190, 194, 197, 205–209, 216, 218–222, 224, 225, 229, 238, 244–255, 265–267, 278 K Kahneman, D., 24, 25, 30, 31 Kangaroo, 6, 27, 130, 131, 135–142 Kazan River, 99–105, 109–113, 115, 118, 120, 122, 123 Knapping, 198 L Lake Federsee, 15, 278, 279, 281 Lakes, 14, 31, 33, 97–103, 105, 107, 109–113, 117, 118, 121–123, 204, 216, 218, 219, 222, 226, 227, 229, 244, 245, 249–256, 262, 265–267, 278, 279, 281, 283–290 Landscape change, 157 Last Glacial Maximum (LGM), 32 Late Mesolithic, 195, 197, 198, 200, 201, 203, 204, 207, 208, 216, 218, 220–227, 229, 230, 232–236, 238, 259–261, 263 Late Neolithic, 244–246, 249, 253, 254, 259, 260, 263, 266–269, 278, 279, 281, 283–286, 289, 290 Late Paleolithic, 216, 218–224, 226–230, 232–238, 247, 248, 262, 265, 266 Linear programming, 27, 31 Lithic debitage, 217, 236, 238 Lithic reduction, 208, 216–239, 246 Lithic technology, 8, 15, 196, 200, 205–208, 220, 221, 236, 238 Livestock management, 150 Lizard, 6, 130, 131, 135, 136, 140 Location, 7, 10, 16, 43–47, 49, 51–53, 57, 58, 60–62, 64–67, 73, 75, 80–82, 86, 97, 99, 101–103, 105, 107–110, 113, 117, 118, 120–123, 135, 140, 152, 154, 157, 166, 168–174, 178, 181, 182, 198, 199, 216, 218–222, 224, 229, 236, 245, 246, 248, 250, 251, 254, 255, 257, 258, 260–262, 264–268, 284, 286 Locational analysis, 44, 46 Logistic regression, 43, 46, 60–64, 67

298 M Mahalanobis distance, 55 Martu, 6, 134–137, 139–141 Maximum entropy model, 61, 63, 67 Mediterranean, 155 Men resource choice, 132 Mesolithic, 8, 14, 15, 30, 32, 33, 96, 186–190, 193–209, 216, 218–238, 247, 252, 253, 259–264, 266–268, 278, 289 Method, 3, 5, 9, 11–16, 43, 50, 51, 55, 58, 62, 64, 66, 72, 77, 81, 87, 169, 223, 225, 231, 233, 236, 238, 239, 246, 250–251 Methodology, 44, 51, 56, 66, 76, 226 Microlith, 190, 195–198, 200, 206–208, 220–222, 224, 225, 238, 259, 261, 263, 264 Middle Mesolithic, 187, 190, 195, 197, 199, 201, 206–208 Middle Neolithic, 246, 249, 252, 253, 257, 258, 260, 263, 264, 268, 278 Migration, 12, 78, 81, 100, 102, 110, 113, 150, 155, 285, 289 Mobility, 71, 74–76, 80, 83, 87, 88, 96, 98, 99, 123, 177, 180–181, 197, 200, 206–208, 219, 220, 222, 244, 245, 267, 284–285 Model, 1–3, 5–10, 15, 16, 19–35, 41–67, 75, 131, 132, 134, 136, 138, 140, 142, 148–154, 159, 169, 177–180, 187, 189–191, 200–204, 207–209, 267, 285, 290, 291 N Neolithic, 14–16, 149, 155, 218, 229, 234, 235, 239, 244–269 New Archaeology, 215, 278 Niche construction, 7, 8, 10, 148–160 9300 cal. BP event, 189, 200 Nitrogen isotopes, 74, 77, 80, 83, 84, 281 Non-site archaeology, 43, 46, 66, 67 North America, 14, 26, 32, 74, 75, 149 North Sea Basin, 186–209 Nunavut, 9 O Optimal foraging theory (OFT), 5, 6, 21–26, 29, 30, 32 Optimization, 21–27, 31, 34, 142 Organization, 10, 16, 29, 178, 180, 197, 208, 209 Oxygen isotopes, 73, 75, 78, 80

Index P Paleoclimate, 8, 192 Paleoecology, 8 Paleoenvironment, 7, 189, 199, 204, 209 Pardo River, 10, 170–173, 177, 178, 180, 181 Pastoralism, 7, 148, 154 Plowzone survey, 250–251, 265 Pollen/palynology, 192, 193, 251, 253 Population, 5–7, 9, 10, 12–14, 16, 21, 28, 32, 44, 56, 65, 67, 80, 85–87, 96, 99, 102, 134–137, 140, 149–155, 158, 159, 166–171, 177–181, 186, 187, 189, 191, 192, 194, 200, 204, 205, 208, 220, 221, 225, 231, 290 Preservation, 13, 15, 33, 34, 101, 193, 221, 239, 251, 253, 257, 265, 279 Principal components analysis (PCA), 49–58, 67 Private collections, 244, 246, 248, 264, 268 Processual archaeology, 13, 44 Q Qualitative, 22, 53 Quantitative, 8, 11, 22, 50, 132, 134, 136, 138, 216 Quarry, 15, 246, 247, 266–268 R Radiocarbon, 73, 81, 175, 187, 193, 194, 199–201, 203, 205, 246, 253, 281, 282 Rainforest vegetation, 165 Raw material, 31, 165, 172, 190, 191, 194, 196–199, 207, 244, 246, 251, 257, 267, 268 Regional survey, 215, 216, 244–269 Residential site, 97, 100, 103, 117, 122, 123 Rhine, 187, 190, 249, 251, 252, 268, 278, 290 Rhine-Meuse-Scheldt culture, 188 S Scale, 5, 8, 10–16, 23, 28, 29, 31–34, 43, 47, 48, 50, 57, 59, 60, 72, 80, 88, 96, 98, 102, 119, 133, 139, 148, 149, 152–154, 166–168, 180, 182, 186, 187, 189, 191–193, 200, 204, 205, 207, 209, 244 Seasonal activity, 109 Seasonality, 101–103, 116 Seasonal round, 33, 88, 98 Sedentism, 11, 179

Index Settlement, 2, 4, 9, 13–17, 31, 34, 41–67, 95–123, 131, 154, 168, 216, 244, 277–291 Settlement area, 278–280 Settlement change, 277 Settlement choice, 9, 44, 49–58, 60, 66, 123 Settlement theory, 43, 64, 66 Settlement types, 47, 218, 283, 290 Sexual division of labor, 133 Social aggregation, 30, 116, 120, 122 Social-ecological systems, 16, 187, 189, 193–195, 199, 204, 205, 207 Social environment, 2, 9, 15, 16, 45, 46, 65, 186 Social landscape, 6, 45, 46, 49, 65, 66, 130, 143 Social organization, 9 Sociocultural change, 245 Soil, 10, 13, 14, 45, 49, 50, 54, 57, 62, 63, 65, 66, 73, 74, 122, 165–167, 169, 172–174, 176, 177, 246, 249, 251, 252, 254, 262, 268, 278, 284, 285, 289, 290 Southwest German Archaeological Survey Project (SWGASP), 216, 218, 222, 226, 236, 245–248, 250, 251, 255–257, 259–262, 266 Spatial analysis, 3, 197 Spatial scale, 9, 32, 159 Statistics, 3, 58, 62, 64–66, 223 Stratigraphy, 174, 233, 236, 239 Strontium isotopes, 12, 77, 78, 81, 83 Subsistence risk, 267 Suitability, 7, 16, 58–66, 149–155, 157–159, 223 Surface assemblage, 223, 239, 246, 247, 266, 268 Surface scatter, 13, 14, 226, 236, 245, 254, 258, 266, 267

299 Survey, 5, 14, 33, 46, 47, 98, 99, 103, 104, 113, 118, 172, 177, 216, 218, 222–224, 226–230, 236, 237, 239, 245–248, 250, 254–267, 269 Swabia, 15, 245, 246 Swabian Alb, 14, 15, 218, 244–253, 255–260, 262–269 T Taphonomy, 34, 138, 196 Technological organization, 209 Technology, 2, 4, 5, 7, 8, 27, 43, 99, 148–150, 157, 181, 194, 195, 198, 200, 205–208, 221, 224, 234, 238 Temporal scale, 5, 6, 11, 33, 34, 72, 73 Tent ring, 105, 106, 109, 115, 117, 120 Theory, 3, 5, 6, 9, 11–13, 20–23, 26–30, 44, 58, 60, 64, 67, 102, 130–132, 134 Tienen quartzite, 197, 198 Transhumance, 8, 155, 157–160 Trapeze, 195, 207, 208, 221, 225, 259 Tundra, 96, 100, 111, 123 U United States, 28 Upper Paleolithic, 32, 235, 252, 259–262, 264, 265 Upper Swabia, 15, 245–247, 249–254, 262, 266–269, 286, 289, 290 W Western Desert (Australia), 135, 142 Women resource choice, 132 Wommersom quartzite, 198, 199