A Critical Analysis of the Adoption of Maize in Southern Ontario and Its

A CRITICAL ANALYSIS OF THE ADOPTION OF MAIZE IN SOUTHERN ONTARIO AND ITS

SPATIAL, DEMOGRAPHIC, AND ECOLOGICAL SIGNATURES

A thesis submitted to the Committee of Graduate Studies in partial fulfillment of the requirements for the degree of Master of Arts in the Faculty of Arts and Science.

TRENT UNIVERSITY Peterborough, Ontario, Canada

ABSTRACT:

A Critical Analysis of the Adoption of Maize in Southern Ontario and its Spatial, Demographic, and Ecological Signatures

Eric Beales

This thesis centers on analyzing the spatial, temporal, and ecological patterns associated with the introduction of maize horticulture into Southern Ontario – contextualized against social and demographic models of agricultural transition. Two separate analyses are undertaken: a regional analysis of the spread of maize across the

Northeast using linear regression of radiocarbon data and a standard Wave of Advance model; and a local analysis of village locational trends in Southern Ontario using a landscape ecological framework, environmental data and known village sites. Through the integration of these two spatial and temporal scales of analysis, this research finds strong support for both migration and local development. A third model of competition and coalescence is presented to describe the patterning in the data.

Keywords: Ontario Archaeology, Spread of Agriculture, Maize, Geostastical Analysis, Geographic Information Systems, GIS, Environmental Modeling, Demographic Modeling, Middle Woodland, Late Woodland, Southern Ontario

ACKNOWLEDGEMENTS:

This study would not have been possible without the generous support of several people and institutions. First and foremost, I would like to express my utmost gratitude to my supervisor, Dr. James Conolly. James first suggested this topic to me three years ago and has provided me with a constant stream of research, ideas, and expertise in geostatistical modelling, people-plant interaction, and paleodemography ever since.

James’ contagious enthusiasm for asking big questions sustained my resolve to conduct a research program of this magnitude over the last three years and, ultimately, made this thesis a much more cohesive piece of work.

I would also like to thank the other members of my committee: Dr. Rodney

Fitzsimons and William Fox. While a self-described “outsider” to Ontario archaeology,

Rod provided many helpful comments, edits, and suggestions along the way and encouraged me to question entrenched beliefs and theories and to clarify my assumptions.

Similarly, Bill was always willing to discuss my research with me and his encyclopedic knowledge of Ontario’s past was of immeasurable help as I developed ideas and collected data. Dr. Robert MacDonald graciously accepted to serve as my external examiner and provided many helpful suggestions that have been incorporated into this final draft.

Thanks must also go to many other members of the Anthropology Graduate

Program at Trent. I have had the pleasure of working with a number of fantastic people while here and their friendship and advice have helped me become a better researcher and student.

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This study would not have been possible without financial support from a number of sources. External funding was provided through an Ontario Graduate Scholarship by the Ontario Ministry of Training, Colleges, and Universities as well as a Canada

Graduate Scholarship by the Social Sciences and Humanities Research Council.

Scholarships were also provided by Trent University, the Sandi Carr Graduate

Scholarship, the Richard B Johnston fund for Archaeology, and ESRI Canada.

Similarly, many people and institutions were incredibly generous in providing data for this research. Special thanks to Dr. Matthew Betts at the Canadian Museum of

Civilization, Dr. Jonathan Lothrop at the New York State Museum, and Robert von Bitter at the Ontario Ministry of Tourism, Culture and Sport for locational data. Additionally, I would like to express my thanks to the many researchers who provided site data and reports for those sites which slipped through the institutional cracks. Thanks also go to

Dr. Lawrence Jackson, Dr. Helen Haines, Dr. Gary Crawford, and Dr. David Smith for providing maize samples from the Dawson Creek site and the Princess Point site for radiocarbon dating.

To my family, I extend my heartfelt thanks for your unending support over this entire process. You always provided an ear when I needed to talk or encouragement when

I thought that I would never be finished. To my wife and stalwart editor, Jenny, thank you for your unwavering support. Through all of the life changes we have seen over the past three years, you always made time for my research. Without your help, this thesis would not have been realized. Lastly, to our daughter Daphne, thank you for providing much-needed distraction and comic relief throughout this process. Hopefully Daddy will be able to play more often now that it is finished.

TABLE OF CONTENTS

ABSTRACT ii

ACKNOWLEDGEMENTS iii

TABLE OF CONTENTS v

LIST OF TABLES xi

LIST OF FIGURES xii

1 A CRITICAL ANALYSIS OF THE ADOPTION OF MAIZE IN 1 SOUTHERN ONTARIO

1.1 INTRODUCTION 1

1.2 DATA AND METHODS 3

1.3 OUTLINE OF CHAPTERS IN THESIS 4

2 MAIZE AND CULTURE IN THE NORTHEAST 6

2.1 THE EARLIEST EVIDENCE FOR MAIZE CONSUMPTION IN THE 8 NORTHEAST 2.1.1 Earliest Evidence for the Introduction of Maize into the 8 Northeast 2.1.2 Stable Carbon Isotope Studies and the Initial 11 Introduction of Maize 2.1.3 Summary 13

2.2 WHERE DID MAIZE IN THE NORTHEAST COME FROM? 14 2.2.1 Northern Flint Maize and its Developmental History 14 2.2.2 Southwestern Origin for Northeastern Maize 16 2.2.3 Summary 19

2.3 DEMOGRAPHIC MODELS FOR THE INTRODUCTION OF MAIZE 21 INTO THE NORTHEAST 2.3.1 Origins and Emergence of Iroquoian Horticultural 21 Patterns

2.3.2 In-Situ vs. Migration Theories of Iroquoian Origins 22 2.3.3 Summary 28

2.4 SUMMARY OF MAIZE AND CULTURE IN THE NORTHEAST 30

3 THEORETICAL MODELS FOR THE ADOPTION OF 32 AGRICULTURE 3.0.1 Origins of Agriculture in Neolithic Europe 33

3.1 MIGRATIONIST THEORIES OF AGRICULTURE ADOPTION 35 3.1.1 Demic Diffusion and the Wave of Advance 36 3.1.2 Long-Distance Migration, Chain Migration, and 41 Leapfrog Development 3.1.3 Summary 45

3.2 INDIGENIST THEORIES OF AGRICULTURAL ADOPTION AND 46 DEVELOPMENT 3.2.1 Socioeconomic Explanations for the Adoption of 47 Agriculture 3.2.2 Integrationist Models, Availability, and Agricultural 50 Frontiers 3.2.3 Summary 55

3.3 CONCLUSIONS 56

4 IDENTIFYING MIGRATION AND CULTURAL DIFFUSION 58

4.1 IDENTIFYING MIGRATION AND CULTURAL DIFFUSION 58 PROCESSES 4.1.1 Determining Migration in the Archaeological Record 58 4.1.2 Determining Cultural Diffusion in the Archaeological 60 Record

4.2 THE EFFECTS OF SPATIAL AND TEMPORAL RESOLUTION ON 63 IDENTIFYING MIGRATION AND CULTURAL DIFFUSION PROCESSES 4.2.1 Regional Analysis of the Spread of Maize Across the 64 Northeast 4.2.2 Local Analysis of the Spread of Maize into Southern 65 Ontario

4.3 SUMMARY 68

5 REGIONAL ANALYSIS OF THE SPREAD OF MAIZE INTO THE 69 NORTHEAST – DATA AND METHODS

5.1 DATASETS USED FOR TESTING DEMIC DIFFUSION IN THE NORTHEAST 5.1.1 Sampling Groups 74

5.2 POINT OF ORIGIN ANALYSIS 76 5.2.1 Point of Origin Methods 76

5.3 LINEAR REGRESSION OF ALL SITES FROM PROPOSED POINTS 79 OF ORIGIN 5.3.1 Linear Regression Methods 79 5.3.2 Evaluating a Demic Model for the Northeast 80 5.3.3 Hypothesis of Demic Diffusion in the Northeast 83

6 LOCAL ANALYSIS OF THE SPREAD OF MAIZE INTO 84 SOUTHERN ONTARIO – DATA AND METHODS

6.1 VILLAGE SITES AS A PROXY FOR SOCIOECONOMIC PATTERNS 85 6.1.1 Using Presence-only Data to Evaluate Settlement 85 Continuity / Discontinuity 6.1.2 Horticultural Site Selection Patterns in Southern 86 Ontario

6.2 ECOLOGICAL ANALYSIS OF SETTLEMENT CHOICE THROUGH 88 TIME

6.3 DATASETS USED FOR TESTING MIGRATION AND CULTURAL 90 DIFFUSION PROCESSES IN SOUTHERN ONTARIO 6.3.1 Datasets Used 90 6.3.1.1 Site Data 90 6.3.1.2 Temporal Groups 93 6.3.1.3 Environmental Data 94

6.4 MAXIMUM ENTROPY ANALYSIS OF VILLAGE LOCATIONAL 97 CHOICES THROUGH TIME 6.4.1 MaxEnt Methods and Parameters 98

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6.4.2 Stepwise Reduction of Environmental Variables 99

6.5 TESTS OF INDEPENDENCE IN ECOLOGICAL VARIABLES 100 BETWEEN TEMPORAL GROUPS 6.5.1 Test of Independent Samples Methods and Parameters 102

6.6 SUMMARY 102 6.6.1 Hypotheses of Migration and Diffusion for Southern 103 Ontario

7 REGIONAL ANALYSIS OF THE SPREAD OF MAIZE INTO THE 104 NORTHEAST – RESULTS

7.1 GLOBAL REGRESSION OF ALL MAIZE-BEARING SITES 105 AGAINST DISTANCE FROM MEXICO 7.1.1 Global Regression Results 105 7.1.2 Summary 107

7.2 POINT OF ORIGIN ANALYSIS 108 7.2.1 Results for Most Probable Origin Areas for Groups A 108 to H 7.2.2 Internal Distance Matrices 110 7.2.3 Summary 112

7.3 LINEAR REGRESSION ANALYSIS AND EVALUATION OF A 114 DEMIC MODEL 7.3.1 Results for the Reduced Major Axis Regression for 114 Groups A to H 7.3.1.1 Dataset A (Earliest Uncontested Date) 115 7.3.1.2 Dataset B (Earliest Dates and Pooled Dates, where 116 Appropriate) 7.3.1.3 Dataset C (Earliest Direct Dates on Maize) 117 7.3.1.4 Dataset D (Pooled Direct Dates on Maize) 118 7.3.1.5 Dataset E (Earliest Dates by Sampling Grid) 119 7.3.1.6 Dataset F (Pooled Dates by Sampling Grid) 120 7.3.1.7 Dataset G (Earliest Direct Dates by Sampling 121 Grid) 7.3.1.8 Dataset H (Pooled Direct Dates by Sampling Grid) 122

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7.3.1.9 Reduced Datasets 123 7.3.2 Summary 125

7.4 CONCLUSIONS 129

8 LOCAL ANALYSIS OF THE SPREAD OF MAIZE INTO 130 SOUTHERN ONTARIO – RESULTS

8.1 GROWING REQUIREMENTS FOR MAIZE 131 8.1.1 Climatic Requirements of Maize 131 8.1.1.1 Frost-free Days and Solar Heat 131 8.1.1.2 Precipitation 135 8.1.2 Soil and Topographic Requirements of Maize 136 8.1.2.1 Soil 136 8.1.2.2 Topography 136 8.1.3 Primary Limiting Variables 137 8.1.4 Summary 138

8.2 MAXIMUM ENTROPY ANALYSIS OF VILLAGE LOCATIONAL 139 CHOICES THROUGH TIME 8.2.1 Stepwise Reduction of Environmental Variables 140 8.2.3 Results of Maximum Entropy Modeling 141 8.2.3.1 MaxEnt Results for Stage 1 144 8.2.3.2 MaxEnt Results for Stage 2 147 8.2.3.3 MaxEnt Results for Stage 3 150 8.2.3.4 Summary of MaxEnt Results 153

8.3 TESTS OF INDEPENDENT SAMPLES 155 8.3.1 Results of Kruskal-Wallis Test of Independent Samples 156 8.3.2 Results of Wilcoxon Mann-Whitney Test of Independent 158 Samples 8.3.3 Summary of Results for Tests of Independence 161

8.4 CONCLUSIONS 163

9 DISCUSSION OF RESULTS 165

9.1 DISCUSSION OF RESULTS OF REGIONAL ANALYSIS OF THE 166 SPREAD OF MAIZE INTO THE NORTHEAST 9.1.1 Point of Origin Analysis 167 9.1.2 Linear Regression 169

9.2 DISCUSSION OF RESULTS OF LOCAL ANALYSIS OF THE SPREAD 173 OF MAIZE INTO SOUTHERN ONTARIO 9.2.1 Maximum Entropy Analyses 175 9.2.2 Tests of Independent Samples 176 9.2.3 Summary of Local Analysis results 179

9.3 AN INTEGRATIVE MODEL FOR THE INTRODUCTION OF MAIZE 182 HORTICULTURE INTO SOUTHERN ONTARIO 9.3.1 The Availability Model, Competition, and Agricultural 183 Frontiers

9.4 CONCLUSIONS 188

10 CONCLUSIONS 190

10.1 SUMMARY OF RESEARCH 190

10.2 FUTURE RESEARCH DIRECTIONS 193

REFERENCES 196

APPENDIX 1: REGIONAL ANALYSIS DATA DESCRIPTION 223

APPENDIX 2: LOCAL ANALYSIS DATA DESCRIPTION 344

LIST OF TABLES:

Table Description Page

5.1 Site summary for regional analysis 72

5.2 Description of sampling groups for regional analysis 75

5.3 Results of a Northeastern calculation of Fisher’s Wave of 83 Advance equation and Fort’s time-delay model

6.1 All sites used in local analysis 92

6.2 Temporal groups used in local analysis 93

6.3 Preliminary environmental variables for analysis of settlement 95-96 trends

7.1 Results of point of origin analysis by sampling group 110

8.1 Final environmental variables 141

8.2 AUC scores for three models based on 500 replicate runs 142

8.3 Variable importance and contribution for the three final MaxEnt 154 models

8.4 Results of Kruskal-Wallis test for difference between temporal 156 groups

8.5 Results of Kruskal-Wallis test for difference between temporal 157 groups and random locations

8.6 Results of Wilcoxon Mann-Whitney test of independent samples 158 for three temporal groups

8.7 Results of Wilcoxon Mann-Whitney test of independent samples 159 for three temporal groups against randomly-located sites

LIST OF FIGURES:

Figure Description Page

2.1 Earliest Evidence of maize in Eastern North America 10

13 2.2 δ C (‰, PDB) for sites in Northeastern North America 12

2.3 Archaeologically recovered Eastern Eight-Row specimens from the 16 Northeast

2.4 Results from Matsuoka et al.’s (2002) microsatellite study of maize 18 variation

2.5 Development of the Iroquois based on ceramic information after 23 MacNeish

3.1 Map Showing the Spread of Early Neolithic Dates in Europe 35

3.2 The spatiotemporal expression of Fisher’s Wave of Advance model 38

3.3 van Andel and Runnels’ Modified Wave of Advance model 42

3.4 Anthony’s Migration Process 44

3.5 The three stage Availability model of the transition to farming 53

3.6 Zvelebil and Rowley-Conwy’s Availability model as an 54 integrationist treatment of the transition to agriculture

5.1 Northeast Study Region as defined for this analysis 71

5.2 Site distribution by time period and dating method 72

5.3 Sites in full Northeast study region dataset 73

5.4 HOA and POA locations for point of origin analysis 78

6.1 All sites used in local analysis 91

6.2 location of random and archaeological sites for tests of independence 101

7.1 Results of global regression for all maize-bearing sites 107

7.2 Internal point of origin analysis HOA locations 111

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Figure Description Page

7.3 Point of origin isochrone 112

7.4 Results of RMA regression, Group A 115

7.5 Results of RMA regression, Group B 116

7.6 Results of RMA regression, Group C 117

7.7 Results of RMA regression, Group D 118

7.8 Results of RMA regression, Group E 119

7.9 Results of RMA regression, Group F 120

7.10 Results of RMA regression, Group G 121

7.11 Results of RMA regression, Group H 122

7.12 Results of RMA regression on dataset D with the three earliest dates 124 removed

7.13 Results of RMA regression on dataset H with the three earliest dates 124 removed

7.14 Results of RMA regression s for datasets D.2 and H.2 with dated 128 material shown

7.15 Results of RMA regression s for dataset H.2 where only directly- 128 dated maize was used

8.1 Distribution of Frost-Free Days in Southern Ontario 133

8.2 Distribution of Growing Degree Days in Southern Ontario 134

8.3 Distribution of Corn Heat Units in Southern Ontario 134

8.4 Distribution of Rainfall during Growing Season in Southern Ontario 135

8.5 Weighted overlay of sand and sandy loam deposits, well-drained 137 soil and adequate GDDs in Southern Ontario

8.6 Final probability outputs of the three MaxEnt models 143

8.7 Results from MaxEnt analysis – Stage 1 145

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Figure Description Page

8.8 Response curves for Stage 1 MaxEnt model 146

8.9 Results from MaxEnt analysis – Stage 2 148

8.10 Response curves for Stage 2 MaxEnt model 149

8.11 Results from MaxEnt analysis – Stage 3 151

8.12 Response curves for Stage 3 MaxEnt model 152

9.1 Results of RMA regression on dataset H.2 against distance from 169 oldest maize-bearing site in region

9.2 Isochrone of calibrated dates in Group H 170

9.3 Predictive surface for best-fitting linear model 170

9.4 Isochrone of calibrated dates in Group H.2 171

9.5 Isochrone of calibrated dates in Group H.2 with predictive surface 171 overlay

9.6 Box plots of environmental variable values at site locations by group 178

9.7 Regression of local terrain ruggedness by Growing Degree Days 179

9.8 The three stage Availability model of the transition to farming 184

9.9 δ13C for bone carbonate from Ontario samples through time 188

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CHAPTER 1: A CRITICAL ANALYSIS OF THE ADOPTION OF MAIZE IN

SOUTHERN ONTARIO

1.1: INTRODUCTION

The transition from foraging to food production should not be understated, having led to profound changes in nearly all aspects of society, from diet and health, to social and symbolic beliefs. While the end result of this transition in Southern Ontario is understood relatively well, the origins, timing, and even mechanisms of adoption are still a matter of much debate. This thesis will focus on analyzing the spatial, temporal, and ecological patterns associated with the introduction of maize horticulture into the region, contextualized against social and demographic models of agricultural transition.

The origins, evolutionary development, and ultimate spread of domesticated corn

(Z. mays L. ssp. mays) across the Americas have been central themes of North American archaeology since at least the middle of the 20th century (Flannery 1973; MacNeish 1967,

1992; Smith 1998, 2001; Willey and Phillips 1958). Maize has always held privileged status in the literature, associated with the development of sociopolitical complexity, changes in social and symbolic life, and the development of patterns that would form the basis of many “formative” lifeways (Flannery 1973; MacNeish 1967, 1992). This was certainly the case for northeastern North America, where the introduction of maize was initially tied to the development of socio-political complexity and, in particular, to the rise of Hopewellian patterns in the Ohio River Valley (c.f. Griffin 1960, 1967; Yarnell

1964). In this, the adoption of maize was seen as a necessary step towards social stratification and ritual power (Fritz 1990:402; B. Smith 1989:1569, 1992:201).

Of course, maize is not indigenous to Eastern North America, having been domesticated for thousands of years before its first arrival in the Northeast between 1500 and 2000 years ago. However, as domesticated corn is fully reliant on humans to disperse, plant, and tend its seeds (Lusteck 2006:524), it acts as an excellent proxy for the social and demographic processes that may have led to its appearance in the region.

Therefore, by tracing the history of maize, as well as the patterns of its spread, researchers can infer systems of community interaction, trade, the diffusion of technologies and practices, or even migration. In fact, other than some evidence for squash in New York and Pennsylvania, and chenopod in the Ohio River Valley (Hart

2001:165; Hart and Asch Sidell 1996, 1997; McConaughy 2003:17; Stothers and Abel

2002), it appears that maize was largely the first cultigen to be grown in the Northeast.

The choice to grow corn, therefore, was likely a very conscious one, one that would require profound changes in settlement, technology, demography, and diet.

The first appearance of maize into the region has been mobilized to argue everything from the migration of agriculturalists into the Northeast from Ohio to the slow adoption of cultigens by local foraging groups (Crawford and Smith 1996; Snow 1995,

1996). While prior evidence suggested the rapid appearance of maize throughout New

York and Ontario around AD 900, new macrobotanical and isotopic data indicate signs of incipient maize horticulture in Ontario around AD 250 and perhaps even earlier in New

York (Crawford et al. 2006; Harrison and Katzenberg 2003; Hart et al. 2007:564). As these new data emerge, a re-evaluation of both the timing and spread of agriculture across

3 the Northeast becomes vital. As many argue, neither migration nor acculturative models of agricultural development properly reflect the archaeological data in the Northeast, which is increasingly showing great regional diversity in the character of agricultural adoption (Hart 2001; Hart and Brumbach 2009; Martin 2008).

Two major theories exist for the appearance of Iroquoian patterns in the

Northeast: one focused on in-situ development, the other on the migration of a foreign group into the region. On the other hand, while maize has often formed a key part of the in-situ / migration debate, little has been done to actually integrate archaeological data with demographic models of agricultural adoption. Instead, the processes of migration or local adoption are often used as shorthand for describing the end result of major demographic change without actually addressing the processes behind the choice to adopt an agricultural lifeway. Without a critical assessment of the processes that led to agricultural uptake, or of what researchers might expect these patterns to reflect in the archaeological record, these arguments run the risk of becoming “just so stories” – based more on historic narratives and shifting research climates than on the growing northeastern dataset.

1.2: DATA AND METHODS

By mobilizing spatial, temporal, and ecological data associated with the spread of maize into the region, this thesis will attempt to critically evaluate the demographic

4 processes that have been proposed for the origins of maize in Southern Ontario. Through a series of hypothesis tests designed to approximate models of short- and long-distance migration as well as local development processes, this research will seek to contextualize the patterning in the data against dichotomizing ideas of forager and farmer. As part of this contextualization, it is necessary to evaluate both the regional and local patterns of maize in Southern Ontario. Two separate analyses are undertaken in this thesis formulated against established demographic models for agricultural change: a regional analysis of the spread of maize across the Northeast using linear regression and a standard Wave of Advance model of agricultural change; and a local analysis of village locational trends in Southern Ontario using a landscape ecological framework. The primary sources of data are the numerous radiocarbon dates associated with maize gathered from journals, site reports, institutional databases and individual data requests, as well as some drawn for this research. Additionally, the local analysis of the spread of maize into Southern Ontario leverages a suite of environmental data consistent with all possible variables associated with maize horticulture, collected from online geospatial databases and individual data requests, or created as part of this research. Through the integration of these two spatial and temporal scales of analysis, this research hopes to develop understanding of the processes behind agricultural adoption in Southern Ontario.

1.3: OUTLINE OF CHAPTERS IN THESIS

This thesis is divided into ten chapters. Chapter Two serves as an introduction to the study of maize’s earliest appearance in the Northeast. It includes a presentation of the earliest evidence of maize in the region mobilizing radiocarbon dates, and isotopic and genetic data, and provides a thorough exegesis of the current theoretical climate in

Southern Ontario and the views of the major processes behind maize’s entry into the region. Chapter Three presents a discussion of models of agricultural adoption, and provides the theoretical basis for the analyses undertaken, while Chapter Four focuses mainly on the methodological means of evaluating agricultural change from both a regional and local perspective. Chapters Five and Six present the particular data and methods for the regional and local analyses, as well as present the hypotheses tested.

Chapters Seven and Eight present the results of these analyses, to be fully discussed and contextualized in Chapter Nine. Finally, Chapter Ten summarizes the major findings of this research and presents fruitful avenues for future studies.

CHAPTER 2: MAIZE AND CULTURE IN THE NORTHEAST

Over twenty years ago, Bruce Smith (1989:1566) noted that the abundance of research in eastern North America had produced one of the most robust records of the origins and development of agriculture worldwide. While much of this scholarship concerns the development of the indigenous Eastern Agricultural Complex, a great deal of attention has also been paid to the adoption of maize agriculture in the region. This is due largely to the fact that the introduction of maize was initially tied to the development of socio-political complexity in eastern North America and, in particular, to the rise of

Hopewellian patterns in the Ohio River Valley (c.f. Griffin 1960, 1967; Yarnell 1964). In this, the adoption of maize was seen as a necessary step towards social stratification and ritual power (Fritz 1990:402; B. Smith 1989:1569, 1992:201).

However, as research progressed, it became clear that maize may not have held as central a role in Middle Woodland diets as was originally assumed (Abrams 2009:179; B.

Smith 1992:200). Once direct dates were obtained from some multicomponent early

Hopewellian sites (Chapman and Crites 1987; Conard et al. 1984), many of the supposed

Early Woodland corn cobs were found to be up to 2,000 years younger than the associated dates suggested (B. Smith 1992:203). Adding to this were numerous stable carbon isotope studies on human remains which indicated that maize was simply not an important component of the Middle Woodland diet, and did not become fully integrated into diets until the end of the first millennium AD (Bender et al. 1981; Katzenberg et al.

1995; Schwarcz et al. 1985; Stothers and Bechtel 1987).

On the other hand, with the addition of phytolith analysis on cooking residues, as well as a small number of direct dates on maize, researchers have continued to find evidence of maize consumption in the Northeast dating to the first four centuries AD, if not earlier (Chapman and Crites 1987; Crawford et al. 1997; 2006; Hart and Brumbach

2009; Fritz 1995; Riley et al. 1994). While these developments have changed how researchers conceptualize the early stages of maize adoption, there are still many unanswered questions. In fact, even though the adoption of maize in the Northeast has seen extensive study over the past fifty years, the origins, timing, and even mechanisms of adoption are still only partially understood (Crawford et al. 1997:112, 2006:549; Hart and Brumbach 2009:369). This chapter will serve as introduction to the current understanding of maize’s earliest appearance in the Northeast and its economic, social, and demographic consequences.

2.1: THE EARLIEST EVIDENCE FOR MAIZE CONSUMPTION IN THE NORTHEAST

2.1.1: Earliest Evidence for the Introduction of Maize into the Northeast

There are very few uncontentious dates on maize from firmly Middle Woodland contexts (roughly 300 BC to AD 500; Figure 2.1) throughout eastern North America. The earliest AMS-dated corn fragments currently come from the Holding site in southeastern

Illinois (ca. 350 BC1-AD 70; King 1999:14; Riley et al. 1994:493-494), followed by the

Icehouse Bottom site in eastern Tennessee (AD 30-530; Chapman and Crites 1987: 353), the Edwin Harness site in southern Ohio (ca. AD 80-550; Fritz 1990:416), and the

D’Aubigny Park site in southern Ontario (ca. AD 130-380; Archaeological Services Inc.

2006:66-67). Importantly, D’Aubigny Park is located in a region of southern Ontario where the next earliest evidence for maize has also been recovered, namely the Princess

Point Complex site of Grand Banks (ca. AD 260-650: Crawford et al. 1997; Crawford and Smith 1996). As seen in these dates, a clear southwest-to-northeast cline in the earliest evidence of maize is apparent, where maize first arrives in the American bottom and diffuses up into Ohio and Southern Ontario.

However, two other sites in the Northeast have associated dates even earlier than those from Holding: Meadowcroft Rockshelter in southeastern Pennsylvania and Vinette in central New York (Adovasio and Johnson 1981; Adovasio et al. 2003; Hart et al.

2007; Thompson et al. 2004:30-35). At roughly 750-110 BC for the earliest maize- bearing strata at Meadowcroft and ca. 400-200 BC for the earliest dated sample at Vinette

(Figure 2.1), these dates change the observed patterns of the earliest entry of maize into the Northeast to one where it arrives almost simultaneously across eastern North

America, with later dates filling in surrounding areas. On the other hand, while

Meadowcroft has produced many associated dates with overlapping intercepts that are consistent with the site’s stratigraphy, the dates have repeatedly been questioned due to their extreme antiquity (King 1999:13; Klein 2003; B. Smith 1992). In fact, the earliest

1 All dates reported in this chapter as “BC” or “AD” have been calibrated at the 2 sigma range using the program Calib 6.1 (Stuiver and Reimer 1993) using the 2009 calibration curve (Stuiver et al. 2009). Lower case “bp” refers to an uncalibrated date, upper case “BP” refers to calibrated years before present.

9 direct dates on maize currently available in Pennsylvania come from Late Woodland

Susquehanna Valley sites, dating to around AD 750-1000 (Hart and Asch Sidell 1996:4;

King 1999; McConaughy 2008:18-22). While the dates from Meadowcroft may well prove to be correct, most researchers will continue to reject the site as early evidence for maize until direct dates are performed (Fritz 1990:410; King 1999:13).

The dates from Vinette provide an altogether different line of evidence for the earliest appearance of corn. Through the identification of maize phytoliths in cooking residues on pot sherds, Hart and colleagues (Hart and Brumbach 2005; Hart et al. 2003,

2007; Thompson et al. 2004) have developed a useful method for identifying maize consumption in the absence of macrobotanical data. Furthermore, as AMS dating can be performed on the same carbonized cooking residues, the maize phytoliths can essentially be dated. Interestingly, the earliest direct dates currently held for maize in New York are similar to those in Pennsylvania, or roughly AD 750-1000 (Hart and Asch-Sidell 1996;

Knapp 2009:104). Therefore, the fact that the phytolith evidence in New York points to a much earlier date for maize consumption may well indicate that the Meadowcroft samples are indeed correct.

Direct Dates on Maize Site Lab No. Sample 14C Date (bp) Calibrated Date* Holding AA-8717 Cob fragment 2077 ± 70 350 BC-AD 70 Holding AA-8718 Kernel 2017 ± 50 170 BC-AD 70 Icehouse Bottom Beta-16576 Kernel 1770 ± 100 AD 30-AD 530 Edwin Harness N/A Kernel 1730 ± 85 AD 90-AD 530 Edwin Harness N/A Kernel 1720 ± 105 AD 80-AD 550 D’Aubigny Park Beta-217154 Kernel 1780 ± 50 AD 130-AD 380 Grand Banks TO-5307 Kernel 1570 ± 90 AD 260-AD 650 Grand Banks TO-5308 Kernel 1500 ± 150 AD 230-AD 870 Associated Dates on Maize Site Lab No. Sample 14C Date (bp) Calibrated Date* Meadowcroft SI-2051 Charcoal 2325 ± 75 750 BC-200 BC Meadowcroft SI-1674 Charcoal 2290 ± 90 750 BC-110 BC Vinette A-0500 Cooking Residue 2270 ± 35 400 BC-200 BC Vinette A-0455 Cooking Residue 1990 ± 40 90 BC-AD 90 Vinette A-0452 Cooking Residue 1940 ± 35 40 BC-AD 130 * Calibrated at 2 sigma with CALIB 6.1 (Stuiver and Reimer 1993). Calibrations are rounded to the nearest 10 years

Figure 2.1: Earliest evidence of maize in the Eastern North America (calibrated dates in parentheses)

2.1.2: Stable Carbon Isotope Studies and the Initial Introduction of Maize

Apart from the radiocarbon evidence, perhaps the best confirmation of when maize first entered the Northeast comes from the numerous stable carbon isotope analyses performed on dated skeletal samples from New York, Ohio, and Ontario

(Katzenberg 2006; Katzenberg et al. 1995; Schwarcz et al. 1985; van der Merwe and

Vogel 1978; Vogel and van der Merwe 1977). The use of carbon isotopes as an indicator of maize consumption is based on the observation that different groups of plants differ markedly in their isotopic composition. In particular, most indigenous plants in temperate

13 regions belong to the C3 photosynthetic group of plants and have δ C isotopic values between -22 and -38‰ (Katzenberg et al. 1995:337; Schwarcz et al. 1985:189). Being a tropical grass, maize belongs to the C4 group of plants, and was likely the only C4 plant consumed in large quantities by groups in the Northeast (Katzenberg 1989:327).

13 C4 photosynthetic pathway plants generally have much higher δ C values – between -9 and -11‰ (Bender et al.1981:350; Schwarcz et al. 1985:195). Therefore, as maize consumption increases in a population, a marked increase in the δ13C values of skeletal samples should also be observed (van der Merwe and Vogel 1978; Vogel and van der

Merwe 1977). From these studies, it appears that maize first appeared in local diets around AD 500, eventually to comprise roughly 50% of all food eaten by AD 1200, and possibly as much as 70% by historic times (Bender et al. 1981; Buikstra and Milner

1991; Katzenberg et al. 1995; Lynott et al. 1986; Stothers and Bechtel 1987; Vogel and van der Merwe 1977). Additionally, as Katzenberg et al. (1995:345) note, a clear clinal pattern of maize uptake is evident, where elevated δ13C values appear first in Illinois and spread northeast to New York and Ontario (Figure 2.2).

13 Figure 2.2: δ C (‰, PDB) for sites in Northeastern North America (redrawn from Katzenberg et al. 1995: figure 7)

In a recent bone carbonate analysis of 49 individuals from 13 archaeological sites in southern Ontario dating between roughly 2,500 BC and AD 1686 (Harrison and

Katzenberg 2003, Katzenberg 2006), researchers identified very low-level consumption of maize from about AD 500 with a gradual increase in the proportion of maize to local diets until the contact period. Furthermore, Katzenberg (2006:270-271) concludes, based on the bone carbonate evidence, that the main route of diffusion for maize into Ontario was through the corridor between Lakes Erie and Ontario, later to expand along the St

Lawrence River into New England, as well as southwest into Michigan.

2.1.3: Summary

Judging from the present collection of direct dates, therefore, the earliest movement of maize across eastern North America appears to have followed a west-to- northeast trend (Figure 2.1). Starting around 200 BC in the Midwest and arriving almost simultaneously at many sites across the Northeast between AD 100 and 500, it also appears that maize spread rapidly across a large range of habitats, covering nearly ten degrees of latitude and travelling up to 1,000 kilometres to southern Ontario in perhaps as little as two to three hundred years. However, the patterns produced by the earliest evidence for maize across northeastern North America are ultimately contingent on the data being used.

If associated dates are incorporated, on the other hand, it appears that maize may have first arrived in southern Pennsylvania and central New York, and then radiated outwards. While the stable carbon isotope evidence supports a later date of introduction than is suggested from the associated dates, it also suggests that the original vector of maize diffusion into Ontario was not from the southwest, but rather through central New

York. This diffusionary vector is also reflected in the isotopic evidence. In this, maize likely entered Southern Ontario in small quantities around AD 500, only to become fully incorporated into local diets by the beginning of the first millennium AD.

2.2: WHERE DID MAIZE IN THE NORTHEAST COME FROM?

An additional line of evidence for the entry of maize into the Northeast comes from morphological and genetic analyses of northeastern landraces of maize. Unlike most other domesticates, maize does not continue as escape vegetation: it will not move into an area without humans to disperse, plant, and tend its seeds (Lusteck 2006:524). Therefore, the ecogeographic and genetic history of maize populations can be used as a proxy for the human transmission of the cultigen. While certain arguments have been made for multiple introduction events of maize in the Northeast, following at least two routes of diffusion (Lathrap 1987; Riley et al. 1990; Sears 1982), little support has actually been found in the archaeological remains or in the genetic signature of northeastern maize

(Crawford and Smith 2003:220; Matsuoka et al. 2002; Vigouroux et al. 2008). Instead, the data overwhelmingly support a single diffusionary origin in the southwestern United

States. Additionally, the extreme genetic divergence of northeastern maize from all other varieties in the Americas has suggested to many that the introduction of the cultigen to the region took place as a single event, likely with a relatively small founding population of maize.

2.2.1: Northern Flint Maize and its Developmental History

All archaeological specimens of maize in the Northeast appear to belong to a variety variously classified as Northern or Eastern Flint (Brown and Anderson 1948),

Maiz de Ocho (Galinat and Gunnerson 1963), or more recently, Eastern Eight Row

(Crawford and Smith 2003; Cutler and Blake 1973). Northern Flint maize has as its primary morphological characteristics ears which are long, slender and straight, usually bearing eight but sometimes ten or twelve rows of grain. Rows generally exhibit strong pairing and cobs appear square or rectangular in cross section. Frequently, the cobs are widest at their base and gradually taper towards the tip. Kernels are crescent-shaped, wider than they are long, and relatively thin (Figure 2.3; Brown and Anderson 1948;

Crawford and Smith 2003:220-225). However, as Crawford and colleagues (Crawford et al. 1997:117, 2006:552-556, Crawford and Smith 2003:220-225) have pointed out, the developmental history of Northern Flint maize is still quite difficult to trace. While this is mainly due to the paucity of diagnostic specimens from pre-AD 1,000 contexts

(Crawford et al. 2006; Sykes 1981:23; Fritz 1990), another difficulty is the absence of clear antecedent varieties from archaeological assemblages anywhere in the Northeast. In fact, without the long developmental sequences such as those that are available at cave sites in highland Mexico or the Southwest, Eastern Eight-Row appears to arrive in the archaeological record quite suddenly (Crawford and Smith 2003:219-221).

This lack of clear developmental evidence has led some scholars to suggest a hybridization event. For example, Riley et al. (1990) argue that the main variety present in the Northeast prior to AD 700 was a low-yielding twelve to sixteen-row maize when hybridization with a South American landrace produced the first Northern Flint maize.

However, arguments for an antecedent Middle Woodland variety of “Midwestern Twelve

Row” (cf Cutler and Blake 1973; Wagner 1993) in the Northeast are largely unsubstantiated, and may instead represent the natural variation of row numbers in

Northern Flint populations (Crawford and Smith 2003:222-225).

Figure 2.3 Archaeologically recovered Eastern Eight-Row specimens from the Northeast. (A) tip of a 10-row maize cob, (B) rectangular, 8-row cob cross section, (C) maize kernel (redrawn from Crawford and Smith 2003, Figure 6.7)

2.2.2: Southwestern Origin for Northeastern Maize

Generally, the most widely accepted hypothesis for the origin of Northern Flint maize is that it stems from varieties in the Southwest, such as Chapalote, Basketmaker II, or the high-altitude-adapted Maiz de Ocho (Crawford et al. 1997:117; Doebley et al.

1988:67; Fritz 1990:409; Galinat 1965:355; Jaenicke-Després et al. 2001). Of course, the diffusion of maize as a cultigen northward into the Eastern Woodlands would have required a series of adaptations in relation to the length of growing season, rainfall, soil, temperature, and day length (Hart 1999; Riley et al. 1990:528). By 1,000 BC, however,

17 maize was already being grown in many locations around the Southwest and was adapted to latitudes as far north as Colorado, as well as to high altitudes (Galinat 1965:355;

Galinat and Campbell 1967; Huckell 2006). Day-length sensitivity or growing season length would therefore not have been a problem for maize transported to the mid- latitudinal Eastern Woodlands from these highland and northern locales, nor would it have been limited to use in its green state (Fritz 1990:409).

Possibly the strongest evidence for the ecogeographic origins of northeastern varieties comes from the extensive genetic and cladistic analyses of maize phylogenies that have been performed in the last decade (van Heerwarden et al. 2011; Matsuoka et al.

2002; Vigouroux et al. 2008). In Matsuoka et al.’s (2002) study of microsatellite loci of all known varieties of maize, the authors found unequivocal support for the connection between southwestern and northeastern varieties. In the cladograms produced from their analysis (Figure 2.4), all known landraces of Northern Flints and Northern Flours were shown to be closely related to southwestern varieties, and not closely related to any other landraces. Additionally, a Principal Components Analysis of the allelic diversity shows a clear separation between southwestern and northeastern maize varieties and all other varieties of maize (Figure 4.4). This suggests that the genetic divergence of southwestern and northeastern varieties of maize took place as a single trajectory leading out of highland Mexico (Matsuoka et al. 2002).

Figure 2.4: Results from Matsuoka et al.’s (2002) microsatellite study of maize variation. (A) results of cladistic analyses, (B) results of Principal Components Analysis (redrawn from Matsuoka et al 2002: figures 2 and 3)

What is perhaps most interesting about the genetic evidence for the development of Northern Flint maize is that, while the majority of the Northern Flint genome is consistent with a southwestern origin, all varieties of eight-rowed maize in the Southwest have chromosomal knobs while Northern Flints do not (Crawford et al. 2006:554;

Doebley et al. 1986; Fritz 1990). Additionally, based on the isoenzymatic signatures,

Doebley et al. (1986:67, 1988:120-122) remark that Northern Flints are so different from all other varieties of maize that they could almost be classified as a separate species. This leads most researchers to conclude that the initial separation of Northern Flint varieties from their southwestern relatives likely involved a very small founding population and a significant genetic bottleneck (Benz and Staller 2006:668; Crawford et al. 2006:554;

Doebley et al. 1986, 1988; Hart 1999:149). As Doebley et al. (1986:65, see also Ford

1985:353) argue, this would mean that all varieties of maize grown in the Northeast until the time of European contact stemmed from a single base population of maize, and likely from a single introduction event.

2.2.3: Summary

While the exact diffusionary pathway is still a matter of some debate, the majority of genetic evidence suggests that all maize found in the Northeast came from varieties originally grown in the Southwest. According to the current state of research, it also appears that all varieties of maize in the Northeast came out of a very small founding population, and possibly, a single introduction event. This of course has profound implications on discussions of the introduction of maize into the Northeast.

For example, did the first variety of maize enter the region with migrating farmers?

Similarly, why is there no evidence of multiple introduction events, considering the abundant evidence for the long-distance exchange of goods across North America during the Middle Woodland period?

However, it must be remembered that the genetic analysis of maize phylogenies is based on extant landraces rather than archaeological ones. Therefore, while there is no genetic evidence to support the view that modern Northern Flint varieties are the result of multiple introduction events, there is no way of knowing whether this was also the case for all Eastern Eight-Row specimens. Similarly, isoenzymatic studies may show that modern varieties were the result of a single founding population but cannot speak to whether this was also the case for all archaeological specimens. Based on morphological traits, however, Crawford and Smith (2003:220) see no evidence to support the hypothesis that there were originally multiple distinct landraces in the Northeast.

According to the authors, the sixth century AD maize recovered from the Grand Banks site in southern Ontario appears consistent with Eastern Eight-Row proportions.

Certainly, if a distinct variety of eight-rowed maize had already begun to be established in the Northeast by the sixth century AD, this would go far to explain the findings of

Doebley et al. (1986, 1988) for the extreme genetic divergence of northeastern landraces. However, the question remains of how and why northeastern maize maintained its relative genetic isolation over the following millennium.

2.3: DEMOGRAPHIC MODELS FOR THE INTRODUCTION OF MAIZE INTO THE NORTHEAST

Of course, one of the main reasons why the spread of maize holds such significance in the literature rests on the simple fact that it cannot propagate on its own.

Therefore, by tracing the history of maize, as well as the patterns of its spread, researchers can infer systems of community interaction, trade, the diffusion of technologies and practices, or even migration. In fact, other than some evidence for squash in Michigan, New York, and Pennsylvania, and chenopod in the Ohio River

Valley (Hart 2001:165; Hart and Asch Sidell 1996, 1997; McConaughy 2003:17; Stothers and Abel 2002), it appears that maize may have been the first cultigen to be grown in large amounts in the Northeast. The choice to grow corn, therefore, was likely a very conscious one, one that would require profound changes in settlement, technology, demography, and diet.

2.3.1: Origins and Emergence of Iroquoian Horticultural Patterns

This connection between maize agriculture and demographic and cultural change can certainly be seen in debates surrounding the origins of historic Northern Iroquoian groups in Ontario, Quebec, and New York. The presence by historic times of a distinct

“island” of Iroquoian speakers in the middle of a “sea” of Algonquian speakers has been of great interest to the anthropological community, as it is one of the few occurrences where two groups with distinct languages, social practices, and subsistence strategies have been found to coexist in the same region (Bursey 1995:43; Fiedel 1999:201; Martin

2008:444; Snow 1995:60). For many researchers, maize agriculture is seen as the critical variable in the development of Northern Iroquoian patterns, where the choice to grow crops is inexorably connected to important changes in settlement and social organization

(Crawford and Smith 1995, 1996, 2002; Hart 2001; Hart and Brumbach 2003, 2009; Hart and Means 2002; Snow1995, 1996; Williamson 1990). Therefore, the origins and introduction of maize into the Northeast are of critical importance, as they also involve the origins and introduction of certain key social and technological patterns.

2.3.2: In-Situ vs. Migration Theories of Iroquoian Origins

Two major theories exist for the appearance of Iroquoian patterns in the

Northeast: one focused on in-situ development, the other on the migration of a foreign group into the region. Early anthropological accounts of Iroquoian origins revolved around the idea that Iroquoian speakers migrated into New York and Ontario from Ohio during the 14th and 15th centuries AD, bringing with them longhouses, matrilineal social organization, distinctive pottery, and of course, maize (Parker 1916:503-507; Griffin

1944). However, by the 1940s, a growing body of evidence began to support the idea that many of the hallmark traits consistent with Iroquoian lifeways appeared to have a long developmental history throughout the Northeast (Griffin 1944; Ritchie 1944).

In 1952, Richard MacNeish formalized this view with his publication of Iroquois

Pottery Types. Using ceramic seriation and Direct Historic associations with historically- documented Iroquoian sites, MacNeish (1952:80-88, 1976:80-82) traced out a long history of local development of characteristic “Iroquoian” decorative treatments. In

23 particular, by looking at the pottery characteristic of the Point Peninsula period (ca. 300

BC - AD 600) and linking these forms to Owasco (AD 900 - 1100) and Early Iroquoian forms (AD 1100-1350), MacNeish challenged the notions of separate migration events for a model that stemmed from a single “proto-Iroquoian” base (Figure 2.5; MacNeish

1952:89; Smith 1990:283; Starna and Funk 1994:46-48). The in-situ theory of Iroquoian cultural development, therefore, describes the transition from hunter-gatherer to horticulturalist as a gradual process that started during the Middle Woodland Point

Peninsula period and was essentially complete by the end of the first millennium AD.

The in-situ model has since become more-or-less accepted as fact within most Iroquoian research (c.f. Engelbrecht 2003; Ferris and Spence 1995:104-106; Pihl et al. 2008; Smith

1990; Smith and Crawford 1996, 1997; Warrick 2000:422; Williamson 1990) and is of course central to J. V. Wright’s (1966) Ontario Iroquois traditions.

Figure 2.5: Development of the Iroquois based on ceramic information after MacNeish (1952:87) redrawn from Hart and Engelbrecht (2011:Figure 2)

In the past twenty years, however, some researchers have begun to question the validity of in-situ development. More recently, Dean Snow (1995a, 1996) has reintroduced a migrationist viewpoint, arguing that the vast differences observed historically between Iroquoian and Algonquian groups could not have come about through gradual local development. According to Snow (1996:795, see also Bursey 1995;

Fiedel 1991, 1999), Iroquoian groups entered New York and Ontario in the sixth century

AD from Pennsylvania, bringing with them maize agriculture, palisaded settlements, matrilineal social organization and residence patterns, and ceramic traditions.

In contradistinction to MacNeish (1952), Snow (1994, 1995a, 1995b) mobilizes ceramic changes as clear evidence of migration. For example, a classic division between

Middle and Late Woodland periods in the Northeast is often made on key differences in the style and manufacture of ceramics: Middle Woodland pottery is characterized by coil- building, conical form, and pseudo-scallop shell and dentate-stamped decorations, while

Late Woodland pottery is characterized by paddle-and-anvil manufacturing techniques, globular bodies, and cord-wrapped-stick impressions (Fox 1990:172; Pihl et al.

2008:153; Snow 1995a:71; D. Smith 1997a). Making pottery by coil-building, Snow

(1995a:71) asserts, is entirely different from modeling techniques, requiring different motor habits, clay preparation and construction methods, and therefore cannot simply be the result of gradual development, but rather indicates the intrusion of a foreign group into the region. Similarly, Fiedel (1991:24-25) points to the striking similarities between early Late Woodland ceramics across the Northeast, such as Princess Point ceramics in

Southern Ontario, Hunter’s Home in New York, and Clemson’s Island in Pennsylvania.

For Fiedel, this sudden change to a completely different style in many locations across the Northeast at roughly the same time suggests a common origin for all groups.

However, as many researchers have pointed out (Crawford and Smith 1995:788;

Fox 1990:172, 1995:145; Hart and Brumbach 2009:378; Stothers 1977:58), there is in fact abundant evidence for gradual change in pottery manufacture techniques between

AD 500-800 and little evidence for a sudden appearance of new methods or designs.

Furthermore, Brumbach (1995:63) suggests that the change from Middle to Late

Woodland ceramic forms in the Northeast likely has more to do with changes in diet and mobility than the actual influx of new groups. In this sense, it was the shift to a maize- based diet and an increase in family-unit size that may have led to larger-bodied pots.

Linguistic data hold perhaps the strongest evidence for the migration of people into the region, and are seen by many as the obvious shortfall of the in-situ theory (Fiedel

1999:201; Ramsden 2006:27; Snow 1995a:70). As Fiedel (1999:201) argues, Iroquoian languages are “totally unlike” Algonquian in vocabulary, phonology, and grammar, and must therefore represent a fairly new language to the region. By tracing out a long history for Algonquian language development in southern Ontario and northern New York,

Fiedel (1991:19, 1999) argues that the glottochronological evidence suggests a split between central and eastern Algonquian languages around AD 500-1000. Fiedel

(1991:18-20, 1999:200) believes that the division of the two major Algonquin language families could only have been achieved if a “foreign” linguistic barrier – such as the

Northern Iroquoian language stock – separated them. Similarly, Snow (1995a:70) illustrates that certain horticultural terms such as “maize”, “corn cob” and “bread” are similar in all Northern Iroquoian languages, suggesting that the founding language group

26 had already adopted maize agriculture before the split occurred. Snow (1995a:76) has even taken this argument further to suggest that that merely 500 migrants into the region as late as AD 900 could have easily produced the population of roughly 95,000 Iroquoian speakers observed in the seventeenth century. The only way to account for the relative homogeneity of Northern Iroquoian languages, Snow asserts, is if a fairly small and geographically circumscribed group entered the region and began to diversify.

Inherent to Snow’s migration model is the idea that these key defining Iroquoian traits were in fact a necessary precondition to migration. Maize becomes ubiquitous throughout much of the Northeast by about AD 1000, and to many, this sudden and uniform appearance of the cultigen across the region suggests that maize entered with a front of colonizing agriculturalists rather than through the piecemeal adoption by erstwhile hunter-gatherers (Bursey 1995:48; Fiedel 1991:22, 1999; Snow 1995a:72-74,

1995b). Snow (1995a:74) even goes so far as to argue that the combination of compact settlements, maize horticulture, and matrilocal social organization would have

“preadapted” foreign pre-Iroquoian groups for effective migration into the region:

A combination of related cultural and environmental factors produced the conditions favorable to the Northern Iroquoian expansion. They cultivated maize and lived in compact hamlets by at least A.D. 900 and perhaps as early as A.D. 750. This preadaptation made them dominant over the smaller, less compact, and less sedentary Point Peninsula communities that they were about to displace. By adopting an aggressive matrilocal settlement system, they would have been equipped to settle anywhere in the thinly populated region to the north, even if they were initially outnumbered on a regional scale.

Within Snow’s model, therefore, matrilocality developed as a response to conquest colonization and hostilities with indigenous groups. With each new migration, an

Iroquoian group would establish itself either by displacing an existing population or perhaps by absorbing women into matrifamilies through warfare (Snow 1995a:76, 1996).

However, this blend of matrilocality, warfare, and maize horticulture has yet to be demonstrated in the archaeological record. While the earliest evidence for maize in

Ontario dates to at least the 4th century AD, the earliest evidence for defensive structures and longhouses are found at the 11th century Porteous site in the Grand River Valley (Pihl et al. 2008:153; Stothers 1977:125-134; Warrick 2000:425). It appears instead, as Trigger

(1978:61) suggests, that matrilocality and internecine conflict rose out of a greater reliance on grown food, rather than the other way around.

While the foreign origin of the Iroquoian language stock presents a compelling case for migration, we must remember that this does not necessarily indicate the movement of people, only the movement of a language. Both Engelbrecht (1999:58) and

Trigger (1985:82; 1999:317-318) point out that language and culture should only be associated in cases of clear demographic continuity, and often follow different diffusionary pathways. A relatively small group of Iroquoian speakers could conceivably have introduced the language to the resident Algonquian speakers, after which it gained widespread acceptance (Engelbrecht 1999, 2003; Martin 2008; Ramsden 2006; Warrick

2000). This indeed may have been a byproduct of the introduction of maize into the region, and as Ramsden (2006:30) suggests, the uptake of both Iroquoian languages and maize in the Northeast may have been due to their inherent “foreign” status. In this sense, maize agriculture and the Iroquoian language may have entered as a package, but not necessarily with migrating peoples.

2.3.3: Summary

The introduction of maize agriculture into the Northeast plays a key role in both in-situ and migration arguments. According to the in-situ hypothesis, at the time of maize adoption, ancestral Iroquoian groups consisted of dispersed to seasonally-aggregated, mobile, patrilocal populations. The adoption of maize agriculture would have started slowly as groups experimented during the summer months, eventually leading to changes in mobility, technology, population size, and social structure (Hart 1999, 2001:165).

Inherent to the migration model is the idea that maize agriculture came into southern

Ontario as a fully-formed cultural package – along with matrilocality, dense settlements, and likely the Iroquoian language stock. For this hypothesis to hold true, there would need to be a clear break in the material record, displaying none of these traits before this transition and all of them after (Fiedel 1991, 1999; Snow 1994a, 1995a).

However, many researchers have begun to argue that the dichotomy of migration versus in-situ hypotheses is unrealistic and ultimately does not represent the rich variability in the archaeological record (Hart and Brumbach 2009:378; Martin 2008:443;

Starna and Funk 1994; Williamson 1990:312). Jamieson (1999:176) points out that when viewed on a regional scale, one cannot really argue that cultures migrate – instead, myriad different diffusion vectors and intergroup interaction networks can be observed.

Engelbrecht (1999:52-56, 2003:112-114) argues for an “ethnogenetic” perspective on

Iroquoian origins as it accommodates population movements, acculturation, diffusion of ideas, and continuity – allowing for a more agentive view of Iroquoian development.

Central in Engelbrecht’s argument is the use of maize as a product of political, religious, and economic power. Peter Ramsden (2006) has even suggested that eastern Iroquoian

29 groups have in-situ origins while the western Iroquoian groups may represent migrants from the Mississippian empire. To him (Ramsden 2006:29), the idea that all horticultural groups in the Northeast must follow the same developmental ontology is based more on previous uni-causal social evolutionary models rather than any archaeological evidence.

As Crawford and Smith (2003:216) point out, however, this “Neoparticularistic” trend in research has led to a certain reticence towards regional syntheses, as researchers worry about extending their demographic interpretations beyond the archaeological evidence (cf Fritz 1990, Gebaur and Price 1992:3). Despite this, some research continues to look at the larger scale of maize development in the Northeast and its demographic source(s). Scott Martin (2004, 2006) argues for an “enchainment” process of maize development where knowledge and technologies, as well as cultigens, were passed through social networks, and in particular, women moving between communities. This general view has also been reiterated by Hart’s (2001) agroecological model of the development of matrilocality. Based on a general model of maize evolution and historically-observed genetic variation in Northern Flint maize populations, Hart (1999,

2001:158) argues that northeastern maize must have been subjected to a combination of isolation and interbreeding events. The principal way that this would have been achieved,

Hart contends, would be through a patrilocal residence pattern whereby women from outside communities would share seed-stock as well as knowledge. As maize rose in importance as a foodstuff, a matrilocal and sedentary lifestyle would have held clear benefits, thus providing selective pressure for the development of characteristic Iroquoian lifeways.

2.4: SUMMARY OF MAIZE AND CULTURE IN THE NORTHEAST

Based on the archaeological evidence from directly dated corn, it appears as though maize first entered the Northeast between 1500 and 2000 years ago. Current evidence suggests that this maize likely came from sources in the Midwest and followed a general southwest-to-northeast trend, although with a significant amount of regional variation. As Hart (1999:161, 2001:173) notes, however, these earliest direct dates may not reflect the first occurrence of maize in the region, but simply the point at which the level of maize consumption makes it archaeologically visible. Bonzani and Oyuela-

Caycedo (2006:351) argue that the earliest maize will be nearly invisible due simply to the fact that we are looking in the wrong places:

The difficulty in locating early remains of maize may also be because of a bias of excavating sites found on landscapes that are stable and highly visible. These locations are probably not where early maize use and exploitation first occurred (for instance, also consumed in green state), as such landscapes would be expected to be in natural floodplain settings.

Strikingly, this floodplain setting is exactly where early maize has been recovered in

Ontario, namely at Princess Point sites along the Grand River (Crawford and Smith 1996;

Crawford et al. 1998; D. Smith 1997b).

The stable carbon isotope evidence does suggest that maize had a long developmental history in the Northeast before seeing an explosion in use between AD

1000 and 1200 (Bender et al. 1981; Buikstra and Milner 1991; Katzenberg et al. 1995;

Lynott et al. 1986; Stothers and Bechtel 1987; Vogel and van der Merwe 1977).

Additionally, Katzenberg’s recent isotopic analysis from bone carbonate material

(Katzenberg 2006:270) has identified low level maize intake at three late Middle

Woodland sites in Ontario where maize consumption was originally rejected by

31 researchers. If maize was indeed present at many sites through the Northeast by the fifth century AD, it begs the question why it took five hundred years for it to become a central part of local diets and ubiquitous across archaeological sites. As research progresses, it becomes clearer that earlier views on the introduction of maize into the Northeast may not be illustrative of the entire process. In fact, recent isotopic studies for Late Woodland foraging Western Basin sites in Michigan and Ontario (Dewar et al. 2011; Watts et al.

2011) show diets heavily reliant on maize, with consumption levels equal to thirteenth century Iroquoian groups. If Algonquian groups were in fact eating as much corn as

Iroquoian ones, then perhaps the in situ vs. migration debate is in need of revision.

CHAPTER 3: THEORETICAL MODELS FOR THE ADOPTION OF

AGRICULTURE

While maize has often formed a key part of the in-situ / migration debate, little has been done to actually integrate archaeological data with demographic models of agricultural adoption. Instead, the in-situ / migration debate is often used as shorthand for describing the end result of major demographic change without actually addressing the process(es) behind the choice to adopt an agricultural lifeway. Without a critical assessment of the processes that led to agricultural uptake, or how these patterns may be reflected in the archaeological record, these arguments run the risk of becoming “just so stories” – based more on historic narratives and shifting research climates than on the growing northeastern dataset.

Interestingly, on a broad scale, the standard models of explanation for the introduction of farming practices into Europe share many similarities with those proposed for the emergence of Iroquoian patterns in the Northeast. In particular, models of the

Neolithic transition in Europe can be broken down into two major classes: those describing the migration of farmers into temperate regions from centers of agricultural innovation, and those describing the local uptake of farming practices by indigenous hunter-gatherers (c.f. chapters in Harris 1996, Price 2000a, and Zvelebil 1986a).

Due to differing research climates between old and new world archaeology, however, the origins and spread of specific cultigens through Europe is rarely looked on as an event in itself (c.f. Thomas 1996), but rather the result of larger social and demographic processes (Staller 2006:xxi, 2010:92). The study of agricultural adoption

33 within North American archaeology, on the other hand, has focused almost entirely on the earliest presence of the cultigen within a region and its local evolution, without an explicit emphasis on the social and cultural contexts that led to its appearance (Staller

2010:91; Zohary and Hopf 1993:230-234). Historically, this has meant that maize has been treated as the catalyst for social change within “Formative” typologies, and in this sense, is treated as an end in itself rather than a part of a larger process (Flannery 1973;

Griffin 1960, 1967; MacNeish 1992; Yarnell 1964).

3.0.1: Origins of Agriculture in Neolithic Europe

The first evidence worldwide for plant and animal domestication is found at Pre-

Pottery Neolithic B sites in southwest Asia – dating to roughly 9500 to 10000 BC2

(Colledge et al. 2004a:S35, 2004b:137). By roughly 7000 BC the first evidence for the spread of farming out of southwest Asia is found on mainland Greece and by 3500 BC nearly all inhabited areas of Europe were under cultivation (Bellwood 2005:68-73;

Colledge et al. 2004a:S35, 2004b:137, 2007; Price 2000b:3). Two processes are commonly invoked to explain the spread of early farming in Europe. The first view involves cultural diffusion, by which cereals, livestock, and farming techniques are passed from one group to the next without the geographic displacement of indigenous populations. Demic diffusion, on the other hand, posits that the spread of agriculture arose from the movement of farmers into either unoccupied regions or areas occupied by mobile groups. Childe (1925) first proposed, based on similarities at early Neolithic sites across Europe, that the patterns exhibited were not consistent with the diffusion of

Neolithic practices from southwest Asia, but rather the movement of agriculturalists. The

2 Dates listed as “BC” are in calibrated calendar years.

34 apparent regularity of this spread, along with the monotonic cline in dates for the earliest

Neolithic across Europe from the southeast to northwest has led subsequent researchers to adopt a similar view. The best known treatment of migration processes in Neolithic

Europe is certainly Ammerman and Cavalli-Sforza’s seminal “Wave of Advance” model.

Through the analysis of the earliest Neolithic dates across Europe, the authors concluded that the predominant pattern was that of a gradual movement of agriculture across the continent, consistent with the slow expansion of an agricultural population (Figure 3.1).

However, while Ammerman and Cavalli-Sforza’s “Wave of Advance” is still the most parsimonious model for discussing the spread of agriculture through Europe on a continental scale, recent studies highlight that there is in fact much regional variation

(Bocquet-Appel et al. 2009; Gkiasta et al. 2003; Pinhasi et al. 2005; Russell 2004; Zilhão

2000). Gkiasta et al. (2003) illustrate that many regions exhibit a co-presence of

Mesolithic and Neolithic sites, and not the rapid transition from one lifeway to the next consistent with the migration of farmers into a region. Furthermore, a complete transition to farming apparently took a significantly longer time than was originally thought – or than a migrationist model would dictate – where the first farming groups in many regions did not appear to adopt farming indiscriminately, but selectively, to fit local needs (Price et al. 1995:107; Price and Gebaur 1992:105-110; Zvelebil 1986c:167; Zvelebil and

Rowley-Conwy 1984; Zvelebil & Zvelebil 1988:578).

The current belief is that the Neolithic transition was likely the result of migration processes in certain regions of Europe, while in others it was the result of the introduction of practices and technologies to extant foraging groups – as well as many other combinations of these two extremes (c.f. chapters in Harris 1996 and Price 2000a).

Figure 3.1: Map Showing the Spread of Early Neolithic Dates in Europe based on Ammerman and Cavalli-Sforza’s 1971 analysis (redrawn from Ammerman and Cavalli-Sforza 1973:Fig. 6)

3.1: MIGRATIONIST THEORIES OF AGRICULTURAL ADOPTION

As many argue (Adams et al. 1978:483; Anthony 1990:895, 1997, Chapman and

Hamerow 1997; Fiedel and Anthony 2003; Sutton 1996), migration as a demographic process is one of the most persistent yet least understood models in archaeology. While migration is generally accepted as a major feature in human prehistory (e.g. the spread of

Homo sapiens out of Africa and the first peopling of North America and Australia), it has suffered from not being critically considered as process. As Anthony (2000:554) states, migration is often considered “a lazy person’s explanation for culture change, used by archaeologists who could not or chose not to deploy more demanding models”.

While Wave of Advance continues to be a valid explanatory model for the spread of agriculture at a continental scale, many argue that the spatiotemporal resolution of the model precludes it from actually describing data in any meaningful way (Anthony

1990:901; Gkiasta et al 2003:46; Pinhasi et al. 2005:2226; Zilhão 2001:14181). Anthony

(1990:901) argues that what appears as a slow gradual spread of agriculture across the continent may in fact be a series of directed colonization events. This highlights the two main migrationist models used to study the spread of agriculture: short-distance expansion and integration, or demic diffusion; and long-distance chain migration and colonization (Anthony 1990, 1997, Bocquet-Appel and Naji 2006; Burmeister 2000;

Chapman and Hamerow 1997; Clark 2001; Hamilton and Buchanan 2007; Hazelwood and Steele 2004; Snow 1995a; Steele 2009, 2010; Sutton 1996).

3.1.1: Demic Diffusion and the Wave of Advance

Although demic diffusion can take place in several different forms, the main point is that early farmers, by relocating their settlements, brought about the transition to agriculture. Demic diffusion involves a standard Malthusian demographic model wherein as a population expands it eventually leads to the random migration and sequential colonization of the surrounding area by daughter communities (Zvelebil and Lillie

2000:62). By definition it is a very slow process, taking place on a generational scale with each generation moving less than 50 kilometers from their place of birth (Russell

2004:76; Price 2000b:31; Zilhão 2000:14180; Zvelebil and Lillie 2000:62). In this sense, the demic model treats the extant foraging communities as passive recipients of

37 agricultural, cultural, and linguistic change (Renfrew 1987), unable to resist the moving front of expanding and incorporating agricultural communities (Cavalli-Sforza 1996:54;

Donahue 1992:73; Zvelebil 1986c:175).

Ammerman and Cavalli-Sforza (1971, 1973; 1984) were the first to explicitly apply and quantify the concept of demic expansion in their “Wave of Advance” model for Neolithic Europe. The Wave of Advance model is based on the assumption that while regional variation may exist, the primary mode of agricultural spread through Neolithic

Europe was that of short-distance movements. In this, Ammerman and Cavalli-Sforza

(1973; 1984:67) posit that high population growth rates in pioneer communities at the front of agricultural spread would produce a population expansion that, when viewed at a continental scale, appears to move out in all directions at a steady radial rate. This steady rate of agricultural front speed is exactly what was observed by Ammerman and Cavalli-

Sforza (1971) in their regression analysis of 53 early Neolithic sites against their distance from a point of origin in southwest Asia. The results of their analysis produced an average front speed of about 1 km per year, with very high correlation coefficients (r =

0.83-0.89). This indicated that not only does the distance of any given early Neolithic site in Europe from southwest Asia provide a good explanation of its date, but that this spread appeared to have taken a very long time.

In order to interpret the demographic processes behind this gradual spread of dates, Ammerman and Cavalli-Sforza (1973, 1984) applied a model first developed by

Fisher (1937) to describe the movement of an advantageous gene through a population.

Fisher’s Wave of Advance model makes two main assumptions: 1) that population growth occurs in a logistic manner with higher growth rates experienced at the wave front

38 and lower replacement rates in densely-settled areas (Figure 3.2 A); and 2) that migration would take place at a constant radial rate and would not be directed towards specific areas (Figure 3.2 B; Ammerman and Cavalli-Sforza 1984:68; Fisher 1937:367).

A B

Figure A shows the rise in local population densities expected as time elapses.

Figure B represents the treatment of migratory activity in Fisher’s model. While the migratory pattern is essentially random, over the larger scale it takes on a Gaussian character.

Figure C represents Ammerman and Cavalli-Sforza’s evaluation of Fisher’s model. Using different values for m and a, the authors determined the range of front speeds.

Figure 3.2: The spatiotemporal expression of Fisher’s Wave of Advance model (redrawn from Ammerman and Cavalli-Sforza 1984: Figs 5.2, 5.7, 5.9)

The Wave of Advance model has been particularly resilient for discussing large- scale population movements in archaeology, and even with the thirty-fold increase in the number of radiocarbon dates for the earliest Neolithic in Europe since the 1970s, the basic parameters of the Wave of Advance model still hold (Bocquet-Appel et al. 2009;

Gkiasta et al. 2003; Pinhasi et al. 2005; Russell 2004; Zilhão 2000). In addition to the spread of agriculture through Europe, the Wave of Advance model has since been used to describe the spread of maize through the southwestern United States (Bellwood 2001;

Hill 2001:916-917), the movement of anatomically modern humans into Europe (Mellars

2006), the late glacial colonization of northern Europe (Fort et al. 2004; Housley et al.

1997), and the diffusion of Clovis point technology through North America (Hamilton and Buchanan 2007).

Of course, the argument for demic diffusion in Neolithic Europe is not based solely on the regression of a site’s date against distance from the Near East. Numerous genetic, linguistic, and archaeological data support the movement of agriculturalists across Neolithic Europe from Southwest Asia (Balaresque et al. 2010; Bramanti et al.

2009; Chikhi et al. 2002; Renfrew 1987; Richards 2003; Semino et al. 2000). For the

Northeast, on the other hand, there is little genetic data to either support or refute the migration of agriculturalists into the region. While Snow (1994, 1995a, 1995b, 1996) did not argue explicitly for demic diffusion, the basis of his migrationist model is essentially that the embedded cultural and economic practices of the first horticulturalists in the region pre-adapted them to effective expansion and incorporation of local groups.

Some support for this view can also be seen in settlement patterns. As Warrick

(2000:423) notes, no sites have been found in Southern Ontario which show clear

40 archaeological evidence of a transition from the hunter-gatherer Point Peninsula patterns to the horticultural Princess Point ones. Instead, the regional pattern tends to illustrate

Point Peninsula occupation immediately succeeded by Princess Point occupations – with no apparent intermixing of the two traditions. Ramsden (1996:107) has noted that the dense concentrations of Middle Woodland sites in the Rice Lake area of Southern

Ontario do not continue into the Late Woodland period, and as such, a clear break in settlement continuity is observed between foraging and agricultural sites.

Short-distance migration appears to have been the primary form of community movement from the middle Late Woodland to Contact period in Southern Ontario

(roughly AD 1300 to AD 1650). Archaeological and ethnohistoric data indicate that

Iroquoian village sites were occupied for a period of 10 to 40 years, after which time the community moved to a new location, often within one kilometer of the previous village

(Heidenreich 1971:213-216; Sykes 1980; Warrick 2000:437, 2008). The spread of communities into Simcoe County during the Middle Iroquoian period (Latta 1976:55) and the Contact period (Emerson 1961:181; Heidenreich 1971:89; Trigger 1962:141) has been interpreted as a pattern of short distance migrations and Warrick (1990:360) has even gone so far as to state that the Middle Iroquoian expansion into Simcoe County closely resembles a Wave of Advance process – although he has more recently

(2008:184) changed this point of view to suggest that the patterns were more likely the result of leapfrog colonization. Therefore, if short-distance community-level migration was present from the middle Late Woodland period onwards then it is possible that similar processes with fewer communities were also behind the apparent discontinuity in settlement / subsistence patterns in Southern Ontario during the Princess Point period.

3.1.2: Long-Distance Migration, Chain Migration, and Leapfrog Development

While demic diffusion may describe the overall patterning of the European dataset particularly well, when viewed at a regional scale very few regions appear to be the result of community fissioning and the slow expansion of a wave of agriculturalists.

When looking at site patterning for the earliest Neolithic in many regions of Europe, a more stochastic pattern of agricultural spread emerges. While the first cluster of Neolithic dates in Europe is found in Thessaly at roughly 7000 BC, agriculture apparently did not spread northwards for 500 years, arriving in the Balkans around 6500 BC (Colledge et al.

2004:s35; Perlès 2001:94; Tringham 2000:27). At the same time, a rapid expansion of

Cardial groups can be seen along the Mediterranean coast, with dates ranging from as early as 6200 BC in southern Italy to 5800 to 5500 BC in southern France and Spain

(Zilhão 2001:14183). In fact, Zilhão (2000:14184) comments that in order to apply the

Wave of Advance model to Cardial groups through Spain and Portugal, the average front speed would need to be in excess of 10 kilometers per year, with migratory activity of

60,000 km2 per generation – far outside of the bounds of Fisher’s formulation.

Furthermore, one of the striking features of the earliest Neolithic along the

Mediterranean Sea is the presence of entire regions with no evidence of late Mesolithic occupation but a sudden appearance of farming communities (Broodbank and Strasser

1991; Perlès 2001; van Andel and Runnels 1995; Zilhão 2000, 2001). In their study of the earliest appearance of agriculture on mainland Greece, van Andel and Runnels (1995) argued that the sudden appearance of Neolithic practices on the Thessalian floodplains indicated the movement of colonists for two main reasons: a choice to settle in areas that were otherwise uninhabited, and a clear preference for arable floodplain soils. From this,

42 the authors present a “modified Wave of Advance” model where the Mediterranean Sea is seen as a barrier to demic expansion and daughter communities travel by boat over long distances to new locales (Figure 3.3). Once arriving in these new locales, however, the authors argue that the spread of agricultural communities operated in a gradual demic manner (van Andel and Runnels 1995:495-498). This expands on Ammerman and

Cavalli-Sforza’s model in that it accounts for differential agricultural productivity in the study region and the desire of emigrants to choose specific locales suited for agriculture.

On a larger scale, Dolukhanov (1973) found a strong link between early agricultural sites in Europe and the choice to settle on arable loess soils. While it is not surprising that agricultural communities would choose to settle in areas of high productivity, this pattern appears much closer to directed colonization events than the random short-distance dispersal of daughter communities (Bogucki 1988:95, 2003:263).

Figure 3.3: van Andel and Runnels’ Modified Wave of Advance model (redrawn from van Andel and Runnels 1995: Fig. 12)

The closest analytical correlate to these patterns of long-distance directed migrations is Anthony’s concept of “chain migration” (Anthony 1990:902-903, 1997:26-

27; Fiedel and Anthony 2003:153). Anthony (1990:903) argues that the pattern of

Neolithic agricultural advance does not appear as a wave, but rather as a series of

“leapfrog” colonization events with later infilling. Within Anthony’s model, great distances may be travelled by pioneer “scouts” who find suitable areas for colonization and relay this information back to pioneer communities (Figure 3.4). In chain migration, less desirable areas (due to economic, climatic, or social barriers) are bypassed in favour of more optimal locations. As these favourable areas become colonized, subsequent colonization events will take place in the immediate vicinity of the initial colony.

Therefore a radial spread of sites continues out from the earliest agricultural site in an area (Anthony 1990:902-903, 1997:26-27). While not explicitly related to a migrationist model, this general pattern has also been formalized by Shennan (2007, 2008) in his treatment of the ideal despotic distribution from population ecology (Sutherland 1996). In the ideal despotic scenario, the initial settlers of a region will select an area that provides the most economic return. As local populations increase (whether from internal growth or immigration) and new villages are formed, however, first-order locales may be occupied.

The choice for each subsequent daughter community will be either to locate in a sub- optimal location and maintain close social ties or to move their community far away to a new optimal locale – thus starting the process again (Shennan 2007:147).

Figure 3.4: Anthony’s Migration Process (redrawn from Anthony 1990)

Long-distance colonization events have also been argued for Southern Ontario, particularly during the Late Iroquoian Period (ca. AD 1400-1650), such as the movement of Neutral groups to the western coast of Lake Ontario (Lennox and Fitzgerald 1990:437-

439), and the movement of Huron communities from the north coast of Lake Ontario into the Balsam Lake region and Huronia (Ramsden 1988, 1990; Sutton 1996:27; Warrick

2000: 441). Additionally, Sutton (1996) applied Anthony’s chain-migration model to the development of pioneer communities near Lake Simcoe in Southern Ontario during the

Middle Iroquoian Period (ca. AD 1300-1450) and found strong support for the targeting of optimal locations by pioneer groups followed by in-filling around the initial settlements (Sutton 1996:144-151). Furthermore, it appears that these pioneer

45 communities were the first in the region and migrants would have likely travelled in excess of 50 kilometers to establish these initial settlements, bypassing many sub-optimal locations along the north coast of Lake Ontario and the Oak Ridges Moraine (Sutton

1996:144-147; Warrick 2000:440).

3.1.3: Summary

Clark (2001:2) defines migration as “long-term residential relocation beyond community boundaries by one or more discrete social units as the result of the perceived decrease in the benefits of remaining residentially stable or a perceived increase in the benefits of relocating to prospective destinations”. This highlights the basis of most migration theories, namely, that migration processes are the result of a combination of push factors in the emigration area and pull factors in the immigration area (Anthony

1990, 1997; Burmeister 2000:546). For migrationist models of the spread of agricultural communities, push factors include population growth and resource depletion in areas under domestication, while pull factors generally focus on the search for arable land

(Bogucki 1988, 2003; Dolukhanov 1973; Stothers 1977, Stothers and Yarnell 1977:219-

222; van Andel and Runnels 1995), or perhaps the presence of culturally-related pioneer communities (Sutton 1996:146; Zilhão 2000, 2001). While the archaeological evidence for the earliest appearance of agriculture through Mediterranean Europe and perhaps some portions of Southern Ontario does appear similar to the process of long-distance colonization, Anthony (1990:901, 1997:26) comments that the large majority of migratory moves in the present day, and presumably also in the past, consist of short-

46 distance movements within a local area. These movements are primarily the result of change of residence at marriage, changes in employment, and the result of social networks between and within communities. While the Wave of Advance model still may account for much of the evidence for migratory activity in Neolithic Europe, its largest fault may be a focus on too large an area over too long a developmental period.

3.2: INDIGENIST THEORIES OF AGRICULTURAL ADOPTION AND DEVELOPMENT

While migration processes still remains the dominant explanatory model for the movement of agriculture through Mediterranean Europe, many argue that few regions show any evidence for colonization, where instead continuity can be observed between

Mesolithic and Neolithic sites (Dolukanov 1986:113; Gkiasta et al. 2003; Price 2000c;

Zvelebil 1989:380, Zvelebil and Lillie 2000:61). As Dennell (1992:91) points out,

Neolithization almost always occurred in areas where hunter-gatherers were already present, and therefore, must involve at least some level of local innovation and adoption.

Northern Europe provides the best evidence for the adoption of farming practices by local hunter-gatherer groups. While domesticated cereals and animals first arrived in southern Scandinavia around 4000 BC, it is not until roughly 3400 BC that major agricultural expansion took place. In this, large settlements of closely-spaced farming communities emerged with characteristic Neolithic settlement patterns but often with the same ceramic and lithic forms of the preceding Mesolithic period (Price and Gebaur

1992:105). Once agriculture entered Northern Europe, however, it saw explosive movement throughout Scandinavia. In fact, as Price (2003:280) comments, the radiocarbon dates between the earliest dates in southern Denmark and those 800 kilometers away in Sweden are nearly indistinguishable. This pattern has been described as being both fast and slow: while the period from first contact with agricultural groups to the full adoption of agricultural practices in Northern Europe extends over 1000 years, once adopted these practices see rapid movement across Denmark, Sweden, and Norway in just over 100 years. This pattern suggests that these practices were following extant community relations and not the slow advance of an expanding and integrating population (Price 2000c, 2003:280, Price and Gebaur 1992:110; Zvelebil and

Dolukhanov 1991: 265; Zvelebil and Rowley-Conwy 1986:112).

3.2.1: Socioeconomic Explanations for the Adoption of Agriculture

While the characteristic ‘fast and slow’ patterns of agricultural appearance and the evidence for social continuity between foraging and farming communities in Northern

Europe may indicate the introduction of farming practices and technologies rather than foreign farmers, it fails to explain why local Mesolithic people would have been willing to undertake such a transformation (Zilhão 2001:14181). Of course, the basis of many diffusionist models – whether demic or cultural – lie in the perceived superiority of farming over foraging. In this sense, the apparent benefits of farming were such that they either led to the adoption of farming practices by receptive cultural groups or the displacement of unwilling groups by expanding agricultural populations (Zvelebil

1986b:9). A variant of this model presented by David Clarke (1976:467) argued for the existence of a Mesolithic industry of plant husbandry focused on the tending of fruit and nut trees as well as rich marine resources. Clarke argued that, as groups began to focus more on plant and marine resources and less on large migratory herbivores, people began to settle longer in a single place, thus leading to increases in local population levels. This created a feedback loop where with increasing sedentism there was greater ability, and perhaps need, to control and manipulate such resources. It was at this exact time, Clarke argues, that Neolithic domesticates became available. Because of their advantages in productivity and storage, cultigens rapidly replaced wild resources – leading to the emergence of an agricultural lifeway within the community.

This connection between the tending of local resources and the eventual adoption of maize has often been posited for Southern Ontario (Chapdelaine 1993; Clermont 1990;

Ferris 1999; Trigger 1985:24; Warrick 2000). For Princess Point sites in particular, it has been argued that the earliest settlement patterns in lowland marshes and along the Grand

River floodplains likely reflected wild rice and nut harvesting practices rather than requirements for maize horticulture (Crawford and Smith 2003:204; Lee et al. 2004;

Lennox and Fitzgerald 1990; Pihl et al. 2008; Warrick 2000:431-433, 2007:139). As

Ferris (1999:31) argues, corn was simply “plugged-in” to an extant procurement strategy already focused on the harvesting of relatively reliable native plants. As many contend, maize likely held clear benefits over more seasonally-variable resources such as wild rice in that it took much less time to harvest, was less sensitive to seasonal climatic variation, and could be stored for years in a dried state – thus allowing for resource buffering

(Ferris 1991:31; Stothers 1977:123; Trigger 1985:109; Warrick 2000:432). As Trigger

(1978:59-61, 1985:87) argues, this greater availability of stored food allowed groups to eliminate the need for cold-season macroband dispersal, and as such, led to the eventual development of year-round settlement patterns. As groups began living in year-round settlements, this likely created greater stress on local resources – thus leading to a greater dependence on grown food (Ferris 1999:31).

A similar view has also been developed by Wills (1988) for the American

Southwest. In this, Wills (1988:37) predicts that the earliest period of agricultural introduction into a region should show no major changes in the settlement patterns or material culture but may be preceded by the experimentation of plant domestication, either with foreign domesticates or the tending of local plants. Furthermore, Wills (1988:

32-33) predicts that, once accepted into local diets, the transition into a horticultural lifeway must, by necessity, be a relatively rapid process:

While plant domestication under cultivation may have been a long-term process… the acceptance of fully domesticated plants by erstwhile hunter- gatherers must have been relatively rapid. The requirements of domesticates for mere survival would prohibit any adoption strategy which did not immediately meet plant needs through planting, harvesting and storage. With anything less than a commitment to the minimal requirements of the specific domesticates, there could have been no sustained involvement with these species.

Therefore, this “fast and slow” pattern can be conceived as a period of initial introduction

(or at least knowledge of) maize, followed by a period of experimentation, and finally, full scale adoption as communities become dependent on the year-round security that maize horticulture provides.

As Price (2000c:309) comments, however, economic arguments for agricultural transition are in fact very difficult to evaluate from a causal standpoint. Instead, Price

(2000c:309-310) argues that the transition to agriculture in Northern Europe should be

50 conceptualized as a social process first and foremost; one where the choice to grow crops was symptomatic of ideological shifts (c.f. Ingold 1988; Hodder 1990, Thomas 1999).

This is indeed the kernel of Trigger’s model for the development of year-round settlement: that maize provided a means of maintaining social cohesion for groups and, in this sense, was incorporated out of social needs rather than strictly economic ones.

3.2.2: Integrationist Models, Availability, and Agricultural Frontiers

While any discussion of the spread of agriculture into occupied areas must include the role of local foraging groups, the fact still remains that all the hallmark traits of the

Neolithic (and maize in Southern Ontario) have foreign origins, and therefore, must also account for the arrival of the foreign domesticate into the region. Zvelebil and Rowley-

Conwy (1984) have developed the ‘Availability’ model for the spread of farming through regions populated by hunter-gatherer communities. This model incorporates processes of migration, demic expansion, intercommunity networks, and local responses by foraging communities in order to conceptualize the processes of agricultural adoption from both sides of the forager-farmer divide. The key to the availability model is the concept of an agricultural frontier, where agricultural practices, and indeed domesticates, were known and available to foraging groups. While this agricultural frontier zone could be

Ammerman and Cavalli-Sforza’s wave front, Anthony’s pioneer community, or the emergence of local farming communities, the point is that the choice to adopt agriculture in a given region is not simply an issue of proximity, but rather involves a suite of social, economic, and demographic choices (Figures 3.5, 3.6).

The availability model distinguishes three phases in the transition to agriculture within the shifting agricultural frontier: availability, substitution, and consolidation, beginning with contact between foragers and farmers and ending with agriculture as the principle source of food (Figure 3.5). The availability model argues that hunter-gatherers were familiar with agricultural products and practices long before adoption, and that the patterns produced by the earliest appearance of agriculture in a given region can be used to discuss the processes at work (Zvelebil 1986b:12, 1996:334; Zvelebil and Dolukhanov

1991:241). The very rapid spread of the farming in Northern Europe, therefore, reflects the fact that pathways for exchange and interaction were operating in the late Mesolithic, while evidence for the resistance of agricultural adoption in other regions reflects group decision-making and the relative stability of certain beliefs and practices (Zvelebil

1996:338; Zvelebil and Dolukhanov 1991:241; Zvelebil and Lillie 2000; Zvelebil and

Rowley-Conwy 1984:124).

In the availability phase, farming is known and familiar to local foraging groups, with some exchange of information, goods, and perhaps people occurring between the hunter-gatherer and farming communities along the agricultural frontier. This likely takes place at the first emergence of farming in a given region, where groups are developing contacts, but still operate as culturally and economically autonomous units. Domesticates may be present on hunter-gatherer sites at this phase, although should only represent a minute portion of diets. The availability phase ends either with the adoption of at least some farming practices by the foraging community or with the settlement of farmers in an area within the hunter-gatherer group territory. As Zvelebil and Rowley-Conwy

(1984:105) argue, this phase is often completely ignored in traditional studies of the

Neolithic in favour of the search for the earliest agricultural communities. In Northern

Europe, this phase lasted for nearly 1000 years, where farming communities existed within 100 kilometers of Mesolithic groups yet each group maintained its relative place in the landscape (Price 2000c, 2003:280, Price and Gebaur 1992:110; Zvelebil and

Dolukhanov 1991: 265; Zvelebil and Rowley-Conwy 1986:112).

The substitution phase marks the rapid adoption of farming practices within foraging groups, leading to the eventual establishment of a fully agricultural lifestyle. The substitution phase can best be described under the rubric of conflict and competition, incorporating ideas of social relations, ideologies, and intercommunity dynamics

(Zvelebil 1986b:12, 1996:333; Zvelebil and Rowley-Conwy 1984:105). As Zvelebil and

Rowley-Conwy (1984:105) argue, the key to any discussion around the transition to farming lies in the competition between two essentially incompatible ways of life.

…foraging and farming compete at several levels: directly for land; food resources and raw materials (Anderson 1976, Alexander 1978); indirectly for space, access to information, time, manpower and social status (Moore 1981, Bender 1978, Sahlins 1974).

There are several indications of conflict between farmers and hunter-gatherers along the frontier zone of southern Scandinavia between 4000 and 3500 BC. These include markers of increased social competition and status differentiation, territoriality, fortifications in farming communities, and even the existence of a “no man’s land” along the wave front

(Zvelebil and Lillie 2000:85). Within the availability model, competition only arises in the substitution phase when there is increased demand on frontier areas from both groups.

Therefore, competition acts as the catalyst for the development of a fully agricultural lifestyle and presents an equifinality to diverse foraging and semi-horticultural strategies.

The substitution phase ends when hunter-gatherers no longer compete with farmers for

53 resources – either through the displacement of remaining hunter-gatherers to environments unsuitable for agricultural production, or the incorporation of foraging groups into farming communities (Zvelebil and Rowley-Conwy 1984:106).

The consolidation phase denotes the final stage in the transition to farming - where groups are fully reliant on agriculture - and is marked by both the extensive and intensive growth of food production. This intensiveness of food production, as well as concomitant rises in local population levels, eventually leads to the spread of farming communities to secondary areas, and ultimately, to the colonization of new regions – thus starting the process again. For this reason, the availability model adapts well to spatiotemporal patterning, as the spread of a single phase can be studied through time, or the patterning of all three phases can be studied through space.

Figure 3.5: The three stage Availability model of the transition to farming. Note the sigmoidal pattern to the incorporation of cultigens into the diet (redrawn from Zvelebil 1996: fig 18.1)

Figure 3.6: Zvelebil and Rowley-Conwy’s Availability model as an integrationist treatment of the transition to agriculture. Essentially, the Availability model can be used to discuss a Wave of Advance scenario or a pioneer colonization scenario (redrawn from Zvelebil 1986b: fig 2)

3.2.3: Summary

While the availability model has also been criticized for not properly addressing causation (Price 2000d:308), its focus is more on providing a descriptive framework for agricultural change from both sides of the forager-farmer divide. The availability model rests squarely on the recognition of the time-depth of agricultural transition, and that processes which manifest quite suddenly may have been preceded by very long periods of experimentation and exchange. As Zvelebil (1986b:13) comments, the adoption of farming likely had a number of causes, variable from region to region and contingent on local environmental and socioeconomic conditions. In fact, Zvelebil (1996:323) argues that the question of whether changes in the mode of subsistence corresponding to ideological or symbolic shifts would be, by necessity, contingent on individual regional and historical contexts. In this sense, the shift to farming is in many ways the only universal in the Neolithic transition, and therefore, the only system of change that can be adequately modeled from a supra-regional perspective.

The main fault of the economic approach lies in the assumption of the inherent superiority of agricultural lifeways over foraging ways. Time and time again, it has been demonstrated that hunter-gatherers are just as healthy as horticulturalists, suffering no ill effects from a foraging lifestyle (Lee and DeVore 1968, Sahlins 1972). Furthermore, these divisions of hunters-versus-farmers suffer from treating both groups as singular entities and neglect the rich variability of groups along this continuum (Ingold 1988;

Layton 2001; Layton et al. 1991; B. Smith 2001; Zvelebil 1996). On the other hand, while a continuous view of agricultural transition is certainly a useful heuristic concept, the transition to agricultural lifeways is almost inevitably unidirectional – with farmers

56 rarely becoming foragers. Furthermore, as Rowley-Conwy (2001:64) highlights, most hunter-gatherers who became farmers did so as the result of stimuli from agricultural neighbours:

Hunter-gatherers with no agricultural neighbours originated agriculture very rarely, perhaps only three or four times… most hunter-gatherer historical trajectories would never have resulted in agriculture had that way of life not impinged on them from the outside.

Therefore, the inter-group dynamics of diffusion are critical in any discussion of agricultural development, and consequently, any discussion of the cultural diffusion of agricultural practices must account for both external and internal pressures.

3.3: CONCLUSIONS

The recognition that migration and local adoption represent two poles on a continuum of strategies of agricultural transition goes far to highlight that the differences between theories of agricultural transition are more of degree than kind (Zvelebil

1989:380). Domestication does not, as B. Smith (2001:17) states, “define the boundary of either agriculture or hunting and gathering” but instead acts as a point from which to view either side. The decision to adopt cultigens, therefore, should be the central point in the discussion of agricultural transition, not from a typological point of view, but from a methodological standpoint. From this boundary, both local and external pressures can be discussed along with their varying archaeological expectations. This is perhaps best conceived as a frontier, both between conflicting lifeways, and often as a spatiotemporal

57 signature. Boundaries and frontiers are interpretive devices that can be used to consider

‘inside’ and ‘outside’ in one sense, and transition and stasis in lifeways in another. This view of an agricultural frontier is central to Ammerman and Cavalli-Sforza’s Wave of

Advance model, Anthony’s long-distance migration model, and of course, Zvelebil and

Rowley-Conwy’s availability model (Figure 3.6). It is the actual nature of these boundaries – whether they are ‘stationary’ or ‘mobile’ (c.f. Dennell 1985, Zvelebil 1996), whether they involve slow or rapid change, and whether they involve cooperation or competition – that can be modeled and discussed on a larger level.

CHAPTER 4: IDENTIFYING MIGRATION AND CULTURAL DIFFUSION

While the processes of migration and cultural diffusion are by no means mutually exclusive, they warrant being separated at a conceptual level, as they give rise to different expectations in terms of human behavioural ecology and interaction, and the relative flexibility of established lifeways (Ammerman and Cavalli-Sforza 1973:343, 1984:62;

Bellwood 2005:252-272; Thomas 1996). In order to conceptualize how best to identify and evaluate these processes for the Northeast, therefore, the following chapter will focus largely on the major signifiers of migration and local development processes.

Furthermore, as seen in the previous chapter, the identification of migration and in-situ processes is largely contingent on the scale of analysis. Therefore, a discussion of the effects of spatial and temporal scale on methods of analysis is of utmost importance.

4.1: IDENTIFYING MIGRATION AND CULTURAL DIFFUSION PROCESSES

4.1.1: Determining Migration in the Archaeological Record

As migration began to fall out of favour as an adequate explanatory model in the mid-twentieth century, researchers developed criteria by which to identify migratory activity in the archaeological record (Haury 1958; Rouse 1958, 1986). Without a doubt, the clearest material correlate of migration is change. As Haury (1958:1) outlined, if a fully-developed culture system (including settlement patterns, subsistence, material

59 culture, and social/symbolic beliefs) appears in an area suddenly and without local precedents, and if this system can be traced to an antecedent group from another region, then migration may well be the process at work. Rouse (1958:64-66) expanded these criteria to five conditions for identifying migration events in the archaeological record:

1) The migrating people, as represented by their material culture, must be identified as an intrusive unit in the region they have colonized;

2) The archaeologist must identify the source area for the migrants and, if possible, locate the transportation route they followed into the new region;

3) It must be determined that all components of the intrusive culture are contemporaneous;

4) The causes of migration must be established; and finally,

5) Other factors which might account for the sudden appearance of a new cultural system, such as independent invention or stimulus diffusion must be eliminated.

However, as many researchers have highlighted (Anthony 1990:897, Burmeister

2000:540; Cabana 2002:17; Sutton 1995:20), the Haury-Rouse approach is flawed in its assumption that material culture stands for actual culture. Furthermore, migration does not necessarily lead to population replacement, and therefore, concepts of ethnogenesis and power relations must also play a part in any discussion of material culture change.

Identifying why migration may have occurred in the past is particularly difficult, and as Anthony (1990:900, 2000:554) argues, nearly impossible in the prehistoric record.

In general, migration is most likely to occur when there are negative (push) stresses in the home region and positive (pull) attractions in the destination region, with relatively low transportation costs between the two (Anthony 1990:900, 1997:23). This is the basis of standard “push-pull” migration models, although often untenable for archaeological data.

Given that source areas are often unknown, push forces are regularly reduced to general problems affecting agricultural communities, such as population pressure, disease, and resource depletion. Similarly, while economic pull factors are easily modeled, these are only relevant in the context of the push factors in the source area. For this reason,

Anthony (1990) chooses to focus on the identification and description of migration processes, with discussions of causality limited to the available knowledge.

As Anthony (1990:902) argues, long-distance migration should result in changes that would have distinct effects on the archeological record. “Migration across an ecological or cultural boundary would require considerable planning on the part of the migrants, and should leave the clearest archaeological evidence”. As evidenced by arguments for long-distance colonization in Mediterranean Europe, one should see an intrusive settlement enter a region, with wholly different subsistence-settlement practices and material culture (Van Andel and Runnels 1995; Zilhão 2000). A clear correlation is expected between the locales chosen by pioneer groups and agricultural strategies, such as the presence of productive soils and the choice to settle on uninhabited land.

4.1.2: Determining Cultural Diffusion in the Archaeological Record

Identifying processes of cultural diffusion in the past is much more difficult to summarize, largely because of the locally-contingent nature of most models. However, the critical concept in any instance is that of continuity. This includes continuity in social and symbolic beliefs, language, settlement patterns and mortuary behaviour, and material culture industries not associated with food production. Furthermore, on a regional scale,

61 greater variability is to be expected between groups as agricultural practices and technologies are adopted piecemeal to suit local needs (Ammerman 1989:164;

Ammerman and Cavalli-Sforza 1984:62; Price et al. 1995:107; Price and Gebaur

1992:105-110; Zvelebil 1986c:167). Ammerman (1989:164) highlights three criteria for identifying cultural diffusion in the archaeological record:

1) The presence of a sedentary foraging population ready to accept farming;

2) That population densities of foraging and farming communities were similar in a given region; and finally,

3) That continuity existed in settlements across the region for the period before and after the introduction of farming.

Ammerman (1989:164) formalizes this into an interpretive model by suggesting that, if the numbers and locations of sites immediately before and directly after the transition to agriculture in a region are similar, continuity can be inferred. Conversely, if there is a large difference between these two periods, researchers should seek other means to describe the patterns.

Another way of identifying cultural diffusion from a regional context is by employing the “fast and slow” pattern used by Price (Price 2000c, 2003:280, Price and

Gebaur 1992:110) to discuss the adoption of farming practices in Neolithic Scandinavia.

In their analysis of the modern radiocarbon evidence for migration in Neolithic Europe,

Pinhasi et al. (2005:2221) noted that no cultural diffusion model has been able to produce a speed compatible with the range of front speed postulated by Ammerman and Cavalli-

Sforza in their Wave of Advance model (roughly 0.6-1.3 km/yr; Figure 3.2C). Therefore, the Wave of Advance model can be constructed as a null hypothesis for cultural diffusion

62 in that if the rate of spread of the earliest agricultural sites is significantly faster or slower on a regional or sub-regional scale than a Wave of Advance model would dictate, this may be an indication of the transmission of farming through social networks, or the agentive refusal of agriculture (Price 2000c, 2003:280, Price and Gebaur 1992:110;

Zvelebil and Zvelebil 1988:578).

Lastly, the kernel to economic models of agricultural adoption as well as the initial stage of the availability model is that local foraging communities likely underwent a period of experimentation with domesticates before deciding to adopt an agricultural lifeway. As Wills (1988:36) states, “foragers adopt domesticated plants not to become farmers but to remain effective foragers.” In this sense, these resources initially act to diversify subsistence and buffer against scarcity, rather than representing a desire to become farmers. Therefore, one should expect to see domesticates being introduced as a resource buffer initially, with low levels of consumption and virtually no change to dietary practices, settlement location and seasonality, or material culture.

However, once horticultural strategies become integrated into local lifeways, one should expect to see a convergence between the settlement and material culture patterns of fully horticultural migrants and local groups who initially attached horticulture to an otherwise mobile subsistence-settlement pattern. This assumption is based partly on

Wills’ (1988: 32-33) comment that the specific ecological requirements of maize would have placed constraints on where people could have effectively grown it – without commitment to these minimum growing requirements, any experimentation would ultimately be unsuccessful. In this sense, maize would have placed selective pressure on communities to either change settlement patterns or choose not to grow maize. Therefore,

63 if cultural diffusion were the primary mechanism by which maize entered Southern

Ontario, one would expect that the distribution of sites with evidence of maize in Ontario should show no correlation to areas of high maize productivity initially, with a trend towards the choice of site locations amenable to maize agriculture through time. In other words, the locational choices of the earliest horticulturalists in the region should reflect pre-horticultural strategies and landscape use – only to change once maize became fully entrenched in local lifeways.

4.2: THE EFFECTS OF SPATIAL AND TEMPORAL RESOLUTION ON IDENTIFYING MIGRATION

AND CULTURAL DIFFUSION PROCESSES

A critical issue in the identification of migration and local development processes in the archaeological record is that the method of analysis is largely contingent on the scale of analysis. While the Wave of Advance remains the most valid explanatory model for Neolithic Europe on a continental scale, many researchers have shown that there are in fact very few instances where the data support demic diffusion at a sub-regional scale

(Anthony 1990:901; Gkiasta et al 2003:46; Pinhasi et al. 2005:2226; Zilhão 2001:14181).

Instead, the pattern appears closer to a series of directed colonization events in some regions and a process of local uptake in others.

In order to test hypotheses of migration and in-situ development in southern

Ontario, therefore, it is necessary to evaluate patterns on local, regional, and supra-

64 regional scales. Indeed, what appears to be the primary process at work on a local scale is only interpretable when contextualized against regional patterns – and vice versa.

Therefore, the analysis of patterns associated with the development of maize horticulture in southern Ontario will be performed in two spatial resolutions: the first evaluating the spread of the earliest evidence of maize across the Northeast; and the second evaluating the local-scale patterns of agricultural adoption within southern Ontario.

4.2.1: Regional Analysis of the Spread of Maize across the Northeast

As discussed earlier, the most direct means of evaluating migration and local development on a regional scale is by looking at the speed and direction of the spread.

Just as a gradual cline in dates associated with the earliest spread of maize across the

Northeast could be an indication of a demic process of migration, any divergence from a demic model can be used to identify other processes at work. The true value of the Wave of Advance model lies in its parsimony – as the simplest mathematical model by which to describe demographic change, and agricultural transition in particular, this model can be incorporated into research as a null hypothesis before moving on to more complex explanatory models. This is common practice for geostatistical studies of the Neolithic transition in Europe (c.f. Fort et al. 2012; Gkiasta et al. 2003; Pinhasi et al. 2005). The means of testing migration and cultural diffusion in the Northeast will therefore be based on the identification of a demic process: the analysis will attempt to evaluate whether the spatiotemporal patterning in the earliest evidence of maize in the region is consistent with the Wave of Advance model, and if not, this may provide evidence of cultural

65 diffusionary processes at work. Importantly, there is at present no means of testing a cultural diffusion process on a regional or supra-regional scale – largely because the locally-specific nature of most local development processes precludes the formalization of a universal model. Therefore, the goal of this analysis is not to test explicitly for an in- situ process, but rather to attempt to refute a demic one.

Identifying demic diffusion, at least as seen in the Wave of Advance model, is relatively straight-forward. In order to fit with Fisher’s (1937) model, logistic population growth in source areas and random short-distance migratory moves are central to the identification of this process. A monotonic cline in dates and gene frequencies should be identifiable on a regional and supra-regional scale, with evidence for slow, gradual change. Following Ammerman and Cavalli-Sforza’s application of the Wave of Advance model, a front speed between 0.5 and 2 km/year should be evident in the spread of radiocarbon dates associated with the earliest evidence of maize. Additionally, an intrusive cultural package should be evident – preferably with an identifiable source locale and route of entry.

4.2.2: Local Analysis of the Spread of Maize into Southern Ontario

For a local analysis of the spread of maize into southern Ontario, the primary signifiers of migration or local development are that of continuity or change. By exploring the subsistence-settlement patterns throughout the period of agricultural development in the region, the assumption is that the patterns produced by the cultural diffusion of maize growing practices into the region should be measurably different from

66 those produced by the migration of foreign farmers. If cultural diffusion were the primary mechanism by which maize entered Southern Ontario, one would expect that the distribution of sites with evidence of maize should show no correlation to areas of high maize productivity initially, with a trend towards the choice of site locations amenable to maize agriculture through time. In other words, the locational choices of the earliest horticulturalists in the region should reflect pre-horticultural strategies and landscape use

– only to change once maize became fully entrenched in local lifeways. On the other hand, if migration were the primary mechanism by which maize entered the region, one would expect to see the sudden appearance of an entirely new settlement and material culture pattern with a preference for the location of sites amenable to maize horticulture from the very earliest sites. Furthermore, these locational preferences should show little change through time, regardless of the proportion of maize in the diet. This is based on the assumption that migrating horticulturalists would have already developed a successful settlement pattern and that this pattern would require little alteration in a new locale.

The most succinct means of evaluating change in this sense would be to look at the locational preferences for horticultural sites in southern Ontario and evaluate this against previous patterns of landscape use. If major change is observed, then migration processes may be at work. However, as pointed out by many researchers, many areas of southern Ontario show no evidence of local foraging groups before the introduction of maize horticulture (Dieterman 2001; Sutton 1996:144-151), or at least show a lengthy interregnum between Middle and Late Woodland patterns (Ramsden 1990:106; Warrick

2000:423).This strategy would require making problematic assumptions that landscape

67 use several millennia before the introduction of maize is representative of all foraging groups, and as such, undermine the locally-contingent nature of cultural diffusion models.

Therefore, the models tested in this thesis will be based solely on the analysis of maize-bearing sites. Under the assumption that settlement locations will change once maize becomes fully entrenched into local lifeways, the analysis will therefore look at settlement choices through time in order to evaluate whether there is measurable change in locational strategies between the earliest and latest horticultural sites. If a change to more productive locales through time can be seen, it may provide evidence for the local development of maize horticultural practices. If the same strategies are visible throughout the agricultural period in southern Ontario, on the other hand, migration is inferred.

Once again, the migration scenario is formulated much like a null hypothesis, in that change through time must be observed in order to disprove migration as the primary process at work for the introduction of maize into Ontario. While the actual pattern of horticultural development in Southern Ontario is likely a combination of both, and perhaps many other, processes (Ingold 1988; B. Smith 2001; Smith and Crawford

1997:29), the point of this analysis is not to propagate simplistic views on causality and the transition to agriculture, but rather to evaluate the evidence against the backdrop of these two opposing views in order to contextualize the patterning in the data.

4.3: SUMMARY

While the primary archaeological correlate of migration and cultural diffusion processes is that of continuity versus change, this is often untenable on a regional or supra-regional scale. Evaluating change through material culture requires that researchers hold many assumptions about the relationship between material culture and group membership and the universality of foraging and farming strategies across an entire region. Furthermore, this approach is difficult to formalize into hypothesis tests or to compare across differing spatial and temporal scales. Evaluating the process of agricultural development in southern Ontario from both a global and local perspective, on the other hand, allows for results to be contextualized against each other – leading to a more nuanced understanding of the processes at work. The following chapters (Chapter 5 and 6) will discuss the data and methods used to test hypotheses of migration and in-situ processes at both a regional and local scale. Chapter 5 will outline the process of testing a demic model for the Northeast using the distribution of radiocarbon dates associated with the earliest evidence of maize, while Chapter 6 will outline the process of testing site location strategies through time for the entire agricultural period in southern Ontario.

CHAPTER 5: REGIONAL ANALYSIS OF THE SPREAD OF MAIZE INTO THE

NORTHEAST – DATA AND METHODS

This chapter presents the data and methods for an analysis of the spatiotemporal patterns associated with the earliest appearance of maize across the Northeast. In particular, the analysis of maize’s spread across the region is contextualized against a demic scenario of agricultural advance in order to evaluate whether the regional patterns better reflect a demic process or a local adoption of farming practices. Following the framework of studies on the spread of agriculture through Neolithic Europe (Ammerman and Cavalli-Sforza 1971, 1973, 1984; Bocquet-Appel et al. 2009; Fort 2009; Fort et al.

2012; Pinhasi et al. 2005; Russell 2004), the primary analytical tool with which to identify demic diffusion in the archaeological record is that of a linear regression performed on the calibrated radiocarbon dates from maize-bearing sites in the Northeast against the distance from potential points of agricultural origin.

However, while Ammerman and Cavalli-Sforza (1971, 1984) used the distance of all sites in their dataset from known centres of agricultural origin in Southwest Asia, there is no universally-accepted source location for the diffusion of maize into the

Northeast. Therefore, part of this analysis will be an evaluation of the best-fitting source locale based on the distribution of dates in the Northeast. The distance from this hypothetical point of origin is then used as the locational variable in the linear regression analysis in order to ensure that the best-fitting linear model is used.

The coefficients produced from the best-fitting regression analyses can then be used to evaluate the suitability of the data to a demic model. Just as a gradual cline in

70 dates associated with the earliest spread of maize across the Northeast could be an indication of a demic process of migration, any divergence from a demic model can be used to indicate that other processes were likely at work. In this sense, the goal of this analysis is not to test explicitly for an in-situ process, but rather to attempt to refute a demic one.

5.1: DATASETS USED FOR TESTING DEMIC DIFFUSION IN THE NORTHEAST

In order to evaluate the spatiotemporal patterns of the earliest entry of maize into the Northeast, a master dataset was constructed using published radiocarbon dates from journals, site reports, and institutional databases. This equates to 166 radiocarbon dates from 83 sites of which 62 radiocarbon dates from 38 sites derived from directly-dated maize remains (Table 5.1, Figures 5.1-5.3). For the purposes of this analysis, the

Northeast is defined by the lower peninsula of Michigan, New York State, Ohio, and

Pennsylvania in the United States, and Southern Ontario below 45°N Latitude in Canada

(Figure 5.1). While modern administrative boundaries admittedly have little relationship to real or perceived boundaries by pre-contact groups in the region, this spatial extent has been previously used to define the Northeast catchment area for the diffusion of maize

(c.f. “the Lower Great Lakes Region”: Crawford et al. 1997; Martin 2004, 2006), as well as in parts in various palaeobotanical studies (see Appendix 1, section A1.1.1; Hart

1999a, 2008; Hart and Rieth 2002). Given that this analysis attempts to evaluate the earliest entry of maize, a temporal limit is set at AD 1200 (see Appendix 1, A1.1.2).

At present, this database is the most comprehensive record of maize-bearing sites in the region. For a full description of the dataset, as well as the procedures in collecting and verifying appropriate dates, justification for the geographic and temporal boundaries, and data manipulation strategies, please see Appendix 1: Regional Analysis Data

Description. Once collected and verified, the final selection of dates was calibrated in the radiocarbon calibration program, OxCal 4.1, using the IntCal09 calibration curve (Reimer et al. 2009), where the median calibrated BP date was recorded. A median date was chosen instead of a mean or modal value as the probabilistic and multimodal nature of radiocarbon calibration precludes any numerical measure of centre (Russell 2004:21).

NY ON

Figure 5.1: Northeast Study Region as defined for this analysis

Number of Sites / Time Period State/ (of which number of dates from directly-dated maize remains) Total Sites Province 500 BC-AD1 AD 1-500 AD 500-1000 AD 1000-1250

Michigan 0(0) 1(0) 4(0) 4(1) 9(1) New York 1(1) 1(1) 10(9) 5(1) 17(12) Ohio (0) 1(1) 7(0) 6(4) 14(5) Ontario 0(0) 1(1) 7(4) 16(9) 24(14) Pennsylvania 1(0)3 (0) 8(1) 10(5) 19(6)

All Regions 2(1) 4(3) 36(14) 41(20) 83(38)

Table 5.1: Site summary for this analysis; for site locations, see Figure 5.3, and Appendix 1

Figure 5.2: Site distribution by time period and dating method

3 It was decided to keep Meadowcroft Rockshelter in this sample, even though many researchers question the validity of the associated dates from the site (Fritz 1990:410; King 1999:13; Klein 2003:123; B. Smith 1992:202) because the dates recovered from the maize-bearing levels are internally consistent, and given the pre-2000 BP date from Vinette in New York, these dates may well prove to be correct. This, however, is the value of creating different sampling groups (see section 4.2.1) in that the Meadowcroft samples can both be included and rejected, depending on whether associated dates are included in a particular dataset.

Figure 5.3: Sites in full Northeast study region dataset

5.1.1: Sampling Groups

A critical issue in the analysis of radiocarbon dates associated with the earliest evidence of maize lies in the security of the date for a given site, and whether it is actually representative of the event in question. In particular, the recent drawing of direct dates on maize from key sites across Mexico, the southwestern United States, and eastern

North America has repeatedly shown that dates based on associated remains are often unsubstantiated and true dates may be significantly more recent (Conard et al. 1984; Ford

1987; Fritz 1994:305; Long et al. 1989). On the other hand, while most researchers are critical of the inherent inaccuracies of conventional dating, they are still reluctant to dismiss associated dates (Crawford et al. 1997:117; Hart 1999b:160).

Following methods laid out by Russell (2004) in her analysis of the radiocarbon evidence for the spread of farming through Neolithic Europe, four separate sampling groups were constructed in order to evaluate the effects of dating and calibration methods on interpretations: a group containing only the earliest accepted date for each site; a group containing a combination of the earliest dates and pooled (or weighted means) dates; a group containing only direct-dates on maize in the region, where the earliest date was used; and, finally, a group of both the earliest and pooled direct-dates on maize

(Table 5.2; Appendix 1, section A1.3-A1.5.4). Dividing the data into these separate sampling groups allows for a more heuristic approach in that contentious sites can be both included and rejected in analysis – thus reducing the risk that particular sampling decisions may preference certain results and interpretations.

Additionally, upon inspection of the location and date of all sites in the database, another set of sampling groups was created in the attempt to alleviate temporal variation on the sub-regional scale. A 50km-by-50km sampling grid was placed onto the region where only the earliest site date within a given grid square was retained. A 2,500 km2 search area was chosen as this was the maximum generational migration distance suggested by Ammerman and Cavalli-Sforza (1984:78-81). Therefore, any sites beyond this range should in theory represent a separation of at least one generation, and, in this sense, be important to this particular analysis. This process was repeated for all of the sampling groups (Table 5.2).

Group Description Sites (n)

Earliest uncontested date of all sites in database (Appendix 1, section A1.5.1; 83 A Table Al.4). Combination of direct and associated dates.

Pooled dates of all sites in database (Appendix 1, section A1.5.2; Table A1.5). 82 B Combination of direct and associated dates.

Earliest direct date on maize, including dates on maize, pottery encrustation, 39 C and bone collagen dates. (Appendix 1, section A1.5.3; Table A1.6)

Pooled dates for sites with direct dates on maize, including dates on maize, D pottery encrustation, and bone collagen dates. (Appendix 1, section A1.5.4; 38 Table A1.7)

50 x 50 km Grid Sampling Groups

The earliest site date per grid square for Group A (Appendix 1, section A1.6.1; 50 E Table A1.8)

The earliest site date per grid square for Group B (Appendix 1, section A1.6.2; 49 F Table A1.9)

The earliest site date per grid square for Group C (Appendix 1, section A1.6.3; 28 G Table A1.10)

The earliest site date per grid square for Group D (Appendix 1, section A1.6.4; 27 H Table A1.11)

Table 5.2: Description of sampling groups

5.2: POINT OF ORIGIN ANALYSIS

While Ammerman and Cavalli-Sforza (1971, 1984) used five sites in Southwest

Asia as assumed points of origin for their regression analysis, this required that a specific point of origin be determined (Pinhasi et al. 2005; Russell 2004:42). While substantial evidence supports a Southwest Asian point of origin for the Neolithic agricultural package, there is no a priori reason to assume that the diffusionary origins of maize in the

Northeast lay in Southern Mexico, or the American Southwest (Benz 2001; Crawford et al. 1997:117; Doebley et al. 1988:67; Fritz 1990:409; Galinat 1965:355; Piperno and

Flannery 2001). This has profound implications not only for assumptions around the geographic origins of migrants, but also for the results of the regression analysis.

Following geostatistical analyses of the spread of farming through Neolithic

Europe (Russell 2004:42-58; Pinhasi et al. 2005:2223-7), a routine was developed to place 136 equally spaced hypothetical points of origin (as well as four known early maize sites) across continental North America in order to find the location which best fit the variation in each of the eight sampling groups (Groups A-H). This ensures that the best- fitting point of origin is used in analysis, and as such, provides the strongest possible linear model for evaluating the likelihood of a demic scenario.

5.2.1: Point of Origin Methods

In order to explore the spatiotemporal variation in the dataset, a hypothetical point of origin (“HOA” [following terminology used in Pinhasi et al. (2005)]) was placed at

77 every 5 degrees of latitude and longitude across continental North America (equivalent to roughly 550 km distance between each HOA) from the southern tip of Mexico (10°N latitude) to the Arctic Circle (60°N latitude). While maize cannot be effectively grown above roughly 45°N latitude (Campbell and Campbell 1992; Fecteau 1985:25), the diffusionary vector of maize may have little to do with its growing requirements, and therefore, it was decided that a larger region should be encompassed (see Boyd et al.

2008 and Boyd and Surette 2010 for evidence of archaeologically-recovered maize above the 49th parallel). Additionally, four archaeological points of origin (“POA” [sensu

Pinhasi et al. (2005)]) were used in order to evaluate whether there is any agreement between the location of some of the earliest evidence of maize in North America and the timing of its entry into the Northeast. These sites are: Guilá Naquitz, the oldest known maize in the Americas (Benz 2001; Piperno and Flannery 2001); Old Corn, the oldest known maize in the southwestern United States (Merrill et al. 2009); and Holding and

Icehouse Bottom, the oldest known maize-bearing sites directly outside of the Northeast

(Figure 5.4; Chapman and Crites 1987; Riley et al. 1994).

The location in decimal degrees of the 140 points of origin as well as the location of the each of the sites in Groups A-H were then imported into the freeware statistical program ‘R’ (R Core Team 2013) in order to calculate the great-circle distance of each site from each of the 136 HOAs and 4 POAs. The equation used to calculate the geodesic distance, d, between two points on a sphere is4:

4 Equation from: http://mathworld.wolfram.com/GreatCircle.html, (Weisstein 2012)

-1 d = acos [cosδ1 cosδ2 cos(λ1 – λ2) + sinδ1 sinδ2] (1) where: a = 6378 km (equatorial radius of the earth) δ = longitude (radians) λ = latitude (radians)

This was performed in R using the “r.dist” function (fields package), where the program creates a distance matrix for the distances between all sites in each sampling group against the 140 points of origin. Once the distance matrices are complete, the relative goodness-of-fit for each location is tested using Pearson’s product moment of correlation, r, using the distance and the calibrated median date BP.

Figure 5.4: HOA and POA locations for point of origin analysis

5.3: LINEAR REGRESSION OF ALL SITES FROM PROPOSED POINTS OF ORIGIN:

In order to test the hypothesis of demic diffusion for the Northeast, Reduced

Major Axis regression (also called “geometric mean regression” or “standard major axis regression”, Russell 2004:60-63) is performed on the date of each site against its distance from the best-fitting point of origin, as determined in the Point of Origin analysis. This was the form of regression used by Ammerman and Cavalli-Sforza (1971, 1984), and has been used by many other researchers (Pinhasi et al. 2005; Russell 2004; Steele 2010) in order to determine the front speed for evaluating demographic process.

5.3.1: Linear Regression Methods

Reduced Major Axis (RMA) regression is performed in R using the “lmodel2” function, with “SMA” as the method of analysis (lmodel2 package). For each of the eight sampling groups, the median calibrated date BP for each site is used as dependent variable and the geodesic distance from that group’s best-fitting point of origin is used as the independent variable. This is essentially testing the scenario that the distance of all sites from a putative origin point provides the best explanation of its date. This is indeed the expectation of a demic scenario, where slow logistic population growth should produce a clinal distribution of dates away from the source location when viewed on a regional scale. Furthermore, one of the benefits of RMA regression over standard

Ordinary Least Squares (OLS) regression is that it represents the mean of both OLS slopes (i.e. the regression of x on y and y on x), and therefore is less affected by scalar

80 differences between variables – as well as the error-prone nature of radiocarbon data

(Steele 2010:2022; although, see Buchanan et al. 2011:2117 for support of OLS over

RMA for estimating front speed). The coefficients produced from the linear regression analyses are then evaluated against a Wave of Advance model for the Northeast in order to test whether the patterns produced from the earliest movement of maize across the region is consistent with a demic process.

5.3.2: Evaluating a Demic Model for the Northeast

In particular, the three main outputs from the linear regression analysis will be used to evaluate a demic model: The intercept; the coefficient of determination (R2) for the regression analysis; and the slope of the regression line.

The intercept value essentially describes the inception date of the migration process, as modelled by the linear regression. In Ammerman and Cavalli-Sforza’s (1971) analysis, the intercept date for their best-fitting linear model was found to be in close agreement with the earliest evidence of domestication in Southwest Asia – therefore, the intercept acted as confirmatory evidence of the applicability of their model to the data.

Ideally, the best-fitting source location as determined in the Point of Origin analysis will come from an area where early maize dates have also been drawn.

Similarly, the coefficient of determination can be used to evaluate the applicability of the linear model to the data. In particular, the R2 value of a linear regression can be described as the percent of variation in the dependent variable that is described by variation in the independent variable, with an R2 of 1 representing a perfect

81 fit and an R2 of 0 representing no fit of the linear model to the data. For this analysis, the

R2 value is essentially describing the effect that distance from the source location has on the date of maize’s appearance. Regression analyses for the European Neolithic produce

R2 values between 0.90 and 0.95 (Ammerman and Cavalli-Sforza 1971; Pinhasi et al

2005) – for demic diffusion to be argued for the Northeastern dataset, a similarly high R2 value should also be found.

The observed rate of spread of early agricultural sites – identified by the slope of the regression line – is a key determinant of a demic process (Pinhasi et al. 2005: 2227;

Russell 2004:58, Steele 2010:2020). Following Fisher’s (1937) Wave of Advance model, the speed of a demographic wave front can be expressed as:

(2) where, r = the rate of advance at the wave front (km/year). m = the migratory activity (km2/generation) a = the population growth rate

Ammerman and Cavalli-Sforza (1984:78-82) determined that the 1 km/year rate of spread observed in their analysis was consistent with a Wave of Advance process, where a was determined through archaeological estimates of Linearbandkeramik population growth rates (0.6% - 3% / year); with m and generation length estimated using ethnographic evidence (500-2,000 km2 / generation; generation length = 25 years).

However, while the use of ethnographically-derived migration terms is acceptable for these datasets – and is largely consistent with the historic accounts of Northern

Iroquoian village relocation sequences (Chapter 3.2.1), it would be spurious to use

Neolithic population growth rates when describing demographic processes in the

Northeast. Warrick (1990, 2008:168) suggests a growth rate of roughly 0.35% per annum for Princess Point groups in Southern Ontario between AD 500 and AD 900, while Snow

(1995a, 1995b) has suggested an annual growth rate of 0.7% in his migration model.

According to Warrick (1990; 2008:146, 182) the highest proposed growth rate for agricultural groups in the Northeast was 1.1- 1.2% per annum for fourteenth century

Middleport groups in Simcoe County, Southern Ontario. The range of .035% pa-1.15% pa will be used in this analysis as term a (Table 5.3).

Another issue that has been recently raised (Fort 2009; Fort and Mendez 1999;

Fort et al. 2012; Hamilton and Buchanan 2007; Steele 2009) is that Fisher’s original model does not take into account the fact that children tend to live with their parents until adulthood. Fort and colleagues (Fort 2009; Fort and Mendez 1999) have derived a model to account for generational time delay, while still working within the Wave of Advance framework. This time-delay model is:

(3)

where, r = the rate of advance at the wave front (km/year). m = the migratory activity (km2/T) a = the population growth rate T = generation time

Note that in cases where there is no time delay (T = 0), equation 3 reduces to equation 2.

For this analysis, T is set at 25 years, in order to be consistent with the generation length used to derive m. As seen in table 5.3, the appropriate range of front speeds for a northeastern model is between 0.5 and 1.9 km/year, using Fisher’s Wave of Advance equation, and 0.5 and 1.7 km/year using a time-delayed model.

Fisher’s Wave of Advance Time Delay Model

500 km2 1000 km2 1500 km2 2000 km2 500 km2 1000 km2 1500 km2 2000 km2

0.35% 0.529 0.748 0.917 1.058 0.507 0.717 0.878 1.014

0.55% 0.663 0.938 1.149 1.327 0.621 0.878 1.075 1.241

0.75% 0.775 1.095 1.342 1.549 0.708 1.002 1.227 1.416

0.95% 0.872 1.233 1.510 1.744 0.779 1.102 1.350 1.558

1.15% 0.959 1.356 1.661 1.918 0.839 1.186 1.453 1.677

Table 5.3: results of a northeastern calculation of Fisher’s Wave of Advance equation and Fort’s time-delay model

5.3.3: Hypothesis of Demic Diffusion in the Northeast

HI: that the spatiotemporal variation in the adoption of maize agriculture in the Northeast, as seen in the distribution of radiocarbon dates associated with the earliest appearance of maize, can be described by the Wave of Advance model.

For demic diffusion to be accepted as the source of maize into the Northeast, the following will have to be shown to hold true: 1) a clear direction of diffusion should be evident in the spread of radiocarbon dates associated with the earliest maize when viewed at a regional or supra-regional scale; 2) the temporal distribution of dates should produce a front speed of between 0.5 and 1.9 km / year when regressed against the distance from a source locale; 3) and lastly, the intercept and coefficient of determination of the regression analysis should show a strong fit of the linear model to the data. The results of these analyses are presented in Chapter 7: Results of Regional Analysis of the Spread of

Maize into the Northeast.

CHAPTER 6: LOCAL ANALYSIS OF THE SPREAD OF MAIZE INTO

SOUTHERN ONTARIO – DATA AND METHODS

This chapter presents the data and methods for an analysis of locational preferences of horticultural communities in Southern Ontario through time. By exploring the subsistence-settlement patterns throughout the entire agricultural period in the region

(ca. AD 500-1650), the assumption is that the patterns produced by the cultural diffusion of maize growing practices should be measurably different from those produced by the migration of foreign farmers. The two main scenarios that will be tested are:

1) If cultural diffusion were the primary mechanism by which maize entered Southern Ontario, one would expect that the distribution of sites with evidence of maize should show no correlation to areas of high maize productivity initially, with a trend towards the choice of site locations amenable to maize agriculture through time. In other words, the locational choices of the earliest horticulturalists in the region should reflect pre- horticultural strategies and landscape use – only to change once maize became fully entrenched in local lifeways.

2) If migration were the primary mechanism by which maize entered the region, one would expect to see a preference for the location of sites amenable to maize horticulture from the very earliest sites. Furthermore, these locational preferences should show little change through time, regardless of the proportion of maize in the diet.

The primary means of evaluating these two scenarios is that of a site settlement- distribution model based on climatic, topographic, and edaphic variables for Southern

Ontario contextualized against maize’s specific growing requirements. The basis of the analysis is the question of whether there are observable locational trends in horticultural site selection through time, and if so, if these patterns are more consistent with a local development sequence or the influx of migrants into the region.

6.1: VILLAGE SITES AS A PROXY FOR SOCIOECONOMIC PATTERNS

6.1.1: Using Presence-only Data to Evaluate Settlement Continuity / Discontinuity

While traditional approaches to identifying migrating horticulturalists evaluate the distribution of sites before and after the introduction of agriculture in order to see if a distinct non-local cultural pattern can be identified (Anthony 1990:902; Dennel 1992:91;

Perlès 2001, Sutton 1996:31; Zilhão 2000, 2001), this approach has many drawbacks.

First is the problem of attributing either a pre- or post-agricultural lifeway to a particular site. While this is understandably easier in analyses of Neolithic Europe where farming entered along with other cultural markers (such as distinct settlement patterns, pottery, and livestock), evaluating a site in Southern Ontario would rely almost entirely on the presence of maize. While the regional analyses use all sites in the Northeast with evidence of maize as the means of analyzing its spread, the issue of maize’s ease of portability becomes paramount when analyzing environmental variables associated with maize horticulture. In other words, the fact that maize was recovered at a site does not mean that it was grown at that location. Conversely, the lack of maize at a particular site cannot be used as evidence that it was not grown at that locale, and may instead relate more to taphonomy and recovery biases rather than site use (Hart 1999, 2001).

Another issue in defining presence or absence of maize horticulture relates to the actual function of the site. Precontact groups in Southern Ontario exploited a broad suite of locales based on specific needs, from hunting and fishing camps and resource extraction sites to winter encampments and burial sites. Therefore, a traditional model of looking at changes in site selection between pre- and post-horticultural periods for

86 evidence of change is inappropriate as there are fundamentally different functional requirements of a horticultural village location from that of a quarry or hunting camp.

In order to remedy this problem, it was determined that presence-only data would be a more appropriate way of evaluating temporal changes in site location selection as it avoids potential errors in absence of horticulture attribution and focuses only on the selection of suitable locations for horticultural villages. For this analysis, therefore, village sites in Southern Ontario will be used as a proxy for maize field locales as this has the strongest support from the literature (Birch and Williamson 2013:97-101; Fecteau et al. 1994:3; Heidenreich 1971; Warrick 2008:121). Furthermore, the fact that Iroqouian villages were relocated roughly every generation provided residents an opportunity to change their locational preferences depending on what was important for village life at the time (Warrick 2000:419). Therefore, long-term trends in village location should reflect dominant social patterns through time (Hasenstab 1990:64, 2007:164).

6.1.2: Horticultural Site Selection Patterns in Southern Ontario

This relationship between the location of a village and dominant economic patterns has certainly been observed for Southern Ontario. As Heidenreich (1971:109) states in his study of Huron settlement patterns: “the selection of a village site was anything but a haphazard affair… considerable care seems to have been exercised in regard to a choice of location.” Numerous studies throughout Southern Ontario have reiterated this general observation: that the actual placement of village sites was the product of a defined set of criteria, and that these criteria were fairly consistent across all

Ontario Iroquoian communities (Campbell 1991; Campbell and Campbell 1992; Fecteau et al. 1994; Kapches 1981; Noble 1975; Warrick 2008; Williamson 1985, 1990).

What is perhaps most interesting to this discussion is that site preferences appear to have changed through time. Previous research (Fox 1976; Kapches 1981; Noble 1975;

Williamson 1985, 1990) has shown that most Early Ontario Iroquoian village sites are located on or near sandy soils (i.e. the Caradoc and Norfolk Sand Plains). For Princess

Point sites along the Grand River Valley, Dieterman (2001:260) found a clear correlation between floodplain locations and proximity to sand deposits. While sandy soils are nutrient-poor and do not retain sufficient water for high levels of maize production, it is generally thought that they were targeted by the earliest farmers because they are the easiest to work with digging-stick technology (Creese 2011:37; Williamson 1985, 1990).

Consequently, a shift to heavier and more nutrient-rich loams and clay loams is seen during the Middle Ontario Iroquoian period (ca AD 1300), exactly when isotopic data suggest a major change in the proportion of maize in local diets (Katzenberg 2006;

Katzenberg et al. 1995; Schwarcz et al. 1985). This shift is thought to be due to the higher economic reliance on corn and the need to locate sites in more productive locales

(Dodd et al 1990:357; Pearce 1984:287; Williamson 1985:343).

On the other hand, Heidenreich (1971:110) notes that the Huron retained this preference for sandy loam soils in Huronia, and Konrad (1976:16) came to similar conclusions regarding village sites in the Toronto Area. While many researchers argue for the move to heavier soils based on nutritive grounds (Fox 1976:190; Kapches 1981;

Warrick 1984; Williamson 1985), Konrad (1976:17) suggests the opposite: that the preference for sandy soils never ceased, but rather the availability of sandy locations

88 became limited – thus forcing the movement to other soil types (although, see

MacDonald 2002:136-140 for a good discussion of how soils data can be misinterpreted in regards to settlement trends). This underlies the basic polarity of the two models under analysis here: a local development of maize would have resulted in a change to more productive soil types as local communities fine-tuned their location preferences; the immigration of foreign horticulturalists would be shown in an immediate preference for certain growing conditions and this would not have changed through time except for the reduction of availability of these preferred locales.

Therefore, the general patterns of village location strategies appears to fit well with the analytical model presented here, and as such, village locational patterns through time provide a practical means of testing local models of migration and in-situ processes.

6.2: ECOLOGICAL ANALYSIS OF SETTLEMENT CHOICE THROUGH TIME

Two complementary approaches are employed in order to evaluate changes in settlement criteria through time: a Species Distribution Model (SDM) using Maximum

Entropy (MaxEnt) modeling in order to identify the primary ecological variables for site selection (MaxEnt version 3.3.3k, Dudík et al., 2010), and, once the key environmental determinants are established, non-parametric testing of independent samples. Essentially, the MaxEnt procedure is used as a form of Exploratory Data Analysis in order to identify

89 the primary environmental parameters in site selection through time in Southern Ontario, as well as to assess the relative importance of each of these variables.

The principle of maximum entropy is well-established in ecology for evaluating species distributions where the actual distribution is unknown (Elith et al. 2011; Harte

2011; Phillips et al. 2006; Phillips and Dudík 2008) and is beginning to see application in archaeology (Banks et al 2012, 2013; Bevan and Wilson 2013; Conolly et al. 2012;

Evans et al. 2012). In particular, the MaxEnt software has been used for evaluating the differences in exploited niches between farming and non-farming groups (Conolly et al.

2012), as well as evaluating differences in inter-cultural landscape use over space (Banks et al. 2013 for Neolithic groups) and time (Banks et al. 2012). Additionally, MaxEnt is specifically designed for modelling species distributions using presence-only data and environmental covariates (Elith et al. 2011; Phillips et al. 2006); it performs well with small samples (Hernandez et al. 2006; Pearson et al 2007) and can use both continuous and categorical variables (Phillips et al. 2006; Phillips and Dudík 2008:163).

The results of the MaxEnt procedure are then translated into a test of independent samples in order to evaluate whether earlier and later sites can reasonably be separated based on locational variables. Given the non-normal distribution of archaeological data, as well as many environmental data for Southern Ontario, the tests of independence will use non-parametric methods. This allows for a more conservative evaluation of locational choice through time while accepting the inherent inaccuracies in archaeological datasets.

6.3: DATASETS USED FOR TESTING MIGRATION AND CULTURAL DIFFUSION PROCESSES IN

SOUTHERN ONTARIO

6.3.1: Datasets Used

Two groups of data are used for this analysis: archaeological site data indicating the date and location of village sites in Southern Ontario; and environmental data consistent with all possible variables associated with maize horticulture.

6.3.1.1: Site Data

As noted, village sites in Southern Ontario were used as a proxy for maize field locales as this has the strongest support from the literature. Site dates and locations were gathered through published sources, online databases, and data requests through the

Ministry of Tourism, Culture and Sport (MTCS). Sites were only included if described as a village in the literature or site reports and where there was a firm occupational date – established either through ceramic chronology or radiocarbon assays. On the other hand, for Princess Point and Western Basin sites, where the connection between permanent villages and maize horticulture is still a matter of debate (Dieterman 2001; Fox 1990;

Murphy and Ferris 1990; Watts et al. 2011), only those sites with maize recovered and with strong support for spring-to-fall occupation were included. This equates to a total of

191 sites in Southern Ontario between AD 500 and AD 1650 (Figure 6.1, Table 6.1; see

Appendix A2.1.2, Table A2.1 for a fuller description of dataset creation).

Figure 6.1: All sites used in analysis. For site names, please see Table 6.1

Table 6.1: All sites used in analysis. For site locations, please see Figure 6.1

6.3.1.2: Temporal Groups

Of course, the critical point in this analysis lies in the attribution of relevant temporal categories in which to analyze change. The most common spatio-temporal classification system in Southern Ontario is certainly J.V. Wright’s (1966) Ontario

Iroquois Tradition (OIT). While Wright’s formulation has understandably seen regular critique over the past few decades, it remains the principle means by which researchers conceptualize and divide data for Late Woodland Southern Ontario (chapters in Ellis and

Ferris 1990 are the clearest example). In fact, even when researchers attempt to diverge from Wright’s taxa and approach the data from a new direction (c.f. Fecteau 1985; Ferris

1999; D. Smith 1997), the temporal divisions stay roughly the same. For the purposes of this analysis, the site data are divided into three stages, representing: the period of first introduction of maize horticulture into the region (AD 500-900); the period of horticultural development and spread (AD 900-1300); and the period of full-scale horticulture (AD 1300-AD 1650; See Table 6.2). For a fuller description of these time periods and their justification, please see Appendix A2.1.3

Stage Date Range n Description

Earliest evidence of maize documented, floodplain 1 AD 500-899 3 settlement locations and small community size.

Movement to upland sandy locales, increase in village 2 AD 900-1299 47 size and contribution of maize to diet.

Drastic changes in settlement size and community 3 AD 1300-1650 141 organization, intercropping practices are fully-adopted and maize comprises largest portion of diet.

Table 6.2: Temporal groups used in analysis

6.3.1.3: Environmental Data

Of equal importance to the discussion of temporal trends is the collection of appropriate data representing maize’s particular growing requirements. All potentially informative environmental data for Southern Ontario were collected from online geospatial databases (e.g. Environment Canada’s climate database; GeoConnections.ca;

GeoGratis.ca) and from individual data requests from Trent University’s Maps, Data and

Government Information Centre (MaDGIC), or were created as part of this research (see

Table 6.3, as well as Appendix A2.1.2 for a fuller description of dataset collection and creation). For the relevance of these particular datasets to the analysis of maize’s growing requirements and village locations in Southern Ontario, please see discussion in Chapter

8.1. Additionally, it is assumed that some of these variables will not inform the particular models, and as such will be removed from the iterative MaxEnt process outlined below.

This study uses modern data rather than reconstructed paleoclimatic data as there are at present no adequate reconstructions for Southern Ontario (MacDonald 2002:192-194).

Most of the climatic variables in this study (Table 6.3, variables 1, 2, and 4) were drawn from 50-year averages (Environment Canada’s 1950-2000 Climate Normals). It is assumed that any changes to topographic and edaphic variables over the last two thousand years would be minimal.

Env. Variable Description Source Number of days from last killing frost Natural Resources Frost-Free 1 until first killing frost. Drawn from Env. Canada; Mckenney et al. Days Canada’s 1950-2000 Climate Normals 2012, See Appendix 2 Ontario Ministry of Crop Heat Total CHUs (base-10) for growing 2 Agriculture and Food, Units season See Appendix 2 Total GDDs (base-10) for growing Natural Resources Growing 3 season. Drawn from Env. Canada’s Canada; Mckenney et al. Degree Days 1950-2000 Climate Normals 2012, See Appendix 2 Growing Total precipitation (mm) for Growing Natural Resources 4 Season Season. Drawn from Env. Canada’s Canada; Mckenney et al. Precipitation 1950-2000 Climate Normals 2012, See Appendix 2 Ontario Ministry of Distance to 5 Distance (m) to nearest deposit of clay Natural Resources, See Clay Appendix 2 Ontario Ministry of Distance to Distance (m) to nearest deposit of clay 6 Natural Resources, See Clay Loam loam Appendix 2 Distance to Ontario Ministry of Distance (m) to nearest deposit of silt 7 Silt Clay Natural Resources, See clay loam Loam Appendix 2 Ontario Ministry of Distance to Distance (m) to nearest deposit of silt 8 Natural Resources, See Silt Loam loam Appendix 2 Ontario Ministry of Distance to Distance (m) to nearest deposit of sandy 9 Natural Resources, See Sand soil Appendix 2 Ontario Ministry of Distance to Distance (m) to nearest deposit of sandy 10 Natural Resources, See Sandy Loam loam Appendix 2 Ontario Ministry of Distance to Distance (m) to nearest deposit of loamy 11 Natural Resources, See Loamy Sand sand Appendix 2 Ontario Ministry of Distance to Distance (m) to nearest deposit of loam 12 Natural Resources, See Loam soil Appendix 2 Distance to Ontario Ministry of Distance (m) to nearest well-drained 13 Well- Natural Resources, See soil Drained Soil Appendix 2

Table 6.3: Preliminary environmental variables for analysis of settlement trends

Env. Variable Description Source Distance to Ontario Ministry of Distance (m) to nearest moderately well or 14 Fair- Natural Resources, See imperfectly-drained soil Drained Soil Appendix 2 Predominant soil drainage characteristics. Ontario Ministry of Grouped into 7 ordinal classes 15 Drainage Natural Resources, See representing “very poor” and “very rapid” Appendix 2 to “well-drained” Percent of surface area covered by stones Ontario Ministry of greater than 15 cm diameter. Grouped into 16 Stoniness Natural Resources, See 6 ordinal classes representing “non stony” Appendix 2 to “excessively stony” Predominant slope of the landscape Ontario Ministry of expressed as a percent (%). Grouped into 17 Slope Natural Resources, See 10 ordinal classes representing “level” to Appendix 2 “very steep” Digital Ontario Ministry of Elevation Elevation in meters above sea level, 100 Natural Resources; 18 Model m resolution MaDGIC, See (DEM) Appendix 2 Ontario Ministry of Terrain Created from 100 m DEM. Average Natural Resources; 19 Ruggedness difference in elevation between eight MaDGIC Index – 500m neighbouring cells. Resampled to 500 m See Appendix 2 Ontario Ministry of Terrain Created from 100 m DEM. Average Natural Resources; 20 Ruggedness difference in elevation between eight MaDGIC Index – 1km neighbouring cells. Resampled to 1,000 m See Appendix 2 Ontario Ministry of Terrain Created from 100 m DEM. Average Natural Resources; 21 Ruggedness difference in elevation between eight MaDGIC Index – 2km neighbouring cells. Resampled to 2,000 m See Appendix 2

Table 6.3, cont’d: Preliminary environmental variables for analysis of settlement trends

In order to fit with the necessities of the MaxEnt software, all datasets were transformed where necessary into raster (grid) format. Furthermore, MaxEnt requires all raster files to have the same projection, spatial extent and resolution. The Ontario Soil

Survey Complex dataset, from which all edaphic variables were derived (variables 5-17 in table 6.3; See section A2.2.1), had the smallest spatial extent – therefore this became the total extent for all datasets (see Figure 6.1). Given that the Soil Survey Complex was designed for agronomic purposes, and as such matches closely the range of soils and climate suitable for agriculture, this dataset also effectively represents the maximum extent in which maize could be grown, making absences meaningful in ecological terms

(Banks et al. 2012, 2013:2748; Barve et al. 2011). Spatial resolution was set at 500m as it was deemed that this would adequately capture local-level variation in village catchment distance (Fecteau et al. 1994; MacDonald 2002:141). The Canada Albers Equal Area

Conic projection was used for all datasets.

6.4: MAXIMUM ENTROPY ANALYSIS OF VILLAGE LOCATIONAL CHOICES THROUGH TIME

To test the hypothesis of whether village location choice changed through time, the first step is to identify the primary variables in village location choice for each period.

The MaxEnt software provides a general-purpose method for identifying primary environmental variables in a given species’ distribution, using incomplete information

(Phillips et al. 2006:234). The goal of the program is to fit a probability distribution of

98 maximum entropy (i.e., that which is closest to uniform), given the constraints of incomplete knowledge about the actual distribution of a given species and available environmental covariates. For the purposes of this analysis, early, middle and late period sites are treated as separate “species” so that their own particular trends can be assessed independently (c.f. Banks et al 2012, 2013; Conolly et al. 2012 for similar approaches).

The final output of the MaxEnt modelling procedure is a map of the study region where each cell is given a probability between 0 and 1, representing the likelihood of a species occurring in that cell. A cell with a predicted probability of 1 represents the most suitable habitat, while cells with a probability of 0 represent areas of lowest suitability (Phillips et al. 2006; Phillips and Dudík 2008). For the temporal analysis of village locations, the

MaxEnt output will present the typical habitat conditions for village sites for each time period, given the present data. This presents the real value of the MaxEnt process as it allows for a multivariate representation of the key environmental variables driving village location preferences for each period.

6.4.1: MaxEnt Methods and Parameters

The MaxEnt procedure first calculates the values for each environmental covariate (Table 6.3) at each site location and then works to establish how these values differ from a random sample of background cells where no sites exist (in this case,

10,000 background points were used). In addition, species records will be partitioned into test and training samples so that model fit can be estimated. This process can then be iterated, each time randomly assigning sites as test and training samples, so that a more

99 robust estimation can be reached (Phillips et al. 2006; Phillips and Dudík 2008).

Following previous archaeological analyses using MaxEnt (Banks et al 2012, 2013;

Conolly et al. 2012), the data will be partitioned into equal test and training sets and iterated for 500 random-seed runs using a bootstrapped replication protocol. All other model parameters will be set to MaxEnt defaults (for a fuller explanation of the MaxEnt defaults and their general-purpose value see Elith et al. 2011, Phillips and Dudík 2008).

6.4.2: Stepwise Reduction of Environmental Variables

While all variables listed in Table 6.3 have the potential to inform the MaxEnt models, in practice many will hold little explanatory value. On the other hand, the

MaxEnt software will still attempt to use these variables for evaluating distribution, resulting in poor model fit. Therefore, a protocol was developed to remove unnecessary variables, thus improving the fit of the model by means of having fewer potential confounding or autocorrelated variables. One of the outputs provided by the MaxEnt software is a table showing the relative contribution of each variable to predicting the occurrence of a village site for each period. In the first run, all environmental variables are included in the development of the MaxEnt model. For each respective run, the lowest three variables from the previous run will be removed as long as they contributed less than five percent to any of the temporal groups. This process is then repeated until all variables contribute at least five percent to at least one model. Not only will this ensure a better model fit for the MaxEnt analysis, this will also provide a smaller number of variables for the tests of independence, outlined below.

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6.5: TESTS OF INDEPENDENCE IN ECOLOGICAL VARIABLES BETWEEN TEMPORAL GROUPS

In order to test whether locational strategies changed through time, two non- parametric tests of independent samples are employed: a Kruskal-Wallis (KW) test for all three groups; and a Wilcoxon Mann-Whitney (WMW) test for pairs of groups in order to evaluate change through time. Similar to the Student’s t test of independence, both tests evaluate the distributions of values within two samples in order to assess whether they could come from the same population, although the Kruskal-Wallis test is in fact more similar to a one-way Analysis of Variance (ANOVA) in that multiple samples can be used (Shennan 1997:65). Unlike a t test, however, these tests are designed for non- parametric data and therefore do not assume normality. As many of the environmental covariate values for site location are heavily skewed, and given that the small sample sizes of the groups make evaluating the sample distribution intractable, non-parametric tests are certainly appropriate.

Both the KW test and the WMW test work under three main assumptions: 1) that each observation represents a random sample from a larger population; 2) that observations within each sample are independent; and 3) that the sample groups are independent of each other (i.e., no sites exist in both groups). Each of these assumptions is only partially met for the data in question. The site data represent nearly all published village sites in Southern Ontario and therefore are not entirely random. However, it can be assumed that this is not the total number of village sites that have existed in Ontario and therefore the site data do represent a sample of an unknown population, albeit with significant biases related to excavation and publication. Independence of samples is

101 difficult to achieve with spatial data as the presence of a site at a given location precludes the possibility of another site. Furthermore, as each site represents the earliest accepted village occupation, this excludes the inclusion of later sites (or earlier contentious ones) at the same location. On the other hand, the inclusion of each location only once allows for independence between samples. One way of reducing bias is to test site data against randomly-generated data in order to evaluate whether the observed environmental trends could result from chance. For this analysis, 100 random locations were plotted within the same ecologically-meaningful boundaries as the MaxEnt analyses (Figure 6.2).

Figure 6.2: location of random and archaeological sites for tests of independence

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6.5.1: Test of Independent Samples Methods and Parameters

In order to test change through time for each of the key environmental variables identified in the MaxEnt procedure, the cell value for each covariate was recorded at the location of each site in the three groups as well as the 100 random points (see Table A2.2 for individual values). Once collected, data were imported into the freeware statistical program ‘R’ (R Core Team 2013) in order to perform the tests of independence. Kruskal-

Wallis tests were performed using the “kruskal.test” function (stats package) and

Wilcoxon Mann-Whitney test were performed using the “wilcox.test” function (stats package). Additionally, R produces a p-value for each test, allowing significance testing.

For change through time to be accepted, a significant difference in environmental variable values should be observed between each group. Additionally, these values should be significantly different from the random sample group – indicating that they are not the result of random variation but instead may reflect specific settlement choices.

6.6: SUMMARY

Testing temporal trends in village location, therefore, will be performed through two separate methods: first is an evaluation of the primary ecological variables driving site location selection during each temporal period using Maximum Entropy modeling; second is a test of the relative importance of each of these variables between periods using non-parametric tests of independence. For migration to be accepted as the primary

103 process at work in the introduction of maize in Southern Ontario, a clear locational strategy should be visible from the earliest appearance of maize in the region. Ideally, this strategy should be in agreement with maize’s specific ecological requirements. For cultural diffusion to be accepted as the primary process there should be no clear link to maize’s growing requirements for earlier sites in the region with a trend towards targeting productive locales through time as maize rose in importance in local diets.

6.6.1: Hypotheses of Migration and Diffusion for Southern Ontario

Therefore, this leads to the following two hypotheses:

H1: Consistent with a cultural diffusionary model, the environmental covariates associated with the location of village sites is seen to significantly change through time in line with expectations of maize productivity.

H2: Consistent with a long-distance migration model, no change is observed in the environmental covariates through time whereas the earliest sites exhibit the same environmental preferences as later sites.

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CHAPTER 7: REGIONAL ANALYSIS OF THE SPREAD OF MAIZE INTO THE

NORTHEAST – RESULTS

This chapter presents an analysis of the spatiotemporal pattern of radiocarbon dates associated with the earliest appearance of maize in the Northeast, and tests the hypothesis that the pattern of dates is consistent with a process of demic diffusion.

Following the framework of studies on the spread of agriculture through Neolithic

Europe (Ammerman and Cavalli-Sforza 1971, 1973, 1984; Bocquet-Appel et al. 2009;

Fort 2009; Fort et al. 2012; Pinhasi et al. 2005; Russell 2004), the primary analytical tool in which to identify demic diffusion in the archaeological record is that of a linear regression performed on the calibrated radiocarbon dates from maize-bearing sites in the

Northeast against their distance from potential points of agricultural origin.

As a preliminary stage of analysis, a global regression is performed on all directly-dated maize remains in North America against distance from Guilá Naquitz in southwest Mexico – the putative source for all varieties of maize. After demonstrating a clear pattern of slow movement of maize across the continent, an analysis of the patterns within the Northeast is certainly valid. On the other hand, there is no a priori reason to assume that the direct origins of northeastern maize lay in the southwest, therefore, the preliminary stage of analysis is to establish the best fitting source location given the temporal variability in each of the eight datasets. Finally, Reduced Major Axis (RMA) regression is performed on all sampling groups for the Northeast based on their best- fitting source locale as determined in the Point of Origin analysis. These results show that

105 while demic diffusion is largely untenable for the full datasets, there is some support for a modified Wave of Advance model when using direct dates on maize.

7.1: GLOBAL REGRESSION OF ALL MAIZE-BEARING SITES AGAINST DISTANCE FROM

MEXICO

To evaluate whether demic diffusion is an appropriate model to apply to the movement of maize into the Northeast, I have analyzed the patterns of maize’s movement from Mexico across the United States and Canada (for a description of the dataset used for this analysis, please see Appendix 1, sections A1.1 and A1.1.2, and Table A1.1).

Guilá Naquitz has produced some of the oldest known maize in the Americas, and is used as a proxy location for the origin point for all maize in North America (Benz 2001; Blake

2006:56-58; Piperno and Flannery 2001; Staller 2003:366-367). Following methods laid out in Chapter 5, great circle distances from Guilá Naquitz were measured for all 143 sites in the dataset and RMA regression was then performed on the median calibrated date BP against the measured distance from southwestern Mexico.

7.1.1: Global Regression Results

As seen in the results of the linear regression (Figure 7.1A), a strong linear trend can be observed in the date of a site against its distance from Guilá Naquitz. The

106 correlation coefficient, r = -0.80 (p < 0.001), is remarkably similar to the values observed from the linear regression of European Neolithic sites (Ammerman and Cavalli-Sforza

1971, 1984; Bocquet-Appel et al. 2009; Gkiasta et al. 2003; Pinhasi et al. 2005; Russell

2004), and the slope is well within the range of that predicted by Ammerman and Cavalli-

Sforza for identifying demic diffusion (Ammerman and Cavalli-Sforza 1984:81). That a linear pattern is present in a dataset of this geographic and temporal magnitude is not entirely surprising; however, there is also very good agreement between the intercept date of roughly 5400 calBP and the current literature on the earliest spread of maize out of

Southern Mexico (Benz 2001, 2006; Fritz 1994; Long et al. 1989; Long and Fritz 2001;

Piperno and Flannery 2001; Staller 2006, 2010). On the other hand, while the regression produces a high and significant r value, the actual patterning of the plot shows heteroscedasticity and patterning in the residuals, and thus should be interpreted with caution (Figure 7.1A). Two distinct groups are visible, with sites in Mexico and the

Northeast comprising the lower right and upper left corners of the plot, respectively. This is also mirrored in the distribution of sites sampled for this analysis (Figure 7.1B). While there is likely geographic and temporal biases present in the North American dataset, it is quite likely that the patterns visible in the regression plot signify differential regional patterns that are otherwise averaged into a single regression.

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Figure 7.1: a) RMA regression of all maize-bearing sites in North America against geodesic distance from southern Mexico; b) Distribution of sites used for regression analysis

7.1.2: Summary

A global regression of the date of 143 maize-bearing sites across North America against their distance from Guilá Naquitz – the putative point of origin for all maize landraces – shows a close affinity with the Wave of Advance model. On the other hand, while an r value of -0.80 certainly points to a significant trend in the dataset, there is patterning in the residuals and it may be that different regional patterns are being masked by a global regression. This, in my opinion, justifies a formal testing of a northeastern maize dataset.

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7.2: POINT OF ORIGIN ANALYSIS

While Ammerman and Cavalli-Sforza (1971, 1984) used five sites in Southwest

Asia as assumed points of origin for their regression analysis of the European Neolithic, this required making an assumption about a point of origin. As there is no universally- accepted origin point for Northeastern farming practices, a routine was developed to place 136 equally spaced hypothetical points of origin (as well as four known early maize sites) across continental North America in order to find the location which best fit the temporal variation in each of the eight sampling groups (Groups A-H). This ensures that the best-fitting point of origin is used in analysis, and as such, allows for the strongest possible linear model for evaluating the likelihood of a demic scenario.

7.2.1: Results for Most Probable Origin Areas for Groups A to H

Given that the dates for each site are recorded in calibrated years BP, a strong correlation will produce a negative score (-0.5 to -1.0). This is because as dates get younger (i.e. BP date gets smaller) they should be further in distance away from a source.

As can be seen in the following results, this is only the case for the direct datasets, with the full datasets (Groups A, B, E, F) showing no clear relationship between the distance from any of the 140 points of origin and the date of a site (Table 7.1). While the relationship found in the direct datasets (Groups C, D, G, H,) is not particularly strong

(Table 7.1), what is perhaps most interesting it that the best-fitting sources of origin for

109 all analyses is located in the northeast corner of the region, rather than to the southwest, as current knowledge on the entry of maize into the region would suggest. For a geographic output of these analyses, please see Appendix 1, Section A1.6, Figures

A1.11- A1.18.

Other than Groups A and B (earliest direct and associated dates and pooled direct and associated dates), in which the best-fitting point of origin was found at 45°N latitude,

70°W longitude, all other groups (Groups C – H) had as their best fitting point of origin

45°N latitude, 75°W longitude. From this, it appears that data manipulation has very little effect on the best-fitting source locale for all maize sites in the region, and that the data overwhelmingly support a point of origin to the northeast of the sampling region (See

Table 7.1). On the other hand, none of the groups which contained both associated and direct dates (Groups A, B, E, F) showed a strong linear relationship with a Northeastern point of origin. While this was the best-fitting point of origin, r values were still below r

= -0.15. This suggests that there is no clear linear relationship between any particular point of origin in North America and all maize bearing sites in the Northeast. For the groups containing only direct dates, a moderate relationship was observed (r ~ -0.45 for

Groups C, D, G, H). This suggests an observable relationship between the location of all directly-dated maize-bearing sites in the Northeast and a point of origin at 45°N latitude,

75°W longitude (See Table 7.1).

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Dataset Description Best Fitting Point of Origin r = No. of Sites

A Earliest Date 45°N, 70°W -0.099 83

B Pooled Dates 45°N, 70°W -0.095 82

C Earliest Direct Date 45°N, 75°W -0.428 39

D Pooled Direct Date 45°N, 75°W -0.442 38

E Earliest Date by Grid 45°N, 75°W -0.098 50

F Pooled Date by Grid 45°N, 75°W -0.087 49

G Earliest Direct by Grid 45°N, 75°W -0.417 28

H Pooled Direct by Grid 45°N, 75°W -0.432 27

Table 7.1: Results of point of origin analysis by sampling group

7.2.2: Internal Distance Matrices

Given that the highest correlation coefficients came from a point within the

Northeast, this justifies an additional point of origin analysis using a finer resolution grid of HOAs, placed at each degree of latitude and longitude within the Northeast study region (Figure 7.2; equivalent to 110 km between each point). As there was no linear relationship found in the location and dates of sites in the full dataset (groups A, B, E, and F), only the direct-date datasets (groups C, D, G, and H) were used for this analysis.

The same procedures were undertaken as for the continental point of origin analysis: the locations of each of the sites in the sampling groups were imported into the statistics package, R, where the great circle distances from each of the 99 hypothetical points of origin were recorded using the “r.dist” package.

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No significant differences were found at this finer scale, with the highest correlation coefficients coming consistently from around 45°N, 75°W. For datasets C and

G, the point with the highest correlation was at 44°N, 76°W (r = -0.43 and -0.42, respectively). For dataset D, the point with the highest correlation was at 44°N, 75°W (r

= -0.44), and for dataset H, the point with the highest correlation was at 44°N, 75°W (r =

-0.43). Additionally, one useful function of sampling at this resolution is that it allows for the creation of a surface of point of origin results across the region (Figure 7.3). Using the correlation coefficient at each HOA in the internal analysis, a surface was interpolated using the Inverse Distance Weighting function in ArcGIS (v. 10.1). As seen in the results of the surface creation, there is a clear east–west trend in the dataset with sites in the northeastern corner of the dataset having the strongest negative r values.

Figure 7.2: Internal point of origin analysis HOA locations

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Figure 7.3: Point of origin isochrone – surface represents r value for model D for each HOA in internal distance matrix (see Figure 7.10)

7.2.3: Summary

Based on the point of origin analysis of the eight sampling groups, a few conclusions can be reached. First, there appears to be next to no linear relationship between the date and location of all dates in the dataset, regardless of the data sampling and manipulation strategy used (datasets A, B, E, and F). However, when using only the direct dates on maize in the region (datasets C, D, G, and H), a more significant trend appears (highest negative r values is r = -0.44 for the dataset D, and r = -0.43 for dataset

H using the internal point of origin analysis), with little difference in results between the

113 datasets. This suggests that the datasets are fairly internally consistent, where sampling from the dataset or pooling appropriate dates has little effect on the observed patterns.

The major observed difference is that no pattern appears when using associated dates, and a moderate pattern appears when using only directly-dated remains. Additionally, and perhaps most surprising, the best-fitting point of origin for all of the datasets lies in the northeast corner of the sampling region (Table 7.1). This runs counter to the assumption of a southwestern origin for the diffusion of maize. However, as a caveat, it should be mentioned that even for the best-fitting datasets, a correlation coefficient of 0.45 is admittedly still quite weak – only explaining about 20% of the variation of the dataset (an r value of -0.45 is equal to an R2 value of 0.20). While the best-fitting point of origin will be used for each dataset in the following regression analyses, it is under the provision that it is only being used so that the best possible linear regression can be assessed, and not that this is indeed the diffusionary source for all maize in the Northeast.

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7.3: LINEAR REGRESSION ANALYSIS AND EVALUATION OF A DEMIC MODEL

In order to test the hypothesis of demic diffusion for the Northeast, Reduced

Major Axis regression (also called “geometric mean regression” or “standard major axis regression”, Russell 2004:60-63) will be performed on the date of each site against its distance from the best-fitting point of origin, as determined in the Point of Origin analysis. For demic diffusion to be accepted as the source of maize into the Northeast, the following will have to be shown to hold true: 1) a clear direction of diffusion should be evident in the spread of radiocarbon dates associated with the earliest maize when viewed at a regional or supra-regional scale; 2) the temporal distribution of dates should produce a front speed of between 0.5 and 1.9 km / year when regressed against the distance from a source locale; 3) and lastly, the intercept and coefficient of determination of the regression analysis should show a strong fit of the linear model to the data.

7.3.1: Results for the Reduced Major Axis Regression for Groups A to H

The following section presents the results of the Reduced Major Axis Regression of all datasets from their best-fitting point of origin. For each analysis, the intercept, slope, and R2 value is provided. Given the inherent error in the regression of calibrated dates against distance, 95% confidence intervals are also provided. As in the point of origin analysis, no statistically meaningful relationship was found in the full datasets

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(Groups A, B, E, F; Figures 7.4, 7.5, 7.8, 7.9). For the direct-date datasets (Groups C, D,

G, H), however, a moderate linear relationship is present (Figures 7.6, 7.7, 7.10, 7.11).

This relationship becomes even stronger when the three earliest dates are removed from datasets D and H (Section 7.3.1.9, Figure 7.12).

7.3.1.1: Dataset A (Earliest Uncontested Date)

As seen in the RMA regression, no significant relationship exists between the date of the 83 sites in the dataset and their distance from a point of origin in the northeastern portion of the sampling region (Figure 7.4). Therefore, demic diffusion is rejected as an explanatory process using the earliest direct and associated dates on maize in the region.

Figure 7.4: Results of RMA regression, Group A

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7.3.1.2: Dataset B (Earliest Dates and Pooled Dates, where Appropriate)

As seen in the RMA regression, no significant relationship exists between the date of the 82 sites in the dataset and their distance from a point of origin in the northeastern portion of the sampling region (Figure 7.5). Therefore, demic diffusion is rejected as an explanatory process using the earliest and pooled dates on maize in the region.

Figure 7.5: Results of RMA regression, Group B

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7.3.1.3: Dataset C (Earliest Direct Dates on Maize)

As seen in the RMA regression, a moderate relationship exists between the date of the earliest maize in the region and their distance from a point of origin in the northeastern portion of the sampling region (Figure 7.6). The intercept is consistent with current knowledge on the timing of the earliest entry of maize into the region and the slope is consistent with a demic front speed. Therefore, demic diffusion cannot be rejected as an explanatory process using the earliest direct dates on maize in the region.

Figure 7.6: Results of RMA regression, Group C

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7.3.1.4: Dataset D (Pooled Direct Dates on Maize)

As seen in the RMA regression, a moderate relationship exists between the date of the earliest maize in the region and their distance from a point of origin to the northeast

(Figure 7.7). The intercept is consistent with current knowledge on the timing of the earliest entry of maize into the region and the slope is consistent with a demic front speed. Therefore, demic diffusion cannot be rejected as an explanatory process using the earliest and pooled direct dates on maize in the region.

Figure 7.7: Results of RMA regression, Group D

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7.3.1.5: Dataset E (Earliest Dates by Sampling Grid)

As seen in the RMA regression, no significant relationship exists between the date of the 50 earliest sites-by-grid from dataset A and their distance from a point of origin in the northeastern portion of the sampling region (Figure 7.8). Therefore, demic diffusion is rejected as an explanatory process using the earliest direct and associated dates on maize in the Northeast.

Figure 7.8: Results of RMA regression, Group E

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7.3.1.6: Dataset F (Pooled Dates by Sampling Grid)

As seen in the RMA regression, no significant relationship exists between the date of the 49 earliest sites-by-grid from dataset B and their distance from a point of origin in the northeastern portion of the sampling region (Figure 7.9). Therefore, demic diffusion is rejected as an explanatory process using the earliest and pooled dates on maize in the

Northeast.

Figure 7.9: Results of RMA regression, Group F

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7.3.1.7: Dataset G (Earliest Direct Dates by Sampling Grid)

As seen in the RMA regression, a moderate relationship exists between the earliest direct maize dates in the region and their distance from a point of origin in the northeastern portion of the sampling region (Figure 7.10). The intercept is consistent with current knowledge on the timing of the earliest entry of maize into the region and the slope is consistent with a demic front speed. Therefore, demic diffusion cannot be rejected as an explanatory process using the earliest direct dates on maize in the region.

Figure 7.10: Results of RMA regression, Group G

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7.3.1.8: Dataset H (Pooled Direct Dates by Sampling Grid)

As seen in the RMA regression, a moderate relationship between the pooled and earliest direct maize dates in the region and their distance from a point of origin to the northeast (Figure 7.11). The intercept is consistent with current knowledge on the timing of the earliest entry of maize into the region and the slope is consistent with a demic front speed (Table 5.3). Therefore, demic diffusion cannot be rejected as an explanatory process using the earliest and pooled direct dates on maize in the region.

Figure 7.11: Results of RMA regression, Group H

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7.3.1.9: Reduced Datasets

For the direct date datasets (Datasets C, D, G, H), most sites fit fairly closely to the regression line. On the other hand, the three earliest sites in the region (Vinette,

D’Aubigny, and Edwin Harness), do not fit well with the linear model and appear as outliers on the right portion of the scatter plot (Figures 7.6, 7.7, 7.10, 7.11). In order to evaluate the effect of these sites on the regression, an additional two datasets were created from the two strongest linear regressions (Datasets D and H) with the sites in question removed. However, as Dataset H was a sampling of Dataset D by grid location, the next earliest site in each of the three grid locations of Vinette, D’Aubigny, and Edwin

Harness was used instead (replaced with Wickham, Grand Banks, and Blain Village, respectively). As seen in the results of the linear regressions (Figures 7.12 and 7.13), the removal of these three sites has a dramatic effect on the fit of the datasets to a linear model. In particular, the linear model for Dataset H.2 shows a particularly strong relationship between the date of a site and its distance from the point of origin (r = -0.76,

R2 = 0.58, p < 0.001).

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Figure 7.12: Results of RMA regression on dataset D with the three earliest dates removed

Figure 7.13: Results of RMA regression on dataset H with the three earliest dates removed

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7.3.2: Summary

As seen through the linear regression results of the four full datasets (Groups A,

B, E, F; Figures 7.4, 7.5, 7.8, 7.9), a Wave of Advance scenario for the Northeast appears unlikely: no statistically meaningful relationship was found to exist between the date of a site in these datasets and its distance from the best-fitting point of origin. Therefore, a demic process is rejected for the Northeast using direct and associated dates on maize- bearing sites. On the other hand, when using only the direct dates on maize in the region

(Datasets C, D, G, H), a moderate relationship is seen in the earliest date on maize at a site against its distance from the northeastern portion of the region (Figures 7.6, 7.7, 7.10,

7.11). This relationship becomes even stronger when the three earliest dates are removed from the dataset (section 7.3.1.9, Figures 7.12, 7.13). For the datasets displaying a moderate to strong relationship, the observed intercept is consistent with current beliefs around the timing of maize introduction into the region and the front speed is consistent with a Wave of Advance model. On the basis of the linear regression of the direct datasets, therefore, a Wave of Advance scenario cannot be rejected.

However, one issue that is raised by the differences in results between the full and direct-date datasets is that one must decide which dataset to use for interpretations. While there is little doubt that the direct dates are accurate representations of the period of maize use in the region, the associated dates used in this analysis are also generally thought to be a correct representation of the event being dated. As Fritz (1994:305) notes, however, direct AMS dates on maize from Mexico, the Greater Southwest, and eastern

North America have repeatedly shown that previous estimates for the antiquity of maize agriculture at key sites are unsubstantiated and true dates may be significantly younger

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(Conard et al. 1984, Ford 1987, Long et al. 1989). On the other hand, most researchers accept the inherent inaccuracies of associated dates and urge future research programs to draw direct dates wherever possible, although they are reluctant to dismiss the associated dates in the interim (Crawford et al. 1997:117; Hart 1999b:160). The issue of bioturbation of floral assemblages is of course not restricted to the Northeast. At the end of the Venice Meeting on the Neolithic Transition in Europe (October 1998), delegates recommended the inception of a dating program “to undertake the study of the Early

Neolithic in Europe and its Western Asian antecedents through the medium of AMS radiocarbon determinations to be conducted on well-provenienced and well-identified samples [of domesticates]” (Ammerman and Biagi 2003:343). However, the majority of radiocarbon dates used for analyzing the spread of agriculture through Neolithic Europe are still conventional. The fact that the direct-date dataset loses significance when included with the associated dates for the region could be more due to the preferential direct-dating of significant or early sites and not any pattern that is being masked by the inaccuracies of conventional dates.

Of course, an issue with the direct-date dataset is the material used for these direct dates. While this research would ideally only use dated macrobotanical remains, this would have severely reduced the number of available dates. Therefore, a decision was made to include dates on the carbonized residue adhering to pot sherds where maize phytoliths were found, as well as bone collagen dates from two sites where the δC13 ratio indicated unquestionable maize consumption (> - 17.0; see Appendix 1, section A1.5.3-

A1.5.4). While phytoliths from maize are unlikely to be confused with other plants, the issue lies in the fact that the phytoliths themselves are not subject to dating but rather the

127 carbonized residue where the phytoliths were embedded. When the two best-fitting models (Datasets D.2 and H.2) are plotted by material type (Figure 7.14), it is clear that these phytolith dates are driving much of the linear regression as they occupy both ends of the plot. While the directly-dated maize remains occupy the center of the plot, however, when regressed separately using the same point of origin as Dataset H.2, the r value and slope are quite similar (r = -0.66, p = 0.003, slope = 0.7-1.4 km/year; Figure

7.15). It is not surprising that the phytolith dates represent the earliest in the region, as this was the justification of the research program to locate them: namely, that the earliest evidence of maize agriculture in the Northeast would likely not be found in the form of charred macrobotanical remains (see chapter 2.4; Hart 1999b:161, 2001:173; Hart et al.

2003, 2007; Thompson et al. 2004). However, any interpretations based on both macro- and-microbotanical remains must be under the assumption that future phytolith research programs in other regions may produce equally early dates as those found in central New

York State – thus changing the patterns observed in these regressions. On the other hand, the consistency between the regressions with phytolith and bone collagen dates and only directly-dated maize remains does suggest that a similar pattern is being captured with the phytolith evidence only adding to the antiquity of the process.

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Figure 7.14: Results of RMA regression s for datasets D.2 and H.2 (see section 7.3.1.9, Figures 7.12, 7.13) with dated material shown

Figure 7.15: Results of RMA regression s for dataset H.2 (see section 7.3.1.9, Figure 7.13) where only directly-dated maize was used

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7.4: CONCLUSIONS

Through the analysis of the spatiotemporal patterning of the earliest entry of maize into the Northeast, as seen in the distribution of radiocarbon dates, a standard

Wave of Advance scenario seems untenable. While the linear regression of the direct- date datasets (Groups C, D, G, H, D.2, H.2) does suggest that a linear model can describe between 18% and 58% of the variation of the dataset, this situation only holds when a point of origin is placed in the northeastern portion of the region. Interestingly, there is at present no archaeological evidence to support the growing of maize in the area specified in these analyses (45°N, 75°W), nor anywhere north or east of New York State, before roughly AD 1200 (Asch Sidell 1999, 2008; Chapdelaine 1993; Chilton 2008; Clermont

1990; Crawford et al. 2003; Hart and Reith 2002; Morin 2001:68). For a demic model to explain the patterning in the data, it would be expected that the direction of diffusion should be from the Southwest, and that the intercept value would be in agreement with the direct dates on maize drawn directly outside of the sampling region – such as at the

Holding and Icehouse Bottom sites (Chapman and Crites 1987; Riley et al. 1994).

However, the fact that the observed front speed, intercept, and R2 agree with a Wave of

Advance model means that a demic process cannot be fully rejected. The implications of these results will be discussed in Chapter 9.

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CHAPTER 8: LOCAL ANALYSIS OF THE SPREAD OF MAIZE INTO

SOUTHERN ONTARIO – RESULTS

This chapter presents an analysis of locational preferences of horticultural communities in Southern Ontario throughout the entire agricultural period (ca. AD 500-

1650). By exploring the subsistence-settlement patterns of village sites divided into three temporal groups (AD 500-900; AD 900-1300; AD 1300-1650), the assumption is that the patterns produced by the cultural diffusion of maize growing practices should be measurably different from those produced by the migration of foreign farmers.

The primary means of analysis is that of a site settlement-distribution model based on ecological variables for Southern Ontario contextualized against the specific growing requirements of maize. This is approached through two complimentary methods: an evaluation of the primary ecological variables driving site location selection for each period using Maximum Entropy (MaxEnt) modeling; and finally, a test of the relationship of these variables between periods using non-parametric tests of independence.

The results of this analysis demonstrate that change through time is observed in accordance with a greater dietary reliance on maize; however, this is largely concerning the relative importance of a limited set of variables which show no change through time.

In order to contextualize the results of analysis, however, a brief discussion of the particular growing requirements of maize and those variables that will take on critical importance to Southern Ontario models of maize growth is necessary.

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8.1: GROWING REQUIREMENTS FOR MAIZE:

Numerous studies on the environmental requirements of maize have demonstrated that length of frost-free growing season, soil and air temperature during the growing season, precipitation, soil type, drainage, and relief were all important variables in successful maize horticulture (Demeritt 1991; Fecteau 1985; Heidenreich 1971; Konrad

1973; Sykes 1980; Yarnell 1964). In areas such as Southern Ontario, which exist at the outermost range of maize productivity, many of these factors take on critical importance.

8.1.1: Climatic Requirements of Maize

8.1.1.1: Frost-free Days and Solar Heat

Being a tropical crop, maize requires between 60 and 120 frost-free days to reach maturity (Shaw 1955:318). For Northern Flint in particular, the belief is that 100-120 frost-free days were required for it to reach maturity (Campbell 1991:19-21; Fecteau

1985:24; Yarnell 1964). In fact, Yarnell (1964:128-131) believes the 120 day period to be the critical lower limit for precontact groups in the Upper Great Lakes, as this allows for a buffer around years with late-spring or early-fall frosts. Given the marginal nature of the Northeastern climate for maize growth, Yarnell (1964:131) stresses the length of the frost-free season as the most important determining factor for maize growth.

However, it should be noted that frost-free duration is not the best representation of what is needed for successful maize growth, where instead a measure of solar heat is a

132 better predictor of productivity (Demeritt 1991:187; Fecteau 1985:104-109). The optimum air temperature for maize growth during the summer months is between 21˚C and 30˚C, although it will tolerate means as low as 16˚C and as high as 35˚C (Fecteau

1985:24; Shaw 1955:334-335). This amount of solar heat is best captured either through

Growing Degree Days (GDDs) or Corn Heat Units (CHUs, also referred to as Crop Heat

Units). Both methods assume that a plant matures through a daily accumulation of heat only after a certain base minimum temperature has been met (for maize, this base temperature is generally 10˚C). The main difference between these approaches is that, unlike GDDs, CHU calculation assumes that once temperatures rise over 30˚C, they no longer provide any distinct advantage to corn development and therefore 30˚C is treated as a ceiling in calculation. The formulas for both methods are:

Daily GDD = ((Tmax + Tmin)/2) – 10* (1) Where: T max = the daily maximum air temperature T min = the daily minimum air temperature *for all mean daily values below 10˚C, GDD = 0

Daily CHU = (Ymax + Ymin) / 2 (2)

Where:

Y max = (3.33 x (T max-10)) - (0.084 x (T max-10.0)2) (If values are negative, set to 0; if values are above 30˚C, set to 30) Y min = (1.8 x (T min - 4.4)) (If values are negative, set to 0) 5 Tmin = Daily minimum temperature (°C)

5 In the CHU calculation, minimum temperatures are considered to be night-time temperatures so the base temperature is set to 4.4˚C.

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Once each day’s solar heat above 10˚C is calculated, these values are then summed for the entirety of the growing season until the arrival of the first killing frosts. For productive corn growth, the minimum growing season requirements are 2000 GDDs or

2500 CHUs (Demeritt 1991:187; Fecteau 1985:104-107). The following figures (Figure

8.1-8.3) show the distribution of frost-free days, GDDs, and CHUs for Southern Ontario, based on Environment Canada’s 1960-1990 Climate Normals

(www.climate.weatheroffice.gc.ca). As seen in these results, Southern Ontario lies at the functional limits of productive maize horticulture. Furthermore, given the fact that the climate has likely never been warmer in the last 8,000 years (Allen 1996:206; Campbell

1991:27; MacDonald 2002:180), this undoubtedly represents the maximum extent for maize productivity in Ontario.

Figure 8.1: Distribution of Frost-Free Days in Southern Ontario

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Figure 8.2: Distribution of Growing Degree Days in Southern Ontario

Figure 8.3: Distribution of Corn Heat Units in Southern Ontario

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8.1.1.2: Precipitation

Without irrigation, maize requires between 30 and 60 cm of rainfall during the growing season and is particularly susceptible to drought in the silking stage (Fecteau

1985:25-26; Shaw 1955:328). As Fecteau (1985:110) notes, however, the average rainfall for Southern Ontario is well within the acceptable range, and likely would not have been a limiting factor in determining where to grow maize. As seen in Figure 8.4, the average distribution of rainfall in Southern Ontario during the growing season is well within the acceptable limits.

Figure 8.4: Distribution of Rainfall during Growing Season in Southern Ontario

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8.1.2: Soil and Topographic Requirements of Maize

8.1.2.1: Soil

Corn grows best on deep, well-drained fertile loam soils that are moisture retentive. Drainage of soil is more important than soil texture as corn does not do well on poorly-drained soils (Campbell and Campbell 1992:7; Fecteau 1985:26-27; Stringfield

1955:344). In fact, numerous studies in Ontario have shown a strong link between the location of village sites and proximity to well-drained soils – ranging between 80% and

95% correspondence (Fecteau 1985:119; Heidenreich 1971:67; Konrad 1975:14).

8.1.2.2: Topography

While numerous researchers have commented on the preference for rolling topography on Iroquoian village sites in Southern Ontario (Campbell and Campbell

1992:22; Engelbrecht 2003:32; Heidenreich 1971:110; Konrad 1976:14), this does not appear related to any specific agronomic requirement of maize (Kravchenko et al. 2000).

On the other hand, a rolling topography could have some value in areas with a short growing season as cooler temperatures would settle in the depressions, creating a slightly warmer microclimate on the ridges. The use of corn hills as microclimatic buffers against frost has been argued for many areas of North America (Demerritt 1991:195; Doolittle

2000; Engelbrecht 2003:32; Heidenreich 1971:184; MacDonald 2002:195, 350; Riley and

Friemuth 1979:282; Yarnell 1964). This topographic preference could therefore be an extension of this strategy (Demeritt 1991:195; Hasenstab 1996:21).

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8.1.3: Primary Limiting Variables

From this discussion, it is clear that some of the key variables for maize growth will be limiting factors in horticultural site selection in Southern Ontario while others will likely not play any significant role. In particular, it is assumed that growing season length and intensity will be critical given the northern location of Southern Ontario. Soil texture and drainage, and perhaps terrain relief, may also prove important, while precipitation will likely have little to no explanatory value. This is in close agreement with other ecological analyses of Iroquoian site selection criteria within the Northeast (Allen 1996;

Campbell 1991; Hasenstab 1996; Heidenreich 1971; MacDonald 2002). Figure 8.5 shows the results of a weighted overlay analysis of sufficient GDDs, and the location of well- drained, easily-worked soils in Southern Ontario. Note that the distribution of areas of high productivity is in close agreement with actual regional patterns of village settlement.

Figure 8.5: Weighted overlay of sand and sandy loam deposits, well-drained soil and adequate GDDs

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This observation has certainly been made before by researchers in Southern

Ontario. Warrick (1990:111-112; 2008:19) highlights the overwhelming correspondence between the distribution of Iroquoian village sites in Southern Ontario and the presence of easily worked, well-drained soils. The assumption by many (Campbell 1991:14-22;

Heidenreich 1971; Konrad 1976:13-16; Warrick 2008:19; Williamson 1985:329) is that the primary determinant of village location in Southern Ontario appears to have been the preference for arable land for maize horticulture.

8.1.4: Summary

Given the remarkable correspondence between village locations in Southern

Ontario and areas amenable to maize horticulture (Figure 8.5; Warrick 1990:111-112;

2008:19), an analysis of settlement choice based on environmental criteria seems appropriate. Furthermore, as Southern Ontario exists at the limits of maize productivity, it is assumed that some of these variables will take on critical importance. This provides an avenue for evaluating whether specific locales were targeted for village location, and indeed, whether these preferences appear to have changed through time. This leads to the following two hypotheses tested in this analysis:

H1: Consistent with a cultural diffusionary model, the environmental covariates associated with the location of village sites are seen to significantly change through time in line with expectations of maize productivity

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For migration to be accepted as the primary process at work in the introduction of maize in Southern Ontario, a clear locational strategy should be visible from the earliest appearance of maize in the region. Ideally, this strategy should be in agreement with maize’s specific ecological requirements, such as adequate growing season, drainage, and rich soils. For cultural diffusion to be accepted as the primary process there should be no clear link to maize’s growing requirements for earlier sites in the region with a trend towards targeting productive locales through time as maize rose in dietary importance.

8.2: MAXIMUM ENTROPY ANALYSIS OF VILLAGE LOCATIONAL CHOICES THROUGH TIME

The real value of the MaxEnt modeling procedure for this analysis is that it allows for a multivariate representation of the key environmental variables driving village location preferences for each period. In particular, the MaxEnt modeling procedure allows for the identification of key variables, bolstered by 500 replicated runs. As outlined in Chapter 6.3, the first step of the MaxEnt protocol is to remove unnecessary variables, thus ensuring better fit of the model to the data. It is these key variables that will later be used for tests of independence. After the stepwise reduction of variables, the next stage of analysis is an evaluation of the key environmental variables for village location selection for each temporal group. This will serve to highlight any major changes in the relative importance of each variable for site selection.

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8.2.1: Stepwise Reduction of Environmental Variables

While all environmental variables initially collected for this analysis (Table 6.3) have the potential to inform the MaxEnt models, in practice many will hold little explanatory value – thus adding unnecessary complexity to the modeling procedure. The final set of environmental variables is listed in table 8.1. One reason why variables may not have had a strong influence on a particular model was that they were highly correlated with other variables. For example, all variables which describe growing season length and intensity (Table 6.3, variables 1-3) are essentially describing the same phenomenon, albeit in slightly different ways. Similarly, three measures of terrain ruggedness were created (Table 6.3, variables 19-21) in order to assess differences in spatial resolution on village location (See section A2.2.3.2 for a fuller discussion of dataset creation and justification). Essentially, these three datasets approximate a 500m,

1km, and 2km catchment around a site in order to evaluate whether different ranges were exploited during the three periods. The one exception to this process was variable # 4, growing season precipitation. This variable contributed roughly 20% to the model for late period sites. However, it was determined that this may be capturing something other than higher precipitation. Therefore, this variable was removed in order to avoid potentially confounding results. While the greater interest in areas of high precipitation during the later period does agree with the belief that groups may have experienced higher occurrences of drought during the Little Ice Age (ca. AD 1400-1800; Allen 1996:210;

Demerritt 1991:195;; Warrick 2008), the fact that no reliable climatic reconstructions exist for this period makes precise modeling of this particular hypothesis intractable.

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Environmental Variable Data Type Source Natural Resources Canada, Growing Degree Days Continuous See Appendix 2 Ontario Ministry of Natural Resources, Distance to Clay (m) Continuous See Appendix 2 Ontario Ministry of Natural Resources, Distance to Clay Loam (m) Continuous See Appendix 2 Ontario Ministry of Natural Resources, Distance to Silt Clay Loam (m) Continuous See Appendix 2 Ontario Ministry of Natural Resources, Distance to Sand (m) Continuous See Appendix 2 Ontario Ministry of Natural Resources, Distance to Sandy Loam (m) Continuous See Appendix 2 Ontario Ministry of Natural Resources, Distance to Well-Drained Soil (m) Continuous See Appendix 2 Ontario Ministry of Natural Resources; Digital Elevation Model (masl) Continuous MaDGIC

Terrain Ruggedness Index – 1km Continuous See Appendix 2

Table 8.1: Final environmental variables

8.2.3: Results of Maximum Entropy Modeling

The final output of the MaxEnt modeling procedure is a map of the study region where each cell is given a probability between 0 and 1, representing the likelihood of a species occurring in that cell. A cell with a predicted probability of 1 represents the most suitable habitat, while cells with a probability of 0 represent areas of lowest suitability

(Phillips et al. 2006; Phillips and Dudík 2008). As seen in Figure 8.6, the distribution of suitable areas is very different for each period. However, given that the number and distribution of sites vary significantly between each period, this is not surprising. MaxEnt has been designed to model the closest distribution to the observed records, and has proven to be more conservative than other species distribution modeling programs

(Banks 2012, 2013; Phillips et al. 2006; Phillips and Dudík 2008). Therefore, the output

142 distributions are simply demonstrating that the MaxEnt software is performing in the manner in which it is intended, and should not be interpreted as an actual “cultural” settlement pattern. As an additional measure of model fit, MaxEnt calculates the area under the receiver operating characteristic curve (AUC) which measures the probability of the model to predict site presence (Phillips et al. 2006:245; Phillips and Dudík

2008:166). An AUC of 0.5 represents random chance and an AUC of 1 represents perfect model fit. Models with an AUC value above 0.75 are considered potentially useful and those with an AUC above 0.9 are considered to have an excellent fit (Elith 2002; Phillips and Dudík 2008:166; Soto Berelov 2011:174). As seen in Table 8.2, the three models have AUC values between 0.87 and 0.99, indicating good to excellent model fit.

However, it should be noted that the AUC value of 0.99 for the model for Stage 1 is likely due to the small sample size rather its inherent superiority as a model.

On the other hand, those areas with high probability far outside the actual distribution of sites represent locations with a similar combination of key environmental characteristics to those identified in the source area for each period (see Figures 8.7-

8.12). More relevant to this analysis are the particular environmental covariates that inform each final MaxEnt model, their relative contribution, and response curves. The following section will present the results of each temporal group.

MaxEnt Time Period AUC Standard Deviation Model Stage 1 AD 500-899 0.995 0.003 Stage 2 AD 900-1299 0.970 0.008 Stage 3 AD 1300-1650 0.879 0.018

Table 8.2: AUC scores for three models based on 500 replicate runs

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Figure 8.6: Final probability outputs of the three MaxEnt models

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8.2.3.1: MaxEnt Results for Stage 1

The final output probability distribution for 500 replicate runs (Figure 8.7a) shows a concentration of suitable environmental characteristics around the Lower Grand River

Valley and the western edge of Lake Ontario, near Hamilton, and along the northern portion of the Niagara Peninsula. The three primary environmental variables in final model design are distance to silt clay loam, Growing Degree Days (GDD), and distance to sand (Figure 8.7b). However, the response curve for distance to sand (Figure 8.8d) indicates a positive relationship, meaning that the probability of site presence increases as one moves farther away from sand. As it is unlikely that groups would have chosen sites based on their distance away from sand deposits, this variable has little explanatory value for the early model. Proximity to well-drained soil has the next highest contribution

(Figure 8.7b). When viewing the jackknife plot for the final MaxEnt model (Figure 8.7c), the environmental variable with highest gain when used in isolation is distance to silt clay loam, which therefore appears to provide the most useful information by itself. The variable that decreases gain the most when it is omitted is also distance to silt clay loam, which therefore appears to provide the most information not present in other variables.

The response curves for distance to silt clay loam and distance to well-drained soil (Figures 8.8f, g) both show strong negative relationships – precisely what one would expect if these variables had influence on site selection, as they indicate that groups were locating sites closer to these resources. Elevation also produces a negative response curve, although this is likely reflecting the floodplain location of the sites rather than any other locational strategy (Figure 8.8a). GDD shows a positive relationship, indicating that groups may have been targeting warmer locations for growing corn (Figure 8.8h).

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Figure 8.7: Results from MaxEnt analysis – Stage 1

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Figure 8.8: Response curves for Stage 1 MaxEnt model

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8.2.3.2: MaxEnt Results for Stage 2

The final output probability distribution for 500 replicate runs (Figure 8.9a) shows a concentration of suitable environmental characteristics largely west of Lake Ontario, although there are some areas north of Lake Ontario. The three primary environmental variables in final model design are GDD, terrain relief, and distance to silt clay loam, although the majority of the model can be described by GDD alone (Figure 8.9b).

Distance to sand and distance to well-drained soil have the next highest contributions.

When viewing the jackknife plot for the final MaxEnt model (Figure 8.9c), the environmental variable with highest gain when used in isolation is GDD, which therefore appears to provide the most information by itself. The variable which decreases the gain the most when omitted is also GDD, which therefore appears to provide the most information not present in other variables.

The response curves for distance to silt clay loam, distance to sand, and distance to well-drained soil (Figures 8.10d, f, g) show strong negative relationships. Again, this is what one would expect if these variables had influence on site selection as it indicates that groups were locating sites closer to these resources. Terrain relief response peaks at about 10m, indicating a preference for gently rolling to nearly level terrain (Figure 8.10i).

The most interesting and informative response, however, is GDD. As the response curve shows (Figure 8.10h), probability of presence peaks almost exactly at 2000 GDDs – the minimum requirement for maize (see section 8.1.1.1). This may indicate that groups were targeting their locations largely on the minimum climatic requirements for maize.

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Figure 8.9: Results from MaxEnt analysis – Stage 2

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Figure 8.10: Response curves for Stage 2 MaxEnt model

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8.2.3.3: MaxEnt Results for Stage 3

The final output probability distribution for 500 replicate runs (Figure 8.11a) shows suitable environmental characteristics throughout much of Southern Ontario, with notable exceptions in the southernmost portion of the study region as well as the Dundalk uplands and Bruce Peninsula region. The three primary environmental variables in final model design are distance to silt clay loam, distance to well-drained soil, and GDD

(Figure 8.11b). Distance to clay and terrain relief have the next highest contributions to the final model. When viewing the jackknife plot for the final MaxEnt model (Figure

8.11c), the environmental variable with highest gain in isolation is distance to silt clay loam, which therefore appears to provide the most useful information by itself. The variable that decreases the gain the most when it is omitted is GDD, which therefore appears to provide the most information not present in other variables.

The response curves for distance to silt clay loam and distance to well-drained soil (Figures 8.12f, g) show strong negative relationships. This is what one would expect if these variables had influence on site selection as it indicates that groups were locating sites closer to these resources. Like the results for the previous model, terrain relief response peaks at about 10m, indicating a preference for gently rolling to nearly level terrain (Figure 8.12i). The response curve for GDD (Figure 8.12h) also has a similar pattern to the previous model, although it peaks between 1800 and 2000 Growing Degree

Days. This may indicate changes in the minimum requirements for maize horticulture or possibly a move to more marginal locations.

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Figure 8.11: Results from MaxEnt analysis – Stage 3

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Figure 8.12: Response curves for Stage 3 MaxEnt model

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8.2.3.4: Summary of MaxEnt Results

These results indicate that there are significant differences between the three periods in the importance and relative contribution of key environmental variables (Table

8.1). GDD informs a large portion of all models, although is the strongest determinant of village location only for the middle period. Additionally, there is a fairly strong correspondence in all three models between the minimum required GDDs for maize and the likelihood of site-presence. This suggests a clear locational strategy tailored to the climatic requirements of maize. While this pattern is weakest for the earliest period, there is a distinct change in the response of the models to this variable by the middle period, exactly when maize has been argued to form a significant part of local lifeways.

Distance to silt clay loam also informs a large portion of both early and late periods, and is the second highest informing variable for the middle period model – albeit at a much lower significance. Distance to well-drained soil is a moderately important variable for all three models. Importantly, both distance to silt clay loam and distance to well-drained soil exhibit strong negative response curves for all three periods, indicating a preference to live as close as possible to these resources. A preference for gently rolling terrain is evident in the middle and late periods (Stage 2 and 3) while elevational preference varies between each model. Based on these results, it appears that locational strategies did change through time, but only within a limited set of variables and only in terms of which variable was most important to site selection. Otherwise, it appears from these results that distance to silt clay loam, distance to well-drained soil, and GDD were important factors for site selection in all periods, suggesting continuity between the earliest introduction of maize and the final distribution of village sites (see Table 8.3).

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Stage 1: Stage 2: Stage 3: Percent Permutation Percent Permutation Percent Permutation Environmental Variable Contribution Importance Contribution Importance Contribution Importance

Dist to Clay 2 2.6 3.5 3.5 9.3 15.3

Dist to Clay Loam 0.8 0.2 3.2 3.1 6.2 11.1

Dist to Sand 17.9* 19.3* 7.6 1.5 4.3 6

Dist to Sandy Loam 4.2 2.2 5.1 2.1 3 2

Dist to Silt Clay Loam 34.7 37.5 8 5.3 20 17

Dist to Well-Drained 8.9 1.8 6.8 4.4 19.6 7.5 Soil

Elevation 5.4 10.6 1.2 1.1 8.3 9.9

GDD 21.9 24.1 55 73.7 18.4 19.7

Terrain Ruggedness 4.3 1.8 9.6 5.1 11.1 11.6

* This variable was determined to not contribute to the Stage 1 model as response curve showed a positive relationship between distance and location

Table 8.3: Variable importance and contribution for the three final MaxEnt models

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8.3: TESTS OF INDEPENDENT SAMPLES

Now that the key environmental covariates driving village location selection through time have been identified, the next step of analysis is an evaluation of whether significant difference can be observed in these variables between periods. For change through time to be accepted, a significant difference in environmental variable values should be observed between each group. Additionally, these values should be significantly different from the randomly-generated dataset (see Figure 6.2) – indicating that they are not the result of random variation but instead may reflect specific settlement choices. Two non-parametric tests of independent samples are employed in this analysis: a Kruskal-Wallis (KW) test for all three groups; and a Wilcoxon Mann-Whitney (WMW) test for pairs of groups.

In standard statistical practice, if differences are found to exist between multiple groups in a Kruskal-Wallis test (or an ANOVA test, for normally-distributed data), a paired test of difference between individual groups is performed in order to locate the divergent group. While the KW test is able to assess whether the three groups could come from the same population, it performs poorly when differences are observed. In other words, there is no way of evaluating from these results whether two of the three groups represent a similar pattern while the third is divergent or if directionality is present in the environmental values through time. Therefore, by testing each individual group against another as well as against randomly-generated data, a more nuanced understanding of local change in site selection and environmental covariates can be achieved.

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8.3.1: Results of Kruskal-Wallis Test of Independent Samples

The following section outlines the results of a Kruskal-Wallis rank sum test of independence between the three temporal groups (Table 8.4), and between the three temporal groups and a group of 100 randomly-located points within the study region

(Table 8.5). The results show that most of the variables cannot be separated between groups but can be separated from the random sample.

χ2 Diff? Variable P Conclusion (df = 2) ( = 0.05) No difference through time observed for distance Dist to Clay 2.431 0.297 N to clay soils Dist to Clay No difference through time observed for distance 4.108 0.128 N Loam to clay loam soils There is a change through time in distance to Dist to Sand 12.679 0.002 Y sandy soils Dist to Sandy No difference through time observed for distance 2.21 0.331 N Loam to sandy loam soils Dist to Silt Clay No difference through time observed for distance 4.577 0.101 N Loam to silt clay loam soils Dist to Well- No difference through time observed for distance 1.03 0.598 N Drained Soil to well-drained soils

Elevation 12.252 0.002 Y There is change through time in site elevation

Growing There is a difference through time in Growing 56.158 <0.001 Y Degree Days Degree Days at site locations Terrain No difference through time observed in terrain 4.225 0.121 N Ruggedness relief 1 km around sites

Table 8.4: Results of Kruskal-Wallis test for difference between temporal groups

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χ2 Diff? Variable P Conclusion (df = 3) ( = 0.05) No difference observed between archaeological Dist to Clay 2.54 0.468 N sites and random locations Dist to Clay No difference observed between archaeological 5.742 0.125 N Loam sites and random locations There is observable difference between three Dist to Sand 12.328 0.006 Y temporal groups and random locations Dist to Sandy There is observable difference between three 17.279 <0.001 Y Loam temporal groups and random locations Dist to Silt Clay There is observable difference between three 36.761 <0.001 Y Loam temporal groups and random locations Dist to Well- There is observable difference between three 15.512 0.001 Y Drained Soil temporal groups and random locations There is observable difference between three Elevation 10.623 0.014 Y temporal groups and random locations Growing There is observable difference between three 73.798 <0.001 Y Degree Days temporal groups and random locations Terrain There is observable difference between three 12.645 0.005 Y Ruggedness temporal groups and random locations

Table 8.5: Results of Kruskal-Wallis test for difference between temporal groups and random locations

As seen in Tables 8.4 and 8.5, distance to clay and distance to clay loam could not be separated between groups or from the random dataset. Therefore, these variables were likely not a factor in settlement choice and variation may in fact be due to background variation across the study region. For distance to silt clay loam, sandy loam, and well- drained soil, as well as terrain relief, covariates were not significantly different between temporal groups but were different from random locations. This indicates that these factors may well have had cultural importance and that this remained fairly steady through time. Lastly, distance to sand, site elevation and GDD all varied significantly among groups as well as between groups and the random dataset. This may indicate culturally-important settlement factors that did not remain constant through time.

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8.3.2: Results of Wilcoxon Mann-Whitney Test of Independent Samples

The following section outlines the results of a Wilcoxon Mann-Whitney test of independence performed pair-wise between the three temporal groups (Table 8.6) and between each of the three temporal groups and the group of 100 randomly-located points within the study region (Table 8.7). As the KW test found no difference between distance to clay and distance to clay loam either in the temporal categories or with random data, these were not included in the WMW test. The results are understandably quite similar to the KW tests above, although some temporal patterning is also present.

Variable Early vs. Late Early vs. Middle Middle vs. Late W p Diff? W p Diff? W p Diff?

Dist to Sand 341 0.071 N 127 0.022 Y 2392 0.004 Y

Dist to 312 0.154 N 107 0.133 N 3275 .905 N Sandy Loam Dist to Silt 91 0.093 N 34 0.141 N 2917 0.22 N Clay Loam Dist to Well- 268.5 0.379 N 84.5 0.555 N 3595 0.337 N Drained Elevation 59 0.034 Y 31 0.116 N 4268 0.003 Y Growing 401.5 0.008 Y 125 0.027 Y 5620 <0.001 Y Degree Days Terrain 233 0.769 N 101 0.22 N 2658 0.043 Y Ruggedness

Table 8.6: Results of Wilcoxon Mann-Whitney test of independent samples for three temporal groups

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Variable Early vs. Random Middle vs. Random Late vs. Random W p Diff? W p Diff? W p Diff?

Dist to Sand 233 0.106 N 1787.5 0.019 Y 7312 0.624 N

Dist to 161 0.836 N 1610 0.002 Y 5118 <0.001 Y Sandy Loam Dist to Silt 26.5 0.016 Y 1241.5 <0.001 Y 4365 <0.001 Y Clay Loam Dist to Well- 133 0.739 N 1867 0.036 Y 5136 <0.001 Y Drained Elevation 74 0.1387 N 2070 0.246 N 7729 0.203 N Growing 280 0.01 Y 4158.5 <0.001 Y 8560 0.005 Y Degree Days Terrain 202 0.313 N 2590.5 0.319 N 8837 <0.001 Y Ruggedness

Table 8.7: Results of Wilcoxon Mann-Whitney test of independent samples for three temporal groups against randomly-located sites

Distance to Sand:

Results show that middle period sites can be separated from early and late groups on the basis of their distance to sand. Additionally, the middle group can be separated from the random point dataset based on distance to sand while early and late sites cannot.

This indicates that proximity to sand may have been an important site-selection factor for middle sites, but not for early or late sites.

Distance to Sandy Loam:

Results show that early, middle, and late groups cannot be separated on the basis of their distance to sandy loam. However, both middle and late sites show a significant separation from the random point dataset, while the early group could not be separated.

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Therefore, it appears that proximity to sandy loam was a determining factor for both middle and late groups but may not have been for early groups.

Distance to Silt Clay Loam:

Results show that early, middle, and late groups cannot be divided on the basis of their distance to silt clay loam. However, early, middle, and late sites show a significant separation from the random point dataset. Therefore, it appears that the distance to silt clay loam was a determining factor for early, middle, and late groups, but that this settlement choice remained relatively stable through time.

Distance to Well-drained Soil:

Results show that early, middle, and late groups cannot be divided on the basis of their distance to well-drained soil. However, both middle and late sites show a significant separation from the random point dataset, while early sites do not. Therefore, it appears that the distance to well-drained soil was a determining factor for both middle and late groups, but not necessarily for early groups.

Elevation:

Results show that late groups can be divided from early and middle groups based on site elevation, while early and middle groups cannot be separated based on elevation.

Furthermore, none of the groups can be separated from the random point dataset.

Therefore, this variation in elevation between periods may be reflecting natural background variation in elevation rather than a culturally-significant difference.

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Growing Degree Days:

Results show that early, middle, and late groups can be divided based on the number of GDDs at each locale. Furthermore, each group can be separated from the random point dataset. This indicates that number of Growing Degree Days was an important site selection factor in all three groups and that this relationship changed through time. However, given the fact that GDD changes through the entire study region and that sites are distributed throughout this range, this may be reflecting the geographic location of each of the group sites rather than any actual settlement change.

Terrain Ruggedness:

Results show that early and late groups and early and middle groups cannot be divided based on the average terrain relief around each site, while middle and late groups can. Furthermore, neither early nor middle groups could be separated from the random dataset while late groups showed significant difference. This indicates that terrain relief around the settlement may not have been a determining factor during early or middle stages, while it was a settlement factor for later groups.

8.3.3: Summary of Results for Tests of Independence

A few key conclusions can be drawn from these results. For distance to silt clay loam, sandy loam, and well-drained soil, both the KW and WMW tests found no significant difference between temporal periods, suggesting a stable site-selection pattern through time. Furthermore, while the KW test suggests that all periods were significantly

162 different from the random site dataset for these three variables, the WMW test indicates that the early period could not be separated from background variation for distance to sandy loam or distance to well-drained soil, but was significantly different for distance to silt clay loam. This may indicate that distance to sandy loam and well-drained soil only gained real importance in site selection by AD 900. While the KW tests indicated that terrain ruggedness could not be separated between groups but could from random locations, the WMW test revealed that only the later group is divergent with early and middle sites not being significantly different from each other or from background variation. While the KW test indicated that distance to sand, site elevation, and GDD varied significantly between groups and from the random dataset, on closer analysis the

WMW tests indicate that elevation does not differ significantly from the random dataset for any of the periods and distance to sand is only significantly different for the middle period. Given the location of many Glen Meyer sites on upland sand plains during the middle period, this is not surprising. Lastly, GDD were found to differ significantly between periods and differ from the random dataset. Again, this is not entirely surprising given the variability in site locations across periods and the clinal distribution of Growing

Degree Days across Ontario. These results indicate that only one variable remained constant and significant through time – proximity to silt clay loam. All other variables either did not differ from background variation or were found to differ between periods.

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8.4: CONCLUSIONS

Based on the environmental evidence for village location strategies through time, the hypotheses of migration and local development can provisionally be both accepted and rejected. Change through time is observed and there is some evidence that suggests that early period sites do not differ significantly from random samples and that there is a significant shift in settlement location strategies by the middle period – a shift in which many of the same variables maintain importance until the historic period. This is exactly what would be expected if maize horticulture were attached to a mobile subsistence- settlement strategy, only to change once it gained dietary importance in local lifeways.

On the other hand, this change through time largely concerns the relative importance of a limited set of environmental criteria. Proximity to silt clay loam remains an important site selection factor for all periods as do proximity to well-drained soil and the minimum solar heat requirements for growing maize, as represented by Growing

Degree Days. This may indicate a preference for the same major site selection strategies throughout the agricultural period in Southern Ontario.

However, the large difference in sample sizes between each group certainly complicates any interpretation of temporal trends. With only three sites in the early group, 47 in the middle group, and 191 in the late, the fact that later sites were able to be separated from the random dataset for many variables, while earlier sites were not, could simply be an artifact of sample numbers rather than any specific locational trend.

While the differences in sample size between groups may have an effect on the patterns observed by the MaxEnt analysis as well as the tests of independent samples,

164 there is no a priori reason for this to be the case. In particular, if migration were the primary mechanism by which maize entered the region, there should be strong evidence for continuity in site selection criteria through time regardless of sample size. In fact, the large difference between the number of village sites by time period is perhaps the strongest evidence against a migration scenario by simple fact that a group which migrated into an occupied area should have a clearer archaeological signature from the first arrival (Anthony 1990:902, 1997; Burmeister 2000:354; Ferris 2001:25; Sutton

1996:31). The implications of these results will be discussed further in the next chapter.

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CHAPTER 9: DISCUSSION OF RESULTS

As seen in the regional and local analyses of the spread of maize into Southern

Ontario (Chapters 7 and 8), the results are not wholly consistent with either a typical migration or local development scenario. This is not surprising given the nature of archaeological data and the spatial and temporal coverage of this thesis. Indeed the true value of this research is not in determining a singular process for agricultural uptake in

Southern Ontario, but rather in contextualizing the results against these competing models in order to reach a better understanding of the economic, social, and demographic processes behind the arrival of maize horticulture. In this vein, the following discussion will act as a contextual analysis of the regional and local results. After presenting the major results from both a regional and local analysis of the spread of maize into Southern

Ontario, as well as the patterns which diverge from the hypotheses tested in this thesis, an integrationist model will then be presented which mobilizes all data in order to reach a better understanding. In particular, an availability scenario is presented for the spread of maize into Southern Ontario whereby the local development and rapid adoption of maize growing practices were the result of local decision making as well as perhaps competition with fully horticultural migrants.

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9.1: DISCUSSION OF RESULTS OF REGIONAL ANALYSIS OF THE SPREAD OF MAIZE INTO THE

NORTHEAST

As seen through the results of the point of origin and linear regression analyses, a standard Wave of Advance scenario for the Northeast is not well-supported. When using all available direct and associated dates for maize in the region, no clear linear relationship is observed and the pattern overwhelmingly suggests that another means of describing the data must be sought. On the other hand, when using only the direct dates on maize in the region, a moderate relationship is seen in the earliest date at a given site against its distance from the northeastern portion of the region. This, however, is still not consistent with a standard demic scenario in that the expected origin of diffusion should be from the southwest as horticultural groups slowly spread across the continent from

Mexico via the Southwest. Furthermore, the fact that this relationship becomes even stronger when the three earliest dates are removed from the dataset suggests that there may have been a period of initial experimentation before maize horticulture began to spread through the region. This section will therefore attempt to contextualize the results against other processes that may have led to the observed patterns. In particular, a modified Wave of Advance scenario is presented where maize first arrived in the region in central New York, perhaps with migrants. However, once established in the Northeast, it does appear that maize may have spread gradually through the region – radiating out from the Finger Lakes district into Southern Ontario, Pennsylvania, Ohio, and Michigan.

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9.1.1: Point of Origin Analysis

The first conclusion that can be drawn from the point of origin analysis is that there is a poor linear fit between the date of the earliest evidence of maize at sites across the Northeast and any single point of origin across continental North America. The most parsimonious explanation for this lack of linear fit to a single origin point is that maize was likely introduced multiple times into the region, following different diffusionary pathways and at different times. However, this is at odds with the genetic and isoenzymatic evidence for the extreme genetic divergence of Northern Flint maize populations in the Northeast, which privileges a single introduction event and a relatively small founding population (see Chapter 2.2.2; Benz and Staller 2006:668; Crawford et al.

2006:554; Doebley et al. 1986, 1988; Ford 1985:353). As Hart (1999b:149-156,

2001:156-159) notes, however, due to maize’s particular susceptibility to inbreeding depression it is very unlikely that a small founding population of maize (whether introduced to existing populations or brought in with horticulturalists) would have allowed for successful maize growing in the region:

Given the obstacles to the establishment of founder populations, it is very unlikely that a single founder event was responsible for the adoption of maize in the Eastern Woodlands. There were probably many founder events in many locations in the East, the majority of which likely ended over varying lengths of time in extinction (Hart 1999b:152).

Instead, Hart (1999b:152) argues that for a maize founder population to be successful it would be necessary for it to be split into many partially-isolated demes, whereby regular gene flow between populations allowed for the regional development of adapted maize landraces. Importantly, Hart’s application of evolutionary theory to maize agriculture does not preclude a single period of initial introduction of maize into the Northeast, only that it would, by necessity, need to be divided between many groups in order to ensure its

168 long-term success. Furthermore, Hart (2001:158) states that this maize population could have remained in relative isolation from other landraces south and west of the Eastern

Woodlands – thus allowing for the extreme genetic diversity of Northern Flint landraces observed by Doebley et al. (1986, 1988).

What is perhaps most surprising from the point of origin analysis is that all sampling groups found a closer link to a point of origin to the northeast of the study region than to the southwest. Considering that areas to the north or east of the study region show no evidence of maize before the thirteenth century AD (Asch Sidell 1999,

2008; Chapdelaine 1993; Chilton 2008; Clermont 1990; Crawford et al. 2003), a more likely candidate for a point of origin would therefore be from the Finger Lakes region of central New York, where the earliest evidence for maize in the region is currently found

(Hart et al. 2007; Thompson et al. 2004:30-35). As seen in Figure 9.1, regressing the date of all sites in Dataset H.2 (reduced direct date dataset by sampling grid) against their distance from Vinette – the oldest maize in the region (site 24 in figure 5.3) – produces similar results to a regression from 45°N, 75°W. However, the intercept date provided by the regression from Vinette is significantly later than the direct dates drawn from the site.

Even using the most recent phytolith dates from Vinette (Sample A-0452: 1940 BP ± 35, ca. 1820-1990 CalBP) would require a period of over 300 years where maize would not have left the Finger Lakes region. Therefore, even when using the best fitting point of origin from the earliest site in the region, a demic model seems unlikely.

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Figure 9.1: Results of RMA regression on dataset H.2 (see section 4.4.3.9 and figure 4.25) against distance from oldest maize-bearing site in region

9.1.2: Linear Regression

This period of stasis where maize did not leave its original point of entry into the region is also reflected in the regression results. In particular, as seen in datasets D.2 and

H.2, a strong linear relationship is only observed when the three earliest sites in the region are removed from the dataset. What is remarkable, however, is the overall fit of the linear model to the data once these earliest sites in the region are removed. Figure 9.2 shows an isochrone for the region using the site dates in Dataset H. Compare this against a predictive surface for the date of the earliest entry in the region of maize based on the linear model for Dataset H.2 (Figure 9.3), and an isochrone of the dates from Dataset H.2 where the earliest three dates have been removed (Figures 9.4 and 9.5), and it is clear why such a strong linear relationship is observed.

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Figure 9.2: Isochrone of Calibrated Dates in Group H

Figure 9.3: Predictive Surface for Best-fitting Linear Model (Group H.2)

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Figure 9.4: Isochrone of Calibrated Dates in Group H.2 – note the improvement of the fit of this model once the three earliest dates in the region are removed (see Figure 9.2)

Figure 9.5: Isochrone of Calibrated Dates in Group H.2 with Predictive Surface Overlay (see Figure 9.4) in white contours – note correspondence between date surface and linear model

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The significance of these results should not be understated, and in fact, the coefficients produced for the pooled direct datasets (Datasets D.2 and H.2) are very close to those observed in Ammerman and Cavalli-Sforza’s (1971, 1984) analyses of the spread of agriculture through Neolithic Europe. As Pinhasi et al. (2005:2221) noted, no cultural diffusion model has been able to derive coefficients compatible with those observed by Ammerman and Cavalli-Sforza in their Wave of Advance model. Therefore the patterns produced from these regression analyses provide strong evidence for a demic process operating in the Northeast, albeit from an unlikely point of origin. As a test of whether these patterns could have been produced through random chance, sensitivity testing was performed on the grid-sampled dataset (Dataset H.2) using permutation analysis. By randomly sorting the dates associated with each site but keeping locations the same, 8,000 linear regressions were performed on Dataset H.2. The results of the permutation analysis unequivocally support the significance of these patterns (F = 34.59; df = 25, 1; p < 0.001), patterns that could not have come about from chance alone or site selection biases.

On the other hand, it should be noted that the removal of Vinette, D’Aubigny, and Edwin Harness was not due to any perceived inaccuracies in the dates, but rather was undertaken to evaluate how the data would need to be transformed in order to fit with a demic scenario. However, it is then necessary to find ways to explain the presence of these three early sites outside of a demic mechanism. Once again, Hart’s application of

Shifting Balance Theory to the success of local maize varieties holds value. Given maize’s susceptibility to inbreeding depression, it should be expected that many early growing attempts in the region were unsuccessful. Therefore, the earliest occurrence of

173 maize may well represent an agricultural dead-end, not leading to further propagation throughout the region.

If a small successful population of maize were able to take hold in this area, bolstered by regular gene flow between populations, this could have led to the productive variety of maize that eventually spread across the Northeast. This is understandably not a standard demic scenario, whereby farmers carrying a productive variety of maize slowly moved into the Northeast from centers of agricultural innovation to the Southwest – outcompeting and incorporating local mobile groups. However, it does hold affinity with a leapfrog colonization model (Anthony 1990, 1997; Sutton 1996), or perhaps a modified demic process (van Andel and Runnels 1995) whereby a far-flung pioneer community is established, eventually to spread once certain social and economic factors are in place.

While the sample size of Dataset H.2 is far too small to make any strong conclusions from these results, a genuine pattern does exist in the earliest direct evidence, suggesting a slow logistic spread of maize associated with population expansion.

9.2: DISCUSSION OF RESULTS OF LOCAL ANALYSIS OF THE SPREAD OF MAIZE INTO

SOUTHERN ONTARIO

Interpreting the results from the local analyses presents difficulty as both continuity and change were observed, and in this sense, both migration and local development models can be supported. While significant differences were observed through time for some of the key environmental covariates, the general pattern from the

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Maximum Entropy analyses suggests a strong correspondence between the location of village sites and ecological variables throughout the agricultural period in Southern

Ontario. This is not surprising, however, given that previous research has supported a connection between village locations and horticultural strategies in Southern Ontario

(Chapter 6.1.2; Campbell 1991; Fecteau et al. 1994; Heidenriech 1971; Konrad 1976;

Noble 1975; Warrick 2008; Williamson 1985).

Furthermore, under the rubric of a socioeconomic model of local development, it is expected that village locational choice throughout the agricultural period would correlate to the minimum growing requirements of maize. In this sense, the earliest experimentation with maize horticulture may not be captured by the analysis of village location as it can be assumed that the first forays into growing maize were undertaken by mobile groups and not at established village sites. However, the fact that the village locational strategies for Princess Point sites are somewhat divergent from later patterns does corroborate this model, suggesting that they may still reflect pre-horticultural land use strategies. Overall, the regional analysis supports the hypothesis that maize growing practices were introduced to local groups in Southern Ontario and that settlement strategies undertook a rapid shift around AD 900 as maize became an important part of local diets. On the other hand, some of the results may in fact point to the opposite process – that the rapid changes in settlement strategies during the tenth century AD were in fact the result of migrants entering the region. The consistency of site settlement strategies in Southern Ontario in the middle and late periods, as well as evidence for the gradual movement to marginal locations aligns well with a leapfrog colonization model and therefore must also be considered.

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9.2.1: Maximum Entropy Analyses

While the MaxEnt results for each period exhibit distinct differences in the importance and relative contribution of environmental covariates, there are also striking consistencies between the three periods. In particular, Growing Degree Days, distance to silt clay loam, and distance to well-drained soil were significant variables in determining site presence for all three models. On the other hand, the relative importance of these variables changed slightly between periods, with the most notable change being that the middle period model can almost entirely be described by the presence of at least 2,000

GDDs. This steep response to Growing Degree Days is also present in the model for late period sites, although with a minimum of 1,800 GDDs, possibly indicating movement to marginal locations during the later period. Regardless, the striking correspondence between site presence and minimum solar heat requirements for maize does provide confirmation that maize productivity was an important locational factor, as does the strong negative response of the models to distance to well-drained soil and silt clay loam.

On the other hand, it is surprising that distance to sandy loam was not a strong contributing factor in any of the models, considering that much of the literature on village site selection patterns in Ontario stresses the preference for sandy loams. However, as

MacDonald (2002:137) remarks, the connection between sandy soils and village location has never been quantitatively analyzed for Southern Ontario and may in fact be based more on entrenched beliefs rather than any strong evidence. Silt clay loams would certainly provide better a nutrient load for maize, and in this sense, a preference for these soils is more aligned with expectations based on maize productivity (Fecteau 1985:26).

On the other hand, one reason why sandy loams may not have informed the MaxEnt

176 models to any great degree could relate to their abundance throughout the study area.

Roughly 25% of the study area is comprised of sandy loam soils and, as the response curves for the three models show (Figures 8.8, 8.10, and 8.12), distance to sandy loam never exceeds 30 kilometers for the entire region – while the other soil types often have maximum ranges over 100 kilometers to the closest deposit. This abundance would make it more difficult for the MaxEnt software to use sandy loam as a limiting factor as there are undoubtedly numerous sandy loam deposits in Southern Ontario without village sites.

Of the 191 sites used in this dataset, 155 are located within two kilometers of sandy loam with 100 situated within one kilometer (See Appendix 2, Table A2.2). Silt clay loams, conversely, only comprise about 5% of the study area and 55 sites are located within two kilometers (35 within one kilometer). Therefore, there appears in this dataset to be a stronger relative preference for silt clay loams over sandy loams.

9.2.2: Tests of Independent Samples

The dominant pattern observable from the tests of independent samples is that of a change through time, albeit with a clear preference for a small number of environmental covariates. Continuity was only observed for one variable: distance to silt clay loam.

However, as the WMW tests reveal, distance to sandy loam and distance to well-drained soil did not differ significantly between middle (AD 900-1300) and late periods (AD

1300-1650), but were significantly different from the random sample. This suggests that these variables were important factors for site selection after AD 900, but were not

177 important to early groups – either because of a preference for other soil qualities or because maize requirements were not a determining factor in village location choice.

Perhaps the best illustration of the relationship of the environmental variables to site location through time is found by looking at the range of environmental values for each period (Figure 9.6). As seen in Figure 9.6, early period sites generally have a narrow range of values with a trend towards greater diversity of habitats through time. This trend is most visible in distance to silt clay loam, distance to well-drained soil, elevation, terrain ruggedness, and GDDs. Most striking amongst these distributions is a clear trend through time for areas with fewer GDDs. Once again, this suggests that groups may have been moving to marginal locations by the 14th century, either because of population pressure in areas of high productivity or perhaps due to the development of a variety of maize which could tolerate a shorter growing season.

One interesting correlation is that a significant difference is also observed in terrain ruggedness preferences during the late period. In fact, when terrain ruggedness around site locations is regressed against GDDs, a weak to moderate linear relationship can be observed (r = -0.49, p < 0.001; Figure 9.7). This relationship between increasing terrain ruggedness and decreasing GDDs may indicate the use of rolling terrain in areas at the limit of maize productivity for the creation of microclimatic buffers. Heidenreich

(1971:375-378) noted the Huron preference for hilltop locations, suggesting that they provided better defense. If Huronia existed in a marginal location for maize growth, these valleys around the villages may have also served as frost sinks, extending the length of the growing season on the hilltops (Demerritt 1991:195; Doolittle 2000; Engelbrecht

2003:32; Hasenstab 1996:21; MacDonald 2002:195.350).

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Figure 9.6: Box plots of environmental variable values at site locations by group

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Terrain Ruggedness by Growing Degree Days

terrain ruggedness (average variation inm) variation (average ruggedness terrain

1700 1800 1900 2000 2100 2200 2300

GDDs

Figure 9.7: Regression of local terrain ruggedness by Growing Degree Days

9.2.3: Summary of Local Analysis results

Overall, the pattern of village site selection through time in Southern Ontario is largely consistent with a local development scenario where foraging groups begin to experiment piecemeal with maize horticulture followed by a rapid shift in settlement strategies concomitant with a shift to greater reliance on horticulture (Wills 1988:30-35,

1992:169, 1995:238-242). This of course is corroborated by the isotopic evidence for a growing dietary reliance on maize through time (Katzenberg 2006; Katzenberg et al.

1997; Schwarz et al. 1985), and in particular, the change in local diets to a greater reliance on maize between 900 and 1200 AD. The sudden change in response curves to

GDD in the MaxEnt model for the middle developmental stage certainly points to this, as does the shift to an interest in locating sites close to well-drained soils and sandy loams –

180 a pattern that continues into the late stage. In Dieterman’s (2001) analysis of Princess

Point settlement patterns, he concluded that Princess Point sites represented an early pattern of maize horticultural development: a pattern which only became stronger by the

10th century. Similarly Ounjian’s (1998) extensive analysis of Glen Meyer and Neutral palaeobotanical assemblages found no identifiable difference between the Middle and

Late Ontario Iroquoian periods in either wild plant foods or cultigens. In fact, given the remarkable consistency between these periods, Ounjian (1998:265) concludes that a stable system of subsistence was already established in Southern Ontario by AD 1000 – continuing virtually unchanged to the historic period. This would certainly be consistent with the view that the growing requirements of maize would place strong selective pressure on horticulturalists, thus leading to a rapid stabilization of subsistence strategies.

On the other hand, while change through time is observed in the tests of independence, there is no clear reduction in the number or importance of variables associated with maize. Therefore, the idea that groups would have fine-tuned site location preferences through time as they became more familiar with maize’s particular requirements is not supported. Instead, it appears that groups may have been increasing the range of environments where maize could effectively be grown. This may indicate a pattern of infilling where, as the landscape became more densely occupied by horticultural sites, subsequent villages were forced to locate in marginal areas (Anthony

1990:902-906; Sutton 1996; Van Andel and Runnels 1995).

There is the possibility that middle period Glen Meyer groups represent migrants into the region as this would certainly fit with a migration model: groups exploiting a totally new locale in the region with an eventual broadening of niches over time as

181 colonists became familiar with the local landscape. Similarly, the pattern of daughter communities being forced to either locate in sub-optimal locales or initiate another migration event is consistent with a leapfrog development model. While the possibility of

Glen Meyer migrants into Ontario has certainly been argued (Snow 1995a, 1996), it is generally considered an unlikely scenario based on material cultural continuity between

Princess Point and Glen Meyer sites (Dieterman 2001:105-106; Pihl et al. 2008:153-154;

Smith and Crawford 1995:, 1997; Williamson 1990:308). As Warrick (2000:434) states,

“The coincidence and overlap of Princess Point and later Iroquoian village sites in the lower Grand River valley is remarkable… demonstrating cultural and demographic continuity and an identical land-use pattern.” However, as Creese (2011:30) remarks,

…it is important not to exaggerate Early Iroquoian continuity from Princess Point settlement-subsistence patterns, particularly after A.D. 1100. The relatively rapid settlement transition (from ca. A.D. 900 to 1100) from extensive, long-term reoccupation of floodplain, wetland margin, and river terrace locales, to nucleated upland settlements with shorter and more variable occupation histories, must have been associated with a significant change in village economies and the organization of labour required to support them… Furthermore, instead of taking advantage of the relatively open and disturbed habitats of river floodplains for maize gardening, shifting to cultivation in upland locations would have required, even at a modest scale, considerably more labour in clearing old growth deciduous forest. This, along with the construction of durable settlement architecture, suggests a commitment to the creation of a new kind of settlement space by a group of associated households.

Therefore, the confirmation of an in-situ process of maize uptake in Southern Ontario is understandably contingent on the assumption of continuity from the Princess Point period until the Contact period. If Princess Point groups were removed from this analysis (thus removing the early group of sites from the local analyses), the results would largely support the migration of a group into the region with a defined set of site location strategies that remained relatively constant through time.

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9.3: AN INTEGRATIVE MODEL FOR THE INTRODUCTION OF MAIZE HORTICULTURE INTO

SOUTHERN ONTARIO

A few key conclusions can be drawn from these results. While there is little evidence to support the migration of horticultural groups from the Southwest across the region, there is strong support in the direct date dataset for a demic expansion of maize horticulture out of the Finger Lakes region in central New York. According to the best- fitting linear model, this process would have begun around 1500 BP and spread at a steady rate of roughly 1km per year – arriving in Southern Ontario between AD 800 and

900. Strikingly, this period coincides with a rapid shift in settlement strategies in

Southern Ontario in which village sites begin to show a strong locational preference for areas suitable to maize horticulture. This section will therefore attempt to contextualize the results against an availability model of agricultural development (Zvelebil 1996;

Zvelebil and Dolukhanov 1991; Zvelebil and Lillie 2000; Zvelebil and Rowley-Conwy

1984). In this scenario, local groups already familiar with maize horticulture in Southern

Ontario may have come into close contact with migrating horticulturalists. Added pressure on key resources and settlement location thus led to increasing competition between fully horticultural groups and those using maize principally as a resource buffer.

This placed selective pressure on local groups, leading to the acceptance of a fully horticultural lifeway.

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9.3.1: The Availability Model, Competition, and Agricultural Frontiers

The key to the availability model is the concept of an agricultural frontier, where agricultural practices, and indeed domesticates, were known and available to foraging groups before the period of initial adoption. According to Zvelebil and colleagues

(Zvelebil 1996; Zvelebil and Dolukhanov 1991; Zvelebil and Lillie 2000; Zvelebil and

Rowley-Conwy 1984), the zone of forager-farmer interaction will undergo three phases of transition: the availability phase, the substitution phase, and the consolidation phase. In the availability phase, farming is known to the foraging groups and there is perhaps some exchange of materials and information between them and farming communities, but without the adoption of farming. In this period, farmers and foragers have close contact with each other but operate as culturally and economically autonomous units. This phase ends with the adoption of at least some elements of farming. While it is expected that agriculture will still only contribute marginally to local diets during the substitution phase, the critical point is that it is during this period that groups begin experimenting with farming practices, while still likely maintaining many social, material cultural, and economic traditions (Zvelebil 1986b:12, 1996:333; Zvelebil and Rowley-Conwy

1984:105). Lastly, the consolidation phase begins when the society becomes fully dependent on farming. In this, the social and economic structures of the now horticultural group stabilize to a predominantly agricultural lifeway and this frontier zone is now indiscernible from areas within the farming territory (Zvelebil and Dolukhanov

1991:240). Economically, this stage is marked by the extensive and the intensive growth of food production in communities and the change in societal structures to allow for changes in seasonality, scheduling, and governance (Figure 9.7).

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Figure 9.7: The three stage Availability model of the transition to farming. Note the sigmoidal pattern to the incorporation of cultigens into the diet. (redrawn from Zvelebil 1996: fig 18.1)

The availability model can best be described under the rubric of conflict and competition. The basis to this is that farming places entirely different pressures on a landscape than a foraging strategy would, which is made only more acute by the increase in population levels concomitant with the presence of agricultural communities. This competition for critical resources then acts as the catalyst for the development of a fully agricultural lifestyle and presents an equifinality to diverse foraging and semi- horticultural strategies in a region – either through the displacement of remaining hunter- gatherers to environments unsuitable for agricultural production, or the incorporation of foraging groups into farming communities (Zvelebil and Rowley-Conwy 1984:106). This exact situation has been used to explain the eventual adoption of maize horticulture by groups in the higher altitudes of the Colorado Plateau in the American Southwest

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(Netting 1990:40; Wills 1992:161-162, 1995:238-242). While the choice to grow maize in high altitude regions would have presented a high-risk strategy by Archaic foraging groups in the Southwest, Wills (1995:239) describes a scenario whereby low-altitude farmers would have placed greater pressure on these groups through specialized resource depletion. In particular, the surpluses and seasonal mobility of these desert farming groups would have allowed them to undertake extremely specific hunting expeditions into upland areas during autumn months targeting high-ranked faunal and floral resources. For mobile populations in these upland areas, which relied heavily on these resources in the winter, the presence of specialized hunting parties targeting the same resources would have had devastating consequences (Wills 1995:240). As Richerson et al. (2001:395) argue, when foragers and farmers are forced to coexist in the same territory, agriculture inevitably wins:

The reason is simple: all else being equal, any group that can use a tract of land more efficiently will be able to evict residents that use it less efficiently (Boserup 1981; Sahlins and Service 1960:75-87). …An agricultural frontier will tend to expand at the expense of hunter-gatherers as rising population densities on the farming side of the frontier motivate pioneers to invest in acquiring land from less-efficient users. Farmers may offer hunter-gatherers an attractive purchase price, a compelling idea about how to become richer through farming, or a dismal choice of flight, submission, or military defense at long odds against a more numerous foe.

There is some evidence for increasing intergroup competition in Southern Ontario around AD 900. In particular, it is during this time that the first evidence of palisades begin to appear in the region (Pihl et al. 2008:153; Stothers 1977:125-134; Warrick

2000:425). The tenth century Porteous village near Brantford, Ontario (Noble and

Kenyon 1972; Stothers 1977) provides an excellent example. The Porteous site is variously described as either a late Princess Point tradition village (Stothers 1977:125;

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Timmins 1985:7) or an early Glen Meyer village (Fox 1990:173; Williamson 1990:308).

While the diminutive size of the site and its ceramics suggest a strong connection to

Princess Point patterns (Dieterman 2000:142; Smith and Crawford 1997:20: Warrick

2000:425), it is the first Princess Point village with definitive evidence for longhouses and palisades (Dieterman 2000:142; Warrick 2000:425). Furthermore, as many have noted (Dieterman 2000; Noble and Kenyon 1972; Williamson 1990), the upland sandy location of Porteous fits better with Glen Meyer settlement strategies than the floodplain locations of the preceding Princess Point groups. Lastly, the abundance of carbonized maize at the site suggests a central focus on horticulture: whereas Stothers (1977;

Stothers and Yarnell 1977) originally identified one carbonized kernel from the Grand

Banks site and four from the multicomponent Princess Point type site, 44 kernels and one cob were recovered from Porteous (Stothers 1977:308). Therefore, the patterns exhibited at Porteous could be pointing to a new type of settlement in Southern Ontario, and with this a new social organization (Kapches 1990; Warrick 2000:425), a diet focused increasingly on maize, and perhaps better defensibility through the upland location and a double row of palisades.

While later in time, the presence of a forager-farmer “no man’s land” has also been argued for the region directly west of London, Ontario from the early fourteenth century onwards (Murphy and Ferris 1990:256; Stothers and Graves 1983:120; Warrick

2000:451). In this, a distinct buffer zone is posited for the area just east of Lake St. Clair to immediately west of London, with evidence for increasing hostilities between Western

Basin foraging groups and Neutral horticulturalists on each side of the divide as Neutral groups expanded westwards (Lennox and Fitzgerald 1990:418-419; Murphy and Ferris

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1990:257-259; Stothers and Graves 1983:120). However, considering that recent isotopic analyses from fourteenth century Western Basin sites in Michigan and Ontario (Dewar et al. 2011; Watts et al. 2011) suggest diets heavily reliant on maize, this dichotomy of

Western Basin foragers and Iroquoian farmers may be unwarranted.

While increasing evidence of internecine conflict is not definitive proof of an availability scenario, the appearance of defensive structures exactly during the period that major settlement changes were taking place is compelling. Adding to this is the isotopic evidence for Southern Ontario which shows a sharp rise in the contribution of maize to local diets during the period of AD 900 to 1300 (Harrison and Katzenberg 2003;

Katzenberg 2006; Katzenberg et al. 1995). In fact, the sigmoidal response seen in δ13C values on dated human remains in Southern Ontario (Figure 9.8) is remarkably similar to that postulated by Zvelebil for the substitution phase of the availability model (Figure

9.7). In discussing the evidence from Grand River Valley Princess Point sites, Smith and

Crawford (1997:11) have even suggested that an availability scenario may best fit the

Southern Ontario data for the earliest evidence of maize, although they comment that the particular resolution of data make it hard to test this model.

Figure 9.8: δ13C for bone carbonate from Ontario samples through time (redrawn from Harrison and Katzenberg 2003 – red dashed line and percent contribution to diet added)

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9.4: CONCLUSIONS:

Of course, the main difficulty with the availability model lies in its explicitly descriptive rather than analytical focus. As a means of describing the process of agricultural adoption in Southern Ontario, however, it is remarkably compelling. In this scenario, an expanding wave of agricultural communities first entered Southern Ontario between AD 800 and 900, placing their villages on upland sandy locations with adequate drainage and solar heat for maize horticulture. The influx of these new migrants then placed strong resource pressure on resident Princess Point groups in the Grand River

Valley, who may have been already familiar with and perhaps experimenting with low- level maize production as a resource buffer. This external pressure then provided the catalyst for resident groups to change their settlement patterns to one which focused largely on growing maize – as seen in the upland sandy locations of late Princess Point villages such as Porteous or Lone Pine. Accordingly, this shift to a fully horticultural settlement strategy took place over a relatively short period of time, leading to a stabilization of regional patterns from the tenth century until the Contact period.

The true value of the availability model rests squarely on the recognition of the time-depth of agricultural transition, and that processes which manifest quite suddenly may have been preceded by very long periods of experimentation and exchange. In this, local decision making and resident foraging groups are not seen as the vanquished in a battle between opposing lifeways, but rather conscious agents forced to change their economic and cultural patterns in order to compete with an entirely different way of life.

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Pihl et al. (2008:154) have an interesting interpretation in regards to the relative contribution of migration and in-situ processes in the development of Iroquoian patterns:

…rather than being indicative of population replacement, such patterns are more consistent with the idea that if Iroquoians truly were recent arrivals to Southern Ontario, then it is more likely that they influenced the technologies, economies, and language of the local populations rather than replaced them.

Ultimately, the dichotomization of foragers and farmers does more to inhibit our understanding of diverse economic strategies than to elucidate. While this thesis has largely been constructed under the rubric of this dichotomy, this was in order to contextualize the patterns produced from testing these hypotheses rather than to argue for any implicit acceptance of archaeological models as fact. As seen in the results from both the regional and local analyses of the introduction of maize into Southern Ontario, the patterns are indeed complex. However, there are a few general conclusions that can be drawn. First, there is a genuine pattern of slow logistic growth of maize-bearing sites out of Central New York in the fifteenth century BP, a pattern that aligns well with a demic mechanism and eventually sees the distribution of maize across the entire Northeast by

800 years ago. Second, the local patterns of maize’s entry into Southern Ontario suggest that maize was likely first introduced to local groups who may have attached it to an otherwise mobile subsistence-settlement strategy. However, once maize took hold in the area around AD 900, rapid changes are seen in settlement strategies, changes that remained relatively stable throughout the remainder of the agricultural period. The fact that the timing of these changes intersects with the exact period that an expanding wave front of horticulturalists may have entered the region is remarkable, and may indicate that both processes of migration and in-situ development were involved.

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CHAPTER 10: CONCLUSIONS

10.1: SUMMARY OF RESEARCH

This research has sought to evaluate the patterns of maize’s introduction into

Southern Ontario in order to assess the demographic, social, and economic processes behind its spread. In particular, two scales of analysis were undertaken representing differing spatial and temporal scales of analysis as well as different demographic models of agricultural transition. By contextualizing the results of these analyses against each other, a more nuanced understanding of the particular process of agricultural adoption is reached.

The regional analysis of the spread of maize into Northeast – defined here as the lower peninsula of Michigan, New York State, Ohio, Pennsylvania, and Southern Ontario below 45°N – was performed through two related approaches: a Point of Origin analysis of the best-fitting source locale for all maize-bearing sites in the Northeast between 250

BC and AD 1200 and a linear regression of the date of these sites against the best-fitting point of origin. This analysis was performed under a model of demic diffusion, and in particular, a Wave of Advance scenario. The hypothesis tested in the regional analysis is:

Overall, the demic model was rejected for the regional analysis, with the results showing little to no linear relationship between the date of a site and its distance from a hypothetical source location. On the other hand, when using only the direct dates on

191 maize in the region, a moderate relationship is seen in the earliest date at a given site against its distance from the northeastern portion of the region. This relationship was particularly strong when the three earliest sites in the Northeast were removed from the dataset, with an observed r value of -0.76 (R2 = 0.58, p <0.001). This may reflect a modified demic mechanism where maize first entered the region in central New York whereby, after a period of local adaptation of maize to the environs, it eventually spread out of this point of origin to the rest of the region.

The local analysis of the spread of maize into Southern Ontario was performed through two complimentary methods: an ecological analysis using Maximum Entropy modeling of the primary environmental variables driving village locational choices throughout the agricultural period (ca. AD 500-1650), and non-parametric tests of independence to evaluate whether change through time can be observed in site selection criteria. This analysis was performed largely under an economic model of maize development where the assumption was that local development processes and migration processes should produce entirely different patterns. The hypotheses tested in the local analysis are:

H2: Consistent with a long-distance migration model. No change is observed in the environmental covariates through time whereas the earliest sites exhibit the same environmental preferences as later sites.

Overall, the results of the local analysis support the hypothesis of cultural diffusion of technologies and the local adoption of maize growing practices rather than the migration of horticulturalists into the region. In particular, the pattern of village site

192 selection through time in Southern Ontario is largely consistent with a local development scenario where foraging groups begin to experiment piecemeal with maize horticulture followed by a rapid shift in settlement strategies concomitant with a shift to greater reliance on horticulture (Wills 1988:30-35, 1992:169, 1995:238-242). Change through time is observed for many key environmental variables and there is some evidence which suggests that early period sites do not differ significantly from random samples and that there is a significant shift in settlement location strategies by AD 900 – a shift in which many of the same variables maintain importance until the historic period. Additionally, this rapid change to new locales during this middle period (AD 900-1300) shows a remarkable correspondence to environmental variables associated with maize horticulture, suggesting the targeting of specific locales for growing maize.

1990:902-906; Sutton 1996; Van Andel and Runnels 1995).

However, it must be recalled that the confirmation of an in-situ process of maize uptake in Southern Ontario is predicated on the assumption of cultural continuity between all village sites throughout the agricultural period. If the earliest Princess Point sites – the

193 exact sites which show divergence from an explicitly horticultural land use strategy – were removed from this analysis, the results would largely support the migration of a group into the region.

Lastly, the combination of a demic mechanism observed on a regional scale and evidence of a rapid change in settlement locations and diets on a local scale at the same time that the demic model suggests that maize would have first entered Southern Ontario presents another option, namely, an availability scenario. In this, an expanding wave of agricultural communities first entered Southern Ontario between AD 800 and 900, placing their villages on upland sandy locations with adequate drainage and solar heat for maize horticulture. The influx of these new migrants then placed strong resource pressure on resident groups, who may have been already familiar with and perhaps experimenting with low-level maize production as a resource buffer. This external pressure then provided the catalyst for resident groups to change their settlement patterns to one which focused largely on growing maize, leading to a stabilization of regional patterns from the tenth century until the Contact period.

10.2: FUTURE RESEARCH DIRECTIONS

Perhaps the greatest value in this research lies in the identification of key problems and divergences observed through analysis. As mentioned in the results of the linear regression analysis (Section 4.3.2), the patterns observed by the earliest direct

194 evidence for maize in the Northeast were largely being driven by the numerous residue analyses that have been undertaken on assemblages in the Finger Lakes region of New

York (Hart et al. 2003, 2007; Thompson et al. 2004:30-35). Without similar research programs, it is impossible to know whether similarly early phyotlith evidence could be found elsewhere. In fact, given that the earliest direct dates on maize in New York currently come from AD 750-1000 contexts, yet maize has been found in pre-AD 500 contexts in Southern Ontario and Ohio, it is possible that even earlier evidence could be found elsewhere if phytolith research were performed. If earlier, or even contemporary, evidence was found in Ontario or Ohio this would have profound effects on the patterns observed in the regression analyses.

Southern Ontario provides the best avenue for focussed research on the identification of maize phytoliths from cooking residues. In particular, the connection between maize horticulture and Princess Point groups has long been established

(Crawford et al. 1997, 1998; Crawford and Smith 1996, 2003; Smith and Crawford 1997;

Stothers 1977) and the extensive research on Princess Point patterns has developed fine- grained ceramic chronologies based on large datasets (Smith 1997a, 1997b; Smith and

Crawford 1995, 1997, 2002; Bekerman 1995). Furthermore, Vinette II pottery is often found on Middle Woodland sites in Southern Ontario, particularly in the Rice Lake area

(Jackson 1988; Johnston 1968). Given that phytoliths were found on Vinette II pottery only 200 kilometers to the south in the Finger Lakes region of New York, it is conceivable that it may have also been present in Ontario at this time.

Another avenue of future research concerns village relocation sequences in

Southern Ontario and whether they are in line with an ideal despotic scenario (Shennan

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2007, 2008). While the pattern of village sites occupying a greater variety of environs through time and the possible movement to marginal locations could be pointing to this, testing this scenario in this analysis is intractable given the gross temporal resolution and spatial coverage. In order to confirm an ideal despotic scenario, a much smaller spatial extent and decadal temporal resolution would be required as well as strong evidence for: a) the direct connection between communities; b) overpopulation in areas of high maize productivity; and c) the movement of later communities to sub-optimal locales.

Middle Iroquoian Uren and Middleport sites provide the best opportunity for testing this model, in that they appear to be largely single-component with occupational periods of less than 30 years, are thought to date to a period of intense population growth, and have been described as occupying the largest variety of terrains in Southern Ontario for any time during the Late Woodland period (Warrick 2000:439-446, 2008). While the ideal despotic model is not contingent on either a migration or diffusionary scenario but simply relates to the effects of population pressure and social cohesion on the development of new communities, it may provide a new means of evaluating hypotheses of community interaction, fissioning, and amalgamation during this period.

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REFERENCES:

Adams, W. Y., van Gerven, D. P., and Levy, R. S. 1978 The Retreat from Migrationism. Annual Review of Anthropology 7:483–532.

Adovasio, J. M., Fryman, R., Quinn, A.G., and Pedler, D. R. 2003 The Appearance of Cultigens and the Early and Middle Woodland Periods in Southwestern Pennsylvania. In Foragers and Farmers of the Early and Middle Woodland Periods in Pennsylvania, edited by P. A. Raber and V. L. Cowin, pp. 67-84. Recent Research in Pennsylvania Archaeology Number 3, Pennsylvania Historical and Museum Commision, Harrisburg, Pennsylvania.

Adovasio, J. M., and Johnson, W. C. 1981 The Appearance of Cultigens in the Upper Ohio Valley: A View from the Meadowcroft Rockshelter. Pennsylvania Archaeologist 59(1-2):63-80.

Allen, K. M. S. 1996 Iroquoian Landscapes: People, Environments and the GIS Context. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, edited by H. D. G. Maschner, pp. 198-222. Center for Archaeological Investigations Occasional Paper 23, Carbondale.

Ambrose, S.H., and Norr, L. 1993 Experimental Evidence for the Relationship of the Carbon Isotope Ratios of Whole Diet and Dietary Protein to those of Bone Collagen and Carbonate. In Prehistoric Human Bone: Archaeology at the Molecular Level, edited by J.B. Lambert and G. Grupe, pp. 1-38. Springer-Verlag, Berlin.

Ammerman, A. J. 1989 On the Neolithic Transition in Europe: A Comment on Zvelebil & Zvelebil (1988). Antiquity 63:162-165.

Ammerman, A. J., and Biagi, P. 2003 Summary of the Discussion. In The Widening Harvest. The Neolithic Transition in Europe: Looking Back, Looking Forward. Edited by A. J. Ammerman and P. Biagi. pp.337-343. Archaeological Institute of America, Boston.

Ammerman, A. J. and Cavalli-Sforza, L. L. 1971 Measuring the Rate and Spread of Early Farming in Europe. Man, New Series, 6(4):674-688.

1973 A Population Model for the Diffusion of Early Farming in Europe. In The Explanation of Culture Change: Models in Prehistory. Proceedings of a meeting of the Research Seminar in Archaeology and Related Subjects held at the University

197

of Sheffield, edited by Colin Renfrew, pp. 343-357. University of Pittsburg Press, U.S.A.

1984 The Neolithic Transition and the Genetics of Populations in Europe. Princeton University Press, Princeton.

Anthony, D. W. 1990 Migration in Archeology: The Baby and the Bathwater. American Anthropologist, New Series 92(4):895-914.

1997 Prehistoric Migration as Social Process. In Migrations and Invasions in Archaeological Explanation, edited by J. Chapman and H. Hamerow, pp. 21-32. vol. 664. British Archaeological Reports International Series.

2000 Comments, in Burmeister, S. 2000 Archaeology and Migration. Current Anthropology 41(4):539-567.

Archaeological Services Inc. 2006 Stage 3-4 Archaeological Assessment and Mitigation of the D‘Aubigny Park Site. ASI File: 05EA-208

Asch Siddell, N. 1999 Prehistoric Plant Use in Maine: Paleoindian to Contact Period. In Current Northeast Paleoethnobotany, edited by J. Hart, pp. 191-224. New York State Museum Bulletin No. 494. Albany: New York State Museum.

2008 The Impact of Maize-based Agriculture on Prehistoric Plant Communities in the Northeast. In Current Northeast Paleoethnobotany II, edited by J. P. Hart, pp. 29-52. New York State Bulletin Series 512, Albany.

Balaresque, P., Bowden, G. R., Adams, S. M., Leung, H-L., King, T. E., Rosser, Z. H., Goodwin, J., Moisan, J. P., Richard, C., Millward, A., Demaine, A. G., Barbujani, G., Previderè, C., Wilson, I. J., Tyler-Smith, C., Jobling, M. A. 2010 A Predominantly Neolithic Origin for European Paternal Lineages. PLoS Biology 8:1–9.

Banks, W. E., Antunes, N., Rigaud, S., d’Errico, F. 2013a Ecological constraints on the first prehistoric farmers in Europe. Journal of Archaeological Science 40:2746-2753.

Banks, W.E., d’Errico, F., Zilhão, J. 2013b Human-climate interaction during the Early Upper Paleolithic: testing the hypothesis of an adaptive shift between the Proto-Aurignacian and the Early Aurignacian. Journal of Human Evolution 64:39-55.

Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S.P., Peterson, A.T., Soberón, J., Villalobos, F. 2011 The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222 ;1810-1819.

198

Beaton, J. M. 1991 Colonizing Continents: Some Problems from Australia and the Americas. In The First Americans: Search and Research, edited by T. D. Dillehay and D. J. Meltzer, pp. 209-230. Boca Raton.

Bellwood, P. 2001 Early Agriculturalist Population Diasporas? Farming, Languages, and Genes. Annual Review of Anthropology 30:181-207.

2005 First Farmers: The Origins of Agricultural Societies. Blackwell, Oxford.

Bender, M. M., Baereis, D. A., and Steventon, A. L. 1981 Further light on carbon isotopes and Hopewell agriculture. American Antiquity 46: 346-353.

Benz, B. F. 2001 Archaeological evidence of teosinte domestication from Guila Naquitz, Oaxaca. Proceedings of the National Academy of Sciences 98(4):2104-2106.

2006 Maize in the Americas. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 9-18. . Academic Press, San Diego.

Benz, B.F., and Staller, J. E. 2006 The Antiquity, Biogeography, and Culture History of Maize in the Americas. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 665-673. Academic Press, San Diego.

Bevan, A., and Wilson, A. 2013 Models of settlement hierarchy based on partial evidence. Journal of Archaeological Science 40:2415-2427.

Blake, M. 2006 Dating the initial spread of Zea mays. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 55–72. Academic Press, San Diego.

Blake, M., B. Benz, N. Jakobsen, R. Wallace, S. Formosa, K. Supernant, D. Moreiras and A. Wong 2012 Ancient Maize Map, Version 1.1: An Online Database and Mapping Program for Studying the Archaeology of Maize in the Americas. http://en.ancientmaize.com/. Laboratory of Archaeology, University of B.C., Vancouver.

199

Bocquet-Appel, J. P. and Naji, S. 2006 Testing the Hypothesis of a Worldwide Neolithic Demographic Transition. Current Anthropology 47(2):341-365.

Bocquet-Appel, J. P., Naji, S., Vander Linden, M., and Kozlowski, J. K. 2009 Detection of Diffusion of Contact Zones of Early Farming in Europe from the Space-time Distribution of 14C dates. Journal of Archaeological Science (36): 807-820.

Bogucki, P. 1988 Forest Farmers and Stockherders: Early Agriculture and its Consequences in North-Central Europe. Cambridge University Press, Cambridge.

1996 The Spread of Early Farming in Europe. American Scientist 84:242-253.

2000 How Agriculture came to North-central Europe. In Europe’s First Farmers, edited by T. D. Price, pp. 197-218. Cambridge University Press, Cambridge.

2003 Neolithic Dispersals in Riverine Interior Central Europe. In The Widening Harvest: The Neolithic Transition in Europe – Looking Back, Looking Forward, edited by A. J. Ammerman and P. Biagi, pp. 249-272. Archaeological Institute of America, Colloquia and Conference Papers 6, Boston.

Bonzani, R. M., and Oyuela-Caycedo, A. 2006 The Gift of Variation and Dispersion of Maize: Social and Technological Context in Amerindian Societies. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 344-356. Academic Press, San Diego.

Boyd, M. and Surette, C. 2010 Northernmost Precontact Maize in North America. American Antiquity 75(1):117-133.

Boyd, M., Varney, T., Surette, C., and Surette, J. 2008 Reassessing the Northern Limit of Maize Consumption in North America: Stable Isotope, Plant Microfossil, and Trace Element Content of Carbonized Cooking Residue. Journal of Archaeological Science 35:2545-2556.

Bramanti, B., Thomas, M. G., Haak, W., Unterlaender, M., Jores, P., Tambets, K., Antanaitis-Jacobs, I., Haidle, M. N., Jankauskas, R., Kind, C. J., Lueth, F., Terberger, T., Hiller, J., Matsumura, S., Forster, P., Burger, J. 2009 Genetic Discontinuity between Local Hunter-Gatherers and Central Europe’s First Farmers. Science 326:137–140.

Broodbank, C., and Strasser, T. F. 1991 Migrant Farmers and the Neolithic Colonization of Crete. Antiquity 65:233- 245.

200

Brown, T. A. 2006 Differing Approaches and Perceptions in the Study of New and Old World Crops. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 3-8. Academic Press, San Diego.

Brown, W. L., and Anderson, E. 1947 The Northern Flint Corns. Annals of the Missouri Botanical Garden 34(1):1`- 29.

Brumbach, H. J. 1995 Algonquian and Iroquoian Ceramics in the Upper Hudson River Drainage. Northeast Anthropology 49:55-66.

Buchanan, B., Hamilton, M., Edinborough, K., O’Brien, M. J., Collard, M. 2011 A Comment on Steele’s (2010) “Radiocarbon Dates as Data: Quantitative Strategies For Estimating Colonization Front Speeds and Event Densities”. Journal of Archaeological Science 38:2116-2122.

Buikstra, J. E., and Milner, G. R. 1991 Isotopic and Archaeological Interpretations of Diet in the Central Mississippi Valley. Journal of Archaeological Science 18:319-329.

Burmeister, S. 2000 Archaeology and Migration. Current Anthropology 41(4):539-567.

Bursey, J. A. 1995 The Transition from the Middle to Late Woodland Periods: A Re-evaluation. In Origins of the People of the Longhouse: Proceedings of the 21st Annual Symposium of the Ontario Archaeological Society, edited by A. Bekerman and G. Warrick, pp. 43–54. Ontario Archaeological Society, North York, Ontario.

Cabana, G. S. 2002 A Demographic Simulation Model to Assess Prehistoric Migrations. Unpublished Ph.D. Dissertation. Department of Anthropology, The University of Michigan.

Campbell, C. 1991 Pre-Contact Settlement Pattern in Southern Ontario: an Edaphic- Climatic Simulation Model for Maize-Based Iroquoian Village Horticulture. Unpublished M.A. Thesis, Department of Geography, York University. North York, Ontario.

Campbell, C., and Campbell, I. D. 1992 Pre-contact settlement pattern in southern Ontario: Simulation model for maize-based village horticulture. Ontario Archaeology 53: 3–26.

201

Cavalli-Sforza, L. L. 1996 The Spread of Agriculture and Nomadic Pastoralism: Insights from Genetics, Linguistics, and Archaeology. In The Origins and Spread of Agriculture and Pastoralism in Eurasia, edited by D. R. Harris, pp. 51-69. UCL Press, London.

Chapdelaine, C. 1993 The Sedentarization of the Prehistoric Iroquoians: A Slow or Rapid Transformation? Journal of Anthropological Archaeology 12:173-209.

Chapman, J. and Crites, G. D. 1987 Evidence for Early Maize (Zea Mays) from the Icehouse Bottom Site, Tennessee. American Antiquity 52(2):352-354.

Chapman, J. and Hamerow, H. (eds.) 1997 Migrations and Invasions in Archaeological Explanation. vol. 664. British Archaeological Reports International Series.

Chikhi L., Nichols R. A., Barbujani G., Beaumont M. A. 2002 Y Genetic Data Support the Neolithic Demic Diffusion Model. Proceedings of the National Academy of Sciences 99:11008–11013.

Childe, V. G. 1925 The Dawn of European Civilization. Kegan Paul, London.

Chilton, E. S. 2008 So Little Maize, So Much Time: Understanding Maize Adoption in New England. In Current Northeast Paleoethnobotany II, edited by J. P. Hart, pp. 53-60. New York State Bulletin Series 512, Albany.

Clark, J. G. D. 1965 Radiocarbon Dating and the Spread of Expansion of Farming Culture from the Near East over Europe. Proceedings of the Prehistoric Society 31:58-73.

Clark, J. J. 2001 Tracking Prehistoric Migrations: Pueblo Settlers among the Tonto Basin Hohokam. Anthropological Papers of the University of Arizona no. 65. University of Arizona Press, Tuscon.

Clarke, D. 1976 Mesolithic Europe: The Economic Basis. In Problems in Economic and Social Archaeology, edited by G. de G. Sieveking, I. H. Longworth, and K. E. Wilson, pp. 449-481. Duckworth, London.

Clermont, N. 1990 Why did the Saint Lawrence Iroquoians Become Horticulturalists? Man in the Northeast 40:75-79.

202

Colledge, S., Conolly, J., and Shennan, S. 2004a Archaeobotanical Evidence for the Spread of Farming in the Eastern Mediterranean. Current Anthropology 45 Supplement: Agricultural Origins and Dispersal into Europe:S35-S58

2004b The Evolution of Neolithic Farming from SW Asian Origins to NW European Limits. European Journal of Archaeology 8(2):137-156.

Conard, N., Asch, D. L., Asch, N. B., Elmore, D., Gove, H., Rubin, M., Brown, J. A., Wiant, M. D., Farnsworth, K. B., and Cook, T. G. 1984 Accelerator Radiocarbon Dating of Evidence for Prehistoric Horticulture in Illinois. Nature 308:443-446.

Conolly, J., Manning, K., Colledge, S., Dobney, K., and Shennan, S. 2012 Species distribution modelling of ancient cattle from early Neolithic sites in SW Asia and Europe. The Holocene 22(9):997-1010.

Cowan, C. W. and Watson, P. J. 1992 Some Concluding Remarks. In The Origins of Agriculture: An International Perspective, edited by C.W. Cowan and P.J. Watson, pp.207-212. Smithsonian Institution Press, Washington, DC.

Coward, F., Shennan, S., Colledge, S., Conolly, J., Collard, M. 2008 The spread of Neolithic plant economies from the Near East to northwest Europe: a phylogenetic analysis. Journal of Archaeological Science 35:42-56.

Crawford, G W., Saunders, D., and Smith, D. G. 2006 Pre-Contact Maize from Ontario, Canada: Context, Chronology, Variation, and Plant Association. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot, and B.Benz , pp. 549-559. Academic Press , San Diego, CA.

Crawford, G. W. and Smith, D. G. 1996 Migration in Prehistory: Princess Point and the Northern Iroquoian Case. American Antiquity 61(4):782-790.

2003 Paleoethnobotany in the Northeast. In People and Plants in Ancient Eastern North America, edited by P. E. Minnis, pp. 172-257. Smithsonian Institution Press, Washington, D.C..

Crawford, G. W., Smith, D. G., and Bowyer, V. E. 1997 Dating the Entry of Corn (Zea mays) into the Lower Great Lakes Region. American Antiquity 62:112-119.

Crawford, G. W., Smith, D. G., Desloges, J., and Davis, A. 1998 Floodplains and Agricultural Origins: A Case Study in South-Central Ontario, Canada. Journal of Field Archaeology 25:123-137.

203

Creese, J. L. 2011 Deyughnyonkwarakda – “At the Wood’s Edge”: The Development of the Iroquoian Village in Southern Ontario, A.D. 900-1500. Unpublished PhD dissertation, Department of Anthropology, University of Toronto.

Dennell, R. 1985 The Hunter-Gatherer/Agricultural Frontier in Prehistoric Temperate Europe. In The Archaeology of Frontiers and Boundaries, edited by S. Green and S. Perlman, pp. 113-139. Academic Press, London.

1992 The Origins of Crop Agriculture in Europe. In The Origins of Agriculture: An International Perspective, edited by C.W. Cowan and P.J. Watson, pp. 71-100. Washington: Smithsonian Institution Press.

Demerrit, D. 1991 Agriculture, Climate, and Agricultural Adaptation in the Prehistoric Northeast. Archaeology of Eastern North America 19:183-202.

Dewar, G., Ginter, J. K., Shook, B. A. S., Ferris, N., Henderson, H. 2011 A Bioarchaeological Study of a Western Basin Tradition Cemetery on the Detroit River. Journal of Archaeological Science 37:2245-2254.

Dieterman, F. A. 2001 Princess Point: A Landscape of Place. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto

Dodd, C. F., Poulton, D. R., Lennox, P. A., Smith, D. G., and Warrick, G. A. 1990 The Middle Ontario Iroquoian Stage. In The Archaeology of Southern Ontario to AD 1650, edited by C. Ellis and N. Ferris, pp. 321-360, Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London.

Doebley, J. F., Goodman, M. M., and Stuber, C. W. 1986 Exceptional Genetic Divergence of Northern Flint Corn. American Journal of Botany 73: 64-69.

Doebley, J. F., Wendel, J. D., Smith, J. S. C., Stuber, C. W. and Goodman, M. M. 1988 The Origin of Cornbelt Maize: The Isozyme Evidence. Economic Botany 42(1):120-131.

Dolukhanov, P. M. 1973 The Neolithization of Europe: A Chronological and Ecological Approach. In The Explanation of Culture Change: Models in Prehistory. Proceedings of a meeting of the Research Seminar in Archaeology and Related Subjects held at the University of Sheffield, edited by Colin Renfrew, pp. 329-342. University of Pittsburg Press, U.S.A.

204

1986 Foragers and Farmers in West-Central Asia. In Hunters in Transition: Mesolithic Societies of Temperate Eurasia and their Transition to Farming, edited by M. Zvelebil, pp. 121-132. Cambridge University Press, Cambridge.

Donahue, R. E. 1992 Desperately Seeking Ceres: A Critical Examination of Current Models for the Transition to Agriculture in Mediterranean Europe. In Transitions to Agriculture in Prehistory, edited by A. B. Gebaur and T. D. Price, pp. 73-80. Monographs in World Archaeology No. 4. Prehistory Press, Madison, WI.

Doolittle, W. E. 2000 Cultivated Landscapes of Native North America. Oxford; Oxford University Press

Dudík M., Phillips, S., and Schapire, R. 2010 Maximum Entropy Modeling of Species Geographic Distributions. Version 3.3.3k. http://www.cs.princeton.edu/~schapire/maxent

Edmonson, M. S. 1960 Neolithic Diffusion Rates. Current Anthropology 2:71-102.

Elith, J., Phillips, S. J., Hastie, T., Dudík M., Chee, Y. E., and Yates, C. J. 2011 A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17: 43–57.

Emerson, J. N. 1961 Problems of Huron Origins. Anthropologica 3:181-201.

Engelbrecht, W. 2003 Iroquoia: The Development of a Native World. Syracuse University Press, Syracuse, N.Y.

Engelbrecht, W. 1999 Iroquoian Ethnicity and Archaeological Taxa. In Taming the Taxonomy: Toward a New Understanding of Great Lakes Archaeology, edited by Ronald F. Williamson and Christopher M. Watts. Pp. 51-59. Eastendbooks, Toronto

2003 Iroquoia: The Development of a Native World. Syracuse University Press, Syracuse, N.Y.

Fecteau, R. 1985 The Introduction and Diffusion of Cultivated Plants in Southern Ontario. Unpublished MA dissertation, York University, Toronto, Ontario.

Fecteau, R., Molnar, J., and Warrick, G. 1994 Iroquoian Village Ecology. KEWA 94(8):2-16.

205

Ferris, N. 1999 Telling Tales: Interpretive Trends in Southern Ontario Late Woodland Archaeology. Ontario Archaeology 68:1–62.

Ferris, N., and Spence, M. W. 1995 The Woodland Traditions in Southern Ontario. Revista de Archeologia Americana 9:83–138.

Fiedel, S.J. 1991 Correlating Archaeology and Linguistics: The Algonquian Case. Man in the Northeast 41: 9-32.

1999 Algonquians and Iroquoians: Taxonomy, Chronology and Archaeological Implications. In Taming the Taxonomy: Toward a New Understanding of Great Lakes Archaeology, edited by R.F. Williamson and C.M. Watts, pp. 193-204. eastendbooks, Toronto.

Fiedel, S. J., and Anthony, D. W. 2003 Deerslayers, Pathfinders, and Icemen: Origins of the European Neolithic as seen from the Frontier. In Colonization of Unfamiliar Landscapes: The Archaeology of Adaptation, edited by M. Rockman and J. Steele, pp. 144-168. Routledge, London.

Fisher, R. A. 1937 The Wave of Advance of Advantageous Genes. Annals of Eugenics 7:355-369.

Flannery, K. V. 1973 The origins of agriculture. Annual Review of Anthropology 2:271-310.

Ford, R. 1985 Patterns of Prehistoric Food Production in North America. In Prehistoric Food Production in North America, edited by R. Ford, pp. 341-364. University of Michigan Museum of Anthropology, Anthropological Paper No. 75. Ann Arbor.

Fort, J. 2009 Mathematical models of the Neolithic transition: a review for non- mathematicians. British Archaeological Reports S1964: 211–216.

Fort J., and Méndez, V 1999 Time-Delayed Theory of the Neolithic Transition in Europe. Phyical Review Letters 82:867–870.

Fort, J., Pujol, T., and Cavalli-Sforza, L. L. 2004 Palaeolithic Populations and Waves of Advance. Cambridge Archaeological Journal 14:53–61.

206

Fort, J., Pujol, T., and Vander Linden, M. 2012 Modelling the Neolithic Transition in the Near East and Europe.American Antiquity 77(2):203-219.

Fox, W. A. 1976 The Central North Shore. In The Late Prehistory of the Lake Erie Drainage Basin, edited by D. Brose, pp. 162-192. The Cleveland Museum of Natural History, Cleveland.

1990 The Middle Woodland to Late Woodland Transition. In The Archaeology of Southern Ontario To AD 1650, edited by C. Ellis and N. Ferris, pp. 171-188. Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London, Ontario.

1995 Concluding Remarks. In Origins of the People of the Longhouse: Proceedings of the 21st Annual Symposium of the Ontario Archaeological Society, edited by A. Bekerman and G. Warrick, pp.144–151. Ontario Archaeological Society, North York, Ontario.

Fritz, G. J. 1990 Multiple Pathways to Farming in Precontact Eastern North America. Journal of World Prehistory 4(4):387-435.

1994 Are the First American Farmers Getting Younger? Current anthropology 35(3):305-309.

1995 New Dates and Data on Early Agriculture: The Legacy of Complex Hunter- Gatherers. Annals of the Missouri Botanical Garden 82(1):3-15.

Galinat, W. C. 1965 The evolution of corn and culture in North America. Economic Botany 19(4):350-357.

Galinat, W. C., and Campbell R. G. 1967 The Diffusion of Eight-Rowed Maize from the Southwest to the Central Plains. Massachusetts Agricultural Experiment Station Monograph Series 1:1-16.

Gebaur, A. B., and Price, T. D. (eds.) 1992 Transitions to Agriculture in Prehistory. Monographs in World Archaeology No. 4. Prehistory Press, Madison, WI.

Gkiasta, M. Russell, T., Shennan, S., and Steele, J. 2003 Neolithic Transition in Europe: The Radiocarbon Record Revisited. Antiquity 77:45-62.

207

Gregg, S. A. 1988 Foragers and Farmers: Population Interaction and Agricultural Expansion in Prehistoric Europe. The University of Chicago Press, Chicago.

Griffin, J. 1944 The Iroquois in American Prehistory. Papers of the Michigan Academy of Science, Arts and Letters, 29:357-374.

1960 Climatic Change: A Contributing Cause of the Growth and Decline of Northern Hopewellian Culture. Wisconsin Archaeologist 41(1):21-33.

1967 Eastern North American Archaeology: A Summary. Science 156(3772):175- 191.

Hamilton, M. J., and Buchanan, B. 2007 Spatial Gradients in Clovis-age Radiocarbon Dates across North America Suggest Rapid Colonization from the North. Proceedings of the National Academy of Science 104(40):15625-15630.

Hansen, J. M. 1991 The Palaeoethnobotany of Franchthi Cave. Indiana University Press: Excavations at Franchthi Cave, Greece, Fascicle 7, Bloomington.

Harris, D. R. (ed.) 1996 The Origins and Spread of Agriculture and Pastoralism in Eurasia. UCL Press, London.

Harrison, R. G., and Katzenberg, M. A. 2003 Paleodiet Studies Using Stable Carbon Isotopes from Bone Apatite and Collagen: examples from Southern Ontario and San Nicolas Island, California. Journal of Anthropological Archaeology 22:227-244.

Hart, J. P. 1999 Maize Agricultural Evolution in the Eastern Woodlands: A Darwinian Perspective. Journal of Archaeological Method and Theory 6:137-180.

2001 Maize, Matrilocality, Migration, and Northern Iroquoian Evolution. Journal of Archaeological Method and Theory, Vol. 8, No. 2: 151-181.

Hart, J. P. (Ed.) 1999a Current Northeast Paleoethnobotany. New York State Museum Bulletin No. 494. The New York State Education Department, Albany, New York.

2008 Current Northeast Paleoethnobotany II. New York State Museum Bulletin No. 512. The New York State Education Department, Albany, New York.

208

Hart, J. and Asch Sidell. N. 1996 Prehistoric Agricultural Systems in the West Branch of the Susquehanna River Basin, A.D. 800 to A.D. 1350. Northeast Anthroology 52:1-30.

1997 Additional Evidence for Early Cucurbit use in the Northern Eastern Woodlands East of the Allegheny Front. American Antiquity 62:523-37.

Hart, J. P. and Brumbach, H.J. 2005 Cooking Residues, AMS Dates, and the Middle-to-Late Woodland Transition in Central New York. N ortheast Anthropology 69:1-33.

2009 On Pottery Change and Northern Iroquoian Origins: An Assessment from the Finger Lakes Region of Central New York. Journal of Anthropological Archaeology 28(4):367‐381.

Hart, J. P., Brumbach, H. J., and Lusteck, R. 2007 Extending the Phytolith Evidence for Early Maize (Zea mays ssp. mays) and Squash (Cucurbita sp.) in Central New York. American Antiquity 72(3):563-583.

Hart, J. P., and Means, B. K. 2002 Maize and Villages: A Summary and Critical Assessment of Current Northeast Early Late Prehistoric Evidence. In Northeast Subsistence-Settlement Change: A.D. 700-1300, edited by J. Hart and C. Rieth, pp.345-358. Bulletin 496. New York State Museum, Albany.

Hart, J. P., and Rieth, C. B. (Eds.) 2002 Northeast Subsistence-Settlement Change: A.D. 700-1300. New York State Museum Bulletin No. 496. The New York State Education Department, Albany, New York.

Hart, J. P., Thompson, R. G., Brumbach, H.J. 2003 Phytolith Evidence for Early Maize (Zea Mays) in the Northern Finger Lakes Region of New York. American Antiquity 68(4):619-640.

Harte, J. 2011 Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics. Oxford: Oxford University Press

Hasenstab, R. J. 1990 Agriculture, Warfare, and Tribalization in the Iroquois Homeland of New York: A G.I.S. Analysis of Late Woodland Settlement. Unpublished PhD, Anthropology Department, University of Massachusetts. 2007 Aboriginal Settlement Patterns in Late Woodland Upper New York State. In Archaeology of the Iroquois: Selected Readings and Research Sources, edited by J. E. Kerber, pp. 164-173. Syracuse University Press, Syracuse, N.Y.

209

Haury, E. W. 1958 Evidence at Point Pines for a Prehistoric Migration from Northern Arizona. In Migrations in New World Culture History, edited by R. H. Thompson, pp. 1-8. Social Science Bulletin no. 27, University of Arizona Bulletin no. 29. University of Arizona Press, Tuscon, Arizona.

Hazelwood, L., and Steele, J. 2004 Spatial Dynamics of Human Dispersals: Constraints on Modeling and Archaeological Detection. Journal of Archaeological Science 31:669–679. van Heerwaarden, J., Doebley, J., Briggs, W. H., Glaubitz, J. C., Goodman, M. M., Gonzales, J. J. S., Ross-Ibarra, J. 2011 Genetic Signals of Origin, Spread, and Introgression in a large sample of Maize Landraces. Proceedings of the National Academy of Sciences of the United States of America 108(3):1088-1092.

Heidenreich, C. E. 1971 Huronia: A History and Geography of the Huron Indians, 1600–1650, McClelland and Stewart, Toronto.

Hernandez, P.A., Graham, C. H., Master, L. L., and Albert, D. L. 2006 The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773-785.

Hodder, I. 1990 The Domestication of Europe. Blackwell, Oxford.

Housley, R. A., Gamble, C. S., Street, M., Pettitt, P. 1997 Radiocarbon Evidence for the Late Glacial Human Recolonization of Northern Europe. Proceedings of the Prehistoric Society 63:25-54.

Huckell, L. W. 2006 Ancient maize in the American Southwest: What does it look like and what can it tell us. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 97–108. Academic Press, San Diego

Ingold, T. 1988 Notes on the Foraging Mode of Production. In Hunters and Gatherers: History, Evolution, and Social Change, edited by T. Ingold, D. Riches, and J. Woodburn, pp. 269-285. Berg, Oxford.

210

Jaenicke-Després, V., E. S. Buckler, B. D. Smith, M. T. P. Gilbert, A. Cooper, J. Doebley, and S. Pääbo 2003 Early allelic selection in maize as revealed by ancient DNA. Science 302(5648):1206-1208.

Jaenicke-Després, V., and Smith, B. D. 2006 Ancient DNA and the integration of archaeological and genetic approaches to the study of maize domestication. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. E. Staller, R. H. Tykot and B. F. Benz, pp. 83–97. Academic Press, San Diego.

Jamieson, S. 1999 A Brief History of Aboriginal Social Interactions in Southern Ontario and their Taxonomic Implications. In Taming the Taxonomy: Toward a New Understanding of Great Lakes Archaeology, edited by R. A. Williamson and C. W. Watts, pp. 175-192. Eastendbooks, Toronto.

Jones, E. E. 2008 Iroquoian Population History and Settlement Ecology, AD 1500-1700. Unpublished PhD. Dissertation, College of the Liberal Arts, Pennsylvania State University

Kapches, M. 1981 The Middleport Pattern in Ontario Iroquoian Prehistory. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto, Toronto.

Katzenberg, M. A., Schwarcz, A.H., Knyf, M. and Melbye, F. J. 1995 Stable Isotope Evidence for Maize Horticulture and Paleodiet in Southern Ontario, Canada. American Antiquity 60:335-350.

Katzenberg, M. A. 2006 Prehistoric Maize in Southern Ontario: Contributions from Stable Isotope Studies. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 263-275. Academic Press, San Diego.

Keegan, W. F. 1987 Diffusion of Maize from South America: The Antillean Connection Reconsidered. In Emergent Horticultural Economies of the Eastern Woodlands. Edited by W. F. Keegan, pp. 329-44. Southern Illinois University Center for Archaeological Investigations Occasional Paper 7.

211

King, F. 1999 Changing Evidence for Prehistoric Plant Use in Pennsylvania. In Current Northeast Paleoethnobotany, edited by J. Hart, pp. 11-26. Albany: New York State Museum.

Klein, M. 2003 Of Time and Three Rivers: Comments on Early and Middle Woodland Archaeology in Pennsylvania. In Foragers and Farmers of the Early and Middle Woodland Periods in Pennsylvania, edited by P. A. Raber and V. L. Cowin, pp. 117-129. Recent Research in Pennsylvania Archaeology Number 3, Pennsylvania Historical and Museum Commision, Harrisburg, Pennsylvania.

Knapp, T. D. 2009 An Unbounded future? Ceramic Types, “Cultures,” and Scale in Late Prehistoric Research. In Iroquoian Archaeology and Analytic Scale, edited by L. E. Miroff and T. D. Knapp, pp. 101-131. The University of Tennessee Press, Knoxville.

Konrad, V. A. 1976 Distribution, Site and Morphology of Prehistorical Settlements in the Toronto Area. In Perspectives on Landscape and Settlement in Nineteenth Century Ontario, edited by J. D. Wood, pp. 6-31. McClelland and Stewart, Toronto.

Kravchenko, A. N., Bullock, D. G., and Boast, C. W. 2000 Joint Mulifractal Analysis of Crop Yield and Terrain Slope. Agronomy Journal 92:1279-1290.

Lathrap, D. W. 1987 The Introduction of Maize in Prehistoric Eastern North America: The View from Amazonia and the Santa Elena Peninsula. In Emergent Horticultural Economies of the Eastern Woodlands, edited by W. F. Keegan, pp. 345-360. Southern Illinois University Center for Archaeological Investigations Occasional Paper 7.

Latta, M. 1976 The Iroquoian Cultures of Huronia: A Study of Acculturation through Archaeology. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto.

Layton, R. H. 2001 Hunter-Gatherers, their Neighbours and the Nation State. In Hunter- Gatherers: An Interdisciplinary Perspective, edited by C. Panter-Brick, R. H. Layton, and P. Rowley-Conwy, pp. 292-321. Cambridge University Press, Cambridge.

212

Layton, R. Foley, R., and Williams, E. 1991 The Transition between Hunting and Gathering and the Specialized Husbandry of Resources. Current Anthropology 32:255-263.

Lee, R. B., and DeVore, I. 1968 Problems in the Study of Hunters and Gatherers. In Man the Hunter, edited by R. B. Lee and I DeVore, pp. 3-12. Aldine, Chicago

Lee, G.-A., Davis, A. M., Smith, D. G., and McAndrews, J. H. 2004 Identifying fossil wild rice (Zizania) pollen from Cootes Paradise, Ontario: a new approach using scanning electron microscopy. Journal of Archaeological Science 31:411-421.

Lennox, P. A. and Fitzgerald, W. A. 1990 The Culture History and Archaeology of the Neutral Iroquoians. In The Archaeology of Southern Ontario to AD 1650, edited by C. Ellis and N. Ferris, pp. 405-456, Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London.

Long, A., Benz, B. F., Donahue, D. J., Jull, J. T., and Toolin, L. J. 1989 First Direct AMS dates on early maize from Tehuacan Mexico, Radiocarbon 31(3):1035-1040.

Long, A., and Fritz, G. J. 2001 Validity of AMS dates on maize from the Tehuacán Valley: A Comment on MacNeish and Eubanks. Latin American Antiquity 12(1):87-90.

Lusteck, R. 2006 The Migrations of Maize into the Southeastern United States. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. 521-528. Academic Press, San Diego.

MacDonald, R. I. 2002 Late Woodland Settlement Trends in South-Central Ontario: A Study of Ecological Relationships and Culture Change. Unpublished PhD Dissertation, Department of Anthropology, McGill University, Montreal.

MacNeish, R. S. 1952 Iroquois Pottery Types: A Technique for the Study of Iroquois Prehistory. National Museum of Canada Bulletin 124, Anthropological Series No. 31. Ottawa

1992 The origins of agriculture and settled life. University of Oklahoma Press, Norman, OK.

213

Martin, S. W. J. 2004 Lower Great Lakes Maize and Enchainment in the First Millenium A.D.. Ontario Archaeology 77/78:135-159.

2006 Earmarked: Maize, Materiality and Agricultural Frontiers in the Lower Great Lakes Region of North America. Unpublished Ph.D. Dissertation, Department of Archaeology, University of Cambridge.

2008 Languages Past and Present: Archaeological Approaches to the Appearance of Northern Iroquoian Speakers in the Lower Great Lakes Region of North America. American Antiquity 73(3):441‐463.

Matsuoka, Y., Vigouroux, Y., Goodman, M. M., and Sanchez, G. 2002 A Single Domestication for Maize shown by Multilocus Microsatellite Genotyping. Proceedings of the National Academy of Sciences of the United States of America 99(9):6080-6084.

McConaughy, M. A. 2008 Current Issues in Paleoethnobotanical Research from Pennsylvania and Vicinity. In Current Northeast Paleoethnobotany II, edited by J. P. Hart, pp. 9-28. New York State Bulletin Series 512, Albany.

Mellars, P. 2006 A New Radiocarbon Revolution and the Dispersal of Modern Humans in Europe. Nature 439:931–935.

Merrill, W. L., R. J. Hard, J. B. Mabry, G. J. Fritz, K. R. Adams, J. R. Roney, and A. C. MacWilliams 2009 The diffusion of maize to the southwestern United States and its impact. Proceedings of the National Academy of Sciences 106(50):21019-21026.

Morin, E. 2001 Early Late Woodland Social Interaction in the St. Lawrence River Valley. Archaeology of Eastern North America 29:65-100.

Morlan R., and Betts, M. 2005 Canadian Archaeological Radiocarbon Database. Eletronic Document. http://card.canadianarchaeology.ca/

Murphy, C., and Ferris, N. 1990 The Late Woodland Western Basin Tradition in Southwestern Ontario. In The Archaeology of Southern Ontario to AD 1650, edited by C. Ellis and N. Ferris, pp. 189-278., Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London.

Noble, W.C. 1975 Corn and the Development of Village Life in Southern Ontario. Ontario Archaeology 25:37-46.

214

Ounjian, G. L. 1998 Glen Meyer and Prehistoric Neutral Paleoethnobotany. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto.

Parker, A. C. 1916 The Origin of the Iroquois as Suggested by Their Archaeology. American Anthropologist 18:479-507.

Pearce, R. J. 1984 Mapping Middleport: A Case Study in Societal Archaeology. Unpublished PhD Dissertation, Department of Anthropology, McGill University, Montreal.

Perlès, C. 2001 The Early Neolithic in Greece: The First Farming Communities in Europe. Cambridge: Cambridge University Press.

Phillips S.J., Anderson, R.P., and Schapire, R.E. 2006 Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231–259.

Phillips, S., and Dudík M. 2008 Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31:161-75.

Pihl, R. H., Monckton, S. G., Robertson, D. A., and Williamson, R. A. 2008 Settlement and Subsistence Change in the Turn of the First Millennium: A View From the Holmedale Site, Brantford, Ontario. In Current Northeast Paleoethnobotany II, edited by J. P. Hart, pp. 151-172. New York State Bulletin Series 512, Albany.

Pinhasi, R., Fort, J, and Ammerman, A. J. 2005 Tracing the Origin and Spread of Agriculture in Europe. PLOS Biology 21(3): 2220–2228.

Piperno, D. R., and Flannery, K. V. 2001 The earliest archaeological maize (Zea mays L.) from highland Mexico: new accelerator mass spectrometry dates and their implications. Proceedings of the National Academy of Sciences 98(4):2101.

Price, T. D. 1996 The First Farmers of Southern Scandinavia. In The Origins and Spread of Agriculture and Pastoralism in Eurasia, edited by D. R. Harris, pp. 346-362. UCL Press, London.

2000b Europe’s First Farmers: An Introduction. In Europe’s First Farmers, edited by T. D. Price, pp. 1-18. Cambridge University Press, Cambridge.

215

2000c The Introduction of Farming in Northern Europe. In Europe’s First Farmers, edited by T. D. Price, pp. 260-300. Cambridge University Press, Cambridge.

2000d Lessons in the Transition to Agriculture. In Europe’s First Farmers, edited by T. D. Price, pp. 301-318. Cambridge University Press, Cambridge.

2003 The Arrival of Agriculture in Europe as Seen from the North. In The Widening Harvest: The Neolithic Transition in Europe – Looking Back, Looking Forward, edited by A. J. Ammerman and P. Biagi, pp. 273-294. Archaeological Institute of America, Colloquia and Conference Papers 6, Boston.

Price, T. D. (ed.) 2000a Europe’s First Farmers. Cambridge University Press, Cambridge.

Price, T. D., Bentley, A., Lüning, J., Gronenborn, D., Wahl, J. 2001 Prehistoric Human Migration in the Linearbandkeramik of Central Europe. Antiquity 75:593-603.

Price, T. D., and Gebaur, A. B. 1992 The Final Frontier: First Farmers in Northern Europe. In Transitions to Agriculture in Prehistory, edited by A. B. Gebaur and T. D. Price, pp. 97-116. Monographs in World Archaeology No. 4. Prehistory Press, Madison, WI.

Price, T. D., and Gebaur, A. B. (eds) 1995 Last Hunters – First Farmers: New Perspectives on the Prehistoric Transition to Agriculture. School for American Research Press, Santa Fe.

Price, T. D., Gebaur, A. B., and Keeley, L. H. 1995 The Spread of Farming into Europe North of the Alps. In Last Hunters – First Farmers: New Perspectives on the Prehistoric Transition to Agriculture, edited by T. D. Price and A. B. Gebaur, pp. 95-126. School for American Research Press, Santa Fe.

R Core Team 2013 R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org

Ramsden, P. G. 1996 The current state of Huron archaeology. Northeast Anthropology 51:101–112.

Ramsden, P. 2006 But Once the Twain Did Meet: A Speculation About Iroquoian Origins. In From the Arctic to Avalon: Papers in Honour of Jim Tuck. Edited by Lisa Rankin and Peter Ramsden. BAR International Series 1507.

Renfrew, C. 1987 Archaeology and Language: The Puzzle of Indo-European Origins. Penguin Books, London.

216

Richards, M. 2003 The Neolithic Invasion of Europe. Annual Review of Anthropology 32:135- 162.

Riley, T.J. Edging, R., Rossen, J., Carter, G. F., Knapp, G., O’Brien, M. J., and Scherwin, K. H. 1990 Cultigens in Prehistoric Eastern North America: Changing Paradigms. Current Anthropology 31(5):525-541.

Riley, T. J., and Freimuth, G. 1979 Field Systems and Frost Drainage in the Prehistoric Agriculture of the Upper Great Lakes. American Antiquity 44(2):271-285.

Riley, T. J., Walz, G. R., Bareis, C. J., Fortier, A. C., and Parker, K. E. 1994 Accelerator Mass Spectrometry (AMS) Dates Confirm Early Zea Mays in the Mississippi River Valley. American Antiquity 59(3):490-498.

Rouse, I. 1958 The Inference of Migrations from Anthropological Evidence. In Migrations in New World Culture History, edited by R. H. Thompson, pp. 63-68. Social Science Bulletin no. 27, University of Arizona Bulletin no. 29. University of Arizona Press, Tuscon, Arizona.

1986 Migrations in Prehistory: Inferring Population Movement from Cultural Remains. Yale University Press, New Haven, New Jersey.

Rowley-Conwy, P. 1984 The Laziness of the Short-distance Hunter: The Origin of Agriculture in Western Denmark. Journal of Anthropological Archaeology 3:300-324.

2001 Time, Change, and the Archaeology of Hunter-Gatherers: How Original is the ‘Original Affluent Society’?, In Hunter-Gatherers: An Interdisciplinary Perspective, edited by C. Panter-Brick, R. H. Layton, and P. Rowley-Conwy, pp. 39-72. Cambridge University Press, Cambridge.

Russell, T. M. 2004 The Spatial Analysis of Radiocarbon Databases: The Spread of the First Farmers in Europe and of the Fat-tailed Sheep in Southern Africa. BAR International Series 1294. Archaeopress, Oxford.

Sahlins, M. 1972 Stone Age Economics. Aldine, Chicago.

Saunders, D. 2001 Princess Point Palaeoethnobotany. Unpublished Ph.D. Dissertation, Graduate Department of Anthropology, University of Toronto.

217

Semino, O., Passarino, G., Oefner, P. J., Lin, A. A., Arbuzova, S., Beckman, L. E., Benedictis, G. D., Francalacci, P. Kouvatsi, A., Limborska, S. Marciki, M., Mika, A., Mika, B., Primorac, D. Santachiara-Benerecetti, A. S., Cavalli-Sforza, L. and Underhill, P. A. 2000 The Genetic Legacy of Paleolithic Homo sapiens sapiens in Extant Europeans: A Y Chromosome Perspective. Science 290:1155-1159.

Schwarcz, H. P., Melbye, F. J. Katzenberg, M., Knyf, M. 1985 Stable Isotopes of Human Skeletons of Southern Ontario: Reconstructing Paleodiet. Journal of Archaeological Science, 12:187-206.

Shaw, S. H. 1955 Climatic Requirements. In Corn and Corn Improvement, edited by G. F. Sprague, pp. 312-348. Agricultural Research Service, U. S. Department of Agriculture and Department of Agronomy, Iowa State College, Ames, Iowa. New York: Academic Press.

Shennan, S. 1997 Quantifying Archaeology, 2nd ed. Iowa City: University of Iowa Press 2007 The Spread of Farming into Central Europe and its Consequences: Evolutionary Models. In The Model-based Archaeology of Socionatural Systems, edited by T. A. Kohler and S. van der Leeuw, pp. 141-156. Santa Fe: School for Advanced Research Press 2008 Population Processes and their Consequences in Early Neolithic Central Europe. In The Neolithic Demographic Transition and its Consequences, edited by J.-P. Bocquet-Appel and O. Bar-Yosef, pp. 315-332. Springer

Smith, B. D. 1989 Origins of Agriculture in Eastern North America. Science, New Series 246(4937):1566-71.

1992 Rivers of Change: Essays on Early Agriculture in Eastern North America. Smithsonian Institution Press, Washington.

2001 Low-Level Food Production. Journal of Archaeological Research 9(1):1-43.

Smith, D. 1997a Radiocarbon Dating the Middle to Late Woodland Transition and Earliest Maize in Southern Ontario. Northeast Anthropology 54:37-73.

1997b Recent Investigations of Late Woodland Occupations at Cootes Paradise, Ontario. Ontario Archaeology 63:4-17.

Smith, D. G. and Crawford, G. W. 1997 Recent Developments in the Archaeology of the Princess Point Complex in Southern Ontario. Canadian Journal of Archaeology 21(1):9-32.

218

2002 Recent Developments in the Archaeology of the Princess Point Complex in Southern Ontario. In Northeast Subsistence-Settlement Change: A.D. 700-1300, edited by J. Hart and C. Rieth, pp. 97-116. Bulletin 496. New York State Museum, Albany.

Snow, D. R. 1994 The Iroquois. Blackwell, Cambridge.

1995a Migration in Prehistory: The Northern Iroquoian Case. American Antiquity 60:59-79.

1995b Population Movements during the Woodland Period: The Intrusion of Iroquoian Peoples. In Origins of the People of the Longhouse: Proceedings of the 21st Annual Symposium of the Ontario Archaeological Society, edited by A. Bekerman and G. Warrick, pp. 5-8. The Ontario Archaeological Society Inc., North York, Ontario.

1996 More on Migration in Prehistory: Accommodating New Evidence in the Northern Iroquoian Case. American Antiquity 61(4): 791-796

1999 Comments: Toward a New Understanding of Great Lakes Archaeology. In Taming the Taxonomy: Toward a New Understanding of Great Lakes Archaeology, edited by R. Williamson and C. Watts, pp. 267-274. Eastendbooks, Toronto.

Soto-Berelov, M. 2011 Vegetation Modeling of Holocene Landscapes in the Southern Levant. Unpublished PhD Dissertation, Arizona State University.

Staller, J. E. 2003 An examination of the palaeobotanical and chronological evidence for an early introduction of maize (Zea mays L.) into South America: a response to Pearsall. Journal of archaeological science 30(3):373-380.

2006 An Introduction to the Histories of Maize. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Linguistics, Biogeography, Domestication, and Evolution of Maize, edited by J. Staller, R. Tykot and B. Benz, pp. xxi-xxv. Academic Press, San Diego.

2010 Maize Cobs and Cultures: History of Zea mays L. Springer Heidelberg Dordrecht, New York.

Starna, W. A., and Funk, R. E. 1994 The Place of the In Situ Hypothesis in Iroquoian Archaeology. Northeast Anthropology 47:45-54.

Steele, J. 2009 Human Dispersals: Mathematical Models and the Archaeological Record. Human Biology 81(2-3):121-140.

219

2010 Radiocarbon Dates as Data: Quantitative Strategies for Estimating Colonization Front Speeds and Event Densities. Journal of Archaeological Science 37: 2017-2030.

Stoskopf. N. C. 1985 Cereal Grain Crops. Reston Publishing Company, V. A.

Stothers, D. 1977 The Princess Point Complex. Archaeological Survey of Canada, Mercury Series 58. Ottawa: National Museum of Man.

Stothers, D., and Abel, T. 2002 The Early Late Woodland in the Southwestern Lake Erie Littoral Region. In Northeast Subsistence-Settlement Change: A.D. 700-1300, edited by J. Hart and C. Rieth, pp.73-96. Bulletin 496. New York State Museum, Albany.

Stothers, D., and Bechtel, S. 1987 Stable Carbon Isotope Analysis: An Inter-Regional Perspective. Archaeology of Eastern North America 15:137-154.

Sutton, R. 1996 The Middle Iroquoian Colonization of Huronia, Unpublished PhD dissertation, Department of Anthropology, McMaster University.

Sykes, C. M. 1981 Northern Iroquoian Maize Remains. Ontario Archaeology 35(2):23-33.

Thomas, J. 1996 The Cultural Context of the First Use of Domesticates in Continental Central and Northwest Europe. In The Origins and Spread of Agriculture and Pastoralism in Eurasia, edited by D. R. Harris, pp. 310-322. UCL Press, London.

1999 Understanding the Neolithic. Routledge, London.

Thompson, R. G., Hart, J. P., Brumbach, H. J., Lusteck, R. 2004 Phytolith Evidence for Twentieth-Century B.P. Maize in Northern Iroquoia. Northeast Anthropology 68:24-40.

Trigger, B. G. 1962 The Historic Location of the Huron. Ontario History 54:137-148.

1976 Children of Aataentsic: A History of Huron People to 1660, 2 Volumes. McGill-Queens University Press, Montreal, Quebec.

1985 Natives and Newcomers: Canada’s “Heroic Age” Reconsidered. Montreal: McGill – Queen’s University Press

220

1999 Master and Servant: a Conference Overview. In Taming the Taxonomy: Toward a New Understanding of Great Lakes Archaeology, edited by R. Williamson and C. W. Watts, pp. 302-322. Eastendbooks, Toronto.

Tringham, R. 2000 Southeastern Europe in the Transition to Agriculture in Europe: Bridge, Buffer, or Mosaic. In Europe’s First Farmers, edited by T. D. Price, pp. 19-56. Cambridge University Press, Cambridge. van Andel, T. H., and Runnels, C. N. 1995 The Earliest Farmers in Europe. Antiquity 69:481-500.

Van der Merwe, N.J., and Vogel, J.C. 1978 13C Content of Human Collagen as a Measure of Prehistoric Diet in Woodland North America. Nature 276:815-816.

Van der Merwe, N. J., Pfieffer, S., Williamson, R. F., and Thomas, S. C. 2003 Isotopic Analysis and the Diet of the Moatfield Community. In Bones of the Ancestors: The Archaeology and Osteobiography of the Moatfield Ossuary, edited by R. F. Williamson and S. Pfieffer, pp. 205-222. Mercury Series Archaeology Paper 163. Gatineau, Quebec: Canadian Museum of Civilisation.

Vencl, S. 1986 The Role of Hunting-gathering Populations in the Transition to Farming: A Central European Perspective. In Hunters in Transition: Mesolithic Societies of Temperate Eurasia and their Transition to Farming, edited by M. Zvelebil, pp. 43- 51. Cambridge University Press, Cambridge.

Vigouroux, Y., J. C. Glaubitz, Y. Matsuoka, M. M. Goodman, and G. Sánchez 2008 Population Structure and Genetic Diversity of New World Maize Races Assessed by DNA Microsatellites. American Journal of Botany 95(10):1240.

Vogel, J.C., and van der Merwe, N. J. 1977 Isotopic Evidence for Early Maize Cultivation in New York State. American Antiquity 42(2):238-242.

Warrick,G. 1990 A Population History of the Huron-Petun, A.D. 500-1650, Unpublished PhD Dissertation, Department of Anthropology, McGill University 2000 Precontact Iroquoian Occupation of Southern Ontario. Journal of World Prehistory 14(4):415-466.

2008 A Population History of the Huron-Petun, A.D. 500-1650. Cambridge University Press.

221

Watts, C.M. White, C.D. and Longstaffe, F.J. 2011 Childhood Diet and Western Basin Tradition Foodways at the Krieger Site, Southwestern Ontario, Canada. American Antiquity 76(3):446-472.

Weisstein, E. W. 2012 Great Circle. From MathWorld-- A Wolfram Web Resource. http://mathworld.wolfram.com/GreatCircle.html

Williamson, Ronald, F. 1985 Glen Meyer: People in Transition. Unpublished PhD Dissertation, Department of Anthropology, McGill University, Montreal.

1990 The Early Iroquoian Period of Southern Ontario. In The Archaeology of Southern Ontario To AD 1650, edited by C. Ellis and N. Ferris, pp. 291-320, Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London.

Williamson, R. F., and Robertson, D. A. 1994 Peer Polities Beyond the Periphery: Early and Middle Iroquoian Regional Interaction. Ontario Archaeology 58:27–48.

Wills, W.H. 1988 Early Prehistoric Agriculture in the American Southwest. School of American Research Press, Santa Fe.

1995 Archaic foraging and the beginning of food production in the American Southwest. In Last Hunters, First Farmers, edited by T. Doug Price and Anne Birgitte Gebauer, pp. 215-242. Santa Fe: School of American Research Press.

Wright, J. V. 1966 The Ontario Iroquois Tradition. National Museum of Canada, Bulletin # 210.

Yarnell, R. A. 1964 Aboriginal Relationships between Culture and Plant Life in the Upper Great Lakes Region. University of Michigan Museum of Anthropology Anthropological Papers 23, Ann Arbor.

1976 Early Plant Husbandry in Eastern North America. In Cultural Changes and Continuity: Essays in Honor of James Bennett Griffin, edited by C. E. Cleland, pp. 265-273. Academic Press, New York.

Zilhão, J. 2000 From the Mesolithic to the Neolithic in the Iberian Peninsula. In Europe’s First Farmers, edited by T. D. Price, pp. 144-182. Cambridge University Press, Cambridge.

2001 Radiocarbon Evidence for Maritime Pioneer Colonization at the Origins of Farming in West Mediterranean Europe. Proceedings of the National Academy of Sciences 98(24): 14180–14185.

222

Zohary, D. 1996 The Mode of Domestication of the Founder Crops of Southwest Asian Agriculture. In The Origins and Spread of Agriculture and Pastoralism in Eurasia, edited by D. R. Harris, pp. 142-158. UCL Press, London.

Zohary D., and Hopf, M. 1993 Domestication of Plants in the Old World, 2nd ed. Oxford University Press, Oxford.

Zvelebil, M. (ed.) 1986a Hunters in Transition: Mesolithic Societies of Temperate Eurasia and their Transition to Farming. Cambridge University Press, Cambridge.

Zvelebil, M. 1986b Mesolithic Prelude and Neolithic Revolution. In Hunters in Transition: Mesolithic Societies of Temperate Eurasia and their Transition to Farming, edited by M. Zvelebil, pp. 5-17. Cambridge University Press, Cambridge.

1986c Mesolithic Societies and the Transition to Farming: Problems of Time, Scale and Organization. In Hunters in Transition: Mesolithic Societies of Temperate Eurasia and their Transition to Farming, edited by M. Zvelebil, pp. 167-188. Cambridge University Press, Cambridge.

1989 On the Transition to Farming in Europe, or what was Spreading with the Neolithic: A Reply to Ammerman. Antiquity 63:379-383.

1994 Plant use in the Mesolithic and its Role in the Transition to Farming. Proceedings of the Prehistoric Society 60:35-74.

1996 The Agricultural Frontier and the Transition to Farming in the Circum-Baltic Region. In The Origins and Spread of Agriculture and Pastoralism in Eurasia, edited by D. R. Harris, pp. 323-345. UCL Press, London.

Zvelebil, M., and Dolukhanov, P. 1991 The Transition to Farming in Eastern and Northern Europe. Journal of World Prehistory 5(3):233-278.

Zvelebil, M., and Lillie, M. 2000 Transition to Agriculture in Eastern Europe. In Europe’s First Farmers, edited by T. D. Price, pp. 57-92. Cambridge University Press, Cambridge.

Zvelebil, M., and Rowley-Conwy, P. 1984 Transition to Farming in Northern Europe: A Hunter-Gatherer Perspective. Norwegian Archaeological Review 17(2):104-128. Zvelebil, M., and Zvelebil, K. V. 1988 Agricultural Transition and Indo-European Dispersals. Antiquity 62:574-583

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APPENDIX 1: REGIONAL ANALYSIS DATA DESCRIPTION

This section outlines the creation of multiple radiocarbon datasets in order to test the spatiotemporal patterning associated with the earliest spread of maize through North

America, and in particular, into the Northeast. These are the datasets used in Chapter 7 to show the spread of dates associated with maize against distance from southwestern

Mexico (section 7.1, Figure 7.1) and the dataset used for the linear regression and point of origin analyses. Following the framework of numerous studies of the spread of agriculture through Neolithic Europe (Ammerman and Cavalli-Sforza 1971, 1973, 1984;

Bocquet-Appel et al. 2009; Fort 2009; Fort et al. 2012; Gkiasta et al. 2003; Pinhasi et al.

2005; Russell 2004), the primary dataset consists of radiocarbon assays taken either from directly-dated maize remains or closely associated materials. A full list of all datasets is provided, as well as the analytical decisions and data manipulation process used in the creation of these datasets.

In order to critically assess both global and local trends, several datasets were constructed representing a range of spatial coverage and radiocarbon sampling procedures: from a global dataset of all dates associated with maize from Mexico to the

Northeast between 5600 BC and 1200 AD to a group of datasets representing only northeastern North America.

Furthermore, any study of spatiotemporal patterning must focus a large amount of attention on developing a firm chronological basis by which to test demographic hypotheses. Therefore, a discussion of radiocarbon data, its information potential as well as the potential errors and issues associated with radiocarbon datasets is discussed.

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Several analytical procedures were employed to increase confidence in a radiocarbon dataset of this magnitude in order to transform the dataset into one which only represents the earliest patterns of maize diffusion through the region, while at the same time incorporating as many samples as possible.

A1.1: MASTER DATASET CREATION

The data used to perform regression analysis of the temporal and spatial character of agricultural adoption are entirely tabular in format, with no physical materials being used. Large online databases of radiocarbon dates from archaeological sites throughout

North America, such as the Canadian Archaeological Radiocarbon Database

(http://card.canadianarchaeology.ca, Morlan and Betts 2005) and Ancient Maize Maps

(http://en.ancientmaize.com, Blake et al. 2012), provided the foundation for this research.

Additionally, a new dataset was also compiled for the Northeast based on a focussed search of previous research on the spread of maize into the region, including published papers, site reports, institutional and governmental databases, as well as personal communications with various researchers throughout Michigan, New York, Ohio,

Ontario, and Pennsylvania (see associated references in Table A1.2). While geographic coordinates are available for each radiocarbon sample from Ancient Maize Maps, the spatial location for all other sites was either requested through an institutional database administrator or located through the georectification of site maps in ArcGIS (v. 10.1). In accordance with data-sharing agreements with both the Canadian Museum of Civilization

225 and the New York State Museum, the locational data gathered may not be published in any numerical format, and all maps generated showing exact site locations may not exceed a scale of 1:2,000,000. The reader is directed to contact Dr. Matthew Betts at the

Canadian Museum of Civilization or Dr. Jonathan Lothrop of the New York State

Museum for geographic coordinates of the appropriate sites.

A1.1.1: Study Region

There are two principle study regions used in Chapter 7: an inclusive region representing the entirety of continental North America in order to look at global trends in the radiocarbon dataset (section 7.1, Figure 7.1), and an exclusive region representing only northeastern North America. Southern Mexico was chosen as a southern limit for this dataset based on numerous archaeological, cladistic, and genetic studies which place the origin of all modern maize landraces to southwestern Mexico (Benz 2001; Blake

2006:56-58; Piperno and Flannery 2001; Staller 2003:366-367). Similarly, there is no evidence for the movement of domesticated corn into North America from either South or Central America, and as such, all maize in the continent appears to have developed out of varieties first developed in Mexico and then the southwestern United States (see chapter 2, section 4.2; Crawford et al. 1997:117; Doebley et al. 1988:67; Fritz 1990:409;

Galinat 1965:355; Jaenicke-Després et al. 2001).

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A1.1.2: Defining the Extent of the Northeast

For this particular analysis, the Northeast is defined by the modern administrative boundaries of the lower peninsula of Michigan, New York State, Ohio, and Pennsylvania in the United States and Southern Ontario below 45° N Latitude in Canada. Originally, this spatial extent was chosen as to encompass all regions surrounding Southern Ontario, although it also has precedent in the literature surrounding the spread of maize into

Southern Ontario. This spatial extent has previously been used to define the Northeast catchment area for the diffusion of maize (c.f. the Lower Great Lakes Region: Crawford et al. 1997; Martin 2004, 2006), as well as in parts in various palaeoethnobotanical volumes (Hart 1999a, 2008; Hart and Rieth 2002). A northern boundary of 45° N was determined based on an absolute northern limit of maize productivity (Campbell and

Campbell 1992; Fecteau 1985:25; although, see Boyd et al. 2008 and Boyd and Surette

2010 for evidence of archaeologically-recovered maize above the 49th parallel), and the eastern boundary was drawn at New York State largely because there is at present no significant evidence for maize horticulture along the eastern seaboard prior to AD 1100-

1200 – a period in which maize was likely fully available to all local groups in the region and was a significant part of many local diets (Asch Sidell 1999, 2008; Bender et al.

1981; Buikstra and Milner 1991; Chilton 2008; Crawford et al. 2003; Hart and Reith

2002; Katzenberg et al. 1995; Lynott et al. 1986; Stothers and Bechtel 1987; Vogel and van der Merwe 1977). While modern administrative boundaries admittedly have little relationship to real or perceived boundaries by pre-contact groups in the region, using provincial/state classifications helped to facilitate the collection of grey literature sources as well as the collection of site data through institutional database queries.

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A1.1.2: Temporal Range

For the northeastern dataset, a temporal end point was set at AD 1200. While there is much variation throughout the Northeast as to the exact period of agricultural uptake, isotopic evidence in Michigan, New York, Ohio, and Ontario has suggested that maize was fully available and becoming widely incorporated into local diets by this time

(Katzenberg 2006; Katzenberg et al. 1995; Schwarcz et al. 1985; Stothers and Bechtel

1987; van der Merwe and Vogel 1978; Vogel and van der Merwe 1977). As the goal of this thesis is to analyze the patterns associated with the earliest diffusion of maize into the region, I feel that including sites after this period could introduce unnecessary temporal noise into the dataset – potentially masking patterns of maize’s earliest entry. However, given the inherent error associated with radiocarbon calibration process, any site where the two-sigma calibrated date intersected with the AD 1200 cut-off was included in the northeastern dataset (see section A1.4.1 below).

The data for the continental dataset outside of the Northeast spatial extent came entirely from the Ancient Maize Maps database. The primary goal of the Ancient Maize database is to create an online repository for information about accurately dated maize samples in order to help trace its geographic spread out of Mexico. A two-sigma temporal limit was also placed at AD 1200 for this dataset. No attempts were made to verify the authenticity of the individual samples in this dataset, under the assumption that this was done by the site administrators, although the reader is directed to the references associated with each sample in Table A1.1.

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A1.1.3: Variables

The primary variables necessary for the regression analyses largely revolve around the spatial location of the site, the site name and administrative code (Borden number in Ontario), as well as the radiocarbon date in uncalibrated years BP, one-sigma error in uncalibrated years BP, and laboratory code for each radiocarbon assay. Spatial coordinates were collected in Latitude and Longitude and converted into Decimal

Degrees using the NAD 1983 datum in order to locate the sites in ArcGIS. Any other transformations of spatial coordinates (such as map projection) were done from the untransformed geographic coordinates to avoid any rounding errors that can arise from the repeated transformation of the same locations. Normalized radiocarbon dates were used where reported; otherwise the assumption was made that the lab-reported radiocarbon date had been corrected for isotopic fractionation.

A1.1.3.1: Additional Variables

Additional variables were added in order to judge the accuracy of each sample, as well as to facilitate the division of the full dataset into smaller samples. These additional variables were the material dated in the radiocarbon assay, the method of radiocarbon analysis (i.e. conventional vs. Accelerator Mass Spectrometry dating), and the δC13 ratio if available. A note field was also added so that any specifics to a particular date not captured in the previous variables could be included. This often included information on the context of associated dates, or when a normalized radiocarbon date had been reported, the uncorrected date was included in this field.

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A1.1.4: Limiting the Dataset

In order to mitigate against potential interpretive problems due to errors inherent in radiocarbon analysis, all problematic dates were removed from the master dataset following the procedure outlined in Shennan and Steele (2000). In their compilation of a radiocarbon dataset for Mesolithic and Neolithic sites across Europe, the authors present five criteria for exclusion of radiocarbon data from a dataset:

(1) Samples had an error term greater than +/- 150 years

(2) Samples were from bad or unreliable cultural associations

(3) Samples were refused by the contributing archaeologists/submitters

(4) Samples were from bad context or of dubious reliability

(5) Samples were from outside the temporal limits of the project

Similar criteria were also advocated by Russell (2004), Byrd (2005), and Jankuta (2011) in their collection and analysis of radiocarbon data. While an original master dataset was compiled of 236 radiocarbon dates from 109 archaeological sites, the final version of this limited dataset contained 166 radiocarbon assays from 83 archaeological sites (Table

A1.2).

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A1.2: ADDITIONAL DATES

As part of the research plan for this particular analysis, funding was generously provided through the Richard B. Johnston Award for Ontario Archaeology in order to send eight additional maize samples in for Accelerator Mass Spectrometry (AMS) dating.

Through conversations with many researchers in Ontario, maize was obtained from two sites in Southern Ontario, Princess Point and Dawson Creek, which were both originally described as early maize-bearing sites in Ontario (Crawford and Smith 1996; Jackson

1983, 1988; Smith 1997; Smith and Crawford 1997). Princess Point has long been considered a candidate for early maize agriculture in Southern Ontario (Stothers 1977), and it was fully expected that the remains dated would be between 1,200 and 1,500 years old (G. Crawford, pers. comm.). The maize was analyzed by Dr. G. Crawford of the

University of Toronto, and determined to be eight-rowed Northern Flint: all kernels were consistent in size and morphology with other pre-1000 AD maize samples in the

Northeast and the four samples were taken from the three lowest levels of the Princess

Point site midden. Until now, no radiocarbon dates existed for the multicomponent

Princess Point site and all age estimates were determined through pottery seriation, maize kernel morphology and dates from other Princess Point tradition sites. Dawson Creek, on the other hand, had an associated charcoal date of 1405 BP ± 60 from a feature containing maize and appeared to be firmly Middle Woodland in context (Jackson 1983,

1988). When the samples were dated, however, the maize from both sites was found to be significantly younger than expected – with the Dawson Creek maize dating to roughly

1450 – 1700 AD and the Princess Point material dating to roughly 1290 – 1450 AD (see

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Figures A1.1 and A1.2). These results provide a clear illustration of the problems of bioturbation in floral assemblages and the importance of directly-dating maize where possible.

However, given that the samples are outside of the range of this present study, the sites were not included in the final master dataset. Dawson Creek has long been questioned as to whether the maize found at the site was intrusive (Crawford et al.

1997:114-115), but the associated date has continued to be used in the literature as one of the earliest evidence maize in the region, and the only occurrence of maize in Ontario within a firmly Middle Woodland context. Therefore, these dates do have value in the sense that two sites that may have been spuriously associated with early maize have now been removed.

A1.3: POOLING DATES

One of the requirements of spatial analyses in general, and regression analysis of spatial data in particular, is the need for a single point value at a particular set of latitude and longitude coordinates. As this research concerns the earliest evidence for maize horticulture across the Northeast, ideally only the earliest dates would be used. However, when sites contain multiple dates, the issue of the inaccuracies of radiocarbon dating methods becomes central. As there is a greater confidence in a radiocarbon date given by the presence of many other similar dates from the same context, pooled dates provide

232 more security in the assumption that the date drawn from the radiocarbon analysis is indeed correct (Bowman 1990:21; McIntosh 1999:136; Shott 1992:212; Ward and

Wilson 1978:21). In order to determine whether multiple dates from a single site could be pooled, the reduced dataset of 166 entries was imported into the radiocarbon calibration software Calib 6.0. The site-grouped dates were tested using a Chi square test (also known as the Ward and Wilson technique) within the software, which also provided the pooled mean date for the site. Of the 166 dates, 113 were tested and pooled. This resulted in 32 site dates coming from pooled contexts, and 50 coming from single dates. While the obvious problem with this approach lies in the difference in central tendencies between pooled and unpooled dates, I believe this offers a more statistically rigorous treatment of the dataset than that provided by only taking the earliest date from each site (Russell

2004:7; Ward and Wilson 1978).

The formula for the Chi square test is:

2 x2 = ∑ (di – d) 2 σi where:

di are the radiocarbon dates (d1, d2, di… dn)

d is the pooled mean, given by the formula:

2 d = ∑ di / σi s 2 ∑ 1 / σi

σi are the standard deviations (σ1, σ2, σi… σn)

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Essentially, this test compares the internal similarity between radiocarbon dates using their standard deviations. Dates from a single site that were statistically similar at the 0.05 confidence level were pooled (Table A1.3). The main issue with this test arises when multiple dates from a single site reflect long-term occupation, and therefore, have too large of a range to be effectively tested. This was the case for 5 of the sites included in the pooled dataset. However, due to the length of occupation (sometimes more than 1,000 radiocarbon years), these sites each exhibited multimodal characteristics. To compensate, each modal group was tested for internal similarity and the earliest modal group was pooled for the final database. For sites with multiple dates where pooling was inappropriate (i.e. a large date range without multimodality or no two similar materials dated), the earliest accepted radiocarbon date was used.

A1.3.1: Pooled Variance

The ‘test significance’ function of Calib 6.0 not only provides a mean pooled date, but also a mean pooled variance. However, the assumption of the pooled variance is that all dates being tested came from the same sample (Shott 1992:212; Ward and Wilson

1978: 21). As this could not be determined for many of the samples, and was unlikely for most, an average of all error terms for the samples being pooled from a given site was used instead as this would provide a more conservative estimate for calibration.

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A1.4: RADIOCARBON CALIBRATION PROCEDURES

While it was initially standard practice to use the modal value of an uncalibrated radiocarbon determination (i.e. radiocarbon date bp without error term) as the point value in regression analyses (c.f. Ammerman and Cavalli-Sforza, 1971, 1984), this is generally considered problematic (Fort et al. 2012; Pinhasi 2005; Russell 2004:118-127; Steele

2010:2019). Given that there is no mathematically-definable relationship between the radiocarbon measurement and the calendar age of the sample (Bowman 1990:21), it was determined that analyzing only the calibrated patterns would be more representative of the temporal processes under analysis, rather than relying on the assumption that the differences in radiocarbon values between sites is equivalent to differences in actual site dates.

A1.4.1: The Calibration of Radiocarbon Dates

Levels of atmospheric 14C concentrations are now known to have frequently fluctuated, due to variations in the rate of isotopic 14C production in the atmosphere.

Therefore, the amount of 14C that an organism will have at death changes depending on what the atmospheric levels were at that time (Bowman 1990:17). The calibration of a radiocarbon age determination is therefore largely contingent on the shape of the calibration curve at the particular point of intersection with the radiocarbon age (Bowman

1990:17-20; Russell 2004:19). Radiocarbon calibration curves are constantly being revised; therefore the uncalibrated date and error term is also provided in tables A1.4 –

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A1.11 so that future researchers may recalibrate the dates. The final selection of dates for each sampling group was calibrated in the radiocarbon calibration package OxCal 4.1 using the IntCal09 calibration curve (Reimer et al. 2009), where the 95.4% probability range was recorded as was the median value. A median date was chosen instead of a mean or modal value as the probabilistic nature of radiocarbon calibration precludes any numerical measure of center (Russell 2004:21).

Lastly, as calibrated dates represent an equal probability across the 2 sigma range, a decision was made to include all sites where the probability range intersected with the temporal range determined for this study. In other words, any dates that had a “calibrated from” date older than 750 BP (ca. AD 1200), were included in the final dataset, although it was the median date that was ultimately recorded for these sites.

A1.4.2: Conventional versus Accelerator Mass Spectrometry Radiocarbon Dating

Methods

There are two techniques used to measure the depletion of 14C in a sample.

Conventional radiocarbon dating measures the amount of beta decay emitted by a sample over a certain amount of time and measures this against a known rate of decay.

Essentially, the older the sample is, the less 14C will be present, and therefore, the less beta decay it will emit. Accelerator Mass Spectrometry (AMS) dating measures directly the proportions of 14C relative to certain stable carbon isotopes such as 13C or 12C, and as such, can achieve greater precision and use much smaller samples (Bowman 1990:33).

While conventional radiocarbon dating typically is performed on wood, charcoal, and

236 other remains capable of providing a sample large enough for dating, AMS dating can be used on minute samples, such as individual seeds, or pottery encrustation (Bowman

1990:34-36).

For palaeobotanical remains, AMS dating has the distinct benefit over conventional dating in that the flora in question can be directly dated, and thus avoid the issue of bioturbation in the archaeological record as well as the known inaccuracies of associated wood dates such as the “old wood effect” (Schiffer 1986). As Fritz (1994:305) notes, direct AMS dates on maize from Mexico, the Greater Southwest, and eastern North

America have repeatedly shown that previous estimates for the antiquity of maize agriculture at key sites are unsubstantiated and true dates may be significantly more recent (Conard et al. 1984, Ford 1987, Long et al. 1989, Wills 1988). At the end of the

Venice Meeting on the Neolithic Transition in Europe (October 1998), delegates recommended the inception of a dating program “to undertake the study of the Early

Neolithic in Europe and its Western Asian antecedents through the medium of AMS radiocarbon determinations to be conducted on well-provenienced and well-identified samples [of domesticates]”. (Ammerman and Biagi 2003:343). However, while AMS dating offers the opportunity to directly date the event in question, there is no a priori reason that the conventional associated dates are not correct. In fact, Scott et al. (2004) found no evidence for a significant difference between AMS and conventional dates in a collection of samples with known ages.

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A1.5: SAMPLING GROUPS

As Russell (2004:6) points out in her analysis of the radiocarbon evidence for the spread of farming through Neolithic Europe, the issue of appropriate date selection in radiocarbon databases is still largely unresolved. In analyzing the earliest movement of a population or technology, is it justified to use the earliest accepted date for each site? On the other hand, while being able to average a collection of site dates through pooling may increase one’s certainty in the age determination, this will invariably also make a site date younger than would be the case if only using the earliest determination – potentially masking the behaviour under analysis: namely, the earliest occurrence of maize horticulture. Lastly, the issue of bioturbation of archaeobotanical specimens highlights the issue of whether associated radiocarbon dates should be used at all. Even on sites with relatively secure stratigraphy, there is no absolute way of determining whether the maize recovered from archaeological strata can in fact be directly associated with other materials (McConaughy 2008:14-15). On the other hand, while the direct-dating of maize remains is becoming increasingly more common, to reduce the dataset to only those direct dates would severely limit the amount of samples available.

For this reason, it was decided that the master dataset would be divided into separate sampling groups representing each of these issues. If there was little difference in the regression results, it could be assumed that the particular method of date selection had little effect on the results.

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A1.5.1: Group A – Earliest dates

- This represents the earliest uncontested date at each site. Once collected, the dates were imported into the radiocarbon calibration software Oxcal (vers. 4.1) for calibration where the 2 sigma range was recorded as well as the median date. The median date was used as the probabilistic nature of radiocarbon calibration precludes any mathematical measure of center that is based on a Gaussian distribution.

- This was done for all 109 sites in the master dataset. After calibration, only sites where the 2 sigma range intersected with the absolute end date were used (i.e. where the "Cal from" date was older than 750 CalBP). This resulted in 83 sites for the region.

- See Figure A1.3 and Table A1.4

A1.5.2: Group B – Pooled Dates

- This represents the pooled date and average error for each site where possible and the earliest date where pooling could not be performed (see section A1.3). The pooling procedure was done in the radiocarbon calibration program Calib (vers. 6.02), where sites were tested using the chi-square test for whether dates were from the same population. For the results of all of the pooling tests, see Table A1.3. For each positive result, the pooled mean date, and average error for all dates pooled, was recorded, as well as the chi-square scores. Dates were only pooled if they passed at the 0.05 confidence level. The average error was used instead of the pooled variance given by Calib as the underlying assumption behind the pooled variance is that all pooled dates came from the same physical sample. As this was likely rarely the case in this dataset, an average error term was used instead. Once collected, the dates were imported into the radiocarbon

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calibration software Oxcal (vers. 4.1) for calibration where the 2 sigma range was recorded as well as the median date.

- See Figure A1.4 and Table A1.5

A1.5.3: Group C – Direct Dates Only (Earliest)

- This represents the earliest direct date on maize for each site that has directly dated maize. While ideally, only AMS-dated maize would be used for this group, this has been done on too few sites in the region and therefore would have resulted in a severely reduced dataset. Instead, dates on the carbonized residue adhering to pot sherds where maize phytoliths were found was also included, as were bone collagen dates (from two sites) where the δC13 ratio indicated unquestionable maize consumption (> - 17.0), and on two occasions, a conventional date drawn from a collection of maize cobs from a site. Once collected, the dates were imported into the radiocarbon calibration software Oxcal (vers. 4.1) for calibration where the 2 sigma range was recorded as well as the median date.

- This was done for the 56 sites in the master dataset that had direct dates drawn. After calibration, only sites where the 2 sigma range intersected with the absolute end date were used (i.e. where the "Cal from" date was older than 750 CalBP). This resulted in 38 sites for the region.

- See Figure A1.5 and Table A1.6

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A1.5.4: Group D - Direct Dates Only (Pooled)

- This represents the pooled date and average error for each site, where possible, and the earliest date where pooling could not be performed (see sections A1.3 and A1.5.2). While ideally, only AMS-dated maize would be used for this group, this has been done on too few sites in the region and therefore would have resulted in a severely reduced dataset. Instead, dates on the carbonized residue adhering to pot sherds where maize phytoliths were found was also included, as were bone collagen dates where the δC13 ratio indicated maize consumption (> - 17.0), and on two occasions, a conventional date drawn from a collection of maize cobs from a site.

- At times, the pooled date represented the pooled result of both direct and indirect dates; however, only sites which had direct dates and where these direct dates were successfully pooled with associated dates were included. Once collected, the dates were imported into the radiocarbon calibration software Oxcal (vers. 4.1) for calibration where the 2 sigma range was recorded as well as the median date.

- This was done for the 56 sites in the master dataset that had direct dates drawn. After calibration, only sites where the 2 sigma range intersected with the absolute end date were used (ie, where the "Cal from" date was older than 750 CalBP). This resulted in 37 sites for the region.

- See Figure A1.6 and Table A1.7

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A1.6: SAMPLING GRID GROUPS

Upon inspection of the location and date of all sites in the database, I decided to run another set of sampling groups in the attempt to alleviate some of the temporal variation on the sub-regional scale. There is a large degree of variation within the dataset as to the earliest date of maize in any given subregion. For example, the Finger Lakes district of New York has many assays dated to between 2100 CalBP and 1600 CalBP, while, in the rest of New York the earliest dates on maize are from roughly 1300 to 1100

CalBP. This is also the case for Southern Ontario, where the earliest dates are in the

Grand River valley at around 1600 CalBP, while in the Southwest of the province and into Michigan, the earliest occurrence is thought to be between 1200 and 1000 CalBP.

Therefore, it was decided that only the earliest date within a 50 km by 50 km sampling grid would be used, and all later groups would be removed. A 2,500 square kilometre search area was chosen as this was used as the maximum generational migration distance in Ammerman and Cavalli-Sforza’s Wave of Advance model (Ammerman and Cavalli-

Sforza 1984:81). Therefore, any sites outside of this range should in theory represent a separation of at least one generation, and in this sense be important to this particular analysis.

The sampling grid was created in ArcGIS (vers. 10.1) using the "fishnet" add-in

(available from http://arcscripts.esri.com/details.asp?dbid=12807). The grid was created in UTM projection (Zone 17N) in order to reduce on-the-ground distortion, and was then imported into the master dataframe which uses an Albers Equal Area Conic projection

(NAD 1983 datum). From this, only the earliest calibrated site date within each grid

242 square was taken. This process was repeated for all of the sampling groups outlined in the previous section (A1.5.1 – A1.5.4).

A1.6.1: Group E – Earliest Date by Sampling Grid

- The sites in Group A (section A1.5.1) were placed onto the sampling grid and only the earliest site date per grid square was retained. This resulted in 50 sites, from the 83 sites in the source dataset.

- See Figure A1.7 and Table A1.8

A1.6.2: Group F – Earliest Pooled Date by Sampling Grid

- The sites in Group B (section A1.5.2) were placed onto the sampling grid and only the earliest site date per grid square was retained. This resulted in 49 sites, from the 82 sites in the source dataset

- See Figure A1.8 and Table A1.9

A1.6.3: Group G – Earliest Direct Date by Sampling Grid

- The sites in Group C (section A1.5.3) were placed onto the sampling grid and only the earliest site date per grid square was retained. This resulted in 28 sites, from the 38 sites in the source dataset.

- See Figure A1.9 and Table A1.10

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A1.6.4: Group H – Earliest Pooled Direct Date by Sampling Grid

- The sites in Group D (section A1.5.4) were placed onto the sampling grid and only the earliest site date per grid square was retained. This resulted in 27 sites, from the 37 sites in the source dataset.

- See Figure A1.10 and Table A1.11

A1.7: Additional Groups

As part of the regression analysis of site dates against distance, many other subsamples of these groups were created. However, the description of these subsamples and the procedures in creating them is contained within the body of Chapter 4.

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Figure A1.1: Princess Point AMS Results

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Figure A1.2: Dawson Creek AMS Results

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Table A1.1: Dataset for all sites across North America with Associated Maize and Radiocarbon Dates

Uncal Median Prov / Lab Dating Direct / Date Calibrated Site Name Site Code Country State Number Material method Indirect D13C BP Error Date BP Reference 11h10 AmHp-10 Canada ON I-4009 Charcoal Conventional I 1070 95 995 Smith 1997a Berg and Anderson AfGx-54 Canada ON TO-7033 Kernel AMS D 720 80 671 Bursey 2000 Reid 1975; Smith 1997a; Boys AlGs-10 Canada ON I-7322 Charcoal Conventional I 985 120 900 Williamson 1990 Smith 1997b; Bull's Point AhGx-9 Canada ON TO-6341 Cupule AMS D 960 60 859 Smith et al 1997 Archaeological Services Beta- Incorporated D'Aubigny AgHb-276 Canada ON 217154 Kernel AMS D -28.6 1780 50 951 2006 Campbell and Campbell 1989; Smith 1997a; DeWaele AfHd-1 Canada ON I-6411 Charcoal Conventional I 900 90 848 Williamson 1990 Kernel Ferris 1988; Dick AaHp-1 Canada ON I-13242 AMS D 930 110 1136 Smith 1997a Crawford et al 2006; Murphy and Ferris 1990; Dymock AeHj-2 Canada ON I-12479 Charcoal Conventional I 1030 80 1647 Smith 1997a Bursey and Smith 1997; TO- Crawford and Forster AgGx-134 Canada ON 70392 Cupule AMS D 1150 100 1078 Smith 2002 Smith 1997c, G. BGS- Dibbs, Personal Grafton BaGm-9 Canada ON 1916 unknown ? I 1065 80 987 Communication Crawford and Smith, 1996, 2002, 2003; Crawford, et al. 1997, 2006; Grand Banks AfGx-3 Canada ON TO-5307 Cupule AMS D 1570 90 1468 Smith and

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Crawford 1997 Brink 2008; Head- Meyer and Smashed-In Beta- Starch grain Walde 2009; Buffalo Jump Canada AB 222822 AMS D 1030 40 971 Zarillo 2008 Pihl et al Holmedale AgHb-191 Canada ON TO-6079 Kernel AMS D 1010 70 2053 2008:161 Lakeshore Charcoal Fox 1990; Lodge AlGh-32 Canada ON S-2194 Conventional I 1110 60 4304 Martin 2004 Crawford and Smith 1996; Smith and Lone Pine AfGx-113 Canada ON TO-4586 Kernel AMS D 1040 60 4003 Crawford 1997 Crawford and Smith 2002; TO- Martin 2004; Meyer AfGx-26 Canada ON 81502 Cupule AMS D 1270 100 2353 Saunders 2002) Timmins 1985; Miller AlGs-1 Canada ON S-108 charcoal Conventional I -25 835 70 1184 Williamson 1990 Fox 1990a, 1990b; Moyer Flats AiHc-24 Canada ON I-13078 charcoal Conventional I 1050 80 763 Williamson 1990 Timmins 1985; Wright 1974, Nodwell BcHi-3 Canada ON S-1720 Kernel AMS D -10 1035 80 969 1985 Monckton 1998; WAT- Williamson and Parsons AkGv-8 Canada ON 2871 Kernel AMS D 860 110 754 Robertson 1998 Martin 2004; Williamson and Pottery MacDonald Peace Bridge AfGr-9 Canada ON encrustation AMS D 1330 60 819 1997 Crawford et al. 1997; Noble and Kenyon 1972; Stothers 1977;Williamson Porteous AgHb-1 Canada ON I-4972 Charcoal Conventional I -25 1130 100 784 1990 Kernels and Crane and Stafford AeHg-3 Canada ON M-1553 cobs AMS D -10 735 110 690 Griffin 1965;

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Williamson 1990 WSU- Kernel timmins 1985; Uren AfHd-3 Canada ON 1957 AMS D 830 70 759 Williamson 1990 Noble 1975; Van Besien AfHd-2 Canada ON I-6848 Charcoal Conventional I 1175 140 1098 Williamson 1990 Aquiles Beta- Clark 1994; Serdán Mexico Chiapas 62915 Kernel AMS D 3145 55 3371 Feddema 1993 Cerro NSRL- Hard and Roney Juanaqueña Mexico Chihuahua 12484 Unknown AMS D 3130 55 3354 1998 Beta- Chilo MZ-17 Mexico Chiapas 62919 Kernel AMS D 3080 50 3296 Clark 1994 Sluyter and Coastal Plain Beta- Dominguez Lake Mexico Veracruz 130582 Pollen AMS I 4150 50 4689 2006 Coxcatlán Mexico Puebla AA-3308 Cob AMS D 4090 50 1402 Long et al. 1989 UCIAMS- Rosenswig Cuauhtémoc Mexico Chiapas 7033 Unknown AMS D 3225 15 1703 2009, 2010 Beta- Clark and Lowe El Cerrito Mexico Chiapas 22623 Cob AMS D 2185 95 2185 1980 Benz et al. 2006; AA- Mangelsdorf et El Riego Mexico Puebla 52684 Cob AMS D 3850 240 4272 al. 1967 Benz 2001; Flannery 1986; Kirkby et al, 1986; Piperno Beta- and Flannery Guilá Naquitz Mexico Oaxaca 132511 Cob AMS D 5420 60 1468 2001 Hard and Roney Not Avail. 1998; Matson La Playa Mexico Sonora C Unknown AMS D 3000 50 1358 2003 Laguna CAMS- Goman and Pompal Mexico Veracruz 1770 Pollen AMS I 4250 70 3147 Byrne 1998 Quintana Leyden et al. Lake Cobá Mexico Roo OS-61 Pollen AMS I 3880 70 3195 1998 Lake Watts and Pátzcuaro Mexico Michoacįn QL-1342 Pollen conventional I 3640 80 4792 Bradbury 1982 Paso de la Beta- Blake et al. Amada Mexico Chiapas 62917 Kernel AMS D 3240 55 4207 1995; Clark

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1994 AA- Kennett et al. Pijijiapan SOC05-2 Mexico Chiapas 63354 Phytolith AMS I 5789 44 1002 2010 Jaenicke- Despres and Smith 2006; Romero's Beta- MacNeish 1958; Cave Mexico Tamaulipas 85431 Cob AMS D 3930 50 2981 Smith 1997 Pohl 2001; Pohl AA- et al. 2007; San Andres Mexico Tabasco 38771 Pollen AMS I 6208 47 880 Pope et al. 2001 Blake et al. Beta- 1995; Clark San Carlos Mexico Chiapas 62911 Kernel AMS D 3365 55 3604 1994 Long et al. 1989; Mangelsdorf et San Marcos Mexico Puebla AA-3311 Cob AMS D 4700 110 5434 al. 1967 Jaenicke- Despres and Valenzuela's Beta- Smith 2006; Cave Mexico Tamaulipas 85433 Cob AMS D 3890 60 4318 Smith 1997 Niederberger 1979; Piperno et Zohapilco Mexico Mexico I-4404 Pollen conventional I 4250 110 4795 al. 2007 McConaughy Backstrum 1 36WM453 USA PN DIC-3028 Charcoal Conventional I 1490 60 1383 2008 UGa- King 1999; Bald Eagle 36CN102 USA PN 4753 Charcoal Conventional I 1175 100 1102 Martin 2004 Dick 1965; Ford 1985; Matson 1991; Mertrill et Bat Cave USA NM A-4187 Unknown AMS D 3740 70 3388 al. 2009 Martin 2004; Beta- Stothers and Bear Fort 33SA08 USA OH 22864 Charcoal Conventional I 790 60 720 Abel 2001 Brashler et al. Birch Run Beta- 2000 Stothers et Road 20SA393 USA MI 4517 Charcoal Conventional I 1120 90 1046 al. 1994 Blackwell 36TI58 USA PN Charcoal AMS I 1070 60 989 Miller 1993

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Bridge OWU- Maslowski et al., Blain Village 33RO128 USA OH 247 A Kernel AMS D 645 150 627 1995 Bendremer and Dewar 1994; Beta- Cassedy and Boland NY USA NY 24510 Charcoal Conventional I 1270 90 1187 Webb 1999 Bendremer and Dewar 1994; Cassedy and Bowmans Beta- Webb 1999; Brook NY USA NY 15770 Charcoal Conventional I -26.9 920 70 836 Little 2002 McConaughy Campbell AA- 2008; Hart et al Farm 36FA26 USA PN 40133 Kernel AMS D 795 40 714 2002 King 1999; Martin 2004; Catawissa McConaughy Bridge, str 3 36C09 USA PN PITT-8 Charcoal Conventional I 1455 45 1348 2008 Chama Beta- Vierra and Ford Alcove USA NM 84648 Cob AMS D 1840 50 1776 2006 Chenango Point NY USA NY N/A? Charcoal Conventional I 1000 70 908 Kuhn 1994 Mabry 1998, 2006, 2008; Beta- Thiel and Mabry Clearwater USA AZ 175842 Kernel AMS D -10.9 3690 40 4030 2006 Hard and Roney 1998; Mabry Cortaro Fan USA AZ AA-2782 Kernel AMS D 2790 60 2897 2008 Ezzo and Deaver 1998; Beta- Huckell 2006; Costello-King USA AZ 89859 Cupule AMS D -11.5 2780 60 2884 Mabry 2008 MacDonald and Cremeens 2002, Coverts Beta- McConaughy Crossing 36Lr75 USA PN 142247 Charcoal Conventional I 840 50 4609 2008 Crane USA IL NSRL- Kernel AMS D 1450 350 3432 Conard et al

251

302 1984; Crawford et al. 1997 Creel and Long 1986; Story and Davis 41CE19 USA TX C-153 Cob conventional D 1800 180 1136 Valastro 1977 Deposit Airport I USA NY Kernel AMS D 1210 40 823 Knapp 2009 Stothers and Abel 2002; Beta- Stothers et al. Dillon OH USA OH 30054 Charcoal Conventional I -25.9 1210 70 2580 1994 Eddy and Cooley 1983; Huckell 1995, Donaldson / 2001; Huckell Matty AA- 2006; Mabry Canyon USA AZ 13125 Cupule AMS D 2505 50 774 2008 Buker 1970; Drew 36AL62 USA PN M-2198 Kernel AMS D -10 835 100 946 King 1999 Crawford et al. Edwin 1997; Riley et Harness 33 USA OH N/A Kernel AMS D 1730 85 1551 al. 1994 Crawford et al. 1997; Martin Beta- 2004; Raviele Eidson 20BE122 USA MI 6154 Charcoal Conventional I 1650 70 2051 2010 Mabry 2008; Beta- Wöcherl 2007a, El Taller USA AZ 161854 Cupule AMS D -10.3 3080 50 3296 2007b Huckell 1990; Fairbank USA AZ AA-4457 Kernel AMS D 2815 80 2950 Mabry 2008 NYSM- Felix 1373 USA NY A-0497 Phytolith AMS D -27.2 1575 35 1465 Hart et al 2007 Crawford et al. UGa- Plant 1997; Hatch Fisher Farm 36CE35 USA PN 2683 remains Conventional I 1245 70 1172 1980; King 1999 Beta- - Fletcher 20BY-28 USA MI 17750 Phytolith AMS D 31.99 785 85 727 Lovis et al 1996 NYSM Thompson et al Fortin 2 72699 USA NY A-0406 Phytolith AMS D -29 1525 35 1945 2004

252

Bendremer and Fortin Locus Dewar 1994; 2 NY USA NY DIC-166 Charcoal Conventional I 870 75 795 Hart 1999c Brose 1993; Franks / Mill Beta- Human bone Maslowski et al Hollow 33LN13 USA OH 43133 collagen AMS D -13 1070 50 985 1995 Franktown AA- Cave USA CO 60692 Cob AMS D 892 36 816 Gilmore 2005 Hard and Roney 1998; Mabry Fresnal 2008; Tagg Shelter USA NM AA-6402 Cob AMS D 2945 55 3112 1996 Crawford et al. 1997; Schurr and Redmond Gard Island # Human Bone 1991; Stothers 2 20MR162 USA MI DC-416 collagen Conventional I -19 1440 80 1351 and Abel 2002 Martin 2004; Stothers and Human Bone Abel, 1993, Gladieux 33LU10 USA OH DIC-797 collagen ? I 1330 70 1247 2002; Martin 2004; GaK- McConaughy Gnagey 36SO55 USA PN 5150 Charcoal Conventional I 1030 80 946 2008:20 East et al. 2001; Martin 2004, Harding Flats 36WO55 USA PN ? Kernel AMS D 1060 40 6229 2006 Holding (Cahokia) USA IL AA-8717 Cob AMS D 2077 70 947 Riley et al. 1994 Asch Sidell Beta- 1999; Crawford Hudson River 211-1-1 USA NY 53452 Cupule AMS D 1130 70 922 and Smith 2003 Hart et al. 2003; Martin 2004; Hunter's NYSM Schulenberg Home 1538 USA NY A-0192 Phytolith AMS D -26.7 1231 44 1050 2002 Icehouse Beta- Chapman and Bottom USA TN 16576 Kernel AMS D 1775 100 1163 Crites 1987 Indian Island Stothers and # 3 20MR153 USA MI DIC-413 Charcoal Conventional i 990 80 1698 Abel 2002

253

Crawford et al. Indian Island 1997; Stothers # 4 20MR154 USA MI DIC-414 Charcoal Conventional I 1410 95 894 and Abel 2002 Adams 1994; Alexander and Reiter 1935; Crane and Griffen 1958; Ford 1975; Beta- Vierra and Ford Jemez Cave USA NM 183771 Unknown AMS D 2990 40 1326 2006 Hart et al. 2003; Martin 2004; NYSM Schulenberg Kipp Island 2084 USA NY A-0225 Phytolith AMS D -26.4 1470 43 3181 2002 UGa- LA 18091 USA NM 4179 Unknown conventional D 2965 267 1358 Simmons 1986 Diehl 2005; Hesse 2005; Mabry 1998, Beta- 2005, 2008; Las Capas USA AZ 148409 Kernel AMS D 3670 40 3964 Whittlesey 2009 Martin 2004; Ritchie 1969; NYSM GX- Schulenberg Levanna 2092 USA NY 28193 Phytolith AMS D 1090 40 1026 2002 Huckell 1984, 2001; Mabry Los Ojitos USA AZ A-3500 Kernel AMS D 2170 150 1000 2008 Gregory 1999, 2001; Gregory et al 2007; Mabty 2005, CAMS- 2008; Merrill et Los Pozos USA AZ 34923 Cupule AMS D 4050 50 957 al. 2009 Mabry 2008; CAMS- Merrill et al. McEuen Cave USA AZ 43177 Cob AMS D 3690 50 2169 2009 UCLA- Cutler 1965; McGraw 33RO108 USA OH 679 Charcoal Conventional I 1510 80 3575 Martin 2004;

254

Prufer 1965 Adovasio and Johnson 1981; Adovasio et al. 2003; Hart and Meadowcroft Brumbach 2005; Rockshelter 36WH297 USA PN SI-2051 Charcoal Conventional I 2325 75 4030 Hart et al. 2007; Asch Sidell 2008; Crawford et al. 1997; Hart Memorial PITT- and Asch Sidell Park 36CN164 USA PN 1073 Charcoal Conventional I 1190 40 1410 1996 Hard and Roney 1998, Huckell et AA- al. 1995; Mabry Milagro USA AZ 12055 Kernel AMS D 2930 45 1116 2008 Martin 2004; Morin 20MR40 USA MI M-2087 Charcoal I -25 880 110 3090 Prahl 1974 George 2005; Murphy's Old Beta- Charcoal McConaughy House 39AR129 USA PN 78747 Conventional I 1080 70 812 2008 Beta Rogers et al. Oak Hill 41RK214 USA TX 73939 Unknown AMS D 810 100 1002 1997 Huber 2005, Huber and West Beta- 2005; Miljour Old Corn LA 137258 USA NM 185023 Kernel AMS D 3810 50 951 and Huber 2005 Stothers and Abel 2002; GX- Human Bone Stothers and Patli-Dowling 33FU5 USA OH 10742 collagen ? D -13.4 885 125 795 Bechtel 1987 Beta- Payne 31MR15 USA NC 18410 Cob conventional D 910 60 3464 Mountjoy 1989 Beta- Hart et al. 2005; Petenbrink 36SO62 USA PN 104103 Unknown ? I 1080 70 830 Means 2002 Carskadden and Morton 1996; Philo II 33MU76 USA OH I-7868 Charcoal AMS I 890 80 1251 Martin 2004 Pony Farm Beta- Hart et al. 2005; Triangle East 36SO243 USA PN 97738 Unknown ? I 860 70 815 Means 2002

255

Hart et al. 2005; Hart and Means Beta- 2002; Means Railroad 36SO113 USA PN 104124 Charcoal Conventional I 1150 50 1057 2002; Mabry 2008; Beta- Wöcherl 2007a, Rillito Fan USA AZ 90317 Cupule AMS D 2860 40 1065 2007b Hart 1999b, 2000; Hart et al. 2005; Timmins Roundtop NY USA NY Y-1534 Charcoal Conventional I -25 880 60 4363 1985 Hellman 2003; Wood and Bowen 1995; Beta- Wood and Rush 9FL164 USA GA 22834 Cupule AMS D 1535 105 802 Ledbetter 1990 Cutler and Blake 1973; George 2004; McConaughy Ryan 36WM23 USA PN I-16,727 Charcoal Conventional I 980 80 1444 2008 Huckell 2006; San Luis de AA- Vieera and Ford Cabezon USA NM 34173 Unknown AMS D 3125 45 3352 2006 Martin 2004; WIS- Niquette and Sand Ridge I 33HA17 USA OH 1748 Charcoal Conventional I -26 1080 70 1002 Crites 1993 Lovis et al. 2001; Martin Schultz 20SA2 USA MI M-1648 Charcoal Conventional I -25 770 100 719 2004 Sheep Camp Shelter USA NM A-3396 Kernel AMS D 2290 210 2326 Simmons 1986 Hart et al 2007; NYSM- J. Hart pers Simmons 1507 USA NY A-0452 Phytolith AMS D -28.7 1620 35 1503 comm. Crawford et al. 1997; Stothers 1977; Stothers and Yarnell Sissung 20MR5 USA MI M-1519 Charcoal Conventional I -25 1250 120 1163 1977

256

Bendremer and Smithfield Beta- Dewar 1994; Beach 36MR5 USA PN 21548 Unknown ? I 930 80 843 Herbstritt 1988 Solar Well USA AZ AA-6641 Kernel AMS D 2835 85 2965 Mabry 2008 Brose 1973, Charcoal and 1994; charred Zea Maslowski et al. South Park 33CU8 USA OH WIS-576 mays cob Conventional D -17.4 950 65 854 1995 Beta- King 1999; St Anthony 36UN11 USA PN 22813 Kernel AMS D 950 80 855 Rieth 2002 Street NY USA NY A-0229 Phytolith AMS D -26.1 1043 40 957 Hart et al. 2007 Three Fir Beta- Shelter USA AZ 26275 Cob conventional D 3610 170 3940 Smiley 1994 Tornillo GX- Shelter USA NM 12720 Cob conventional D 3175 240 3396 Blake 2006 Jaenicke- Després and Smith 2006; Jaenicke- Després et al. Tularosa Beta- 2003; Martin et Cave USA NM 166755 Cob AMS D 1920 40 1867 al. 1952 Blake and Benz 2010; Matson 1991; Matson and Chisolm Turkey Pen WSU- 1991; Speller et Cave USA UT 3513 Cob conventional D 2050 80 2024 al. 2010 Twin Mounds West 33HA24 USA OH Unknown ? I 1100 130 1028 Martin 2004 AA- Valley Farms USA AZ 28496 unknown unknown D 3145 50 3372 Wellman 2008 Vinette NY USA NY A-0500 Phytolith AMS D -28.1 2270 35 1941 Hart et al. 2007 Maslowski et al. 1995; Stothers CWRU- Human Bone and Bechtel Waterworks 33LU06 USA OH 36 Collagen AMS I -25 1460 55 1356 1987 Martin 2004; Stothers and Weilnau 33ER409 USA OH I-16454 Charcoal Conventional I 1380 80 1297 Abel 2002

257

West End USA AZ AA-4810 Kernel AMS D 2735 75 2847 Mabry 2008 Hart et al. 2007; Ritchie and Westheimer NY USA NY A-0498 Phytolith AMS D -25.9 1600 35 1475 Funk 1973 Hart et al. 2003; Wickham NY USA NY A-0190 Phytolith AMS D -28.1 1425 45 1329 Martin 2004 Woodrat Beta- Midden CC-2 USA NM 219354 Pollen AMS I 3090 50 3306 Hall 2010 Woodrat Beta- Midden CC-3 USA NM 218109 Pollen AMS I 3940 40 4383 Hall 2010

258

Table A1.2: All Dates associated with maize in the Northeast Before AD 1200

Prov Lab Dating Uncal Date Site Name Site Code / Material 13 Error Reference Number method δ C BP State Charcoal (associated Zea Murphy and Ferris 11h10 AmHp-10 ON I-4009 Conventional 1070 95 mays) 1990; Smith 1997a Berg and Bursey Anderson AfGx-54 ON TO-7033 Zea mays kernel fragment AMS 720 80 2000 Charcoal in Feature 14 Backstrum 1 36WM453 PN DIC-3028 associated with two maize Conventional 1490 60 McConaughy 2008 kernels Charcoal in Feature 14 Backstrum 1 36WM453 PN DIC-3059 associated with two maize Conventional 1260 50 McConaughy 2008 kernels UGa- Charcoal (associated with King 1999; Martin Bald Eagle 36CN102 PN Conventional 1175 100 4753 Zea mays kernel) 2004 UGa- Charcoal (associated with King 1999; Martin Bald Eagle 36CN102 PN Conventional 1040 85 4754 Zea mays kernel) 2004 Charcoal from wall trench Martin 2004; Beta- Bear Fort 33SA08 OH (associated with maize Conventional 790 60 Stothers and Abel 22864 kernel) 2001 Charcoal (associated with Birch Run Beta- Brashler et al. 2000 20SA393 MI Human Bone and Zea mays Conventional 1120 90 Road 4517 Stothers et al. 1994 kernel) Blackwell Charcoal (associated with 36TI58 PN N/A AMS 1070 60 Miller 1993 Bridge maize kernel in feature) Blackwell Charcoal (associated with 36TI58 PN N/A AMS 1065 75 Miller 1993 Bridge maize kernel in feature) Blackwell 36TI58 PN N/A Zea mays kernel AMS 1040 65 Miller 1993 Bridge OWU- Maslowski et al. Blain Village 33RO128 OH charred Zea mays kernels AMS 645 150 247 A 1995 Bendremer and Beta- Charcoal (associated with Dewar 1994; Boland NY NY Conventional 1270 90 24510 Zea mays kernel) Cassedy and Webb 1999 Beta- Charcoal (associated with Bendremer and Boland NY NY Conventional 940 80 21533 Zea mays kernel) Dewar 1994;

259

Cassedy and Webb 1999 Bendremer and Dewar 1994; Bowmans Beta- Charcoal (associated with NY NY Conventional -26.9 920 70 Cassedy and Brook 15770 Zea mays kernel) Webb 1999; Little 2002 Reid 1975; Smith Charcoal (associated Zea Boys AlGs-10 ON I-7322 Conventional 985 120 1997a; Williamson mays) 1990 Reid 1975; Smith Charcoal (associated Zea Boys AlGs-10 ON I-n/a-2 Conventional 725 95 1997a; Williamson mays) 1990 plant remains (Zea mays Smith 1997b; Bull's Point AhGx-9 ON TO-6341 AMS 960 60 Cupule) Smith et al 1997 Campbell AA- McConaughy 2008; 36FA26 PN Zea mays kernel AMS 795 40 Farm 40133 Hart et al 2002 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN PITT-8 Conventional 1455 45 2004; McConaughy Bridge, str3 Zea mays kernel) 2008 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN PITT-76 Conventional 1280 35 2004; McConaughy Bridge, str3 Zea mays kernel) 2008 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN PITT-11 Conventional 1245 70 2004; McConaughy Bridge, str3 Zea mays kernel) 2008 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN PITT-12 Conventional 1040 45 2004; McConaughy Bridge, str4 Zea mays kernel) 2008 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN DIC-3151 Conventional 1030 70 2004; McConaughy Bridge, str4 Zea mays kernel) 2008 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN PITT-74 Conventional 995 55 2004; McConaughy Bridge, str4 Zea mays kernel) 2008 King 1999; Martin Catawissa Charcoal (associated with 36C09 PN PITT-77 Conventional 795 75 2004; McConaughy Bridge, str4 Zea mays kernel) 2008 Chenango Charcoal (associated with NY NY N/A? Conventional 1000 70 Kuhn 1994 Point Zea mays kernel)

260

MacDonald and Coverts Beta- Charcoal (associated with 36Lr75 PN Conventional 840 50 Cremeens 2002, Crossing 142247 Zea mays kernel) McConaughy 2008 Archaeological Beta- D'Aubigny AgHb-276 ON Zea mays kernel AMS -28.6 1780 50 Services 217154 Incorporated 2006 Deposit NY N/A Zea mays kernel AMS 1210 40 Knapp 2009 Airport I Campbell and Charcoal (associated Zea Campbell 1989; DeWaele AfHd-1 ON I-6411 Conventional 900 90 mays) Smith 1997a; Williamson 1990 Campbell and Charcoal (associated Zea Campbell 1989; DeWaele AfHd-1 ON I-6412 Conventional 855 90 mays) Smith 1997a; Williamson 1990 Ferris 1988; Smith Dick AaHp-1 ON I-13242 Zea mays kernel AMS 930 110 1997a Charcoal associated with Stothers and Abel Beta- sucker cob and small grit Dillon OH OH Conventional -25.9 1210 70 2002; Stothers et 30054 tempered Vase Dentate rim al. 1994 (Stothers and Abel 2002:81) Buker 1970; King Drew 36AL62 PN M-2198 charred Zea mays kernels AMS -10 835 100 1999 Crawford et al Charcoal (taken from feature 2006; Murphy and Dymock AeHj-2 ON I-12479 63-3 which had 0.06 g of Conventional 1030 80 Ferris 1990; Smith Zea mays kernel recovered) 1997a Crawford et al Charcoal (taken from feature 2006; Murphy and Dymock AeHj-2 ON I-12149 12 which had 0.08 g of Zea Conventional 945 80 Ferris 1990; Smith mays kernel recovered) 1997a Crawford et al Charcoal (taken from feature 2006; Murphy and Dymock AeHj-2 ON I-12478 28 - 1 which had 0.65 g of Conventional 940 80 Ferris 1990; Smith Zea mays kernel recovered) 1997a Crawford et al Charcoal (taken from feature 2006; Murphy and Dymock AeHj-2 ON I-12150 29-2 which had 137 maize Conventional 910 80 Ferris 1990; Smith kernels recovered) 1997a

261

Crawford et al Charcoal (associated Zea 2006; Murphy and Dymock AeHj-2 ON I-12152 Conventional 890 80 mays) Ferris 1990; Smith 1997a Crawford et al Charcoal (associated Zea 2006; Murphy and Dymock AeHj-2 ON I-12151 Conventional 850 80 mays) Ferris 1990; Smith 1997a Crawford et al. Edwin 33 OH N/A Zea mays kernel AMS 1730 85 1997; Riley et al. Harness 1994 Crawford et al. Edwin 33 OH N/A Zea mays kernel AMS 1720 105 1997; Riley et al. Harness 1994 Charcoal (associated with Crawford et al. Beta- Eidson 20BE122 MI Zea mays cupule in feature Conventional 1650 70 1997; Martin 2004; 6154 #306) Raviele 2010 NYSM- Felix NY A-0497 Phytolith on potsherd AMS -27.2 1575 35 Hart et al 2007 1373 NYSM- Felix NY A-0503 Phytolith on potsherd AMS -27.9 1525 40 Hart et al 2007 1373 NYSM- Felix NY A-0499 Phytolith on potsherd AMS -36.8 1430 40 Hart et al 2007 1373 NYSM- Felix NY A-0506 Phytolith on potsherd AMS -26.3 1315 50 Hart et al 2007 1373 Crawford et al. UGa- Plant remains (associated Fisher Farm 36CE35 PN Conventional 1245 70 1997; Hatch 1980; 2683 with Zea mays) King 1999 Beta- Fletcher 20BY-28 MI Pottery encrustation AMS -31.99 785 85 Lovis et al 1996 17750 Bursey and Smith TO- Forster AgGx-134 ON Zea mays cupule AMS 1150 100 1997; Crawford 70392 and Smith 2002 Bursey and Smith plant remains (Zea mays Forster AgGx-134 ON TO-6343 AMS 840 90 1997; Crawford Cupule) and Smith 2002 NYSM Thompson et al Fortin 2 NY A-0410 Phytolith on potsherd AMS 1995 35 72699 2004 NYSM Thompson et al Fortin 2 NY A-0406 Phytolith on potsherd AMS -29 1525 35 72699 2004

262

NYSM Hart and Brumbach Fortin 2 NY A-0407 Phytolith on potsherd AMS 29 1505 35 72699 2005 Bendremer and Fortin Locus NY NY DIC-166 Charcoal Conventional 870 75 Dewar 1994; Hart 2 1999c Brose 1993; Franks / Mill Beta- human bone collagen 33LN13 OH AMS -13 1070 50 Maslowski et al Hollow 43133 associated with maize 1995 Crawford et al. 1997; Schurr and Gard Island # Human Bone collagen 20MR162 MI DC-416 Conventional -19 1440 80 Redmond 1991; 2 (Associated with Zea mays) Stothers and Abel 2002 Crawford et al. 1997; Schurr and Gard Island # Human Bone collagen 20MR162 MI DC-419 Conventional -19 1220 125 Redmond 1991; 2 (Associated with Zea mays) Stothers and Abel 2002 Crawford et al. 1997; Schurr and Gard Island # Human Bone collagen 20MR162 MI DC-418 Conventional -19 1200 75 Redmond 1991; 2 (Associated with Zea mays) Stothers and Abel 2002 Martin 2004; bone associated with maize Gladieux 33LU10 OH DIC-797 ? 1330 70 Stothers and Abel, (Martin 2009) 1993, 2002; Martin 2004; bone associated with maize Gladieux 33LU10 OH DIC-428 ? 1210 75 Stothers and Abel, (Martin 2009) 1993, 2002; Martin 2004; bone associated with maize Gladieux 33LU10 OH DIC-429 ? 1080 60 Stothers and Abel, (Martin 2009) 1993, 2002; Martin 2004; GaK- associated Charcoal from Gnagey 36SO55 PN Conventional 1030 80 McConaughy 5150 features 13D 2008:20 Martin 2004; UGa- associated Charcoal from Gnagey 36SO55 PN Conventional 920 80 McConaughy 1599 features 13D 2008:20 BGS- Unknown (but associated Smith 1997, G. Grafton BaGm-9 ON Conventional 1065 80 1846 with Zea mays) Dibbs, Personal

263

Communication Smith 1997, G. BGS- Unknown (but associated Grafton BaGm-9 ON Conventional 1030 80 Dibbs, Personal 1844 with Zea mays) Communication Smith 1997, G. BGS- Unknown (but associated Grafton BaGm-9 ON Conventional 970 110 Dibbs, Personal 1845 with Zea mays) Communication Smith 1997, G. BGS- Unknown (but associated Grafton BaGm-9 ON Conventional 935 85 Dibbs, Personal 1843 with Zea mays) Communication Crawford and Smith 1996, 2002, 2003; Crawford et Grand Banks AfGx-3 ON TO-5307 Zea mays cupule AMS 1570 90 al. 1997, 2006; Smith and Crawford 1997 Crawford and Smith 1996, 2002, 2003; Crawford et Grand Banks AfGx-3 ON TO-5308 Zea mays cupule AMS 1500 150 al. 1997, 2006; Smith and Crawford 1997 Crawford and Smith 1996, 2002, 2003; Crawford et Grand Banks AfGx-3 ON TO-4585 Zea mays kernel AMS 1250 80 al. 1997, 2006; Smith and Crawford 1997 Crawford and Smith 1996, 2002, 2003; Crawford et Grand Banks AfGx-3 ON TO-4584 Zea mays kernel AMS 1060 60 al. 1997, 2006; Smith and Crawford 1997 Crawford and Smith 1996, 2002, 2003; Crawford et Grand Banks AfGx-3 ON TO-5875 Zea mays cupule AMS 970 50 al. 1997, 2006; Smith and Crawford 1997

264

East et al. 2001; Harding Flats 36WO55 PN N/A Zea mays kernel AMS 1060 40 Martin 2004, 2006 Holmedale AgHb-191 ON TO-6079 Carbonized Zea mays kernel AMS 1010 70 Pihl et al 2008:161 Asch Sidell 1999; Beta- Hudson River 211-1-1 NY Zea mays cupules AMS 1130 70 Crawford and 53452 Smith 2003 Asch Sidell 1999; Beta- Hudson River 211-1-1 NY Zea mays cupules AMS 1090 60 Crawford and 53451 Smith 2003 Asch Sidell 1999; Beta- plant remains (Zea mays Hudson River 211-1-1 NY AMS 1050 50 Crawford and 84969 kernel) Smith 2003 Asch Sidell 1999; Beta- plant remains (Zea mays Hudson River 211-1-1 NY AMS 850 60 Crawford and 84970 kernel) Smith 2003 Charred residue extracted from cooking pot (pottery Hart et al. 2003; Hunter's NYSM NY A-0192 type: Owasco Corded AMS -26.7 1231 44 Martin 2004; Home 1538 Horizontal) (Hart et al. Schulenberg 2002 2003:624). Charred residue extracted from cooking pot (pottery Hart et al. 2003; Hunter's NYSM NY A-0198 type: Owasco Corded AMS -27.8 1211 46 Martin 2004; Home 1538 Horizontal) (Hart et al. Schulenberg 2002 2003:624). Hart et al. 2003; Hunter's NYSM NY A-0196 Phytolith on potsherd AMS -24.9 1138 40 Martin 2004; Home 1538 Schulenberg 2002 Crawford et al. Indian Island Charcoal (associated with 3 20MR153 MI DIC-413 Conventional 990 80 1997; Stothers and # 3 Zea mays kernels) Abel 2002 Crawford et al. Indian Island Charcoal (associated with 20MR154 MI DIC-414 Conventional 1410 95 1997; Stothers and # 4 Zea mays kernel) Abel 2002 Crawford et al. Indian Island Charcoal (associated with 20MR154 MI DIC-415 Conventional 1360 55 1997; Stothers and # 4 Zea mays kernel) Abel 2002 Crawford et al. Indian Island 20MR154 MI DIC-300 Charcoal Conventional 1300 75 1997; Stothers and # 4 Abel 2002

265

Crawford et al. Indian Island Charcoal (associated with 20MR154 MI DIC-301 Conventional 1110 85 1997; Stothers and # 4 Zea mays kernel) Abel 2002 Charred residue extracted Hart et al. 2003; NYSM from cooking pot (pottery Kipp Island NY A-0225 AMS -26.4 1470 43 Martin 2004; 2084 type:Jacks Reef Corded) Schulenberg 2002 (Hart et al. 2003:624). Charred residue extracted Hart et al. 2003; NYSM from cooking pot (pottery Kipp Island NY A-0227 AMS -27 1428 41 Martin 2004; 2084 type:Jacks Reef Corded) Schulenberg 2002 (Hart et al. 2003:624). Lakeshore Fox 1990; Martin AlGh-32 ON S-2194 associated Charcoal Conventional 1110 60 Lodge 2004 Martin 2004; NYSM GX- residue on Levanna cord-on- Levanna NY AMS 1090 40 Ritchie 1969; 2092 28193 cord potsherd Schulenberg 2002 Crawford and Lone Pine AfGx-113 ON TO-4586 Zea mays Kernel AMS 1040 60 Smith 1996; Smith and Crawford 1997 Crawford and Lone Pine AfGx-113 ON TO-4083 Zea mays Cupule AMS 800 50 Smith 1996; Smith and Crawford 1997 UCLA- Cutler 1965; Martin McGraw 33RO108 OH Charcoal Conventional 1510 80 679 2004; Prufer 1965 Cutler 1965; Martin McGraw 33RO108 OH OWU-61 Charcoal Conventional -25 1470 65 2004; Prufer 1965 Adovasio and Johnson 1981; Adovasio et al. Charcoal associated with Meadowcroft 2003; Hart and 36WH297 PN SI-2051 Stratum IV and 16 row Conventional 2325 75 Rockshelter Brumbach 2005; popcorn Hart et al. 2007; Thompson et al. 2004 Adovasio and Johnson 1981; Charcoal associated with Meadowcroft Adovasio et al. 36WH297 PN SI-1674 Stratum IV and 16 row Conventional 2290 90 Rockshelter 2003; Hart and popcorn Brumbach 2005; Hart et al. 2007;

266

Thompson et al. 2004 Adovasio and Johnson 1981; Adovasio et al. Charcoal associated with Meadowcroft 2003; Hart and 36WH297 PN SI-2487 Stratum V and ten, twelve, Conventional 2155 65 Rockshelter Brumbach 2005; and fourteen-row corn Hart et al. 2007; Thompson et al. 2004 Adovasio and Johnson 1981; Adovasio et al. Charcoal associated with Meadowcroft 2003; Hart and 36WH297 PN SI-2362 Stratum V and ten, twelve, Conventional 2075 125 Rockshelter Brumbach 2005; and fourteen-row corn Hart et al. 2007; Thompson et al. 2004 Asch Sidell 2008; Memorial PITT- Charcoal (associated with Crawford et al. 36CN164 PN Conventional 1190 40 Park 1073 Zea mays kernel) 1997; Hart and Asch Sidell 1996 Asch Sidell 2008; Memorial AA- Crawford et al. 36CN164 PN Zea mays cupule AMS -10.6 985 45 Park 19127 1997; Hart and Asch Sidell 1996 Asch Sidell 2008; Memorial Beta- Charcoal (associated with Crawford et al. 36CN164 PN Conventional 870 50 Park 46544 Zea mays kernel) 1997; Hart and Asch Sidell 1996 Crawford and TO- Smith 2002; Martin Meyer AfGx-26 ON Zea mays cupule AMS 1270 100 81502 2004; Saunders 2002) Charcoal (although, according to Noble 1973, Timmins 1985; Miller AlGs-1 ON S-108 Conventional -25 835 70 this site produced evidence Williamson 1990 of 8-row northern flint) Charcoal (associated with Martin 2004; Prahl Morin 20MR40 MI M-2087 maize and vase dentate Conventional -25 880 110 1974 ware [believed to date to

267

around 700 AD]) Fox 1990a, 1990b; Moyer Flats AiHc-24 ON I-13078 Charcoal Conventional 1050 80 Williamson 1990 Murphy's Old Beta- Charcoal (associated with George 2005; 39AR129 PN Conventional 1080 70 House 78747 Zea mays) McConaughy 2008 Timmins 1985; Nodwell BcHi-3 ON S-1720 Carbonized Zea mays kernel AMS -10 1035 80 Wright 1974, 1985 Monckton 1998; WAT- Parsons AkGv-8 ON Carbonized Zea mays kernel AMS 860 110 Williamson and 2871 Robertson 1998 Stothers and Abel GX- Bone Collagen associated Patli-Dowling 33FU5 OH ? -13.4 885 125 2002; Stothers and 10742 with 3 kernels maize Bechtel 1987 Martin 2004; charred cooking residue Peace Bridge AfGr-9 ON N/A AMS 1330 60 Williamson and from a conoidal vessel MacDonald 1997 Beta- Unknown (but associated Hart et al. 2005; Petenbrink 36SO62 PN ? 1080 70 104103 with Zea mays) Means 2002 Beta- Unknown (but associated Hart et al. 2005; Petenbrink 36SO62 PN ? 1010 60 77677 with Zea mays) Means 2002 Charcoal (from pit C-28, Carskadden and which there was a single Philo II 33MU76 OH I-7868 AMS 890 80 Morton 1996; maize kernel found in direct Martin 2004 association) Charcoal (from pit C-28, Carskadden and which there was a single Philo II 33MU76 OH I-14101 AMS 880 80 Morton 1996; maize kernel found in direct Martin 2004 association) Pony Farm Beta- Unknown (but associated Hart et al. 2005; 36SO243 PN Conventional 860 70 Triangle East 97738 with Zea mays) Means 2002 Pony Farm Beta- Unknown (but associated Hart et al. 2005; 36SO243 PN Conventional 770 60 Triangle East 97737 with Zea mays) Means 2002 Crawford et al. 1997; Noble and Charcoal (associated Zea Kenyon 1972; Porteous AgHb-1 ON I-5820 Conventional 1370 90 mays) Stothers 1977; Timmins 1997; Williamson 1990 Charcoal (associated Zea Crawford et al. Porteous AgHb-1 ON I-4972 Conventional -25 1130 100 mays) 1997; Noble and

268

Kenyon 1972; Stothers 1977; Timmins 1997; Williamson 1990 Beta- Charcoal associated with Hart and Means Railroad 36SO113 PN Conventional 1150 50 104124 Zea mays 2002 Hart et al. 2005; Beta- Unknown (but associated Railroad 36SO113 PN Conventional 970 80 Hart and Means 104134 with Zea mays) 2002; Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 960 60 104120 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 960 70 104131 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 900 60 104122 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 880 80 77680 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 860 80 104119 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 850 80 104135 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 840 80 104123 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 830 80 104130 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 820 80 104118 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 780 80 104121 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 770 80 104129 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 760 60 104128 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 730 50 92578 with Zea mays) Means 2002; Beta- Unknown (but associated Hart et al. 2005; Railroad 36SO113 PN Conventional 730 50 104125 with Zea mays) Means 2002; Hart 1999b, 2000; Charcoal (associated with Roundtop NY NY Y-1534 Conventional -25 880 60 Hart et al. 2005; Zea mays kernel) Timmins 1985

269

Hart 1999b, 2000; AA- Roundtop NY NY Zea mays AMS 830 45 Hart et al. 2005; 26541 Timmins 1985 Cutler and Blake Charcoal (associated with 1973; George Ryan 36WM23 PN I-16,727 Conventional 980 80 Zea mays) 2004; McConaughy 2008 Cutler and Blake GaK- Charcoal (associated with 1973; George Ryan 36WM23 PN Conventional 830 80 3729 Zea mays) 2004; McConaughy 2008 Martin 2004; WIS- Sand Ridge I 33HA17 OH Charcoal Conventional -26 1080 70 Niquette and Crites 1748 1993 Lovis et al. 2001; Schultz 20SA2 MI M-1648 Charcoal Conventional -25 770 100 Martin 2004 NYSM- Hart et al 2007; J. Simmons NY A-0452 Phytolith on potsherd AMS -28.7 1620 35 1507 Hart pers comm. NYSM- Hart et al 2007; J. Simmons NY A-0501 Phytolith on potsherd AMS -29.7 1390 35 1507 Hart pers comm. Crawford et al. Charcoal (associated with 1997; Stothers Sissung 20MR5 MI M-1519 Conventional -25 1250 120 Zea mays kernel) 1977; Stothers and Yarnell 1977 Bendremer and Smithfield Beta- Unknown (but associated 36MR5 PN Conventional 930 80 Dewar 1994; Beach 21548 with Zea mays) Herbstritt 1988 Bendremer and Smithfield Beta- Unknown (but associated 36MR5 PN Conventional 890 60 Dewar 1994; Beach 15573 with Zea mays) Herbstritt 1988 Brose 1973, 1994; Charcoal and charred Zea South Park 33CU8 OH WIS-576 Conventional -17.4 950 65 Maslowski et al. mays cob 1995 Beta- King 1999; Rieth St Anthony 36UN11 PN Zea mays kernel AMS 950 80 22813 2002 Crane and Griffin, 1965; Dodd, et al. Stafford AeHg-3 ON M-1553 Zea mays kernels and cobs AMS -10 735 110 1990; Fox, 1978; McAllister, 1962; Williamson, 1990;

270

Wright, 1966b Hart, Brumbach, Street NY NY A-0229 Phytolith on potsherd AMS -26.1 1043 40 and Lustek 2007; Wellman 1993:123 Martin 2004(Ford Twin Mounds Unknown (but associated 33HA24 OH N/A unknown 1100 130 1979; Hawkins West with Zea mays) 1996) Timmins 1985; Uren AfHd-3 ON DIC-905 Zea mays kernels AMS 1390 75 Williamson 1990 WSU- Timmins 1985; Uren AfHd-3 ON Zea mays kernels AMS 830 70 1957 Williamson 1990 Charcoal (associated Zea Noble 1975; Van Besien AfHd-2 ON I-6848 Conventional 1175 140 mays) Williamson 1990 Charcoal (associated Zea Noble 1975; Van Besien AfHd-2 ON I-6847 Conventional 1010 90 mays) Williamson 1990 Charcoal (associated Zea Noble 1975; Van Besien AfHd-2 ON I-6167 Conventional 1005 90 mays) Williamson 1990 Vinette NY NY A-0500 Phytolith on potsherd AMS -28.1 2270 35 Hart et al. 2007 Vinette NY NY A-0455 Phytolith on potsherd AMS -29.8 1990 40 Hart et al. 2007 Vinette NY NY A-0452 Phytolith on potsherd AMS -29.3 1940 35 Hart et al. 2007 Maslowski et al. CWRU- Waterworks 33LU06 OH Human Bone Collagen AMS -25 1460 55 1995; Stothers and 36 Bechtel 1987 Maslowski et al. CWRU- Waterworks 33LU06 OH Human Bone Collagen AMS -25 1460 55 1995; Stothers and 48 Bechtel 1987 Martin 2004; Charcoal (associated Zea Weilnau 33ER409 OH I-16454 Conventional 1380 80 Stothers and Abel mays) 2002 Hart et al. 2007; Westheimer NY NY A-0498 Phytolith on potsherd AMS -25.9 1600 35 Ritchie and Funk 1973 Hart et al. 2003; Wickham NY NY A-0190 Phytolith on potsherd AMS -28.1 1425 45 Martin 2004 Hart et al. 2003; Wickham NY NY A-0191 Phytolith on potsherd AMS -25.8 1228 42 Martin 2004

271

Table A1.3: Pooling Results for Full Northeastern Dataset – All Dates

Uncal Pooled Pooled Average x2 x2 Site Name Material Error Pooled? T Statistic df Date BP Mean Variance Error (0.05) (0.025) 11h10 Charcoal 1070 95 n Anderson Zea mays 720 80 n Backstrum 1 Charcoal 1490 60 n 8.67 3.84 5.02 1 Backstrum 1 Charcoal 1260 50 n 8.67 3.84 5.02 1 Bald Eagle Charcoal 1175 100 y 1095 65 93 1.06 3.84 5.02 1 Bald Eagle Charcoal 1040 85 y Barrie Zea mays 530 40 n Bear Fort Charcoal 790 60 n Birch Run Road Charcoal 1120 90 n Blackwell Bridge Charcoal 1070 60 y 1060 38 70 0.13 5.99 7.38 2 Blackwell Bridge Charcoal 1065 75 y Blackwell Bridge Zea mays 1040 65 y Blackwell Bridge Zea mays 660 70 n Blain Village Zea mays 645 150 n Bloody Hill Charcoal 530 80 n Boland Charcoal 1270 90 n 7.51 3.84 5.02 1 Boland Charcoal 940 80 n Bowmans Brook Charcoal 920 70 n 11.31 3.84 5.02 1 Bowmans Brook Zea mays 610 60 n Boys Charcoal 985 120 y 825 74 107 2.88 3.84 5.02 1 Boys Charcoal 725 95 y Briggs Run Zea mays 635 45 n Bull's Point Zea mays 960 60 n Campbell Farm Zea mays 795 40 n Catawissa Bridge, Charcoal 1455 45 n str3

272

Catawissa Bridge, Charcoal 1280 35 y 1275 31 53 0.20 3.85 5.02 1 str3 Catawissa Bridge, Charcoal 1245 70 y str3 Catawissa Bridge, Charcoal 1040 45 y 990 29 61 8.33 7.81 9.35 3 str4 Catawissa Bridge, Charcoal 1030 70 y str4 Catawissa Bridge, Charcoal 995 55 y str4 Catawissa Bridge, Charcoal 795 75 y str4 Cayadutta Zea mays 557 58 y 485 40 57 3.10 3.84 5.02 1 Cayadutta Zea mays 415 56 y Chenango Point Charcoal 1000 70 n Coverts Crossing Charcoal 840 50 n Cromwell 1 Zea mays 488 52 n D'Aubigny Zea mays 1780 50 n Dawson creek Zea mays 350 30 n Dawson creek Zea mays 240 30 n Dawson creek Zea mays 190 30 n Deposit Airport I Zea mays 1210 40 n DeWaele Charcoal 900 90 y 880 64 90 0.13 3.84 5.02 1 DeWaele Charcoal 855 90 y Dick Zea mays 930 110 n Dillon Charcoal 1210 70 n Drew Zea mays 835 100 n Dymock Charcoal 1030 80 y 930 32 80 2.92 11.10 12.83 5 Dymock Charcoal 945 80 y Dymock Charcoal 940 80 y Dymock Charcoal 910 80 y

273

Dymock Charcoal 890 80 y Dymock Charcoal 850 80 y Edwin Harness Zea mays 1730 85 y 1730 66 95 0.01 3.84 5.02 1 Edwin Harness Zea mays 1720 105 y Eidson Charcoal 1650 70 n Elwood Zea mays 409 49 n Pottery Felix 1575 35 y 1555 26 35 0.89 3.84 5.02 1 encrustation Pottery Felix 1525 40 y encrustation Pottery Felix 1430 40 y 1385 31 45 3.23 3.84 5.02 1 encrustation Pottery Felix 1315 50 y encrustation Fisher Farm Floral remains 1245 70 n Pottery Fletcher 785 85 n encrustation Forster Zea mays 1150 100 n 980 67 95 5.04 3.84 5.02 1 Forster Zea mays 840 90 n Pottery Fortin 2 1995 35 n encrustation Pottery Fortin 2 1525 35 y 1515 25 35 0.16 3.84 5.02 1 encrustation Pottery Fortin 2 1505 35 y encrustation Fortin Locus 2 Charcoal 870 75 n Franks / Mill Hollow Bone Collagen 1070 50 n Furnace Brook Charcoal 650 60 n Gard Island # 2 Bone Collagen 1440 80 y 1295 50 93 5.25 5.99 7.38 2 Gard Island # 2 Bone Collagen 1220 125 y Gard Island # 2 Bone Collagen 1200 75 y Garoga Zea mays 585 40 n

274

Garoga Zea mays 430 49 y 425 31 45 0.01 3.84 5.02 1 Pottery Garoga 425 40 y encrustation Getman Charcoal 560 75 n Gladieux Bone Collagen 1330 70 y 1275 51 72 1.37 3.84 5.02 1 Gladieux Bone Collagen 1210 75 y Gladieux Bone Collagen 1080 60 n 7.42 5.99 7.38 2 Gnagey Charcoal 1030 80 y 975 56 80 0.95 3.84 5.02 1 Gnagey Charcoal 920 80 y Grafton Unknown 1065 80 y 1005 43 90 1.44 7.81 9.35 3 Grafton Unknown 1030 80 y Grafton Unknown 970 110 y Grafton Unknown 935 85 y Grand Banks Zea mays 1570 90 y 1550 77 120 0.16 3.84 5.02 1 Grand Banks Zea mays 1500 150 y Grand Banks Zea mays 1250 80 y 1130 56 70 3.61 3.84 5.02 1 Grand Banks Zea mays 1060 60 y Grand Banks Zea mays 970 50 n Harding Flats Zea mays 1060 40 n Hibou Zea mays 730 50 y 660 35 50 3.92 3.84 5.02 1 Hibou Zea mays 590 50 y Holmedale Zea mays 1010 70 n Hudson River Zea mays 1130 70 y 1080 33 60 0.90 5.99 7.38 2 Hudson River Zea mays 1090 60 y Hudson River Zea mays 1050 50 y Hudson River Zea mays 850 60 n Hudson River Zea mays 390 50 n Pottery Hunter's Home 1231 44 y 1190 25 43 2.77 5.99 7.38 2 encrustation

275

Pottery Hunter's Home 1211 46 y encrustation Pottery Hunter's Home 1138 40 y encrustation Indian Island # 3 Charcoal 990 80 n Indian Island # 4 Charcoal 1410 95 y 1285 43 88 6.63 7.81 9.35 3 Indian Island # 4 Charcoal 1360 95 y Indian Island # 4 Charcoal 1300 75 y Indian Island # 4 Charcoal 1110 85 y Kelso Charcoal 560 100 n Pottery Kipp Island 1470 43 y 1450 30 42 0.50 3.84 5.02 1 encrustation Pottery Kipp Island 1428 41 y encrustation Klock Zea mays 520 75 y 490 35 58 0.22 3.84 5.02 1 Pottery Klock 480 40 y encrustation Lakeshore Lodge Charcoal 1110 60 n Lawson Zea mays 445 110 n Pottery Levanna 1090 40 n encrustation Lone Pine Zea mays 1040 60 n 9.44 3.84 5.02 1 Lone Pine Zea mays 800 50 n McGraw Charcoal 1510 80 y 1485 50 73 0.15 3.84 5.02 1 McGraw Charcoal 1470 65 y Meadowcroft Charcoal 2325 75 y 2225 41 88 4.90 7.81 9.35 3 Rockshelter Meadowcroft Charcoal 2290 90 y Rockshelter Meadowcroft Charcoal 2155 65 y Rockshelter Meadowcroft Charcoal 2075 125 y Rockshelter

276

Memorial Park Charcoal 1190 40 n 27.12 5.99 7.38 2 Memorial Park Zea mays 985 45 n Memorial Park Charcoal 870 50 n Memorial Park Zea mays 420 40 n Meyer Zea mays 1270 100 n Miller Charcoal 835 70 n Morin Charcoal 880 110 n Charcoal and Morrison charred Zea 400 115 n mays Moyer Flats Charcoal 1050 80 n Murphy's Old House Charcoal 1080 70 n Nahrwold Charcoal 640 95 y 560 61 88 1.27 3.84 5.02 1 Nahrwold Charcoal 500 80 y Nodwell Zea mays 1035 80 n Nodwell Zea mays 335 70 n Otstungo Zea mays 415 50 y 415 35 50 0.01 3.84 5.02 1 Otstungo Zea mays 410 50 y Parsons Zea mays 860 110 y 625 45 95 5.54 5.99 7.38 2 Parsons Zea mays 586 80 y Parsons Charcoal 569 65 y Patli-Dowling Bone Collagen 885 125 n Pottery Peace Bridge 1330 60 n encrustation Petenbrink Unknown 1080 70 y 1040 45 65 0.58 3.84 5.02 1 Petenbrink Unknown 1010 60 y Philo II Charcoal 890 80 y 885 57 80 0.01 3.84 5.02 1 Philo II Charcoal 880 80 y Pleasant Hill Charcoal 595 90 n

277

Pony Farm Triangle Unknown 860 70 y 810 46 65 0.95 3.84 5.02 1 East Pony Farm Triangle Unknown 770 60 y East Porteous Charcoal 1370 90 y 1265 67 95 3.18 3.84 5.02 1 Porteous Charcoal 1130 100 y Princess Point Zea mays 620 30 n 13.86 7.81 9.35 3 Princess Point Zea mays 600 30 n Princess Point Zea mays 530 30 n Princess Point Zea mays 480 30 n Railroad Charcoal 1150 50 y 1035 31 65 8.66 7.81 9.35 3 Railroad Unknown 970 80 n 140.50 30.10 19 Railroad Unknown 960 70 n Railroad Unknown 960 60 n Railroad Unknown 900 60 n Railroad Unknown 880 80 n Railroad Unknown 860 80 n Railroad Unknown 850 80 n Railroad Unknown 840 80 n Railroad Unknown 830 80 n Railroad Unknown 820 80 n Railroad Unknown 780 80 n Railroad Unknown 770 80 n Railroad Unknown 760 60 n Railroad Unknown 730 50 n Railroad Unknown 730 50 n Railroad Unknown 650 60 n Railroad Unknown 550 90 n Railroad Unknown 550 60 n

278

Railroad Unknown 510 50 n Roundtop Charcoal 880 60 y 850 36 53 0.44 3.84 5.02 1 Roundtop Zea mays 830 45 y Roundtop Zea mays 675 55 n Roundtop Zea mays 440 45 n Roundtop Zea mays 330 45 n Ryan Charcoal 980 80 y 905 56 80 1.76 3.84 5.02 1 Ryan Charcoal 830 80 y Sand Ridge I Charcoal 1080 70 n Schultz Charcoal 770 100 n Sebonac Zea mays 555 85 n Pottery Simmons 1620 35 n 21.59 3.84 5.02 1 encrustation Pottery Simmons 1390 35 n encrustation Sissung Charcoal 1250 120 n Smithfield Beach Unknown 930 80 y Smithfield Beach Unknown 890 60 y 905 48 70 0.16 3.84 5.02 1 Smithfield Beach Unknown 670 70 y 620 41 76 0.85 5.99 7.38 2 Smithfield Beach Unknown 590 100 y Smithfield Beach Unknown 590 60 y Smith-Pagerie Zea mays 430 50 y 420 35 50 0.13 3.84 5.02 1 Smith-Pagerie Zea mays 405 50 y South Park Charcoal 950 65 n St Anthony Zea mays 950 80 n Stafford Zea mays 735 110 n Pottery Street 1043 40 n encrustation Turnbull Zea mays 435 50 n Twin Mounds West Unknown 1100 130 n

279

Uren Zea mays 1390 75 n Uren Zea mays 830 70 n Van Besien Charcoal 1175 140 y 1040 58 107 1.18 5.99 7.38 2 Van Besien Charcoal 1010 90 y Van Besien Charcoal 1005 90 y Pottery Vinette 2270 35 n encrustation Pottery Vinette 1990 40 y 1960 26 38 0.89 3.84 5.02 1 encrustation Pottery Vinette 1940 35 y encrustation Waterworks Bone Collagen 1460 55 y 1460 39 55 0.00 3.84 5.02 1 Waterworks Bone Collagen 1460 55 y Weilnau Charcoal 1380 80 n Pottery Westheimer 1600 35 n encrustation Pottery Wickham 1425 45 n 10.24 3.84 5.02 1 encrustation Pottery Wickham 1228 42 n encrustation

280

Figure A1.3: Sites in Group A (for site names, please see Map ID reference in Table A1.4)

281

Table A1.4: Sites in Group A (Earliest Date)

Prov Median Map Site Lab Dating Direct / Uncal Site Name / Material 13 Error Caibrated Reference ID Code Number method Indirect δ C Date BP State Date BP Birch Run Brashler et al. 2000 1 20SA393 MI Beta-4517 Charcoal Conventional I 1120 90 1046 Road Stothers et al. 1994 Crawford et al. 1997; 2 Eidson 20BE122 MI Beta-6154 Charcoal Conventional I 1650 70 1551 Martin 2004; Raviele 2010 Beta- Pottery 3 Fletcher 20BY-28 MI AMS D -31.99 785 85 727 Lovis et al 1996 17750 encrustation Crawford et al. 1997; Gard Island Bone Schurr and Redmond 4 20MR162 MI DC-416 Conventional I -19 1440 80 1351 # 2 Collagen 1991; Stothers and Abel 2002 Indian Stothers and Abel 5 20MR153 MI DIC-413 Charcoal Conventional I 990 80 894 Island # 3 2002 Crawford et al. 1997; Indian 6 20MR154 MI DIC-414 Charcoal Conventional I 1410 95 1326 Stothers and Abel Island # 4 2002 Martin 2004; Prahl 7 Morin 20MR40 MI M-2087 Charcoal Conventional I -25 880 110 812 1974 Lovis et al. 2001; 8 Schultz 20SA2 MI M-1648 Charcoal Conventional I -25 770 100 719 Martin 2004 Crawford et al. 1997; Stothers 1977; 9 Sissung 20MR5 MI M-1519 Charcoal Conventional I -25 1250 120 1163 Stothers and Yarnell 1977 Bendremer and Beta- 10 Boland NY NY Charcoal Conventional I 1270 90 1187 Dewar 1994; Cassedy 24510 and Webb 1999 Bendremer and Bowmans Beta- Dewar 1994; Cassedy 11 NY NY Charcoal Conventional I -26.9 920 70 836 Brook 15770 and Webb 1999; Little 2002 Chenango 12 NY NY N/A Charcoal Conventional I 1000 70 908 Kuhn 1994 Point Deposit 13 NY N/A Zea mays AMS D 1210 40 1136 Knapp 2009 Airport I

282

NYSM- Pottery 14 Felix NY A-0497 AMS D -27.2 1575 35 1465 Hart et al 2007 1373 encrustation NYSM Pottery 15 Fortin 2 NY A-0406 AMS D -29 1525 35 1409 Thompson et al 2004 72699 encrustation Bendremer and Fortin 16 NY NY DIC-166 Charcoal Conventional I 870 75 795 Dewar 1994; Hart Locus 2 1999c Asch Sidell 1999; Hudson Beta- 17 211-1-1 NY Zea mays AMS D 1130 70 1050 Crawford and Smith River 53452 2003 Hart et al. 2003; Hunter's NYSM Pottery 18 NY A-0192 AMS D -26.7 1231 44 1163 Martin 2004; Home 1538 encrustation Schulenberg 2002 Hart et al. 2003; NYSM Pottery 19 Kipp Island NY A-0225 AMS D -26.4 1470 43 1358 Martin 2004; 2084 encrustation Schulenberg 2002 Martin 2004; Ritchie NYSM Pottery 20 Levanna NY GX-28193 AMS D 1090 40 1000 1969; Schulenberg 2092 encrustation 2002 Hart 1999b, 2000; 21 Roundtop NY NY Y-1534 Charcoal Conventional I -25 880 60 802 Hart et al. 2005; Timmins 1985 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 AMS D -28.7 1620 35 1503 1507 encrustation Hart pers comm. Pottery 23 Street NY NY A-0229 AMS D -26.1 1043 40 957 Hart et al. 2007 encrustation Pottery 24 Vinette NY NY A-0500 AMS D -28.1 2270 35 2246 Hart et al. 2007 encrustation Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 AMS D -25.9 1600 35 1475 Ritchie and Funk encrustation 1973 Pottery Hart et al. 2003; 26 Wickham NY NY A-0190 AMS D -28.1 1425 45 1329 encrustation Martin 2004 Beta- Martin 2004; Stothers 27 Bear Fort 33SA08 OH Charcoal Conventional I 790 60 720 22864 and Abel 2001 Blain OWU-247 28 33RO128 OH Zea mays AMS D 645 150 627 Maslowski et al., 1995 Village A Stothers and Abel Beta- 29 Dillon OH OH Charcoal Conventional I -25.9 1210 70 1136 2002; Stothers et al. 30054 1994

283

Edwin Crawford et al. 1997; 30 33 OH N/A Zea mays AMS D 1730 85 1647 Harness Riley et al. 1994 Franks / Beta- Bone Brose 1993; 31 33LN13 OH AMS D -13 1070 50 985 Mill Hollow 43133 Collagen Maslowski et al 1995 Bone Martin 2004; Stothers 32 Gladieux 33LU10 OH DIC-797 ? I 1330 70 1247 Collagen and Abel, 1993, 2002; Cutler 1965; Martin 33 McGraw 33RO108 OH UCLA-679 Charcoal Conventional I 1510 80 1410 2004; Prufer 1965 Stothers and Abel Patli- Bone 34 33FU5 OH GX-10742 ? D -13.4 885 125 819 2002; Stothers and Dowling Collagen Bechtel 1987 Carskadden and 35 Philo II 33MU76 OH I-7868 Charcoal AMS I 890 80 815 Morton 1996; Martin 2004 Sand Ridge Martin 2004; Niquette 36 33HA17 OH WIS-1748 Charcoal Conventional I -26 1080 70 1002 I and Crites 1993 Brose 1973, 1994; 37 South Park 33CU8 OH WIS-576 Zea mays Conventional D -17.4 950 65 854 Maslowski et al. 1995 Twin 38 Mounds 33HA24 OH N/A Unknown ? I 1100 130 1028 Martin 2004 West Maslowski et al. 1995; Water Bone 39 33LU06 OH CWRU-36 AMS I -25 1460 55 1356 Stothers and Bechtel works Collagen 1987 Martin 2004; Stothers 40 Weilnau 33ER409 OH I-16454 Charcoal Conventional I 1380 80 1297 and Abel 2002 41 11h10 AmHp-10 ON I-4009 Charcoal Conventional I 1070 95 995 Smith 1997a 42 Anderson AfGx-54 ON TO-7033 Zea mays AMS D 720 80 671 Berg and Bursey 2000 Reid 1975; Smith 43 Boys AlGs-10 ON I-7322 Zea mays Conventional I 985 120 900 1997a; Williamson 1990 Smith 1997b; Smith et 44 Bull's Point AhGx-9 ON TO-6341 Zea mays AMS D 960 60 859 al 1997 Archaeological AgHb- Beta- 45 D'Aubigny ON Zea mays AMS D -28.6 1780 50 1703 Services Incorporated 276 217154 2006 Campbell and Campbell 1989; Smith 46 DeWaele AfHd-1 ON I-6411 Charcoal Conventional I 900 90 823 1997a; Williamson 1990

284

Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 Zea mays AMS D 930 110 848 1997a Crawford et al 2006; 48 Dymock AeHj-2 ON I-12479 Charcoal Conventional I 1030 80 946 Murphy and Ferris 1990; Smith 1997a Bursey and Smith AgGx- 49 Forster ON TO-70392 Zea mays AMS D 1150 100 1078 1997; Crawford and 134 Smith 2002 Smith 1997c, G. 50 Grafton BaGm-9 ON BGS-1916 Unknown ? I 1065 80 987 Dibbs, Personal Communication Crawford and Smith, Grand 1996, 2002, 2003; 51 AfGx-3 ON TO-5307 Zea mays AMS D 1570 90 1468 Banks Crawford, et al. 1997, 2006; AgHb- 52 Holmedale ON TO-6079 Zea mays AMS D 1010 70 922 Pihl et al 2008:161 191 Lakeshore Fox 1990; Martin 53 AlGh-32 ON S-2194 Charcoal Conventional I 1110 60 1026 Lodge 2004 Crawford and Smith 54 Lone Pine AfGx-113 ON TO-4586 Zea mays AMS D 1040 60 957 1996; Smith and Crawford 1997 Crawford and Smith 55 Meyer AfGx-26 ON TO-81502 Zea mays AMS D 1270 100 1184 2002; Martin 2004; Saunders 2002) Timmins 1985; 56 Miller AlGs-1 ON S-108 Charcoal Conventional I -25 835 70 763 Williamson 1990 Fox 1990a, 1990b; 57 Moyer Flats AiHc-24 ON I-13078 Charcoal Conventional I 1050 80 969 Williamson 1990 Timmins 1985; Wright 58 Nodwell BcHi-3 ON S-1720 Zea mays AMS D -10 1035 80 951 1974, 1985 Monckton 1998; WAT- 59 Parsons AkGv-8 ON Zea mays AMS D 860 110 795 Williamson and 2871 Robertson 1998 Martin 2004; Peace Pottery 60 AfGr-9 ON N/A AMS D 1330 60 1251 Williamson and Bridge encrustation MacDonald 1997 Crawford et al. 1997; 61 Porteous AgHb-1 ON I-4972 Charcoal Conventional I -25 1130 100 1057 Noble and Kenyon 1972; Stothers 1977;

285

Timmins 1997; Williamson 1990 Crane and Griffin 62 Stafford AeHg-3 ON M-1553 Zea mays AMS D -10 735 110 690 1965; Williamson 1990 WSU- timmins 1985; 63 Uren AfHd-3 ON Zea mays AMS D 830 70 759 1957 Williamson 1990 Noble 1975; 64 Van Besien AfHd-2 ON I-6848 Charcoal Conventional I 1175 140 1098 Williamson 1990 Backstrum 36WM45 65 PN DIC-3028 Charcoal Conventional I 1490 60 1383 McConaughy 2008 1 3 King 1999; Martin 66 Bald Eagle 36CN102 PN UGa-4753 Charcoal Conventional I 1175 100 1102 2004 Blackwell 67 36TI58 PN N/A Zea mays AMS D 1070 60 989 Miller 1993 Bridge Campbell McConaughy 2008; 68 36FA26 PN AA-40133 Zea mays AMS D 795 40 714 Farm Hart et al 2002 Catawissa King 1999; Martin 69 Bridge, str 36C09 PN PITT-8 Charcoal Conventional I 1455 45 1348 2004; McConaughy 3 2008 MacDonald and Coverts Beta- 70 36Lr75 PN Charcoal Conventional I 840 50 756 Cremeens 2002, Crossing 142247 McConaughy 2008 Buker 1970; King 71 Drew 36AL62 PN M-2198 Zea mays AMS D -10 835 100 774 1999 Crawford et al. 1997; Plant 72 Fisher Farm 36CE35 PN UGa-2683 Conventional I 1245 70 1172 Hatch 1980; King remains 1999 Martin 2004; 73 Gnagey 36SO55 PN GaK-5150 Charcoal Conventional I 1030 80 946 McConaughy 2008:20 Harding East et al. 2001; 74 36WO55 PN N/A Zea mays AMS D 1060 40 971 Flats Martin 2004, 2006 Adovasio and Johnson 1981; Meadow- 36WH29 Adovasio et al. 2003; 75 croft PN SI-2051 Charcoal Conventional I 2325 75 2353 7 Hart and Brumbach Rockshelter 2005; Hart et al. 2007; Thompson et al. 2004 Memorial PITT- Asch Sidell 2008; 76 36CN164 PN Charcoal Conventional I 1190 40 1116 Park 1073 Crawford et al. 1997;

286

Hart and Asch Sidell 1996 Murphy's Beta- George 2005; 77 39AR129 PN Charcoal Conventional I 1080 70 1002 Old House 78747 McConaughy 2008 Beta- Hart et al. 2005; 78 Petenbrink 36SO62 PN Unknown ? I 1080 70 1002 104103 Means 2002 Pony Farm Beta- Hart et al. 2005; 79 Triangle 36SO243 PN Unknown ? I 860 70 784 97738 Means 2002 East Hart et al. 2005; Hart Beta- 80 Railroad 36SO113 PN Charcoal Conventional I 1150 50 1065 and Means 2002; 104124 Means 2002; Cutler and Blake 81 Ryan 36WM23 PN I-16,727 Charcoal Conventional I 980 80 880 1973; George 2004; McConaughy 2008 Bendremer and Smithfield Beta- 82 36MR5 PN Unknown ? I 930 80 843 Dewar 1994; Beach 21548 Herbstritt 1988 Beta- 83 St Anthony 36UN11 PN Zea mays AMS D 950 80 855 King 1999; Rieth 2002 22813

287

Figure A1.4: Sites in Group B (for site names, please see Map ID reference in Table A1.5)

288

Table A1.5: Sites in Group B (Pooled Date)

Prov Median Map Site Lab Dating Direct / Uncal Site Name / Pooled? Material 13 Error Caibrated Reference ID Code Number method Indirect δ C Date BP State Date (BP) Brashler et al. Birch Run Beta- 1 20SA393 MI n Charcoal Conventional I 1120 90 1046 2000 Stothers et Road 4517 al. 1994 Crawford et al. Beta- 2 Eidson 20BE122 MI n Charcoal Conventional I 1650 70 1551 1997; Martin 2004; 6154 Raviele 2010 Beta- Pottery 3 Fletcher 20BY-28 MI n AMS D -31.99 785 85 727 Lovis et al 1996 17750 encrustation Crawford et al. DIC-416, Human 1997; Schurr and Gard Island 4 20MR162 MI DIC-418, y Bone Conventional I -19 1295 93 1206 Redmond 1991; # 2 DIC-419 Collagen Stothers and Abel 2002 Indian Stothers and Abel 5 20MR153 MI DIC-413 n Charcoal Conventional I 990 80 894 Island # 3 2002 DIC-300, Crawford et al. Indian DIC-301, 6 20MR154 MI y Charcoal Conventional I 1285 88 1200 1997; Stothers and Island # 4 DIC-414, Abel 2002 DIC-415 Martin 2004; Prahl 7 Morin 20MR40 MI M-2087 n Charcoal Conventional I -25 880 110 812 1974 Lovis et al. 2001; 8 Schultz 20SA2 MI M-1648 n Charcoal Conventional I -25 770 100 719 Martin 2004 Crawford et al. 1997; Stothers 9 Sissung 20MR5 MI M-1519 n Charcoal Conventional I -25 1250 120 1163 1977; Stothers and Yarnell 1977 Bendremer and Beta- Dewar 1994; 10 Boland NY NY n Charcoal Conventional I 1270 90 1187 24510 Cassedy and Webb 1999 Bowmans Beta- Bendremer and 11 NY NY n Charcoal Conventional I -26.9 920 70 836 Brook 15770 Dewar 1994;

289

Cassedy and Webb 1999; Little 2002 Chenango 12 NY NY N/A? n Charcoal Conventional I 1000 70 908 Kuhn 1994 Point Deposit 13 NY N/A n Zea mays AMS D 1210 40 1136 Knapp 2009 Airport I A-0497, NYSM- A-0499, Pottery 14 Felix NY y AMS D -27.2 1555 35 1457 Hart et al 2007 1373 A-0503, encrustation A-0506 NYSM A-0406, Pottery Thompson et al 15 Fortin 2 NY y AMS D -29 1515 35 1397 72699 A-0407 encrustation 2004 Bendremer and Fortin 16 NY NY DIC-166 n Charcoal Conventional I 870 75 795 Dewar 1994; Hart Locus 2 1999c Beta- 53451, Asch Sidell 1999; Hudson Beta- 17 211-1-1 NY y Zea mays AMS D 1080 60 999 Crawford and River 53452, Smith 2003 Beta- 84969 A-0192 Hart et al. 2003; Hunter's NYSM Pottery 18 NY A-0196, y AMS D -26.7 1190 43 1116 Martin 2004; Home 1538 encrustation A-0198 Schulenberg 2002 Hart et al. 2003; NYSM A-0225. Pottery 19 Kipp Island NY y AMS D -26.4 1450 42 1343 Martin 2004; 2084 A-0227 encrustation Schulenberg 2002 Martin 2004; NYSM GX- Pottery 20 Levanna NY n AMS D 1090 40 1000 Ritchie 1969; 2092 28193 encrustation Schulenberg 2002 Y-1534, Hart 1999b, 2000; 21 Roundtop NY NY AA- y Charcoal Conventional I -25 850 53 767 Hart et al. 2005; 26541 Timmins 1985 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 n AMS D -28.7 1620 35 1503 1507 encrustation Hart pers comm. Pottery 23 Street NY NY A-0229 n AMS D -26.1 1043 40 957 Hart et al. 2007 encrustation A-0452, Pottery 24 Vinette NY NY y AMS D -28.1 1960 38 1911 Hart et al. 2007 A-0455 encrustation

290

Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 n AMS D -25.9 1600 35 1475 Ritchie and Funk encrustation 1973 Pottery Hart et al. 2003; 26 Wickham NY NY A-0190 n AMS D -28.1 1425 45 1329 encrustation Martin 2004 Martin 2004; Beta- 27 Bear Fort 33SA08 OH n Charcoal Conventional I 790 60 720 Stothers and Abel 22864 2001 OWU- Maslowski et al., 28 Blain Village 33RO128 OH n Zea mays AMS D 645 150 627 247 A 1995 Stothers and Abel Beta- 29 Dillon OH OH n Charcoal Conventional I -25.9 1210 70 1136 2002; Stothers et 30054 al. 1994 Crawford et al. Edwin 30 33 OH N/A y Zea mays AMS D 1730 95 1648 1997; Riley et al. Harness 1994 Human Brose 1993; Franks / Mill Beta- 31 33LN13 OH n Bone AMS D -13 1070 50 985 Maslowski et al Hollow 43133 Collagen 1995 DIC-428, Human Martin 2004; 32 Gladieux 33LU10 OH DIC-429, y Bone ? I 1275 72 1198 Stothers and Abel, DIC,797 Collagen 1993, 2002; OWU-61, Cutler 1965; 33 McGraw 33RO108 OH UCLA- y Charcoal Conventional I 1485 73 1385 Martin 2004; 679 Prufer 1965 Human Stothers and Abel Patli- GX- 34 33FU5 OH n Bone ? D -13.4 885 125 819 2002; Stothers and Dowling 10742 Collagen Bechtel 1987 Carskadden and I-7868, i- 35 Philo II 33MU76 OH y Charcoal AMS I 885 80 811 Morton 1996; 14101 Martin 2004 Martin 2004; Sand Ridge WIS- 36 33HA17 OH n Charcoal Conventional I -26 1080 70 1002 Niquette and I 1748 Crites 1993 Brose 1973, 1994; 37 South Park 33CU8 OH WIS-576 n Zea mays Conventional D -17.4 950 65 854 Maslowski et al. 1995 Twin 38 Mounds 33HA24 OH N/A n Unknown ? I 1100 130 1028 Martin 2004 West

291

CWRU- Human Maslowski et al. 36, 39 Waterworks 33LU06 OH y Bone AMS I -25 1460 55 1356 1995; Stothers and CWRU- Collagen Bechtel 1987 48 Martin 2004; 40 Weilnau 33ER409 OH I-16454 n Charcoal Conventional I 1380 80 1297 Stothers and Abel 2002 41 11h10 AmHp-10 ON I-4009 n Charcoal Conventional I 1070 95 995 Smith 1997a Berg and Bursey 42 Anderson AfGx-54 ON TO-7033 n Zea mays AMS D 720 80 671 2000 Reid 1975; Smith I-7322, I- 43 Boys AlGs-10 ON y Zea mays Conventional I 825 107 768 1997a; Williamson n/a-2 1990 Smith 1997b; 44 Bull's Point AhGx-9 ON TO-6341 n Zea mays AMS D 960 60 859 Smith et al 1997 Archaeological Beta- 45 D'Aubigny AgHb-276 ON n Zea mays AMS D -28.6 1780 50 1703 Services 217154 Incorporated 2006 Campbell and I-6411, I- Campbell 1989; 46 DeWaele AfHd-1 ON y Charcoal Conventional I 880 90 808 6412 Smith 1997a; Williamson 1990 Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 n Zea mays AMS D 930 110 848 1997a I-12479,I- 12149, I- Crawford et al 12478, I- 2006; Murphy and 48 Dymock AeHj-2 ON y Charcoal Conventional I 930 80 843 12150,, I- Ferris 1990; Smith 12151, I- 1997a 12152 Bursey and Smith TO- 49 Forster AgGx-134 ON n Zea mays AMS D 1150 100 1078 1997; Crawford 70392 and Smith 2002 BGS- 1843, BGS- Smith 1997c, G. 50 Grafton BaGm-9 ON 1844, y Unknown ? I 1005 90 916 Dibbs, Personal BGS- Communication 1845, BGS-

292

1845 Crawford and Smith, 1996, 2002, Grand TO-5307, 2003; Crawford, et 51 AfGx-3 ON y Zea mays AMS D 1550 120 1462 Banks TO-5038 al. 1997, 2006; Smith and Crawford 1997 52 Holmedale AgHb-191 ON TO-6079 n Zea mays AMS D 1010 70 922 Pihl et al 2008:161 Lakeshore Fox 1990; Martin 53 AlGh-32 ON S-2194 n Charcoal Conventional I 1110 60 1026 Lodge 2004 Crawford and Smith 1996; Smith 54 Lone Pine AfGx-113 ON TO-4586 n Zea mays AMS D 1040 60 957 and Crawford 1997 Crawford and TO- Smith 2002; Martin 55 Meyer AfGx-26 ON n Zea mays AMS D 1270 100 1184 81502 2004; Saunders 2002) Timmins 1985; 56 Miller AlGs-1 ON S-108 n Charcoal Conventional I -25 835 70 763 Williamson 1990 Fox 1990a, 1990b; 57 Moyer Flats AiHc-24 ON I-13078 n Charcoal Conventional I 1050 80 969 Williamson 1990 Timmins 1985; 58 Nodwell BcHi-3 ON S-1720 n Zea mays AMS D -10 1035 80 951 Wright 1974, 1985 Martin 2004; Peace Pottery 60 AfGr-9 ON N/A n AMS D 1330 60 1251 Williamson and Bridge encrustation MacDonald 1997 Crawford et al. 1997; Noble and I-4972, i- Kenyon 1972; 61 Porteous AgHb-1 ON y Charcoal Conventional I -25 1265 95 1181 5820 Stothers 1977; Timmins 1997; Williamson 1990 Crane and Griffin 62 Stafford AeHg-3 ON M-1553 n Zea mays AMS D -10 735 110 690 1965; Williamson 1990 WSU- timmins 1985; 63 Uren AfHd-3 ON n Zea mays AMS D 830 70 759 1957 Williamson 1990 I-6167, I- Noble 1975; 64 Van Besien AfHd-2 ON y Charcoal Conventional I 1040 107 960 6847, I- Williamson 1990

293

6848 36WM45 65 Backstrum 1 PN DIC-3028 n Charcoal Conventional I 1490 60 1383 McConaughy 2008 3 UGa- 4753, King 1999; Martin 66 Bald Eagle 36CN102 PN y Charcoal Conventional I 1095 93 1022 Uga- 2004 4754 Blackwell 67 36TI58 PN N/A y Zea mays AMS D 1060 70 979 Miller 1993 Bridge McConaughy Campbell AA- 68 36FA26 PN n Zea mays AMS D 795 40 714 2008; Hart et al Farm 40133 2002 King 1999; Martin Catawissa PITT-76, 69 36C09 PN y Charcoal Conventional I 1275 53 1209 2004; Bridge, str 3 PITT-11 McConaughy 2008 MacDonald and Coverts Beta- 70 36Lr75 PN n Charcoal Conventional I 840 50 756 Cremeens 2002, Crossing 142247 McConaughy 2008 Buker 1970; King 71 Drew 36AL62 PN M-2198 n Zea mays AMS D -10 835 100 774 1999 Crawford et al. UGa- Plant 72 Fisher Farm 36CE35 PN n Conventional I 1245 70 1172 1997; Hatch 1980; 2683 remains King 1999 GaK- Martin 2004; 5150, 73 Gnagey 36SO55 PN y Charcoal Conventional I 975 80 875 McConaughy Uga- 2008:20 1599 Harding East et al. 2001; 74 36WO55 PN N/A n Zea mays AMS D 1060 40 971 Flats Martin 2004, 2006 Adovasio and Johnson 1981;

Adovasio et al. Meadow- SI-2051, 2003; Hart and 75 croft 36WH297 PN SI1674, y Charcoal Conventional I 2225 88 2222 Brumbach 2005; Rockshelter SI-2487, Hart et al. 2007; SI-2362 Thompson et al. 2004 Asch Sidell 2008; Memorial PITT- 76 36CN164 PN n Charcoal Conventional I 1190 40 1116 Crawford et al. Park 1073 1997; Hart and

294

Asch Sidell 1996 Murphy's Beta- George 2005; 77 39AR129 PN n Charcoal Conventional I 1080 70 1002 Old House 78747 McConaughy 2008 Beta- 104103, Hart et al. 2005; 78 Petenbrink 36SO62 PN y Unknown ? I 1040 65 957 Beta- Means 2002 77677 Beta- Pony Farm 97737, Hart et al. 2005; 79 Triangle 36SO243 PN y Unknown ? I 810 65 738 Beta- Means 2002 East 97738 Beta- 104124, Beta- Hart et al. 2005; 104134, Hart and Means 80 Railroad 36SO113 PN y Charcoal Conventional I 1035 59 952 Beta- 2002; Means 104131, 2002; Beta- 104120 Cutler and Blake I-16727, 1973; George 81 Ryan 36WM23 PN GAK- y Charcoal Conventional I 905 80 826 2004; 3729 McConaughy 2008 Beta- Bendremer and Smithfield 21548, 82 36MR5 PN y Unknown ? I 905 70 826 Dewar 1994; Beach Beta- Herbstritt 1988 15573 Beta- King 1999; Rieth 83 St Anthony 36UN11 PN n Zea mays AMS D 950 80 855 22813 2002

295

Figure A1.5: Sites in Group B (for site names, please see Map ID reference in Table A1.6)

296

Table A1.6: Sites in Group C (Earliest Direct Date)

Prov Median Map Site Lab Dating Uncal Site Name / Material 13 Error Caibrated Reference ID Code Number method δ C Date BP State Date BP Beta- Pottery - 3 Fletcher 20BY-28 MI AMS 785 85 727 Lovis et al 1996 17750 encrustation 31.99 Deposit 13 NY N/A Zea mays AMS 1210 40 1136 Knapp 2009 Airport I NYSM- Pottery 14 Felix NY A-0497 AMS -27.2 1575 35 1465 Hart et al 2007 1373 encrustation NYSM Pottery Thompson et al 15 Fortin 2 NY A-0406 AMS -29 1525 35 1409 72699 encrustation 2004 Asch Sidell 1999; Hudson Beta- 17 211-1-1 NY Zea mays AMS 1130 70 1050 Crawford and Smith River 53452 2003 Hart et al. 2003; Hunter's NYSM Pottery 18 NY A-0192 AMS -26.7 1231 44 1163 Martin 2004; Home 1538 encrustation Schulenberg 2002 Hart et al. 2003; NYSM Pottery 19 Kipp Island NY A-0225 AMS -26.4 1470 43 1358 Martin 2004; 2084 encrustation Schulenberg 2002 Martin 2004; Ritchie NYSM GX- Pottery 20 Levanna NY AMS 1090 40 1000 1969; Schulenberg 2092 28193 encrustation 2002 Hart 1999b, 2000; AA- 21 Roundtop NY NY Zea mays AMS 830 45 743 Hart et al. 2005; 26541 Timmins 1985 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 AMS -28.7 1620 35 1503 1507 encrustation Hart pers comm. Pottery 23 Street NY NY A-0229 AMS -26.1 1043 40 957 Hart et al. 2007 encrustation Pottery 24 Vinette NY NY A-0500 AMS -28.1 2270 35 2246 Hart et al. 2007 encrustation Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 AMS -25.9 1600 35 1475 Ritchie and Funk encrustation 1973 Pottery Hart et al. 2003; 26 Wickham NY NY A-0190 AMS -28.1 1425 45 1329 encrustation Martin 2004

297

Blain OWU- Maslowski et al., 28 33RO128 OH Zea mays AMS 645 150 627 Village 247 A 1995 Crawford et al. Edwin 30 33 OH N/A Zea mays AMS 1730 85 1647 1997; Riley et al. Harness 1994 Brose 1993; Franks / Beta- Bone 31 33LN13 OH AMS -13 1070 50 985 Maslowski et al Mill Hollow 43133 Collagen 1995 Stothers and Abel Patli- GX- Bone 34 33FU5 OH ? -13.4 885 125 819 2002; Stothers and Dowling 10742 Collagen Bechtel 1987 Brose 1973, 1994; WIS- 37 South Park 33CU8 OH Zea mays Conventional -17.4 950 65 854 Maslowski et al. 576 1995 TO- Berg and Bursey 42 Anderson AfGx-54 ON Zea mays AMS 720 80 671 7033 2000 TO- Smith 1997b; Smith 44 Bull's Point AhGx-9 ON Zea mays AMS 960 60 859 6341 et al 1997 Archaeological AgHb- Beta- 45 D'Aubigny ON Zea mays AMS -28.6 1780 50 1703 Services 276 217154 Incorporated 2006 Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 Zea mays AMS 930 110 848 1997a Bursey and Smith AgGx- TO- 49 Forster ON Zea mays AMS 1150 100 1078 1997; Crawford and 134 70392 Smith 2002 Crawford and Grand TO- Smith, 1996, 2002, 51 AfGx-3 ON Zea mays AMS 1570 90 1468 Banks 5307 2003; Crawford, et al. 1997, 2006; AgHb- TO- 52 Holmedale ON Zea mays AMS 1010 70 922 Pihl et al 2008:161 191 6079 Crawford and Smith TO- 54 Lone Pine AfGx-113 ON Zea mays AMS 1040 60 957 1996; Smith and 4586 Crawford 1997 Crawford and Smith TO- 55 Meyer AfGx-26 ON Zea mays AMS 1270 100 1184 2002; Martin 2004; 81502 Saunders 2002 Timmins 1985; 58 Nodwell BcHi-3 ON S-1720 Zea mays AMS -10 1035 80 951 Wright 1974, 1985

298

Monckton 1998; WAT- 59 Parsons AkGv-8 ON Zea mays AMS 860 110 795 Williamson and 2871 Robertson 1998 Martin 2004; Peace Pottery 60 AfGr-9 ON N/A AMS 1330 60 1251 Williamson and Bridge encrustation MacDonald 1997 Crane and Griffin 62 Stafford AeHg-3 ON M-1553 Zea mays AMS -10 735 110 690 1965; Williamson 1990 WSU- timmins 1985; 63 Uren AfHd-3 ON Zea mays AMS 830 70 759 1957 Williamson 1990 Blackwell 67 36TI58 PN N/A Zea mays AMS 1040 65 957 Miller 1993 Bridge Campbell AA- McConaughy 2008; 68 36FA26 PN Zea mays AMS 795 40 714 Farm 40133 Hart et al 2002 Buker 1970; King 71 Drew 36AL62 PN M-2198 Zea mays AMS -10 835 100 774 1999 Harding East et al. 2001; 74 36WO55 PN N/A Zea mays AMS 1060 40 971 Flats Martin 2004, 2006 Asch Sidell 2008; Memorial AA- Crawford et al. 76 36CN164 PN Zea mays AMS -10.6 985 45 883 Park 19127 1997; Hart and Asch Sidell 1996 Beta- King 1999; Rieth 83 St Anthony 36UN11 PN Zea mays AMS 950 80 855 22813 2002

299

Figure A1.6: Sites in Group D (for site names, please see Map ID reference in Table A1.7)

300

Table A1.7: Sites in Group D (Pooled Direct Date)

Prov Median Map Lab Dating Uncal Site Name Site Code / Pooled? Material 13 Error Caibrated Reference ID Number method δ C Date (BP) State Date (BP) Beta- Pottery 3 Fletcher 20BY-28 MI n AMS -31.99 785 85 727 Lovis et al 1996 17750 encrustation Deposit 13 N/A NY N/A n Zea mays AMS 1210 40 1136 Knapp 2009 Airport I A-0497, NYSM- A-0499, Pottery 14 Felix NY y AMS -27.2 1555 35 1457 Hart et al 2007 1373 A-0503, encrustation A-0506 NYSM A-0406, Pottery Thompson et al 15 Fortin 2 NY y AMS -29 1515 35 1397 72699 A-0407 encrustation 2004 Beta- 53451, Asch Sidell 1999; Beta- 17 Hudson River 211-1-1 NY y Zea mays AMS 1080 60 999 Crawford and 53452, Smith 2003 Beta- 84969 A-0192 Hart et al. 2003; Hunter's NYSM Pottery 18 NY A-0196, y AMS -26.7 1190 43 1116 Martin 2004; Home 1538 encrustation A-0198 Schulenberg 2002 Hart et al. 2003; NYSM A-0225. Pottery 19 Kipp Island NY y AMS -26.4 1450 42 1343 Martin 2004; 2084 A-0227 encrustation Schulenberg 2002 Martin 2004; NYSM GX- Pottery 20 Levanna NY n AMS 1090 40 1000 Ritchie 1969; 2092 28193 encrustation Schulenberg 2002 Y-1534, Hart 1999b, 2000; 21 Roundtop NY NY AA- y Zea mays Conventional -25 850 53 767 Hart et al. 2005; 26541 Timmins 1985 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 n AMS -28.7 1620 35 1503 1507 encrustation Hart pers comm. 23 Street NY NY A-0229 n Pottery AMS -26.1 1043 40 957 Hart et al. 2007

301

encrustation A-0452, Pottery 24 Vinette NY NY y AMS -28.1 1960 38 1911 Hart et al. 2007 A-0455 encrustation Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 n AMS -25.9 1600 35 1475 Ritchie and Funk encrustation 1973 Pottery Hart et al. 2003; 26 Wickham NY NY A-0190 n AMS -28.1 1425 45 1329 encrustation Martin 2004 OWU- Maslowski et al., 28 Blain Village 33RO128 OH n Zea mays AMS 645 150 627 247 A 1995 Crawford et al. Edwin 30 33 OH N/A y Zea mays AMS 1730 95 1648 1997; Riley et al. Harness 1994 Brose 1993; Franks / Mill Beta- Bone 31 33LN13 OH n AMS -13 1070 50 985 Maslowski et al Hollow 43133 Collagen 1995 Stothers and Abel GX- Bone 34 Patli-Dowling 33FU5 OH n ? -13.4 885 125 819 2002; Stothers and 10742 Collagen Bechtel 1987 Brose 1973, 1994; 37 South Park 33CU8 OH WIS-576 n Zea mays Conventional -17.4 950 65 854 Maslowski et al. 1995 Berg and Bursey 42 Anderson AfGx-54 ON TO-7033 n Zea mays AMS 720 80 671 2000 Smith 1997b; 44 Bull's Point AhGx-9 ON TO-6341 n Zea mays AMS 960 60 859 Smith et al 1997 Archaeological Beta- 45 D'Aubigny AgHb-276 ON n Zea mays AMS -28.6 1780 50 1703 Services 217154 Incorporated 2006 Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 n Zea mays AMS 930 110 848 1997a Bursey and Smith TO- 49 Forster AgGx-134 ON n Zea mays AMS 1150 100 1078 1997; Crawford 70392 and Smith 2002 Crawford and Smith, 1996, 2002, TO- 2003; Crawford, et 51 Grand Banks AfGx-3 ON 5307, y Zea mays AMS 1550 120 1462 al. 1997, 2006; TO-5038 Smith and Crawford 1997

302

52 Holmedale AgHb-191 ON TO-6079 n Zea mays AMS 1010 70 922 Pihl et al 2008:161 Crawford and 54 Lone Pine AfGx-113 ON TO-4586 n Zea mays AMS 1040 60 957 Smith 1996; Smith and Crawford 1997 Crawford and TO- Smith 2002; Martin 55 Meyer AfGx-26 ON n Zea mays AMS 1270 100 1184 81502 2004; Saunders 2002) Timmins 1985; 58 Nodwell BcHi-3 ON S-1720 n Zea mays AMS -10 1035 80 951 Wright 1974, 1985 Martin 2004; Pottery 60 Peace Bridge AfGr-9 ON N/A n AMS 1330 60 1251 Williamson and encrustation MacDonald 1997 Crane and Griffin 62 Stafford AeHg-3 ON M-1553 n Zea mays AMS -10 735 110 690 1965; Williamson 1990 WSU- timmins 1985; 63 Uren AfHd-3 ON n Zea mays AMS 830 70 759 1957 Williamson 1990 Blackwell 67 36TI58 PN N/A y Zea mays AMS 1060 70 979 Miller 1993 Bridge McConaughy Campbell AA- 68 36FA26 PN n Zea mays AMS 795 40 714 2008; Hart et al Farm 40133 2002 Buker 1970; King 71 Drew 36AL62 PN M-2198 n Zea mays AMS -10 835 100 774 1999 East et al. 2001; 74 Harding Flats 36WO55 PN N/A n Zea mays AMS 1060 40 971 Martin 2004, 2006 Asch Sidell 2008; Memorial AA- Crawford et al. 76 36CN164 PN n Zea mays AMS -10.6 985 45 883 Park 19127 1997; Hart and Asch Sidell 1996 Beta- King 1999; Rieth 83 St Anthony 36UN11 PN n Zea mays AMS 950 80 855 22813 2002

303

Figure A1.7: Sites in Group E (for site names, please see Map ID reference in Table A1.8)

304

Table A1.8: Sites in Group E (Earliest Date by Sampling Grid)

Prov Median Map Site Lab Dating Direct / Uncal Site Name / Material 13 Error Caibrated Reference ID Code Number method Indirect δ C Date BP State Date BP Birch Run Beta- Brashler et al. 2000 1 20SA393 MI Charcoal Conventional I 1120 90 1046 Road 4517 Stothers et al. 1994 Crawford et al. 1997; Beta- 2 Eidson 20BE122 MI Charcoal Conventional I 1650 70 1551 Martin 2004; Raviele 6154 2010 Beta- Pottery - 3 Fletcher 20BY-28 MI AMS D 785 85 727 Lovis et al 1996 17750 encrustation 31.99 Crawford et al. 1997; Gard Island Bone Schurr and Redmond 4 20MR162 MI DC-416 Conventional I -19 1440 80 1351 # 2 Collagen 1991; Stothers and Abel 2002 Crawford et al. 1997; Stothers 1977; 9 Sissung 20MR5 MI M-1519 Charcoal Conventional I -25 1250 120 1163 Stothers and Yarnell 1977 Bendremer and Beta- Dewar 1994; 10 Boland NY NY Charcoal Conventional I 1270 90 1187 24510 Cassedy and Webb 1999 Bendremer and Bowmans Beta- Dewar 1994; 11 NY NY Charcoal Conventional I -26.9 920 70 836 Brook 15770 Cassedy and Webb 1999; Little 2002 Deposit 13 NY N/A Zea mays AMS D 1210 40 1136 Knapp 2009 Airport I NYSM- Pottery 14 Felix NY A-0497 AMS D -27.2 1575 35 1465 Hart et al 2007 1373 encrustation Asch Sidell 1999; Hudson Beta- 17 211-1-1 NY Zea mays AMS D 1130 70 1050 Crawford and Smith River 53452 2003 Hart et al. 2003; NYSM Pottery 19 Kipp Island NY A-0225 AMS D -26.4 1470 43 1358 Martin 2004; 2084 encrustation Schulenberg 2002

305

Martin 2004; Ritchie NYSM GX- Pottery 20 Levanna NY AMS D 1090 40 1000 1969; Schulenberg 2092 28193 encrustation 2002 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 AMS D -28.7 1620 35 1503 1507 encrustation Hart pers comm. Pottery 24 Vinette NY NY A-0500 AMS D -28.1 2270 35 2246 Hart et al. 2007 encrustation Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 AMS D -25.9 1600 35 1475 Ritchie and Funk encrustation 1973 Beta- Martin 2004; Stothers 27 Bear Fort 33SA08 OH Charcoal Conventional I 790 60 720 22864 and Abel 2001 Edwin Crawford et al. 1997; 30 33 OH N/A Zea mays AMS D 1730 85 1647 Harness Riley et al. 1994 Carskadden and 35 Philo II 33MU76 OH I-7868 Charcoal AMS I 890 80 815 Morton 1996; Martin 2004 WIS- Brose 1973, 1994; 37 South Park 33CU8 OH Zea mays Conventional D -17.4 950 65 854 576 Maslowski et al. 1995 Twin 38 Mounds 33HA24 OH N/A Unknown ? I 1100 130 1028 Martin 2004 West Maslowski et al. CWRU- Bone 39 Waterworks 33LU06 OH AMS I -25 1460 55 1356 1995; Stothers and 36 Collagen Bechtel 1987 Pottery Martin 2004; Stothers 40 Weilnau 33ER409 OH I-16454 AMS I 1380 80 1297 encrustation and Abel 2002 41 11h10 AmHp-10 ON I-4009 Charcoal Conventional I 1070 95 995 Smith 1997a Reid 1975; Smith 43 Boys AlGs-10 ON I-7322 Charcoal Conventional I 985 120 900 1997a; Williamson 1990 Archaeological Beta- 45 D'Aubigny AgHb-276 ON Zea mays AMS D -28.6 1780 50 1703 Services 217154 Incorporated 2006 Campbell and Campbell 1989; 46 DeWaele AfHd-1 ON I-6411 Charcoal Conventional I 900 90 823 Smith 1997a; Williamson 1990 Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 Zea mays AMS D 930 110 848 1997a

306

Crawford et al 2006; 48 Dymock AeHj-2 ON I-12479 Charcoal Conventional I 1030 80 946 Murphy and Ferris 1990; Smith 1997a Bursey and Smith NYSM Pottery 49 Fortin 2 NY A-0406 AMS D -29 1525 35 1409 1997; Crawford and 72699 encrustation Smith 2002 Smith 1997c, G. BGS- 50 Grafton BaGm-9 ON Unknown ? I 1065 80 987 Dibbs, Personal 1916 Communication Lakeshore Fox 1990; Martin 53 AlGh-32 ON S-2194 Charcoal Conventional I 1110 60 1026 Lodge 2004 Fox 1990a, 1990b; 57 Moyer Flats AiHc-24 ON I-13078 Charcoal Conventional I 1050 80 969 Williamson 1990 Timmins 1985; 58 Nodwell BcHi-3 ON S-1720 Zea mays AMS D -10 1035 80 951 Wright 1974, 1985 Monckton 1998; WAT- 59 Parsons AkGv-8 ON Zea mays AMS D 860 110 795 Williamson and 2871 Robertson 1998 Martin 2004; Peace Pottery 60 AfGr-9 ON N/A AMS D 1330 60 1251 Williamson and Bridge encrustation MacDonald 1997 Noble 1975; 64 Van Besien AfHd-2 ON I-6848 Charcoal Conventional I 1175 140 1098 Williamson 1990 DIC- 65 Backstrum 1 36WM453 PN Charcoal Conventional I 1490 60 1383 McConaughy 2008 3028 Blackwell 67 36TI58 PN N/A Charcoal AMS I 1070 60 989 Miller 1993 Bridge Campbell AA- McConaughy 2008; 68 36FA26 PN Zea mays AMS D 795 40 714 Farm 40133 Hart et al 2002 King 1999; Martin Catawissa 69 36C09 PN PITT-8 Charcoal Conventional I 1455 45 1348 2004; McConaughy Bridge, str 3 2008 Crawford et al. 1997; UGa- 72 Fisher Farm 36CE35 PN Zea mays Conventional I 1245 70 1172 Hatch 1980; King 2683 1999 Martin 2004; GaK- 73 Gnagey 36SO55 PN Charcoal Conventional I 1030 80 946 McConaughy 5150 2008:20 Harding East et al. 2001; 74 36WO55 PN N/A Zea mays AMS D 1060 40 971 Flats Martin 2004, 2006

307

Adovasio and Johnson 1981; Adovasio et al. 2003; Meadowcroft 75 36WH297 PN SI-2051 Charcoal Conventional I 2325 75 2353 Hart and Brumbach Rockshelter 2005; Hart et al. 2007; Thompson et al. 2004 Asch Sidell 2008; Memorial PITT- Crawford et al. 1997; 76 36CN164 PN Charcoal Conventional I 1190 40 1116 Park 1073 Hart and Asch Sidell 1996 Murphy's Beta- George 2005; 77 39AR129 PN Charcoal Conventional I 1080 70 1002 Old House 78747 McConaughy 2008 Beta- Hart et al. 2005; 78 Petenbrink 36SO62 PN Unknown ? I 1080 70 1002 104103 Means 2002 Cutler and Blake 81 Ryan 36WM23 PN I-16,727 Charcoal Conventional I 980 80 880 1973; George 2004; McConaughy 2008 Bendremer and Smithfield Beta- 82 36MR5 PN Unknown ? I 930 80 843 Dewar 1994; Beach 21548 Herbstritt 1988 Beta- Zea King 1999; Rieth 83 St Anthony 36UN11 PN AMS D 950 80 855 22813 mays 2002

308

Figure A1.8: Sites in Group B (for site names, please see Map ID reference in Table A1.9)

309

Table A1.9: Sites in Group F (Earliest Pooled Date by Sampling Grid)

Prov Median Map Site Lab Dating Direct / Uncal Site Name / Material 13 Error Pooled? Caibrated Reference ID Code Number method Indirect δ C Date BP State Date (BP) Brashler et al. Birch Run Beta- 1 20SA393 MI Charcoal Conventional I 1120 90 n 1046 2000 Stothers Road 4517 et al. 1994 Crawford et al. Beta- 1997; Martin 2 Eidson 20BE122 MI Charcoal Conventional I 1650 70 n 1551 6154 2004; Raviele 2010 Beta- Pottery - Lovis et al 3 Fletcher 20BY-28 MI AMS D 785 85 n 727 17750 encrustation 31.99 1996 DIC- Crawford et al. 416, Human 1997; Schurr Gard Island 4 20MR162 MI DIC- Bone Conventional I -19 1295 93 y 1206 and Redmond # 2 418, Collagen 1991; Stothers DIC-419 and Abel 2002 Crawford et al. 1997; Stothers 9 Sissung 20MR5 MI M-1519 Charcoal Conventional I -25 1250 120 n 1163 1977; Stothers and Yarnell 1977 Bendremer and Beta- Dewar 1994; 10 Boland NY NY Charcoal Conventional I 1270 90 n 1187 24510 Cassedy and Webb 1999 Bendremer and Dewar 1994; Bowmans Beta- 11 NY NY Charcoal Conventional I -26.9 920 70 n 836 Cassedy and Brook 15770 Webb 1999; Little 2002 Deposit 13 NY N/A Zea mays AMS D 1210 40 n 1136 Knapp 2009 Airport I A-0497, NYSM- A-0499, Pottery 14 Felix NY AMS D -27.2 1555 35 y 1457 Hart et al 2007 1373 A-0503, encrustation A-0506

310

Beta- 53451, Asch Sidell Hudson Beta- 1999; Crawford 17 211-1-1 NY Zea mays AMS D 1080 60 y 999 River 53452, and Smith Beta- 2003 84969 Hart et al. 2003; Martin NYSM A-0225. Pottery 19 Kipp Island NY AMS D -26.4 1450 42 y 1343 2004; 2084 A-0227 encrustation Schulenberg 2002 Martin 2004; NYSM GX- Pottery Ritchie 1969; 20 Levanna NY AMS D 1090 40 n 1000 2092 28193 encrustation Schulenberg 2002 Hart et al 2007; NYSM- Pottery 22 Simmons NY A-0452 AMS D -28.7 1620 35 n 1503 J. Hart pers 1507 encrustation comm. A-0452, Pottery 24 Vinette NY NY AMS D -28.1 1960 38 y 1911 Hart et al. 2007 A-0455 encrustation Hart et al. Pottery 25 Westheimer NY NY A-0498 AMS D -25.9 1600 35 n 1475 2007; Ritchie encrustation and Funk 1973 Martin 2004; Beta- 27 Bear Fort 33SA08 OH Charcoal Conventional I 790 60 n 720 Stothers and 22864 Abel 2001 Crawford et al. Edwin 30 33 OH N/A Zea mays AMS D 1730 95 y 1648 1997; Riley et Harness al. 1994 Carskadden I-7868, and Morton 35 Philo II 33MU76 OH Charcoal AMS I 885 80 y 811 i-14101 1996; Martin 2004 Brose 1973, WIS- 1994; 37 South Park 33CU8 OH Charcoal Conventional D -17.4 950 65 n 854 576 Maslowski et al. 1995 Twin 38 Mounds 33HA24 OH N/A Unknown ? I 1100 130 n 1028 Martin 2004 West

311

CWRU- Maslowski et Human 36, al. 1995; 39 Waterworks 33LU06 OH Bone AMS I -25 1460 55 y 1356 CWRU- Stothers and Collagen 48 Bechtel 1987 Martin 2004; Pottery 40 Weilnau 33ER409 OH I-16454 AMS I 1380 80 n 1297 Stothers and encrustation Abel 2002 41 11h10 AmHp-10 ON I-4009 Charcoal Conventional I 1070 95 n 995 Smith 1997a Reid 1975; I-7322, Smith 1997a; 43 Boys AlGs-10 ON Charcoal Conventional I 825 107 y 768 I-n/a-2 Williamson 1990 Archaeological Beta- Services 45 D'Aubigny AgHb-276 ON Zea mays AMS D -28.6 1780 50 n 1703 217154 Incorporated 2006 Campbell and Campbell I-6411, 1989; Smith 46 DeWaele AfHd-1 ON Charcoal Conventional I 880 90 y 808 I-6412 1997a; Williamson 1990 Ferris 1988; 47 Dick AaHp-1 ON I-13242 Zea mays AMS D 930 110 n 848 Smith 1997a I- 12479,I- Crawford et al 12149, 2006; Murphy I-12478, 48 Dymock AeHj-2 ON Charcoal Conventional I 930 80 y 843 and Ferris I- 1990; Smith 12150,, 1997a I-12151, I-12152 Bursey and NYSM A-0406, Pottery Smith 1997; 49 Fortin 2 NY AMS D -29 1515 35 y 1397 72699 A-0407 encrustation Crawford and Smith 2002 BGS- Smith 1997c, 1843, G. Dibbs, 50 Grafton BaGm-9 ON Unknown ? I 1005 90 y 916 BGS- Personal 1844, Communication

312

BGS- 1845, BGS- 1845 Lakeshore Fox 1990; 53 AlGh-32 ON S-2194 Charcoal Conventional I 1110 60 n 1026 Lodge Martin 2004 Fox 1990a, 1990b; 57 Moyer Flats AiHc-24 ON I-13078 Charcoal Conventional I 1050 80 n 969 Williamson 1990 Timmins 1985; 58 Nodwell BcHi-3 ON S-1720 Zea mays AMS D -10 1035 80 n 951 Wright 1974, 1985 Martin 2004; Peace Williamson and 60 AfGr-9 ON N/A Charcoal AMS D 1330 60 n 1251 Bridge MacDonald 1997 I-6167, Noble 1975; 64 Van Besien AfHd-2 ON I-6847, Charcoal Conventional I 1040 107 y 960 Williamson I-6848 1990 DIC- McConaughy 65 Backstrum 1 36WM453 PN Charcoal Conventional I 1490 60 n 1383 3028 2008 Blackwell 67 36TI58 PN N/A Charcoal AMS I 1060 70 y 979 Miller 1993 Bridge McConaughy Campbell AA- 68 36FA26 PN Zea mays AMS D 795 40 n 714 2008; Hart et al Farm 40133 2002 King 1999; PITT- Catawissa Martin 2004; 69 36C09 PN 76, Charcoal Conventional I 1275 53 y 1209 Bridge, str 3 McConaughy PITT-11 2008 Crawford et al. UGa- 1997; Hatch 72 Fisher Farm 36CE35 PN Zea mays Conventional I 1245 70 n 1172 2683 1980; King 1999 GaK- Martin 2004; 5150, 73 Gnagey 36SO55 PN Charcoal Conventional I 975 80 y 875 McConaughy Uga- 2008:20 1599 74 Harding 36WO55 PN N/A Zea mays AMS D 1060 40 n 971 East et al.

313

Flats 2001; Martin 2004, 2006 Adovasio and Johnson 1981; Adovasio et al. SI-2051, 2003; Hart and Meadowcroft SI1674, 75 36WH297 PN Charcoal Conventional I 2225 88 y 2222 Brumbach Rockshelter SI-2487, 2005; Hart et SI-2362 al. 2007; Thompson et al. 2004 Asch Sidell 2008; Crawford Memorial PITT- 76 36CN164 PN Charcoal Conventional I 1190 40 n 1116 et al. 1997; Park 1073 Hart and Asch Sidell 1996 George 2005; Murphy's Beta- 77 39AR129 PN Charcoal Conventional I 1080 70 n 1002 McConaughy Old House 78747 2008 Beta- Hart et al. 104103, 78 Petenbrink 36SO62 PN Unknown ? I 1040 65 y 957 2005; Means Beta- 2002 77677 Cutler and I-16727, Blake 1973; 81 Ryan 36WM23 PN GAK- Charcoal Conventional I 905 80 y 826 George 2004; 3729 McConaughy 2008 Beta- Bendremer and Smithfield 21548, 82 36MR5 PN Unknown ? I 905 70 y 826 Dewar 1994; Beach Beta- Herbstritt 1988 15573 Beta- King 1999; 83 St Anthony 36UN11 PN Zea mays AMS D 950 80 n 855 22813 Rieth 2002

314

Figure A1.9: Sites in Group G (for site names, please see Map ID reference in Table A1.10)

315

Table A1.10: Sites in Group G (Earliest Direct Date by Sampling Grid)

Prov Median Map Site Lab Dating Uncal Site Name / Material 13 Error Caibrated Reference ID Code Number method δ C Date BP State Date BP Beta- Pottery 3 Fletcher 20BY-28 MI AMS -31.99 785 85 727 Lovis et al 1996 17750 encrustation Deposit 13 NY N/A Zea mays AMS 0 1210 40 1136 Knapp 2009 Airport I NYSM- Pottery 14 Felix NY A-0497 AMS -27.2 1575 35 1465 Hart et al 2007 1373 encrustation NYSM Pottery Thompson et al 15 Fortin 2 NY A-0406 AMS -29 1525 35 1409 72699 encrustation 2004 Asch Sidell 1999; Hudson Beta- 17 211-1-1 NY Zea mays AMS 0 1130 70 1050 Crawford and Smith River 53452 2003 Hart et al. 2003; NYSM Pottery 19 Kipp Island NY A-0225 AMS -26.4 1470 43 1358 Martin 2004; 2084 encrustation Schulenberg 2002 Martin 2004; Ritchie NYSM GX- Pottery 20 Levanna NY AMS 0 1090 40 1000 1969; Schulenberg 2092 28193 encrustation 2002 Hart 1999b, 2000; AA- 21 Roundtop NY NY Zea mays AMS 0 830 45 743 Hart et al. 2005; 26541 Timmins 1985 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 AMS -28.7 1620 35 1503 1507 encrustation Hart pers comm. Pottery 24 Vinette NY NY A-0500 AMS -28.1 2270 35 2246 Hart et al. 2007 encrustation Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 AMS -25.9 1600 35 1475 Ritchie and Funk encrustation 1973 Crawford et al. Edwin 30 33 OH N/A Zea mays AMS 0 1730 85 1647 1997; Riley et al. Harness 1994 Brose 1993; Franks / Beta- Bone 31 33LN13 OH AMS -13 1070 50 985 Maslowski et al Mill Hollow 43133 Collagen 1995 34 Patli- 33FU5 OH GX- Bone AMS -13.4 885 125 819 Stothers and Abel

316

Dowling 10742 Collagen 2002; Stothers and Bechtel 1987 Brose 1973, 1994; WIS- 37 South Park 33CU8 OH Zea mays Conventional -17.4 950 65 854 Maslowski et al. 576 1995 Archaeological AgHb- Beta- 45 D'Aubigny ON Zea mays AMS -28.6 1780 50 1703 Services 276 217154 Incorporated 2006 Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 Zea mays AMS 0 930 110 848 1997a Timmins 1985; 58 Nodwell BcHi-3 ON S-1720 Zea mays AMS -10 1035 80 951 Wright 1974, 1985 Monckton 1998; WAT- 59 Parsons AkGv-8 ON Zea mays AMS 0 860 110 795 Williamson and 2871 Robertson 1998 Martin 2004; Peace Pottery 60 AfGr-9 ON N/A AMS 0 1330 60 1251 Williamson and Bridge encrustation MacDonald 1997 Crane and Griffin 62 Stafford AeHg-3 ON M-1553 Zea mays AMS -10 735 110 690 1965; Williamson 1990 WSU- timmins 1985; 63 Uren AfHd-3 ON Zea mays AMS 0 830 70 759 1957 Williamson 1990 Blackwell 67 36TI58 PN N/A Zea mays AMS 0 1040 65 957 Miller 1993 Bridge Campbell AA- McConaughy 2008; 68 36FA26 PN Zea mays AMS 0 795 40 714 Farm 40133 Hart et al 2002 Buker 1970; King 71 Drew 36AL62 PN M-2198 Zea mays AMS -10 835 100 774 1999 Harding East et al. 2001; 74 36WO55 PN N/A Zea mays AMS 0 1060 40 971 Flats Martin 2004, 2006 Asch Sidell 2008; Memorial AA- Crawford et al. 76 36CN164 PN Zea mays AMS -10.6 985 45 883 Park 19127 1997; Hart and Asch Sidell 1996 Beta- King 1999; Rieth 83 St Anthony 36UN11 PN Zea mays AMS 0 950 80 855 22813 2002

317

Figure A1.10: Sites in Group H (for site names, please see Map ID reference in Table A1.11)

318

Table A1.7: Sites in Group H (Earliest Pooled Direct Date by Sampling Grid)

Prov Median Map Site Lab Dating Uncal Site Name / Pooled? Material 13 Error Caibrated Reference ID Code Number method δ C Date (BP) State Date (BP) Beta- Pottery 3 Fletcher 20BY-28 MI n AMS -31.99 785 85 727 Lovis et al 1996 17750 encrustation Deposit 13 NY N/A n Zea mays AMS 1210 40 1136 Knapp 2009 Airport I A-0497, NYSM- A-0499, Pottery 14 Felix NY y AMS -27.2 1555 35 1457 Hart et al 2007 1373 A-0503, encrustation A-0506 NYSM A-0406, Pottery 15 Fortin 2 NY y AMS -29 1515 35 1397 Thompson et al 2004 72699 A-0407 encrustation Beta- 53451, Asch Sidell 1999; Hudson Beta- 17 211-1-1 NY y Zea mays AMS 1080 60 999 Crawford and Smith River 53452, 2003 Beta- 84969 Hart et al. 2003; NYSM A-0225. Pottery 19 Kipp Island NY y AMS -26.4 1450 42 1343 Martin 2004; 2084 A-0227 encrustation Schulenberg 2002 Martin 2004; Ritchie NYSM GX- Pottery 20 Levanna NY n AMS 1090 40 1000 1969; Schulenberg 2092 28193 encrustation 2002 Y-1534, Hart 1999b, 2000; 21 Roundtop NY NY AA- y Zea mays AMS -25 850 53 767 Hart et al. 2005; 26541 Timmins 1985 NYSM- Pottery Hart et al 2007; J. 22 Simmons NY A-0452 n AMS -28.7 1620 35 1503 1507 encrustation Hart pers comm. A-0452, Pottery 24 Vinette NY NY y AMS -28.1 1960 38 1911 Hart et al. 2007 A-0455 encrustation Hart et al. 2007; Pottery 25 Westheimer NY NY A-0498 n AMS -25.9 1600 35 1475 Ritchie and Funk encrustation 1973

319

Edwin Crawford et al. 1997; 30 33 OH N/A y Zea mays AMS 1730 95 1648 Harness Riley et al. 1994 Franks / Mill Beta- Bone Brose 1993; 31 33LN13 OH n AMS -13 1070 50 985 Hollow 43133 Collagen Maslowski et al 1995 Stothers and Abel Patli- GX- Bone 34 33FU5 OH n AMS -13.4 885 125 819 2002; Stothers and Dowling 10742 Collagen Bechtel 1987 WIS- Brose 1973, 1994; 37 South Park 33CU8 OH n Zea mays Conventional -17.4 950 65 854 576 Maslowski et al. 1995 Archaeological AgHb- Beta- 45 D'Aubigny ON n Zea mays AMS -28.6 1780 50 1703 Services Incorporated 276 217154 2006 Ferris 1988; Smith 47 Dick AaHp-1 ON I-13242 n Zea mays AMS 930 110 848 1997a Timmins 1985; Wright 58 Nodwell BcHi-3 ON S-1720 n Zea mays AMS -10 1035 80 951 1974, 1985 Martin 2004; Peace Pottery 60 AfGr-9 ON N/A n AMS 1330 60 1251 Williamson and Bridge encrustation MacDonald 1997 Crane and Griffin 62 Stafford AeHg-3 ON M-1553 n Zea mays AMS -10 735 110 690 1965; Williamson 1990 WSU- timmins 1985; 63 Uren AfHd-3 ON n Zea mays AMS 830 70 759 1957 Williamson 1990 Blackwell 67 36TI58 PN N/A y Zea mays AMS 1060 70 979 Miller 1993 Bridge Campbell AA- McConaughy 2008; 68 36FA26 PN n Zea mays AMS 795 40 714 Farm 40133 Hart et al 2002 Buker 1970; King 71 Drew 36AL62 PN M-2198 n Zea mays AMS -10 835 100 774 1999 Harding East et al. 2001; 74 36WO55 PN N/A n Zea mays AMS 1060 40 971 Flats Martin 2004, 2006 Asch Sidell 2008; Memorial AA- Crawford et al. 1997; 76 36CN164 PN n Zea mays AMS -10.6 985 45 883 Park 19127 Hart and Asch Sidell 1996 Beta- King 1999; Rieth 83 St Anthony 36UN11 PN n Zea mays AMS 950 80 855 22813 2002

320

A1.7: RESULTS OF POINT OF ORIGIN ANALYSIS

A1.7.1: Dataset A (Earliest Uncontested Date)

For dataset A, no significant point of origin was found based on the distribution of dates. The highest correlation coefficient for the point of origin analysis was r =-0.10 at

45°N latitude, 70°W longitude, but this is too low a value to be instructive. There were slightly stronger positive r values just northwest of the sampling region (45°N, 85°W to

50°N, 95°W; r ~ 0.12 to 0.15), suggesting that there is a slight trend for dates to get younger as one travels northwest; however these values were also too low to be instructive of any patterns (Figure A1.11). This indicates that no linear pattern can effectively describe the variation in the earliest date of all 83 sites in Group A, and therefore, no definitive point of origin can be established.

Figure A1.11: Results of point of origin analysis for Group A

321

A1.7.2: Dataset B (Earliest Dates and Pooled Dates, where Appropriate)

For dataset B, no significant point of origin was found based on the distribution of dates. The highest correlation coefficient for the point of origin analysis was r = -0.10, also at 45°N latitude, 70°W longitude; however this is too low a value to be instructive.

Similar to dataset A, there was a slightly stronger positive trend northwest of the sampling region (45°N, 85°W to 50°N, 90°W; r ~ 0.10 to 0.13), although a slightly weaker relationship was observed (Figure A1.12). This indicates that no linear pattern can effectively describe the variation in the earliest date of all 82 sites in Group B, and therefore, no definitive point of origin can be established.

Figure A1.12: Results of point of origin analysis for Group B

322

1.7.3: Dataset C (Earliest Direct Dates on Maize)

For dataset C, the strongest correlation coefficient was found at 45°N latitude,

75°W longitude (r = -0.43). This only represents a moderate linear relationship between date and location, although there was a fair amount of grouping of similar negative r values in the area directly northeast of the sampling region (50°N, 65°W to 45°N, 75°W; r ~ -0.40 to -0.43). There is an equally strong positive trend in southwestern Mexico (r ~

0.39), with Guilá Naquitz providing one of the highest positive values (r = 0.39). While r values of 0.4 and -0.4 do not indicate a strong linear trend by which to establish a point of origin, what is perhaps most interesting is that it appears that dates in the Northeast get older the further they are from Mexico (Figure A1.13). This is contradictory to what one would expect from a demic spread out of Mexico via the southwestern United States.

Figure A1.13: Results of point of origin analysis for Group C

323

A1.7.4: Dataset D (Pooled Direct Dates on Maize)

For dataset D, the strongest correlation coefficient was also found at 45°N latitude, 75°W longitude (r = -0.44). Similar to dataset C, the strongest negative coefficients were found northeast of the sampling region (50°N, 65°W to 45°N, 75°W; r

~ -0.42 to -0.44) and there is an equally strong positive trend in southwestern Mexico (r ~

0.40), with Guilá Naquitz providing one of the highest positive values (r = 0.40). This suggests – based on direct dates on maize in the dataset – that there is a correlation between the earliest entry of maize in the region and a diffusionary origin directly to the northeast of the sampling region (Figure A1.14).

Figure A1.14: Results of point of origin analysis for Group D

324

A1.7.5: Dataset E (Earliest Dates by Sampling Grid)

For dataset E, no significant point of origin was found based on the distribution of dates. The highest correlation coefficient for the point of origin analysis was r = -0.10 at

45°N latitude, 75°W longitude; however this is too low a value to be instructive. Slightly stronger positive r values were observed just northwest of the sampling region (45°N,

85°W to 50°N, 90°W; r ~ 0.10 to 0.14), suggesting that there is a slight trend for dates to get younger as one travels northwest; however these values were also too low to be instructive of any patterns (Figure A1.15). This indicates that no linear pattern can effectively describe the variation in the earliest date of 50 earliest sites by region from

Group A, and no point of origin can be established. While sampling by grid has removed

30 later sites from the dataset, little change is seen in the results.

Figure A1.15: Results of point of origin analysis for Group E

325

A1.7.6: Dataset F (Pooled Dates by Sampling Grid)

For dataset F, no significant point of origin was found based on the distribution of dates. Similar to dataset B (the pooled dataset of all sites in the region), the points with the strongest negative relationship was at 45°N latitude, 75°W longitude (r = -0.087), however this is too low a value to be instructive. Similar to dataset B, there was a slightly stronger positive trend northwest of the sampling region (45°N, 80°W to 50°N, 85°W; r ~

0.10 to 0.13). This indicates that no linear pattern can effectively describe the variation in the earliest date of all 49 sites in Group F, and no point of origin can be established

(Figure A1.16). Furthermore, this indicates that this lack of relationship is likely not due to inherent temporal noise on the regional scale in the dataset.

Figure A1.16: Results of point of origin analysis for Group F

326

A1.7.7: Dataset G (Earliest Direct Dates by Sampling Grid)

For dataset G, the strongest correlation coefficient was found at 45°N latitude,

75°W longitude (r = -0.42). There is an equally strong positive trend in southwestern

Mexico, with Guilá Naquitz providing one of the highest positive values (r = 0.36); however the strongest positive value came surprisingly from the center of the sampling region, at 40°N, 80°W. This location had similarly high positive values in datasets C and

D. Therefore, based on the temporal variation in the direct dataset, it appears that there is a pattern not only of dates getting older in the northeast portion of the dataset, but also of their getting older as they move away from the centre of the sampling region (Figure

A1.17). Additionally, it appears that the sampling of the direct datasets by grid location had little effect on the observed temporal variability in the dataset.

Figure A1.17: Results of point of origin analysis for Group G

327

1.6.8: Dataset H (Pooled Direct Dates by Sampling Grid)

For dataset H, the strongest correlation coefficient was found at 45°N latitude,

75°W longitude (r = -0.43). There is an equally strong positive trend in southwestern

Mexico, with Guilá Naquitz providing one of the highest positive values (r = 0.38). Like dataset G, however, the strongest positive value came from the center of the sampling region, at 40°N, 80°W (Figure A1.18).

Figure A1.18: Results of point of origin analysis for Group H

328

REFERENCES

Adams, K. R. 1994 A Regional Synthesis of Zea mays in the Prehistoric American Southwest. In Corn and Culture in the Prehistoric New World, edited by S. Johannessen and C. A. Hastorf, pp. 273-302. Westview Press, Boulder.

Alexander, H. G. and P. Reiter 1935 Report on the excavation of Jemez Cave, New Mexico. Monograph No. 4. University of New Mexico and School of American Research, Santa Fe.

Bendremer, J.C.M. and R.E. Dewar 1994 The advent of prehistoric maize in New England. In Corn and Culture in the Prehistoric New World, edited by S. Johannessen and C.A. Hastorf. pp. 369-393. University of Minnesota Publications in Anthropology, No. 5, Boulder, Westview Press.

Benz, B. F. 2001 Archaeological evidence of teosinte domestication from Guilá Naquitz, Oaxaca. Proceedings of the National Academy of Sciences, USA 98(4):2104- 2106.

Benz, B. F., L. Cheng, S. W. Leavitt and C. Eastoe 2006 El Riego and Early Maize Agricultural Evolution. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Biogeography, Domestication, and Evolution of Maize, edited by J. E. Staller, R. H. Tykot and B. F. Benz, pp. 73-82. Academic Press, Amsterdam.

Berg, D. J. and J. A. Bursey 2000 The worked faunal material from the Anderson site: a Uren village on the lower Grand River, Ontario. Ontario Archaeology 69:7-18.

Blake, M. and B. Benz 2010 AMS Radiocarbon date and morphological measurements for maize samples from Turkey Pen Cave, UT. Unpublished preliminary report submitted to R.G. Matson. On file with Laboratory of Archaeology, University of B.C.

Blake, M., J. E. Clark, B. Voorhies, G. Michaels, M. W. Love, M. E. Pye, A. A. Demarest and B. Arroyo 1995 Radiocarbon Chronology for the Late Archaic and Formative Periods on the Pacific Coast of Southeastern Mesoamerica. Ancient Mesoamerica 6:161- 183.

329

Bowman, S. 1990 Interpreting the Past: Radiocarbon Dating. London: British Museum Publications

Brashler, J. G., Garland, E. B., Holman, M. B., Lovis, W. A., and Martin S. R. 2000 Adaptive Strategies and Socioeconimic Systems in Northern Great Lakes Riverine Environments: The Late Woodland of Michigan. In Late Woodland Societies: Tradition and Transformation across the Midcontinent, edited by T. E. Emerson, D. L:. McElrath, and A. C. Fortier, pp. 543–582. University of Nebraska Press.

Brink, J. 2008 Imagining Head-Smashed-In: Aboriginal Buffalo Hunting on the Northern Plains. AU Press, Edmonton, AB.

Brose, D. 1973 A preliminary analysis of recent excavations at the South Park site, Cuyahoga County, Ohio. Pennsylvania Archaeologist 43(1): 25-42.

1993 Early Mississippian Connections at the Late Woodland Mill Hollow Site in Lorain County, Ohio. Midcontinental Journal of Archaeology 18:97-130.

Buker, W. E. 1970 The Drew Site (36-AL-62). The Pennsylvania Archaeologist 40(3-4):21-66

Bursey, J. A., and Smith, D.G. 1999 Report on the 1997 excavations at the Forster site (AgGx-134), Caledonia, Ontario. Annual Archaeological Report, Ontario (New Series) 9:95–97.

Byrd, B. F. 2005 Reassessing the Emergence of Village Live in the Near East. Journal of Archaeological Research. 13(3):231-290.

Campbell, C. and I.D. Campbell 1989 The Little Ice Age and Neutral faunal assemblages. Ontario Archaeology 49:13-33.

Carskadden, J, and J. Morton 1996 The Middle Woodland-Late Woodland Transition in the Central Muskingum Valley of Eastern Ohio: a View From the Philo Archaeological District. In A View From the Core: A Synthesis of Ohio Hopewell Archaeology, edited by P. Pacheco, pp. 316-338. Ohio Archaeological Council, Columbus.

330

Cassedy, D. and Webb, P. 1999 New Data on the Chronology of Maize Horticulture in Eastern New York and Southern New England. In Current Northeast Paleoethnobotany, edited by John Hart, pp. 85-99. New York State Museum Bulletin No. 494. Albany: New York State Museum.

Clark, J. E. 1994 The Development of Early Formative Rank Societies in the Soconusco, Chiapas, Mexico. Unpublished Ph.D. dissertation, Department of Department of Anthropology, University of Michigan, Ann Arbor.

Clark, John E. and Ronald W. Lowe 1980 Archaeological Investigations in the Morelos Piedmont, Municipio de Trinitaria, Mexico. Report on file with the New World Archaeological Foundation, San Cristobal de las Casas, Chiapas.

Conard, N., D. L. Asch, N. B. Asch, D. Elmore, H. Gove, M. Rubin, J. A. Brown, M. D. Wiant, K. B. Farnsworth and T. G. Cook 1984 Accelerator Radiocarbon Dating of Evidence for Prehistoric Horticulture in Illinois. Nature308(5958):443-446.

Creel, Darrell and Austin Long 1986 Radiocarbon Dating of Corn. American Antiquity 51(4):826-837.

Crane, H. R. and J. B. Griffin 1958 University of Michigan radiocarbon dates, part 3. Science 128:1117-1122.

Crawford, G. W. and David G. Smith 2002 Early Late Woodland in Southern Ontario: an Update. In Northeast Subsistence-Settlement Change: A.D. 700 –A.D. 1300, edited by John Hart and Christina Rieth. pp. 117-133. New York State Museum, Albany.

Cutler, Hugh C. 1965 Cultivated Plants. In: The McGraw Site (by Olaf H. Prufer et al), pp. 107- 112. Scientific Publications of the Cleveland Natural History Museum, Volume 4, No. 1.

Dick, H. W. 1965 Bat Cave. Monograph No. 27. School of American Research, Santa Fe.

331

Diehl, M. W. 2005 Morphological observations on recently recovered early agricultural period maize cob fragments from southern Arizona. American Antiquity 70(2):361- 375.

East, T., C. Espenshade, P. Fitzgibbons, and F. King 2001 Wyoming County, S.R. 0006, Tunkhannock Bypass: Harding Flats Site 36WO55, Phase II/III Data Recovery Investigations: Volume 5. Interpretations and Conclusions. Skelly and Loy, Inc. Monroeville, PA. Ms. on file Pennsylvania Department of Transportation, Engineering District 3-0, Montoursville.

Eddy, F. W. and M. E. Cooley 1983 Cultural and Environmental History of Cienega Valley, Southeastern Arizona.Anthropological Papers No. 43. University of Arizona Press, Tucson.

Ezzo, J. A. and W. L. Deaver 1998 Watering the Desert: Late Archaic Farming at the Costello-King Site. Technical Series No. 68. Statistical Research, Inc., Tucson.

Fecteau, R. 1985 The Introduction and Diffusion of Cultivated Plants in Southern Ontario. Unpublished MA dissertation, York University, Toronto, Ontario.

Feddema, Vicki L. 1993 Early Formative Subsistence and Agriculture in Southeastern Mesoamerica. Unpublished M.A. Thesis, Department of Department of Anthropology and Sociology, University of British Columbia, Vancouver. [https://circle.ubc.ca/handle/2429/2254].

Flannery, Kent V. 1986 Guilá Naquitz in Spatial, Temporal, and Cultural Context. In Guilá Naquitz: Archaic Foraging and Early Agriculture in Oaxaca, Mexico, edited by K.V. Flannery, pp. 31-42. Academic Press, Orlando.

Ford, R. I. 1975 Re-excavation of Jemez Cave. Awanyu 3:13-27.

Ford, R. I. 1985 Patterns of prehistoric food production in North America. In Prehistoric Food Production in North America, edited by R. I. Ford, pp. 341-364. Anthropological Papers No. 75. University of Michigan Museum of Anthropology, Ann Arbor.

332

Gilmore, K. P. 2005 National Register of Historic Places Registration Form for the Franktown Cave Site (5DA272). On file at the Office of Archaeology and Historic Preservation, Denver.

Fox, W. A. 1990a The Middle Woodland to Late Woodland transition. In The Archaeology of Southern Ontario to A.D. 1650, edited by C. J. Ellis and N. Ferris, pp. 171- 188. Occasional Papers of the London Chapter, Ontario Archaeological Society, No. 5. London, Ontario: Ontario Archaeological Society.

1990b Salvage excavation of the Moyer Flats site. Kewa 90(2):2-13.

Garland, E. B. 1990 Features at the Eidson site. In Late Archaic and Early Woodland adaptation in the lower St/ Joseph River Valley, Berrien County, Michigan, edited by E.B. Garland, Volume 2. pp. 301-332. Kalamazoo: Michigan Cultural Resource Investigation Series.

George, R. L. 2004 A Closer Look at the Monongahela Drew Tradition. Pennsylvania Archaeologist 74(1):63–91.

George, R. L. 2005 The Murphy’s Old House Site (36AR129): A Historic and Prehistoric Multi-component Site. Pennsylvania Archaeologist 75(1):1–28.

Goman, M. and R. Byrne 1998 A 5000-year record of agriculture and tropical forest clearance in the Tuxtlas, Veracruz, Mexico. The Holocene 8(1):83-89.

Gregory, D. A. (editor) 1999 The Early Agricultural Period Component at Los Pozos: Feature Descriptions and Data Tables. Technical Reports 1999-4. Center for Desert Archaeology, Tucson.

Gregory, D. A. (editor) 2001 Excavations in the Santa Cruz River Floodplain: The Early Agricultural Period Component at Los Pozos. Anthropological Papers 21. Center for Desert Archaeology, Tucson.

333

Gregory, D. A., M. N. Stevens, F. L. Nials, M. R.Schurr and M. W.Diehl 2007 Excavations in the Santa Cruz River Floodplain: Further Investigations at Los Pozos. Anthropological Papers. No. 27. Center for Desert Archaeology, Tucson.

Hall, Stephen A. 2010 Early Maize Pollen from Chaco Canyon, New Mexico, USA. Palynology34(1):125-137.

Hard, R. J. and J. R. Roney 1998 A Massive Terraced Village Complex in Chihuahua, Mexico, 3000 Years Before Present.Science 279:1661-1664.

Hart, J. P. 1999c Dating Roundtop’s Domesticates: Implications for Northeast Late Prehistory. In Current Northeast Paleoethnobotany, edited by J. Hart, pp. 47- 68. New York State Museum Bulletin No. 494. Albany: New York State Museum.

Hart, J. P. 2000b New Dates from Old Collections: The Roundtop Site and Maize-Beans- Squash Agriculture in the Northeast. North American Archaeologist 21:7–17.

Hart, J. P., and N. Asch Sidell. 1996 Prehistoric Agricultural Systems in the West Branch of the Susquehanna River Basin, A.D. 800 to A.D. 1350. Northeast Anthropology 52:l-30.

Hart, J. P., Nass, J. P., Means, B. K. 2005 Monongahela Subsistence-settlement Change? Midcontinental Journal of Archaeology 30(2):327-365.

Hatch, J. W. (editor) 1980 The Fisher Farm Site: A Late Woodland Hamlet in Context. Occasional Papers in Anthropology No. 3. Department of Anthropology, The Pennsylvania State University, University Park.

Hellman, R. 2003 Kennesaw Mountain National Battlefield Park: Cultural Overview. National Park Service, Tallahassee. http://www.nps.gov/seac/SoutheastChronicles/sechronicles.htm accessed July 2010.

334

Hesse, S. J. and M. S. Foster (editors) 2005 Investigation of Middle Archaic and Early Agricultural Period Components at Las Capas: The Treatment Plant Locus (Draft). Cultural Resources Report 05-165. SWCA, Inc., Tucson.

Huber, E. K. 2005 Chapter 36: Early maize at the Old Corn Site (LA 137258). In Fence Lake Project. Volume 1: Introduction and Site Descriptions, edited by E. K. Huber and C. R. V. West, pp. 36.1-36.33. Technical Series No. 84. Statistical Research Inc., Tempe.

Huber, E. K. and C. R. V. West (editors) 2005 Fence Lake Project. Archaeological Data Recovery in the New Mexico Transportation Corridor and First Five-Year Permit Area, Fence Lake Coal Mine Project, Catron County, New Mexico. Volume 1: Introduction and Site Descriptions. Technical Series No. 84. Statistical Research Inc., Tempe.

Huckell, B. B. 1984 The Paleo-Indian and Archaic occupation of the Tucson Basin: an overview. The Kiva 49:133-145.

Huckell, B. B. 1990 Late Preceramic Farmer-foragers in South-eastern Arizona: A Cultural and Ecological Consideration of the Spread of Agriculture into the Arid Southwestern United States. Unpublished Ph.D dissertation, Department of Arid Lands Resource Sciences, University of Arizona, Tucson.

Huckell, B. B. 1995 Of marshes and maize: preceramic agricultural settlements in the Cienega Valley, southeastern Arizona. Anthropological Papers No. 59. University of Arizona Press, Tucson.

Huckell, B. B. 2001 History of archaeological research in the Cienega Valley. Archaeology Southwest 15(4):2.

Huckell, B. B., L. Huckell and S. K. Fish 1995 Investigations at Milagro, a Late Preceramic Site in the Eastern Tucson Basin. Technical Report 94-5. Center for Desert Archaeology, Tucson.

335

Jankuta, K. 2011 Radiocarbon Dates as Data: Spatial-Temporal Analysis of the Late Dorset to Thule Transition. Unpublished MA thesis, Department of Anthropology, Trent University, Peterborough, Ontario.

Jaenicke-Després, V., E. S. Buckler, B. D. Smith, M. Thomas, P. Gilbert, A. Cooper, J. Doebley and S. Pääblo 2003 Early Allelic Selection in Maize as Revealed by Ancient DNA. Science302:1206-1208.

Jaenicke-Després, V. and B. D. Smith 2006 Ancient DNA and the Integration of Archaeological and Genetic Approaches to the Study of Maize Domestication. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Biogeography, Domestication, and Evolution of Maize, edited by J. E. Staller, R. H. Tykot and B. F. Benz, pp. 83-96. Academic Press, Amsterdam.

Kennett, Douglas J., Dolores R. Piperno, John G. Jones, Hector Neff, Barbara Voorhies, Megan K. Walsh and Brendan J. Culleton 2010 Pre-Pottery Farmers on the Pacific Coast of Southern Mexico. Journal of Archaeological Science 37(12):3401-3411.

Kirkby, Michael J., Anne V. Whyte and Kent V. Flannery 1986 The Physical Environment of the Guilá Naquitz Cave Group. In Guilá Naquitz: Archaic Foraging and Early Agriculture in Oaxaca, Mexico, edited by K.V. Flannery, pp. 43-61. Academic Press, Orlando.

Knapp, T. D. 2009 An Unbounded Future? Ceramic Types, Cultures, and Scale in Late Prehistoric Research. In Iroquoian Archaeology and Analytic Scale, edited by Laurie E. Miroff and Timothy D. Knapp, pp. 101-129. University of Tennessee Press.

Kuhn, R. D. 1994 Recent CRM contributions to Iroquoian archeology in New York State. Archaeology of Eastern North America 22:73-88.

Leyden, B. W., M. Brenner and B. H. Dahlin 1998 Cultural and climatic history of Cobá, a Lowland Maya city in Quintana Roo, Mexico. Quaternary Research 49(1):111-122.

Little, E. A. 2002 Kautantouwit's legacy: calibrated dates on prehistoric maize in New England. American Antiquity 67(1):109-118. Long, A., B. F. Benz, D. J. Donahue, A. J. T. Jull and L. J. Toolin

336

1989 First Direct AMS Dates on Early Maize from Tehuacán, Mexico.Radiocarbon 31(3):1035-1040.

Lovis, W.A., K.C. Egan, G.W. Monaghan, B.A. Smith, E.J. Prahl 1996 Environment and Subsistence at the Marquette Viaduct Locale of the Fletcher Site. In Investigating the Archaeological Record of the Great Lakes State, edited by M.B. Holman, J.G. Brashler, and K.E. Parker, pp. 251-306. New Issues Press, Kalamazoo, MI.

Lovis, W. A., K.C. Egan-Bruhy, B.A. Smith, G.W. Monaghan, 2001 Wetlands and Emergent Horticultural Economies in the Upper Great Lakes: A new Perspective from the Schultz Site, American Antiquity 66 (2001) 615–632.

Mabry, J. B. 1998 Introduction. In Archaeological Investigations of Early Village Sites in the Middle Santa Cruz Valley: Analyses and Synthesis, Part I, edited by J. B. Mabry, pp. 1-29. Anthropological Papers No. 19. Center for Desert Archaeology, Tucson.

2005 Diversity in early Southwestern farming and optimization models of transitions to agriculture. In Subsistence and Resource Use Strategies of Early Agricultural Communities in Southern Arizona, edited by M. W. Diehl, pp. 115-152. Anthropological Papers, No. 34. Center for Desert Archaeology, Tucson.

2006 Chapter 19: Radiocarbon dating of the early occupations. In Rio Nuevo Archaeology, 2000-2003: Investigations at the San Agustín Mission and Mission Gardens, Tucson Presidio, Tucson Pressed Brick Company, and Clearwater Site, edited by J. H. Thiel and J. B. Mabry, pp.19:1-5. Technical Report No. 2004-11. Desert Archaeology, Inc., Tucson.

Mabry, J. B. (editor) 2008 Las Capas: Early Irrigation and Sedentism in a Southwestern Floodplain.Anthropological Papers No. 28. Center for Desert Archaeology, Tucson.

MacNeish, R. S. 1958 Preliminary Archaeological Investigations in the Sierra De Tamaulipas, Mexico. Transactions of the American Philosophical Society, No. 48(6), Philadelphia.

337

Mangelsdorf, P. C., R. S. MacNeish and W. C. Galinat 1967 Prehistoric Wild and Cultivated Maize. In The Prehistory of the Tehuacan Valley, Volume 1. Environment and Subsistence, edited by D. S. Byers, pp. 178-200. University of Texas Press, Austin.

Martin, P. S., J. B. Rinaldo, E. Bluhm, H. C. Cutler and J. R. Grange 1952 Mogollon Cultural Continuity and Change: The Stratigraphic Analysis of Tularosa and Cordova Caves. Fieldiana Anthropology. No. 40. Chicago Natural History Museum, Chicago.

Maslowski, R.F., C.M. Niquette, and D.M. Wingfield 1995 The Kentucky, Ohio and West Virginia radiocarbon database. West Virginia Archeologist 47(1&2):1-75.

Matson, R. G. 1991 The Origins of Southwestern Agriculture. University of Arizona Press, Tucson.

Matson, R. G. 2003 The Spread of Maize Agriculture into the U.S. Southwest. In Examining the Farming/Language Dispersal Hypothesis, edited by P. Bellwood and C. Renfrew, pp. 341-356. McDonald Institute for Archaeological Research, University of Cambridge, Cambridge.

Matson, R. G. and B. Chisholm 1991 Basketmaker II subsistence: carbon isotopes and other dietary indicators from Cedar Mesa, Utah. American Antiquity 56(3):444-459.

McIntosh, Jane. 1999 The Practical Archaeologist: How we know what we know about the past (2nd Ed.). Checkmark Books: New York

Means, B. K. 2002 The Later Prehistory of Meyersdale, Pennsylvania and its Surroundings: An Overview. North American Archaeologist 23(4):281-307.

Merrill, W. L., R. J. Hard, J. B. Mabry, G. J. Fritz, K. R. Adams, J. R. Roney and A. C. MacWilliams 2009 The diffusion of maize to the southwestern United States and its impact. Proceedings of the National Academy of Sciences USA 106(50):21019- 21026.

338

Meyer, D. and D. Walde 2009 Rethinking Avonlea: Pottery Wares and Cultural Phases. Plains Anthropologist 54(209):49-73.

Miljour, H. J. and E. K. Huber 2005 Chapter 5: LA 137258 (Old Corn Site). In Fence Lake Project. Volume 1: Introduction and Site Descriptions, edited by E. K. Huber and C. R. V. West, pp. 5.1-5.86. Technical Series No. 84. Statistical Research Inc., Tempe.

Monckton, S. G. 1998 Parsons Site Plant Remains. Ontario Archaeology 65/66:111–120.

Mountjoy, J. B. 1989 Early Radiocarbon Dates from a Site on the Pee Dee-Siouan Frontier in the Piedmont of Central North Carolina. Southern Indian Studies 38:7-22.

Murphy, C. and N. Ferris 1990 The Late Woodland Western Basin tradition in southwestern Ontario. In The archaeology of southern Ontario to A.D. 1650, edited by C.J. Ellis and N. Ferris. pp. 189-278. Occasional Publication of the London Chapter, Ontario Archaeological Society 5, London, Ontario.

Noble, W.C, and Kenyon, I. T. 1972 Porteous (AgHb-1): A Probable Early Glen Meyer Village in Brant County, Ontario. Ontario Archaeology 19:11-38. Niederberger, C. 1979 Early Sedentary Economy in the Basin of Mexico. Science203(4376):131- 142.

Piperno, D. R. and K. V. Flannery 2001 The earliest archaeological maize (Zea mays L.) from Highland Mexico: New accelerator mass spectrometry dates and their implications. Proceedings of the National Academy of Sciences, USA 98(4):2101-2103.

Pohl, M. 2001 Economic Foundations of Olmec Civilization in the Gulf Coast Lowlands of México. FAMSI Online Reports. http://www.famsi.org/reports/99069/ accessed October 23 2010.

Pohl, M. E. D., D. R. Piperno, K. O. Pope and J. G. Jones 2007 Microfossil Evidence for Pre-Columbian Maize Dispersals in the Neotropics from San Andrés, Tabasco, Mexico. Proceedings of the National Academy of Sciences USA 104(16):6870-6875.

339

Pope, K. O., M. D. Pohl, J. G. Jones, D. L. Lentz, C. v. Nagy, F. J. Vega and I. R. Quitmeyer 2001 Origin and environmental setting of ancient agriculture in the lowlands of Mesoamerica. Science 292:1370-1373.

Prahl, E. 1974 The Morin Site (20MO40), Monroe County, Michigan. Michigan Archaeologist 20(2):65-95.

Prufer, O. (editor) 1965 The McGraw Site: a Study in Hopewellian Dynamics. Scientific Publications 4(1). Cleveland Museum of Natural History, Cleveland.

Raviele, M. E. 2010 Assessing Carbonized Archaeological Cooking Residues: Evaluation of Maize Phytolith Taphonomy and Density through Experimental Residue Analysis. Unpublished Ph.D. Dissertation, Department of Anthropology, Michigan State University.

Reid, C.S. 1975 The Boys Site and 'the Early Ontario Iroquois Tradition.Mercury Series Paper 42. Archaeological Survey of Canada. National Museum of Man, Ottawa.

Ritchie, W. 1969 The Archaeology of New York State. Revised ed. Natural History Press, Garden City.

Ritchie, W. and Funk, R. 1973 Aboriginal Settlement Patterns in the Northeast. New York Museum & Science Service Memoir 20. Albany: The University of the State of New York.

Rogers, R., B. Cruse, M. Cruse, K. Reese-Taylor, J. M. Tanner, T. L. Gosdin and W. P. Glande 1997 Archaeological Investigations at the Oak Hill Village Site (41RK214), Rusk County, Texas. Paper presented at the 14th Annual Meeting of the American Society for Surface Mining and Reclamation, Austin, TX.

Rosenswig, R. M. 2009 Early Mesoamerican garbage: ceramic and daub discard patterns from Cuauhtémoc, Soconusco, Mexico. Journal of Archaeological Method and Theory 16(1):1-32.

340

Rosenswig, R. M. 2010 The Beginnings of Mesoamerican Civilization: Inter-Regional Interaction and the Olmec. Cambridge University Press, New York.

Simmons, A. H. 1986 New Evidence for the Early Use of Cultigens in the American Southwest.American Antiquity 51(1):73-89.

Schiffer, M. B. 1982 Radiocarbon Dating and the “Old Wood” Problem: The Case of the Hohokam Chronology. Journal of Archaeological Science 13:13-30.

Schulenberg, J. 2002 New Dates for Owasco Pots. In Northeast Subsistence-Settlement Change: A.D. 700-1300, edited by J. Hart and C. Rieth, pp. 153-165. Bulletin 496. New York State Museum, Albany.

Schurr, M. and B. Redmond 1991 Stable Isotope Analysis of Incipient Maize Horticulturalists from the Gard Island No. 2 Site. Midcontinental Journal of Archaeology 16(1):69-84.

Shott, M.J. 1992 Radiocarbon Dating as a Probabilistic Technique: The Childers Site and Late Woodland Occupation in the Ohio Valley. American Antiquity 57(2):202- 230

Sluyter, A. and G. Dominguez 2006 Early maize (Zea mays L.) cultivation in Mexico: dating sedimentary pollen records and its implications. Proceedings of the National Academy of Sciences, USA 103(4):1147-1151.

Smiley, Francis E. 1994 The Agricultural Transition in the Northern Southwest: Patterns in the Current Chronometric Data. Kiva 60(2):165-189.

Smith, B. D. 1997 Reconsidering the Ocampo Caves and the Era of Incipient Cultivation in Mesoamerica. Latin American Antiquity 8(4):342-383.

Speller, C. F., B. M. Kemp, S. D. Wyatt, C. Monroe, W. D. Lipe, U. M. Arndt and D. Y. Yang 2010 Ancient mitochondrial DNA analysis reveals complexity of indigenous North American turkey domestication. Proceedings of the National Academy of Sciences, USA 107(7):2807-2812.

341

Story, Dee Ann and S. Valastro, Jr. 1977 Radiocarbon Dating and the George C. Davis Site, Texas. Journal of Field Archaeology4(1):63-89.

Stothers, D. M., and R. A. Yarnell 1977 An Agricultural Revolution in the Lower Great Lakes. In Geobotany, edited by R.D. Romans, pp. 209-232. Plenum Press, New York.

Stothers, D., Graves, J., Bechtel, S. and Abel, T. 1994 Current Perspectives on the Late Prehistory of the Western Lake Erie Region: An Alternative to Murphy and Ferris. Archaeology of Eastern North America 22:135-196.

Tagg, M. D. 1996 Early Cultigens from Fresnal Shelter, Southeastern New Mexico.American Antiquity 61(2):311-324.

Thiel, J. H. and J. B. Mabry (editors) 2006 Rio Nuevo Archaeology, 2000-2003: Investigations at the San Agustín Mission and Mission Gardens, Tucson Presidio, Tucson Pressed Brick Company, and Clearwater Site. Technical Report No. 2004-11. Desert Archaeology, Inc., Tucson.

Timmins, P. 1985 The Analysis and Interpretation of Radiocarbon Dates in Iroquoian Prehistory. Research Report 19. London, Ontario: Museum of Indian Archaeology.

Vierra, B. J. and R. I. Ford 2006 Early maize agriculture in the northern Rio Grande Valley, New Mexico. In Histories of Maize: Multidisciplinary Approaches to the Prehistory, Biogeography, Domestication, and Evolution of Maize, edited by J. E. Staller, R. H. Tykot and B. F. Benz, pp. 497-510. Elsevier/Academic Press, Amsterdam.

Ward, G. K., and Wilson, S. R. 1978 Procedures for Comparing and Combining Radiocarbon Age Determinations: A Critique. Archaeometry 20(1):19-31.

Watts, W. A. and J. Platt Bradbury 1982 Paleoecological Studies at Lake Patzcuaro on the West-Central Mexican Plateau and at Chalco in the Basin of Mexico. Quaternary Research 17:56-70.

342

Wellman, K. D. (editor) 2000 Farming Through the Ages: 3400 Years of Agriculture at the Valley Farms Site in the Northern Tucson Basin. Cultural Resource Report 98-226. SWCA Environmental Consultants, Tucson.

Whittlesey, S. M., S. J. Hesse and M. S. Foster (editors) 2009 Recurrent Sedentism and the Making of Place. SWCA Environmental Consultants, Tucson.

Williamson, R. 1990 The Early Iroquoian Period of Southern Ontario. In The Archaeology of Southern Ontario to A.D. 1650, edited by C. Ellis and N. Ferris, pp. 291-320. Occasional Papers of the London Chapter, Ontario Archaeological Society, No. 5. London, Ontario: Ontario Archaeological Society.

Williamson, R., and R. MacDonald (editors) 1997 In the Shadow of the Bridge: the Archaeology of the Peace Bridge Site (AfGr-9), 1994-1996 Investigations. Occasional Publication 1. Archaeological Services Inc., Toronto.

Williamson, R. F., and Robertson, D. A. (eds.) 1998 The Archaeology of the Parsons site: A fifty year perspective. Ontario Archaeology 65/66: 1–161.

Wöcherl, Helga (editor) 2007a Archaeological Investigations at the El Taller, AZ AA:12:92 (ASM), and Rillito Fan, AZ AA:12:788 (ASM), Sites Along Eastbound I-10 between Sunset and Ruthrauff Roads, Tucson, Pima County, Arizona (TR2003-08). Technical Report 2003-08. Center for Desert Archaeology, Tucson.

Wöcherl, Helga 2007b Chronology and Sequence of Occupation. In Archaeological Investigations at the El Taller, AZ AA:12:92 (ASM), and Rillito Fan, AZ AA:12:788 (ASM), Sites Along Eastbound I-10 between Sunset and Ruthrauff Roads, Tucson, Pima County, Arizona (TR2003-08). Technical Report 2003-08, edited by H. Wöcherl, pp. 83-86. Center for Desert Archaeology, Tucson.

Wood, W. D. and W. R. Bowen 1995 Woodland Period Archaeology in Northern Georgia. University of Georgia Laboratory of Archaeology Series, Report No. 33. University of Georgia, Athens.

343

Wood, W. D. and R. J. Ledbetter 1990 Rush: An Early Woodland Period Site in Northwest Georgia. Occasional Papers in Cultural Resource Management, No. 4. Georgia Department of Transportation, Office of Environmental Analysis, Atlanta.

Wright, J.V. 1974 The Nodwell Site. Mercury Series Paper 22. Archaeological Survey of Canada, National Museum of Man, Ottawa.

Zarrillo, S. 2008 The Amaizing Trace II: More on Precontact Maize in Alberta and the Canadian Plains. Lecture presented at the Archaeological Society of Alberta, Lethbridge, Alberta, February 5.

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APPENDIX 2: LOCAL ANALYSIS DATA DESCRIPTION

This section outlines the creation of multiple environmental datasets in order to test the hypothesis of differing settlement location strategies through time in Southern

Ontario. In particular, these environmental datasets were designed in order to align with beliefs on the minimum edaphic and climatic requirements of maize in order to approximate horticultural decision making by pre-contact horticultural groups in

Southern Ontario during the entire agricultural period (ca. AD 500 – AD 1650). This appendix will first discuss the collection of relevant site data to describe horticultural site locations as well as the division of these sites into three temporal groups in order to evaluate change. Secondly, the creation of multiple environmental datasets consistent with maize growing requirements in Southern Ontario will be described as well as the associated metadata. Lastly, the values of each relevant environmental dataset at the location of each village site are provided.

A2.1: HORTICULTURAL SITE SELECTION FOR SOUTHERN ONTARIO

A2.1.1: Villages as a proxy for horticultural site locations

Of critical importance in this analysis is the attribution of relevant site locations for maize horticulture. While the regional analysis (Chapters 5, 7, Appendix 1) used all sites in the Northeast with evidence of maize as the means of analyzing its spread, the

345 issue of maize’s ease of portability becomes paramount when analyzing environmental variables associated with maize horticulture. In other words, the fact that maize was recovered at a site does not mean that it was grown at that location. Conversely, the lack of any evidence of maize at a particular site cannot be used as evidence that it was not grown at that locale, and may instead relate more to taphonomy, sampling, and recovery biases rather than site use (Hart 1999, 2001). For this analysis, therefore, village sites in

Southern Ontario were used as a proxy for maize field locales as this has the strongest support from the literature (Fecteau et al. 1994:3; Heidenreich 1971; Warrick 2008:121).

Furthermore, due to the fact that villages relocated roughly every generation, this provided residents an opportunity to change their locational preferences depending on what was important for village life at the time. Therefore, long-term trends in village location should reflect dominant social patterns through time (Hasenstab 1990:64,

2007:164). While it is expected that maize was not grown within the limits of the actual village, it can be assumed that it was grown within close proximity to the village. In fact recent excavations at the expansive Mantle site North of Lake Ontario has suggested that corn fields extended more than 1.5 kilometers from the village site in each direction, encompassing thousands of hectares (Birch and Williamson 2013:98-101; Williamson

2012:281). Therefore, while villages are used only as proxies for maize field locales, the assumption is that they were likely located within 500 to 2,000 meters of the actual maize fields. As Heidenreich (1971:214) states, even for the largest Huron villages a maximum distance to the outermost corn fields was never more than 2 kilometers.

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A2.1.2: Village Site Database

Site dates and locations were gathered through published sources, online databases, and data requests through the Ministry of Tourism, Culture and Sport (MTC).

Sites were only included if described as villages in the literature or site reports and where there was a firm occupational date – established either through ceramic chronology or radiocarbon assays. On the other hand, for Princess Point and Western Basin sites, where the connection between permanent villages and maize horticulture is still a matter of debate (Dieterman 2001; Fox 1990; Murphy and Ferris 1990; Watts et al. 2011), only those sites with maize recovered and with strong support for spring/summer/fall occupation were included. This equates to 191 sites in Southern Ontario between AD 500 and AD 1650 (Table A2.1). Often, the location of a site, its date, and its attribution as a village site were gathered from three different sources. For this reason, Table A2.1 provides three separate fields for each site listing where the locational data was drawn from, where the date was drawn from, and the primary source for the village site.

As Warrick (2000:419) contends, the short occupation span of villages in

Southern Ontario has resulted in an astonishing number of village sites – with a greater expected density of sites per square kilometer over Neolithic Britain or the Valley of

Mexico. Warrick (1990:157-159) estimates that there are likely over 1500 village sites in

Southern Ontario, many of which have been destroyed and undocumented due to development. While this collection of sites represents a sample of the total number of village sites which at one point existed in Southern Ontario, it is my belief that additional sites would only add to the density of sites in a given locale and not necessarily change the breadth of exploited locales represented in this analysis.

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A2.1.3: Temporal Groups

Of course, the critical point in this analysis lies in the attribution of relevant temporal categories. The most common spatio-temporal classification system in Southern

Ontario for village sites is certainly J.V. Wright’s (1966) Ontario Iroquois Tradition

(OIT). While Wright’s formulation has understandably seen regular critique over the past few decades, it remains the principle means by which researchers conceptualize and divide data for Late Woodland Southern Ontario (chapters in Ellis and Ferris 1990 are the clearest example). In fact, even when researchers attempt to diverge from Wright’s taxa and approach the data from a new direction (Fecteau 1985; Ferris 1999; D. Smith 1997a), the temporal divisions stay roughly the same. For the purposes of this analysis, the site data are divided into three stages, representing: the period of first introduction of maize horticulture into the region (AD 500 – 900); the period of horticultural development and spread (AD 900 – 1300); and the period of full-scale horticulture (AD 1300 – AD 1650).

Stage 1: Early Horticulture (AD 500 – 900)

This period was marked by distinct changes in settlement and subsistence strategies from Middle Woodland foraging patterns (Ferris and Spence 1995; Fox 1990;

Crawford and Smith 2003; Dieterman 2001; Smith and Crawford 1997). These changes are specifically associated with Princess Point sites along the Grand River Valley and on the western end of Lake Ontario. Most researchers describe this period by a focus on riverine and lacustrian resources with limited investment in maize horticulture (Smith and

Crawford 1997:16; Fecteau 1985:130; Warrick 2000:421; Ferris 1999). While Princess

Point sites have been dated up to AD 1050 (Dieterman 2001; D. Smith 1997a; Timmins

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1985), sites after AD 900 (e.g. Holmedale, Lone Pine, Porteous) all exhibit a different settlement strategy more consistent with the following period – namely, a move to upland sandy locations, larger settlement size, the appearance of longhouses, and evidence for year-round occupation (Fox 1990:181; Smith and Crawford 1997:24; Pihl et al.

2008:151-152; Warrick 2000:427; Williamson 1990:310).

Stage 2: Horticultural Expansion and Consolidation (AD 900 – 1300)

This period, represented largely by Pickering and Glen Meyer sites throughout

Southern Ontario, signifies a major shift in settlement-subsistence strategies – equivalent to Wright’s Early Ontario Iroquoian stage (Warrick 2000:434; Williamson 1990; Wright

1966). In particular, formal palisaded villages appear during this time with a shift away from rivers to drier upland locations and an apparently greater investment in maize horticulture (Creese 2012:31; Fecteau 1985:137; Warrick 2000:434; Williamson 1985:80,

1990:306). Isotopic data (Katzenberg 2006; Katzenberg et al. 1997; Schwarz et al. 1985) indicate that maize comprised roughly 25-35% of total diets during this time, although many researchers still argue for a relatively diffuse economy, supplemented heavily by maize (Fecteau 1985:137; Warrick 2000:435; Williamson 1990:306). However,

Ounjian’s (1998) extensive analysis of Glen Meyer and Neutral palaeobotanical assemblages found no identifiable difference between the early and late stages in either wild plant foods or cultigens. In fact, given the remarkable consistency between these periods, Ounjian (1998:265) concludes that a stable system of subsistence was already established in Southern Ontario by Early Iroquoian times – a system that continued virtually unchanged to the historic period.

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Stage 3: Established Horticulture (AD 1300 – 1650)

This final period of agricultural development – equivalent to Wright’s Middle and

Late Ontario Iroquoian stages – is marked by a substantial increase in the number of horticultural sites across Ontario, as well as drastic changes in village and house size, settlement location, distribution, and village organization (Creese 2011:37; Dodd et al.

1990; Fecteau 1985:139-150; Kapches 1981; Warrick 2000:439-446). By the end of the

13th century all historically-documented cultigens were present on village sites and isotopic data suggests that maize comprised 50-70% of diets (Fecteau 1985; Katzenberg

2006; Ounjian 1998:263-265; van der Merwe et al. 2003:258; Watts et al. 2011).

Furthermore, these changes appear to have taken place within the span of one or two generations (van der Merwe 2003:248; Warrick 2000:452), associated with a massive population explosion (Warrick 1990:353 suggests 14th century growth rates of about

1.1% per annum., while Sutton 1996:173-176 has argued for a 5.7% p.a. growth rate in

Simcoe County between AD 1330 and AD 1355). This period also saw the expansion of communities into new territories throughout Southern Ontario and an apparent shift to heavier, more productive soils (Dodd et al. 1990:343, 350; Warrick 2000:441).

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Figure A2.1: All sites used in analysis. For site names, please see Table A2.1

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Table A2.1: Sites in Village Site Database

Map Cultural Temporal Dating ID Borden Site Name Period Date (AD) Period Method Date Reference Location Ref Village Reference Murphy and Ferris 1 AaHp-4 Dick WBT 1020 - 1215 middle AMS Campbell 1991 Campbell 1991 1990 ceramic Lennox and 2 AcHk-1 Clearville Neutral 1535 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Murphy and Ferris 3 AcHm-1 Krieger Younge 1200-1290 middle seriation Watts et al. 2012 Campbell 1991 1990 ceramic Lennox and 4 AcHm-2 McGeachy Neutral 1550 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 5 AcHm-3 Wolfe Creek Neutral 1500-1600 late seriation Campbell 1991 MTC* Fitzgerald 1990 ceramic Lennox and 6 AeHf-l Pound Neutral 1535 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 Middle ceramic 7 AeHg-20 Charles White Iroquioan 1375 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990

8 AeHg-3 Stafford Glen Meyer 1175 - 1390 middle AMS CARD*** MTC* Williamson 1990 Southwold ceramic Lennox and 9 AeHi-1 Earthworks Neutral 1500 late seriation Campbell 1991 MTC* Fitzgerald 1990 ceramic 10 AeHk-1 Goessens Glen Meyer 1125 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 Princess 11 AfGx-3 Grand Banks Point 400 - 630 early AMS Campbell 1991 Campbell 1991 Fox 1990

12 AfGx-54 Anderson Uren 1060 - 1270 middle AMS Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 13 AfHc-3 Vanessa Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990

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14 AfHd-1 DeWaele Glen Meyer 1040 - 1220 middle conventional CARD*** See Appendix 1 Williamson 1990

15 AfHd-2 Van Besien Glen Meyer 885 - 1155 middle conventional CARD*** Campbell 1991 Williamson 1990

16 AfHd-3 Uren Uren 1225 - 1385 middle conventional CARD*** Campbell 1991 Dodd et al 1990

17 AfHg-1 Calvert Glen Meyer 1050 - 1270 middle conventional CARD*** MTC* Williamson 1990 Dorchester ceramic 18 AfHg-24 Village Glen Meyer 1250-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic Lennox and 19 AfHh-2 Pond Mills Neutral 1425 late seriation Campbell 1991 MTC* Fitzgerald 1990 ceramic Lennox and 20 AfHh-24 Pincombe 2 Neutral 1450-1550 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 21 AfHh-72 Pincombe 6 Neutral 1400-1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990

22 AfHi-20 Kelly Glen Meyer 1160 - 1285 middle AMS CARD*** MTC* Williamson 1990

23 AfHi-21 Yaworsky Glen Meyer 1045 - 1220 middle conventional CARD*** MTC* Williamson 1990 ceramic 24 AfHi-22 Drumholm Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990

25 AfHi-23 Edwards Middleport 1255 - 1395 middle conventional CARD*** Campbell 1991 Dodd et al 1990 ceramic 26 AfHi-32 Berkmortel Glen Meyer 1000-1200 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 ceramic 27 AfHi-50 Dunn Glen Meyer 900-1300 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 ceramic 28 AfHj-18 Smale Glen Meyer 1000-1200 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 ceramic 29 AfHj-19 MiV18 Glen Meyer 1100-1200 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 ceramic 30 AfHj-24 Hardy Glen Meyer 1100-1200 middle seriation Campbell 1991 Campbell 1991 Williamson 1990

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ceramic 31 AfHj-26 AfHj-26 Glen Meyer 1100-1200 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 ceramic 32 AfHj-28 Little Glen Meyer 1100-1200 middle seriation Campbell 1991 Campbell 1991 Williamson 1985

33 AfHj-29 Thoren Neutral 1290 - 1400 late conventional CARD*** Campbell 1991 CARD*** Middle ceramic 34 AfHk-7 Metcalf Iroquioan 1400-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 35 AgGx-6 Ben Soules Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 36 AgHa-2 Middleport Middleport 1325 late seriation Campbell 1991 MTC* Dodd et al 1990 ceramic 37 AgHa-41 Roadside Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Princess Point / Glen 38 AgHb-1 Porteous Meyer 665 - 865 middle conventional CARD*** Campbell 1991 Williamson 1990

39 AgHb-18 Cooper Glen Meyer 1160 - 1280 middle conventional CARD*** Campbell 1991 Williamson 1990 ceramic Lennox and 40 AgHh-1 Lawson Neutral 1500 late seriation Campbell 1991 MTC* Fitzgerald 1990 ceramic Lennox and 41 AgHh-10 Ronto Neutral 1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 42 AgHh-9 Windermere Neutral 1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic 43 AhGw-2 Pergentile Glen Meyer 1125 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 Princess ceramic 44 AhGx-1 Princess Point Point 600 - 800 early seriation Campbell 1991 Campbell 1991 Fox 1990 ceramic 45 AhGx-14 G. Smith Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990

354

ceramic 46 AhGx-95 Clish Glen Meyer 1250 middle seriation Campbell 1991 Campbell 1991 Williamson 1990 ceramic City of Hamilton 47 AhHa-29 Richer Neutral 1580-1600 late seriation Campbell 1991 Campbell 1991 2004 ceramic Lennox and 48 AhHb-1 Knight-Tucker Neutral 1575 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic City of Hamilton 49 AhHb-20 Pottruff Neutral 1550-1600 late seriation Campbell 1991 Campbell 1991 2004 ceramic City of Hamilton 50 AhHb-21 Hunter Village Neutral 1550-1600 late seriation Campbell 1991 Campbell 1991 2004 ceramic 51 AhHd-1 Ovington Uren 1250-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990

52 AiGt-2 Draper Huron 1315 - 1480 late conventional CARD*** MTC* Ramsden 1990

53 AiGx-1 Bennett Pickering 1230 - 1405 late conventional CARD*** MTC* Williamson 1990 ceramic 54 AiGx-11 Unick Middleport 1375 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990

55 AiGx-12 Pipeline Neutral 1300 - 1440 late conventional CARD*** MTC* Dodd et al 1990 Late Pickering / 56 AiGx-5 Gunby Uren 1290 - 1420 late conventional CARD*** MTC* Dodd et al 1990 Crawford ceramic 57 AiGx-6 Lake Middleport 1435-1460 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 58 AiGx-7 Rife Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 59 AiGx-73 Chypchar Middleport 1375 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 60 AiGx-8 Van Eden Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Middle ceramic 61 AiHb-13 Myers Road Iroquioan 1280-1340 late seriation Campbell 1991 Campbell 1991 Creese 2011

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Coleman Late ceramic 62 AiHd-7 Village Woodland 1300-1450 late seriation Campbell 1991 Campbell 1991 Macdonald 1986 Suraras 63 AiHd-8 Spring Neutral 1310 - 1430 late conventional CARD*** Campbell 1991 Dodd et al 1990 ceramic 64 AkGt-2 Elliot Middleport 1375 late seriation Campbell 1991 MTC* Dodd et al 1990 ceramic 65 AkGt-20 Thompson Uren 1250 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 66 AkGu-10 Risebrough Huron 1550 late seriation Campbell 1991 Campbell 1991 Ramsden 1990 ceramic 67 AkGu-13 Downsview Huron 1460 late seriation Campbell 1991 MTC* Ramsden 1990 ceramic 68 AkGu-3 Jackes Huron 1480 late seriation Campbell 1991 MTC* Ramsden 1990 ceramic 69 AkGu-9 Doncaster Huron 1480 late seriation Campbell 1991 MTC* Ramsden 1990 ceramic 70 AkGv-1 Seed-Barker Huron 1575 late seriation Campbell 1991 MTC* Ramsden 1990 McKenzie- ceramic 71 AkGv-2 Woodbridge Huron 1520 late seriation Campbell 1991 MTC* Ramsden 1990 ceramic 72 AkGv-8 E.A. Parson Huron 1535 late seriation Campbell 1991 MTC* Ramsden 1990 ceramic 73 AkGx-1 Wallace Huron 1500-1550 late seriation Campbell 1991 Campbell 1991 Ramsden 1990 Emmerson Late ceramic 74 AkGx-5 Springs Woodland 1550-1580 late seriation Campbell 1991 Campbell 1991 Hawkins 2004

75 AlGo-29 Auda Pickering 780 - 1150 middle conventional CARD*** MTC* Williamson 1990 Glen Meyer Madonald and 76 AlGo-50 Hibou / Middleport 1280 - 1390 late AMS CARD*** See Appendix 1 Williamson 1994

77 AlGs-1 Miller Pickering 1060 - 1270 middle conventional CARD*** MTC* Williamson 1990

356

78 AlGs-10 Boys Pickering 1050 - 1280 middle conventional CARD*** Campbell 1991 Williamson 1990 ceramic 79 AlGs-101 Delancey Pickering 1200 middle seriation Campbell 1991 MTC* Williamson 1990 ceramic 80 AlGs-102 Bolitho Pickering 1000 middle seriation Campbell 1991 MTC* Williamson 1990 ceramic 81 AlGs-73 Peter Webb 2 Middleport 1300-1450 late seriation Campbell 1991 MTC* Dodd et al 1990 ceramic 82 AlGt-1 Milroy Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 83 AlGt-4 Robb Middleport 1350-1400 late seriation Campbell 1991 MTC* Dodd et al 1990 ceramic 84 AlGt-96 Robin Hood Huron 1535 late seriation Campbell 1991 MTC* Ramsden 1990 St. ceramic 85 BaFp-4 Salem Lawrence 1400 late seriation Campbell 1991 Campbell 1991 Jamieson 1990 St. ceramic 86 BaGg-I Waupoos Lawrence 1535 late seriation Campbell 1991 MTC* Ramsden 1990 ceramic Timmins Martelle 87 BaGp-36 Fleetwood 2 Huron 1500-1550 late seriation Campbell 1991 Campbell 1991 2010 ceramic Latta and Reed 88 BaGw-1 Beeton Huron 1480 late seriation Campbell 1991 MTC* 1993 ceramic 89 BbGi-1 Lite Huron 1400-1500 late seriation Campbell 1991 MTC* Ramsden 1990

90 BbGl-4 Richardson Pickering 1285 - 1400 late conventional CARD*** MTC* Williamson 1990 ceramic 91 BbGr-2 Thomas Huron 1500-1550 late seriation Campbell 1991 MTC* Ramsden 1990 Late ceramic 92 BbGs-11 Markson Woodland 1400-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Late ceramic 93 BbGv-19 Brassington Woodland 1450-1480 late seriation Campbell 1991 Campbell 1991 Bursey 1993

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Late ceramic 94 BbGv-20 Cooper Woodland 1580-1600 late seriation Campbell 1991 Campbell 1991 Warrick 2008 Late ceramic 95 BbGv-22 Lucas Woodland 1500-1550 late seriation Campbell 1991 Campbell 1991 Bursey 1993 Late ceramic 96 BbGv-30 Blu Meanie Woodland 1490-1520 late seriation Campbell 1991 Campbell 1991 Bursey 1993 ceramic 97 BbGw-14 Paisley Huron 1480-1500 late seriation Campbell 1991 Campbell 1991 Bursey 1993 ceramic 98 BbGw-5 Dykstra Middleport 1250 - 1350 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 99 BcGv-11 McDonald Huron 1440-1470 late seriation Campbell 1991 Campbell 1991 Bursey 1993

100 BcGw-1 Beswetherick Middleport 1300 - 1415 late conventional CARD*** Campbell 1991 Dodd et al 1990 ceramic 101 BcGw-15 Little I Middleport 1450 - 1500 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990

102 BcGw-18 Barrie Uren 1330 - 1435 late AMS CARD*** Campbell 1991 Dodd et al 1990 ceramic 103 BcGw-21 Cundles Creek Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 104 BcGw-26 Wiacek Middleport 1425 late seriation Campbell 1991 MTC* Dodd et al 1990 ceramic 105 BcGw-5 Gervais Middleport 1350-1400 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 106 BcGw-8 Sparrow Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 107 BcGx-15 Kenney Middleport 1300-1400 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 108 BcHi-3 Nodwell Middleport 1350-1380 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 109 BdGm-l Quackenbush Huron 1535 late seriation Campbell 1991 MTC* Ramsden 1990

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110 BdGv-14 Bauman Huron 1330 - 1460 late conventional CARD*** MTC* Ramsden 1990 John ceramic 111 BdGw-11 Thompson Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 112 BdGw-5 W. Thompson Huron 1500 late seriation Campbell 1991 Campbell 1991 Bursey 1993 ceramic 113 BdGx-12 McRae Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 114 BdGx-13 Webb Middleport 1375 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 115 BdGx-7 Flos #9 Middleport 1300-1400 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 St. ceramic 116 BeFv-2 Crystal Rock Lawrence 1540 late seriation Campbell 1991 Campbell 1991 Jamieson 1990 St. 117 BeFv-4 Roebuck Lawrence 1300 - 1440 late AMS CARD*** MTC* Jamieson 1990 ceramic 118 BeGx-5 Farlain Lake Huron 1480 late seriation Fecteau 1985 MTC* Bursey 1993 Copeland ceramic 119 BeGx-3 Creek Huron 1500 late seriation Campbell 1991 Campbell 1991 Ramsden 1990 ceramic 120 BeGx-4 Deshambault Huron 1480 late seriation Campbell 1991 MTC* Bursey 1993 ceramic 121 BeHa-11 Davey Middleport 1300-1450 late seriation Campbell 1991 Campbell 1991 Dodd et al 1990 St. ceramic 122 BfFt-1 Beckstead Lawrence 1480 late seriation Campbell 1991 Campbell 1991 Jamieson 1990 St. 123 BfFv-1 McIvor Lawrence 1400 -1650 late AMS CARD*** MTC* Jamieson 1990 St. 124 BgFp-5 Glenbrook Lawrence 1440 - 1650 late conventional CARD*** MTC* Jamieson 1990

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Murphy and Ferris 125 AeHj-2 Dymock Younge 1025 - 1175 middle conventional CARD*** Google Earth** 1990 Riviere au Vase - ceramic Murphy and Murphy and Ferris 126 AaHp-20 Robson Road Springwells 1200 - 1400 late seriation Ferris 1990 Google Earth** 1990 Younge / Murphy and Murphy and Ferris 127 AaHp-21 Cherry Lane Springwells 1200 - 1400 late conventional Ferris 1990 Google Earth** 1990 King's Forest 128 AhGw-1 Park Glen Meyer 1255 - 1390 middle conventional CARD*** Google Earth** Williamson 1990

129 AfHj-14 Roeland Glen Meyer 1215 - 1385 middle conventional CARD*** Google Earth** Williamson 1990 Glen Meyer 130 AfGu-2 Bonisteel / Middleport 1220 - 1390 middle AMS CARD*** Google Earth** Dodd et al 1990 ceramic 131 AkGv-3 Boyd Huron 1600 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 132 BdGr-2 Hardrock Huron 1550-1600 late seriation Fecteau 1985 Google Earth** Ramsden 1990 ceramic 133 BeGx-19 Lalonde Huron 1400-1500 late seriation Campbell 1991 Campbell 1991 Ramsden 1990 Middle ceramic 134 AeHg-10 D. Cook Iroquoian 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Middle ceramic 135 AeHg-19 Frank Zavitz Iroquoian 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Middle ceramic 136 AeHg-12 Knowles II Iroquoian 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Middle ceramic 137 AeHg-18 Macillius Iroquoian 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990

138 BaGo-29 Gibbs Middleport 1325 - 1440 late conventional CARD*** Google Earth** Dodd et al 1990 ceramic 139 AlGh-9 Huff Middleport 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990

140 AlGt-36 New Middleport 1285 - 1395 late conventional CARD*** Google Earth** Dodd et al 1990

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ceramic Lennox and 141 AiHd-18 Baden Neutral 1400-1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 142 AgHh-26 Black Kat Neutral 1400-1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 143 AiHa-1 Ivan Elliott Neutral 1500-1600 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 144 AiHd-15 Mannheim Neutral 1400-1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 145 AgHh-29 Matthews Neutral 1400-1500 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 ceramic Lennox and 146 AiHd-1 Waterloo Neutral 1500-1600 late seriation Campbell 1991 Campbell 1991 Fitzgerald 1990 AiGw- 147 100 Five Acre Field Pickering 1050 - 1280 middle conventional CARD*** Google Earth** Fecteau et al 1994

148 AiGx-39 Ireland Pickering 1050 - 1280 middle conventional CARD*** Google Earth** Dodd et al 1990 AiGw- 149 124 Tara Pickering 1160 - 1380 middle conventional CARD*** Google Earth** Williamson 1990 ceramic 150 AjGw-71 Mullet Pond Uren 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 ceramic 151 AhHd-46 Walton Uren 1200-1300 middle seriation Campbell 1991 Campbell 1991 Dodd et al 1990 Princess AgGx- Point / Glen 152 134 Forster Meyer 660-1150 early AMS CARD*** Google Earth** Bursey 2003 ceramic 153 BeGw-15 Alonso Huron 1600-1630 late seriation Fecteau 1985 MTC* Bursey 1993 ceramic 154 BdGw-3 Auger-Yates Huron 1640 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 155 BaGu-2 Aurora Huron 1575 late seriation Fecteau 1985 MTC* Ramsden 1990

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ceramic 156 BdGv-3 Ball Huron 1545-1616 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 157 BdGr-1 Benson Huron 1550-1600 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 158 AkGv-11 Black Creek Huron 1550 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 159 BdGv-1 Cahiague Huron 1545-1616 late seriation Fecteau 1985 MTC* Warrick 2008 ceramic 160 BdGw-27 Flanagan Huron 1640 late seriation Fecteau 1985 MTC* Bursey 1993 ceramic 161 BfGx-2 Gignac Lake Huron 1545-1616 late seriation Fecteau 1985 MTC* Obrien 1976 ceramic 162 BeGx-15 Le Caron Huron 1640 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 163 AlGh-2 Payne Huron 1535 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 164 BeHa-3 Robitaille Huron 1545-1616 late seriation Fecteau 1985 MTC* Ramsden 1990 Methodist ceramic 165 BeGx-11 Point 1 Huron 1535 late seriation Fecteau 1985 Google Earth** Obrien 1976 ceramic 166 BfGx-1 Second Lake Huron 1575 late seriation Fecteau 1985 MTC* Obrien 1976 ceramic 167 BfGx-3 Second Lake 2 Huron 1535 late seriation Fecteau 1985 MTC* Obrien 1976 historic 168 Ste Marie I Huron 1650 late sources Fecteau 1985 Google Earth** Ramsden 1990

169 BcGw-10 Dunsmore Huron 1295 - 1395 late conventional Fecteau 1985 MTC* CARD*** Middleport / Proto ceramic Jamieson 1990 170 AfHa-1 Slack - Caswell Neutral 1375-1415 late seriation Fecteau 1985 MTC* 1986

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Harrietsville ceramic Lennox and 171 AfHf-10 Earthworks Neutral 1480 late seriation Fecteau 1985 Google Earth** Fitzgerald 1990 ceramic 172 AhHb-8 Fonger Neutral 1600-1615 late seriation Fecteau 1985 Google Earth** Warrick 1983 Connor - Petun / ceramic 173 BcHb-3 Rolling Odawa 1640 late seriation Fecteau 1985 MTC* Garrad 1987 Petun / ceramic 174 BcHb-1 Glebe Odawa 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 Graham - ceramic 175 BcHb-7 Ferguson Petun 1640 late seriation Fecteau 1985 MTC* Garrad 1987 Hamilton - ceramic 176 BbHa-10 Lougheed Petun 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 ceramic 177 BcHb-27 Haney - Cook Petun 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 Kelly - ceramic 178 BcHb-10 Campbell Petun 1650 late seriation Fecteau 1985 MTC* Garrad 1987 ceramic 179 BcHb-26 MacMurchy Petun 1545-1616 late seriation Fecteau 1985 MTC* Ramsden 1990 ceramic 180 BcHb-25 McAllister Petun 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 McQueen - ceramic 181 BcHb-31 McConnell Petun 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 ceramic 182 BbHa-7 Melville Petun 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 Plater - Petun / ceramic 183 BdHb-2 Fleming Odawa 1650 late seriation Fecteau 1985 MTC* Garrad 1987 Petun / ceramic 184 BdHb-1 Plater - Martin Odawa 1650 late seriation Fecteau 1985 MTC* Garrad 1987 ceramic 185 BcHb-20 Rock Bottom Petun 1650 late seriation Fecteau 1985 MTC* Garrad 1987

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ceramic 186 BbHa-6 Sidey - Mackay Petun 1600 late seriation Fecteau 1985 MTC* Ramsden 1990 Young - ceramic 187 BcHb-19 McQueen Petun 1545-1616 late seriation Fecteau 1985 MTC* Garrad 1987 AgHb- Princess 188 191 Holmedale Point 905 - 1150 middle AMS Pihl et al. 2008 Google Earth** Pihl etal 2008 Princess 189 AfGx-113 Lone Pine Point 895 - 1035 middle AMS CARD*** Google Earth** Pihl etal 2008 ceramic Murphy and Murphy and Ferris 190 AcHo-1 Liahn I Springwells 1325 late seriation Ferris 1990 MTC* 1990 Wolf / ceramic Murphy and Murphy and Ferris 191 AdHo-1 Weiser Sandusky 1550 late seriation Ferris 1990 MTC* 1990 Elliott Fox personal Fecteau 1985; 192 AfHc-2 Village**** Glen Meyer 800 - 1100 middle conventional communication Google Earth** Ounjian 1998

* MTC refers to an Archaeological Site Data Request from the Ministry of Tourism, Culture and Sport

** Google Earth refers to heads-up digitized location drawn from locating the site within Google Earth. This was only done for 17 sites and only when a sufficiently high-resolution map was available for site location. In general, site location for these sites would be a maximum of 200m away from their true location.

*** CARD refers to a data request from the Canadian Archaeological Radiocarbon Database administered by the Canadian Museum of Civilisations.

**** At the time of analysis, no accurate spatial data was available for Elliot Village. This site was therefore not included in analyses. However, this site exists in a region with many other middle period sites and it is therefore assumed that the inclusion of this site would not significantly change results. On the other hand, as Ounjian (1998) demonstrates, Elliott Village is a significant site in terms of early horticultural patterns and should be included in further research (Fox 2013, personal communication).

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A2.2: ENVIRONMENTAL VARIABLES

Given the marginal location of southern Ontario for maize horticulture, many environmental variables will take on key importance. In accordance with the discussion of key environmental variables for maize horticulture (Chapter 6.3.1.3), several spatial datasets were collected and created for the analysis of village site selection in terms of key environmental covariates. This section outlines the creation of each of these datasets for Southern Ontario and the sources from which the data was collected.

A2.2.1: Edaphic Variables

All data related to soil types, quality, composition, and drainage were drawn from the Soil Survey Complex (2009) created by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMaFRA). This dataset was retrieved under a data request to the

Maps, Data and Government Information Centre (MaDGIC) at Trent University. The digital soils data were automated from soil survey reports and maps that were produced over the past few decades. The current soil database of Ontario is based upon a set of 44 soil reports, and their accompanying maps for Southern Ontario. All of these soil maps, and their classifications of soil and land attributes, have been digitized and electronically

"stitched" together to produce a single digital coverage. In order to query soils data from the geospatial dataset, a relational join was created between the soils complex shapefile and the associated SSCmplx.txt table and “OBJECTID” as the primary and foreign key.

365

A2.2.1.1: Distance to Clay

This dataset was created from the master Soil Survey Complex (2009) dataset where all clay deposits in Southern Ontario were selected and a Euclidean Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to clay deposits.

A2.2.1.2: Distance to Clay Loam

This dataset was created from the master Soil Survey Complex (2009) dataset where all clay loam deposits in Southern Ontario were selected and a Euclidean Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to clay loam deposits.

A2.2.1.3: Distance to Silt Clay Loam

This dataset was created from the master Soil Survey Complex (2009) dataset where all silt clay loam deposits in Southern Ontario were selected and a Euclidean

Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a

500m cell size and the Soil Survey Complex extent as the maximum limits. This

366 effectively creates a surface across the study region where every cell has as its value the closest distance to silt clay loam deposits.

A2.2.1.4: Distance to Silt Loam

This dataset was created from the master Soil Survey Complex (2009) dataset where all silt loam deposits in Southern Ontario were selected and a Euclidean Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to silt loam deposits.

A2.2.1.5: Distance to Sand

This dataset was created from the master Soil Survey Complex (2009) dataset where all sand and fine sand deposits in Southern Ontario were selected and a Euclidean

Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a

500m cell size and the Soil Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to sand deposits.

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A2.2.1.6: Distance to Sandy Loam

This dataset was created from the master Soil Survey Complex (2009) dataset where all sandy loam, fine sandy loam, and very fine sandy loam deposits in Southern

Ontario were selected and a Euclidean Distance surface was created within ArcGIS v.

10.1 (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to sandy loam deposits.

A2.2.1.7: Distance to Loamy Sand

This dataset was created from the master Soil Survey Complex (2009) dataset where all loamy sand, loamy fine sand, and loamy very fine sand deposits in Southern

Ontario were selected and a Euclidean Distance surface was created within ArcGIS v.

A2.2.1.8: Distance to Loam

This dataset was created from the master Soil Survey Complex (2009) dataset where all loam deposits in Southern Ontario were selected and a Euclidean Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey Complex extent as the maximum limits. This effectively creates

368 a surface across the study region where every cell has as its value the closest distance to loam deposits.

A2.2.1.9: Drainage

This dataset was created from the master Soil Survey Complex (2009) dataset using the “DRAINAGE” category. This category divides drainage into seven nominal drainage categories: very poorly drained; poorly drained; imperfectly drained; moderately well drained; well drained, rapidly drained, and very rapidly drained. This was converted into an ordinal dataset where:

1 = very poorly drained soils and very rapidly drained soils

2 = poorly drained and rapidly drained soils

3 = imperfectly drained soils

4 = moderately well drained soils

5 = well drained soils

This was then converted to a raster surface within ArcGIS v. 10.1 using “Raster

Calculator” tool (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey

Complex extent as the maximum limits.

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A2.2.1.10: Distance to Fair-Drained Soil

Given the preference of maize for well-drained soils, a decision was made to change the scale of measurement from ordinal to ratio by changing the values from discrete to continuous. In this, all areas within the master Soil Survey Complex (2009) classed as moderately well drained were selected and a Euclidean Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a 500m cell size and the

Soil Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to moderately well-drained soil.

A2.2.1.11: Distance to Well-Drained Soil

Given the preference of maize for well-drained soils, a decision was made to change the scale of measurement from ordinal to ratio by changing the values from discrete to continuous. In this, all areas within the master Soil Survey Complex (2009) classed as well drained were selected and a Euclidean Distance surface was created within ArcGIS v. 10.1 (Spatial Analyst toolbox) using a 500m cell size and the Soil

Survey Complex extent as the maximum limits. This effectively creates a surface across the study region where every cell has as its value the closest distance to well-drained soil.

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A2.2.1.12: Stoniness

This dataset was created from the master Soil Survey Complex (2009) dataset using the “STONINESS” category. This category divides drainage into six ordinal categories representing the occurrence of surface stoniness. The stoniness classes are based upon the percent of surface area coverage of stones greater than 15 cm diameter where:

0 = Non-stony (<0.01% surface coverage)

1 = Slightly stony (0.01-0.1%)

2 = Moderately stony (0.1-3.0%)

3 = Very stony (3.0-15%)

4 = Exceedingly stony (15-50%)

5 = Excessively stony (>50%)

This was then converted to a raster surface within ArcGIS v. 10.1 using “Raster

Calculator” tool (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey

Complex extent as the maximum limits.

A2.2.1.13: Slope

This dataset was created from the master Soil Survey Complex (2009) dataset using the “SLOPE” category. This category divides slope into 10 nominal categories representing the predominant slope of the landscape expressed as a percent (%). The value stored in this field will be derived from the Class Mean (%) values represented in the chart below. While expressed as a percent (Numeric with one decimal place; 5 characters including the decimal), slope steepness is often referred to by Class:

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Class Slope Range (%) Class Mean (%) Terminology

A 0 - 0.5 0.2 Level

B 0.5 - 2 1.2 Nearly level

C 2 - 5 3.5 Very gentle slopes

D 5 - 9 7.0 Gentle slopes

E 9 - 15 12.0 Moderate slopes

F 15 - 30 22.5 Strong slopes

G 30 - 45 37.5 Very strong slopes

H 45 - 70 57.5 Extreme slopes

I 70 - 100 85.0 Steep slopes

J > 100 125.0 Very steep slopes

This category was then transformed to ordinal data where class A was given a value of 0 and class J was given a value of 9. This was then converted to a raster surface within

ArcGIS v. 10.1 using “Raster Calculator” tool (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey Complex extent as the maximum limits.

A2.2.2: Climatic Variables

Other than the dataset representing Crop Heat Units, all other climatic variables were drawn from high-quality longitudinal climatic reconstructions produced by Natural

Resources Canada (http://cfs.nrcan.gc.ca/projects/3?lang=en_CA). These were retrieved under a data request to the administrator of the dataset. These climate grids are the result of over a decade of research undertaken by the Canadian Forest Service in partnership

372 with Environment Canada and the Australian National University in order to develop high resolution climate reconstructions for areas far away from meteorological stations.

The surfaces requested for this analysis were custom built for Southern Ontario using fifty year averages (1951 – 2001) drawn from Environment Canada’s Climate Normals datasets and fitted to the region using the ANUSPLIN interpolation method. Cell size was provided at 500m. In total, this dataset contains 19 climatic variables and 16 bioclimatic variables. The particular bioclimatic variables used in this analysis will be outlined below.

A2.2.2.1: Frost Free Days

This surface represents the total number of frost-free days in Southern Ontario as represented by the 50-year averages in the ANUSPLIN model (see section A2.2.2). The frost free period can be described as the length of time in days between the last spring frost (temperature below -2 degrees Celsius) and the first autumn frost (temperature below -2 degrees Celsius). Cell size was set at 500m and spatial extent was clipped to the

Soil Survey Complex in order to be in line with the other datasets.

A2.2.2.2: Growing Degree Days

This surface represents the total Growing Degree Days value (base 10) for the growing season in Southern Ontario as represented by the 50-year averages in the

ANUSPLIN model (see section A2.2.2). The growing season here was determined using

373 temperature-based rules, starting when the mean daily temperature was greater than or equal to 5 degrees Celsius for 5 consecutive days beginning March 1. The growing season ends when the average minimum temperature is less than -2 degrees Celsius beginning August 1. Cell size was set at 500m and spatial extent was clipped to the Soil

Survey Complex in order to be in line with the other datasets.

A2.2.2.3: Growing Season Precipitation

This represents the total precipitation in millimeters during the growing season as represented by the 50-year averages in the ANUSPLIN model (see section A2.2.2). The growing season here was determined using temperature-based rules, starting when the mean daily temperature was greater than or equal to 5 degrees Celsius for 5 consecutive days beginning March 1. The growing season ends when the average minimum temperature is less than -2 degrees Celsius beginning August 1. Cell size was set at 500m and spatial extent was clipped to the Soil Survey Complex in order to be in line with the other datasets.

A2.2.2.4: Crop Heat Units

The surface representing Crop Heat Units during the growing season for Southern

Ontario (base 10) was retrieved through a data request with the Ontario Ministry of

Agriculture, Food and Rural Affairs (OMaFRA: [[email protected]]). No metadata was provided as to the source data used to create this surface although the data

374 administrator believed that it was created from Environment Canada’s 1961-1991

Climate Normals Data. The data was provided in vector format as a polygon shapefile.

This was then converted to a raster surface within ArcGIS v. 10.1 using “Raster

Calculator” tool (Spatial Analyst toolbox) using a 500m cell size and the Soil Survey

Complex extent as the maximum limits.

A2.2.3: Topographic Variables

All data related to topography and landform were drawn from a 10 meter resolution Digital Elevation Model (DEM) for Southern Ontario created by the Ontario

Ministry of Natural Resources (2006). This dataset was retrieved under a data request to the Maps, Data and Government Information Centre (MaDGIC) at Trent University. The dataset was provided in tile format – from this, 82 separate tiles were stitched together for the entire study region in ArcGIS v. 10.1 using the “Mosaic” function (Data Management toolbox). This dataset represents a high resolution interpolated three dimensional surface for Southern Ontario.

A2.2.3.1: Elevation

Elevation data was gathered from the stitched 10 metre DEM surface using the

“Resample” function in ArcGIS v. 10.1 (Spatial Analyst toolbox) in order to create a single raster grid for the entire study area. Cell size was resampled to 100m in order to remove much elevational noise present in the dataset such as roadways and architecture.

375

Finally, spatial extent was clipped to the Soil Survey Complex in order to be in line with the other datasets.

A2.2.3.2: Terrain Ruggedness Index

This dataset was created in response to the need for a high resolution dataset representing variation in elevation over Southern Ontario, and in particular, a means of evaluating topographic preferences in site selection. While there is a slope category found in the Soil Survey Complex dataset (Section A2.2.1.12), this describes the predominant slope in a given soil region and is not of sufficient resolution to capture local-level changes in topography. Designed initially by Riley et al. (1999), the Terrain Ruggedness

Index (TRI) represents the root of the squared difference between the value of a cell and the mean of an 8-cell neighbourhood of surrounding cells. In other words, this represents the total elevational variance around a given cell without needing to account for positive or negative elevational change. This dataset was created using the 100m resampled DEM as base surface. The actual creation of the TRI surface was performed within the freeware

GIS program GRASS (v.6.4.3.2) as coding was more accessible in GRASS than in the

Python environment of ArcGIS. Using the r.map.calc function in GRASS, the syntax used was:

'TRI=sqrt(((E[0,1]-E[0,0])^2)+((E[0,-1]-E[0,0])^2)+((E[1,0]- E[0,0])^2)+((E[-1,0]-E[0,0])^2)+((E[1,1]-E[0,0])^2)+((E[-1,-1]- E[0,0])^2)+((E[1,-1]- E[0,0])^2)+((E[-1,1]-E[0,0])^2))'

Projection was set at Albers Equal Area Conic and cell size was set at 100m. This created the base surface for all three TRI datasets outlined below.

376

A2.2.3.2.1: Terrain Ruggedness Index – 500m

The dataset was drawn from the 100m TRI dataset outlined above and was resampled to a 500m cell size using the mean value in the “Focal Statistics” function in

ArcGIS v. 10.1 (Spatial Analyst toolbox). This essentially creates a dataset where the average elevational variance over a 500m catchment area is represented. Cell size was set at 500m and spatial extent was clipped to the Soil Survey Complex in order to be in line with the other datasets.

A2.2.3.2.2: Terrain Ruggedness Index – 1,000m

The dataset was drawn from the 100m TRI dataset outlined above and was resampled to a 1000m cell size using the mean value in the “Focal Statistics” function in

ArcGIS v. 10.1 (Spatial Analyst toolbox). This essentially creates a dataset where the average elevational variance over a 1 kilometre catchment area is represented. Cell size was re-set to 500m and spatial extent was clipped to the Soil Survey Complex in order to be in line with the other datasets.

A2.2.3.2.3: Terrain Ruggedness Index – 2,000m

The dataset was drawn from the 100m TRI dataset outlined above and resampled to a 2000m cell size using the mean value in the “Focal Statistics” function in ArcGIS v.

10.1 (Spatial Analyst toolbox). This essentially creates a dataset where the average elevational variance over a 2 kilometre catchment area is represented. Cell size was re-set

377 to 500m and spatial extent was clipped to the Soil Survey Complex in order to be in line with the other datasets.

A2.3: ENVIRONMENTAL COVARIATE VALUES FOR EACH SITE IN VILLAGE DATABASE

A2.2). Once collected, data were imported into the freeware statistical program ‘R’ in order to perform the tests of independence. The cell value for each environmental covariate at each site location, including the 100 random points, is presented below

(Table A2.2). It was these particular values, separated by group designation, that were tested in the Wilcoxon Mann-Whitney test and the Kruskall-Wallis test.

378

Table A2.2: Environmental Covariate Values for each Site in Village Database

Silt- Med Clay Clay- Silt Sandy- Well- Map Cultural Elevation Clay- TRI Borden Site Date Dist Loam Dist Loam Drain GDD ID Period (m) Loam 1k (m) (AD) (m) Dist (m) (m) Dist (m) Dist (m) Dist (m) 11 AfGx-3 Grand Banks early 489 183.351 15305.2 4527.69 21053.5 0 1118.03 0 2073 8.86 Princess 44 AhGx-1 early 700 71.185 15500 8077.75 30016.7 4924.43 707.107 707.107 2125 9.27 Point AgGx- 152 Forster early 890 189.759 16867.1 10111.9 22627.4 500 5522.68 707.107 2061 9.99 134

98 BbGw-5 Dykstra late 1300 316.046 13000 20500 31084.6 2692.58 0 0 1800 4.46

126 AaHp-20 Robson Road late 1300 176.907 707.107 4301.16 1118.03 30413.8 0 1000 2366 2.54

127 AaHp-21 Cherry Lane late 1300 176.907 707.107 4301.16 1118.03 30413.8 0 1000 2366 2.54

53 AiGx-1 Bennett late 1305 267.205 8631.34 2500 24698.2 14705.4 500 0 1909 4.22

61 AiHb-13 Myers Road late 1310 277.717 37138.3 18741.7 4924.43 6020.8 1802.78 1000 1871 9.12

36 AgHa-2 Middleport late 1325 197.396 28111.4 14577.4 17464.3 707.107 500 707.107 2043 4.29

190 AcHo-1 Liahn I late 1325 176.239 15700.3 15628.5 14008.9 500 1000 14008.9 2297 1.28

76 AlGo-50 Hibou late 1335 143.494 38422 7071.07 1118.03 12619.4 500 1118.03 1862 14.35

140 AlGt-36 New late 1342 180.953 3162.28 4924.43 17557.1 38108.4 707.107 0 1958 5.73

90 BbGl-4 Richardson late 1343 227.987 25553.9 11543.4 500 8631.34 0 0 1830 24.47

33 AfHj-29 Thoren late 1345 224.602 10920.2 11180.3 707.107 1118.03 1000 0 2058 4.63

169 BcGw-10 Dunsmore late 1348 287.292 10606.6 23717.1 26925.8 2061.55 707.107 0 1791 4.83

379

107 BcGx-15 Kenney late 1350 217.363 2000 21840.3 9708.24 1000 500 500 1831 1.22

115 BdGx-7 Flos #9 late 1350 225.091 1000 23717.1 10920.2 500 0 0 1832 4.71

86 BaGg-I Waupoos late 1353 129.751 1000 1118.03 10307.8 46671.7 500 500 1975 9.6

56 AiGx-5 Gunby late 1354 262.309 8276.47 3535.53 24596.7 12776.9 0 500 1909 16.26

100 BcGw-1 Beswetherick late 1357 285.663 8860.02 24515.3 26400.8 3640.05 500 0 1771 23.35 Suraras 63 AiHd-8 late 1363 346.63 52654.5 3905.12 9708.24 9013.88 0 0 1840 7.94 Spring 108 BcHi-3 Nodwell late 1365 200.101 41773.2 4924.43 1000 3162.28 500 707.107 1730 2.62

117 BeFv-4 Roebuck late 1373 94.5592 20408.3 707.107 500 25104.8 2692.58 500 1917 3.19 Charles 7 AeHg-20 late 1375 217.237 3041.38 5147.81 1581.14 4123.11 0 4123.11 2021 5.35 White 13 AfHc-3 Vanessa late 1375 234.208 47900.9 9552.49 4472.14 7905.69 707.107 500 2008 1.45

24 AfHi-22 Drumholm late 1375 264.196 19981.2 3201.56 1500 707.107 0 500 2007 5.71

35 AgGx-6 Ben Soules late 1375 215.064 12349.1 19729.4 28040.2 0 7433.03 500 2032 2.75

37 AgHa-41 Roadside late 1375 204.21 30565.5 16401.2 17102.6 500 1000 500 2031 7.17

45 AhGx-14 G. Smith late 1375 220.86 16378.3 17007.4 32318.7 500 4301.16 0 2024 6.96

54 AiGx-11 Unick late 1375 282.39 8077.75 2000 25224 17734.1 500 0 1894 6.22

58 AiGx-7 Rife late 1375 293.343 7500 3807.89 25104.8 19849.4 1118.03 0 1871 8.06

59 AiGx-73 Chypchar late 1375 273.874 11280.5 4609.77 22051.1 16194.1 1500 500 1883 4.75

60 AiGx-8 Van Eden late 1375 299.267 6800.74 3605.55 25811.8 19811.6 1118.03 0 1856 7.6

62 AiHd-7 Coleman late 1375 361.917 57552.1 2236.07 4949.75 4123.11 0 0 1831 9.25

380

64 AkGt-2 Elliot late 1375 177.969 1581.14 4743.42 24382.4 40056.2 2500 0 1973 5.52

81 AlGs-73 Peter Webb 2 late 1375 188.031 7632.17 4123.11 12093.4 42807.7 1118.03 0 1917 13.88

82 AlGt-1 Milroy late 1375 184.852 3535.53 4609.77 18794.9 40929.8 3354.1 500 1954 6.52

83 AlGt-4 Robb late 1375 174.728 1118.03 6324.56 19313.2 37619.8 1802.78 500 1950 7.67 Cundles 103 BcGw-21 late 1375 264.231 11500 22299.1 28306.4 1414.21 707.107 0 1790 12.13 Creek 105 BcGw-5 Gervais late 1375 285.032 6576.47 26800.2 23963.5 4609.77 0 0 1761 18.2

106 BcGw-8 Sparrow late 1375 304.463 8077.75 25401.8 25714.8 3000 0 0 1788 7.24 John 111 BdGw-11 late 1375 272.419 4716.99 34132.1 15532.2 2692.58 0 0 1771 20.15 Thompson 113 BdGx-12 McRae late 1375 208.506 3162.28 24274.5 1500 2061.55 1000 0 1812 4.51

114 BdGx-13 Webb late 1375 201.839 5220.15 21914.6 1000 3535.53 1500 0 1820 11.7

121 BeHa-11 Davey late 1375 197.765 18027.8 29504.2 1000 6500 500 0 1756 13.95

55 AiGx-12 Pipeline late 1382 204.242 6041.52 0 24010.4 15402.9 500 500 1978 13.78 Slack – 170 AfHa-1 late 1395 204.585 38600.5 5522.68 4242.64 0 500 0 2036 9.76 Caswell 85 BaFp-4 Salem late 1400 55.7923 24829.4 1118.03 9013.88 47034.6 1000 500 1887 2.74

102 BcGw-18 Barrie late 1406 235.215 10770.3 22770.6 28398.9 3201.56 0 0 1824 1.97

138 BaGo-29 Gibbs late 1406 218.725 27060.1 4031.13 1414.21 10735.5 0 0 1817 13.54

19 AfHh-2 Pond Mills late 1425 269.316 19039.4 1500 14500 4609.77 2121.32 2500 1976 5.35

34 AfHk-7 Metcalf late 1425 225.306 8732.13 9055.38 500 1000 500 0 2061 4.42

381

92 BbGs-11 Markson late 1425 296.898 10816.7 3354.1 16031.2 36773 0 500 1791 15.57

104 BcGw-26 Wiacek late 1425 305.077 15532.2 18027.8 28861.7 3162.28 0 0 1796 3.81

110 BdGv-14 Bauman late 1425 263.05 2061.55 17356.6 32756.7 3041.38 0 0 1761 13.6

52 AiGt-2 Draper late 1428 229.111 3041.38 707.107 10500 36503.4 0 0 1876 6.51

21 AfHh-72 Pincombe 6 late 1450 273.615 15508.1 0 12206.6 2500 1581.14 2061.55 1987 4.11 Crawford 57 AiGx-6 late 1450 282.926 4743.42 2692.58 25739.1 18821.5 0 0 1870 23.52 Lake 89 BbGi-1 Lite late 1450 119.807 500 707.107 12903.5 14396.2 1118.03 0 1937 9.45

133 BeGx-19 Lalonde late 1450 221.526 3640.05 26655.2 1414.21 3041.38 500 0 1801 6.42

141 AiHd-18 Baden late 1450 371.995 59506.3 2500 5000 4500 0 500 1815 6.36

142 AgHh-26 Black Kat late 1450 212.53 25347.6 6264.98 13453.6 500 4500 500 2048 1.7

144 AiHd-15 Mannheim late 1450 346.305 53665.6 5500 9708.24 8139.41 0 0 1833 10.73

145 AgHh-29 Matthews late 1450 212.203 25164.5 6708.2 13829.3 0 4527.69 707.107 2048 1.7

67 AkGu-13 Downsview late 1460 173.076 2500 500 17182.8 33354.2 1414.21 707.107 1969 8.44

93 BbGv-19 Brassington late 1465 238.725 10307.8 7905.69 19313.2 500 0 0 1819 5.29

99 BcGv-11 McDonald late 1465 262.227 14534.4 12020.8 23505.3 2500 0 0 1817 6.28

101 BcGw-15 Little I late 1475 310.539 14577.4 19105 30083.2 4000 0 0 1796 6.67

68 AkGu-3 Jackes late 1480 171.856 5590.17 3535.53 18179.7 39246 3500 3500 1984 11.37

69 AkGu-9 Doncaster late 1480 181.397 0 500 21505.8 33589.4 2692.58 2692.58 1970 25.76

88 BaGw-1 Beeton late 1480 298.964 19144.2 7810.25 31384.7 1118.03 1581.14 0 1785 16.59

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118 BeGx-5 Farlain Lake late 1480 209.625 12806.2 37366.4 10198 7632.17 0 0 1744 12.79

120 BeGx-4 Deshambault late 1480 236.521 10965.9 35531.7 8381.53 6363.96 0 0 1760 10.74

122 BfFt-1 Beckstead late 1480 80.0257 24233.2 0 6363.96 24397.7 3041.38 500 1885 0.98 Harrietsville 171 AfHf-10 late 1480 275.001 17356.6 500 12093.4 2500 0 1414.21 1976 3.04 Earthworks 97 BbGw-14 Paisley late 1490 282.618 16530.3 17029.4 27892.7 2236.07 500 0 1798 8.93 Southwold 9 AeHi-1 late 1500 212.886 6020.8 2121.32 10124.2 2000 2692.58 1118.03 2043 2.09 Earthworks 20 AfHh-24 Pincombe 2 late 1500 273.615 15508.1 0 12206.6 2500 1581.14 2061.55 1987 4.11

40 AgHh-1 Lawson late 1500 265.223 23264.8 8077.75 10404.3 6000 2000 1500 2005 11.98

41 AgHh-10 Ronto late 1500 205.237 26613 4949.75 11672.6 0 4031.13 500 2046 5.08

42 AgHh-9 Windermere late 1500 200.429 27064.7 5315.07 11313.7 500 3535.53 0 2046 5.08 W. 112 BdGw-5 late 1500 284.878 8500 28640 21377.6 2061.55 0 0 1752 15.69 Thompson Copeland 119 BeGx-3 late 1500 180.744 8139.41 34154.1 7500 4527.69 707.107 0 1806 9.33 Creek 96 BbGv-30 Blu Meanie late 1505 267.741 13901.4 12510 18027.8 707.107 0 0 1799 13.89 McKenzie- 71 AkGv-2 late 1520 143.942 500 3535.53 20742.5 25739.1 500 0 1985 14.18 Woodbridge 124 BgFp-5 Glenbrook late 1522 51.1914 20670 1802.78 6800.74 42110 1000 500 1870 6.33

73 AkGx-1 Wallace late 1525 347.699 707.107 2692.58 32268.4 27459.1 1118.03 500 1788 20.47

87 BaGp-36 Fleetwood 2 late 1525 316.436 13928.4 2549.51 0 20573 500 500 1774 16.15

91 BbGr-2 Thomas late 1525 284.826 5147.81 2061.55 16378.3 44141.8 0 0 1801 11.59

383

95 BbGv-22 Lucas late 1525 281.062 10793.5 9394.15 18607.8 2500 500 0 1802 6.69

2 AcHk-1 Clearville late 1535 199.801 11045.4 500 3500 2061.55 0 2061.55 2148 5.83

6 AeHf-l Pound late 1535 232.9 3535.53 2000 707.107 5700.88 0 3201.56 2010 1.57

72 AkGv-8 E.A. Parson late 1535 192.382 2236.07 500 19506.4 30153.8 3535.53 0 1968 11.27

84 AlGt-96 Robin Hood late 1535 232.494 500 0 10735.5 34630.2 3201.56 0 1888 6.56

109 BdGm-l Quackenbush late 1535 271.575 17269.9 29702.7 23005.4 24186.8 707.107 0 1767 8.35

163 AlGh-2 Payne late 1535 94.5002 1000 0 5099.02 26076.8 707.107 0 1993 2.25 Methodist 165 BeGx-11 late 1535 227.719 15116.2 39714.6 12589.7 9656.6 0 0 1735 21.89 Point 1 Second Lake 167 BfGx-3 late 1535 210.16 16800.3 40447.5 13462.9 10198 500 0 1749 13.99 2 116 BeFv-2 Crystal Rock late 1540 93.3042 22472.2 6324.56 500 30805.8 2061.55 1000 1906 5.16

4 AcHm-2 McGeachy late 1550 181.752 5656.85 15206.9 8500 2828.43 4272 8485.28 2301 2.17

5 AcHm-3 Wolfe Creek late 1550 181.566 7071.07 16552.9 7158.91 1414.21 3905.12 7071.07 2303 1.43

66 AkGu-10 Risebrough late 1550 192.007 0 500 23838 29706.9 3905.12 2000 1953 3.17

143 AiHa-1 Ivan Elliott late 1550 315.543 16800.3 12659 16225 14866.1 0 0 1842 11.2

146 AiHd-1 Waterloo late 1550 348.625 52376 9013.88 7071.07 10500 1802.78 1414.21 1802 9

158 AkGv-11 Black Creek late 1550 129.931 707.107 500 15074.8 34300.1 500 500 2011 9.85

191 AdHo-1 Weiser late 1550 175.591 4500 6103.28 7382.41 1000 707.107 11068 2263 0.38 Emmerson 74 AkGx-5 late 1565 306.563 0 707.107 30594.1 29261.8 1000 500 1834 37.45 Springs

384

123 BfFv-1 McIvor late 1572 100.445 25298.2 2692.58 1414.21 24382.4 1414.21 0 1897 5

48 AhHb-1 Knight-Tucke late 1575 243.635 31800.2 22983.7 6020.8 1802.78 2236.07 500 1961 3.61

49 AhHb-20 Pottruff late 1575 235.422 30777.4 23900.8 9552.49 4272 1414.21 0 1986 6.08 Hunter 50 AhHb-21 late 1575 231.373 42638 14713.9 18668.2 1802.78 3354.1 1000 1988 2.21 Village 70 AkGv-1 Seed-Barker late 1575 170.518 1000 0 25000 21523.2 707.107 0 1936 22.19

132 BdGr-2 Hardrock late 1575 256.142 16007.8 16530.3 4743.42 43011.6 3201.56 3605.55 1770 3.21

155 BaGu-2 Aurora late 1575 316.009 1581.14 4031.13 500 21476.7 0 0 1781 18.08

157 BdGr-1 Benson late 1575 263.915 13462.9 13647.3 0 35418.9 3640.05 1500 1771 3.87

166 BfGx-1 Second Lake late 1575 197.044 16225 40607.9 13537 10440.3 500 500 1726 15.09

156 BdGv-3 Ball late 1580 263.05 2061.55 17356.6 32756.7 3041.38 0 0 1761 13.6

159 BdGv-1 Cahiague late 1580 237.59 1414.21 16688.3 31988.3 2236.07 500 0 1778 15.39

161 BfGx-2 Gignac Lake late 1580 190.213 17240.9 42172.3 15074.8 12020.8 500 0 1774 7.93

164 BeHa-3 Robitaille late 1580 245.003 17613.9 33503.7 5590.17 5656.85 0 0 1750 16.54

174 BcHb-1 Glebe late 1580 269.976 6000 3354.1 8860.02 3807.89 707.107 0 1779 11.01 Hamilton – 176 BbHa-10 late 1580 395.631 10049.9 10307.8 12165.5 5147.81 1500 0 1721 34.34 Lougheed 177 BcHb-27 Haney - Cook late 1580 391.113 0 2500 2549.51 500 3000 0 1700 40.77

179 BcHb-26 MacMurchy late 1580 246.734 707.107 707.107 3162.28 0 2236.07 0 1788 10.9

180 BcHb-25 McAllister late 1580 271.254 500 1118.03 4123.11 0 3162.28 0 1756 9.1

181 BcHb-31 McQueen-McConn late 1580 244.742 1500 0 7810.25 2121.32 500 0 1792 8.69

385

182 BbHa-7 Melville late 1580 336.733 11068 11715.4 14713.9 2828.43 2000 0 1787 26.82 Young – 187 BcHb-19 late 1580 252.945 2061.55 500 8514.69 2236.07 0 500 1792 11.02 McQueen 47 AhHa-29 Richer late 1590 241.938 22203.6 17691.8 16867.1 707.107 500 500 1964 7.22

94 BbGv-20 Cooper late 1590 298.771 16286.5 14983.3 23500 1581.14 0 0 1813 9.77

131 AkGv-3 Boyd late 1600 158.973 707.107 1118.03 23162.5 23585 0 0 1962 16.86 Sidey – 186 BbHa-6 late 1600 288.061 9656.6 12727.9 14534.4 4031.13 2500 0 1758 26.71 Mackay 172 AhHb-8 Fonger late 1610 220.126 35514.1 21982.9 17066 1414.21 500 500 2010 12.94

153 BeGw-15 Alonso late 1615 238.283 3500 28570.1 23323.8 2915.48 0 0 1771 5.46

154 BdGw-3 Auger-Yates late 1640 266.457 10000 25179.4 24520.4 1414.21 0 0 1808 14.81

160 BdGw-27 Flanagan late 1640 320.622 10049.9 26925.8 23048.9 707.107 0 0 1800 18.78

162 BeGx-15 Le Caron late 1640 238.54 6946.22 31622.8 5315.07 2236.07 500 500 1789 10.69 Connor – 173 BcHb-3 late 1640 359.532 7810.25 7158.91 9192.39 7382.41 1000 0 1700 20.08 Rolling Graham – 175 BcHb-7 late 1640 383.57 4716.99 4123.11 8845.9 5700.88 1118.03 0 1655 13.67 Ferguson 168 Ste Marie I late 1650 219.125 2236.07 37312.9 13086.3 2500 0 500 1819 20.16 Kelly – 178 BcHb-10 late 1650 267.243 2692.58 500 9604.69 2000 1414.21 0 1761 13.03 Campbell Plater – 183 BdHb-2 late 1650 183.401 1000 4272 707.107 0 500 0 1836 12.05 Fleming Plater – 184 BdHb-1 late 1650 209.99 707.107 3640.05 707.107 0 500 500 1803 10.39 Martin

386

185 BcHb-20 Rock Bottom late 1650 252.171 1118.03 0 5315.07 1500 2000 0 1798 9.49

38 AgHb-1 Porteous middle 769 193.493 40577.1 19293.8 16918.9 3000 1500 1118.03 2027 8.03

75 AlGo-29 Auda middle 955 139.604 38108.4 6363.96 1500 11926.9 0 1118.03 1870 20.97

15 AfHd-2 Van Besien middle 991 241.377 34340.2 1581.14 1000 3000 1118.03 0 1988 5.25

189 AfGx-113 Lone Pine middle 994 189.46 16620.8 4000 22803.5 500 500 0 2059 13.17

80 AlGs-102 Bolitho middle 1000 130.956 6576.47 3535.53 16552.9 44922.2 0 0 1973 8.22 AgHb- 188 Holmedale middle 1029 205.054 46097.7 14221.5 19006.6 4123.11 1802.78 2121.32 2019 4.29 191

26 AfHi-32 Berkmortel middle 1100 235.366 14326.5 5830.95 707.107 2236.07 0 0 2036 6.11

27 AfHi-50 Dunn middle 1100 272.966 18200.3 1500 5500 2915.48 0 0 1980 16.87

28 AfHj-18 Smale middle 1100 224.14 15524.2 15976.5 500 1118.03 1118.03 500 2049 3.99

1 AaHp-4 Dick middle 1103 176.018 0 2500 1000 29090.4 1118.03 3000 2364 1.13

125 AeHj-2 Dymock middle 1108 209.052 0 4031.13 2121.32 1414.21 1000 2236.07 2087 10.75

10 AeHk-1 Goessens middle 1125 216.055 17755.3 3354.1 0 11543.4 2121.32 0 2033 2.64

43 AhGw-2 Pergentile middle 1125 100.506 13209.8 12619.4 32015.6 2236.07 500 500 2134 4.58

14 AfHd-1 DeWaele middle 1142 231.28 32237.4 2692.58 1118.03 5000 500 500 2005 8.33

23 AfHi-21 Yaworsky middle 1149 233.956 14983.3 6946.22 500 2500 0 0 2039 9.46

29 AfHj-19 MiV18 middle 1150 238.581 16378.3 15890.2 500 1118.03 500 0 2051 4.33

30 AfHj-24 Hardy middle 1150 238.356 17219.2 15041.6 500 1000 500 500 2048 1.54

31 AfHj-26 AfHj-26 middle 1150 223.692 13829.3 15206.9 1118.03 2915.48 0 707.107 2055 2.37

387

32 AfHj-28 Little middle 1150 234.937 16101.2 15976.5 0 2549.51 500 0 2052 2.61

17 AfHg-1 Calvert middle 1179 260.684 24418.2 1581.14 4609.77 1414.21 0 0 1986 7.81

78 AlGs-10 Boys middle 1183 131.142 8544 3201.56 15532.2 45839.4 0 0 1970 10.59

77 AlGs-1 Miller middle 1187 125.876 7516.65 2500 18337.1 46327.6 0 0 1988 15.01

12 AfGx-54 Anderson middle 1192 212.98 20615.5 3041.38 20597.3 0 500 500 2050 2.71 AiGw- Five Acre 147 middle 1195 175.53 2236.07 0 20303.9 9055.38 4242.64 500 2031 9.9 100 Field 148 AiGx-39 Ireland middle 1195 175.633 2828.43 500 21100.9 8062.26 3605.55 0 2016 9.1

79 AlGs-101 Delancey middle 1200 157.096 6184.66 4123.11 15890.2 44300.1 500 500 1964 13.68

39 AgHb-18 Cooper middle 1216 215.391 39000 20814.7 18927.5 707.107 1000 1581.14 2016 14.05

22 AfHi-20 Kelly middle 1221 239.951 15206.9 6708.2 0 2828.43 500 0 2039 9.46 AiGw- 149 Tara middle 1238 159.185 3201.56 500 20808.7 8015.61 4031.13 0 2043 3.65 124

3 AcHm-1 Krieger middle 1245 181.056 8062.26 17182.8 5315.07 500 2061.55 5315.07 2296 2.39

46 AhGx-95 Clish middle 1250 235.553 21783 14430.9 30438.5 2500 1000 0 2002 7.1

65 AkGt-20 Thompson middle 1250 160.395 2549.51 2915.48 27459.1 42267 500 0 1983 9.72

134 AeHg-10 D. Cook middle 1250 240 500 3162.28 2500 1000 1118.03 500 1993 4.23

135 AeHg-19 Frank Zavitz middle 1250 249.712 1118.03 4123.11 1802.78 1414.21 1118.03 0 1994 11.68

136 AeHg-12 Knowles II middle 1250 230.444 500 3605.55 1802.78 0 1118.03 0 1991 18.91

137 AeHg-18 Macillius middle 1250 220.157 5590.17 4716.99 0 2500 1500 4031.13 2018 5.09

139 AlGh-9 Huff middle 1250 81.0381 500 1802.78 1118.03 33533.6 500 500 1998 2.56

388

150 AjGw-71 Mullet Pond middle 1250 189.669 5522.68 500 11543.4 31504 2500 0 1973 6.45

151 AhHd-46 Walton middle 1250 276.614 50440.6 6184.66 15508.1 6500 500 0 1952 5.41

8 AeHg-3 Stafford middle 1260 227.332 6670.83 3041.38 500 4031.13 0 1414.21 2013 3.92 Dorchester 18 AfHg-24 middle 1275 250.287 25124.7 2549.51 3640.05 3000 500 500 1993 8.22 Village 51 AhHd-1 Ovington middle 1275 285.491 50480.2 6324.56 15652.5 7158.91 0 500 1951 2.29

129 AfHj-14 Roeland middle 1275 236.299 16770.5 15182.2 707.107 1500 0 500 2050 1.54

16 AfHd-3 Uren middle 1288 243.482 30153.8 1000 5147.81 6519.2 707.107 0 2013 8.49

130 AfGu-2 Bonisteel middle 1290 189.473 3201.56 500 11672.6 1000 2549.51 2549.51 2107 5.12 King's Forest 128 AhGw-1 middle 1291 107.221 14115.6 12747.5 32221.9 1581.14 1118.03 500 2130 8.87 Park 25 AfHi-23 Edwards middle 1299 263.115 20248.5 3535.53 1000 1000 500 0 2013 5.71

389

REFERENCES:

Bursey, J. A. 1993 Prehistoric Huronia: Relative Chronology through Ceramic Seriation. Ontario Archaeology 55:

City of Hamilton 2004 Hamilton`s Heritage Volume 4: Inventory of Registered Archaeological Sites. Planning and Development Department, Development and Real Estate Division, Community Planning and Design Section. December 2004.

Crawford, G. W. and Smith, D. G. 2003 Paleoethnobotany in the Northeast. In People and Plants in Ancient Eastern North America, edited by P. E. Minnis, pp. 172-257. Smithsonian Institution Press, Washington, D.C..

Creese, J. L. 2011 Deyughnyonkwarakda – “At the Wood’s Edge”: The Development of the Iroquoian Village in Southern Ontario, A.D. 900 – 1500. Unpublished PhD dissertation, Department of Anthropology, University of Toronto.

Dieterman, F. A. 2001 Princess Point: A Landscape of Place. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto

Ellis, C., and Ferris, N (Eds) 1990 The Archaeology of Southern Ontario To AD 1650. Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London, Ontario.

Fecteau, R. 1985 The Introduction and Diffusion of Cultivated Plants in Southern Ontario. Unpublished MA dissertation, York University, Toronto, Ontario.

390

Fecteau, R., Molnar, J., and Warrick, G. 1994 Iroquoian Village Ecology. KEWA 94(8):2-16.

Ferris, N. 1999 Telling Tales: Interpretive Trends in Southern Ontario Late Woodland Archaeology. Ontario Archaeology 68:1–62.

Ferris, N., and Spence, M. W. 1995 The Woodland Traditions in Southern Ontario. Revista de Archeologia Americana 9:83–138.

Fox, W. A. 1990 The Middle Woodland to Late Woodland Transition. In The Archaeology of Southern Ontario To AD 1650, edited by C. Ellis and N. Ferris, pp. 171 – 188. Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London, Ontario.

Hart, J. P. 1999 Maize Agricultural Evolution in the Eastern Woodlands: A Darwinian Perspective. Journal of Archaeological Method and Theory 6:137-180.

2001 Maize, Matrilocality, Migration, and Northern Iroquoian Evolution. Journal of Archaeological Method and Theory, Vol. 8, No. 2: 151-181.

Hawkins, A. L. 2004 Report on the 2004 Investigations at the Emmerson Springs Village (AkGx- 5), Town of Halton Hills, Ontario Under Licence P081-002 and P081-004.

Heidenreich, C. E. 1971 Huronia: A History and Geography of the Huron Indians, 1600–1650, McClelland and Stewart, Toronto.

Jamieson, J.B. 1990 The Archaeology of the St. Lawrence Iroquoians. In The Archaeology of Southern Ontario to AD 1650, edited by C. Ellis and N. Ferris, pp. 385-404, Occasional Publication of the London Chapter, Ontario Archaeological Society Number 5, London.

391

Kapches, M. 1981 The Middleport Pattern in Ontario Iroquoian Prehistory. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto, Toronto.

Katzenberg, M. A., Schwarcz, A.H., Knyf, M. and Melbye, F. J. 1995 Stable Isotope Evidence for Maize Horticulture and Paleodiet in Southern Ontario, Canada. American Antiquity 60:335-350.

MacDonald, R. I. 1986 The Coleman Site (AiHd-7): A late prehistoric Iroquoian village in the Waterloo region . Unpublished MA thesis, Trent University, Peterborough, Ontario.

MacDonald, R. I., and Williamson, R. F. 1993 Summary fo 1992 research by Archaeological Services Inc. Annual Archaeological Report, Ontario 4:29-36

McKenney, D. W., Hutchinson, M. F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E. Hopkinson, R. F., Price, D. and Owen, T. 2011 Customized Spatial Climate Models for North America. Bulletin of the American Meteorological Society, 92: 1611–1622

Latta, M.A., and Reed, P. 1991 The Beeton Site, BaGw-1. Volume 1: Environment and Excavation History. Submitted to the Ontario Heritage Foundation; 1993 The Beeton Site, BaGw-1. Volume 2: Material Culture. Submitted to the Ontario Heritage Foundation

392

O’Brien, R. F. 1976 An Archaeological Survey of the Methodist Point Park Reserve. Unpublished Master’s Thesis, Department of Anthropology, University of Manitoba

Ounjian, G. L. 1998 Glen Meyer and Prehistoric Neutral Paleoethnobotany. Unpublished PhD Dissertation, Department of Anthropology, University of Toronto.

Riley, S. J., DeGloria, S. D., and Elliot, R. 1999 A Terrain Ruggedness Index that Quantifies Topographic Heterogeneity. Intermountain Journal of Sciences. V. 5(1-4): 23-27.

Smith, D. 1997 Radiocarbon Dating the Middle to Late Woodland Transition and Earliest Maize in Southern Ontario. Northeast Anthropology 54:37-73.

Smith, D. G. and Crawford, G. W. 1997 Recent Developments in the Archaeology of the Princess Point Complex in Southern Ontario. Canadian Journal of Archaeology 21(1):9-32.

Sutton, R. 1996 The Middle Iroquoian Colonization of Huronia, Unpublished PhD dissertation, Department of Anthropology, McMaster University.

Timmins, P. A. 1985 The Analysis and Interpretation of Radiocarbon Dates in Iroquoian Prehistory, Museum of Indian Archaeology Research Report No. 19

Timmins Martelle Heritcage Consultants Inc. 2010 Stage 1 Archaeological Assessment Sumac Ridge Wind Farm Geographic Township of Manvers City of Kawartha Lakes, Ontario.

393

2008 A Population History of the Huron-Petun, A.D. 500-1650. Cambridge University Press.

Watts, C.M. White, C.D. and Longstaffe, F.J. 2011 Childhood Diet and Western Basin Tradition Foodways at the Krieger Site, Southwestern Ontario, Canada. American Antiquity 76(3):446-472.

Williamson, R. F. 1985 Glen Meyer: People in Transition. Unpublished PhD Dissertation, Department of Anthropology, McGill University, Montreal.

2012 What will be has always been: The past and present of Northern Iroquoians in The Oxford Handbook of North American Archaeology, edited by T.R. Pauketat. Pp. 273-284. New York: Oxford University Press

Wright, J. V. 1966 The Ontario Iroquois Tradition. National Museum of Canada, Bulletin # 210.