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Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2012 Florida's Paleoindian and Early Archaic: A GIS Approach to Modeling Submerged Landscapes and Site Distribution on the Continental Shelf Ryan M. Duggins Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES FLORIDA’S PALEOINDIAN AND EARLY ARCHAIC: A GIS APPROACH TO MODELING SUBMERGED LANDSCAPES AND SITE DISTRIBUTION ON THE CONTINENTAL SHELF By RYAN M. DUGGINS A Dissertation Submitted to the Department of Anthropology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Degree Awarded: Summer Semester, 2012 Ryan Duggins defended this dissertation on April 17th, 2012. The members of the supervisory committee were: Glen H. Doran Professor Directing Dissertation Joseph F. Donoghue University Representative Rochelle A. Marrinan Committee Member Frank W. Marlowe Committee Member The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with the university requirements. ii I dedicate this work to my parents, without whose support and encouragement it would never have happened. iii ACKNOWLEDGEMENTS Many people have helped me during the course of this project, and they all deserve recognition. I would like to start by thanking my committee - Joe Donoghue: for opening my eyes to sea level change; Rochelle Marrinan: for her thorough editing and always being supportive; Frank Marlowe: for teaching me the value of parsimonious explanation; and Glen Doran: for taking me under his wing, providing help when I needed it, for being so relaxed, and getting edits back to me so quickly. I greatly appreciate the help of Vincent Birdsong at the Florida Master Site File for his help in my data acquisition. Thanks are also due to Michael Bryan, without whose help I would surely have been defeated by GIS. Thanks go out to Jim Clark, and the rest of the West Marine team for being so accommodating with my schedule. Raphael Kampmann provided me with endless hardware and software support and formatting help, and I greatly appreciate his willingness to lend his expertise. Thanks also go out to my mother for editing the document. Andy Hemmings planted the seed for this project, and I thank him dearly. For all the times I needed to play ball during the writing process, I thank Maple for always being there for me. This project would never have been completed were it not for Julie Byrd. Her help, guidance, support and love made this possible. iv TABLE OF CONTENTS List of Tables x List of Figures xi Abstract xv 1 Introduction 1.1 1 Purpose Of This Research . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Stage 1: Analysis of Terrestrial Site Distribution . . . . . . . . 2 1.1.2 Stage 2: GIS Reconstruction of the Continental Shelf . . . . . 3 1.1.3 Stage 3: Data Incorporation and Eco-Cultural Niche Modeling 4 1.2 Implications Of This Research . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Submerged Prehistoric Archaeology . . . . . . . . . . . . . . . . . . . 5 1.4 Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Background 8 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Paleoindian and Early Archaic Period Archaeological Research . . . . 9 2.2.1 Settlement Models . . . . . . . . . . . . . . . . . . . . . . . . v 11 2.2.2 2.3 2.4 2.5 2.6 Paleoindian Subsistence . . . . . . . . . . . . . . . . . . . . . 12 Coastal Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2 Worldwide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.3 Americas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.4 Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3.5 Resource Availability . . . . . . . . . . . . . . . . . . . . . . . 17 Underwater Archaeology . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.4.1 Survey Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Study Area: Why Florida? . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5.2 Geology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5.3 Sea Level Change . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.5.4 Florida Sea Level . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.5 Florida’s Hydrology . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5.6 Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.7 Springs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.8 Hydrology and Sea Levels . . . . . . . . . . . . . . . . . . . . 32 2.5.9 Bathymetry of the Gulf of Mexico . . . . . . . . . . . . . . . . 36 GIS and Predictive Modeling . . . . . . . . . . . . . . . . . . . . . . 37 2.6.1 GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6.2 Predictive Modeling . . . . . . . . . . . . . . . . . . . . . . . 38 2.6.3 Maxent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 40 vi 3 Methods 42 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2 A GIS Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2.1 Hardware and Software . . . . . . . . . . . . . . . . . . . . . . 43 3.2.2 Modeling Terrestrial Site Data . . . . . . . . . . . . . . . . . . 43 3.2.3 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.4 Data Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.5 Terrestrial Environmental Layers . . . . . . . . . . . . . . . . 46 3.2.6 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.2.7 Site Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.8 Proximity Analysis . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2.9 Paleoindian and Early Archaic Proximity . . . . . . . . . . . . 52 Modeling Continental Shelf Data . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Data Aqcuisition . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.2 Environmental Layers . . . . . . . . . . . . . . . . . . . . . . 54 Maxent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.4.2 Data Requirements . . . . . . . . . . . . . . . . . . . . . . . . 58 3.4.3 Running the Model . . . . . . . . . . . . . . . . . . . . . . . . 59 3.4.4 Data Output . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.3 3.4 3.5 4 Results and Discussion Part I: Site Distribution 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 62 62 4.2 Results: Terrestrial Site Distribution Analysis . . . . . . . . . . . . . 63 4.3 Paleoindian Sites and Water Features . . . . . . . . . . . . . . . . . . 65 4.4 Early Archaic Sites and Water Features . . . . . . . . . . . . . . . . . 71 4.5 Site Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.6 Site Proximity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.7 Chert Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.8.1 The Eglin Air Force Base Problem . . . . . . . . . . . . . . . 85 4.8.2 Water Proximity . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.8.3 Site Proximity . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.8.4 Chert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.8.5 Hydro-Highway Hypothesis . . . . . . . . . . . . . . . . . . . 92 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.9 5 Results Part II: Submerged Landscape Reconstruction 96 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.2 Submerged Landscape Reconstruction . . . . . . . . . . . . . . . . . . 97 5.3 Drainage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.4.1 Benefits to Archaeologists . . . . . . . . . . . . . . . . . . . . 120 5.4.2 Ground Truthing . . . . . . . . . . . . . . . . . . . . . . . . . 121 5.4.3 Water Features . . . . . . . . . . . . . . . . . . . . . . . . . . 122 5.4.4 High Preservation Potential . . . . . . . . . . . . . . . . . . . 122 5.4.5 Site Distribution and Prehistoric Landscapes . . . . . . . . . . 129 viii 5.5 Maxent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 5.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6 Conclusions 145 6.0.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 6.0.2 Project Summary . . . . . . . . . . . . . . . . . . . . . . . . . 146 Bibliography 149 Biographical Sketch 168 ix LIST OF TABLES 2.1 4.1 Approximate Sea Level Stand Below Present Between 15,000 and 10,000 BP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Nearest Neighbor Site Proximity (km) . . . . . . . . . . . . . . . . . 78 x LIST OF FIGURES 2.1 Florida Sea Level Curve (Donoghue 2011) . . . . . . . . . . . . . . . 26 2.2 Distribution of Florida’s Aquifers (FDEP 2012) . . . . . . . . . . . . 30 2.3 Relationship Between Inland and Coastal Aquifer (Barlow 2003) . . . 33 2.4 Continental Shelf and Aquifer Discharge: Arrows Represent Water Flow, While Triangles Show Springs (Faure et al. 2002). . . . . . . . 34 Paleoindian Site Distribution n=234 (DEM: U.S. Geological Survey 1999). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Early Archaic Site Distribution n=349 (DEM: U.S. Geological Survey 1999). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Major Rivers and 1st Magnitude Springs of Florida (DEM: U.S. Geological Survey 1999; Rivers: FDEP 2001; Springs FDEP 2001). . . . . 66 4.4 Paleoindian Sites and Distance from 1st Magnitude Springs. . . . . . 67 4.5 Paleoindian Site Frequency and Distance From 1st Magnitude Springs. 67 4.6 Paleoindian Site Frequency Change and Distance From 1st Magnitude Springs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.7 Paleoindian Sites and Distance Away from Rivers. . . . . . . . . . . . 69 4.8 Paleoindian Site Frequency and Distance from Rivers. . . . . . . . . . 69 4.9 Paleoindian Site Frequency Change and Distance From Rivers. . . . . 70 4.1 4.2 4.3 4.10 Paleoindian Sites and Distance Away From Rivers, Streams and Creeks. 70 xi 4.11 Paleoindian Site Frequency and Distance From Rivers, Streams, and Creeks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.12 Paleoindian Site Frequency Change and Distance From Rivers, Streams and Creeks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.13 Early Archaic Sites and Distance From Springs. . . . . . . . . . . . . 72 4.14 Early Archaic Site Frequency and Distance From Springs. . . . . . . . 73 4.15 Early Archaic Frequency Change and Distance From Springs. . . . . 73 4.16 Early Archaic Sites and Distance From Rivers. . . . . . . . . . . . . . 74 4.17 Early Archaic Site Frequency and Distance From Rivers. . . . . . . . 74 4.18 Early Archaic Frequency Change and Distance From Rivers. . . . . . 75 4.19 Early Archaic Sites and Distance From Rivers, Streams, and Creeks. . 75 4.20 Early Archaic Site Frequency and Distance From Rivers, Streams, and Creeks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.21 Early Archaic Site Frequency Change and Distance From Rivers, Streams and Creeks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.22 Paleoindian Site Density Home Range 175 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). . . . . . . . . . . . . . . . . . . . . 79 4.23 Paleoindian Site Density Home Range 4500 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). . . . . . . . . . . . . . . . . . . . . 80 4.24 Early Archaic Site Density Home Range 175 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). . . . . . . . . . . . . . . . . . . 81 4.25 Early Archaic Site Density Home Range 4500 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). . . . . . . . . . . . . . . . . 82 4.26 Chert Distribution and Paleoindian Sites (Upchurch et al. 1982). . . . 83 4.27 Chert Distribution and Early Archaic Sites (Upchurch et al. 1982). . 84 4.28 Chert Distribution and Paleoindian Site Density (Upchurch et al. 1982). 85 xii 4.29 Chert Distribution and Early Archaic Site Density (Upchurch et al. 1982). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.1 Florida’s Shoreline and Elevation 8,000 BP. . . . . . . . . . . . . . . . 98 5.2 Florida’s Shoreline and Elevation 9,000 BP. . . . . . . . . . . . . . . . 99 5.3 Florida’s Shoreline and Elevation 10,000 BP. . . . . . . . . . . . . . . 100 5.4 Florida’s Shoreline and Elevation 11,000 BP. . . . . . . . . . . . . . . 101 5.5 Florida’s Shoreline and Elevation 12,000 BP. . . . . . . . . . . . . . . 102 5.6 Florida’s Shoreline and Elevation 13,000 BP. . . . . . . . . . . . . . . 103 5.7 Florida’s Shoreline and Elevation 14,000 BP. . . . . . . . . . . . . . . 104 5.8 Florida’s Shoreline and Elevation 15,000 BP. . . . . . . . . . . . . . . 105 5.9 Location of Terrestrial Rivers and Exposed Land at 15,000 BP. . . . . 108 5.10 FDEP Major Rivers and Predicted River Channels. . . . . . . . . . . 109 5.11 Florida at 8,000 BP and River Channels. . . . . . . . . . . . . . . . . 110 5.12 Florida at 9,000 BP and River Channels. . . . . . . . . . . . . . . . . 111 5.13 Florida at 10,000 BP and River Channels. . . . . . . . . . . . . . . . 112 5.14 Florida at 11,000 BP and River Channels. . . . . . . . . . . . . . . . 113 5.15 Florida at 12,000 BP and River Channels. . . . . . . . . . . . . . . . 114 5.16 Florida at 13,000 BP and River Channels. . . . . . . . . . . . . . . . 115 5.17 Florida at 14,000 BP and River Channels. . . . . . . . . . . . . . . . 116 5.18 Florida at 15,000 BP and River Channels. . . . . . . . . . . . . . . . 117 5.19 Florida’s Rivers and Paleorivers. . . . . . . . . . . . . . . . . . . . . . 118 5.20 Paleoriver Channel Buffer and Submerged Springs. . . . . . . . . . . 119 5.21 Inundated Land 9,000-8,000 BP, With High Preservation Potential. . 125 xiii 5.22 Inundated Land 11,000-10,000 BP, With High Preservation Potential. 126 5.23 Inundated Land 9,000-8,000 BP HPP Areas and River Channels. . . . 127 5.24 Inundated land 11,000-10,000 BP HPP Areas and River Channels. . . 128 5.25 Early Archaic Site Density, Low Mobility, at 10,000 BP. . . . . . . . . 130 5.26 Early Archaic Site Density, High Mobility, at 10,000 BP. . . . . . . . 131 5.27 Paleoindian Site Density, Low Mobility, at 13,000 BP. . . . . . . . . . 132 5.28 Paleoindian Site Density, High Mobility, at 13,000 BP. . . . . . . . . 133 5.29 River Confluences and Paleoindian Site Density. . . . . . . . . . . . . 134 5.30 Maxent Predicted Paleoindian Site Distribution. . . . . . . . . . . . . 138 5.31 Maxent Predicted Model: Highest Probability of Paleoindian Sites. . 139 5.32 Maxent Model Effectiveness: AUC Curve and Standard Deviation vs. Random. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 5.33 Maxent Probability of Site Distribution Past the Present Shoreline. . 141 xiv ABSTRACT Recent advances in coastal and offshore bathymetric data, coupled with the computational power of GIS, provide the necessary platform to reconstruct submerged paleolandscapes. This has far reaching implications for submerged prehistoric survey as well as site modeling. As Florida sea levels have risen 98m in the past 15,000 years, the extent of landscape available to prehistoric populations drastically changed. Florida Master Site File distribution data for Florida’s Paleoindian and Early Archaic periods provide the dataset to analyze the relationship between landscape features and site distribution. This study aims to guide future offshore prehistoric research in the Gulf of Mexico by identifying patterns present in Florida’s inland sites. xv CHAPTER 1 INTRODUCTION Interpretation of Florida’s Paleoindian and Early Archaic cultures is inherently hindered by an incomplete data set. Rising sea levels over the past 15,000 years submerged archaeological sites on Florida’s continental shelves. Although the archaeological potential of continental shelves was identified over 50 years ago (Emery and Edwards 1966; Goggin 1960), archaeologists have only scratched the surface of that potential. Excellent work has and is being conducted on Florida’s submerged sites (Adovasio and Hemmings 2009; Dunbar et al. 1988; Faught 2002, 2003; Faught and Donoghue 1997; Latvis and Quitmyer 2006; Webb 2006). New technology and better resolution of data are expanding possibilities for the field. This study employs cutting-edge Geographic Information Systems (GIS) analysis to improve previous data constrictions in two ways: 1) incorporating high-resolution bathymetric data never before used in archaeological research and 2) using the exact locations of terrestrial sites rather than county level data. The predictive model created from this research outlines areas of high priority for future underwater surveys, and offers a user-friendly shapefile to guide future work. 1 1.1 Purpose Of This Research This research seeks to develop a methodology to facilitate the process of locating submerged prehistoric sites in the Gulf of Mexico. The project uses known archaeological site locations in terrestrial Florida to build a predictive model of settlement patterns that can be used to predict areas of high probability of site occurrence on submerged lands. Terrestrial archaeology has had over one hundred years to refine survey methods. The relatively recent emergence of prehistoric submerged archaeology, the variation in underwater environments, and the cost of investigating these submerged landscapes, has meant that a ”systematic search strategy to broadly investigate submerged prehistoric landscapes is lacking” (Benjamin 2010:255). Ruppe (1988:57) identified three areas of research that should guide the study of submerged prehistoric sites: sea level change, coastal geomorphology, and coastal settlement patterns. Ruppe’s standards form the foundation of this study and are made stronger with the modern analytical capabilities of GIS. Because this study builds on itself, it is conducted in three stages: 1) finding patterns in terrestrial site distribution; 2) mapping the submerged paleolandscape, and 3) incorporating data from stages 1 and 2 in a model of probable locations for underwater sites. 1.1.1 Stage 1: Analysis of Terrestrial Site Distribution Human settlement is patterned and therefore archaeological sites are not distributed evenly across the landscape (Jochim 1976). Quantifying the relationships between sites and the environment helps elucidate patterns in prehistoric site distribution. The most efficient way to build a model of potential submerged site location is to use available data from terrestrial analogs. The dichotomous distinction between 2 terrestrial archaeological sites and submerged archaeological sites is a product of the modern environment and not the environment of prehistoric populations. Using GIS, I have analyzed all recorded Paleoindian and Early Archaic site locations that are within the state of Florida – the inhabitants of Florida when sea level was the most different from today. Research on Paleoindian and Early Archaic occupation of Florida stresses the importance of proximity to water in site distribution (Dunbar and Waller 1983; Neil 1964; Thulman 2006, 2009). Stage 1 quantifies this relationship and tests the validity of the hypothesis. 1.1.2 Stage 2: GIS Reconstruction of the Continental Shelf Modern advances in sea-floor bathymetric data analysis allow for the detailed reconstruction of submerged landscapes. Florida’s eastern and western continental shelves will be reconstructed from the highest resolution bathymetric data available. GIS allows for the integration of terrestrial and submerged landscape analysis, thereby treating the entire landmass as one entity. This study will analyze drainage patterns on the continental shelf, thereby identifying the probable location of submerged paleoriver channels and prehistoric topography. Florida will be recreated at eight sea level stands between 15,000 and 8,000 BP. Recreating the geography of Florida during the Terminal Pleistocene/Early Holocene creates a hypothetical environment on which to apply GIS analysis. GIS analysis incorporating both sea level and continental shelf morphology will assist in identifying submerged areas likely to have preserved archaeological sites. 3 1.1.3 Stage 3: Data Incorporation and Eco-Cultural Niche Modeling As step one will quantify relationships between sites and landscape features, and step two will recreate the inundated prehistoric landscape, step three will incorporate the data. Patterns identified in terrestrial sites will be extended onto submerged areas. A newly developed method of identifying the probability distribution of archaeological sites, Eco-Cultural Niche modeling, will be used to predict site distribution on submerged lands. Using the known location of terrestrial sites, along with a limited number of environmental variables, a machine-learning algorithm based on Bayesian principles (called Maxent) will be used to identify high probability survey areas (Banks et al. 2006; Banks et al. 2008; Phillips et al. 2006). 1.2 Implications Of This Research This research lays the foundation upon which large unanswered anthropological questions may one day be answered. Paleoindian research is in the midst of a paradigm shift away from Clovis-first, ice-free corridor traveling, and big game hunting models of colonization. Previous models are proving to be incompatible with recent finds, and Paleoindian researchers are struggling to answer the most basic questions about the first Americans: when they arrived, how they arrived, from where they came. These questions, as Holliday (2009:310) notes, ”have been some of the most fundamental issues in American archaeology since initial European colonization of the continent.” Given that terrestrial archaeological approaches have yet to provide the necessary data to answer these questions, it is time to explore other options. The earliest inhabitants in 4 the Americas interacted with an environment that is strikingly different from today. This study aims to provide a methodology to locate submerged sites that may help resolve these lingering questions. On a regional scale, this research presents the opportunity to greatly enhance our understanding of Florida’s Paleoindian and Early Archaic populations. While current evidence supports a pre-Clovis occupation of the Americas, the abundance and clarity of these data do not compare to the substantial amount of Paleoindian data (Adovasio et al. 1990; Dillehay 1999; Goebel et al. 2008; Waters et al. 2007). Since Paleoindians were most likely the first cultural groups to inhabit the region, this project will illustrate how they explored the landscape. The Early Archaic is often considered to be a transitional, readaptave cultural phase between the Paleoindian and the Middle Archaic (Anderson and Sassaman 1996; Daniel 1998). This study focuses on Paleoindian and Early Archaic sites because these cultures interacted with a landscape that was distinctly different from that of today. Sea level stabilization did not occur until the Middle Archaic, thereby hindering interpretation of earlier cultures. By comparing the distribution patterns between these two cultural groups this study will identify the similarities and differences and ultimately question the Paleoindian to Early Archaic boundary. 1.3 Submerged Prehistoric Archaeology This study seeks to define a new methodology directed toward the analysis of submerged landscapes. High-resolution coastal relief models provide the necessary data to analyze drowned landscapes. These data have been utilized for geological and geographic purposes, but have not yet been utilized within the archaeological community. Just as GIS analysis revolutionized terrestrial archaeological predictive modeling 5 (Brandt et al. 1992; Canning 2005; Duncan and Beckman 2000; Ebert 2004), GIS will transform the field of submerged prehistoric archaeology. Submerged prehistoric archaeological sites are needles in the proverbial haystack (Faught and Flemming 2008) and this project demonstrates how GIS analysis is a tool to find those needles. 1.4 Chapter Overview Chapter 1 identifies the goal of this study and the problems it address. The regional and general implications of this work are identified. Chapter 2 will provide the background information relevant to this project and a literature review. The chapter is organized by relevant topics and includes: a review of Paleoindian migration, settlement and subsistence; a summary of coastal studies including archaeological evidence for aquatic resource use and a discussion of submerged prehistoric archaeological survey methods; the rationale for selecting Florida as a survey area, including relevant geological and hydrological data; and finally a review of GIS modeling and Maxent. Chapter 3 will detail the methods used in this study. The chapter is organized by relevant topic and includes: archaeological site data used, spatial data used, submerged bathymetric data used, an explanation of drainage analysis, an explanation of correcting elevation changes due to sea level, and the procedures to running Maxent. Chapter 4 will contain results and a discussion of the terrestrial site distribution analysis. This will include: modeling of terrestrial Paleoindian and Early Archaic site distribution data, quantifying the distance between sites and water features, nearest neighbor analysis between sites, site density analysis, and site distribution in relation to chert distribution. Chapter 5 will contain results and a discussion of the submerged landscape 6 reconstruction. This will include: the identification of shorelines between 15,000 and 8,000 BP, identification of submerged river channels, extending terrestrial patterns to offshore landscapes, identifying areas with the highest probability of site integrity, and Maxent results. Chapter 6 will conclude this dissertation and will provide a brief summary of the findings. Most importantly, this chapter will highlight the potential use of this study’s data to locate and identify submerged prehistoric sites. 7 CHAPTER 2 BACKGROUND 2.1 Introduction This chapter will discuss the background information and theoretical foundations upon which this project was built. While the ultimate objective is anthropological interpretation, computer-based GIS is the tool used to analyze geological and cultural data. This chapter is organized into four sections. Section one provides a brief introduction into Paleoindian and Early Archaic archaeological research. Section two focuses on coastal studies and is divided into two segments: prehistoric marine and aquatic resources utilization; and prehistoric underwater archaeological research. Section three provides the rationale for choosing Florida as a study area and provides critical information necessary to analysis. Section four provides background information on the archaeological use of GIS systems and predictive models. 8 2.2 Paleoindian and Early Archaic Period Archaeological Research This section will briefly outline the trajectory of Paleoindian and Early Archaic research. Entire volumes (Bonnichsen et al. 2005; Bonnichsen and Turnmire 1999; Brown 1994; Dixon 1999; Meltzer 2009) have been written on the historiography of North American Paleoindian research. It is not the objective of this section to either condense or repeat previous summaries. This section will however, include a critical analysis arguing that the dominant notions and interpretations of Terminal Pleistocene/Early Holocene inhabitation were dictated by early site finds. Dominant paradigms influenced interpretation of settlement, subsistence, and migration and prevented early researchers from seeing new data. As recent data questions the validity of many traditional models, new methods and theories must be developed. Initial discoveries of Paleoindian cultural material in association with Pleistocene megafaunal remains resulted in the long-standing paradigm of Paleoindians as big game specialists (Holliday 2009; Meltzer 1989). Paleoindian cultural material was found throughout central North America and in association with highly visible, well preserved mammoth and bison bones. The location of these first finds and their association with Pleistocene megafauna shaped our notions of Paleoindian migration, settlement and subsistence. There is widespread acceptance of a Paleoindian presence in the Americas by at least 11,500 BP (Buchanan and Collard 2007; Kelly 2003; Meltzer 2009). One of the earliest questions asked is from where, and by which route, these populations took to get to the Americas. Since the majority of the first Paleoindian sites were found in the Great Plains, archaeologists constructed migration theories, which accounted for the 9 site distribution at the time. Since early site finds were concentrated in the interior, coastal routes were not considered. The Big-Game hunter model further prevented interpretations in which Paleoindians used aquatic resources, much less boats. The dogma for much of the 20th century centered on a Paleoindian migration from Asia via the Bering Land Bridge (Haynes 1964; Dixon 2001). This theory of migration fit nicely with dating of the earliest sites temporally occurring during sea level low stands and glacial retreat. A narrative of big game hunting Paleoindians migrating over the Bering Land Bridge and south into deglaciated areas of North America dominated all aspects of Paleoindian research. The Bering land bridge theory was an appropriate explanation at the time, but recent evidence of early sites, coastal sites, and broad subsistence strategies is revising the theory (Dillehay 1999; Erlandson et al. 2008; Erlandson et al. 2009; Walker 1998). Recent evidence of glacial chronology suggests that the ice sheets had not retreated sufficiently to permit migration south from Beringia before 11,000 BP (Dixon 2001; Mandryk et al. 2001). Paleoindian sites exist throughout North America that predate 11,000 BP, suggesting that prehistoric populations arrived while the ice-free corridor was closed. Although the consensus against a single migration over the over the Bering Land Bridge is growing, anthropologists have not yet agreed on an updated model (Anderson and Gillam 2000; Anderson et al. 2010; Bradley and Stanford 2004; Erlandson and Braje 2011; Erlandson et al. 2007; Holliday 2009; Stanford and Bradley 2012). With the widespread acceptance of a pre-Clovis presence in the Americas (Anderson and Faught 1998; Dillehay 1999; Goebel et al. 2008; Meltzer et al. 1997) a major topic of discussion within Paleoindian research has been alternative migration routes and colonization processes (Anderson and Gillam 2000; Anderson et al. 2010; Erlandson and Braje 2011; Erlandson et al. 2007; Holliday 2009; Stanford 10 and Bradley 2004, 2012; Steele et al. 1998). A northwestern coastal migration route (Fladmark 1979) recently gained momentum because of a number of well-dated, early coastal sites (Erlandson 2001, 2008). Recognizing similarities between Clovis and Solutrean lithic technology, Bradley and Stanford (2004, 2012) have proposed an Atlantic coastal migration along the edge of the north Atlantic glacier. Genetic MTDNA analyses are providing clues about the ancestry of Native America populations, however these results are often conflicting (Fagundes et al. 2008; Hey 2005; Kemp et al. 2007; O’Rourke and Raff 2010; Szathmary 1993). As anthropologists are used to finding a single answer to the migration question, many see these alternative routes as being in competition with one another. Constrained by our roots in the single route model occurring at a single time, the field is reluctant to consider multiple migrations routes at multiple time frames and stubbornly seeks the perfect model to replace the Bering Land Bridge paradigm. 2.2.1 Settlement Models Settlement models help describe how Paleoindian populations colonized and dispersed throughout the continent. Models vary based on the importance researchers place on certain variables. Kelly and Todd’s (1988) high technology forager model proposes high residential and logistical mobility and stresses the fact that Paleoindian populations did not know their landscape well and followed resources. Others argue that procurement of lithic raw material shaped settlement and colonization (Daniel 1998, 2001; Goodyear 1979). Because of the low population density of colonizing populations, Anderson (1995, 2000) stresses the requirement that reproductive mating networks would have had. The band-microband model (Anderson and Hanson 1988) proposes that regional settlement structure consisted of local bands that would coa- 11 lesce for resource and mating activities. Low population density is a common thread within many models, but numerous models exist and none prevails. Fewer models pertain specifically to Florida’s Paleoindian record. Florida settlement models build on the low population density and are unified by water as a resource (Dunbar 1991; Neil 1964; Thulman 2006, 2009; Waller 1970). 2.2.2 Paleoindian Subsistence The prevailing idea of Paleoindians as big game hunting specialists continues to dominate settlement and subsistence theories, despite evidence otherwise (but see Speth et al. 2010). The specialist/generalist debate of Paleoindian subsistence currently lingers, but it is only a matter of time before there is widespread acceptance of Paleoindians as generalist foragers. Analysis of faunal assemblage breadth at sites provides increasing evidence that Paleoindians did not specialize in big game to the extent of excluding other faunal material (Hemmings 2004; Walker 1998). Hemmings’ (2004) comprehensive analysis of Clovis faunal material represents a subsistence strategy that is more similar to ethnographic accounts of modern hunter-gatherers. If Paleoindian subsistence was not solely focused on big game, then these populations would have been exploiting a variety of resources, and this generalist subsistence approach would have facilitated movement between regions. 2.3 2.3.1 Coastal Studies Introduction This section will provide an overview of some of the earliest evidence of aquatic and marine resource use. Exploiting aquatic resources and occupying aquatic environ12 ments is not a recent adaptation, but prehistoric subsistence models are dominated by narratives of terrestrial resource exploitation (Erlandson et al. 2008:2232). Utilization of aquatic resources or habitats is often attributed to mid-Holocene population increase and the need to exploit secondary resources to support their growing population. Recent finds, however, highlight the need to revise this (see: Bailey 2002; Rick et al. 2001). Lingering paradigms of terrestrial resource use are a product of present day coastlines and sea levels (Westley and Dix 2006:22). Due to changes in sea level, the global and regional archaeological evidence for aquatic resource use is sparse (Westley and Dix 2006:10). However, a growing number of finds show that prehistoric populations were using aquatic resources earlier than previously thought. Aquatic environments contain dense populations of flora and fauna and coastal wetlands are the most productive of all ecosystems (Michener et al. 1997:771). Coastal and aquatic ecosystems contain a combination of both marine and terrestrial resources that would have been attractive to prehistoric populations (Erlandson et al 2007:164). Coastal areas generally provide more equable climates than inland areas (Bailey and Milner 2002:2; Erlandson et al. 2007:170). Higher water tables result in higher concentration of freshwater sources compared to inland areas. This results in coastal and aquatic environments offering more abundant and reliable plant and animal resources than inland areas (Erlandson et al. 2007:170). If present human settlement patterns can offer any insight into prehistoric land use and transportation preferences, it is important to note that over 50 percent of the United States population lives within 80 km of the coast, and approximately 70 percent of the world’s population lives in the coastal zone (Michener et al. 1997). 13 2.3.2 Worldwide Establishing the antiquity of aquatic resource utilization is difficult. With direct evidence often lacking, indirect associations provide necessary clues. Analyzing lithic material on a number of Greek Islands, Ferentinos et al. (2012) document that the history of seafaring extends beyond Homo sapiens and that Neanderthals made significant ocean crossing in the Mediterranean by at least 200,000 BP. While this does not directly indicate aquatic resource use, it recognizes a very early maritime adaptation. Marean et al. (2007) document extensive shellfish exploitation at Pinnacle Point, South Africa by 164 ka BP. The recovery of extensive fish remains and barbed bone harpoons by Yellen et al. (1995) documents the widespread utilization of aquatic resources in Zaire by at least 90,000 years ago. The colonization of Australia, approximately 50,000 years ago, was a significant ocean voyage that required a seaworthy craft as well as navigational knowledge (Bailey and Milnder 2002:7). Aquatic resource use and maritime migration are documented across the world and deep in time, setting the stage for the adaptation of aquatic resources use in the Americas. 2.3.3 Americas Archaeological evidence for Terminal Pleistocene aquatic resource utilization is found in the Americas. The Monte Verde site, located in Chile, contains evidence of marine resource use. Dillehay et al. (2008) note the extensive use of marine seaweed at the site, and suggest seaweed and estuarine resources were important to Terminal Pleistocene populations. The South American Peruvian site of Quebrada Tacahuay contains evidence of extensive maritime resource utilization. The site contains extensive faunal evidence of fish and sea birds with direct evidence of anthropogenic butchering (Keefer et al. 1998:1835). Dating to 10,770 to 10,530 radiocarbon years 14 BP, Quebrada Tacahuay represents a specialized adaptation to coastal foraging. The faunal remains are indicative of the intentional acquisition of a few key items, and do not represent a pattern of opportunistic collection (DeFrance et al. 2011:414). The Andean coast (a non-stable region that experienced substantial regional Quaternary uplift) contains shell middens dating to the Late Pleistocene (DeFrance et al. 2011:414; Keefer et al. 1998; Saillard et al. 2011). North American evidence of Terminal Pleistocene aquatic resource utilization is limited to the west coast. California’s Channel Islands contain the earliest evidence of seafaring in the New World (Erlandson et al. 2009:712). These islands were occupied by at least 13,000 years ago and provide evidence for Terminal Pleistocene seafaring in North America (Erlandson et al. 2009). The Channel Islands show extensive marine resource utilization. Early sites have been documented that contain bone bipoints that are interpreted to have been used as fish gorges (Erlandson et al. 2009). At one Channel Island site, Daisy Cave, over 27,000 fish bones were recovered (Erlandson et al. 2009; Rick et al. 2001). Walker’s (1998) analysis of the faunal assemblage at Dust Cave, Alabama, provide a record of a Southeastern continuum of Paleoindian to Middle Archaic subsistence patterns. Walker’s (1998) analysis shows wide diet breadth, including both large and small mammals, freshwater fish species, amphibians, but waterfowl remains were the most abundant. These resources would have been available in and around riverine regions. Hemmings’ (2004) summary of Clovis subsistence also supports a generalized diet that includes aquatic resources. These sites document a broad use of both marine and freshwater aquatic resources during the Terminal Pleistocene, an adaptation that would have been highly beneficial in Florida where there was an abundance of coastline. 15 2.3.4 Florida The long-standing assumption regarding prehistoric aquatic resource utilization in Florida is that populations only began exploiting coastal resources in the Middle Archaic and increasingly used these resources in the Late Archaic (Miller 1988; Widmer 2005). Coastal and near coastal shell middens indicate extensive marine resource extraction and date to the Middle Archaic or after. It is important to note that the dates of many of these sites correspond with relative sea level stabilization. Conclusions were drawn, upon this limited evidence, that Florida’s prehistoric populations did not exploit aquatic resources until after sea levels stabilized. This implies that both aquatic ecosystems and prehistoric populations were unable to adapt to rising sea levels. There is growing evidence for marine resource use prior to sea level stabilization (Thompson and Worth 2011). The earliest date from a Gulf shell midden comes from Texas and dates from 8200-8050 cal BP (Saunders and Russo 2011; Weinstein 2009). Goodyear and Warren (1972) documented an extensive inundated shell deposit in Tampa Bay. This early evidence, combined with an increasing awareness of the resilience of aquatic resources to sea level rise, has led Saunders and Russo to conclude: a pronounced slowing of sea level rise was not necessary for robust estuarine adaptations to arise during the Middle and Late Archaic in Florida: both estuarine species and human beings can adapt to rapid changes in sea level. The large scale shell constructions of the Late Archaic resulted from centuries of familiarity with estuaries and resulted in social traditions that, in part, celebrated the abundance of the aquatic resources that had been a part of their coastal landscape for millennia (Saunders and Russo 2011:49). 16 The archaeological evidence to support Terminal Pleistocene/Early Holocene aquatic resource utilization is currently submerged. Many researchers (Milanich 1994; Goodyear 1983; Saunders and Russo 2011; Ruppe 1988) note that there is great potential for submerged archaeological sites to indicate earlier aquatic resource use. The potential for this research to guide the detection of submerged shell middens will be discussed in Chapter Five. 2.3.5 Resource Availability Terminal Pleistocene/Early Holocene changes in global temperature and sea levels have altered the composition of fish species present in Florida’s waters. Today, Florida contains one of the most complex ichthuofaunas in the western Atlantic (Gilmore 2001). The vast majority of these species are tropical, and migrated into Florida’s waters during deglacial sea level rise. Colder Pleistocene ocean temperatures, coupled with steeper continental shelves, resulted in a Pleistocene fish population that most closely resembled that of modern day Carolina and Virginia (Gilmore 2001:723). Florida’s Terminal Pleistocene fish population would have included Atlantic sturgeon, striped bass, Atlantic menhaden, and black sea bass and other more cold water adapted species (Gilmore 2001:725). With rising sea levels and ocean temperatures, Terminal Pleistocene fish populations migrated northward, and tropical Caribbean species migrated into Florida’s waters. These warm temperature species, having arrived during Holocene sea level rise, include lane snapper, mangrove snapper, scamp grouper, red grouper, tarpon snook, dog snapper, and black grouper (Gilmore 2001:725). Many of these species incorporate extensive seagrass beds in their life cycle. Seagrasses grow only in shallow waters and provide protection and habitat for many juvenile fish (Shenker 2009). Given Florida’s gentle bathymetric contours, ris- 17 ing sea levels would have created the ideal environment for shallow seagrass beds on Florida’s western continental shelf. During the Terminal Pleistocene/Early Holocene Florida’s ichthuofauna was undergoing a transitional period. During this transition, its coastal waters would have contained numerous fish species. 2.4 Underwater Archaeology Underwater archaeology is a subfield within archaeology that denotes a specialized method of site investigation and recording. Underwater archaeology is, simply put, archaeology that is conducted underwater. Within the broader field of underwater archaeology exist two primary divisions: maritime (or nautical) archaeology, and prehistoric underwater archaeology. Maritime archaeology, as defined by Muckelroy (1978:6), is the scientific study, through the surviving material evidence, of all aspects of seafaring: ships, boats, and their equipment; cargoes, catches, or passengers carried on them, and the economic systems within which they were operating; their officers and crew, especially utensils and other possessions reflecting their specialized lifestyles. Maritime archaeological research is by far the most common form of underwater archaeology. The pioneering work by George Bass, Peter Throckmorton, and Keith Muckelroy led to the creation of the field itself, as these researchers stressed the need to apply systematic, archaeological methods to the excavation and recovery of submerged shipwrecks (Bass 1966; Bass and Van Doornick 1971; Muckelroy 1978; Throckmorton 1987). Specialized recording methods were developed and standardized which are unique to the study of submerged shipwrecks. As a result, maritime archaeological 18 research has provided great insight into previously unknown shipbuilding technologies, seafaring cultures, and trade networks. Prehistoric underwater archaeology combines the specialized methods first developed for maritime archaeological research (underwater site location, underwater site mapping) and uses them to address broader anthropological questions beyond historic shipwreck sites. Prehistoric underwater archaeology is a very small research field. A small but growing number of graduate programs exist worldwide that provide specialized training in maritime archaeology; however, there are no programs that offer a specialized degree in prehistoric underwater archaeology. Given the global extent of land inundated by sea level rise the anthropological community is hindered by a lack of a specialized degree program. Because of the variation in regional underwater environments, coupled with the small number of researchers there exists no well-defined survey methodology specific to submerged prehistoric archaeological research (Benjamin 2010:255). This is not caused by a lack of quality research. Chapter Five will demonstrate how technological innovation can enhance and standardize submerged prehistoric archaeological methods. Because of the small number of researchers within this field and research methods that are unsystematized, submerged prehistoric archaeological sites and finds are relatively rare when compared to the number of terrestrial prehistoric sites that are known to exist. Submerged prehistoric sites are generally located by one or more of the following methods: diver survey, remote sensing, and submerged paleolandscape reconstruction. 19 2.4.1 Survey Methods Diver survey, the systematic searching of the sea, lake, or river bottom is perhaps the most common method by which sites have been found. This method can be used on its own, or be incorporated as one aspect within broader survey methods. This method requires capable divers with a desire to look for artifacts and, therefore, is often used by recreational divers and avocational collectors. For example, using this method divers have been able to locate Acheulean handaxes off the coast of South Africa and hundreds, if not thousands, of worked stone points in Florida’s rivers (Dunbar and Waller 1983; Flemming 2004). Remote sensing surveys are frequently used by both prehistoric and maritime archaeologists and involve towing remote sensing devices behind a boat. While maritime archaeological surveys often use a magentometer to locate ferrous metal, they are of no use when surveying for prehistoric sites. Side scan sonar provides an acoustic reflection of the sea floor, allowing the identification of landscape features, structures, and shipwrecks. Side scan sonar also has been used on a number of submerged prehistoric projects and can effectively provide critical submerged landscape information (Coleman and Ballard 2007; Adovasio and Hemmings 2009; Faught 2006; O’Shea and Meadows 2009). Subbottom profilers are sonar systems that are able to penetrate below the seabed (Green 2004). This tool is therefore very useful in identifying buried landscape features, and has successfully been used to identify submerged features (Adovasio and Hemmings 2009; Coleman and Ballard 2007; Faught 2002; O’Shea and Meadows 2009). The combination of side scan sonar data with sub bottom profiler data provides the most comprehensive remote sensing data gathering method for submerged prehistoric archaeological survey. Higher end machines combine side scan sonar and subbottom profiling into one towfish thereby reducing the need to 20 duplicate survey lines to gather complementary data. Remote sensing operations are capable of providing real time data reflecting what lies under a boat. However, the use of these tools requires substantial funding. Investigative analysis of submerged bathymetry has played a vital role in the identification of submerged prehistoric sites. Topography denotes terrain and elevation contours and bathymetry denotes change in water depth as a function of submerged contour lines. The resolution of the bathymetric data used will directly determine the accuracy of the predicted submerged topography. These data can be used to approximate the location of lowered sea level stands (Anderson et al. 2010) and identify drainage networks (Conti and Furtado 2009; Faught and Donoghue 1997; Knundu and Pattnaik 2011). Technological advancements in the collection of bathymetric data as well as advancements in the computational power of GIS software to analyze these data represent a breakthrough in the analysis of submerged landscapes. Faught (2004) conducted bathymetric analysis by digitizing depths from nautical charts and used these data along with remote sensing to locate submerged segments of paleoriver channels. Even though the contour data on nautical charts is relatively low by modern standards, this method guided remote sensing operations that proved fruitful. Fedge and Christensen (1999) used bathymetric data to reconstruct the paleoenviroment. This aided in locating and interpreting sites, as they note that ”modeling paleotopography offers considerable promise for locating early coastal occupation sites” (Fedge and Christensen 1999:649). O’Shea and Meadows (2009) analyzed high-resolution bathometry as a means of identifying likely survey areas in the Great Lakes. Anderson et al. (2010) analyzed high-resolution bathymetric data to reconstruct the paleoenvironment and to infer possible Pleistocene migration routes from Beringia. 21 2.5 2.5.1 Study Area: Why Florida? Introduction Compared to other regions in North America, Florida offers several unique features that make it the ideal location for modeling prehistoric site distribution on the continental shelf. Florida contains an extensive record of prehistoric archaeological sites, many of these directly associated with Pleistocene fauna (Anderson and Faught 1998, 2000; Bullen 1975; Daniel and Wisenbaker 1983; Dunbar et al. 1988; Neil 1964; Sellards 1916; Simpson 1948; Webb et al. 1984). The preservation potential for Florida’s prehistoric sites is high as indicated by the high density of wet sites and underwater sites compared to other regions in North America (Balsillie et al. 2006; Cockrell and Murphy 1978; Clausen et al. 1979; Dunbar and Waller 1983; Latvis and Faught 2000; Waller 1970; Webb 2006). Dates from Paleoindian sites in Florida are some of the earliest in North America, yet Florida’s location does not fit nicely into any existing migration model. Unlike other coastal states, most of Florida contains an extensive continental shelf that has already proven to hold archaeological sites (Faught 2004; Marks 2006). The prehistoric coastline is now hidden beneath the water and is inaccessible to terrestrial archaeologists. Terrestrial site interpretation is only providing some of the evidence. 2.5.2 Geology The Florida visible today on any map of the United States does not convey the true extent of the landmass. The Florida Platform consists of the landmass currently above sea level as well as the submerged continental shelves that extend into the Atlantic Ocean and the Gulf of Mexico. 22 The Florida Platform has a long geological history. It is thought to have originally been part of the African continent approximately 200 million years ago, when all of Earth’s landmasses were connected in the landmass called Pangaea (Smith and Lord 2007:21). The Florida Platform has repeatedly been flooded by shallow seas during interglacials, and exposed as dry land during glacial periods (Schmidt 1997:2). To the South and East, the Florida platform quickly drops off into the Atlantic Ocean. To the West, however, the Gulf of Mexico covers a vast area of the Florida Platform. The edge of the Platform extends approximately 320 km (200 miles) west from the current Gulf shoreline and at its deepest is currently covered by approximately 200 meters (650 feet) of water (Merrit 2004: 43). Because of the gentle slope of Florida’s western platform, changes in sea level have a drastic effect on the amount of land exposed (Schmidt 1997:2). During periods of lowered sea level, Florida gained extensive amounts of land westward of the peninsula, and minor amounts of land southeastward. 2.5.3 Sea Level Change Analysis of sea level change and its effect on prehistoric land use is central to this project. Global sea level is directly linked with global climate. An understanding of the basic principles behind global climate change is therefore necessary. The following section is a condensed discussion of global cyclical climate change and the effect it has on climate and sea level. Earth’s climate is unstable and cyclical. Long-term climate changes have been attributed to cyclical variations in the shape of Earth’s orbit around the sun, changes in the tilt of the Earth’s axis, and changes in the orientation of the Earth’s axis. Collectively these ever changing cyclical patterns are known as Milankovitch cycles 23 (Bennett 1990; Short et al. 1991). Ice core data showing patterns in Earth’s past climate indicate a relationship between Earth’s climate and these orbital variations (Masson et al. 2000; Steig et al. 2000). The collective effect of these three variables changes the intensity with which solar radiation hits the planet and the distance from which Earth orbits the sun. These changes affect global temperature and ultimately the percentage of land covered by ice. Long term cooling episodes result in cooler winter periods with lower average temperatures. Over thousands of years, this results in the formation of glacial ice sheets. During these glacial periods much water is stored in ice. Snow falls, but with lower average temperatures, less of it melts and drains into the world’s oceans. As a result global (or eustatic) sea level fall occurs. The amount of water draining into the world’s oceans is less than the amount of water being evaporated. These glacial advances have the effect of lowering global sea levels, and thereby exposing continental shelves. Just as changes in the Earth’s orbital parameters cause long term cooling episodes, the continuation of these changes results in long term warming periods. These warming periods, known as interglacials, have the effect of increasing global temperatures. Warmer temperatures result in glacial melting and the reintroduction of liquid water into the global hydrological cycle. As glacial meltwater flows into the world’s oceans, global sea levels rise (Donoghue 2011; Flower et al. 2004). These rising seas inundate low lying coastal zones on the continental shelves (Balsillie and Donoghue 2004). This process continues until Earth’s orbital parameters change and the process of glaciation repeats. There are two types of sea level change that affect the amount of land inundated during periods of transgression: eustatic and isostatic (Lewis 2000:526). Eustatic sea level change is a global phenomenon resulting from the process outlined 24 above wherein a stable land surface is being inundated. Isostatic sea level change results from regional variations in the vertical movement of landmasses (Lambeck and Chappell 2001:679; Lewis 2000:526). Isostatic uplift occurs following glacial melting as a region rebounds (positive vertical increase relative to sea level) as a result of the removal of the weight of a glacier. The Earth’s surface is actually deformed by the weight of a glacier and rebounds to its pre-glacial position (Lambeck and Chappell 2001:681). Therefore sea level rise, while a global phenomenon, is variable across regions. As a result, sea level history must be calculated on a regional basis as the geological character of the coast influences the degree of change (Balsillie and Donoghue 2004:3). 2.5.4 Florida Sea Level The necessity of using a regional sea level curve has already been addressed. The sea level curve data used in this study is derived from Balsillie and Donoghue (2004). This regional sea level curve for the northern and eastern coasts of the Gulf of Mexico aligns with the study area of this project. This sea level curve is the result of 23 data sources combined for a total of 353 dated paleo-sea-level samples going back 20,000 years (Balsillie and Donoghue 2004). Their detailed analysis implied that the Florida Platform is stable as global sea level closely mirrors the regional Gulf sea level curve (Balsillie and Donoghue 2004:17; Donoghue 2011:22). Balsillie and Donoghue’s sea level curve (Figure 2.1) indicates that Gulf sea level rise following the Last Glacial Maximum did not occur at a constant rate. The general trend over the past 20,000 years is one of sea level rise, but the Gulf of Mexico experienced brief periods of substantial rise, slowdown, and retreat. As this project is focused on the period between 15,000 and 8,000 years ago this period’s sea level 25 history will be summarized. Figure 2.1: Florida Sea Level Curve (Donoghue 2011) At 15,000 cal yr BP Gulf sea levels were approximately 98 meters below present. There was a rapid rise in sea level as result of meltwater pulse-1A beginning shortly before 14,000 cal yr BP (Donoghue 2011). As glacial ice melted, large lakes of meltwater formed within glaciers. Large meltwater pulses are attributed to the sudden release of this stored water as the glacial bowl containing the water broke. The rapid rise in Gulf sea level starting just after 14,000 BP is attributed to a meltwater release from the Laurentide ice sheet flowing down the Mississippi River and into the Gulf of Mexico. This release resulted in sea level rise of approximately 40 mm/yr (Donoghue 2011; Zarikian et al. 2005). A second high rate of sea level rise occurred between approximately 13,000 and 12,000 BP, with an average increase of 45 mm/yr. Between approximately 12,000 and 11,000 BP sea levels fluctuated as the ice sheets advanced and retreated. The period between 11,000 BP and 8,000 BP experienced a 26 relatively slow rate of 26 mm/yr. By approximately 8,000 cal year BP Gulf sea levels were approximately 8 m below present sea levels, and experience slight fluctuations before reaching present levels by approximately 6,000 BP (Donoghue 2011:22). To summarize, this project will use the regional sea level curve data compiled by Balsillie and Donoghue (2004) as an estimate to determine paleoshorelines. Sea level curves are a compilation of data and may contain some level of error. These sea level values are not absolute. Nevertheless the sea level curve used represents the best approximation of sea level change available and are certainly still valuable data. The values used in this analysis are shown in Table 2.1. Table 2.1: Approximate Sea Level Stand Below Present Between 15,000 and 10,000 BP Age (cal yr BP) 15,000 14,000 13,000 12,000 11,000 10,000 9,000 8,000 2.5.5 Sea Level Below Present in Meters (approximate) 98 77 66 42 44 30 24 8 Florida’s Hydrology As this project models Florida’s paleo-landscape it is necessary to summarize Florida’s unique hydrological features. The Florida Platform is composed of carbonate rocks and is highly karstic (Miller 1997). Karst topography is formed by the dissolution of limestone by water and is characterized by springs, sinkholes, and caves (Schmidt 1997; Van Beynen et al 2012). Florida’s hydro-geological network and karst topog27 raphy resembles a block of Swiss cheese, as water has dissolved substantial passages throughout the Florida Platform (Fleury et al. 2007; Florea et al. 2007). All of Florida’s freshwater is replenished by precipitation that falls on the Florida Platform as well as the southern regions of Georgia and Alabama (Miller 1997: 69). Some of this precipitation will flow as runoff, following natural depression in the landscape before it reaches streams, rivers and lakes. Some of the precipitation will be absorbed into the soil and make its way into shallow aquifers where it will either be discharged to surface features or continue to penetrate deeper aquifers (Miller 1997:69). The general principle underlying all of Florida aquifers is that water flows downhill and from areas of higher pressure to areas of lower pressure. Florida contains five different aquifers that surface at different parts of the state: the Floridan aquifer, the Intermediate aquifer, the Surfical aquifer, the Sand and Gravel aquifer, the Biscayne aquifer (see Figure 2.2). Paleoindian and Early Archaic site distribution only overlaps four of these and are not associated with the Biscayne aquifer system. It will therefore not be discussed. The general flow of each of these aquifers is one from higher (inland) elevations toward lower, or coastal areas (www.dep.state.fl.us). As each of these aquifers exists in three-dimensional space and are interconnected, displaying their exact spatial distribution is difficult. A generalized summary of these aquifers is necessary to understand the interplay between precipitation, groundwater, and sea level. The Floridan Aquifer is the largest and deepest extending over the entire Florida peninsula and extending into Alabama, Georgia, and South Carolina. Stratigraphically, it is the lowest aquifer system in the state. It is divided into the Upper and Lower Floridan aquifers that are separated by a confining unit (Miller 1990). The Floridan Aquifer feeds all of Florida’s first magnitude springs. [A first magnitude spring is defined as having a flow of at least 100 cubic feet per second] (Miller 1990). In these areas, the upper Floridan aquifer is 28 confined and under immense pressure. This causes groundwater flow upward, through dissolved passages in the limestone, to the surface. The high output of this aquifer makes it one of the most productive aquifers in the world (Scott et al. 2002). Sitting on top of the Floridan aquifer is the Intermediate aquifer system that is limited in distribution to areas in southwestern Florida. This aquifer is under confined (artisian) conditions, but does not produce as much water as the Floridan aquifer (Miller 1990). The intermediate aquifer is recharged both from upward leakage from the Floridan aquifer and from downward seepage from the Surfical aquifer (Zarikian et al. 2005:138). The Surfical aquifer system is comprised of the collection of any undefined aquifers present at the surface and represents water table conditions. However, the presence of clay beds in some locations can result in local confined conditions (Miller 1997). Most of the precipitation that falls enters the surficial aquifer system and is discharged as baseflow to streams (Miller 1997). The relationship between the surfical aquifer and lower units is dependent upon hydraulic head pressure. When the head pressure of lower aquifers is less than the hydraulic head of the surfical aquifer, water will flow downward. When the hydraulic head pressure of lower aquifers is greater than the surfical aquifer, water will seep up from lower units into the surficial aquifer (Miller 1997). The Sand and Gravel aquifer is limited to the western section of Florida’s panhandle. The aquifer system itself extends northwestward into Alabama and Mississippi. It is replenished by precipitation and water flow moves coastward. Depending on local conditions, the sand and gravel aquifer is under both confined and unconfined conditions. Upper sections of the aquifer contain hardpan layers that create perched water tables under artesian pressure (Miller 1990). The section of this aquifer that is within the state of Florida is under confined conditions and discharges into streams 29 and bays (Miller 1990). Figure 2.2: Distribution of Florida’s Aquifers (FDEP 2012) 2.5.6 Rivers Rivers in Florida are fed by a combination of precipitation runoff and by aquifer discharge. When the water level of an aquifer is higher than that of the bottom of a river channel, it is known as an effluent, or gaining stream. This condition occurs when the river is being recharged by runoff as well as positive head pressure from an aquifer. When the water level of an aquifer is lower than the base of a river, the opposite process occurs. This is known as a influent, or losing stream. As the head pressure of the aquifer is less than that exerted by the river’s baseflow, the river recharges the aquifer (Miller 1997). During periods of prolonged drought, where there is little precipitation that provides rivers with recharge via runoff, river and stream flow will continue as a result of baseflow until aquifer levels drop below the level of 30 river channels. Therefore river and stream levels are directly related to aquifer water levels (Miller 1997:70). 2.5.7 Springs One of Florida’s predominant hydrological features is the large number of springs that exist throughout the state. These springs are found on exposed areas of the platform, as well as on submerged sections of the eastern and western continental shelves of the state. Spring formation is common in karstic terrain. Springs discharge water under artesian pressure when the aquifer’s point of recharge is at a higher elevation than a point of release. Currently, FDEP has an inventory of 44 submerged springheads. While only springs within one mile of the coastline have been validated, FDEP’s inventory also contains submerged spring location as reported by local fishermen. The knowledge of submerged springs in Florida goes back at least to the 1950s, and their freshwater flow is so abundant that some recommend the direct capture and use of this water to meet Florida’s growing freshwater demand (Jordon 1960:264). While springs are readily identifiable, groundwater discharge occurs in other settings. As already discussed, groundwater provides baseflow to rivers and streams. Many low-lying wetlands in Florida are also areas of groundwater discharge (Miller 1997:69). These areas, while not containing a single high output spring head, represent areas where the aquifer is discharging across a large region at the surface. Submerged groundwater discharge (SGWD) is difficult to detect when it is not isolated as a springhead, however, it occurs on submerged landscapes just as it does on terrestrial ones (Burnett et al. 2003:4). If an aquifer extends on the continental shelf beyond the current shoreline to areas presently covered by seawater, and the pressure head of the aquifer exceeds that of the pressure of the ocean water, the 31 aquifer will release water (Burnett et al. 2003:3). The process of SGWD, the direct flow of freshwater into the ocean through rocks and sediments, is a process that has been documented on all of the world’s continental shelves (Faure et al. 2002:52; Taniguchi et al. 2002). The discharge of submerged freshwater from the continental shelf indicates that freshwater aquifers extend beyond current shorelines. 2.5.8 Hydrology and Sea Levels As a general rule, groundwater flows from higher elevations to lower elevations. Eventually lower elevations become shorelines and the interface between land and sea beings (Faure et al. 2002:52). A strong relationship exists between sea levels and freshwater table levels. A drop in sea level will result in lower freshwater tables, and a rise in sea level will result in higher freshwater tables (Donoghue 2006; Dunbar 2002; Morrissey et al. 2010). As saltwater is denser than freshwater, coastal aquifers are constrained by areas of saltwater. There exists a transitional, or mixing zone, wherein the freshwater contained in an aquifer mixes with the ocean’s salt water (Figure 2.3). This is most notably apparent when coastal aquifers are over pumped and a decrease in head pressure results in saltwater intrusion (Burnett et al. 2003:14). As sea levels fall, the saltwater that contains freshwater retreats. This process allows recently exposed lands to begin interacting in the hydrological system. Inland freshwater tables drop, since a greater area becomes available for the water table to occupy. When sea levels rise, the process is reversed and inland freshwater tables rise as well (Morrissey et al. 2010; Titus 1988). The hydrological properties of the Gulf continental shelf during times of lowered sea level are of critical importance when ascertaining the ecological productivity of that land as it relates to potential human habitation. Currently information does 32 Figure 2.3: Relationship Between Inland and Coastal Aquifer (Barlow 2003) not exist concerning the exact stratigraphy of the Floridan aquifer system beneath the submerged portions of the Florida Platform (Merritt 2004:43). Previous hydrological studies make the assumption that the aquifer and confining zone depths present on terrestrial Florida, continue across the Florida Platform (Merrit 2004:43). During periods of lowering sea levels, new dry land is exposed. The maximum extent of land formed from marine regression occurred during the Last Glacial Maximum (approximately 18,000 BP). At this point much of Florida’s continental shelf was terrestrially exposed. The same hydrological and geological properties that govern modern aspects of Florida’s landscape were driving the development of the continental shelf’s landscape. Precipitation fell on the emerged land and flowed as runoff towards natural depressions as it made its way down to lower elevations and into the groundwater (Schmidt 1997:4). Just as the rivers present in Florida today have eroded channels into the landscape, drainage networks of rivers and streams present on the continental shelf also left marks on the now submerged landscape. The hydrological effects of falling sea levels extended beyond the development of drainage systems on the continental shelf. Faure et al. (2002) argue that emergent 33 continental shelves would be exceedingly rich in freshwater sources. Lowered sea levels have the effect of increasing the hydraulic gradient across coastal aquifers resulting in enormous volumes of groundwater discharge on the shelf (Faure et al. 2002:52). This positive hydraulic head will continue to discharge freshwater in low-lying coastal areas until sea level stabilization occurs and hydraulic head stabilization follows (Figure 2.4). Despite the water table level dropping in inland areas, in coastal areas it will still remain significantly higher than sea level (Faure et al. 2002:52). As Faure et al. (2002:52) note, ”a 120m sea-level fall is equivalent to raising the continental water table 120 m above its stable base level.” Figure 2.4: Continental Shelf and Aquifer Discharge: Arrows Represent Water Flow, While Triangles Show Springs (Faure et al. 2002). The effect of hyper-rich freshwater resources on near coastal areas of the emerging shelf has drastic implications for the development of coastal ecosystems. Despite arid conditions that were present in Florida’s interior (Grimm et al. 2006; Watts et al. 1992; Willard et al. 2007), areas along Florida’s coastline during falling sea levels would have been wet and vibrant (Faure et al. 2002:52). These resource rich 34 coastal environments would have been attractive to flora and fauna, especially given the less hospitable conditions in the interior. This would have resulted in a significantly higher biomass on the continental shelves compared to the interior regions (Bailey et al. 2007; Faure et al. 2002:48). By the LGM, Florida’s coastal areas would have been well-developed, resource rich ecological zones. Much has been written regarding the effects of modern sea level rise on coastal environments, however, little has been written specifically regarding post LGM sea level rise and coastal ecosystems. For the purposes of this discussion, sea level rise after the LGM will have a similar affect on coastal ecosystems as modern sea level rise. This will be discussed in Chapter Five after the general principles are introduced here. Wetlands form in low-lying areas where the aquifer is at the surface. Analysis of Florida’s current wetlands shows that they formed after sea level stabilization. Originally it was thought that wetlands only formed after water tables rose following sea level stabilization (Willard and Bernhardt 2011). As previously discussed, the freshwater table is closer to the surface at lower elevations (i.e., at or near the coast)(Bildstein et al. 1991: 219). During sea level rise, the saltwater barrier that limits the dispersal of freshwater through coastal aquifers moves inland. This results in freshwater tables at coastal zones rising. Areas of dry land become wetlands and coastal marshes (Titus 1988). For vast areas of shoreline with little gradient, like much of Florida’s western shelf, saltwater intrusion into the fringes of coastal aquifers is likely (Titus 1988:18). This results in coastal ecosystems that are defined by their proximity to the shoreline. Areas closer to the coast are more brackish and become increasingly fresh inland. Areas closest to the shore are fed by water with more salinity. In these locations, salt marshes develop. Inland from the immediate coastal zone, a higher influx of freshwater reduces salinity and wetlands are found 35 (Titus 1988:18). Further inland, the freshwater flow prohibits saltwater intrusion, and freshwater marshes and swamps develop (Titus 1988:18). This process is documented from cores collected from Florida Bay where the evolution of coastal environments as a result of Holocene sea level rise is visible. These cores show the transition from freshwater marshes (formed during lower sea levels), to transitional vegetation (during a period of some saltwater intrusion as a result of rising sea level) to mangrove swamp development (that reflects a higher salinity as a result of greater saltwater intrusion from to sea level rise) (Willard and Bernhardt 2011). The possibility of the continual development and evolution of coastal ecosystems as a result of rising sea levels hinges on the ecosystem’s ability to develop at a rate greater than the rise in sea level. The previous section has outlined how Florida’s geography and hydrology responded to Terminal Pleistocene/Early Holocene environmental change. This is of critical importance when assessing the potential ecological productivity of the continental shelf. To quickly summarize, vast areas of land were exposed on Florida’s western continental shelf due to lowered sea levels. Lowered water tables resulted in drier conditions inland and wetter conditions near the coast. Coastal ecosystems may have developed along previous shorelines and migrated inland toward their current location. 2.5.9 Bathymetry of the Gulf of Mexico The bathymetry of the Gulf of Mexico provides an ideal location for the preservation of prehistoric archaeological remains. As previously noted, the inundated continental shelf in the Gulf of Mexico is of gentle slope and low relief. Compared to the east coast of Florida, the west coast is significantly more protected from wave action. 36 Discounting tectonic movement all waves are created either by tidal forces or wind (Rousmaniere 199:137). Wind driven waves develop as a result of sustained wind. The depth of the water limits the speed of a wave. The East coast of Florida, like the Pacific coast, has a steep continental shelf that quickly drops off into the Atlantic. Wave development therefore has an extended surface area to develop, and comparatively little shallow shoreline to slow waves down. Florida’s Gulf Coast, however, is a protected basin with a gently sloping continental shelf. Wave development in the Gulf of Mexico, and particularly over the Florida Platform, is limited by ocean depth and wind development and as a result, the coast is considered low-energy (Lewis 2000:525). Whereas high-energy coastlines will be more prone to eroding or re-depositing archaeological sites, low-energy coastlines are more likely to preserve sites intact (Lewis 2000: 535). 2.6 2.6.1 GIS and Predictive Modeling GIS Geographic Information Systems (GIS) have become a vital tool used in any science with spatially distributed data (Arbia 1993). Providing a singular definition of GIS is difficult, as the term is used widely to denote any number of GIS software packages and spatial datasets (Kvamme 1999:157). Fundamentally, GIS functions as a spatially referenced database (Ebert 2004: 319). Individual variables, or ”layers” as they are referred to in GIS applications, contain spatially referenced information (Kvamme 1999:157). Spatial datasets are referenced using differing coordinate systems and datums. The successful integration of discrete data requires transforming all data within the geographically defined region into the same coordinate system and having 37 the same datum. Individual layers can be overlaid thereby allowing spatial analysis of numerous variables simultaneously. The output provides a visual representation (a map) of the incorporated data. GIS capabilities extend far beyond the creation of maps. Modern GIS software suites contain extensive analytical tools that permit data generation and manipulation (Kvamme 1999:154). Anthropological and archaeological studies often contain spatially distributed data that require analysis. GIS analysis has become a wellestablished archaeological tool that represents a significant technological innovation in archaeological analysis (Bevan and Conolly 2002:123; Ebert 2004:319). As archaeology focuses on the spatial dimension of human behavior, there are many archaeological applications of GIS analysis (Ebert 2004:319). Fundamentally, GIS allows archaeologists to correlate site location with cultural material and any number of environmental variables (Bevan and Conolly 2002:132). Visually displaying the distribution of archaeological sites and their association with other spatial variables frequently results in the discovery of spatial relationships (Kvamme 1999:160). GIS analysis can be used at any stage in archaeological research. It is often used in survey planning, field survey, and post survey data analysis at the site as well as regional level (Arbia 1993:341; Bevan and Conolly 2002). This project employs GIS at the survey planning level and the results will be output as a layer file to use in survey data analysis, and refinement of future models. 2.6.2 Predictive Modeling Archaeological sites are not distributed evenly across the environment (Jochim 1976). Predictive models seek to determine whether there are discernable environmental factors that correlate with site location (Duncan and Beckman 2000; Kvamme 1999). 38 The spatial distribution of archaeological sites is often used in conjunction with environmental layers to create predictive models. A successful predictive model is one that is able to predict the location of sites on lands that have not been archaeologically tested. Archaeological predictive models are therefore highly useful in guiding survey work. Using patterns visible via GIS analysis, predictive models are rooted in the notion that sites are not randomly scattered across the landscape (Warren and Asch 2000). Archaeological sites occur in locations that were favorable to both prehistoric human occupation as well as archaeological preservation (Warren and Asch 2000). There exists great variability in the theoretical underpinnings and methodological approaches of different predictive models. Since there are many ways to classify GIS models, what follows will be a brief discussion of the two primary theoretical approaches to GIS modeling (DeMers 2002). GIS predictive models can be constructed using either a deductive or inductive theoretical approach (Kvamme 1999). Deductive models begin with an a priori theory, and use existing archaeological data to explain and predict site location as a function of the theory (Canning 2005). Inductive models begin with detailed data analysis and lead to the construction of a theory to explain the data. Inductive archaeological models attempt to find correlations between the location of archaeological sites and their relevant environmental variables (Canning 2005:7; Kohler and Parker 1986:402). Although inductive archaeological models are more common than deductive, there is a concern that inductive models may misrepresent the relationship between variables (Kvamme 1999). Inductive models progress from data to theory. This is a concern that will be addressed in Chapter Five. It is vital, as the history of previous research demonstrates, that dominant paradigms do not overshadow data analysis. Therefore, this project uses an inductive approach. All predictive models are not created in the same way. The most common 39 form of archaeological predictive modeling is the weighted map layer (Ebert 2004; Krist 2001). The methodology behind this approach involves the modeler creating a weighted scale wherein each environmental variable is ranked based on its relative importance. The environmental layers are summed, and the areas with the highest cumulative ranking are deemed the most probable. Of critical importance in this type of model becomes the method by which these weighted variables are calculated. Changes in the perceived importance of one variable will result in a model with drastically different results (Ebert 2004). Ranking the importance of environmental factors is too subjective of an approach, even if statistical methods are used to determine the values. This method implies that the modeler not only knows all of the environmental variables available to prehistoric populations, but also that the modeler can rank their relative importance. This type of modeling was rejected for this project since it incorporates too many assumptions. 2.6.3 Maxent Modeling Maxent modeling is a relatively new methodological approach to archaeological predictive models. Based on a machine learning algorithm, Maxent uses presence only data to approximate unknown probability (Phillips 2006; Sakaguchi et al. 2010). Originally developed to model species distribution, Maxent modeling has been called Eco-Cultural Niche modeling when used in association with the archaeological record (Gillam et al. 2007). Maxent modeling functions in a similar way to Genetic Algorithm Rule Set Prediction (GARP) modeling, yet due to Maxent’s increased performance it was selected for this project (Peterson et al. 2007). The benefits of using Maxent modeling over weighted map layer modeling rest in the objectivity of Maxent. Maxent modeling requires a set of known site locations 40 and a set of environmental variables (Phillips and Dudic 2008). The approach of Maxent modeling is to estimate a target probability distribution that contains the maximum entropy (the most evenly distributed) subject to the variable constraints (Phillips 2006). Using a raster dataset of environmental variables, Maxent creates an output that shows a probability distribution for each pixel. The sample of site data is divided into training and testing data. Training data are used to determine the probability distribution, while testing data are used to validate the model’s effectiveness. While this method of modeling has been used with archaeological data, the number of environmental variables has also been quite high (Anderson et al. 2010; Banks et al. 2008) but only a few variables can be predicted from the continental shelf with certainty. While Maxent modeling has successfully been used with continental shelf data in non-archaeological applications, previous researchers were able to include climate data into their analysis. Detailed climate data for Florida’s continental shelf between 15,000 and 8,000 years ago does not exist. 41 CHAPTER 3 METHODS 3.1 Introduction The first section of this chapter will describe and detail the process of GIS modeling Florida’s Paleoindian and Early Archaic site distribution on terrestrial landscapes. A description of GIS reconstruction of continental shelf landscapes will follow. The second section of this chapter will describe the parameters used for Maxent modeling of both terrestrial and continental shelf data. While the primary aim of this chapter is to record the process used, the secondary aim of this chapter is to provide a manual for others to duplicate and expand upon this work. Because there is a steep learning curve associated with GIS analyses and Maxent modeling, this chapter is a comprehensive presentation of each step in the process. 42 3.2 3.2.1 A GIS Approach Hardware and Software This study is fundamentally intertwined with technology and innovation. A few years ago, it would not have been possible to complete this analysis because computer power and storage space were too limited. Given the instrumental role hardware and software played in this project it is necessary to define the systems used. The computer used for all analysis was a 2011 MacBook Pro with a 2.4 GHz Intel Core 2 Duo processor viewed on a 27-inch monitor. It quickly became apparent that the standard hardware specifications were not sufficient, requiring a RAM upgrade from four gigabytes to eight, and a replacement of the stock hard drive with a terabyte of storage. VMWare Fusion was installed in the Mac operating system allowing the computer to simultaneously run as a Mac and as a Windows operating system. Windows 7 (64 bit) provided the operating system in which ArcGIS 9.3.1 license type ArcEditor and Maxent V 3.3.3 operated. ArcMap was the geographic information system platform used to analyze data and create maps. Model and map output resulted in over 400 gigabytes of data, six geodatabases, and many map layer files. Each Maxent model run produced output files between two and ten gigabytes each. 3.2.2 Modeling Terrestrial Site Data All data used for site locations was from the Florida Master Site File. Florida Statute 267 Section 5 charges the Florida Division of Historical Resources to establish and maintain a central inventory of historic resources that has come to be known as the Florida Master Site File (Florida Statute 267.5). The Florida Master Site File is the State of Florida’s official catalog for all known cultural resources located within state 43 boundaries. The Florida Master Site File contains records of both prehistoric and historic archaeological sites, historical structures, cemeteries, bridges and districts. To date, the Florida Master Site File houses over 180,000 entries (Florida Division of Historical Resources 2011). In order for an archaeological site to be included in the Florida Master Site File, it must meet specific criteria established by the Florida Division of Historical Resources. The Florida Department of State Division of Historical Resources Bureau of Historic Places defines a prehistoric archaeological site for documentation purposes as: artifacts not obviously redeposited which collectively meet at least one of the following criteria: 1) At least three prehistoric artifacts (diagnostic or not) fit within a circle of thirty meters diameter 2) At least one prehistoric artifact is diagnostic (a formally defined type with chronological significant) 3) In the archaeologist’s reasonable professional judgment, at least one artifact occurs which is of possible value in understanding the human prehistoric past (Florida Bureau of Historic Preservation:30). Florida Master Site File prehistoric archaeological site records include (among other things) cultural designation of materials present at the site. For example, the record for a multi-component site containing Paleoindian, Early Archaic, and Middle Archaic diagnostics will have one site file identification number but will identify each cultural period present at the site. The Florida Master Site File database provides the necessary dataset for analyzing the spatial distribution of archaeological sites of particular cultural affinity. 44 3.2.3 Data Acquisition The Florida Master Site File is an open record as required under the ”sunshine” laws of the State of Florida. Fear of looting, however, has meant that site location information has been exempted from public record. To gain access to the restricted information regarding archaeological site location, a research plan must be approved for clearance. Exact coordinate locations of sites, while instrumental in spatial analysis, are therefore excluded from this document. Florida Master Site File data in this study include sites recorded as of fall 2010. At this time, the database contained over 42,000 entries representing all of Florida’s documented cultural resources. Site File data pertaining to Paleoindian and Early Archaic cultures were extracted. The Florida Master Site File subdivides cultural affinity within these cultures as: Paleoindian, Possible Paleoindian or Late Paleoindian/Early Archaic. Early Archaic cultures are subdivided: Early Archaic Big Sandy, Early Archaic Kirk, and Early Archaic. For this research, the term ’site’ is used to denote a positive occurrence location of a cultural group. Multi-component Site File records represent repeated land use that is separated across time and cultural group. By treating each component as a distinctive occurrence, it is possible to detect the spatial distribution of groups as well as changes and preferences in prehistoric landscape use. 3.2.4 Data Used Since this study seeks to explore broad patterns in site distribution during the Terminal Pleistocene and Early Holocene, I have eliminated the subdivisions and group all Paleoindian sites together and all Early Archaic sites together. The Site File data had 298 recorded Paleoindian sites and 55 possible Paleoindian sites. Of these 353 45 records, 235 contained exact coordinate locations. These 235 records provide the site distribution data for Florida’s Paleoindian period. Early Archaic, Big Sandy, and Early Archaic Kirk records totaled 588. Of these, 349 records included exact coordinates. These 349 records provide the site distribution data for Florida’s Early Archaic period. Excluding sites without exact coordinates (those with only township, range, or county level data) was intentional. Given the general dearth of data available regarding prehistoric site distribution, settlement patterns, and subsistence, it is inexcusable not to use all the data that are available. To predict site location beyond the county level it is necessary to input data with resolution higher than the county level. Inclusion of low-resolution data only weakens the predictive capability of the model. A discussion of this topic will be included in Chapter Four. Florida Master Site File coordinate data are recorded as Universal Transverse Mercator (UTM). Florida falls under two UTM zones. UTM zone 16 covers Florida’s panhandle region, while UTM zone 17 covers the rest of the state. Modeling the spatial distribution of sites required site data in both UTM format as well as degrees of latitude and longitude (Lat/Long). Terrestrial spatial analysis was conducted as UTM coordinates because that allowed for measurements in kilometers. Continental shelf analysis was in degrees of latitude and longitude because that allowed for conversion to nautical miles. Converting UTM coordinates to Lat/Long coordinates is an overly complicated 28-step process, but it was necessary to plot site distribution on NOAA’s Coastal Relief Model (discussed below). Transformations were done with ArcGIS 9.3. 3.2.5 Terrestrial Environmental Layers Terrestrial geospatial environmental layers were used to construct Florida’s geography within ArcGIS. Federal and State organizations that create geospatial environmen- 46 tal layers often post them for public use and these files are available for download. The Florida Geographic Data Library (FGDL) (www.fgdl.org) provided access to environmental layers pertaining to Florida. Digital elevation models (DEM’s) have become the standard method to analyze elevation and terrain data within GIS (Do et al. 2011; Ebert 2004; Jenson and Domingue 1988:1593; O’Callaghan and Mark 1984). DEM’s consist of a high-resolution raster dataset wherein each pixel, or cell, contains a data point relating to elevation. The United States Geological Survey (USGS) National Elevation Dataset raster DEM of Florida was downloaded from the FGDL website and provides the terrestrial backdrop for analysis. The Florida Department of Environmental Protection (FDEP) constructed a database containing Florida’s 50 major rivers. This vector shapefile geospatial layer was downloaded from (FGDL) and provided critical information pertaining to water drainage patterns. The (FDEP) compiled geospatial information on Florida’s springs and made the data available as a vector shapefile. These data are composed of two separate classes and corresponding layers: 1) first magnitude springs, and 2) all springs, sinks, and seeps. According to FDEP Florida contains 33 first magnitude springs. FDEP has identified 1014 spring vents across the state. These data were also downloaded from FGDL. The distribution of Florida State Park land, Federal State Park land, as well as National Military installation boundaries were also obtained from FGDL as vector polygon files. The Florida Geological Survey, a division of the Florida Department of Environmental Protection, has geospatial data pertaining to Florida’s surficial geology. This vector polygon file was downloaded from FGDL. No geospatial data exist regarding Florida’s distribution of chert. While chert is found in limestone beds, and limestone distribution is mapped in the FGS surficial geology file, the data were not detailed enough for analysis. Upchurch et al. (1982) published a map of Florida chert outcrops that provides a broad scale depiction of chert distribution across the state. 47 This map was uploaded as a JPEG and georeferenced within ArcMap. 3.2.6 Data Analysis Analysis of discrete geospatial layers within ArcGIS requires that each individual data layer have the same geographic coordinate system and projection. Difficulties in compiling geospatial data from multiple sources and having each of them project and display properly is greatly eased via the creation of geodatabases. Each geospatial data layer was transformed into the same UTM coordinate system and projection. This geospatial referencing ensures the correct overlay of each layer. The USGS National Elevation Dataset DEM of Florida provided the necessary data layer from which other terrain data could be extracted. ArcMap’s Surface Tools, located within the Spatial Analyst Toolbox, provide the capability to infer surface features from a DEM. Using the DEM of Florida, Spatial Analyst tools were used to construct aspect, slope, and hillshade rasters. These datasets were desirable for three primary reasons: 1) these variables, while not directly indicating driving factors in settlement choices, are used as a proxy for other variables; 2) these landscape features are the most commonly used in predictive models (Ebert 2004; Wescott and Brandon 2000); 3) these are variables which can be extracted from continental shelf data, and are therefore useful in comparison. While FDEP has geospatial data on rivers and springs, no dataset exists pertaining to streams, creeks, and other small-scale drainage features. While rivers usually flow along natural troughs present in the landscape, smaller first and second order waterway distribution provides useful environmental data. Drainage networks can be extracted from DEM’s (Knundu and Pattnaik 2011; Tarboton et al. 1991: 81; Al-Sulaimi et al. 1997). Modern technological advancements have automated the 48 process. Drainage networks were identified in the DEM using ArcMap’s Hydrology tools located within the Spatial Analyst Toolbox. Creation of a drainage analysis is a four-step process. First, the DEM under analysis must be filled. This step removes any raster elevation errors by filling in depressions in the surface (Jensen and Dominque 1988; Temme et al. 2006). The output of DEM filling is a raster dataset that is ”cleaned up” and ready for analysis. The second step in drainage analysis is Flow Direction. Using the D-8 method, ArcMap calculates the elevation of each cell and its eight adjacent neighbors (O’Callaghan and Mark 1984). Flow direction is calculated using the hydrological principle that surface water flows from higher elevation to lower. The output of this analysis is a raster that represents the least cost method of drainage. The third step used is flow accumulation. This analysis calculates the total number of upstream cells that flow into each downstream cell, and as an output presents the flow accumulation of the area. The output flow accumulation raster is very detailed, as it represents the drainage network of all cells in the DEM (Jenson and Domingue 1988:1596). To best identify meaningful drainages, it is necessary to set a stream threshold. This fourth step in the drainage analysis process allows the user to select the cut off threshold by which drainage analysis should be displayed. Instead of displaying the continuous relationship between raster cells, setting a stream threshold allows the user to only view drainages which have a raster cell input above a user-defined number. It is possible to make a conditional ”con” statement using the raster calculator within ArcMap’s Spatial Analyst tools. Creation of a stream raster that only shows the drainages with at least 5,000 cells flowing into them is accomplished with the formula [Stream = con(FlowAccumulation) > 5000,)] in the raster calculator. The output Stream raster now represents only the most meaningful drainage networks. 49 Obviously, outcome varies depending upon where a stream threshold is set. While there are numerous methods describing alternative ways of finding the optimal threshold, the default value of 1 percent of Flow Accumulation is useful. This is a subjective step, and as Tarboton et al. (1991) note, ”the drawing of blue lines on maps usually involves some subjective judgment (Tarboton et al. 1991:81).” Maps were made with numerous threshold settings to determine the scale at which the data would be most meaningful. This was found to correspond to 1/500 of the total number of cells in the DEM. The drainage analysis raster overlaid the Florida river data and showed that the drainage analysis was valid. 3.2.7 Site Distribution It was necessary to merge all Paleoindian subdivisions and all Early Archaic subdivisions into two broad cultural groups. This resulted in two discrete datasets, the first containing all Paleoindian sites, and the second containing all Early Archaic sites. This was done for three primary reasons. First, this project aims to detect broad scale patterns within site distribution of Florida’s Terminal Pleistocene early Holocene populations. Dividing the sample into component parts would require absolute confidence in the cultural affinity the point represents, absolute confidence in the time period to which the point dates, and finally absolute confidence in the designation given to the point by the person who submitted the Site File Report. As outlined in Chapter Two, broad consensus does not exist regarding the exact chronology of Florida’s Paleoindian period. Therefore, dividing the sample might actually hinder rather than strengthen analysis. Since exact chronologies are absent, it is most logical to group point and site type into their broadest classification. There is generall acceptance that the Paleoindian period began by at least 11,500 B.P. (Anderson 50 et al. 1996; Anderson and Faught 1998; Meltzer 2009). However, exact dating of the termination of the Paleoindian period and the beginning of the Early Archaic is still debated. Chapter Two explained the logic behind lumping all Paleoindian sites and all Early Archaic sites into two component groups based on cultural similarity. The method underlying this project reflects the temporal and cultural distinctions between Paleoindian and Early Archaic populations. The focus therefore, is not to split these cultural periods into their component parts, but to group classes into the broadest categories they represent. Site distribution was plotted using all point data present for Paleoindian and Early Archaic site location. Point density distribution was used to analyze site plotting and patterning (Ebert 2004). To extract further patterns in the distributional data, kernel density analysis was run with a 1km threshold set. This allowed for regional hot spots to be more easily detected (Gibin et al. 2007). 3.2.8 Proximity Analysis The ultimate goal of this research was focused on predicting site location, thus proximity analysis between sites and landscape features was of critical importance. The only relevant terrestrial landscape features included in this analysis are those features also discernible on the continental shelf. These features are limited to: 1) elevation, 2) slope, 3) hillshade, 4) aspect, and 5) drainages. Elevation is measured as meters above sea level. Slope reflects the incline of each raster cell. Hillshade reflects the amount of direct sunlight each cell receives. Aspect refers to the cardinal direction that each raster cell faces. Drainages, as described above, indicate areas of water flow. While surficial geological data exists for terrestrial Florida, that dataset does not exist in detail for the continental shelf, and therefore was excluded from the final 51 analysis. 3.2.9 Paleoindian and Early Archaic Proximity Proximity analysis proceeded in two ways: 1) exact calculations were made between sites and their nearest landscape features, and 2) exact calculations were made which show the average distance from landscape features to sites. Distances between sites and their nearest feature were calculated using ArcMap’s Analysis Tool Box>Proximity Tools>Near Feature. This analysis allowed for an exact measure (in kilometers) between each Paleoindian site and the nearest spring, major river, stream, Paleoindian site, and Early Archaic site. The Near Feature tool outputs data into a shapefile. Shapefile data were then exported as a database file, and then analyzed in Microsoft Excel. Calculating the average distance from sites to features was completed using the Analysis Tool Box>Proximity Tools>Buffer Feature. Each feature included in analysis (springs, major rivers, streams, Paleoindian sites, and Early Archaic sites) was given incrementally increasing buffers with the objective of finding the most probable search radius. The spatial relationship between first magnitude springs and Paleoindian sites was calculated using a series of buffers. First magnitude spring data points were given a buffer of .1 km using the Buffer Feature (all distance data will be presented in kilometers as this was the format used in analysis). This results in a new output raster layer that depicts each site with a .1km buffer. Within the Paleoindian site’s attribute table, a new column labeled ”count” was created and the value was set to equal one. The buffered spring layer was then joined to the Paleoindian site layer. As a result, opening the .1 km spring buffer layer’s attribute table, provided a total count of the number of Paleoindian sites that were located within that buffer. 52 That number was divided by the total number of Paleoindian sites in the sample and multiplied by 100 to give a total percentage. This method provided the measurements used to quantify site distribution. The same processes were undertaken at buffer distances of .2, .3, .4, .5, 1, 2, 3, 4, and 5, kilometers on major river features, first magnitude springs, all springs, and on streams and rivers and their distances to Paleoindian and Early Archaic sites were graphed. This analysis was exceedingly useful because it allowed for quantification of broad scale patterning and provided probability guidelines for the location of unknown sites. The buffers made it possible to test the Oasis Hypothesis using generalized distance from water. The Oasis Hypothesis, first proposed by Childe (1928), and applied specifically to Florida by Neill (1964) and Dunbar and Waller (1983) stressed the influence of constant water sources. The exact relationship between site location and water features was quantified using graduated buffer distances. 3.3 3.3.1 Modeling Continental Shelf Data Data Aqcuisition Florida Master Site File Data of Paleoindian and Early Archaic site distribution on the continental shelf are limited. Of the 235 Paleoindian sites included in this analysis, three sites (1.3 percent of sample) are located on the continental shelf. Of the 349 Early Archaic sites included in this analysis, four sites (1.5 percent of sample) are located on the continental shelf. Seven sites is too small of a sample for meaningful analysis. Therefore, all Paleoindian and Early Archaic site distribution data was used for offshore modeling. As previously discussed, the perceived dichotomy between terrestrial sites and underwater sites is flawed. We therefore must conceive underwater 53 site distribution as merely being an extension of terrestrial site patterning. 3.3.2 Environmental Layers Historically, analysis of submerged terrain was limited to bathymetric contour lines and paper charts with poor resolution. Any analysis of submerged lands was both conceptually and actually separated from its terrestrial counterpart. Technological advancements allow for improvements in prehistoric underwater archaeological site detection. As a method to analyze and model the potential effects of natural disasters on coastal communities, the National Oceanic and Atmospheric Association (NOAA) created high resolution coastal relief DEM’s. These data, which NOAA refers to as Coastal Relief Models (CRM’s), integrate terrestrial DEM’s with seafloor bathymetry (Eakins and Taylor 2010:40). Coastal Relief Models of the entire U.S. coastline were constructed with the best available digital elevation and bathymetric data collected from federal and state agencies, universities, and private organizations (Eakins and Taylor 2010:44). The revolutionary aspect of the NOAA CRM’s is that terrestrial and submerged topographic data are seamlessly integrated. Exposed and submerged areas of the continental shelf can be analyzed as one continuous surface. This dataset proved to be vital to this project because it provided improved, continuous resolution of bathymetric and terrestrial data. NOAA allows access to download these coastal relief models at www.ngdc. noaa.gov. Standard data access results in a 1-minute resolution raster. 3-arc second (90 meter) resolution is available by extracting individual Coastal Relief Model tiles. This is accomplished via NOAA’s GEODAS Grid Translator-Design-a-Grid interface wherein the user must enter grid areas in degrees and minutes for upper and lower lat- 54 itude, as well as left and right longitude. Of the output grid format options available, selecting binary raster format facilitates data integration within ArcMap. In this study, 49 individual 3-arc second Coastal Relief Model tiles were created and downloaded into a geodatabase. Each tile then needed to be converted from binary floating point to raster data. This conversion was done using ArcMap’s Conversion Toolbox> To Raster Tools> Float to Raster Feature. Once each tile was converted, it was georeferenced using the World Geodetic System (WGS 1984) coordinate system and opened within ArcMap. Once all 49 tiles were converted, georeferenced, and opened within ArcMap there remained the problem of integrating each raster tile so that there would be a continuous surface as opposed to a patchwork quilt of data. To combine each of these raster tiles, ArcMap’s Data Management Toolbox> Raster Tool> Raster Dataset Sub-Tool> Mosaic To New Raster Feature was used. It is imperative that when mosaicing these tiles to specify that the pixels are 32-bit floating points and that the data should be blended. Failure to do so will result in poor resolution. The output file is an integrated 3-arc second DEM that provides continuous coverage on all of terrestrial Florida, as well as the submerged Florida Platform extending into both the Atlantic and Gulf of Mexico. This process is, of course, time intensive and tedious, but it is the only method for obtaining 3-arc second resolution data. The resulting DEM functions like any other DEM, yet it is unique in that its data extends beyond the current coastline (where all other DEM data abruptly stops and prevents integrated underwater and terrestrial analysis). This Coastal Relief Model, coupled with the computational powers of ArcGIS, provide the necessary foundations to begin site modeling. A similar pattern of data analysis undertaken on Florida’s terrestrial DEM was undertaken on Florida’s Coastal Relief DEM. Using ArcMap’s Surface Analysis Tools, 55 Aspect, Slope, and Hillshade for the entire Florida Platform were calculated. Unlike terrestrial geospatial data, there exists no dataset that defines or describes continental shelf features. The Florida Geological Survey (FGS) does have a geospatial dataset of 44 submerged springs on the Florida Platform. The vast majority of these submerged springs have not been validated by FGS as they have only confirmed submarine spring discharge within one mile of the present coast. The location data of these submerged springs is a result of fishermen reporting. As submerged springs are fish havens, it is very likely that these data do in fact represent the location of submerged springs, but it is also likely that spring location is withheld to ensure fishing territory. To further model the landscape of the continental shelf, drainage analysis was conducted. GIS analysis of submerged paleo drainages is a new area of study because the data to construct submerged drainages was only recently developed. Drainage analysis of submerged areas follows the same process outlined earlier for identifying drainages in terrestrial areas. This process involves filling the DEM of any voids, flow direction analysis, flow accumulation analysis, and setting a stream threshold. Given the significant number of pixels in this dataset as compared to the terrestrial data, a much higher stream threshold was set. The resulting layer represents the likely drainage areas of the Florida Platform that were formed when it was exposed during times of lowered sea level. Previous research validates this method. Conti and Furtado (2009) conducted similar paleo-drainage analysis on the Brazilian continental shelf with 100-meter resolution. Using these data, they were able to ground truth the location of the channels with a subbottom profiler (Conti and Furtado 2009) thereby both validating the method and its accuracy. Kunudu and Pattnaik (2011) used similar methods to identify submerged channels on India’s continental shelf. The archaeological implications of these data will be discussed in Chapter Five. The resulting DEM of the Florida Platform contains the present elevation data 56 representing Florida’s current coastline. To effectively analyze this landscape as it was during the Terminal Pleistocene/Early Holocene transition, sea level curve data must be incorporated. Regional sea level curve data were taken from Balsillie and Donoghue (2004) in meters below present in 1,000-year increments between 15,000 and 8,000 cal yr. B.P. Since elevation is a function of sea level, it is possible to adjust the elevation data of the Coastal Relief Model to represent sea level at past time periods. Anderson et al. (2010) describe how this method results in a series of DEM’s which have zero elevation values representing the coastline at previous sea level stands. To change the DEM’s elevation values, ArcMap’s Spatial Analyst>Raster Calculator was used. Within Raster Calculator, the DEM was selected and the change in sea level was added to the raster. For example, to recreate sea levels 10 meters lower than present, 10 meters were added to the DEM in the raster calculator, shifting the data uniformly. For example, a point at 10 m below sea level becomes zero meters at sea level. The highest elevation in the original DEM becomes 10 meters higher to reflect lower sea levels. The resulting rasters representing past Florida shorelines and each have a different range of positive elevation values. As a result, the automated classification of contours results in markedly different visual representations of the DEM’s. While this does not change the data values within each raster cell, these visual groupings are very useful in visual analysis. To minimize the contour differences between rasters and maximize the visual detail of each raster, contours for each time period were manually entered. The break for contour lines at each time period was calculated as the total positive elevation of the raster, divided by 32 (the total number of allowable raster classification breaks in ArcMap). Despite the slight differences in contour lines between maps, the resulting DEM’s all display the maximum amount of landscape detail. 57 3.4 3.4.1 Maxent Modeling Introduction Mexent modeling provides an objective measure to predict the probability of distribution. This method was originally developed to model species distribution (Warren and Seifert 2011), however its applications extend to modeling archaeological sites (Banks et al. 2006; Gillam et al. 2007; Phillips et al. 2006). To make predictions, Maxent uses presence only data, and does not use absence data (see Elith et al. 2010, Jaunes 1982 for a statistical and methodological explanation). In this study, site location and environmental variables were the presence only data used (Elith et al. 2006). Environmental variables were limited to those that are discernible on the continental shelf. 3.4.2 Data Requirements The Maxent software package is available free to download at www.cs.princeton. edu/~schapire/maxent. This program requires that the operating system be running the latest Java version. The program itself is an executable jar file that opens to the main input screen wherein data layers can be added, and model parameters can be adjusted. Maxent requires presence locations (in this study Paleoindian and Early Archaic site locations) in latitude and longitude degrees. These data must be in a .csv (comma separated value) format and must be in a dedicated folder. Maxent imposes strict requirements on environmental layer data input. Each layer must be in the exact same geographic projection and coordinate system, contain the same cell size and pixel depth, and each layer must have the same extent. All too frequently Maxent would cancel a run because of differences in geographical 58 areas. It is imperative that care be taken when creating Maxent environmental layers. Something seemingly harmless, such as reclassifying, will result in a layer that has a different pixel depth and is therefore rejected by the program. Furthermore, environmental layers must be in ASCII file format. The environmental raster data layers described above were converted to ASCII files using ArcMap’s Conversion Tool Box>From Raster Tool>to ASCII Feature. These ASCII files must be in a dedicated folder. 3.4.3 Running the Model Data input to Maxent involves selecting the dedicated folder of positive presence data and the dedicated folder of environmental layers. A data output folder must be created and specified. User defined parameters include (but are not limited to) default prevalence, threshold, the number of runs, the option of visual output, the type of output (raw, logistic, or cumulative), the type of validation tests to run (bootstrapping, cross validation), and the option for jackknife analysis. Maxent comes pre-programmed with default settings which strive to make a model based on small data sets. For this project, the default settings in Maxent were not ideal for high model performance. Models were run with various permutations to evaluate model performance. Maxent provides an evaluation of the model on a one point scale called an AUC curve. A common problem that occurred was that of Maxent over-fitting. It was found that by adjusting Maxent’s default settings to reflect a larger sample size, over-fitting was reduced. Hinge and threshold features were turned off. Both of these features convert continuous environmental data into binary categories, which is useful when modeling low occurrence data. The adjustment of these parameters resulted in Maxent probability runs that produced effective models 59 without over-fitting. 3.4.4 Data Output Maxent results will be discussed in Chapter Five. Maxent summarizes each model’s run in an Html file format. This output contains analysis on the model’s predictive accuracy in the form of an AUC curve. Replicate runs of 5, 10, 15, and 20 were run and it was found that predictive accuracy does not necessarily increase after 10 runs. When analyzing replicate runs, Maxent output allows visualization of each individual run, as well as the minimum, maximum, and standard deviation. Most useful to this project was the visual output, which contains a map of the environmental layers used, with the predicted probability of occurrence mapped over it. This file is stored as an ASCII file that can be imported into ArcMap and converted into a raster file. 3.5 Summary GIS analysis of these data required two primary datasets in two different datums. All data used for proximity analysis and site density were transformed into UTM zone 16, North American Dataset 1983 (NAD83) datum. This was necessary to measure proximity in kilometers of terrestrial features. Offshore data were transformed into World Geodetic System 1984 (WGS84) datum. As these data are designed to be integrated into offshore research, distances away from features were displayed in nautical miles, the standard unit of maritime measurement. These data are designed to enhance submerged prehistoric research, and can be integrated as a shapefile into standard marine chart plotters. The ultimate objective of this research is to identify patterns in terrestrial site distribution and to reconstruct the submerged environment allowing the extension of terrestrial site patterns onto submerged lands. GIS was the 60 tool employed to achieve this objective. 61 CHAPTER 4 RESULTS AND DISCUSSION PART I: SITE DISTRIBUTION 4.1 Introduction This chapter will illustrate patterns found in Florida’s Paleoindian and Early Archaic site distribution. Analysis of terrestrial site location is necessary to successfully locate sites in submerged contexts. The utility of this analysis, however, extends beyond the realm of submerged prehistoric archaeology. Quantifying the spatial relationship between sites and making comparisons between time periods helps archaeologists better understand Florida’s Paleoindian and Early Archaic populations. Changes in site distribution within and between cultural periods can offer clues regarding changes in settlement strategies, subsistence, social organization, and resource allocation, whether caused by environmental or cultural factors. 62 4.2 Results: Terrestrial Site Distribution Analysis This study incorporated all recorded Paleoindian and Early Archaic site data from the Florida Master Site File with exact location coordinates for a total of 235 Paleoindian sites and 349 Early Archaic sites. Therefore, the size of the total sample of sites studied is 584. Data were lumped together to include all Paleoindian point typologies and all Early Archaic point typologies. All sites were treated equally in this analysis. Larger stratified and dated sites, (e.g. Page/Ladson, Colorado Springs, Salt Springs, Harney Flats) were not weighted higher than sites consisting of isolated surface finds. Previous large-scale Paleoindian analyses in Florida (Thulman 2006; Tyler 2008) included large samples of lithic material obtained by collectors. Since, these data do not have exact provenience, their inclusion in the present type of analysis would be problematic and they were omitted. Provenience information is critical in this analysis. Florida’s Paleoindian sites (Figure 4.1) extend from the panhandle and down the peninsula but generally do not extend south of Tampa Bay. Initial observation shows that sites are located in both interior regions and near areas of the present coast, but site density appears to favor the Gulf coast more than the Atlantic coast. There are two areas of site clustering: the western panhandle and on the Gulf coast. Florida’s Early Archaic Sites (figure 4.2) are dispersed across the state and initial observation indicates that they follow the same regional distribution patterns as Paleoindian sites. This study quantified the relationship between sites and water features (figure 4.3) for two reasons: 1) to test Neill’s Oasis theory and Dunbar’s semi-sedentary Oasis theory; 2) the data are useful for modeling submerged sites because inundated springs and river channels can be detected in submerged environments by remote 63 Figure 4.1: Paleoindian Site Distribution n=234 (DEM: U.S. Geological Survey 1999). sensing equipment. This study quantified the relationship between sites and 1st magnitude springs, major rivers, and streams and creeks. During this analysis, major rivers were used to denote the channel feature itself, and do not imply that the rivers were flowing during the Terminal Pleistocene/Early Holocene. 64 Figure 4.2: Early Archaic Site Distribution n=349 (DEM: U.S. Geological Survey 1999). 4.3 Paleoindian Sites and Water Features Figures 4.4, 4.5, and 4.6 show the relationship between Paleoindian sites and distance from 1st magnitude springs. Seventeen percent of sites occur within 3 km of a 1st magnitude spring. Frequency change data shows diminished returns after the 2 km peak. First-magnitude springs are often riverheads, forming the start of a river channel. First-magnitude springs also can be located adjacent to river channels 65 Figure 4.3: Major Rivers and 1st Magnitude Springs of Florida (DEM: U.S. Geological Survey 1999; Rivers: FDEP 2001; Springs FDEP 2001). and discharge their water into another channel. The distribution of sites around 1st magnitude springs effectively levels out at a 3 km buffer. This 3 km buffer would also include the resulting river channel. Therefore, the most meaningful data from this analysis are the sites that lay in close proximity to springs. Only 10 percent of Paleoindian sites lie within 2 km of a 1st magnitude spring, and only five percent of Paleoindian sites lie within 1 km of a first magnitude spring. This low percentage 66 suggests that Paleoindians were not using 1st magnitude springs as frequent resource bases. Figure 4.4: Paleoindian Sites and Distance from 1st Magnitude Springs. Figure 4.5: Paleoindian Site Frequency and Distance From 1st Magnitude Springs. 67 Figure 4.6: Paleoindian Site Frequency Change and Distance From 1st Magnitude Springs. There is a relationship between Paleoindian sites and major rivers in Florida (Figures 4.7, 4.8, and 4.9). Over 30 percent of Paleoindian sites occur within 2 km of a major river (Figure 4.8). Frequency change data shows diminished returns outside of a 0.1 km buffer, and then again outside of a 1 km buffer. Twelve percent of Paleoindian sites are within a 0.1 km buffer of major rivers. These data support the general notion of existing settlement hypotheses (Dunbar 1991; Neill 1964; Thulman 2009) which suggest that Florida’s Paleoindians used resources within a close proximity of major river channel features. A relationship also exists between Paleoindian sites and smaller scale drainages and rivers (Figures 4.10, 4.11, and 4.12). Fifty percent of Paleoindian sites occur within 1.2 km of rivers, streams and creek features. Frequency change data shows a drop in return outside of 0.3 km and again outside of 1 km. The area under consideration when buffering rivers and smaller waterways is considerably larger in area than that of only buffering rivers. However, the small buffer size of 1.2 km 68 Figure 4.7: Paleoindian Sites and Distance Away from Rivers. Figure 4.8: Paleoindian Site Frequency and Distance from Rivers. encompassing 50 percent of sites indicates that Paleoindians were also utilizing smaller drainage features along with major ones. 69 Figure 4.9: Paleoindian Site Frequency Change and Distance From Rivers. Figure 4.10: Paleoindian Sites and Distance Away From Rivers, Streams and Creeks. 70 Figure 4.11: Paleoindian Site Frequency and Distance From Rivers, Streams, and Creeks. 4.4 Early Archaic Sites and Water Features There is a relationship between Early Archaic sites and 1st magnitude springs (Figures 4.13, 4.14, and 4.15). Ten percent of Early Archaic sites are found within 2 km of a 1st magnitude springs, and only six percent are found within 1 km. These data closely mirror the Paleoindian frequency, where 10 percent of sites are located within 2 km of a 1st magnitude spring. Frequency return rates show a drop outside of 1 km, and another outside of 2 km. These patterns suggest that Paleoindian and Early Archaic populations were utilizing 1st magnitude spring features in a similar, infrequent way. The relationship between Early Archaic sites and major rivers is more apparent (Figures 4.16, 4.17, and 4.18). Fifty percent of Early Archaic sites are located within 2 km of a major river (only 30 percent of Paleoindian sites are within 2 km of a major river). Sixteen percent of Early Archaic sites are located within 0.1 km of a major 71 Figure 4.12: Paleoindian Site Frequency Change and Distance From Rivers, Streams and Creeks. Figure 4.13: Early Archaic Sites and Distance From Springs. river. Frequency return rates show a drop outside of 0.1 km, and again outside of 1.5 km. These data suggest that Early Archaic populations were more tethered to 72 Figure 4.14: Early Archaic Site Frequency and Distance From Springs. Figure 4.15: Early Archaic Frequency Change and Distance From Springs. major river features than Paleoindian populations, perhaps indicating lower mobility. Alternatively these patterns might be a result of increased watercraft use by Early Archaic populations. 73 Figure 4.16: Early Archaic Sites and Distance From Rivers. Figure 4.17: Early Archaic Site Frequency and Distance From Rivers. There is a relationship between Early Archaic sites and rivers, streams and creeks (Figures 4.19, 4.20, and 4.21). Thirty-six percent of Early Archaic sites are found within 1 km of large and small drainages and 50 percent of sites are within 2 km of rivers and smaller drainage features. Frequency return rates show a maximum 74 Figure 4.18: Early Archaic Frequency Change and Distance From Rivers. peak at 1 km, and a drop off at 2 km. Whereas 50 percent of Paleoindian sites occur within 1.2 km, 50 percent of Early Archaic sites occur at the 2 km buffer. Figure 4.19: Early Archaic Sites and Distance From Rivers, Streams, and Creeks. 75 Figure 4.20: Early Archaic Site Frequency and Distance From Rivers, Streams, and Creeks. 4.5 Site Density Paleoindian and Early Archaic sites are not evenly dispersed across the landscape. Plotting the distribution of sites is informative, but, it is difficult to visualize patterns without density analysis. Kernel density analysis is well-suited for use in archaeological applications and home range analysis (Baxter and Beardah 1997; Seaman and Powell 1996) and was employed in this project. In kernel density analyses, each point is given a search radius. Where more points fall within the given search radius, a higher density is assigned. User-determined search radius is of critical importance to density analysis and greatly alters the output. To determine the search radius, I relied on the home range data from Marlowe’s (2005) comprehensive analysis of documented hunter-gatherer populations. Marlowe 76 Figure 4.21: Early Archaic Site Frequency Change and Distance From Rivers, Streams and Creeks. calculated the home range of warm climate, non-equestrian foragers, and these data provide the search radius necessary for kernel site density analysis. Site density analysis was conducted using the median (175 km2 ) and maximum (4,500 km2 ) home range values (Marlowe 2005:63). This translates into search radii of 7.5 km and 38 km. Figure 4.22 shows the density of Paleoindian sites with an estimated home range of 175 km2 . Traditional Paleoindian models suggest a high degree of mobility (Kelly and Todd 1988; Anderson 1996). While this value represents the median home range value from ethnographic data, in all likelihood it represents too small of a range to accurately depict Paleoindian mobility (Anderson 1996). Figure 4.23 shows the density of Paleoindian sites with an estimated home range of 4,500 km2 . This value represents the maximum-recorded value from ethnographic data of hunter-gatherers, 77 and is most likely more representative of Paleoindian mobility than the lower estimate. Paleoindian populations are frequently characterized as being highly mobile, and this home range estimate is representative of a radius of 38 km. This analysis shows three high-density areas and two lower density areas (Figure 4.23). Figure 4.24 shows the density of Early Archaic sites with an estimated home range of 175 km2 . Middle Archaic settlement models also stress high mobility, however perhaps at lower levels than Paleoindian (Anderson 1996), and this home range likely represents a value that is too low. Figure 4.25 shows the density of Early Archaic sites with an estimated home range of 4,500 km2 . The Early Archaic kernel density maps created during this study are very similar to the Paleoindian density maps. A few of the less dense Paleoindian areas show an increase in density during the Early Archaic, but these changes are increases in density rather than new areas of density. There is little difference in the geographic distribution of Paleoindian and Early Archaic sites. 4.6 Site Proximity Measuring distances from Paleoindian and Early Archaic sites to their nearest neighboring site (Table 2.1) provides an alternative approach to the kernel density analysis discussed above. Table 4.1: Nearest Neighbor Site Proximity (km) Paleoindian to Paleoindian Early Archaic Median 1.17 0.09 Average 5.96 4.24 Min 0.001 0.001 Max 49.35 118.4 78 Early Archaic to Paleoindian Early Archaic 3 2.18 7.96 5.17 0.001 0.001 69.34 62.19 Figure 4.22: Paleoindian Site Density Home Range 175 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). The nearest neighbor site proximity data illuminates subtle differences in site distribution and reflects a population expanding into nearby areas over time. As there are 113 more Early Archaic sites than Paleoindian sites, the nearest neighbor analysis between cultures (i.e. Paleoindian to Early Achaic vs. Early Archaic to Paleoindian) differ. The median distance between sites is especially informative. The median distance between Paleoindian sites is 1.17 km and the median distance between Paleoindian and Early Archaic sites is 0.09 km. The similarity in mean 79 Figure 4.23: Paleoindian Site Density Home Range 4500 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). distances between sites may indicate that Paleoindians and Early Archaic groups made similar decisions about site locations with respect to other campsites. The Early Archaic to Paleoindian median distance is 3 km, and reflects Early Archaic populations expanding into areas not used by Paleoindians. 80 Figure 4.24: Early Archaic Site Density Home Range 175 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). 4.7 Chert Distribution When Paleoindian and Early Archaic site distribution overlays are placed on top of Florida’s chert distribution, several patterns emerge (Figures 4.26, 4.27). Aside from the clustering of sites on the western most panhandle (which will be addressed in the discussion section of this chapter), many sites fall within chert rich areas. Paleoindian sites appear to cluster in chert-rich areas. However, more Early Archaic sites are 81 Figure 4.25: Early Archaic Site Density Home Range 4500 km2 (DEM: U.S. Geological Survey 1999; Rivers FDEP 2001). located outside of chert-rich areas. This suggests that Early Archaic populations were perhaps more familiar with the landscape and able to use resources that existed outside of chert-rich areas. Alternatively, these data might suggest that Paleoindian populations were more tied to chert resources than Early Archaic populations. When kernel density maps of Paleoindian and Early Archaic sites are placed on top of Florida’s chert distribution, the association becomes even clearer (Figures 4.28, 4.29). The areas of highest site density line up with the chert-rich regions of 82 Figure 4.26: Chert Distribution and Paleoindian Sites (Upchurch et al. 1982). Florida. 4.8 Discussion The results presented in this chapter outlined Paleoindian and Early Archaic site distributions with respect to each other, with respect to water features, and with respect to chert. Below I discuss possible explanations for these patterns and place them in the context of existing settlement models. First, this discussion interprets the 83 Figure 4.27: Chert Distribution and Early Archaic Sites (Upchurch et al. 1982). overall distribution of sites, and then it progresses to their distribution in relationship to particular types of water features. Site density data and site proximity data will be discussed in relationship to cultural change between the two periods. The implication of site distribution in relationship to chert outcrops will be addressed. The discussion ends by proposing a new settlement hypothesis that combines several factors. 84 Figure 4.28: Chert Distribution and Paleoindian Site Density (Upchurch et al. 1982). 4.8.1 The Eglin Air Force Base Problem The first factor to consider when analyzing site distribution is the bias towards surveyed areas. The westernmost panhandle cluster of sites corresponds with the boundaries of Eglin Air Force Base, illustrating this problem. An intensive historic resource survey was conducted within the bounds of the base for compliance with Section 110 of the NHPA (Thomas and Campbell 1993). This area represents one of the largest, concentrated areas that have been intensively surveyed in the state. The survey re- 85 Figure 4.29: Chert Distribution and Early Archaic Site Density (Upchurch et al. 1982). sulted in a large number of Paleoindian and Early Archaic finds which we can use to predict site locations in under-surveyed areas. Perhaps surprisingly, there are no chert outcrops on the base at Eglin. The high density of sites on Eglin may represent prehistoric use of Alabama chert resources. This superior chert is found in outcrops just to the north of Eglin and across the state line. High mobility for stone resources is implied in the Eglin sites. Unlike all other regions in Florida, Eglin sits atop the sand and gravel aquifer. This aquifer extends north into Alabama and Mississippi and contains perched water tables under artesian pressure (Miller 1991). This location 86 suggests that the area around Eglin may have been significantly richer in freshwater water resources than other areas of Florida. Many sites within Eglin are located along the Yellow River drainage system and follow the same distribution patterns as the rest of the state. 4.8.2 Water Proximity This section will address the validity of previous settlement theories in light of the data produced by this study. Existing theories that explain the distribution of Florida’s Paleoindian sites include: the Oasis Hypothesis, the River Crossing Hypothesis, the Karst Hypothesis and the Semi-sedentary Hypothesis. Neill (1964) proposed the Oasis Hypothesis, arguing that drier conditions during the Pleistocene concentrated freshwater resources. As the theory goes, limited freshwater locations (termed oases) would have been central focal points for animals, and the humans that hunted them. This theory helped to explain the high concentration of diagnostic points that were recovered from river bottoms. Waller (1970) proposed the River Crossing hypothesis. This theory proposed that Paleoindian sites were concentrated in Florida rivers near shallower areas that would have permitted animals to cross. Prehistoric populations, so the theory explains, hunted these animals at these crossing sites. Dunbar and Waller (1983) plotted the distribution of Paleoindian sites and found a strong correlation between karst features and sites. Dunbar (1991) further expanded the karst connection by proposing that Paleoindians may have been semi-sedentary, focusing their occupation around karstic rivers. While different theoretical approaches have been used to explain Paleoindian site distribution, they all share a common thread of water proximity. The oasis hypothesis and the river-crossing hypothesis are in many ways fun- 87 damentally opposed. If water resources were limited to low lying oases, then all rivers would be relatively shallow, and animals would not have needed to cross only at specific locations. Today, Florida contains abundant freshwater resources (Figure 4.3). During the Terminal Pleistocene/Early Holocene, the distribution of these resources was different. Pollen analysis, taken from inland sites, indicates that Terminal Pleistocene/Early Holocene Florida was drier (Grimm et al. 2006; Watts et al. 1992). Lowered sea levels in turn lowered the freshwater table (See Chapter Two). Many of today’s springs (which were inland locations at the Terminal Pleistocene) would have been sinkholes. Many of Florida’s rivers would have been significantly smaller, and perhaps only offered continuous flow during the wet season. Smaller streams and creeks most likely would have only offered flow during the wet season. Twenty-two percent of Paleoindian sites are located within 0.5 km of a river, and 25 percent of recorded Early Archaic sites are located within 0.3 km of a river. The high percentage of sites found in such close proximity to river features suggests a pattern of resource use around river channels. This discussion must include the very high number of diagnostic artifacts that divers have recovered from Florida’s river bottoms. The precise number of such finds is undocumented and unknown. However, for the past 50 years, SCUBA divers have been frequently and regularly removing artifacts from Florida’s rivers. Dunbar and Waller (1983) note the removal of over 100 projectile points in just one day from the Suwannee-Santa Fe river junction. The awareness that Florida’s rivers contain abundant artifacts is widely known throughout the local diving population and anthropological community. This information, while not quantifiable, suggests that prehistoric populations were extensively utilizing river features. River channel features therefore, must have offered an attractive alternative to other areas on the landscape. The data from this project confirm that prehistoric use of river features was 88 concentrated toward the middle of the river channel. The percentage of site frequency change is at the highest at the 0.1 km river buffer and quickly drops off as the distance away from a river increases. Including the unquantified, yet abundant, number of sites that recreational divers have found on the river bottoms only increases the assertion that site frequency is highest in and immediately around river channels. These data are in general agreement with the Oasis Hypothesis and Semi-sedentary hypothesis. The low frequency of Paleoindian and Early Archaic sites around first magnitude springs suggests the limited availability of resources around these features during the Terminal Pleistocene/Early Holocene when inland springs were sinkholes. While it is likely that the sinkholes functioned as catch basins, perhaps difficulty in accessing that water drove animal and human populations to more easily accessible water sources. Alternatively, there may be many more sites that are yet to be discovered that are deep within Florida’s springs. The increase in site frequency when streams and creeks were added to the river buffers is significant. Florida’s major rivers, during the Terminal Pleistocene/Early Holocene, at best only offered continuous flow similar to small streams and creeks. During the wet season, flow would have increased as a result of runoff. Similarly, smaller drainages would probably have been relatively dry except during the wet season. The frequency of sites around streams and creeks, therefore, can be interpreted in three ways: Paleoindian and Early Archaic populations used stream and creek resources during the wet season, during the dry season, or during both seasons. The use of these features during the wet season needs little explanation, as these smaller features would have attracted plant and animal resources. The use of these features during the dry season however, requires a more complex interpretation and will be discussed at the end of this chapter as part of a new settlement hypothesis. The Early Archaic is often deemed a transitional period between the Paleoin89 dian and Middle Archaic (Anderson and Hanson 1988). Settlement models for the Early Archaic stress the importance of river drainages (Anderson 1996; Anderson and Sassaman 1996). However, since Florida is geologically and hydrologically different from the rest of the Southeast, the application of non-Florida based models may be questionable (Webb 2006). By first quantifying Early Archaic sites in relationship to water features, it is then possible to compare these data with Paleoindian site data. Differences between the data may represent environmental, cultural, or population changes; similarities may indicate subsistence and cultural uniformity. Early Archaic sites were measured using the same methods and criteria as Paleoindian sites. The patterns between Early Archaic and Paleoindian site distribution in relationship to water features are very similar. This suggests a high degree of similarity in land use and resource allocation between the two time periods. Comparing the site frequency change and distance from rivers between the Paleoindian and the Early Archaic suggests that a continued reliance on river channel features existed in both the periods (Figures 4.9, 4.18). Ultimately, this suggests similar distributional patterns. 4.8.3 Site Proximity The distance between sites offers insight into patterns of localized land use among, and between, cultural groups. Furthermore, these data will be useful in future survey work. When a site is found, knowing the average distance between sites will help guide operations. The most informative data from the site proximity analysis are the changes in the median distance between sites from the Paleoindain to the Middle Archaic. The median distance from Paleoindian site to the nearest Paleoindian site is 1.17 km. This suggests a high concentration of sites in nearby areas. The median 90 distance from Paleoindian to the nearest Early Archaic site is 0.9 km, reflecting a very similar pattern in land use between the two periods. However, the median distance from Early Archaic sites to the nearest Paleoindian site is 3 km. This not only reflects the increased number of Early Archaic sites in the sample, but also adds evidence to support a population increase (Anderson 1996). Most importantly however, this figure shows expansion into areas not used by Paleoindians. 4.8.4 Chert Daniel (1998; 2001) argues the importance of lithic material in Paleoindian and Early Archaic settlement. Despite the superior preservation that is found in Florida’s wet sites, the vast majority of Pleistocene and Early Holocene finds contain only lithic material. Chert is a hard, workable stone that was used to make a variety of tools by prehistoric populations. In Florida, chert is found within limestone and karstic areas (Austin and Estabrook 2000). Upchurch et al. (1982) document the distribution of chert on the exposed portion of the Florida Platform. Plotting the distribution of sites over chert distribution shows a pattern of high correlation. Does the concentration of lithic artifacts found in chert-rich areas reflect the use of an available resource by an occupying population? Or does the concentration of lithic artifacts found in chertrich areas reflect population density that clusters around this central resource? Or, does site density on top of chert outcrops simply reflect the limitations of preservation and we are only seeing rock that preserves uniformly across the Pleistocene-Holocene boundary? Daniel (1998; 2001) and Goodyear (1979) argue that access to high quality lithic resources was a driving factor in Paleoindian and Early Archaic settlement. This may be the case for some populations but not all. Windover is an extensive mortuary pond with excellent preservation and many preserved artifacts. However, 91 the Windover site only contained five lithic artifacts (Penders 1997:2, 193). The degree to which chert resources drove settlement is outside the scope of this project. However, chert was being used and therefore must be considered as a variable in settlement models. Inundated rock outcrops can be detected using standard remote sensing techniques (Adovasio and Hemmings 2009). 4.8.5 Hydro-Highway Hypothesis Patterns exposed by this study encourage the development of a more comprehensive settlement model of Florida’s Paleoindian and Early Archaic populations. The data suggest that river channel features were central elements in Paleoindian and Early Archaic settlement. During the drier Terminal Pleistocene/Early Holocene, these river channel features would have been stream-like corridors that flowed along the deepest avenues of today’s river channels. These continuous channel features would have been attractive to Paleoindian and Early Archaic populations for the following reasons: 1) close proximity to water and animal resources; 2) exposed chert in eroded channel banks; and 3) channel features in Florida represent the geographic reference point for navigation by which prehistoric populations conceptualized their environment. River channels offered Paleoindian and Early Archaic populations food, water, lithic raw material, and a defining geographic reference point. Scarce water availability as a result of lowered sea levels and water tables would have concentrated plant and animal resources. River channels represent the continuous line of lowest elevation and therefore would have collected whatever water resources were available. Large and small game would therefore be limited in their range to these areas and, prehistoric populations would have exploited this fact. Furthermore, these water resources would have provided Paleoindian and Early Ar- 92 chaic populations with their necessary water requirements. In mild temperatures, the average adult human requires between two and three liters of water per day (Department of the Army), this requirement increases due to greater activity level or higher temperature. Florida’s chert resources are not evenly dispersed across the landscape and Paleoindian and Early Archaic sites are located in chert-rich areas (Figures 4.28 and 4.29). The ease of accessibility of chert resources, however, is limited. Upchurch notes that chert is exposed in stream channels as a result of bank erosion (Upchurch 1982). During the Terminal Pleistocene/Early Holocene, Florida’s river channels would have looked like small streams flowing in deep wide trenches. The banks of the river would have provided access to chert resources in areas where river channels flow through chert outcrop areas. Furthermore, the site distribution data shows a correlation between sites and streams and creeks. During the Terminal Pleistocene/Early Holocene, these stream and creek beds would have been dry for much of the year and, therefore, would have allowed further access to exposed chert resources. Modern navigation is aided by the use of maps, roads, compasses, GPS, and aerial pictures. Prehistoric population’s navigation relied solely on landscape features, and celestial, solar, and lunar cycles. Conceptualizing one’s location is dependent upon notable and relatively unchanging geographical features. Florida is distinctly flat, and therefore does not contain easily identifiable fixed reference points like mountains or hills. I argue that Florida’s waterways represented the central landscape features by which prehistoric populations conceptualized and mentally mapped their landscape. Distance and direction would have been measured in relation to these features. Spatial information regarding resource distribution would have been shared in relation to these features. Unlike landscape features that only have meaning to one individual (i.e., the 93 location where a particular successful hunt occurred; the location of a particularly painful injury), rivers provide a universal feature that can be conceptualized by all parties (Golledge 2003). I argue that Florida’s river channels may have represented conduits, or ”Hydro-Highways” that facilitated dispersal and movement. River channels would have provided prehistoric populations with geographic boundaries by which to reference the landscape, and their place within it. The need for geographic reference points is especially high for colonizing populations (Kelly 2003; Rockman 2003). Without previous cultural knowledge of where resources exist on the landscape, colonizing populations would have had to explore new land. A waterway is a fixed, yet continuous, reference point that would have been central to colonizing populations’ mental maps. Furthermore, this vital reference point is also a resource hot spot where populations are guaranteed water and access to animal resources. Expansion along waterways guarantees the ability of populations to retrace their steps and easily relocate particular areas. Given the benefits that waterways offer, the question arises as to why a colonizing population would not follow these naturally occurring paths? Regardless of where or how the first Americans entered the continent, I argue that rivers would have been a central resource that would have been exploited. Recent faunal assemblages show that Paleoindians were in fact generalists and not specialized big game hunters. Focusing the subsistence strategy on riverine resources would have provided a broad and plentiful resource base. The role of rivers in the colonization process has not received the attention it deserves. How might Florida’s Paleoindain and Early Archaic site distribution differ from other regions? Were Florida’s populations more strictly linked with river systems than populations elsewhere? Perhaps so many diagnostic artifacts have been recovered from Florida’s rivers simply because Florida’s rivers are conducive to diving 94 and have a low sediment load. If colonizing populations across the continent utilized river channels at the same frequency as Florida’s colonizing populations, then perhaps the answers to some of American anthropology’s lingering questions lay submerged in rivers and along abandoned river channels. These are quantifiable data that can, and should, be analyzed on a nationwide scale. This analysis required the use of exact site location data. County level data are useful in addressing particular questions, such as continental patterns and clusters. However, county level resolution data do not permit site analysis at this resolution. High-resolution site location data provide direct evidence of where prehistoric populations used the landscape. 4.9 Summary Clearly, the distributional data used in this analysis does not represent all Paleoindian and Early Archaic finds, but it does represent all distributional data with recorded provenience. Therefore conclusions must be made from the data at hand. This analysis shows that there is a strong relationship between Paleoindian and Early Archaic sites and their proximity to river channels. Previous settlement theories have stressed the importance of one resource over another but this study has developed a multi-component theory of Florida’s Paleoindian and Early Archaic settlement. The ”Hydro-Highway” hypothesis posits that Florida’s river channels offered Paleoindian and Early Archaic populations access to food, water, lithic raw material, and also provided the central landscape feature by which their cognitive maps were centered. If Florida’s data are representative of colonizing populations across the continent, we will be forced to address the potentially vital role rivers had in the colonization of the Americas. 95 CHAPTER 5 RESULTS PART II: SUBMERGED LANDSCAPE RECONSTRUCTION 5.1 Introduction This chapter will present the results of the GIS analysis on submerged sections of Florida’s continental shelf. Using GIS to model submerged landscapes is a new approach, and these data represent the first of their kind for the Florida Platform. This study used hydrological drainage analysis on the continental shelf and identified over 40 inundated paleo-river channels as well as 29 submerged springs. With the goal of isolating survey work, patterns found in terrestrial site distribution were used to identify areas that may contain Paleoindian and Early Archaic sites. Furthermore, high preservation potential (HPP) areas were identified. All of these data were georeferenced and can be incorporated into marine GPS. 96 5.2 Submerged Landscape Reconstruction The National Oceanic and Atmospheric Administrations (NOAA) Coastal Relief Model (www.ngdc.noaa.gov/mgg/coastal/coastal.html) provided the foundation for this analysis. As outlined in Chapter Three, 49 custom created 3-arc second (90m) resolution raster tiles were created and downloaded from NOAA’s GEODAS Grid Translator-Design-a-Grid interface. These tiles were then georeferenced and moasiced together to create one high-resolution surface that includes both exposed land and the submerged continental shelves of Florida. Functionally, it can be analyzed and manipulated like traditional terrestrial digital elevation models (DEMs). To model the extent of land exposed during the Terminal Pleistocene/Early Holocene transition, the elevation values were adjusted to represent approximate shoreline location and elevation between 8,000 and 15,000 BP (Figures 5.1-5.8). 97 98 Figure 5.1: Florida’s Shoreline and Elevation 8,000 BP. 99 Figure 5.2: Florida’s Shoreline and Elevation 9,000 BP. 100 Figure 5.3: Florida’s Shoreline and Elevation 10,000 BP. 101 Figure 5.4: Florida’s Shoreline and Elevation 11,000 BP. 102 Figure 5.5: Florida’s Shoreline and Elevation 12,000 BP. 103 Figure 5.6: Florida’s Shoreline and Elevation 13,000 BP. 104 Figure 5.7: Florida’s Shoreline and Elevation 14,000 BP. 105 Figure 5.8: Florida’s Shoreline and Elevation 15,000 BP. 5.3 Drainage Analysis Drainage analysis is frequently used on terrestrial DEMs. Using elevation data, this process (outlined in Chapter 3) identifies continuous low-lying channels that represent rivers, streams and creeks. While this method is frequently used in terrestrial drainage analysis and terrestrial paleo-drainage analysis, the application of this analysis to submerged landscapes has been limited due to a general lack of large scale, high-resolution data (Quinn et al. 1991). Drainage analysis was used to identify inundated river channels on the Florida Platform. Figure 5.9 illustrates the distribution of terrestrial rivers superimposed on top of Florida’s terrestrial landmass at 15,000 BP. Rivers flow from higher elevation to lower elevation and empty into oceans and bays. Therefore river channels should be present on the submerged continental shelf. Figures 5.9-5.16 show the location of Florida’s river channels between 8,000-15,000 BP. To validate the effectiveness of drainage analysis on 90 meter resolution raster DEMs, drainage analysis was conducted on Florida’s landmass as it appears today (Figure 5.10). The agreement between estimated drainage analysis and current river location is high. Figure 5.10 shows the similarities between FDEP river location and predicted river channels. The calculated location and extent of the river channels are a product of the stream threshold (see Chapter Three). Threshold levels were intentionally set high, resulting in fewer total channels identified. The benefit of a conservative approach is twofold. First, this approach diminishes the chance for false positives; predicted locations do not exceed the actual location of known rivers, and most predicted locations lie within the bounds of known rivers. Second, larger channel features are likely to be easier to identify during survey. The estimated routes of Florida’s paleorivers are therefore conservative in their extent and do not 106 identify smaller second order drainage. Lower threshold analysis was conducted that did identify smaller channel features, but those predictions were removed from this analysis given the benefits previously outlined. Similar analysis was conducted with the continental shelf data, and Figures 5.11-5.18 show the location of Florida’s river channels between 8,000-15,000 BP. Some of these river channels represent the continuation of modern channels. However, many inundated channel features represent rivers that only existed on the continental shelf. Figure 5.19 shows the distribution of Florida’s modern river system, and how it relates to Florida’s submerged paleoriver system. The names this project assigned to paleorivers correspond with rivers or cities on the adjacent modern shoreline. Modern rivers that continued offshore were given the prefix ”paleo-” but are assigned the same name. Rivers that have their headwaters on the continental shelf indicate the probable location of submerged springheads. This project predicts the probable location of 29 submerged springs. The springs are identified and shown in Figure 5.20 107 108 Figure 5.9: Location of Terrestrial Rivers and Exposed Land at 15,000 BP. 109 Figure 5.10: FDEP Major Rivers and Predicted River Channels. 110 Figure 5.11: Florida at 8,000 BP and River Channels. 111 Figure 5.12: Florida at 9,000 BP and River Channels. 112 Figure 5.13: Florida at 10,000 BP and River Channels. 113 Figure 5.14: Florida at 11,000 BP and River Channels. 114 Figure 5.15: Florida at 12,000 BP and River Channels. 115 Figure 5.16: Florida at 13,000 BP and River Channels. 116 Figure 5.17: Florida at 14,000 BP and River Channels. 117 Figure 5.18: Florida at 15,000 BP and River Channels. 118 Figure 5.19: Florida’s Rivers and Paleorivers. 119 Figure 5.20: Paleoriver Channel Buffer and Submerged Springs. 5.4 5.4.1 Discussion Benefits to Archaeologists These data represent the most comprehensive map of submerged river channels ever created for the Florida Platform. Previous methods used to identify submerged channel features required the costly and time-consuming process of remote sensing operations from a boat or were done without the capabilities of GIS analysis (Adovasio and Hemmings 2009; Faught 2002, 2004) . Remote sensing only provides channel information location along transects. A remote sensing operation will run north-south, or east-west survey lines and therefore channel features that have been identified are isolated in space. This results in known location points being connected and may not actually represent the true course of the river. This dissertation uses highresolution DEMs to predict flow accumulation resulting in a complete map of Florida’s submerged river channels. This approach eliminates the costly and time-consuming process of remote sensing for the survey portion of future projects. This data can be incorporated into all marine-based GPS chartplotters allowing researchers to navigate their vessel directly to channel features. Survey work can then be targeted around these features whenever favorable conditions exist. The terrestrial site data analyzed in Chapter Four indicate that 16 percent of Early Archaic and 13 percent of Paleoindain sites are located within 0.1 km of a river. These values increase to 30 percent of Paleoindian sites, and 46 percent of Early Archaic sites occurring within 1.5 km of a river. Figure 5.20 shows Florida’s paleorivers with a 1.5 km (0.81 nautical mile) buffer. If terrestrial site patterning continues to submerged areas, then this buffer suggests that upwards of 30 percent of Paleoindian and 46 percent of Early Archaic sites will be located within it. The distinction be120 tween terrestrial sites and underwater sites is a product of the modern shoreline, and therefore does not represent a factor that would have affected prehistoric land use. This study indicates exact areas with the highest likelihood of Paleoindian and Early Archaic sites on the continental shelf. 5.4.2 Ground Truthing The results of this study have not been ground-truthed, but the method of using GIS to predict the locations of offshore river channels has been successfully groundtruthed off the coast of Brazil (Conti and Furtado 2009). Conti and Furtado (2009) validated the location of submerged river channels using a sub-bottom profiler, and I propose to follow their lead and use a sub-bottom profiler to ground-truth the river channels in the Gulf. Conti and Furtado (2009) used data with 100-m accuracy, while this study uses data with 90-m accuracy. Findings of correlation elsewhere suggest that the channel features identified by this project’s drainage analysis are accurate and show the true course of Florida’s paleoriver system. One potential problem with predicting inundated river channel location, based on current submerged bathymetry, is that the ocean has flattened the land it has inundated in the last 15,000 years. Sea floor morphology is in a constant state of flux, as storm action is responsible for the movement of sand. This is an important factor at the micro level, but my research analyzed the elevation changes on a macro level so that the low-lying drainage areas should remain constant regardless of sand movement. However, rivers that have high sediment load, such as the Apalachicola River, most likely result in an accumulation of sediment and infilling of the drainage channel (Donoghue and White 1995). Drainage estimates of rivers with lower sediment load most likely will be more accurate than those with high sediment load. 121 The preservation and identification of East Coast paleo-channels despite the high-energy coastline of the Atlantic, was surprising. Since the paleo-channels are preserved, we may expect some level of archaeological site integrity on the Atlantic shelf. Because Paleoindian and Early Archaic site distribution favors the Gulf coast, those areas should be given priority over Atlantic channel features. 5.4.3 Water Features Faure et al. (2002) proposed that during lowered sea levels, springs would have emerged on exposed continental shelves. Figure 5.20 identifies 29 likely springheads at the upland head of inundated paleorivers. These channel features do not extend onto areas of present day Florida and suggest that spring flow was significant enough on both the eastern and western continental shelves to create river channels. My predictive data support the hypothesis of Faure et al. (2002) and is in agreement with the current hypothesis that Florida’s aquifers extend across the Florida Platform. Furthermore, these data call into question the prevailing notions that all of Florida was significantly drier during the Terminal Pleistocene/Early Holocene. The data used to infer prehistoric precipitation has been collected from areas that would have been upland; inland regions during the Terminal Pleistocene/Early Holocene and may very well not represent conditions on the continental shelves. 5.4.4 High Preservation Potential Despite the isolated survey areas proposed by my research, the area under consideration is still vast. By isolating areas with high preservation potential (HPP), I argue that it is possible to isolate areas with greater probability of containing sites with high integrity. Terminal Pleistocene/Early Holocene sea level rise was not constant, 122 and frequently involved periods of advance and retreat (Figure 2.1). Geographic areas that experienced multiple waves of sea level advance and retreat should not be targeted for survey. I argue that the highest preservation potential for archaeological material on submerged lands will be located in areas that only experienced a singular sea advance. Figures 5.21 and 5.22 show high preservation potential areas that represent areas that experienced a singular, swift, inundation event. Multiple inundation events would have both disturbed artifact assemblages and covered them with marine overburden. Gulf sea level experienced two waves of rapid advance between approximately 9,000-8,000 BP and 11,000-10,000 BP (Figure 2.1). These periods of rapid sea level rise did not involve periods of retreat and thus would have the highest potential for artifact preservation and site integrity. It should be noted that areas in yellow (Figures 5.21 and 5.22) represent the approximate location of land inundated in the 1000-year period between 9,000-8,000 BP (Figure 5.21) and 11,000-10,000 BP (Figure 5.22). This does not imply that sites found within the yellow HPP areas will date from those periods; it only suggests that any sites that existed prior to the inundation period would have been potentially better preserved than in other areas. Areas of high preservation potential can be further narrowed if we consider the high probability that sites will be located near rivers. I propose that survey should be targeted within a 1.5 km (0.81 nautical mile) buffer of paleoriver channels that lie in high preservation potential areas. The terrestrial site data analyzed in Chapter Four indicates that 16 percent of Early Archaic and 13 percent of Paleoindian sites are located within 0.1 km of a river. These values increase to 30 percent of Paleoindian sites, and 46 percent of Early Archaic sites occurring within 1.5 km of a river. Figure 5.20 shows Florida’s paleo-rivers with a 1.5 km (0.81 nautical mile) buffer. If terrestrial site patterning continues onto submerged areas, then this buffer 123 suggests that upwards of 30 percent of Paleoindian and nearly 50 percent of Early Archaic sites will be located within it. This study defines the exact areas with the highest likelihood of containing Paleoindian and Early Archaic sites on the continental shelf. Figures 5.23 and 5.24 show the intersection of Florida’s paleo-rivers and high preservation potential areas. 124 125 Figure 5.21: Inundated Land 9,000-8,000 BP, With High Preservation Potential. 126 Figure 5.22: Inundated Land 11,000-10,000 BP, With High Preservation Potential. 127 Figure 5.23: Inundated Land 9,000-8,000 BP HPP Areas and River Channels. 128 Figure 5.24: Inundated land 11,000-10,000 BP HPP Areas and River Channels. 5.4.5 Site Distribution and Prehistoric Landscapes The extent to which Paleoindian and Early Archaic populations inhabited drowned areas of the Florida Platform is technically unknown. While sites have been located relatively near the modern coastline, no sites have been identified in waters deeper than 40 feet (Faught 1996, 2004). It would be both highly coincidental, and unlikely, that Paleoindian and Early Archaic populations only inhabited the landscape that is presently above water. We can logically reason that populations used resources and inhabited lands that are now inundated. Using the site density calculated in Chapter Four, it is possible to display the known population densities of Paleoindian and Early Archaic sites on the landscape as they would have existed during occupation. Figures 5.25 and 5.26 display the predicted home range density calculations for Early Archaic sites on the available landscape at 10,000 BP. Figures 5.27 and 5.28 display the home range density calculations for Paleoindian sites on the available landscape at 13,000 BP. The site density that reflects a higher degree of mobility for Paleoindian and Early Archaic cultures extends past the boundaries of present-day Florida. Extension of site density onto the coast is visible on the westernmost panhandle near Fort Walton Beach, south of Tallahassee extending into Apalachee Bay, and extending westward from Tampa Bay. Of the three areas, Apalachee Bay is unique since the site density directly overlays three river channels and two confluences (Figure 5.29). Apalachee Bay’s submerged prehistoric archaeological potential has been previously identified (Dunbar 1991; Faught 2002, 2004), and the results of this study concur. 129 130 Figure 5.25: Early Archaic Site Density, Low Mobility, at 10,000 BP. 131 Figure 5.26: Early Archaic Site Density, High Mobility, at 10,000 BP. 132 Figure 5.27: Paleoindian Site Density, Low Mobility, at 13,000 BP. 133 Figure 5.28: Paleoindian Site Density, High Mobility, at 13,000 BP. 134 Figure 5.29: River Confluences and Paleoindian Site Density. 5.5 Maxent Modeling To increase the predictability of spatial patterning and site density analysis beyond that of Chapter Four, this project used a new GIS modeling method. Developed in 2006, the machine learning algorithm program Maxent creates a probability distribution based on site occurrence location data and environmental data. This method is frequently used in the biological and ecological sciences (Phillips and Dudil 2008; Warren and Seifert 2001), and its application to archaeology is growing (Banks et al. 2006; Banks et al. 2008; Gillam et al. 2007). When used with archaeological data, this process is called Eco-Cultural Niche Modeling. Banks et al. (2006:69) note, that eco-cultural niche modeling turns ”centuries of archaeological description into prediction.” Maxent seeks to predict the distribution of the culture (or species in question) based on the known distribution and the constraints of the environmental variables present. This project used Maxent for two reasons: 1) to fully exploit the landscape data present in the Coastal Relief Model, and 2) to test whether it is possible to predict offshore site location using new high resolution Coastal Relief Model data. The DEM created from the Coastal Relief Model was used to calculate landscape features of the continental shelf. Elevation, aspect, hillshade, and river features were calculated for inundated areas and entered into Maxent as the environmental layers. Since no other environmental data exist for the continental shelf, this study represents all the available data. Previous applications of Maxent modeling have used climate data, but use of such data was not a possibility since detailed paleoclimate data for Florida between 15,000 and 8,000 BP do not exist. Furthermore, the climatic conditions on the continental shelf may have been strikingly different from that of more inland areas. By using all known terrestrial Paleoindian and Early Archaic site location data from the Florida Master Site File, and by limiting the environmen135 tal variables, this analysis provides a large sample set of occurrence data while also reducing model complexity, resulting in a more accurate model (Phillips 2010). Approximately 45 Maxent runs were conducted during this study, for a total exceeding 300 individual models. To obtain a more accurate model, the standard program settings were altered. The maximum number of iterations was increased from 500 to 10,000 thereby allowing the program sufficient attempts per run to predict probability distribution. Ten replicate runs were used to average the distribution. Because almost all of the sites from the Florida Master Site File are terrestrial, Maxent overestimated the importance of elevation in constructing distribution. This resulted in high probability areas being limited primarily to the extent of present day Florida, and favorable conditions only extend to the near shore regions. This represents a hurdle in the creation of submerged probability-based predictive modeling for the entire continental shelf, as too few submerged sites exist to provide an adequate training sample. However, high probability areas do align with the course of Florida’s rivers (Figure 5.10 and 5.30). The probability distribution models do not extend further onto the continental shelf because of the lack of recorded offshore sites. Maxent models use known occurrence data to predict a probability distribution, and these models therefore reflect the fact that there are no recorded occurrence data offshore. This does not suggest that sites do not exist further offshore, merely that the results reflect the data used to construct the model. Maxent results show that higher probability areas (50 percent and higher probability, shown in green) follow the course of Florida’s rivers (Figure 5.31). Higher probability areas do extend past the modern shoreline. Figure 5.32 shows the near shore high probability areas for locating Paleoindian sites. The effectiveness of this model, measured as the AUC, estimates the accuracy of the model versus random selection. Any value over 50 percent indicates that the model performs better than 136 chance. The mean AUC value for this model is 0.88 indicating a high ability to predict the location of sites (Figure 5.33). The results of this prediction support the analysis in Chapter Four and stress the relationship between sites and river features. 137 138 Figure 5.30: Maxent Predicted Paleoindian Site Distribution. 139 Figure 5.31: Maxent Predicted Model: Highest Probability of Paleoindian Sites. 140 Figure 5.32: Maxent Model Effectiveness: AUC Curve and Standard Deviation vs. Random. Figure 5.33: Maxent Probability of Site Distribution Past the Present Shoreline. The primary goal of creating probability distribution models was to identify areas for survey. However, Maxent modeling also provides a more unbiased method of analyzing site distribution patterns than those used in Chapter Four. Whereas only site distribution as it relates to water features was analyzed in Chapter Four, the probability models created using Maxent represent analysis that factors in all available landscape data for the continental shelf. Combined, these lines of evidence suggest that proximity to river channels is indeed a driving factor in site distribution, as high probability areas align with river channels in both types of analysis. Like all modeling, these data should be approached cautiously. This analysis only used elevation, distance to river, aspect, and hillshade as environmental factors in analysis as this is the extent of available data for the continental shelf. There is the strong possibility that other factors – environmental and cultural – not available for inclusion in 141 this analysis influenced site distribution. Despite this possibility, those factors cannot be quantified for inundated areas during the Terminal Pleistocene, and therefore the models presented in this chapter represent the best predications possible using the available data. 5.6 Future Work The data created for this project have significant potential for future archaeological work. In many ways, this entire study is a blueprint for future archaeological survey in Florida’s waters. For the first time, a GIS shapefile exists which depicts the locations of Florida’s inundated paleo-river channels. Adovasio and Hemmings’ (2009) approach to identifying potential inundated site locations involves locating inundated channel features and rock outcrops. This approach focuses survey on two resources that were being used by prehistoric populations and that are also visible on the seabed and is promising. Using the channel data developed in this study, it is possible to navigate a vessel over specific channel features. Surface relief consistent with hard bottom (rock) can be identified with a common fish-finder/bottom machine, that has become standard equipment on most boats. This approach would allow any researcher with a boat, the channel data, and a fish-finder to locate areas where rock outcrops occur near inundated river channels. Sometimes lithic material is located on the seafloor surface (Faught 1996; 2004; Flemming 2004), thereby removing the need for dredging operations. Diver survey along the identified river channels, and especially where rock outcrops or other surface features exist, therefore has the potential to be a cost effective means of surface survey. I argue that survey efforts should be concentrated within the 0.81 nm buffer surrounding Florida’s paleo-river channels. Special focus should be placed on high 142 preservation potential areas. The confluence of paleo-river channels would have been major landscape features and therefore are likely to contain archaeological sites. The area of Apalachee Bay is especially promising because site density and river confluences intersect. Therefore, I suggest a concentrated survey effort around the confluence of the Paleo-Ochlocknee and Paleo-St. Marks, and the confluence of the Paleo-Aucilla and the Paleo-St. Ochlocilla. Inundated shell middens can be easily identified by remote sensing equipment. Following the method used in this project, it would be possible to calculate the average distance between Florida’s shell middens and rivers. These data could then be used to create a buffer around the inundated paleo-channels that represents the highest potential for shell middens. If the relationship between rivers and middens were to be identified, remote sensing surveys that followed the channel buffer would then be most likely to identify submerged shell middens. This could facilitate the discovery of extensive aquatic resource use prior to sea level stabilization at the modern shoreline. 5.7 Summary This chapter has presented the results obtained from GIS analysis of the Florida Platform. Paleoshoreline location was estimated by adjusting elevation values to represent Florida’s regional sea level curve between 15,000 and 8,000 BP. GIS drainage analysis was conducted on Florida’s Terminal Pleistocene/Early Holocene landscape and identified 40 inundated river channels and 29 likely submerged springheads. Areas on the continental shelf that experienced singular sea level advance were identified, and are considered more likely to preserve site integrity than areas that experienced multiple waves of sea level advance and retreat. Using the terrestrial site data analyzed in Chapter Four, a 1.5 km (0.82 nm) river buffer file was created and extended over 143 the continental shelf. If Paleoindian and Early Archaic site patterning on the continental shelf is similar to that of terrestrial Florida, then approximately 50 percent of sites should be found within this buffer. Extending Paleoindian and Early Archaic site density analysis onto the continental shelf identifies inundated areas that are the most likely to contain sites. Three factors were identified that are the most relevant for predicting inundated prehistoric sites: rivers, known site density, and HPP areas. The confluence of inundated river channels in Apalachee Bay has been proposed as being especially promising, as these waterways lie within predicted site density. The location of 15 paleo-river channels intersect areas of high preservation potential and should be the primary locations targeted for survey. Maxent probability distribution modeling was used to identify areas with high site potential, yet the limited number of inundated sites that have been located hinders the model’s ability to predict site location on the continental shelf. Maxent probability models reconfirm the relationship between site distribution and river features. 144 CHAPTER 6 CONCLUSIONS 6.0.1 Introduction The archaeological potential of the continental shelves was realized long ago (Edwards and Merrill 1977; Emery and Edwards 1966; Goggin 1960). Post-glacial sea level rise has prevented archaeologists from gathering a more complete archaeological record. Although sites exist on inundated land, the ability to locate them is hindered by the ocean that now separates archaeologists from the prehistoric landscape. This project presents a method that digitally removes the ocean from the equation, thereby allowing landscape analysis. The effects of sea level rise have significant implications for our understanding of Florida’s Paleoindian and Early Archaic populations. Because of Florida’s gentle contours, the state was affected by sea level rise more than any other in the southeast. Florida also contains a substantial number of Paleoindian and Early Archaic sites. These two factors combined make it the ideal location for submerged prehistoric archaeological research. Furthermore, the pioneering work by Faught (1998, 2002) resulted in the confirmation that prehistoric sites do exist in the Gulf of Mexico and would be found along inundated paleo-channels. 145 The goal of this project was to explore the application of new technology to identify areas likely to contain submerged Paleoindian and Early Archaic sites in Florida’s Gulf of Mexico. Because of technological innovation, this study analyzed the sea floor in ways that previous researchers could not. NOAA’s Coastal Relief Model provided the ideal digital elevation model to analyze Florida’s terrestrial and submerged areas. GIS hydrological drainage analysis and proximity analysis allowed for detailed, quantified analysis. Key to identifying submerged prehistoric sites is the analysis of terrestrial analogs. However, this analysis is only useful if it is conducted using variables that can be detected underwater. Scatters of lithic flakes cannot be identified without diving, but inundated river channels and springs can be detected via remote sensing. Previous findings revealed the connection between Paleoindian and Early Archaic sites and water. By quantifying the relationship between Terminal Pleistocene/Early Holocene populations and water features, this study tests the validity of previous hypotheses, while also identifying search radii around submerged features. 6.0.2 Project Summary This three-stage study resulted in three products: 1) quantified terrestrial site distribution for Florida’s Paleoindian and Early Archaic sites; 2) a GIS reconstruction of Florida’s continental shelf; and 3) an Eco-Cultural Niche Model predicting probable site location. Florida’s Paleoindian and Early Archaic sites were analyzed in relation to water features. These quantified data suggest a high reliance on river channel features. Florida’s Paleoindian and Early Archaic sites were also analyzed in relation to each other, and these data suggest population growth and expansion into nearby areas. Finally, Florida’s Paleoindian and Early Archaic sites were analyzed in relation 146 to chert distribution, and the results suggest that Paleoindian populations were more tied to chert-rich areas than Early Archaic populations. GIS drainage analysis on NOAA’s Coastal Relief Model identified over 40 inundated paleo-river channels and the location of 29 potential inundated spring heads. These data represent the first of their kind for the Florida Platform. This dissertation’s method of analysis, conducted entirely on a computer, does not require costly and fieldwork-heavy remote sensing operations to identify channel features and represents a breakthrough in understanding Florida’s paleolandscape. By extrapolating patterns in the terrestrial site distribution of Florida’s Paleoindian and Early Archaic sites, this study identified areas in the Gulf of Mexico which are the most likely to contain sites. By combining the location data of inundated river channels with terrestrial archaeological site patterns, this study also has identified areas with high archaeological site potential. Terminal Pleistocene/Early Holocene sea level rise was not constant, it consisted of periods of rapid rise, retreat, and advance. Areas that were only inundated once by a quickly advancing sea are more likely to possess a higher degree of archaeological site integrity. These areas, labeled in this study as ’High Preservation Potential’ (HPP) were identified on the continental shelf. Areas on the continental shelf where inundated paleo-river channels cross over high preservation potential areas were identified. These data are compatible with marine electronics, and will allow researchers to navigate directly to inundated channel features. By combining the archaeological site distribution data with the inundated landscape data it was possible to model the probability of site occurrence on the continental shelf. Maxent modeling was used to provide a secondary, non-biased form of data analysis and prediction. This represents the first application of Maxent to model archaeological sites on submerged lands. Maxent results concur with the 147 proximity analysis, showing that the areas of highest probability follow Florida’s river channels. Too frequently smaller sites and isolated finds are quickly discounted as being non-informative because they lack context, preventing their contribution to anthropological theories. I argue that this is not always the case. The precise location where sites are found represents a direct choice made by prehistoric individuals and their mere existence provide valuable data. Even if vertical context is lost, horizontal context can be informative. Patterns found in large scale site location analysis such as this one reflect the accumulation of repeated behaviors and preferences. Hypotheses must be made which offer explanations of these repeated behaviors. This dissertation shows that there is a strong relationship between Paleoindian and Early Archaic sites and their proximity to river channels. Previous settlement theories have stressed the importance of one resource over another but I propose a multi-component theory of Florida’s Paleoindian and Early Archaic settlement. The Hydro-Highway Hypothesis’ posits that Florida’s river channels offered Paleoindian and Early Archaic populations access to food, water, lithic raw material, and also provided the central landscape feature by which their cognitive maps were centered. 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Ryan earned a B.A. in Anthropology from the University of Georgia. He later earned an M.A. in Maritime Archaeology and History from the University of Bristol. Ryan enjoys sailing, fishing, diving, woodworking, and playing with his dog. 168