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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
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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
5.5
5.6
5.7
5.8
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
5.19 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
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
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
100
101
102
103
104
105
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
112
113
114
115
116
117
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.
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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.
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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.
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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.
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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.
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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.
If Florida’s data are representative of colonizing populations across the continent we
will be forced to acknowledge the potentially vital role rivers had in the colonization
of the Americas.
148
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BIOGRAPHICAL SKETCH
Ryan Duggins was born on September 20th 1981. He spent his early childhood living
in Gainesville, Florida and later moved to Kennesaw, Georgia. 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.
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