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The patterning of human behavior: A test of a prehistoric archeological subsistence and settlement model for Delaware

Gelburd, Diane Elizabeth, Ph.D. The American University, 1988

Copyright ©1988 by Gelburd, Diane Elizabeth. All rights reserved.

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UMI

r

THE PATTERNING OF HUMAN BEHAVIOR; A TEST OF

A PREHISTORIC ARCHEOLOGICAL SUBSISTENCE

AND SETTLEMENT MODEL FOR DELAWARE

By

Diane Elizabeth Gelburd

submitted to the

Faculty of the College of Arts and Sciences

of The American University

in Partial Fulfillment of

The Requirements for the Degree

of

Doctor of Philosophy

in

Anthropology

Signatures of Committee:

Chairman :

t r . of fthe Co llegeDean of fthe'the CollegeDean College

Date 1988

The American University

Washington, D.C. 20016

SEE AMÉSICAJJ UNIVERSITY LIBRARY r © COPYRIGHT

BY

DIANE ELIZABETH GELBURD

1988

ALL RIGHTS RESERVED For Stephen, with many thanks THE PATTERNING OF HUMAN BEHAVIOR: A TEST OF

A PREHISTORIC ARCHEOLOGICAL SUBSISTENCE

AND SETTLEMENT MODEL FOR DELAWARE

BY

Diane Elizabeth Gelburd

ABSTRACT

This dissertation presents a test of a Delaware model of prehistoric human subsistence and settlement patterns (Custer 1983a,

1984a) and a review of archeological models. The test was conducted by surveying a five percent stratified random quadrat sample of four environmental zones in the Saint Jones and Murderkill River watersheds, Kent County, Delaware, The problem which is addressed is the extent to which prehistoric human subsistence and settlement patterns and their resulting site locations can be predicted by using contemporary environmental variables and paleoenvironmental reconstructions. The research tested the following hypothesis:

Assuming human adaptation to the changing environment, it is postulated that there will be concomitant changes in prehistoric subsistence and settlement patterns in the watersheds, and both these changes and the subsistence and settlement patterns can be correlated with contemporary environmental variables. Differences in numbers and locations of sites and correlations of site types with environmental variables, as predicted by a Delaware LANDSAT-generated model (Custer

ii et al. 1986), are indicators of these changes. The null hypothesis is

that: There is no correlation between prehistoric subsistence and

settlement patterns and specific environmental variables as predicted

by the Delaware model.

The field survey tested high, medium and low probability

predictions of archeological site locations based on the model. The model was determined to be 84% accurate, which is not possible by

random chance. Therefore, the null hypothesis is rejected and the hypothesis is accepted.

Environmental information was collected to identify any other

influencing factors and recommend improvements in the model. Five

recommendations for refinement of the Delaware model are offered.

They are: (1) to better identify high probability areas related to marshes; (2) modify the model for the Delaware Bay Shore Zone for which the model had a low 33% accuracy rate; (3) closely analyze

terraces with well drained soils as a possible predictive factor in

future tests; (4) consider lower elevations (0-30 feet) as a possible

predictor variable; and (5) adjust the logistical regression formula

to enlarge the probability classes for high and medium probability, and reduce it for low probability.

iii ACKNOWLEDGMENTS

Acknowledgments are the most rewarding and most difficult

section of any piece of work. Rewarding, because you can officially

thank all those wonderful people who helped pull you through.

Difficult, because simple words often don't do justice to the

kindness, help, and support unselfishly given by so many people.

I feel particularly fortunate in the assistance I received in

conducting this dissertation. So many people helped in so many ways

that I want to list each of them as first and foremost. And, in

reality, each of them are. But, since no two atoms can occupy the

same space, and no two names can be listed first, I will start with

the person who is frequently listed last in acknowledgments. That is my husband, Stephen R. Potter, who pushed me through all kinds of

difficulties and withstood all kinds of abuse in getting me through.

And to my many colleagues, friends and bosses at the Soil Conservation

Service, who tolerated my moments of insanity and provided me the

opportunity to take a leave of absence from my position so that

I could complete the dissertation. Without that time, I probably would have never completed it. In particular, I would like to thank my supervisors Gail Updegraff, Barbara Osgood, Robert Shaw, and Joe

Haas, who supported my educational efforts every step of the way. And

Michael Kaczor, who disrupted his own life to take care of my job

iv while I was gone. And to all my coworkers at SGS who encouraged and helped me in many different ways.

I greatly appreciate the support of the faculty at the

Anthropology Department and of The American University. The award of a Dissertation Fellowship enabled me to conduct the fieldwork and take a leave of absence from my job.

Jay Custer was extremely helpful in allowing me to test his model, providing me with maps and other related data, and advising and commenting on the fieldwork and dissertation. Daniel Griffith; Cara

Wise; Charles McNett, Jr.; June Evans; and Joe Dent also provided much advice and assistance.

Much is owed to my parents, Peggy and Irving Gelburd, who always encouraged me and gave me the freedom to pursue my career. And to my parent-in-laws, Louise and Robert Potter, who tramped through the fields with me, washed artifacts and provided other kinds of assistance while I worked on the dissertation.

Many thanks go to Malcolm Richardson who taught me how to use dBase III and helped me print the data, Deborah Milstead who helped me with SAS and the University computer, and Andrea Kefera who educated me on statistics. Also to Lee Shields for squeezing time from his busy schedule to edit this monster, Jody Choate for showing me how to prepare a proper map, and J. D. Ross, for helping with last-minute typing and the final printing of this document.

And finally, I want to thank the landowners and lessees who allowed me on their land and let me bother them time and time again. TABLE OF CONTENTS

ABSTRACT...... ii

ACKNOWLEDGMENTS...... iv

Chapter I. INTRODUCTION ...... 1

Purpose Statement of the Problem The Study Area

II. MODELING IN ARCHEOIDGY...... 9

Introduction Historical Background Archeological Models Optimal Foraging Theory Models Decision Making Theory Models Central Place Theory Models Site Catchment Analysis Statistical Approaches Eastern Woodlands Archeological Models Discussion

III. DELAWARE PREHISTORY AND THE SUBSISTENCE AND SETTLEMENT M O D E L ...... 68

Background of Archeological Research Delaware Prehistory Paleo-Indian Period (15,000-6500 B.C.) Archaic Period (6500-3000 B.C.) Woodland I Period (3000 B.C.-A.D. 1000) Woodland II Period (A.D, 1000-A.D, 1600) Early European Contact Complex

IV. RESEARCH STRATEGY AND FIELD METHODS ...... 135

Research Strategy and Scope Field Survey

Quadrat Probability Determinations LANDSAT-generated Predictive Model

VI V. DATA A N A L Y S I S ...... 154

Introduction ...... The Data. Bases The Quadrat Data Base The Sites Data Base Data Analysis Objective One; To Test the Model's Predictions Objective Two: To Test a Random Model's Predictions in Comparison with the LANDSAT- generated Model's Predictions Objective Three: Recommendations Based on an Analysis of Specific Environmental Variables Objective Four: To Test the Empirically- Described Model Against the LANDSAT- generated Model

VI. SUMMARY AND CONC L U S I O N S...... 217

Summary Conclusions Related to the Research Hypothesis Conclusions Related to Archeological Models

APPENDIX A ...... 235

APPENDIX B ...... 237

APPENDIX C ...... 241

APPENDIX D ...... 263

APPENDIX E ...... 276

BIBLIOGRAPHY...... 281

Vll LIST OF TABLES

1. Sitepresence by Probability Frequency Tabulations...... 163 2. Environmental Zone (ENVZONE) by Probability Controlling for Sitepresence Frequency Tabulations...... 165 3. Sitepresence by Random Model Frequency Tabulations ...... 168 4. Model’s Results (MODAC) Compared to Random Model’s Results (RANAC) Frequency Tabulations and Chi-Square Analysis...... 170 5. Dominant Soil Type Within Quadrat (SOILQl) ...... 173 6. Secondary Soil Type Within Quadrat (S0ILQ2) ...... 174 7. Quadrats With Surface W a t e r ...... 177 8. Nearest Water Source for Quadrats Without W a t e r ...... 178 9. Distance to Nearest Water Source for Quadrats Without W a t e r ...... 179 10. Quadrat Distance to Murderkill or Saint Jones R i v e r ...... 181 11. Quadrat E l e v a t i o n s ...... 185 12. Degree of Slope Within Q u a d r a t ...... 186 13. Dominant Soil Type Associated With Each Site ( S S O I L l ) ...... 188 14. Site Distance to Nearest Surface Water ...... 190 15. Site Elevations ...... 192 16. Quadrat Data Base Positive Correlations With Sitepresence Variable ...... 194 17. Quadrat Data Base Negative Correlations With Sitepresence Variable ...... 195 18. Sites Data Base Positive Correlations...... 197 19. Sites Data Base Negative Correlations . 198 20. Correlations by Cultural Time Periods and Environmental Z o n e s ...... 200 21. Archeological Sites ...... 213

viii LIST OF FIGURES

1. Murderkill and Saint Jones RiversStudy Area, Delaware ...... 5 2. Environmental Zones in the Study Ar e a ...... 7 3. Paleo-Indian Cyclical Model ..... 77 4. Paleo-Indian Serial M o d e l ...... 78 5. Archaic M o d e l ...... 84 6. Delmarva Adena M o d e l ...... 104 7. Woodland I Model ...... 112 8. Woodland I Mortuary-Exchange M o d e l ...... 116 9. Woodland II Model 1 ...... 128 10. Woodland II Model 2 ...... 129 11. Woodland II Model 3 ...... 130 12. Quadrats S u r v e y e d ...... 142 13. Archeological S i t e s ...... 147

IX CHAPTER I

INTRODUCTION

Purpose

The objective of this research is to test a Delaware model of

prehistoric human subsistence and settlement patterns which is based on technoeconomic theory and cultural ecology. By conducting this test, one is also evaluating the use of technoeconomic theory and the cultural ecology theoretical orientation as an explanation of prehistoric culture change in the Middle Atlantic region of the United

States.

In technoeconomic theory (Kaplan and Manners 1972:91), a society maintains itself and undergoes change according to the tools and knowledge available to that society and how it applies those tools and knowledge to production, distribution and consumption of goods and services. Under technoeconomic theory, the most common theoretical orientation is that of cultural ecology: the relationship of humans and other living organisms and their physical milieu as integrated systems.

Cultural ecologists seek "to ascertain whether the adjustments of human societies to their environments require particular modes of behavior or whether they permit latitude for a certain range of possible behavior patterns" (Steward 1955:36). Cultural ecology considers adaptation on two levels : (1) the way cultural systems adapt

1 2 to their total environment; and (2) the way a culture's institutions adapt or adjust to one another. Technology, ecology, demography, and economics are considered the primary mechanisms in shaping and changing human behavior. The three fundamental procedures of cultural ecology are to; (1) analyze the interrelationship of exploitative technology (i.e., material culture) and the environment; (2) analyze the behavior patterns used in exploiting a geographic area by means of a particular technology; and (3) ascertain the extent to which the behavior patterns involved in exploiting the particular environment affect other aspects of the cultural system (Steward 1955:40-42).

In recent years, archeologists, using technoeconomic theory and cultural ecology, have formulated models of human subsistence and settlement patterns in terms of their temporal and spatial variation.

Models are "hypotheses or sets of hypotheses which simplify complex observations whilst offering a largely accurate predictive framework structuring these observations" (Clarke 1968:32). The purpose of modeling is to generate a set of predictions about the nature of a cultural system in a given environment. Models are valuable because they: (1) make operational our expectations about how a culture uses a given environment; (2) allow us to compare these expectations to actual patterns of utilization; and (3) generate predictions and explanations about a great variety of activities and their products

(Jochim 1976:xiii). The significance of this recent emphasis on modeling is the assumption that we now understand enough about prehistoric human behavior to describe it and project it into the past. These projections are made not only for areas where much 3

information is known about the past, but also In geographical areas of

limited information.

There are, however, many problems associated with this recent

emphasis on modeling. The objectives and uses of modeling are of

great controversy among archeologists (Sebastian and Judge 1985;

Kohler and Parker 1986; M. Berry 1984). There has been a proliferation of models without evaluation of the adequacy of the base data, and without evaluation of existing models. Very few models are based on systematic survey data. Most models have been developed from existing site files of varying quality. Many archeologists are generating their own models and even different models for the same geographic areas.

Few archeologists are independently testing existing models. In cultural resource management, predictive models are being increasingly used to identify high probability areas for locating archeological

sites and to eliminate low probability areas from archeological

surveys. Because most models are not independently tested and their reliability is therefore questionable, many archeological sites and valuable information may be lost or destroyed as a consequence.

Independent tests are needed to determine the validity and reliability of predictive modeling for archeology. This research is an

independent test of one model.

Statement of the Problem

The general problem addressed in this study is the extent to which prehistoric human subsistence and settlement patterns and their 4 resulting site locations can be predicted by using contemporary environmental variables and paleoenvironmental reconstructions. To examine this problem, a model of Delaware prehistoric subsistence and settlement patterns (Custer 1984a; Custer et al, 1986) is tested using data generated from a sample archeological survey of the Saint Jones and Murderkill River watersheds, Kent County, Delaware (see fig. 1).

According to Kohler and Parker (1986:431), a sample archeological survey is the most rigorous validation procedure one can conduct on a model. In this way, one can test predictions directly, and assess the degree of fit between expected and observed values.

The following hypothesis is tested: Assuming human adaptation to the changing environment, which has been documented for the Middle

Atlantic region from 12,000 B.C. to A.D. 1500, it is postulated that there will be concomitant changes in prehistoric subsistence and settlement patterns in the watersheds, and both these changes and the subsistence and settlement patterns can be correlated with contemporary environmental variables. Differences in numbers and “ locations of sites and correlations of site types with environmental variables, as predicted by the Delaware LANDSAT-generated model

(Custer et al. 1986), are indicators of these changes. The null hypothesis is that: There is no correlation between prehistoric subsistence and settlement patterns and specific environmental variables as predicted by the Delaware LANDSAT-generated model.

Several attendant considerations may be important in the interpretation of the results: (1) the use of modern resource distribution and paleoenvironmental reconstructions to project Dover

4- Study Area

M iles 10

K ilom eters

Fig. 1. Murderkill and St. Jones Rivers study area, Delaware 6 prehistoric resource distribution may not be completely accurate; and

(2) site location decisions may be partially affected by social factors that may be difficult to recognize in the archeological record.

The Study Area

The subsistence and settlement model was developed for the

Delaware Bay Shore and Mid-drainage Environmental Zones and two transitional zones (Custer 1983a, 1984a). The test of the model is conducted in the same geographic area for which it was originally developed. The area encompasses all but the upper reaches of the

Saint Jones and Murderkill River watersheds. These two rivers would have been joined into one major river system during the early part of the Holocene epoch, before sea level rise inundated their confluence in the nearshore Delaware Bay area some time after 1000 B.C. (J. C.

Kraft 1977:52). The study area is in the central portion of Kent

County, and includes Saint Jones, Murderkill, and Milford Necks (see fig. 2). The study area is bordered by the Delaware Bay to the east,

Lewis Ditch and Route 10 to the north. Route 13 to the west, and Route

14 and Clark Point to the south. The upper reaches of the watersheds were not included in the study because it was in the Mid-peninsular

Drainage Divide Zone, an area not considered for the model.

Historically, the study area is that of Algonkian-speaking Indian societies.

In presenting this test of a prehistoric subsistence and settlement model, it is first necessary to review the history of CnWronm*ntat Environmtntal En vifOfWiwntolEnvironmtntal Zone 4 Z ont3 ZontZ Zonal

F o r e t Bmw

Kitts H um m ock

Dataware Bay

DELAWARE

Fig. 2. Environmental zones in the study area 8 modeling in archeology and a number of related models. This is set forth in Chapter II, Chapter III is a discussion of the Delaware model and Delaware prehistory. Chapter IV presents the research strategy and field methods. Chapter V discusses the data analysis and

Chapter VI presents the summary and conclusions. CHAPTER II

MODELING IN ARCHEOLOGY

Introduction

The purpose of this chapter is to provide some background on models in archeology. First, I will review the history of archeological models, then discuss some major types of models, and, third, describe a number of different kinds of models developed for the Eastern Woodlands region of the United States.

Historical Background

In the past seven years, modeling has become an important component of the archeological discipline (Sebastian and Judge

1985:1). Archeological models, and their underlying assumptions, are an outgrowth of settlement analyses beginning with Julian Steward’s

(1938) study of North American Basin-Plateau aboriginal sociopolitical groups and Gordon Willey's (1953) prehistoric settlement pattern studies in the Viru Valley of Peru.

Steward’s (1938, 1955) advances in environmental concepts laid the foundation for current modeling efforts. Specifically, he encouraged interest in causal explanation instead of correlation, stressed the effects of local environments on particular aspects of cultures, and identified ways in which the environment can influence cultures by means of his ’culture core’ concept.

9 10

The 'culture core' includes those basic cultural features with similar functional interrelationships closely associated with environmental exploitation and local ecological adaptations (Steward

1955:6). Core features and secondary features are identified empirically and change with different cultures and environments. In studying a particular culture, one needs to examine the relationship between the natural environment and the culture's economy, and then the behavior patterns in exploiting the environment (Steward 1938:2;

1955:37,40). Steward (1938:2) determined that the natural environmental variables influencing the Indian cultures of the Great

Basin and adjacent Columbian and Colorado Plateaus were topography, climate, water sources and distribution of certain animals and plants. By interviewing informants. Steward was able to determine environmental factors influencing village site locations. For example, the Northern Paiute of Owens Valley villages were centrally located with respect to critical floral resources and established on alluvial fans near permanent water sources. Some resource extraction locations, such as pinyon gathering areas, depended on the distribution of those resources which could not be consistently predicted.

Steward demonstrated that certain site types correlated with specific environmental variables among the Indians of the Great Basin and adjacent areas. This thesis, and his argument for environmental influences on the 'culture core', form the underlying theoretical basis for settlement pattern studies and current modeling efforts. 11

Influenced by Steward's research, Willey conducted what is considered to be one of the first prehistoric settlement surveys

(Willey 1953:xviii). He opened up a new field of archeological enquiry, that of settlement patterning. In his study of the Viru

Valley of Peru, Willey outlined changing settlement types and locations for thousands of years. In so doing, he recognized the role of environment, technology and demography in influencing settlement patterns. Willey defined the term settlement pattern "as the way in which man disposed himself over the landscape in which he lived"

(Willey 1953:1). To Willey, settlement patterns include the nature and disposition of houses and other buildings and their arrangement within communities. In studying the settlements in the Viru Valley, he determined that Cerro Prieto and early and middle Guanape settlements were located on the coast, late Guanape sites were focused inland, and La Plata and Estero period sites were situated on dunes behind the coast. All settlements were located within or next to the limits of cultivation (Willey 1953:371-89). His identification of specific properties associated with types of sites is a basic premise used in modeling today.

Since Willey's (1953) seminal work, settlement analysis has become an important focus of archeology (R. M. Adams 1965; Chang 1958;

Harn 1971; Kurjack 1974; and Munson 1971). The objectives of these studies include the identification of systematic relationships between particular environmental variables and archeological sites. As correlations between archeological sites and specific environmental indicators became more evident, the plotting of archeological site 12 densities by ecological zones became popular In settlement studies

(Gumerman 1971; Plog 1974) and cultural resource management research

(Schiffer and House 1975).

In the 1970s, archeologists began to address the factors which caused the perceived structures in the settlement systems (Gumerman

1971). Archeologists began to look beyond the site location, itself, to the territory in which it was located. Such studies are usually called site-catchment analyses, as originally introduced by Vita-Finzi and Higgs (1970). Site-catchment analyses investigate the economic activities of a site’s occupants in terms of their location to important economic resources. A site is analyzed in terms of its exploitable territory. These analyses are based on the Mini-Max assumption derived from Zipf’s (1949) principal of least effort and locational analysis (Dacey 1966; Chisolm 1970). The Mini-Max assumption (also termed the Principal of Least Effort by some archeologists) is that people behave and live in a way that allows them to obtain maximum returns for minimum input (Lafferty et al.

1985:83-91).

One of the first efforts at a systematic, rigorous statistical analysis of the degree of relationship between site location and environmental variables was conducted by the Southwestern

Anthropological Research Group. Using the Mini-Max assumption, they tested three hypotheses on activity loci. The hypotheses were that activity loci are situated: (a) in relation to critical on-site resources; (b) to minimize energy expenditures in acquiring critical resources; and (c) to minimize the cost of resources and information 13

flow among loci used by interacting populations (Plog and Hill

1971:12). Plog and Hill (1971:11) believed the research would lead to

predicting site locations.

Archeological Models

The plotting of site densities in settlement pattern research,

combined with the growing awareness of archeological site correlations

with certain environmental characteristics, led to settlement models.

In the past ten years, over one hundred models have been developed for

various geographic areas of the United States. The models exhibit a

variety of approaches and techniques. They deal with peoples*

ordering of space as either human relationships or human-land

relationships. Very few models attempt to address both social and

natural environmental interactions.

This section provides an overview of some of the important

features and different types of archeological models. It is not

intended to be a comprehensive discussion of models, the different

theories, and different statistical analyses used in their

development.

Many archeological models are applications of geographic or

ecological models. Models dealing with human relationships are often

based on central place theory and gravity models. Central place

theory models are geographic models representing the structure of

economic transactions on an unbounded featureless plain in terms of centers and clusters of activity. Gravity models are derived from

social physics and are based on the premise that "the amount of 14 interaction between two cities is directly proportional to the number of people living in those cities, and inversely proportional to the intervening distance" (Crumley 1979:146), Most models, however, rely on human-land relationships, primarily economic transactions with the environment, correlating site locations with environmental features and forecasting the location of other sites based on similar environmental conditions. Different site type locations have been associated with differential distribution of faunal and floral resources, as well as raw material sources.

Models reduce complex adaptive systems to a number of essential features. Those features are assumed to be significant in regards to the problem orientation of the research being conducted. In modeling, archeology seeks to understand and reconstruct nonobservables, that is past behavior and cognition, through the pattern recognition of observables, the archeological data (Carr 1985:20). The goals of modeling are the same as Blalock’s (1979) suggested criteria for good social science theory: generalizability, simplicity, internal consistency, precision and falsifiability. Therefore, they contribute to theory-building. In addition, models are inherently predictive.

However, the term ’predictive modeling’ is widely used in archeology and will be used here to refer to the application of models to predicting archeological site locations in areas where such information is not known.

Kohler and Parker (1986) classify archeological models as either empiric correlative or deductive. "Empiric correlative models work by correlating the locations of a samiple of sites with environmental 15

features and forecasting the location of other, unknown sites in areas

that are similar environmentally" (Kohler and Parker 1986:401). In

empiric correlative models, the relationship between the sample and

the target population is established with formal statistical

inference. In building empiric correlative models, one must select a

probabilistic sample, determine the significant temporal or functional

subsets of sites, choose variables for screening and spatial

resolution of prediction, use statistical procedures for identifying

the determinants, and validate the model (Kohler and Parker

1986:402-03).

Deductive models are derived from theories and assumptions

concerning human behavior and theorems regarding the environmental

structure. A deductive model considers how people make decisions, and

for what goal; identifies the variables involved in location choices;

includes a way to measure the variables and provides predictions which

can be tested through the archeological record (Kohler and Parker

1986:432). The deductive approach focuses on systemic decisions as

usually causing locational behavior. One can then analyze the results

of peoples’ decisions in relation to cognitive and environmental

factors. These types of models often use environmental variables

derived from ethnographic studies or empiric correlative models.

Some archeologists argue that social, cultural and biophysical variables should all be considered in modeling efforts (Kohler and

Parker 1986). Others believe that biophysical variables provide a framework within which social and cultural subsystems function.

Parker (1985:174-75) suggests that the use of biophysical data can 16 provide not only good locational models, but explanatory models as well. While most builders of empiric correlative models would like to think their model contributes to theory-building, they usually contribute more to what is termed 'middle-range theory', or bridging arguments (Binford 1977). Middle-range theory can specify the archeological variables that lead to particular behavioral phenomena.

Correlative models are limited because they only provide statements of relationships between the locations of cultural remains and particular features of the modern environment. They do not explain causal factors. Deductive, or explanatory, models are difficult to build but, I believe, are the ultimate goal of current modeling efforts.

A number of models are based on optimal foraging theory (e.g.,

Keene 1979; Winterhalder 1981), decision making theory (e.g., Jochim

1976; Limp and Carr 1985), or central place theory (e.g., Johnson

1972, V. Steponaitis 1978). These theoretical approaches often use the concept of site catchment, as well.

Optimal Foraging Theory Models

Optimal foraging theory models are derived from the field of evolutionary ecology and its assumptions that competition for resources gives advantages to organisms with the most efficient techniques of acquiring energy and nutrients which can be passed on in the gene pool or through behavior, or used to avoid predators

(Winterhalder 1981). Optimal strategy analysis attempts to specify the effects of complex goal- or fitness-related behavior to theoretically derived predictions. Strategy analysis looks for the 17

origins of behaviors in the effects of selection in patterned environments (Levins 1968). When an animal is faced with a certain

situation, it determines its strategy in the light of alternative future conditions, given its current physiological state and its evaluation of present and predicted environmental conditions (Schoener

1971:375). While behavior is believed to be patterned, organisms evaluate and make decisions in response to changing conditions.

The term foraging is used in reference to resources not directly produced by humans, such as plants which are gathered without cultivation and animals which are hunted (Winterhalder 1981:16-17).

Foraging usually considers questions related to a species' diet breadth and choice of items, feeding period, foraging space and group size (Winterhalder 1981:14).

Optimal diet models analyze the forager's food selection in relation to its environmental resource possibilities. Environments may be heterogeneous (patchy) or homogenous (uniform). The diet breadth model by MacArthur and Pianka (1966) is designed to determine an optimal forager's resource use selection. The model predicts that search time will decrease as resources are added to the diet, but average pursuit costs will increase. The assumption is that resource types will be added until the pursuit time is greater than the search time.

The spatial characteristic of an organism's foraging relative to particular resource distributions is examined in optimal foraging space models (Schoener 1971:386). One of these models, the optimal patch choice model (MacArthur and Pianka 1966), predicts an expansion 18

in patch types until average hunting time or energy within the additional patch type exceeds the average decrease in travel time or energy between patches.

In the Horn model for optimal group size and dispersion (Horn

1968), optimum forager dispersion requires the least travel for food requirements. When food resources are stable and evenly distributed, foragers will tend towards regular dispersal over the landscape. When resources are centrally located or unevenly distributed, the foraging population aggregates at a central location (Winterhalder 1981).

Decision Making Theory Models

Decision making theory models are based on the general economic theory assumption that individuals make choices in order to maximize their satisfactions (Jochim 1976:4). Decision making includes both the recognition of values, resources, priorities, and constraints, and the application of decision rules (Saaty 1972:1061). Decision making deals with the need to make choices. These choices include the proportional use and time of utilizing resources, and the demographic and spatial arrangements necessary to accomplish the exploitation.

All these choices require human time and energy which structure the subsistence and settlement patterns of a human group (Jochim 1976:4).

Herskovits states that there is a universal need for choice in allocating resources among alternative ends and "on the whole, the individual tends to maximize his satisfaction in terms of the choices he makes" (Herskovits 1952:18). 19

There are a number of different approaches in using decision

making theory. The underlying assumption of all the different

approaches and 'sub-theories' is that the decision maker is rational.

The decision maker considers the consequences of the different courses

of action available (Jochim 1976:4-5). One of the sub-theories which

is often used in decision making models is rational choice theory. In

rational choice theory (Arrow 1951), preference, ordering and

selection are the important features of choice. The decision maker

then selects alternatives from the highest attainable preference set.

Using a hierarchical decision framework, rational choice theory has

been applied to the question of locational choice by Limp and Carr

(1985). In their analysis, Zipf's (1949) Mini-Max assumption is

rejected because not all choices are based on minimum effort. Some

choices, for example, may be selected for power or for the quality of

life. In using the hierarchical decision framework, each conditional

preference variable is evaluated sequentially. Additional variables

are considered only if prior variables have the correct values

(Gladwin 1975:160).

Limp and Carr (1985) predict archeological site locations using

a hierarchical decision model. They first selected thirteen primary

physical locational measurements which were considered to be

potentially important in structuring the choice process. Related measurement variables were then combined into decision trees to form a

potential preference ordering. The variables were evaluated according

to 149 combinations. Two decision trees were developed for floodplain

location choice in the Arkansas study area. The first decision tree 20

considered the soil permeability variable as primary and the flood

risks variable as secondary. The second decision tree involved

proximity to water and flood risk. In both cases, the data supported

the postulated ordering preference of soil permeability and proximity

to water over flood risk considerations.

Limp and Carr (1985) then applied this method to location

choices of Early Archaic, Late Archaic, Early Woodland and Late

Woodland/Mississippian cultures. In generating a number of decision

tree models, they found proximity to water a better predictor of Late

Archaic sites than Early Archaic. While they predicted that Late

Woodland/Mississippian cultures would place a higher preference on

highly permeable soils because of their agricultural focus, it was

found that all cultural groups had a predilection towards locating on

highly permeable soils. Limp and Carr (1985:162) believe the reason

for this is because highly permeable soils support a vegetation type which provides a high faunal biomass.

Parker (1985) also developed a locational model for the Sparta

area of southern Arkansas using decision making theory. Rather than

using a sequential hierarchical approach, she used multivariate

statistical analysis. In multivariate analysis, a set of variables is

considered as a system, rather than considering one variable at a

time. Her reason for using this approach is based on the assumption

that biophysical variables are treated as a system in choosing

settlement locations. The method recognizes all possible site

locations and predicts both high and low probability site areas. 21

The biophysical variables selected for analysis included five soil variables (Bermuda grass index, loblolly pine index, available soil moisture, depth to water, and soil capability class) and nonsoil variables such as slope, order of nearest stream, order of second nearest stream, order of nearest stream junction, distance to nearest stream, distance to second nearest stream, distance to nearest stream junction, distance to upland/lowland divide, distance to nearest fourth order stream, and elevation. As a result of the multivariate analysis, Parker (1985:188) found the most significant variable to be the Bermuda grass index. Prehistoric people were more likely to choose their settlement location in areas of soils with a low Bermuda grass index. Soil capability class, distance to fourth order stream, and distance to nearest stream, were the next three significant variables, in that order. In addition to the Bermuda index, sites are therefore likely to be located near more productive soils by smaller tributaries.

Jochim (1976:10) developed a model of hunter-gatherer subsistence and settlement using human ecology and systems analysis coupled with decision making theory. The model is based on the assumptions that human economic behavior results from conscious choices which are deliberate and not opportunistic; the deliberate choices are rational, calculated according to preferences among consequences which are uncertain and are estimated; the choices allow mixed strategy solutions and are not aimed at maximizing specific measures, but are aimed at satisfying predetermined aspiration levels; and underlying all economic decisions is the desire to limit effort. 22

The three major problem areas addressed were resource use schedule,

demographic arrangement and site placement. The two basic subsistence

goals are a secure income and low cost maintenance of the population.

Using ethnographic reports, Jochim (1976:23) identifies the most important resource attributes as weight (w), density (^, aggregation

size (a), mobility (m), fat content (f), and nonfood yield (n). One can calculate a secure income by using the formula wnd/m. The population aggregation at minimum cost can be calculated according to the formula wna/m. One can then calculate the proportional use of each resource by adding the secure income scores and the low cost aggregation scores separately over all resources, calculating the amount each resource contributes to the sums, each resource's mean of the two proportions then being the amount of subsistence contribution.

Jochim (1976:52-63) models settlement location using a gravity model concept. The gravity model concept assumes that unevenly distributed or clustered resources will exert a 'puli' or gravity effect on people using those resources. In relation to his model,

Jochim (1976:53) believes that the more security provided by a resource, the greater its 'puli' on a settlement. The gravity model he developed predicts that settlements will be closer to less clustered, more dense, and less mobile resources, all other attributes 2 being equal. The model formula is ^ = wna/p where ^ is the distance between two sej:tlements, wna being the resource cluster mass, and being the dietary proportion of a resource. 23

Jochim (1976:83-188) then applied his model to data from

Mesolithic archeological sites in southwestern Germany, The data were analyzed according to resources, location, size and artifacts. He found that by analyzing the proportion of resource remains, he could determine the seasonal procurement strategy. Site aspect indicated seasonal occupation. Using this information, Jochim (1976:172-75) was able to determine that the Mesolithic subsistence-settlement system in southwestern Germany was a pattern of seasonal movement with fission and fusion. Winter camps were located deep in the Danube Valley, spring settlements down river near a tributary mouth, summer locations along the wider parts of the main river or on lakes, and fall settlements on the upper river or its tributaries. Summer population peaked at an estimated 54 to 108 people, with fall and winter population being smaller (17-33 people), and spring populations the smallest. Jochim (1976:180-89) concludes that the model helps to elucidate the possible functional roles of various archeological sites within a subsistence-settlement system. One of the problems with

Jochim*s model, however, is that it takes all resources into account without considering peoples' selection process for specific resources which have been documented for hunter-gatherers in the ethnographic record. It also does not consider changes in resource use and resource scarcity (Hettinger 1980:210).

Central Place Theory Models

Central place theory is "the theory of the location, size, nature and spacing of . . . clusters of activity " (B. Berry 1967:3). 24

Central place, according to Christaller, is a locus where centralized

goods and services are available to people living in the surrounding

region. The relevant features of the theory are that "the basic

function of a city [or central place] is to be a central place

providing goods and services to a surrounding tributary area," and

these central places are situated in a hierarchy of groups of centers

(Berry and Pred 1965:4-5). Central place theory includes a set of

related models which predict that, in a market economy, central places

develop across the landscape in a regularly spaced hierarchically

nested lattice. Central places are located at the midpoint of their

tributary area to ensure minimum aggregate travel. Each central place

of the same functional size is equidistant from each other. To

determine which settlements may be served by a center and therefore

located in relation to that center, nearest neighbor analysis is often

used. Nearest neighbor analysis proposes a calculation which can be

used to determine nonrandom spatial relationships of populations.

V. Steponaitis (1978) adapted the concept of central place

theory to an analysis of complex Mississippian chiefdoms. Because

Christaller's (1966) model is based on true market economies,

Steponaitis argues that the model cannot be directly applied to

complex chiefdoms. There are three reasons for this. First, market

centers develop as central locations for the distribution of goods and

services desired by the general populace. Chiefly centers arise as

administrative districts for political and social factors independent

from the desires of the general populace. Secondly, market centers offering equivalent goods tend to be regularly spaced across the 25

landscape to reduce direct competition and Increase profits. Chiefly

centers, on the other hand, have well defined administrative districts

which reduce competitive concerns within a well integrated political

system. Third, in market systems, households travel to lower-order

centers for frequently needed common goods, and to higher-order

centers for goods and services not available at the lower-order

centers. In chiefly systems, households do not often have access to

higher-order centers.

Using ethnographic data on the Natchez political hierarchy and

the Society Islanders at time of European contact, Steponaitis

develops a locational model based on political and social concerns.

His model consists of one major center and four subordinate minor

centers. The location of centers is established by the cost or effort

invested in (1) surplus production and labor, and (2) moving people,

goods, and tribute to and from the centers. The model is based on the assumptions that the demands for goods and services are in proportion

to each settlement's and center's population, and the movement cost is

in proportion to the distance squared. Minor centers have movement

costs from interacting with settlements within their districts, and with the capital. If there was no movement to the capital, the least-cost situation would exist when the minor center is geographically centered within its population district. Steponaitis

(1985:432) terms this ideal central location as the demographic center of gravity (DCG). However, because the minor center pays tribute to the capital, the ideal location is closer to the capital. Minor centers are therefore not equally spaced throughout the landscape. 26

This becomes more significant with the degree of political

centralization. Therefore, the model predicts that the minor centers

will cluster in location towards the capital. It then follows that

the capital will always be located at the center of gravity of the

minor centers (CGMC). The actual location of a capital can be

measured according to the model by using the index of spatial

efficiency (E). Steponaitis' (1985:436) formula for the index is: E = YlelEil

where R. is the distance from the CGMC to the minor center in the l±h district, and is the distance from the capital to the minor center in the _ith district. Because by d e f i n i t i o nSR ^ 2 is less than or equal toS^i^, this index equals 1.0 when the capital is ideally located, and becomes smaller as the distance between the observed and ideal location increases (see Massam 1972:6).

Steponaitis then applies the model to Mississippian settlement

data. The data are of the Moundville phase in west-central Alabama.

First, he looked at distances between ten adjacent minor centers. If

spatial competition similar to Christaller's market system was

present, the centers should be equally spaced throughout the area.

He found they were not. In fact, the distance ranged from .8 to 22.3 kilometers with a mean distance of 6.3 kilometers and a standard

deviation of 5.5 kilometers. Using the index of spatial efficiency and measuring straight-line distances, Steponaitis found Moundville's

spatial efficiency with minor centers very high, with E being valued at .94. Most of the minor site locations were valued at less than

.94. Steponaitis then measured the sites according to distance by river travel and distance by land, whichever way seemed most 27 appropriate for the site. The spatial efficiency of river sites was

.996 and .89 for land sites. Moundville had the highest spatial efficiency of all sites in both groups. The location of the capital,

Moundville, thus approximates the ideal model (V. Steponaitis

1985:439-44).

Using nearest neighbor analysis, Steponaitis found the minor centers to cluster towards the capital. These centers were found to not be located near the best arable soil, thus negating a common biophysical variable.

One of the model's limitations is its restrictive set of sociopolitical factors. It does not consider numerous biophysical variables which may also be responsible for site location. It is important, however, in that it considers a dimension not usually addressed in models, the sociopolitical determinants.

Site Catchment Analysis

Vita-Finzi and Higgs (1970:5) first proposed the term site catchment analysis and defined it as "the study of relationships between technology and those natural resources lying within economic range of individual sites." Catchment is a geomorphological term denoting the water source area of a river or stream. For an archeological site, the catchment area is the area from which the inhabitants obtain their resources. Site catchment analysis is usually used with Zipf's (1949) Mini-Max assumption. The assumption related to site catchment analysis is that the intensity of resource extraction activities decreases as the energy costs increase. The 28

energy costs are considered to increase as one moves away from their

central locus, that is site area. Resource extraction activities

stop beyond the point where they are no longer profitable.

Supporting data for this assumption comes from the ethnographic

studies of the IRung San who do not usually pursue resources more

than ten kilometers from their base camp (Lee 1969:61; Roper

1979a:121).

The concept of site catchment analysis was formulated when

Vita-Finzi and Higgs realized that people could exploit different

resources at different times within their natural environment, but

that there was a certain distance beyond which people would not

travel to obtain resources. A resulting basic premise of site

catchment analysis is that there is a correlation between site

function and site location. An individual or group's site catchment

area shape, size and location is dependent upon the spacing, zonation

and seasonality of resources.

In using site catchment analysis, a site's territory or set of

territories is first identified. Higgs (1975:ix) defines territory

as the area regularly exploited and immediately surrounding the

site's inhabitants and defines catchment as the total area exploited as represented in the site's archeological record. The size of the

territory may be defined in three different ways: (1) by drawing a predetermined distance around the site (such as a ten kilometer circle based on ethnographic studies); (2) drawing a perimeter around the resources used by the site's inhabitants; or (3) by analysis of the site's location to its neighbors. The territory is considered to 29

be the area of greatest resource use potential. The area of each

resource zone is then measured. Statistical analyses are conducted

which often involve estimating caloric yields and energy costs of

different resources, differential weighting of various resources

based on a cost/benefit analysis related to defined values, and/or

calculating seasonal availability (Roper 1979a;122-30).

In deliminating territories, Higgs et al. (1967) used a ten

kilometer radius in their study of Paleolithic sites in Epirus,

Greece. Seasonal resource extraction activities were calculated to

determine human population movement, the function of the site of

Kastritsa, and its position in its settlement system. In a site

catchment analysis of prehistoric populations in the Mount Carmel

area of Palestine, Vita-Finzi and Higgs (1970) used time contours

instead of a ten kilometer radius. They believed the ten kilometer

radius to be inadequate because it did not consider terrain changes

and the resulting energy expenditures in acquiring different

resources. The time contour radius was determined to be one hour walks for agriculturalists and two hour walks for hunter-gatherers.

They then predicted seasonal movement based on the resource potential of the time contour and terrain radius they calculated (Vita-Finzi and Higgs 1970:22-26).

Two interesting studies involving site catchment analysis are those of K. R. Adams (1977) and Flannery (1976). In analyzing five sites in the Rio Puerco Valley, New Mexico, Adams (1977) assessed the wild plant resource potential. Seventy-five plants were considered to be useful by scoring their parts, seasonality, reliability, and 30 energy costs in procurement and preparation. These plant scores were then used to determine a catchment area around the sites. Flannery

(1976), on the other hand, looked at the data from the archeological sites in Oaxaca and Tehuacan, Mexico, and calculated where the resource areas were for the floral, faunal, and mineral materials.

This is the same concept, but a reverse procedure, from that used by

K. R. Adams (1977). Flannery (1976:117) found that prehistoric site catchment consisted of a series of three concentric rings, with the innermost area around the village (1-2.5 kilometer radius) providing most of the agricultural products, and a second ring of about five kilometers providing many mineral resources, wild plants and small game. The third ring, beyond five kilometers, was needed for exotic trade and construction materials, and larger game. These materials required a catchment area much larger than most studies conclude.

While the site catchment concept is used frequently in modeling efforts, it is not without its problems. Among its problems, Roper

(1979a) points out the acceptance of commonly-used circular radii distance figures of ten kilometers or 1-2 hours walking distance, as if the sites' inhabitants were on dog leashes; the problem of calculating irregular catchment sizes; the frequent reliability on one or a few resources for catchment analysis; and the dependence on modern resource distributions to project prehistoric resource distributions. However, site catchment is a useful concept and is applied in conjunction with a number of different theoretical and statistical approaches. 31

Statistical Approaches

In addition to there being different theoretical approaches to modeling, there are also different statistical approaches. The choice of the statistical technique depends on what one is trying to determine. Correlation coefficient calculations, for example, only provides the strength of relationship between two variables.

Regression analysis, on the other hand, determines the importance of the predictor variables' contributions to the variation in the dependent variable. Most archeological models deal with the geographic location of archeological sites. In statistically analyzing these locations, a number of significant variables are usually identified. A variable is a measurable entity which can be assigned two or more different values (Kachigan 1982:10). A variable can be measured on a nominal, ordinal, interval or ratio scale. With nominal scales, each variable is mutually exclusive and cannot be arranged in any meaningful order. In ordinal scales, variables may be related and ranked according to some criterion. When variables are ranked and distances between scale values have equal meaning, they are considered to be on an interval scale. Interval scales do not measure absolute differences because they do not include a zero point. Ratio scales are like interval scales with the addition of a zero point (Kachigan 1982:10-15).

Statistical tests are either parametric or nonparametric.

Nonparametric, or distribution-free, tests are usually applied to data measured on a nominal, ordinal, interval, or ratio scale.

Parametric tests are often applied to data measured on an interval or 32

ratio scale. Statistical tests can also be univariate, bivariate or multivariate (Rose and Altschul 1985),

In building a statistical model, or in validating a model, the individual variable characteristics are first defined and measured.

Then relationships between variables and sets of variables are analyzed. The analyses can be conducted using a number of different techniques, depending upon what is being measured.

In conducting a statistical analysis, frequency tabulations are often the first method employed. Frequency tabulations provide a basic analysis of all the data collected. This method provides a count of how often each variable value occurs within the data set

(Kachigan 1982:21).

Cluster analysis includes a set of techniques which are used to group data into homogenous subsets according to inter-object similarities (Kachigan 1982:262). These techniques usually begin with similarity and distance coefficient calculations.

Correlation coefficients measure the strength of relationship between two variables (Helwig 1983:53). Two variables are considered to be positively correlated if cases have either correspondingly low values or high values for both variables. Variables are negatively correlated if the higher the first variable, the lower the second variable, or vice versa (Norusis 1983:57).

Because correlation coefficients only tells you the strength of relationship between two variables, factor analysis is often used to remove the redundancy from a set of correlated variables and reduce the number of correlated variables into a set of factors, or 33

’derived* variables. This statistical technique is particularly

useful when a large number of variables are under study (Kachigan

1982I236-37).

Various regression analyses are among the most frequently used

in modeling efforts. They are usually used when it is expected that there is a dependence relationship between one dependent variable and one or more independent variables. In other words, for every dependent variable X, there is a distribution of Y's. Regression analysis is an equation which describes the nature of a relationship between two or more variables. It can either be simple or multiple.

Simple regression predicts an object’s value on a criterion, or dependent, variable, based on one predictor variable value. Multiple regression calculates the criterion variable’s value based on a number of predictor variables. R-square analysis measures how much variation in the dependent variable can be explained by the predictor variables. Stepwise regression searches for the best model by analyzing the independent variables one at a time (Kachigan

1982:160-93). Logistic regression, which is distribution independent, creates an equation for predicting the classification probability of a categorical dependent variable (Anderson 1972).

In developing a linear program model, choices are represented as a series of linear algebraic equalities or inequalities that are analyzed simultaneously. Variables must be quantified at least at the ordinal scale (Keene 1985:243-44). The objective of linear programming is to find the least-cost solution to an economic problem. This is accomplished by considering a series of production 34 activities with costs and benefits. There may be several benefits for each cost. For example, procuring a deer benefits in calories, protein, prestige and material items such as hide, sinew and antler.

Stipulations such as limited catchment area may be included in the calculations. The optimum solution is calculated to meet the desired benefits or maximize the benefits at least cost (Bettinger

1980:217-18).

Discriminant function analysis defines differences among two or more groups in relation to several variables simultaneously. This type of analysis usually requires variables measured on the interval or ratio scales and combines and weighs variables so each group's central tendency is distinct (Rose and Altschul 1985:32). Linear discriminant function analysis assumes equal variance-covariance matrices and multivariate normal distributions for all classes. By recognizing and rating variables which cause the distinction between groups, discriminant function analysis is used to identify and predict site and nonsite locations.

Kohler and Parker (1986:421-31) present an interesting test of four different statistical approaches to modeling, or what they call predictive techniques. Using data from the Sparta mine area of

Arkansas, they employed four different decision rules to come up with four simulated site distributions based on twelve biophysical variables. The biophysical variables were: slope, distance to nearest stream, distance to second nearest stream, order of nearest stream, order of second nearest stream, distance to nearest stream junction, order of nearest stream junction, distance to upland or 35

lowland divide, distance to a fourth-order stream, feet above mean

sea level, depth to water, and soil capability class. Five samples

were drawn at random from each of the simulated distributions. The

multivariate statistical techniques of multiple regression, linear

and quadratic discriminant function analysis, and logistic regression

were then applied to the same sample of sites and nonsites. This

approach created eighty predictive functions (4 techniques x

5 samples x 4 distributions) (Kohler and Parker 1986:421).

Classification error rates were then calculated by applying the

techniques to the training sample on which the models were based, and

a test sample of simulated sites and nonsites which were not part of

the model data base.

All four models were developed with similar criteria. They were: distance to nearest water < 1000 meters, distance to upland or

lowland ecotone < 1000 meters, distance to fourth-order stream > 1000 meters, depth to groundwater > 26 inches and soil capability ^ 3. In

some cases the distance to water and type of water source varied.

The first decision model which was developed was called a 'fatal flaw decision model' because all criteria had to be met or the site location would be rejected. A second model, termed a 'hierarchical decision model' was developed by generating a sequential analysis of environmental location characteristics. The criteria were the same as the first model but, in this case, a location was rejected if any of the sequentially considered criterion is not met. The third decision model was an 'unweighted additive decision model' and the fourth model was a 'weighted additive decision model '. 36

Classification error rates were then computed for both the test

and training samples. In applying the statistical techniques to the

fatal flaw decision model, the average total error rate for the test

samples were .162 for logistic regression, .106 for quadratic

discriminant function, .171 for for linear discriminant function and

.174 for multiple regression. Therefore, quadratic discriminant

function analysis exhibited the lowest error rate and multiple

regression the highest. For the hierarchical decision model, linear

discriminant function analysis with .133 had the highest error rate, and logistic regression with .082, the lowest. Error rates for quadratic discriminant function was .085 and for multiple regression was .120. For the unweighted linear additive decision model, error rates were .143 for quadratic discriminant function and multiple regression, .139 for linear discriminant function, and .118 for logistic regression. In the case of the weighted linear additive decision model, multiple regression had an error rate of .163, linear discriminant function had .175, quadratic discriminant function had

.165, and logistic regression had .132. From this analysis, Kohler and Parker (1986:427) concluded that quadratic discriminant analysis and logistic regression yielded better results than linear discriminant analysis or multiple regression. The important point in conducting this test, however, is that different models can be developed using the same data base. They can have somewhat different results, not only by using the different modeling approaches, but also by using different statistical tests in evaluating their effectiveness. 37

Eastern Woodlands Archeological Models

Although most of the predictive models have been developed for

the western United States, Brose (1981), Custer (1984a), Gardner

(1978, 1980a, 1980b, 1981, 1982), Hay et al. (1982), McManamon (1981,

1982), Perlman (1980), Reidhead (1980), Roper (1979b), R. M. Stewart

(1980b) and Wolynec et al. (1983) have developed models for the

Northeast and Middle Atlantic regions. While all of them correlate

site locations with environmental variables, Custer's (1984a) is the

only one that includes social variables. Before describing the

Delaware model developed by Custer, it is useful to discuss some of

the other models developed for the Northeast and Middle Atlantic

regions. These models represent different approaches or different

ecological situations, and are not necessarily considered the best or

the only models that have been developed.

In conducting a sample survey of outer Cape Cod, Massachusetts,

McManamon (1981, 1982) developed a model of prehistoric land use.

The objectives of his initial survey included: (1) estimate the

frequency and locations of prehistoric sites and their associated

environmental variables; (2) estimate the structures and contents of

identified sites; (3) determine any differential use of the area by

prehistoric populations; and (4) determine the activity range,

intensity and frequency of the prehistoric populations (McManamon

1982:3). The survey involved a stratified random sample design based on the known archeological record and environmental variables.

Because 80% of the known prehistoric sites were located within 200 meters of a fresh water source or tidal marsh, areas with these 38 characteristics were first established as stratum I. Areas more than

200 meters away from such water sources were considered stratum II.

After conducting an initial pilot sample, stratum I was subdivided into stratum IA, IB and IC. Stratum lA was established as a 200 meter wide area surrounding Nauset Marsh, Salt Pond Bay, Pilgrim

Heights, and the west side of Salt Meadow. Stratum IB was a 200 meter area surrounding fresh water ponds and rivers. Stratum IC was a 200 meter area surrounding fresh water wetlands and hollows.

Environmental areas were identified using 1:24,000 U. S. Geological

Survey topographic maps and a 1962 vegetation map of the area.

The sample units were 100 meter by 200 meter (2 hectares or 4.9 acres) quadrats. The sample unit size was selected to coincide with the stratum I 200 meter designation and to enable testing in a relatively short time by a single crew. Because most areas were heavily vegetated, shovel tests were conducted using a twenty-five meter interval grid.

Analysis of the contents and structure of the identified sites was conducted first using the relative frequencies of lithic artifacts (such as bifaces, unifaces, flakes, decortication flakes, trimming flakes, thinning flakes, rough stone, shatter, blocks and cores) to establish a matrix of Euclidian distances between each pair of sites. Sites with similar contents were then grouped together using a cluster analysis and furthest neighbor linking method. The resulting groups were considered to represent different activities and/or lengths of occupations. The structural characteristics of each site were then independently analyzed using the mean frequency 39

of materials such as lithics, shell and/or fire-cracked rock

recovered from each shovel test pit. From these analyses, five

clusters of activities were recognized as representing three activity

patterns. Clusters A and 6 included a wide range of activities

related to the large percentage of flakes indicating cutting,

scraping, sawing, manufacturing and maintenance activities. These

assemblages infer recurrent or long-term activities and a year-round

or seasonal occupation. The small lithic debitage comprising cluster

C was interpreted to represent limited activity areas with late stage

tool manufacturing or rejuvenating. Cluster D and E materials were

considered to be similar to A and B but not representing as wide an array of activities. Cluster E contained a small average number of artifacts and was considered to represent short-term activities or occupations.

It was discovered that stratum lA, which comprised only 4% of the study area, was the most densely and/or recurrently populated stratum. It was the only area where prehistoric ceramics and shell middens were identified. The resulting estimated frequency of settlements and activities which can be used as a model for prehistoric land use activity on Cape Cod is as follows. Stratum lA,

Nauset area, with 31 sample units, had .01-.07 long term or recurrent sites (clusters A and B with high structural variable scores) per acre; .01-.06 long term/recurrent, possible cold-weather sites

(clusters A and B with high fire-cracked rock scores) per acre;

.03-.11 intensive hard shell clam exploitation sites (assemblages with high hard shell clam scores) per acre; .06-.14 moderate length 40

occupation sites (cluster D assemblages) per acre; .03-.08 short term

sites (cluster E assemblages) per acre; and .02-.07 short term, late

stage manufacturing sites (cluster C assemblages) per acre. Short

term, late stage lithic manufacturing sites and intensive hard shell

clam exploitation sites were considered rare for all other stratums.

Stratum lA, Pilgrim Spring, with seven sample units, had .005-.07 moderate length occupation sites and short term sites per acre.

Forty-six units were sampled in stratum IB, but only .003-.01 short term sites per acre were found. In stratum IC, .006-.03 long term/recurrent sites and short term sites per acre were found out of thirty-four sample units. Of the twenty-three sample units in stratum II, .002-.04 long-term/recurrent sites and long-term/recurrent, possibly cold weather sites per acre were discovered. McManamon (1982:19) concludes that the general pattern of prehistoric land use on Cape Cod is one of small intensive settlement in very small areas, and little use of large intervening areas.

While McManamon mentions that these data can be applied to other areas, he does not actually specify a model. One can formulate from these data, however, a model of prehistoric land use and settlement involving intensive use of highly productive marsh and bay areas; some long terra or recurrent use of fresh water wetlands and hollows; and primarily moderate or short term use of most other areas. The one exception to McManamon's (1982:18) original hypothesis that most sites would be found within 200 meters of fresh water and marsh areas, was the location of sites 300 meters from 41

water sources in stratum II, Such results allow for revision of

original hypotheses and the generation of new ones for testing.

Another model of coastal hunter-gatherer behavior for the

eastern United States was developed by Perlman (1980). Perlman first

develops a model of coastal productivity, and then presents a model

of optimum diet and behavior for coastal hunter-gatherers. The

behavior model is based on Zipf's (1949) Mini-Max assumption combined

with risk minimization. In this model, hunter-gatherers are expected

to minimize both their energy expenditure and risk in acquiring

resources.

Perlman first analyzes coastal areas in terms of their

productivity. For the purposes of his model, coastal areas are

defined as those terrestrial areas in direct contact with ocean waters; that is the shore, tidal zone and brackish waters. Organic and inorganic resources within those areas are considered coastal resources, and those people who obtain part or all of their resources from these areas are considered adapted to the coastal zone (Perlman

1980:259). Populations are assumed to operate at carrying capacity within these areas. Energy is measured in calories and time-inputs.

The coastal systems model identifies energy inputs related to sea level change, topography and bathymetry. Increasing stress in these systems reduces species diversity and productivity (Odum and

Copeland 1974:36). Coastal systems may be considered of low, medium or high stress depending on energy input factors. For example, the force of waves on a shoreline is considered high stress. Therefore, sand beaches and rocky coasts are considered low productivity areas. 42

The most productive coastal zones are those with salinity-stressed or

estuarine areas because they generate high fish productivity; areas

with broad shallow bathymetry (underwater topography); and extensive

tidal areas with large marshlands. In calculating net productivity of

different environments, coastal estuarine, marsh and swamp areas are

the most productive, averaging 1750 grams per square meter per year

(Perlman 1980:272).

These data are then applied to an optimum diet model based on

the premise that predators select prey with the highest return for

the least amount of energy and risk. Perlman identifies eight

relationships on a continuum of increasing energy costs. They are

that: (1) predators expend more energy in selecting smaller prey; (2)

some small prey will not be selected unless new technologies, requiring greater energy inputs increase the competitive returns of the prey; (3) return rates decline as prey density declines, requiring more energy in pursuit of that particular prey; (4) a predator's diet becomes more generalized as favored prey become less abundant; (5) energy input requirements increase as the time required in pursuit of food resources increase; (6) return rates plateau when a species' density increases above a specific number, and drop too low when species' density decreases below a certain number; (7) group size costs increase as one moves away from minimum cost group size; and (8) as resources become concentrated and productive, group size is optimized. From these relationships, one can rank the costs of social systems. A least-cost ranking of environments can be calculated by determining the caloric return rate of various 43

resources. For example, the least-cost hunter-gatherer habitat Is a

highly productive environment with numerous, large, concentrated

resources.

Perlman calculates return rates for deer, small game, nuts, and

aquatic resources. From his calculations, he determines that

estuarine environments, especially those above forty degrees

latitude, provide the highest resource return rate with the least

risk, minimizes mobility, and support a higher population. Secondary

occupation areas would include riverine, interior, and other coastal

environments (Perlman 1980:283). Given this model, the following

hypotheses are presented for archeological sites: (1) evidence of

coastal occupation and exploitation will exist on highly productive

Holocene coastal zones; (2) shellfish are considered a reliable,

intermediate-raturn resource, which were used by prehistoric people

as a dietary supplement or backup resource when other resources were

not available; (3) evidence of increased sedentism should exist in

highly productive areas, such as estuarine environments, because

transhumance is the result of low productivity, dispersed resource

environments; (4) indications of storage techniques for resources

such as anadromous fish should be found in coastal zone areas because

storage reduces energy and risk costs; (5) prehistoric coastal zone

inhabitants may have had larger group size and nonegalitarian social

organization if contemporary hunter-gatherer group size is considered a function of environmental productivity and not social costs

(Perlman 1980:284). Perlman (1980:298) concludes that the archeological record supports the model's predictions that many 44 coastal ecological systems would provide a least-cost and least-risk environment and not interior ecological systems. His conclusions are reached, however, by presenting archeological data in support of his arguments, not by actually testing the model against a nonbiased sample of data.

Brose (1981) developed a model of prehistoric human activity of the Cuyahoga Valley National Recreation Area in Summit and Cuyahoga counties of Ohio. His model was developed for the National Park

Service management activities in Cuyahoga National Recreation Area.

The model is based on Zipf's (1949) Mini-Max assumption. That is, that prehistoric settlements would have been located to minimize social and economic costs in using the area's resources. In developing the model, Brose (1981:41) correlated known site locations with environmental variables such as topography, floral communities, ranked drainage systems, winter/summer climatic isoclines, soil textures, and surface geomorphology. Microenvironmental zones were then identified by calculating the intersection of six variables: topography/physiography/geomorphology; floral cover, related to pre-contact community; drainage system characteristics; soil classes; slope class; and flood potential. The topography variable included alluvial bottom lands, colluvium-covered terraces. Pleistocene high terraces isolated by Post-Pleistocene activity, poorly dissected interfluvial plateau segments, and all other areas. Under the floral cover category were oak openings, mixed mesophytic forests, beech-maple forests, A/B or A/C ecotones, and all others. Drainage system characteristics included locations within fifty meters of: 45

secondary tributary junction, primary tributary junction, primary

tributary, Cuyahoga River; and all other areas. Sands or gravelly

sands, sandy loams, silt loam, clayey loams/silty clay/clayey silt

loams, and all others were considered as the general soil classes.

Slope classes were separated out as 0 to 6%, 6 to 18%, 18 to 25%, and

above 25%. Flood potential was either flood prone or not flood

prone. These variable attributes were ranked in the order

presented. From these six variables, 216 ecotones were identified.

These ecotones were condensed into a 6 x 3 matrix indicating the

probabilities for containing a prehistoric archeological site as either higher than expected, normally distributed, or lower than expected, in relation to a random distribution of known site frequencies.

In analyzing the variables, high probability, modal or moderate probability, and low probability zones were identified. High probability zones included flood prone alluvial bottomlands within fifty meters of a secondary tributary junction, having open oak forests and 0 to 6% slopes of sands or gravelly sands. Moderate probability areas were on high isolated Pleistocene terraces of sandy or silty loam soils, having 6 to 18% slopes and mixed mesophytic forests. Using this model, it was predicted that 29% of the area would be high probability and contain 65% of the archeological sites, and 47% of the area was low probability containing 28% of the sites

(Brose 1981:51-57).

The predictions were then tested by conducting four fifty meter wide transects crossing the environmental zones from east to west. 46

By conducting a Kolmogorov-Smirnov test of cumulative frequency

distributions of these data, Brose concluded that this initial

approach had been conceptually oversimplified because the observed

distribution of sites differed significantly from the predicted

distribution (p<.001), Brose (1981:59) believed the reason for this

was that the prehistoric site selection process was much more complex

than the normative model he developed. This he postulates may be due

to limited function site areas, A Poisson distribution of areal

frequencies led to the identification of five ecological strata,

ranging from high to low probability. The high probability variables

remained the same, however,

Brose (1981:92-98) conducted a discriminant function analysis

of his data. Three models were developed by weighting combinations

of various variable attributes. The first model, the 'conservation' model, weighted variable attributes in the order of: texture of the

second soil horizon, pH of the second soil horizon, drainage, vegetation, and named U.S.D.A. soil type. The resulting algorithm which was run off of base map files, resulted in a model which had a low overall accuracy limit of 62%. It identified 87% of all known site areas, but also selected 56% of the known nonsite areas as having a high probability of containing an archeological site. The second model provided a high overall accuracy of 85% by weighting the combination of variable attributes for agricultural capability classes, texture of the second soil horizon, drainage, named U.S.D.A. soil types, and vegetation, in the order presented. This

'prospecting' model selected 24% of the project area as high 47 probability. However, it identified 45% of the known site area as not containing archeological sites. The ’double-ended' model, having a 78% overall accuracy rate, predicted the project area as 16% high probability, 36% low probability, and 48% unknown status. These results led Brose (1981:97) to the difficult conclusion that there are many ways to develop predictive models in archeology, but no

'best' way. His 'conservation' model works in identifying high probability areas as long as one is willing to include large areas of low probability. This kind of approach is good for identifying areas to avoid construction impacts on archeological sites. The 'prospect' model is good for locating archeological sites, if one is willing to omit part of the archeological universe. If one is willing to deal accurately with only half of the project area, the 'double-ended' model is the best choice for identifying high and low probability areas.

Wolynec et al. (1983) developed a model for the Lake Erie coastal zone by first using relevant attributes from four models developed for areas of Pennsylvania and Ohio. The resulting summary of synthetic environmental rankings were for variables related to geomorphology, soil texture class, topography, area distance to known site, first drainage system (less than 350 meters) by junction of second system, and relative degree of dissection system (sixteen hectare areas). Attributes considered under the geomorphology variable included fossil glacial beach ridge (weighted ranking 1), kame and/or kame terrace (4), recent alluvial formations (9), outwash plain/morain (16), and lake bed deposits (25). Soil texture class 48

included well drained/sands over gravel (weighted ranking 1),

moderately well drained/sands or sandy loam (4), poorly drained/silt

loams or clay loams (9), very poorly drained/silty clays or clay

(16), and no drainage/bogs, muck, gleyed soils (25). Topography

variable attributes were modern lakeshore bar, beach ridge or dunes

(weighted ranking 1); active river terrace (4); river bluff edge (9);

floodplain (16); and other (25). Ranked area distance to known site

in meters were 150 to 350 (1), less than 149 (4), 351 to 500 (9), 501

to 700 (16), and greater than 700 (25). The first drainage system

ranking multiplied by junction of second system included primary

tributary (1x2), Lake Erie (2x1), second order creeks (3x4),

bog/kettle/pond (4x3), third order stream (5x5), and none (6x6). The

last variable ranking, relative degree of dissection system, was

obtained by cross multiplying difference in elevation (in feet) by

average slope of area (in degrees). The variable characteristics

were 30 to 60 by 0 to 6 (1x1), 60 to 80 by 6 to 12 (2x2), 0 to 30 by

12 to 17 (3x3), 80 to 120 by 17 to 22 (4x4), and 120 feet by >22

degrees (5x5). After conducting a summary synthetic environmental

ranking, it was determined that areas with a value of 0 to 2.08 were

of extreme sensitivity (high potential for containing an archeological site), a value of 2.08 to 4.16 indicated high

sensitivity, 4.16 to 6.59 was of moderate sensitivity, 6,59 to 7.45 of fair sensitivity, 7.45 to 8.32 of poor sensitivity, and 8.32 to

9.84 of minimal sensitivity (Wolynec et al. 1983:2-43 to 2-45). The model was first tested on known site locations and was found to have a 98% accuracy rate. A second test of the model was conducted using 49

data collected from a surface survey, test excavations and informant

interviews. Of the fourteen archeological sites identified in this

manner, six were found in high sensitivity areas, six sites in

moderately sensitive areas, and two possible sites (reported by

informants but not field checked) in areas of low sensitivity

(Wolynec et al. 1983:2-54). The authors conclude that the model will

predict areas of high, medium and low probability with good accuracy.

Using site catchment analysis, Roper (1979b) developed a model

of Woodland settlement patterns for the Sangamon River Valley in

central Illinois. In conducting her study, she first established

seven propositions. They were: (1) spatial and seasonal differences

exist in the biophysical environment; (2) people are refuging

animals; (3) people organize themselves into communities; (4)

communities do not use all the resources available; (5) in exploiting

its natural environment, a community will tend to act in a rational manner; (6) archeological data reflects the behavioral structure

involved in its deposition, at least partially; and (7) settlements are adaptations to two sets of conditions, situation and site. By

refuging animal, Roper means that people demonstrate a rhythmic dispersal from and return to a certain fixed point. The Mlni-îfax assumption is at work in the proposition that a community acts in a rational manner. Situation is both the physical and cultural conditions surrounding a site. Site characteristics are the local environmental features on which the settlement is established (Roper

1979b:10-11). 50

Roper conducted an archeological survey of the Sangamon River

Valley study area. The survey involved locating all recorded

archeological sites and conducting a fifteen meter interval transect

survey of the valley. She identified numerous biophysical variables

related to what she determined to be the most relevant environmental

variables: topography, drainage, soil, flora and fauna (Roper

1979b:77). For example, the topographic characteristics included

bottomland prairie, upland prairie, floodplain forest, upland forest,

floodplain, valley, valley slope, and terrace. Soil texture included

silty clay, silty clay loam, silt, silt loam, loam, fine sandy loam,

sandy loam, loamy sand, and sand. Reconstructed plant communities were based on the ecological literature, J. Johnson’s (1972) study of

proto-European-American phytogeography of the Sangamon River Valley and historic travellers’ accounts. For each site, situation characteristics were calculated for a one mile radius. The areas of each major landform (floodplain, terrace, slope, and upland) were measured. Vegetation was determined using a series of one mile concentric circles up to three miles. The four vegetation zones were upland forest, floodplain forest, upland prairie, and bottomland prairie. Each site was given a score for the rank of largest stream within the three concentric circles. Rank of nearest stream, and the horizontal and vertical distance to that stream were also calculated. Other variables included Middle or Late Woodland temporal periods, material evidence (ceramics and projectile points), and spatial considerations determined by using the natural divisions map of Illinois (Schwegman n.d.) (Roper 1979b:81-82). 51

Roper (1979b:84) then analyzed twenty-three variables on

sixty-three Middle Woodland sites, A matrix of Euclidean distances

was calculated for cluster analysis and non-metric multidimensional

scaling. From the cluster analysis, three preliminary conclusions

were reached: (1) people selected certain situations for functionally

distinct sites during the Middle Woodland; (2) functionally

equivalent site type (i.e., ceramic sites) locations vary within the

natural environmental structure; and (3) different areas of the

Sangamon River Valley were used in different ways. For example, the

upper valley area has more projectile point sites without ceramics.

Ceramic sites are considered to be habitation sites, while projectile

point sites are considered to represent primarily hunting camps used

by men.

Using multidimensional scaling, three Middle Woodland site

location strategies were hypothesized. The first strategy was restricted to below the river junction with Salt Creek and to the

south side of the river drainage. Sites are located on a bluff base, back of terrace or a slope near a Sangamon River tributary in the upland forest, but within two or three miles of all major environmental zones along the river. The second strategy is similar to the first, but used in areas where there are no linear terrace features. With this strategy sites are on a slope near the river.

Sites using the third strategy are bottomland oriented, in contrast to the first two strategies, and do not have access to the upland forest as sites using the first two strategies do. These third strategy sites are located on the north side of the river, below the 52

confluence with Salt Creek, and are on the front edge of a terrace or

near lakes (Roper 1979b;94). In general Roper concludes that the

Middle Woodland settlement pattern model is one of a village or base

camp at the base of a bluff with seasonal bottomland camps at the

confluence of the Sangamon and Illinois Rivers, and temporary hunting

camps along the tributary streams.

The same type of analysis was conducted for fifty-seven Late

Woodland components. From the analyses, Roper (1979b;101) found that

sites are more evenly distributed throughout the drainage and there

is less evidence of situation selection than there was during the

Middle Woodland. She hypothesizes the reasons for this may include

that the site selection variables important during Middle Woodland

times may not have been important during Late Woodland times due to

subsistence or settlement changes or that sampling error was a

factor. Roper (1979b:141) determines the Late Woodland settlement

pattern model to consist of two types of components: (1) sites in

upland forests, on a slope, often near a bluff crest; and (2)

floodplain or terrace bottomland sites located within the bottomland

prairie or floodplain forest. In general, the Late Woodland exhibits more seasonal movement than the Middle Woodland, with bluff base villages or camps and bottomland villages or camps. Bluff base sites are on slopes within the upland forest, often near the bluff crest.

These two types of Late Woodland sites may represent seasonally interchangeable occupations, as opposed to the base camp-ancillary camp settlement pattern of the Middle Woodland. The general distribution of Late Woodland sites indicates to Roper (1979b:141) 53

that the Late Woodland settlement system may have had a smaller

catchment area than during Middle Woodland times, and a major

territorial reorganization may have occurred between the Middle

Woodland and Late Woodland.

Reidhead (1980) developed an economic model of subsistence

change using rational choice theory, the Mini-Max assumption and

estimated nutritional requirements for Late Woodland and Fort Ancient

cultures of the middle Ohio Valley. The objective was to develop a

model of balanced diet at the lowest resource extraction cost to

humans. The model predicts how people would behave if they chose to

minimize their labor energy costs in meeting nutrient requirements.

It is a deductive optimizing model.

The model was developed by estimating: (1) the population and

nutritional requirements of both the Late Woodland and Fort Ancient

settlements at the Leonard Haag site; (2) the available food

resources within a 38.8 square kilometer (fifteen square miles) area

on a seasonal basis; (3) the nutrient value of the resources under

consideration; and (4) the average cost of resource use according to

the technological capabilities of both the Late Woodland and Fort

Ancient cultures. For each cultural period, resource mixes were calculated according to seasonal variability, nutrient availability, labor cost, and other considerations. Linear programming was used to develop the actual model.

The optimal procurement strategy mix for a population of 144

Late Woodland people in the middle Ohio Valley was determined to be

210 kilogram units (kgu) of stored fish, 2350 kgu of stored fruit. 54

6160 kgu of fresh maple sugar, 2280 kgu of stored squash, 5210 kgu of fresh tubers, 240 kgu of fresh turtle, and 2050 kgu of stored weed seeds for the winter season. Spring requirements were 9170 kgu of fresh fish, 30 kgu of fresh greens, 4880 kgu of stored maple sugar,

450 kgu of fresh tubers, and 1430 kgu of fresh turtles. Summer food needs included fresh fish (7370 kgu), fresh fruit (1100 kgu), fresh greens (130 kgu), stored hickory butter (1190 kgu), fresh mussels

(2000 kgu) and fresh squash (5520 kgu). Fall nutrient requirements were calculated as being 2080 kgu of fresh acoms, 5350 kgu of fresh deer, 1130 kgu of fresh fruit, 2300 kgu of fresh mussels, and 15410 kgu of fresh squash (Reidhead 1980:158).

Reidhead (1980:163-72) then applies the model to empirical data from the Leonard Haag site. He finds that the original model does not fit the resource use identified at the site. The predicted rank of animal utilization in order of Importance was fish, mussel, turtle, deer, turkey, elk, raccoon, beaver, waterfowl, bear, opossum, squirrel, and muskrat. The actual use in order of importance was deer, elk, bear, raccoon, turkey, waterfowl, beaver, squirrel, turtle, fish, mussel, muskrat, and opossum.

By using linear programming, Reidhead then analyzed the model's assumptions to identify possible errors. He found that if he eliminated the emphasis on calcium nutrients from animals, that is he eliminated mussels and turtles from the model, fifty percent of the error was also eliminated^ Reidhead speculates that the calcium requirement may have been met by plant resources or water, the population may have suffered from a calcium deficiency, or another 55

resource not considered in this model may have met their calcium

requirements. Without mussels and turtles, fish are predicted as the

most important resource, and deer the second most important. The

data, however, indicates that deer is the most important resource and

fish is ranked ninth. Reidhead cannot explain the discrepancy.

However, two reasons could be the extensive use of deer for nonfood uses, and the preservation problem with fish remains.

The calculations for plant resources were more difficult due to the lack of archeological remains resulting from the use of tubers, maple sugar and greens. Reidhead (1980:172) analyzed them according to a presence/absence scale. The major difference between predicted and actual plant resource use was with hazelnuts and walnuts. The model predicted no use of those nuts because of the labor cost.

However, he found a higher use of hazelnut than any other nut, and some use of walnut.

Reidhead describes the resulting picture of Late Woodland resource use as one that optimizes most predicted resources, such as deer, squash, fruits, weed seeds and hickory nuts, but deviates from the model in several ways. They used more hazelnuts and walnuts, even though it cost them more on labor, and they used much less fish than would have if they were primarily concerned with maximum nutrient return for minimum labor costs.

Reidhead (1980:173) also developed a model for Fort Ancient optimal resource utilization. It was similar to the Late Woodland model except for the introduction of com. Resource use in Fort

Ancient times would have relied heavily on corn in the summer, using 56

3420 kgu of it, and 9030 kgu of fresh fish, 170 kgu of fresh greens,

2150 kgu of fresh mussels, and 6240 kgu of fresh squash. Resource use would have been similar in the fall, except for the addition of deer. Fall resources included dried c o m (6410 kgu), deer (1180 kgu), fresh fish (320 kgu), fresh mussels (2700 kgu), and fresh squash (17330 kgu). Winter nutrient requirements included stored fish (30 kgu), stored fruits (7240 kgu), stored squash (2630 kgu), fresh tubers (940 kgu), fresh turtles (240 kgu), and stored weed seeds (11080 kgu). In the spring, resource procurement was calculated as 9170 kgu of fresh fish, 700 kgu of fresh greens, 3890 kgu of stored maple sugar, 1630 kgu of fresh turtles, and 2400 of stored weed seeds. Reidhead found the same problems with this model as he did with his Late Woodland model. The Fort Ancient people used corn, squash, deer, fruits and weeds that minimized labor costs, but they did not use fish as much as was predicted. They also used more hazelnuts and walnuts than predicted.

In comparing the Late Woodland to Fort Ancient resource procurement strategies, Reidhead believes that Fort Ancient people would have to emphasize more protein-rich resources because of their use of poor quality protein in corn. If they did not, they would have been protein deficient and nutritional stress would be evident in the population. He also believes that the model indicates why

Late Woodland people would have adopted corn agriculture. Economic benefits are provided by the low energy input versus the nutrient return. 57

Reidhead (1980:178-79) concludes that the Late Woodland and

early Fort Ancient people at the Leonard Haag site did minimize their

labor cost in resource procurement decisions to some extent*

However, they did not exercise a purely optimizing strategy because

they used the more expensive nuts and avoided the cheaper fish. This

data indicates that archeologists should use the Mini-Max assumption

with a critical eye. People do not necessarily operate in a purely

least-effort manner. Human behavior is the result of many

considerations, some of which are rational, but not all.

Hay et al. (1982) developed a model of prehistoric

archeological site locations for New Hanover County, North Carolina.

This coastal county is similar environmentally to the study area.

Using existing site information, they predicted high, medium, low and

nonprobability probability areas based on soil type, nearness to

surface water, type of nearest water and elevation. High probability

areas were within 98 to 389 meters to streams, at four to seven meters elevation, and had three well drained soil types. Medium

probability areas were less than 98 meters or between 389 to 680 meters from saltwater ponds, were 1.2-4 or 7-10 meters in elevation, and associated with a number of different soil types. Low probability areas were 680-971 meters from springs, lakes or swamps; were less than 1.2 meters or between 10-12.8 meters in elevation, and associated with a number of soil types. Nonprobability areas were greater than 971 meters from sloughs and other water sources, 12.8 meters or higher in elevation, and associated with a few soil types

(Hay et al. 1982:6,22). The county was then stratified according to 58 these four probability zones and a sample survey conducted. They found that, while the model predicted site locations, it did not discriminate between site and nonsite areas. Similar amounts of archeological materials were found in each zone (Hay et al.

1982:43). In analyzing the results, they determined that soil type was the only statistically significant variable for predicting site location, specifically agricultural suitability and soil drainage.

Well drained soils suitable for agriculture were found to contain the most sites and be the best predictor for archeological site potential

(Hay et al, 1982:78).

Gardner (1978, 1980a, 1980b, 1981, 1982) has developed a model of Middle Atlantic prehistoric settlement systems based on his research in Virginia. The model has been tested by R. Michael

Stewart (1980b) in the Great Valley of Maryland. First, prehistoric site locations can be correlated with a number of environmental variables in different physiographic zones. In the floodplains, archeological sites are correlated with surface water, particularly backwater swamps or poorly drained areas, and low order streams bisecting the floodplains; lithic raw material sources; zones of maximum habitat overlap, such as floodplain/terrace margins, alluvial fans, river banks, levees; areas of maximum sunlight exposure; and well drained areas of low topographic relief. In the Pleistocene terrace zones of high order streams, sites are correlated with the distribution of surface water, primarily in proximity to two or more low order streams or their junctions; game-attractive habitats and maximum habitat overlap; lithic raw material sources; well drained 59

low relief topographic areas; and distance away from high order

streams. In the upland areas, sites are correlated with the

distribution of cobbles; surface water Including various stream

orders and springs; distance away from high order streams; and

distribution of primary outcrops of chert. In the foothill zone, the

variables are distribution of surface water, high order streams, and

lithlc raw material sources. In the mountains, sites are associated with surface water, especially in close proximity to springs, low

order streams, bogs and swamps; distribution of high order.streams and distance away from such streams; areas of maximum sunlight

exposure; lithic raw material sources; game-attractive habitats and maximum habitat overlap; and well drained low relief topographic areas (Gardner 1978:14-17).

In addition to identifying environmental correlates of prehistoric site locations, Gardner (1978, 1980a, 1980b, 1981, 1982) also presents a model of subsistence and settlement patterns, some of which is used by Custer (1984a) in his Delaware model. Paleo—Indian cultures (11,500-9000 B.P.) are considered to be small band organizations who hunted large game and collected various plant foods. Gardner (1980b) includes what is commonly referred to as the

Early Archaic cultures within the Paleo-Indian because of the basic continuation of the same settlement pattern. Paleo-Indian settlement pattern was one oriented around cryptocrystalline lithic sources and game-attractive habitats. Their site types included: (1) quarry sites, which were lithic material source areas and contain large waste flakes, cores and crude bifaces; (2) quarry reduction stations 60

at the nearest available water source to the quarries, where lithic

raw materials were further reduced and tools were manufactured; (3)

quarry-related base camps, usually in floodplain terrace/upland

ecotones of maximum habitat overlap, maximum sunlight and minimal

wind exposure, where a wide range of artifacts and manufacturing

activities indicate temporary habitation sites; (4) base camp

maintenance stations associated with hunting and food procuring,

recognizable by butchering and related processing tools, located In

game-attractive areas of maximum habitat overlap; (5) outlying

hunting sites which are resource extraction areas on a lower order

stream at or near its confluence with a higher order stream in less

ecologically favorable zones and up to twenty miles from base camps

and quarries; and (6) isolated point finds indicating individual

hunting situations. The quarry-related base camps were probably used

by macro-band social units and the maintenance camps by micro-band

social units (Gardner 1981:55-59). Gardner (1981:68) tested this

Paleo-Indian model using data from other locations in the Middle

Atlantic and Southeastern United States. He determined that the

model basically works, but more research is needed to adequately test

it.

The settlement pattern changes in the Middle Archaic period

(6500-3000 B.C.) with a move towards more generalized hunting and

gathering and exploitative use of the environment. With the more generalized exploitative strategy comes a more generalized tool kit, with the addition of chipped and ground stone axes, grinding tools, drills. Previous projectile point styles (e.g., Clovis, Folsom, 61

Dalton-Hardaway, Kirk and Palmer) are abandoned for a more widespread

use of the LeCroy style. There is also a reduced emphasis on

cryptocrystalline lithic materials and therefore less reliance on

quarry-related settlement patterns. The main site type is the base

camp, with its focal point being inland swamps in the coastal plain,

and floodplain and Pleistocene terrace zones in the Piedmont and

upland areas. These site locations are in areas of maximum habitat

overlap and high biomass. Transitory resource procurement camps

occur in other resource locations, but never far away from higher

order streams (Gardner 1978). The Late Archaic (3000-1000 B.C.),

subsistence and settlement pattern is similar to the Middle Archaic,

with a move away from poorly drained swamps, and more exploitation of

mountain and upland habitats.

A significant change occurs between the Late Archaic and the

Woodland periods with the introduction of pottery reflecting

increased sedentism and reduced dependence on trade to obtain

steatite. The settlement pattern during the Early Woodland

(1000-500 B.C.) and Middle Woodland (500 B.C.-A.D. 900) is one of

macro-social unit base camps at the junction of major estuaries and

freshwater streams, and interior micro-group transient camps in the

outer coastal plain; seasonal macro-social unit base camps at

strategic fishing locations in the inner coastal plain; and micro-social unit base camps with smaller exploitative foray camps in

the upland valley areas (Gardner 1982).

During the Late Woodland (A.D, 900 to European contact), maize agriculture caused great changes in the subsistence and settlement 62 pattern. People began to rely on agriculture and located in sedentary base camps/farming villages characterized by a wide variety of artifact types and usually found on floodplains of major drainages in conjunction with tributary streams. Some occurrences of hunting/exploitative camps and individual point find locations in floodplains and adjacent Pleistocene terrace and upland areas also exist (R. M. Stewart 1980b:118-19).

R. M. Stewart (1980b) tested Gardner's model in the Great

Valley of Maryland by conducting a survey of nine transects which represented the environmental variability of the area as a whole.

The transects were the western Clear Spring, eastern Clear Spring,

Conococheague, St. James, Dargan, Crampton Gap, southern Marsh Run,

Smithsburg, and Antietam-Potomac areas. The transects were each about one mile long and a half mile wide, and 100% survey was attempted on all of them (R. M. Stewart 1980b:21,28). R. M. Stewart

(1980b:398) concludes that the Gardner model is predictively powerful from the aspect of site locations. However, three major deviations were identified in the Great Valley of Maryland. First, during the end of the Early Archaic, there is evidence for a broader use of the environment than has been modeled by Gardner. Sites were found in association with the total range of stream orders. Secondly, the

Pleistocene/high terrace zone of high order streams was not used to the extent predicted in the model. Third, the selective use of rhyolite lithic raw materials from the late Middle Archaic through the Middle Woodland was not predicted and indicates a decision to use 63

rhyolite instead of equally useful lithic materials located

throughout the Valley,

Hughes and Weissman (1982) analyzed the models developed for

western Maryland by Gardner (1978), Kavanaugh (1980), Peck (1979),

R. M. Stewart (1980b) and Wall and Lacoste (1981), They (1982:89)

found that most prehistoric archeological sites in western Maryland

are within 200 meters from flowing or sizeable bodies of water, on

well drained soils with slopes of less than 15%, and have southerly

sloping aspects.

Discussion

It is evident from this review that there are many different

theoretical and methodological approaches to modeling. Most

importantly, there is no one right approach or one 'true' model.

Models only mirror selected aspects of observations (Clarke 1972:4).

As Brose (1981:98) has so cogently pointed out, there has been little

recognition of the fact that very different predictive models can be

developed from the same archeological resource data base. As was

discussed in detail previously, Brose (1981:95-98) discovered that by

combining different high potential variables, he could develop three

locational models of archeological site areas. Each one of those models had an overall accuracy rate of over 61%, but each had some serious limitations. Kohler and Parker (1986) demonstrated that same point in their test of four different statistical approaches using the same data. In a similar coastal environment, Custer (1984a) 64

found surface water to be one of the primary site location variables,

while Hay et al. (1982) found it to be fertile soils.

There is no way to really classify archeological models.

Kohler and Parker (1986) lump them into empiric correlative and

deductive. However, some empiric correlative models are based on

assumptions of human behavior (e.g., Parker 1985), which is a

criterion of deductive models. And deductive models often use the

physical variables derived from empiric correlative models (e.g.,

Reidhead 1980). Some models combine both approaches, like Custer’s

(1984a) Delaware model. Hay et al. (1982:13-14) categorize models as

either descriptive, behavioral or statistical. Bettinger (1980)

identifies four categories of hunter-gatherer models. They are

models of environment, subsistence, settlement location and

territoriality, and population. But MacArthur and Pianka’s (1966)

optimal foraging models are considered under both Bettinger's

environment and subsistence model categories, and Jochim's (1976)

decision making model is included under his subsistence model and

settlement location and territoriality categories. As this review of models indicates, the optimal foraging models also use decision making theory, most models are based on the Mini-Max assumption and

involve site catchment analysis, and, sooner or later, they deal with physical locations. Reidhead's (1980) and Perlman's (1980) models are called optimum foraging models, but they both include decision making theory and the Mini-Max assumption.

The fact that archeological models cannot be categorized may be advantageous. It may mean that archeology is experiencing a 65

formulation or reformulation of concepts, ideas, approaches, etc,,

which has not yet crystallized. It could also mean that there is an

underlying premise to all the models that has yet to be realized. Or

it could just mean that the state of the art in archeological

modeling is one of chaos and confusion.

There are a number of problems ivith current modeling efforts.

The analyses conducted by Kohler and Parker (1986) and Brose (1981)

demonstrate two major problem areas. They are: (1) the biases

inherent in whatever theoretical and/or methodological approach the

particular model is based on, and (2) the lack of independent

evaluation and verification. With respect to the first problem,

archeologists usually select one theoretical and/or methodological

approach, and then, maybe, field test it. There is little attempt to

test and improve existing models for the area, or for similar areas,

and there is little attempt to test different approaches on the same

data base. Assumptions and techniques are not critically evaluated.

For example, if one looks at one's own life, there are many

activities that would not necessarily fit Zipf's (1949) Mini-Max

assumption. As Reidhead (1980) demonstrated, his resource

optimization model did not fit reality. People make some decisions

based on factors other than least effort. While the Mini-Max

assumption may play a part in peoples' decisions, it is too

simplistic to use as the only motivational factor in peoples'

behavior. Such an assumption is too much a reflection of current western civilization cognition. The Mini-Max assumption, however, is 66 an underlying assumption of most archeological models. It is uncritically accepted in so many models.

Another assumption that should also be looked at more critically is the acceptance of ten kilometer and two hour walk radii in site catchment analysis. Such limiting factors should be evaluated. Variable selection and reliance on a few key variables as determinants in site selection are also problematic. It is limiting, and perhaps unreliable, to select a few resources, such as soil and vegetation as the determinants in site selection. Variables are sometimes selected based on intuitive judgement. Various variables are treated of equal or unequal weight that are solely at the discretion of the investigator. The use of modern environmental variables to predict the location of prehistoric environmental variables is questionable, at best. Land use, geomorphic and climatic change, and sea level fluctuations have caused drastic changes which may not allow us to infer past environments. Often there is no indication of how variables contribute to locational decisions. Our own cultural biases may not allow adequate interpretations of past cognitive and behavioral systems.

The second major problem area involves developing models using existing data that were not collected in a systematic fashion, and then testing the models on additional existing data. In such cases, a model is developed using data not representative of the archeological universe, and therefore not nearly representative of prehistoric human behavior. Then the model is tested on data not representative of the realm of human behavior. When one considers 67

the differential preservation problems inherent in the archeological record, using data that are not, at minimum, a representative sample, is extremely problematic. Even if the model is developed using a sampling technique, the sampling technique may be inappropriate or have low spatial resolution. The statistical techniques may not be appropriate. In addition, the problem of site contemporaneity or lack of contemporaneity is often not addressed. Models are usually tested by the model-builder, therefore not providing an independent test for better verification. One purpose of this study is to address some of these problem areas. CHAPTER III

DELAWARE PREHISTORY AND THE SUBSISTENCE AND

SETTLEMENT MODEL

Background of Archeological Research

Interest in Delaware prehistory began in 1865 with Dr. Joseph

Leidy’s investigations of shell middens near Lewes on Cape Henlopen.

Dr. Leidy was a parasitologist and anatomy professor at the

University of Pennsylvania Medical School in Philadelphia. Leidy became interested in the middens during a boat tour on the Delaware

Bay. He returned to conduct some excavations of the middens with friends the following year and reported his findings to the Academy of Natural Sciences in Philadelphia (Weslager 1968:10-15).

A number of Philadelphians followed in Leidy's footsteps.

Francis Jordan began excavating shell middens in Rehoboth Beach in

1870. Hilborne Cresson began excavations in Claymont. Cresson's work in Delaware earned him a position on the research staff at the

Peabody Museum in Cambridge, Massachusetts, from where he continued his work in Delaware. Eventually, Delawareans began to appreciate their prehistory and join the Philadelphians primarily in excavating shell middens and attempting to determine the age of the remains they found. Among the first was Joseph Wigglesworth who had training in archeological field methods under Warren K. Moorehead in the

68 69

1889 excavations of the Fort Ancient site in the Ohio Valley.

Wigglesworth encouraged the interest of other people, such as C, A.

Weslager who has researched and written many books on Delaware

history and prehistory (Weslager 1953, 1968, 1972, 1983).

The history of Delaware archeology is primarily that of

interested and knowledgable amateurs who occasionally involved

professionals, such as Dr. T. Dale Stewart from the Smithsonian

Institution. In 1965, Ronald A. Thomas was hired as the first state

archeologist. A number of excavations were conducted under his

direction by members of the Archaeological Society of Delaware. The

Archaeological Society had been formed in the 1930s. One of the most

significant archeological sites excavated by them and R. Thomas

(1974b) was the Island Field site at South Bowers. The site contains

two components; a Woodland II Slaughter Creek complex occupation; and

a Woodland I Webb complex mortuary center. It is now part of a

museum and archeological research center.

In the 1970s and 1980s, much archeological work has been

accomplished in Delaware as part of environmental impact assessments

and historic preservation requirements related to Federally involved

development. Some of the major projects have been conducted under

the direction of Dr. Jay Custer at the University of Delaware. They have included surveys and excavations related to the Route 13

transportation corridor development which crosses Delaware from north

to south (Custer et al. 1984, 1985, 1987; Custer and Bachman 1986;

Custer and Cunningham 1986). This corridor cuts through the third environmental zone of the study area, the Mid-drainage Zone. Other 70

large scale projects Include a survey of the duallzation of Route 113

in Dover (Cunningham et al, 1980), a survey of cultural resources

north of the Saint Jones River (Griffith et al, 1979), a sample

survey of selected portions of the Saint Jones and Murderkill

drainages (Custer and Gallasso 1983), a survey of a proposed

alignment of the West Dover By-Pass (Griffith and Artusy 1976), and a

survey of Killens Pond State Park (Wise 1984). A number of studies have been conducted by R, Thomas (1966a, 1966b, 1966c, 1966d, 1970,

1973a, 1973b, 1974a, 1974b). In addition, state and county archaeological societies have conducted numerous surveys which have added greatly to our knowledge of Delaware prehistory (Delaware

Division of Historical and Cultural Affairs 1977, 1978, 1980, n.d.).

Delaware Prehistory

From this research, a good outline of Delaware prehistory, beginning with the Paleo-Indian period, can be described.

Paleo-Indian Period (15,000-6500 B.C.)

Human settlement in Delaware begins during the end of the

Pleistocene, around 15,000 B.C. The first cultural time period, termed the Paleo-Indian period, continues until the Holocene environment becomes well established, around 6500 B.C. This

Paleo-Indian period spans the Late Glacial, Pre-Boreal and Boreal environmental episodes. The Delaware Paleo-Indian period is similar to that for the rest of the Middle Atlantic region. The oldest sites are usually associated with the eastern Clovis fluted point 71 tradition. Dates for the eastern Clovis period come from a number of sites, including the Debert site at 9000 B.C. (MacDonald 1968) in

Nova Scotia, and from Shawnee-Minisink at 8700 B.C. in Pennsylvania

(McNett 1985). However, possibly older assemblages of stone tools have been found at Meadowcroft Rockshelter dating to 13,000 B.C.

(Adovasio et al. 1977). Because of this, and other research in North

America, an approximate date of 15,000 to 6500 B.C. has been established for the Paleo-Indian period (Custer 1984a).

The Paleo-Indian period is considered to include four phases.

The four phases include three identified by Gardner (1974, 1977,

1980b) and one added by Custer (1984a). The phases are Clovis,

Mid-Paleo, Dalton-Hardaway, and what Custer (1984a;59) calls

Notched-Point phase. These phases are different from those other archeologists have used in categorizing Paleo-Indian complexes because they include what used to be considered the Early Archaic time period with side- and corner-notched projectile points such as

Kirk, Palmer, and Amos. The reason Gardner (1980b) includes the

Early Archaic in the Paleo-Indian is because he found settlement pattern continuity throughout the time periods at the Fifty and

Thunderbird sites in Virginia. He also found the same basic use of jasper for the nonprojectile portions of the stone tool kit.

Although projectile point styles change from fluted to notched,

Gardner and Custer believe there are no substantial life-style changes (Custer 1984a;43). This application to the Middle Atlantic region is supported by research conducted by Gardner and Verrey 72

(1979) in western Virginia, Brown (1979) in Maryland, and Goodyear

(1979) for North America.

Because the prehistory of Delaware is tied with the environment, a discussion of the environment is essential in understanding human adaptations and cultural changes. During the

Late Glacial, the climate was much colder, averaging 5*P, with cloudy and wet conditions. The Laurentide ice sheet was located just north of the Delaware River headwaters at about 12,000 B.C. (Ogden 1977:24) and greatly affected the Delmarva peninsula climate. At this time, the Delmarva peninsula was covered by grassland within a broader coniferous forest matrix. Some deciduous trees were also present.

In addition to large Pleistocene mammals such as mammoth, mastodon, elk, and caribou, small tundra mammals such as voles, lemmings, mice and ground squirrels were present. The Delaware Bay did not exist at this time. Instead, the ancestral Delaware River extended fifty kilometers south of the present mouth of the Delaware Bay as a deeply incised river channel (J. C. Kraft 1977:620). Estuarine resources in the rivers should have been minimal because of the rapidly rising cold water level and constant change in salinity (Ogden 1977:26;

Gardner and Stewart 1977:4).

The rapid melting of the Laurentide ice sheet after 8500 B.C. and resulting shifts in air mass patterns caused wetter conditions which eventually gave way to a warmer and drier climate as westerly winds became more prevalent. With these changes, vegetation changed from pine, birch, spruce and grasses to predominantly pine and spruce with a little oak (Carbone 1976), This climatic time period is 73

called the Pre-Boreal/Boreal (8080-6540 B.C.) episode and is a

transition between the end of the Pleistocene and beginning of the

Holocene. Pleistocene megafauna became extinct with the reduction of

the open grassland habitats and the increase of closed boreal

forests. While the sea level was rising at about 3.3 centimeters a

year, it was still 24 to 19 meters below present sea level (Belknap

and Kraft 1977:620). Estuarine resources should have been limited at

this time.

Fluted point surveys of the Delmarva peninsula have been

conducted by Custer (1984b), Brown (1979), R. Thomas (1966b, 1974a),

Reynolds and Dilks (1965), Kinsey (1958, 1959a), and Mason (1959).

Fifty-one Paleo-Indian projectile points have been located in

Delaware, although no in-situ Paleo-Indian site component has been

found (Custer 1984b:17-18). While the projectile points do not

exhibit a complete subsistence and settlement pattern, such a pattern

can be inferred from research conducted in the Middle Atlantic

region. In describing Delaware Paleo-Indian prehistory, however,

Custer (1984a) relies primarily on the Flint Run Paleo-Indian complex

(Gardner 1974, 1977).

The Paleo-Indian life-style is considered to be one of hunters

and gatherers adapted to the Late Glacial mosaic parkland and

Pre-Boreal and Boreal environments. Included in their stone tool kit are projectile points for killing and butchering animals, biface knives for butchering and tool manufacturing, scrapers, and multiple purpose flake tools. Their tool kit was basically one of a biface technology using high quality cryptocrystalline silicates. It is 74

assumed that Paleo-Indians subsisted on large mammals such as musk

ox, mammoth, mastodon, giant moose, woodland peccaries, white-tailed

deer, caribou, elk and giant beaver, and on collected plant and

aquatic foods such as have been identified at the Shawnee-Minisink

site in Pennsylvania (Dent and Kauffman 1985:67), At Shawnee-

Minisink, a Paleo-Indian hearth and occupation level revealed the

remains of hackberry, hawthorn plum, blackberry, grape, chenopod,

acalypha, amaranth, smartweed, buckbean, and fish bones.

Assuming that the Paleo-Indlans had a flexible band level social organization and no efficient technologies for long term food

storage, their settlement pattern was based on movement from one location to another as critical resources became available or were depleted. Critical resources would have included food, water and shelter. Gardner (1974, 1977, 1979) argues, however, that the most critical resource would have been stone material for tool manufacture. Assuming this to be true, Custer (1984a) describes the

Delaware Paleo-Indian subsistence and settlement system to be one focused on lithic raw materials. The main habitation sites were base camps located close to lithic sources, water, areas of maximum habitat overlap and on well drained ridges with a southern exposure.

Periodically revisited, base camp maintenance stations were resource procurement sites near rich gathering or game-attractive hunting locations such as swamps, bay/basin features and other poorly drained areas within fifteen kilometers of base camp (Gardner 1980b, 1981).

In the northern Delmarva peninsula there are three major concentrations of Paleo-Indian sites. The first concentration, in 75

northeastern Cecil County, Maryland, and northwestern New Castle

County, Delaware, is associated with a cryptocrystalline outcrop

called the Delaware Chalcedony Complex (Custer and Galasso 1980).

This local material was used in manufacturing fluted points found in

this area. Unfortunately, no diagnostic artifacts have been

retrieved from one of the quarry-related sites, 7NC-D-34, in

excavations conducted by the Delaware Bureau of Archaeology and

Historic Preservation. The area around the mouths of the Choptank

and Nanticoke Rivers is the location of the second concentration of

Paleo-Indian sites. During the Late Pleistocene, this area would

have been a good observation point and resource location at the

headlands overlooking the ancestral Choptank, Nanticoke, Potomac, and

Susquehanna Rivers. The area contains high quality lithic materials

in the form of cobbles associated with gravel point bar deposits

(Gardner 1979). The last concentration of Paleo-Indian sites is in the Mid-peninsular Drainage Divide, exhibited by the Hughes complex and associated with poorly drained high resource locations such as swamps and bay/basin features. The Hughes complex (7K-E-10,24,33) in

Kent County, Delaware, consists of six surface artifact concentrations on well drained knolls adjacent to a large freshwater swamp and poorly drained areas. The artifacts include a Clovis projectile point; notched projectile points such as Palmer and Kirk; scrapers; late stage bifaces; and flake tools. These sites are west of the study area.

In analyzing Paleo-Indian sites in Delaware, Custer (1984a:59) believes that the quarry site complex in northern New Castle county 76

may have been the center of settlement systems that ranged south Into

the Mid-peninsular Drainage Divide and north into the Piedmont

uplands. The Choptank and Nanticoke quarry complex could have been

another focal point in the Mid-peninsular Drainage Divide, It could

also be that there was a peninsular-wide settlement system, with

groups ranging between 60 and 170 kilometers for various resources between the two quarry sources.

The Paleo-Indian Model

The Paleo-Indian subsistence and settlement model involves two alternatives: a cyclical model, and a serial model (Custer et al.

1983). The alternatives depend on whether the lithic resources are large and dispersed, as in the cyclical model, or are small and clustered in an area relatively close together, as in the serial model. In the cyclical model (see fig. 3), groups use a cycle of base camps with a focal point of one quarry-related base camp. In the serial model (see fig. 4), groups would procure lithic resources in conjunction with other activities on an as-needed basis. The serial model does not include quarry-related base camps and quarry reduction sites because there would be no need for them. Both models, however, include base camps, quarries, hunting sites and base camp maintenance stations. While not all types of sites have been found in Delaware, the different potential Paleo-Indian site types are as follows (Custer 1984a:39-60).

Quarry Sites

Quarry sites are resource extraction locations of primary or 77

QR QB

Key

% Quarry related Quarry ©B 1 Base camp .'« o / base camp

Quarry reduction Hunting site M Base camp site maintenance station

► Partial group -► Group movement foray

Fig. 3. Paleo-Indian cyclical model (after Custer 1984a:54) 78

À

Key

Base camp Quarry M I Base camp maintenance © station

Hunting site -► Partial group Group movement foray

Fig. 4. Paleo-Indian serial model (after Custer 1984a;55) 79 secondary cobble outcrops of high quality lithic material for stone tool manufacture. There are usually a large number of waste flakes and rejected bifaces and few, if any, diagnostic artifacts. The sites are usually not near water sources.

Quarry Reduction Stations

Quarry reduction stations are locations where further stone material reduction, often of bifaces into smaller primary-thinned bifaces, occurred. They are usually on level ground by water sources near the quarries.

Base Camps

Base camps are the main habitation sites with houses and a wide variety of artifacts indicating domestic activities. They are usually located near water with a southern exposure and maximum habitat overlap for resource extraction. Quarry-related base camps are very large and contain the remains of tool manufacturing activities, particularly late stage biface reduction and tool finishing.

Base Camp Maintenance Stations

Base camp maintenance stations are faunal and floral resource procurement areas within ten to fifteen kilometers of the base camp which would be periodically revisited. Artifacts associated with resource processing and unmodified flake tools are common. 80

Outlying Hunting Sites

Outlying hunting sites are resource procurement sites in particularly game-attractive areas, such as swamps and bay/basin features, within forty kilometers of base camps. Artifacts found are similar to those at base camp maintenance stations, but less numerous.

Isolated Point Finds

Isolated point finds are individual projectile points which may indicate individual hunting sites, ephemeral occupations, or remnants of partially destroyed sites. They are the most numerous site type.

Archaic Period (6500-3000 B.C.)

With the emergence of the Holocene environment with a continental climate and distinct seasonal changes came a change in human adaptation and culture. This time period is referred to as the

Archaic, originally indicating preceramic hunter-gatherer cultures of mobile small band organization (Ritchie 1965:32; Willey and Phillips

1958:107). Because more recent studies indicate greater complexity during the Archaic, in both social organization and technology,

Custer (1984a:61) and a number of other archeologists use the term to represent a time period between the Paleo-Indian and Woodland periods. The Archaic time period for Delaware does not include what has been traditionally called the Early Archaic and the Late or

Terminal Archaic time periods. Because the cultural prehistory of

Delaware is analyzed according to cultural and technological 81 adaptations to a changing environment, the Early Archaic has been included in the Paleo-Indian period, as discussed previously, and the

Late Archaic has been included in the Woodland I period to be discussed in the next section.

The Archaic period is associated with the Atlantic climatic episode and the establishment of a continental climate with distinct seasonal changes and widespread dense mesic forests (Carbone

1976:75). During the early portion of this time period, from 6500

B.C. to 5000 B.C., precipitation increased and the mean annual temperature became warmer. First hemlock, and then oak, moved into the area (Bernabo and Webb 1977:77). The entire Delmarva peninsula was probably covered in dense, mesic forests, with large poorly drained areas. Faunal distribution was similar to today's, with deer and turkey dominant. Sea level rise was slightly slower, at three centimeters annually, but still too rapid to allow the establishment of stable estuarine resources.

Unfortunately, only forty-two Archaic surface sites have been found in Delaware, and no intact ones have been excavated. However, it is most likely that the Archaic period in Delaware is similar to that for the Middle Atlantic region. Particularly, Chapman

(1975:248-69) has commented on the pan-eastern spread of Archaic bifurcate projectile points, as has Dincauze (1976:140-42) on the similarity of Archaic stemmed points on the Atlantic Slope from South

Carolina to New England, In addition, Michlovic (1976) believes the widespread distribution of similar Archaic projectile points indicates reciprocal exchange among small band social organizations. 82

Therefore, archeological sites from elsewhere in the region (Broyles,

1971; K. W. Carr 1975; Kinsey 1959b, 1975; H. Kraft 1970, 1975;

Ritchie 1965; Ritchie and Funk 1973) are used in describing the

Archaic period for Delaware,

During this time, people settled in a wider variety of

environmental settings in response to an increased variety of

seasonally available resources. People began to focus on resources

related to the spread of mesic forests, sea level rise and water

table changes. With the wider array of resources available, new

tools were added to the tool kit. In addition to new bifurcate and

Archaic stemmed projectile points came ground stone tools such as

axes, gouges, grinding stones and plant processing tools (Chapman

1975:275-76). Numerous flake and biface tools were also used.

People were no longer dependent on high quality lithic quarry sources

as they began using a wide variety of lithic materials, including

rhyolite, quartz, quartzite, chert, and jasper, much of it from

cobble sources.

A major indication of adaptation changes comes from the

settlement pattern. Archaic sites are located in a wider variety of

environmental settings, different from those of Paleo-Indians. Using

contemporary hunter-gatherer studies (Steward 1955; Lee and DeVore

1968), the Archaic subsistence and settlement system was probably one of seasonal fission and fusion with multiple family units living at macro-band base camps for extended periods when many resources were available and splitting into family units located at micro-band base camps when resources were less plentiful. Macro-band base camps may 83

have Included twenty to thirty related nuclear families. They may

have had carefully planned seasonal scheduling and a more elaborate

system than the Paleo-Indians (Snow 1980:183).

Interior swamps may have been the most important resource focus

for Archaic peoples (Custer 1984a:69) and would have been likely

locations for macro-band base camps. Such sites in Delaware include

the Clyde Farm site (7NC-E-6) and the Julian Powerline site

(7NC-E-42) on high terraces at the confluence of White Clay Creek and

Churchman’s Marsh in New Castle County. A number of Archaic surface sites may be micro-band base camps including 7S-E-21 and 7S-E-20 along the upper Nanticoke River, 7NC-E-14 on the upper terraces of the Delaware River, and sites located during archeological surveys by

R. Thomas (1980) and Gardner and Stewart (1978) in New Castle

County. Unfortunately, diagnostic materials have not been recovered from these sites that would indicate these were undoubtedly micro-band base camps. A number of Archaic procurement sites have been found close to bay/basin features, such as 7NC-J-3,14; 7NC-G-68; and 7K-C-25, indicating the desirous game-attractive location (Custer

1984a:61-74).

The Archaic Period Model

During the Archaic, people lived in large social units called macro-bands during seasons of greatest environmental productivity.

These social units may have included twenty to thirty related nuclear families. Small work groups would make occasional trips to resource procurement areas (see fig, 5). Using the analogies of contemporary 84

Key

Macro-band base (© b J Micro-band base /pA \ Procurement site

► Periodic foray -4------► Group reiocation

Fig. 5. Archaic model (after Custer 1984a:68) 85

hunter-gatherers, work groups may have been organized on the basis of

sex and age. As the resources immediately surrounding the macro-band

base camp were depleted, either from human exploitation or seasonal

factors, social groups would disperse as micro-bands. The

micro-bands would locate to smaller base camps in less productive

environmental zones (Custer et al. 1984:59).

The different potential Archaic type sites that may be found

are listed below (Custer 1984a:67; Custer et al. 1984:59).

Macro-band Base Camps

Macro-band base camps are living areas for multiple family units located in areas of maximum habitat overlap and characterized by a wide variety of tool classes and extensive debris covering an area of 10,000 square meters or more. They would have been located on low terraces along major drainages, especially near lower order stream confluences.

Micro-band Base Camps

Micro-band base camps are smaller living areas occupied by one, or a small number, of family units in areas of limited resource distribution and characterized by little variety of tool classes and minimal debris covering less than 10,000 square meters. They were located on upper terraces of major drainages which are along lower order tributaries and at low order stream confluences up to ten kilometers from major drainages. 86

Procurement Sites

Procurement sites are resource extraction locations indicating

a limited range of activities. They are usually located near swamps,

floodplains of major and minor drainages, and alluvial fans associated with swamps, bogs or lithic sources.

Of the forty-two known Archaic sites in Delaware, two can be identified as possible macro-band base camp sites, three may be micro-band base camps, and three may be defined as procurement sites. Not enough information is available to determine the nature of the other sites.

Woodland I Period (3000 B.C.- A.D. 1000)

By 3000 B.C. a number of changes were occurring in both the environment and prehistoric lifeways. After a warm, dry period, the environment became essentially the same as today. The rate of sea level rise began to slow and estuarine environments stabilized to the point where shellfish and anadromous fish appeared. People began to exploit the newly established estuarine and riverine resources, in addition to deer, turkey, small mammal, and seed and other plant foods. This time period indicates a drastic change from previous mobile hunter-gatherer social systems. People became relatively sedentary, with less portable storage technologies, nonportable facilities, ranked societies, elaborate exchange systems, and complex burial patterns.

The Woodland I period, as established for the State of

Delaware, is different from the traditional Woodland time period for 87 eastern North America. It includes what are usually called the Late

Archaic, Early Woodland and Middle Woodland time periods. The reason these time periods have been combined into Woodland I is because the cultures exhibit similar archeological manifestations: macro-band base camp focus on estuarine and riverine resources at freshwater/saltwater interface zones of major drainages; large increases in population from Archaic times at macro-band base camps; new foraging and collecting adaptations in less productive areas; participation in resource exchange networks over large areas; and occasional complex mortuary ceremonies resulting in cemeteries with exotic grave goods (Custer 1984a:77).

The Woodland I period corresponds with the Sub-Boreal (3110

B.C.-810 B.C.) and Sub-Atlantic (810 B.C.-A.D. 1000) climatic episodes. A warm and dry period, called the midpostglacial xerothermic, lasted from 2700 B.C. to 200 B.C. and affected the distribution of plant and animal communities and probably human subsistence and settlement as well. The mesic forests gave way to increased hickory, oak and pine, and spreading grasslands. The ranges of animal species intolerant of dry environments were reduced. Deer populations would have increased with the early serial stage vegetation, however. Reduced moisture would have caused hydrologie fluctuations which would have affected estuarine and riverine resources, particularly temperature and salinity sensitive fauna such as shellfish and anadromous fish. At this time, the rate of sea level rise decreases to two centimeters per year, or less, and estuarine resources become established. By the Sub-Atlantic 88

environmental episode, Increasing moisture and slowly decreasing

temperature gradually led to a close approximation of modern

conditions. Vegetation became that of an oak-chestnut forest region

in the high coastal plain and evergreen forest in the low coastal

plain. Loblolly pine dominated, but areas of raised elevation

included Virginia pine and deciduous species. Poorly drained uplands

were dominated by white oak, sweet gum, willow oak, pin oak, and sour

gum (Braun 1967). The major game resources included deer, turkey,

rabbit, squirrel, and waterfowl.

The study area is a focus for Woodland I period activity.

Factors that distinguish this time period from earlier Paleo-Indian and Archaic time periods included population growth, development of exchange networks, and changes in tool kits, social organization, and subsistence and settlement patterns. Technological changes associated with the new subsistence patterns were large stemmed and narrow projectile points; proliferation of broadspear knives and woodworking tools such as adzes, celts, gouges and axes; plant processing tools; and stone and ceramic containers. The intensified use of estuarine and riverine resources and increased storage facilities produced focal adaptations which led to increased sedentism and higher population densities. The increased population densities at macro-band base camps effected a major modification of the social environment leading to ranked societies with higher status individuals considered to be 'big men' and well developed trade and exchange networks. 89

The Woodland I settlement system was, in some ways, similar to the Archaic system, in that they were both focused on macro-band base camps. However, with the increased reliance on a few local resources such as shellfish, anadromous fish, deer and plant foods, people became more sedentary. The ratio of macro- to micro-band base camps and variety of activities would have increased. The variety of activities at procurement sites would have decreased. People tended to focus on predictable water sources and faunal and floral resources. In general, the adaptive trend was toward focusing on a more specific reliable range of resources and locations. The higher population densities at macro-band base camps led to a major change in social organization, according to Custer (1984a:97). For the first time, nonegalitarian societies probably developed in Delaware.

The increased population would have led to a decrease in the number of high status leadership positions per capita. When leadership becomes ascribed rather than achieved because of special leadership talents, the social system becomes one of a ranked society (Fried

1967). In addition, Custer (1984a:98) believes that the Woodland I complexes exhibit ranked social systems because of their elaborate trade and exchange systems and symbolic use of particular artifacts.

Custer identifies two types of exchange patterns: one of exchange for cryptocrystalline materials for making certain broadspears like

Perkiomen projectile points; and another for noncryptocrystalline stone materials, such as rhyolite and argillite, for other broadspear forms. Also linked to this trading network was the importation of steatite stone bowls or raw material. 90

The earliest Woodland I complexes are the Clyde Farm complex,

from site 7NC-E-6 in northern New Castle County (R. Thomas 1977:53),

and the Barker's Landing complex, from the Barker's Landing site

(7K-D-13) on the Saint Jones River in Kent County. The Clyde Farm

site is one of the largest remaining Woodland I macro-hand base camps

in Delaware. It is located at the confluence of Churchman's Marsh

and White Clay Creek and extends over a one square kilometer area.

Studies have been conducted at this site since Crozier (1938b) first

located it in 1938. The major occupation occurred prior to 500 B.C.

Large amounts of lithic debitage, ground stone tools, unfinished

bifaces, stone tools made out of nonlocal materials, experimental

ceramics, and steatite bowl fragments have been recovered from the

site. Three kilometers upstream from the Clyde Farm site is another

macro-band base camp of the same complex known as the Delaware Park

site (7NC-E-41). It included two semisubterranean pithouses dating

to 1850 B.C. (3800 + 100 B.P., UGa-3440) and 790 B.C. (2740 + 65

B.P., UGa-3559) (R. Thomas 1981). Associated with the first

structure were a ground stone grooved axe and bifaces. A number of

Bare Island/Lackawaxen stemmed projectile points, broadspears, and fishtail points were also found at the site. Clyde Farm complex macro-band base camps are also located on the floodplains and upper terraces of the Delaware River where higher order drainages enter.

Two of these sites are Crane Hook (7NC-E-18) and one at the mouth of

Naaman's Creek (7NC-C-2). Argillite biface caches were found at both these sites. The closest source of argillite is near Trenton, New

Jersey. Argillite caches are significant because they show special 91 treatment of nonlocal materials and may have played a symbolic role or indicated scarcity of raw material. No clear evidence for Clyde

Farm complex macro-band base camps exists south of the Chesapeake and

Delaware Canal, although there are some potential locations (Custer

1984a:102).

Clyde Farm complex micro-band base camps are defined as smaller, containing fewer tool types and artifacts than macro-band base camps, and unlikely to contain diagnostic artifacts. They are difficult to identify, although Custer (1984a:110) hypothesizes that they should occur close to necessary resources, such as rich hunting and gathering areas and lithic sources, that are far enough away from macro-band base camps to warrant a separate camp. Some potential

Clyde Farm complex micro-band base camps include the Green Valley sites (7NC-D-54, 7NC-D-55, 7NC-D-62) and sites 7NC-E-3, 7NC-E-23,

7—NC—E—24, and 7NC—F—1.

Woodland I procurement sites are similar to Archaic procurement sites. They include 7NC-D-3, 7NC-D-5, 7NC-D-19, 7NC-F-18, 7NC-H-2 on the high coastal plain drainage divide; and 7NC-G-2, 7NC-G-3,

7NC-G-6, 7K-A-2, and 7K-B-8 associated with marshes and swamps.

The Barker's Landing complex is well represented in the study area. This complex has a similar subsistence-settlement pattern to the Clyde Farm complex, but the macro-band base camps are smaller and they have more artifacts made out of nonlocal materials. The two macro-band base camps located in the Mid-drainage Zone, Barker's

Landing (7K-D-13) and Coverdale (7K-F-38), are in the study area.

Barker's Landing is on the Saint Jones River, and the Coverdale site 92

is on the Murderkill River. Both would have been situated at the

freshwater/saltwater interface at the time of occupation, according

to Belknap and Kraft's (1977) sea level rise curve. This area would

have been the richest and possibly most predictable resource zone

during the midpostglacial xerothermic. Therefore, it would have been

a good location for people to populate as is evidenced by the two

sites. The surrounding areas would have been less rich than

surrounding areas of the Clyde Farm complex in the Interior Swamp and

Delaware Shore Zones. Custer (1984a:108) proposes that a ranked

social system was more established in the Barker's Landing complex

than in the Clyde Farm complex because higher local population growth

and less food resources would have led to an intensification of estuarine food production in the immediate area. Temporary food

surpluses may have been used to 'invest' in status symbols, such as the caches of nonlocal stone materials and other items obtained

through an elaborate trade network. Custer uses materials recovered from the Barker's Landing site as evidence to support his thesis.

Preliminary results from studies conducted by the University of

Delaware Department of Anthropology, indicate that the most prevalent raw material at the Barker's Landing site is argillite, a nonlocal material which must have been procured through trade or through special procurement trips involving supralocal interpersonal relationships. The closest source for another prevalent raw material, rhyolite, would have been the Blue Ridge physiographic zone in Maryland and Pennsylvania (R. M. Stewart 1980a, 1980b). Almost all of the broadspear projectile points were made of the nonlocal 93 materials, argillite and rhyolite. Local cobble cherts and Jaspers were primarily used to make flake tools. From the remains, it is evident that argillite was brought into Delaware as large prepared bifaces or cores. Production of stone tools was then carried out at

Barker's Landing and perhaps redistributed through their trade network.

Containers recovered at Barker's Landing include steatite bowl fragments, experimental ceramics, and Marcey Creek Plain pottery.

The steatite was probably brought in as finished bowls. The closest steatite sources are in the Piedmont areas of Pennsylvania and

Maryland (Wilkins 1962; Holland et al. 1981:203-304). Custer

(1984a:109) believes the steatite trading involved a network different from the argillite and rhyolite network because the stone bowls were finished pieces. The Marcey Creek Plain Ware dates from

2000 B.C. to 700 B.C.

The Coverdale site (7K-F-38) is similar to the Barker's Landing site with large amounts of argillite and rhyolite. The Coverdale site consists of an uncontrolled surface collection made by the landowner. While Custer (1984a:109) notes that the site is similar to Barker's Landing except for the presence of steatite bowls, the author has found steatite bowl fragments near the site during the course of this survey. The Coverdale site revealed a large number of early stage bifaces up to twenty-three centimeters long and three pounds in weight. Because they show little modification except to prepare them into an oval shape, Custer (1984a:109) argues that this indicates that they are in the form which must have been used for 94

trading and were handled with special treatment. Further indication

of this is a number of late stage argillite bifaces resembling large

Koens-Crispin projectile points that show no indication of tool use.

A number of micro-band base camps related to these two

complexes have been identified in the study area. Sites 7K-F-12,

7K—F—45» 7K—F—46, 7K—F—49, 7K—F—52, 7K—F—53, and 7K—F—55 are along

the Murderkill River upstream from the Coverdale macro-band base

camp. They were probably associated with the hunting and gathering

of freshwater-related resources and represent camp sites on slight

rises above the floodplain at that time. No surveys have been

conducted along the Saint Jones River above the Barker's Landing

macro-band base camp. However, surveys downstream from the site

identified two micro-band base camps (7K-D-42, 7K-D-52) related to

brackish or saltwater resources (Delaware Division of Historical and

Cultural Affairs 1978; Griffith and Artusy n.d.). Two sites

considered to be general base camps which have been recently

discovered in the study area are 7K-F-136 and 7K-F-137 (Custer et al.

1985:167,288). Procurement sites for both the Barker's Landing and

Coverdale sites have been found upstream from the macro-band base camps and include 7K-C-33, 7K-C-53, and 7K-C-57 along the Saint Jones

River and 7K-F-37, 7K-F-44, 7K-F-47, and 7K-F-48 along the Murderkill

River.

The micro-band base camps include tools made from the nonlocal materials argillite, rhyolite and steatite; projectile points, bifaces, flakes, and steatite bowl fragments, Custer (1984a:110) again points to the artifacts found at the micro-band base camps as 95 indicating the processing and redistributing of nonlocal stone materials at the macro-band base camps for tool manufacture and use throughout the settlement system. These artifacts had a technomic function, as defined by Binford (1962), but may have had a sociotechnic function as well. Artifacts with sociotechnic functions serve as status symbols which help individuals form cohesive social groups. The nonlocal materials used for the artifacts indicate an elaborate trade or procurement network which would have required some type of supralocal social relationships at the very least. The high status individuals were probably organizers or major traders in the trade network. Using analogies from contemporary cultures of similar social complexity, the traders may have been adult male heads of extended families or lineages (Harris 1979:92-94). Indications at the Barker's Landing and Coverdale sites lead Custer (1984a:112) to believe that this transformation of artifacts to sociotechnic functions was occurring during Woodland I times in the study area.

Further evidence of this occurs at the Kiunk Ditch site (7K-F— 18) where a cache of 156 argillite bifaces was found (Omwake 1955) that is similar to that found at the Coverdale site. The cache was in an isolated pit that was probably dug for storing the bifaces. The bifaces had been neatly laid in the pit. This indicates special treatment of these artifacts and the removal of these valuable materials from public access for at least certain periods of time.

The survey conducted by the author indicates that there may be a similar cache near the Coverdale site. Eight bifaces were found 96

clustered together on the surface in a nearby site similar to the way

the Kiunk Ditch cache was found.

Following the Clyde Farm and Barker's Landing complexes are the

Wolfe Neck and Delmarva Adena complexes (500 B.C.-A.D. 1). The basic

sociocultural adaptations are the same. The differences are marked

by changes in projectile point and pottery styles, extensive

development of trade networks in some areas, increased use of

estuarine resources, and intensification of food gathering. The

Wolfe Neck complex develops out of the Clyde Farm complex and the

settlement pattern continues to be similar. The Delmarva Adena

complex develops out of the Barker's Landing complex (R. Thomas

1977).

Diagnostic artifacts of the Wolfe Neck complex include

Rossville and other stemmed projectile points, and a grit-tempered cord and net-marked ceramic called Wolfe Neck Ware in southern

Delaware and Susquehanna series in New Castle County, The type site for the Wolfe Neck complex, the Wolfe Neck site (7S-D-10), is a shell midden site located in the Coastal Bay Zone of eastern Sussex County

(Griffith and Artusy 1977). The site dates from 375 B.C. (2325 +, 60

B.P., UGa-1224) to A.D, 330 (1620 + 65 B,P., UGa-1273a). It is - probably a micro-band base camp where periwinkle, mussels, clams, and oysters were procured.

The Delmarva Adena complex is represented by artifacts similar to Ohio Valley Adena sites in both style and raw material (Griffin

1967), They include late stage bifaces made of Ohio Valley cryptocrystalline stone materials, pendants, gorgets, tubular pipes. 97

copper beads, and side- and corner-notched projectile points

(R. Thomas 1970). In addition, Delaware Coulbourne ceramics,

Rossville-like stemmed points, and side-notched points are often

found in these sites. The majority of these Adena artifacts have

been recovered from cemeteries as grave goods, indicating high status

individuals and a ranked social organization.

Both the Wolfe Neck and Delmarva Adena complexes continue with

the same subsistence-settlement pattern of the earlier Woodland I

groups. In fact, Wolfe Neck and Delmarva Adena components have been

found at most of the earlier Woodland I macro-band base camps.

Macro-band base camp sites include the Clyde Farm site (7NC-E-6),

Mitchell site (7NC-A-2), Crane Hook site (7NC-E-18), Naaman's Creek

site (7NC-C-2), Coverdale site (7K-F-38), and Delaware Park site

(7NC-E-41). Four features found at the Delaware Park site were

determined to be associated with the Wolfe Neck complex. The two

most interesting features were cylindrical pits containing stemmed

projectile points, flakes, seed remains, and pollen from various

grasses. Dating to 425 B.C. (2375 + 60 B.P., UGa-3560) and 10 B.C.

(1960 + 80 B.P., UGa-3500), the pits were determined to function as

food storage pits (R. Thomas 1981: chapter 5, 20). Micro-band base

camps include a number of shell midden sites, such as 7S-K-21,

7S—D—8, 7S—D—9, 7S—D—22, 7S—D—27, 7S—D—29, 7S—D—30, and 7S—D—34.

Delmarva micro-band base camps in the study area are 7K-D-37 and

7K-D-38 on the lower Saint Jones River, and 7K-F-55, 7K-F-44,

7K-F-54, 7K-F-45, 7K-F-46, 7K-F-47, 7K-F-53, and 7K-F-56 on the

Murderkill River (Griffith and Artusy n.d.). Procurement sites 98

include 7K-E-92, 7K-E-93, 7K-E-97, 7K-E-138, 7K-E-162, 7K-E-163, and

7K-E-164 in Killens Pond State Park (Wise 1984:33).

There does not seem to be the heavy reliance on argillite

materials at the Delmarva Adena habitation sites as there was during

the Barker's Landing complex time period. No evidence of unreduced

primary bifaces or unutilized tools is present. For the Wolfe Neck

complex, it seems that tools have returned to a primarily technomic

function. Less indication of high status items exists at the

habitation sites of both the Wolfe Neck and Delmarva Adena complexes.

The habitation sites for the Delmarva Adena complex reveal

little of their social organization, however. A number of Delmarva

Adena cemeteries are much more revealing. These cemeteries, called mortuary-exchange centers by Custer (1984a:118), appear in the central Delaware Mid-drainage Zone by 500 B.C. and include numerous individual graves with Adena artifacts made from nonlocal materials such as Ohio cryptocrystalline stone materials and Great Lakes copper

(R. Thomas 1970, 1976; Dunn 1966). The mortuary-exchange centers have been discovered during development and farming operations. One of the first to be discovered was the Killens Pond site (7K-E-3) during a borrow pit excavation in 1938 (R. Thomas 1970:60).

Artifacts and information obtained from the workmen indicate that there were at least two concentrations of burials, large Flint Ridge chalcedony late stage bifaces, blocked-end tubular pipes, and gorget grave goods. No other evidence of the cemetery has been found.

However, a number of habitation sites (7K-E-95, 7K-E-152, 7K-E-98) have been found within two kilometers of the cemetery and may be 99

micro-band base camps, some with Coulbourne ceramics. Recent

research by Wise (1984:32-33) indicates the possibility of a

macro-band base camp (7K-E-3) associated with the cemetery.

Coulbourne ceramics, fire-cracked rocks, and various stone tools were

recovered.

The Saint Jones site (7K-D-1) was discovered during borrow pit

excavations in 1960. Controlled excavations were conducted on that

site by Leon de Valinger (1970). A reanalysis by R. Thomas (1976)

indicates that about fifty burials were uncovered and 250 artifacts

recovered. From this reanalysis, it was determined that a number of

different burial treatments were practiced, with cremation prevalent

in some site loci and the placement of disarticulated bone remains in

others. An area of high concentrations of unburned bone. Loci G and

H, contained the only two lithic artifact caches, primarily of Flint

Ridge chalcedony bifaces. Red ochre was found to cover some of the

bifaces, beads, copper ornaments and drilled animal teeth associated

with the burials. There appeared to be no status stratification

based on age or sex. Cremation, however, was associated with few

grave goods and may indicate lower status individuals (R. Thomas

1976). Another similar mortuary-exchange center, the Frederica site

(7K-F-2) was discovered during borrow pit excavations in 1964. While most of it was destroyed before records could be made, the cemetery was determined to be at least thirty meters in diameter. Among the

250 artifacts recovered were bifaces of Ohio Valley chalcedony and local lithic materials, gorgets and blocked-end tubular pipes. In addition, 700 copper beads were found (R. Thomas 1970:60-85). 100

Other Adena-related sites have been found east of the Ohio

Valley. Such sites include the Nassawango site (Wise 1974) near

Salisbury, Maryland and the Sandy Hill site (Ford 1976) near

Cambridge, Maryland, on the Delmarva Peninsula; the West River site

(Ford 1976) near Annapolis, Maryland; the Rosencrans site (H. Kraft

1976) in the Upper Delaware Valley of New Jersey; and similar New

England complexes described by Snow (1980:268-74). Adena-related

caches of Ohio Valley cryptocrystalline and local lithic materials

have also been found in the Middle Atlantic area (Crozier 1934,

Dunn 1966, Flegel 1954, Eggen 1954, Howard 1969, Marine 1966,

R. Thomas 1973b, Weslager 1939a).

The term, Delmarva Adena complex, is used in relation to these

sites because of the particular styles of grave goods, primarily

large ovate late stage bifaces made of high quality Ohio Valley

chalcedony and cherts, Ohio pipestone tubular pipes, and Upper Great

Lakes copper ornaments. Both Tuck (1978:41) and Griffin (1967:183)

describe Adena cultures as a development from local Archaic complexes with an extensive elaboration of mortuary ceremonialism and exchange

(Custer 1984a:123). The largest accumulation of these late stage

bifaces, copper ornaments and tubular pipes in Delaware is found in

the mortuary-exchange centers. Occasionally, some of these artifacts

of Ohio Valley lithic materials will be found on micro-band or macro-band base camps (7S-K-21, 7K-F-38, 7K-D-37, 7K-D-38, 7NC-E-18), but never in significant accumulations (R. Thomas 1970:61). Because the Killens Pond site is smaller than the other two mortuary-exchange centers and may be associated with habitation sites, Custer 101

(1984a:124) terms it a minor mortuary-exchange center, while the

Saint Jones and Frederica sites are major mortuary-exchange centers.

All three mortuary-exchange centers are located in the study area.

Custer (1984a:126-28) suggests that the Delmarva Adena complex

represents an incipient ranked society with a 'big-man’ organization

(Harris 1979:92-95) operating on a supralocal basis. In a ranked

society, people have structured unequal access to prestige and status

(Ember and Ember 1973:142). People who achieve a special leadership

status, often by organizing temporary surpluses of material items,

are considered 'big men' in an incipient ranked society. The

Delmarva Adena mortuary exchange centers and the special grave goods

are a significant development. By removing exotic artifacts from

circulation through their burial in graves, the artifacts must have had a sociotechnic and probably an ideotechnic function (Binford

1962). Similar situations have been found in Middle Atlantic sites such as the Savich Farm site (Regensburg 1970) and the Koens Crispin site (Cross 1941:81-90; Hawkes and Linton 1916) in New Jersey. The artifact distribution pattern in the cemeteries indicate ranked status, particularly the cremated interments with few artifacts are compared with the noncremated interments with elaborate grave goods.

Because the major mortuary-exchange centers are not associated with any villages, they may have served a number of social groups. The fact that most grave goods are nonlocal finished artifacts indicates a complex ritual and exchange system. The earlier Barker's Landing complex indicates a simple exchange relationship of perhaps trading partners obtaining rhyolite and argillite. Because Adena artifacts 102

have not been found in sites located between the Ohio Valley and the

Atlantic Coast, some sort of long distance relationship must have

been established. The Delmarva Adena special commodities trade may

have involved a ritualized chainlike organization. This may be

similar to the Pacific long distance trade networks as reported by

Malinowski (1922), Gould (1971), Harding (1970) and Stanner (1933).

As has been reported for the Pacific, these trade partnerships could

have been inherited, involving fictive kinship relationships and

rules of conduct and exchange. The exchange items may have been

frequently traded and included rights and obligations.

This Delmarva Adena social development occurred primarily in

the Mid-drainage Zone of the study area, along the Murderkill and

Saint Jones Rivers in Kent County. This development may have occurred because of population growth and intensified food production related to the productive estuarine environment. By A.D. 1, however, the distinctive aspects of the Delmarva Adena complex disappear from the archeological record (Custer 1984a:130). The cause of this disappearance is unknown, but may be attributed to a breakdown in the trade relationships.

Delmarva Adena Complex Settlement System Model

The Delmarva Adena complex is considered to be an outgrowth of the local Archaic complexes and the Barker's Landing Woodland I complex. It is similar to the Woodland I settlement system except for the addition of complex mortuary-exchange centers and caches.

The settlement system included major mortuary-exchange centers with 103

large accumulations of Adena artifacts and exotic raw materials

associated with mortuary ceremonialism; minor mortuary-exchange

centers with small amounts of Adena artifacts and exotic raw

materials associated with mortuary ceremonialism; cache sites with

mostly late stage bifaces buried in cache pits near macro-band base

camps; and spot find sites where single side-notched and

corner—notched projectile points of Ohio chalcedonies or their

debitage occur (see fig. 6). Major mortuary-exchange centers may not

have been associated with habitation sites, indicating a dramatic

change in the social organization of the Delmarva Adena people,

probably related to ranked society. Minor mortuary-exchange centers,

however, were associated with a series of micro-band base camps.

These Delmarva Adena sites are located in the Mid-drainage Zone of

Delaware and may be associated with a subsistence focus on resources

from the freshwater/saltwater interface areas of river drainages.

In addition to the typical Woodland I macro-band and micro-band

base camps and procurement sites, the following types of Delmarva

Adena sites may be found (Custer 1984a;124; Custer et al.

1984:92-100).

Major Mortuary-Exchange Centers

Major mortuary-exchange centers are cemeteries containing large

numbers of Adena artifacts and nonlocal raw materials, particularly

chalcedonies and cherts from the Ohio Valley and Great Lakes copper.

These artifacts are associated with evidence of mortuary

ceremonialism. These sites are found along major drainages at 104

/ / t \ / \ / k\ /

\ \/ 0 A T 0

Key

Major mortuary Minor mortuary Micro-band M exchange center M / exchange center ® base camp

Cache Isoiated —^ Direct sociai iink b ) Macro-band base camp find Indirect sociai link

Fig. 6. Delmarva Adena model (after Custer 1984a:125) 105

a central location to several macro-band base camps in the

Mid-drainage Zone.

Minor Mortuary-Exchange Centers

Minor mortuary-exchange centers are cemeteries with small

numbers of Adena artifacts and nonlocal materials, usually of high

quality Ohio Valley cryptocrystalline lithic materials and Great

Lakes copper. Burials are associated with mortuary ceremonialism.

Such sites are associated with micro-band base camps or at a central

location to a number of micro-band base camps along major drainages.

Cache Sites

Cache sites are small concentrations of Adena artifacts

associated with macro-band base camps. The artifacts are primarily

late stage bifaces made of Ohio Valley chalcedonies or local materials and are buried in holes dug below ground which are called

cache pits.

Spot Find Sites

Spot find sites are individual side-notched and corner-notched projectile points made of Ohio Valley chalcedonies or debitage concentration areas resulting from the reworking of these stone tools. These sites are associated with macro-band and micro-band base camps (Delaware Division of Historical and Cultural Affairs

1978). 106

By 1 A.D., the elaborate exchange and mortuary systems appear

to break down, indicating perhaps a change in social organization

from incipient ranked societies to more egalitarian ones. The Carey

complex (A.D, 1-A,D. 600) is a continuation of the Woodland I

settlement system with the absence of the mortuary-exchange centers

and elaborate nonlocal artifacts. Diagnostic artifacts of this

period include Rossville-like stemmed. Fox Creek and large triangular

projectile points and Mockley ceramics (Custer 1984a:l31). The Carey

Farm site (7K-D-3), for which the complex is named, is located in the

study area on the Saint Jones River in the Mid-drainage Zone, The

site was dated to A,D. 200 (1750 ± 90 B,P,, 1-5817), Among the

remains recovered from the site were Mockley ceramics. Fox Creek

projectile points, deer, shellfish, dog, muskrat, turkey, box turtle,

diamond back turtle, beaver, woodchuck and hickory nuts (Delaware

Division of Cultural and Historical Affairs 1977). The site is

considered to be a macro-band base camp from which procurement trips were occasionally made and occupied at least from midfall to midwinter (Griffith 1974:78-79). Other Carey complex macro-band base

camps are located at some earlier Woodland I macro-band base camps,

including the Clyde Farm site (7NC-E-6), and the Delaware Park site

(7NC-E-41). At the Delaware Park site, the Carey complex component dates from A.D. 65 (1885 + 170 B.P,, UGa-3504) to A.D. 455 (1495 +

160 B.P,, UGa-3438), Stemmed and side-notched projectile points, seven hearths and four storage features with pollen and plant remains were uncovered (Custer 1984a:131),

Micro-band base camps include the Green Valley sites (7NC-E-54,

7NC-D-55, 7NC-D-62); the Wolfe Neck Midden site (7NC-D-10), the 107

Wilgus site (7S-K-21) and sites 7K-F-12,44,45,46,53,54,55 which are

in the study area. Other possible macro-band or micro-band base

camps in the study area are 7K-E-95 and 7K-E-3. Possible procurement

sites are 7K-E-92, 7K-E-138, 7K-E-162, and 7K-E-163 (Wise 1984:33).

Cultural changes begin to appear around A.D. 500 with evidence of intensified food production and complex societies. In northern

Delaware, this cultural change is indicated by the Delaware Park complex. The same cultural change is termed the Webb complex in central Delaware. These two complexes are associated with grit and crush rock-tempered Hell Island Ware ceramics. Hell Island Ware is different from the earlier shell-tempered Mockley ceramics. Except for recent indications of Hell Island ceramics found by Wise

(1984:34) at a Killens Pond State Park site (7K-E-152), these cultural changes are not evident in southern Delaware where the Carey complex seems to continue until A.D. 1000. The sites dating between

A.D. 500 and A.D. 1000 in southern Delaware are considered to be a

Late Carey complex. This cultural continuity is supported by the technological continuity from Mockley ceramics to the Woodland II

Townsend ceramics (Custer 1984a:185).

The Delaware Park complex, identified at the Delaware Park site

(7NC-E-41) is a macro-band base camp that has been dated to A.D. 605

(1345 + 400 B.P., UGa-3437) and A.D. 640 (1310 + 155 B.P.,

UGa-3439). Among the materials recovered from the site are Hell

Island ceramics; and Rossville stemmed. Jack's Reef pentagonal, side-notched and large triangular projectile points (R. Thomas

1981). Other Delaware Park macro-band base camps are the Clyde Farm 108

site (7NC-E-6), and the Green Valley sites (7NC-D-54,55,62) (Custer

et al. 1981). The Delaware Park complex has only been identified In

New Castle County north of the Chesapeake and Delaware Canal.

The Webb complex was identified at the Island Field site

(7K-F-17) (Thomas and Warren 1970a) which is located on the

Murderkill River in the Delaware Bay Shore Zone of the study area.

This complex is similar to the Delaware Park complex in basic

subsistence and settlement pattern and tool kit, but is defined

separately because of mortuary ceremonialism, indication of extensive

trade networks, and sociotechnic and ideotechnic artifacts. People

of the Webb complex had an intensive gathering subsistence base. Hell

Island ceramics. Jack's Reef projectile points, a mortuary complex with exotic grave goods, and long distance trade networks (Thomas and

Warren 1970a: 22-23; R. Thomas 1974b). Webb complex sites have been

identified from southern Kent County to south of the Chesapeake and

Delaware Canal. However, the only definite macro-band base camp is

the Hell Island site (7NC-F-7). Site 7K-C-94 in the study area may also be a possible macro-band base camp. Located on a small island in the Mid-drainage Zone of the Appoquinnimink River, the Hell Island

site was excavated by Wright (1962) and R, Thomas (1966c), Among the artifacts recovered were Hell Island Corded and Hell Island

Fabric—impressed ceramics, Rossville-like stemmed and Jack's Reef projectile points, a clay platform pipe, and a variety of stone tools.

During this time, micro-band base camps seem to be more prevalent. Among those used were 7NC-F-15 and 7NC-F-19 on the 109

Appoquinnimink River, and 7-S-C-17. Micro-band base camps in the

study area include 7K-D-42,47,48 and 7K-F-86 on the Saint Jones

River; and 7K-F-45,46,47,53,54,55,56 on the Murderkill River. One

Webb complex procurement site (7K-F-48) has been identified on the

Murderkill River.

The Island Field site (7K-F-17) is a large mortuary center which is located on the Murderkill River less than one kilometer from the Delaware Bay because of local sea level rise and land subsidence

(J. C. Kraft 1974, J. C. Kraft et al. 1976). During its prehistoric use, however, it would have been further from the Bay. It was excavated in the late 1960s by the Delaware Section of Archaeology

(Thomas and Warren 1970a). The site contained a Woodland II

Slaughter Creek component over a Webb complex cemetery. About half the site was excavated and a total of 120 burials uncovered. The similarity of grave goods indicates that the burials probably date to around the same time, A.D. 740 (1210 Hh 90 B.P., 1-6338). A number of different kinds of treatments were used: 62% flexed burials, 20% disarticulated burials, 8% redeposited cremations, 2% in situ cremations, 2% bundle burials (Thomas and Warren 1970:4), There appeared to be no preferential treatment based on sex or age. There is, however, the possibility that six graves may represent a special status group because they are buried more deeply, are all adult males with their bodies oriented in the same direction, and each are associated with one to 208 artifacts (R. Thomas 1974b). The grave goods include artifacts with technomic, ideotechnic and sociotechnic functions. Various technomic artifacts are projectile points, celts. 110

pestles, and bone and antler tools. Steatite platform pipes and

large pentagonal bifaces made from Ohio Valley and other nonlocal materials may be sociotechnic and possibly ideotechnic artifacts. An

antler headdress and a cup made from part of a human skull were

probably ideotechnic. The large amount of nonlocal materials indicates an extensive trade network and is suggestive of the

Delmarva Adena mortuary-exchange centers (Thomas and Warren

1970a:8-23).

With the reappearance of the mortuary-exchange centers probably came a change in settlement patterns back to the kind that existed during the Delmarva Adena complex. As with the Delmarva Adena cemeteries, no habitation sites were found in association with them.

This may indicate the development of an incipient ranked society,

'big-man* organization, again. The social changes occurring during the Woodland I period may be related to increased population. Cohen

(1978) has found that when a society's population increases beyond a point its political system can handle, it either fissions into smaller groups or develops more complex forms of social organization. It is easy for social groups to fission when their social and natural environment can accommodate them. However, when resource distribution or the existence of other social groups limits expansion (Carneiro 1970), fissioning cannot occur. One or both of these situations may have led to the development of the Webb complex

(Custer 1984a:142-43).

Woodland I Period Model

While the Woodland I settlement system was, in general, similar Ill

to the Archaic system in its focus around macro-band base camps.

Woodland I macro-band base camps would have been much larger and occupied for longer time periods by larger groups of people. This is evident in the archeological record by storage/refuse features and remains of house structures. The emergence of a focal adaptation and settlement centered around macro-band base camps are common characteristics of the Woodland I period and adaptations to people's biosocial environment. The ratio of macro- to micro-band base camps and variety of activities would have increased. Variety of activities at procurement sites would have decreased. People tended to focus on predictable water sources and faunal and floral resources. The continued occupation of a number of macro-band base camps (Clyde Farm, Crane Hook, and Delaware Park) throughout the period attests to this. In general, the adaptive trend was toward focusing on a more specific, reliable range of resources, such as shellfish, anadromous fish, deer, and plant foods (see fig. 7).

With increased sedentism and higher population densities, came a major change in social organization (Custer 1984a:97). For the first time, nonegalitarian societies probably developed in Delaware.

In addition, Custer (1984a:98) believes that the Woodland I complexes exhibit ranked social systems because of their elaborate trade and exchange systems and symbolic use of particular artifacts. This became most developed in the study area, along the Saint Jones and

Murderkill Rivers. In the study area, one can see a cultural continuity from the Barker's Landing and the Delmarva Adena complex to the Webb complex. The trade networks developed during the 112

Macro-Band Base Camp ---

\ 4 V J

•A A/

Key ( ■► Periodic foray Procurement site ■------► Group relocation

Fig. 7. Woodland I model (after Custer 1984a:96) 113

Barker's Landing complex become much more extensive and far-reaching

during the Delmarva Adena and Webb complex. During this time, the

focus continues to be on resources located at the

saltwater/freshwater interface along the major rivers.

The following are Woodland I site types (Custer 1984a;67;

Custer et al. 1984:68).

Macro-band Base Camps

Macro-band base camps are living areas for multiple family units located in areas of maximum habitat overlap and characterized by a wide variety of tool classes and extensive debris covering an area of over 10,000 square meters. They would have been located on low terraces along major drainages, especially near lower order stream confluences. They were primarily located in the Mid-drainage

Zone at the freshwater/saltwater interface.

Micro-band Base Camps

Micro-band base camps are smaller living areas occupied by one, or a small number, of family units in areas of limited resource distribution and characterized by little variety of tool classes and minimal debris covering less than 10,000 square meters. They were located on upper terraces of major drainages which are along lower order tributaries and at low order stream confluences up to ten kilometers from major drainages (Delaware River Shore Zone). In the

Mid-drainage Zone they were located at the confluences of low order streams and tidal marshes. In the Interior Zone they were located on 114

well drained knolls at spring and stream confluences. In the

Delaware Bay Shore Zone they were located on upper terraces near

freshwater sources and tidal marshes.

Procurement Sites

Procurement sites are resource extraction locations indicating

a limited range of activities. They are usually located near swampy

floodplains of major and minor drainages and alluvial fans associated

with swamps, bogs or lithic sources (Delaware River Shore Zone); on

well drained knolls near swamps and springs (Interior Zone); along

minor and ephemeral drainages adjacent to poorly drained woodlands

and on small sand ridges and knolls (Mid-drainage Zone); and along

tidal marshes and swampy low order floodplains (Delaware Bay Shore

Zone).

Major Mortuary-Exchange Centers

Major mortuary-exchange centers are cemeteries containing large

numbers of exotic artifacts and nonlocal raw materials. These

artifacts are associated with evidence of mortuary ceremonialism.

The sites are found along major drainages at a central location to

several macro-band base camps in the Mid-drainage Zone.

Minor Mortuary-Exchange Centers

Minor mortuary-exchange centers are cemeteries with small

numbers of exotic artifacts and nonlocal materials found in the

Mid-drainage Zone. Burials are associated with mortuary 115 ceremonialism. Such sites are associated with micro-band base camps or at a central location to a number of micro-band base camps along major drainages.

Woodland I Mortuary-Exchange Center Model

The location of mortuary-exchange centers is considered to be a factor of cultural considerations and therefore requires a model slightly different from the model presented above (Custer et al.

1984:92-100). The model (see fig. 8) developed by Custer et al.

(1984) is based on central place theory (Christaller 1966) and

Price's (1982, n.d.) analysis of the formulation of ranked societies. According to Price, early ranked societies tend to redistribute labor resulting in surpluses which can be 'invested' in material goods. These material goods found in the mortuary-exchange center graves are seen as symbols of the achieved stature of various organizational 'big-men' in the Delmarva Adena and Webb cultures, similar to the 'big-men' systems described ethnographically for the southwest Pacific (Harris 1979). The mortuary-exchange centers are considered to be locations where higher ranking individuals from a number of base camps are interred. The centers are not considered to be associated with one special group because of the nature of developing ranked societies where trade centers are not established due to individual and group competition. Between 600 B.C. and

200 B.C. three main mortuary-exchange centers are known to have existed in the study area. They are the Saint Jones Adena (7K-D-1),

Frederica Adena (7K-F-2) and Killens Pond (7K-E-3) sites. They may 116

Key

Major mortuary Minor mortuary exchange center exchange center

Fig. 8. Woodland I mortuary-exchange model (after Custer 1983a:94) 117 represent a developing hierarchy of a ranked society with the Saint

Jones and Frederica sites being larger and therefore at the top of the hierarchy, and Killens Pond being at an intermediate level

(Gardner 1982). The habitation sites, the macro-band and micro-band base camps, would be the lowest level of the hierarchy and are considered to be associated with the mortuary-exchange centers because of the presence of exotic trade materials, such as Ohio

Valley chalcedony. A similar pattern is inferred for the Webb complex culture, however, not enough data exists at this point to make any definitive statements on it.

The model to predict the location of mortuary-exchange centers uses Earle's (1976) Mesoamerican locational model of Olmec ceremonial centers in southern Vera Cruz and Tabasco. In Earle’s (1976:219-21) model, the locations of the major centers appear to be regularly spaced. The labor support territorial borders can be approximated by hexagons in that the distance from the hexagon’s central site to a side of the hexagon is equal to half the distance between major site centers. Minor sites are located at two-thirds the distance from the major center to the territorial border (Custer et al. 1984:94).

Assuming that the basic pattern of socio-political organization is the same for all incipient ranked societies, Custer et al.

(1984:94-96) applies this same formula to the Delmarva Adena sites.

This formula is applied with two assumptions: (1) the Saint Jones and

Frederica Adena sites were used contemporaneously and were of similar rank; and (2) the Killens Pond site is of a lower rank and part of the same network of sites (Custer et al. 1984:94). Viewed as a pair 118

of neighboring mortuary-exchange centers, the Saint Jones and

Frederica Adena sites are fourteen kilometers apart. Therefore,

their territories would be composed of a hexagon with seven kilometers from center to side. Second level sites would then be

4.6 kilometers from the central site, or two-thirds the distance.

The Killens Pond site does fall close to this formula, being

4.8 kilometers from the Frederica Adena site. More data, however, are needed to adequately test this model.

There are ninety-nine known Woodland I period sites in

Delaware. Fifty of these are in Kent County. Twelve sites are possibly macro-band base camps, ten sites are considered to be

Micro-band base camps, and at least thirty-two are procurement sites. The Saint Jones Adena, Frederica Adena, Killens Pond Adena, and Island Field Webb mortuary complexes are all located in the study area.

Woodland II Time Period (A.D. 1000-A.D. 1600)

During the Woodland II period, the climate continued to be similar to the Sub-Atlantic environmental episode and modern climatic conditions (Carbone 1976). Vegetation was that of an oak-chestnut forest region in the high coastal plain and evergreen forest in the low coastal plain. Loblolly pine dominated the region, but areas of raised elevation include Virginia pine and deciduous species. Poorly drained uplands were dominated by white oak, sweet gum, willow oak, pin oak, and sour gum (Braun 1967). The major game resources 119 included deer, turkey, rabbit, squirrel, and waterfowl. Estuarine resources were prevalent as the sea level pretty much stabilized.

By A.D. 1000, a number of changes in peoples' subsistence and settlement patterns and social organizations were occurring. The subsistence and settlement system changed greatly with the development of agricultural food production, the breakdown of trade and exchange networks, and more sedentary life-styles. Both the extensive trade networks evident in the Delmarva Adena and Webb complexes, and the rhyolite and argillite trade networks that existed throughout the Woodland I time period disappeared after A.D. 1000.

The process of fissioning which may have occurred during the Webb complex, may have disrupted trade relationships or social organization (Custer and Griffith 1986; Custer 1984a;146; R. M.

Stewart 1980b;394-95). Agricultural food production effected a change in settlement systems away from saltwater/freshwater interface zones to fertile floodplains of major drainages. Semisedentary villages with multiple social units were established. These would have been much larger than the earlier macro-band base camps, but many of them were established at the same location as Woodland I macro-band base camps. Increased storage facilities and more permanent house structures appeared. Agriculture intensified as populations increased.

Technology changed to the exclusive use of triangular projectile points and more finely-made and thinner-walled ceramics.

Projectile points were frequently made out of cryptocrystalline lithic material. The ceramics have more complex decorations and 120

Include two major types: Townsend Ware and Mlnguannan Ware (Griffith

1977, 1981). Townsend ceramics are similar to Mockley Ware but are

more finely made, have thinner walls and different surface

treatments. Townsend ceramics are crushed shell-tempered with

fabric-impressed exterior surfaces. They have a variety of design

motifs which tend to change from incised to cord-wrapped stick and

direct cord impression through time. Minguannan ceramics have sand,

grit and crushed quartz temper with smoothed, corded and

smoothed-over corded exterior surfaces. They have incised,

cord-wrapped stick and direct cord impressions like the Townsend

ceramics, but more complex design motifs.

The Minguannan complex appears to be an outgrowth of the

Woodland I Delaware Park complex and is found at many of the same sites. The Minguannan complex is found at the Clyde Farm site

(7NC-E-6), the Crane Hook site (7NC-E-18), Julian Powerline site

(7NC-D-42), and the Mitchell site (7NC-A-2). Minguannan sites in nearby Maryland include the Hollingsworth Farm site (36-CE-129) near

Elkton; and the Minguannan (36-CH-5) and Webb sites (36-CH-51) on

White Clay Creek. Recently discovered Minguannan sites in the study area include a macro-band base camp (7K-F-143) and two possible micro-band base camps, 7K-F-136 and 7K-F-139 (Custer et al.

1985:167,288). From the information gained from these sites, there appears to be no settlement shifts to large sedentary villages and arable land. There is no evidence of increased population or abandonment of previous hunter-gatherer subsistence focus.

Agriculture was probably a minor portion of the subsistence base, if 121

any at all. This Woodland II adaptation in northern Delaware

continues through the time of European contact and is considered to

represent the lifeways of the historic Lenape (Unami) Delaware

Indians (Weslager 1972; Becker 1976, 1980a, 1980b, 1981a, 1981b,

1981c; Custer 1984a;156).

The Slaughter Creek complex is found from Sussex County into central Kent County and is identified by triangular projectile points, Townsend ceramics, large macro-band base camps, and probably sedentary villages with large numbers of storage features (Custer and

Griffith 1986; R. Thomas 1973, 1977; Weslager 1938b;8). A number of

Slaughter Creek complex sites have been excavated and two basic site types have been identified. These sites are called base camps and seasonal camps by the original investigators (Thomas et al.

1975;62), They are termed macro-band base camps and micro-band base camps, respectively, by Custer (1984a;157). The original research on the Slaughter Creek complex was conducted at a series of macro-band base camps on Slaughter Creek (Davidson 1935a, 1935b, 1936; Weslager

1968:58-61). One very large site, 7S-C-1, had numerous storage features, a large number of plant processing and other tools, and ceramics. Deer, bear and small mammal remains were recovered. There may have been at least one semisubterranean pithouse. In one area, a number of burials with different treatments were found. Several sites on the north side of Slaughter Creek (7S-C-7,30) had shell-filled features and may have been micro-band base camps. On the south side of Slaughter Creek, a number of sites (7S-C-8,9,29) were located that are similar to site 7S-C-2, but smaller (Zukerman 122

1979a, 1979b). During Woodland II times, the Slaughter Creek area

seems to have an extensive population with at least one macro-band

base camp and a number of micro-band base camps. Custer (1984at161)

points out, however, that the sites may not all be contemporaneous

because radiocarbon dates range between A.D. 975 and A.D. 1345 (975 +

60 B.P., SI-4946; 680 + 50 B.P., SI-4944; 605 + 60 B.P., SI-4943).

Another Slaughter Creek complex macro-band base camp site, the

Townsend site (7S-G-2), was discovered near Lewes in 1947.

Excavations uncovered a grave feature with nineteen individuals,

twelve possible dog burials, and over ninety pits filled with oyster,

clam, conch, and mussel shells (Omwake and Stewart 1963). Artifacts

included chert and jasper triangular projectile points, Townsend

ceramics, and various bone and stone tools. The date of this site is

problematic, but current interpretations identify two occupations: one before A.D. 1300 and one that may be as late as A.D. 1550.

The Mispillion site (7S-A-1) in the watershed immediately south of the study area, is similar to the Townsend and Slaughter Creek sites. It includes a number of shell-filled pits with numerous artifacts and a semisubterranean house feature. Both the Townsend ceramic styles and radiocarbon date of A.D. 1085 (865 ±_ 75 B.P.,

UGa-923) indicate this site was occupied early in the Slaughter Creek complex time. It also indicates a large population increase or concentration early in Woodland II times.

The northernmost site of the Slaughter Creek complex, the

Hughes-Willis site (7K-D-21) is located in the study area. The

Delaware Bureau of Archaeology and Historic Preservation and the 123

Kent County Archaeological Society excavated twenty percent of the

site, including ten features. A wide variety of tools and faunal and

floral remains were recovered. Among those remains were deer,

turtles, birds, hickory nuts, American lotus and black haw. Because

there is evidence of extensive hickory nut processing, a fall through

midwinter occupation is inferred (Thomas et al. 1975:70-78). The

short term occupation of this site, as compared to those further

south, may indicate a slightly different settlement system and social

organization according to Custer (1984a:163). Another northern

Slaughter Creek complex was located at the Island Field site

(7K-F-17), but information on the excavations were never published.

The Slaughter Creek complex includes a number of micro-band

base camps. Some of these were discovered and excavated in the

1800s. These include a number of shell middens in Rehoboth Beach and

Lewes which were found by Leidy (1865) and Jordan.(1880, 1895,

1906). They are determined to be micro-band base camps because of

the artifacts discovered, their small size, and the probable recovery

of Townsend ceramics. Slaughter Creek complex micro-band base camps have been found in the location of Woodland I micro-band base camps and as single component sites. For example, both Webb and Slaughter

Creek components were found at the Millman site (7K-E-4,23)

(R. Thomas 1966d, Delaware Division of Historical and Cultural

Affairs 1980); and at sites 7K-D-45 and 7K-D-48 on Saint Jones Neck in the study area (Delaware Division of Historical and Cultural

Affairs 1978). Single component sites include the Lewes High School site (7S-D-5) (Omwake 1948, Anonymous 1951a); the Ritter site 124

(7S-D2,3) which produced the only corn remains in Delaware (Omwake

1951, 1954b, 1954d); and the Derrickson site (7S-D-6) (Anonymous

1951b).

One micro-band base camp, the Indian Landing site (7S-G-1) on

the north shore of Indian River Bay, was excavated in the 1970s

(Thomas et al. 1975). From thirteen storage and refuse features came

shellfish, deer, turtle, small mammals, fish, birds and hickory nuts, and numerous tools including bone tools which may be related to weaving (Custer 1984a:166). The site is believed to have been used

for a number of food producing activities during the summer and fall

seasons. Other similar sites are the Warrington site (7S-G-14)

(Marine et al. 1964, Griffith n.d.a.), the Poplar Thicket site

(7S-G-22) (Griffith n.d.b.) and the Bay Vista site (7S-G-21). The

Warrington and Poplar Thicket sites both had semisubterranean house features (Artusy and Griffith 1975), indicating that fairly permanent house forms were used at micro-band base camps as well as macro-band ones. Radiocarbon dates from these sites range from A.D. 1100 to

A.D. 1370 and are associated with Townsend ceramics (Griffith 1977).

Procurement sites, while difficult to determine, have been identified on Saint Jones Neck in the study area. They are

7K-D-42,45,49,52, and 7K-F-86, and are associated with saltwater or brackish water resource areas. A limited number of tools and other artifacts have been recovered from these sites (Delaware Division of

Historical and Cultural Affairs 1978). Wise (1984:34) has also identified five procurement sites with Townsend ceramics in Killens

Pond State Park (7K-E-95, 7K-E-98, 7K-E-3, 7K-E-152, and 7K-E-163). 125

It seems that a number of subsistence and settlement patterns

were in operation during Woodland II times. Custer (1984a:167)

describes three he believes existed for the Slaughter Creek complex.

This is modified from five originally proposed by Thomas et al.

(1975). The first settlement pattern is north of the Mispillion

River, and includes the study area. Two macro-band base camps have

been found, the Island Field and Hughes-Willis sites. They indicate

a continuity from Woodland I but have more storage features. They

are smaller than macro-band base camps found south of the Mispillion

River. Custer (1984ai169) proposes that the settlement pattern for

the study area and Kent County north of the Mispillion River was one

of smaller macro-band base camps which were occupied during the fall, winter and spring seasons in the Mid-drainage Zone with summer

coastal occupation at micro-band base camps.

South of the Mispillion River the year-round macro-band base camp model was operating. The largest Slaughter Creek complex macro-band base camp sites were found between the Mispillion River and Cape Henlopen at the interface between the Delaware Shore Zone and the Mid-drainage Zone. These sites, however, are different from the Woodland I sites because of their different location at the interface area, and because they are much larger, with numerous storage features, and an increase in population and/or sedentism.

Intensive utilization of shellfish is evident from the extensive shellfish remains found at the sites. Subsistence activities probably focused on shellfish and plant gathering, and some agriculture. The population concentration at this brackish water 126 area may be due to the environmental configuration with the drainages in this area being more truncated and the resulting estuarine resource area smaller. In such a situation, people would have to intensify their food production and storage facilities. Even with this increased population and food production intensification, there is no definite evidence for ranked societies or trade networks that existed earlier.

The wide range of micro-band base camps south of Cape Henlopen is similar to the study area. The sites also indicate a continuity from Woodland I times and little evidence of agricultural food production. As maximum population densities were reached, Custer

(1984a;171) proposes that groups fissioned, leading to the numerous micro-band base camps. The highly productive estuarine resource areas also precluded the need for agricultural development. The settlement model most applicable to this area is one of Interior Zone macro-band base camps occupied during the fall and winter, and coastal macro-band base camps in the spring and summer seasons

(Custer and Griffith 1986:40-46; Custer 1984a:157-71; Thomas et al.

1975:63).

By A.D. 1300, some changes were occurring on the Delmarva peninsula which reflect trends in the Middle Atlantic region, in general. There appears to be a population shift southward by people using Townsend ceramics (Griffith 1977:145-50). At the same time, nonlocal Woodland II ceramics, such as Potomac Creek and Keyser Farm, were appearing in central Kent County as evidenced at the Robbins

Farm site (Stocum 1977). This appearance of nonlocal ceramics is in 127 an area believed to be of low population during Woodland II times, and may indicate the movement of nonlocal groups into the area. This correlates with other population movements and disruption which was believed to occur during Woodland II times in the Middle Atlantic.

Townsend ceramic designs in southern Delaware change from complex incised motifs to corded horizontal motifs similar to Potomac Creek ceramics from the Chesapeake Bay western shore (Stephenson 1963).

Woodland II Period Model

A number of different subsistence and settlement patterns were in operation during the Woodland II time period. Semisedentary villages with multiple social units were established. In some areas, these were much larger than the earlier macro-band base camps, but many of them were established at the same location as Woodland I macro-band base camps. Different cultural groups practiced different settlement patterns during this period. Three different settlement pattern models that probably existed at the time were: (1) year-round sedentary macro-band base camps in the Mid-drainage Zone (see fig.

9); (2) a seasonal round of macro-band base camps in the Mid-drainage

Zone during winter, spring and fall and movement to coastal micro-band base camps in the summer (see fig. 10); and (3) macro-band base camps in the Interior Zone in the fall and winter with movement to coastal (Delaware Bay Shore Zone) macro-band base camps in the spring and summer (see fig. 11) (Custer and Griffith 1986:41-45;

Custer 1984a:157-71; Thomas et al. 1975). 128

Poorly Drained Well Drained Tidal Woodlands Woodlands Marsh

River

Year round

Key

Transient Base camp /p \ Procurement site foray

Fig. 9. Woodland II model 1 (after Custer 1984a:160) 129

Poorly Drained Well Drained Tidal Woodlands Woodlands Marsh

Fall/Winter/Spring

Summer

Key © Base camp Seasonal camp Procurement site ------► Transient foray Seasonal group movement

Fig. 10. Woodland II model 2 (after Custer 1984a:159) 130

Poorly Drained Well Drained Tidal Woodlands Woodlands Marsh

River

Fall/Winter

Spring/Summer

Key Transient foray

B 1 Base camp Procurement Seasonal group © site movement

Fig. 11. Woodland II model 3 (after Custer 1984a:158) 131

Woodland II site types are as follows (Custer et al.

1984:75-76).

Macro-band Base Camps

Macro-band base camps are living areas for multiple family units located in areas of maximum habitat overlap and characterized by a wide variety of tool classes and extensive debris covering an area of over 10,000 square meters. They would have been located on low terraces along major drainages, especially near lower order stream confluences in the Delaware River Shore Zone, on low terraces of major drainages at the freshwater/saltwater interface with marshes in the Mid-drainage Zone, and on well drained knolls with access to fresh water springs or streams in the Delaware Bay Shore Zone.

Micro-band Base Camps

Micro-band base camps are smaller living areas occupied by one, or a small number, of family units in areas of limited resource distribution and characterized by little variety of tool classes and minimal debris covering less than 10,000 square meters. They were located on upper terraces along lower order tributaries and at low order stream confluences up to ten kilometers from major drainages in the Delaware River Shore Zone and on well drained knolls at springs and stream confluences in the Interior Zone. In the Mid-drainage

Zone they were located at the confluences of low order streams and tidal marshes. In the Delaware Bay Shore Zone they were located at the confluences of minor drainages with marsh settings. 132

Procurement Sites

Procurement sites are resource extraction locations indicating

a limited range of activities. They are usually located near swampy

floodplains of major and minor drainages in the Delaware River Shore

Zone, on well drained knolls near swamps and springs in the Interior

Zone, along minor and ephemeral drainages adjacent to poorly drained

woodlands and on small sand ridges and knolls in the Mid-drainage

Zone, and along tidal marshes and swampy low order floodplains in the

Delaware Bay Shore Zone.

Of the fifty known Woodland II sites in Delaware, eleven have

been identified as macro-band base camps, fifteen are considered to

be micro-band base camps, and ten are classified as possible

procurement sites.

Early European Contact Complex

Woodland II subsistence and settlement patterns were completely

disrupted with the arrival of Europeans in the 1600s and the

resulting aboriginal abandonment of the Delaware area as the Indians took refuge with other Indian groups further north and west.

Not much is known archeologically for this time period. Among the sites dating to contact times are a number on Cape Henlopen which are associated with the Dutch settlement of Fort Swanendael. The

Townsend site (7S-G-2), may contain a contact period component according to Witthoft (1963) because of the presence of some late seventeenth century European pipes. Other possible locations are the

Swanendael site (7S-D-11), the Russell site (7S-D-7) and 7K-D-48. 133

However, in all cases, there is no clear association of European

artifacts with Indian artifacts. It is known that the Unami

Delaware, Choptank, Nanticoke, and other groups of the Delmarva

peninsula shared a common egalitarian band or tribal level

organization and lived in small extended family groups. Their

subsistence was based on hunting and gathering with some agriculture

(Weslager 1972, Feest 1978). As European settlement increased in the

area, Delaware Indian groups moved north and west. By the

mid-eighteenth century most Indians had moved out of Delaware and

their lifeways were completely disrupted.

As can be seen from the above discussion, a fair amount of

research has been conducted on Delaware archeology. One point that

needs to be mentioned, however, is that there is a large gap in

Delaware prehistory. That gap is related to the drainage banks and coastal areas which have been inundated during the past 12,000 years. During the Holocene Epoch, the Atlantic Ocean has transgressed over Delaware from a position 160 kilometers east of the present shoreline. The sea level has risen 104 meters (J. C. Kraft

1971:5), It has been estimated that the Delaware rivers were thirty-one meters lower than their present level (J. C. Kraft

1971:46,115). From the initial rapid sea level rise of one meter per century between 10,000 and 6000 B.C., the rate decreased to .3 meter per century between 6000 B.C. and 1750 B.C., and to .1 meter per century after 1750 B.C. (J, C. Kraft 1971). During the ca. A.D, 700 major occupation of the Island Field site (7K-F-17), for example, the

Delaware Bay shoreline was probably one mile east of its current 134

position adjacent to the site (J. C. Kraft 1971:46). Obviously,

then, sites considered to be in coastal areas today may have been in

more upland woodland settings during occupation. Therefore, although

much is known about Delaware prehistory, a portion of the

archeological record is missing due to sea level rise and erosion

processes over the past 12,000 years.

I: CHAPTER IV

RESEARCH STRATEGY AND FIELD METHODS

Research Strategy and Scope

The research objective is to test a Delaware model of prehistoric human subsistence and settlement patterns with systematically collected, independent data in the same geographic region for which the model was developed. The reason for this is that the model is based on information contained in the Delaware

State archeological survey site files, which is primarily composed of surface collections made by amateurs between the 1940s and 1960s.

The model therefore needs to be tested by a representative sample of the potential archeological record.

The geographic region encompasses the Delaware Bay Shore and

Mid-drainage Environmental Zones of the Saint Jones and Murderkill

River watersheds in Kent County, Delaware. In addition to the major portions of the Saint Jones and Murderkill Rivers, the area includes

Saint Jones, Murderkill and Milford Necks; Cypress Branch; Tidbury

Creek; Double Run; Hudson Branch; Pratt Branch; Spring Creek; Browns

Branch; Big Cripple Swamp; Derby, Voshell, McGinnis, Coursey,

Killens, and McColley Ponds; and Andrews Lake. The area is bordered by the Delaware Bay to the east, Lewis Ditch and Route 10 to the north. Route 13 to the west, and Route 14 and Clark Point to the south (see fig. 2, p. 7).

135 136

The hypothesis was tested by surveying a five percent

stratified random quadrat sample of the Saint Jones and Murderkill

River watersheds. The stratified random quadrat sample approach was

selected for two reasons. First, Judge, Ebert and Hitchcock

(1975:122) recommend random quadrat surveys within ecological strata

as the best second-stage sampling method. This study is considered a

second-stage sampling design because the model is based on existing

data from a first sampling stage. Properly stratified samples are

considered to provide more accurate estimates of the locations and

frequency of sites than nonstratified samples (Cochran 1977: Redman

1974). Custer (1983b:80) also found stratified random quadrat

surveys to be the most accurate and precise sample survey technique

for Eastern Woodland environments. Secondly, the regional transect

alternative was not logistically feasible because of the large number

of individual trespass permissions one would have to obtain, and the

resulting high probability of incomplete transects due to trespass

denials and unsurveyable areas.

In a stratified random quadrat sample, the survey area is

divided into strata, usually according to environmental variables.

Sample units are selected randomly from within each stratum. A five percent sample was selected because it is one of the more efficient sampling percentiles for locating sites (Plog et al. 1978:396). It is also considered to be a statistically representative sample within the constraints of the large area under consideration (247,360 square kilometers total surveyable area) and the lack of field assistance.

Each quadrat was 400 meters on a side, comprising a total of 160,000 square meters. This quadrat size was chosen because it is a 137

manageable survey size (16 hectares, or 39.5 acres), and large enough

to encompass most archeological sites but small enough to allow for a

large number of sample units. According to Cowgill (1975:266),

sample units should be bigger than the units of interest (i.e.,

archeological sites), yet small enough to allow recognition of

settlement patterning. For example, the largest archeological site

type expected is a macro-band base camp averaging 10,000 square

meters. By obtaining a large number of sample units, this study

should provide a more accurate representation of the population

according to Judge et al. (1975:85) and Cowgill (1975:269).

The research area is located in the Low Coastal Plain of

Delaware. The Low Coastal Plain is one of three physiographic

regions in Delaware. The other two are the High Coastal Plain and

the Piedmont. The Low Coastal Plain is underlain by the Columbian

formation sands which have been extensively reworked to a very flat

and somewhat featureless landscape (Delaware Geological Survey

1976). Elevation ranges from sea level to thirty-five meters above

sea level. Extensive marshes exist and the lower and middle reaches of the rivers are tidal. The area includes well drained to poorly drained soils.

Because the major physiographic regions mask a number of significant environmental differences within the state, six environmental zones within the state were delineated for cultural resource planning purposes. Specified in the Management Plan for

Delaware's Prehistoric Archaeological Resources (Custer 1983a), these zones are the Delaware Bay Shore, Mid-drainage, Interior, 138

Mid-peninsular Drainage Divide, Interior Swamp, and Atlantic

Coast/Small Bay,

The research area is located within two of the state

environmental zones, the Delaware Bay Shore and Mid-drainage. In

conducting the research, the area was first stratified according to

those two zones, with transition zones between the first two

environmental zones and the third Mid-peninsular Drainage Divide

Zone. The zones are based on edaphic factors (soils, topography,

exposure, and slope) which would not have changed over time. Each

zone would therefore have supported similar plant and animal

communities within it. According to Odum (1971), the composition of

a biotic community depends on two types of factors: climatic

(available atmospheric moisture, solar radiation, and temperature)

and edaphic. While climatic factors change temporally, edaphic

factors are essentially stable, except for the effects of sea level

rise. Therefore the zones have been established according to edaphic

factors. The zones were also developed to correspond with the

variation in salinity of the estuarine drainages, and the order of

the drainages. At any given time during the Holocene, each zone would have been more internally homogenous in comparison with the

other zones (Custer et al. 1986).

For the purposes of this study, the two environmental zones and

two transition zones were identified for analysis. These four zones were numbered one through four, beginning with the easternmost

Delaware Bay Shore Zone and moving west (see fig. 2, p. 7). These zones, and the grid system used, are the same as those used by Custer 139

and Gallasso (1983). This affords equivalent units for comparative

analysis with Custer and Gallasso's findings.

The first zone, the Delaware Bay Shore Zone includes the

remnant terraces of the Delaware River and the tidal marshes along

the Delaware River and Delaware Bay. Marshes are scattered

throughout the zone and usually have sandy barriers. The soil

association is predominantly that of the periodically flooded tidal

marsh soils. They are generally poorly drained soils with occasional

occurrences of well drained soils in higher elevations. Fresh water

is not prevalent.

Zone 2, Bay Front/Mid-drainage Transition Zone, is a transition

zone between the Delaware Bay Shore Zone and the Mid-drainage Zone.

It has extensive marshes along the major drainage channels and large areas of well drained headlands along the western side of the bay front marshes. Some fresh water low order tributaries of the major drainages exist. It is an area of predominantly poorly drained soils of the Othello-Matapeake-Mattapex and Fallsington-Sassafras-Woodstown soil associations (Ü. S. Department of Agriculture 1971).

The Mid-drainage Zone, Zone 3, is considered the most ecologically diverse area with the richest array of natural resources. It includes the central portion of all the coastal plain tributaries of the Delaware River and Bay. The center of this zone marks the modern tidal limit. Therefore, the tributaries contain fresh water throughout the western portion and brackish water in the eastern portion of this zone. There are a number of fresh water tributaries. Large areas of poorly drained woodlands exist in the interior locations away from the major drainages. 140

Sassafras-Fallsington is the dominant soil association.

Floodplains along the major drainages include some tidal marshes and

poorly drained soils. Well drained soils predominate on upper

terraces and headlands between major drainages and their tributaries

(Custer 1984a:27).

Zone 4, the Drainage Divide Transition Zone, is a transition

area between the Mid-drainage Zone and the Mid-peninsular Drainage

Divide. The Mid-peninsular Drainage Divide is considered the

'backbone' of the Delmarva Peninsula (R. Thomas 1966:3). The

drainage divide is an area of low, rolling topography that separates

the headwaters of streams draining into the Chesapeake Bay from those

draining into the Delaware River. Zone 4 is the area of low order

tributaries and sources of the major drainages. Much of the area

does not have surface water under modern conditions. Some bay/basin

features with well drained sand ridges exist. This zone includes the area of Sassafras-Fallsington and Rumsford-Evesboro-Fallsington soil associations. It includes the upper reaches of the tributaries draining into the Delaware River. The tributaries in this area are fresh water and there is relatively little marshland (Custer and

Galasso 1983:5).

One important point to make, however, is that these environmental zones have probably moved continuously west since the end of the Pleistocene Epoch, due to the sea level rise of 104 meters

(J. C. Kraft 1971:5) and other landscape modifications. The river and bay configurations have changed, particularly in the Delaware Bay

Shore and Mid-drainage Zones. The environmental zones were developed 141 in consideration of these changes. For example, the Mid-drainage

Zone includes all Post-Pleistocene oligohaline locations as can be identified using the work of J. C. Kraft (Belknap and Kraft 1977;

J, C, Kraft et al. 1976; Custer et al. 1986).

After laying out the environmental zones, each zone was divided into quadrats of 400 m x 400 m. The grid system was established from the northwest corner of the study area. It was laid out over five

U.S. Geological Survey 7.5 minute series topographic maps. The topographic maps include Frederica, Wyoming, Harrington, Milford and

Bennett's Pier. Quadrats covered by more than fifty percent surface water, or industrial or residential development were eliminated from the study before sample selection. The quadrats were then numbered for each environmental zone. A five percent random sample of quadrats within each environmental zone was selected for survey.

The Delaware Bay Shore Zone, Zone 1, has 122 quadrats and six were chosen for survey. The second zone, a transition zone, has 168 quadrats, with eight quadrats being identified for survey. Seventeen quadrats were selected out of the total 341 in the Mid-drainage Zone,

Zone 3. Forty-six out of a total 917 quadrats were chosen for survey in Zone 4, the transition zone between the Mid-drainage and Drainage

Divide (see fig. 12).

The quadrats were selected using a computer random number generator statistical program called Epistat. Two sets of five percent samples were run for each zone. In order to test the model with new information. State survey maps and records were then researched to eliminate quadrats areas which had been previously 142

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Fig. 12. Quadrats surveyed 143 surveyed. In ten cases, quadrats with recorded sites were surveyed because the quadrat area, itself, had not been surveyed. The archeological sites had been reported by landowners or amateur archeologists.

Except for two quadrats belonging to the State government, all the land selected for survey is in private ownership. A permit to survey Delaware State lands was applied for and obtained. Property maps and tax records were consulted for a two week period at the Kent

County property tax mapping office to identify landowners for each quadrat. At first, attempts were made to request permission from the landowners in person. However, because of the difficulty in locating rural route and box numbers, and the large number of absentee landowners, letters requesting permission were written to most of the landowners. The letters included maps delineating the area needed for survey; permission statements to sign; and stamped, self-addressed return envelopes. Landowners were asked about planting schedules and hunting leases. One hundred and ninety-two landowners were approached for permission to survey. Of those, twenty-two people did not respond and twenty-three denied permission. In addition, six lessees had to be consulted. One lessee refused permission. Permission to survey a total five percent sample of the first three environmental zones was achieved by using the two sets of five percent random sample quadrats. For the fourth zone, a third random sample set had to be generated. Quadrats were then surveyed as permission became available. Because the quadrats were selected by a random number generator, and trespass permissions 144

were provided in a random manner, the third five percent sample in

Zone 4 was not biased.

A total seventy-seven quadrats, 12.32 square kilometers or

1,232 hectares, were surveyed from June of 1986 to July of 1987. In order to ensure optimum survey visibility and avoid damaging crops and upsetting landowners and lessees, quadrats were surveyed according to cropping and hunting schedules. Each field within a quadrat was surveyed after crops were harvested and fields were plowed, whenever feasible. Quadrats also had to be surveyed between hunting seasons. Because of the complex vegetable and grain cropping patterns, often involving triple-cropping, the study area had to be frequently revisited over a fourteen month period. The fieldwork comprised a total of four months. Most of the fieldwork was conducted by myself. However, two weeks of it were accomplished with the assistance of one to three people.

Field Survey

The field survey consisted of walking straight north-south transects using a compass and line-sighting. Transects were spaced ten meters apart. Ten meter transects were selected to ensure total coverage of each quadrat and allow for the identification of relatively small archeological sites. The survey thus consisted of forty transects per quadrat.

The north-south transect direction was adjusted when crop rows had to be followed or when fences and drainage patterns would not allow direct north-south transects. Adjustments were made on 145

sixteen, or twenty-one percent of the quadrats. Ten meter interval

transects were still maintained in those cases, however. Transects

were started from the southwest corner, when feasible. The

infrequently encountered residences and lawns were not surveyed, as

requested by landowners.

All areas of a quadrat were examined by surface reconnaissance. Almost all areas were under cultivation. Forested areas were surveyed if accessible, although most forested areas were steep ravines or swamps with little visibility. Although original plans included shovel-testing areas of less than fifty percent visibility, no quadrats had that little visibility. In addition, landowners would not grant permission for shovel-testing, and shovel-testing would have provided data not equivalent to the surface reconnaissance data. It was considered important to include forested areas in the sample, however, because they represented a potentially unique ecological setting.

Forms were developed to ensure consistent recording of necessary information for each quadrat (see Appendix A). Recorded quadrat information included (1) survey dates, (2) quadrat zone and number, (3) the U.S. Geological Survey 7.5 minute quadrangle map reference, (4) owners' names and addresses, (5) tenants' names and addresses, (6) informants' names and addresses, (7) recorders' names,

(8) description of survey and survey conditions, (9) soil association, (10) water source and type, (11) topographic features,

(12) materials observed, (13) materials collected, (14) time periods represented, and (15) map of sites and features. 146

For the purposes of this study, an archeological site is defined as a locus of past human activity which leaves empirically observable evidence (Potter 1982:110). Specifically, the remains are considered an archeological site if the following is found: one or more identifiable tools or diagnostic artifact; at least one piece of fire-cracked rock with one artifact; or more than one artifact within five meters of each other. Artifact is defined as any prehistoric item modified by humans, including, but not limited to, lithic flakes and tools, ceramic and steatite sherds, and mortars. Each definable concentration of artifacts was recorded as a separate site as requested by the Delaware Bureau of Archaeology and Historic

Preservation. Recorded site information included (1) provenience,

(2) type, (3) time period (if identifiable), (4) owner, (5) size,

(6) soil type, (6) surface and subsurface indicators, and

(7) associated environmental characteristics identified in

Appendix B. State archeological site forms were also completed for each site and the information submitted to the Delaware Bureau of

Archaeology and Historic Preservation.

Thirty-eight archeological sites were identified (see fig. 13).

All diagnostic artifacts and a representative sample of flakes and cores were collected. The sample of flakes and cores were collected according to stone material type. The artifacts were prepared for curation according to the Delaware Bureau of Archaeology and Historic

Preservation standards, and deposited with the Island Field Museum, the designated State repository. Permission to donate the artifacts to the State repository was requested of the landowners. 147

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Fig. 13. Archeological sites 148

Quadrat Probability Determinations

After conducting the field survey, the individual quadrats were analyzed to determine whether they were of high, medium or low probability for subsistence settlement locations according to the

Delaware model. The probability was not determined before conducting the survey in order to eliminate any potential bias. High probability areas include; well drained ridges; swamps, marshes, bay/basin features and other very poorly drained soil areas; low terraces along major drainages, especially in the vicinity of lower order confluences; upper terraces of major drainages along lower order tributaries and at low order stream confluences up to ten kilometers from major drainages; alluvial fans associated with swamps, bogs and lithic sources; well drained knolls at springs, swamps and stream confluences; floodplains of major and minor drainages; swampy floodplains; confluences of low order streams and tidal marshes; along minor and ephemeral drainages adjacent to poorly drained woodlands and on small sand ridges and knolls; upper terraces near freshwater sources and tidal marshes; and low terraces of major stream confluences and at saltwater/freshwater interface of marshes

(Custer 1983a; Custer et al, 1986), All other areas are considered low probability,

LANDSAT-Generated Predictive Model

The probability areas are identified using environmental information generated by LANDSAT satellite. The LANDSAT satellite collects earth resources data using a return beam vidicon system and 149

a four-band multispectral system. This information is collected over

Delaware at 920 kilometer altitude every eighteen days, LANDSAT data

is recorded in digital form and is analyzed by computer to classify

and map environmental areas by first identifying indicator variables

that are special categories of land classes on the ground. These

land classes are called training sets. The training sets can be

identified on LANDSAT images and special spectral characteristics can

be recognized through statistical analyses (Klemas 1977), The

LANDSAT image can then be classified according to the special characteristics. Map resolution using this technique is usually eighty meters and can therefore discriminate closely spaced environmental zones. Probability areas were identified by combining general environmental characteristics associated with known site locations. The model was developed from Wells’ (1981; Wells et al,

1981) model for New Castle County, Delaware, Wells (1981:41-46) used variables such as distance to surface water of various orders; the presence of specific topographic features such as sinkholes, bay/basin features, and river levees; and the distance to interfaces of well drained and poorly drained soils. This approach avoids problems encountered by other researchers who have tried to use

LANDSAT data to identify exact archeological site locations. Many archeologists have tried to look for specific on-the-ground features, such as soil differences, shell scatters, architectural remains, or crop marks (Ebert and Lyons 1976), LANDSAT resolution is not fine enough to identify exact archeological site locations, but can identify probability zones based on environmental variables. 150

The probability that a certain quadrat will contain at least one

site is determined according to the following logistical regression model (Custer et al, 1986:584);

PROB(Y(i)=l) = (E(Y(i))) = ^ x(i)'b 1+e where X(i)’ ~ (1#X^2 ^12’***’^ip^

is a vector of the p predictor variables at grid cell i and b — (b^,b^,...,bp)

is a vector of regression coefficients to be estimated,

Y(i) is the dependent variable between zero and one. The output to the logistical regression model consists of the Y(i) of known test sites (i,e,, the probability value one for locations known to contain archaeological sites and the probability value zero for locations known no to contain archaeological sites) and X(i), the observational values of the environmental variables.

The logistical regression model was used for four reasons: (1) it can be applied to gridded data bases; (2) the dependent variable always lies between zero and one; (3) there are few restrictions on the independent variable distribution; and (4) results can be produced even with noisy data (Wells 1981:23), The logistical regression model was first applied to the Appoquinimink River area of New Castle County,

Using data from an archeological survey of that area (Gardner and

Stewart 1978), a series of computer programs, called the ODESSA system, were developed. The training set included the variables: distance to major stream or river, distance to closest minor stream, distance to present marsh, convexity of the landscape, distance to well drained soil, and local gradient (Wells 1981:41-46), The derived ODESSA 151

equations, then, were a linear series of coefficients so that if the

observed variables and distances were multiplied by the observations

and distances for the variables, a known site location would generate a value of one. A location not containing a site would provide a value

of zero. By applying the analysis of variance. Wells (1981:41) was able to account for 72% of the site location variation in the training set.

The LANDSAT-generated model used in this study was developed by

Custer et al. (1986) using Wells’ ODESSA system with the variables derived from the study area to form a new training program called

FIELD. A March 1979 LANDSAT scene was used because it had very little banding and was clear and cloudless. The March scene was also selected because it provided the best visibility of variables determined useful in Wells’ study. In particular, the March scene had little vegetative cover and the near infrared channel (Band 7) was, therefore, not oversaturated.

A classification was developed on the scene by downloading a subset of data using the University of Pennsylvania Office of Remote

Sensing of Earth Resources (ORSER) computer program and the Earth

Resources Data Analysis Systems (ERDAS) system owned by the University of Delaware Center for Remote Sensing, College of Marine Studies, The

FIELD program then identified spectral characteristics using pixel brightness and a series of statistical indicators. Sixteen specified signatures were first recognized for the study area. After comparing the signatures and spatial distribution to infrared aerial photographs,

U.S. Geological Survey topographic maps, and color aerial photographs. 152

ten variables were used. The variables are deep water (bay and deeper

parts of rivers, high order streams); shallow, turbid water (turbid

sections of rivers, moderate order streams); shallow, clear water

(less turbid sections of rivers, low order streams); salt marsh 1

(tidal wetland with low productivity, high salinity marshes); salt marsh 2 (tidal wetland with high productivity, brackish and low salinity marshes); trees (wooded areas with very poorly drained soils); agricultural 1 (high productivity farm land with well drained soils having some moisture retention); agricultural 2 (low productivity farm land with excessively well drained soils); bare soil

1 (bare soils and dead grass with moderately well drained soils); and bare soil 2 (bare soil and dead grasses with moderately drained soils). These indicator variables are on computer file at the

University of Delaware.

The LANDSAT data was converted into a gridded data base using a series of programs. The resulting data base contained variables which included percentages of ground-truthed grid cells, and a series of minimum distances (converted to log distance measures) to a number of critical environmental variables identified by Custer et al,

(1986:580), The final set of critical variables were determined to be: shallow, turbid water; shallow, clear water; salt marsh 1; salt marsh 2; and trees. The ODESSA model was then applied to the study area by inputting each cell’s environmental variables into the equation, A value between zero or one was then given to each cell.

The value is the probability of that cell containing an archeological site. Three probability classes were mapped: p>,75 is a high 153 probability area, ,50

For quadrats with a percentage of high, medium and low probability areas, the largest area of probability was used. For example, a quadrat with one quarter high, one half medium and one quarter low probability was considered medium probability. The probabilities were not assigned numbers and given an average because that might appear to be too specific and give the reader the impression that it was an exact probability ratio. CHAPTER V

DATA ANALYSIS

Introduction

The data were primarily gathered to determine if the observed

subsistence and settlement pattern met the predicted pattern, in terms

of both the LANDSAT-generated model and the empirically-described model presented in Chapter III. Environmental information was also collected to identify any other possible influencing factors. The environmental variables included those identified by Custer (1984a) and other modeling studies described in Chapter II (Roper 1979b; Hay et al. 1982).

The Data Bases

To conduct the data analysis, two data bases were developed.

The first data base, entitled Quadrat, contained quadrat-related data. It consisted of seventy-seven records, one for each quadrat surveyed, and sixty-four fields or variables. The second data base.

Sites, contained site-specific data. This data base consisted of thirty-eight records and sixty-six fields. While the research was based on the quadrat as a unit of analysis, the second data base was developed to determine if additional information could be gained by analyzing site-specific data.

154 155

The data bases were developed in a way that they could be

merged for analysis. This was accomplished by using the quadrat

number as the common denominator. Unless otherwise noted, all fields

were recorded as one for presence or yes, and zero for absence or no.

The Quadrat Data Base

The major categories of information for the Quadrat data base

(see Appendix C) were site presence or absence, the LANDSAT-generated model probability prediction, a random model predictor, the

environmental zone, the U.S. Geological Survey (U.S.G.S.) topographic map, the primary and secondary soil type, the closest location and

type of water source, topographic features, elevation, and slope.

The model probability prediction recorded high as 3, medium as 2, and low as 1. The random model predictor was recorded in the same manner. The random model predictor was included to determine if site location probability could be predicted as accurately by random chance. The random model was developed by randomly selecting 1, 2, or 3 for each quadrat using the Epistat computer program.

The environmental zones were listed from 1 through 4 with 1 being the Delaware Bay Shore Zone; 2 being the Bay Front/Mid-drainage

Transition Zone; 3, the Mid-drainage Zone; and 4, the Drainage Divide

Transition Zone. The U.S.G.S. topographic map was included to assist in locating quadrats on the maps. The first letter for each topographic map (Bennett's Pier, Frederica, Harrington, Milford, and

Wyoming) was used for recordation purposes.

Soils information was recorded by dominance. The largest and second largest areas of soil types were listed as separate variables 156

In the data base. The twenty-two possibilities were recorded by

number, but do not indicate ranking. They are; (1) Sassafras sandy

loam, 0-2 percent slopes (SaA); (2) Sassafras sandy loam, 2-5 percent

slopes (SaB); (3) Mattapex silt loam (Mt); (4) Sassafras loam, 0-2

percent slopes (SfA); (5) Matapeake silt loam, 0-2 percent slopes

(MeA); (6) Rumford loamy sand, 0-2 percent slopes (RuA); (7) Rumford

loamy sand, 2-5 percent slopes (RuB); (8) Keyport sandy loam (Ka);

(9) Woodstown sandy loam (Wo); (10) Evesboro loamy sand, clayey

substratum (Ev); (11) Fallsington sandy loam (Fa); (12) Evesboro

loamy sand, 2-5 percent slopes (EsB); (13) Sassafras sandy loam,

10-15 percent slopes, moderately eroded (SaD2); (14) Sassafras sandy

loam, 5-10 percent slopes, severely eroded (SaC3); (15) Othello silt

loam (Ot); (16) Woodstown loam (Ws); (17) Pocomoke loam (Po); (18)

Johnston silt loam (Jo); (19) Rumford loamy sand, 5-10 percent slopes

(RuC2); (20) Tidal marsh (Tm); (21) Matapeake silt loam, 2-5 percent

slopes (MeB); and (22) Sassafras loam, 2-5 percent slopes (SfB). The

soils information was obtained from the Soil Survev of Kent Countv.

Delaware (U, S. Department of Agriculture 1971).

The nearest water source type was recorded as a separate

variable according to whether it was within or outside the quadrat.

If the water source was outside the quadrat, distance in meters from

the nearest quadrat side was recorded. The possible types of water

sources are: river, low order stream, swamp, marsh, bay/basin

feature, spring, bog, low order stream confluence up to ten kilometers from a major drainage, stream confluence, confluence of low order stream and tidal marsh, minor and ephemeral drainage, and

saltwater/freshwater interface of marsh. 157

For the purpose of this study, the water source types were

defined so the results could be compared with other studies.

Definitions were developed using the Dictionary of Geological Terms

(American Geological Institute 1962) and Webster's Seventh New

Collegiate Dictionary (Webster 1967). 'River' is either the

Murderkill or Saint Jones River. 'Low order stream' is any body of

perennially flowing water other than the Murderkill or Saint Jones

River. 'Swamp' is low, spongy land, frequently inundated with water,

that contains trees and other aquatic vegetation, 'Marsh' is a shallow lake, usually with stagnant or very slowly moving water, that contains rushes, reeds or sedge. 'Bay/basin feature' is a circular depression of poorly drained soils, usually inundated with water, containing trees and aquatic types of vegetation. 'Spring' is a place where water flows naturally from a rock crevice or soil onto the land or into surface water. 'Bog' is a tract of wet land consisting of decaying matter or peat. 'Low order stream confluence up to ten kilometers from a major drainage' uses the same definition for 'low order stream' with a 'major drainage' being any other 'low order stream' or the two rivers. 'Stream confluence' is the location where two streams meet. 'Confluence of low order stream and tidal marsh' is the location where a low order stream meets a marshy area that is regularly inundated by the rise and fall of the tide. 'Minor and ephemeral drainage' is a water course channel or gully that flows only in direct response to precipitation. 'Saltwater/freshwater interface of marsh' is the estuarine area of a marsh.

No ranking of water source types was conducted. That is, if a marsh was closer than a river, the marsh was selected. The water 158

source information was obtained from U.S.G.S. topographic maps and the

Soil Survev of Kent County, Delaware (Ü. S. Department of Agriculture

1971). In three cases, quadrats 3.185, 3.212 and 3.253, springs were

recorded based on field survey observations. All water sources within

a quadrat were recorded. However, only the closest water source was

recorded for those quadrats that did not contain water sources within

them. For each quadrat, distance to either the Murderkill or Saint

Jones Rivers was recorded in meters. This variable was included to

determine if major rivers were an influencing factor in settlement

patterns.

Topographic features within the quadrat were recorded as

separate variables. They are; ridge; lower terrace along a drainage

system; upper terrace along a drainage system; alluvial fan associated with swamps, bogs, and lithic sources; well drained knolls; floodplain

of major or minor drainage; swampy floodplain; and small sand ridges

or knolls. The U.S.G.S. topographic maps and the Soil Survev of Kent

County. Delaware (U. S. Department of Agriculture 1971) were used to

identify topographic features.

As with the water source types, the various topographic features were defined using the Dictionary of Geological Terms (American

Geological Institute 1962) and Webster's Seventh New Collegiate

Dictionary (Webster 1967). 'Ridge' is a narrow elongate elevation rising at steep angles, indicated on the U.S.G.S. topographic maps by contour interval lines. 'Lower terrace along a drainage system' is the first level of a narrow plain with a fairly steep front bordering a watercourse or body of water. 'Upper terrace along a drainage 159

system’ is the second and higher levels of a narrow plain with a

fairly steep front bordering a watercourse or body of water. Both

upper and lower terraces were identified by contour interval lines on

U.S.G.S, topographic maps, ’Alluvial fan associated with swamps, bogs, and lithic sources’ is a cone-shaped deposit of sediment formed by stream action located near a swamp, bog, or cryptocrystalline and other major rock outcrops, ’Well drained knolls’ are small rounded hills of well drained soils indicated by contour lines on U.S.G.S, topographic maps, ’Floodplain of major or minor drainage’ is the area alongside a river or stream that is covered with water when the water course overflows its banks. ’Swampy floodplain’ is a floodplain composed of saturated poorly drained soils. ’Small sand ridges or knolls’ are small ridges or knolls as previously defined which are composed of sandy soils.

Quadrat elevation was recorded using twenty different possible ranges from zero to seventy feet. Elevation was determined from

U.S.G.S. topographic maps.

Slope was recorded using the Soil Survev of Kent Countv.

Delaware (U. S. Department of Agriculture 1971). Seven possible slope ranges were identified.

After conducting the initial data analyses, it became apparent that better information might be obtained by making all the variables equivalent units. Therefore, a second set of data containing seventeen variables was merged with the first set, comprising the total sixty-four fields. This second set divided fields that contained more than one variable possibility into presence/absence 160

categories. Environmental zones, model probability prediction,

elevation, slope, and soil type were the fields which were separated

out in this manner. Soil type was compressed into a primary well

drained or poorly drained soil and a secondary well drained or poorly

drained soil. Elevation was consolidated into three variables: 0-30

feet elevation, 31-50 feet elevation, and 51-70 feet elevation. Slope

variables are: 0-5% slope, ranging up to 10% slope, and ranging up to

15% slope.

The Sites Data Base

For the Sites data base, each discrete site locus was recorded

separately. The two reasons for recording each site locus separately

are: (1) maximum site-specific information could be analyzed; and

(2) the larger the number of cases, the more powerful the statistical

test (Siegel 1956:11).

The major categories of information for the Sites data base (see

Appendix D) include the quadrat number, environmental zone, U.S.G.S.

topographic map, site numbers, cultural time period, site type, site

Landsat-generated model probability, primary and secondary soil type,

distance to nearest water source in meters, type of water source,

distance to the Murderkill or Saint Jones Rivers, location on

topographic feature, site elevation, site slope, and site aspect.

All variables similar to the Quadrat data base were recorded in the same manner as the Quadrat data base. Each cultural time period was recorded as a separate variable. The cultural time periods are

Paleo-Indian, Archaic, Woodland I and Woodland II. They were recorded 161

only when a diagnostic artifact attributable to a certain time period

could be determined. Site types were recorded individually as

macro-band base camp, micro-band base camp, procurement site,

Paleo-Indian base camp maintenance station, Paleo-Indian hunting site,

major mortuary-exchange center, minor mortuary-exchange center, cache

site, and spot find. Site type was determined according to Custer's

(1984a) definitions discussed in Chapter IV, Site aspect was recorded

as either north, east, south, west, northeast, northwest, southeast,

or southwest according to the direction of slope away from the site.

If no slope existed, then aspect was determined as being towards the nearest water source. A description of each site is in Appendix E.

As with the quadrat data base, a second set of data containing

seventeen variables was merged with the first set, to total sixty-six

fields. This was done for the same reasons as the quadrat data base,

to make all the variables equivalent units. The second set of data contained the same fields as was added for the quadrat data base.

Data Analysis

To test the research hypothesis, the data analysis was divided into four objectives. They are to determine if: (1) the observed data met the LANDSAT-generated model's predictions; (2) a random model's predictions could predict site locations as accurately as the

LANDSAT-generated model; (3) recommendations for improving the model could be made through an analysis of specific environmental variables; and (4) predictions might be different between the LANDSAT-generated model and the empirically-described model from Chapter III. 162

Objective One: To Test the

Model’s Predictions

Objective One is the primary objective of the research. The hypothesis for Objective One is: The LANDSAT-generated model predicts the presence and absence of prehistoric archeological sites in the

Saint Jones and Murderkill River watersheds with at least a 75% accuracy. The null hypothesis is: The LANDSAT-generated model predicts the presence and absence of prehistoric archeological sites in the Saint Jones and Murderkill River watersheds with less than 75% accuracy. A 75% accuracy rate was chosen to ensure that the results could not be due to chance.

According to the model's logistical regression formula, the average predicted probability that a site will be found in a high probability area is 88%. For medium probability areas, the average predicted probability is 63%. For low probability areas, it is 25%.

A frequency tabulation of the SITEPRES (site presence/absence) variable and the model PROBability variable was first conducted (see table 1). It was found that for the 4 quadrats which were identified as high probability (3), all 4 had sites. Therefore the model's high probability prediction was 100% accurate, better than the 88% average predicted probability. For the 17 quadrats predicted to have a medium probability (2), 15 of them had archeological sites and 2 of them did not. The model therefore predicted archeological sites in 88% of the medium probability areas. This is better than the average predicted probability of 63%. Of the 56 quadrats predicted to have a low probability (1) of containing archeological sites, no sites were found 163

TABLE 1

SITEPRESENCE BY PROBABILITY FREQUENCY TABULATIONS

SITEPRES PROB Frequency Percent Row Pet Col Pet I Total

0 46 2 0 48 59.74 2.60 0.00 62.34 95.83 4.17 0.00 82.14 11.76 0.00

1 10 15 4 29 12.99 19.48 5.19 37.66 34.48 51.72 13.79 17.86 88.24 100.00

Total 56 17 4 77 72.73 22.08 5.19 100.00 164

in 82% of the quadrats, with 10 quadrats containing archeological

sites and 46 not containing sites. This, again, is better than the

average predicted low probability of 75% nonsite areas.

For the purposes of this analysis, high and medium probability

predictions were considered accurate if a site was located in the

those areas. Low probability predictions were considered accurate if no site was found in the low probability areas. This is stricter than the model's average predicted probabilities. In particular, the low probability does not mean no probability, even though it is analyzed as such in this study. The reason for approaching the analysis in this manner is to better identify correlations between site locations and environmental variables and recommend improvements in the model.

A frequency tabulation of the model PROBability variable and the

SITEPRES variable according to environmental zone (ENVZONE) was then conducted to determine if the model was a better predictor for certain environmental zones (see table 2). It was found that the model's predictions were accurate for: 33% of the quadrats (2 out of 6 quadrats) in Zone 1, 75% of the quadrats (6 out of 8) in Zone 2, 88% of the quadrats (15 out of 17) in Zone 3, and 91% of the quadrats (42 out of 46) in Zone 4. Correlation coefficient computations also showed a negative relationship between the model's accuracy (MODAG) and Zone 1 (EZl) with a -0.41 strength of relationship at the ,01 level of significance and a positive relationship between MODAG and

Zone 4 (EZ4) with a 0.23 strength of relationship at the .04 level of significance. Although the relatively small number of quadrats may be an influencing factor, it is obvious that the model's predictive power 165

TABLE 2

ENVIRONMENTAL ZONE (ENVZONE) BY PROBABILITY CONTROLLING FOR SITEPRESENCE FREQUENCY TABULATIONS

SITEPRES=0 ENVZONE PROB Frequency Percent Row Pet Col Pet 3 I Total

1 2 1 3 4.17 2.08 6.25 66.67 33.33 4.35 50.00

2 0 2 4.17 0.00 4.17 100.00 0.00 4.35 0.00

10 0 10 20.83 0.00 20.83 100.00 0.00 21.74 0.00 - + 32 1 33 66.67 2.08 68.75 96.97 3.03 69.57 50.00

Total 46 2 48 95.83 4.17 100.00 166

TABLE 2— Continued

SITEPRES=1 ENVZONE PROB Frequency Percent Row Pet Col Pet 1 3 1 Total |b .^B ^BB BB BB B^B MB BM B^ 1 3 0 0 3 10.34 0.00 0.00 10.34 100.00 0.00 0.00 30.00 0.00 0.00 B ^^B ^^B BB. ^^B ^^B I^M BBB ^^B . IB BBB .^B MB MB BBB BBB BB. . 2 2 3 1 6 6.90 10.34 3.45 20.69 33.33 50.00 16.67 20.00 20.00 25.00 MB ^^B BM .MB ^^B ^^B # 3 2 4 1 7 6.90 13.79 3.45 24.14 28.57 57.14 14.29 20.00 26.67 25.00

4 3 8 2 13 10.34 27.59 6.90 44.83 23.08 61.54 15.38 30.00 53.33 50.00

Total 10 15 4 29 34.48 51.72 13.79 100.00 167

for Environmental Zone 1 is much smaller than that of the other

environmental zones. This may be because all three low probability

quadrats with sites in Zone 1 have either primary or secondary poorly

drained soils and two are associated with marshes. It may also be

because the rise in sea level has altered this zone more than any

other.

In analyzing the model's predictive power by the number of

quadrats accurately predicted, the overall accuracy rate is 84%.

Therefore, the null hypothesis for Objective One is rejected.

Objective Two: To Test a Random Model's Predictions

in Comparison with the LANDSAT-Generated

Model's Predictions

The purpose of Objective Two is to determine if one could obtain as accurate a predictive model by chance, as one could obtain using the LANDSAT-generated model. The hypothesis for Objective Two is: The random model predicts the presence and absence of prehistoric archeological sites in the Saint Jones and Murderkill River watersheds with at least the same 84% accuracy as the LANDSAT-generated model.

The null hypothesis is: The random model predicts the presence and absence of prehistoric archeological sites in the Saint Jones and

Murderkill River watersheds with significantly less than an 84% accuracy rate, as indicated by a chi-square analysis.

A frequency tabulation of the RANDOM model variable and the

SITEPRES variable was conducted (see table 3). It was found that for the 44 quadrats which were identified as high probability (3), 16 had archeological sites and 28 did not. Therefore the random model's high 168

TABLE 3

SITEPRESENCE BY RANDOM MODEL FREQUENCY TABULATIONS

SITEPRES RANDOM Frequency Percent Row Pet Col Pet 3 I Total

5 15 28 48 6.49 19.48 36.36 62.34 10.42 31.25 58.33 62.50 60.00 63.64

3 10 16 29 3.90 12.99 20.78 37.66 10.34 34.48 55.17 37.50 40.00 36.36

Total 8 25 44 77 10.39 32.47 57.14 100.00 169

probability prediction was 36% accurate. For the 25 quadrats

predicted to have a medium probability (2), 10 of them had

archeological sites and 15 of them did not. The random model

therefore predicted archeological sites in 40% of the medium

probability areas. Of the 8 quadrats predicted to have a low

probability (1) of containing archeological sites, the random model

was accurate 63% of the time, with 5 quadrats not containing

archeological sites and 3 containing sites. In analyzing the random

model's predictive power by the frequency of quadrats accurately

predicted, the overall accuracy rate is 40%.

To determine if the difference between the LANDSAT-generated

model's results (MODAG) and the random model's results (RANAC) are

significant, a chi-square analysis was conducted. The chi-square test

determines whether there is a significant difference between an

observed number of cases and an expected number for two or more

categories (Siegel 1956:43). The chi-square test determined that the

difference is significant at the .0001 level (see table 4).

For this second objective, the null hypothesis is accepted and

the hypothesis is rejected. The random model predicts archeological

site presence and absence at a 40% accuracy rate which is

significantly less accurate than the LANDSAT-generated model.

Objective Three: Recommendations Based on an Analysis

of Specific Environmental Variables

To go beyond a test of the model and determine if any recommendations could be made to improve the model, a number of statistical analyses were conducted. The statistical analyses were 170

TABLE 4

MODEL'S RESULTS (MODAC) COMPARED TO RANDOM MODEL'S RESULTS (RANAC) FREQUENCY TABULATIONS AND CHI-SQUARE ANALYSIS

MODAC RANAC Frequency Percent Row Pet Col Pet 0 Total

1 11 12 1.30 14.29 15.58 8.33 91.67 2.17 35.48

45 20 65 58.44 25.97 84.42 69.23 30.77 97.83 64.52

Total 46 31 77 59.74 40.26 100.00

Statistics for 2-Way Tables Chi-Square 15.619 DF= 1 Prob=0.000 Phi -0.450 Contingency Coefficient 0.411 Cramer's V 0.450 Likelihood Ratio Chi-Square 16.678 DF= 1 Prob=0.000 Continuity Adj. Chi-Square 13.190 DF= 1 Prob=0.000 Fisher's Exact Test (1-Tail) Prob=0.000 (2-Tail) Prob=0.000 171 conducted not only to clarify the relationship between environmental variables and site locations, but also to clarify the relationship between environmental variables and site absence. For this third objective, the hypothesis is; Specific environmental variables that strongly correlate with site presence or absence can be identified and recommended for improvement of the model. The null hypothesis is; No environmental variables that strongly correlate with site presence or absence can be identified and recommended for improvement of the model.

Step 1; Frequencv Tabulations

Frequency tabulations of the SITEPRES variable by the environmental variables were first conducted. Frequency tabulations provide a count of how often each variable value occurs within the data set (Kachigan 1982:21). Because of the different levels of measurement (nominal, ordinal and ratio), frequency tabulations provide a basic statistical analysis of all the data collected.

Quadrat Data Base Frequency Tabulations

For S0IIX)1, the dominant soil type within each quadrat.

Sassafras sandy loam was the most prevalent soil series. It was the dominant soil in 75% of the quadrats, with Sassafras sandy loam, 0-2% slopes (SaA), being the predominant soil type in 15 quadrats (19% of the total number of cases) without sites and 16 quadrats (21%) with sites. Sassafras sandy loam, 2-5% slopes (SaB), was the predominant soil type in 18 quadrats (23%) without sites and 9 quadrats (12%) with 172

sites. A list of all the dominant soil types are presented in

table 5.

Sassafras sandy loam was also the most prevalent soil series for

S0ILQ2, the secondary soil type within a quadrat. It was the

secondary soil series in 55% of the quadrats. SaA was in 9 quadrats

(12% of the total number of cases) without sites and 3 quadrats (4%)

with sites; SaB in 16 quadrats (21%) without sites and 11 quadrats

(14%) with sites; Sassafras sandy loam, 5-10% slopes, severely eroded

(SaC3), in 1 (1%) without a site; and Sassafras sandy loam, 10-15%

slopes, moderately eroded (SaD2), in 2 (3%) with sites. The range of

secondary soil types are listed in table 6.

As can be seen from the frequency of primary and secondary soil

types, there is a heavy weighting of quadrats containing the well

drained soils SaA and SaB with site presence. Of the 29 quadrats

containing archeological sites, 25 of them are predominantly SaA or

SaB soils (86%). However, when you compare this to quadrats without

sites, 33 out of 48 (69%) also contain predominantly SaA or SaB. If

one was to analyze only site locations, one would find a heavy weighting of SaA and SaB. However, with the percentage of quadrats without sites containing SaA and SaB being over 50%, SaA and SaB soils

are not a reliable predictor, at least by itself. By comparing site and nonsite quadrats, no weighting of a specific soil type could be clearly identified. Therefore, to determine if well drained versus

poorly drained soil was a better way to clarify influencing factors, the data were consolidated in such a manner and frequency tabulations were run. 173

TABLE 5

DOMINANT SOIL TYPE WITHIN QUADRAT (SOILQl)

Quadrats Quadrats Total Percent of Type With Sites Without Sites Quadrats Total Cases

Sassafras sandy loam, 16 (21%) 15 (19%) 31 40 0-2% slopes (SaA)

Sassafras sandy loam, 9 (12%) 18 (23%) 27 35 2-5% slopes (SaB)

Rumford loamy sand, 1 (1%) 4 (5%) 5 6 2-5% slopes (RuB)

Sassafras loam, 1 (1%) 3 (4%) 4 5 0-2% slopes (SfA)

Mattapex silt loam 1 (1%) 2 (3%) 3 4 (Mt)

Woodstown sandy loam 0 2 (3%) 2 3 (Wo)

Matapeake silt loam, 1 (1%) 0 1 1 0-2% slopes (MeA)

Evesboro loamy sand, 0 1 (1%) 1 1 clayey substratum (Ev)

Fallsington sandy loam 0 1 (1%) 1 1 (Fa)

Evesboro loamy sand, 0 1 (1%) 1 1 2-5% slopes (EsB)

Rumford loamy sand, 0 1 (1%) 1 1 0-2% slopes (RuA)

Total 29 (37%) 48 (61%) 77 100*

*May not exactly equal 100% due to rounding. 174

TABLE 6

SECONDARY SOIL TYPE WITHIN QUADRAT (S0ILQ2)

Quadrats Quadrats Total Percent of Type With Sites Without Sites Quadrats Total Cases

Sassafras sandy loam, 11 (14%) 16 (21%) 27 35 2-5% slopes (SaB)

Sassafras sandy loam, 3 (4%) 9 (12%) 12 16 0-2% slopes (SaA)

Fallsington sandy loam 2 (3%) 3 (4%) 5 7 (Fa)

Woodstown sandy loam 1 (1%) 3 (4%) 4 5 (Wo)

Johnston silt loam 2 (3%) 2 (3%) 4 6 (Jo)

Sassafras loam, 2 (3%) 1 (1%) 3 4 0-2% slopes (SfA)

Rumford loamy sand, 3 (4%) 0 3 4 2-5% slopes (RuB)

Woodstown loam (Ws) 1 (1%) 2 (3%) 3 4

Sassafras loam, 0 3 (4%) 3 4 2-5% slopes (SfB)

Evesboro loamy sand, 0 2 (3%) 2 3 2-5% slopes (EsB)

Sassafras sandy loam, 2 (3%) 0 2 3 10-15% slopes, moderately eroded (SaD2)

No secondary soil type 0 2 (3%) 2 3

Matapeake silt loam, 1 (1%) 1 (1%) 2 2 0-2% slopes (MeA) 175

TABLE 6— Continued

Quadrats Quadrats Total Percent of Type With Sites Without Sites Quadrats Total Cases

Rumford loamy sand, 0 1 (1%) 1 1 0-2% slopes (RuA)

Sassafras sandy loam, 0 1 (1%) 1 1 5-10% slope, severely eroded (SaC3)

Othello silt loam 0 1 (1%) 1 1 (Ot)

Pocomoke loam (Po) 0 1 (1%) 1 1

Rumford loamy sand, 1 (1%) 0 1 1 5-10% slopes (RuC2)

Total 29 (38%) 48 (63%) 77 100*

*May not exactly equal 100% due to rounding. 176

A total of 71 quadrats contained primarily well drained soils.

Of those quadrats, 43 (61%) did not contain an archeological site and

28 (38%) did. Six quadrats contained primarily poorly drained soils

with 1 (17%) containing a site and 5 (83%) not containing any sites.

Out of 75 quadrats that contained a secondary soil type, 34 (60%)

quadrats containing well drained soils did not contain sites and 23

(40%) quadrats did contain sites. Twelve (67%) quadrats with poorly

drained secondary soils did not contain sites and 6 (33%) did. Again,

no clear delineation of a soil type predictor variable in terms of

relative frequencies could be determined.

Thirty-three quadrats contained surface water. Of those, 19

(58%) contained archeological sites and 14 (42%) did not. In 4 cases, more than one kind of surface water was recorded within the quadrat.

Six quadrats (18%) with sites contained low order streams; 4 (12%) quadrats with sites and 2 (6%) without sites contained a marsh; and 11

(33%) quadrats with minor and ephemeral drainages did not contain sites while 4 (12%) did. The frequency and different kinds of surface water are listed in table 7.

Forty-four quadrats did not include a surface water source.

With those quadrats the nearest water source and distance to it were measured. Of those 44 quadrats, 34 (77%) did not contain sites and 10

(23%) did. The frequency of nearest water source types is presented in table 8 and the distance to those sources is listed in table 9.

Because of the observed concentration of archeological sites along the Murderkill and Saint Jones Rivers, quadrat distance to those rivers was calculated. This was to determine if location near a major 177

TABLE 7

QUADRATS WITH SURFACE WATER

Water Quadrats Quadrats Total Percent of Type With Sites Without Sites Quadrats Total Cases

Minor and ephemeral 4 (12%) 11 (33%) 15 45 drainage

Marsh 4 (12%) 2 (6%) 6 18

Low order stream 6 (18%) 0 6 18

Spring 1 (3%) 0 1 3

Low order stream 1 (3%) 0 1 3 confluence

Marsh and low order 1 (3%) 0 1 3 stream

Marsh and bay/basin 0 1 (3%) 1 3

Low order stream and 1 (3%) 0 1 3 spring

Low order stream, 1 (3%) 0 1 3 marsh and spring

Total 19 (57%) 14 (42%) 33 100*

*May not exactly equal 100% due to rounding. 178

TABLE 8

NEAREST WATER SOURCE FOR QUADRATS WITHOUT WATER

Water Quadrats Quadrats Total Percent of Type With Sites Without Sites Quadrats Total Cases

River 3 (7%) 3 (7%) 6 14

Low order stream 2 (5%) 8 (18%) 10 23

Swamp 0 1 (2%) 1 2

Marsh 4 (9%) 2 (5%) 6 14

Spring 0 2 (5%) 2 5

Low order stream 1 (2%) 0 1 2 confluence up to 10k from major drainage

Minor and ephemeral 1 (2%) 17 (39%) 18 41 drainage

Total 11 (25%) 33 (76%) 44 100*

*May not exactly equal 100% due to rounding. 179

TABLE 9

DISTANCE TO NEAREST WATER SOURCE FOR QUADRATS WITHOUT WATER

Distance Quadrats Quadrats Total Percent of in Meters With Sites Without Sites Quadrats Total Cases

25 0 1 (2%) 1 2

100 2 (5%) 5 (11%) 7 16

120 0 3 (7%) 3 7

150 0 1 (2%) 1 2

200 2 (5%) 2 (5%) 4 10

220 0 1 (2%) 1 2

250 1 (2%) 0 1 2

300 1 (2%) 4 (9%) 5 11

380 1 (2%) 0 1 2

400 2 (5%) 6 (14%) 8 19

500 0 3 (7%) 3 7

600 0 2 (5%) 2 5

650 0 1 (2%) 1 2

950 1 (2%) 0 1 2

1,000 0 2 (5%) 2 5

1,300 0 1 (2%) 1 2

1,400 0 2 (5%) 2 5

Total 10 (23%) 34 (78%) 44 100*

*May not exactly equal 100% due to rounding. 180

river might be an influencing factor. Nearness was measured from the

closest quadrat side to whichever river was closest. For quadrats

with sites, the distance to the nearest major river ranged from 0 to

3600 meters. For quadrats without sites, the range was AGO to 6600

(see table 10).

As with the soil types, there is no clear indication of

preference for a certain type of water source. There is no

significant difference in the quadrat distance from the major rivers

in relation to archeological sites. The fact that 58% of the quadrats

containing surface water sources also contained archeological sites while 77% of those quadrats not containing surface water also did not

contain sites, may indicate that nearness to surface water is a predictor variable. To determine if this is indeed the case, site distance to water (rather than quadrat which may mask the actual predictive power) is analyzed under the Sites data base which follows this section.

In considering topographic features (TOPOQÜAD), 23 quadrats

(30%) which contained an archeological site also contained a topographic feature, while 6 (8%) with archeological sites did not.

Forty-five quadrats (58%) which did not contain an archeological site also did not contain a topographic feature, while 3 (4%) did. Of those quadrats containing topographic features, lower terraces were present in 11 quadrats (42% out of the 26) with sites and 2 (8%) without, a lower terrace and knoll were present in 1 quadrat (4%) with a site, upper terraces were present in 9 quadrats (35%) with sites and

1 quadrat (4%) without, swampy floodplains were present in 1 quadrat 181

TABLE 10

QUADRAT DISTANCE TO MURDERKILL OR SAINT JONES RIVER

Distance Quadrats Quadrats Total Percent of in Meters With Sites Without Sites Quadrats Total Cases

0 1 (1.3%) 0 1 1.3

100 2 (3%) 0 2 3

200 1 (1.3%) 0 1 1.3

400 3 (4%) 2 (3%) 5 7

500 1 (1.3%) 1 (1.3%) 2 2.6

600 1 (1.3%) 0 1 1.3

680 0 1 (1.3%) 1 1.3

800 2 (3%) 1 (1.3%) 3 4.3

850 1 (1.3%) 0 1 1.3

900 1 (1.3%) 1 (1.3%) 2 2.6

920 0 1 (1.3%) 1 1.3

950 1 (1.3%) 0 1 1.3

1,000 2 (3%) 0 2 3

1,100 1 (1.3%) 2 (3%) 3 4.3

1,200 0 1 (1.3%) 1 1.3

1,300 0 1 (1.3%) 1 1.3

1,400 0 2 (3%) 2 3

1,500 0 1 (1.3%) 1 1.3

1,600 0 2 (3%) 2 3

1,650 0 1 (1.3%) 1 1.3

1,700 2 (3%) 1 (1.3%) 3 4.3 182

TABLE 10— Continued

Distance Quadrats Quadrats Total Percent of in Meters With Sites Without Sites Quadrats Total Cases

1,800 2 (3%) 1 (1.3%) 3 4.3

1,850 0 1 (1.3%) 1 1.3

1,900 1 (1.3%) 0 1 1.3

2,000 1 (1.3%) 0 1 1.3

2,200 0 1 (1.3%) 1 1.3

2,330 0 1 (1.3%) 1 1.3

2,400 1 (1.3%) 1 (1.3%) 2.6

2,430 0 1 (1.3%) 1 1.3

2,500 0 2 (3%) 3

2,700 0 1 (1.3%) 1 1.3

2,800 0 1 (1.3%) 1 1.3

2,820 0 1 (1.3%) 1 1.3

2,900 2 (3%) 1 (1.3%) 4.3

2,950 1 (1.3%) 0 1 1.3

3,000 0 1 (1.3%) 1 1.3

3,300 0 1 (1.3%) 1 1.3

3,400 1 (1.3%) 0 1 1.3

3,500 0 1 (1.3%) 1 1.3

3,550 0 1 (1.3%) 1 1.3

3,600 1 (1.3%) 1 (1.3%) 2 2.6

3,650 0 1 (1.3%) 1 1.3

3,700 0 2 (3%) 2 3 183

TABLE 10— Continued

Distance Quadrats Quadrats Total Percent of in Meters With Sites Without Sites Quadrats Total Cases

3,800 0 1 (1.3%) 1 1.3

3,900 0 1 (1.3%) 1 1.3

4,000 0 3 (4%) 3 4

4,200 0 1 (1.3%) 1 1.3

4,650 0 1 (1.3%) 1 1.3

4,700 0 1 (1.3%) 1 1.3

5,700 0 1 (1.3%) 1 1.3

6,600 0 1 (1.3%) 1 1.3

Total 29 (40%) 48 (65%) 77 100%*

♦May not exactly equal 100% due to rounding. 184

(4%) with a site, and a swampy floodplain and lower terrace were

present in 1 quadrat (4%) with a site. Topographic features,

primarily upper and lower terraces, do appear to be a factor in site

location. Of the 29 quadrats containing archeological sites, 23 (79%)

contained a topographic feature while only 6 (21%) did not.

A number of different quadrat elevations were recorded. Because

the elevation information came from Ü.S.G.S. topographic maps,

elevation was recorded in feet. Archeological sites were found from 0

to 50 feet elevations. However, quadrat elevations ranged from 0 to

70 feet (see table 11). Because of the broad range of quadrat

elevations which did not clarify any potential predictor variables,

elevation variables were then reduced to three: 0-30 feet, 31-50 feet,

and 51-70 feet. Of 34 quadrats in the 0-30 feet elevation area, 19

(56%) contained archeological sites and 15 (44%) did not. Of 36

quadrats in the 31-50 feet area, 10 (28%) contained archeological

sites and 26 (72%) did not. All 7 quadrats (100%) in the 51-70 feet area did not contain sites. By reducing the elevation variables, it does appear that there are more archeological sites at the lower elevations than the higher elevations.

Slope ranged from 0 to 15% (see table 12). The slope variable was also reduced to three choices: 0-5% slope, ranging to 10% slope, and ranging to 15% slope. For the first choice, 41 quadrats (67%) did not contain archeological sites and 20 (33%) did, for a total of 61 quadrats. Of the 14 quadrats with the second choice, 7 (50%) contained archeological sites and 7 (50%) did not. The 2 quadrats

(100%) with the third choice did not contain archeological sites. 185

TABLE 11

QUADRAT ELEVATIONS

Elevation Quadrats Quadrats Total Percent of in Feet With Sites Without Sites Quadrats Total Cases

0-5 3 (4%) 1 (1%) 4 5

0-10 1 (1%) 1 (1%) 2 2

0-20 4 (5%) 1 (1%) 5 6

0-30 4 (5%) 0 4 5

6-10 2 (3%) 2 (3%) 4 6

10 3 (4%) 1 (1%) 4 5

11-20 1 (1%) 3 (4%) 4 5

11-30 1 (1%) 0 1 1

21-30 0 6 (8%) 6 8

21-40 3 (4%) 1 (1%) 4 5

31-40 2 (3%) 7 (9%) 9 12

31-50 2 (3%) 6 (8%) 8 11

41-50 1 (1%) 9 (12%) 10 13

50 2 (3%) 3 (4%) 5 7

51-60 0 5 (6%) 5 6

60-70 0 2 (3%) 2 3

Total 29 (38%) 48 (62%) 77 100^

♦May not exactly equal 100% due to rounding. 186

TABLE 12

DEGREE OF SLOPE WITHIN QUADRAT

Slope Quadrats Quadrats Total Percent of Range With Sites Without Sites Quadrats Total Cases

0-2% 4 (5%) 4 (5%) 8 10

2-5% 1 (1%) 10 (13%) 11 14

0-5% 15 (19%) 27 (35%) 42 54

0-10% 4 (5%) 3 (4%) 7 9

5-10% 3 (4%) 4 (5%) 7 9

5-15% 0 2 (3%) 2 3

Total 27 (34%) 50 (65%) 77 lOO*

♦May not exactly equal 100% due to rounding. 187

Although no archeological sites were found in areas with slopes up to

15%, the small sample of two for that category, combined with the

large number of quadrats without sites for the other categories, does

not allow slope to be considered a predictor variable.

Sites Data Base Frequency Tabulations

Thirty-eight archeological sites were located during the

survey. Site-specific data were collected on each one for the Sites

data base. Twenty of those sites could be identified by a cultural

time period due to diagnostic artifacts recovered. One (5%) is a

Paleo-Indian site, one (5%) is Archaic, 15 (75%) are Woodland I, 2

(10%) are Woodland II, and 1 (5%) contains both a Woodland I and

Woodland II component. One Woodland I site is interpreted to be a

macro-band base camp, one a micro-band base camp, and the Woodland

I/II site is a micro-band base camp. Three Woodland I sites and one

site containing a mortar are considered procurement sites. The

Paleo-Indian site, consisting of an isolated Kirk-Palmer projectile

point with 2 flakes, is interpreted as a hunting site.

For the dominant soil type associated with each site (SSOILl);

Sassafras sandy loam, 0-2% slopes (SaA), with 11 sites (29%); and

Sassafras sandy loam, 2-5% slopes (SaB), with 19 sites (50%); are the

predominant soil type for a total of 79% of the cases. The remaining

soil types are listed in table 13.

Twenty-six archeological sites are not associated with a

secondary soil type. For the remaining 12, 4 (33%) are associated

with SaA, 3 (25%) with SaB, 1 (8%) with Fallsington sandy loam (Fa),

EL 188

TABLE 13

DOMINANT SOIL TYPE ASSOCIATED WITH EACH SITE (SSOILl)

Soil Number of Sites Percent of Total Cases

Sassafras sandy loam, 19 50 2-5% slope (SaB)

Sassafras sandy loam, 11 29 0-2% slope (SaA)

Rumford loamy sand, 2 5 2-5% slope (RuB)

Johnston silt loam (Jo) 3

Sassafras loam, 1 3 0-2% slope (SfA)

Mattapex silt loam (Mt) 1 3

Matapeake silt loam, 1 3 0-2% slope (MeA)

Evesboro loamy sand, 1 3 2-5% slope (EsB)

Tidal marsh (Tm) 3

Total 38 100+

♦May not exactly equal 100% due to rounding. 189

3 (25%) with Tidal marsh (Tm), and 1 (8%) with Matapeake silt loam,

2-5% slopes (MeB),

The most prevalent primary and secondary soil series is

Sassafras sandy loam. To determine whether well drained versus poorly

drained soils could provide an even clearer picture of soil

association, the soils were reduced accordingly. Thirty-five (92%)

out of the 38 archeological sites are located primarily on well

drained soils. Three (8%) are located on primarily poorly drained

soils. Of the 12 secondary soil associations, 9 (75%) are located on

well drained soils and 3 (25%) with poorly drained soils. Therefore,

the archeological sites are primarily associated with well drained

soils. As was noted previously, however, the association is not

significant when compared to nonsite areas.

Distance to nearest surface water was measured in meters and ranged from 1 to 1000 (see table 14). Only one type of surface water,

the closest, was identified for each site. Sixty-nine percent of the

sites are located near marshes and low order streams. This may

indicate that such water types are predictor variables. If the one

Paleo-Indian hunting site is eliminated, the rest of the sites are within 600 meters of surface water. The 600 meter distance corresponds with some other models, such as Wolynec et al. (1983) and

Hay et al. (1982).

Thirty (80%) of the 38 sites are located on a topographic feature. Of those 30, 18 (60%) are on lower terraces and 12 (40%) on upper terraces. These two topographic features are significantly associated with archeological sites. 190

TABLE 14

SITE DISTANCE TO NEAREST SURFACE WATER

Distance in Meters Number of Sites Percent of Total Cases

1 1 3

2 3 8

10 8 21

20 1 3

40 1 3

50 3 8

70 1 3

100 8 21

120 1 3

150 1 3

200 4 11

250 1 3

300 2 5

400 1 3

600 1 3

1,000 1 3

Total 38 100*

♦May not exactly equal 100% due to rounding. 191

In analyzing site frequencies related to elevation, the entire

range was first assessed (see table 15) and then the elevation

variables were reduced to three; 0-30, 31-50, and 51-70 feet.

Twenty-nine sites (76%) are within the 0-30 feet elevation, 9 (24%) are in the 31-50 feet elevation, and none are in the third category.

This provides a stronger site association with lower elevations than the Quadrat data base did.

Sixteen archeological sites (42%) are in the 0-2% slope range,

17 (45%) are in the 2-5% slope range, and 5 (13%) are in the 0-5% slope range. Although quadrat slopes ranged up to 15%, these data identify a tighter fit between archeological sites and the lower slope categories.

Site aspect included 9 (24%) north, 9 (24%) east, 7 (18%) south,

3 (8%) west, 2 (5%) northeast, 4 (11%) northwest, 2 (5%) southeast, and 2 (5%) southwest. No strong preference for a site aspect is indicated.

Step 2: Spearman Correlation Coefficients

For a better determination of strength of relationship between the various environmental variables and archeological site locations,

Spearman correlation coefficients were computed. Correlation coefficients measure the strength of relationship between two variables (Helwig 1983:53). Two variables are considered to be positively correlated if cases have either correspondingly low values or high values for both variables. Variables are considered to be negatively correlated if the higher the first variable, the lower the 192

TABLE 15

SITE ELEVATIONS

Elevation in Feet Number of Sites Percent of Total Cases

0-5 3 8

0-10 1 3

6-10 2 5

10 10 26

11-20 7 18

11-30 1 3

20 1 3

30 4 11

31-40 3 8

35 1 3

40 2 5

50 3 8

Total 38 100*

♦May not exactly equal 100% due to rounding. 193

second variable, or vice versa (Norusis 1983:57). Spearman rank

correlation coefficient statistical test is a nonparametric means of

correlation which was determined to be a better choice than Pearson

correlation coefficients, considering the nature of the data. For the

purposes of this study, variables were considered to be correlated if

they were at least at the .05 level of significance.

Quadrat Data Base Correlations

For the 77 quadrat cases, the SITEPRESence variable was found to be positively correlated with: water source within the quadrat

(V/WATQUAD), low order stream within the quadrat (WLOSTRE), spring within the quadrat (WSPRING), topographic feature within the quadrat

(TOPOQUAD), low terrace along a drainage (LTERRACE), upper terrace along a drainage (UTERRACE), and 0-30 feet elevation zone (El) (see table 16). TOPOQUAD is the only variable above a 0.5 value in strength of relationship.

The SITEPRESence variable was found to be negatively correlated with: quadrat distance to water (WATDISQ), water not present in the quadrat and therefore closest water source measured from quadrat

(WATPRES), minor and ephemeral drainage measured as closest water source outside of the quadrat (DDRAIN), distance to quadrat from either the Murderkill or Saint Jones River (QDISTR), and 51-70 feet elevation zone (E3) (see table 17). No variable is greater than -0.5 value in strength of relationship.

Sites Data Base Correlations

For the 38 site cases, the following pairs of variables were 194

TABLE 16

QUADRAT DATA BASE POSITIVE CORRELATIONS WITH SITEPRESENCE VARIABLE

Strength of Level of Variable Relationship Significance

Water source within 0.36 .01 quadrat (WWATQUAD)

Low order stream within 0.47 .01 quadrat (WLOSTRE)

Spring Within Quadrat 0.26 .02 (WSPRING)

Topographic feature within 0.75 .01 quadrat (TOPOQUAD)

Low terrace along drainage 0.50 .01 (LTERRACE)

Upper terrace along 0.42 .01 drainage (UTERRACE)

0-30 feet elevation 0.33 .01 zone (El) 195

TABLE 17

QUADRAT DATA BASE NEGATIVE CORRELATIONS WITH SITEPRESENCE v a r i a b l e

Strength of Level of Variable Relationship Significance

Quadrat distance to -0.35 .01 water (WATDISQ)

Water not present in -0.36 .01 quadrat and closest source recorded (WATPRES)

Minor and ephemeral -0.37 .01 drainage measured as closest water source outside quadrat (DDRAIN)

Distance to Murderkill -0.42 .01 or Saint Jones River from quadrat (QDISTR)

51-70 feet elevation -0.25 .03 zone (E3) 196

found to be positively correlated; site distance from water (SWATDIS)

with 31-50 feet elevation (SE2), dominant poorly drained soil (SSPDl)

with marsh (SMARSH), marsh (SMARSH) with secondary poorly drained soil

(SSPD2), marsh (SMARSH) with 0-30 feet elevation (SEl), site distance

from the Murderkill or Saint Jones River (SDISTR) with site distance

from water (SWATDIS), site distance from the Murderkill or Saint Jones

River (SDISTR) with low order stream water source (SLOSTRE), site distance from water (SWATDIS) with upper terrace (SUTERRAC), site distance from the Murderkill or Saint Jones River (SDISTR) with 31-50 feet elevation (SE2), upper terrace (SUTERRAC) with 31-50 feet elevation (SE2), and lower terrace (SLTERRAC) with 0-30 feet elevation

(SEl) (see table 18).

The following pairs of variables were found to be negatively correlated (see table 19): low order stream water source (SLOSTRE) with marsh (SMARSH), dominant well drained soil (SSPD2) with marsh

(SMARSH), site distance from water (SWATDIS) with secondary poorly drained soil (SSPD2), site distance from water (SWATDIS) with 0-30 feet elevation (SEl), marsh (SMARSH) with 31-50 feet elevation (SE2), site distance from the Murderkill or Saint Jones River (SDISTR) with the closest water source being a river (SRIVER), low terrace along a drainage (SLTERRAC) with site distance from water (SWATDIS), upper terrace along a drainage (SUTERRAC) with marsh (SMARSH), low terrace along a drainage (SLTERRAC) with upper terrace along a drainage

(SUTERRAC), dominant well drained soil (SSPD2) with site distance from the Murderkill or Saint Jones River (SDISTR), site distance from the

Murderkill or Saint Jones River (SDISTR) with 0-30 feet elevation zone 197

TABLE 18

SITES DATA BASE POSITIVE COTTELATIONS

Strength of Level of Variable 1 Variable 2 Relationship Significance

Site distance from 31-50 feet elevation 0.47 .01 water (SWATDIS) (SE2)

Dominant poorly Marsh (SMARSH) 0.43 .01 drained soil (SSPDl)

Marsh (SMARSH) Secondary poorly 0.51 .01 drained soil (SSPD2)

Marsh (SMARSH) 0-30 feet elevation 0.38 .02 (SEl)

Distance from Distance from water 0.42 .01 Murderkill or St. (SWATDIS) Jones River (SDISTR)

Distance from Low order stream 0.41 .01 Murderkill or St. water source (SLOSTRE) Jones River (SDISTR)

Distance from water Upper terrace 0.43 .01 (SWATDIS) (SUTERRAC)

Distance from 31—50 feet elevation 0.36 .03 Murderkill or St. (SE2) Jones River (SDISTR)

Upper terrace 31-50 feet elevation 0.69 .01 (SUTERRAC) (SE2)

Lower terrace 0-30 feet elevation 0.53 .01 (SLTERRAC) (SEl) 198

TABLE 19

SITES DATA BASE NEGATIVE CORRELATIONS

Strength of Level of Variable 1 Variable 2 Relationship Significance

Low order stream Marsh (SMARSH) -0.52 .01 water source (SLOSTRE)

Dominant well Marsh (SMARSH) -0.43 .01 drained soil (SSWDl)

Distance from water Secondary poorly -0.48 .01 (SWATDIS) drained soil (SSPD2)

Distance from water 0-30 feet elevation -0.47 .01 (SWATDIS) (SEl)

Marsh (SMARSH) 31-50 feet elevation -0.38 .02 (SE2)

Distance from River as closest water -0.40 .01 Murderkill or St. source (SRIVER) Jones River (SDISTR)

Low terrace along Distance from water -0.47 .01 drainage (SLTERRAC) (SWATDIS)

Upper terrace along Marsh (SMARSH) -0.34 .04 drainage (SUTERRAC)

Low terrace along Upper terrace along -0.65 .01 drainage (SLTERRAC) drainage (SUTERRAC)

Secondary poorly Distance from -0.42 .01 drained soil (SSPD2) Murderkill or St. Jones River (SDISTR)

Distance from 0-30 feet elevation -0.36 .03 Murderkill or St. zone (SEl) Jones River (SDISTR)

Upper terrace along 0-30 feet elevation -0.69 .01 drainage (SUTERRAC) zone (SEl)

Low terrace along 31-50 feet elevation -0,53 .01 drainage (SLTERRAC) zone (SE2) 199

(SEl), upper terrace along a drainage (SUTERRAC) with 0-30 feet

elevation zone (SEl), low terrace along a drainage (SLTERRAC) with

31-50 feet elevation (SE2).

Variables that were found to be positively correlated are;

SWATDIS with SE2, SDISTR and SUTERRAC; SMARSH with SSPDl, SSPD2 and

SEl; SDISTR with SLOSTRE, SWATDIS, and SE2; SLTERRAC with SE2; and

SUTERRAC with SWATDIS and SEl. Positively correlated variables that

demonstrated a strength of relationship greater than 0.5 include;

SMARSH with SSPD2 (0.51), SUTERRAC with SE2 (0.69) and SLTERRAC with

SEl (0.53).

Variables that are negatively correlated are; SMARSH with

SLOSTRE, SSPD2, SE2 and SUTERRAC; SWATDIS with SSPD2, SEl, and

SLTERRAC; SDISTR with SRIVER, SSPD2 and SEl; SLTERRAC with SUTERRAC,

SWATDIS and SE2; and SUTERRAC with SEl, SLTERRAC and SMARSH.

Negatively correlated variables with a greater than 0.5 strength of

relationship are; SMARSH with SLOSTRE (-0.52), SLTERRAC with SUTERRAC

(-0.65), SUTERRAC with SEl (-0.69) and SLTERRAC with SE2 (-0.53).

One obvious problem with the correlations computed for the Sites data base is that a number of the variables are mutually exclusive,

providing potentially misleading information in relation to the overall objective. For example, upper and lower terraces are negatively correlated with each other. However, in terms of frequency related to site locations, they are both strong predictor variables.

In computing correlations for the various environmental zones and cultural time periods, some interesting correlations become evident (see table 20). Woodland II sites (WII) correlate with 200

TABLE 20

CORRELATIONS BY CULTURAL TIME PERIODS AND ENVIRONMENTAL ZONES

Strength of Level of Variable 1 Variable 2 Relationship Significance

Paleo-Indian site River as closest 0.48 .01 (PALEO) water source (SRIVER)

Archaic site River as closest 0.48 .01 (ARCHAIC) water source (SRIVER)

Woodland I sites Lower terraces 0.47 .01 (WI) (SLTERRAC)

Woodland I sites Third environmental 0.56 .01 (WI) zone (SEZ3)

Woodland I sites Fourth environmental -0.36 .03 (WI) zone (SEZ4)

Woodland II sites Lower terraces 0.47 .01 (WII) (SLTERRAC)

Woodland II sites Microband base camps 0.67 .02 (WII) (MICROBAS)

Macro-band base Secondary poorly 0.48 .01 camp (MACROBAS) drained soils (SSPD2)

Environmental zone 1 Topographic features -0.33 .05 (SEZl) (TOPOSITE)

Environmental zone 1 Primarily well drained -0.64 .01 (SEZl) soils (SSWDl)

Environmental zone 1 Primarily poorly 0.64 .01 (SEZl) drained soils (SSPDl)

Environmental zone 2 Marsh (SMARSH) 0.48 .01 (SEZ2)

Environmental zone 2 Secondary well drained 0.44 .01 (SEZ2) soils (SSWD2) 201

TABLE 20— Continued

Strength of Level of Variable 1 Variable 2 Relationship Significance

Environmental zone 2 Low order stream as -0.33 .04 (SEZ2) nearest water source (SLOSTRE)

Environmental zone 3 Water source distance -0.33 .04 (SEZ3) from site (SWATDIS)

Environmental zone 3 Elevation zone 2 -0.43 .01 (SEZ3) (SE2)

Environmental zone 3 Lower terraces 0.59 .01 (SEZ3) (SLTERRAC)

Environmental zone 3 0-30 feet elevation 0.43 .01 (SEZ3) (SEl)

Environmental zone 4 Water source distance 0.46 .01 (SEZ4) from site (SWATDIS)

Environmental zone 4 River as closest water 0.42 .01 (SEZ4) source (SRIVER)

Environmental zone 4 31-50 feet elevation 0.69 .01 (SEZ4) (SE2)

Environmental zone 4 Upper terraces 0.61 .01 (SEZ4) (SUTERRAC)

Environmental zone 4 Marsh (SMARSH) -0.55 .01 (SEZ4)

Environmental zone 4 0-30 feet elevation -0.69 .01 (SEZ4) (SEl)

Environmental zone 4 Lower terraces -0.44 .01 (SEZ4) (SLTERRAC) 202 micro-band base camps (MICROBAS). However, it should be pointed out that there is only one case with these variables. SRIVER (site distance to river) correlates with the one Paleo-Indian site (PALEO) and the one Archaic site (ARCHAIC). Both Woodland I (WI) and Woodland

II sites (WII) are positively associated with lower terraces

(SLTERRAC). The one macro-band base camp (MACROBAS) is associated with secondary poorly drained soils (SSPD2). Woodland I sites (WI) are positively associated with the third environmental zone (SEZ3) and negatively associated with the fourth environmental zone (SEZ4).

Environmental Zone 1 (SEZl) is negatively correlated with topographic features (TOPOSITE) and SSWDl (primarily well drained soils), and positively correlated with SSPDl (primarily poorly drained soils).

Environmental Zone 2 (SEZ2) is positively correlated with SMARSH and

SSWD2 (secondary well drained soils); and negatively correlated with

SLOSTRE (low order stream nearest water source). Environmental Zone 3 is also negatively correlated with SWATDIS (water source distance from site) and elevation zone 2 (SE2); and positively correlated with

SLTERRAC and elevation zone 1 (SEl). Environmental Zone 4 (SEZ4) is positively correlated with SWATDIS, SRIVER, SE2; and SUTERRAC; and negatively correlated with SMARSH, SEl and SLTERRAC.

Step 3; Multiple Regression

Regression analysis is an equation which describes the nature of a relationship between two or more variables. It also provides variance measures for assessing the accuracy with which the equation can predict values on the criterion variable. With multiple 203

regression analysis, one predicts an object's value on a criterion variable in relation to its value on each of several predictor variables. Its objective is to determine the relative importance of the predictor variables as they contribute to the variation in the criterion, or dependent, variable (Kachigan 1982:160-61).

Because regression analysis analyzes the predictor variable's contributions to variation in the dependent variable, this analysis was conducted on the Quadrat data base. The Quadrat data base provides information on site presence and absence so that the variation can be compared. The Sites data base only provides site presence information. In conducting the analysis, the number of variables were limited to less than nine to avoid the possibility of regression to the mean.

Two multiple linear regression analyses, one R-square analysis and three stepwise regression analyses were conducted. The first multiple regression analysis included the variables WLOSTRE, WSPRING,

WATDISQ, DDRAIN, LTERRACE, and UTERRACE. They were selected according to the results of the correlation coefficient computations. Because including them all would provide too many variables for analysis, six of the highly correlated variables were eliminated. The ones which were eliminated were considered to be redundant, mutually exclusive from each other, or representing too small a number of cases.

The results of the first analysis indicates that the model is significant at the .01 level. The R-square, which measures how much variation in the SITEPRES variable can be explained by the predictor variables, is 55% and the adjusted R-square is 52%. The two variables 204 which contribute significantly to the model are LTERRACE and UTERRACE at the ,01 significance level.

The second multiple linear regression analysis included the variables WWATQUAD, WLOSTRE, WSPRING, TOPOQUAD, LTERRACE, UTERRACE and

El. The results of this second analysis indicates that the model is significant at the .01 level. The R-square is 61% and the adjusted

R-square is 57%. The two variables which contribute significantly to the model are TOPOQUAD at the .03 significance level and El at the .03 significance level.

Because the multiple linear regression models had few variables which contributed significantly to the model, an R-square analysis was conducted. R-square analyses measure how much the dependent variable's variation can be explained by each variable individually, and then in combination. Considering SITEPRES to be the dependent variable, the independent variables WWATQUAD, WLOSTRE, WSPRING,

WATDISQ, DDRAIN, QDISTR, TOPOQUAD, LTERRACE, UTERRACE, El and E3 were analyzed. With each variable analyzed separately for the model,

TOPOQUAD explained 56% of the variation. The next closest variable is

LTERRACE at 25%. As various combinations of variables are added, the variation explained increases to 62%. The 62% level is reached with five variables in the following order: WLOSTRE, DDRAIN, TOPOQUAD,

LTERRACE, and El. However, TOPOQUAD and El explain 58%.

To determine if one could obtain more information by using a stepwise regression analysis, three were conducted. Stepwise regression searches for the best model by analyzing the independent variables one at a time. Variables have to meet the .15 significance level to be considered for entry into the model. 205

The first stepwise regression analysis studied the variables

WWATQUAD, WLOSTRE, WSPRING, TOPOQUAD, LTERRACE, UTERRACE and El.

TOPOQUAD was the first variable entered with an R-square of 56% and El was the second variable entered at a cumulative R-square of 58%.

The second stepwise regression analysis studied the variables

WWATQUAD, WLOSTRE, WSPRING, WATDISQ, WATPRES, DDRAIN, QDISTR,

TOPOQUAD, LTERRACE, UTERRACE, El and E3. TOPOQUAD was the first variable entered with an R-square of 56%, El was the second variable entered at an R-square of 58%, and DDRAIN at 60%.

The third stepwise regression analysis studied the variables

WWATQUAD, WLOSTRE, WMARSH, WSPRING, WATPRES, TOPOQUAD, LTERRACE,

UTERRACE, SWDl, SPDl, SWD2, SPD2, El, E2, E3, SLl, SL2, and SL3.

Again, TOPOQUAD was the first variable entered with an R-square of 56% and El was the second variable entered at an R-square of 58%.

From these multiple regression analyses, LTERRACE and UTERRACE are the most significant variables identified in the first multiple linear regression analysis. However, TOPOQUAD and El are the most significant variables identified in the second multiple linear regression analysis and the stepwise regression procedures.

Discussion

In conducting this study, it was first thought that the predictor variables would be nearness to surface water (i.e., all archeological sites would be found within a certain distance of surface water), well drained soils, low slope gradient, and topographic features. This expectation was based on previous archeological modeling studies discussed in Chapter II. 206

From the frequency tabulations, some variables were identified

as potential predictor variables, such as nearness to water, 0-5%

slopes, below 50 foot elevation, and topographic features (terraces).

However, strength of relationship needed to be determined using

Spearman correlation coefficients and multiple regression analysis.

The Spearman correlation coefficient computations for the

Quadrat data base identified site presence as positively correlated with the WWATQUAD, WLOSTRE, WSPRING, TOPOQUAD, LTERRACE, UTERRACE and

El variables. TOPOQUAD was the only variable above a 0.5 value in strength of relationship. It also found site presence as negatively correlated with the WATDISQ, WATPRES, DDRAIN, QDISTR, and E3 variables.

LTERRACE and UTERRACE were also found to be the strongest variables in one of the multiple regression analyses. However,

TOPOQUAD and El are the most significant variables identified in the second multiple linear regression analysis and the stepwise regression procedures.

In addition to analyzing the environmental variables to identify predictor variables, quadrat site probabilities which were not well predicted by the model were analyzed. Ten quadrats which were predicted to have a low probability contained archeological sites.

They are; 1.040, 1.083, 1.115, 2.123, 2.124, 3.185, 3.212, 4.426,

4.519, and 4.623. Of those, six (60%) had well drained soils, three

(30%) had primarily well drained soils with secondary poorly drained soils, and one (10%) had primarily poorly drained soils. The within quadrat or nearest water source is marsh for three quadrats (30%), 207 minor and ephemeral drainage for three quadrats (30%), low order

stream for two (20%), river for one (10%) and spring for one (10%).

Five (50%) quadrats did not contain topographic features, one contained a swampy floodplain (10%), two (20%) contained lower terraces and two (20%) contained upper terraces. Nine quadrats (90%) are in 0-30 feet elevation zone (El), and one (10%) is in the 31-50 foot elevation zone (E2). Eight quadrats (80%) have 0-5% slope and two (20%) have 0-10% slope.

Two quadrats which were predicted as having a medium probability and did not contain archeological sites are 1.088 and 4.521. One quadrat (50%) had well drained soils and one (50%) had primarily well drained soils with secondary poorly drained soils. The within quadrat or nearest water source is marsh and bay/basin for one quadrat (50%) and minor and ephemeral drainage for one quadrat (50%). Neither quadrat (100%) contains a topographic feature. Both quadrats (100%) are in the 0-30 feet elevation zone (El). One quadrat (50%) has 0-5% slope and one (50%) has 0-10% slope.

In considering Custer et al.'s (1984:92-100) Woodland I Delmarva

Adena mortuary-exchange model, an analysis of the macro-band and micro-band base camps in relation to the Saint Jones (7K-D-1),

Frederica Adena (7K-F-2) and the Island Field (Webb) (7K-F-17) mortuary-exchange centers was conducted. The macro-band base camp

(3.253B) contains components from throughout the Woodland I period as indicated by the recovery of Wolfe Neck and Hell Island sherds; steatite bowl fragments; Susquehanna broadspear, stemmed, and Snyder's projectile points; and a cache of argillite preforms. The macro-band 208

base camp (3.253B) is 1,9 kilometers from the Frederica site, 13.6

kilometers from the Saint Jones site, and 7.1 kilometers from the

Island Field site. The micro-band base camp 3.253A also spans the

Woodland I period with Wolfe Neck sherds and Savannah River and

triangular projectile points. It is 2 kilometers from the Frederica

site, 13.5 kilometers from the Saint Jones site, and 7 kilometers from the Island Field site. The second micro-band base camp (3.011) dates to early in the Woodland I period with Bare Island and square stemmed projectile points. It is 14.8 kilometers from the Frederica site, 1.7 kilometers from the Saint Jones site, and 12.8 kilometers from the

Island Field site. Assuming the macro-band base camp 3.253B and micro-band base camp 3.253A are contemporaneous with the Island Field site Webb phase, these distances comply with the territorial radii of

7 kilometers for major centers and 4.8 kilometers for minor centers.

They are also within the 7 kilometer territory of the Frederica mortuary-exchange center. If the micro-band base camp 3.011 is contemporaneous with the Saint Jones Adena occupation, it would have been within the territory of the Saint Jones mortuary-exchange center. While these sites are located in relation to environmental predictor variables, they may also be located for purposes of social organization. It should also be pointed out that all but the Island

Field site are located within Environmental Zone 3, the Mid-drainage

Zone, which includes all Post-Pleistocene oligohaline locations.

From this analysis, some recommendations can be made for possible improvement of the model. First, the sites identified in low probability areas are primarily close to marshes, minor and ephemeral 209

drainages, and spring water sources (70%). However, minor and

ephemeral drainages are negatively correlated with site presence when

one analyzes the entire Quadrat data base. Marshes are positively

correlated. An attempt should be made to better identify high

probability areas related to marshes.

Secondly, the model did not work well for the first

environmental zone. As was stated previously, this may be a factor of

the small number of quadrats for that zone. Assuming that the problem

is with the model and not the sample size, it is suggested that the

model be modified for Environmental Zone 1. The modifications should

be to better predict sites associated with marshes and poorly drained

soils. All three low probability quadrats with sites in Zone 1 had

either primarily or secondarily poorly drained soils and two are

associated with marshes.

Third, this analysis found sites to be located on terraces with

primarily well drained soils. While well drained soils were

determined to not be a predictor variable, the combination of terraces and well drained soils may be a predictor variable when located adjacent to the other critical predictor variables. It is recommended that such situations be analyzed in future studies to determine its significance as a predictor factor.

Fourth, elevation should be considered as a possible predictor variable because lower elevations (El) are identified as strongly correlated with site presence in both the correlation coefficient computations and the regression analyses. Higher elevations (E3) are identified as negatively correlated with site presence by the correlation coefficient computations. 210

Fifth, the model logistical regression formula should be slightly adjusted to enlarge the probability classes for high and medium probability and reduce it for low probability. In other words, the p>.75 for high probability and .50

Because specific environmental variables can be identified and recommended for improvement of the model, the null hypothesis is rejected.

Problems

There are a number of potential problems with the data analysis which should be mentioned. These problems point out potential difficulties with all archeological models. First is the subjectivity with which the potential predictor variables are selected and defined. One chooses predictor variables which one believes are important or other archeologists have stated are important. A good example of the problems with this is site association with well drained soils. Many models and archeologists associate site location with well drained soils. However, one of the surprises in this analysis is that well drained soils is not a predictor variable. That is because there are just as many nonsite quadrats with well drained 211

soils as there are site quadrats. This may be because of the sample area selected. However, the soil survey maps of the area seem to indicate that the two watersheds contain primarily well drained soils. This demonstrates the importance of looking at nonsite areas in addition to site areas.

In addition to looking at which variables are selected, how one defines, or does not define, the variables is also very important. In identifying water source types and topographic features for this study, it became apparent that subjective decisions are involved in selecting variable terminology and priority type. For example, in one quadrat, the same topographic feature was termed a low terrace by one archeologist and a ridge by another. Also, one can select different water source types as being the closest depending on the importance one places on different water types (e.g., a marsh versus a river).

It therefore is critical that one carefully define variable terminology and selection process. Yet very few archeological model descriptions do just that.

Another problem is with the statistical analyses. Results of the multiple regression analysis depend on the variables entered into the equation. Correlation coefficient computations, on the other hand, look at the strength of relationship of all potential variables and eliminate a second level of subjective selection. Conducting frequencies and correlation coefficient analyses are a good first step in identifying important variables that can be considered in other statistical analyses such as multiple regression. 212

Objective Four: To Test the Empirically-Described Model

Against the LANDSAT-Generated Model

The final objective of the data analysis is to determine if predictions might be different between the empirically-described model presented in Chapter III and the LANDSAT-generated model. The hypothesis is: There is a significant difference between the predictions of the LANDSAT-generated model and the empirically- described model. The null hypothesis is: There is no significant difference between the predictions of the LANDSAT-generated model and the empirically-described model.

To test this hypothesis, only those sites which have been identified by cultural time period in the Sites data base are used.

This provides a test of the empirically-described model which varies according to cultural time periods. The sites include one Paleo-

Indian site, one Archaic site, 16 Woodland I components and 3 Woodland

II components (see table 21).

In analyzing the empirically-described model, the model did not predict the Paleo-Indian site which is not near a game-attractive locale or on a ridge, but did predict the Archaic site which is on an upper terrace 200 meters from a river.

For both the Woodland I and II sites in the Delaware Bay Shore

Zone, micro-band base camps are predicted to be on upper terraces near freshwater sources and tidal marshes and procurement sites are along tidal marshes and swampy low order floodplains. For the Mid-drainage

Zone, macro-band base camps are predicted to be on low terraces of major drainages at stream confluences and at saltwater/freshwater 213

TABLE 21

ARCHEOLOGICAL SITES

Distance Site Proba­ to Water Water Topographic Nimber Type bility in Meters Type Feature

Paleo-Indian: 4.623 hunting low 1,000 river none

Archaic: 4.609 medium 300 river u. terrace

Woodland I: 2.105 high 100 marsh 1. terrace 2.114 medium 100 marsh 1. terrace 3.011 micro-band medium 70 stream 1. terrace base camp 3.012 high 100 stream 1. terrace 3.185A procurement medium 50 stream 1. terrace 3.185B procurement low 40 stream 1. terrace 3.212 medium 150 spring 1. terrace 3.253A micro—band high 10 marsh 1. terrace base camp 3.253B macro—band high 2 marsh 1. terrace base camp 3.253C medium 10 spring 1. terrace 3.254A medium 2 marsh 1. terrace 3.254B medium 200 marsh u. terrace 3.254C medium 2 marsh 1. terrace 4.519 low 100 drain u. terrace 4.520 low 300 drain u. terrace 4.6470 medium 200 stream u. terrace

Woodland II: 2.116 medium 10 marsh 1. terrace 3.253A micro-band high 10 marsh 1. terrace base camp 4.543 medium 100 stream u. terrace 214

TABLE 21— -Continued

Distance to Site River in Soil Types: Number Meters SWDl SPDl SWD2 SPD2 Elevation Slope

Paleo-Indian: 4.623 1,000 Y 2 1

Archaic: 4.609 300 YY 2 1

Woodland I: 2.105 620 YY 1 1 2.114 800 YY 1 1 3.011 500 YY 1 1 3.012 900 Y Y 1 1 3.185A 1,920 Y Y 1 1 3.185B 2,100 YY 1 1 3.212 1,000 Y Y 1 2 3.253A 500 YY 1 3.253B 400 YY 1 1 3.253C 700 YY 1 1 3.254A 480 YY 1 1 3.254B 680 YY 1 1 3.254C 500 Y Y 1 1 4.519 1,900 YY 1 1 4.520 1,900 Y Y 1 1 4.647C 1,400 Y Y 2 1

Woodland II: 2.116 850 YY 1 1 3.253A 500 YY 1 1 4.543 800 Y Y 1 1

NOTE: Soil types recorded as *Y' for presence as follows: SWDl is primary well drained soil; SWD2 is secondary well drained soil; SPDl is primary poorly drained soil; and SPD2 is secondary poorly drained soil. Elevation is 1 for 0-30 feet and 2 for 31—50 feet. Slope is 1 for 0-5% and 2 for ranging to 10%.

NOTES: Drain is minor and ephemeral drainage. Stream is low order stream. L. terrace is lower terrace, U. terrace is upper terrace. 215

Interface of marshes; micro-band base camps are at confluences of low

order streams and tidal marshes; and procurement sites are along minor

ephemeral drainages adjacent to poorly drained woodlands and on small

sand ridges and knolls.

For the Woodland I sites, 7 (44%) are near marshes, 5 (31%) are

near low order streams, 2 (13%) are near springs and 2 (13%) are near

minor and ephemeral drainages. Twelve (75%) are situated on lower

terraces and 4 (25%) are on upper terraces. None are located at

confluences of streams and other water sources. Two (13%) of the

sites are in the Bay Front/Mid-drainage Transition Zone (Environmental

Zone 2), 11 (67%) are in the Mid-drainage Zone (Zone 3), and 3 (19%)

are in the Drainage Divide Transition Zone (Zone 4). Applying the

model prediction for the Delaware Bay Shore Zone to Zone 2 works well

because both sites are located near marshes and are on lower

terraces. Although the Delaware Bay Shore Zone model is not supposed

to be applied to Zone 3, that model would work for Zone 3 because 5

(45%) of the sites are also located on lower or upper terraces near

marshes. Four (36%) are on lower terraces near low order streams and

2 (18%) are on lower terraces by springs. Therefore, the model does not predict sites in Zone 3, the Mid-drainage Zone. Because 2 (67%) of the 3 sites in Zone 4 are on upper terraces along minor and ephemeral drainages and the third one (33%) is on an upper terrace along a low order stream, the Mid-drainage Zone model may have predicted the first two sites. Although they are not located at stream confluences, it should be noted that nine of the sites are between 480 and 800 meters from the Murderkill or Saint Jones River. 216

Of the three Woodland XI sites, one is located in each of the

last three environmental zones. The sites in Zone 2 and Zone 3 are

located on lower terraces near marshes. As with the Woodland I sites,

the Delaware Bay Shore Zone model would have predicted site locations

along marshes, but not the Mid-drainage Zone model. The site in Zone

4 is on an upper terrace along a low order stream. Although not at a

confluence, it may have been predicted by the Mid-drainage Zone

model. All three sites are between 500 and 850 meters from the

Murderkill or Saint Jones River.

The empirically-described model would have predicted 6 out of

the 21 sites, or 29%. For the sites analyzed, the model is too

dependent on confluences with streams and other water sources. Most

sites are located primarily along marshes and low order streams.

In analyzing the results of the LANDSAT-generated model, four of

the sites are in a low probability area, twelve are in a medium

probability area, and five are in a high probability area. Therefore

the LANDSAT-generated model predicted site locations 81% of the time.

For the entire Sites data base of thirty-eight, sites were accurately

predicted in twenty-seven cases, or 71% accuracy rate. In comparing only the sites with diagnostics, the LANDSAT-generated model

predictions were significantly more accurate than the empirically-described model.

For this fourth objective, the null hypothesis that there is no significant difference between the predictions of the LANDSAT- generated model and the empirically-described model is rejected and the hypothesis is accepted. The Landsat-generated model’s predictions are significantly more accurate than the empirically-described model. CHAPTER VI

SUMMARY AND CONCLUSIONS

Summary

The major purpose of this dissertation is to test a Delaware model of prehistoric human subsistence and settlement patterns which is based on technoeconomic theory and cultural ecology. To achieve this purpose, a review of the history of archeological models and different kinds of models was conducted. Then Delaware prehistory and the subsistence and settlement model were discussed. Finally, the field survey and test of the model were presented.

This research tested the following hypothesis; Assuming human adaptation to the changing environment, which has been documented for the Middle Atlantic region from 12,000 B.C. to A.D. 1500, it is postulated that there will be concomitant changes in prehistoric subsistence and settlement patterns in the watersheds, and both these changes and the subsistence and settlement patterns can be correlated with contemporary environmental variables. Differences in numbers and locations of sites and correlations of site types with environmental variables, as predicted by the Delaware LANDSAT-generated model

(Custer et al. 1986), are indicators of these changes. The null hypothesis is that: There is no correlation between prehistoric subsistence and settlement patterns and specific environmental variables as predicted by the Delaware LANDSAT-generated model.

217 218

The hypothesis was tested using data generated from a sample

archeological survey. The data were primarily gathered to determine if

the observed subsistence and settlement pattern met the predicted

pattern, in terms of both the LANDSAT-generated model and the

empirically-described model in Chapter III. In addition to analyzing

the observed and predicted data, environmental information was

collected to identify any other possible influencing factors.

The test data were collected by surveying a five percent

stratified random quadrat sample of the Saint Jones and Murderkill

River watersheds in Kent County, Delaware. Each quadrat was 400 meters

on a side, comprising a total 160,000 square meters, or sixteen hectares. Four environmental zones were surveyed; the Delaware Bay

Shore Zone (Zone 1); the Bay front/Mid-drainage Transition Zone

(Zone 2); the Mid-drainage Zone (Zone 3); and the Drainage Divide

Transition Zone (Zone 4). A five percent random sample of each environmental zone was surveyed for a total of 77 quadrats; 1,232 hectares. The survey took four months and was conducted between June of 1986 to July of 1987. Quadrats were surveyed according to cropping and hunting schedules. Surveys were conducted after fields were plowed for maximum visibility, whenever possible. Thirty-eight archeological sites were located during the survey.

To conduct the data analysis, two data bases were developed.

The first data base, entitled Quadrat, contained quadrat-related data.

It consisted of seventy-seven records, one for each quadrat surveyed, and sixty-four fields or variables. The second data base. Sites, contained site-specific data. This data base consisted of thirty-eight 219

records and sixty-six fields. While the research was based on the

quadrat as a unit of analysis, the second data base was developed to

determine if additional information could be gained by analyzing

site-specific data. Some of the variables (e.g., aspect and elevation)

and the statistical techniques (e.g., multiple regression) included in

the data analysis were selected as a result of the review of

archeological models presented in Chapter II.

To test the hypothesis, the data analysis was divided into four

objectives. They were to determine if: (1) the observed data met the

LANDSAT-generated model’s predictions; (2) a random model's predictions

could predict site locations as accurately as the LANDSAT-generated

model; (3) recommendations for improving the model could be made

through ah analysis of specific environmental variables; and

(4) predictions might be different between the LANDSAT-generated model

and the empirically-described model.

The first objective is the primary objective of the research.

The hypothesis for Objective One is: The LANDSAT-generated model

predicts the presence and absence of prehistoric archeological sites in

the Saint Jones and Murderkill River watersheds with at least a 75%

accuracy. The null hypothesis is: The LANDSAT-generated model predicts

the presence and absence of prehistoric archeological sites in the

Saint Jones and Murderkill River watersheds with less than 75% accuracy.

According to the model's logistical regression formula, the average predicted probability that a site will be found in a high probability area is 88%. For medium probability areas, the average 220

predicted probability is 63%, For low probability areas, it is 25%.

For the purpose of this study, however, high and medium probability

predictions were considered accurate if a site was located in the those

areas. Low probability predictions were considered accurate if no site

was found in the low probability areas. This is stricter than the

model's average predicted probabilities but was considered the best way

to identify correlations of site locations and environmental variables,

and recommend improvements in the model.

The study found the model's high probability prediction to be

100% accurate; the medium probability predictions to be 88% accurate;

and the low probability predictions to be 82% accurate. In analyzing

the model's predictive power by the number of quadrats accurately

predicted, the overall accuracy rate is 84%.

In determining if the model was a better predictor for certain

environmental zones, it was found that the model's predictions were:

33% accurate in Zone 1; 75% accurate in Zone 2; 88% accurate in Zone 3;

and 91% accurate in Zone 4. Correlation coefficient computations

supported these findings by showing a significant negative relationship

between the model's accuracy and Zone 1 and a significant positive

relationship between the model's accuracy and Zone 4. Although the

relatively small number of quadrats in Zone 1 may be an Influencing

factor, it is obvious that the model's predictive power for Zone 1 is much smaller than that of the other environmental zones. This may be due to two reasons. First, the rise in sea level has probably caused more changes in Zone 1 than in any other environmental zone during the

Holocene. Secondly, as was indicated by the correlation coefficient 221

computations, Zone 1 is negatively correlated with topographic features and primarily well drained soils; and positively correlated with primarily poorly drained soils. It may be that the logistical regression model has difficulty differentiating the critical variables in areas of primarily poorly drained soils. Perhaps the model works best in Zone 4 because of the clear delineation of rivers and low order streams, topographic relief and well drained soils.

In concluding the analysis for Objective One, the null hypothesis is rejected because the LANDSAT-generated model realized an overall 84% accuracy rate.

The purpose of Objective Two is to determine if one could obtain as accurate a predictive model by chance, as one could obtain using the

LANDSAT-generated model. The hypothesis for Objective Two is: The random model predicts the presence and absence of prehistoric archeological sites in the Saint Jones and Murderkill River watersheds with at least the same 84% accuracy as the LANDSAT-generated model.

The null hypothesis is: The random model predicts the presence and absence of prehistoric archeological sites in the Saint Jones and

Murderkill River watersheds with significantly less than an 84% accuracy rate. .

It was determined that the random model's overall accuracy rate was 40%. A chi-square analysis found the difference between the results of the LANDSAT-generated model and the random model to be significant at the .0001 level. For this second objective, the null hypothesis is accepted and the hypothesis is rejected. The random model predicts the presence and absence of archeological sites with significantly less than an 84% accuracy rate. 222

The third objective is to go beyond a test of the model and

determine if any recommendations could be made to improve the model.

For this third objective, the hypothesis is: Specific environmental

variables that strongly correlate with site presence or absence can be

identified and recommended for improvement of the model. The null

hypothesis is: No environmental variables that strongly correlate with

site presence or absence can be identified and recommended for

improvement of the model.

Three different statistical analyses were conducted: frequency

tabulations, correlation coefficients, and multiple regression. The

statistical analyses found topographic features, primarily upper and lower terraces, to be the strongest individual predictor variable for site location in all four statistical analyses. In the regression analyses, for example, TOPOQUAD was found to explain 56% of the variation in both the R-square and stepwise regression.

The correlation coefficient computations found that site presence is positively correlated with the water source within quadrat

(WWATQTJAD), low order stream water source within quadrat (WLOSTRE), spring water source within quadrat (WSPRING), topographic feature within quadrat (TOPOQUAD), lower terrace (LTERRACE), upper terrace

(UTERRACE) and 0-30 feet elevation zone (El) variables, with TOPOQUAD being the only variable above a 0.5 value in strength of relationship.

Site presence is negatively correlated with water distance from quadrat

(WATDISQ), water not present in quadrat but closest source measured from quadrat (WATPRES), minor and ephemeral drainage as closest water source from quadrat (DDRAIN), quadrat distance from either the 223

Murderkill or Saint Jones River (QDISTR), and 51-70 feet elevation zone

(E3) variables, with no variables being above a -0.5 value in strength of relationship.

Elevation zone 1 (0-30 feet) was also found to be positively correlated with site presence in the correlation coefficient and the regression analyses. Elevation zone 3 (51-70 feet) had a negative correlation with site presence.

A number of variables which were expected to be predictor variables turned out not to be. For example, well drained soils was expected to be a predictor variable. It was not. While frequency tabulations found a heavy weighting of quadrats with sites (86%) containing the well drained soil Sassafras sandy loam (SaA and SaB), it found almost as many quadrats without sites (69%) containing that soil type. Also, slope was not a predictor variable, even though no archeological site was found on a greater than 5% slope. This demonstrates the importance of analyzing nonsite locations in addition to site locations to determine predictor variables. From this analysis, recommendations were made for improvements to the

LANDSAT-generated model. Therefore, for Objective Three, the null hypothesis is rejected.

The fourth objective of the data analysis is to determine if predictions might be different between the empirically-described model presented in Chapter III or the LANDSAT-generated model. The hypothesis is: There is a significant difference between the predictions of the LANDSAT-generated model and the 224

empirically-described model. The null hypothesis is: There is no

significant difference between the predictions of the LANDSAT-generated

model and the empirically-described model.

In testing this hypothesis, only those twenty sites identified by

cultural time period were used. It was found that the empirically-

described model would have predicted 29% of the sites and the

LANDSAT-generated model predicted 81%. The empirically-described model

is too dependent on confluences of streams and other water sources.

Most sites are not located at confluences. The LANDSAT-generated

model, on the other hand, may be too dependent on water-related

variables.

For this fourth objective, the null hypothesis that there is no

significant difference between the predictions of the LANDSAT-generated

model and the empirically- described model is rejected and the

hypothesis is accepted. The LANDSAT-generated model's predictions are

significantly more accurate than the empirically-described model.

Conclusions Related to the Research Hypothesis

Based on the analyses discussed above, the research's null

hypothesis is rejected and the hypothesis is accepted. The research

found the LANDSAT-generated model, which is based on environmental

predictor variables, to be 84% accurate. Such accuracy cannot be

attained by random chance.

It was also found that changes in the subsistence and settlement patterns could be inferred from differences in numbers and locations of

sites and correlations of site types with environmental variables. 225

Both the Paleo—Indian and Archaic sites are In Environmental Zone 4.

The nearest water source for the Paleo-Indian site is a river 1000

meters away; and for the Archaic site a river 300 meters away. Of the

16 Woodland I components, 69% (11) are in Environmental Zone 3, 19% (3)

are in Environmental Zone 4, and 13% (2) are in Environmental Zone 2.

This Woodland I focus in Environmental Zone 3 is supported by the

correlation coefficient computations which found Woodland I sites

positively associated with Environmental Zone 3 and negatively

associated with Environmental Zone 4. Also, the one Woodland I macro-band base camp and two micro-band base camps are in Zone 3. All

three Woodland I sites are on upper terraces in Zone 4, and all but one are on lower terraces in the other two zones. Again, in the correlation coefficient computations, both Woodland I and Woodland II sites are positively associated with lower terraces. The Woodland I sites are associated with marshes, low order streams or springs, except in Zone 4 where two are associated with minor and ephemeral drainages.

The three Woodland II sites are scattered throughout the watersheds, with one being in each of the last three environmental zones.

Although most of the data relate to the Woodland I time period, they seem to support the settlement- subsistence pattern based on technoeconomic theory and cultural ecology. The Paleo-Indian and

Archaic sites support the relatively mobile hunting and gathering pattern with no indication of long term sedentary macro-band base camps. There is a drastic change with the Woodland I sites, however.

The sites do indicate a focus on riverine and estuarine resources with larger, more sedentary habitation sites. Most sites are located 226

adjacent to a marsh or low order stream. One macro-band and two

micro-band base camps were identified. A large variety of nonlocal

materials including steatite bowl fragments, a Snyder's projectile

point of nonlocal chert, and a cache of argillite preforms, were

recovered. These types of sites were not found for the Woodland II period. No large macro-band base camps were located for the Woodland

II time period. There is, however, the possibility of one micro-band base camp which is a second component to a Woodland I micro-band base camp. The sites may exhibit more of the Woodland II settlement pattern model 2 or 3, with seasonal base camps. The apparent emphasis on estuarine resources during the Woodland I time period does support the cultural ecology and technoeconomic theory as an explanation of prehistoric culture change in the Middle Atlantic Region. The evidence of extensive trading networks and possible social stratification indicate social adaptations as well. There are not enough data, however, to determine how much of the adaptation was due to the cultural environment and how much was due to the natural environment.

In considering Custer et al.'s (1984:92-100) Woodland I Delmarva

Adena mortuary-exchange model, an analysis of the macro-band and micro-band base camps in relation to the Saint Jones (7K-D-1),

Frederica Adena (7K-F-2) and the Island Field (Webb) (7K-F-17) mortuary-exchange centers was conducted. The Woodland I macro-band base camp 3.253B is 1.9 kilometers from the Frederica site, 13.6 kilometers from the Saint Jones site, and 7.1 kilometers from the

Island Field site. The Woodland I micro-band base camp 3.253A is

2 kilometers from the Frederica site, 13.5 kilometers from the Saint 227

Jones site, and 7 kilometers from the Island Field site. The second

micro-band base camp 3.011 is 14.8 kilometers from the Frederica site,

1.7 kilometers from the Saint Jones site, and 12.8 kilometers from the

Island Field site. Assuming the macro-band base camp 3.253B and micro-band base camp 3.253A are contemporaneous with the Island Field

site Webb phase, these distances comply with the territorial radii of

7 kilometers for major centers and 4.8 kilometers for minor centers.

They are also within the 7 kilometer territory of the Frederica mortuary-exchange center. If the micro-band base camp 3.011 is contemporaneous with the Saint Jones Adena occupation, it would have been within the territory of the Saint Jones mortuary-exchange center.

While these sites are located in relation to environmental predictor variables, they may also be located for purposes of social organization. It should also be pointed out that all but the Island

Field site are located within Environmental Zone 3, the Mid-drainage

Zone, which includes all Post-Pleistocene oligohaline locations.

While there is a strong correlation between site locations and the environmental predictor variables, it should be noted that the use of modern resource distribution and paleoenvironmental reconstructions to project prehistoric resource distribution may not be completely accurate. This may be one of the reasons why the model did not work well for the first environmental zone, the Delaware Bay Shore Zone.

The Delaware Bay Shore Zone has probably undergone the most change during the Holocene. In addition, site locations which have been correlated with environmental variables may actually have been selected for social reasons which are not recognizable in the archeological record. 228

This empirical test and the statistical analyses indicate that

the model explains about 82% of the variability in site presence and

absence, depending on which statistical analysis is uSed. In addition

to the survey finding an 84% accuracy rate, the regression analyses

accounted for 56% to 82% of the variance. The analysis of variance

conducted by Custer et al. (1986:580) found the model accounting for up

to 82% with a .407 standard deviation of the residuals. The variance

which is not explained may be due to social variables as has. been

suggested by Custer (1984a, 1987) or by environmental variables which

have yet to be elicited. Much more work needs to be done before one

can say whether the remaining variance is due to social or

environmental variables. The only possible social considerations

revealed in this survey are those related to the macro-band and

micro-band base camps. Their locations, however, were predicted by the

LANDSAT-generated model. Refinement of some environmental variables in

the LANDSAT-generated model is recommended. The suggestions are as

follows.

First, the sites identified in low probability areas are

primarily close to marshes, which are positively correlated with site

presence. An attempt should be made to better identify high

probability areas related to marsh areas.

Secondly, the model did not work well for the first environmental

zone. It is suggested that the model be modified for Environmental

Zone 1, the Delaware Bay Shore Zone. The modifications should be to

better predict sites associated with marshes and poorly drained soils.

Third, the combination of terraces and well drained soils may be

a predictor variable when located adjacent to the other critical

K.. 229

predictor variables. It is recommended that such situations be

analyzed in future studies to determine its significance as a predictor

factor.

Fourth, lower elevations (0-30 feet) should be considered as a

possible predictor variable because they are identified as strongly

correlated with site presence in both the correlation coefficient

computations and the regression analyses.

Fifth, the model logistical regression formula should be slightly

adjusted to enlarge the probability classes for high and medium

probability and reduce it for low probability.

Conclusions Related to Archeological Models

It is evident from this study that there are many different

theoretical and methodological approaches to modeling. There is no one right approach or one 'true' model. Models only mirror selected aspects of observations (Clarke 1972:4). While great progress has been made in understanding prehistoric human subsistence and settlement patterns, much research is still needed in modeling those patterns.

There are a number of problems with current modeling efforts.

This study, and the analyses conducted by Kohler and Parker (1986) and

Brose (1981), indicate three major problem areas. They are: (1) the biases inherent in whatever theoretical and/or methodological approach the particular model is based on; (2) the subjective selection and definition of variables; and (3) the lack of independent evaluation and verification. With respect to the first problem, archeologists usually select one theoretical and/or methodological approach, and then, maybe. 230

field test it. There is little attempt to test and improve existing

models for the area, or for similar areas. There is little attempt to

test different theoretical approaches on the same data base.

Assumptions and techniques upon which the models are based are not

critically evaluated. For example, Reidhead’s (1980) resource

optimization model, which was based on Zipf's (1949) Mini-Max

assumption, did not fit reality in terms of the archeological record.

His model predicted a rank of animal utilization that did not fit the resource use identified at the Leonard Haag site (Reidhead

1980:163-72), In analyzing the assumptions to identify possible errors, Reidhead could not completely explain the discrepancy. Testing models can reveal problems with assumptions that need more research to be resolved. Decision making processes can be very complex. People make some decisions based on factors other than least effort or maximum gain. While the Mini-Max assumption may play a part in people's decisions, it is too simplistic to use it as the only motivational factor in people's behavior. Such an assumption is too much a reflection of current western civilization cognition. The Mini-Max assumption, however, is an underlying assumption of many archeological models. It is uncritically accepted in too many models. More work needs to be done in using a variety of theoretical approaches to determine if any approach provides a better explanation than another.

As Brose (1981:98) has so cogently pointed out, there has been little recognition of the fact that very different predictive models can be developed from the same archeological resource data base. As was discussed in Chapter II, Brose (1981:95-98) discovered that by 231

combining different high potential variables, he could develop three

locational models of archeological site areas. Each one of those

models had an overall accuracy rate of over 61%, but each had some

serious limitations. Kohler and Parker (1986) demonstrated that same

point in their test of four different statistical approaches using the

same data. In a similar coastal environment, Custer (1984a) found

surface water to be one of the primary site location variables, while

Hay et al. (1982) found it to be fertile soils. In this study,

different variables were identified as predictor variables depending on which statistical analysis was used. For example, the correlation coefficient computations identified site presence as positively

correlated with WWATQUAD, WLOSTRE, WSPRING, TOPOQUAD, LTERRACE,

UTERRACE, and El; and negatively correlated with WATDISQ, WATPRES,

DDRAIN, QDISTR, and E3. Most of the regression analyses, on the other hand, only identified TOPOQUAD and El as major predictor variables.

Conducting a number of different statistical analyses, then, can reinforce certain variable selections and provide better information for model development.

A second problem area is the subjective selection and definition of variables. With both variable selection and definition, biases are entered into the model. In many models, important variables are selected for analysis without consideration of other variables which the investigator may not think are important, but could turn out to be. Various variables are treated of equal or unequal weight that are solely at the discretion of the investigator. The use of modem environmental variables to predict the location of prehistoric 232

environmental variables is questionable, at best. Land use, geomorphic

and climatic change, and sea level fluctuations have caused drastic

changes which may not allow us to infer past environments. Often there

is no indication of how variables contribute to locational decisions.

Our own cultural biases may not allow adequate interpretations of past

cognitive and behavioral systems.

In this study, elevation was entered as a variable, not because

it was considered important, but because other models had considered it

important. Surprisingly, it surfaced as a predictor variable that was

not anticipated. Well drained soils, which was expected to be a

predictor variable on the other hand, was not found to be a predictor

variable. It would have been considered one if a comparison with

nonsite locations had not been conducted. Because water was considered

to be a predictor variable in this study, it became a constant

variable. In other words, if it was not recorded within the quadrat,

it was recorded as outside the quadrat. This may have caused problems

with some of the statistical analyses. In the regression analyses,

both those variables could not be included in the same analysis because

they became a constant variable.

Definition of variables was also found to be important, and

rarely provided in discussions on models. How one identifies and

defines such variables as topographic relief and water sources can

vary. This is particularly true if one is using onsite observations, topographic maps or satellite imagery. Springs, for example, are often not identifiable on maps and satellite imagery. It is therefore important in discussing the variables involved in a model to also define how they are identified. 233

The third problem area is the lack of independent evaluation and verification. Models are often developed using existing data that were not collected in a systematic fashion, and then tested on additional existing data. In such cases, the model is developed using data not representative of the archeological universe, and therefore not nearly representative of prehistoric human behavior. Then the model is tested on data not representative of the realm of human behavior. When one considers the differential preservation problems inherent in the archeological record, using data that are not, at minimum, a representative sample, is extremely problematic. Even if the model is developed using a sampling technique, the sampling technique may be inappropriate or have low spatial resolution. The statistical techniques may also not be appropriate. In addition, the problem of site contemporaneity or lack of contemporaneity is often not addressed. Models are usually tested by the model-builder, therefore not providing an independent test for better verification.

Why should archeologists be concerned with models? Clarke

(1972:3) gives five good reasons. The first two are that:

(1) , , . our personal archaeological opinions, approaches, aims and selection of projects are controlled by largely subconscious mind models which we accumulate through time. We should realize that we are thus controlled. (2). . . w e always operate conceptual models in the interpretation of observations. . . . We should make these operational models explicit and testable.

Third, the essence of scientific research is the development, testing, verification and improvement of models. Such endeavors provide the 234

progressive cycle by which new insight and information are revealed and

theory is developed. Fourth, model definition provides a mechanism in

formulating an explicit theory, and formulating explicit theories

defines a vigorous discipline, "The existence of a model presupposes

the existence of an underlying theory, since a model is but one

simplified, formalized , , , expression of a theory,"

(5) Finally; Hypotheses are generated from the model expression of a theory. Explanation comes from tested hypotheses. Hypotheses are tested by using relevant analyses on meaningful categories of data. Thus models are a vital element in all archaeological attempts at hypothesis, theory, explanation, experiment and classification (Clarke 1972:3),

While modeling is fraught with problems, it has great potential.

Modeling has provided a tremendous amount of data on settlement

patterning and on how we look at settlement patterning. Models have

indicated the important role that our natural and social environment plays in site selection. It has moved us from describing archeological

sites to trying to explain archeological site locations and prehistoric human behavior in a systemic context. As mentioned previously, models contribute to theory building and testing. The goals of modeling are the same as Blalock's (1979) suggested criteria for good social science theory: generalizability, simplicity, internal consistency, precision, and falsifiability. Only by developing and testing models can we move forward in the explanation of human behavior (Clarke 1972:43), It is exciting that we have gathered enough information on prehistoric human behavior to begin attempts at explaining it. This is the greatest contribution archeology can make to anthropology, and to human history. APPENDIX A

QUADRAT DATA SURVEY FORM

Instructions; Each quadrat is 400m on a side. Survey transects at 10m north/south intervals, using a compass. Describe the artifact scatter, material types and estimated # of artifacts per square meter. Describe and map in features and artifact scatter locations.

Date:______Ouadrat Zone: # Map Reference:______Owner and Address:______

Tenant:____ Informants: Recorded Bv: Description of Survev and Survev Conditions (note vegetation problems encountered, etc.):

Description of Biophysical Setting: 1. Soil Association: Soil Series:

2. Water Source: within quadrat;______meters from______(N,S,E,W) side, 3, Water Source Type (get from map): river, low order stream, swamp, marsh. bay/basin feature, spring, ____ bog, poorly drained soil area, low order stream confluence up to 10 kilometers from major drainage, stream confluence, ____ confluence of low order stream and tidal marsh, minor and ephemeral drainage, at saltwater/freshwater interface of marsh. 4. Topographic Features within the Quadrat: well drained ridge; low terrace along major drainage; upper terrace; ____ alluvial fan associated with swamps, bogs and lithic sources; well drained knolls; floodplain of major or minor drainage; swampy floodplain; small sand ridges or knolls, 5, Other Features:

Material Observed in Field (attach site form); Site(s) found. Description:

235 236

Material Collected (attach Inventory by site);

Time Periods Represented;

Map: On the graph below, note sites, features, artifact scatter location, landmarks, etc. If only part of the area was surveyed, note which part.

Graph attached. APPENDIX B

MODEL OF PREHISTORIC SUBSISTENCE AND

SETTLEMENT PATTERNS FOR DELAWARE

Paleo-Indlan Period (12.000-6500 B.C.)

Site types Locations Base camp well drained ridge in areas of maximum habitat overlap

Base camp game-attractive locale close to base camp (swamps, maintenance bay/basin) station

Hunting site game-attractive locale away from base camp (swamps, bay/basin)

Archaic Period (6500-3000 B.C.)

Geographic area Site types Locations Major Drainage macro-band low terraces along major drainage, base camp especially at yicinity of lower order confluences

micro-band upper terraces of major drainage, base camp along lower order tributaries and at low order stream confluences up to 10 km from major drainage

procurement swamps, floodplains of major and site minor drainages, alluvial fans associated with swamps, bogs and lithic sources

All remaining unknown areas of Delaware

In this model, macro-bands (large social units) live in rich resource zones during seasons of high environmental productivity. Small work groups would occasionally leave these macro—band base camps for hunting and gathering procurement locations. As local resources

237 238

were exhausted, social groups would break Into micro-bands and relocate in other, less rich, resource areas (Custer et al. 1984:59-60).

Woodland I Period (3000 B.C.-A.D. 1000)

Geographic areas Site types Locations

Delaware River macro-band low terraces, especially at low Shore Zone base camp order confluences

micro-band upper terraces along low order base camp tributaries and at low order stream confluences up to 10 km from major drainage

procurement swampy floodplains of major and site minor drainages, alluvial fans associated with swamps, bogs, and lithic sources

Interior Zone micro-band well drained knolls at springs base camp and stream confluences

procurement well drained knolls at swamps and site springs

Mid-drainage Zone macro-band low terraces of major drainages base camp at stream confluences and at saltwater/freshwater interface of marshes

micro-band confluences of low order streams base camp and tidal marshes

procurement along minor and ephemeral site drainages adjacent to poorly drained woodlands and on small sand ridges and knolls

major mortuary along major drainages at central exchange location to several macro-band center base camps

minor mortuary associated with micro-band base exchange camps or along major drainage at center central location to several micro-band base camps

Bay Shore Zone micro-band upper terraces near fresh water base camp sources and tidal marshes 239

procurement along tidal marshes and swampy site low order floodplains

mortuary centralized location to several site micro-band base camps

The Woodland I model has the same general settlement pattern as the Archaic model. Woodland I macro-band base camps are larger and inhabited for longer periods of time as is reflected in the remains of house structures, storage/refuse pits, and middens not seen in Archaic archeological sites (Custer et al. 1984:66-68).

Woodland II Period (A.D. 1000-A.D. 1600)

Geographic areas Site tvpes Locations

Delaware River macro-band low terraces, especially at low Shore Zone base camp order confluences

micro-band upper terraces along low order base camp tributaries and at low order stream confluences up to 10 km from major drainage

procurement swampy floodplains of major and site minor drainages

Interior Zone micro-band well drained knolls at springs and base camp stream confluences

procurement well drained knolls at swamps and site springs

Mid-drainage Zone macro-band low terraces of major drainages at base camp stream confluences and at saltwater/freshwater interface of marshes

micro-band confluences of low order streams base camp and tidal marshes

procurement along minor and ephemeral drainages site adjacent to poorly drained woodlands and on small sand ridges and knolls

Bay Shore Zone macro-band well drained knolls with access to base camp freshwater springs and streams

micro-band confluences of minor drainages with base camp marsh settings 240

procurement along tidal marshes and swampy low site order floodplains

The Woodland II model is similar to the Woodland I model. However, three possible variations in the settlement pattern occurred: (1) year-round sedentary macro-band base camps in the Mid-drainage Zone; (2) a seasonal round of macro-band base camps in the Mid-Drainage Zone during winter, spring and fall, and movement to coastal micro-band base camps in the summer; or (3) macro-band base camps in the Interior Zone in the fall and winter with movement to coastal macro-band base camps in spring and summer. No mortuary-exchange centers existed during Woodland II times (Custer 1984a:157-71; Custer et al. 1984:75-76). APPENDIX C

QUADRAT DATABASE

This data base involves quadrat-related data and contains 77 records and 47 fields for each record. The following are the field definitions.

1. QUADRAT number, e.g., 4.999. 5 numeric spaces, 3 decimals.

2. ENVZONE - Environmental zone, 1-4. 1 character space.

3. SITEPRES - site presence/absence. l=yes, 0=no. 1 numeric space.

4. PROBability - the site probabilityarea according to the LANDSAT-generated model. 3=High, 2=Medium, l=Low. 1 numeric space.

5. MAP code - U.S.G.S. topographic map Harrington, Milford, Frederica, Wyoming, Bennett's Pier. H,M,F,W,B. 1 character space.

6. SOILQl - soil type dominant within quadrat. l=>SaA, 2=SaB, 3=Mt, 4=SfA, 5=>MeA, 6=RuA, 7=RuB, 8=Ka, 9=Wo, 10=Ev, ll=Fa, 12=:EsB. 2 numeric.

7. S0ILQ2 - soil type secondary within quadrat. l=SaA, 2=SaB, 3=Mt, 4=>SfA, 5=MeA, 6=RuA, 7=RuB, 8=Xa, 9=Wo, 10=Ev, ll=Fa, 12=EsB, 13=SaD2, 14=SaC3, 15=0t, 16=Ws, 17=Po, 18=Jo, 19=RuC2, 20=»Tnu, 21=MeB, 22=SfB. 2 numeric.

8. WWATQUAD - water source within quadrat. l=yes, 0=no. 1 numeric.

NOTE; The following fields (9-20) are water source types within quadrat, each one numeric, l=yes, 0=no:

9. WRIVER - river.

10. WLOSTRE - low order stream.

11. WSWAMP - swamp.

12. WMARSH - marsh.

13. WBAYBAS - bay/basin feature.

14. WSPRING - spring.

241 242

15. WBOG - bog.

16. WLOSTCON - low order stream confluence up to 10 kilometers from major drainage.

17. WSTRCONF - stream confluence.

18. WCONSTMR - confluence of low order stream and tidal marsh.

19. WDRAIN - minor and ephemeral drainage.

20. WINTERFA - at saltwater/freshwater interface of marsh.

21. WATDISQ - quadrat distance to water in meters from quadrat side, if no water source within quadrat. 4 numeric space.

22. WATPRES - yes/no as to whether the distance to water from quadrat is measured. This would be No if water is present in quadrat and Yes if water is not present in quadrat and therefore closest water source is measured. l=yes, 0=no. 1 numeric.

NOTE: The following fields (23-34) are the closest water source types from quadrat. l=yes, 0=no. 1 numeric.

23. DRIVER - river.

24. DLOSTRE - low order stream.

25. DSWAMP - swamp.

26. DMARSH - marsh.

27. DBAYBAS - bay/basin feature.

28. DSPRING - spring.

29. DBOG — bog.

30. DLOSTCON - low order stream confluence up to 10 kilometers from major drainage.

31. DSTRCONF - stream confluence.

32. DCONSTMR - confluence of low order stream and tidalmarsh.

33. DDRAIN - minor and ephemeral drainage.

34. DINTERFA - at saltwater/freshwater interface of marsh.

35. QDISTR - distance of quadrat to either the Murderkill or St. Jones River, whichever is nearer, in meters. 4 numeric. 243

36. TOPOQUAD - Y/N as to whether topographic features exist within the quadrat. l=yes, 0=no. 1 numeric.

NOTE: The following fields (37-44) are the possible topographic field types within the quadrat. l=yes, 0=no. 1 numeric.

37. RIDGE - well drained ridge.

38. LTERRACE - low terrace along drainage.

39. ÜTERRACE - upper terrace along drainage.

40. FAN - alluvial fan associated with swamps, bogs and lithic sources.

41. KNOLL - well drained knolls,

42. FLODPL - floodplain of major or minor drainage.

43. SWFLOD - swampy floodplain.

44. RIDGKNOL - small sand ridges or knolls,

45. ELEVQ - Quadrat elevation in feet according to U.S.G.S. topo maps. 1=0-5', 2=0-10', 3=0-20', 4=0-30', 5=6-10', 6=10', 7=11-20', 8=11-30', 9=20', 10=21-30', 11=21-40', 12=30', 13=31-40', 14=31-50', 15=35', 16=40', 17=41-50', 18=50', 19=51-60', 20=60-70'. 2 numeric.

46. SLOPEQ - quadrat slope calculatedfrom soil map types. 1=A (0-2%), 2=B (2-5%), 3=AB (0-5%), 4=ABC (0-10%), 5=BC (2-10%), 6=BCD (2-15%), 7=BD (2-15%). 1 numeric.

47. RANDOM - the high, medium, low probability according to a random model. H=3,M=2,L=1. 1 numeric.

Quadrat Data Base 2

This is a second data base on quadrat-related data created to eliminate potential problems with multiple answers for the same field in the first data base. It was merged with the first data base on the IBM mainframe computer. It contains 77 records and 18 fields for each record.

1. QUADRAT number, e.g. 4.999. 5 numeric spaces, 3 decimals.

NOTE: The following are all recorded as either yes(l) or no(0). Each are 1 numeric.

2. EZl - environmental zone 1.

3. EZ2 - environmental zone 2. 244

4. EZ3 - environmental zone 3.

5. EZ4 - environmental zone 4.

6. PHI - high probability area.

7. PMED - medium probability area.

8. FIX) - low probability area.

9. SWDl- dominant soil type in quadrat is a well drained soil. Well drained soils are; l=SaA, 2=SaB, 4=SfA, 5=MeA, 6=RuA, 7=RuB, 8=Ka, 10=Ev, 12=EsB, 13=SaD2, 14=SaC3, 19«RuC2, 21=MeB, and 22=SfB (numbers are according to Data base 1, fields SOILQl & 2).

10. SPDl - dominant soil type in quadrat is a poorly drained soil. Poorly drained soils are: 3=Mt, 9=Wo, ll=Fa, 15=0t, 16=Ws, 17=Po, 18=Jo, and 20='Rn,

11. SWD2 - secondary soil type in quadrat is a well drained soil.

12. SPD2 - secondary soil type in quadrat is a poorly drained soil.

13. El - 0-30 feet elevation (1-10, 12 of original elevation field choices),

14. E2 - 31-50 feet elevation (11, 13-18 of original elevation field choices).

15. E3 - 51-70 feet elevation (19-20 of original elevation field choices).

16. SLl - 0-5% slope (1-3 of original slope field choices).

17. SL2 - ranging to 10% slope (4-5 of original slope field choices).

18. SL3 - ranging to 15% slope (6-7 of original slope field choices).

19. MODAC - model accuracy, with 1 for yes the prediction was accurate for this quadrat, and 0 it was not.

20. RANAC - random model accuracy, with 1 for yes the prediction was accurate for this quadrat, and 0 it was not. 245

QUADRAT DATA

Quadrat Env- Site- Prob Map Soilql Soilq2 Wwat- Wriver zone pres quad

1.004 1 0 1 F 3 15 0 0 1.031 1 0 1 F 3 5 1 0 1.040 1 1 1 F 3 5 0 0 1.083 1 1 1 F 1 11 1 0 1.088 1 2 F 2 11 1 G 1.115 1 1 1 F 4 16 1 G 2.105 2 1 3 F 1 11 1 0 2.114 2 1 2 F 1 2 0 0 2.116 2 1 2 F 1 2 G 0 2.119 2 1 F 1 11 1 0 2.120 2 1 2 F 1 9 0 0 2.123 2 1 1 F 5 4 1 0 2.124 2 1 1 F 1 2 1 0 2.135 2 1 F 1 2 G 0 3.011 3 1 2 W 2 4 1 0 3.012 3 1 3 W 2 1 1 0 3.051 3 0 1 F 2 16 G 0 3.101 3 0 1 F 2 1 G 0 3.132 3 0 1 F 7 2 G 0 3.146 3 0 1 F 11 2 G 0 3.151 3 0 1 F 2 1 1 0 3.185 3 1 1 F 2 19 1 0 3.186 3 1 2 F 1 2 1 0 3.189 3 0 1 F 1 2 G 0 3.200 3 0 1 F 1 2 G 0 3.212 3 1 1 F 2 1 1 0 3.213 3 0 1 F 1 2 1 0 3.253 3 1 2 F 1 2 1 0 3.254 3 1 2 F 1 2 1 0 3.307 3 0 1 M 1 0 G 0 3.308 3 0 1 M 1 2 G 0 4.094 4 0 1 W 1 2 G 0 4.095 4 0 1 W 2 0 G 0 4.115 4 0 1 W 2 1 G 0 4.132 4 0 1 W 2 6 0 0 4.152 4 0 1 W 2 1 G 0 4.187 4 0 1 F 4 22 G 0 4.202 4 0 1 W 4 22 1 0 4.233 4 0 1 W 4 2 G 0 4.300 4 0 1 w 2 16 1 0 4.395 4 1 3 w 2 7 1 0 4.409 4 0 1 w 2 18 0 0 4.410 4 0 1 w 2 18 1 0 4.426 4 1 1 w 2 18 1 0 246

QUADRAT DATA

Quadrat Env- Site- Prob Map Soilql Soilq2 Wwat- Wriver zone pres quad

4.448 4 1 2 W 1 2 0 0 4.449 4 1 2 W 1 2 1 0 4.480 4 0 1 W 2 1 0 0 4.481 4 0 1 W 1 2 0 0 4. 485 4 0 1 W 2 1 0 0 4. 504 4 0 1 W 2 4 1 0 4. 505 4 0 1 W 2 14 0 0 4.511 4 0 1 H 2 22 1 0 4. 519 4 1 1 H 1 7 1 0 4. 520 4 1 2 H 1 2 0 0 4. 521 4 2 H 2 1 1 0 4. 543 4 1 2 H 7 18 1 0 4.588 4 1 2 H 1 2 0 0 4.589 4 1 2 M 2 13 1 0 4.607 4 1 H 1 2 0 0 4.609 4 1 2 M 1 2 0 0 4.623 4 1 1 H 1 7 0 0 4.638 4 1 H 7 12 0 0 4.646 4 1 3 M 2 1 0 0 4.647 4 1 2 M 2 13 1 0 4.654 4 0 1 M 1 2 1 0 4.685 4 0 1 H 7 9 0 0 4.686 4 0 1 H 7 9 0 0 4. 701 4 0 1 M 1 2 0 0 4.702 4 0 1 M 1 2 1 0 4.725 4 0 1 M 9 1 0 0 4. 728 4 0 1 H 10 12 0 0 4.743 4 0 1 M 1 2 0 0 4.751 4 0 1 H 6 9 0 0 4.752 4 0 1 H 12 11 1 0 4. 795 4 0 1 M 1 2 0 0 4.838 4 0 1 M 2 1 0 0 4.841 4 0 1 H 9 17 0 0 247

QUADRAT DATA

Quadrat Wlostre Wswamp Vfmarsh Wbay- Wspring Wbog bas

1.004 0 0 0 0 0 0 1.031 0 0 1 0 0 0 1.040 0 0 0 0 0 0 1.083 0 0 1 0 0 0 1.088 0 0 1 1 0 0 1.115 0 0 0 0 0 0 2.105 0 0 1 0 0 0 2.114 0 0 0 0 0 0 2.116 0 0 0 0 0 0 2.119 0 0 0 0 0 0 2.120 0 0 0 0 0 0 2.123 0 0 1 0 0 0 2.124 0 0 0 0 0 0 2.135 0 0 0 0 0 0 3.011 1 0 0 0 0 0 3.012 1 0 1 0 0 0 3.051 0 0 0 0 0 0 3.101 0 0 0 0 0 0 3.132 0 0 0 0 0 0 3.146 0 0 0 0 0 0 3.151 0 0 0 0 0 0 3.185 1 0 0 0 1 0 3.186 1 0 0 0 0 0 3.189 0 0 0 0 0 0 3.200 0 0 0 0 0 0 3.212 0 0 0 0 1 0 3.213 0 0 1 0 0 0 3.253 1 0 1 0 1 0 3.254 0 0 1 0 0 0 3.307 0 0 0 0 0 0 3.308 0 0 0 0 0 0 4.094 0 0 0 0 0 0 4.095 0 0 0 0 0 0 4.115 0 0 0 0 0 0 4.132 0 0 0 0 0 0 4.152 0 0 0 0 0 0 4.187 0 0 0 0 0 0 4.202 0 0 0 0 0 0 4.233 0 0 0 0 0 0 4.300 0 0 0 0 0 0 4.395 0 0 0 0 0 0 4.409 0 0 0 0 0 0 4.410 0 0 0 0 0 0 4.426 1 0 0 0 0 0 248

QUADRAT DATA

Quadrat Wlostre Wswamp Wmarsh Wbay- Wspring Wbog bas

4.448 0 0 0 0 0 0 4.449 1 0 0 0 0 0 4.480 0 0 0 0 0 0 4.481 0 0 0 0 0 0 4.485 0 0 0 0 0 0 4.504 0 0 0 0 0 0 4.505 0 0 0 0 0 0 4.511 0 0 0 0 0 0 4.519 0 0 0 0 0 0 4. 520 0 0 0 0 0 0 4.521 0 0 0 0 0 0 4.543 1 0 0 0 0 0 4.588 0 0 0 0 0 0 4.589 0 0 0 0 0 0 4.607 0 0 0 0 0 0 4.609 0 0 0 0 0 0 4.623 0 0 0 0 0 0 4.638 0 0 0 0 0 0 4.646 0 0 0 0 0 0 4.647 1 0 0 0 0 0 4.654 0 0 0 0 0 0 4.685 0 0 0 0 0 0 4.686 0 0 0 0 0 0 4.701 0 0 0 0 0 0 4.702 0 0 0 0 0 0 4.725 0 0 0 0 0 0 4.728 0 0 0 0 0 0 4.743 0 0 0 0 0 0 4.751 0 0 0 0 0 0 4.752 0 0 0 0 0 0 4.795 0 0 0 0 0 0 4.838 0 0 0 0 0 0 4.841 0 0 0 0 0 0 249

QUADRAT DATA

Quadrat Wlo- Wstr- Wcon- Wdrain Wint­ Wat- Wat- stcon conf stmr er fa disq pres

1.004 0 0 0 0 0 500 1 1.031 0 0 0 0 0 0 0 1 .040 0 0 0 0 0 400 1 1 .083 0 0 0 0 0 0 0 1 .088 0 0 0 0 0 0 0 1 .115 0 0 0 1 0 0 0 2 .105 0 0 0 0 0 0 0 2.114 0 0 0 0 0 100 1 2 .116 0 0 0 0 0 400 1 2.119 0 0 0 1 0 0 0 2.120 0 0 0 0 0 250 1 2 .123 0 0 0 0 0 0 0 2 .124 0 0 0 1 0 0 0 2.135 0 0 0 0 0 100 1 3.011 0 0 0 0 0 0 0 3 .012 0 0 0 0 0 0 0 3 .051 0 0 0 0 0 100 1 3.101 0 0 0 0 0 1000 1 3 .132 0 0 0 0 0 1300 1 3.146 0 0 0 0 0 100 1 3.151 0 0 0 1 0 0 0 3.185 0 0 0 0 0 0 0 3.186 0 0 0 0 0 0 0 3 .189 0 0 0 0 0 400 1 3.200 0 0 0 0 0 100 1 3 .212 0 0 0 0 0 0 0 3.213 0 0 0 0 0 0 0 3 .253 0 0 0 0 0 0 0 3 .254 0 0 0 0 . 0 0 0 3.307 0 0 0 0 0 400 1 3 .308 0 0 0 0 0 500 1 4.094 0 0 0 0 0 120 1 4.095 0 0 0 0 0 220 1 4.115 0 0 0 0 0 600 1 4.132 0 0 0 0 0 400 1 4.152 0 0 0 0 0 1000 1 4.187 0 0 0 0 0 400 1 4.202 0 0 0 1 0 0 0 4.233 0 0 0 0 0 300 1 4.300 0 0 0 1 0 0 0 4.395 0 1 0 0 0 0 0 4.409 0 0 0 0 0 200 1 4.410 0 0 0 1 0 0 0 4.426 0 0 0 0 0 0 0 250

QUADRAT DATA

Quadrat Wlo- Wstr- Wcon- Wdrain Wint- Wat- Wat- stcon conf stmr erf a disq pres

4.448 0 0 0 0 0 100 1 4.449 0 0 0 0 0 0 0 4.480 0 0 0 0 0 600 1 4.481 0 0 0 0 0 650 1 4.485 0 0 0 0 0 400 1 4.504 0 0 0 1 0 0 0 4. 505 0 0 0 0 0 25 1 4.511 0 0 0 1 0 0 0 4.519 0 0 0 1 0 0 0 4.520 0 0 0 0 0 380 1 4.521 0 0 0 1 0 0 0 4.543 0 0 0 0 0 0 0 4.588 0 0 0 0 0 200 1 4.589 0 0 0 1 0 0 0 4.607 0 0 0 0 0 400 1 4.609 0 0 0 0 0 200 1 4.623 0 0 0 0 0 950 1 4.638 0 0 0 0 0 500 1 4 .646 0 0 0 0 0 300 1 4.647 0 0 0 0 0 0 0 4.654 0 0 0 1 0 0 0 4.685 0 0 0 0 0 1400 1 4.686 0 0 0 0 0 1400 1 4.701 0 0 0 0 0 100 1 4.702 0 0 0 1 0 0 0 4.725 0 0 0 0 0 300 1 4.728 0 0 0 0 0 150 1 4.743 0 0 0 0 0 300 1 4.751 0 0 0 0 0 200 1 4.752 0 0 0 1 0 0 0 4.795 0 0 0 0 0 300 1 4.838 0 0 0 0 0 120 1 4.841 0 0 0 0 0 120 1 251

QUADRAT DATA

Quadrat Driver Dlos- Dswamp Dmarsh Dbay- Dspring tre bas

1.004 0 1 0 0 0 0 1.031 0 0 0 0 0 0 1.040 0 0 0 1 0 0 1.083 0 0 0 0 0 0 1.088 0 0 0 0 0 0 1.115 0 0 0 0 0 0 2.105 0 0 0 0 0 0 2.114 0 0 0 1 0 0 2.116 0 0 0 1 0 0 2.119 0 0 0 0 0 0 2. 120 0 0 0 1 0 0 2.123 0 0 0 0 0 0 2.124 0 0 0 0 0 0 2.135 0 0 0 0 0 0 3.011 0 0 0 0 0 0 3.012 0 0 0 0 0 0 3.051 0 0 0 1 0 0 3.101 0 0 0 0 0 0 3.132 0 1 0 0 0 0 3. 146 0 0 0 0 0 0 3.151 0 0 0 0 0 0 3.185 0 0 0 0 0 0 3. 186 0 0 0 0 0 0 3.189 0 0 0 1 0 0 3.200 0 0 0 0 0 0 3.212 0 0 0 0 0 0 3.213 0 0 0 0 0 0 3.253 0 0 0 0 0 0 3.254 0 0 0 0 0 0 3. 307 0 0 0 0 0 1 3.308 0 0 0 0 0 1 4.094 0 0 0 0 0 0 4.095 0 0 0 0 0 0 4.115 0 0 0 0 0 0 4. 132 0 0 0 0 0 0 4.152 0 0 0 0 0 0 4.187 0 0 0 0 0 0 4.202 0 0 0 0 0 0 4.233 0 1 0 0 0 0 4.300 0 0 0 0 0 0 4.395 0 0 0 0 0 0 4.409 0 0 0 0 0 0 4.410 0 0 0 0 0 0 4.426 0 0 0 0 0 0 252

QUADRAT DATA

Quadrat Driver Dlos- Dswamp Dmarsh Dbay- Dspring tre bas

4.448 0 0 0 0 0 0 4.449 0 0 0 0 0 0 4.480 0 1 0 0 0 0 4,481 0 1 0 0 0 0 4.485 0 1 0 0 0 0 4.504 0 0 0 0 0 0 4.505 0 0 0 0 0 0 4.511 0 0 0 0 0 0 4.519 0 0 0 0 0 0 4.520 0 1 0 0 0 0 4. 521 0 0 0 0 0 0 4. 543 0 0 0 0 0 0 4.588 1 0 0 0 0 0 4.589 0 0 0 0 0 0 4.607 1 0 0 0 0 0 4.609 1 0 0 0 0 0 4.623 1 0 0 0 0 0 4.638 1 0 0 0 0 0 4.646 0 1 0 0 0 0 4.647 0 0 0 0 0 0 4.654 0 0 0 0 0 0 4.685 1 0 0 0 0 0 4.686 0 0 0 0 0 0 4.701 0 0 0 0 0 0 4. 702 0 0 0 0 0 0 4. 725 0 0 0 0 0 0 4. 728 0 0 0 0 0 0 4.743 0 1 0 0 0 0 4.751 0 0 0 0 0 0 4.752 0 0 0 0 0 0 4.795 0 1 0 0 0 0 4.838 0 0 1 0 0 0 4.841 0 0 0 0 0 0 253

QUADRAT DATA

Quadrat Dbog Dlos- Dstr- Dcon- Ddrain Dint- Qdi — tcon conf stmr erfa str

1.004 0 0 0 0 0 0 3800 1.031 0 0 0 0 0 0 2900 1.040 0 0 0 0 0 0 2000 1.083 0 0 0 0 0 0 100 1.088 0 0 0 0 0 0 680 1.115 0 0 0 0 0 0 2400 2. 105 0 0 0 0 0 0 400 2. 114 0 0 0 0 0 0 800 2.116 0 0 0 0 1 0 850 2. 119 0 0 0 0 0 0 1600 2. 120 0 0 0 0 0 0 1900 2. 123 0 0 0 0 0 0 2950 2.124 0 0 0 0 0 0 1100 2. 135 0 0 0 0 1 0 1500 3.011 0 0 0 0 0 0 500 3.012 0 0 0 0 0 0 900 3.051 0 0 0 0 0 0 800 3. 101 0 0 0 0 1 0 1400 3. 132 0 0 0 0 0 0 4000 3. 146 0 0 0 0 1 0 3550 3.151 0 0 0 0 0 0 3000 3. 185 0 0 0 0 0 0 1800 3.186 0 0 0 0 0 0 1800 3.189 0 0 0 0 0 0 1100 3.200 0 0 0 0 1 0 1100 3.212 0 0 0 0 0 0 600 3.213 0 0 0 0 0 0 400 3.253 0 0 0 0 0 0 400 3.254 0 0 0 0 0 0 400 3.307 0 0 0 0 0 0 920 3 . 308 0 0 0 0 0 0 1200 4 .094 0 0 0 0 1 0 3700 4.095 0 0 0 0 1 0 3300 4. 115 0 0 0 0 1 0 3500 4. 132 0 0 0 0 1 0 4700 4.152 0 0 0 0 1 0 4200 4.187 0 0 0 0 1 0 3650 4.202 0 0 0 0 0 0 3900 4.233 0 0 0 0 0 0 5700 4.300 0 0 0 0 0 0 6600 4.395 0 0 0 0 0 0 3400 4.409 0 0 0 0 1 0 3700 4.410 0 0 0 0 0 0 3600 4.426 0 0 0 0 0 0 3600 254

QUADRAT DATA

Quadrat Dbog Dlos- Dstr- Dcon- Ddrain Dint- Qdi- tcon conf stmr erfa str

4.448 0 1 0 0 0 0 2900 4.449 0 0 0 0 0 0 2900 4.480 0 0 0 0 0 0 2500 4.481 0 0 0 0 0 0 2500 4.485 0 0 0 0 0 0 2200 4.504 0 0 0 0 0 0 1800 4.505 0 0 0 0 1 0 1700 4.511 0 0 0 0 0 0 900 4.519 0 0 0 0 0 0 1700 4.520 0 0 0 0 0 0 1700 4.521 0 0 0 0 0 0 1600 4.543 0 0 0 0 0 0 800 4.588 0 0 0 0 0 0 100 4.589 0 0 0 0 0 0 0 4.607 0 0 0 0 0 0 500 4.609 0 0 0 0 0 0 200 4.623 0 0 0 0 0 0 950 4.638 0 0 0 0 0 0 400 4.646 0 0 0 0 0 0 1000 4.647 0 0 0 0 0 0 1000 4.654 0 0 0 0 0 0 1650 4.685 0 0 0 0 0 0 1400 4.686 0 0 0 0 0 0 1300 4.701 0 0 0 0 1 0 2400 4.702 0 0 0 0 0 0 2700 4.725 0 0 0 0 1 0 2800 4.728 0 0 0 0 1 0 1850 4.743 0 0 0 0 0 0 2820 4.751 0 0 0 0 1 0 2330 4.752 ' 0 0 0 0 0 0 2430 4.795 0 0 0 0 0 0 4000 4.838 0 0 0 0 0 0 4650 4.841 0 0 0 0 1 0 4000 255

QUADRAT DATA

Quadrat Topo- Ridge Lter- Uter- Fan Knoll Flo- Swf- quad race race dpi lod

1.004 0 0 0 0 0 0 0 0 1.031 0 0 0 0 0 0 0 0 1.040 0 0 0 0 0 0 0 0 1.083 1 0 0 0 0 0 0 1 1.088 0 0 0 0 0 0 0 0 1.115 0 0 0 0 0 0 0 0 2.105 1 0 1 0 0 0 0 1 2.114 1 0 1 0 0 0 0 0 2. 116 1 0 1 0 0 0 0 0 2. 119 0 0 0 0 0 0 0 0 2.120 0 0 0 0 0 0 0 0 2.123 0 0 0 0 0 0 0 0 2.124 0 0 0 0 0 0 0 0 2.135 0 0 0 0 0 0 0 0 3.011 1 0 1 0 0 0 0 0 3.012 1 0 1 0 0 0 0 0 3.051 1 0 0 1 0 0 0 0 3.101 0 0 0 0 0 0 0 0 3.132 0 0 0 0 0 0 0 0 3. 146 0 0 0 0 0 0 0 0 3.151 0 0 0 0 0 0 0 0 3. 185 1 0 1 0 0 0 0 0 3. 186 1 0 1 0 0 0 0 0 3.189 0 0 0 0 0 0 0 0 3.200 0 0 0 0 0 0 0 0 3.212 1 0 1 0 0 0 0 0 3.213 1 0 1 0 0 0 0 0 3.253 1 0 1 0 0 1 0 0 3.254 1 0 1 0 0 0 0 0 3.307 0 0 0 0 0 0 0 0 3.308 0 0 0 0 0 0 0 0 4.094 0 0 0 0 0 0 0 0 4.095 0 0 0 0 0 0 0 0 4.115 0 0 0 0 0 0 0 0 4.132 0 0 0 0 0 0 0 0 4.152 0 0 0 0 0 0 0 0 4.187 0 0 0 0 0 0 0 0 4.202 1 0 1 0 0 0 0 0 4.233 0 0 0 0 0 0 0 0 4.300 0 0 0 0 0 0 0 0 4.395 1 0 0 1 0 0 0 0 4.409 0 0 0 0 0 0 0 0 4.410 0 0 0 0 0 0 0 0 4.426 1 0 0 1 0 0 0 0 256

QUADRAT DATA

Quadrat Topo- Ridge Lter- Uter- Fan Knoll Flo- Swf- quad race race dpl lod

4.448 1 0 0 1 0 0 0 0 4.449 1 0 0 1 0 0 0 0 4.480 0 0 0 0 0 0 0 0 4.481 0 0 0 0 0 0 0 0 4.485 0 0 0 0 0 0 0 0 4. 504 0 0 0 0 0 0 0 0 4.505 0 0 0 0 0 0 0 0 4.511 0 0 0 0 0 0 0 0 4.519 1 0 0 1 0 0 0 0 4.520 1 0 0 1 0 0 0 0 4.521 0 0 0 0 0 0 0 0 4.543 1 0 1 0 0 0 0 0 4. 588 1 0 0 1 0 0 0 0 4. 589 1 0 1 0 0 0 0 0 4.607 0 0 0 0 0 0 0 0 4.609 1 0 0 1 0 0 0 0 4.623 0 0 0 0 0 0 0 0 4.638 0 0 0 0 0 0 0 0 4.646 1 0 0 1 0 0 0 0 4.647 1 0 1 0 0 0 0 0 4.654 0 0 0 0 0 0 0 0 4.685 0 0 0 0 0 0 0 0 4.686 0 0 0 0 0 0 0 0 4.701 0 0 0 0 0 0 0 0 4. 702 0 0 0 0 0 0 0 0 4.725 0 0 0 0 0 0 0 0 4.728 0 0 0 0 0 0 0 0 4.743 0 0 0 0 0 0 0 0 4.751 0 0 0 0 0 0 0 0 4.752 0 0 0 0 0 0 0 0 4.795 0 0 0 0 0 0 0 0 4.838 0 0 0 0 0 0 0 0 4.841 0 0 0 0 0 0 0 0 257

QUADRAT DATA

Quadrat Ridg- Elevq Slopeq Random knol

1.004 0 2 3 3 1.031 0 1 1 3 1.040 0 6 3 2 1.083 0 1 1 2 1.088 0 5 2 2 1.115 O i l 2 2.105 0 5 1 3 2.114 0 5 3 3 2.116 0 6 3 3 2.119 0 6 1 3 2.120 0 6 1 3 2.123 0 1 3 3 2.124 0 7 3 3 2.135 0 7 3 3 3.011 0 4 3 3 3.012 0 3 4 2 3.051 0 3 2 2 3.101 0 7 3 3 3.132 0 13 2 3 3.146 0 10 2 3 3.151 0 10 3 1 3.185 0 8 5 3 3.186 0 3 3 3 3.189 0 10 3 3 3.200 0 7 3 1 3.212 0 3 3 3 3.213 0 5 3 3 3.253 0 2 3 2 3.254 0 3 3 1 3.307 0 10 1 3 3.308 0 10 3 2 4.094 0 14 3 3 4.095 0 17 2 1 4.115 0 14 3 3 4.132 0 17 3 3 4.152 0 17 3 2 4.187 0 13 3 2 4.202 0 11 4 2 4.233 0 14 4 2 4.300 0 13 5 3 4.395 0 4 5 2 4.409 0 17 5 2 4.410 0 14 5 3 4.426 0 14 2 2 258

QUADRAT DATA

Quadrat Ridg- Elevq Slopeq Random knol

4.448 0 14 4 3 4.449 0 11 4 1 4.480 0 20 3 3 4.481 0 20 3 2 4.485 0 17 3 3 4.504 0 17 3 3 4.505 0 17 5 2 4.511 0 13 2 2 4.519 0 18 4 3 4.520 0 18 3 3 4.521 0 18 4 1 4.543 0 11 5 2 4.588 0 13 3 2 4.589 0 4 6 2 4.607 0 17 3 3 4.609 0 13 3 3 4.623 0 17 3 3 4.638 0 19 2 3 4.646 0 11 3 3 4.647 0 4 7 1 4.654 0 10 3 2 4.685 0 18 2 1 4.686 0 18 2 3 4.701 0 13 3 3 4.702 0 13 3 2 4.725 0 13 1 3 4.728 0 19 3 2 4.743 0 14 3 3 4.751 0 19 3 3 4.752 0 19 3 3 4.795 0 17 3 3 4.838 0 14 3 3 4.841 0 19 2 2 259

QUADRAT DATA

Quadrat Ezl Ez2 Ez3 Ez4 Phi Pmed Plo Swdl Spdl Swd2

1.004 1 0 0 0 0 0 1 0 1 0 1.031 1 0 0 0 0 0 1 0 1 1 1.040 1 0 0 0 0 0 1 0 1 1 1.083 1 0 0 0 0 0 1 1 0 0 1.088 1 0 0 0 0 1 0 1 0 0 1.115 1 0 0 0 0 0 1 1 0 0 2. 105 0 1 0 0 1 0 0 1 0 0 2.114 0 1 0 0 0 1 0 1 0 2.116 0 1 0 0 0 1 0 1 0 2.119 0 1 0 0 0 0 1 1 0 2 .120 0 1 0 0 0 1 0 1 0 2.123 0 1 0 0 0 0 1 1 0 2.124 0 1 0 0 0 0 1 1 0 2.135 0 1 0 0 0 0 1 1 0 3.011 0 0 1 0 0 1 1 0 3.012 0 0 1 0 1 0 1 0 3.051 0 0 1 0 0 0 1 1 0 3.101 0 0 1 0 0 0 1 1 0 3.132 0 0 1 0 0 0 1 1 0 3.146 0 0 1 0 0 0 1 1 3.151 0 0 1 0 0 0 1 1 0 3.185 0 0 1 0 0 0 1 I 0 3.186 0 0 1 0 0 1 1 0 3.189 0 0 1 0 0 0 1 1 0 3.200 0 0 1 0 0 0 1 1 0 3.212 0 0 1 0 0 0 1 1 0 3.213 0 0 1 0 0 0 1 1 0 3.253 0 0 1 0 0 1 1 0 3.254 0 0 1 0 0 1 1 0 3.307 0 0 1 0 0 0 1 1 0 3.308 0 0 1 0 0 0 1 1 0 4.094 0 0 0 1 0 0 1 1 0 4.095 0 0 0 1 0 0 1 1 0 4.115 0 0 0 1 0 0 1 1 0 4.132 0 0 0 1 0 0 1 1 0 4.152 0 0 0 1 0 0 1 1 0 4.187 0 0 0 1 0 0 1 1 0 4.202 0 0 0 1 0 0 1 1 0 4.233 0 0 0 1 0 0 1 1 0 4.300 0 0 0 1 0 0 1 1 0 0 4.395 0 0 0 1 1 0 1 0 1 4.409 0 0 0 1 0 0 1 1 0 0 4.410 0 0 0 1 0 0 1 1 0 0 4.426 0 0 0 1 0 0 1 1 0 0 4.448 0 0 0 1 0 1 0 1 0 1 260

QUADRAT DATA

Quadrat Ezl Ez2 Ez3 Ez4 Phi Pmed Plo Swdl Spdl Swd2

4.449 0 0 0 1 0 1 0 1 0 4.480 0 0 0 1 0 0 1 1 0 4.481 0 0 0 1 0 0 1 1 0 4.485 0 0 0 1 0 0 1 1 0 4.504 0 0 0 1 0 0 1 1 0 4.505 0 0 0 1 0 0 1 1 0 4.511 0 0 0 1 0 0 1 1 0 4.519 0 0 0 1 0 0 1 1 0 4.520 0 0 0 1 0 1 0 1 0 4.521 0 0 0 1 0 1 0 1 0 4.543 0 0 0 1 0 1 0 1 0 4.588 0 0 0 1 0 1 0 1 0 4.589 0 0 0 1 0 1 0 1 0 4.607 0 0 0 1 0 0 1 1 0 4.609 0 0 0 1 0 1 0 1 0 4.623 0 0 0 1 0 0 1 1 0 4.638 0 0 0 1 0 0 1 1 0 4.646 0 0 0 1 1 0 1 0 4.647 0 0 0 1 0 1 1 0 4.654 0 0 0 1 0 0 1 1 0 4.685 0 0 0 1 0 0 1 1 0 4.686 0 0 0 1 0 0 1 1 0 4.701 0 0 0 1 0 0 1 1 0 4.702 0 0 0 1 0 0 1 1 0 4.725 0 0 0 1 0 0 1 1 4.728 0 0 0 1 0 0 1 1 0 4.743 0 0 0 1 0 0 1 1 0 4.751 0 0 0 1 0 0 1 1 0 4.752 0 0 0 1 0 0 1 1 0 4.795 0 0 0 1 0 0 1 1 0 4.838 0 0 0 1 0 0 1 1 0 4.841 0 0 0 1 0 0 1 0 1 0 261

QUADRAT DATA

Quadrat Spd2 El E2 E3 Sll 812 813 Modac Ranac

1.004 1 1 0 0 1 0 0 1 0 1.031 0 1 0 0 1 0 0 1 0 1.040 0 1 0 0 1 0 0 0 1.083 1 1 0 0 0 0 0 1.088 1 1 0 0 1 0 0 0 1.115 1 1 0 0 1 0 0 0 2.105 1 1 0 0 1 0 0 1 2.114 0 1 0 0 1 0 0 1 2.116 0 1 0 0 1 0 0 1 2.119 1 1 0 0 1 0 0 1 2.120 1 1 0 0 1 0 0 1 2.123 0 1 0 0 1 0 0 2.124 0 1 0 0 1 0 0 2. 135 0 1 0 0 1 0 0 1 3.011 0 1 0 0 1 0 0 1 3.012 0 1 0 0 1 0 1 3.051 1 1 0 0 1 0 0 1 0 3.101 0 1 0 0 1 0 0 1 0 3.132 0 1 0 1 0 0 1 0 3.146 0 1 0 0 1 0 0 1 0 3.151 0 1 0 0 1 0 0 1 1 3.185 0 1 0 0 1 0 1 3.186 0 1 0 0 1 0 0 1 1 3.189 0 1 0 0 1 0 0 1 0 3.200 0 1 0 0 1 0 0 1 1 3.212 0 1 0 0 1 0 0 1 3.213 0 1 0 0 1 0 0 1 . 0 3.253 0 1 0 0 1 0 0 1 1 3.254 0 1 0 0 1 0 0 1 0 3. 307 0 1 0 0 1 0 0 1 0 3.308 0 1 0 0 1 0 0 1 0 4.094 0 0 1 0 1 0 0 1 0 4.095 0 0 1 0 1 0 0 1 1 4.115 0 0 1 0 1 0 0 1 0 4.132 0 0 1 0 1 0 0 1 0 4.152 0 0 1 0 1 0 0 1 0 4.187 0 0 1 0 1 0 0 1 0 4.202 0 0 1 0 0 1 0 1 0 4.233 0 0 1 0 0 1 0 1 0 4.300 1 0 1 0 0 1 0 1 0 4.395 0 1 0 0 0 1 0 1 1 4.409 1 0 1 0 0 1 0 1 0 4.410 1 0 1 0 0 1 0 1 0 4.426 1 0 1 0 1 0 0 0 1 4.448 0 0 1 0 0 1 0 1 1 262

QUADRAT DATA

Quadrat Spd2 El E2 E3 Sll S12 813 Modac Ranac

4.449 0 0 1 0 0 1 0 1 0 4.480 0 0 0 1 1 0 0 1 0 4.481 0 0 0 1 1 0 0 1 0 4.485 0 0 1 0 1 0 0 1 0 4.504 0 0 1 0 1 0 0 1 0 4.505 0 0 1 0 0 1 0 1 0 4.511 0 0 1 0 1 0 0 1 0 4.519 0 0 1 0 0 1 0 0 1 4.520 0 0 1 0 1 0 0 1 1 4.521 0 0 1 0 0 1 0 0 1 4.543 1 0 1 0 0 1 0 1 1 4.588 0 0 1 0 1 0 0 1 1 4.589 0 1 0 0 0 0 1 1 1 4.607 0 0 1 0 1 0 0 1 0 4.609 0 0 1 0 1 0 0 1 1 4.623 0 0 1 0 1 0 0 0 1 4.638 0 0 0 1 1 0 0 1 0 4.646 0 0 1 0 1 0 0 1 1 4.647 0 1 0 0 0 0 1 1 0 4.654 0 1 0 0 1 0 0 1 0 4.685 1 0 1 0 1 0 0 1 1 4.686 1 0 1 0 1 0 0 1 0 4.701 0 0 1 0 1 0 0 1 0 4.702 0 0 1 0 1 0 0 1 0 4.725 0 0 1 0 1 0 0 1 0 4.728 0 0 0 1 1 0 0 1 0 4.743 0 0 1 0 1 0 0 1 0 4.751 1 0 0 1 1 0 0 1 0 4.752 1 0 0 1 1 0 0 1 0 4.795 0 0 1 0 1 0 0 1 0 4.838 0 0 1 0 1 0 0 1 0 4.841 1 0 0 1 1 0 0 1 0 APPENDIX D

SITES DATA BASE

This second data base is site-specific with a quadrat correlation to allow merging with the quadrat data base. This data base contains 38 records and 48 fields. The following are the field definitions.

1. QUADRAT number, e.g., 4.999. 5 numeric spaces, 3 decimals.

2. ENVZONE - Environmental zone, 1-4. 1 character space.

3. MAP code - Harrington, Milford, Frederica, Wyoming, Bennett's Pier. H,M,F,W,B. 1 character space.

4. TEMPNUM - temporary field site number, e.g., 3.253A. 6 character spaces.

5. SITENUM - Delaware's internal site numbering system. 9 character spaces.

6. CRSNUM - Delaware's Smithsonian Institution site numbering system. 6 character spaces.

NOTE: The following are yes/no answers. l=yes, 0=no. 1 numeric. 7. PALEO - Site time period Paleo-Indian.

8. ARCHAIC - Site time period Archaic.

9. WI - Site time period Woodland I (WI).

10. WII - Site time period Woodland II (WII).

NOTE: The following fields (11-19) are site types when identified. l=yes, 0=no. 1 numeric space.

11. MACROBAS - macro-band base camp.

12. MICROBAS - micro-band base camp.

13. PROCUR - procurement site.

14. PIMAINT - Paleo-Indian base camp maintenance station.

15. PIHUNT - Paleo-Indian hunting site.

263 264

16. MAJMORT - major mortuary-exchange center.

17. MINMORT - minor mortuary-exchange center.

18. CACHE - cache site.

19. SPOT - spot find.

20. SITEPROB - the site probability area according to the LANDSAT-generated model, 3=High, 2=Medium, l=Low. 1 numeric.

21. SSOILl - soil type dominant within quadrat. l=SaA, 2=SaB, 3=Mt, 4=SfA, 5=MeA, 6=RuA, 7=RuB, 8=Ka, 9=Wo, 10=Ev, ll=Fa, 12=EsB, 13=SaD2, 14=SaC3, 15=0t, 16=Ws, 17=Po, 18=Jo, 19=RuC2, 20=Tm, 21=MeB, 22=SfB. 2 numeric.

22. SS0IL2 - soil type secondary within quadrat. l=SaA, 2=SaB, 3=Mt, 4=SfA, 5=MeA, 6=RuA, 7=RuB, 8=Ka, 9=Wo, 10=Ev, ll=Fa, 12=EsB, 13=SaD2, 14=SaC3, 15=0t, 16=Ws, 17=Po, 18=Jo, 19=RuC2, 20=Tm, 21=MeB, 22=SfB. 2 numeric.

23. SWATDIS - water distance from site in meters. 4 numeric.

NOTE; The following fields (24-35) are the closest water source types from site. l=yes, 0=no. 1 numeric.

24. SRIVER - river.

25. SLOSTRE - low order stream.

26. SSWAMP - swamp.

27. SMARSH - marsh.

28. SBAYBAS - bay/basin feature.

29. SSPRING - spring.

30. SBOG - bog.

31. SLOSTCON - low order stream confluence up to 10 kilometers from major drainage.

32. SSTRCONF - stream confluence,

33. SCONSTMR - confluence of low order stream and tidal marsh.

34. SDRAIN - minor and ephemeral drainage.

35. SINTERFA - at saltwater/freshwater interface of marsh. 265

36. SDISTR - distance of site to either the Murderkill or St. Jones River, whichever is hearer, in meters. 4 numeric.

37. TOPOSITE - Y/N as to whether the site is located on a topographic feature. l=yes, 0=no. 1 numeric.

NOTE; The following fields (38-45) are the possible topographic feature types. l=yes, 0=no. 1 numeric.

38. BRIDGE - well drained ridge.

39. SLTERRAC - low terrace along drainage.

40. SUTERRAC - upper terrace along drainage.

41. SPAN - alluvial fan associated with swamps, bogs and lithic sources,

42. SKNOLL - well drained knolls.

43. SFLODPL - floodplain of major or minor drainage.

44. SSWFLOD - swampy floodplain.

45. SRIDGKNL - small sand ridges or knolls.

46. ELEVS - site elevation in feet according to USGS topographic maps. 1=0-5', 2=0-10', 3=0-20', 4=0-30', 5=6-10', 6=10', 7=11-20', 8=11-30', 9=20', 10=21-30', 11=21-40', 12=30', 13=31-40', 14=31-50', 15=35', 16=40', 17=41-50', 18=50', 19=51-60', 20=60-70'. 2 numeric.

47. SLOPES - site slope determined from soil map type. 1=A (0-2%), 2=B (2-5%), 3=AB (0-5%). 1 numeric.

48. ASPECTS - site aspect. l=north, 2=east, 3=south, 4=west, 5=northeast, 6=northwest, 7=southeast, 8=southwest. 1 numeric.

Sites Data Base 2

This is a second data base on site-related data created to eliminate potential problems with multiple answers for the same field. It was merged with the first data base on the IBM mainframe computer. It contains 38 records and 18 fields for each record.

1. TEMPNUM - temporary field site number, e.g., 3.253A. 6 character spaces.

NOTE; The following are all recorded as either yes(l) or no(0). Each are 1 numeric.

2. SEZl - environmental zone 1. 266

3. SEZ2 - environmental zone 2.

4. SEZ3 - environmental zone 3.

5. SEZ4 - environmental zone 4.

6. SPHI - high probability area.

7. SPMED - medium probability area.

8. SPLO - low probability area.

9. SSWDl - dominant soil type in quadrat is a well drained soil (see Quadrat Data Base 2 for definition of well drained soil).

10. SSPDl - dominant soil type in quadrat is a poorly drained soil (see Quadrat Data Base 2 for definition of poorly drained soil).

11. SSWD2 - secondary soil type in quadrat is a well drained soil.

12. SSPD2 - secondary soil type in quadrat is a poorly drained soil.

13. SEl - 0-30 feet elevation (1-10, 12 of original field elevation choices).

14. SE2 - 31-50 feet elevation (11, 13-18 of original elevation field choices).

15. SE3 - 51-70 feet elevation (19-20 of original elevation field choices).

16. SSLl - 0-5% slope (1-3 of original slope field choices).

17. SSL2 - ranging to 10% slope (4-5 of original slope field choices).

18. SSL3 - ranging to 15% slope (6-7 of original slope field choices). 267

SITE DATA

Quadrat Env- Map Tempnum Sitenum Crsnum Paleo zone

1.040 1 F 1.040 7K-D-39 K-877 0 1 .083 1 F 1.083 7K-F-77 K-616 0 1 .115 1 F 1.115 7K-F-68 K-655 0 2. 105 2 F 2.105 7K-F-15 K-642 0 2 .114 2 F 2.114 7K-F-18 K-640 0 2.116 2 F 2.116 7K-F-15 K-642 0 2.120 2 F 2.120 7K-F-112 K-5436 0 2 . 123 2 F 2.123 7K-F-62 K-650 0 2.124 2 F 2.124 7K-F-18 K-640 0 3.011 3 W 3.011 7K-C-358 K-6363 0 3.012 3 W 3.012 7K-C-114 K-5937 0 3. 185 3 F 3.185A 7K-F-148 K-6360 0 3. 185 3 F 3 . 185B 7K-F-148 K-6360 0 3. 186 3 F 3.186A 7K-F-149 K-6361 0 3 . 186 3 F 3.186B 7K-F-149 K-6361 0 3.212 3 F 3.212 7K-F-150 K-6362 0 3 . 253 3 F 3.253A 7K-F-146 K-6353 0 3 . 253 3 F 3.253B 7K-F-147 K-6354 0 3 . 253 3 F 3 .253C 7K-F-147 K-6354 0 3.254 3 F 3.254A 7K-F-146 K-6353 0 3 .254 3 F 3.254B 7K-F-38 K-638 0 3 . 254 3 F 3.254C 7K-F-38 K-638 0 4. 395 4 W 4.395 7K-E-168 K-6364 0 4.426 4 W 4.426 7K-E-169 K-6365 0 4. 448 4 W 4.448 7K-E-170 K-6366 0 4. 449 4 w 4.449 7K-E-170 K-6366 0 4. 519 4 H 4.519 7K-E-171 K-6367 0 4. 520 4 H 4.520 7K-E-171 K-6367 0 4 . 543 4 H 4.543 7K-E-113 K-716 0 4. 588 4 H 4.588 7K-E-107 K-722 0 4.589 4 M 4.589 7K-F-9 K-768 0 4.609 4 M 4.609 7K-F-151 K-6369 0 4.623 4 H 4.623 7K-E-172 K-6368 1 4.646 4 M 4.646 7K-F-126 K-5466 0 4.647 4 M 4.647A 7K-F-152 K-6370 0 4.647 4 M 4.647B 7K-F-152 K-6370 0 4.647 4 M 4.647C 7K-F-152 K-6370 0 3. 253 3 F 3.253D 7K-F-1 K-637 0 268

SITE DATA

Quadrat Arch­ WI WII Macro- Micro- Pro- Pima- Pihunt aic bas bas cur int

1.040 0 0 0 0 0 0 0 0 1.083 0 0 0 0 0 0 0 0 1.115 0 0 0 0 0 0 0 0 2.105 0 1 0 0 0 1 0 0 2.114 0 1 0 0 0 0 0 0 2.116 0 0 1 0 0 0 0 0 2.120 0 0 0 0 0 0 0 0 2.123 0 0 0 0 0 0 0 0 2.124 0 0 0 0 0 0 0 0 3.011 0 1 0 0 1 0 0 0 3.012 0 1 0 0 0 0 0 0 3.185 0 1 0 0 0 1 0 0 3.185 0 1 0 0 0 1 0 0 3.186 0 0 0 0 0 0 0 0 3.186 0 0 0 0 0 0 0 0 3.212 0 1 0 0 0 0 0 0 3.253 0 1 1 0 1 0 0 0 3.253 0 1 0 1 0 0 0 0 3.253 0 1 0 0 0 0 0 0 3.254 0 1 0 0 0 0 0 0 3.254 0 1 0 0 0 0 0 0 3.254 0 1 0 0 0 0 0 0 4.395 0 0 0 0 0 0 0 0 4.426 0 0 0 0 0 0 0 0 4.448 0 0 0 0 0 0 0 0 4.449 0 0 0 0 0 1 0 0 4.519 0 1 0 0 0 0 0 0 4.520 0 1 0 0 0 0 0 0 4.543 0 0 1 0 0 0 0 0 4.588 0 0 0 0 0 0 0 0 4.589 0 0 0 0 0 0 0 0 4.609 1 0 0 0 0 0 0 0 4.623 0 0 0 0 0 0 0 1 4.646 0 0 0 0 0 0 0 0 4.647 0 0 0 0 0 0 0 0 4.647 0 0 0 0 0 0 0 0 4.647 0 1 0 0 0 0 0 0 3.253 0 0 0 0 0 0 0 0 269

SITE DATA

Quadrat Maj- Min- Cache Spot Site- Ssoill Ssoil2 mort mort prob

1.040 0 0 0 0 1 3 0 1.083 0 0 0 0 1 20 11 1.115 0 0 0 0 1 4 0 2.105 0 0 0 0 3 1 0 2.114 . 0 0 0 0 2 18 1 2.116 0 0 0 0 2 7 1 2.120 0 0 0 0 1 1 0 2.123 0 0 0 0 1 5 21 2.124 0 0 0 0 1 12 0 3.011 0 0 0 0 2 2 0 3.012 0 0 0 0 3 1 2 3.185 0 0 0 0 2 2 0 3.185 0 0 0 0 1 2 0 3.186 0 0 0 0 2 2 0 3.186 0 0 0 0 1 1 0 3.212 0 0 0 0 2 2 1 3.253 0 0 0 0 3 2 0 3.253 0 0 0 0 3 2 20 3.253 0 0 0 0 2 2 0 3.254 0 0 0 0 2 2 20 3.254 0 0 0 0 2 1 0 3.254 0 0 0 0 2 2 20 4.395 0 0 0 0 3 2 0 4.426 0 0 0 0 2 2 0 4.448 0 0 0 0 3 1 2 4.449 0 0 0 0 2 1 0 4.519 0 0 0 0 1 1 0 4.520 0 0 0 0 1 1 0 4.543 0 0 0 0 2 7 0 4.588 0 0 0 0 2 2 1 4.589 0 0 0 0 2 2 0 4.609 0 0 0 0 2 1 2 4.623 0 0 0 0 1 1 0 4.646 0 0 0 0 3 2 0 4.647 0 0 0 0 2 2 0 4.647 0 0 0 0 2 2 0 4.647 0 0 0 0 2 2 0 3.253 0 0 0 0 2 2 0 270

SITE DATA

Quadrat Swat- Sriver Slos- Sswamp Smarsh Sbay- dis tre bas

1.040 400 0 0 0 1 0 1.083 10 0 0 0 1 0 1.115 10 0 0 0 0 2. 105 100 0 0 0 1 0 2. 114 100 0 0 0 1 0 2. 116 10 0 0 0 1 0 2.120 250 0 0 0 1 0 2. 123 1 0 0 0 1 0 2 . 124 10 0 0 0 0 0 3.011 70 0 1 0 0 0 3.012 100 0 1 0 0 0 3.185 50 0 1 0 0 0 3 . 185 40 0 1 0 0 0 3 . 186 10 0 1 0 0 0 3. 186 120 0 _____4^ 0 0 0 3.21-2— ■ 150 0 0 0 3 . 253 10 0 0 0 1 0 3 .253 2 0 0 0 1 0 3 . 253 10 0 0 0 0 0 3.254 2 0 0 0 1 0 3 . 254 200 0 0 0 1 0 3 . 254 2 0 0 0 1 0 4.395 300 0 0 0 0 0 4.426 100 0 1 0 0 0 4 . 448 100 0 1 0 0 0 4.449 200 0 1 0 0 0 4. 519 100 0 0 0 0 0 4 . 520 300 0 0 0 0 0 4. 543 100 0 1 0 0 0 4.588 10 1 0 0 0 0 4. 589 20 1 0 0 0 0 4 .609 300 1 0 0 0 0 4.623 1000 1 0 0 0 0 4.646 600 0 1 0 0 0 4.647 50 0 1 0 0 0 4.647 100 0 1 0 0 0 4.647 200 0 1 0 0 0 3.253 50 0 0 0 0 0 271

SITE DATA

Quadrat Sspring Sbog Slos- Sstr- Scon- Sdrain Sint- tcon conf stmr erfa

1.040 0 0 0 0 0 0 0 1.083 0 0 0 0 0 0 0 1.115 0 0 0 0 0 1 0 2.105 0 0 0 0 0 0 0 2.114 0 0 0 0 0 0 0 2.116 0 0 0 0 0 0 0 2.120 0 0 0 0 0 0 0 2.123 0 0 0 0 0 0 0 2.124 0 0 0 0 0 1 0 3.011 0 0 0 0 0 0 0 3.012 0 0 0 0 0 0 0 3.185 0 0 0 0 0 0 0 3.185 0 0 0 0 0 0 0 3.186 0 0 0 0 0 0 0 3.186 0 0 0 0 0 0 0 3.212 1 0 0 0 0 0 0 3.253 0 0 0 0 0 0 0 3.253 0 0 0 0 0 0 0 3.253 1 0 0 0 0 0 0 3.254 0 0 0 0 0 0 0 3.254 0 0 0 0 0 0 0 3.254 0 0 0 0 0 0 0 4.395 0 0 0 1 0 0 0 4.426 0 0 0 0 0 0 0 4.448 0 0 0 0 0 0 0 4.449 0 0 0 0 0 0 0 4.519 0 0 0 0 0 1 0 4.520 0 0 0 0 0 1 0 4.543 0 0 0 0 0 0 0 4.588 0 0 0 0 0 0 0 4.589 0 0 0 0 0 0 0 4.609 0 0 0 0 0 0 0 4.623 0 0 0 0 0 0 0 4.646 0 0 0 0 0 0 0 4.647 0 0 0 0 0 0 0 4.647 0 0 0 0 0 0 0 4.647 0 0 0 0 0 0 0 3.253 0 0 0 0 0 1 0 272

SITE DATA

Quadrat Sdistr Topo- Bridge Slter- Suter- Sfan Skn- site rac rac oll

1.040 2200 0 0 0 0 0 0 1.083 120 0 0 0 0 0 0 1.115 350 0 0 0 0 0 0 2.105 620 1 0 1 0 0 0 2.114 800 1 0 1 0 0 0 2.116 850 1 0 1 0 0 0 2.120 2400 0 0 0 0 0 0 2.123 3100 0 0 0 0 0 0 2.124 1400 0 0 0 0 0 0 3.011 500 1 0 1 0 0 0 3.012 900 1 0 1 0 0 0 3.185 1920 1 0 1 0 0 0 3.185 2100 1 0 1 0 0 0 3.186 1800 1 0 1 0 0 0 3.186 2100 1 0 0 1 0 0 3.212 1000 1 0 1 0 0 0 3.253 500 1 0 1 0 0 0 3.253 400 1 0 1 0 0 0 3. 253 700 1 0 1 0 0 0 3.254 480 1 0 1 0 0 0 3.254 680 1 0 0 1 0 0 3.254 500 1 0 1 0 0 0 4. 395 3500 1 0 0 1 0 0 4.426 3900 1 0 0 1 0 0 4.448 2900 1 0 0 1 0 0 4.449 3050 1 0 0 1 0 0 4. 519 1900 1 0 0 1 0 0 4.520 1900 1 0 0 1 0 0 4.543 800 1 0 0 1 0 0 4.588 120 1 0 0 1 0 0 4. 589 10 1 0 1 0 0 0 4.609 300 1 0 0 1 0 0 4.623 1000 0 0 0 0 0 4.646 1000 0 0 0 0 0 4.647 1350 1 0 1 0 0 0 4.647 1400 1 0 1 0 0 0 4.647 1400 1 0 0 1 0 0 3.253 700 1 0 1 0 0 0 273

SITE DATA

Quadrat Sflo- Ssw- Sridg- Elevs Slo­ Asp­ dpl flod knl pes ects

1.040 0 0 0 6 1 2 1.083 0 0 0 1 1 6 1.115 0 0 0 1 5 2.105 0 0 0 5 1 6 2.114 0 0 0 6 1 5 2.116 0 0 0 6 1 4 2.120 0 0 0 6 1 2 2.123 0 0 0 1 3 4 2.124 0 0 0 6 2 8 3.011 0 0 0 8 2 3 3.012 0 0 0 7 3 3 3.185 0 0 0 7 2 2 3. 185 0 0 0 7 2 2 3.186 0 0 0 5 2 8 3.186 0 0 0 9 1 4 3.212 0 0 0 7 3 7 3. 253 0 0 0 2 2 1 3.253 0 0 0 6 2 1 3.253 0 0 0 6 2 1 3.254 0 0 0 6 2 1 3.254 0 0 0 7 1 1 3.254 0 0 0 6 2 1 4.395 0 0 0 15 2 2 4.426 0 0 0 18 2 3 4.448 0 0 0 13 3 3 4.449 0 0 0 16 1 3 4.519 0 0 0 18 1 3 4.520 0 0 0 18 1 3 4.543 0 0 0 13 1 4. 588 0 0 0 12 1 6 4.589 0 0 0 12 1 6 4.609 0 0 0 13 1 4.623 0 0 0 16 1 1 4.646 0 0 0 12 1 7 4.647 0 0 0 7 2 2 4.647 0 0 0 7 2 2 4.647 0 0 0 12 2 2 3.253 0 0 0 6 2 2 274

SITE DATA

Tempnum Ss- 8s- Sel Se2 Se3 Ssll Ssl2 Ssl3 wd2 pd2

1.040 0 0 1 0 0 1 0 0 1.083 0 1 1 0 0 1 0 0 1.115 0 0 1 0 0 1 0 0 2.105 0 0 1 0 0 1 0 0 2.114 1 0 1 0 0 1 0 0 2.116 1 0 1 0 0 1 0 0 2.120 1 0 1 0 0 1 0 0 2.123 1 0 1 0 0 1 0 0 2. 124 0 0 1 0 0 1 0 0 3.011 0 0 1 0 0 1 0 0 3.012 1 0 1 0 0 1 0 0 3.185A 0 0 1 0 0 1 0 0 3.185B 0 0 1 0 0 1 0 0 3.186A 0 0 1 0 0 1 0 0 3 . 186B 0 0 1 0 0 1 0 0 3.212 1 0 1 0 0 1 0 0 3.253A 0 0 1 0 0 1 0 0 3.253B 0 1 1 0 0 1 0 0 3.253C 0 0 1 0 0 1 0 0 3.254A 0 1 1 0 0 1 0 0 3.254B 0 0 1 0 0 1 0 0 3.254C 0 1 1 0 0 1 0 0 4 . 395 0 0 0 1 0 1 0 0 4.426 0 0 0 1 0 1 0 0 4 . 448 1 0 0 1 0 1 0 0 4.449 0 0 0 1 0 1 0 0 4.519 0 0 0 1 0 1 0 0 4.520 0 0 0 1 0 1 0 0 4.543 0 0 0 1 0 1 0 0 4.588 1 0 1 0 0 1 0 0 4 . 589 0 0 1 0 0 1 0 0 4.609 1 0 0 1 0 1 0 0 4.623 0 0 0 1 0 1 0 0 4.646 0 0 1 0 0 1 0 0 4.647A 0 0 1 0 0 1 0 0 4.647B 0 0 1 0 0 1 0 0 4.647C 0 0 1 0 0 1 0 0 3.253D 0 0 1 0 0 1 0 0 275

SITE DATA

Tempnum Sezl Sez2 Sez3 Sez4 Sphi Sp- Splo Ss- 8s- med wdl pdl

1.040 1 0 0 0 0 0 1 0 1 1.083 1 0 0 0 0 0 1 0 1 1.115 1 0 0 0 0 0 1 1 0 2.105 0 1 0 0 1 0 0 1 0 2.114 0 1 0 0 0 1 0 1 2.116 0 1 0 0 0 1 0 1 0 2.120 0 1 0 0 0 0 1 1 0 2.123 0 1 0 0 0 0 1 1 0 2.124 0 1 0 0 0 0 1 1 0 3.011 0 0 1 0 0 1 0 1 0 3.012 0 0 1 0 1 0 0 1 0 3 . 185A 0 0 1 0 0 1 0 1 0 3.185B 0 0 1 0 0 0 1 1 0 3.186A 0 0 1 0 0 1 0 1 0 3.186B 0 0 1 0 0 0 1 1 0 3.212 0 0 1 0 0 1 0 1 0 3.253A 0 0 1 0 1 0 0 1 0 3.253B 0 0 1 0 1 0 0 1 0 3.253C 0 0 1 0 0 1 0 1 0 3.254A 0 0 1 0 0 1 0 1 0 3.254B 0 0 1 0 0 1 0 1 0 3 .254C 0 0 1 0 0 1 0 1 0 4.395 0 0 0 1 1 0 0 1 0 4.426 0 0 0 1 0 1 0 1 0 4.448 0 0 0 1 1 0 0 1 0 4.449 0 0 0 1 0 1 0 0 4.519 0 0 0 1 0 0 1 1 0 4.520 0 0 0 1 0 0 1 1 0 4.543 0 0 0 1 0 1 0 1 0 4.588 0 0 0 1 0 1 0 1 0 4.589 0 0 0 1 0 1 0 1 0 4.609 0 0 0 1 0 1 0 1 0 4.623 0 0 0 1 0 0 1 1 0 4.646 0 0 0 1 1 0 0 1 0 4.647A 0 0 0 1 0 1 0 1 0 4.647B 0 0 0 1 0 1 0 1 0 4.647C 0 0 0 1 0 1 0 1 0 3.253D 0 0 1 0 0 1 0 1 0 APPENDIX E

ARCHEOLOGICAL SITE DESCRIPTIONS

Environmental Zone 1 Site 1.040 This is previously recorded site 7K-D-39 consisting of an oyster shell scatter over a 20 square meter area and no diagnostics.

Site 1.083 This is previously recorded site 7K-F-77 consisting of an oyster shell scatter over a 100 square meter area and no other information.

Site 1.115 Adjacent to a marsh, the site consists of 1 quartz flake, 4 fire-cracked rock, and an oyster shell scatter of an average 2 per square meter over a 25 meter area.

Environmental Zone 2 Site 2.105, Woodland I Procurement Site Located 100 meters east of a marsh, adjacent to woods, on a low terrace, 1 quartzite Lagoon-like projectile point (extensively retouched) and three flakes were found.

Site 2.114, Woodland I This is previously recorded site 7K-F-18 which contained no diagnostic information in the original site file. During this survey, I soapstone bowl fragment was found and an oyster shell scatter (1 per 5 square meters) was observed over a 100 square meter area. It is located on a low terrace 100 meters from a marsh.

Site 2.116, Woodland II This is previously recorded site 7K-F-15 identified as a Woodland II site. No further information is provided on the site record. The site is located on a low terrace 10 meters from a marsh.

Site 2.120 This is previously recorded site 7K-F-112. Located near Brockonbridge Gut, it consists of 3 cobble cores and fire-cracked rock.

Site 2.123 Adjacent to a marsh, 3 fire-cracked rock, 2 flakes, and a scatter of oyster shells (1 per 2 square meters) over a 25 meter area were found.

276 277

Site 2.124 One broken grinding stone, one quartz flake, and an oyster shell scatter (1 per 1 square meter) over a 10 square meter area were observed. It is located on the northwest side of an ephemeral drainage.

Environmental Zone 3 Site 3.011, Woodland I Micro-band Base Camp This site is located on a low terrace on the north side of Tidbury Creek near its confluence with the Saint Jones River. The following collection was made: 2 green jasper square-stemmed projectile points, 1 quartzite Bare Island projectile point, 1 full-grooved ax, 1 celt fragment, 1 pestle fragment, 2 battering tools, 2 yellow jasper and 1 quartz pebble cores, and 3 bifaces. Artifacts and fire-cracked rock are scattered over a 200 square meter area.

Site 3.012, Woodland I Located on a low terrace on the north side of Tidbury Creek near its confluence with the Saint Jones River, this site could be part of site 3.011, although it contains a discrete locus of activity separated by 200 meters. One Calvert quartz projectile point, 2 bifaces, 1 uniface, 3 pestle fragments, 4 battering tools, 4 pebble cores, 2 grinding stone fragments, and a scatter of flakes and fire-cracked rock were found over a 75 square meter area.

Site 3.185A, Woodland I Procurement Site Two Rossville projectile points (1 jasper, 1 quartz), 2 mortars, and a flake and fire-cracked rock scatter (1 per 5 square meters) were found over a 15 square meter area. This site is located on a lower terrace west of a small tributary of the Murderkill River.

Site 3.185B, Woodland I Procurement Site Located on a low terrace west of a small tributary of the Murderkill River, this site was indicated by 1 quartzite Bare Island projectile point, 1 net-impressed Coulbourne ceramic sherd, 1 mortar, 4 bifaces, 2 pebble cores, 1 utilized flake, and a flake and fire-cracked rock scatter (3 per square meter) over a 25 square meter area.

Site 3.186A This site includes 1 hammerstone, 3 fire-cracked rock, and 5 flakes scattered over a 50 meter square area. It is on a low terrace east of a small tributary of the Murderkill River.

Site 3.186B The collection from this site consists of 2 hammerstones, 5 fire-cracked rock, and 12 flakes scattered over a 100 meter square area, this site is located on an upper terrace east of a small tributary of the Murderkill River. 278

Site 3.212, Woodland I Located on a low terrace near a spring, this site includes 1 smoothed-over Wolfe Neck ceramic sherd, 1 mortar, 1 pestle, 1 hammerstone, and a scatter of fire-cracked rock (2 per square meter), over a 10 square meter area.

Site 3.253A, Woodland I/II Micro-band Base Camp On a low terrace adjacent to a marsh and the Murderkill River, this site includes 1 quartzite Savannah River Contracting Stemmed Variant projectile point; 1 Snyder's projectile point (grey chert); 2 jasper triangular projectile points; 1 net-impressed and 1 cordmarked Wolfe Neck ceramic sherds; 1 biface; 1 adze; 1 abrading stone; 4 pebble cores; 1 core spall fragment; 2 unifacially-worked flakes; and a scatter of flakes and fire-cracked rock (2 per square meter) over a 100 square meter area.

Site 3.253B, Woodland I Macro-band Base Camp This site contains an argillite preform cache of 8 preforms found clustered on the surface and an additional 4 scattered nearby, 3 Susquehanna Broadspear projectile points (2 of jasper, 1 of rhyolite), 1 unfinished jasper Late Archaic small-stemmed tradition projectile point, 2 soapstone bowl fragments, 1 Hell Island cordmarked ceramic sherd, 2 quartzite bifaces, 5 hammerstones, and a scatter of fire-cracked rock and flakes (10 per square meter) over a 100 square meter area. It is considered a macro-band base camp even though it does not meet the size criteria of 10,000 square meters because of the preform cache, the variety and density of artifacts, and the numerous site loci in the area which may actually be part of the same site. It is on a low terrace on the south side of the Murderkill River, 2 meters from a marsh on a lower terrace.

Site 3.253C, Woodland I Located near a spring on a low terrace, the artifacts from this site include 1 rhyolite Susquehanna Broadspear base, 1 triangular quartz projectile point, 1 soapstone bowl fragment, 4 Wolfe Neck cordmarked ceramic sherds, 2 Hell Island cordmarked ceramic sherds, 1 anvil stone, 2 hammerstones, 1 pebble core, and a fire-cracked rock and flake (11 per square meter) scatter over a 10 square meter area.

Site 3.253D This site is located on the south side of the Murderkill River and consists of a fire-cracked rock and flake scatter (1 per 2 square meters) over a 15 square meter area.

3.254A, Woodland I One soapstone bowl fragment, 1 pebble core and a scatter of fire-cracked rock, flakes, oyster and clam shell (1 per square meter) over a 15 square meter area were found. It is located on a low terrace, 2 meters from a marsh, on the south side of the Murderkill River. 279

3.254B, Woodland I A rhyolite Susquehanna Broadspear projectile point base and a scatter of fire-cracked rocks and flakes (1 per square meter) over a 10 square meter area were found. The site is 200 meters from a marsh on the south side of the Murderkill River.

3.254C; Woodland I Adjacent to a marsh on the south side of the Murderkill River, the site includes 1 eroded Wolfe Neck ceramic sherd, 1 Mockley net-impressed ceramic sherd, 3 pebble cores, and a scatter of fire-cracked rock and flakes (1 per square meter) over a 25 square meter area. The landowner reported finding a jasper Lagoon projectile point, 1 full-grooved axe, 1 mortar, 1 pestle, and 1 grinding stone from this site.

Environmental Zone 4 Site 4.395 This site consists of 3 fire-cracked rock and 1 utilized flake over a 10 square meter area on an upper terrace west of Andrews Lake.

Site 4.426 Located on an upper terrace on the north side of Pratt Branch, 2 pestle fragments, 1 mortar fragment, 2 hammerstones, 1 cores, 2 fire-cracked rock and a flake scatter (1 per square meter) were found over a 10 square meter area.

Site 4.448 One scraper, 2 cores, 3 flakes and 4 fire-cracked rock were found over a 10 square meter area on an upper terrace on the north side of Pratt Branch.

Site 4.449, Procurement Site One isolated mortar found on an upper terrace on the north side of Pratt Branch.

Site 4.519, Woodland I Located 100 meters from an ephemeral drainage, 1 Hell Island ceramic sherd and 1 flake were found.

Site 4.520, Woodland I Located 300 meters from an ephemeral drainage, 1 Wolfe Neck cordmarked ceramic sherd and 2 fire-cracked rock were found.

Site 4.543, Woodland II Fourteen Townsend Ware incised ceramic sherds, 1 pestle, 5 fire-cracked rock and 8 flakes were found over a 50 square meter area. It is on an upper terrace on the south side of Spring Branch.

Site 4.588 This is previously recorded site 7K-E-107 for which no description is provided in the State site records. 280

Site 4.589 This is previously recorded site 7K-F-9 for which no diagnostics were found.

Site 4.609, Archaic One jasper LeCroy bifurcate projectile point and a scatter of flakes (1 per 25 square meters) over a 175 square meter area were found. The site is 300 meters south of the Murderkill River.

Site 4.623, Paleo-Indian Hunting Site One black chert Kirk/Palmer projectile point and 2 flakes were found 1000 meters south of the Murderkill River.

Site 4.646 This is previously recorded site 7K-F-126, consisting of a lithic scatter over a 10 square meter area.

Site 4.647A Located on a low terrace on the northeast side of Brown's Branch, this site is a fire-cracked rock and flake scatter (1 per square meter) over a 10 square meter area.

Site 4.647B Two pebble cores and a fire-cracked rock and flake scatter (1 per 2 square meters) were found over a 15 square meter area. The site is on a low terrace on the northeast side of Brown's Branch.

Site 4.647C, Woodland I One Mockley cordmarked ceramic sherd, 1 chopper and 2 flakes were found at this site which is on an upper terrace 200 meters northeast of Brown's Branch. BIBLIOGRAPHY

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