The Pennsylvania State University
The Graduate School
LONG TERM AVERAGE RATE MAXIMIZATION OF CREEK INDIAN
RESIDENTIAL MOBILITY: A TEST OF THE MARGINAL VALUE THEOREM
A Thesis in
Anthropology
by
H. Thomas Foster, II
2001 H. Thomas Foster, II
Submitted in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
December 2001
We approve the thesis of H. Thomas Foster, II.
Date of Signature
Dean R. Snow Professor of Anthropology and Head of Department of Anthropology Thesis Advisor Chair of Committee
Marc D. Abrams Professor of Forest Ecology and Physiology
Steven J. Beckerman Associate Professor of Anthropology
Kenneth G. Hirth Professor of Anthropology
ABSTRACT
This dissertation is about the variables that explain small-scale population migration. These small-scale population movements contribute to cultural transmission, population interaction, and biological evolution. I reviewed the ecological research on variables that contribute to population dispersion and then applied an ecological model to population movement.
Specifically, I measured the resource utility for catchments surrounding ten Creek Indian towns. Based on Creek ethnohistoric sources, I characterized resource utility as agricultural productivity and fuel wood availability. I quantified fuel wood availability and forest resources from federal public land surveys and agricultural productivity from William Baden’s (1987) model of crop yield depletion among Mississippian agricultural systems.
I found that fuel wood is not the most significant and limiting variable contributing to town abandonment among late historic Creek Indians.
Consequently, I applied the marginal value theorem (1976) test solely to agricultural productivity. These analyses were consistent with the proposition that Creek Indians were optimizing the productivity of land surrounding their towns. They abandoned towns when agricultural productivity surrounding the town decreased to the average optimum yield for the entire region.
In two cases, however, Creek towns were not abandoned at the time of optimal instantaneous resources gain. These two towns were the only towns iv directly on a major east-west trade route. The inhabitants of these two towns did not optimize agricultural productivity in their town catchments because the value of the trade route increased the marginal utility of their settlements.
TABLE OF CONTENTS
LIST OF FIGURES ...... vi
LIST OF TABLES ...... x
ACKNOWLEDGMENTS ...... xv
Chapter 1 Introduction and Background ...... 1
Chapter 2 Theory...... 11
Chapter 3 Materials and Methods...... 42
Chapter 4 The Study Population...... 75
Chapter 5 Forest Analysis...... 118
Chapter 6 Analysis and Discussion ...... 173
BIBLIOGRAPHY ...... 221
Appendix A: Creek Town Occupation Times ...... 240
Appendix B: Town Catchment Corn Yields by Soil Type ...... 252
LIST OF FIGURES
Figure 2–1: Variables used in the optimal diet model ...... 21
Figure 2–2: This figure shows different forms that the gain function can assume. Time is along the abscissa and net return is along the ordinal axis...... 26
Figure 2–3: Variables used in the patch model (MVT)...... 28
Figure 3–3–1: Example of Survey Maps and Witness Trees. Photograph by Thomas Foster, courtesy of the Georgia Department of Archives and History. Note the circles at the corner of each square. Each circle is a witness tree and is labeled by the tree’s species...... 48
Figure 3–2: Apalachicola Old Town soil types by 2km, 1km, 1/2km, and 1/4km catchments ...... 56
Figure 3–3: Apalachicola soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 57
Figure 3–4: Broken Arrow soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 58
Figure 3–5: Chiaha soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 59
Figure 3–6: Coweta Old Town soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 60
Figure 3–7: Cusseta Old Town soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 61
Figure 3–8: Cusseta soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 62
Figure 3–9: Osochi soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 63 vii
Figure 3–10: Upatoi soil types by 2km, 1km, 1/2km, and 1/4km catchments...... 64
Figure 3–11: Yuchi soil types by 2km, 1km, 1/2km, and 1/4km catchments. 65
Figure 4–1: Ephram G. Squire's engraving of "Creek Towns and Dwellings" (From Waselkov and Braund 1995) ...... 81
Figure 4–2: "How they till the soul and plant" Theodore De Bry ...... 92
Figure 4–3: Creek House and path to Agricultural Fields Drawn on Georgia Land Survey Map. Photograph by Thomas Foster. Courtesy of the Georgia Department of Archives and History...... 97
Figure 4–4: Lower Creek towns included in Study...... 115
Figure 4–5: Muscogee County, District 10, Section 252, Geogia Land Survey platt map, Georgia Surveyors General, Department of Archives and History, Atlatnta, Georgia. The map section shows the Creek town of Upatoi drawn on the land survey map for Muscogee County, 1827. Surveyor’s handwriting is outlined in white for clarity. North is to the right. The square lines around the Upatoi area represent a section. A section is 45 chains to a side. Forty-five chains is 66 feet (20.1168 meters)...... 116
Figure 5–1: Phsiographic Regions and Subdistricts used in Study...... 120
Figure 5–2: Standardized Residuals by Physiographic Region ...... 130
Figure 5–3: Standardized Residuals for Coosa Valley Topography...... 132
Figure 6–1: Available fuel wood around Apalachicola per year in cubic meters ...... 178
Figure 6–2: Available fuel wood around Apalachicola Old Town per year in cubic meters...... 178
Figure 6–3: Available fuel wood around Broken Arrow per year in cubic meters ...... 179
Figure 6–4: Available fuel wood around Chiaha per year in cubic meters ..... 179
Figure 6–5: Available fuel wood around Coweta Tallahassee per year in cubic meters...... 180
Figure 6–6: Available fuel wood around Cusseta per year in cubic meters.... 180 viii
Figure 6–7: Available fuel wood around Cusseta Old Town per year in cubic meters...... 181
Figure 6–8: Available fuel wood around Osochi per year in cubic meters ..... 181
Figure 6–9: Available fuel wood around Upatoi per year in cubic meters ...... 182
Figure 6–10: Available fuel wood around Yuchi per year in cubic meters...... 182
Figure 6–11: Estimate of total corn yield for Apalachicola according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 187
Figure 6–12: Estimate of total corn yield for Apalachicola Old Town according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 187
Figure 6–13: Estimate of total corn yield for Broken Arrow according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 188
Figure 6–14: Estimate of total corn yield for Chiaha according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 188
Figure 6–15: Estimate of total corn yield for Coweta Tallahssee according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 189
Figure 6–16: Estimate of total corn yield for Cusseta according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 189
Figure 6–17: Estimate of total corn yield for Cusseta Old Town according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 190
Figure 6–18: Estimate of total corn yield for Osochi according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 190
Figure 6–19: Estimate of total corn yield for Upatoi according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 191 ix
Figure 6–20: Estimate of total corn yield for Yuchi according to the population labor potential and the Baden (1987) model of corn depletion over time ...... 191
Figure 6–21: Inverse of Gain Function for Apalachicola by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot ...... 195
Figure 6–22: Inverse of Gain Function for Apalachicola Old Town by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot ...... 196
Figure 6–23: Inverse of Gain Function for Broken Arrow by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot ...... 197
Figure 6–24: Inverse of Gain Function for Chiaha by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot...... 198
Figure 6–25: Inverse of Gain Function for Coweta Tallahassee by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot ...... 199
Figure 6–26: Inverse of Gain Function for Cusseta by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot...... 200
Figure 6–27: Inverse of Gain Function for Cusseta Old Town by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot ...... 201
Figure 6–28: Inverse of Gain Function for Osochi by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot...... 202
Figure 6–29: Inverse of Gain Function for Upatoi by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot...... 203
Figure 6–30: Inverse of Gain Function for Yuchi by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot...... 204
LIST OF TABLES
Table 1: Apalachicola Catchment soil types and corn yields by soil type in a 2 km catchment. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 68
Table 4–1: Towns Included in Study and Respective Occupation Period (archaeological and ethnohistoric justification for the dates of occupation is in the appendix). *Estimated average population during period of occupation based on Swanton (1922) and a gunman ratio of 1:4...... 114
Table 5–1: Species Frequencies and Common Names for witness trees in Study Area...... 127
Table 5–2: Species frequency by physiographic region...... 128
Table 5–3: Coosa Valley species frequency by topography ...... 133
Table 5–4: Species frequency for Coosa Valley mineralogy...... 137
Table 5–5: Species frequency for Piedmont physiographic districts ...... 143
Table 5–6: Species frequency for Ashland Plateau mineralogy...... 145
Table 5–7: Species frequency for Opelika Plateau mineralogy...... 147
Table 5–8: Species frequency for Coastal Plain physgiographic districts ...... 151
Table 5–9: Species frequencies for Black Prairie mineralogy...... 164
Table 5–10: Species frequencies for Fall Line Hills mineralogy...... 166
Table 5–11: Species frequencies for Red Hills mineralogy...... 169
Table 6–1: Variables used to calculate annual corn yield based on the labor available in each Creek town...... 186 xi
Table 6–2: Population size, forest type (according to types used by McMinn and Hardt, 1993), occupation period, and instantaneous rate of returns for each town assuming a 2 km catchment...... 209
Table 6–3: The range of yield at abandonment within one sigma standard deviation from the mean for eight Creek Indian town catchments...... 212
Table 6–4: Yield at abandonment for 2km, 1km, 1/2km, and 1/4km catchments for eight towns and the respective mean and standard deviation...... 213
Table 5: Apalachicola Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 252
Table 6: Apalachicola Old Town Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 253
Table 7: Broken Arrow Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 254
Table 8: Chiaha Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 255
Table 9: Coweta Tallahassee Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 256
Table 10: Cusseta Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 257 xii
Table 11: Cusseta Old Town Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 258
Table 12: Osochi Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 259
Table 13: Upatoi Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 260
Table 14: Yuchi Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a)...... 261 ACKNOWLEDGMENTS
This has been a long project in the making. Many people have contributed significantly to its success. First of all, thank you to my committee,
Drs. Dean Snow, Marc Abrams, Stephen Beckerman, and Ken Hirth.
Particularly, thank you to Dean Snow, my dissertation chair, for your support, advice, and open mind. Stephen Beckerman has influenced this dissertation from its very infancy when I was just exploring ideas about behavioral ecology and learning about optimal foraging theory. I wouldn’t be doing this kind of work today without his support and guidance. Thank you to the late Jim Hatch for encouragement and support. Thank you to Jim Wood for teaching me how to conduct quality research or at least recognize when I don’t. Thank you to Henry
Harpending for guiding me during the early stages of this work. This project has changed considerably since he was assisting me but it is the same at the core.
Many others offered support in various forms. Christopher Hamilton at
Fort Benning provided access to countless reports that improved the quality and validity of this dissertation. James Jameson (National Park Service), Eugene
Futato (University of Alabama, Office of Archaeological Research), Mark Williams
(University of Georgia), Greg Waselkov (University of South Alabama), Dean
Wood and Dan Elliot (Southern Research), Frank Schnell (Columbus Museum)
Chad Braley (Southeastern Archaeological Services), John Cottier (Auburn), xiv
Ernest Seckinger (Army Corps of Engineers, Mobile), and John Moore
(Coosawattee Foundation) also provided access to valuable reports. Thanks to
Shelby Huffman of the Bureau of Land Management for sorting through my requests for microfilm. The staff at the Georgia Department of Archives and
History were more than gracious. I particularly thank them for allowing me to photograph the plat maps. Thank you to the Nature Conservancy at Fort
Benning and Nick Tew at the Alabama Geological Survey for access to data layers. Thank you to Eugene Futato and Mark Williams for access to site files data.
Thank you to Greg Waselkov, Vernon Knight, Dean Wood, Dan Elliot,
Frank Schnell, Robbie Ethridge, Charles Hudson, Richard Polhemus, John
Cable, Craig Sheldon, John Cottier, Brian Black, Marvin Smith, and William
Baden all assisted with interpretation and offered advice. Thanks to Dave Hally for advice and encouraging me more than he realizes from the very beginning of my interest in archaeology.
Funding for this project was primarily provided by a contract from the
Conservation Program of the Strategic Environmental Research and
Development Program (SERDP) with Oak Ridge National Laboratory (ORNL).
ORNL is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. Assistance was also received from the Hill
Fellowship, Department of Anthropology, Pennsylvania State University. Relief xv from teaching was provided by a Dissertation Fellowship from the Graduate
School, Pennsylvania State University.
Finally I offer many thanks to my family and to my wife Kara for love, encouragement and for putting up with all of this.
Chapter 1
Introduction and Background
Behavioral ecology is the study of animal behavior in response to evolutionary forces and environmental variation. It is not necessarily a unified field but rather a collection of theories and models, which place the processes of organic evolution and quantitative analysis at the core of investigation (Krebs and Davies
1978).
One area of study in behavioral ecology is the application of optimization theory to the explanation and prediction of evolved feeding behaviors. This focus is the application of optimal foraging theory (Stephens and Krebs 1986). Optimal foraging theory is the application of formal models to the explanation and prediction of animal behavior in response to environmental fluctuation; it is the focus of this dissertation.
The purpose of this study is to test the application of the marginal value theorem (Charnov 1976), a model derived from optimal foraging theory, for its applicability to population migration among humans. This optimal resource use model is applied to the residential mobility of a population of late historic period
(circa 1600-1830) Creek Indians in the southeastern United States. Human applications of the marginal value theorem (MVT) have been few and mostly 2 qualitative. To my knowledge no anthropological application has fully satisfied all of the model’s assumptions and this application is no exception. Furthermore, there have been no archaeological tests of the MVT. Some have used it to explain a pattern but have not tested it (Etnier 1997, Fitzhugh 1997). The application in this dissertation is the most quantitative and a priori anthropological application of the marginal value theorem to date.
Optimality models in behavioral ecology assume that an organism has evolved to maximize some currency in relation to a specific behavioral phenotype.
A phenotype is “any measurable trait of an individual that arises from the interaction of the individual’s genes and environment”(Alcock 1998, g-4). Most behavioral scientists are interested in explaining and predicting phenotypes as opposed to genotypes. Phenotypes are the outward manifestation of evolution and environment on the traits of an individual. They are the traits that we observe. An individual’s genes and environment affect the individual traits such as morphology or behavior. The models of behavioral ecology and this dissertation are concerned with behavioral phenotypes such as economic, subsistence, or social decisions.
The marginal value theorem in optimal foraging theory predicts the instantaneous rate of gain for a given patch of resources at the time of abandonment of that patch for an optimal forager (Charnov 1976). This theorem says that there is some theoretical optimal instantaneous rate of gain at abandonment for each patch of resources in a consumer’s habitat. That optimal instantaneous rate of gain is defined as the average instantaneous rate of gain for 3 the entire habitat or all patches combined. This formulation leads to the prediction that an optimal consumer will abandon any given patch when the instantaneous rate of gain for that patch reaches to the average for the entire habitat. This is the case because, after that point the consumer will gain at least the average rate of gain by moving to some other patch. Remaining in the patch will produce returns that are below average. I used existing ecological field research and ethnohistoric data for the Creek Indians to identify the most significant and most limiting variables contributing to the value of a given patch of land that was occupied by the Indians.
These sources indicate that fuel wood availability and agricultural productivity were the ecological variables that most influenced population movement and town residence abandonment over the long term. Occasionally, hostility and warfare were important and these variables are controlled in this study. I created use area catchments around each town to represent a patch of resources as defined in the marginal value theorem. Following standard procedures for testing the marginal value theorem, I calculated the resource gain curves for each town’s use area catchment, a patch. The resource gain curve is a mathematical function that describes the returns that a consumer would receive in a given patch of resources over time. These variables were then used to test the hypothesis that Creek Indians were optimizing their long-term average rate of returns for the patch of resources immediately surrounding their respective towns. Specifically, the marginal value theorem predicts that the rate of return at the point of abandonment will be the same for all instances of patch abandonment. In the case of the Creek Indians, this 4 idea translates to town abandonment. In other words, for every time a town was abandoned, I measured the rate of resource returns in the towns use area catchment. No matter how long the town was occupied, the rate of returns at the point of abandonment should have been the same as the rate of returns at the point of abandonment for any other town.
I calculated the resource consumption curves for each town’s catchment use area. Then, using ethnohistoric and archaeological data, I determined the occupation duration for each town. Since the occupation duration is known for each town, I then calculated the rate of returns that consumers in that town catchment would have been receiving. The marginal value theorem predicts that the rate of returns at the point of abandonment, which is known for each town, should be the same. They all should be the same rates of return if the Creek consumers were optimizing the long-term average rate of returns, which was the average for the entire habitat. There is only one average rate for the entire habitat. Theoretically, the Creek consumers knew this average rate of returns and they abandon a given town location when they begin extracting less than the average rate of return because remaining in the location would have been sub-optimal.
The Creek Indians lived in relatively centralized towns in the southeast
United States from about 1600 to 1830. These Indians were an amalgam of populations that gradually migrated to what is now Alabama and Georgia from across the southeast region during the historic period. By applying the marginal value theorem to the issue of Creek Indian population movement, this study helps 5 refine the use of behavioral ecology in predicting human resource use.
Furthermore, it will help explain and quantify the processes that contributed to population movement and cultural interaction among these southeastern Indians.
Ecological Research on Population Dispersion
Current human diversity is the product of millions of years of population interaction and selection. This population interaction is partly both cause and effect of population movement. Consequently, population movement is likely to be an important variable in the explanation of any evolutionary event at both the proximate and ultimate levels. Stenseth and Lidicker (1992: 12-14) describe population movement as a “conceptual glue” because it affects and ties together a number of behavioral and evolutionary fields of study. The explanation of almost all cultural or biological phenotypes must include population migration as a significant variable.
Causal Variables of Population Migrations
There have been very few studies of human migration using evolutionary theory or behavioral ecology. Biologists have studied the causes of population movement, or “dispersion,” among non-human animals for decades, however. At a long-term evolutionary scale, the processes that contribute to human versus non-human population movement should be relatively the same. In order to elucidate the variables that contribute to the evolution of migratory behaviors, I briefly discuss that research. Since different organisms have had different evolutionary environments, not all of these variables are necessarily directly 6 related to human migration. It is useful to describe each, however, in order to give a complete perspective on the evolutionary mechanisms involved in migratory adaptations. Review of the variables that affect migration leads to methods for their control so that one of them, resource depression, can be used to test the marginal value theorem (Charnov 1976).
The causal variables for animal movement can be categorized as intrinsic or extrinsic, relative to the dispersing individual. Intrinsic variables are causes that are internal to the individual migrator such as an evolved tendency to migrate away from a natal home at a particular age. Extrinsic variables are migratory causes that are stimulated from outside the individual such as conspecific aggression. Howard (1960) first made the intrinsic and extrinsic distinction. This distinction is an arbitrary but heuristically useful one.
Intrinsic Factors
Age and Sex Variation. Age and sex biased dispersion implies that there are physiological mechanisms that cause individuals at a particular age or sex to disperse. Theories on the evolution of this kind of dispersion vary. They generally show a genetic advantage in the form of decreased inbreeding, increased probability of surviving, or increased probability of finding a mate. For example, sex biased dispersion is often attributed to extreme inbreeding (Dobson 1982;
Greenwood 1980; Shields 1982) or mate-defense (Greenwood 1980).
In general sex biased dispersion should occur when moving to another area would increase fitness for one sex. This advantage would result in a sex bias when 7 access to mates is greater in some other home range. This advantage may occur under numerous conditions. For example, the 1:1 ratio of the sexes may be broken when there is sex-biased mortality or when there is little dispersal where males are competing for females. In the latter case, the sex ratio would be in favor of females
(Hamilton 1967). Clark (1978) observed a sex bias in favor of males among primates as a result of resource competition between females.
Reproduction. Reproduction can be an intrinsic cause of migration if a particular species has evolved to disperse in order to find non-related mates
(Howard 1960). Traditionally in dispersion biology, it is observed that males are the primary natal dispersers. Sexual bias in dispersion is usually attributed to reproductive efforts. Generally speaking, humans have a more various reproductive dispersal pattern but it is often exogamous, particularly in band and tribal level societies. For example, Smouse and Wood (1987) analyzed 11 parishes in New
Guinea for intermigration. They collected genetic and demographic data and observed that "far more females migrate before reproducing than males: over
91%..."(Smouse and Wood 1987: 213). Whether or not reproductive dispersion among humans is the result of some physiological mechanism is unknown.
According to the terminology of Howard (1960) extrinsic factors are dependant on variables external to the migrating individual. The migration behavior is dependant on some environmental stimulus.
8
Extrinsic Factors
Conspecific Aggression. Conspecific aggression is an often-mentioned proximate cause of migration among mammals. Conspecific aggression is hostility between individuals of the same species. There appears to be a large amount of variation in the conclusions of these studies. However, increased population movement has been observed in numerous species and appears to be a consistent response to aggression. The sex of the forced migrator varies (Pusey 1992).
Conspecific aggression has been connected with migration in a number of experiments with mammals. Indirect evidence for the connection was found by
Myers and Krebs (1971). They observed that voles' migration coincided with increases in population. Consistently, in numerous experiments, dispersal was connected with population density. When migration was denied, population density increased dramatically, which indicates that migration is a normal mechanism of population regulation (Lidicker 1975). There are countless examples of human population movement as a result of warfare and hostility. So common are these cases that it is not necessary to provide specific examples.
Resource Depression. The simplest cause of migratory dispersion is a decrease of resources that are important to the individual’s inclusive fitness. When the rate of return for a significant resource declines to a threshold, the individual should move to another, resource richer area. This variable is likely to have had a significant and regular influence on population movement over the course of evolutionary history. 9
This dissertation focuses on resource depression because of all the variables this one is most likely to be significant over the spatial and temporal scale of human migration events. Resource depression should be a significant variable contributing to migration because all animals need to subsist and reproduce and they must all respond to fluctuations in the environment.
Dissertation Organization
This dissertation is organized from a general problem to theory to a model derived from the theory to a specific test of the model. I introduce the general, cross-species variables that contribute to the evolution of population movement.
Then, since I am interested in long-term processes, I focus on one of those variables, environmental resource depression. Optimization theory and the marginal value theorem are introduced as a model of resource depression in relation to animal migration and movement. I argue that the marginal value theorem is general and applicable to human migratory situations, specifically town abandonment. Then I introduce a human study population and apply a model prediction to that population.
This chapter introduces the ecological research that exists pertaining to the issue of population migration or “dispersal.” It reviews the variables in terms of intrinsic and extrinsic divisions (Howard 1960). Chapter two introduces the marginal value theorem and derives the formula’s predictions. Chapter three is a discussion of the methods used to perform the marginal value theorem analysis. Chapter four reviews the study population, the Creek Indians during the eighteenth and 10 nineteenth centuries. Creek Indian settlement, agricultural practices, resource use, and decision-making are discussed. Chapter five is an in depth analysis of the ecology, specifically the forests in the Creek Indian habitat. These data are derived from historic land survey maps and notes that were made at the time of the Creek
Indian removal in the early nineteenth century. Lastly, chapter six is a presentation and interpretation of the results of the test and a discussion of their meaning.
Chapter 2
Theory
Introduction
This chapter introduces optimal foraging theory, the marginal value theorem, and their evolutionary logic. Criticisms of optimal foraging theory are also relevant to the marginal value theorem and are reviewed. The marginal value theorem is mathematically derived so that the reader can understand its assumptions and limitations. This step is necessary in order to point out how the hypothesis tested in this dissertation is deduced from the model and to point out the model’s simplicity, advantages, and disadvantages. Lastly, I review human applications of the marginal value theorem and discuss the uniqueness of the application in this dissertation.
Optimization Theory
Optimization theory has been used in fields as diverse as engineering, economics, and biology. In the behavioral sciences, an organism is assumed to act optimally. This assumption is logically deduced from the theory of natural selection and allows one to identify adaptive phenotypes. Under natural selection, those individuals who manage limited resources the most efficiently should have the greatest overall reproductive success. They will have more time or resources for increasing inclusive fitness in other ways (e.g. protecting offspring, mating, etc.). 12
These optimizers and the phenotypes that contribute to the more efficient behaviors will thus become more dominant in the evolving population.
This body of theory is used as a method for identifying mechanisms of adaptation and researchers derive models of behavior from these assumptions.
These models are applied to living organisms to determine the most accurate description of the organisms’ behavior. Failure to find results consistent with hypotheses testing an optimal foraging theory model is not a failure to find results consistent with optimal foraging theory because it is the model hypotheses that are being tested, not the theory or its assumptions. Adaptive phenotypes are assumed to be optimal according to the tenants of evolution according to natural selection.
This point may seem obvious but it has been a source of criticism in the literature (Stephens and Krebs 1986: 207-212). Critics have suggested that optimal foraging theory is tautological and therefore “unscientific” because optimal foraging theory is not refuted when a model is not supported by observation and testing.
Consequently, it is necessary to point out here that optimal foraging theory itself is not being tested. The model, which is derived from optimal foraging theory, is being tested for its utility in predicting some behavior. Not all models are useful or accurate but that inaccuracy does not necessarily refute the evolutionary or other assumptions that underlie them.
Most models to date have been applied to foraging and feeding behavior.
Each of these models has a specific form. Each has decision assumptions, 13 currency assumptions, and constraint assumptions. These must be discussed and specified in turn.
Decision Assumptions
The decision assumption specifies the behavioral choice or morphology being analyzed. “Decision here refers to the type of choice …the animal is assumed to make (or that natural selection has made for it)…” (Stephens and Krebs 1986, 6; original italics). In a behavioral model, the decision assumption is a formal mathematical model describing some behavior. In a formal model, the decision assumption has specific decision variables. The number of variables included in the decision determines the accuracy and complexity of the model.
Optimization Currency
All optimization models have to deal with the issue of currency. The organism has to optimize something that is common to all resources. Ultimately the currency must be inclusive fitness; however, it is impractical to measure the success of particular genes. Instead we measure a particular currency that is assumed to be a proxy for genetic success. Energy is the most commonly used currency in foraging models. However, it is not the only currency or even the best currency that the researcher may choose. The currency chosen should depend on the demands of the particular individual being studied and the environmental situation. In some environments, energy is very abundant and it is not the limiting resource. The particular behavior under consideration might be optimizing a unique resource. Energy is convenient because it is common to all matter and easy to 14 measure. If we chose offspring as the currency (which may be more valid), we would have to measure the contribution that each resource contributed to the production of additional offspring.
Optimization behaviors and the decision assumption can occur in numerous forms. Optimization is not always maximizing some currency. A consumer that minimizes time (or some other currency) invested per unit returned is maximizing a negative currency (Stephens and Krebs 1986, 7). In mathematical terms, a negative currency is the negative of the maximized positive value. Similarly, a risk minimizer is maximizing negative risk. The maximizing behavior is the same but the currency is different. The species’ adaptations and environment thus drive decisions about the best currency to use and about decision assumptions used in the model.
In addition to the currency decision, the time over which the individual is making a decision must be chosen for the model. Many models use long-term rate maximization with “long-term” being much greater than the time needed to execute the behavior. If the individual is maximizing over one minute and it takes five minutes to perform the behavior, the decision will be different than if the times were reversed. For example, a bee foraging bout may last only a few minutes.
Consequently, the optimization decision and its model duration are only a few minutes. A human foraging bout, however, may last days or weeks.
15
Constraint Assumptions
Constraints are parameters within which the organism must operate.
Constraints consequently determine the boundaries of the optimal behavior model.
Among classic optimal foraging models, three constraints are normally assumed:
(1) exclusivity of search and exploitation, (2) sequential encounters, and (3) complete information. Exclusivity of search and exploitation indicates that the optimizer does not search for a resource and consume the resource at the same time. “Sequential encounters” refers to the assumption that resources are encountered one after the other not at the same time. Lastly, complete information assumes that the optimizer knows all of the decision variables.
All organisms have different environmental and physiological limitations; consequently, the models of their behaviors have to be modeled to the individual situation of every species. A bird foraging in a berry bush has different constraints than a lion hunting a herd of zebra. Each modeled animal must be analyzed with its own set of assumptions, which are tailored to the organism’s environment and phenotype. Nevertheless, some models that have been created are very general and explain a wide number of behaviors in a wide number of species. The marginal value theorem (Charnov 1976) is one of those models.
Marginal Value Theory
Eric Charnov developed the marginal value theorem in the early 1970’s in his Ph.D. dissertation at the University of Washington (Charnov 1973). It was designed to predict the resource use of a forager over space and time. The general 16 form of the marginal value theorem (Charnov 1976) comes from economic theory.
In microeconomics, the utility of a resource is the gain derived from it. As the number of resources per individual increases, the amount of utility of the additional resource unit decreases. This principal conforms to the law of diminishing returns.
An optimizing consumer will maximize the utility gained from each product.
The optimal consumer will obtain resources at some rate. The rate diminishes over time as the resources are locally depleted. The consumer obtains products until the rate of return drops to the average rate of returns that could be expected for all localities available to the consumer. By doing this, the consumer is assured that s(he) is not receiving lower utility return rates from any one locale. The consumer is not spending too much time on a given resource that gives a lower utility rate of return. This rule allows the consumer to obtain the maximal utility returns from each locale because s(he) can obtain just the right amount of each product.
This formulation makes intuitive sense. The forager feeds at some rate.
When the rate of feeding drops to what could be expected, on average, from some other patch then the forager will do at least as well somewhere else and possibly better. Consequently, the maximizing forager should feed in a patch until the rate of feeding decreases to the average rate of feeding that can be expected from all patches combined.
This concept was applied to foragers using resources by ecologists in the
1960’s and 1970’s. Eric Charnov (1976) developed the Marginal Value Theorem 17
(MVT) to predict when a forager should be expected to abandon one resource and start using another. If resources are distributed patchily, the forager should optimize time contributed to each patch and abandon patches when the return from a given patch drops to the overall return rate for all patches in the habitat. It is more advantageous to the forager to move on to a new patch that would produce higher initial rates of return.
The model
Eric Charnov first formally defined the MVT in his dissertation from the
University of Washington (1973). It was a variation from models that were designed to predict feeding behavior in foragers. There was no widely established foraging theory before Charnov and colleagues developed the diet choice models in the early 1960’s and 1970’s. These models started from simple logical deductions derived from Darwinian biology. Charnov and Orians (1973: 2) stated
Foraging behavior should be molded by natural selection because the kinds of choices made by a predator will influence (a) survival during periods of food shortage, if and when they occur, (b) the rate of accumulation of energy reserves for reproduction and, hence, the number of offspring that can be provisioned, (c) and the amount of time that must be allocated to foraging activities. This in turn, influences the amount of time available for other activities which also contribute to fitness.
Given these assumptions of natural selection in an environment of limited resources, Charnov and Orians (1973) used optimization models to predict feeding behaviors that would result in the highest returns. They created an optimal diet model and an optimal patch use model. The optimal diet model measures the costs and benefits of various items in a potential diet, then ranks them, and predicts 18 which items should be included in a diet that provides the optimal returns per investment. Each potential diet item is viewed as a single prey unit that contributes to an optimal diet. The optimal patch use model, the marginal value theorem, is an extension of the optimal diet choice models because a single patch can be viewed as a prey that has diminishing returns instead of a single return. In the diet model, prey yield a single unit of currency (e.g. 100 calories). However, in the patch model, the “prey” is a clump of resources that has diminishing returns over time. The models are very similar although the value of the prey is different. Consequently, the explanation of the MVT requires explaining the derivation of the optimal diet choice model.
Optimal Diet Model
As explained above, every optimization model has to be designed for a particular environmental situation. The optimal diet model is no exception. A model of grasshopper mating may not be an adequate model for wolf hunting because the decision assumption, constraints, and currency are different even though they are both models of optimization behavior. There are many versions of the following model that are specific for different types of foragers and environments. The form developed here is the basic model. It is analogous to that of a “typical predator”(Charnov and Orians 1973: 21). Typical predators “handle their prey individually and many decisions about individual prey items must normally be made during the lifetime of a predator, or even within a single feeding period”(Charnov and Orians 1973: 21). Since the model to be developed here is for a human 19 migrator, the predator’s prey is analogous to the resources obtained at a specific residential location. Stephens and Krebs (1986) provide a review of the variations on the basic model that describe different foragers and assumptions.
Optimal diet models were developed out of predator-prey models in the
1950’s and 1960’s. C. S. Holling (1959) identified the functional relationships between predators and prey. He derived an equation that describes the diminishing rates of return for a predator. The basic diet rate models used today are largely based on his work. The following derivation is from Charnov (1973), Charnov and
Orians (1973), Charnov (1976), and Stephens and Krebs (1986). These equations and their symbolic notation are based on Stephens and Krebs (1986).
The rate of return, R, of a maximized currency (e.g. energy or offspring) can be expressed as follows
E R = , (1) T
where, E is the total energy gained while foraging and T is the time expended. T can be divided into search time, Ts, and handling time, Th. Search time is the time expended up until a prey is encountered and handling time is the average time expended on a given prey. Handling time includes collecting the prey, eating the prey, and any maintenance of devices used to handle the prey. Prey handling devices could be tools like webs for spiders or arrows for humans. Substituting 20 the handling time and search time variables and expressing the equation in the form of a net rate of return produces the following equation
E − E R = f s , (2) + Ts Th
where Es is the average energy expended while searching for prey. Ef is the net amount of energy gained in Tf, the total time while foraging.
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Figure 2–1: Variables used in the optimal diet model
λi = the encounter rate of prey of type i. In the earlier forms of the model, this variable was a direct function of density. Charnov (1976) calls it
“proportion.” Whether it is directly a function of prey density depends on the forager. These conditions will be discussed later. In addition, for the basic foraging equation, this variable has a Poisson distribution. This is not to imply that the forager is moving randomly; but, rather, the resources are not geographically dependent. The consequences of autocorrelation could be determined through simulation; ei = average energy obtained from prey of type i; s = average cost of searching for prey of type i; h = average handling time for prey of type i;
Ts = average search time;
Th = average handling time;
Ef = average gross energy from foraging;
Es = average cost of searching
R = rate of return
22
If λ is the encounter rate of items (prey per time or patches per time), then
λTs is the average number of prey items encountered. If e is the average energy gained from a single prey item, then λTse is the average gross energy gained while foraging. If s is the average energy expended per unit time, then sTs is the average energy expended while searching. If the average time spent handling an item is represented by h then λTsh is the average time spent handling all items. See Figure
2-1 for a description of all variables used in the diet model.
Substituting these relationships into (2) results in the following
eλT − sT R = s s . (3) + λ Ts h Ts
The Ts cancel out and the following equation is left
eλ − s R = . (4) 1 + hλ
However, as Stephens and Krebs (1986: 15-17) point out, the jump from
Holling’s equation, (1) to equation (2) and eventually (3) requires some assumptions and shortcuts. They prove that (4) is equal to Holling’s rate equation only when the number of encounters is infinite. 23
Given that, in a stochastic situation, E(G) is an expected energy gain per encounter and E(T) is an expected time spent per encounter and gi and ti are particular realizations, respectively. Then the expectation quotient, in a stochastic environment, is equal to Holling’s rate equation when the number of encounters is infinite,
1 n ∑ g1 E f g + g + ... + g n = = 1 2 n = i 1 (5) + + + n T f t1 t2 ... tn 1 ∑t1 n i=1
Infinite sampling proves that,
n n 1 = 1 = lim gi E(G) and lim ti E(T ) . n→∞ ∑ n→∞ ∑ n i=1 n i=1
E E(G) λe − s So, f approaches = as n approaches infinity. + λ T f E(T ) 1 h
However, Stephens and Charnov (1982) showed that the energy gains also approach Holling’s rate equation as foraging time increases. For the situation of a human migrator using an area of land, this assumption is valid because of the length of time involved. The temporal scale of a migration or regional population movements is large. In other words, the “foraging time increases” enough to 24 approximate Holling’s rate equation. Consequently, applying this model to human population movement does not have to absolutely reproduce the model’s assumption of random encounters since the assumption of random encounters is dependant on the fact that Holling’s rate equation and equation (5) are continuous. This is important because of the MVT’s assumption of random encounters. A predator is assumed to encounter prey at random. This is a necessary assumption due to the probabilistic basis of Charnov’s model. He used a continuous probability function in a stochastic environment. Stephens and
Charnov (1982) demonstrated that, as foraging time increases, non-random encounters approximate the random encounter model. Human foraging is most likely non-random however, as foraging time increases, the fact that humans do not forage randomly becomes less important and the MVT remains a useful model.
Patch model
Equation (4) is the basic diet rate model. In order to apply this model to a patch and derive the MVT, three adjustments must be made to equation (4).
First, the average energy per item will be written as a function, gi(ti). This is the net energy gain function for a given patch type, i. For this model, it is assumed to have the following characteristics (Stephens and Krebs 1986: 25):
1. The net energy gain when zero time is spent in a patch is zero [gi(0)=0];
2. The function is at least initially increasing [gi′(0)>0];
3. The gain function is eventually negatively accelerated. 25
These are the conditions of the gain function for each patch according to the marginal value theorem. They stipulate that the gain function is initially increasing from a zero point and that it eventually decelerates. The gain curves in Figure 2.2 satisfy these requirements and are provided as examples of potential gain curves. In Figure 2-2, the gain measured in energy is increasing along the y-axis and patch residence time is measured along the x-axis. 26
Figure 2–2: This figure shows different forms that the gain function can assume. Time is along the abscissa and net return is along the ordinal axis.
27
Second, the encounter rate, λi, of a prey type is changed to the encounter rate of patches of type, i. Finally, the handling time, hi, for a given prey type becomes, ti, the residence time for a given patch type, i. Variables specific to the patch model
(MVT) are listed in Figure 2-3.
Substituting these variables and writing the rate equation in terms of all patch types results in equation (6).
n λ − ∑ i g(ti ) s = i=1 R n (6) + λ 1 ∑ iti i=1
g(ti) = the gain function for patch type i. This takes the place of the 28 average energy gain from a prey in the diet model. It is a diminishing function with respect to time, t;
ti = time spent in patch of type i;
λI = encounter rate for patches of type I. This variable has, like in the diet model, a Poisson distribution. This is not to say that the forager is traveling randomly. Indeed they may choose which patch to visit next. Instead, it says that, during the foraging time period, the patches are distributed such that the presence of one patch does not predict the presence of another. This assumption, in reality, may or may not occur very often. If not, it would be necessary to characterize the effects of relaxing this assumption. Stephens and Krebs (1986) review some of those attempts.
Figure 2–3: Variables used in the patch model (MVT)
29
Equation (6), written terms of a single patch type, results in the following
λ g(t ) − k R = i i i (7) + λ ci iti
where ki is the sum of all variables in the numerator not involving i, the patch under consideration, and ci is the sum of all variables not involving ti in the denominator. In order to maximize R, we differentiate (7) with respect to a given ti:
∂R λ g '(t )(λ t + c ) − λ (λ g(t ) + k ) = i i i i i i i i i i (8) ∂ 2 ti λ + ( iti ci )
Equation (8) gives the equation of the instantaneous rate of gain for a particular patch type. If equation (8) is set equal to zero, it gives a local maximum. This is proven by taking the second derivative of (7) and looking for points of inflection. It can be shown (Stephens and Krebs 1986: box 2.5) that the second derivative of (7) is positively accelerating as it approaches zero; hence, zero is a local maximum.
When equation (8) is set equal to zero and it is algebraically simplified, it results in the following
∂g(t ) λ g (t ) + k i = i i i i , (9) ∂ λ + ti iti ci 30
which is the basic, multi-patch, equation for the Marginal Value Theorem as defined by Charnov (1976). For simplicity, equation (9) can be written in terms of a single patch type and no search costs
∂g(t ) λ g (t ) i = i i i . (10) ∂ λ + ti iti 1
Given one patch type, all gain functions, g(t), are the same. This final equation is
Charnov’s Marginal Value Theorem as used in most tests of the model.
It says that a forager should be expected to abandon a given patch, i, when the net rate of return, g(ti), is equal to the average rate of return for the habitat. This result is intuitive. An optimal forager should abandon a resource patch when the rate of return for that patch falls to the average for the habitat.
The forager would, on average, be more efficient by migrating to a new patch.
Tests of the Marginal Value Theorem
The most extensive review of the utility and success of this model was published by Stephens and Krebs (1986) in their review of foraging theory in general. They characterized how MVT has been applied and its success under various conditions. Out of 45 field and laboratory tests, 70% had results that were at least “partially or qualitatively consistent with the model” and 21% had
“quantitative agreement with the model” (Stephens and Krebs 1986: 196). They also noted whether the assumptions of the model were upheld. When all assumptions were met, 91% of the tests supported the model (Stephens and Krebs 31
1986: 197). They comment that assumptions were often probably not met because the model was applied post hoc. The investigators typically did not design the experiments specifically to test the model.
Most of the above tests of the MVT were made on non-human animals.
Applications to humans have been few. I only know of five (Beckerman 1983;
DeBoer and Prins 1989; Keegan 1986; O’Connell and Hawkes 1981; Smith 1991).
All of these studies were all applied to ethnographic data.
Ethnographic data have distinct advantages. The most significant advantage of ethnographic tests of the marginal value theorem is that direct observations can be made of the subjects. Accurate measurements can be made of the gain function for each patch and of the decision assumptions. This study uses archaeological data, however. Archaeological data are disadvantaged because it is impossible to make direct measurements of the model variables. Archaeological data are significantly advantaged though because it is practical to apply this model to long time periods and large regions. This study exploits the advantages of archaeological data by applying the marginal value theorem to a historic situation of migration over a relatively long time duration.
Application of the MVT to migration
In this study, I argue that the process of foragers moving from a patch of resources is analogous to resource dependent migrations. I show in Chapter 4 that the Creek Indians settled in towns along rivers. They farmed and used a catchment of land immediately surrounding these villages. A village and its catchment can be 32 viewed as a patch of resources. The resources as they are harvested define the gain curves for each town catchment. There are multiple resources that contribute to the resource gain curve for such a residential catchment/patch (agricultural produce, firewood, foraged foods, etc.). However, theoretically, there is a single, overall gain curve that is the sum of all of these resources and that defines the diminishing returns that the Creek Indians received while utilizing a given town’s catchment. On the other hand, there may be one or two resources that were most important to the Creek Indians and would have had the greatest influence on the value of a town’s catchment. After some time period (town residence occupation), the rate of those returns would have diminished to a point that was optimal to abandon the town and move to a new town location. This situation of town settlement and catchment resource use and the situation of a forager feeding on a patch of resources are conceptually similar. The scale is different but the behavioral process is the same. A forager feeding on a patch of resources and human settlement mobility, therefore, can be similarly modeled with the marginal value theorem.
The MVT was designed for a forager moving from patch to patch for a time, t. The forager could stay in a patch for a minute, a day, a month, a year, etc. In a static environment, the process would be the same as a town’s catchment being abandoned. If the environment is dynamic, the habitat gain function would be constantly changing. Nevertheless, the process is the same. Therefore, we should 33 be able to apply the MVT structure to the process of migration. As with any model
(foraging or otherwise), the MVT needs to be adjusted for the specific situation.
MVT of Human Settlement
For reference, the marginal value theorem for a single patch type is stated again
∂g(t ) λ g (t ) i = i i i . (10) ∂ λ + ti iti 1
The encounter rate variable should be adjusted for the application of human foragers (or migrators). Applied to an event of residential mobility such as Creek
Indians abandoning a town and migrating to a new residence, the area of use that immediately surrounds the residence becomes the forager’s patch. Foragers move from patch to patch (residence town to residence town) according to the predictions that I have outlined above. The time in each patch can be any time period (on the scale of minutes, days, or months). Consequently, residence time for the patch, t, becomes residence time for the town.
The gain function (or its inverse, the depreciation function) for the patch becomes the gain function for the area of use that surrounds the town. The concept of catchment area could be substituted, however the shape of the catchment area does not have to be circular. The catchment in this situation is equivalent to the patch. It is as if the foragers move from patch to patch and stay at a single patch for 34 a lengthy period. They use the resources in the town’s catchment until they have been depleted to a particular level and then move residence.
The gain function is defined as the net rate of return for a given patch. If the patch is the area of resource use surrounding the residence, the gain function becomes the net rate of return for the sum of resources in a given patch/catchment area.
The encounter rate, λ, is the element in the model that is altered the most.
The encounter rate of a patch type in the original model was envisioned for a mobile forager that was traveling over the landscape during a single foraging bout of a small bird (Charnov 1976). Charnov and Orians designed this variable after
Paloheimo’s (1971) modeling on search theory. The following notation is from
Charnov and Orians (1973).
Paloheimo included a number of variables that accounted for particular visual characteristics of mobile foragers. He defined the encounter rate as
λ = i ai Di (11)
where, ai is a function of predator movement, predator visual field, etc. It is defined with the following variables
= aa 2rv (12)
where r is visual radius and v is predatory velocity. Di is prey density or patch density. 35
For this model of human residential migration, it is assumed that the foragers know where they intend to place a residence/catchment area. In other words, they know where the location of potential habitation sites. They are not encountering potential residence areas as they walk across space. This makes the variable, ai, not relevant. Encounter rate becomes simply a function of patch/catchment area density. This is equivalent to a ratio of the number of habitable catchment areas to the total habitation area. Consequently, Di will be used in place of λI.
These changes result in the following model of forager residential mobility
∂g(t ) D g (t ) i = i i i (13) ∂ + ti Diti 1
where g(ti)j is the net rate of return for a single resource in patch type, i, and m is the number of resources in said patch. Equation (14) represents the relationship between individual resources in a catchment area and the gain function of the entire catchment area.
m = g(ti ) ∑ g(ti ) j (14) 1= j
Tests of the marginal value theorem have been varied (Stephens and
Krebs 1986: Table 9.1). The formal model makes a specific quantitative prediction. The instantaneous rate of gain in a given patch at the point of abandonment is the same as the instantaneous rate of gain for the entire habitat or for the average of all patches. This is a difficult hypothesis to test because the 36 rate of gain for each patch and the overall average rate of gain for the entire habitat must be measured quantitatively. To my knowledge no one has yet measured the instantaneous rate of gain for the entire habitat in a study.
Other tests that have been applied are more qualitative. For example, a forager should stay longer in patches when the environment as a whole is poorer or when travel time is longer. This test has been applied in human cases
(O’Connell and Hawkes 1981). O’Connell and Hawkes tested a central place version of the marginal value theorem (See Krebs and Stephens 1986: 53-60) on
Alyawara foragers in Australia. They identified two patches, sandhills and woodlands. They found that Alyawara forager behavior did appear to fit the patch use models.
Eric Smith applied a related patch use question to Inujjuamiut hunting and fishing strategies (1991). He tested whether given two or more patch alternatives, those with higher average return rates receive greater foraging allocation time.
He applied this test to hunting types instead of patch types. This is basically the rank order predictions of the diet choice model applied to patches and isn’t a test of the MVT as Smith points out (1991: 258). It is similar to the combined prey- patch model developed by Stephens and Krebs (1986). Smith stated a number of times that the data requirements of the MVT were too great to collect and implement in his ethnographic study (1991: 268). He found “general” agreement of Inujjuamiut foraging behavior with optimal foraging theory predictions. 37
Stephen Beckerman (1983) analyzed fishing and hunting among the Bari of Columbia. He found results that were inconsistent with the MVT. He suggested that daily changes may have affected the predictions which assume “long-term average rates.”
Kaplan and Hill (1992: 180-181) also recognize that all human applications of the MVT have not met the rigorous requirements of the model. Almost all tests have been of patch choice, not patch utilization time as in this dissertation. This is not a proper test and is an incorrect application of the MVT. The archaeological and historical application in this dissertation has its own weaknesses as a result of the data set but it also has significant and unique strengths as a result of the archaeological data set used.
There have been no explicit tests, qualitative or quantitative, of the marginal value theorem using archaeological data. This is in part because of the strict quantitative variables that must be measured in order to test it. Some archaeologists (Etnier 1997, Fitzhugh 1997) have used it to qualitatively explain archaeological phenomena but none have explicitly applied the MVT to a natural experiment as I am in this dissertation. To my knowledge, this dissertation is the first a priori, quantitative test of the MVT on a human population particularly on an archaeological data set.
Next to the applications mentioned above, the most quantitative test of the marginal value theorem predicts that the instantaneous rate of gain at patch abandonment will be the same for all instances of abandonment. Since all 38 optimal consumers are abandoning resource patches when the rate of return, g(t), for the given patch approaches the rate of return for the entire habitat, the rate of return at each abandonment should be the same for all observed abandonments. The consumers should all occupy a given resource patch until they could obtain a better return on average from another resource patch.
Hypotheses Tested
The MVT makes multiple predictions as stated above. One prediction is that the rate of gain (the instantaneous rate of return) being obtained in a patch by a forager should be the same value every time that a patch is abandoned
(equation 10), ceteris paribus. This is because the forager always abandons patches when the rate of return depreciates to the optimal/average rate of returns for all patches in the forager’s habitat. Again, the logic of the MVT states that the long-term average rate maximizer will abandon a patch when the instantaneous gain function drops to the average instantaneous rate of returns for all patches combined because, on average, the forager can obtain better returns in another patch. Stated in terms of the MVT as derived above, the second derivative of the rate equation (7) is positively accelerating as it approaches zero; hence, zero is a local maximum. The instantaneous rate of gain (first derivative of equation eight) set equal to zero states that the optimal rate of gain for a patch is equal to the first derivative (instantaneous rate of gain) of the gain function for all patches in the habitat combined. 39
As applied to Creek Indian town residential mobility, the rate of returns for all observations of town abandonment should be the same (left side of equation
10). In other words, if I know how long a town was occupied and I know what the rate of returns were for the town’s catchment at the time of abandonment, I can test that the instantaneous rate of returns at the point of abandonment were the same every time that a town was abandoned. For example, a Creek Indian town,
Apalachicola Old Town was abandoned after forty years. I can measure the rate of return for this town’s resource catchment after forty years of use. The MVT predicts that the respective catchment’s instantaneous rate of gains will be the same value for all observations of town abandonment if the Creek Indians are maximizing their long-term average rate of gain in the respective town’s catchment. Chapter 3, Methods and Materials, details how the town’s occupational duration is determined and how the gain curve for each town’s catchment can be calculated.
Summary
This chapter introduced optimization theory and its evolutionary logic. The marginal value theorem was mathematically derived so that the reader could understand its mathematical basis and the hypothesis being tested in this dissertation. Lastly, I reviewed human applications of the marginal value theorem and potential hypotheses that can be derived from the theorem.
The MVT has been very useful in predicting resource use behavior in a wide number of species. It is a general model that can be applied to different 40 species and environments. The model’s strength lies in its quantitative and predictive power. It predicts that an optimal resource consumer will utilize a patch of resources until the rate of returns in that patch depletes to the overall rate of returns for the entire habitat in which the consumer lives. Consequently the MVT can predict the point of abandonment or length of patch residence time for a given area. The optimal rate of return and length of patch residence time can be quantitatively predicted if the rate of returns, g(t), for each patch occupied by the consumer can be measured. This prediction is defined in equation nine above.
This study uses the MVT to predict when Creek Indians should have abandoned their residential towns according to optimal foraging theory. In general each town had its own resource use area, a catchment, with its own rate of returns. The rate of returns is defined in Figure 2-3. Optimal consumers, Creek
Indians residing in the town, used the town’s resource area until the rate of returns decreased to the optimal rate of returns. The optimal point along a continuum of returns over time can be determined mathematically by setting the rate function (equation 8) equal to zero. This is the instantaneous rate of return at the local maximum and is shown in equation nine to be equal to the average instantaneous rate of returns for the entire habitat. The instantaneous rate of returns for the entire habitat is the point at which the Creek Indians should have abandoned the given town and moved to another location. Conceptually, this prediction makes sense. A consumer uses a resource until, on average, the 41 consumer could obtain better returns somewhere else. The average is the average return for the entire habitat, or all patches combined.
If Creek Indians were optimally utilizing a town’s resource area according to the MVT, then the instantaneous rate of returns for harvested resources in the abandoned town catchment should be the same every time that a town is abandoned. In other words, Creek Indians used a town’s immediate vicinity to harvest necessary foods and materials. They obtained these resources at some measurable rate that diminished over time. By measuring the diminishing rate of resource returns over time in that town’s vicinity, I can generate predictions of the rate of returns, g(t), at any time while the area was occupied and used. If I know when the Creek Indians decided to abandon the town’s resource area and move on to another location, I can compare the rate of returns at that abandonment point for a number of towns. Each town’s abandonment is an observation of patch abandonment. If the Creek Indians are abandoning these towns according to the MVT, then the instantaneous rate of returns at each observation of abandonment will be the same value. This is predicted by the MVT because all towns will be abandoned at the optimal (average) rate of returns for the entire habitat. If the Indians remained in the town longer, they would have harvested products at a rate lower than the optimal/average for the entire habitat. This dissertation is a test that ten Creek towns were abandoned at the optimal rate of returns for the entire habitat according to the MVT. 42
Chapter 3
Materials and Methods
Introduction
In previous chapters, I introduced the general ecological variables that contribute to the evolution of migration in various species. Then I identified a human population, Creek Indians. The Creek Indians have their own environmental situation that dictates the form and condition of an application of the marginal value theorem. This chapter describes how I will measure the variables needed to test the hypothesis outlined in chapter two.
As I have explained in previous chapters, the marginal value theorem is a theory based on optimization theory and natural selection designed to aid in the identification and characterization of adaptations. It states that a long-term average rate optimizer in a patchy resource environment will use a patch of resources until the rate of gains from the resource decreases to the average rate of gains for all of the patches in the habitat. This is because the optimal resource consumer would obtain a higher rate of gain by moving on to another resource patch after that time. One hypothesis deduced from this theory is that all observations of resource patch abandonment in a singe habitat will occur at the same rates of gain. In other words the rate of gain being obtained will be the same for all consumers when they are observed abandoning a patch of resources. 43
Measurement of variables
The hypothesis in this study requires measuring
(1) the resource gains for a given patch over time (knowing the gain function, g(t)) and
(2) the patch occupation duration (the patch habitation time).
The resource gain for a given patch, g(t), is a continuous function that is a composite of the resources that are most limiting or contribute most to the inclusive fitness for the consumer in the patch. I am defining patches as use areas around a residential town in this study. The most limiting variables are the variables that the town consumers consider to be most important for that particular location. In Chapter Four, I will identify a number of variables that were observed to be important for determining how and when a given Creek town was abandoned or when a Creek population migrated. The two most important ecological variables mentioned in the historical literature were fuel wood availability and agricultural productivity. Since these factors are not easily combined into a single currency, they are analyzed separately below.
Returns from Fuel Wood
Quantifying fuel wood available to Indians is a difficult task and to my knowledge has never been done before. I used historic land survey maps and field notes to create a digital model of the relative frequency of the tree species alive in the early nineteenth century. 44
The composition of the pre-European settlement forests can be estimated with the use of land survey notes, maps, and witness (bearing) tree analysis
(Bourdo 1956). These data are often the best source of information regarding the pre-European settlement forest composition in regions like this one where significant change has occurred due to European economic practices.
Witness (or bearing) tree studies use maps and notes made during the process of land surveys by various agencies, usually state or federal governments. The federal government performed the surveys in Alabama in the
1830’s and 1840’s. These land surveys divided federal land up into square townships that were six miles on a side. Each township was further divided into square sections of one mile on a side. Thus, each township had thirty-six sections. The boundary of each of these sections was defined by placing a marker at each corner. The actual corner boundary marker was usually a post or some other indication like a pile of rocks. These boundary markers were defined by noting and mapping “witness trees.” Witness trees were trees that were relatively close to the boundary markers and were used in each case to define the location of the marker. For each marker the witness trees were mapped and noted in the surveyor notes. By recording the trees from these maps and notes, a detailed map of the forests can be created that reflects a sample of the trees at the time of the survey. Usually, four witness trees were observed at each corner and two were observed at each quarter-corner. The result is a map of the forests taken at regular intervals with an observation density of about twenty-four trees 45 for a given section, which is a square mile. The entire region of Creek Indian habitation was surveyed by this grid survey system. It therefore provides a systematic and quantitative estimate of the forest composition at the time of
Creek Indian occupation.
These surveys were usually made at the time of land acquisition in preparation for land distribution by the federal government. This preceded the majority of intensive European settlement and consequently also preceded
European agricultural practices that drastically altered the forests. In the Georgia section of the study area, the land was also surveyed with a rectangular system but was conducted by the state of Georgia instead of the federal government.
Each section was divided into 202 ½ acres (81.95 ha), roughly a half mile on one side. At each corner, four trees were observed. The Georgia surveys in the study area were conducted in 1827, two years after the Creek Indians were removed from the region. Figure 3-1 shows a section of the Georgia survey plat map. The squares are sections of a district survey. Each section was 45 chains on a side
(2970 feet, 905.25 meters). At the corner of each section a corner tree and four
“witness trees” are marked on the map. For example, the lower left corner of section 77 has a “Pine P” listed as the corner post. Surrounding the corner post are two pines, a maple, and a white oak. These corner trees and witness trees are observed and mapped every 2970 feet (905.26 m) in the Georgia surveys and every half-mile (.80 km) in the Alabama surveys. 46
It has been pointed out that witness tree studies can be biased (Bourdo
1956). Occasionally there was surveyor fraud. For example, it has been discovered that surveyors would sometimes record species of trees that were considered to be more economically valuable (hardwoods) in order to elevate the value of the land. Most cases of large-scale fraud are known. None are known in the area of this study. It is often assumed that surveyors would be biased toward longer-living hardwoods when choosing a witness tree for a marker. The diversity and number of small understory trees in a witness tree study is often interpreted as an indication that there is relatively little bias toward the long-lived hardwoods
(Bourdo 1956; Whitney 1994). In Chapter five, I will show that surveyors in the study area recorded 65 separate species. This is one indication that bias toward long-living hardwoods in the study area is minimal. Even with these possible biases, the witness tree data in these land surveys is the largest, most systematic, and most accurate form of data available for the pre-European settlement forests available.
The land survey documents have been microfilmed and I obtained them for this study from the United States Bureau of Land Management and from the
Georgia Department of Archives. I extracted witness trees from the microfilm record and then digitized the data onto Public Land Survey System (PLSS) digital line graph (DLG) files that are available from the United States Geological Survey
(USGS). I used Arcview 3.2a and Arc/Info 8.1 (ESRI) programs to perform this work. Tree species are listed in common names in the survey notes. These 47 common names were transcribed from Godfrey (1988). When I encountered ambiguous species names, I used physiographic and habitat associations to clarify which species were named. 48
Figure 3–3–1: Example of Survey Maps and Witness Trees. Photograph by Thomas Foster, courtesy of the Georgia Department of Archives and History. Note the circles at the corner of each square. Each circle is a witness tree and is labeled by the tree’s species. 49
The witness tree surveys provide the relative frequency of the tree species in the study region. However, in order to estimate the gain function for a given patch, I had to calculate the firewood available to the Indians in the use area or patch around towns.
According to ethnohistoric references of eastern North American Indians, women and children were collecting the firewood and probably only collecting deadfall or coarse woody debris around towns (Buffalohead, 1983; Silver 1990:
57; Wallace 1972, 190). . “The work of the women was to collect fuel, usually only dry sticks gathered in the woods” (Beauchamp 1900, 82). The collection area for firewood collected in this way was probably not very large since all wood would have been carried by hand. For example, John Swanton (cited in Hudson
1976, 264) recorded that “Natchez women carried firewood and other burdens using two strips of bearskin, each about the width of a hand.”
References to the distance traveled for fuel wood collected are scant.
However, two references will suffice as justification for a two-kilometer fuel wood catchment. “Every day one can see, about four o'clock in the afternoon, long strings of women, each with her ax and packing strap, going out into the woods perhaps a mile; soon the woods are vocal with the axes; and then equally long strings of women are seen issuing from the woods…”(Gilfillan quoted in Hilger
1951). Similarly the Iroquois are described as “The task of collecting firewood belongs to the women and girls of the house. They spent much of their time in the summer hauling wood from distances up to half of a mile, carrying it in large 50 bales on the back supported by a belt around the forehead, spitting it into short narrow lengths…” (Wallace 1972).
I created catchment areas of two kilometers around each town to define the patch of fuel wood resources for each residential area. This distance is consistent with ethnohistoric accounts of the distance traveled to collect firewood
(Wallace 1972, 190). When a catchment area bordered the Chattahoochee
River, I included only the section of the catchment that was on the same side of the river as the town. My assumption was that wood collection would be primarily on the same side of the river as the town. I used the species frequencies from the witness tree survey maps to characterize the forest composition and then used forestry data from the southeast to estimate the amount of coarse woody debris by forest composition. There are no data on the coarse woody debris
(dead fall) on the ground in the study area during the Creek Indian occupation.
Consequently, I estimated the amount of fuel wood available to the
Indians from current forestry data in the southeast (McMinn and Hardt 1993).
These data are collected from forest research stations all over the southeast in managed and unmanaged field stations. Table 3-1 shows this data for coarse woody debris by forest type in the southeast region. Coarse woody debris is a forestry term for branches and wood litter fall on the ground in a forest. McMinn and Hardt compiled data from a number of forest studies. This table shows the average amount of coarse woody debris that exists on the ground (Standing
Crop) and the average amount of annual input of coarse woody debris (dead fall) 51 per year. Standing crop is the coarse woody debris that exists on the forest floor of the specified forest type and annual input is the volume of coarse woody debris that falls to the floor annually. For example, in a managed pine plantation, there is an average of 7.860 cubic meters of coarse woody debris per hectare existing on the ground and 0.524 cubic meters of coarse woody debris that falls to the ground annually. Coarse woody debris varies by forest type because different tree species have varying litter rates.
With these data, it is possible to estimate the amount of firewood that was available to the Indians to collect from the ground over a period of years. I can use the witness tree analyses to identify the forest type surrounding a town and then use the coarse woody debris statistics from McMinn and Hardt (Table 3-1) to estimate the standing crop and annual input of firewood available to the
Indians. For example, a Creek town may have been situated in an Oak-Pine forest according to the witness tree records. I can use the current forestry research on coarse woody debris for Oak-Pine forests to estimate the amount of fuel wood that would be expected for such a forest type. I am assuming that an
Oak-Pine forest, for example, in the southeast produces a similar amount of coarse woody debris in the present time as it did in the 18th to 19th century. 52
Forest Type Annual Input (m3/ha) Standing Crop (m3/ha)
Pine Plantation 0.524 7.860
Natural Pine 1.275 19.125
Oak Pine 0.5202 7.530
Upland Hardwood 0.306 4.590
Lowland Hardwood 1.267 19.005
Table 3-1: Estimated Volume (cubic meters per hectare) of Coarse Woody
Debris on Public land in the southeast (McMinn and Hardt 1993: Table 3)
53
After I knew how much firewood was available, I used estimates of fuel wood consumption derived from cross-cultural comparisons of people using subsistence strategies similar to the southeastern Indians (Kalifa 1984). This gave an annual fire wood consumption rate per capita. I then calculated the depletion of firewood according to consumption rate by the Creek population figures from Swanton (1922) and Wright (1999). The data and results of these calculations are in the results section in Chapter 6.
Available wood per catchment per year was calculated based on the following formula and iterated over one hundred years.
Standing crop + (annual input)(year) – (annual consumption per capita)
(population)(year) = available wood per year
This formula estimates the amount of coarse woody debris that was available in a given catchment over time for a particular population size and forest type. This is not the same as the rate of gain, g(t), of wood consumption. However, because there are no known data on the rate of gain of wood consumption by forest type for southeastern Indians, this is my best estimate of g(t) of fuel wood by catchment. It is sufficiently accurate to allow me to show in the results section that fuel wood is not a significant and limiting variable relative to agricultural productivity and therefore was not included into the gain function for the MVT test. 54
Agricultural field preparation techniques (see below) and natural forest fires may have affected the quantity of fuel wood on the ground because Indians occasionally burned the forests in preparation for agricultural fields (Krech 1998,
Foster, Black and Abrams 2001). This behavior would be expected to reduce the quantity of fuel wood available. It will be seen, however, in chapter six that the amount of fuel wood being contributed annually by the tree litter (Annual Input in
Table 3-1) exceeds the amount of fuel wood being consumed by the Indians.
Even if forest fires from agricultural field burning did reduce the amount of fuel wood on the ground, the annual tree litter exceeds the amount of wood being consumed per year.
Returns from Agricultural Productivity
Quantifying the agricultural productivity of a given catchment according to southeastern Indian agricultural methods is not a simple task. The most extensive study of this process to date in the southeast is by William Baden in his doctoral dissertation (1987) and reported later (Baden 1995, Baden and
Beekman 2001).
In his model of Mississippian period agricultural systems, Baden used a number of resources including ethnohistoric sources and historic data on crop productivity in non-fertilized farming to calculate the depletion curve of soil in an aboriginal economy. He incorporated nutrient loss, pests, weeds, and Indian agricultural techniques. The model is general and can be applied to different soil types in different regions. I used this model of maize crop depletion over time to 55 estimate the resource gains (or, the inverse, depletion of gains) over time by catchment area in this study. For catchment size consistency, I initially used the same size agricultural use area catchment as I used in for the fuel wood calculations (2 km). However, as will be seen below, the model that I used to calculate agricultural productivity for each catchment assumes that the entire catchment was being cultivated. Since it is unlikely that a single town cultivated the entire 2 km catchment at any one time (see Chapter 4), I also used 1 km, ½ km, and ¼ km catchment sizes (see figures 3-2 to 3-11).
Figure 3–2: Apalachicola Old Town soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–3: Apalachicola soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–4: Broken Arrow soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–5: Chiaha soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–6: Coweta Old Town soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–7: Cusseta Old Town soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–8: Cusseta soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–9: Osochi soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–10: Upatoi soil types by 2km, 1km, 1/2km, and 1/4km catchments
Figure 3–11: Yuchi soil types by 2km, 1km, 1/2km, and 1/4km catchments I included only maize productivity in this study because it was the most utilized crop (Hudson 1976, 291). Other crops were cultivated but maize was the most important crop to southeastern Indians. It probably contributed up to 50% of the diet. I am arguing that maize was, therefore, the most limiting food currency that contributed to subsistence decisions.
Since there are no known data available of corn yields by soil type using southeastern Indian agricultural methods, I calculated the average aboriginal corn yield by soil type from the modern soil series corn yields for each catchment. The corn yield by soil type in those soil series is for modern agricultural techniques. Therefore, an average Indian yield is estimated by weighting each modern corn yield by 30/MAX bushels per acre. The numerator in this ratio is the maximum corn yield under American Indian agricultural practices in eastern North America (Baden 1987. This value of 30 bushels per acre as an average maximum corn yield for eastern North American Indians is not inconsistent with Schroeder’s (1999) recent review of North American Indian agricultural productivity. The denominator in the ratio, MAX, is the maximum corn yield expected within the catchment. This is determined by identifying the highest yield in the modern soil series for all of the soil types in the catchment.
For example, table 1 shows the soil types for the Apalachicola catchment. The third column, “Corn Yield,” was obtained from the Soil Survey of Russell County,
Alabama (USDA 1998, table 6). These yields are those “to be expected under a high level of management” (USDA 1998, table 6). Column six is an estimate of the aboriginal/Indian corn yield on the soil type in this catchment. This is 67 calculated by weighting column three by 30/MAX, where MAX is 130 bushels per acre (8087.3 kg/ha). Remember that MAX is the maximum corn yield expected in the respective catchment. In this case, that is 130 bushels per acre on CtB and
RvA soils (Table 1, Corn Yield). Lastly, the soil type area (Table 1, Acres) is multiplied by the Aboriginal Corn Yield to calculate the expected Aboriginal Corn
Yield in the catchment for the respective soil type (Table 1, Aboriginal Corn Yield by Area).
Capability Corn Yield Aboriginal Corn Aboriginal Corn Soil Type Hectares Acres Index (bushels) Yield (bu) Yield by Area (bu)
DoA I 115.00 54.81 135.37 26.54 3592.60 KoA I 115.00 51.63 127.52 26.54 3384.15 MxA I 110.00 45.25 111.77 25.38 2837.30 OrA I 110.00 25.02 61.79 25.38 1568.57 RbA I 110.00 10.28 25.40 25.38 644.68 WkA I 125.00 34.84 86.06 28.85 2482.49 DoB ii e 105.00 5.20 12.84 24.23 311.10 SbB ii e 100.00 12.08 29.84 23.08 688.67 FuB ii s 85.00 89.60 221.31 19.62 4341.07 AnA ii w 100.00 59.45 146.85 23.08 3388.82 CtB ii w 130.00 30.88 76.27 30.00 2288.13 DgA ii w 100.00 11.40 28.17 23.08 650.03 RvA ii w 130.00 0.86 2.13 30.00 63.87 BnB iii s 60.00 65.93 162.84 13.85 2254.67 TaB iii s 60.00 26.54 65.56 13.85 907.77 KMA v w 0.00 81.79 202.01 0.00 0.00 UcD vi e 75.00 17.40 42.97 17.31 743.64 BeA vi w 0.00 18.58 45.90 0.00 0.00 TsE vii e 0.00 124.69 307.99 0.00 0.00 Pt viii s 0.00 1.32 3.26 0.00 0.00 Ur viii s 0.00 2.95 7.29 0.00 0.00 BLANK 0.00 425.06 1049.89 0.00 0.00 W 0.00 54.70 135.12 0.00 0.00 1065.32a 26241.49b
24.63c Table 1: Apalachicola Catchment soil types and corn yields by soil type in a 2 km catchment. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
69
After aboriginal yields have been calculated for each soil type in the catchment by area, the total catchment yield depletion, Et, can be estimated with equation 3.8 in Baden (1987). T is time in years.
–0.2249 Et = (AVE/30) 27.78t (bushels/acre) (3.1)
This equation is a weighted regression curve. The regression curve is calculated from an historic agricultural study comparing fertilized corn yields to unfertilized corn yields on a silt loam in Wooster, Ohio between 1894 and 1913 (Baden 1987, table 4). The purpose of the study was to demonstrate the value of crop rotation.
It compared fertilized to unfertilized continuously planted plots over a twenty year period. Equation 3.1 represents the difference between the fertilized and fertilized plots; consequently it holds local environmental variables constant. It reflects the depletion of nitrogen, phosphorous, and potassium in the soil over time. Variation of these elements in the soil is mostly a function of microorganisms in the soil.
Mineralogical variation would result in yield variations on the order of fractions of a bushel per acres and are therefore ignored in this study. This regression equation is an average of the process of nutrient removal in the soil while controlling for local environmental conditions.
Soil nutrients may have been regenerated at some interval due to annual floods in the river bottoms. The nutrient depletion curve used in the model accounts for this regeneration because the Wooster, Ohio data from which it is 70 modeled were also on river bottom soils. These data were collected over a twenty year period therefore it is reasonable to expect that the effects of annual flooding were incorporated into the average yield per year. In fact, Baden noted that the Wooster, Ohio experiments did show variations in yield due to annual fluctuations in the soil nutrients (Baden 1987). His model is an average regression of the entire twenty-year period and therefore incorporates these fluctuations in yield due to annual flood regeneration.
The regression curve is weighted by AVE/30 where AVE is the average aboriginal corn yield for the catchment in bushels per acre divided by 30, which is the maximum aboriginal expected corn yield as explained above. The average aboriginal corn yield for the catchment (AVE) can be calculated by summing the aboriginal corn yields by acre in Table 1 (26241.49, column seven) and dividing by the total acreage for those soil types in the catchment (1065.32, column five). It should be stated here that North American Indians would have planted only on type I and II soils because of the limitations of poorer soils. Consequently, the average aboriginal corn yield is calculated only for type I and II soils and their area. This results in an average aboriginal corn yield for the entire catchment of
24.63 bu/acre (1532.23 kg/ha) in the Apalachicola example above. All ten town catchments and their respective soil values are listed in Appendix 2.
The end result of equation 3.1 above is a depletion curve of corn yields for the catchment surrounding a town. The depletion curve represents the change in corn yields over time for a specified acreage of soil. Since the yield is a function 71 of time, I can identify the yield in a town’s catchment (patch in the MVT) at the point of abandonment. Since the Creek Indian towns were abandoned in the past, this is a retroactive natural experiment. I identify how long a town and its catchment were occupied; then I use equation 3.1 weighted for the town’s catchment soils to identify what the corn yield was after the occupied time, the abandonment point.
It should be noted that Baden’s model produces depletion curves of resource gain. The depletion curve is the inverse of the gain curve as stipulated in the marginal value theorem (Stephens and Krebs 1986). The inverse of the depletion curve fulfills the assumptions of the marginal value theorem as stipulated in Chapter Two and in Stephens and Krebs (1986). Furthermore, this is a model of “optimal potential yields”(Baden and Beekman 2001, Schroeder
2001).
Patch Habitation Time
After I estimated the resource gain for each town catchment, I measured the occupational time for each respective catchment. I determined town origin and abandonment from ethnohistoric sources and archaeological research in the study region.
Protocol for measuring town habitation
The late historic Creek Indians were chosen as a study sample because of the detailed historic information available about them. Benjamin Hawkins and
William Bartram provide the most detail regarding the occupational histories of 72 specific towns. Benjamin Hawkins was the United States Indian Agent and lived among the Creek Indians from 1796 to 1816. His Letters and Journals and
Sketch of the Creek Country provide detailed descriptions of the locations of towns and villages including environmental features. Modern scholars have used his descriptions to retrace his travels and locate Creek towns (Ethridge 1996;
Wood and Elliot 1997). Maps covering the eighteenth and nineteenth centuries were consulted for presence of the town in its general location. Most of the maps are not detailed enough to reveal the precise location. Terry Lolly (1996) has provided the most comprehensive study of the location of Creek Indian towns and villages from maps of the southeast. I used Lolly as a general guide for the presence of towns and villages on a given map.
Lastly, the region occupied by the Creek Indians has witnessed over one hundred years of archaeological research. The area of the Lower Creeks in particular has been intensively investigated because of federally mandated cultural resource management in association with Fort Benning, an army base in
Georgia and Alabama, and a number of manmade reservoirs. I verified that the archaeological investigations were consistent with the historic references. In cases where reliable historic references were not existent regarding the occupational history of a given town, the results of archaeological investigation was used to identify the occupation time. Table 4-2 shows the occupational times for each town in the study. 73
Town Name* Occupational Time (years) Occupation Dates
Apalachicola 45 1755-1800
Apalachicola Old Town 40 1715-1755
Broken Arrow (“Uchee”) 85 1730-1815
Chiaha 110 1715-1825
Coweta Tallahassee 85 1715-1800
Cusseta 85 1740-1825
Cusseta Old Town 25 1715-1740
Osochi 70 1755-1825
Upatoi 35 1790-1825
Yuchi 85 1715-1800
*Names in use during late eighteenth century
Table 4-2: Towns in Study and Their Respective Occupation Dates
74
Towns were sometimes abandoned slowly. In these cases, I used the approximate date of the beginning of the abandonment. Although this date is not the absolute abandonment time, it should represent the beginning of the decision to abandon. Ultimately what this decision reflects is that individuals are making their own decision about maximization of inclusive fitness at slightly different times. The documentary and archaeological justification for the occupational time for each town is listed in the appendix.
Conclusion
An application of the marginal value theorem must be tailored to the environmental and behavioral situation of the specific species under investigation. Ethnohistoric literature in chapter four will demonstrate that fuel wood and agricultural productivity are the two most significant variables contributing to Creek town abandonment over the long-term. Agricultural productivity will be estimated from William Baden’s study of Mississippian period agricultural productivity. His model is general and applicable to the Creek Indian region. Fuel wood availability will be estimated from land survey maps and cross-cultural observations of fuel wood consumption. These variables will be used to calculate the gain curves (see chapter two) for each patch. The gain curve will be used in chapter six to determine the rate of returns at the point of abandonment, which is known from ethnohistoric and archaeological investigation, for each respective town in the sample.
Chapter 4
The Study Population
Introduction
This chapter introduces the study population, the Creek Indians of the late historic period (1600-1830) of the southeastern United States. It gives a brief history and culture of the populations that make up the Creek Indians followed by a special focus on the characteristics that are specific to this study, Creek settlement, agriculture and resource use, and decision-making. Lastly, I identify and describe ten Creek towns to be used in the study.
Creek Indians
The Creek Indians were an amalgam of linguistically diverse populations living in the southeastern United States during the late historic period (1600-
1830). They coalesced during that period in response to population decline and
European contact. The core ethnicity of the Creek Indians was made up of
Muscogee speakers but the new amalgamated population included Hitchiti,
Uche, Yamasee, and Alabama speakers as well (Swanton 1922).
These Indians were settled in what is now central Georgia and Alabama at the time of European contact and settlement. They were divided into two regions called the Upper Creeks and the Lower Creeks by early European settlers. The
Upper Creeks were located along the Tallapoosa, Coosa, and Alabama rivers in
Alabama and the Lower Creeks were located along the Chattahoochee River on 76 the Georgia/Alabama border. This dissertation study deals with towns of the
Lower Creeks.
Since the Creek Indians were an amalgam of linguistically diverse populations, generalization is not always accurate. Nevertheless, the majority of the Creek Indians were Muscogee speakers and had similar cultural characteristics. The following description is about the core Muscogee speaking population of the Creek Indians. The reader is referred to other references for a more complete ethnography of the Creek Indian populations (Braund 1993;
Crane 1956; Swanton 1922; Waselkov and Braund 1995).
The earliest documentary evidence of the populations that would become the Creek Indians was in 1540 when Hernando De Soto, a Spanish explorer, traveled through what is now the southeast United States. In the areas of northern Georgia and central Alabama, he encountered and described Muscogee speakers that were politically organized into Mississippian period (1000-1500
AD) chiefdoms. A chiefdom is a hierarchical political form with a chief at the top with the power of life and death over the subjects of the chiefdom. These populations were settled in tightly, well defined towns along the rivers and were practicing river bottom horticulture.
The Spanish explorers of the early 16th century brought foreign diseases to the Indians. Consequently there was severe depopulation, population movement and political reorganization (Smith 1987). Remnants of the Coosa chiefdom populations from northwest Georgia gradually migrated down the 77
Coosa River into Upper Creek territory during the 17th century (Smith 2000).
Around the same time Hitchiti speakers migrated up from the Gulf Coast region onto the Chattahoochee River (Hann 1988). Diffused Alabama speaking tribes settled along the confluence of the Coosa and Tallapoosa Rivers from the southwest. Yamasee migrated from the South Carolina coast. Shawnee refugees that were pushed out of the Midwest by Iroquois settled into the heart of
Creek country. Natchez Indians migrated from the Louisiana area because of
French settlements among the Natchez chiefdoms. The result was migration and coalescence of Indian groups into the populations that were subsequently identified as Creek Indians by the Europeans of Georgia and South Carolina
(Crane 1956).
Population size dropped significantly after the initial European contact
(16th century) but began to rebound during the 19th century (Foster 2000). The
1715 South Carolina census counted 8,777 Creek Indians (Braund 1993). In
1836, when the Creek Indians were forcibly removed to reservations in
Oklahoma, their population was counted at roughly 26,000 (Muller 1997,
Swanton 1922). Individual towns had populations varying from about one hundred to around 1000 (Swanton 1922).
Although the political organization changed and the population decreased, material and subsistence culture did not change significantly during the early historic period (1500-1800 AD). The Creek Indians of the 18th century were organized into independent towns or tribes. This is in contrast to the chiefdom 78 political organization of the previous period (circa 1000-1500 AD). In the 18th century, the period of this dissertation study, each town held it’s own council of male elders, who made collective decisions about the town. The council and its members did not have absolute power over the town’s individuals, however.
They could only suggest recommendations (Braund 1993, Waselkov and Braund
1995).
The majority of the Creek Indian populations were matrilineal. Property, control over the upbringing of children, and kinship affiliation was traced through the female line. A woman owned a house and its land. After marriage, her husband moved in with the woman and her family. The result was an extended family compound of multiple house structures located in close proximity. Ideally, the extended family included a woman, her husband, their unmarried children, their married daughters, and sons-in-law, grandchildren, and elderly male relatives of the woman (Braund 1993, 11). Caleb Swan described such a compound.
“These houses stand in clusters of four, five, six, seven and eight together, irregularly distributed up and down the banks of rivers or small streams; each cluster of houses contains a clan, or family of relations, who eat and live in common (Swan 1855).”
A Creek town, or talwa, was the political unit. Each town was independent of the others and represented the tribe of its inhabitants. These towns were described as being very stable throughout the historic period. Some towns were 79 in existence for over hundreds of years although the location of the town may have changed.
Most towns had a similar layout. At the center was a square ground, chunky yard, and a council house. The square ground was an area with four rectangular structures on four sides and a ceremonial fire in the middle. This square ground was used in warmer weather for council decisions and ceremonies. In 1772, David Taitt, a British officer, gave the best description of a square ground. “The Square is formed by four houses about forty feet in Length and ten wide. Open in front and devided into three different Cabins each. The seats are made of Canes Split and worked together raised about three feet off the Ground; and half the width of the House, the back half being raised abouve the other about one foot; these Cabins serve for beds as well as seats in
Summer (Taitt 503).” The chunky yard was adjacent to the square ground and was a large flat and cleared field. This yard was often swept clean so that a slight mound of earth encircled it from the sweeping. The chunky yard was the location of a ball game that was played by the Creek Indians. Also located adjacent to the square ground was the council house, or hothouse or winter council rotunda. This was a large circular structure that could house hundreds of individuals. David Taitt again described the Tukabatchee council house. “The hot house is generally built at the north west Corner of the Square having the door fronting the South East. The one in this Town is a square building about 30 feet diameter rounded a little at the Corners; the walls are about four feet high; from 80 these walls the roof rises about twelve feet, terminating in a point at top…In this house the Indians Consult about the affairs of their Nation in the Winter Season and in their Square in the Summer (Taitt 503).” William Bartram provided an idealized town layout in 1789 (Figure 4-1; Waselkov and Braund 1995). In the figure, A is the council house, B is the square ground, and C is the chunky yard. 81
Figure 4–1: Ephram G. Squire's engraving of "Creek Towns and Dwellings" (From Waselkov and Braund 1995) 82
Surrounding the square ground, chunky yard, and council house were the individual residences (see Figure 4-1). In the figure, the small rectangles represent individual house structures within a house lot indicated by the dotted lines. Each family ideally had four house structures organized around a courtyard forming a square about the size of a half-acre (Waselkov and Braund 1995, 93).
This was like a miniaturized “square ground” as described above. Note, however, that not all families were large enough or wealthy enough to subscribe to this idealized house compound layout as seen in Bartram’s sketch of individual house lots in Figure 4-1. Individual families had small gardens immediately adjacent to their house.
Towns were located along the major rivers in the area, the Tallapoosa,
Coosa, Alabama, and Chattahoochee. Again Bartram described the variables contributing to Creek Indian town location.
“An Indian town is generally so situated, as to be convenient for procuring game, secure from sudden invasion, a large district of excellent arable land adjoining, or in its vicintity, if possible on an isthmus betwixt two waters, or where the doubling of a river forms a peninsula; such a situation generally comprises a sufficient body of excellent land for planting Corn, Potatoes, Beans, Squash, Pumpkins, Citruls, Melons, &c. and is taken in with a small expence and trouble of fencing, to secure their crops from the invasion of predatory animals. At other times however they choose such a convenient fertile spot at some distance from their town, when circumstances will not admit of having both together” (Waselkov and Braund 1995, 126-127).
In the 1770’s towns were described as very compact. Few households are
“out of sight of the town” (Bartram in Waselkov and Braund 1995, 156). This pattern of a population tightly focused on a central town location may have been 83 changing in the very late historic period (1800-1830; Ethridge 1996). Beyond the town’s border and house structures were the town’s agricultural fields. These fields were located immediately adjacent to the town along the river bottoms.
The soil in the river bottoms was relatively rich and afforded the Indians to plant with minimal effort. Agricultural techniques will be described in greater detail below.
Settlement
The Creek Indians were descended from Mississippian period (circa 1000-
1500 AD) populations. During this period the southeastern Indian populations were organized into tightly defined towns that were oriented around chiefly political centers. European contact brought diseases that were unknown to the
Indians. These diseases disrupted political organization and reduced the population size.
References to migration among the Creek Indians have been documented historically. For example, Louis Le Clerc Milfort (1972) recorded the migratory history of the Muscogee Indians as it was related to him by an old man transcribing the story from a mnemonic strand of beads. It documented an origin in Mexico and a gradual migration east until the Muscogee speakers settled in what is now Alabama. Similarly, a chief from a town of Cusseta recounted the migration history of that town. The chief documented a migration from the west to the east (Gatschet 1969: 222). The validity of these and other migration origin histories is not known. Some of the migration myths do generally parallel what is 84 understood from archaeological investigations. For example, the Cusseta town inhabitants are documented historically and archaeologically to have moved from the Chattahoochee River in the west to the Ocmulgee River in the east around
1685 (Knight 1994). Marvin Smith (1987; 2000) has documented archaeological evidence for population dispersion from northwest Georgia into central Alabama immediately following European contact and the presumed consequent population disruption due to European diseases. While there were probably mass population movements during the history of the southeastern Indians, the majority of population dispersion was probably at the level of small-scale movements and residential relocations that were designed to optimize resources surrounding a town.
As discussed above these towns were generally distributed along river bottoms of the major rivers in the region like the Chattahoochee, Tallapoosa, and
Coosa. These Indians practiced “riverine” agriculture that necessitated placement of towns along the rivers (Smith 1978). Agriculture techniques were horticultural and carried out without the use of fertilizer. Because fertilizer was not used, crops rapidly depleted the soils resulting in the need to shift agricultural plots periodically. Over time all the available agricultural land surrounding a town became exhausted and the town was moved. Population density was low enough in the historic southeast that movement of a town was less costly than intensifying agricultural techniques and remaining in one location. 85
Creek Indians were politically organized at the level of towns or “talwas”.
Each town made its own political decisions and was physically organized around a council house and square ground (Figure 4-1). Decisions about town level events took place in the council in each town. Although the optimal foraging model being tested in this study is designed for the decisions of a single individual, it is argued that the town council was making an optimal decision and that this was the average of all optimal decisions for each individual in the town.
In other situations, groups of individuals collectively made their own decisions and sometimes split from the main town. There is support for this in the record of observations by European residents of the towns. Individual towns were moved when it was collectively decided that conditions warranted such a decision
(Hudson 1976).
Small-scale movements over time resulted in apparent large-scale migrations. Thomas Nairne was a soldier and early chronicler of the southeastern
Indians. He traveled and wrote about the Creek Indians in the early 1700’s. In
1708 he described the process of town division and population mobility in a letter regarding the customs of southern Indians. In the following quote, he is describing Indian towns in general, not a specific instance.
86
Sir that at once you may have a notion of the Indian Government and the progression of on Village out of another, I’le Illustrate by an Example:
C ______1 2 ______A B D E
Suppose 1:2 to be a river, A: a populous flourishing Town on the river side, straightned for planting ground. Upon some disgust, or other reason 2 Leading men lead out Colonies of 30 or 40 fameiles Each and sattle 2 New Villages B: C: Bechancing to florish and increase much, out of it by the same means arise D and E (Moore 1988, 62-63).
Variables contributing to Residential Mobility
There are a number of variables identified in the ethnohistoric literature as contributing to residential mobility and population dispersion. These are factors usually identified by some Indian informant and related to a European traveler or resident. Such data may be biased by the informant or by the historian and are usually retrospective. However there are consistencies throughout the available sources.
There are two ecological variables that are cited most as contributing to small-scale population dispersion and residential mobility. The first, which was mentioned briefly above, is decline in agricultural productivity. Horticultural plots were placed near towns and these formed a patch, or catchment, of agricultural land use surrounding each town. As the land became more depleted in nutrients, the crop return declined. Benjamin Hawkins, Indian Agent to the Creek Indians 87 between 1796 and 1816, observed that the land surrounding older towns had been depleted significantly (Hawkins 1982: 58).
The other often-mentioned variable is access to fuel wood. The Mohawk moved because of fire wood depletion. “By 1714 the soil at Caughnawaga was exhausted as was the wood that could be gathered nearby, and the Indians decided to move their village two leageues farther up the Saint Lawrence. In
1716, they began the process of moving to the new village” (Fenton and Tooker
1978). An example specific to the Creek Indians is Tukabatchee. Tukabatchee was an older Creek Indian town situated on the Tallapoosa River in Alabama occupied during from the late seventeenth to the beginning of the nineteenth centuries. According to the U.S. Indian Agent living among the Tukabatchee
Indians, the women were beginning to complain because they had to travel too far from the village to gather firewood. The use area or patch of resources around the town had been depleted significantly by the time of these recorded complaints.
Women and children collected the firewood in eastern U.S. Indian cultures
(Hudson 1976, 264; Silver 1990, 59; Wallace 1972, 190). For example the
Chippewa are described by Gilfillan (quoted in Hilger 1951) “Every day one can see, about four o’clock in the afternoon, long strings of women, each with her ax and packing strap, going out into the woods perhaps a mile; soon the woods are vocal with the axes; and then equally long strings of women are seen issuing 88 from the woods, each with her load upon her back, and each woman packs an immense quantity.”
Since chopping trees was very labor intensive given the technology of the
Indians, only deadfall from trees (coarse woody debris) was collected. “The work of the women was to collect fuel, usually only dry sticks gathered in the woods
(Beauchamp 1900, 82).
Other reasons for population movement are pests (Harper 1998: 246), spiritual reasons (Harper 1998: 246), aggression (Bartram in Waselkov and
Braund 1995, Huscher 1972), and internal conflict (Moore 1988: 63). These factors are not inconsistent with the factors identified from the ethological field studies described in chapter one. Internal conflict and aggression are conspecific
(same species) aggression and soil and wood depletion are environmental degradation. Since this dissertation is only concerned with the ultimate behavioral ecological causes for population dispersion and mobility over the long term, only agricultural productivity and fuel wood availability will be used as variables contributing to the resource gains from a given patch included in the model hypotheses.
Agriculture and Forest Food Resources
The effects of Indian agriculture on the forests are variable over space and time. Southeastern Indian agriculture was small-scale horticulture. Small fields were cleared, owned, and worked by single towns. Among the Creek Indians, a single town had use rights to a plot of land that was communally farmed by the 89 town members (Bartram in Waselkov and Braund 1995, 158). These plots were located in the river bottoms immediately adjacent to the town during the historic period. However, there is some evidence that some southeastern Indians in the very late historic period (1800-1830) were abandoning river bottom settlements and “settling out” into the uplands for a variety of reasons (Waselkov 1997). The degree of this change in settlement has not been quantified over space or time, however.
A town’s communal agricultural fields were distributed among the families of the town. Since Creek Indians were matrilineal, adult females owned and worked the farmland. Individual plots were identified in the large communal fields by borders. However everyone worked together on everyone else’s plot.
“Every town, or Community, assigns a piece, or parcels, of Land, as near as may be tot the Town, for the sake of conveniency – This is called the Town Plantation, where every Family or Citizen has his parcel or share, according to desire or conveniency, or largeness of his Family – The shares are divided or bounded by a strip of grass ground, poles set up, or any other natural or artificial boundary, - thus the whole plantation is a collection of lots joining each other, comprised in one inclosure, or general boundary. In the Spring when the Season arrives, all the Citizens as one Family, prepare the ground & begin to plant, beginning at one end or the other, as conveniency may direct for the general good; & so continue on until finished; & when the young plants arise & require culture, they dress and husband them until their crops are ripe. The work is directed by an overseer, elected or appointed annually, I suppose, in rotation throughout the Families of the Town. He rises by day break, makes his progress through the Town, and, with a singular loud cry, awakens the people to their daily labour, who, by surise, assemble at the public square, each one with his hoe & axe, where they form themselves into one body or band, headed by their superintendant, who leads them to the field in the same order as if they were going to battle, where they begin their work & return at evening. The females do not march out with the men, but follow after, in detached parties, bearing the provisions for the day. When the Fruits of their labours are ripe, and in fit order to gather in, they all on the same day 90
repair to the plantation, each gathering the produce of his own proper lot, brings it to town & deposits it in his own crib, allotting a certain proportion for the Public Granary which is called the King’s Crib because its contents is at his disposal, tho’ not his private property, but is to be considered the Tribute, or Free Contribution of the Citizens for the State, at the disposal of the King” (Bartram in Waselkov and Braund 1995, 158-159; his underlining).
Techniques for field preparation were relatively consistent over the
Southeast. The Indians used fire to clear the understory (Ethridge 1996: 229) and reused old fields before new ones were cleared because of the labor involved.
Virgin forests were rarely cleared for agricultural land. Large trees were girdled and left to fall in the winter storms (Silver 1991; Williams 1930: 435). On average the communal agricultural fields for a single southeastern Indian town were several hundred acres (approximately 100 ha) in size and immediately adjacent to the towns (Ethridge 1996). James Adair was a trader who lived among the southeastern Indians and wrote about them in the 18th century. He said, “Now, in the first Clearing of their plantations, they only bark the large timber, cut down the saplings and underwood, and burn them in heaps; as the suckers shoot up, they chop them off close by the stump, of which they make fires to deaden the roots, till in time they decay” (1930, 435). Similar procedures were used by Indians all over the eastern United States.
After the field was relatively clear, the women created “hillocks” (Lafitau
1977, 54), small hills of dirt about two to three feet apart, in which to plant seeds.
Three to ten seeds were observed to be planted in each “hillock” (Lafitau 1977,
54; Lescarbot 1968, 248-249; Will and Hyde 1917, 81 cited in Baden 1987, 22). 91
The hillocks gave support to the growing plants. James Adair, again, described these rows of hills as relatively compact. “[The Indians] plant the corn-hills so close, as to thereby choak up the field. They plant their corn in straight rows, putting five or six grains into one hole, about two inches distant. They cover them with clay in the form of a small hill. Each row is a yard asunder, and in the vacant ground they plant pumpkins, water-melons, marsh-mallows, sunflowers, and sundry sorts of beans and peas, at least two of which yield a large increase”
(1930, 439). Figure 4-2 shows a Theodore De Bry woodcut of a field being worked by Algonquian men and women.
92
Figure 4–2: "How they till the soul and plant" Theodore De Bry 93
These hills were formed and worked with digging sticks and hoes made of stone, shell, or the shoulder blade of a large animal. William Baden, using mostly northeastern ethnohistoric sources, noted that Indians worked only about six weeks in the fields (1987, 24). This work included only occasional weeding.
He also points out that the techniques and labor involved in this type of agriculture have important effects on the yield of corn in a single plot of land over time. Since the Indians were not tilling the ground, the ash from burned and harvested plant matter on the ground was not being reincorporated back into the soil. Nitrogen and other important nutrients under this system were rapidly lost to erosion (Baden 1987).
In the southeast, two crops of corn were planted, and “early corn” or “little corn” and a “great corn” (Hudson 1976, 295). The early corn was planted in the individual family plots around the houses. This corn was planted around March and harvested around May. A Natchez Indian from Louisiana described the season for harvesting the “little corn. “The third moon is that of the Little Corn.
This month is often awaited with impatience, their harvest of the great corn never sufficing to nourish them from one harvest to another” (Swanton quoted in
Hudson 1976, 366). The “great corn” was harvested around September and was planted in the larger, communal fields on the edge of the towns.
Every household had an individual garden adjacent to the house compound inside the town. The size of these small gardens is unknown. They 94 are only described as “small” (Waselkov and Bruand 1995, 54). “Besides this general plantation, each Habitation in the Town incloses a garden spot adjoining his House, where he plants, Corn, Rice, Squashes, &c, which, by early planting & closer attention, affords an earlier supply than their distant plantations” (Bartram in Waselkov and Braund 1995, 160; his underlining). In general, the women tended these small gardens and the larger communal gardens. Men helped in clearing the fields but rarely assisted in the daily agricultural work.
Corn, beans, squash, tomatoes, potatoes, and sunflowers were planted in these fields. Creeks began to experiment with European crops such as potatoes, wheat, cotton, and rice but these were only supplemental (Ethridge 1996). The traveler William Bartram was a botanist and consequently was very attentive to the plants when he passed through the southeast in the 1770’s. His detailed descriptions of plant foods of the Indians are invaluable and the reader is referred to his writings for lengthy descriptions of plant foods, agricultural and non- agricultural, used by the Indians (Harper 1998, Waselkov and Braund 1995).
The general locations of these agricultural fields are relatively well known.
However, the specific location and number of all agricultural fields is much more difficult to characterize. Benjamin Hawkins, the United States Indian Agent between 1796 and 1816, lived among the Creek Indians and made detailed descriptions of the land and agricultural practices of the Indians. From his writings, researchers have reconstructed the location of these towns and agricultural fields (Ethridge 1996; Waselkov 1981). In addition, some of the early 95 maps, particularly survey maps, show many of the Indian fields. Figure 4-3 is section 59 and 60 of Muscogee County District six, Georgia Land Survey plat map. The surveyor drew the location of a Creek house compound of four structures and a path to the agricultural fields. For clarity, these features have been outlined in white. The large squares on the map are survey sections and are 45 chains to a side (2970 feet; 905.25 meters).
The size of these fields is unknown. However, we can derive an estimate from observations of early explorers and scholars of eastern United States
Indians. Will and Hyde (1917, 99) provide the most specific data on the layout of a Mandan field. The Mandan measured fields in a “nupka” which consisted of seven rows of corn with beans between the cornrows. The nupka had no specific length but the average size of these fields was 0.25 acres (.10 ha). One mature Mandan woman had three to four of these “nupka” fields. Sissel
Schroeder notes that maps of 19th century “…Hidatsa family garden beds along the Missouri River …give field dimensions [which indicated] that these gardens averaged about 0.62 acres (0.25 ha) in size with a range from 0.16 to 1.35 acres
(0.07 – 0.54 ha)” (Wilson 1987, 128 cited in Schroeder 1999, 508). Arikara plots ranged between 0.5 and 2.0 acres (.20 and .81 ha; Cutright 1969, 98 cited in
Baden 1987, 28). Cherokee Indians of the southeast that did not grow corn for commercial purposes had fields of about 1.25 acres (.51 ha; Schoolcraft 1847,
32-28 cited in Baden 1987 29). In a summary of various North American Indians,
Baden estimates that the agricultural fields of eastern U.S. Native American 96
Indians in a subsistence economy were between 0.3 and 1.5 acres (.12 and .61 ha) per person (Baden 1987, 33). Schroeder (1999) points out that garden size is dependant on agricultural technology. Mean garden size of Native American groups is 1.32 acres (0.49 ha). However, mean garden size of Native American groups with plows is 1.92 acres (0.78 ha) and mean garden size of Native
American groups without plows is 0.59 acres (0.24 ha; Schroeder 1999, 510).
The Creek Indians did not use plows until very close to removal (1836). Even then, the only Indians that used plows were in the eastern section of the Creek
Indian territory because they were closer to whites and European agricultural practices (Ethridge 1996). In summary of these variable field size estimates, an average garden size of 2 acres (4.94 ha) per family of five individuals will be assumed for further calculations in this dissertation. This field size is within the range of the various sources mentioned above while accounting for the fact that the diet of the Creek Indians probably included a higher percentage of corn than most of the ethnohistoric sources mentioned above.
Figure 4–3: Creek House and path to Agricultural Fields Drawn on Georgia Land Survey Map. Photograph by
Thomas Foster. Courtesy of the Georgia Department of Archives and History. 98
According to historical references such as William Bartram, the botanist who wrote extensively about the southeastern Indians in the 1770’s, the Indians do not appear to have intensively planted trees. They definitely encouraged the growth of particular species that were economically and religiously important. For example, southeastern Indians were occasionally described as having peach trees (Ethridge 1996, 214) and orange trees (Waselkov and Braund 1995, 48-49) in close proximity to towns. While it seems likely that these trees may have been planted, to my knowledge there is no historic reference that indicates so. Greg
Laden (1992) observed among Efe hunter-gatherers of the Ituri Rain Forest in central African that fruit trees were more frequent along pedestrian paths simply as a function of traveling and dropping seeds. The only active tree cultivation that
Southeastern Indians apparently took part in was for a particular tree that was important for religious and social purposes, Casine yapon. “[T]he Indians call it the beloved tree, and are very careful to keep them pruned and cultivated
(Bartram in Waselkov and Braund 1995, 82). This holly tree, (Ilex vomitoria Ait), was used to make a strong “black drink” that was imbibed daily for religious purification and social reasons (Hudson 1976: 226-227).
There are no references to actual planting of fruit bearing trees but they were definitely encouraging the growth of these resources. It is difficult to estimate how long this encouragement has been occurring but if it is generally a trait of relatively sedentary populations, Indians may have been encouraging nut and fruit bearing trees for thousands of years. 99
Nuts were a major subsistence resource and the Indians encouraged the trees that bore them. Coweta Tallahassee, a Creek town on the Chattahoochee
River in Alabama, maintained a 2000 acre stand of hickory and oak trees just west of the town (Hawkins Letters 309 in Ethridge 1996: 217). William Bartram noted that the Indians used live oak acorns to make oil for seasoning and for roasting (Waselkov and Braund 1995: 44). He observed that
“in the antient cultivated fields, 1. Diospyros, 2. Gleditsia triacanthos, 3.
Prunus Chicasaw, 4. Callicarpa, 5. Morus rubra, 6. Juglans exaltata, 7. Juglans nigra, which inform us, that these trees were cultivated by the ancients, on account of their fruit, as being wholesome and nourishing food. Tho’ these are natives of the forest, yet they thrive better, and are more fruitful, in cultivated plantations, and the fruit is in great estimation with the present generation of
Indians, particularly Juglans exaltata commonly called shell-barked hiccory; the
Creeks store up the latter in their towns. I have seen above an hundred bushels of these nuts belonging to one family” (Waselkov and Braund 1995, 39).
Firewood was mostly from oak or hickory. Pine was sometimes used for kindling and is also mentioned as a source of firewood. Wood wasn’t cut for firewood. Instead, dead fall or “coarse woody debris” was collected by hand.
From ethnohistoric sources, Timothy Silver documents that women and children 100 collected the firewood by hand in the area immediately surrounding their town
(Silver 1990: 57-60).
Further evidence of the species of wood that was used for fuel wood comes from archaeological investigations of households. Hearths that have preserved wood residues have been recovered from residential areas of Indian villages. For example, Richard Polhemus reported at Toqua, a culturally similar town occupied in Tennessee between 1300 and 1600, a general trend of pine being used for building material and hardwood used for firewood (Polehemus
1987).
Decision Making among Creek Indians
The marginal value theorem is derived from optimal foraging theory. It predicts the resource use behavior of an individual forager. The application in this dissertation is, however, applied to a village or population of individuals. I am not arguing that a village is acting as an entity or that evolution has selected optimal behaving groups of populations. Instead, I am arguing that the tribal level society of the Creek Indians is a collection of optimally behaving individuals who make their own individual decision about fitness maximization within the constraints of a village setting. This use is not inconsistent with other applications of evolutionary biology applied to populations. For example Bonahge-Freund and
Kurland (1994) use another behavioral ecological model that models individual behavior to explain population interaction. They use game theory to model the development of the League of the Iroquois. 101
Each individual comes to his or her optimal decision about the proper abandonment time of a residence. For some it is sooner and for some it is later.
Individuals come to a consensus about the optimal abandonment time and then make the decision to move. Sometimes there is no consensus and towns split or only a few individuals abandon the town location. The end result of a group of individuals making personal optimal decisions about large-scale environmental variables is an average optimal decision about the surrounding resource patch.
The town is abandoned when the returns for the resource patch are an average optimal level for all individuals in the town.
This type of decision-making is possible under a relatively egalitarian political structure. As previously mentioned, the Creek Indians lived in a tribal level political organization. Town level decisions were made by a collection of council members. The council members did not have absolute control over any individual’s behavior. The council could only persuade individuals to do something (Braund 1993: 22). In this situation, then, the marginal value theorem is viewed as predicting the average decision of all individual optimal decisions in a group of Creek Indians living in a single town.
The Sample
A sample of ten Creek towns was selected for this study. These ten towns were selected because I could quantitatively measure the necessary temporal and environmental variables with enough precision to include them in the 102 sample. It was necessary to measure the resource gains over time for each patch and to measure as well the occupational period for each patch. The towns included in the sample are listed in table 1. The names used are those in use during the late eighteenth century. The associated technical documentation of each town is listed in the appendix. The location of each town is illustrated in
Figure 4-4. I will follow with a brief description of each town as defined in this study.
Apalachicola
The town in this study by the name of Apalachicola was settled around
1755 by the former inhabitants of Apalachicola Old Town (Bartram in Waselkov and Braund 1955). These Indians were Hitichiti speakers from the Apalachee area on the Gulf Coast of Florida. Hitchiti is related to the Muscogee languages of the core of the Creek Indians. The Apalachee Indians gradually migrated to the lower Chattahoochee during the 16th century (Hann 1988) and settled in a number of villages, one of which was Apalachicola although not in the location of the Apalachicola as defined in this study.
According to Bartram’s informant in the 1770’s, the site of Apalachicola of the late 19th century in this study was settled because of spiritual reasons. “This old town [the former location of Apalachicola – Apalachicola Old Town] was evacuated about twenty years ago by the general consent of the inhabitants, on account of its unhealthy situation, owing to the frequent inundations of the river over the low grounds; and moreover they grew timorous and dejected, 103 apprehending themselves to be haunted and possessed with vengeful spirits, on account of human blood that had been undeservedly spilt in this old town, having been repeatedly warned by apparitions and dreams to leave it” (Harper 1998,
246-247). The dating of this event places the origin of the Apalachicola town around 1755. In 1799, Benjamin Hawkins, the United States Indian Agent who lived among the Creek Indians from 1796-1816, described Apalachicola as “poor” and “not much in estimation” (Hawkins in Swanton 1922,134). I am interpreting this to indicate that Apalachicola was almost completely abandoned by 1800.
This town has been identified archaeologically (Worth 2000; see Appendix).
Apalachicola Old Town is related to the Apalachicola town mentioned above. This is the same Hitchiti population that moved north from the Gulf Coast area but was settled before the Apalachicola mentioned above. Its origin date is well documented. It was one of the towns listed in a mass migration from the east, central Georgia, in 1715. During the late 17th century, most of the Creek towns, including Apalachicola, moved east to be closer to the English traders in
South Carolina. Apalachicola moved to the Savannah River and its location is known archaeologically (Caldwell 1948). After the Yamasee War in 1716, these
Creek towns moved back west to the Chattahoochee River. Joseph Caldwell’s archaeological investigations at the Savannah River site confirmed that site’s identification as the Apalachicola population that moved from the Chattahoochee
River to the east by 1684 and then back to the Chattahoochee River location in
1716. Consequently, there is significant historic and archaeological 104 documentation to suggest a 1716 origin date for the Chattahoochee River
Apalachicola town (in this study called by its late 19th century name, Apalachicola
Old Town). In the 1770’s as mentioned above William Bartram was told by an
Apalachicola resident that Apalachicola Old Town was one and one-half miles down river from the current (Apalachicola) town and that the former location
(Apalachicola Old Town) was abandoned around 1755 (Harper 1998, 246).
David Taitt, a British Agent to John Stuart who was commissioned to survey and map all of the Creek towns, confirms the distance of Apalachicola Old Town relative to Apalachicola. He said, “I went this forenoon to view the point where the Pallachocola Town formerly stood about a mile and a half below this
[Apalachicola], but Could not get an Opertunity to Survey any part of it on
Account of ten or Twelve Eutchie Women who were gathering Strawberries all over the Old Town” (Mereness 1961, 557). The location of Apalachicola Old
Town is known archaeologically (Worth 2000). See the Appendix for the historical and archaeological justification for this identification.
Broken Arrow is a smaller town located on the Chattahoochee River. It is associated with Yuchi town and Coweta. Benjamin Hawkins, Indian Agent, was told in 1796 that it was founded around 1729. Swanton (1922, 229) records that it was destroyed in 1814 in the Creek War. Its location is known archaeologically and historically (Appendix).
Chiaha was a large town located on the Chattahoochee River during the
18th and 19th centuries. Similar to Apalachicola, it was one of the Chattahoochee 105
River Creek Indian towns that moved to the Ocmulgee River in central Georgia to be closer to English traders in South Carolina in the late 17th century (Crane
1956). After the Yamasee war in 1716, this town moved back to the
Chattahoochee River. This migration is well documented on historic documents and maps of the 18th century and establishes an origin date for the Chiaha town on the Chattahoochee of the 18th century. Bartram, Hawkins, and David Taitt visited Chiaha in the late 18th century and described its location. All of these documentary sources consistently place it in the same location. An archaeological site in that location has been extensively excavated and confirms the occupation dates and location (see Appendix). The location of this town did not move from its original settlement around 1715 until the federal government removed the Creek Indians from the Chattahoochee River around 1825.
Coweta Tallahassee was one of the most important and older Creek towns on the Chattahoochee. It, historically, held the power to declare war.
Coweta Tallahassee was the original location of the town of Coweta. In 1772,
David Taitt, a British Agent in the Americas, was commissioned to survey all of the Creek towns and map them. His sources are some of the most accurate and detailed documents for the identification of Creek towns in the 18th century. He listed a Coweta and a Little Coweta (Mereness 1961). Taitt said that Coweta was about five miles north of Cusseta and on the west side of the river (Mereness
1961, 549). Roughly twenty-five years later, Benjamin Hawkins described a
Coweta and a Coweta Tallahassee. Cusseta’s location is well established (see 106
Appendix). Based on Cusseta’s location and the location of physical geographic features, the locations of Coweta and Coweta Tallahassee are non ambiguous
(Fairbanks 1955, Hemperly 1969; Hurt 1975). Coweta Tallahassee’s date of original settlement is documented. The Coweta inhabitants migrated from the
Ocmulgee River in central Georgia to the Chattahoochee River in 1716 with a number of other Creek towns after the Yamassee war (see Apalachicola description above). Following this migration, they are consistently listed and mapped in the same location until David Taitt and Benjamin Hawkins describe them in the late 18th century.
In 1796 Benjamin Hawkins described it as “…two and a half miles below
Cowetuh, on the right [west] bank of the river. In going down the path between the two towns, in half a mile cross Kotes-ke-le-jau [current Cochgalechee Creek], ten feet wide, running to the left is a fine little creek sufficiently large for a mill, in all but the dry seasons. On the right bank enter the flat lands between the towns.
These are good, with oak, hard-shelled hickory and pine: they extend two miles to Che-lec-in-ti-getuh [current Mill Creek], a small creek five feet wide, bordering on the town. The town is a half mile from the river, on the right bank of the creek; it is on a high flat…”(Hawkins in Swanton 1922, 228).
Cusseta and Cusseta Old Town will be discussed together because they are the same town in two different locations. This town was another very old and important town among the Lower Creek Indians. They were Muscogee speakers.
Cusseta was settled on the Chattahoochee River during the 17th century as 107 noted in Spanish mission documents. In 1685 it was burned along with a number of nearby Lower Creek towns by a Spanish expedition from Florida. These towns migrated east to the Ocmulgee River in central Georgia until 1716. Shortly after 1716, Cusseta and a number of other Creek towns resettled on the
Chattahoochee River.
Benjamin Hawkins, in 1769, visited Cusseta on the Chattahoochee.
“…[T]he land rises to a high flat, formerly the Cussetuh town, and afterwards a
Chickasaw town. This flat is intersected with one branch. From the southern border of this flat, the Cussetuh town is seen below, on a flat, just above flood mark, surrounded with this high flat to the north and east, the river to the west; the land about the town is poor, and much exhausted; the principal dependence is on the rich fields above the creek; to call them rich must be understood in a limited sense; they have been so, but being cultivated beyond the memory of the oldest man in Cussetuh, they are almost exhausted…”(Hawkins in Swanton
1922, 223). The location of Cusseta at the time that Hawkins observed it has been interpreted as the second location (Swanton 1922, O’Steen 1997) of settlement since the 1716 migration from the Ocmulgee to the Chattahoochee
Rivers mentioned above. The current geography of the area is the same as when
Hawkins observed it and is easy to identify the location of the Cusseta town and the former Cusseta location (Cusseta Old town) mentioned by Hawkins in 1796.
It is also consistent with David Taitt’s description in 1772. “As soon as I Crossed the River and Entering into the Cussita Kings yard, he Caused a Cow horn to be 108 fired, and one when I went into his house where I Smoked and eat with him, and then went to his Square where his beloved men and Warriours were present.
After Smoking with them I went with the head warriour to his house on the Top of a high hill and from thence viewed the Town which Stands on a fine plain
Extending along the side of the River and about half a mile back, is bound by high hills. The flood two years ago overflowed all this Town , but at present the banks are fifty feet above the Surface of the water” (Mereness 1961, 550). The head warrior, or Mico in Muscogee language, resided in the location of the
Cusseta Old Town up on the bluffs above the Cusseta town in 1772.
In addition the location of Cusseta (as referred to by Hawkins in 1796) has been extensively excavated (O’Steen 1997, Willey and Sears 1952). O’Steen
(1997) archaeologically investigated the late 18th century site of Cusseta (9CE1) and found no indication of settlement between 1715 and about 1740. This is consistent with the interpretation of Cusseta settling in two different locations during the 18th century and with what Hawkins was told of a “former” location of
Cusseta up on the bluff above the “present” location (Hawkins in Swanton 1922,
223). Consequently, there is little ambiguity about the location of the Cusseta
Old town (1715-1740) on the bluffs above 9CE1 and the Cusseta town (9CE1) in the floodplain.
Almost all travelers, Indian and non-Indian, entered the Cusseta town and
Cusseta Old Town when passing through the Creek Indian country. This is because a major travel road and trade route, which became the United States 109
Federal Road, and connected the far east to the far west passed directly through these two towns (Southerland and Brown 1989). Hawkins observed, “…the main trading path, from the upper towns, passes through here…”(quoted in Swanton
1922, 223). Adam Hodgeson, an English missionary made an description of
Cusseta town in 1820 while traveling on this road that is valuable for its ethnographic description.
“[W]e looked down into a savannah [onto the floodplain from the bluffs as quoted above by Hawkins in 1796] in which is situated the Indian town of Co-se-ta, on the Chatahouchy. It appeared to consist of about 100 houses, many of them elevated on poles from two to six feet high, and built of unhewn logs, with roofs of bark, and little patches of Indian corn before the doors. The women were hard at work, digging the ground, pounding Indian corn, or carrying heavy loads of water from the river; the men were either setting out to the woods with their guns or lying idle before the doors; and the children were amusing themselves in little groups. The whole scene reminded me strongly of some of the African towns, described by Mungo Park. In the center of the town, we passed a large building, with a conical roof, supported by a circular wall about three feet high [council house described above]: close to it was a quadrangular space, enclosed by four open buildings, with rows of benches rising one above the other [square ground]. The whole was appropriated, we were informed, to the great council of the town, who meet, under shelter, or in the open air, according to the weather. Near the spot was a high pole, like our May-poles, with a bird at the top, round which the Indians celebrate their green-corn dance [ball field]”(Benton 1998, 5-6).
According to archaeological investigations at Cusseta town and Benjamin
Hawkins’s informants in 1796, the occupational history of these two towns can be reconstructed. Cusseta Old Town was settled after the 1715 migration to the
Chattahoochee River from the Ocmulgee River. According to archaeological investigations, Cusseta town in the flats was settled around 1740 after Cusseta
Old town. This is consistent with Hawkins and indicates an occupation of about 110
1715 to 1740 for Cusseta Old Town. Cusseta town was settled around 1740 and occupied until 1825 (Swanton 1922). See the Appendix for a more thorough description.
Osochi also migrated to the Chattahoochee from the Flint and Ocmulgee
Rivers after the Yamasee war in 1716. However, they apparently did not settle on the Chattahoochee River until around 1755. Swanton documented through maps and censuses of the 18th century that the Osochi town was on the Ocmulgee
River in central Georgia during the 1738 Spanish census and the 1750 French census. In the 1761 English census, they had moved and are listed with the
“Point towns” on the Chattahoochee, however (Swanton 1922, 166). The “Point towns” was a name given to a collection of towns that were settled in a large bend, a point, of the Chattahoochee River. Hawkins says of them, “Oose-oo- che; is about two miles below Uchee, on the right [east] bank of Chat-to-ho-chee; they formerly lived on Flint river, and settling down here, they built a hot house in
1794” (quoted in Swanton 1922, 166). The description by Hawkins is consistent with the map and documentary reconstruction by Swanton (1922). This town has been identified archaeologically (see Appendix).
Upatoi was settled the shortest period but has one of the most tightly dated and defined settlements. The inhabitants of this town originally lived at
Cusseta but moved up the Upatoi Creek around 1790. In 1796, Benjamin
Hawkins wrote that it was “…twenty miles from the river [Chattahoochee], on
Hat-che thluc-co [Upatoi Creek]; they have good fences, and the settlers under 111
[enjoy?] the best characters of any among the Lower Creeks…The village is in the forks of Hatche thlucco, and the situation is well chosen; the land is rich, on the margins of the creeks and the cane flats; the timber is large, of poplar, white oak, and hickory; the uplands to the south are the long-leaf pine; and to the north waving oak, pine, and hickory; cane is on the creeks and reed in all the branches” (quoted in Swanton 1922, 223). This town has been extensively studied both archaeologically and geophysically (Elliot et al 1996). In addition, the council house and some of the residential structures were mapped on the
1827 land survey maps (Figure 4-5). Consequently, I am confident of its location.
It was settled around 1790 according to Hawkins’s informant (Swanton 1922,
223) and was abandoned in 1825 (Appendix, Elliot et al 1996).
Yuchi town was an old and important Lower Creek town. Its inhabitants however spoke a language very different than the core of the Creek Indians. Its inhabitants settled on the Chattahoochee River after the Yamasee war in 1716 like the other towns in this study (Braley 1998). It is listed in the Diego Peña
Spanish expedition of 1716 as being settled on the Chattahoochee (Hann 1988,
363). This is consistent with an interpretation of Yuchi town migrating west with the rest of the Lower Creek towns from the central Georgia regions (see above).
In 1776, William Bartram visited the town located at the mouth of Uchee
Creek. He described it as “situated in a low ground immediately bordering on the river; it is the largest, most compact, and best situated Indian town I ever saw; the habitations are large and neatly built; the walls of the houses are constructed 112 of a wooden frame, the lathed and plastered inside and out with a reddish well- tempered clay or mortar, which gives them the appearance of red brick walls; and these houses are neatly covered or roofed with Cypress bark or shingles of that tree. The town appears to be populous and thriving, full of youth and young children…Their own national language is altogether or radically different from the
Creek or Muscogulge tongue…They are in confederacy with the Creeks, but do not mix with them…”(Bartram in Swanton 1922, 309). Twenty years later, this town was still in the same location as noted by Benjamin Hawkins in 1796. “U- chee: is on the right bank of Chat-to-ho-che, ten and a half miles below Cow-e- tuh tal-lau-has-see, on a flat or rich land, with hickory, oak, blackjack, and long- leaf pine; the flat extends from one to two miles back from the river. Above the town, and bordering on it, Uchee Creek, eighty-five feet wide, joins the river”(Hawkins in Swanton 1922, 309). Regarding its abandonment, in 1796
Hawkins says “[t]hey have lately begun to settle out in villages”(Hawkins in
Swanton 1922, 310.
The Pena reference provides evidence of the presence of Yuchi on the
Chattahoochee by 1716. Bartram and Hawkins confirm its location as of 1776.
The location identified by Bartram and Hawkins has been investigated archaeologically (Braley 1994, 1998; Hargrave 1998). These investigations provide solid archaeological evidence of continuous occupation from the beginning of the 18th century with a gradual abandonment by the end of the 18th century (Braely 1998). Braley’s ceramic analysis of the Yuchi town site indicates 113 a lack of occupation during the late 19th century. This is consistent with
Hawkins’s observation that the Yuchi town residents had “lately begun to settle out in villages.” He was indicating that Yuchi was beginning to be abandoned by
1796. These archaeological data combined with the historical references indicate an origin date of 1716 and an abandonment date of about 1800.
114
Occupation Town Name Occupation (years) Population* Dates
Apalachicola 45 1755-1800 200
Apalachicola Old Town 40 1715-1755 240
Broken Arrow 85 1730-1815 100
Chiaha 110 1715-1825 200
Coweta Tallahassee 85 1715-1800 200
Cusseta 85 1740-1825 200
Cusseta Old Town 25 1715-1740 200
Osochi 70 1755-1825 200
Upatoi 35 1790-1825 100
Yuchi 85 1715-1800 200
Table 4–1: Towns Included in Study and Respective Occupation Period (archaeological and ethnohistoric justification for the dates of occupation is in the appendix). *Estimated average population during period of occupation based on Swanton (1922) and a gunman ratio of 1:4.
115
Figure 4–4: Lower Creek towns included in Study
116
Figure 4–5: Muscogee County, District 10, Section 252, Geogia Land Survey platt map, Georgia Surveyors General, Department of Archives and History, Atlatnta, Georgia. The map section shows the Creek town of Upatoi drawn on the land survey map for Muscogee County, 1827. Surveyor’s handwriting is outlined in white for clarity. North is to the right. The square lines around the Upatoi area represent a section. A section is 45 chains to a side. Forty-five chains is 66 feet (20.1168 meters). 117
These Creek towns were included in the study sample because of the available archaeological, historic, and environmental data, which are necessary for the natural experiment in this dissertation. Previous applications of the MVT have used ethnographic data with various results as described in chapter two.
Most of these applications have been qualitative tests. The temporal and regional span of these data offer significant advantages for testing the MVT in a way that is impractical with ethnographic data.
Conclusion
This chapter introduced the study population, the Creek Indians of the late historic period (1600-1830) of the southeastern United States. I reviewed the ethnohistoric evidence for town population dynamics and abandonment. The marginal value theorem predicts that a consumer will optimize the resources in a given patch. Ultimately the optimized resource is inclusive fitness. However, the environmental variables that contribute to inclusive fitness vary between and among species. In this study, I am assuming from ethnohistoric references reviewed above that agricultural productivity and fuel wood availability are the long-term environmental variables that are the most limiting and contribute the most value to a town’s location. Finally, I identifed ten Creek towns to be used in the study and reviewed their individual histories. Chapter 5
Forest Analysis
Introduction
This chapter provides a thorough analysis of the geo-morphological and forest environment surrounding the Creek Indians. This is important for comparitive purposes and because it controls the factors contributing to resource depression as used in this study.
The region occupied by the Creek Indians during the historic period is environmentally diverse. It encompasses the Coastal Plain, the Piedmont, and the Ridge and Valley physiographic provinces (Fenneman 1938; Figure 5-1).
These physiographic variations contributed to variations in the forest and consequently to variations in the Indian settlement. A thorough analysis of the forest variation is warranted due to the large-scale ecological effects of physiography, soil, and forest distribution on human settlement.
The Coastal Plain is bordered on the north by the fall line and is underlain by Cretaceous period marine sediments. Elevations range between about 100 feet and about 600 feet. Major physiographic regions of the Coastal Plain in this region are the Fall Line Hills, the Black Belt, and the Red Hills (Fenneman 1938;
Sapp and Emplaincourt 1975). See Figure 5-1.
The Fall Line Hills are uplands dissected by streams flowing from more resistant Paleozoic sedimentary and crystalline rocks to less resistant 119
Cretaceous sand and clay sediments. The Black Belt is an east-west belt about twenty miles wide of black soils underlain by chalks. The Red Hills are southward sloping undulating hills of more resistant siliceous clay and sandstones
(Fenneman 1938; Sapp and Emplaincourt 1975). 120
Figure 5–1: Phsiographic Regions and Subdistricts used in Study 121
The Piedmont region in central Alabama has two major topographic regions, the Opelika Plateau and the Ashland Plateau. The Opelika Plateau is underlain by Archean rocks and is incised on the eastern boundary by the
Chattahoochee River. These Archean rocks are metamorphosed gneisses and shists. The Ashland Plateau is diversified by ridges. Surfaces range from about
1100 feet above sea level in the northern section of the Piedmont to about 500 feet above sea level in the southern section.
A small portion of the study region is within the Coosa Valley district of the
Ridge and Valley physiographic region. Elevations range from about 300 feet at the valley floors to about 1500 feet on the ridges. Ridges are generally northeast- southeast lines of sandstone whereas the valley plains are formed on limestones and shales.
The study region consists of two forest regions, the Oak-Pine and the
Southeastern Evergreen Region (Braun 1950; Waggoner 1975). The Oak-Pine region is dominated by Oaks but Pines (Pinus) are a significant addition in areas of poorer soils. The Southeastern Evergreen Region is dominated by broad- leaved evergreens and coniferous species, particularly longleaf pine (Pinus palustris; Braun 1950).
The present makeup of the forests in this region the consequence of extensive European settlement and agricultural activity since the early nineteenth 122 century. Logging reached its peak in Alabama during the early 20th century and today no virgin forests exist in this region (Waggoner 1975).
The composition of the pre-European settlement forests can be estimated with the use of land survey notes, maps, and witness (bearing) tree analysis
(Abrams and Ruffner 1995, Bourdo 1956). These data are often the best source of information regarding the pre-European settlement forest composition in regions like this one where significant change has occurred due to economic practices.
Witness (or bearing) tree studies use maps and notes made during the process of land surveys by various agencies, usually state or federal government. In Alabama, land surveys were conducted by the federal government in accordance with federal laws. These land surveys divided federal land up into square townships that were six miles on a side. Each township was further divided into square sections of one mile on a side. Thus, each township had thirty-six sections. The boundary of each of these sections was defined by marking or creating a boundary at each corner and quarter-corner. These boundaries were defined by noting and mapping “witness trees.” These “witness trees” were trees that were relatively close to the boundary marker. Each witness tree was mapped and noted in the surveyor notes. A very detailed map of the forests can be created that reflects a sample of the forests at the time of the survey by using these surveyor notes. Usually, four witness trees were observed at each corner and two were observed at each quarter-corner. The result is a 123 map of the forests taken at regular intervals with an observation density of twenty-four trees in a given square mile. These surveys were usually made at the time of land acquisition in preparation for land distribution by the federal government. This preceded the majority of intensive European settlement and consequent European agricultural practices that drastically altered the forests.
It has been pointed out that witness tree studies can be biased (Bourdo
1956). Occasionally there was surveyor fraud. For example, it has been discovered that surveyors would sometimes record species of trees that were considered to be more economically valuable (hardwoods) in order to elevate the value of the land. Most cases of large-scale fraud are known and can be avoided.
None are known in the area of this study. However, in addition, it is often assumed that surveyors would have been biased toward longer living hardwoods when choosing witness trees for markers.
Methods
I reconstructed pre-European settlement forest species frequencies using witness trees from the land survey notes conducted by the General Land Office in Alabama. These documents have been microfilmed and I obtained them from the United States Bureau of Land Management. I extracted data on witness trees from the microfilm and then digitized onto the Public Land Survey System digital line graph (DLG) from the United States Geological Survey (USGS) using
Arcview 3.2a and Arc/Info 8.02 (ESRI). 124
Tree species are listed by common names in the survey notes. I translated the common names to scientific ones using Godfrey (1988). When I encountered ambiguous entries, I used physiographic and habitat associations to determine which species were determined. Common names and the associated scientific names for the species that I used in this study are listed in table 5-1.
I defined six physiographic regions based on Fenneman (1938) and Sapp and Emplaincourt (1975). The Fall Line Hills, the Black Belt, and the Red Hills. In the Piedmont are the Opelika Plateau and the Ashland Plateau are in the Coastal
Plain. I defined one physiographic region, the Coosa Valley, in the Ridge and
Valley province. I split the the Coosa Valley into three topographic divisions; (1) the valley floor, defined as elevations under 300 meters, (2) side slopes, defined as elevations between 300 meters and 400 meters, and (3) ridge tops, defined as elevations over 400 meters.
I classified soil parent material with the State Soil Geographic Data Base
(STATSGO) digital soil layer from the United States Department of Agriculture
Natural Resource Conservation Service (USDA-NRCS 1991). I identified five parent material mineralogies in the study region: kaolinitic, micaceous, mixed, montmorillonitic, and siliceous.
I used Binary Discriminate Analysis (BDA) to identify species relationships with the environmental parameters (Strahler 1978). The methods I used were modeled after Black and Abrams (2001). I constructed presence or absence frequency tables for each species and environmental class. When the sample 125 size was less than six in at least two thirds of the expected individuals per class, I dropped the species due to insufficient sample size following Steele et al (1997).
I used the G2 statistic to test for independence between species distribution and the environmental variables (Sokal and Rolf 1995). Species without a significant
(α=0.05) response were removed from further analysis. Lastly, I created standardized residuals from the statistically significant classes according to the method of Haberman (1973) and the residuals were graphed. A positive residual indicates a preference for the environmental parameter and a negative residual indicates an avoidance of the parameter.
Results
The forest composition and spatial distribution corresponds to variations in physiography, topography, and soil parent material. The composition and relative frequency of all species in the study area including their common names are listed in table 5-1. A total of 43610 trees were included in this section of the analysis. Pinus (pine) dominates at 44% with the second most abundant species being Quercus stellata (post oak) at 11%. The species of pine is unknown. The surveyors simply recorded “pine.” William Bartram, an eighteenth century botanist who traveled and wrote extensively about the vegetation in the southeastern United States, mentioned Pinus palustris (long leaf or yellow pine) as being most common. However, he also mentions loblolly, black pine, shortleaf, slash, and white pines (Harper 1998). 126
I searched for relationships between the various tree species and three major physiographic regions, six physiographic subdivisions, three topological divisions and various soil parent material attributes. I found that physiographic divisions explain much of the species variation in the area. Major physiographic preferences are indicated in figure 5-2. Positive standardized residuals indicate a positive preference for the environmental class and negative standardized residuals reflect avoidance. Carya (hickory), Castanea (chestnut), Quercus stellata (post oak), and Quercus velutina (black oak) have a strong preference for the Piedmont versus the Coastal Plain. Ilex (holly) and Pinus (pine), however, have a strong preference for the Coastal Plain.
The species distribution by major physiographic regions, Coastal Plain,
Piedmont, and Ridge and Valley, are listed in table 5-2. The Coastal Plain contains a sample of 17184 trees over 686569 hectares and is dominated by
Pinus (pine) at 52% with the second most common species being Quercus stellata (post oak) at 8%. The Piedmont is dominated by Pinus (pine) at 37% with the next most abundant species being Quercus stellata (post oak) at 14% and
Carya (hickory) at 12%. The sample from the Piedmont included 21386 trees over 741191 hectares. The Ridge and Valley section of the study was 191553 hectares and included 5040 trees. This physiographic region was dominated by
Pinus (pine) at 47% with Quercus stellata (post oak), 11%, and Carya (hickory),
10%, being the next most abundant species. 127
Table 5–1: Species Frequencies and Common Nyssa gum 745 0.026 Oxydendrum arboreum sourwood 213 0.005 Names for witness trees in Study Area Persea bay 285 0.007 Species Common Name Count Frequency Persea borbonia red bay 3 0.000 Acer negundo box elder 15 0.000 Pinus pine 19182 0.440 Acer saccharinum sugar tree 47 0.001 Pinus glabra spruce pine 2 0.000 Aceraceae maple 385 0.009 Platanus occidentalis sycamore 23 0.001 Anacardiaceae sumac 3 0.000 Populus poplar 313 0.007 Annonaceae apple tree 3 0.000 Prunus wild cherry 1 0.000 Bitula nigra birch 39 0.001 Prunus americana plum 10 0.000 Carpinus caroliniana ironwood 202 0.005 Prunus persica peach 21 0.000 Carya hickory 3826 0.088 Prunus serotina cherry 26 0.001 Carya illinoiensis pecan 3 0.000 Quercus oak 118 0.003 Castanea chestnut 1013 0.023 Quercus alba white oak 1262 0.029 Castanea pumila chinquapin 81 0.002 Quercus marilandica black jack 1723 0.040 Celtis hackberry 42 0.001 Quercus nigra water oak 227 0.005 Cercis canadensis red bud 13 0.000 Quercus phellos willow oak 130 0.003 Citrus wild orange 1 0.000 Quercus rubra red oak 3233 0.073 Clethraceae alder 9 0.000 Quercus stellata post oak 5065 0.116 Cornus florida dogwood 436 0.010 Quercus velutina black oak 2541 0.058 Crataegus haw 6 0.000 Salicaceae willow 11 0.000 Crataegus spathulata red haw 1 0.000 Salix swamp willow 1 0.000 Cupressaceae cypress 7 0.000 Sambucus elder 3 0.000 Diospyros virginiana persimmon 128 0.003 Sassafras albidum sassafras 211 0.005 Euonymous atropurpureus wahoo 15 0.000 Tilia americana lynn, bass 87 0.002 Fagus grandifola beech 551 0.013 Ulmus elm 167 0.004 Fraxinus ash 318 0.007 Ulmus rubra red elm 16 0.000 Gleditsia triacanthos honey locust 2 0.000 Vaccinium arboreum huckleberry 10 0.000 Hamamelis virginiana hazel tree 2 0.000 Viburnum rufidulum black haw 2 0.000 Ilex holly 239 0.005 Total 43610 1.000 Juglans walnut 12 0.000
Juglans nigra black walnut 3 0.000
Juniperus virginiana cedar 7 0.000 Lagerstroemia myrtle 1 0.000 Liquidamber styraciflua sweet gum 454 .0010 Magnolia acuminata cucumber 20 0.000 Magnolia grandiflora magnolia 37 0.001 Malus angustifolia crabapple tree 3 0.000 Moraceae mulberry 65 0.001 128
Table 5–2: Species frequency by physiographic region Coastal Plain Piedmont Ridge and Valley Species Count Frequency Count Frequeny Count Frequency
Acer negundo 13 0.001 2 0 Acer saccharinum 18 0.001 20 0.001 9 0.002 Aceraceae 156 0.009 198 0.009 31 0.006 Anacardiaceae 1 0 2 0 1 0 Annonaceae 2 0 0 0 0 0 Bitula nigra 15 0.001 21 0.001 3 0.001 Carpinus caroliniana 130 0.008 59 0.003 13 0.003 Carya 778 0.045 2570 0.12 478 0.095 Carya illinoiensis 3 0 0 0 0 0 Castanea 125 0.007 757 0.035 131 0.026 Castanea pumila 27 0.002 50 0.002 4 0.001 Celtis 38 0.002 4 0 0 0 Cercis canadensis 8 0 4 0 1 0 Citrus 1 0 0 0 0 0 Clethraceae 3 0 6 0 0 0 Cornus florida 165 0.01 237 0.011 34 0.007 Crataegus 6 0 0 0 0 0 Crataegus spathulata 1 0 0 0 0 0 Cupressaceae 6 0 1 0 0 0 Diospyros virginiana 42 0.002 71 0.003 15 0.003 Euonymous 14 0.001 1 0 0 0 Fagus grandifola 335 0.019 189 0.009 27 0.005 Fraxinus 160 0.009 118 0.006 40 0.008 Moraceae 0 0 2 0 0 0 Hamamelis virginiana 1 0 1 0 0 0 Ilex 202 0.012 32 0.001 5 0.001 Juglans 10 0.001 1 0 1 0 Juglans nigra 1 0 1 0 1 0 Juniperus virginiana 4 0 1 0 2 0 Lagerstroemia 1 0 0 0 0 0 Magnolia acuminata 5 0 14 0.001 1 0 Magnolia grandiflora 37 0.002 0 0 0 0 Malus angustifolia 2 0 1 0 Moraceae 39 0.002 17 0.001 9 0.002 Nyssa 7 0 7 0 0 0 Nyssa sylvatica 590 0.034 489 0.023 106 0.021 Oxydendrum arboreum 82 0.005 102 0.005 29 0.006 Persea 198 0.012 86 0.004 1 0 Persea borbonia 3 0 0 0 0 0 129
Pinus 8918 0.519 7889 0.369 2375 0.471 Pinus glabra 2 0 0 0 0 0 Platanus occidentalis 15 0.001 5 0 3 0.001 Populus 105 0.006 182 0.009 26 0.005 Prunus 1 0 0 0 0 0 Prunus americana 7 0 1 0 2 0 Prunus persica 13 0.001 7 0 1 0 Prunus serotina 14 0.001 7 0 5 0.001 Quercus 72 0.004 36 0.002 10 0.002 Quercus alba 585 0.034 559 0.026 118 0.023 Quercus falcata 49 0.003 175 0.008 56 0.011 Quercus marilandica 850 0.049 621 0.029 252 0.05 Quercus nigra 142 0.008 69 0.003 16 0.003 Quercus phellos 56 0.003 65 0.003 9 0.002 Quercus rubra 982 0.057 1756 0.082 205 0.041 Quercus stellata 1425 0.083 3104 0.145 536 0.106 Quercus velutina 444 0.026 1661 0.078 436 0.087 Salicaceae 6 0 5 0 0 0 Salix 0 0 1 0 0 0 Sambucus 2 0 1 0 0 0 Sassafras albidum 80 0.005 114 0.005 17 0.003 Tilia americana 59 0.003 21 0.001 7 0.001 Ulmus 107 0.006 44 0.002 16 0.003 Ulmus rubra 11 0.001 1 0 4 0.001 Vaccinium arboreum 9 0.001 0 0 1 0 Viburnum rufidulum 1 0 1 0 0 0 Totals 17184 1 21386 1 5040 1
130
Physiographic Regions
15.00 11.97 10.00 8.43
4.41 5.00 4.11 1.68 1.22 1.17 1.14 0.00 Coastal Plain -2.02 -0.68 -0.41 Piedmont -1.65 -2.07 -5.00 -2.88 -2.61 -2.18 Carpinus Carya Castanea Castanea pumila Celtis Cornus florida Ridge and Valley caroliniana -10.00 -10.81 -15.00 -15.31 -20.00
20.00 16.28 15.00
10.00 8.67 6.27 6.32 4.57 4.49 4.21 5.00 2.42 2.92 2.06 1.54 1.65 Coastal Plain 0.51 0.51 0.20 0.00 -1.95 -0.93 Piedmont
-2.18 -1.88 -2.52 -1.34 -1.29 -2.05 Ridge and Valley -3.04 -1.60 -5.00 -3.55 -4.06 -2.77 -3.26 -4.35 -5.63 -5.25 -10.00
-15.00 -14.94 -20.00
Fagus Fraxinus Ilex Magnolia Moraceae Nyssa Persea Pinus Platanus Populus Quercus grandifola grandiflora sylvatica occidentalis
15.00 9.45 10.00 8.658.05 6.09 -0.91 5.21 4.34 3.11 3.60 3.29 3.96 5.00 1.46 -0.71 0.00 -2.20 1.49 -1.74 -1.88 -7.14 -1.75 -2.87 -2.04 -2.37 -3.00 -5.00 -4.21 -5.61 -10.00 -10.58 -15.00 -14.13 -20.00
Quercus Quercus Quercus Quercus Quercus Quercus Tilia Ulmus Ulmus alba marilandica nigra rubra stellata velutina americana rubra
Figure 5–2: Standardized Residuals by Physiographic Region
131
I analyzed topographic variation and soil parent material in the Coosa
Valley district of the Ridge and Valley physiographic region. The frequencies of species by topographic subdivision are listed in table 5-3 and the standardized residuals from the significant species are displayed in figure 5-3. Pinus (pine) dominates on the ridges in the Coosa Valley with 47% with Castanea (chestnut) being the next most dominant species at 12%. Sideslopes are dominated by
Pinus (pine) at 52%, and Quercus velutina (black oak) at 8%. The valley floors are dominated by Pinus (pine) at 44%, Quercus stellata (post oak) at 13%, and
Carya (hickory) at 12%.
Topographic preferences for the Coosa Valley are illustrated in figure 5-3.
Castanea (chestnut) and Quercus marilandica (black jack oak) have a strong preference for the ridges whereas Carya (hickory) and Quercus stellata (black oak) have a preference for the valleys. Castanea (chestnut), Pinus (pine),
Quercus marilandica (black jack oak), and Quercus rubra (red oak), have a preference for the side slopes or middle elevations.
132
Coosa Valley Topography
8.00 7.26
6.00 3.78 3.61 4.00 3.24 2.49 2.81 2.57 2.88 2.00 Ridge 0.59 0.87 0.16 Side Slope 0.00 Valley -0.22 -0.81 -2.00 -1.27 -1.95 -1.85 -1.93 -4.00 -2.97 -3.32 -4.58 -6.00 -4.80 Carya Castanea Pinus Quercus Quercus Quercus Quercus alba marilandica rubra stellata
Figure 5–3: Standardized Residuals for Coosa Valley Topography
133
Table 5–3: Coosa Valley species frequency by topography Ridge Count Frequency Side Slope Count Frequency Carya 16 0.109 Acer saccharinum 1 0.001 Castanea 18 0.122 Aceraceae 16 0.009 Castanea pumila 2 0.014 Annonaceae 1 0.001 Diospyros virginiana 1 0.007 Carpinus caroliniana 1 0.001 Nyssa sylvatica 2 0.014 Carya 89 0.053 Oxydendrum arboreum 2 0.014 Castanea 74 0.044 Pinus 70 0.476 Castanea pumila 2 0.001 Quercus marilandica 16 0.109 Cornus florida 9 0.005 Quercus rubra 6 0.041 Diospyros virginiana 3 0.002 Quercus stellata 3 0.020 Fagus grandifola 10 0.006 Quercus velutina 11 0.075 Fraxinus 2 0.001 147 1.000 Ilex 1 0.001 Moraceae 1 0.001 Nyssa sylvatica 40 0.024 Oxydendrum arboreum 14 0.008 Persea 1 0.001 Pinus 884 0.523 Populus 12 0.007 Prunus serotina 1 0.001 Quercus 1 0.001 Quercus alba 46 0.027 Quercus marilandica 115 0.068 Quercus nigra 7 0.004 Quercus rubra 116 0.069 Quercus stellata 108 0.064 Quercus velutina 128 0.076 Sassafras albidum 4 0.002 Tilia americana 2 0.001 Ulmus 1 0.001 1690 1.000
134
Table 5–3: Coosa Valley Species by Topography Continued
Valley Count Frequency Acer negundo 2 0.001 Acer saccharinum 8 0.002 Aceraceae 15 0.005 Bitula nigra 3 0.001 Carpinus caroliniana 12 0.004 Carya 373 0.116 Castanea 39 0.012 Cercis canadensis 1 0.000 Cornus florida 25 0.008 Diospyros virginiana 11 0.003 Fagus grandifola 17 0.005 Fraxinus 38 0.012 Ilex 4 0.001 Juglans 1 0.000 Juglans nigra 1 0.000 Juniperus virginiana 2 0.001 Magnolia acuminata 1 0.000 Malus angustifolia 1 0.000 Moraceae 8 0.002 Nyssa sylvatica 64 0.020 Oxydendrum arboreum 13 0.004 Pinus 1421 0.441 Platanus occidentalis 3 0.001 Populus 14 0.004 Prunus americana 2 0.001 Prunus persica 1 0.000 Prunus serotina 3 0.001 Quercus 9 0.003 Quercus alba 72 0.022 Quercus marilandica 121 0.038 Quercus nigra 9 0.003 Quercus phellos 9 0.003 Quercus rubra 139 0.043 Quercus stellata 425 0.132 Quercus velutina 297 0.092 Sassafras albidum 13 0.004 Tilia americana 5 0.002 Ulmus 15 0.005 Ulmus rubra 4 0.001 Vaccinium arboreum 1 0.000 Ulmus 16 0.005 Ulmus rubra 4 0.001 Vaccinium arboreum 1 0.000 3223 1.000
135
Soil parent material in the Coosa Valley is of four types. The species frequencies for each type are listed in table 5-4. Kaolinitic soils are dominated by
Pinus (pine) at 49%. The next most frequent species are Carya (hickory) at 9%,
Quercus stellata (post oak) at 8%, and Quercus velutina (black oak) at 8%.
Micaceous soils are infrequent in the Coosa Valley but are dominated by Pinus
(pine) at 57%. Mixed parent material soils are dominated by Pinus (pine) at 45% and Quercus stellata (post oak) at 12%. Silica based soils are dominated by
Pinus (pine) at 48% with Quercus stellata (post oak) and Carya (hickory) next in frequency.
The Coosa Valley does not display strong preferences between soil types and tree species. This may be due to a significantly smaller sample size in the
Coosa Valley relative to the other physiographic divisions. The strongest associations were by Pinus (pine), which prefers clay, mica, and mixed soils while avoiding silica soils in the Coosa Valley. Quercus nigra (water oak) prefers siliceous soils in this region (Figure 5-4). 136
Coosa Valley Mineralogy
4.00 3.48
3.00 2.88
2.07 1.96 2.00 1.17 1.05 kaolinitic 1.00 0.55 0.41 0.31 micaceous mixed 0.00 siliceous -0.26 -0.48 -1.00 -1.10 -1.32 -2.00 -1.56
-2.40 -2.29 -3.00 Fagus grandifola Pinus Quercus nigra Quercus stellata
Figure 5–4: Standardized Residuals for Coosa Valley Mineralogy
137
Table 5–4: Species frequency for Coosa Valley mineralogy Kaolinitic Count Frequency Micaceous Count Frequency Mixed Count Frequency Silecous Count Frequency Acer saccharinum 2 0.00 Carya 90.08 Acer negundo 2 0.00 Aceraceae 2 0.00 Aceraceae 20 0.01 Castanea 20.02Acer saccharinum 4 0.00 Carpinus caroliniana 1 0.00 Annonaceae 1 0.00 Fagus grandifola 30.03 Aceraceae 9 0.00 Carya 70 0.11 Bitula nigra 3 0.00 Nyssa sylvatica 50.05Carpinus caroliniana 10 0.00 Castanea 15 0.02 Carpinus caroliniana 2 0.00 Pinus 61 0.57 Carya 233 0.10 Cercis canadensis 1 0.00 Carya 163 0.09 Quercus marilandica 70.07 Castanea 52 0.02 Cornus florida 1 0.00 Castanea 54 0.03 Quercus nigra 20.02Castanea pumila 1 0.00 Diospyros virginiana 1 0.00 Castanea pumila 3 0.00 Quercus rubra 70.07 Cornus florida 13 0.01 Fagus grandifola 1 0.00 Cornus florida 16 0.01 Quercus stellata 40.04Diospyros virginiana 7 0.00 Fraxinus 4 0.01 Diospyros virginiana 7 0.00 Quercus velutina 70.07Fagus grandifola 12 0.01 Juglans 1 0.00 Fagus grandifola 7 0.00 107 1.00 Fraxinus 22 0.01 Malus angustifolia 1 0.00 Fraxinus 13 0.01 Ilex 3 0.00 Nyssa sylvatica 16 0.02 Ilex 1 0.00 Juniperus virginiana 1 0.00 Oxydendrum arboreum 4 0.01 Juglans nigra 1 0.00 Moraceae 6 0.00 Pinus 312 0.48 Juniperus virginiana 1 0.00 Nyssa sylvatica 42 0.02 Populus 2 0.00 Magnolia acuminata 1 0.00 Oxydendrum arboreum 12 0.01 Prunus persica 1 0.00 Moraceae 3 0.00 Pinus 1006 0.45 Quercus 2 0.00 Nyssa sylvatica 41 0.02 Platanus occidentalis 3 0.00 Quercus alba 19 0.03 Oxydendrum arboreum 11 0.01 Populus 10 0.00 Quercus marilandica 23 0.04 Persea 1 0.00 Prunus americana 1 0.00 Quercus phellos 2 0.00 Pinus 907 0.49 Prunus serotina 3 0.00 Quercus rubra 25 0.03 Populus 14 0.01 Quercus 1 0.00 Quercus stellata 78 0.12 Prunus americana 1 0.00 Quercus alba 54 0.02 Quercus velutina 61 0.09 Quercus 7 0.00 Quercus marilandica 106 0.05 Sassafras albidum 1 0.00 Quercus alba 37 0.02 Quercus nigra 6 0.00 Tilia americana 2 0.00 Quercus marilandica 111 0.06 Quercus phellos 1 0.00 Ulmus 3 0.00 Quercus nigra 7 0.00 Quercus rubra 117 0.05 649 1.00 Quercus phellos 3 0.00 Quercus stellata 279 0.12 Quercus rubra 106 0.05 Quercus velutina 215 0.10 Quercus stellata 158 0.08 Sassafras albidum 9 0.00 Quercus velutina 146 0.08 Tilia americana 1 0.00 Sassafras albidum 6 0.00 Ulmus 8 0.00 Tilia americana 4 0.00 Ulmus rubra 3 0.00 Ulmus 5 0.00 2252 1.00 Ulmus rubra 1 0.00 Vaccinium arboreum 1 0.00 1865 1.00
138
I have defined the Piedmont as having two physiographic subdivisions in this study, the Ashland Plateau and the Opelika Plateau. There are no significant topographic differences between the two sections. Consequently, I analyzed the subdivisions by soil mineralogy only. The species distribution for the Piedmont is listed in table 5-5. The Ashland Plateau is dominated by Pinus (pine) at 39 %.
Carya (hickory) at 11%, Quercus stellata (post oak) at 12%, and Quercus rubra
(red oak) at 10% are the next most frequent species. The Opelika Plateau is dominated by 35% Pinus (pine), 17% Quercus stellata (post oak), 13% Carya
(hickory), and 10% Quercus velutina (black oak). Aceraceae (maple), Castanea
(chestnut), Fagus grandifola (beech), Oxydendrum arboreum (sourwood), Persea
(bay), Pinus (pine) and Quercus rubra (red oak) all had significant preference for the Ashland Plateau. The Opelika plateau was preferred by Carya (hickory),
Quercus stellata (post oak), and Quercus velutina (black oak). Almost all of these species shows a significant and strong preference for one or the other physiographic subdivision in the Piedmont region (Figure 5-5).
Soils in the Piedmont are kaolinitic, micaceous, mixed, and siliceous. Tree species on the Ashland Plateau soils are described in table 5-6. Kaolinitic soils are dominated by 40% Pinus (pine). The species with the next highest frequencies are Quercus stellata (post oak) with 10%, Quercus rubra (red oak) with 9%, and Carya (hickory) with 9%. Micaceous soils are dominated by 34%
Pinus (pine) and Quercus stellata (post oak) at 14%. Mixed soils are dominated by Pinus (pine) at 39% and Quercus stellata (post oak) and Quercus rubra (red 139 oak) both at 12%. Last, the siliceous soils in the Ashland Plateau are greatly dominated by Pinus at 60%. The next highest frequency is Qeurcus stellata at
7%.
Statistically significant soil preferences in the Ashland Plateau are illustrated in Figure 5-6. The strongest associations were with the micaceous and siliceous soils. Quercus rubra and Quercus stellata had the strongest association with micaceous soils whereas Aceraceae (maple), Persea (bay), and Pinus had strong associations with silica-based soils.
The Opelika Plateau soils are similar to those of the Ashland Plateau.
Kaolinitic soils are dominated by Pinus at 27% and Quercus stellata at 21%.
Micaceous soils are dominated by Pinus at 45%, Quercus stellata at 10%, and
Quercus velutina at 11%. Mixed soils are both dominated by 46% Pinus and 13%
Quercus stellata. Lastly, siliciceous soils are dominated by 41% Pinus and 13%
Quercus stellata.
The Opelika Plateau soils are listed in Table 5-7. Pinus (27%) dominates the kaolinitic soils with Quercus stellata (21%) next in frequency. Pinus is also highest on micaceous (45%), mixed (46%), and siliceous (41%). Quercus velutina and stellata were the next most frequent species on these soils.
Significant soil class associations are illustrated in figure 5-7. Carya,
Quercus stellata, and Quercus velutina have a strong preference for kaolinitic soils. Micaceous soils are preferred by Carpinus caroliniana (ironwood), Cornus florida (dogwood), Fagus grandifola (beech), Pinus, and Quercus phellos (willow 140 oak). Pinus, Quercus falcatta, and Quercus marilandica had strong associations with mixed soils whereas Pinus, Quercus alba, and Quercus rubra had strong associations with siliceous soils 141
Piedmont Physiographic Divisions
4.00 2.90 2.61 3.00 2.37 2.39 1.99 1.96 2.00
1.00 Ashland Plateau 0.00 Opelika Plateau
-1.00
-1.49 -2.00 -1.78 -1.79 -1.96 -2.17 -3.00 -2.61 Aceraceae Carya Castanea Fagus grandifola Oxydendrum Persea arboreum
Figure 5–5: Standard Residuals for Piedmont Physiographic Divisions 142
8.00
5.59 6.00 4.93 4.26 4.00 2.91
2.00 1.29 1.21
0.00
-0.97 -2.00 -1.61 -2.18 -4.00 -4.19
-6.00 -5.68 -6.58 -8.00 Pinus Populus Quercus nigra Quercus rubra Quercus stellata Quercus velutina
Figure 5–5: Standard Residuals for Piedmont Physiographic Divisions Continued 143
Table 5–5: Species frequency for Piedmont physiographic districts Ashland Plateau Count Frequency Opelika Plateau Count Frequency Acer saccharinum Count 12 0.00 Acer saccharinum Count 8 0.00 Aceraceae Count 109 0.01 Aceraceae Count 89 0.01 Bitula nigra Count 12 0.00 Anacardiaceae Count 2 0.00 Carpinus caroliniana Count 28 0.00 Bitula nigra Count 9 0.00 Carya Count 994 0.11 Carpinus caroliniana Count 31 0.00 Castanea Count 380 0.04 Carya Count 1576 0.13 Castanea pumila Count 18 0.00 Castanea Count 377 0.03 Celtis Count 3 0.00 Castanea pumila Count 32 0.00 Cercis canadensis Count 3 0.00 Celtis Count 1 0.00 Clethraceae Count 2 0.00 Cercis canadensis Count 1 0.00 Cornus florida Count 111 0.01 Clethraceae Count 4 0.00 Cupressaceae Count 1 0.00 Cornus florida Count 126 0.01 Diospyros virginiana Count 29 0.00 Diospyros virginiana Count 42 0.00 Fagus grandifola Count 112 0.01 Euonmus atropurpureus Count 1 0.00 Fraxinus Count 55 0.01 Fagus grandifola Count 77 0.01 Gleditsia triacanthos Count 2 0.00 Fraxinus Count 63 0.01 Ilex Count 17 0.00 Hamamelis virginiana Count 1 0.00 Juglans Count 1 0.00 Ilex Count 15 0.00 Juniperus virginiana Count 1 0.00 Juglans nigra Count 1 0.00 Liquidamber styraciflua 71 0.00 Liquidamber styraciflua 64 0.00 Magnolia acuminata Count 7 0.00 Magnolia acuminata Count 7 0.00 Moraceae Count 11 0.00 Moraceae Count 6 0.00 Nyssa Count 153 0.02 Nyssa Count 208 0.02 Oxydendrum arboreum Count 69 0.01 Oxydendrum arboreum Count 33 0.00 Persea Count 56 0.01 Persea Count 30 0.00 Pinus Count 3559 0.39 Pinus Count 4330 0.35 Platanus occidentalis Count 4 0.00 Platanus occidentalis Count 1 0.00 Populus Count 93 0.01 Populus Count 89 0.01 Prunus persica Count 4 0.00 Prunus americana Count 1 0.00 Prunus serotina Count 4 0.00 Prunus persica Count 3 0.00 Quercus Count 11 0.00 Prunus serotina Count 3 0.00 Quercus alba Count 248 0.03 Quercus Count 25 0.00 Quercus marilandica Count 261 0.03 Quercus alba Count 311 0.03 Quercus nigra Count 18 0.00 Quercus marilandica Count 360 0.03 Quercus phellos Count 30 0.00 Quercus nigra Count 51 0.00 Quercus rubra Count 1033 0.11 Quercus phellos Count 35 0.00 Quercus stellata Count 1077 0.12 Quercus rubra Count 898 0.08 Quercus velutina Count 489 0.05 Quercus stellata Count 2027 0.17 Salicaceae Count 3 0.00 Quercus velutina Count 1172 0.10 Sassafras albidum Count 44 0.00 Salicaceae Count 2 0.00 Tilia americana Count 12 0.00 Salix Count 1 0.00 Ulmus Count 18 0.00 Sambucus Count 1 0.00 Ulmus rubra Count 1 0.00 Sassafras albidum Count 70 0.01 Total 9166 1.00 Tilia americana Count 9 0.00 Ulmus Count26 0.00 Viburnum rufidulum Count 1 0.00 Total 12220 1.00
144
Ashland Plateau Mineralogy
8.00 7.32
6.00
4.00 3.44 3.16 2.16 2.09 1.75 kaolinitic 2.00 1.36 1.38 1.50 0.27 0.99 1.04 micaceous 0.27 0.07 -0.62 -0.21 mixed 0.00 siliceous 1.44 -0.24 -0.20 0.33 -0.84 -2.00 -1.15 -1.55 -1.78 -1.52 -2.44 -2.34 -2.76 -2.70 -4.00 -3.69 -3.80 -4.51 -6.00 Aceraceae Carya Castanea Persea Pinus Quercus Quercus Quercus rubra stellata velutina
Figure 5–6: Standard Residuals for Ashland Plateau Mineralogy
145
Table 5–6: Species frequency for Ashland Plateau mineralogy Kaolinitic Count Frequency Micaceous Count Frequency Mixed Count Frequency Siliceous Count Frequency Acer saccharinum 2 0.001 Acer saccharinum 4 0.002 Acer saccharinum 4 0.001 Acer saccharinum 2 0.008 Aceraceae 24 0.016 Aceraceae 33 0.016 Aceraceae 42 0.008 Aceraceae 7 0.026 Bitula nigra 2 0.001 Bitula nigra 5 0.002 Bitula nigra 4 0.001 Bitula nigra 1 0.004 Carpinus caroliniana 7 0.005 Carpinus caroliniana 8 0.004 Carpinus caroliniana 11 0.002 Carya 10 0.038 Carya 141 0.094 Carya 226 0.107 Carya 605 0.118 Castanea 2 0.008 Castanea 76 0.050 Castanea 89 0.042 Castanea 210 0.041 Cornus florida 2 0.008 Castanea pumila 1 0.001 Castanea pumila 6 0.003 Castanea pumila 10 0.002 Fagus grandifola 5 0.019 Celtis 2 0.001 Cercis canadensis 1 0.000 Celtis 1 0.000 Fraxinus 3 0.011 Cornus florida 25 0.017 Cornus florida 24 0.011 Cercis canadensis 2 0.000 Gleditsia triacanthos 2 0.008 Diospyros virginiana 5 0.003 Diospyros virginiana 9 0.004 Clethraceae 2 0.000 Ilex 1 0.004 Fagus grandifola 19 0.013 Fagus grandifola 27 0.013 Cornus florida 58 0.011 Moraceae 2 0.008 Fraxinus 13 0.009 Fraxinus 12 0.006 Diospyros virginiana 14 0.003 Nyssa sylvatica 7 0.026 Ilex 1 0.001 Ilex 5 0.002 Fagus grandifola 55 0.011 Oxydendrum arboreum 1 0.004 Moraceae 2 0.001 Juglans 1 0.000 Fraxinus 25 0.005 Persea 6 0.023 Nyssa sylvatica 35 0.023 Moraceae 2 0.001 Ilex 8 0.002 Pinus 162 0.609 Oxydendrum arboreum 13 0.009 Nyssa sylvatica 47 0.022 Juniperus virginiana 1 0.000 Populus 6 0.023 Persea 7 0.005 Oxydendrum arboreum 19 0.009 Magnolia acuminata 7 0.001 Prunus persica 1 0.004 Pinus 604 0.401 Persea 8 0.004 Moraceae 4 0.001 Quercus alba 4 0.015 Platanus occidentalis 3 0.002 Pinus 718 0.341 Nyssa sylvatica 126 0.025 Quercus marilandica 6 0.023 Populus 10 0.007 Populus 19 0.009 Oxydendrum arboreum 35 0.007 Quercus phellos 4 0.015 Prunus persica 2 0.001 Prunus serotina 1 0.000 Persea 33 0.006 Quercus rubra 7 0.027 Prunus serotina 1 0.001 Quercus 3 0.001 Pinus 1996 0.391 Quercus stellata 19 0.071 Quercus 2 0.001 Quercus alba 57 0.027 Platanus occidentalis 1 0.000 Quercus velutina 4 0.015 Quercus alba 52 0.035 Quercus marilandica 48 0.023 Populus 57 0.011 Ulmus 2 0.008 Quercus marilandica 50 0.033 Quercus nigra 1 0.000 Prunus persica 1 0.000 266 1.000 Quercus nigra 5 0.003 Quercus phellos 10 0.005 Prunus serotina 2 0.000 Quercus rubra 154 0.103 Quercus rubra 273 0.130 Quercus 6 0.001 Quercus stellata 145 0.096 Quercus stellata 303 0.144 Quercus alba 132 0.026 Quercus velutina 89 0.059 Quercus velutina 131 0.062 Quercus marilandica 153 0.030 Sassafras albidum 10 0.007 Salicaceae 2 0.001 Quercus nigra 11 0.002 Tilia americana 2 0.001 Sassafras albidum 8 0.004 Quercus phellos 16 0.003 Ulmus 3 0.002 Tilia americana 3 0.001 Quercus rubra 590 0.132 1507 1.000 Ulmus 1 0.000 Quercus stellata 598 0.117 Ulmus rubra 1 0.000 Quercus velutina 248 0.049 2105 1.000 Salicaceae 1 0.000 Sassafras albidum 26 0.005 Tilia americana 6 0.001 Ulmus 9 0.002 5110 1.000
146
Opelika Plateau Mineralogy
12.00 9.58 10.00 8.00 5.89 6.00 4.84 4.35 3.58 4.03 4.00 2.52 1.88 kaolinitic 2.00 0.72 0.24 micaceous -0.18 -0.21 0.00 mixed 1.53 -0.20 -0.69 siliceous -2.00 -0.89 -1.20 -1.32 -1.11 -2.03 -4.00 -2.61 -4.05 -6.00 -4.76 -8.00 -10.00 -8.57 Carpinus Carya Castanea Cornus florida Fagus Pinus caroliniana grandifola
8.00
6.00 5.95
4.00 3.48 3.09 2.73 2.65 2.08 2.00 1.02 1.16-3.59 0.74 -0.65
0.00 -0.99 0.75 -0.13 -0.95 -0.95 -1.00 -2.00 -1.43
-2.75 -3.01 -4.00 -4.17 -4.12 -4.74 -6.00 -5.35 Quercus alba Quercus Quercus phellos Quercus rubra Quercus stellata Quercus velutina marilandica
Figure 5–7: Standardized Residuals of Opelika Plateau Mineralogy
147
Table 5–7: Species frequency for Opelika Plateau mineralogy kaolinitic Count Frequency micaceous Count Frequency mixed Count Frequency siliceous Count Frequency Acer saccharinum 2 0.00 Acer saccharinum 5 0.01 Aceraceae 17 0.01 Aceraceae 29 0.01 Aceraceae 34 0.01 Aceraceae 4 0.01 Anacardiaceae 10.00 Bitula nigra 1 0.00 Anacardiaceae 1 0.00 Bitula nigra 2 0.00 Bitula nigra 30.00Carpinus caroliniana 6 0.00 Carpinus caroliniana 13 0.00 Carpinus caroliniana 5 0.01 Carpinus caroliniana 30.00 Carya 285 0.10 Carya 978 0.16 Carya 48 0.10 Carya 212 0.09 Castanea 91 0.03 Castanea 205 0.03 Castanea 5 0.01 Castanea 65 0.03 Castanea pumila 7 0.00 Castanea pumila 10 0.00 Castanea pumila 2 0.00 Castanea pumila 60.00 Celtis 1 0.00 Cercis canadensis 1 0.00 Cornus florida 14 0.03 Cornus florida 16 0.01 Cornus florida 40 0.01 Clethraceae 4 0.00 Diospyros virginiana 3 0.01 Diospyros virginiana 90.00Diospyros virginiana 6 0.00 Cornus florida 48 0.01 Fagus grandifola 7 0.01 Fagus grandifola 19 0.01 Euonmus atropurpureus 1 0.00 Diospyros virginiana 21 0.00 Fraxinus 2 0.00 Fraxinus 15 0.01 Fagus grandifola 15 0.01 Fagus grandifola 25 0.00 Ilex 2 0.00 Ilex 10.00 Fraxinus 10 0.00 Fraxinus 32 0.01 Nyssa sylvatica 14 0.03 Magnolia acuminata 20.00 Ilex 1 0.00 Ilex 10 0.00 Oxydendrum arboreum 3 0.01 Nyssa 10.00Magnolia acuminata 1 0.00 Magnolia acuminata 4 0.00 Persea 1 0.00 Nyssa sylvatica 53 0.02 Moraceae 5 0.00 Moraceae 1 0.00 Pinus 216 0.45 Oxydendrum arboreum 70.00 Nyssa 6 0.00 Nyssa sylvatica 119 0.02 Populus 2 0.00 Persea 30.00 Nyssa sylvatica 62 0.02 Oxydendrum arboreum 12 0.00 Prunus persica 1 0.00 Pinus 1037 0.46 Oxydendrum arboreum 7 0.00 Persea 14 0.00 Quercus 2 0.00 Populus 17 0.01 Persea 5 0.00 Pinus 1661 0.27 Quercus alba 15 0.03 Prunus serotina 10.00 Pinus 1155 0.41 Populus 40 0.01 Quercus marilandica 4 0.01 Quercus 30.00 Populus 20 0.01 Prunus americana 1 0.00 Quercus nigra 1 0.00 Quercus alba 46 0.02 Quercus 3 0.00 Prunus persica 2 0.00 Quercus phellos 5 0.01 Quercus marilandica 87 0.04 Quercus alba 98 0.03 Prunus serotina 1 0.00 Quercus rubra 11 0.03 Quercus nigra 10 0.00 Quercus marilandica 95 0.03 Quercus 15 0.00 Quercus stellata 48 0.10 Quercus phellos 30.00 Quercus nigra 10 0.00 Quercus alba 138 0.02 Quercus velutina 55 0.11 Quercus rubra 166 0.07 Quercus phellos 9 0.00 Quercus marilandica 163 0.03 Sassafras albidum 4 0.01 Quercus stellata 294 0.13 Quercus rubra 241 0.08 Quercus nigra 22 0.00 Tilia americana 1 0.00 Quercus velutina 165 0.07 Quercus stellata 359 0.13 Quercus phellos 11 0.00 Ulmus 2 0.00 Sassafras albidum 90.00 Quercus velutina 220 0.08 Quercus rubra 446 0.08 Total 485 1.00 Tilia americana 10.00 Sassafras albidum 19 0.01 Quercus stellata 1291 0.21 Ulmus 20.00 Tilia americana 1 0.00 Quercus velutina 695 0.11 Total 2274 1.00 Ulmus 7 0.00 Salicaceae 1 0.00 Viburnum rufidulum 1 0.00 Sambucus 1 0.00 Total 2817 1 Sassafras albidum 34 0.01 Tilia americana 6 0.00 Ulmus 12 0.00 Total 6074 1.00
148
The last physiographic region in the study area is the Coastal Plain. The
Coastal Plain in this area has three major physiographic subdivisions, the Black
Prairie (Selma chalk), the Fall Line Hills, and the Red Hills. The species frequencies for each of these subdivisions are listed in table 5-8. Pinus (40%),
Quercus stellata (9%), Quercus rubra (8%), and Carya (8%) dominate the Black
Prairie. The Fall Line Hills are covered by 67% Pinus. The next most frequent species is Quercus marilandica at 6%. The Red Hills are 44% Pinus and 7%
Quercus rubra.
The statistically significant physiographic-species associations for the
Coastal plain are illustrated in figure 5-8. Some of the stronger associations are
Carpus caroliniana, Carya, Fagus grandifola, and Quercus rubra for the chalky
Black Prairie. Pinus and Quercus marilandica have strong associations for the
Fall Line Hills relative to the Black Prairie and the Red Hills.
149
Coastal Plain Physiography
10.00 8.25 8.00 6.00 4.05 4.23 3.76 4.00 2.12 2.48 1.71 1.68 1.25 Black Prairie 2.00 0.72 0.80 0.51 0.19 0.21 Fall Line Hills 0.00 Red Hills -2.00 -1.56 -4.00 -2.62 -2.91 -2.95 -6.00 -4.64 -5.71 -5.36 -8.00 Carpinus Carya Castanea Cornus Diospyros Fagus Fraxinus caroliniana florida virginiana grandifola
25.00
19.10 20.00
15.00
10.00
3.78 4.35 5.00 2.68 2.24 2.68 2.04 2.24 0.85 0.09 -0.35 0.45 0.00 -0.51 -1.69 -0.92 -1.00 -1.72 -5.00 -3.21 -6.33 -10.00 -11.49 -10.69 -15.00 Ilex Magnolia Nyssa Persea Pinus Populus Quercus alba grandiflora sylvatica
Figure 5–8: Standardized Residuals of Coastal Plain Physiographic Regions 150
8.00 5.81 6.00 4.56 4.04 4.04 4.00 2.61 2.72 2.09 2.00 1.28 0.98 0.54 0.00 -0.16 -2.00 -0.58 -1.37 -1.40-1.58 -1.98 -2.49 -2.79 -4.00 -3.33 -6.00
-8.00 -7.35 -7.27 -10.00 Quercus Quercus nigra Quercus phellos Quercus rubra Quercus stellata Quercus velutina Ulmus marilandica
Figure 5–7: Standardized Residuals of Coastal Plain Physiographic Regions Continued
151
Table 5–8: Species frequency for Coastal Plain physgiographic districts Black Prairie Count Frequenc Fall Line Hills Count Frequency Acer negundo 1 0.000 Acer negundo 2 0.000 Acer saccharinum 4 0.001 Acer saccharinum 1 0.000 Aceraceae 24 0.008 Aceraceae 62 0.010 Anacardiaceae 1 0.000 Bitula nigra 11 0.002 Annonaceae 1 0.000 Carpinus caroliniana 26 0.004 Bitula nigra 2 0.001 Carya 173 0.027 Carpinus caroliniana 44 0.015 Castanea 22 0.003 Carya 240 0.080 Castanea pumila 8 0.001 Carya illinoiensis 2 0.001 Celtis 15 0.002 Castanea 26 0.009 Citrus 1 0.000 Castanea pumila 6 0.002 Clethraceae 1 0.000 Celtis 4 0.001 Cornus florida 33 0.005 Clethraceae 1 0.000 Crataegus 1 0.000 Cornus florida 39 0.013 Crataegus spathulata 1 0.000 Cupressaceae 1 0.000 Cupressaceae 3 0.000 Diospyros virginiana 8 0.003 Diospyros virginiana 8 0.001 Euonmus atropurpureus 6 0.002 Fagus grandifola 51 0.008 Fagus grandifola 94 0.031 Fraxinus 15 0.002 Fraxinus 31 0.010 Ilex 41 0.006 Ilex 60 0.020 Juglans 2 0.000 Juglans 3 0.001 Juglans nigra 1 0.000 Juniperus virginiana 3 0.001 Liquidamber styraciflua 96 0.015 Liquidamber styraciflua 56 0.019 Magnolia acuminata 2 0.000 Magnolia grandiflora 14 0.005 Magnolia grandiflora 6 0.001 Moraceae 10 0.003 Moraceae 15 0.002 Nyssa 76 0.025 Nyssa 113 0.017 Oxydendrum arboreum 15 0.005 Oxydendrum arboreum 28 0.004 Persea 52 0.017 Persea 64 0.010 Pinus 1216 0.404 Persea borbonia 2 0.000 Platanus occidentalis 2 0.001 Pinus 4350 0.669 Populus 28 0.009 Platanus occidentalis 7 0.001 Prunus americana 3 0.001 Populus 26 0.004 Prunus persica 4 0.001 Prunus 1 0.000 Prunus serotina 5 0.002 Prunus persica 5 0.001 Quercus 15 0.005 Prunus serotina 3 0.000 Quercus alba 127 0.042 Quercus 29 0.004 Quercus marilandica 123 0.041 Quercus alba 104 0.016 Quercus nigra 24 0.008 Quercus marilandica 411 0.063 Quercus phellos 5 0.002 Quercus nigra 41 0.006 Quercus rubra 249 0.082 Quercus phellos 12 0.002 Quercus stellata 266 0.088 Quercus rubra 211 0.032 Quercus velutina 83 0.028 Quercus stellata 334 0.051 Salicaceae 1 0.000 Quercus velutina 114 0.018 Sassafras albidum 14 0.005 Salicaceae 2 0.000 Tilia americana 5 0.002 Sambucus 2 0.000 Ulmus 16 0.005 Sassafras albidum 22 0.003 Ulmus rubra 1 0.000 Tilia americana 3 0.000 Total 3011 1.000 Ulmus 18 0.003 Ulmus rubra 1 0.000 Total 6500 1.000
152
Table 5–8: Species Frequency Counts for Coastal Plain Physiograhic Region Continued Red Hills Count Frequency Acer negundo 10 0.001 Acer saccharinum 13 0.002 Aceraceae 70 0.009 Annonaceae 1 0.000 Bitula nigra 2 0.000 Carpinus caroliniana 60 0.008 Carya 365 0.048 Carya illinoiensis 1 0.000 Castanea 77 0.010 Castanea pumila 13 0.002 Celtis 19 0.002 Cercis canadensis 8 0.001 Clethraceae 1 0.000 Cornus florida 93 0.012 Crataegus 5 0.001 Cupressaceae 2 0.000 Diospyros virginiana 26 0.003 Euonmus atropurpureus 3 0.000 Euonymous atropurpureu 5 0.001 Fagus grandifola 190 0.025 Fraxinus 114 0.015 Hamamelis virginiana 1 0.000 Ilex 101 0.013 Juglans 5 0.001 Juniperus virginiana 1 0.000 Lagerstroemia 1 0.000 Liquidamber styraciflua 125 0.016 Magnolia acuminata 3 0.000 Magnolia grandiflora 17 0.002 Malus angustifolia 2 0.000 Moraceae 14 0.002 Nyssa 131 0.017 Oxydendrum arboreum 39 0.005 Persea 82 0.011 Persea borbonia 1 0.000 Pinus 3352 0.437 Pinus glabra 2 0.000 Platanus occidentalis 6 0.001 Populus 51 0.007 Prunus americana 4 0.001 Prunus persica 4 0.001 Prunus serotina 6 0.001 Quercus 28 0.004 Quercus alba 354 0.046 Quercus marilandica 316 0.041 Quercus nigra 77 0.010 Quercus phellos 39 0.005 Quercus rubra 571 0.075 Quercus stellata 825 0.108 Quercus velutina 247 0.032 Salicaceae 3 0.000 Sassafras albidum 44 0.006 Tilia americana 51 0.007 Ulmus 73 0.010 Ulmus rubra 9 0.001 Vaccinium arboreum 9 0.001 Viburnum rufidulum 1 0.000 Total 7673 1.000
153
Soil parent material is a significant defining variable for physiographic subdivisions in the Coastal Plain of the study area. The major soils of the Black
Prairie are siliceous and mixed soils. The frequency of trees on each are listed in table 5-9. Pinus again dominates the mixed soil region with 39% while the next most dominant species is Quercus stellata at 10%. Pinus dominates the siliceous soils at 42% and Quercus stellata follows with 9%.
Major class preferences by soil type in the Black Prairie are illustrated in figure 5-9. The statistically significant species seem to be very prefernential to one soil type or the other. Carya and Moracea (mulberry) prefer mixed soils.
However, Ilex (holly), Oxydendrum arboreum (sourwood), and Quercus marilandica (black jack oak) prefer the siliceous soils.
The Fall Line Hills are characterized by clays, silicas, and mixed soils.
Silicas dominate. The tree species frequencies for this subdivision of the Coastal
Plain are listed in table 5-10. The sample for kaolinitic soils was very small (32); nevertheless, Pinus contributed 72% of the trees. Mixed soils were covered by
66% Pinus with the next highest frequency of trees being Quercus marilandica at
7%. Siliceous soils were dominated by Pinus (69%) and Quercus marilandica
(6%).
Soil mineralogy class preferences are graphed in figure 5-10. Pinus and
Quercus nigra had the only preference, although weak, for clay soils.
Oxydendrum arboreum and Pinus had a relative prefernce for siliceous soils over mixed soils whereas Aceraceae and Quercus velutina show a preference for 154 mixed soils. All of these associations are weak relative to the rest of the results in this study.
Last, the Red Hills species frequencies are listed in table 5-11 by soil mineralogy. Soils in this region are predominanty mixed, montmorilinitic, and siliceous. Mixed soils were dominated by Pinus at 39% and Quercus stellata at
14%. Montmorilinitic soils in this region showed the only instance where Pinus was not the dominant species. Quercus stellata and Quercus alba were the two most dominant species constituting 20% and 10% respectively. Pinus dominated siliceous soils at 49%. The next most represented species were Quercus stellata at 9% and Quercus alba at 8%.
Species associations for the statistically significant classes are represented in figure 5-11. The strongest associations were Fraxinus, Quercus alba, Quercus nigra, Quercus stellata, Tilia americana, and Ulmus preference for montmorilinitic soils. Pinus and Quercus stellata showed some preference for siliceous soils.
Discussion
Pine and oak collectively comprised 76.6% of all the witness trees.
Longleaf pine is widely distributed on the Coastal Plain, Piedmont, and Ridge and Valley of the southern U.S. (Braun 1950; Burns and Honkala 1990; Marks and Harcombe 1991). It occurs on a wide range of sites, including wet to dry and sandy plains to rocky ridges, and soils including sands and clays derived from 155 quartzite, sandstone, granite, shale, sandstone, schist, and others (Burns and
Honkala 1990). Loblolly pine grows on the Coastal Plain, Piedmont Plateau, and
Ridge and Valley. Shortleaf pine has the widest range of all the southeastern pines, and is the least exacting in terms of its requirements for temperature and moisture. It is more abundant than loblolly pine on dry, nutrient poor sites in the
Piedmont. Shortleaf pine grows poorly in high calcium, high pH soils (Burns and
Honkala 1990). In the original forests of the Talladega Mountains of east-central
Alabama, it has been suggested that loblolly pine dominated bottomland and northern-lower slopes, shortleaf pine dominated southern-lower, middle elevation and southern-upper slopes, and longleaf pine co-dominated southern-upper slopes and dominated ridge tops (Shankman and Wills 1995). The wide array of sites that these three pine species can grow on results in them being dominant across the diverse topography of this study area.
Oaks occupied three of the top five positions across all physiographic provinces and landforms in the study area. Oak’s dominance was the mirror image of that of pine, oak being highest in the Piedmont and lowest in the
Coastal Plain. The climate, sandy soils, and high frequency and/or intensity of fire on the Coastal Plain were apparently more conducive to pine than oak species (Harper 1943; Garren 1943). A ranking of oak in terms of their percent density in the entire witness tree record is post oak, red oak (including southern red and northern red oak), black oak, blackjack oak, and white oak. Post oak was the dominant oak in all three physiographic provinces. Post oak, blackjack oak 156 and southern red oak are abundant trees on dry, sandy, nutrient poor soils in the
Coastal Plain and Piedmont (Burns and Honkala 1990). A variety of southern red oak, cherrybark oak (Q. falcata var. pagodifolia Ell.), is associated with bottomlands and swamps on the Coastal Plain. Black oak and white oak are among the most widely distributed oaks in the eastern U.S. Black oak is a highly versatile species that grows on almost all soils and site conditions (Burns and
Honkala 1990, Shankman and Wills 1995). In this study, black oak had a strong negative association with the Coastal Plain. White oak grows on a wide range of soil and site conditions, but is thought to have a positive association for limestone, valley floors sites (Abrams and Ruffner 1995; Abrams and McCay
1996). White oak was fairly evenly distributed across all three physiographic provinces in their study, but did have a slight positive association for river bottoms and the Red Hills on the Coastal Plain. In the Talladega Mountains of
Alabama, white oak witness trees had their highest density on bottomland and lower slope sites (Shankman and Wills 1995). Most upland oaks benefit from periodic, low intensity burning, which retards the invasion of later successional tree species, reduces the thickness of the organic seedbed, and increases light levels to the forest floor (Abrams 1992).
The pre-European forests in northern Florida were 77% dominated by pine, with longleaf pine followed by slash, loblolly, and shortleaf pine probably being the most important (Schwartz 1994). In that region, pine dominated almost all sites except for floodplains along the major rivers. The dominant presettlement 157 oak species in northern Florida were blackjack oak, red oak (southern red, black oak, turkey oak (Q. laevis Walt.), and shumard oak (Q. shumardii Buckl.)), and water oak. Collectively, however, oak only accounted for 6% of all witness trees tallied (Schwartz 1994), compared to 33% in this study. Upland pine-oak forest and flatwoods dominated most of the north-central Florida Panhandle except for river bluff, floodplain and bottomland sites, which were comprised of magnolia- beech, bay (Persea borbonia L., Godonia lasianthus, Magnolia virginiana L. and
Pinus palustris)-gum-oak, and bay-holly-beech, respectively (Delcourt and
Delcourt 1977).
After oak and pine, hickory was a recurring dominant and all three physiographic sections in this study. It was also in the top five rank species across all seven landforms as well as in river bottoms. The dominant hickory species in the study area include pignut hickory (Carya glabra Mill.) and mockernut hickory (C. tomentosa Poir. Nutt.) (Burns and Honkala 1990,
Schwartz 1994). Smaller amounts of bitternut hickory (C. cordiformis Wangeng.), pecan (C. illinoensis Wangenh.), and water hickory (C. aquatica (Michx. f.) Nutt.) could have also appeared in the witness tree record. All hickory was noted at the genus level in the witness tree record, except for the mention of three pecan trees, which, ironically, is outside of the present-day range of the species (Burns and Honkala 1990). Hickory had a strong positive association for the Piedmont and a negative association with the Coastal Plain. Most hickories grow on a wide variety of sites, including infertile to fertile soils, moist bottomland and lower 158 slopes, and dry ridge tops, side slopes, and sandy plains. Water hickory, as its name implies, occurs on riparian and bottomland sites in southern forests
(Schwartz 1994), and may have occurred in the river bottom sites of the study. In southern upland forests, hickories grow with longleaf pine, loblolly pine, and shortleaf pine, as well as in the oak-pine type. In the Talladega Mountains, hickory witness trees ranged from 2% to 14% density, being lowest on ridge tops and highest north-facing middle slopes (Shankman and Wills 1995). Hickory was about 10% of the witness trees in the original forests of the Georgia Piedmont, and was widely distributed across topographic gradient from floodplains to upper slopes (Nelson 1957; Cowell 1995). However, hickory accounted for less than one percent of the witness trees in northern Florida and was associated with the mesic to dry upland hardwood type (Schwartz 1994). The same factors that probably reduced the dominance of oak on the Coastal Plain, namely soils, climate, and frequent fire, may also apply to hickory.
The original range of chestnut included almost the entire Appalachian
Plateau, the Ridge and Valley, and the Piedmont (Braun 1950; Elias 1980). The pre-blight distribution of chestnut suggests that it is a calciphobe that mainly occurred on well-drained, acid loam soils, and had little success on the Coastal
Plain (Russell 1987). The relatively high density of chestnut witness trees on the
Piedmont and ridge sites and its low density on the Coastal Plain and in valleys in this study support this description. Chestnut's positive association of river bottom sites over uplands suggests that these soils were acidic sands and clays 159 and not derived from limestone (USDA 1998). However, river bottom soils of the
Coosa Valley were non-acidic and suggest that chestnut could occupy a fairly wide range of soil pH. Indeed, chestnut represented 4.1% of the witness trees on valley floors in the Ridge and Valley Province of east-central West Virginia
(Abrams and McCay 1996: 219). In the Georgia Piedmont, chestnut witness trees represented about 1-2% of forest types ranging from floodplains to ridge tops, and were mentioned as part of the oak-hickory forest (Nelson 1957; Cowell
1995). In the Talladega Mountains of east-central Alabama, chestnut witness tree density was 7-15% of upland landforms from northern lower slopes to ridge tops, and 2% of bottomlands (Shankman and Wills 1995).
Black gum was widely distributed across the study area, and was positively associated with the Coastal Plain and river bottoms and negatively associated with the Piedmont and Ridge and Valley. Black gum is a common species associated with creek bottoms in the Coastal Plain (Burns and Honkala
1990). In southern forests it occurs in upland oak and pine forests, and in bottomlands or cypress (Taxodium Rich.) swamps (Golden 1979; Marks and
Harcombe 1991). Swamp tupelo (Nyssa sylvatica var. biflora (Walt.) Sarg.) and water tupelo (N. aquatica L.) may not have been recorded in the witness tree record to any significant degree because surveyors were not required to survey corner sections in permanently standing water (Bourdo 1956; Schwartz 1994).
Sweet gum (Liquidamber styraciflua L.) also grows on a range of bottomland and riparian sites and it represented 0.8 to 2.0% of the witness trees in the three 160 physiographic provinces of the study region (Table 1). In the Talladega
Mountains, witness trees of black gum represented 4-5% and sweet gum 2-3% in bottomlands, lower slope and/or northern aspect sites (Shankman and Wills
1995). In addition, black gum represented 1-2% of upper slope and ridge top sites in that study. Similarly, black gum and sweet gum were about 3% of the witness trees on bottomlands and only 1% or less of upland slopes in the
Georgia Piedmont (Cowell 1995).
Beech and southern magnolia (Magnolia grandiflora) were a small component of the original forest but did exhibit a positive association with Coastal
Plain sites. Beech was the sixth rank dominant on river bottom sites in the study area, where it had the strongest affinity of all the witness tree species. Similar results were reported for the Talladega Mountains (Shankman and Wills 1995).
Magnolia-beech forests dominated the Apalachicola River Bluff on the Coastal
Plain of the north-central Florida (Delcourt and Delcourt 1977) and were part of the floodplain forests on the Coastal Plain of southeast Texas (Marks and
Harcombe 1981).
A 1970s study of the forest composition of the lower Alabama Piedmont, including the Ashland and Opelika Plateaus used in this study, identified ten different forest types, three dominated by pine (longleaf pine, loblolly pine, and pine-hardwood), five dominated by oak (white oak, chestnut oak, blackjack oak, oak-hickory, and mixed-oak), and two wet site types (swamp or bottomland)
(Golden 1979). The wet sites are dominated by sweet gum, water oak, red maple 161
(Acer rubrum L.), beech, yellow poplar (Liriodendron tulipifera L.) ironwood
(Carpinus caroliniana Walt.), and magnolia. Shortleaf pine and loblolly pine occurred in all ten forest types, but was most important in dry, upland types.
Black gum was also widely distributed on wet and dry sites, which is consistent with the results of this study. Among the hickory species, mockernut hickory was the most dominant, followed by pignut hickory. Bitternut hickory was generally restricted to wet sites (Golden 1979). In general, the presettlement data concur with the contemporary forest composition, with exception of chestnut oak dominated forests and the river bottom sites, which were dominated, instead, by pine, oak, and hickory in the witness tree data.
It is often thought that southeastern pine forests were maintained historically by recurring fire (Shankman and Wills 1995, Cowell 1995). In the absence of fire they would be invaded or replaced by hardwood species (Garren
1943, Braun 1950, Oosting 1942, Quarterman and Keever 1962). For example, fire suppression in north-central Florida is causing a shift from pine to hardwoods on sandy upland sites (Delcourt and Delcourt 1977). A shift from pine to hardwoods was also noted in the lower Alabama Piedmont, and this is occurring at a much more rapid rate on moist sites than dry sites (Golden 1979). In an old loblolly pine forest on the Coastal Plain of Virginia, there was a shift towards increasing dominance of black gum, sweet gum, and holly (Ilex opaca Ait.)
(Abrams and Black 2000). The higher dominance of hardwoods versus pine in bottomland sites in this study and in the Talladega Mountains of Alabama may be 162 due to the lower fire frequency in bottomlands (Shankman and Wills 1995). It is also possible that the higher percentage of pine and lower percentage of oak in the Ashland Plateau versus Opelika Plateau may be due to a combination of drier conditions or more frequent or intense fire in the higher elevations. The high incidence of fire on uplands in the southeast resulted by ignitions from lightning and Native American activity (Komarek 1974; Shankman and Wills 1995). In this study, I found a low density of later successional species, such as red maple, beech, black gum, magnolia, sweet gum, and holly throughout the original forest.
The overwhelming dominance of pine, oak, and hickory, and scarcity of fire sensitive tree species on the uplands is consistent with the fire hypothesis
(Abrams 1992, 1998; Shankman and Wills 1995). Indian occupation was much more intensive on the Ashland Plateau than on the Opelika Plateau (Ethridge
1996; Foster 2001; Knight et al 1984; Waselkov and Smith 2000). Since the
Indians often burned the forests near their towns, this may explain the higher prevalence of pine in the Ashland Plateau relative to the Opelika. 163
Black Prairie Mineralogy
2.00 1.44 1.33 1.32 1.50 1.21 1.00 0.89
0.50 mixed 0.00 siliceous -0.50
-1.00 -1.00 -1.10 -1.08 -1.50 -1.61 -2.00 -1.74 Carya Ilex Moraceae Oxydendrum Quercus marilandica arboreum
Figure 5–9: Standardized Residuals of Black Prairie Mineralogy
164
Table 5–9: Species frequencies for Black Prairie mineralogy mixed Count Frequency siliceous Count Frequency Acer negundo 1 0.001 Acer saccharinum 3 0.002 Acer saccharinum 1 0.001 Aceraceae 11 0.007 Aceraceae 13 0.010 Anacardiaceae 1 0.001 Bitula nigra 1 0.001 Annonaceae 1 0.001 Carpinus caroliniana 17 0.013 Bitula nigra 1 0.001 Carya 124 0.093 Carpinus caroliniana 27 0.017 Castanea 14 0.010 Carya 111 0.069 Castanea pumila 3 0.002 Carya illinoiensis 2 0.001 Celtis 3 0.002 Castanea 12 0.007 Clethraceae 1 0.001 Castanea pumila 3 0.002 Cornus florida 12 0.009 Celtis 1 0.001 Cupressaceae 1 0.001 Cornus florida 26 0.016 Diospyros virginiana 5 0.004 Diospyros virginiana 3 0.002 Euonmus atropurpureus 2 0.001 Euonmus atropurpureus 4 0.002 Fagus grandifola 42 0.031 Fagus grandifola 52 0.032 Fraxinus 15 0.011 Fraxinus 14 0.009 Ilex 15 0.011 Ilex 45 0.028 Juglans 2 0.001 Juglans 1 0.001 Juniperus virginiana 2 0.001 Magnolia grandiflora 10 0.006 Magnolia grandiflora 4 0.003 Moraceae 2 0.001 Moraceae 8 0.006 Nyssa 2 0.001 Nyssa sylvatica 64 0.048 Nyssa sylvatica 62 0.038 Oxydendrum arboreum 3 0.002 Oxydendrum arboreum 12 0.007 Persea 25 0.019 Persea 25 0.015 Pinus 517 0.387 Pinus 678 0.419 Platanus occidentalis 1 0.001 Populus 11 0.007 Populus 17 0.013 Prunus americana 1 0.001 Prunus americana 2 0.001 Prunus serotina 1 0.001 Prunus persica 3 0.002 Quercus 6 0.004 Prunus serotina 2 0.001 Quercus alba 64 0.040 Quercus 9 0.007 Quercus marilandica 82 0.051 Quercus alba 62 0.046 Quercus nigra 9 0.006 Quercus marilandica 39 0.029 Quercus phellos 4 0.002 Quercus nigra 11 0.008 Quercus rubra 136 0.084 Quercus phellos 1 0.001 Quercus stellata 138 0.085 Quercus rubra 108 0.081 Quercus velutina 43 0.027 Quercus stellata 127 0.095 Salicaceae 1 0.001 Quercus velutina 40 0.030 Sassafras albidum 6 0.004 Sassafras albidum 7 0.005 Tilia americana 2 0.001 Tilia americana 3 0.002 Ulmus 6 0.004 Ulmus 10 0.007 Ulmus rubra 1 0.001 1337 1.000 1620 1.000
165
Fall Line Hills Mineralogy
2.50
2.00 1.85 1.41 1.50 1.23 1.26 1.30 1.01 1.00 0.86
0.50 kaolinitic mixed 0.00 siliceous
-0.50 -0.36 -0.54 -1.00 -0.74 -1.17 -1.50 -1.26 -1.29 -1.20 -1.44 -2.00 Aceraceae Oxydendrum Pinus Quercus nigra Quercus velutina arboreum
Figure 5–10: Standardized Residuals of Fall Line Hills Mineralogy
Table 5–10: Species frequencies for Fall Line Hills mineralogy kaolinitic Count Frequency mixed Count Frequency Diospyros virginiana 1 0.031 Aceraceae 41 0.012 Fagus grandifola 1 0.031 Bitula nigra 8 0.002 Pinus 23 0.719 Carpinus caroliniana 14 0.004 Quercus alba 2 0.063 Carya 87 0.026 Quercus nigra 1 0.031 Castanea 14 0.004 Quercus stellata 3 0.094 Castanea pumila 5 0.002 Total 31 0.969 Celtis 9 0.003 Citrus 1 0.000 Clethraceae 1 0.000 Cornus florida 19 0.006 Crataegus 1 0.000 Crataegus spathulata 1 0.000 Diospyros virginiana 4 0.001 Fagus grandifola 23 0.007 Fraxinus 11 0.003 Ilex 14 0.004 Juglans 2 0.001 Magnolia acuminata 1 0.000 Magnolia grandiflora 2 0.001 Moraceae 11 0.003 Nyssa 2 0.001 Nyssa sylvatica 111 0.034 Oxydendrum arboreum 8 0.002 Persea 35 0.011 Pinus 2166 0.654 Platanus occidentalis 4 0.001 Populus 16 0.005 Prunus serotina 2 0.001 Quercus 10 0.003 Quercus alba 50 0.015 Quercus marilandica 216 0.065 Quercus nigra 27 0.008 Quercus phellos 6 0.002 Quercus rubra 109 0.046 Quercus stellata 176 0.053 Quercus velutina 73 0.022 Salicaceae 1 0.000 Sassafras albidum 13 0.004 Tilia americana 3 0.001 Ulmus 14 0.004 Total 3311 1.000
167
Table 5–10: Species Frequencies for Fall Line Hills Mineralogy, Continued
sileceous Count Frequency Acer negundo 2 0.001 Acer saccharinum 1 0.000 Aceraceae 20 0.006 Bitula nigra 3 0.001 Carpinus caroliniana 12 0.004 Carya 86 0.027 Castanea 8 0.003 Castanea pumila 3 0.001 Celtis 6 0.002 Cornus florida 14 0.004 Cupressaceae 3 0.001 Diospyros virginiana 3 0.001 Fagus grandifola 27 0.009 Fraxinus 4 0.001 Ilex 27 0.009 Juglans nigra 1 0.000 Magnolia acuminata 1 0.000 Magnolia grandiflora 4 0.001 Moraceae 4 0.001 Nyssa 1 0.000 Nyssa sylvatica 93 0.030 Oxydendrum arboreum 20 0.006 Persea 29 0.009 Persea borbonia 2 0.001 Pinus 2144 0.685 Platanus occidentalis 3 0.001 Populus 10 0.003 Prunus 1 0.000 Prunus persica 5 0.002 Prunus serotina 1 0.000 Quercus 19 0.006 Quercus alba 52 0.017 Quercus marilandica 195 0.062 Quercus nigra 12 0.004 Quercus phellos 6 0.002 Quercus rubra 101 0.032 Quercus stellata 154 0.049 Quercus velutina 40 0.013 Salicaceae 1 0.000 Sambucus 2 0.001 Sassafras albidum 8 0.003 Ulmus 4 0.001 Total 3132 1.000
168
Red Hills Mineralogy
15.00 11.34 10.00 3.94 5.00 3.18 1.78 1.38 0.98 0.75 0.15 0.29 -2.14 -1.54 -3.50 mixed 0.00 1.70 -0.82 -0.14 -0.50 montmorillinitic -1.88 -2.36 -1.63 -1.97 -5.00 siliceous -10.00
-15.00
-20.00 -16.76 Carpinus Carya Castanea Fraxinus Oxydendrum Persea Pinus caroliniana arboreum
12.00 10.88
10.00 8.74 8.00 6.58 6.16 5.48 6.00 3.58 3.84 3.56 4.00
1.04 1.07 1.04 2.00 0.63 0.26 -0.04 0.00 1.69 1.19 1.21 -0.54 -0.61 -2.00 -1.14 -0.87 -1.45 -1.33 -1.63 -2.30 -2.06 -4.00 -4.51 -6.00 Quercus Quercus Quercus Quercus Quercus Quercus Quercus Tilia Ulmus alba marilandica nigra phellos rubra stellata velutina americana
Figure 5–11: Standardized Residuals for Red Hills Mineralogy
169
Table 5–11: Species frequencies for Red Hills mineralogy mixed Count Frequency montmoillonitic Count Frequency Acer negundo 2 0.00 Acer saccharinum 2 0.00 Acer saccharinum 2 0.00 Aceraceae 3 0.01 Aceraceae 8 0.01 Annonaceae 1 0.00 Bitula nigra 1 0.00 Carpinus caroliniana 8 0.01 Carpinus caroliniana 18 0.01 Carya 43 0.08 Carya 71 0.05 Castanea 1 0.00 Castanea 6 0.00 Castanea pumila 2 0.00 Castanea pumila 2 0.00 Celtis 9 0.02 Celtis 6 0.00 Cornus florida 6 0.01 Cercis canadensis 3 0.00 Crataegus 2 0.00 Cornus florida 10 0.01 Cupressaceae 1 0.00 Crataegus 2 0.00 Diospyros virginiana 7 0.01 Diospyros virginiana 8 0.01 Fagus grandifola 13 0.02 Euonmus atropurpureus 2 0.00 Fraxinus 42 0.08 Euonymous atropurpureu 2 0.00 Ilex 6 0.01 Fagus grandifola 40 0.03 Malus angustifolia 1 0.00 Fraxinus 30 0.02 Moraceae 4 0.01 Ilex 29 0.02 Nyssa sylvatica 28 0.05 Magnolia grandiflora 3 0.00 Persea 1 0.00 Malus angustifolia 1 0.00 Pinus 39 0.07 Moraceae 2 0.00 Platanus occidentalis 1 0.00 Nyssa sylvatica 54 0.04 Populus 5 0.01 Oxydendrum arboreum 3 0.00 Prunus americana 1 0.00 Persea 17 0.01 Prunus persica 1 0.00 Pinus 587 0.39 Quercus 1 0.00 Platanus occidentalis 3 0.00 Quercus alba 54 0.10 Populus 11 0.01 Quercus marilandica 24 0.04 Prunus americana 2 0.00 Quercus nigra 27 0.05 Prunus serotina 1 0.00 Quercus rubra 49 0.09 Quercus 1 0.00 Quercus stellata 110 0.20 Quercus alba 86 0.06 Quercus velutina 9 0.02 Quercus marilandica 93 0.06 Sassafras albidum 5 0.01 Quercus nigra 13 0.01 Tilia americana 16 0.03 Quercus phellos 11 0.01 Ulmus 31 0.06 Quercus rubra 61 0.04 Viburnum rufidulum 1 0.00 Quercus stellata 216 0.14 Total 554 1.00 Quercus velutina 63 0.04 Salicaceae 2 0.00 Sassafras albidum 13 0.01 Tilia americana 8 0.01 Ulmus 17 0.01 Ulmus rubra 5 0.00 Total 1515 1.00
170
Table 5–11: Species Frequencies for Red HIlls Mineralogy, Continued
siliceous Count Frequency Acer negundo 6 0.00 Acer saccharinum 9 0.00 Aceraceae 58 0.01 Bitula nigra 1 0.00 Carpinus caroliniana 32 0.01 Carya 249 0.04 Carya illinoiensis 1 0.00 Castanea 70 0.01 Castanea pumila 7 0.00 Celtis 4 0.00 Cercis canadensis 4 0.00 Clethraceae 1 0.00 Cornus florida 76 0.01 Crataegus 1 0.00 Cupressaceae 1 0.00 Diospyros virginiana 9 0.00 Euonmus atropurpureus 1 0.00 Euonymous atropurpureu 3 0.00 Fagus grandifola 134 0.02 Fraxinus 42 0.01 Hamamelis virginiana 1 0.00 Ilex 65 0.01 Juglans 5 0.00 Juniperus virginiana 1 0.00 Lagerstroemia 1 0.00 Magnolia acuminata 3 0.00 Magnolia grandiflora 14 0.00 Moraceae 6 0.00 Nyssa sylvatica 172 0.03 Oxydendrum arboreum 36 0.01 Persea 64 0.01 Persea borbonia 1 0.00 Pinus 2698 0.49 Pinus glabra 2 0.00 Platanus occidentalis 2 0.00 Populus 33 0.01 Prunus americana 1 0.00 Prunus persica 3 0.00 Prunus serotina 5 0.00 Quercus 26 0.00 Quercus alba 214 0.04 Quercus marilandica 195 0.04 Quercus nigra 37 0.01 Quercus phellos 28 0.01 Quercus rubra 451 0.08 Quercus stellata 498 0.09 Quercus velutina 175 0.03 Sassafras albidum 26 0.00 Tilia americana 27 0.00 Ulmus 24 0.00 Ulmus rubra 4 0.00 Vaccinium arboreum 9 0.00 5536 1.00
171
Conclusion
In conclusion, this study represents the most extensive compilation yet of witness tree data for the State of Alabama. Furthermore, it is the largest witness tree study yet compiled for a multi-physiographic region. The study area includes the Coastal Plain, Piedmont, and Ridge and Valley physiographic provinces. Despite the edaphic diversity of the region, most major forest types were dominated by pine and oak in the pre-European settlement period, including river bottoms. Indeed, the forests differed, for the most part, only by the varying ranks of the four major oak species (post oak, blackjack oak, black oak, and red oak) and hickory. All other species represented less than 4% of the forest composition. Thus, forests in the study area can be classified as the pine- oak type, as described for the southeastern evergreen forest on the Coastal Plain
(Braun 1950). However, the Piedmont and Ridge and Valley portions of the study were dominated by pine-oak, excluding the Opelika Plateau, which contradicts
Braun's (1950) classification of oak-pine. Despite the aforementioned classification of the Piedmont region, Braun (1950) lists the most abundant species as pine (loblolly, longleaf, and shortleaf pine), oak (red, post, black, and blackjack oak), and hickory (mockernut and pignut hickory). Nonetheless, the forests of this study area may be somewhat unique due to the high dominance of pine across most landforms. Clearly, the major pine, oak, and hickory species in the region have a very large ecological breadth that allowed them to dominate the wide range of sites encompassed in this study. Using witness tree data is one 172 of the most robust techniques for the reconstruction of composition of the original forests. The data provided in this study should prove invaluable for studies of forest history and classification, as well as monitoring changes in composition between the pre-European and contemporary forests of east-central Alabama.
Chapter 6
Analysis and Discussion
Introduction
This chapter describes the results of the marginal value analysis in this dissertation. Agricultural productivity and fuel wood were identified in previous chapters as being the most likely long-term variables contributing to the value of the resource use area surrounding the ten Creek Indian towns in the study region. Each of these two variables is analyzed separately in this chapter. It is shown that only agricultural productivity is relevant at the occupational time scale of the ten towns in the study sample. Consequently, only agricultural productivity is used in the marginal value theory test at the end of the chapter.
I am testing the proposition that Creek Indian town abandonments maximized the long-term average rate of intake for the catchment surrounding a given town. More specifically and according to the marginal value theorem, I am testing the proposition that the instantaneous long-term average rates of gain were the same for all ten Creek town abandonments. Each town could have been abandoned after any given occupation period. I am testing the hypothesis that they were all abandoned when the town’s instantaneous rate of gain reached the average for the entire habitat. Since there is only one average value for the habitat, then this value should be the same for all observations of town abandonment. 174
I specified the methods for implementing this test in Chapter 3. In summary, I defined a catchment of two kilometers around ten towns. This distance was chosen for consistency with the fuel wood catchments and because
Creek Indian fields were sometimes up to two kilometers from the town. It will be shown below that two kilometers is too large of an area for an accurate representation of the area that was continuously farmed; therefore smaller catchments of 1 km, ½ km, and ¼ km were also used.
The catchment represents a use area or patch. The marginal value theorem specifies that there exists a “rate of gain” associated with each patch. In the case of the Creek Indians, I showed that historical references suggest that agricultural productivity and fuel wood were two major ecological variables that contributed to the value of land and the length of time that the land was occupied.
The marginal value theorem describes a single rate of gain however. Usually this gain is some generalized currency like energy or reproductive potential.
Agricultural productivity and fuel wood cannot be easily combined into a single currency particularly for a study group that cannot be observed. Consequently, I assessed them separately.
Fuel Wood
Since there are no existing data on the rate of gain of fuel wood consumption for the specific forest composition of the late nineteenth century
Chattahoochee River valley by Creek Indians, I estimated it. I obtained the best possible estimate from existing data on coarse woody debris by forest type 175 combined with observations of fuel wood consumption for economically similar cultures. The specifics of the formula are in Chapter 3. The estimate results in a linear function of fuel wood availability. The results of the fuel wood availability are illustrated in figures 6-1 through 6-10. These figures represent the estimates of fuel wood consumption and availability for the given town over one hundred years. These functions are in direct conflict with two features of the definition of the gain function. The gain function is supposed to be a curve that is initially increasing and eventually negatively accelerated (see chapter two and Stephens and Krebs 1986, 25).
These estimates of availability and consumption are based on the forest composition and the population size of the given town. Note that Apalachicola
Old Town (Figure 6-2), Broken Arrow (Figure 6-3), Cusseta (Figure 6-6), Cusseta
Old Town (Figure 6-7), and Upatoi (Figure 6-9) never run out of fuel wood. In addition, Apalachicola (Figure 6-1) and Chiaha (Figure 6-4) do not run out while the town is occupied. In most cases, the annual input (litter fall from trees) exceeds the annual consumption.
These fuel wood estimates indicate that, for the majority of Creek Indian towns, fuel wood was not a significant variable contributing to town abandonment and migration. By comparing the occupation times in table 6-1, it is apparent that most Creek towns either (1) never ran out of fuel wood, or (2) abandoned the town for other reasons before the fuel wood was exhausted. Only in two cases was the fuel wood gain inconsistent with the known occupation times. These are 176
Yuchi and Coweta Tallahassee (Figures 6-5 and 6-10). In the cases of these two towns, it is probable that fuel wood was gathered outside the two-kilometer catchment defined for these analyses.
Since fuel wood does not seem to have been a significant variable contributing to the value of a given catchment and since these functions do not adhere to the definition of a gain function for use in the marginal value theorem, I did not include fuel wood in further tests of the marginal value theorem.
In any application of optimization theory in behavioral ecology, the investigator must match the model to the organism. The currency and constraints depend on the specific situation of the organism under investigation. I am interpreting these results to indicate that fuel wood was not the most significant and limiting variable for measuring the value of a catchment of resources used by late historic Creek Indians.
Fuel wood was, however, mentioned a number of times by European explorers and observers during the eighteenth and nineteenth centuries. The towns where fuel wood was mentioned as a reason for out migration were the older towns. Tukabatchee, for example, was an Upper Creek Indian town that had been settled for well over one hundred years. The settlers of Tukabatchee told Benjamin Hawkins that people were leaving the town due to the lack of firewood (Hawkins in Swanton 1922, 281). It appears from these analyses that lack of firewood only became a significant issue for the longer-occupied towns.
Most towns were not occupied long enough for firewood depletion to become an 177 issue. However, in the longer-occupied towns, the firewood that was closest to the town was exhausted. In these cases, the women and children had to travel further and further to collect it, beyond the two-kilometer catchment defined in this study. 178
Apalachicola
1800 1600 1400 1200 1000 800 600 400 Cubic Meters of Fuelwood Meters Cubic 200 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–1: Available fuel wood around Apalachicola per year in cubic meters
Apalachicola Old Town
160000
140000
120000
100000
80000
60000
40000
Cubic Meters of Fuelwood 20000
0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–2: Available fuel wood around Apalachicola Old Town per year in cubic meters
179
Broken Arrow
3000
2500
2000
1500
1000
500 Cubic Meters of Fuelwood 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–3: Available fuel wood around Broken Arrow per year in cubic meters
Chiaha
1800
d 1600 1400 1200 1000 800 600 400
Cubic Meters of Fuelwoo 200 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–4: Available fuel wood around Chiaha per year in cubic meters
180
Coweta Tallahassee
3000
2000
1000 Cubic Meters of Fuelwood 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–5: Available fuel wood around Coweta Tallahassee per year in cubic meters
Cusseta
3000
2500
2000
1500
1000
500 Cubic Meters of Fuelwood 0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97
Year
Figure 6–6: Available fuel wood around Cusseta per year in cubic meters
181
Cusseta Old Town
6000
5000
4000
3000
2000
1000 Cubic Meters of Fuelwood 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–7: Available fuel wood around Cusseta Old Town per year in cubic meters
Osochi
1600 1400 1200 1000 800 600 400 200 Cubic Meters of Fuelwood 0 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97
Year
Figure 6–8: Available fuel wood around Osochi per year in cubic meters
182
Upatoi
45000 40000 35000 30000 25000 20000 15000 10000
Cubic Meters of Fuelwood 5000 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–9: Available fuel wood around Upatoi per year in cubic meters
Yuchi
1600 1400 1200 1000 800 600 400 200 Cubic Meters of Fuelwood 0 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Year
Figure 6–10: Available fuel wood around Yuchi per year in cubic meters
183
Agricultural Productivity
Agricultural productivity seems to be the most limiting and significant resource obtained by the Creek Indians on a given patch of land. This is not surprising since the Creek Indians were horticulturalists. It may be expected that changes in the subsistence strategies would affect the contribution of agricultural products. For example, in the early 19th century, cattle ranching became more prevalent and important to some Creek towns (Ethridge 1996). In these situations, the gain function of a given patch may have to include variables relevant to cattle pasturage. The towns included in this study were occupied primarily before cattle ranching became a significant contribution to Creek economy. Therefore it is justified to use only soil productivity as a measure of resource gains for a given town catchment.
William Baden’s (1987, 2001) extensive model of agricultural productivity and resource depletion provides the basis for deriving the long-term average rate of gain function for each catchment. His model estimates resource depletion, the inverse of gain. This model was used in two forms, a labor potential yield and an optimal agricultural potential yield.
Labor Estimate Approach
I calculated the potential agricultural yield given the population size of each town, agricultural field size, the number of females available to cultivate the fields, and the depletion of a continuously planted, unfertilized crop. This calculation resulted in a given area of land that was potentially farmed by a given 184 town (for example 80 acres, 197.6 ha) within the town’s catchment. Then, using the Baden formula as specified in Chapter 3, I calculated the potential yield for those 80 acres (197.6 ha) over time until the yield per town dropped below the nutritional requirements of the town. It is assumed that a family of five (two adults and three children) subsisting on a diet of 55% corn, need to grow 23.56 bushels
(598.35 kg) of corn per year (Schroeder 1999, Table 5). After the corn yield depletes below this per capita level, the model assumes that the town’s inhabitants moved the communal agricultural fields to a new plot within the town’s catchment. The new, previously uncultivated plot would yield higher yields again according to the Baden model. The town’s agricultural plots were moved to new, uncultivated areas every time that the yield dropped below the nutritional requirements for the town until the type I and II soils were used up. At that point, the model assumed that the agricultural fields were moved back to previously cultivated areas. The result is a depletion curve calculated from the soils of the catchment that increases and decreases over time according to the movements of the agricultural fields within the two-kilometer catchment surrounding the town.
Table 6-1 lists each town, the population of each town, number of families in each town (population divided by five), total area cultivated in acres (two acres multiplied by the number of families), the corn requirements for the entire town based on a 55% dietary contribution of corn (Schroeder 1999, Table 4), area of type I and type II soils available within the town’s two kilometer catchment, and 185 the number of years that the town was occupied. The results of this simulation are illustrated in figures 6-11 to 6-20.
Total Total Area Total Town Corn Number agricultural land Years Town Population cultivated Requirements Families available (acres; Occupied (acres; ha) (bu; kg) ha)
Apalachicola 200 40 80; 198 942.4; 23956.3 1065; 2631 45
Apalachicola Old Town 240 44 88; 217 1036.6; 26351.0 1387; 3426 40
Broken Arrow 100 20 40; 99 471.2; 11978.2 85; 210 85
Chiaha 200 40 80; 198 942.4; 23956.3 1805; 4458 110
Coweta Tallahassee 200 40 80; 198 942.4; 23956.3 1234; 3048 85
Cusseta 200 40 80; 198 942.4; 23956.3 395; 976 85
Cusseta Old Town 200 40 80; 198 942.4; 23956.3 577; 1425 25
Osochi 200 40 80; 198 942.4; 23956.3 1762; 4352 70
Upatoi 100 20 40; 99 471.2; 11978.2 278; 687 35
Yuchi 200 40 80; 198 942.4; 23956.3 1571; 3880 85
Table 6–1: Variables used to calculate annual corn yield based on the labor available in each Creek town Annual corn yield for Apalachicola
2000
1500
1000 Bushels 500
0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–11: Estimate of total corn yield for Apalachicola according to the population labor potential and the Baden (1987) model of corn depletion over time
Annual corn yield for Apalachicola Old Town
3000 2500 2000 1500
Bushels 1000 500 0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–12: Estimate of total corn yield for Apalachicola Old Town according to the population labor potential and the Baden (1987) model of corn depletion over time
188
Annual corn yield for Broken Arrow
1200 1000 800 600
Bushels 400 200 0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–13: Estimate of total corn yield for Broken Arrow according to the population labor potential and the Baden (1987) model of corn depletion over time
Annual corn yield for Chiaha
2500
2000
1500
1000 Bushels
500
0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–14: Estimate of total corn yield for Chiaha according to the population labor potential and the Baden (1987) model of corn depletion over time
189
Annual corn yield for Coweta Tallahassee
2500
2000
1500
1000 Bushels
500
0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–15: Estimate of total corn yield for Coweta Tallahssee according to the population labor potential and the Baden (1987) model of corn depletion over time
Annual corn yield for Cusseta
800 700 600 500 400 300 200 100 0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286
Figure 6–16: Estimate of total corn yield for Cusseta according to the population labor potential and the Baden (1987) model of corn depletion over time
190
Annual corn yield for Cusseta Old Town
500
400
300
200 Bushels
100
0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–17: Estimate of total corn yield for Cusseta Old Town according to the population labor potential and the Baden (1987) model of corn depletion over time
Annual corn yield for Osochi
2500
2000
1500
1000 Bushels
500
0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–18: Estimate of total corn yield for Osochi according to the population labor potential and the Baden (1987) model of corn depletion over time 191
Annual corn yield for Upatoi
2000
1500
1000 Bushels 500
0 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 Years
Figure 6–19: Estimate of total corn yield for Upatoi according to the population labor potential and the Baden (1987) model of corn depletion over time
Annual corn yield for Yuchi
2500
2000
1500
1000 Bushels
500
0 1 17 33 49 65 81 97 113 129 145 161 177 193 209 225 241 257 273 289 Years
Figure 6–20: Estimate of total corn yield for Yuchi according to the population labor potential and the Baden (1987) model of corn depletion over time The results of this corn yield simulation demonstrate that all towns in the sample, except Cusseta and Cusseta Old town, had adequate soils available to supply the town’s populations with a diet consisting of 55% corn. Broken Arrow had the least amount of type I and II soils available for cultivation (85 acres; 28.4 ha). In this case, I propose that Broken Arrow cultivated forty acres until the yield depleted below nutritional requirements for 100 individuals (29 years, Figure 6-
13), then the last 40-45 acres of soil was cultivated. After approximately 60 years of cultivation in this catchment, the agricultural fields would have had to be moved back to a previously cultivated area. The town was occupied for only 85 years however. In all other cases, except Cusseta and Cusseta Old Town, there was ample land to continuously shift cultivation to previously uncultivated plots for the duration of the occupation of the town.
Cusseta and Cusseta Old Town do not produce enough corn for their respective populations in the first year of the simulation. These towns may have been depending on a lower percentage of corn for their diet or their agricultural fields may have been outside the two-kilometer catchment as defined in this labor approach. An alternative explanation will be proposed below with the optimal agricultural potential simulation.
These corn yield simulations are dependant on the population of each town. They use the population independent Baden model of corn yield over time and multiply the yield by the number of acres that would have been cultivated by a population of the respective size. This figure is a microenvironment estimate of the yield per town over time. It is not an average yield potential for the entire 193 town’s catchment. Instead it estimates the yield for each successive agricultural field over time for a three hundred year period. Note (Table 6-1) that none of the towns were occupied that long. In fact most towns were abandoned after only a few decades. Most of these towns had enough type I and II soils available to yield enough corn for hundreds of years without using a previously cultivated field. There is, consequently, no correlation between occupation time of these towns and the annual yield of individual agricultural fields. This approach reveals little if anything about the effects of individual field potential as a predictor for the abandonment of a town. It appears that Creek Indians were not using individual field yield potentials as a currency for long-term town catchment optimization.
Consequently, I tested an alternative currency, maximization of average yield for the entire catchment using Baden’s model to estimate optimal agricultural potential of the catchment’s soils.
Optimal Agricultural Potential Approach
The marginal value theorem is a model about long-term average gains for a patch of resources. Therefore, in addition to the labor model, I also calculated the average optimal potential yields over time for the town’s entire catchment.
The long-term average depletion functions for each respective town’s two- kilometer catchment as a whole are illustrated in figures 6-21 to 6-30. These graphs show the inverse of the gain curve for each town’s catchment. The vertical axis shows the depletion of corn in the catchment’s type I and II soils 194 according to the Baden model (1987, equation 3.8). Units are in bushels per acre and are population independent. Since this approach does not consider field size, this is an estimate of the expectation of corn yields for the entire two- kilometer catchment per year. Not all catchments have the same area of type I and II soils. Also, the quality of the soils varies in each catchment. The horizontal axis displays time in years. The corn yield at the point of abandonment can be determined by identifying the value along the curve that corresponds to the town’s occupation duration. For example, Apalachicola was settled for 45 years.
The corn yield after 45 years is 9.7 bushels per acre (Figure 6-21). This value,
9.7 bu/ac, is the long-term instantaneous rate of depletion (the inverse of the rate of gain) at the time of Apalachicola’s abandonment. A large point along the curve with its value labeled indicates the instantaneous rate of depletion for each town catchment. The marginal value theorem predicts that this value, the instantaneous rate of gain at the point of abandonment, is the same for each town’s abandonment because each town should be abandoned at the habitat’s optimal long-term instantaneous rate of gain.
Apalachicola Depletion Curve
25.0
20.0
15.0
9.7 10.0
5.0
0.0 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 Years
Figure 6–21: Inverse of Gain Function for Apalachicola by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 196
Apalachicola Old Town Depletion Curve
30.0
25.0
20.0
15.0 10.8
10.0
5.0
0.0 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 Year
Figure 6–22: Inverse of Gain Function for Apalachicola Old Town by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot
197
Broken Arrow Depletion Curve
30.0
25.0
20.0
15.0
9.2 10.0
5.0
0.0 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 Years
Figure 6–23: Inverse of Gain Function for Broken Arrow by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 198
Chiaha Depletion Curve
30.0
25.0
20.0
15.0
9.0 10.0
5.0
0.0 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 Years
Figure 6–24: Inverse of Gain Function for Chiaha by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 199
Coweta Tallahassee Depletion Curve
30.0
25.0
20.0
15.0
9.9 10.0
5.0
0.0 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 Years
Figure 6–25: Inverse of Gain Function for Coweta Tallahassee by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 200
Cusseta Depletion Curve
9.0
8.0
7.0
6.0
5.0
4.0 3.1
3.0
2.0
1.0
0.0 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 Years
Figure 6–26: Inverse of Gain Function for Cusseta by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 201
Cusseta Old Town Depletion Curve
6.0
5.0
4.0
2.7 3.0
2.0
1.0
0.0 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 Years
Figure 6–27: Inverse of Gain Function for Cusseta Old Town by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 202
Osochi Depletion Curve
30.0
25.0
20.0
15.0
10.0 10.0
5.0
0.0 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 Years
Figure 6–28: Inverse of Gain Function for Osochi by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot
203
Upatoi Depletion Curve
25.0
20.0
15.0
10.3
10.0
5.0
0.0 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 Years
Figure 6–29: Inverse of Gain Function for Upatoi by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot 204
Yuchi Depletion Curve
30.0
25.0
20.0
15.0
9.8 10.0
5.0
0.0 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287 298 Years
Figure 6–30: Inverse of Gain Function for Yuchi by Bushels of Maize per Acre with the instantaneous rate of depletion at the observed abandonment point indicated by the large dot By using the depletion curves and the known occupation time, we can derive the instantaneous rate of depletion (inverse of the rate of gain) at the point of abandonment for each town. It is graphically computed by identifying the point along the function that intersects with the respective occupation year for the given town. The results of these calculations are listed in Table 6-2.
The hypothesis in this dissertation is that all of these instantaneous rates of depletion are the same. Since there are only ten observations in the sample, I used the non-parametric Kolmogorov-Smirnov test for normality about the mean to determine if the values were the same. The results of this test are illustrated in
Figure 6-31.
The results are significant but not very strong (0.123). Note that there are two towns, Cusseta and Cusseta Old Town, which are outliers (see Table 6-2).
While all other towns have a yield at abandonment of about 9.8 bushels per acre,
Cusseta and Cusseta Old Town yields at abandonment are 3.1 and 2.7 bushels per acres, respectively.
If Cusseta and Cusseta Old Town are removed from the sample and the test for normality is re-run, then the results are very significant and strong (0.996;
Figure 6-32). The results from these eight remaining towns are consistent with the stated hypothesis that all catchments were being abandoned at the same instantaneous long-term average rate of return. The remaining eight towns were all abandoned when the respective town’s catchment was reduced to 9.8 bushels per acre. Note that the occupation periods (Table 6-2, column two) are not
(p=0.000) normally distributed about a mean (65) with a standard deviation 206
(27.28). This pattern indicates that there is a significant amount of variation within the occupation periods. In other words, although the occupation time varies significantly, all towns are abandoned when the corn yield drops to about the same return, 9.85 bu/acre.
I decided to go back and rerun the MVT test on smaller catchments for two reasons. First, there is a significant amount of error in the range of potential yields at abandonment for the length of time that each town was occupied. Table
6-3 shows the range of yields within one standard deviation of the mean for each two kilometer catchment. The yield at abandonment for the eight towns
(excluding the outliers, Cusseta and Cusseta Old Town) has a mean of 9.8 bu/acre and a standard deviation of, 0.5 bu/acre. This table shows the length of time, Occupation Range in column four, that the yield at abandonment could be within one standard deviation of the mean, 9.8 bu/acre. Compare the
Occupation Range, column four, to the Occupation Duration, column two. In most of the cases, the range of error is almost half the entire duration of the town’s existence. The second reason that I decided to rerun the tests with smaller catchments than two kilometers is that the model of agricultural depletion by Baden (1987) is designed to calculate the average yield for the entire area. In the application in this dissertation, the entire area is the catchment. This assumes that the Indians were farming the entire two-kilometer catchment.
Based on the labor potential approach explained above and in Chapter 3, this is unlikely. The largest area farmed at one time was probably closer to 88 acres 207
(217 ha; see Table 6-1). This area is close to the area of a ¼ km catchment.
However, not all gardens were equally placed within a circle surrounding the town due to soil productivity variation, location of habitations, and access to the river. For these two reasons, I calculated the agricultural yield of 1 km, ½ km, and ¼ km catchments also. These catchments are illustrated in Chapter 3
(Figures 3-2 to 3-11).
The Baden model used in this study estimates the average continuous yield for a catchment over time. This is precisely what the gain function in the
MVT is supposed to represent. The new catchment sizes may represent the region that was actually farmed by the Creek Indians more accurately.
The instantaneous yield at abandonment for each size catchment for each town is illustrated in Table 6-4. The results do not change significantly from the
2km application. The only difference is the productivity of the Broken Arrow catchment. The availability of type I and II soils drops significantly within each smaller catchment to the point where there are no type I and II soils in the ½ and
¼ km catchments. That is why the yield is zero, 0 (Table 6-4, columns 5 and 6) and the standard deviation for these catchments is so high.
A Kolmogorov-Smirnov test of normality is at the bottom of each respective column. This test is a non-parametric test that the observations, the yields at abandonment, are normally distributed about their respective means. I already reviewed the results of this test for the 2km catchment above. A value larger than 0.05 is significant that the observations fit the distribution of a 208 normally distributed population around the respective mean. The 1km catchment is the strongest response. Next to the 2km catchment, this distribution of yields at abandonment most closely fit the hypothesis that all towns were abandoned at the same yield. In the case of the 1km catchment, all towns were abandoned around the mean yield, 9.5 bu/acre. The other two catchment sizes had large standard deviations due to the fact that Broken Arrow’s catchment had zero yields. This made the strength of the K-S test much lower.
Forest Type Yield Population Occupation Town (surrounding (bushels/acre) at size period (years) town) Abandonment
Apalachicola 200 oak-pine 45 9.6
Apalachicola Old Town 240 oak-pine 40 10.8
Broken Arrow 100 oak-pine 85 9.2
Chiaha 200 oak-pine 110 9.0
Coweta Tallahassee 200 oak-pine 85 9.9
Cusseta 200 oak-pine 85 3.1
Cusseta Old Town 200 oak-pine 25 2.7
Osochi 200 oak-pine 70 10.0
Upatoi 100 natural pine 35 10.3
Yuchi 200 oak-pine 85 9.8
Table 6–2: Population size, forest type (according to types used by McMinn and Hardt, 1993), occupation period, and instantaneous rate of returns for each town assuming a 2 km catchment.
210
One-Sample Kolmogorov-Smirnov Test Yield (bu/acre)
N 10
Normal Parametersab Mean 8.4500
Std. Deviation 2.9744
Most Extreme Differences Absolute .373
Positive .205
Negative -.373
Kolmogorov-Smirnov Z 1.181
Asymp. Sig. (2-tailed) .123 a Test distribution is Normal. b. Calculated from data.
Figure 6-31: Kolmogorov-Smirnov Test of Normality about the Mean (SPSS
10.0.5)
211
One-Sample Kolmogorov-Smirnov Test Yield (bu/acre)
N 8 Normal Parametersab Mean 9.8375
Std. Deviation .6022
Most Extreme Differences Absolute .144
Positive .144
Negative -.100
Kolmogorov-Smirnov Z .406 Asymp. Sig. (2-tailed) .996
a Test distribution is Normal.
b Calculated from data.
Figure 6-32: Kolmogorov-Smirnov Test of Normality (SPSS 10.0.5) Excluding
Cusseta and Cusseta Old Town
212
Occupation Yield Range Occupation time within Occupation Town within one duration one sigma range sigma value value
Apalachicola 45 37-57 20 9.3-10.3
Apalachicola Old Town 40 33-49 16 10.4-11.4
Broken Arrow 85 68-107 39 8.7-9.7
Chiaha 110 88-139 51 8.5-9.5
Coweta Tallahassee 85 69-104 35 9.4-10.4
Osochi 70 56-85 29 9.5-10.5
Upatoi 35 28-44 16 9.5-10.5
Yuchi 85 70-108 38 9.0-10.0
Table 6–3: The range of yield at abandonment within one sigma standard deviation from the mean for eight Creek Indian town catchments Occupation Town 2km 1km 1/2 km 1/4 km duration
Apalachicola 45 9.8 10.3 10.4 10.4
Apalachicola Old Town 40 10.9 10.9 10.8 11.4
Broken Arrow 85 9.2 6 0 0
Chiaha 110 9 9 8.9 9.1
Coweta Tallahassee 85 9.9 9.8 10 10
Osochi 70 10 9.9 9.9 9.7
Upatoi 35 10 10.2 11.2 12.5
Yuchi 85 9.5 9.7 9.7 9.4
Mean 9.8 9.5 8.9 9.1
Standard deviation 0.5 1.5 3.6 3.8
Kolmogorov-Smirnov test of normality .996 .428 .200 .201
Table 6–4: Yield at abandonment for 2km, 1km, 1/2km, and 1/4km catchments for eight towns and the respective mean and standard deviation
Discussion
This natural experiment has demonstrated that the marginal value theorem is a useful model for predicting and explaining residential mobility and small-scale population migration among Creek Indians of the late Historic Period.
Two ecological variables, fuel wood and agricultural productivity, were identified as contributing to town catchment value. These variables have never been directly measured for the Creek Indians. Accordingly, I estimated them as well as possible given the available information.
After reconstructing the available fuel wood and estimating its consumption, I determined that fuel wood was only rarely a significant contributor to Creek town abandonment. Lack of fuel wood was mentioned by the Creeks residing in towns that had been occupied for over one hundred years, such as
Tukabatchee. The majority of towns, however, were never occupied that long.
Consequently, I focused the marginal value application on soil productivity as a measure of the value of a given town catchment.
I estimated agricultural productivity by two methods. First, I determined the crop yield that each town would have produced given the town’s available labor potential, soil productivity, land available, and nutritional requirements given the town’s population. This resulted in a series of yield simulations that depleted over time given the soil’s productivity until the gardens would not have produced enough food for the village. At that point the gardens were assumed to have been moved to a new plot that produced higher yields. 215
The known abandonment periods were compared to these labor yields. In all cases, the Indians did not exhaust land within a two-kilometer catchment during the time that the town was occupied. Therefore, there was no correlation or direct relationship between the yield of the individual gardens and the time that the town was abandoned. These results indicate that the Creek Indians were not maximizing the yield of individual gardens over time when deciding to abandon a town. In the terms of the MVT, the Creek Indians were not maximizing the long- term average rate of individual garden productivity.
Next, I tested whether or not Creek Indians were maximizing the average productivity of the catchment’s soils surrounding each respective town. I began these analyses with a two kilometer catchment to be consistent with the two kilometer catchment of the fuel wood catchments and because some fields may have been up to two kilometers from individual towns. This method assumes that the entire catchment was farmed. Another interpretation of this estimate is that it represents the average expected rate of any randomly placed plot within the catchment.
I used William Baden’s (1987) model of Mississippian Period agricultural productivity to estimate this value for each town’s two-kilometer catchment. The maize yield was measured at the point of abandonment for each town. When two outliers, Cusseta and Cusseta Old Town were dropped from the sample, the results were strongly consistent with the predictions of the marginal value theorem. Creek towns were being abandoned at the same instantaneous long- 216 term average rate of return. In this case that instantaneous long-term average rate of return was about 9.8 bushels per acre.
The results of this test were consistent with the MVT; however, a two- kilometer catchment is probably too large of an area to have been simultaneously farmed over the period of the simulation. For example, the Baden model is an average of the yield for the entire region in the simulation. The region in my simulation was a two-kilometer catchment surrounding each town. Therefore, this simulation estimated the average yield assuming that the entire area was farmed. This is unlikely given the labor potential and population size of these
Indians. Consequently, I estimated the yield potential for smaller catchments also. They were 1km, ½ km, and ¼ km catchments surrounding each town.
The results of these simulations and tests were similar to the 2 km test except for one town, Broken Arrow. In the case of Broken Arrow, the ½ km and
¼ km catchment did not contain any type I or II soils, therefore the yield was zero, 0. In the case of Broken Arrow, the inhabitants either farmed areas outside a ½ km catchment or accepted a lower yield. Although this town was difficult to interpret, the overall results were still consistent with the hypothesis that the
Creek Indians were maximizing average yield potential of soil surrounding their towns over the long term. It is important to emphasize the observation that they were maximizing average yield of the entire catchment in contrast to the yield of individual gardens. This indicates that these horticulturalist Indians knew the average potential of the Chattahoochee River soils where they were settled for 217 hundreds of years and were using this measure as a currency for maximization of town occupation.
The outliers in the Sample
The cases of Cusseta and Cusseta Old Town were inconsistent with the rest of the sample and the model predictions. There is one strong possible explanation for this inconsistency. In each of these two cases, soil productivity was not the most significant and limiting variable contributing to the value of the town’s catchment. The accuracy of the marginal value theorem depends on the ability to measure the gain function for each catchment. It is possible that the value of the use area surrounding Cusseta and Cusseta Old Town originated from some variable other than soil productivity. The most significant and obvious variable that is associated with these two towns is a major road. One of the most significant east-west trade routes in the southeast passed directly through these two towns. This is the path traveled by Bartram and others travelers. Later, the
Federal Highway traversed this old Indian path. Cusseta and Cusseta Old Town are very close to each other and may well have both been drawn to the value of the proximity of this transportation and trade route. No other town in this study was directly on this trade route or any other trade route of similar size.
Alternative or Confounding Variables
Four other causes for migration were mentioned in the ethnohistoric and ethological literature: pests, conspecific aggression, mate access, and spiritual. 218
Pests are mentioned only once in the ethnohistoric literature (see Chapter 4) and are probably related to agricultural productivity. Hence the effect of pests has been incorporated into the agricultural depletion models of this study.
Conspecific aggression is aggression from the same species (for example, warfare). The towns of this study were chosen partly because there were no cases of warfare during their occupation history as defined in this study. Mate access is a significant cause for population movement, however it results in differential migration by the sexes. In the cases of town abandonment in this dissertation, the entire town is being abandoned by both sexes. Consequently, it is argued that mate access is not a significant cause for the abandonment of the
Creek Indian towns in the sample of this dissertation. Lastly, spiritual reasons are occasionally mentioned as a reason for abandoning a town. It is argued that such non-ecological currencies are a cultural, emic explanation for the ultimate currency such as inclusive fitness. The marginal value theorem and these analyses are directed toward explaining ultimate evolutionary adaptation.
Conclusion
This dissertation is about what variables contribute to and how to model small-scale population migration. These small-scale population movements contribute to cultural transmission, population interaction, and biological evolution. I reviewed the ecological research on variables that contribute to 219 population dispersion and then applied an ecological model to population movement.
The ecological model, the marginal value theorem, is a general one that has been very successful when applied to non-humans. Its successful application to humans has produced more ambiguous results, however. I applied the MVT to late historic Creek Indian population movement using ethnohistoric and archaeological data. These temporally and regionally collected data provide a unique application of the MVT.
Specifically, I measured the resource utility for catchments surrounding ten Creek Indian towns. Based on Creek ethnohistoric sources, I characterized resource utility as agricultural productivity and fuel wood availability. I quantified fuel wood availability and forest resources from federal public land surveys and agricultural productivity from William Baden’s model of crop yield depletion among Mississippian agricultural systems.
I found that fuel wood is not the most significant and limiting variable contributing to town abandonment among late historic Creek Indians.
Consequently, I applied the MVT test solely on agricultural productivity. When I tested that Creek Indians were maximizing the productivity of individual garden’s over time for decisions about town abandonment, I found no relationship.
However, there is a correlation between the average yield of the entire catchment surrounding a town and the town’s abandonment. These analyses were consistent with the proposition that Creek Indians were optimizing catchments of 220 land surrounding their towns. They were abandoning towns when agricultural productivity surrounding towns decreased to the average optimum yield for the entire region.
In two cases, however, Creek towns were not abandoned at the optimal instantaneous resources gain. These two towns, Cusseta and Cusseta Old
Town, were the only towns directly on a major east-west trade route. The inhabitants of these two towns were not optimizing agricultural productivity in their town catchments because the value of the trade route increased the marginal utility of their respective settlements.
Behavioral ecology is the application of ecological biology to animal behavior. The marginal value theorem, a model derived from behavioral ecology, was successful in predicting the occupation time for eight of ten towns. The two exceptions point out that the optimal foraging models must be constantly refined for the specific species and environmental situation. In this case, optimal foraging models were useful at explaining population movement, the lack of population movement, and the environmental variables that are most significant to a particular Indian population.
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Appendix: Creek Town Occupation Times
This appendix includes the justification for the occupation times used in the methods and results section of the dissertation. Occupation duration for a group of people is a difficult variable to accurately quantify particularly if those people cannot be observed. These occupation times are an average of the town habitation beginning at the original settlement of the town until the beginning of the town’s abandonment. In some cases the town was not completely abandoned by the start of abandonment. However, this point is when the majority of the town’s inhabitants judged the town’s catchment to be sub-optimal relative to other choices in the habitat. Names of towns are those in use during the William Bartram, David Taitt, and Benjamin Hawkins era, late 18th and early
19th century.
Apalachicola and Apalachicola Old Town
These two towns are related and will be discussed together. These
Hitchiti towns held an influential and old population of Lower Creek Indians.
These are probably different names for the same town that moved locations during the late historic period. This discussion will be about the town occupied during the late 18th and early 19th centuries under the general name of
Apalachicola and Apalachicola Old Town. 241
David Taitt, the English surveyor, visited Apalachicola, Pallachocola, in
1772. He gives little description except that Geehaw (Chiaha) is “six miles NNE”
(Taitt in Mereness 1961, 555). This places the Apalachicola town of 1772 in the vicinity of 1RU69 and 1RU18 if Chiaha is 1RU12/54. The location of Chiaha is in little doubt based on archeological and historical investigation by Dickinson and Wayne (1985), Jackson et al. (1998), Huscher (1959), and Hurt (1956).
Bartram in 1775 visited “Apalachucla town, where we arrived after riding over a level plain, consisting of ancient Indian plantations…” (Harper 1998, 246).
He says that the old town, Apalachicola Old Town, was abandoned “about twenty years ago” around 1755 and “the greatest number, however, chose to sit down and build the present new Apalachucla town, upon a high bank of the river above the inundations” (Harper 1998 246-246). Since this was written only two years after Taitt’s observation, presumably these towns were in the same location.
This places an origin date for Apalachicola (the new) at around 1755.
In 1799, forty four years later, Benjamin Hawkins, the United States Indian agent that lived among the Creek Indians from 1796 to 1816, stated that
“Pa-la-chooc-le is on the right bank of Chat-to-ho-che, one and a half miles below
Au-he-gee creek on a poor, pine barren flat… The Indians are poor, the town has lost its former consequence, and is not now much in estimation” (Hawkins quoted in Swanton 1998, 134). If the town was settled around 1755 and the inhabitants are considered “poor” and not much “in estimation,” it could be interpreted that the town is beginning to be abandoned around the time of Hawkins’s visit. 242
It is necessary to determine the exact location of Apalachicola relative to Apalachicola Old Town because the environmental resources of these two catchments may be different. Since much of the documentation of Apalachicola is made in relation to the location of
Apalachicola Old Town, I will discuss the evidence for the location of
Apalachicola Old Town also. In contrast to the interpretation of Huscher
(1959), Knight and Mistovich (1984), Kurjack (1975), 1RU69 and 1RU18 are the best candidates for the late 18th century Apalachicola of Taitt,
Bartram, and Hawkin era. The Patterson site, 1RU69, has been identified in the literature as late 18th century Apalachicola (Knight and Mistovich
1984, Kurjack 1975). This seems to be primarily based and perpetuated on Hurt’s early ethnohistoric work (1947). However, Bartram says old town Apaluchucla is 1 1/2 miles down river from the current, 1777,
Apachucla. Taitt in 1772 agrees with this distance. Taitt also says that,
Geehaw is six miles NNE from Pallachocola. That places Taitt period
Apalachicola in the vicinity of 1RU69 and 1RU18. This region is almost exactly six miles (shortest distance) from 1RU12/54, which is identified as
Chiaha (Dickinson and Wayne 1985, Jackson et al. 1998). In addition,
Hawkins says that Apalachicola is 1 1/2 miles below Auhegee Creek.
There is a present day creek called Ihagee Creek just north of this area.
The junction of Ihagee Creek at the Chattahoochee River, measured along the river is almost exactly at 1RU18. 243
Apalachicola old town of Taitt and Bartram may be the ruins of the
Fort Apalachicola. Bartram thought he was seeing a mound of about 8 feet tall. The ruins of the Fort trace described in Kurjack and
Pearson (1975) could have been misidentified as a square mound like
Bartram said that he had seen among the Cherokee.
Fort Apalachicola is almost exactly one and one half miles below
1RU69 which is consistant with Bartram and Taitt. In addition, Bartram says that Apaluchucla old town is in a bend formed by the river (Harper
1998, 246) and David Taitt, in 1772, “went to the Old Pallachocola Town below this [Apalachicola new] and Surveyed it and the River round the point where the Town stood” (Mereness 1961, 558). Fort Apalachicola,
1RU101, and the Patterson site, 1RU66, are definitely in a bend.
There is a little distance between 1RU18 and 1RU69. 1RU18 seems to be the Apalachicola of Hawkins time and 1RU69 seems to be the Apalachicola of Bartram/Taitt time. A minor shifting of the town downriver would explain the locational difference between these sites. For these reasons, I am using the location of 1RU69 as the center of the late
18th century Apalachicola town and 1RU66 as the mid to late 18th century
Apalachicola Old Town (early 18th century Apalachicola).
Bartram gives the abandonment date of about 1755 for Apalachicola Old
Town. The origin is more problematic. There was an important town located on the lower Chattahoochee in the 17th century. Fort Apalachicola was placed on 244 the Chattahoochee in 1689 by the Spanish and was abandoned two years later when the Lower Creek Indians migrated east (Crane 1956). There are few documents that cover the transition from the 17th to the 18th century. It is probable that Apalachicola was abandoned in the late 17th century like many other Lower Creek towns. In fact a Palachicola town was settled on the
Savannah River at this time (Caldwell 1948). It is often assumed that this town is the same population that originated from the Chattahoochee Apalachicola town of the 17th century. If this is true then, the Apalachicola Old Town of 1RU66 would have been settled around 1716 after the Creeks who migrated east in the late 17th century migrated back west to the Chattahoochee. Apalachicola is listed among these Creek towns in the list of Pena (Boyd 1949). In his archaeological investigations of the Spanish Fort and associated Creek village, Kurjack (1975) interprets the artifactual investigations of the village as later than the associated and neighboring Fort Apalachicola that is from the late 17th century. He dates the European artifacts found at the village site as middle to late 18th century
(Kurjack 185, 1975). This is consistent with the interpretation that the town located at 1RU66 and called Apalachicola Old Town by Taitt, Bartram, and
Hawkins was settled after 1715. Since Bartram says that it was abandoned around 1755, this gives the occupation time of Apalachicola Old Town as forty years. The occupation time of Apalachicola is about 45 years as stated above.
Broken Arrow 245
The population history of this town is complex and ambiguous due to apparent movements and various names during the historic period. While there appears to have been numerous locations for a town named “Broken Arrow” during the late historic period, the town used in this study is the area settled around site 9CE68.
In his Sketch of the Creek Country written around 1796, Benjamin
Hawkins said that he was told that in 1729, Captain Ellick, a Cusseta chief, brought Uche wives to Cusseta. This was disliked by the Cusseta.
Consequently, he moved down river opposite of the current location of Uche town. The location of Uche town during Hawkins’s time seems to be unambiguous (Braley 1998). It is represented by site 1RU63. The most significant late 18th century site across the river from 1RU63 is 9CE68 (Buchner
1996; Schnell 1982). In 1790, Caleb Swan says that he passes through Broken
Arrow, which is 12 miles below Cusseta and Coweta (Schoolcraft 1854, 254).
This distance is consistent with 9CE68. Also, there is a town on the 1827
Georgia State land survey map, which is labled “Uchee Town.” This is consistent with Broken Arrow being associated with Uche settlers. If Hawkins’s information about the origin of Broken Arrow is to be taken literally, then the origin date is around 1730.
Weisman and Abrosino (1997) noted that Uche was abandoned in 1814 when the settlers left to join the Red Sticks in the Red Stick war of 1813-1814.
In addition, Swanton (1922: 229) indicates that Broken Arrow is burned in 1814. 246
This places a terminal date on the town if Broken Arrow is closely tied with Uche across the river. Later, in the 1832 census, Broken Arrow is listed again but this
Broken Arrow is in Alabama and may or may not be the same population of people.
Chiaha
The location of this town has been confirmed by many independent researchers as being associated with 1RU12 (1RU54). 1RU12 (1RU54?) was visited by Dickinson and Wayne (1985), Jackson et al. (1998), Huscher 1959, and Hurt 1956. Jackson et al (1998) did phase II work and confirmed a Lawson
Field component. English trade goods were found that suggested an 18th to early 19th century occupation. They make very few interpretations other than identifying the location as Chiaha according to Swanton (1922). The European artifacts suggested a late eighteenth century dating but the sample was small.
David Taitt and Benjamin Hawkins visited this town. It seems to not have moved since its settlement in 1715 until removal, around 1825.
Coweta Tallahassee
David Taitt says that Coweta is about five miles from Cussita and on the west side of the river. If Cussita is near 9CE1 then Taitt’s Coweta is in the area of 1RU9, which is Hawkins’s Coweta Tallahassee (Hurt 1975, 18-19). Taitt mentions a Little Coweta, which is presumably Hawkins’s Coweta. Coweta must 247 have been relatively new/small in Taitt’s time. Coweta Tallahassee (of Hawkins’s time) was settled soon after the Yamassee war, 1716, and was almost completely abandoned by Hawkins’s time. However it had split off a number of towns including Coweta (Hawkins 1982). Its occupational duration is therefore
1715 to 1800 or 85 years.
Cusseta
Cusseta is well known to be one of the towns that moved from the
Chattahoochee to the Ocmulgee around 1690 and back to the Chattahoochee around 1715 (Crane 1956). It’s location after moving back to the Chattahoochee was apparently not in the location referred to as “Cusseta” by Hawkins in the late
18th century and representing the population of Indians in this study. By 1725
(Herbert Hunter 1725-1744 map) and 1733, according to the Popple 1733 map, the towns along the lower Chattahoochee in the vicinity of Cusseta were distributed as they are found later by Bartram, Hawkins, etc.
O’Steen et al. 1997 suggest that between 1715 and about 1820, Cusseta had three different locations: Old Town Cusseta (Hawkins), Cusseta at Lawson
Field, and after Hawkins’ time around 1814, back to the Old Town Cusseta location (Early and Melish maps subjected to literal interpretation). O’Stean seems to be saying this based on Swanton’s interpretation of Bartram. Swanton interprets Bartram’s “Usseta” as Cusseta. Bartram says that “Usseta” borders
Chehaw and that they spoke radically different languages (ie. Hitchiti). However, 248 the Melish 1815 and 1816 map don’t seem to show the location of Cusseta and
Coweta is far north than in its past.
My interpretation of the Early 1818 map of Georgia is that Kasihta is immediately south of Upatoi creek and north of Uchee creek. It is on the east side of the Chattahoochee. All of these conditions are exactly as they were at
Hawkins time and apparently since its first settlement after ~1740. I see no evidence that it moved a third time.
Blackmon phase Kasihta town at Lawson Field presumably represents a town that may or may not be Cusseta settled before the easterly migration to the
Ocmulgee River in 1690. Lawson Field Cusseta town at Lawson Field is described by O’Stean (1997). They didn’t find any early Lawson Field phase ceramic components, which indicates that Cusseta wasn’t settled there immediately after 1716. This is consistent with what Hawkins was told about
Kasihta Old Town being up on the bluff. Hawkins was told in 1796 that Cusseta was first settled up on the bluff above the village at first and then settled at it’s current location at 9CE1. O’Steen 1997 speculate that Lawson Field Phase
Kasihta Town was settled around 1740 (386). This was the settlement after the initial post 1715 settlement. Its abandonment was around the time of removal since it is still occupied in 1820 (Peter Brannon 1925, 45; 1922, 10).
Cusseta Old Town 249
The history of the settlement is directly related to the justification for the history of the Cusseta town described above. It was settled soon after 1715 when the Creek towns migrated back to the Chattahoochee River from the
Ocmulgee River. Hawkins was told that the original settlement was up on the bluff above the current (Cusseta town at 9CE1). This location is in the middle of the residential area of the Fort Benning United States Army Base and there has not been much archaeology. Dan Elliot of Southern Research is currently conducting work in the area but results are not available (personal communication, 2000). The verbal location by Hawkins is very consistent with the landforms around Cusseta (9CE1) however. It was settled around 1715 and abandoned for the lower location around 1740.
Osochi
Swanton says that the 1738 and 1750 census shows them on the
Okmulgee and the 1760 census between the Sawokli and Eufaula towns on the
Chattahoochee River (1922). Bartram and Hawkins mention them in 1776 and
1796, respectively. Hawkins says they are about two miles below Uche town on the right bank of the Chattahoochee, formerly on the Flint River. This is consistent with the Swanton’s interpretation of Cochoutehy on the DeCrenay map of 1733. They didn’t move from the “point” are until removal when they resettled with the Chiaha in Oklahoma. If they moved to the Chattahoochee
River between 1750 and 1760, I am interpreting the origin date of the 250
Chattahoochee settlement to be around 1755. This makes the occupation of the
Hawkins period Osochi/Oswichee town lasting about 70 years.
Worth (2000), Huscher (1959), and Dickenson et al (1985) identify this town with site 1Ru52. The site is on the natural levee of the Chattahoochee
River at Bon Acre Landing. Huscher interpreted the site as probably the site of
Oswichee town of the Bartram-Hawkins period. The site has a great deal of trade material, china, stoneware, and metal which is consistent with a late 18th century occupation of the area.
Upatoi
Elliot et al (1996) identified a cluster of sites along on a tributary of the
Upatoi Creek as the town of Upatoi of Hawkins’s time. According to Hawkins,
“Au-put-tau-e” was a town located twenty miles from the Chattahoochee River up the Hat-che-thluc-co creek (Upatoi Creek). This location is consistent with a group of sites and a number of structures drawn on the 1827 Georgia Land
Survey District 10 map. Hawkins, quoted in Elliot, et al (1996: 256), says that the Tus-se-kia Mico moved to Upatoi and settled the town in 1792. Previous to his settlement, the area was settled by Otassees, an Upper Creek town. Since they are listed on the 1827 land survey map, the town was presumably occupied until removal. This makes an occupation duration for Upatoi of about 35 years.
Yuchi 251
The location of Yuchi (Uche) town is well known archaeologically and historically as associated with site 1Ru63. Hawkins in the late 1790’s notes that it has some out settlements and he describes it as being decreased in population
“lately.” I am interpreting this as since around 1790. This is consistent with Chad
Braley’s archaeological interpretation of being gradually abandoned in the late
18th century (1991, 1998). If it was settled after the Yamassee war of 1715 and being abandoned “lately” according to Hawkins and Braley its occupation was about 75 years.
There are two Creek components at Yuchi, Blackmon and Lawson Field.
The Lawson Field component is after the 1715 resettlement. Braley (1991,
1998) argues for gradual abandonment in the late 1700’s based on archaeological evidence. This is consistent with Wagner’s analysis of historic
British trade goods found by USACERL (Hargrave, 1998).
Appendix B: Town Catchment Corn Yields by Soil Type
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
DoA I 115.00 54.81 135.37 26.54 3592.60 KoA I 115.00 51.63 127.52 26.54 3384.15 MxA I 110.00 45.25 111.77 25.38 2837.30 OrA I 110.00 25.02 61.79 25.38 1568.57 RbA I 110.00 10.28 25.40 25.38 644.68 WkA I 125.00 34.84 86.06 28.85 2482.49 DoB ii e 105.00 5.20 12.84 24.23 311.10 SbB ii e 100.00 12.08 29.84 23.08 688.67 FuB ii s 85.00 89.60 221.31 19.62 4341.07 AnA ii w 100.00 59.45 146.85 23.08 3388.82 CtB ii w 130.00 30.88 76.27 30.00 2288.13 DgA ii w 100.00 11.40 28.17 23.08 650.03 RvA ii w 130.00 0.86 2.13 30.00 63.87 BnB iii s 60.00 65.93 162.84 13.85 2254.67 TaB iii s 60.00 26.54 65.56 13.85 907.77 KMA v w 0.00 81.79 202.01 0.00 0.00 UcD vi e 75.00 17.40 42.97 17.31 743.64 BeA vi w 0.00 18.58 45.90 0.00 0.00 TsE vii e 0.00 124.69 307.99 0.00 0.00 Pt viii s 0.00 1.32 3.26 0.00 0.00 Ur viii s 0.00 2.95 7.29 0.00 0.00 BLANK 0.00 425.06 1049.89 0.00 0.00 W 0.00 54.70 135.12 0.00 0.00 1065.32a 26241.49b
24.63c Table 5: Apalachicola Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a). 253
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
DoA I 115.00 17.38 42.94 26.54 1139.52 MxA I 110.00 125.28 309.43 25.38 7854.81 OrA I 110.00 0.84 2.08 25.38 52.73 WkA I 125.00 186.07 459.60 28.85 13257.77 DoB ii e 105.00 10.92 26.97 24.23 653.56 SbB ii e 100.00 6.40 15.80 23.08 364.52 FuB ii s 85.00 0.90 2.23 19.62 43.70 AnA ii w 100.00 140.38 346.75 23.08 8001.89 CtB ii w 130.00 54.27 134.05 30.00 4021.41 RvA ii w 130.00 15.69 38.76 30.00 1162.85 BnB iii s 60.00 25.46 62.88 13.85 870.63 KMA v w 0.00 22.40 55.32 0.00 0.00 UcD vi e 75.00 4.62 11.40 17.31 197.33 BeA vi w 0.00 66.09 163.25 0.00 0.00 TsE vii e 0.00 140.01 345.83 0.00 0.00 BLANK 0.00 375.58 927.67 0.00 0.00 W 0.00 57.98 143.20 0.00 0.00 1378.60a 37423.38b
27.15c Table 6: Apalachicola Old Town Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
254
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
NaB ii e 75.00 31.57 77.99 26.47 2064.32 FuB ii s 85.00 3.20 7.90 30.00 236.90 TrB iii s 60.00 12.21 30.16 21.18 638.71 NaC iv e 50.00 6.01 14.83 17.65 261.75 CoC iv e/ vi s 60.00 0.08 0.19 21.18 3.92 NkC3 vi e 0.00 206.52 510.11 0.00 0.00 NkD3 vi e 0.00 67.31 166.26 0.00 0.00 NnE3 0.00 173.81 429.30 0.00 0.00 W 0.00 56.01 138.33 0.00 0.00 none 0.00 114.98 284.01 0.00 0.00 85.88a 2301.21b
26.80c Table 7: Broken Arrow Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
255
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
KoA I 115.00 127.09 313.91 26.54 8330.68 MxA I 110.00 70.12 173.20 25.38 4396.71 WkA I 125.00 336.03 830.00 28.85 23942.42 AnA ii w 100.00 10.41 25.71 23.08 593.31 CtB ii w 130.00 187.33 462.70 30.00 13881.08 FpA vii w 0.00 1.49 3.69 0.00 0.00 BLANK 0.00 434.94 1074.29 0.00 0.00 UdA 0.00 3.89 9.60 0.00 0.00 W 0.00 78.97 195.05 0.00 0.00 1805.53a 51144.21b
28.33c Table 8: Chiaha Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
256
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
MxA I 110.00 87.87 217.05 25.38 5509.64 WkA I 125.00 147.93 365.38 28.85 10539.80 SbB ii e 100.00 12.17 30.06 23.08 693.63 CtB ii w 130.00 78.54 194.00 30.00 5820.11 DgA ii w 100.00 2.89 7.13 23.08 164.50 RvA ii w 130.00 170.39 420.86 30.00 12625.68 LnC2 iv e 70.00 3.23 7.98 16.15 128.92 KMA v w 0.00 30.17 74.52 0.00 0.00 LsE vii e 0.00 164.39 406.04 0.00 0.00 TsE vii e 0.00 92.61 228.74 0.00 0.00 FpA vii w 0.00 42.91 105.98 0.00 0.00 Pt viii s 0.00 187.18 462.33 0.00 0.00 Ur viii s 0.00 23.00 56.81 0.00 0.00 BLANK 0.00 100.45 248.10 0.00 0.00 W 0.00 106.56 263.19 0.00 0.00 1234.47a 35353.36b
28.64c Table 9: Coweta Tallahassee Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
257
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
NaB ii e 75.00 26.06 64.38 18.75 1207.09 OrB ii e 120.00 29.30 72.36 30.00 2170.76 OuB ii e 0.00 104.79 258.82 0.00 0.00 UoC iii e 0.00 37.05 91.52 0.00 0.00 TrB iii s 60.00 1.27 3.15 15.00 47.20 NaC iv e 50.00 3.36 8.29 12.50 103.62 NkC3 vi e 0.00 30.69 75.81 0.00 0.00 NkD3 vi e 0.00 53.07 131.09 0.00 0.00 CoD vi e/ vii e 0.00 2.36 5.84 0.00 0.00 CwE vii e 0.00 45.91 113.41 0.00 0.00 NnE3 0.00 28.91 71.42 0.00 0.00 RvA 0.00 0.76 1.86 0.00 0.00 Ua 0.00 5.50 13.58 0.00 0.00 UbC 0.00 283.05 699.14 0.00 0.00 Ud 0.00 78.09 192.89 0.00 0.00 W 0.00 41.99 103.71 0.00 0.00 395.56a 3377.8b5
8.54c Table 10: Cusseta Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
258
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
NaB ii e 75.00 12.03 29.70 18.75 556.91 OrB ii e 120.00 39.35 97.20 30.00 2915.98 OuB ii e 0.00 182.46 450.68 0.00 0.00 UoC iii e 0.00 135.95 335.78 0.00 0.00 TrB iii s 60.00 11.33 27.99 15.00 419.89 NaC iv e 50.00 3.36 8.29 12.50 103.62 CoC iv e/ vi s 60.00 4.50 11.11 15.00 166.61 UtC iv s 0.00 15.42 38.09 0.00 0.00 NkC3 vi e 0.00 45.66 112.77 0.00 0.00 NkD3 vi e 0.00 38.08 94.07 0.00 0.00 CoD vi e/ vii e 0.00 13.51 33.37 0.00 0.00 TrC vi s 55.00 11.04 27.27 13.75 374.98 CwE vii e 0.00 46.40 114.61 0.00 0.00 NnE3 0.00 19.22 47.48 0.00 0.00 Ua 0.00 9.00 22.22 0.00 0.00 UbC 0.00 236.35 583.77 0.00 0.00 Ud 0.00 103.48 255.60 0.00 0.00 W 0.00 23.20 57.30 0.00 0.00 none 0.00 200.67 495.65 0.00 0.00 577.58a 3472.89b
6.01c Table 11: Cusseta Old Town Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
259
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
KoA I 115.00 165.21 408.06 26.54 10829.25 MxA I 110.00 152.40 376.42 25.38 9555.23 WkA I 125.00 283.65 700.61 28.85 20209.78 AnA ii w 100.00 4.44 10.96 23.08 253.02 CtB ii w 130.00 107.98 266.70 30.00 8001.02 FpA vii w 0.00 4.59 11.33 0.00 0.00 Pt viii s 0.00 1.33 3.29 0.00 0.00 BLANK 0.00 506.80 1251.81 0.00 0.00 UdA 0.00 5.79 14.29 0.00 0.00 W 0.00 18.09 44.69 0.00 0.00 1762.75a 48848.30b
27.71c Table 12: Osochi Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
260
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
DoB ii e 120.00 12.92 31.91 30.00 957.22 EtA ii w 90.00 80.43 198.66 22.50 4469.84 WbA ii w 100.00 19.58 48.36 25.00 1208.94 DoC iii e 100.00 25.08 61.96 25.00 1548.94 EmB iii e 50.00 42.83 105.78 12.50 1322.22 AaB iii s 50.00 4.40 10.86 12.50 135.73 BoC iii s 80.00 16.33 40.33 20.00 806.55 TrB iii s 60.00 37.37 92.31 15.00 1384.71 CoC iv e/ vi s 60.00 1.87 4.61 15.00 69.17 AaC iv s 45.00 8.75 21.61 11.25 243.11 Bh v w 0.00 67.56 166.87 0.00 0.00 CoD vi e/ vii e 0.00 3.80 9.39 0.00 0.00 TrC vi s 55.00 44.17 109.10 13.75 1500.16 TrD vi s 0.00 12.93 31.93 0.00 0.00 CwE vii e 0.00 1.16 2.85 0.00 0.00 Ck 100.00 75.63 186.80 25.00 4670.09 Pm 0.00 103.10 254.66 0.00 0.00 SeA 80.00 50.50 124.74 20.00 2494.70 SuB 0.00 15.12 37.34 0.00 0.00 SuC 0.00 2.36 5.82 0.00 0.00 TSD 0.00 53.68 132.58 0.00 0.00 TVD 0.00 16.25 40.14 0.00 0.00 To 90.00 97.71 241.33 22.50 5430.01 VeC 60.00 37.84 93.46 15.00 1401.93 VeD 55.00 112.21 277.15 13.75 3810.83 W 0.00 4.42 10.92 0.00 0.00 WaB 75.00 23.30 57.54 18.75 1078.94 WaC 70.00 101.68 251.16 17.50 4395.25 278.92a 6636.01b
23.79c Table 13: Upatoi Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
261
Aboriginal Corn Capability Aboriginal Corn Soil Type Corn Yield Hectares Acres Yield by Area Index Yield (acres)
KoA I 115.00 98.06 242.20 26.54 6427.57 MxA I 110.00 226.35 559.08 25.38 14192.02 WkA I 125.00 123.94 306.14 28.85 8830.87 CtB ii w 130.00 174.97 432.18 30.00 12965.28 DgA ii w 100.00 12.72 31.41 23.08 724.93 Pt viii s 0.00 0.13 0.32 0.00 0.00 BLANK 0.00 555.02 1370.89 0.00 0.00 UdA 0.00 38.17 94.28 0.00 0.00 W 0.00 20.92 51.67 0.00 0.00 1571.00a 43140.66b
27.46c Table 14: Yuchi Catchment soil types and corn yields by soil type. “a” is the sum the acreage of type I and II soils, “b” is the sum of the aboriginal corn yield by area of type I and II soils, and “c” is the average aboriginal corn yield for the entire catchment in bushels per acre ( b/a).
Curriculum Vitae
Howard Thomas Foster, II
Education: 2001, Ph.D. in Anthropology, Pennsylvania State University 1997, M.A. in Anthropology, Pennsylvania State University 1993, B. A. in Anthropology, University of Georgia Selected Awards: 2001, Dissertation Fellowship, Graduate School, Pennsylvania State University 1999-2001, Strategic Environmental Research and Development, Research Grant, Department of Defense 2001, Research Award, Strontium Area, Geochron Laboratories 1998, Hill Fellowship, Department of Anthropology, Pennsylvania State University 1994-1996, Graduate Assistantship, Pennsylvania State University 1993, Phi Kappa Phi, Honor Society 1992, DeSoto Award, Department of Anthropology, University of Georgia Selected Positions Held: 1997, 1999-2001, Part Time Instructor, Pennsylvania State University 1999, Adjunct Faculty, Elizabethtown College 1994-1995, 1999-2001, Vice-President, Anthropology Graduate Student Association, Pennsylvania State University Selected Publications and Presentations: Foster, Thomas, Bryan Black and Marc Abrams. 2001. An analysis of the effects of American Indian behaviors on the forest composition in east- central Alabama. (submitted) Black, Bryan, Thomas Foster, and Marc Abrams. 2001. Combining environmentally dependant and environmentally independent analysis of witness tree data in East-Central Alabama. (submitted) Olsen, Lisa M., Virginia Dale, and Thomas Foster. 2001. Landscape patterns as indicators of ecological change at Fort Benning, Ga. 2001 ESRI Users Conference, San Diego, California. Foster, Thomas. 2000. Temporal trends in the paleodemography of the late historic Creek Indians. Southeastern Archaeological Conference, Macon, Ga. Foster, Thomas. 2000. Evolutionary ecology of Creek Indian residential mobility. Southeastern Archaeological Conference, Macon, Ga. Hatch, James, Jerald Ledbetter, Adam King, Peter Van Rossum, Thomas Foster, and Ervan Garrison. 1997. Excavations at the Marshal Site (9OC25). Early Georgia 25(2): 1-35. Foster, Thomas. 1993. Burial depth at a late Lamar Village. Southeastern Archaeological Conference, Raleigh, North Carolina.