EVIDENCE OF CHANGING SETTLEMENT PATTERNS
|Table 1. Distribution of sample units in comparison to the number of recognized temporal components and classified functional site designations.|
Number of dated|
|Residential Bases||Field Camps||Locations||Total|
To develop a model of land use over time we must begin by identifying, based on the characteristics of the random sample, sets of spatial loci which display differential potential for human habitation and assign them temporally dependent probabilities of occupation. If these spatial loci or topographic types can be quantitatively defined in a manner that permits the classification of any portion of the study area, then a site prediction model can be produced using spatial interpolation procedures. This is accomplished by applying basic pattern recognition techniques to the SU's defined in terms of the four topographic variables.
The first step of this process involves standardizing and simplifying the four variables by applying principal component analysis with varimax rotation to the correlation matrix derived from the 96 datable units. The resulting solution provides dual components which scale the elevation and distance to water variables independently. The two component characterization of the 96 dated units can be used to define minimum variance clusters following Ward's algorithm (Ward 1963). An examination of the fluctuations in the clustering criterion (Figure 6) indicates either a three or nine cluster solution as the best fit for this data. The nine cluster solution was preferred because the smaller grouping tended to oversimplify the topographic variability into merely river terraces, slopes, and uplands. A discriminant analysis incorporating the nine within group covariance matrices derived from the clustered 96 datable SU's can then be used to assign locational type designations to each of the 329 remaining units. (The within group covariance matrices were used rather than the pooled form because the between group variances were not equal). A breakdown of these nine quantitatively defined clusters in terms of nine recognizable land form types is given in Table 2. Table 3 provides mean and standard deviations for the original four variables within each cluster. The clusters appear to reflect the topographic gradient that separates river terraces from the uplands across the study area.
|Table 2. Distribution of cluster groups by major land form types.|
|Older Terraces LTR||4||9||1||0||5||5||1||2||0|
|Tellico R. valley||4||8||5||0||0||0||0||0||0|
|Tellico R. slopes||1||1||1||2||0||2||0||2||0|
|Prim. trib. valley||0||4||0||0||13||0||13||0||0|
|Prim. trib. slopes||0||1||0||4||12||3||9||0||0|
|LTR = Little Tennessee River
Prim. trib. = Primary tributary
|Table 3. Statistics on original topographic variables by cluster group.|
|C = Cluster group
DIST1 = Distance to the nearest major stream (ft)
DIST2 = Distance to the nearest secondary stream (ft)
ELEV1 = Elevation above the nearest water source (ft)
ELEV2 = Elevation above mean sea level (ft)
To produce the original regional maps (Figures 2, 3, 4, and 5), 915 additional unsampled SU locations were quantified in terms of the four topographic variables. For this study component scores are calculated and the discriminant analysis is used to assign locational type designations for each. The combined 1340 unit sample can now be used to define the spatial distribution of each of the nine location types across the study area. By associating probabilities of site occurrence with each cluster group, maps that delineate levels of site concentration for each time period can be interpolated using the piecewise Bessel algorithm of SURFACE II (Sampson 1978).
Calculation of the relative frequency of each temporal component within each location type can be used as an estimate of the probabilities of site occurrence. However, because separate sampling schemes were used to select the 425 SU's, each unit must be weighted by the inverse of its associated sample fraction in order to produce an unbiased estimate of the total population density of each cluster type. Thus, one unit from the below pool sample stratum (f=0.0151) would represent 66.23 SU's from the total population. Units from the second and third strata would correspond with 20.00 and 18.08 units, respectively. The final weighted estimates of the frequency of location types for the 12,663 unit population are:
The expected relative frequency of each temporal component for each location type is provided in Table 4. Each value corresponds with the probability of locating a specific component within a 300 x 300 ft area sampled in the manner described above. These values can be interpreted as the expected density of temporally recognizable sites. To graphically present the shifts in settlement density and location, maps were produced showing the differences between contiguous temporal units (Figures 7, 8, 9, and 10). (Areas not highlighted represent consistent or decreased levels of utilization between time periods and not an expected absence of sites). This provides the basis for an evaluation of settlement changes through time.
|Table 4. Probabilities of site location on nine topographic types by temporal period.|
The approach applied to this data is one of crosstabulation of categorical data to produce estimated probabilities of event occurrence at any given locus. Because the units were randomly selected, each temporal period has a somewhat equal a priori chance of being identified at any one location. Obviously the probability of identifying a component is dependent on the number of diagnostic types associated with each time unit and the total duration of the period. Unfortunately, it is beyond the scope of this paper to fully address these biases. However, this data can be confidently used to identify cultural preferences (trends) for site location, or more explicitly, to identify locations which tend to be correlated with the preservation of temporal markers. Higher probabilities should be directly related to extensive use of loci with lower values indicating less utilization. With this in mind several settlement observations can be made from the data's temporal distribution across space.
Given a standard set of a priori notions of southeastern archaeological cultures we would expect to see an uninterrupted increase in site density from Early Archaic through Mississippian along the terraces of the Lower Little Tennessee River valley. This is based on our assumptions of: 1) continuous occupation of the valley over time, 2) an exponential population growth for its inhabitants, and 3) through time, an increased dependence on plant domesticates grown on river bottom soils. A recent examination (Cridlebaugh 1984) of the effects of aboriginal settlement on the area's vegetation pattern based on paleoethnobotanical and pollen samples supports these contentions. Summarizing her work, prior to the Late Archaic/Woodland I period only minor alterations to the landscape were induced by cultural activities. At about 4000 years B.P. the expanded use of cultigens resulted in the initialization of river terrace clearance. As horticulture became a primary means of food production, populations began to concentrate on soils as a resource. Thus, utilization of more and more river terrace land became necessary to support larger and larger populations. Our changing settlement model should reflect these trends if survey data is to be excepted as a useful source of information.
In general our expectations are supported by the maps showing the areas of increased site density. Figure 7 shows a broad based utilization of site locations across the valley above Fork Greek by Middle Archaic times. The Late Archaic/Woodland I site densities suggest a slight expansion of this pattern with particular emphasis placed on broadening the use of river terraces (Figure 8).
By Woodland II/III (Middle Woodland) times, however, this steady expansion has halted (Figure 9). Based on the survey data, only specific river terrace locations are expected to produce indications of expanded site utilization. It is tempting to suggest that land adjoining the mouths of major streams provide the primary locations for intensive site location. Indeed, the two most extensively excavated Middle Woodland sites from the valley are located near the mouth of Tellico River, Icehouse Bottom (40MR23, Chapman 1973, Cridlebaugh 1981) and Patrick (40MR40, Schroedl 1978). Additionally, pollen data from Tuskegee Pond, located near this area, suggest that steady Zea mays production occurred during this period (Cridlebaugh 1984:143). Given the relatively short 1100 year time span the observance of such a concentrated settlement plan is possible, although not expected. What this map reflects may be the less mobile nature of the Middle Woodland populations over a short period of time which contrasts with the pattern produced by the highly mobile settlement system of the previous 6000 years. This shift marks a major change in settlement strategy and probably social organization,. that culminates in the more intensive occupations of the Mississippian period.
The Mississippian pattern (Figure 10) appears to be an expansion of this earlier concentration on river terraces with the exclusion of those areas most intensively used during the preceding Woodland II/III period. Unlike the pre-Woodland II/III expansions, this pattern is more narrowly confined to the prime agricultural soils and develops over a shorter (900 years) period. The observed increased use of these soils logically follows our expectations given the assumed population levels during this period, the increased dependence on Zea mays by these populations, and the eventual dispersed settlement pattern of the later historic Cherokee.
This brief examination into the changing settlement patterns for the Lower Little Tennessee River valley has been useful for three reasons. First, it demonstrates that survey data can be used to assess settlement changes over time. Second, the results can help direct future research by identifying anomalous situations, like that associated with the Middle Woodland Settlements, that cannot be explained by our current unilineal model for culture change. Finally, the settlement model, reduced to a simple graphic format, has been shown to be a tool for interpretation that readily supplements the results of other studies.
Surveys of this sort provide an important avenue for future research. They alone synthesize various analytical results to produce a predictive model. By striving for a simple representation of the model a useful interpretational tool can be produced, despite the complexity involved in construction.
Acknowledgments. This project is an extension of the original survey funded by the Tennessee Valley Authority. I would like to thank Maxwell Ramsey and Bennett Graham of the T.V.A. for their support and my co-workers, Larry R. Kimball and R.P. Stephen Davis, Jr. for their contributions to the production of the original data used here. I would also like to thank Thomas Whyte, Cliff Boyd, and Brett Riggs for their comments and suggestions which greatly improved the contents of this paper. However, I accept full responsibility for the conclusions presented herein. Financial support for the computational aspects of this research was provided by the University of Tennessee's Computing Center and the Department of Anthropology.
1982a The Nature of Surface Collections. In An Archeological Survey and Assessment of Aboriginal Settlement Within the Lower Little Tennessee River Valley, edited by R.P. Stephen Davis, Jr., Larry R. Kimball, and William W. Baden, pp. 105-119. Report submitted to the Tennessee Valley Authority.
1982b Appendix I: Generation of a Probabilistic Model of Site Location. In An Archeological Survey and Assessment of Aboriginal Settlement Within the Lower Little Tennessee River Valley edited by R.P. Stephen Davis, Jr., Larry R. Kimball, and William W. Baden, pp. 517-524. Report submitted to the Tennessee Valley Authority.
1982 The Archaeology of Place. Journal of Anthropological Archaeology 1:5-31.
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1981 The Icehouse Bottom Site (40MR23) 1977 Excavations. Report of Investigations No. 35. Department of Anthropology, University of Tennessee, Knoxville.
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1980 A Preliminary Report of Probabilistic and Nonprobabilistic Archeological Sampling in Industrial Area II Tellico Reservoir, Tennessee. Tellico Archeological Survey Report No. 2. Department of Anthropology, University of Tennessee, Knoxville.
1982a Sampling Design. In An Archeological Survey and Assessment of Aboriginal Settlement within the Lower Little Tennessee River Valley, edited by R.P. Stephen Davis, Jr., Larry R. Kimball, and William W. Baden, pp. 37-72. Report submitted to the Tennessee Valley Authority.
1982b Ceramic Artifact Analysis. In An Archeological Survey and Assessment of Aboriginal Settlement Within the Lower Little Tennessee River Valley, edited by R.P. Stephen Davis, Jr., Larry R. Kimball, and William W. Baden, pp. 253-332. Report submitted to the Tennessee Valley Authority.
Davis, R.P.S., Jr.
1982c Archeological Settlement Analysis. In An Archeological Survey and Assessment of Aboriginal Settlement Within the Lower Little Tennessee River Valley, edited by R.P. Stephen Davis, Jr., Larry R. Kimball, and William W. Baden, pp. 333-411. Report submitted to the Tennessee Valley Authority.
Davis, R.P.S., Jr., L.R. Kimball, and W.W. Baden (editors)
1982 An Archeological Survey and Assessment of Aboriginal Settlement Within the Lower Little Tennessee River Valley. Report submitted to the Tennessee Valley Authority.
1982 Lithic Artifact Analysis. In An Archeological Survey and Assessment of Aboriginal Settlement Within the Lower Little Tennessee River Valley, edited by R.P. Stephen Davis, Jr., Larry R. Kimball, and William W. Baden, pp. 120-252. Report submitted to the Tennessee Valley Authority.
1978 Surface II Graphics System. Kansas Geological Survey, Lawrence.
1978 The Patrick Site (40MR40), Tellico Reservoir, Tennessee. Report of Investigations No. 25. Department of Anthropology, University of Tennessee, Knoxville.
1963 Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association 58:236-244.