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PREfflSTORIC POPULATION DYNAMICS IN THE SILVER CREEK AREA,
EAST-CENTRAL ARIZONA
by
Joanne Marie Newcomb
A Thesis Submitted to the Faculty of the
DEPARTMENT OF ANTHROPOLOGY
In Partial Fulfillment of the Requirements
For the Degree of
MASTER OF ARTS
In the Graduate College
THE UNIVERSITY OF ARIZONA
1997
X3MX Number: 1387965
UMI Microform 1387965
Copyright 1998, by UMI Company. All rights reserved.
This microform edition is protected against unauthorized
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2
STATEMENT BY AUTHOR
This thesis heis been submitted in partial fulfillment of requirements for an
advanced degree at The University of Arizona and is deposited in the University Library
to be made available to borrowers under rules of the Library.
Brief quotations from this thesis are allowable without special permission,
provided that accurate acknowledgment of source is made. Requests for permission for
extended quotation from or reproduction of this manuscript in whole or in part may be
granted by the head of the major department or the Dean of the Graduate College when in
his or her judgment the proposed use of the material is in the interests of scholarship. In
all other instances, however, permission must be obtained from the author.
SIG
APPROVAL BY THESIS DIRECTOR
This thesis has been approved on the date shown below:
Barbeira J.'Mills
Associate Professor of Anthropology
/6-2?fecfc-r7.-e.g^ l ? ? f Date
ACKNOWLEDGEMENTS
I would like to express my sincerest appreciation to the members of my
committee, Jef&ey S. Dean, Barbara J. Mills, and J. Jefferson Reid, for their help
throughout this project. A special note of thanks goes to my advisor, Barbara Mills, for
her guidance since my arrival in the program. Numerous others provided invaluable
assistance including: Donald J. Breckenfeld, for helping me sift through soil data; Doug
Gann, Chuck Riggs, and Adam Smith, without whom I would still be AutoCAD illiterate;
Michael Zecchino, for his Excel-lent assistance; Mark Elson, for being the voice of
reason; Ken Boden, for having to listen to me talk about this thesis for so long; and Tim
Collier, for much needed moral support. Finally, a word of thanks to my mother, for
always being there.
4
TABLE OF CONTENTS
LIST OF FIGURES
5
LIST OF TABLES
6
ABSTRACT
7
CHAPTER ONE: Introduction
8
CHAPTER TWO: Previous Population Reconstructions in the Silver Creek Region
13
CHAPTER THREE: Methods of Demographic Reconstruction
22
Site-Based and Regional Approaches to Population Reconstruction
24
Recent Approaches to Southwestern Demographic Reconstruction
29
CHAPTER FOUR: The Database and Reconstructions
35
Time Standardizing Sites
38
Environmental Strata
39
Site Size Classes
44
Population Models
47
Model A: Time-Standardized Room Counts
47
Model Bl: Total Rooms Using Site Size Category Median
48
Model B2: Total Rooms Transformed
50
Model B3: Momentary Population from Transformed Total Rooms
52
Model CI: Total Rooms Using Actual Room Counts for Sites with 20 or More
Rooms
57
Model C2: Total Rooms Transformed Using Actual Room Counts for Sites with
20 or More Rooms
59
Model C3: Momentary Population from Transformed Total Rooms Using Actual
Room Counts for 20+ Room Sites
60
Model Dl: The Habitation Room Problem
62
Model D2: Momentary Population Using Plog's Adjustment Factors
63
Model E: Small Sites and the Seasonality Problem
64
Models El and E3: Transformed Total Rooms Minus All 1—4 Room Sites and
Transformed Total Rooms with Only 1-4 Room Sites
65
Models E2 and E4: Momentary Population from Transformed Total Rooms Minus
All 1-4 Room Sites and Only
Room Sites
69
Will the Real Population Curve Please Stand Up?
71
CHAPTER FIVE: Discussion and Conclusion
72
Observed Versus Expected Growth
72
Environmental Variables
82
Conclusion
84
Future Research
88
APPENDIX 1: Site Data
91
APPENDIX 2: Site Counts by Strata
109
APPENDIX 3: Site Locations by Time Period
113
REFERENCES CITED
123
5
LIST OF FIGURES
Figure 1.1: The Silver Creek Watershed, East-Central Arizona
Figure 4.1: Surveys included in the database
Figure 4.2: Soils in the Silver Creek watershed
Figure 4.3: Sites by number of rooms
Figure 4.4: Sites by number of periods of occupation
Figure 4.5: Rooms contributed to the curve by number of periods a site spans
Figxire 4.6: Model A
Figure 4.7: Model B1
Figure 4.8: Model B2
Figure 4.9: Model B3
Figure 4.10: Model CI
Figure 4.11: Model C2
Figure 4.12: Model C3
Figure 4.13: Model D1
Figure 4.14: Model D2
Figure 4.15: Model El
Figure 4.16: Model E3
Figure 4.17: Model E2
Figure 4.18: Model E4
Figure 5.1: Site sizes through time
9
36
42
44
46
46
48
49
51
56
58
60
61
63
64
67
67
70
70
81
6
LIST OF TABLES
Table 2.1: Upper Little Colorado Population, Chicago Natural History Museum
Reconstruction
—
15
Table 2.2: Upper Little Colorado Population, F. Plog Reconstruction
15
Table 2.3: Upper Little Colorado Population. Zubrow Reconstruction
16
Table 2.4: Pinedale Region Population, Jewett Reconstruction
17
Table 2.5: Silver Creek/Hay Hollow Population Comparison, Lightfoot Reconstruction 20
Table 2.6: Upper Little Colorado Population, Lightfoot Reconstruction
21
Table 4.1: Projects Included in the Database
37
Table 4.2: Soil Types
41
Table 4.3: Silver Creek Environmental Strata
43
Table 5.1: Observed and Expected Rates of Population Increase
74
Table 5.1: Continued
75
Table 5.1: Continued
76
Table 5.1: Continued
77
Table 5.1: Continued
78
7
ABSTRACT
The Silver Creek area has been the focus of archaeological research since the late
nineteenth century. Many of the theories resulting finm this work have incorporated
estimates of population, either explicitly or implicitly, into the fabric of their arguments.
Topics such as sociopolitical structure, migration, aggregation, and social integration
require population reconstructions as a foundation for understanding the processes of
culture change.
Numerous population reconstructions have been presented in the past for the
Silver Creek area; however, much of the data incorporated in the present study was
unavailable for the previous reconstructions. In this study, several models based on
numerous plausible assumptions are presented to determine if a best fit can be found. The
results show that there was a major increase in population in the Silver Creek area
between A.D. 900-1100, and population declined steadily after about A.D. 1100-1150
until the region was abandoned by about A.D. 1400.
8
CHAPTER ONE
INTRODUCTION
To discuss many of the issues of interest to archaeologists today, we must have an
idea of population trends. Issues such as migration, aggregation, social integration,
hierarchy, and specialization, for example, all have intrinsic assumptions about the size
and distribution of the population. The size of communities and their distribution on the
landscape are fundamental to understanding cultural change and adaptation. As Powell
(1988:168-169) has noted, "People are both dependent and independent variables in the
study of cultural change—they form part of the environment to which they are adapting
and they actively change that environment with their adaptations. Thus measures of
population size are critical for assessing theories of why and how cultures change".
Understanding the ebb and flow of people is particularly relevant as studies of
migration have seen a resurgence of interest in recent times. Moreover, population studies
must necessarily incorporate the study of migration, since population configurations are
equally affected by not only birth and death rates, but by the rate of migration as well
(Anthony 1990:896-897). However, migration can often be difficult to recognize
archaeologically since short-distance movements were common in the Southwest (Mills
1996:7) and migration streams are often bidirectioneil (Anthony 1990:897-898).
Coincident with the study of migration is the study of the processes of abandonment,
since moving to a new location requires leaving another (Fish et al. 1994:135).
9
Figure 1.1: The Silver Creek Watershed, East-Central Arizona
tan
Low
The resurgence of interest in the process of migration and its implications in
culture change has been particularly relevant for research in the Silver Creek region of
Arizona (Figure 1.1). Several important theories have incorporated migration into the
fabric of their argimients. Crown (1994:203-215) tuis argued that population upheavals
and demographic shifts at the end of the 13th century coincided with the appearance and
dissemination of a Southwestern cult. She cites evidence for a migration from the
10
Tusayan-Kayenta area into the Mogollon Rim region as being responsible for the
development of Salado Polychrome ceramics, the distribution of which signals the
widespread adoption of a religious ideology centered around the functioning of the
universe, the earth, and fertility. She hypothesizes that initially the production of pottery
may have allowed immigrants to integrate more easily into an already populated area
along the Mogollon Rim.
Stinson (1996:11-15) expands on this idea in her study of Pinto Polychrome in
the Silver Creek area. She argues that influxes of people into areas with extant
populations could have an economic impact that would affect systems of agriculture and
craft production; population pressures would amplify these effects. Because migrants are
often marginalized in the host community, and often not privy to adequate subsistence
resources, ceramic production would be a way of supplementing subsistence activities.
Stinson's findings (1996:86-91) support Crown's suggestion that Salado Polychromes
originated in the Mogollon Rim area, and argues that a shift in ceramic technology in the
late 13th century may provide further evidence of an influence from Kayenta-Tusayan
migrants.
Adams (1991:119-120, 151,160) has suggested that the near-simultaneous
appearance of Pinedale-style iconography, small rectangular kivas, and enclosed-plaza
pueblos, signals the origin and development of the pueblo katsina cult. He attributes
demographic shifts in the late 13th century as being responsible for development of the
cult in the middle and upper Little Colorado region, a result of the need to integrate
migrating populations into increasingly aggregated host communities.
11
Herr (1994:66—75) also looked at how communities integrate their populations,
but during an earlier time period. In her study, she suggested that the expansion of
circular great kivas into the Silver Creek area corresponded to large population increases
around A.D. 1000—1100, indicating a migration into the area. Circular great kivas as a
form of integrative architecture lasted until about A.D. I ISO and were replaced by square
kivas and plazas in the A.D. 1200s. Herr suggests that increased population and
aggregation may have resulted in a reorganization of social structure and, consequently,
changing requirements for integrative architecture.
As Herr noted, aggregation can have a significant impact on social structure.
Aggregation, in general, refers to "...the processes that produce spatial clustering of
households, communities, or archaeological habitation sites" (Cordell et al. 1994:109).
From about A.D. 1000, much of the Southwest saw a change from dispersed to
increasingly aggregated commimities, ultimately resulting in the large, highly nucleated
sites of the Pueblo IV period. According to Cordell et al. (1994:129-130), in the nonMimbres Mogollon areas, aggregated sites tend to occur between A.D. 1150 and 1300,
and population increase is assumed to have preceded the shift to an aggregated pattern of
settlement.
Cordell et al. (1994:111-112) noted that there are a few key variables central to
discussions of aggregation, one of which is population density. In addition, they point out
that in order to make inferences about aggregation, population size must be known for
contemporaneous sites. Population studies then should consider issues of aggregation
since population size and aggregation are interrelated, and understanding these is
essential to numerous questions. For example, degrees of aggregation differentially affect
human impact on the environment, and Zack Homer (1996:153), in her study of the
faunal assemblages of two aggregated sites in the Silver Creek area, showed that
increased population and aggregation have a measurable effect on subsistence practices
and impact on faunal resources. In her study of the effects of aggregation at historic
pueblos, Dohm (1990:224) found that with increasing aggregation, floor space per person
increases. This fact is of particular importance to current population reconstruction
methods, and is significant in analyzing the results of the present paper.
The present study looks at the paleodemography of the Silver Creek region in
east-central Arizona, located on the border between the larger regional systems of Chaco
and Hohokam. As illustrated above, the Silver Creek area has played a part in the
formulation of numerous theories integrating issues of population. Consequently an
investigation of the paleodemography of the area may help to inform on these
phenomena. Is there any evidence to support the idea of a migration in the 11th century?
Is there evidence for population upheaval and demographic shift in the late 13th century?
This paper will try to shed some light on these issues for the Silver Creek region.
13
CHAPTER TWO
PREVIOUS POPULATION RECONSTRUCTIONS IN THE
SILVER CREEK REGION
The Silver Creek area has been the focus of archaeological study since the late
nineteenth century. In 1883, Adolf Bandelier (1892:392-393) visited the area and was the
first to map Showlow Ruin (Lange et al. 1970:80-82). Jesse Walter Fewkes (1904)
investigated the region for the Bureau of American Ethnology, and his 1897 field
expedition led him to examine ruins at Pinedale and SnoMrflake, including Fourmile Ruin,
Pinedale Ruin, and Bailey Ruin. Walter Hough (1903), who had accompanied Fewkes on
the 1896 expedition, returned to the Silver Creek area in 1901 with the Museiun-Gates
Expedition. Among the sites Hough visited were the Linden Ruin (also known as Pottery
Hill) and Showlow Ruin.
It was not until the early 1900s, however, that an archaeologist expressed interest
in the demography of the Silver Creek area. Leslie Spier (1918, 1919) visited numerous
sites in the Silver Creek drainage and the surrounding region in an attempt to establish a
ceramic chronology for the Zuni area. With his ceramic sequence constructed. Spier
(1919:386) hypothesized population movements as a means of explaining the observed
variation in the archaeological record. These population movements included a migration
of people from the White Mountains eastward to the Silver Creek area, and later, from
Silver Creek toward Zuni.
After Spier's visit, the next major archaeological research project was in the late
1920s when Haury and Hargrave (1931) excavated at several sites in the Silver Creek
14
area for the National Geographic Society's Third Beam expedition. Their efforts resulted
in the discovery of what has been called ..the Rosetta Stone of southwestern
prehistory..." (Mills 1996:2)—^the missing link in the tree-ring sequence that allowed
absolute dating of prehistoric sites in the Southwest
Starting in the late 1950s, a flurry of activity erupted and a period of intense
archaeological investigation was begun in several areas adjacent to Silver Creek. Most of
this work was conducted by the Chicago Natural History Museum under the direction of
Paul S. Martin (Martin et al. 1962), and consisted of excavations and survey in an
approximately 1500 square mile area. This area stretched from Springerville to St. Johns,
to Snowflake, to Show Low, and also included an area to the south in the White
Mountains. The data from these investigations, which included an intensive survey of
approximately 50 square miles, provided the basis for what became the first attempt at a
demographic reconstruction for the Upper Little Colorado area. The survey data were
grouped according to geographic area, and sites were assigned to six temporal groups
based on ceramics. General population trends were thereby derived for the Upper Little
Colorado for a period spanning from 1300 B.C. to A.D. 1300 (Longacre 1964; Martin et
al. 1962:215-225). Population figures were provided with the understanding that they
were not to be taken literally, but rather as an indication of relative population through
time (Table 2.1).
15
Table 2.1: Upper Little Colorado Population, Chicago Natural
History Museum Reconstruction'
Date
A.D. 275
A.D. 900
A.D. 1000
A.D. 1200 ±50
A.D. 1100-1300
Demographic Interpretation
Regional population small
900 people
Population greatly increased
2600 people in a 1500 square mile area
3800-4000 people clustered around the Little Colorado River and its
major tributaries near Snowflake eind Mesa Redonda
A.D. 1500
Area completely abandoned
(based on Martin et al. 1962:216)
Based on the Chicago Natural History Museum's work in the Hay Hollow Valley,
Fred Plog (1974:93-95) presented a demographic reconstruction for the area, which he
felt typified the population changes for the Upper Little Colorado region (Table 2.2).
Table 2.2 Upper Little Colorado Population, F. Plog Reconstruction^
Date
A.D. 200-400
A.D. 400
A.D. 500-800
A.D. 800-1050
A.D. 1050-1200
Demographic Interpretation
Population increased
Population peak
Population decreased and became stable
Population increased rapidly
Population peaked and remained at a maximum of approximately 50100% more than the previous peak
A.D. 1200-1350 Population declined rapidly and the valley was abandoned although the
Upper Little Colorado region remained occupied for another 150 years
^(based on Plog 1974:93-95)
Plog (1974:93-94) saw no utility in translating habitation units into population
figures because the important point to consider was the shape of the curve and not the
absolute numbers. He did, however, provide a table with the number of dwelling units per
time period which corresponds to the trends expressed above.
16
For his study of prehistoric carrying capacity, Zubrow (1975:57-66) field-checked
the Hay Hollow survey data compiled by the Chicago Natural History Museum personnel
and concluded that the previous dating and room estimates for the sites in the area were
reliable. Zubrow's reconstruction therefore agreed with Longacre's (1964) and Johnson's
(1970) population reconstructions which he summarized (Table 2.3).
Table 23 Upper Little Colorado Population, Zubrow Reconstruction^
Demographic Interpretation
No evidence of architecture
Population increased up to A.D. 500 and decreased after A.D.
600
A.D. 750-900
Population decreased until about A.D. 800 and began to increase
A.D. 900-1100
Period of maximum population growth, peaking at about A.D.
1025 (although possibly later), and then rapidly declining
A.D. 1100-1450
Total population is less than the previous period. Population
continues to decline until the area is completely abandoned
around A.D. 1350-1400
•"(based on Zubrow 1975:55—57)
Date
B.C. 1000-A.D. 200
A.D. 200-750
Plog (1981:68) attributed the differences between his reconstruction and
Longacre's and Zubrow's reconstruction to local conditions and methodological
differences, although he did not elaborate what these were.
In the late 1970s, numerous projects were conducted by personnel from Arizona
State University (ASU), including the Chevelon Archaeological Research Project
(CARP) to the west of the Silver Creek area and surveys in the Silver Creek drainage
itself. Fred Plog (1975) used the data collected as part of the Chevelon Archaeological
Research Project to reconstruct population figures for the area between Purcell and
Larson Draws. He noted that the resulting demographic pattern was similar to that of
17
Longacre (1964) and Zubrow (1975) for the Upper Little Colorado and Hay Hollow
Valley (Flog 1981:68).
The most important projects, however, from the standpoint of the present report,
were those conducted in the Silver Creek drainage itself. In 1974, as part of the Chevelon
Archaeological Research Project, Stephen Plog (1980:35-37) conducted a survey near
Pinedale. This survey investigated approximately 4% of a 260 square mile area on the
Apache-Sitgreaves National Forests, and included transect and block surveys. A total of
118 sites was located, 31 of which were listed as habitation sites containing a total of 218
rooms (Jewett 1978:221—225). Jewett (1978:240) used these data to reconstruct a Jshaped curve of population trends for the Pinedale area based on room numbers (Table
2.4).
Table 2.4 Pinedale Region Population, Jewett Reconstruction'*
Demographic Interpretation
Date
Pre-A.D. 850
less than 25 people
A.D. 850-1050
less than 10 people
less than 10 people
A.D. 1050-1125
A.D. 1125-1200
approximately 50 people
just over 400 people
A.D.1200-1275
"(based on Jewett 1978:240)
Jewett (1978:239-240) noted that "room density taken as an estimate of
population numbers exhibits a...pattern of initial stability preceding 1125 with a seven
fold increase beginning at 1125 and six-fold again between 1200 and 1275."
However, these numbers are somewhat misleading. For instance, Jewett notes that
the density of habitation sites increased fourfold from A.D. 1050-1123 to A.D. 1125-
18
1200; this translates to a total of four habitation sites in the latter time period. There does,
however, appear to be a substantial increase in sites recorded for the period A.D. 12001275, with 36 sites recorded, including 97 rooms. Because the rate of increase was seen
as greater than a natural population growth rate, immigration or colonization was
hypothesized as an explanation for rapid population growth in the area. Jewett
(1978:257-263) posits that changing environmental conditions made the study area
attractive for migrating populations and that environmental stress led to the abandonment
of the area after A.D. 1275.
Perhaps one of the most controversial works to come out of the Silver Creek area
was presented in Lightfoot's 1984 volume Prehistoric Political Dynamics: A Case Study
from the American Southwest. Based on survey information from the Pinedale,
Snowflake, and Hay Hollow areas, the author proposed a sociopolitical model for the area
that included a three-tiered hierarchical decision-making structure to account for
subsistence and settlement changes in prehistory. This work helped touch off a long
standing debate among archaeologists regarding social complexity in the prehistoric
Southwest. Researchers pointed out problems with Lightfoot's model including the
assiunption that site function correlates directly with site size (Whittlesey 1986), and the
assimiption that sites spanning a 200 year period are contemporaneous. Lightfoot and
Most (1989) later re-evaluated some aspects of the study and concluded that site size was
not necessarily a good indicator of site fimction. Nevertheless, they maintained that the
settlement systems of Pinedale and Snowflake reflected the development of simple
decision-making hierarchies.
19
The site information from the projects conducted by ASU personnel in the Silver
Creek area is summarized in Lightfoot 1984, and was incorporated into the database for
the current report, with some exceptions. Lightfoot (1984:56-58) divided the survey
information into what he called the "upland" survey area, meaning the area around
Pinedale, which is at relatively higher elevations, and the "lowland" survey area around
Snowflake, which is at lower elevations. The upland survey included a 100% survey of a
41 square kilometer area around the town of Pinedale, in addition to a 4% sample survey
conducted in 1974 by S. Plog. The major portion of the upland siurvey was subsumed by a
larger, more recent sxuvey, the Lons Timber Sale survey (Oliver and Dosh 1992). This
survey attempted to relocate and update the ASU information and this more recent
information was included in the database for this paper. Original site forms and Universal
Transverse Mercator (UTM) locational information (the means by which the sites were
entered into AutoCAD) were not available for the Nick's Camp portion of the lowland
survey, and consequently these data were not included in the present database. Because
the upland area is well-represented by other surveys in the database, this omission should
not affect the results significantly.
The lowland survey area included a 100% survey of a 25 square kilometer area
directly south of the present town of Snowflake, an adjacent eight square kilometer area,
surveyed earlier by Lyle Stone, and a 5% sample survey of a 65 square kilometer area
adjacent to the 100% surveyed areas. The upland survey located 143 prehistoric sites
dating from about A.D. 700-1375, while the lowland survey identified 136 sites dating
20
from A.D. 300-1475 (Lightfoot 1984:56-57). Locational informatioii was unavailable for
the sites attributed to Lyie Stone and these were not included in the current database.
Lightfoot (1984:87-88) used room counts to look at relative population shifts
through time for the Silver Creek area to support his arguments on sociopolitical
structure. He noted an inverse relationship for population between the Snowflake area and
the Hay Hollow Valley (Table 2.5).
Table 2.5 Silver Creek/Hay Hollow Population Comparison, Lightfoot Reconstruction^
Date
Hay Hollow Population
A.D. 700-900
decreased
A.D. 900-1100
increased (peaked)
A.D. 1100-1250
decreased
A.D. 1250-1475
decreased
^(based on Lightfoot 1984:87-88)
Snowflake Population
increased
decreased
increased (peaked)
decreased
Population for the Pinedale study area was seen to increase steadily from about
A.D. 700 to about A.D. 1250 at which point it began to decline. When population is
considered for the entire region, Lightfoot notes that an S-shaped population curve
emerges: "Between A.D. 300 and 1250, the demographic structure of the region was
characterized by steady growth, which by A.D. 900 to 1100 was increasing at a rate two
to three times that of previous periods—a pattern very similar to the demographic
changes noted by Jewett (1978). After A.D. 1250, the population began to decline, and
most areas were abandoned by around A.D. 1400 to 1450" (Lightfoot 1984:88). Thus
Lightfoot's regional demographic reconstruction is summarized in Table 2.6.
21
Table 2.6 Upper Little Colorado Population, Lightfoot Reconstruction^
Date
Demographic Interpretation
A.D. 300-700
A.D. 700-1000
A.D. 1100-1250
minor increases in population growth
regional population increases about 2.5 times during this period
population increases to about three times the previous period, with
population peaking at about A.D. 1100
A.D.1250
population began declining for the region
^(Lightfoot 1984: 88)
In Fred Plog's (1981:69) summary of the history of demographic studies for the
upper Little Colorado area, he notes that '\..all lines of evidence suggest a major increase
in population between about A.D. 900-1150;" however, "...further analysis is required to
be certain that this last increase is the product of a real increase in human numbers rather
than organizational change or change in the average length of site occupation."
The bulk of the data for the demographic reconstructions in this thesis was
collected from surveys that took place after Lightfoot's work was published. Therefore,
these data do not appear in previous population reconstructions for the area. In the present
study, I do not presimie to be able to reconcile the varied demographic reconstructions for
the region. However, by presenting various models illustrating the prehistoric population
dynamics of the Silver Creek area, I hope to clarify the demographic situation and to
investigate whether there is additional evidence to support the assumption of a major
increase in population between A.D. 900-1 ISO, and the scale of migrations in the late
13th century, now well documented from other evidence (e.g., Adams 1991; Crown 1994;
Dean et al. 1994; Duff 1995; Mills 1995, 1996).
22
CHAPTER THREE
METHODS OF DEMOGRAPHIC RECONSTRUCTION
Prehistoric demographic reconstructions of absolute population size must, by their
nature, be controversial. The ability to transmogrify archaeological evidence on the
distant past into a form that resembles people requires that researchers make certain
assumptions—it is because of the difficulty in making these assumptions that population
reconstructions have been criticized. However, as Fred Flog (1974:91). has noted, "Every
technique of reconstruction involves assumptions. The question that must be asked is
whether the assumptions are good or poor ones." The archaeological record, including
artifacts and architecture, can be synthesized with other factors affecting prehistoric
populations, such as subsistence and environment, to try to establish a relationship
between these factors and population. Cook (1972:2) points out, however, that "...as a
rule, such a relationship does not take the form of an estimate of actual numbers of
inhabitants, but rather describes the limits between which those numbers must have lain"
This idea of estimating a population range is pursued later in this paper.
Cook (1972:2) suggested that there are four essential variables which must be
known or reasonably estimated in order to predict population from material remains. The
first variable is "...the total quantity of the element being considered that is present in the
area," or what Schlanger (1987) has called the "proxy." The proxy is the diing being
counted or measured as a stand-in for people, and can include architecture, ceramics, or
anything a researcher deems significant in representing population.
23
The second variable which must be considered is the turnover rate, or uselife of
the proxy item. In reference to artifacts. Cook (1972:11) points out that "...there must
have existed a quantitative relationship between the rate, or volume, of production and
usage, on the one hand, and the population, on the other. There remains only to discover
this relationship and express it numerically." Nelson et al. (1994:130-135) have
attempted to do just this, and have found a strong correlation between the amount of
artifact deposition and person-years of site occupation, suggesting that this information
could be used to calibrate estimates of population. Schiffer (1987:56) has also noted the
correlation between discard rates and population, and has discussed the value of discard
equations in deriving population estimates. Varien and Mills (1997) have recently
summarized the accumulations research to date, and have proposed a method for
estimating site occupation spans from ceramic discard. This method appears to hold great
promise for refining site occupation spans, and sampling sites in a given area using this
method would do much toward improving the accuracy of population reconstructions.
The third variable is time or the duration of the period being studied. This can
generally be established at varying levels of precision through relative and absolute
chronological controls, such as ceramic seriation or radiocarbon dating. If one could
accurately fix the three variables—^the proxy, the uselife, and the time period—it would
be possible to know the total number of proxy items at any given time. However,
knowing the exact number of proxy items at a given time is not sufficient to determine
how many people are represented. The task then is to discover the relationship between
the proxy and the population—^the fourth and most difficult variable (Cook 1972:3).
24
Site-Based and Regional Approaches to Population Reconstruction
Two basic approaches have been used to estimate prehistoric population, each
with its own set of evidence and assumptions: the single site approach and the regional
approach. In the single site approach, tree-ring information, masonry styles, bond/abut
patterns, burial information, and ceramic accumulations, have been used to trace the
history of population growth at a single site. The most well-known example of this
method in the Southwest is Dean's (1969) tree-ring and architectural analysis of Betatakin
and Kiet Siel.
The second approach is to look at population on a regional scale. This approach
has become important for examining issues such as aggregation, dispersion, migration,
sociopolitical structure, and social integration. Habitation space is generally the most
common proxy for estimations of population at this scede, and Cook (1972:12) notes that
it is "...the criterion which has been employed more extensively and successfully than any
other." Schact (1981:124-125) also mentions that habitation space "...seems to be
regarded by both historical demographers and archaeologists as the best source of nonwritten data for estimating population sizes."
In order to make population estimates based on archaeological evidence at diis
scale, there are again, a number of assumptions that must be made—not the least of
which is that there is a continuous relationship between increasing habitation space (i.e.,
number of structures), and the number of people represented (Plog 1974:87). It is
generally assumed that the more habitation space in a given area, the more people are
25
represented. Cook (1972:12-13) notes that the "...living room tends to be a direct
function of population and that either parameter can, if handled properly, be converted
into the other...," and "...the general rule governing the relationship between dwelling
space and number of occupants is that the magnitudes vary in a direct and parallel
manner." Schact (1981:125) notes too that various studies have supported the opinion that
"...population size is highly correlated with the number of rooms, the nimiber of houses,
and the area occupied by rooms." Given the seemingly sturdy assimiption that habitation
space correlates well with population, the more relevant question would appear to be
whether or not the relationship is linear, logistic, exponential or some other variant
(Hassan 1981:64) for a particular area and economic base.
Researchers have proposed numerous methods to translate habitation space into
population estimates and these can generally be placed into several broad categories
including methods based on the number and area of houses, and methods based on the
number and area of settlements (Schact 1981:125-130). Some examples of these methods
are presented below, and while these few descriptions in no way exhaust the literature on
the subject, they provide an idea of the procedures involved in estimating population
based on habitation space (for detailed simimaries see Cook 1972, Hassan 1981, and
Schact 1981). Site length from surface estimates (i.e., 10 meters equals two population
units) and site volume have also been used in making population estimates (Hassan
1981:64), but since these methods are rarely utilized they are not discussed further here.
Ethnographic data have always been an important part of the study of prehistoric
demography, and most methods use some sort of ethnographic data to establish various
26
parameters including household size, household space, and reproduction and death rates.
Using ethnographic data, Naroll (1962:587-588) was one of the first to describe a method
for estimating prehistoric population using floor area of habitations. He compared
ethnographic information firom 18 societies for the total roofed dwelling space and the
number of people represented. He then suggested that prehistoric population could be
extrapolated as approximately one-tenth the total floor area of a settlement in square
meters. Using additional ethnographic datei, LeBlanc (1971:211) later concluded that the
total floor area per person varied considerably. However, if one took into consideration
room function and only living area was used, then the average floor area per person
remziined about 10 square meters as Naroll suggested. This method, however, has been
criticized as being too simplistic, and Schact (1981:126-128) points out that Naroll's
original hypothesis stated that there was a nonlinear (logarithmic) relationship between
dwelling area and population; that "...the number of square meters per person changes as
floor space increases," but Naroll nevertheless cited an average of 10 square meters per
person as a rule of thumb.
In a more recent example, De Roche (1983:187-191) used ethnographic data
including census figures, and aerial and topographic maps for two different time periods
in an area of Mexico's Central Highlands in order to determine population from surface
remains. She determined the population ranges of known settlement areas and the number
of dwellings and used this to extrapolate population predictions. De Roche found that
underestimating population was more common when using the number of dwellings, but
that overestimation was more common when using site area. According to De Roche,
27
both methods correctly predicted population approximately two-thirds of the time for
individual settlements. When regional populations were estimated, more precise results
were obtained, and both methods were accurate to within 10% of the actual population.
De Roche also foimd that using a constant of six people per house and nimiber of
residences gave accurate predictions for both individual settlements and regional
populations. De Roche attributed this to the "...physical uniformity of most residence
structures—"
While this study apparently confirmed that there is a predictive relationship
between settlement area, number of dwellings, and population, De Roche (1983:192)
warned this method may only be useful where reliable modem data are available and
where a cultural continuity for the area examined can be shown.
Realizing the difficulties with using ethnographic analogy. Turner and Lofgren
(1966) attempted to develop a nonethnographic based method of estimating prehistoric
population by compiling data on the changing ratio of cooking jar capacity to serving
bowl capacity in order to estimate changing household size through time. Population
estimates were then derived by multiplying by the nimiber of families (habitations) for a
given time period. It is difficult to assess the accuracy of this method considering that the
sample of whole jars and bowls was collected from an extremely large area. In addition,
site formation, artifact preservation, and curation processes would no doubt affect the jars
and bowls recovered, and these factors were not fully discussed. Nelson et ai. (1994:136)
felt that the study produced some reasonable estimates, and further work along these lines
in more localized settings may eventually prove useful.
28
Knowledge of site area and size alone is insufficient in determining population
unless the density of houses can be established (Cook 1972:19). In many cases, as in the
cases above, ethnographic analogy is used to provide a basis for comparison with
prehistoric sites, and researchers have devised numerous formulae based on ethnographic
data to convert site area to population. However, the relationship between population and
site area is relative based on site layout and other factors, as numerous examples have
shown (Hassan 1981:67—72); ethnographic data may be of nominal use in deriving
density for archaeological cases unless these factors are known. This has lead Hassan
(1981:67) to conclude that "...correlations between site area and population drawn from
modem contexts caimot be applied to archaeological contexts without reservations."
The problem of spatial arrangement of a site affecting site area, and consequently
population estimates, may be alleviated to some extent by using the number of houses or
dwellings rather than site area. As Cook (1972:19) notes, "...in the case of many
settlements, the size of the site becomes irrelevant when the number of habitations and
their dimensions are known, for with the latter information...population can be calculated
directly." Since the house or dwelling is generally equated with a household or family
group, the critical issue for estimating population using individual dwellings is
determining the number of people that comprise a household (Cook 1972:13; Schact
1981:2S), and the number of rooms used by a household. This generally requires the use
of ethnographic analogy (Hassan 1981:72) or excavation data. Some researchers,
however, have avoided the pitfalls of absolute population estimates by not converting
dwelling area into people, but instead comparing the changes in dwelling area through
29
time (e.g., Orcutt 1987). As we shall see, though, discussing population change in terms
of people has the advantage of being able to compare the observed changes in population
to expected growth rates for prehistoric groups.
Recent Approaches to Southwestern Demographic Reconstruction
Nelson et al. (1994:113, 125, 130-137) have pointed out the difficulties of
demographic reconstruction in the Southwest, noting that the general response is to either
ignore the problems, construct models that do not require population estimates, or to
continue to collect data in the hope that some day an accurate reconstruction will be
possible. Instead of falling back on these responses they propose another: to try to
ascertain the effect of measurement error on population estimates and suggest solutions.
Examining various reconstructions using data for several areas of the Southwest, they
noted the discrepancies between the models and suggested that a 100% survey strategy,
coupled with research into assemblage formation processes, would help to eliminate some
of the problems in estimating population. In their study, they noted a strong correlation
between artifact deposition and site occupation length, attributing the regularity in the
depositional process to person-years of occupation. Based on this they suggested that it
might be possible to calibrate this information to estimate population. Plausible ranges of
absolute population could then be derived by comparing information from architecturebased, assemblage-based, and resource-based population estimates. Varien and Mills
(1997) have shown the utility of accumulations data in estimating site occupation span
given population size. Further research may prove that population may be accurately
30
estimated through deposition rates. In the meantime. Nelson et al. point out that numerous
questions can still be answered using relative population estimates.
Dean et al. (1994:57—58,63, 73) presented just such relative population
reconstructions for the Southwest in order to examine the relationship between
environmental and demographic variables which may have structured prehistoric
sociocultural adaptive strategies. Because the data for these reconstructions were
compiled directly from the literature, adjustments could generally not be made for site
and room f\mction or chronology. Although absolute numbers of people were presented
in the reconstructions, the authors point out that these may be misleading, and it is the
general population trend—or the shape of the curve—that is reasonably representative of
the region. The curve presented for the Little Colorado region encompasses a broad area
stretching from Hopi to Zuni and includes the Silver Creek area. It shows a major
increase in population around Snowflake and Pinedale during the A.D. 1000s to 1100s,
and eventual abandonment of the area that may have been related to environmental
variations, among other factors, during that time.
In the Mimbres area, Bleike et al. (1986:447-455) presented relative and absolute
population estimates based on survey data. In order to make generalizations about the
region, sites were stratified into 12 groups based on environmental characteristics. The
total area of a particular environmental stratum was calcxilated, as was the area surveyed
in that stratum, thereby determining the fraction of the particular environmental stratum
that was surveyed. This percentage was then used to extrapolate the total number of
rooms estimated to be present in the entire stratum. Population estimates were presented
31
in both relative and absolute terms. First, raw data on the numbers of sites, rooms, and
room areas were presented for each time period by environmental strata. This was to
show the general population trend through time, represented as changes in floor area.
Next, these data were time-standardized based on the length of the shortest period, in
order to compare room area across time periods. Because this assumes a constant
population growth rate during all periods, and it does not take into accoimt structure
uselife, the authors then manipulated the data to determine the effect of those factors on
the room-area estimates for each period. This was accomplished by varying the average
room uselife from 40 to 150 years, and the average growth rate from 0.3% to 0.585%.
Absolute population estimates were derived by assimiing a value of four to six square
meters of floor-area per person. Regardless of the adjustments for structure uselife and
growth rate, the general population trend remained the same. This is not surprising
considering that these factors are generally treated as constants and therefore do not affect
the shape of the curve. It should be noted, however, that structure uselife significantly
affects absolute population figures. Cameron (1990:162) recalculated Blake et al.'s
population figures using a 15 year uselife instead of the average of 75 years used in their
study, and found that population totals were reduced by about 75 percent.
In a preliminary effort to document demographic change on the Pajarito Plateau,
in northwest New Mexico, Orcutt (1993:1—3) used total roomblock area as the best proxy
for estimating prehistoric population. Rubble area measurements were translated into
roomblock area using a regression formula based on data from sites that had both rubble
and roomblock measurements. The number of periods during which a site was occupied
32
was detennined and the data were standardized to the shortest period so that population
could be compared across different time intervals. Aggregation was measured by using
the mean roomblock area. Because this was a preliminary study, site uselife, the ratio of
storage to living space through time, and the amount of abandoned space were not
adjusted for in the initial estimates. In previous population estimates for the Pajarito
Plateau, Orcutt (1991, 1987:618) used a site weight index of room-block area as a proxy
for momentary population. This was calculated by dividing the total roon>block area of a
site by the number of periods the site was occupied, and then time-standardizing for the
length of the periods. The number of households represented was determined by dividing
rubble areas by 50 square meters. These values then provided a relative estimate of
population through time.
The use of number of habitations to estimate population is best illustrated by
Schlanger's (1986, 1987, 1988) study of the prehistoric population of the Dolores area of
Colorado. Schlanger (1987:569-571, 597) used the living room as the best proxy unit for
prehistoric population; the living room being defined as the ''...room in which the
household sleeps and performs cooking and food preparation chores." In general, these
rooms can be identified by the artifacts and features found within (for instance, ground
stone and hearths), and by the fact that they were probably roofed and fully enclosed.
Schlanger cites several reasons for using a structure-based method for estimating
population for the Dolores area. Since most of the data for the project is acquired through
survey, the proxy measure must consequently be visible on the surface. In addition,
collection and recording of surface artifacts changed over the course of the lengthy
33
project casting doubt on the comparability of artifactual information. Other factors were
that excavation data could be used to support interpretations of surface architecture, and
that houses and rooms provided a convenient unit of analysis for questions about
changing household or settlement size. The population figures for the Dolores area were
derived by multiplying the total number of dwellings by an adjustment factor resulting in
an estimate of "...average household population at any given time during the period...,"
also known as momentary population. The benefit of this method is that it attempts to
correct for dwelling uselife and the length of the period. The following formula results
(Schlanger 1988:783):
(number of (living room (rebuilding
momentary population = living rooms') x lifespan') x freauencv') x people per living room
(length of period)
Researchers have warned that population estimates can be skewed unless factors
such as site function, room function, and chronology are taken into consideration (Dean et
al. 1994:57—58; Powell 1988). Schlanger's formula takes into account some of these
methodological problems.
Given that the population estimates for the Silver Creek area are based entirely on
survey data, techniques similar to those used by Blake et al. (1986) and Schlanger (1987)
appear to be the most suitable given the variables. Therefore, their methods have been
adapted for the reconstructions presented in this paper. The available information for the
Silver Creek area provided a large and intransigent dataset. To compensate for the relative
lack of archaeological evidence firom excavation and other problems of estimation, a
34
single reconstruction is not presented for the Silver Creek area. Instead, several models
based on a variety of plausible assumptions are proposed in order to suggest a range of
possible population figures, and to ascertain whether or not a "best fit" model can be
found. Such reconstructions provide a way of eliminating implausible interpretations
given the data, allowing us to estimate the range of probable population growth curves for
the area.
35
CHAPTER FOUR
THE DATABASE AND RECONSTRUCTIONS
The database used for this study includes archaeological data acquired through
surveys conducted for the Apache-Sitgreaves National Forests, and for various other
projects in the Silver Creek area (Table 4.1, Figure 4.1). Surveys and sites had to meet
certain criteria in order to be included in the database. Surveys had to cover a broad area
(as opposed to linear surveys), and locational data had to be clear enough to allow the
survey to be plotted on a topographic map. If a survey straddled the boundary of the
Silver Creek watershed, only the portion within the watershed was considered. Surveys
did not have to have sites, but sites had to be associated with a surveyed area due to the
technique used to make generalizations about the entire Silver Creek region. In other
words, if a survey was conducted and no sites were found, the survey was still included in
the database. If a site was known, but was not within a surveyed area, the site was
generally not included in the database. The exception to this is three, large PIV sites that
were included in those models where actual room counts were used for sites with 20 or
more rooms. Sites also had to have clear locational data to allow plotting, and
descriptions with time periods and number of rooms were required. The final database
included 25 surveys and 748 sites; of the 748 sites, 393 contained rooms (Appendix 1).
This translates to about 101 square miles of 100% survey coverage in an area of
approximately 883 square miles; or roughly 11% surveyed area to 89% unsurveyed area
for a ratio of 1:8.
36
Figure 4.1: Surveys included in the database. (Numbers correspond to Table 4.1).
• Surveyed Area
22
23.
rB
Since the population estimating technique used by this study is based on numbers
of rooms, sites without structures were eliminated from further analysis. Site information
was taken from project reports and original survey forms including site maps, and in
those cases where the Silver Creek Archaeological Project (SCARP) resurveyed sites and
made adjustments to the site data, the updated information was used (see Mills et al.
yi
1993, 1994, 1995, 1996). If ranges were given for the number of rooms at a site, the
midpoint was used. The surveyed areas and the associated sites with rooms were digitized
onto a base map of the Silver Creek drainage.
Table 4.1: Projects Included in the Database
(Survey names are numbered to correspond with areas shown in Figure 4.1)
Name
ASM Project #
1. Aztec
2. Bagnal
3. Bailey
4. Burton
5. Colbath I
6. Colbath 11
7. Clay Springs
8. Construction Site
9. Dodson
10. East Side Pigs
11. East Side Pigs I
12. East Side Pigs II
13. Fence
14. Fool Hollow
15. Habitat 89
16. Lons
17. Material Pit
18. McNeil
19. Sackett
20. Schoen's Dam
21. Snowflake
22. Snowflake-Mesa Redonda
23. Stott
24. Wolf-Mullen
25. Wolf II
1984-222
1990-219
1982-222
1992-296
1989-216
1991-296
1990-229
1993-019
1991-305
1988-110
1992-313
1993-388
1990-220
1991-309
1989-224
1992-307
1989-076
1989-227
1990-233
1982-223
none
1984-214
1980-248
1989-230
1989-229
Reference
Green 1984
Neily 1991
Hammack 1984
Gregory 1992
Greenwald et al. 1990
Nightengale and Peterson 1991a
Nightengale and Peterson 1990a
Logan 1993
Peterson and Nightengale 1991
Rozen 1988
Newcomb and Weaver 1992
Spalding and Michelson 1993
Dosh 1991
Nightengale and Peterson 1991b
Seymour 1989
Oliver and Dosh 1992
Weaver 1989
Hohmann and Johnson 1989
Nightengale and Peterson 1990b
Stebbins and Hartman 1988
Lightfoot 1984
Neily 1985
Ciolek-Torrello 1981
Gregory 1989a
Gregory 1989b
38
Time Standardizing Sites
In order to standardize the data by time periods for this study, the period
considered for this thesis, A.D. 400-1400, was divided into SO year intervals; the rooms
from each site were apportioned evenly between those intervals, based on the site
occupation span. A SO year interval was chosen because the dating scheme used by many
archaeologists tends to divide time periods into SO year intervals, and SO years was the
shortest occupation span assigned to any site in the project area. Using phases was
decided against since they may seriously inflate population estimates if the average site
occupation tends to be shorter than the length of the phase (Schlanger 1988:781). In
addition, phase-based estimates "...act to smooth curves and can obscure short-term
population fluctuations and lead to inaccurate estimates of growth rates" (S. Plog
1986:229).
Apportioning rooms to the periods covering the entire length of the site's
occupation has certain advantages over assigning all the rooms from a site to the site
occupation median date. It helps to eliminate the central tendency caused by using a
single site median date, thereby increasing resolution by allowing each site to contribute
over its entire occupation span. This in turn helps balance the contribution of long and
short-occupied sites. For instance, if there is a patterned difference in site occti^iation
length, the differences will be smoothed out since long-occupied sites will contribute to
more time-periods than short-occupied sites—^whereas using median dates alone, longand short-occupied sites would contribute equally. S. Plog (1986:232) used a similar idea
39
in his population reconstructions for Black Mesa. However, instead of apportioning
rooms to the periods of occupation, he added the total number of structures to every
relevant period. Had we used Plog's approach, the result would have only been a
difference in magnitude, with no effect on the shape of the curve.
Environmental Strata
The goal of this study was to derive population curves for the entire Silver Creek
drainage, not just the surveyed portions, because of possible biases in the zones subjected
to intensive survey. The most conunonly used method in traditional site location studies
is where the
..model simply divided the study region into biotic communities and
projected expected numbers of various site types in each conmiunity based on site density
estimates obteiined from sample surveys" (Kvamme 1988:345). It was decided to use a
similar strategy here, subdividing the Silver Creek region into environmental strata, and
then extrapolating into the unsurveyed areas based on what is known about the surveyed
areas. This method draws on the work of Blake et al. (1986:47) in the Mimbres region.
Their reasoning for using this method was that "site types and their densities are more
similar within environmental strata than between different strata...," and that "by making
qualified estimates for each separate stratum and combining them, the regioneil estimates
of site frequencies and distributions are more reliable than if unstratified estimates had
been made across largely unknown heterogeneous areas."
For this project, it was decided to use a combination of soil type and elevation as
the basis for dividing the environmental strata. Soil categories and elevation can be seen
40
as an indication of vegetation or agricultural potential, and may reflect the prehistoric
situation better than modem vegetation types, which have been influenced by the
introduction of cattle and fire suppression policies among other things. Soil data were
acquired in the form of a printout of soils from the Apache-Sitgreaves National Forests
for the Silver Creek portion of the Forests, annotated VA minute orthoquads from the
Natural Resource Conservation Service (formerly the Soil Conservation Service) in
Holbrook, Arizona, and accompanying soil descriptions (Camp 1993; Lainge et al. 1989).
The soil data were simplified, with the help of Donald J. Breckenfeld, Resource
Soil Scientist for the Natural Resource Conservation Service in Tucson. Since the Natural
Resource Conservation Service and the Forest Service each have their own soil
classification schemes, a list was made of all the soil types for the Silver Creek drainage
and soils were reassigned to a general scheme based on their position on the landscape.
This reduced the number of soil categories from over 100 to 10 broad categories (Table
4.2). The soils were then digitized onto a base map of the Silver Creek drainage (Figure
4.2). To further subdivide the area into five approximately equal elevational zones, the
region was subdivided as follows: below 1800 m, 1800—2040 m, 2040—2280 m, 22802520 m, and 2520 m and above. Each zone represents an elevational change of
approximately 240 meters. This resulted in 50 environmental strata; although after
analysis, it was foimd that sites occurred in only 10 of the 50 possible strata.
41
Table 4^: Soil Types
Category
1
2
1-2
3
4
5
6
7
8
0
Description
Bedrock, shallow soil
Bedrock, moderately deep to deep soil (20-40 inches)
Combination of soil 1 and soil 2
Fan terrace
Alluvial fan
Floodplain
Rough, broken land, eroded areas
Sand dune
Wetland
no data available
The total area was calculated for each environmental stratum, as was the total area
surveyed within each stratum (Table 4.3), in order to derive the fraction svu^eyed of each
environmental stratum. Dividing the total area by the surveyed area in each environmental
stratimi provided the means by which the total number of sites for a stratum could be
estimated. As an example, if the ratio of surveyed area to total area in a partictilar stratum
was 1:5, and the number of sites in the surveyed area was two, the total number of sites
for the stratum would be 10.
Figure 4.2: Soils in the Silver Creek watershed.
43
Table 43: Silver Creek Environmental Strata
Total Aiea
Ratio (l:n>
Surveyed area
Unsurvcyed area
(sqlon)
suiveyed:toial
(sqlon)
(sqton)
1.81
3.10
239
IJO
1800
0
439.01
14.46
408.66
30J5
1800
I
0.19
0.20
85.22
0.00
1800
12
14.84
13J7
10.10
1.47
2
1800
140.95
110J9
4.61
30.55
3
1800
43.05
36.53
6.61
6.51
5
1800
0.29
0.29
0.00
6
0.00
1800
2.57
3.05
638
7
0.48
1800
0.00
0.00
0.00
8
0.00
1800
110.72
309.03
11037
0.36
2040
0
76.96
81.93
16.46
4.98
2040
1
56.89
60.21
18.15
3J2
2040
12
256.17
235.48
1238
20.69
2040
2
585.99
7.55
508.40
77.60
2040
3
14.42
14.60
82.54
0.18
5
2040
0.00
0.00
0.00
0.00
6
2040
2J4
237
89.59
7
2040
0.03
0.00
0.00
0.00
0.00
8
2040
62.43
62.43
0.00
0.00
0
2280
7.63
8.54
939
0.91
I
2280
102.96
26.40
26.66
0.26
12
2280
188.29
165.12
8.13
23.17
2280
2
15837
2.99
105J7
53.00
3
2280
0.00
0.00
0.00
0.00
5
2280
0.00
0.00
0.00
0.00
6
2280
0.00
0.00
0.00
7
0.00
2280
1.89
1.74
0.80
1.09
8
2280
0.00
0.00
0.00
0.00
0
2520
10.58
10.73
68.98
0.16
1
2520
0.00
0.00
0.00
0.00
12
2520
60.75
55.91
12.55
4.84
2
2520
2.86
2.92
1.88
0.98
2520
3
0.00
0.00
0.00
0.00
5
2520
0.00
0.00
0.00
0.00
6
2520
0.00
0.00
0.00
7
0.00
2520
3.98
25.09
3.83
0.16
8
2520
3.06
0.00
3.06
O.OO
0
237
2J7
0.00
0.00
1
I.8I
1.81
0.00
0.00
2
2288.22
2025.85
262J7
Sq. km:
228822
202585
26237
Hectares
883.49
782.19
lOIJO
Sq. miles:
565433.60
500601.60
64832.00
Acres:
Elevation
[
1
^
1
j.
•;
252(K
2520^252(HTotals
Soil
Surveyed
41.83
6.91
1.17
9.90
21.68
15.13
0.00
15.68
0.00
032
6.07
5.51
8.08
13.24
1.21
0.00
1.12
0.00
0.00
10.65
0.97
1230
33.47
0.00
0.00
0.00
57.63
0.00
1.45
0.00
7.97
34.28
0.00
0.00
0.00
3.99
0.00
0.00
0.00
44
Site Size Classes
A graph of sites by number of rooms (Figure 4.3) shows that the majority of sites
have 10 or fewer rooms. This corroborates findings from the Southwestern
Anthropological Research Group, which found that sites with greater than 10 rooms are
relatively rare in the Southwest, and the average number of rooms per site is 6.5 (Plog et
al. 1978:141). McAllister and Plog (1978:17) suggested that even six rooms as an average
may be too high for the Southwest. In the Silver Creek database, sites with more than 20
rooms are relatively rare, and are clustered on the scale in increments of five or ten,
probably a result of recorder bias in estimation.
Site Sizes
100
95
90
ss
80
75
70
1
1
la
65
M
60
CM
55
o
k
JS
aa
1
50
!
45
40
'
t
1t
35
30
25
•
l Il Il Il .
(
20
15
t
j
10
5
0
1
^
m
S 8
Roons
Figure 43: Sites by number of rooms.
2 8
45
To facilitate plotting in AutoCAD and subsequent analysis, sites were subdivided
into four size range categories within each 50 year time period: 0.1-4 rooms, 4.1-10
rooms, 10.1-20 rooms, and 20.1+ rooms. Consequently, sites could fall into one or more
of 80 site categories based on 20 time intervals from A.D. 400-A.D.1399, and four size
classes. Because there were only a few small sites dating prior to A.D. 400, and because
the date ranges were so imprecise, spanning thousands of years, these sites were not
included.
A graph of sites by length of occupation (Figure 4.4), shows that the majority of
sites are dated to within four periods and the majority of rooms are in those sites (Figiire
4.5). Since the relative contribution of rooms to each period decreases with an increase in
the number of periods, the effect of imprecisely-dated sites on the shape of the curve is
minimal, assuming they are distributed randomly along the curve.
46
Site Counts by Number of Periods
Figure 4.4: Sites by number of periods of occupation.
Rooms Contributed by Numiier of Periods
1400
1200
1000
800
1
i
400
1
i
1
1
1
1
1
• 1 2
3
4
S
6
7
8
Period!
9
1 0
I I
1 2
1 3
1 4
Figure 4.5: Rooms contributed to the curve by number of periods a site spans. (Note that
sites spanning three periods or less contribute the majority of rooms.)
47
To calculate the total number of sites for the entire Silver Creek drainage, sites by
time period and size category were multiplied by the ratio of surveyed area to total area
for each environmental stratum. The simi of these sites produced the total number of sites
(Appendix 2). This provided the foundation for the models presented below.
Population Models
Model A: Time-Standardized Room Counts
Figure 4.6 shows the room counts after the process of time-standardizing the data
to 50 year intervals and apportioning the rooms from each site to those intervals. If we
were to assume that population resembles the number of rooms, the curve shows a
bimodal pattern with the greatest number of rooms/people around A.D. 1250-1350, and
the next greatest number at around A.D. 1000-1100. There is a sharp decrease in numbers
of rooms between the two peaks with a low point aroimd A.D. 1200-1250. The sharp
increase in rooms between A.D. 900—1050 would seem to signal movement into the area
through migration. These trends are significant for issues of social integration previously
mentioned. Circular great kivas show up in the Silver Creek area around A.D. 1050 (Herr
1994:34), after what appears to be a large influx of people into the area based on this
model. The Southwestern Cult hypothesized by Crown (1994), and the Katsina Cult
proposed by Adams (1991), apparently develop after population shifts in the late
thirteenth century. This model would seem to support the idea of a migration into the area
corresponding to that time period.
48
Model A
600
500
400
M
E 300
e
e
ae
200
100
vt
O
Year
Figure 4.6: Model A.
Model Bl: Total Rooms Using Site Size Category Median
To facilitate analysis, sites were placed in size categories by time period within
AutoCAD. After counting the number of sites by environmental strata in AutoCAD, the
sites were transformed to rooms by multiplying by the median number of rooms for the
particular time period and size class. For instance, if there were 22 sites in the 1-4 room
category in stratum one during period A, and the median number of rooms in the 1-4
room category during period A was two, then the total number of rooms would be 22 x 2,
or 44 rooms.
Using the median for the large sites may have its advantages. First, the large, late
sites tend to be better dated than other sites. This means they contribute a large number of
rooms to one or two periods compared with smaller sites that contribute over numerous
49
time periods because they may not be as well dated. Using the median helps reduce this
bias in the later time periods by reducing the total number of rooms contributed by large,
well-dated sites. Second, if most of the large sites are already known, it is unreasonable to
extrapolate an equal number into unsurveyed areas, and using the median helps to reduce
this effect.
After the calculations were completed, the total number of rooms for each time
period was summed and graphed (Figure 4.7). A noticeable result of multiplying by the
medians is the lowering of the curve in terms of absolute numbers. However, the shape of
the curve remains relatively unchanged, indicating that the method of reconstruction up to
this point has not excessively biased the outcome.
Model B1
Figure 4.7: Model Bl.
50
In this model, the peak of population is around A.D. 1300-1350, similar to Model
A. However, the increase between A.D. 1200-1250 and the next period, A.D. 1250-1300,
is less steep in the current model. This is almost entirely due to a smaller median for sites
in the 20 rooms and above size category for A.D. 1250-1300, compared with a larger
median for the period A.D. 1300-1325. If the same median was used for both periods,
then the shape of the curve would look basically identical to the one shown in Model A.
Model B2: Total Rooms Transformed
Figure 4.8 shows the previous model after extrapolation to the entire area, using
the ratio of surveyed area to unsurveyed area for each environmental zone. This model
takes into account the total watershed of Silver Creek and represents an estimate of the
total rooms for the entire area. The shape of this curve is still quite similar to the previous
model without transformation, although the absolute numbers have increased
significantly, from a peak of 344 rooms in Model B1, to a peak of 2702 rooms in the
present reconstruction. The model still shows a bimodal distribution with the highest peak
around A.D. 1300-1350, and a second, slightly lower peak around A.D. 1000-1100,
however, the peaks are closer to each other in terms of absolute numbers than in the
previous models. Also noticeable is a steeper increase around A.D. 1000-1050, a steeper
decline afterward to A.D. 1200-1250, and a steeper increase around A.O. 1300-1350.
Several factors may contribute to the fact that the curve in this model retains a
similar shape to the model before transformation. First, the sample of surveyed area in
each environmental zone may be proportional to the presence of those zones in the
51
overall area. However, looking at the ratios of surveyed area to total area (Table 4.3), the
ratios are not similar to each other. It is possible though, that sites only occur in those
zones that have similar ratios. Another possibility is that sites occur in zones that have a
high percentage of surveyed area. The closer a zone is to being 100% surveyed, then the
smaller the change in the curve, since the ratio of surveyed area to total area approaches
1:1. A relatively large portion of the Silver Creek watershed has been surveyed compared
to the total area—^about 1 ;8, and this may be part of the reason for the similarity in the
shape of the curves.
Model B2
3000
2500 1
2000 ^
£
1500 ^
1000 J .
500 1
Yean
Figure 4.8: Model B2.
The increase in absolute numbers seems somewhat high, and some reasons why
the numbers may be inflated are discussed in terms of the population model below.
52
Model B3: Momentary Population from Transformed Total Rooms
In order to discuss the population curves in terms of people rather than rooms, we
can use Schlanger's (1988:783) formula, discussed in the previous chapter, to transform
rooms into people. Momentary population, or the number of people at any single point
during the time period, is derived by the formula:
(number of (living room (rebuilding
momentary population = living rooms') x lifespan") x frequency) x people per living room
(length of period)
Because the formula requires the number of habitation rooms rather than all
rooms, we run into the difficulty of having to distinguish between them. Schlanger
(1987:576-578) discusses the problem of interpreting the number of living rooms on sites
with pit structures, surface structures, and a combination of both. Since pit structures
were not easily identified in the Dolores area, her solution was to estimate population
using surface rooms for sites after A.D. 800, and for sites dating between A.D. 600-800,
"...the median number of pitstructure dwelling rooms per site, derived from excavated
sites..." would be used as the proxy measure imless substantial architectural remains are
visible.
Unlike Schlanger, we do not have sufficient excavation data to allow us to
determine the number of living rooms per component, therefore, most of the models
presented assume that all rooms are living rooms. This also makes the untenable
assumption that the ratio of storage rooms to habitation rooms remains constant; although
53
Model D looks at this issue. However, evidence from Grasshopper Pueblo to the south of
the study area shows that only about 33% of all the rooms in the main pueblo were
habitation rooms, either specialized or generalized (Reid and Whittlesey 1982:697). F.
Plog (1974:90), working in the Hay Hollow Valley encountered a similar problem in
trying to distinguish habitation rooms from other rooms. His solution was: "1. Count all
pithouses on pithouse sites; 2. subtract 25% of the total number of rooms from pueblo
villages occupied A.D. 900-1150; 3. subtract 41% of the total number of rooms from
pueblos occupied A.D. 1150-1500." However, it is unclear on what evidence he bases
these percentages.
Most of the models presented here treat the number of living rooms as a constant
percentage, and consequently may inflate population estimates in the pueblo period
relative to the pithouse period, because storage, manufacturing, and ceremonial rooms, in
example, are more likely to be counted as habitation rooms during that time. This is
something to keep in mind when evaluating the population trends.
Schlanger (1987:586-588; 1988:783) also notes the difficulty in estimating room
lifespan for surface structures versus pit structures due to preservation and other factors.
She cites an average of 15 years or less for the lifespan of pit structures in the Dolores
area, 1-15 years for pitstructures in the ethnographic literature, and Alhstrom's estimate
of 12 years or less for pit structures in the Southwest. Based on these figures, Schlanger
estimates the likely average of pitstructure uselife as falling between 6-12 years, and she
uses 15 years as the average living room lifespan for both pit structures and surface
structures. LeBIanc et al. (1986:453—454) estimate an average uselife for both pithouses
54
and pueblo rooms of about 75 years, but their estimate does not seem as well-founded as
Schlanger's, especially considering Cameron's (1990) suggestion that pit structure uselife
was no more than 10-15 years. Since the number is treated as a constant in the models
presented, the actual value used does not affect the shape of the curve, therefore, 15 years
was used for the Silver Creek model. If we were to use a 75 year uselife, the result would
be an increase in magnitude only, and a 10 year uselife would reduce the absolute
numbers.
"Rebuilding frequency" refers to how many times a structure is remodeled to
extend its uselife (for example, by replacing rotting wood supports) (Schlanger
1987:587—588). Schlanger (1987:596;1988:783) used the number of floors per structure
as a proxy for rebuilding frequency. She calculated the average number of floors per
structure for each time period based on a sample of excavated structures, then used this
information to infer rebuilding frequency for unexcavated sites. Unfortunately, data on
structure rebuilding frequency is currently unavailable for the Silver Creek area, and
therefore a constant of one was used. Using a constant of one may not adversely affect the
reconstructions presented here since Schlanger found that in the Dolores area, the
majority of rooms were not rebuilt. She did note, however, that the highest incidence of
rebuilding occurred during the period A.D. 920-1250, with an average rate of about 28
percent, and if this is the case in the Silver Creek area too, then populatioa may be
underestimated for those time periods. However, without data from excavations, it is
impossible to know for certain how rebuilding frequencies change in the Silver Creek
region.
55
For household size, Schlanger (1988:784) used a number of five people per living
room based on ethnographic literature for the Southwest, which suggests about 3—7.5
people per household. However, Dohm (1990:212) suggests that as sites become more
nucleated, the number of rooms per person increases. In her study of historic pueblos in
Arizona and New Mexico, she found the number of people per room ranged from 0.74.6, with an average of 2.16. Because the number of people per room is treated here as a
constant, the actual number has no effect on the shape of the curve. A constant of three
people per room was chosen for the Silver Creek model, which provides a somewhat
more conservative population estimate than if we were to use five people per room as
Schlanger did. Because living rooms are not differentiated from other rooms in most of
the models presented here, this is probably a reasonable assumption. However, we should
keep in mind that if Dohm is proven correct, our population estimates may be inflated for
periods of aggregation.
Using the numbers discussed above, population was plotted using Schlanger's
formula (Figure 4.9). The curve is nearly identical to the tremsformed total rooms, but
with slightly lower absolute numbers—not surprising considering all the variables are
treated as constants in this model. The reconstruction shows a bimodal peak, with the
highest population at about A.D. 1300-1350, and the next highest population at about
A.D. lOOO-l 100. Like Model A, this reconstruction would support the idea of an influx
of people around A.D. 1000 and A.D. 1300. According to this model, at the peak of
population in the Silver Creek drainage, there were just over 2400 people in an area
approximately 884 square miles. This figure may be high given that a large part of the
56
project area is marginal for agriculture due to high elevations resulting in short growing
seasons. An investigation into the potential agricultural productivity of the project area
would be an interesting topic for future research.
Model B3
2500 X
2000 _
1500 i.
1000 -L
500 i.
Figure 4.9: Model B3.
Several factors probably drive up this estimate. Foremost is the fact that it is
unlikely all structures were occupied contemporaneously and year-round. Seasonal
occupation of small sites could easily double the population figures for aggregated
periods if every household during those periods had a contemporaneous field house. In
addition, Cameron (1990:161) noted in her study of structure uselife that "there is
ethnographic evidence that in many communities, at any one time, there are more usable
structures Jhan occupants." Not only does this model assume that every room at a site is
inhabited, but it also assumes that every room is a habitation room and is occupied
57
pennanentiy. Consequently the absolute population figures are undoubtedly inflated. To
compensate for some of these problems, several of the models below look at the effect of
seasonality and changing room function on the shape of the curve.
Another probable reason for overestimation is the method used to extrapolate the
total number of sites. The method, based on environmental zones, relies on broad soil
categories that have not been ground-truthed. Certain soil categories may contain areas
unsuitable for habitation, and the correction factor is currently unknown. In addition,
archaeological surveys may be conducted in some areas because of the high probability
that sites exist there (Bruce Donaldson, personal communication 1997). This may bias the
sample within particular environmental zones resulting in an overestimation of the total
number of sites. It is therefore suggested that the absolute numbers presented in the
majority of these models are overestimated and should be evaluated cautiously.
Model CI: Total Rooms Using Actual Room Counts for Sites with 20 or More Rooms
The process of transformation in Model B2 inflates the absolute numbers,
particularly in the later time periods, by extrapolating additional large sites into similar
environmental strata. That there are many unknown large sites is probably not a
reasonable assumption, and in order to compare the effect of large sites on the curve, a
second graph of the total rooms using site size class medians was prepared. However, for
the size class of 20 or more rooms, actual room counts were used instead of transforming
using medians (Figure 4.10). This model assimies that all large sites are known. Three
large sites, Showlow Ruin, Flake's Ruin, and AZ P:11:133(ASU), not included in
58
Model B because they were not within a surveyed area in the database, were added for
this model.
Model CI
600
500
400
M
E 300
I 200
100
ae
Year
Figure 4.10: Model CI.
The graph of this model shows a bimodal distribution reminiscent of Models A
and B. However, the peak around A.D. 1300-1350 is higher since most of the largest sites
occur in the later periods. The highest point is at A.D. 1300-1350, with a second peak
around A.D. 1000-1100, and a dip between the two peaks at about A.D. 1200-1250. Like
Models A and B, this model also suggests a migration into the area around A.D. 10001100, and a second demographic shift in the late thirteenth century.
Although there may be some sites larger than 20 rooms that are unknown in the
region, it seems highly probable that most of the largest sites in the Silver Creek area are
known; therefore, this model was used as the base for the models that follow.
59
Model C2: Total Rooms Transformed Using Actual Room Counts for Sites with 20 or
More Rooms
In this model, sites with 20 or more rooms are not transformed; instead, actual
room counts are used for these sites, and the remaining sites are transformed using
environmental strata (Figure 4.11). The most noticeable change in the shape of the curve
is the shift from a bimodal to a unimodal distribution. If we compare Model C2 to B2, we
can see that the two models are basically the same curve, except for the later time periods
when the largest sites occur. Not transforming sites with 20 or more rooms results in a
significant lowering of the curve after A.D. 1250. There is also a slight lowering of the
curve between A.D. 700-900 where a few large sites occur, and a slightly steeper drop
off between A.D. 1000 and 1200.
60
1
Model C2
3000
2500
2000
I
ae
1500
1000
500
0
n
v-k
oe
«rv
<N
9^
CN
O
CN
n
n
»n
fS
Year
Figure 4.11: Model C2.
Assuming that most large sites are known, the current model is a more accurate
representation of the population curve, although the absolute numbers are still suspect as
explained above. This being the case, there is only a single peak of population around
A.D. 1000-1100.
Model C3: Momentary Population from Transformed Total Rooms Using Actual
Room Counts for lih- Room Sites
Using Schlanger's formula with the constants discussed in Model B3, population
was plotted using the new parameters from Model C2 (Figure 4.12). The absolute nimiber
of people for the peak at A.D. 1000-1 ICQ remains exactly the same as in Model B3, but
in the current model population drops off more suddenly from the peak around A.D.
1000-1100, and the decline is relatively steady until the abandonment of the region.
61
Model C3
3000
2500
2000
1500
1000
500
I
00
<N
O
Figure 4.12: Model C3.
There are several implications of this model. First, the evidence to support an
influx of people around A.D. 1000-1100 is very robust. In the models above, the sharp
increase during this time period always remains. The second peak, however, seen in
Models A, B, and CI is not as robust. When we assume that all sites with 20 rooms and
above are known, the second peak disappears, and population drops off relatively steadily
after A.D. 1000-1100. This seems to indicate that if there was a population upheaval in
the late thirteenth century as has been suggested, evidence for it in the Silver Creek area
may be more subtle than anticipated.
What might account for the differences between the bimodal distribution seen in
Models A, B, and CI, and the unimodal distribution seen in the other models? One
answer might be the possibility that archaeological surveys occur in areas where large
sites are located, biasing the database. Large, visible sites have always been of interest to
62
the archaeological community and consequently may be overrepresented in
archaeological surveys. For example, Lightfoot's (1984) lowland survey encorapassed the
area around Fourmile Ruin, the largest PIV site in the area, while his upland survey
included the area around Pinedale Ruin, the largest PIV site in that vicinity. The largest
known PIV site in the Silver Creek area not included in a surveyed tract is Showlow
Ruin. Although the ruin itself was investigated by Bandelier in 1883 (in Haury 1931:9),
Hough (1903:301) in 1901, and Haury and Hargrave (1931) in 1929, and is well known,
the surrounding land is private and has not been surveyed.
If we assume close to 100% of the largest sites are known, compared with a much
smaller percentage of small sites known, then the bimodal distribution seen in those
graphs of surveyed area only may be somewhat misleading. Estimating the total nxmiber
of sites with less than 20 rooms for the entire Silver Creek region may help to reduce this
bias. Therefore, the unimodal curve may be a better representation of population.
Model Dl: The Habitation Room Problem
As mentioned above, there is a difRculty in distinguishing habitation rooms from
specialized use rooms such as storage or manufacturing rooms. To determine the effect
on the shape of the curve, in this model Plog's (1974:90) adjustments for the Hay Hollow
Valley were applied to the Silver Creek room counts. To include only habitation rooms in
his population reconstruction, Plog adjusted his room counts by counting all pithouses,
subtracting 25% of the total number of rooms for the period A.D. 900-1150, and
subtracting 41% of the total number of rooms for the period A.D. 1150-1500 (Figure
63
4.13). Since pithouses were not separated from other structures in the current database,
the adjustment for pithouses was not made. Using Model C2 as a base model, 25% of the
total rooms for the period A.D. 900-1150 were subtracted, and 41% of the total rooms for
the period A.D. 1150-1400 were subtracted. The result has little efTect on the shape of the
curve, except for steepening the drop-off around A.D. 1150-1200, and lowering the
absolute numbers.
Model D1
;
2000 _
j
1800 ^
'
1600 I
j
1400 1
I
«
1200 1
1 1000 1
i
J
800 -
;
600 1
400 i
1
200 1
0 «.
<N
r-i *r*
<s
>6
lO
<N
r-
n
oe
•o
fS
o
*r>
n
<N
CN
<N
Year
Figure 4.13: Model Dl.
Model D2: Momentary Population Using Plog's Adjustment Factors
Replotting population using Plog's adjustment factors (Figure 4.14), the curve is
basically the same as the transformed models already presented, except for the steeper
drop-off around A.D. 1150-1200 mentioned above. The absolute numbers are lower, and
according to this model population peaked at approximately 1659-1670 people around
64
A.D. 1000-1100. Using Plog's adjustments to the population curve the absolute numbers
appear more reasonable, although the numbers still seem high. One possible reason for
the absolute numbers still being inflated is the fact that seasonality has yet to be taken
into account in any of the models so far.
Model D2
1800 ^
1600 _
1400 ^
1200 :
1000 ±
800 ^
600 J.
400 J
200
JL
i
Year
Figure 4.14: Model D2.
Model E: Small Sites and the Seasonality Problem
Pilles and Wilcox (1976:1-3) defined a small site as one "...whose size and
artifactual assemblage suggest a limited temporal occupation by a small group of people,
gathered at the locality to carry out a specific, seasonally-oriented set of activities." Small
sites exist in great numbers in the Southwest, and have been recognized since the time of
Mindeleff as often being temporary structures. Haury (quoted in Moore 1976:10-11)
suggested that small sites were the result of aggregation and increased travel time to
65
agricultural fields resulting firom the nucleation of pueblos, and as a consequence few
would be found dating prior to A.D. 1000. Moore (1976:11-12) points out, however, that
aggregation is just one of a number of variables, generally termed "inconveniences,"
which lead to the construction of seasonal structures. He provides several ethnographic
examples of nonurbanized groups that maintain seasonal structures in addition to their
permanent homestead. However, Wilcox (1978:25-27) confirms Haury's hypothesis that
with the advent of nucleated settlements, the single-room, masonry-type fieldhouse
appeared in large numbers.
The presence of these seasonally occupied sites is a significant factor affecting
population estimates. If the majority of small sites were temporary fieldhouses used only
during the agricultural season, and primary habitations were at larger, more permanent
pueblos, using a count of all rooms would double the population numbers. Whether these
sites were occupied for short gathering trips, or the entire agricultural field season, is
unimportant to the present analysis. The important point is that the stmcture is
contemporaneous with a habitation site found elsewhere.
Modeb El and E3: Transformed Total Rooms Minus All 1-4 Room Sites and
Transformed Total Rooms with Only 1~4 Room Sites
In a preliminary analysis of sedentism for the Rye Creek Project, Elson (1992:83,
105) analyzed sites that he classified as Hohokam, Mogollon, Salado, Sinagua, and
Anasazi, in order to determine if there were distinguishing characteristics between
sedentary and seasonal sites. He noted that there is "...a correlation between site size and
66
degree of sedentism, because larger sites tend to be more sedentary than smaller sites." In
addition, Russell's (1978:36) study of Navajo fieldhouses also confirms the correlation
between site size and sedentism: the longer a site was to be occupied, the more energy
that was expended in terms of structure construction.
The average nimiber of structures for seasonal sites in Elson's (1992:106) study
was five, with the median and the mode being three structures. Therefore, the likelihood
that many of the small sites in the Silver Creek database are temporary may be a
reasonable assumption, and worthy of examination as a model. It is understood, however,
that site size does not necessarily correlate with site function, and consequently some of
these small sites may be permanently occupied. This is particularly true for those periods
before aggregation when small sites characterize the settlement pattem, and were more
likely to be permanently occupied. Keeping this in mind, population may be
underestimated for those periods prior to A.D. lOOQ-1100. Conversely, during these
earlier time periods small structures may have been seasonally used during short-term
migrations from adjacent areas.
To determine the effect of small structures on the shape of the curve, a model was
run removing all sites with one to four rooms (Figure 4.15), using Model C2 as a base.
The model shows a sharp increase in large sites around A.D. 1000-1050, a slight decline
around A.D. 1050-1100, and a peak at about A.D. 1100-1150. The number of large
structures declines only slightly from the peak to about A.D. 1200 after which point there
is a sharp drop and fairly steady decline until abandonment of the area.
Model El
1800
1600 ..
1400 _
ee
£
1000 _
800 J_
600 J.
400 J.
200
Year
Figure 4.15: Model El.
Model E3
1600 1.
1400 I.
1
800 L
600 1
Figure 4.16: Model E3.
68
The graph of 1-4 room sites only (Figure 4.16) shows that population is fairly low
and remains steady until a large increase in small sites fix>m A.D. 900-950. There is a
second jump in small sites aroimd A.D. 1000-1050, and after reaching a peak around
A.D. 1050-1100, small sites decline steadily until the abandonment of the area.
One hypothesis that might account for the trends seen in Models El and E3 would
be if the first increase in small sites at A.D. 900-950 was not totally related to seasonal
use, but instead signaled a migration into the area. Population preceding the time period
in question was low in general, and the number of large sites during A.D. 900-950 seems
too low to account for such a large increase in small, seasonally occupied structures.
Therefore, the sudden increase in small structures at A.D. 900-950 could suggest that
these are what Moore refers to as "migration structures"—small structures that are
constructed by new migrants during the initial settlement of an area (Moore 1976:13;
Schwartz 1970:189). The increase in small structures around A.D. 1000-1050 may also
signal a second wave of migrants into the area.
Other hypotheses to explain these trends include the possibility that as population
increases at larger structures, the use of contemporaneous seasonal structures increases.
This could explain the tremendous increase in small sites around A.D. 1000-1050. A
change in number of people per habitation room would also have an effect on the number
of structures. For instance, if sites in the earliest time periods had six people per room
instead of a constant of three as presented in the models, a shift to three people per room
could result in a doubling of room counts given the same number of people. This would
69
appear as a sharp increase in the number of rooms similar to what is seen in A.D. 900950orA.D. 1000-1050.
A similar pattern would be seen if room uselife changed through time. If we
assume room uselife was much longer in the earlier time periods, for instance 30 years,
and aroimd A.D. 900-950 it changed to 15 years, the number of rooms would double,
given a constant rebuilding frequency. Given that the minimum chronologic£d resolution
possible with the available survey data is 50 years, these changes would not be visible
without excavation.
Another possible explanation for the trends seen is that small sites have also been
known to be used cross-culturally as boundary markers or to stake land claims (Preucel
1990:165). This being the case, the incidence of small structures may far exceed the
requirements of the actual population. It is impossible to assess the validity of these
suggestions without additional data, but they should be kept in mind when evaluating the
trends.
Models E2 and E4: Momentary Population from Transformed Total Rooms Minus
All 1-4 Room Sites and Only 1-4 Room Sites
Transforming the above models into population numbers (Figiu-es 4.17 and 4.18),
we see that if we eliminate all 1-4 room sites, population peaks at approximately 864
people around A.D. 1100-1150. After the peak, population starts to decline and decreases
until abandonment of the area around A.D. 1400. Whereas the previous models of
population appear to overestimate population, it seems unreasonable to assume that all
small sites in all time periods are seasonal structures, and consequently this model may
imderestimate population in this regard.
•
i
1
Model E2
1
11
1800
1600
i
1400
i
1
1200
i
800
600
i
400
200
r-
%r\
^
a©
^
Year
Figure 4.17: Model E2.
Model E4
1800
1600 i.
1400
I
1200 i.
1000
400
J-
!
CN
<N
Year
Figure 4.18: Model E4.
1425
«r»
1325
ir»
1225
j
0
1123
&
1000
1025
O&
1
71
Will the Real Population Curve Please Stand Up?
The "truth" about which curve is closest to population probably lies in a
combination of several of the models. Model D2 takes into account changes in room
function, but not seasonal use of structures, and is therefore probably too high. Model E2
takes into account seasonal use of structures, but does not consider permanently occupied
small sites. Consequently, the numbers in this model are probably too low. However,
Model E2 also does not consider room function, which might raise the totals and could
ceincel some of the effect of not considering permanently occupied small sites. If we use
Model D2 as an upper range and Model E2 as a lower range we can estimate that
population was at its peak in the Silver Creek drainage sometime around A.D. 10001100, and was probably somewhere between 774 to 1670 people.
72
CHAPTER FIVE
DISCUSSION AND CONCLUSION
Observed Versus Expected Growth
One advantage of translating rooms into population figures is that we can compare
the numbers to what might be expected based on certain assumptions for prehistoric
groups. Schlanger (1988:786) estimated an average local intrinsic growth of 2.4% per
year for the Dolores area. Hassan (1981:140), on the other hand, suggests a maximum of
0.52% per year increase for prehistoric populations which seems more reasonable based
on a survey of the ethnographic and archaeological literature. Although this figiu'e
assumes "...conditions of high preadult and adult mortality and...a late age at nubility, as
well as a long child-spacing period...," Hassan notes that prehistoric populations were still
capable of rapid population increase.
If we compare the graphs of population, the first event of interest is the large
increase in population around A.D. 900-950. This may in part be a manifestation of the
pithouse to pueblo transition where below ground structures are replaced by above
ground, masonry structures, which may result in differential visibility (Cordell et al.
1994:129). Using Hassan's estimate of the maximum prehistoric population growth rate
to calculate the expected population based on the beginning population at AJD. 875 in
Model C3, there is an unexplained increase of about 443 people during the time period in
73
question (Table 5.1)*. Using the lower figures of Models D2 and E2, there is still an
unexplained increase of about 265 and 109 people respectively. Because these increases
are larger than can be explained by natural population growth, and because the increase in
population is coupled with the construction of numerous small structures, this seems to
provide evidence for a migration into the Silver Creek region during this time period.
Whether this signaled a short-term migration such as seasonal use from adjacent areas or
a more permanent settling of the area is unclear.
•Rate of increase is calculated using the formula: average rate of increase per year =
(end population/beginning population)"'®''*
-I.
Table 5.1: Observed and Expected Rates of Population Increase
MODEL B3: Momentary Population from Transformed Total Room
Period
425
475
525
575
625
675
725
775
825
875
925
975
1025
1075
1125
1175
1225
1275
1325
1375
1425
Momentary
Population
6
6
9
9
14
20
158
158
192
303
714
866
2212
2226
1882
1667
1244
1517
2432
2148
0
Population
Increase from
Preceding
Period
0
3
0
4
6
138
0
34
110
412
151
1346
14
-344
-216
-422
273
915
-283
-2148
Rate
Increase
of
Population
0.0000
0.0090
0.0000
0.0076
0.0073
0.0426
0.0000
0.0040
0.0091
0.0173
0.0039
0.0189
0.0001
-0.0033
-0.0024
-0.0058
0.0040
0.0095
-0.0025
-1.0000
Expected at
.52 Percent
Increase
8
8
12
12
18
26
204
204
248
391
921
1117
2852
2870
2427
2149
1604
1956
3136
2770
Unexplained
Increase or
Decrease
-2
1
-3
2
2
132
-46
-12
55
323
-55
1095
-626
-988
-760
-905
-87
476
-988
-2770
Table 5.1: Continued
MODEL C3: Momentary Population from Transformed Total Rooms with Actual
Room Counts for 20+ Room Sites
Period
Population
Population
Expected at Unexplained
Rate
Using Actual Increase from Increase
.52 Percent Increase or
Decrease
of
Counts for 20+ Preceding
Increase
Population
Sites
Period
425
6
475
6
0.0000
0
8
-2
525
9
0.0090
8
1
3
575
9
0.0000
-3
0
12
625
14
0.0076
4
12
2
675
20
0.0073
18
6
2
725
65
46
0.0243
26
39
775
65
0.0000
0
84
-19
825
100
0.0085
34
84
16
875
210
0.0150
81
110
129
925
714
505
0.0248
271
443
975
866
0.0039
151
921
-55
1025
1346
0.0189
1095
2212
1117
1075
2226
0.0001
2852
-626
14
1620
-606
-1250
1125
-0.0063
2870
1175
1404
-0.0029
-685
-216
2089
1225
846
-558
-0.0101
1810
-964
1275
606
-240
-485
-0.0067
1091
1325
505
-0.0037
-276
-101
781
1375
311
-194
-0.0097
651
-340
1425
0
-311
-1.0000
401
-401
Table 5.1: Continued
MODEL D2: Momentary Population using Plog's Adjustment Factors
Median Population
Date
Using
Adjustment
Factors
425
6
475
6
525
9
575
9
625
14
675
20
725
65
775
65
100
825
875
210
536
925
975
649
1659
1025
1670
1075
1125
1215
828
1175
1225
499
358
1275
298
1325
1375
183
0
1425
Population
late
Increase from Increase
Preceding
of
Population
Period
0
3
0
4
6
46
0
34
110
326
114
1010
11
-455
-386
-329
-142
-60
-114
-183
0.0000
0.0090
0.0000
0.0076
0.0073
0.0243
0.0000
0.0085
0.0150
0.0189
0.0039
0.0189
0.0001
-0.0063
-0.0076
-0.0101
-0.0067
-0.0037
-0.0097
-1.0000
Expected
at .52
Percent
Increase
8
8
12
12
18
26
84
84
129
271
691
837
2139
2153
1567
1068
643
462
384
236
Unexplained
Increase or
decrease
-2
1
-3
2
2
39
-19
16
81
265
-42
822
-469
-938
-739
-569
-285
-164
-201
-236
Table 5.1: Continued
MODEL E2: Momentary Population from Transformed Total Rooms Minus All 1—4
Room Sites
Period
425
475
525
575
625
675
725
775
825
875
925
975
1025
1075
1125
1175
1225
1275
1325
1375
1425
Momentary
Population
Minus 1-4
Room Sites
Rate Increase Expected at
Population
Increase from of Population .52 Percent
Increase
Preceding
Period
0
0
0
0
0
0
26
26
60
60
186
281
774
655
864
830
591
524
478
309
0
Unexplained
Increase or
Decrease
•
0
0
0
0
0
26
0
34
0
127
95
492
-119
209
-34
-238
-67
-46
-169
-309
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0170
0.0000
0.0230
0.0083
0.0204
-0.0033
0.0056
-0.0008
-0.0068
-0.0024
-0.0018
-0.0087
-1.0000
0
0
0
0
0
0
34
34
77
77
240
362
998
845
1114
1070
762
676
616
398
0
0
0
0
0
26
-8
26
-17
109
41
412
-343
19
-284
-479
-238
-198
-307
-398
78
Table 5.1: Continued
MODEL E4: Momentary Population from Transformed Total Rooms, Only 1-4 Room
Sites
Momentary
Population
Rate Increase Expected at Unexplained
Population 1-4 Increase from of Population .52 Percent Increase or
Increase
Decrease
Room Sites
Preceding
Period
425
6
475
6
0
0.0000
8
-2
525
9
3
0.0090
8
1
575
9
0
0.0000
-3
12
625
14
0.0076
12
2
4
675
20
6
0.0073
18
2
725
40
14
20
0.0142
26
775
40
0.0000
0
52
-12
825
40
0
0.0002
52
-12
875
150
110
0.0268
52
98
925
528
378
193
0.0254
335
975
584
56
681
•91
0.0020
1025
1438
854
0.0182
753
685
1075
133
1572
0.0018
1854
-282
1125
756
-815
2027
-1271
-0.0145
1175
574
-182
-0.0055
975
-401
1225
-319
740
255
-0.0161
-485
1275
82
-173
-247
-0.0225
329
1325
-55
26
106
-0.0224
1375
-25
1
-0.0574
34
-33
1425
0
676
-676
-1
-1.0000
1
00
Period
The second event of interest is another increase in population around A.D. 10001050. Again using Hassan's maximum growth rate of 0.52%, population exceeds the
expected by about 1095 people in Model C3; by 822 people in Model D2; and by 412 in
Model E2. This event differs from the first in that there is a large increase in both small
and large sites. The increase during this time period is even greater than the previous and
therefore cannot be explained through natural population growth. Other factors may be at
work, as discussed in the previous chapter. However, assuming the number of people per
79
room and room uselife remain relatively constant until A.D. lOSO, there appears to have
been an influx of people into the area between AJ). 1000-1050.
The third period of interest is A.D. 105Q-1100. There appears to have been a
decline in population at larger sites during this time period, while small sites increase
very slightly. Whether this indicates that some of the population left the area or some of
the population fissioned into smaller sites, is unclear. What is evident is that population
did not increase as expected for natural population growth. It is suggested in the next
section that several years of drought may have been a factor. If drought was an issue, an
increase in the number of seasonal habitations may reflect an effort to expand the
resource base through exploitation of other environmental zones. Investigation into
whether the trends seen here are sociocultural or environmental would be an interesting
topic for future research. The fact that great kivas began to show up during this time
period in the Silver Creek area (Herr 1994:52) may be linked to the population trends
seen here as well. It has been suggested that the integrative structures may have played a
role in labor recruitment (Mills 1995:11); stagnating or declining population numbers
would seem to support such a hypothesis.
The next period of interest is A.D. I lOO-l 150. During this time there was a steep
decline in small sites with a corresponding increase in larger sites. This suggests a
contraction of the population into fewer, larger sites. The majority of great kiva sites in
the Silver Creek area span this time period (Herr 1994:52) and may be indicative of social
reorganization necessitated by increasing aggregation. However, the increase at larger
sites during this time period is explainable by natural population growth, with the
80
observed population at large sites (Model E2) as expected based on a growth rate of
0.52%.
If this is the case, then where did all the people go who were living in the small
sites? One explanation is that the small sites were seasonally occupied, and with
increasing aggregation, seasonal sites began to be abandoned. Cordell et al. (1994:130)
note that by the early 1300s, in the upper Little Colonido River area and around Zuni,
small sites completely disappear, with the majority of the population living in large,
aggregated villages. This could mark the beginning of a similar trend in the Silver Creek
area.
An alternative hypothesis would be that small sites, or at least some of them, were
occupied concurrently with larger sites, then during A.D. 1100-1 ISO, there was a
migration out of the Silver Creek area by many of the people occupying small sites. This
scenario might look at the plausibility of earlier established people having "firstcomer"
status and the economic advantages associated with that position (Herr 1996). These
better-established people could have closed ranks, moving into aggregated sites, while
others were forced to move elsewhere. LaMotta (1996) has looked at issues of land tenure
at HomoFovi to the north of Silver Creek, and suggested that restrictive control of
landholdings would be a viable way of reducing population density through forced fission
during times of subsistence stress.
Population, having peaked sometime around A.D. 1100, shows a steady decline
imtil the abandonment of the region around A.D. 1350-1400. Small sites show a fairly
steady decline after aggregation into larger sites until abandonment of the area, from A.D.
81
1100-1400 (Figiure 5.1). Although population at the larger sites appesirs to remain stable
from A.D. 1100-1200, the feict that there is no growth indicates that either some of the
population is moving out or that factors are a£fecting reproduction or death rates
canceling out population growth.
Number of Sites by Time Period
Grouped by Site Size Classes
300
250
200
,1-4
•
|4.1-I0
J 150
!
110.1-20
,20+
100
:
50
• • • 1 1 -J
fM
>o
lo
fS
•
•
%r\
fS
OP
O
1.LL L.. .
<M
—
*r\
<N
<N
Period
Figure 5.1: Site sizes through time.
These results have some interesting implications for the causes and consequences
of aggregation. Increased numbers of small seasonal structures have been seen as a
response to increased aggregation, and yet in the reconstructions presented, with
82
increasing aggregation small sites become more scarce. Is this trend related to
sociopolitical reorganization, subsistence pattern reorganization, conflict, or some other
combination of factors? Most of the circular great kivas in the area that are tree-ring dated
occur in the early 1100s, around the time that population peaks then begins to stagnate
and decline. What role do these integrative structures play during this period of
population change in the Silver Creek area? These questions are beyond the scope of the
present paper, but would be interesting topics of future research.
Environmental Variables
As an indication of the environmental factors that may have played a role during
this time, the Palmer Drought Severity Index (PDSI) was consulted. PDSI data for the
central mountains of northem Arizona, were provided by the Laboratory of Tree-ring
Research at The University of Arizona. The PDSI serves as an indication of the available
moisture for plants, and consecutive years with lower than average values would indicate
drought conditions. It is possible that short intervals of lower than average PDSI over a
prolonged period might have the same effect as many consecutive years of lower than
average PDSI; and Dean et al. (1994:55) note that excessive rainfall can also reduce
agricultural production. However, for the sake of convenience, periods with five or more
consecutive years of lower than average PDSI values were marked from the beginning of
the index at A.D. 966 to A.D. 1400, as an indication of environmental impact on
population.
83
A span of five years of negative PDSI values was chosen, since this could
conceivably result in five years of high infant mortality. Five years of high infant
mortality could have a significant impact on population within a generation (Wetterstrom
1986:134). Five cases of consecutive PDSI values lower than normal were noted: A.D.
1033-1041 (nine years), A.D. 1131-1137 (seven years), A.D. 1214-1223 (10 years), A.D.
1338—1355 (18 years interspersed with four nonconsecutive years of positive PDSI
values), and A.D. 1387-1391 (five years).
If we compare these periods of drought to Model E2, the graph of population for
large sites, we see that, interestingly, they correspond to periods of population decline. In
addition. Dean et al. (1994:64) observed that "...major population increases in the
Pinedale and Snowflake areas occurred during a period of favorable LFP conditions in the
1000s to 1100s; [and] the population decreases that led to abandonment in the 1300s
occurred during a period of LFP and HFP deterioration." LFP refers to low firequency
processes that have periodicities greater than 25 years such as: "...long-term climate
trends, alluvial groundwater fluctuations, aggradation and degradation of alluvial
floodpleiins, deposition and erosion on slopes, and changes in the composition and
elevational boundaries of vegetation zones." HFP refers to high firequency processes that
have periodicities of less than 25 years; for instance, "...seasonal, annual, and multiyear
fluctuations in various climatic parameters (precipitation, temperature, fi'ost-fi-ee period,
drought), streamflow, vegetative production, and the temporal and spatial aspects of these
factors" (Dean et al. 1994:54).
84
While it is easy to fall back on an environmental interpretation of population
trends, I quote Carla Van West (1993:4-5) that "...while environmental factors may play
an important and sometime causal role in human adaptation and culture change, they are
not sufficient to fully explain all aspects of cultural behavior." Dean et al. (1994:77) also
point out that "...many of the environmental correspondences apparent in local population
curves may be due to behavioral adaptations rather than to environmentally induced
changes in the numbers of people." In other words, cultural responses to environmental
conditions, such as aggregation or increased mobility, would be reflected in the
demographic reconstructions. The important point to keep in mind is that "...populationresource disjimctions [can] trigger behavioral responses that create new adaptive
configurations" (Dean et al. 1994:53).
Conclusion
The models presented in this paper provide several demographic solutions, using a
number of plausible assumptions, regarding the prehistoric population of the Silver Creek
area. Based on these, there appears to have been rapid population growth between A.D.
900-1050, supporting the hypothesis of a migration into the Silver Creek area during that
time. Practically all of the previous reconstructions for the area hypothesized a significant
increase of population during this time period, and the evidence appears to be quite
robust. However, there does not appear to be any evidence to support a population
increase in the later time periods. Based on the current reconstructions, population
reached its peak sometime around A.D. 1100. Every other reconstruction for the Silver
85
Creek area, save one, suggests that population reached its peak in the area much later. The
only reconstruction that agrees with the current model is the one by Longacre (1964) and
Johnson (1970), summarized by Zubrow (1975:55-57). Their model indicates that
maximum population growth occurred during the period A.D. 900-1100 and afterwards
declined rapidly to abandonment. A finding basically identical to the reconstructions
presented here. Although Lightfoot (1984:88) also suggests that population peaks around
A.D. 1100, in his reconstruction, population does not begin to decline until after A.D.
1250—much later than the models presented here indicate.
The evidence for a migration into the Silver Creek area corresponds to the
appearance of large-scale public architecture known as circular great kivas (Herr 1996:1).
During the initial stages of immigration into the area, these structures may have played an
important role in integrating dispersed communities. However, as population growth
stagnated or declined after the population peak around A.D. 1100, the fvmction of these
structures may have changed to an emphasis on recruiting labor and marriage partners
into the area (Mills 1995:7-8). Increasing aggregation in the area after A.D. 1100
corresponds to a pattern of declining population that continues until abandonment of the
Silver Creek region.
By the thirteenth century, circular great kivas have been replaced with rectangular
great kivas and plazas. Mills (1995:9) notes that "...sites of the early to late thirteenth
century in the Silver Creek area, marks a distinctive break with earlier sites in the area.
Not only are they larger and without circular great kivas, they are located on high points
with commanding views of the landscape." This time period does not correspond to any
86
of the drought periods noted above, nor are there any apparent population fluctuations,
apart from a steady decline in population. The shift of population into the Little Colorado
River Valley and the Mogollon Highlands during this time, suggested by Dean (1996:46),
is not evident from the population reconstructions for the Silver Creek area.
Mills (1995:9) hypothesizes that the change in site patterning represents a
contraction of the population into better-protected sites, in response to some real or
perceived threat. The declining population pattern noted in the reconstructions presented
could add weight to this argument. Wilcox and Haas (1994:236) provide data to support
this theory by suggesting that evidence for warfare in the Southwest is apparent and
widespread for the period A.D. 1250-1300; a pattern they believe contributes to
defensively located settlements and increased aggregation. The time period is
characterized by severe population-resource imbalances in other areas, as evidenced by
the abandonment of the San Juan drainage during this time (Dean 1996:46), which may
have contributed to an unstable population dynamic and conflict.
Although there is good evidence of migration from the Tusayan-Kayenta area to
Point of Pines to the south of Silver Creek (Haury 1958), and burgeoning evidence for
destinations within the Silver Creek area (Mills 1996; Stinson 1996:89), there is nothing
in the population reconstruction to support a migration during the late thirteenth and early
fourteenth century. If, however, people moved into the area £is others moved out, this may
not be evident archaeologically (Mills 1995:3). The lack of evidence in the demographic
reconstructions for an influx of people during this time supports Mills (1996:24)
hypothesis that the PFV migrations were the result of the movement of small groups such
87
as individual households, which would be difficult to identify without multiple lines of
evidence.
The cunrent reconstructions also have implications for theories of social
integration such as the development of a Southwestern Cult in the late 1200s (Crown
1994) or the Katsina Cult (Adams 1991). Both Crown (1994:213) and Adams (1991:151,
160) have hypothesized that the development of these cults may have been related to the
need to integrate migrating populations from the Tusayan-Kayenta area into the host
communities of the already populated Mogollon Rim area. The reconstructions presented
here show that by the late thirteenth century, population was relatively low, having
declined significantly from the peak around A.D. 1100. Using Model D2 as an upper
limit, population in the late thirteenth century had been reduced to less than 400 people,
declining to around 300 people by A.D. 1300-1350, and about 200 people by about A.D.
1350-1400.1 have already suggested the estimates of Model D2 are high, and these
numbers would already include the supposed migrants of the late thirteenth century,
meaning the population of the host communities would be lower than the model allows.
With populations so low, is it logical to assume that the main function of these cults was
community integration? It is logical, if we look at it in terms of the need to integrate
different groups where there is a shortage of marriageable partners or labor due to low
population. Mills (1995:7-8) has discussed a similar scenario in relation to the low
populations of the Silver Creek area, and further investigation along these lines may
prove productive.
88
Future Research
The current study points out several areas for future research which would
complement and help to refine the population leconstructions presented. The first would
be to conduct full-coverage surveys for a sample of the environmental strata used in this
study, to test the accuracy of the method of reconstruction. Out of the 50 environmental
strata defined for this project, sites occurred in only 10 strata. Ground-truthlng the
accuracy of the ratios of sites for the ten strata where sites occur would be a useful
exercise, and a sample of the other 40 strata should be checked for the presence of sites.
Additional research on artifact accumulation rates could help to refine population
reconstructions as well. Limited testing at a sample of sites by size class could provide
data to improve estimates of site occupation length. Excavation at a sample of sites by
size and time period could also provide data to estimate rebuilding frequency and the
ratio of habitation rooms to total rooms through time.
Another interesting area of research would be to examine the function of small
sites through time. The current paper suggests that many of the small sites associated with
the large influx of people into the area during A.D. 900-1050 may have served as
migration structures, while small sites during later periods of aggregation, A.D. 1100 to
abandonment, are more likely to be seasonal-use structures. Complete excavation at a
sample of small sites may provide evidence of technological change in ceramics or other
indicators to support or refute these hypotheses.
89
Another productive avenue of research would be to convert the present database
using a geographic information system (CIS) such as ARCInfo. This would allow more
thorough analyses of the spatial aspects of population location and movement, using
variables such as elevation, distance to water, aspect, and slope, for example. Analyses of
soil, topography, and available water, could provide information about the amount of
cultivable land available which could inform discussions of carrying capacity,
sociopolitical organization, and land tenure issues. Changes in inter- and inra-regional
patterning of site locations might provide evidence of demographic change during the late
13th century not visible through population reconstructions alone. View-shed analyses
could provide information on matters relating to conflict and defense. Patterned change of
sites by elevation and distance to water might provide clues to environmental variables at
work in short-distance migrations. For instance, in the Chevelon area, during periods of
drought, people moved between lower and higher elevations (Dean et al. 1994:63), and
similar patterns may show up with additional analysis of the Silver Creek data. Intrasite
analyses of architecture could provide more fine-grained data on population change
through time by looking at the growth of individual sites, for instance. Studying changing
architectural forms and the spatial structure of settlements could be useful in examining
changes in social structure resulting from population dynamics such as population
growth, aggregation, and decline.
This paper has looked at only a few variables in the population dynamics of the
Silver Creek area, and a cursory glimpse at the possibilities for future research shows that
there is still much more that can be done. Population reconstructions using variables other
than the ones presented here would no doubt provide further insights into the
paleodemography of the region. In addition, by applying the knowledge gained from
additional research to the present reconstructions, we can no doubt refine these estimates
in the future.
Demographic reconstruction is an integral and necessary step toward
understanding numerous aspects of prehistory. It would be difficult to discuss issues such
as aggregation and migration in a meaningful way without some reference to population.
At the same time, population reconstructions should be viewed in a critical light, and the
underlying assumptions made explicit. This done, paleodemography can be a useful tool
when combined with multiple lines of evidence for the study of culture change.
APPENDIX 1: SITE DATA
Project
ASM#
Other Site#
Date
Bcgial
Date
End 1 Rooms
Aztec
AZP 11 224(ASM)
1050
1150
3.5
Aztec
AZP 11 225(ASM)
1000
1150
0
Aztec
AZP 11 226(ASM)
850
1370
0
Aztec
A2a» 11 227(ASM)
1010
1120
0
Aztec
A23' 11 228(ASM)
900
IIOO
4.5
Aztec
AZP 11 229(ASM)
900
1100
15
Aztec
AZP 11 230(ASM)
1000
1150
2
Aztec
AZP 11 231(ASM)
900
1150
0
Aztec
AZP 11 232(ASM)
900
1125
0
Aztec
AZP 11 233(ASM)
925
1125
0
Aztec
AZP 11 234(ASM)
850
1120
2.5
Aztec
AZP 11 236(ASM)
900
1150
I
Aztec
AZP 11 238(ASM)
1010
1120
0
Aztec
AZP 11 239(ASM)
950
1120
0
Aztec
AZP II 24I{ASM)
1000
1150
2.5
Aztec
AZP II 242(ASM)
900
1120
0
Aztec
AZP II 242(ASM)
1275
1300
0
Aztec
AZP II 244(ASM)
900
1120
0
Aztec
AZP II 244(ASM)
1275
1300
0
Aztec
AZP II 245(ASM)
900
1125
2
Aztec
AZP II 246(ASM)
950
1120
0
Aztec
AZP 11 247(ASM)
1000
1120
0
Aztec
AZP 11 248(ASM)
1000
1125
0
Aztec
AZP 11 250(ASM)
760
1200
0
0
0
Aztec
AZP 11 251(ASM)
1010
1120
Aztec
AZP 11 252(ASM)
1010
1120
Aztec
AZP 11 253(ASM)
850
1370
0
Aztec
AZP 11 254(ASM)
850
910
0
Aztec
AZP 11 256(ASM)
1150
1250
2.5
Aztec
AZP 11 257(ASM)
900
1125
0
Aztec
AZP 11 258(ASM)
1000
1200
0
Aztec
AZP 11 259(ASM)
850
1000
0
Aztec
AZP 11 260(ASM)
850
910
2
Aztec
AZP II 26I(ASM)
1050
1150
0
Aztec
AZP II 262(ASM)
1000
1125
0
Aztec
AZPtl 1:263(ASM)
1050
1150
0
Aztec
AZPrl l:264(ASM)
1000
1125
0
Aztec
AZP:11:265(ASM)
850
1370
0
Aztec
AZP:n;266(ASM)
950
1120
0
Aztec
AZP:11:269(ASM)
850
1370
0
Aztec
AZP:ll:270(ASM)
850
1370
0
Aztec
AZP;1I:272(ASM)
850
1120
0
Aztec
AZP:ll:274(ASM)
1050
1150
0
Aztec
AZP:11:275(ASM)
850
1120
10
Aztec
AZP:ll:276(ASM)
850
1370
0
Aztec
AZP:11:280(ASM)
900
1100
1
Aztec
AZP:11:281(ASM)
1150
1250
7
Bagnal
AZP:12:129(ASM)
1000
1100
4
Bagnal
AZP;12:I30(ASM)
1000
1100
2.5
Bagnal
AZP;I2:131(ASM)
1050
1150
3
Bagnal
AZP:I2:132(ASM)
1000
1100
1.5
Bagnal
AZP;12:133(ASM)
1000
1100
2.5
Bagnal
AZP:I2:134(ASM)
1000
1100
0
Bagnal
AZP:I6:096{ASM)
1000
1100
5.5
Bagnal
AZP;16:150(ASM)
1000
1100
0
Bagnal
AZP;16:15l(ASM)
1000
1100
0
Bagnal
AZP:16:152(ASM)
1000
1100
1
Bagnal
A2P:I6:I53(ASM)
1000
1100
15
Bagnal
AZP:16:154(ASM)
1000
1100
5.5
Bagnal
AZP:16:155(ASM)
1000
1100
2
Bagnal
AZP:16:156(ASM)
1000
1100
0
Bagnal
AZP:16:157(ASM)
900
1000
0
Bagnal
AZP:16:158(ASM)
850
950
0
Bagnal
AZP;16:159(ASM)
1000
1100
1.5
Bagnal
AZP:16:160(ASM)
1050
1150
7.5
Bagnal
AZP:16:161(ASM)
1000
1100
2.5
Bagnal
AZP;16;162(ASM)
1000
1100
0
Bagnal
AZP;l6;i63(ASM)
1000
1100
0
Bagnal
AZP;I6:I64{ASM)
1000
1100
0
Bagnal
AZP:16:165(ASM)
1050
1150
2.5
Bagnal
AZP:I6:166(ASM)
1000
1100
5.5
Bagnal
AZP:I6:I67(ASM)
1000
1100
2.5
Bagnal
AZP:I6:168(ASM)
1000
1100
0
Bagnal
AZP;16;169(ASM)
1000
1100
0
Bagnal
AZP:16:170(ASM)
1000
1100
2.5
Bagnal
AZP:I6;171(ASM)
1000
1100
4
Bagnal
AZP:16:172(ASM)
800
900
0
Bagnal
AZP 16:173(ASM)
1000
1100
0
1
0
Bagnal
AZP 16:174(ASM)
1000
1100
Bagnal
AZP 16:175(ASM)
1000
1100
Bagnal
AZP 16:176(ASM)
1000
1100
6
Bagnal
AZP 16:177(ASM)
1000
1100
6.5
Bagnal
AZP I6:178(ASM)
1000
1100
I
Bagnal
AZP I6:179(ASM)
1000
1100
0
Bagnal
AZP 16:I80(ASM)
lOSO
1150
0
Bagnal
AZP 16:I81(ASM)
1000
1100
I
Bagnal
AZP 16:I82(ASM)
1000
1100
0
Bagnal
AZP 16:183(ASM)
1000
1100
5.5
Bagnal
AZP 16:I84(ASM)
1000
1100
0
Bagnal
AZP I6:185(ASM)
1000
1100
1
Bagnal
AZP 16:186<ASM)
1000
1100
0
Bagnal
AZP 16:187(ASM)
1000
1100
0
Bagnal
AZP 16;I88(ASM)
1000
1100
0
Bagnal
AZP 16:I89(ASM)
1000
1100
5
Bagnal
AZP I6:190(ASM)
1000
1100
2
Bagnal
AZP 16:191(ASM)
1000
1100
2
Bagnal
AZP I6:192(ASM)
1000
1100
2
Bagnal
AZP 16;I93(ASM)
1000
1100
0
Bagnal
AZP I6:I95(ASM)
1000
1100
1
Bagnal
AZP 16:196(ASM)
1000
1100
4
Bagnal
AZP t6:l97(ASM)
1100
1200
27.5
Bagnal
AZP 16:I98(ASM)
1000
1100
2
Bagnal
AZP 16:199(ASM)
1000
1100
10
Bagnal
AZP I6;200(ASM)
1000
1100
8.5
Bagnal
AZP 16:201(ASM)
1000
1100
1
Bailey
AZP 11:001(ASM)
1275
1325
200
Bailey
AZP M.003(ASM)
1100
1250
0
1150
4.5
1150
0
Bailey
AZP II:28S(ASM)
1050
Bailey
AZP II:286(ASM)
1050
Bailey
AZP 11:289(ASM)
1150
1275
0
Bailey
AZP ll:290(ASM)
1050
1150
0
Bailey
AZP ll:291(ASM)
1050
1150
0
0
Bailey
AZP n:292(ASM)
600
1000
Bailey
AZP ll:293(ASM)
900
1000
0
Bailey
AZP 1!:294(ASM)
1150
1275
8.5
Bailey
AZP
n:295(ASM)
800
900
0
Bailey
AZP 11:296(ASM)
1000
1150
3
0
0
Bailey
AZP 1I:297(ASM)
800
900
Bailey
AZP ll:299(ASM)
1150
1275
Bailey
AZP 11 300(ASM)
600
800
0
Bailey
AZP 11 301(ASM)
1000
1150
0
Bailey
AZP 11 302(ASM)
600
1275
4
Bailey
AZP 11 303(ASM)
1000
1150
1
Bailey
AZP 11 304(ASM)
1000
1150
I
Bailey
AZP 11 305(ASM)
650
800
0
Bailey
AZP 11 306(ASM)
IISO
1275
1
Bailey
AZP 11 307(ASM)
1000
1200
4.5
Bailey
AZP 11 308(ASM)
1000
1150
1
Bailey
AZP 11 309(ASM)
900
1000
0
Bailey
AZP 11 3I0(ASM)
1000
1150
0
Bailey
AZP 11 3I1(ASM)
600
800
0
Bailey
AZP 11 3I2(ASM)
900
1150
0
Bailey
AZP 11 3I3(ASM)
800
900
3
Bailey
AZP 11 314(ASM)
950
1200
0
Bailey
AZP 11 315(ASM)
800
900
0
Bailey
AZP 11 3I6(ASM)
1150
1275
0
Bailey
AZP 11 317(ASM)
900
1000
0
Bailey
AZP 11 3I8(ASM)
800
900
0
Bailey
AZP 11 320(ASM)
1000
1150
8
Bailey
AZP 11 321(ASM)
900
1000
0
Bailey
AZP II 322(ASM)
1150
1275
7
Bailey
AZP 11 323(ASM)
1150
1275
3
Bailey
AZP 11 324(ASM)
1000
1150
1
Bailey
AZP 11 325(ASM)
1100
1175
2.5
Bailey
AZP 11 326(ASM)
800
900
0
Bailey
AZP 11 327(ASM)
1275
1325
0
Bailey
AZP 11 328(ASM)
1050
1175
3
Bailey
AZP 11 329(ASM)
1275
1325
0
Bailey
AZP 11 330(ASM)
1275
1325
5
Bailey
AZP 11 331(ASM)
1275
1325
0
Bailey
AZP 11 332(ASM)
600
1300
0
Bailey
AZP 11 333(ASM)
1000
1150
0
Bailey
AZP 11 334(ASM)
900
1000
0
Bailey
AZP 11 335(ASM)
600
700
0
Bailey
AZP 11 336(ASM)
1150
1325
0
Bailey
AZP 11 337(ASM)
1150
1275
5
Bailey
AZP 11 338(ASM)
1150
1325
9
Bailey
AZP 11 339(ASM)
1150
1325
1
Bailey
AZP 11 340(ASM)
900
1275
1
Bailey
AZP 11 341(ASM)
1100
1275
3.5
Bailey
AZP II 342(ASM)
1275
1325
0
AZP:11:344(ASM)
900
0
AZP:12:135(ASM)
1000
AZP;12;136(ASM)
1000
AZP:12:137(ASM)
1000
T
T
T
AZP:12:138(ASM)
1150
AZP:I2:139(ASM)
1000
AZP;12:I40(ASM)
1000
AZP:12:141(ASM)
1000
A2P:12:142(ASM)
1000
AZP;12:143(ASM)
500
AZP:12:143(ASM)
1000
AZP;12:144(ASM)
1000
AZP:12:145(ASM)
1000
AZP:12:146(ASM)
1000
o"
"o
"o
J
J
"o
"o
AZP:12:147(ASM)
1000
"o
AZP:12:148(ASM)
1000
J
AZP:11:065(ASM)
1000
AZP:11:066(ASM)
1000
AZPtl 1:067(ASM)
1000
"o
"o"
"o
"o
T
"o
"o
AZP:11:068(ASM)
1000
AZP;l 1;069(ASM)
1000
AZP:11:070(ASM)
1000
AZP:11:071(ASM)
1000
AZP:11;074(ASM)
1000
AZP:11:075(ASM)
1000
AZP:12:012(ASM)
1200
AZP:12:209(ASM)
1000
AZP:12:210(ASM)
900
AZP:12:211(ASM)
900
AZP:12:212(ASM)
900
AZP:12:213(ASM)
950
AZP:12:214<ASM)
850
AZP:I2:215(ASM)
950
AZP:12:216(ASM)
950
AZP:12:217(ASM)
850
AZP:12:218(ASM)
850
AZP:12:219(ASM)
950
AZP:12:220(ASM)
1100
AZP;12:22I(ASM)
950
AZP:12:222(ASM)
950
AZP:12:223(ASM)
950
AZP:12:224(ASM)
1100
15
o"
T
T
T
T
T
"o
T
o"
T
"o
T
o"
"o
96
Colbath I
AZP:12:225(ASM)
900
1300
1
Colbath I
AZP:12:226(ASM)
850
1200
5
Colbath I
AZP:12:227(ASM)
950
1200
2
Colbath I
AZP;12;228(ASM)
850
IlOO
0
Colbath I
AZP:12:229(ASM)
850
1200
7
Colbath I
AZP;i2:230(ASM)
1000
1250
2
Colbath I
AZP:12:231(ASM)
600
1100
0
Colbath I
AZP:12:232(ASM)
850
1100
2
Colbath 1
AZP;12:233(ASM)
600
1300
1
Colbath I
AZP:12:234(ASM)
950
1100
4
Colbath I
AZP:I2:235(ASM)
950
1200
0
Colbath I
AZP;12:236(ASM)
950
1300
0
0
Colbath I
AZP;12;237(ASM)
600
1100
Colbath I
AZP:I2:239(ASM)
950
1100
0
Colbath I
AZP;12:240(ASM)
850
1300
0
Colbath 1
AZP:12:241(ASM)
600
1300
0
Colbath I
AZP:I2:242(ASM)
850
1200
4
Colbath I
AZP:12:243(ASM)
1000
1100
0
Colbath I
AZP:12:244(ASM)
850
1200
6
Colbath I
AZP:I2:245(ASM)
850
1200
0
Colbath I
AZP:12:246(ASM)
1100
1200
0
Colbath I
AZP;I2:247(ASM)
1000
1100
0
Colbath I
AZP;12:248(ASM)
950
1200
14
Colbath I
AZP:12;249(ASM)
600
1100
1
Colbath I
AZP:12:250(ASM)
950
1200
0
Colbath I
AZP;12:251(ASM)
950
1200
0
Colbath I
AZP:12;253(ASM)
950
1200
5
Colbath I
AZP:12;254(ASM)
800
1150
0
Colbath I
AZP;12:255(ASM)
950
1100
0
Colbath I
AZP:12:256(ASM)
950
1200
4
Colbath I
AZP:12:257(ASM)
1050
1150
0
Colbath I
AZP:12:258(ASM)
950
1200
8
Colbath I
AZP:12:259(ASM)
950
1200
9
Colbath I
AZP:12:260(ASM)
850
1100
6.5
Colbath I
AZP:12:261(ASM)
950
1100
0
Colbath I
AZP;12;263(ASM)
950
1200
0
Colbath I
AZP:12:264(ASM)
950
1200
3
Colbath I
AZP:12:265(ASM)
1000
1100
2.5
Colbath I
AZP:I2:266(ASM)
1000
1100
0
Colbath I
AZP:12:267(ASM)
950
1200
0
Colbath I
AZP:12;268(ASM)
850
1200
0
Colbath I
AZP;12:269(ASM)
600
1300
1
97
Colbath I
AZP:12:270(ASM)
1000
HOC
3
Colbath I
AZP:I2;271(ASM)
1000
1100
0
Colbath I
AZP:I2:272(ASM)
600
1300
0
Colbath I
AZP:I2:273(ASM)
1000
1100
0
Colbath I
AZP:I6:202(ASM)
600
1100
0
Colbath I
AZP:16:203(ASM)
950
1100
0
Colbath I
AZP:I6:204(ASM)
800
1200
0
Colbath n
AZP:12:193(ASM)
1000
1100
0
Colbath 11
AZP:I2:194(ASM)
1000
1100
3
Colbath II
AZP;I2:195(ASM)
1000
1200
17
Colbath II
AZP:I2:196(ASM)
1000
1100
3.5
Colbath II
AZP:!2:197(ASM)
1000
1100
0
Colbath H
AZP:12;198(ASM)
1000
1100
3
Colbath II
AZP;I2:199(ASM)
1000
1100
10
Colbath II
AZP;12:200(ASM)
1000
1100
2
Colbath II
AZP:12:201(ASM)
1000
1100
1
Colbath II
AZP;12:202(ASM)
1000
1100
2
Colbath II
AZP:12:203(ASM)
1000
1100
0
Colbath II
AZP:12:204(ASM)
1000
1100
0
Colbath II
AZP;12:205(ASM)
1000
1100
0
Colbath II
AZP:12:206{ASM)
1000
1100
0
Colbath II
AZP:12:207(ASM)
1000
1100
0
Colbath II
AZP:12:208(ASM)
1000
1100
15
Dodson
AZP:12:149(ASM)
1000
1100
0
Dodson
AZP;12:150(ASM)
1000
1100
0
Dodson
A2P:12:151(ASM)
1000
1100
0
Dodson
AZP:12:152(ASM)
1000
1100
0
Dodson
AZP:I2:153(ASM)
1000
1100
0
Dodson
AZP;12:154(ASM)
1000
1100
0
Dodson
AZP:12:156{ASM)
1000
1100
0
Dodson
AZP:I2:!57(ASM)
1000
1100
1
Dodson
AZP:I2:I58(ASM)
1000
1100
0
Dodson
AZP;12:I59(ASM)
1000
1100
1
Dodson
AZP:12:I60(ASM)
1000
1100
0
Dodson
AZP:12:I6I(ASM)
1000
1100
0
Dodson
AZP:12:162(ASM)
1000
1100
0
Dodson
AZP:12:163(ASM)
1000
1100
0
Dodson
AZP:12:I64(ASM)
1000
1100
5
Dodson
AZP:I2:16S(ASM)
1000
1100
2
Dodson
AZP:12:I66(ASM)
1000
1100
1
2
2
Dodson
AZP:12:167(ASM)
1000
IISO
Dodson
AZP:12:168(ASM)
1000
1100
Dodson
AZP:12:188(ASM)
Dodson
AZP;12:189(ASM)
Dodson
AZP;I2;I90(ASM)
Dodson
AZP:I2:19I(ASM)
Dodson
AZP:12:I92(ASM)
Eastside Pigs
AZP:8:052(ASM)
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1100
Eastside Pigs
AZP:8:054(ASM)
900
Eastside Pigs
AZP;8:057(ASM)
Fence
AZP:12;046(ASM)
Dodson
AZP:12:I70(ASM)
Dodson
AZP:12:171(ASM)
Dodson
AZP:12:172(ASM)
Dodson
AZP:12:173(ASM)
Dodson
AZP:12:174(ASM)
Dodson
AZP:12:175(ASM)
Dodson
AZP;12:176{ASM)
Dodson
AZP:12:177(ASM)
Dodson
AZP:12:178(ASM)
Dodson
AZP:12:179(ASM)
Dodson
AZP:12:I80(ASM)
Dodson
AZP:I2:I81(ASM)
Dodson
AZP:12:182(ASM)
Dodson
AZP:I2:183(ASM)
Dodson
AZP:12:184(ASVI)
Dodson
AZP:12:185(ASM)
Dodson
AZP:12:I86(ASM)
Dodson
AZP;12:I87(ASM)
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
0
0
0
0
0
0
0
2.5
0
3
0
0
0
1100
1
1
1
1100
0
1100
2
1100
1100
1100
1100
0
1
0
0
1100
0
1275
3
1100
0
1060
1290
3
900
1100
3
Fence
AZP:I2:047(ASM)
900
1100
6
Fence
AZP:12:048(ASM)
1000
1100
3
Fence
AZP:12:049(ASM)
900
llOO
0
Fence
AZP:12:050(ASM)
1000
I
Fence
AZP:12:051(ASM)
1000
1100
1100
1.5
Fence
AZP:12:052(ASM)
700
900
2.75
Fence
AZP:12:052(ASM)
lOSO
1150
2.75
Fence
AZP:12:053(ASM)
900
1000
6
Fence
AZP:12:054(ASM)
1000
1100
5
Fence
AZP:12:055(ASM)
900
1100
4
Fence
AZP;12;056(ASM)
900
uoo
2
Fence
AZP;12:057(ASM)
1000
1150
6.5
Fence
AZP:12:058(ASM)
900
1200
1
Fence
AZP:12:059(ASM)
900
1100
3.5
Fence
AZP:12:060(ASM)
900
1200
0
Fence
AZP:12:061(ASM)
900
1100
2
Fence
AZP:12:062(ASM)
900
1100
0
Fence
AZP:12:063(ASM)
900
1000
0
Fence
AZP:12:064(ASM)
1000
1100
3.5
Fence
AZP:12:065(ASM)
1100
1200
10
Fence
AZP:12:066(ASM)
900
1100
2
Fence
AZP:I2:067(ASM)
900
1100
1
Fence
AZP:12:068(ASM)
900
1100
2
Fence
AZP:I2:069(ASM)
900
1100
3
Fence
AZP;12:070(ASM)
900
1100
4.5
Fence
AZP:12:071(ASM)
900
1100
14.5
Fence
AZP:12:072(ASM)
900
1100
0
Fence
AZP;I2:073(ASM)
900
1100
1
Fence
AZP;12:074(ASM)
900
1100
0
Fence
AZP:12:075(ASM)
900
1100
2.5
Fence
AZP:12:076(ASM)
900
1000
3.5
Fence
AZP:12:077(ASM)
900
1100
0
Fence
AZP:12:078(ASM)
900
1000
0
Fence
AZP:12:079(ASM)
1000
1200
8
Fence
AZP;12:080(ASM)
900
1100
1.5
Fence
AZP:12:08 l(ASM)
1000
1100
1
Fence
AZP;12:082(ASM)
1000
1100
0
Fence
AZP:12:083(ASM)
1000
1100
9
Fence
AZP;12;084(ASM)
1000
1100
5
Fence
AZP:12:085(ASM)
900
1100
9.5
Fence
AZP:12:086(ASM)
900
1100
2.5
I
Fence
AZP:12;087(ASM)
900
1100
Fence
AZP:12:088(ASM)
900
1100
2.5
Fence
AZP:12:089(ASM)
900
1100
0
Fence
AZP:12:090(ASM)
1100
1200
2
Fence
AZP:12:091(ASM)
900
1100
1
Fence
AZP:I2:092(ASM)
900
1000
1
Fence
AZP:12:093(ASM)
700
900
0
Fence
AZP:12:094(ASM)
900
1100
0
Fence
AZP:I2:095(ASM)
900
1000
2
Fence
AZP:12:096(ASM)
900
1100
1
Fence
AZP:12:097(ASM)
900
1100
8.5
Fcncc
AZP:I2:098(ASM)
900
1100
5
Fence
AZP:12:099(ASM)
900
1100
7.5
Fence
AZP;I2:100(ASM)
900
1000
1
Fence
AZP:12:!01(ASM)
900
1100
1
Fence
AZP:12:102(ASM)
1000
1100
4.5
Fcncc
AZP:I2:103(ASM)
1000
1100
3
Fence
AZP:12:104(ASM)
1000
1100
3
Fence
AZP:12:105(ASM)
900j
1100
3
Fence
AZP;12:106(ASM)
900
1100
0
Fence
AZP:12:107(ASM)
900
1100
0
Fence
AZP:12:I08(ASM)
900
1100
0
Fence
AZP:I2:109(ASM)
900
1100
1
Fence
AZP;12:11I(ASM)
1000
1100
0
Fence
AZP:I2:112(ASM)
900
1000
0
Fence
AZP:12:I13(ASM)
1000
1100
6
Fence
AZP:I2:114(ASM)
900
1000
14
Fence
AZP:I2:1I9(ASM)
900
1100
0
Fence
AZP:I2:120(ASM)
900
1000
4
Fence
AZP:12:12l(ASM)
900
1100
0
Fence
AZP:12:I22(ASM)
1000
1100
0
Fence
AZP:12:123(ASM)
1000
1100
2
Fence
A2P:12:I24(ASM)
900
1100
1.5
Fence
AZP:12:125(ASM)
800
1100
0
Fence
AZP:12:I26(ASM)
900
1100
0
Fence
AZP:12:127(ASM)
1000
1100
1.5
Fence
AZP:12:I28(ASM)
1000
1200
23.5
Fence
AZP:16:051(ASM)
900
1200
1
Fence
A2P:I6:090(ASM)
1100
1200
15
Fence
AZP:16:091(ASM)
900
1100
11
Fence
AZP:16;092(ASM)
900
1100
0
Fence
AZP:16:093(ASM)
1100
1200
5
Fence
AZP:16;094(ASM)
lOSO
1150
4
Fence
AZP:16:095(ASM)
1050
1150
5.5
Fence
AZP:16:097(ASM)
900
1100
7.5
Fence
AZP:16;098(ASM)
900
1100
2
Fence
A2P:I6:099(ASM)
950
1050
10
Fence
AZP:I6:I00(ASM)
1000
1100
1
Fence
AZP:16:101(ASM)
900
1100
0
Fence
AZP:16:102(ASM)
900
1200
0
Fence
AZP:16:103(ASM)
1000
1100
0
Fence
A2P:I6:104<ASM)
900
1200
1
Fence
AZP:I6:105(ASM)
900
1000
1
Fence
AZP:16:106(ASM)
500
700
1.5
Fence
AZP;I6:I06<ASM)
900
1100
1.5
Fence
A2P:16:I07(ASM)
900
1000
0
Fence
AZP;16:I08{ASM)
900
1100
0
Fence
AZP:16:I09(ASM)
900
1100
1
i
101
Fcnce
AZP;I6 110(ASM)
700
1050
1
Fence
AZP:16 11I(ASM)
900
1100
1
Fence
AZP:16 112(ASM)
1100
1150
9
Fence
AZP:16 113(ASM)
900
1100
1
Fence
AZP:16 n4(ASM)
900
1100
1.5
Fence
AZP:16 115(ASM)
900
1100
3.5
Fence
AZP:16 116(ASM)
900
1100
0
Fence
AZP:I6 117(ASM)
900
1100
0
Fence
AZP:16 118(ASM)
900
1100
4.5
Fence
AZP;I6 119(ASM)
1000
1200
13
Fence
AZP:16 120(ASM)
900
1100
3.5
Fence
A2P:16 121(ASM)
900
1100
1.5
Fence
A2P:16 122(ASM)
900
1100
8
Fence
AZP:16 123(ASM)
900
1100
6.5
Fence
1050
8
AZP:16 124(ASM)
900
Fence
AZP:16 I25(ASM)
900
1100
2
Fence
AZP:16 126(ASM)
900
1100
19
Fence
AZP:16 127(ASM)
900
1100
2.5
Fence
AZP;16 128(ASM)
950
1100
24
Fcnce
A2P:16 129(ASM)
900
1000
4
Fence
AZP:16 130(ASM)
900
1100
1.5
Fence
AZP:16 131(ASM)
900
1100
1
Fence
AZP:16 132(ASM)
900
1100
2-5
Fence
AZP:I6 133(ASM)
900
1100
1
Fence
AZP:I6 134{ASM)
900
1100
1
Fence
AZP:16 135(ASM)
1000
1100
2
Fcnce
AZP:16 137(ASM)
1000
1100
18.5
Fence
AZP:I6 138(ASM)
1000
1100
0
Fence
AZP:16 139(ASM)
1050
1200
2.5
Fence
AZP:I6 140(ASM)
900
1100
2
Fence
AZP:16 14I(ASM)
900
1100
0
Fence
AZP;16 142(ASM)
900
1000
4
Fence
AZP;16 143(ASM)
900
1000
2
Fence
AZP:16 144(ASM)
900
1100
2.5
Fence
AZP:16 I4S(ASM)
900
1100
2
Fence
AZP:16 I46(ASM)
1000
1100
0
Fence
AZP:16 147(ASM)
900
1100
0
Fence
AZP:16 148(ASM)
1000
1100
0
Fence
AZP:16 I49(ASM)
900
1100
2
Flake's Ruin
none
HH-Sackett
AZP;ll:048(ASM)
1000
1100
0
HH-Sacken
AZP;11:049(ASM)
1000
1100
0
100
HH-Sackctt
AZP I1:050(ASM)
1000
1100
0
HH-Sackett
AZP 1I:0S2(ASM)
1000
1100
1
HH-Sackett
AZP ll;053(ASM)
1000
1100
0
HH-Sackett
AZP 1I:054<ASM)
1000
1100
0
HH-Sackett
AZP ll:055(ASM)
1000
1100
0
HH-Sackett
AZP 11:056(ASM)
1000
1100
0
HH-Sackett
AZP 11:057(ASM)
1000
1100
0
HH-Sackett
AZP I1:058(ASM)
1000
1200
0
HH-Sackett
AZP n:059(ASM)
1000
1200
0
HH-Sundown
AZP ll:061(ASM)
1000
1100
0
HH-Sundown
AZP ll:062(ASM)
1000
1100
0
HH-Sundown
AZP 11:064(ASM)
1000
1100
0
Lons
AZP I1:077(ASM)
1000
1200
0
Lons
AZP II:078(ASM)
900
1200
0
Lons
AZP 11:080(ASM)
8S0
950
2
Lons
AZP ll;08l(ASM)
1000
1100
0
Lons
AZP 1I:082(ASM)
1000
1100
0
Lons
AZP ll:083(ASM)
900
1200
1
Lons
AZP I1:084(ASM)
900
1100
0
Lons
AZP II:08S(ASM)
1000
1100
2
Lons
AZP II:086(ASM)
900
1300
0
Lons
AZP 1I:088(ASM)
900
1100
0
Lons
AZP 1I:089(ASM)
1000
1200
0
Lons
AZP 1I:090(ASM)
1000
1100
0
Lons
AZP I1:091(ASM)
900
1100
0
Lons
AZP 11:092(ASM)
600
950
0
Lons
AZP 11:093(ASM)
1000
1100
0
Lons
AZP 11:094(ASM)
1000
1100
0
Lons
AZP 11;095(ASM)
1000
1100
2.5
Lons
AZP ll:096(ASM)
900
1100
0
Lons
AZP ll:097(ASM)
600
900
0
Lons
AZP II:098(ASM)
900
1100
0
Lons
AZP 11:100(ASM)
1100
1250
15
Lons
AZP II:IOI(ASM)
1000
1100
5
Lons
AZP ll:i02(ASM)
1100
1300
0
Lons
AZP II:103(ASM)
1000
1100
0
Lons
AZP ll:104(ASM)
400
600
4
Lons
AZP 1I;I05(ASM)
1100
1300
17
Lons
AZP II:I06(ASM)
900
1100
0
Lons
AZP ll:I07(ASM)
1000
1100
2
Lons
AZP II:108(ASM)
900
1100
2.5
Lons
AZP ll:109(ASM)
1000
1100
5
103
AZP 11 11 l(ASM)
900
HOC
1
Lons
AZP 11 1I2(ASM)
900
1200
0
Lons
AZP 11 113(ASM)
1100
1200
3
Lons
AZP 11 114<ASM)
1000
1200
0
Lons
AZP 11 115(ASM)
1000
1200
0
Lons
AZP 11 116(ASM)
1000
1200
0
Lons
Lons
AZP 11 117(ASM)
1000
1100
2.5
Lons
AZP 11 118(ASM)
900
1200
0
Lons
AZP II 119(ASM)
900
1200
2
Lons
AZP 11 120(ASM)
1000
1200
1
Lons
AZP 11 121(ASM)
900
1200
2
Lons
AZP 11 122(ASM)
1000
1100
4
Lons
AZP 11 123(ASM)
900
1200
0
Lons
AZP 11 124(ASM)
1000
1100
0
Lons
AZP 11 125(ASM)
900
1200
3
Lons
AZP 11 126(ASM)
900
1200
0
Lons
AZP 11 127(ASM)
800
1200
0
Lons
AZP 11 128(ASM)
800
1100
0
Lons
AZP 11 129(ASM)
1000
1100
1
Lons
AZP 11 130(ASM)
1000
1100
0
Lons
AZP 11 131(ASM)
1000
1100
0
Lons
AZP 11 132(ASM)
900
1100
3
Lons
AZP 11 133(ASM)
1000
1100
0
Lons
AZP 11 134(ASM)
1100
1300
0
Lons
AZP 11 I35(ASM)
1000
1100
0
Lons
AZP 11 136(ASM)
1000
1200
1
Lons
AZP 11 137(ASM)
900
1000
0
Lons
AZP 11 I38(ASM)
600
1000
0
Lons
AZP 11 139(ASM)
900
1200
0
Lons
AZP 11 140(ASM)
900
1200
0
Lons
AZP 11 14I(ASM)
900
1200
0
Lons
AZP 11 142(ASM)
1000
1200
0
Lons
AZP 11 143(ASM)
1000
1100
0
Lons
AZP 11 144(ASM)
1000
llOO
2
Lons
AZP 11 145(ASM)
900
1200
0
Lons
AZP 11 146(ASM)
1200
1300
0
Lons
AZP 11 147(ASM)
1000
1200
10
Lons
AZP 11 148(ASM)
1000
1100
0
Lons
AZP 11 149(ASM)
600
900
0
Lons
AZP 11 150(ASM)
1000
1200
3
Lons
AZP 11 151(ASM)
1100
1300
3.5
Lons
AZP 11 153(ASM)
1000
1100
0
104
Lons
AZP:ll:154(ASM)
1(X)0
Lons
AZP:n;l55(ASM)
900
noo
0
Lons
AZP;1I;1S6(ASM)
900
1100
0
Lons
AZP;II:157(ASM)
1000
1200
11
Lons
AZP:ll:158(ASM)
1000
1200
7.5
Lons
AZP:I1:I59(ASM)
1000
1100
2
Lons
AZP:15:019(ASM)
1000
1200
1
Lons
AZP:I5;020(ASM)
900
1100
0
Lons
AZP:I5:021(ASM)
1000
1200
17
McNeil
AZP:16:206(ASM)
1000
1100
0
McNeil
AZP:I6:207(ASM)
1000
1100
0
McNeil
AZP:16:208(ASM)
1000
1100
0
1300
80
McNeil
AZP:I6:209(ASM)
1100
1200
1
McNeil
AZP:16:2 II (ASM)
1050
1150
0
McNeil
AZP:16:2I2(ASM)
850
1100
0
McNeil
AZP:16:2I3(ASM)
1000
1100
0
McNeil
AZP:I6:214(ASM)
1000
1100
9.5
McNeil
AZP:I6:2I5(ASM)
900
1200
0
McNeil
AZP:16:216(ASM)
1000
1100
1
McNeil
AZP:I6:2I7(ASM)
800
900
10
McNeil
AZP:I6:2I8(ASM)
1100
1200
7
McNeil
AZP:16:219(ASM)
700
800
0
McNeil
AZP:I6:220(ASM)
1000
1100
0
McNeil
AZP:16:221(ASM)
1000
1100
0
McNeil
AZP:16:222(ASM)
1100
1200
4
McNeil
McNeil
AZP:16:223(ASM)
AZP:I6:224(ASM)
1000
1000
1100
1100
5
2
1200
2.5
Nick's Camp
AZP:I1:I33(ASU)
Schoens Dam
AZP:12:298(ASM)
925
40
Schoens Dam
AZP:12:299(ASM)
900
1200
14
Schoens Dam
AZP:I2:300(ASM)
900
1200
8
Schoens Dam
AZP;12:301(ASM)
975
1250
5
Schoens Dam
AZP:12:302(ASM)
950
1125
0
Schoens Dam
AZP:I2:304(ASM)
900
1100
13.5
Schoens Dam
AZP:12:306(ASM)
800
1200
1.5
Schoens Dam
AZP:12:308(ASM)
925
IISO
0
Schoens Dam
AZP:I2:309(ASM)
900
1150
0
Schoens Dam
AZP:12:311(ASM)
400
900
2.5
Schoens [>am
AZP:12:312(ASM)
900
1050
0
Schoens Dam.
AZP:12:314(ASM)
975
1150
0
Schoens Dam
AZP;I2:315(ASM)
975
IISO
12
Schoens Dam
AZP:12:316(ASM)
900
1100
0
Schoens Dam
AZP:12:317(ASM)
825
1050
0
Schoens Dam
AZP;12:3I8(ASM)
950
1125
I
Schoens Dam
AZP;12:321(ASM)
800
1125
2
Schoens Dam
AZP:12322(ASM)
800
1100
3.5
Schoens Dam
AZP:12:324(ASM)
850
1025
0
Schoens Dam
AZP:I2:325(ASM)
825
1050
0
0
Schoens Dam
AZP:12:327(ASM)
825
1050
Schoens Dam
AZP:12:328(ASM)
825
1025
0
Showlow Ruin
AZP:12:003(ASM)
1325
1390
200
Snowflake-Mesa Redonda
AZP:12:025(ASM)
1050
1150
0
Snowflake-Mesa Redonda
AZP;I2:026(ASM)
900
1100
1
Snowflake-Mesa Redonda
AZP:12;027(ASM)
900
1150
6
Snowflake-Mesa Redonda
AZP:I2:028(ASM)
900
1150
1
Snowflake-Mesa Redonda
AZP:12:029(ASM)
700
900
0
Snowflake-Mesa Redonda
AZP:12:030(ASM)
1125
1300
1
Snowflake-Mesa Redonda
AZP:12:031(ASM)
1050
1200
8
Snowflake-Mesa Redonda
AZP:8:013{ASM)
900
1100
33
Snowflake-Mesa Redonda
AZP:8:014(ASM)
900
1100
1
Snowflake-Mesa Redonda
AZP;8:016(ASM)
900
1050
1.5
Snowflake-Mesa Redonda
AZP:8:017(ASM)
900
1150
1
Snowflake-Mesa Redonda
AZP;8:018(ASM)
900
1150
1
Snowflake-Mesa Redonda
AZP;8:019(ASM)
650
800
1
Snowflake-Mesa Redonda
AZP:8:02l(ASM)
1050
1250
1
Snowflake-Mesa Redonda
AZP;8:022(ASM)
1050
1150
1
Snowflake-Mesa Redonda
AZP:8:023(ASM)
1050
1150
3
Snowflake-Mesa Redonda
AZP:8:024(ASM)
1050
1150
1
Snowflake-Mesa Redonda
AZP:8:025(ASM)
900
1100
1
Snowflake-Mesa Redonda
AZP:8:026(ASM)
1070
1225
10
Snowflake-Mesa Redonda
AZP:8;027(ASM)
1050
1150
2
Snowflake-Mesa Redonda
AZP:8:030(ASM)
600
900
16
Snowflake-Mesa Redonda
AZP:8:03I(ASM)
1050
1150
1
Snowflake-Mesa Redonda
AZP:8:034(ASM)
1125
1300
7
Snowflake-Mesa Redonda
AZP:8:036(ASM)
900
1150
0
Snowflake-Mesa Redonda
AZP:8:037(ASM)
900
1150
0
Snowflake-Mesa Redonda
AZP:8:038(ASM)
900
1100
4
Snowflake-Mesa Redonda
AZP;8:039(ASM)
940
1120
0
Snowflake-Mesa Redonda
AZQ:9:018(ASM)
900
1200
2
Snowflake-Mesa Redonda
AZQ:9:020(ASM)
900
1100
0
Snowflake-Mesa Redonda
AZQ:9:021(ASM)
900
1100
1
Snowflake-Mesa Redonda
AZQ:9:022(ASM)
900
1150
1
Snowflake-Mesa Redonda
AZQ:9;024(ASM)
1050
1200
0
Snowflake-Mesa Redonda
AZQ;9;025{ASM)
1050
I ISO
0
Snowflake-Mesa Redonda
AZQ:9;026(ASM)
900
1150
1
Snowflake-Mesa Redonda
AZQ:9;027(ASM)
950
1150
0
Snowflake-Mesa Redonda
AZQ;9;028(ASM)
900
1100
0
Snowflake-Mesa Redonda
AZQ:9:029(ASM)
1050
1150
1
Snowflake-Mesa Redonda
AZQ:9:030(ASM)
900
1100
1
Snowflake-Mesa Redonda
AZQ;9:031(ASM)
1000
1100
3
Snowflake-Mesa Redonda
AZQ;9:032(ASM)
900
1100
3
Snowflake-Mesa Redonda
AZQ:9:033(ASM)
1000
1150
2
1000
1150
9
Snowflake Survey
AZ2:0017
1100
1250
1
Snowflake Survey
AZ2:0019
1100
1250
1
Snowflake Survey
AZ2:0020
900
1100
4
Snowflake Survey
AZ2:0021
1100
1250
4
Snowflake Survey
AZ2:0022
1100
1250
5
Snowflake Survey
AZ2:0023
1100
1250
10
Snowflake Survey
AZ2:0024
1100
1250
4
Snowflake Survey
AZ2:0025
1100
1250
25
Snowflake Survey
AZ2;0026
900
1100
4
Snowflake Survey
AZ2:0027
1100
1250
3
Snowflake Survey
AZ2:0028
IIOO
1250
1.5
Snowflake Survey
AZ2:0029
1100
1250
10
Snowflake Survey
AZ2:0030
1100
1250
2
Snowflake Survey
AZ2:0031
1100
1250
35
Snowflake Survey
AZ2:0032
1100
1250
2
Snowflake Survey
AZ2:0033
900
1250
1
Snowflake Survey
AZ2:0034
1100
1250
4
Snowflake-Mesa Redonda
AZQ:9:034(ASM)
Snowflake Survey
AZ2:0035
1100
1250
2
Snowflake Survey
AZ2:0036
1100
1250
'
Snowflake Survey
AZ2:0037
1100
1250
2
Snowflake Survey
AZ2:0038
1100
1250
15
Snowflake Survey
AZ2:0039
1100
1250
65
Snowflake Survey
AZ2:0040
400
700
1
Snowflake Survey
AZ2-0041
1100
1250
\
Snowflake Survey
AZ2:0042
1250
1390
1
Snowflake Survey
AZ2:0043
700
900
I
Snowflake Survey
AZ2:0044
700
900
114
Snowflake Survey
AZ2;0045
1100
1250
30
Snowflake Survey
AZ2:0046
1100
1250
35
Snowflake Survey
AZ2:0047
1100
1250
3
Snowflake Survey
AZ2;0048
1100
1250
6
Snowflake Survey
AZ2:0049
1100
1250
2
Snowflake Survey
AZ2:0050
700
900
1.25
Snowflake Survey
AZ2:0050
1100
1250
1.25
Snowflake Survey
AZ2:00S1
1100
1250
35
Snowflake Survey
AZ2:00S2
700
900
3
Snowflake Survey
AZ2:0053
900
1100
3
Snowflake Survey
AZ2:0054
1100
1250
10
Snowflake Survey
AZ2:0055
1250
1390
0
Snowflake Survey
AZ2:0056
900
1250
1
Snowflake Survey
AZ2:0057
900
1100
2
Snowflake Survey
AZ2:0058
1100
1250
8
Snowflake Survey
AZ2:0059
900
1100
5
Snowflake Survey
AZ2:0061
1100
1250
2
Snowflake Survey
AZ2:0063
900
1100
5
Snowflake Survey
AZ2;0064
1100
1250
50
Snowflake Survey
AZ2:0065
900
1250
0
Snowflake Survey
AZ2:0066
900
1100
4
Snowflake Survey
AZ2:0069
1100
1250
10
Snowflake Survey
AZ2:0070
900
1100
0
Snowflake Survey
AZ2:0071
700
900
7.5
Snowflake Survey
AZ2:0072
1100
1250
0
Snowflake Survey
AZ2:0073
1100
1250
0
Snowflake Survey
AZ2:0074
700
900
5
Snowflake Survey
AZ2:0075
1100
1250
15
AZ2:0077
1275
1325
10
1275
1390
200
Snowflake Survey
Snowflake Survey
AZP 12:002(ASM)
Snowflake Survey
AZP 12:004(ASM)
1275
1375
450
Snowflake Survey
AZP 12:006(ASM)
1325
1390
54
Stott
AZP ll:160(ASM)
1000
1150
0
Stott
AZP 1I:161(ASM)
1000
1150
5
Stott
AZP ll:I62(ASM)
1000
1150
20
Stott
AZP ll:163(ASM)
1000
1150
9
Stott
AZP 11;I64(ASM)
1000
1150
5
Stott
AZP 1I;165(ASM)
1000
1150
0
Stott
AZP 1I:166(ASM)
1000
1100
1
Stott
AZP I1;167(ASM)
1000
1300
3
Stott
AZP 11:I68(ASM)
1000
1300
0
Stott
AZP I1:171(ASM)
1000
1150
0
Stott
AZP I1:I72(ASM)
1000
1150
0
Stott
AZP 1I:173(ASM)
1150
1275
16
Stott
AZP I1:I74(ASM)
1000
1300
2
Stott
AZP 11:I75(ASM)
1000
1300
0
Stott
AZP I1:176(ASM)
1000
1300
0.5
Ston
AZP n:l77(ASM)
1000
1150
0
Ston
AZP 11 178(ASM)
1000
1150
0
Ston
AZP 11 I79(ASM)
1000
1150,
6
2
Ston
AZP 11 I80(ASM)
1000
1150
Ston
AZP 11 I8I(ASM)
1000
1150
0
Ston
AZP 11 182(ASM)
1000
1150
0
Ston
AZP 11 I83(ASM)
1000
1300
0
Ston
AZP 11 I84(ASM)
1000
1150
0
Ston
AZP 11 18S(ASM)
1000
1300
0
Ston
AZP 11 186(ASM)
1000
1150
0
Ston
AZP 11 I87(ASM)
1000
1150
0
Ston
AZP 11 I88(ASM)
1000
1150
0
Ston
AZP 11 I89(ASM)
1000
1150
0
Ston
AZP 11 190(ASM)
1000
1150
0
Ston
AZP 11 I91(ASM)
1000
1150
0
Ston
AZP 11 I92{ASM)
1000
1150
0
Ston
AZP 11 193(ASM)
1000
1150
0
Ston
AZP 11 194(ASM)
1000
1150
0
Ston
AZP 11 196{ASM)
1000
1150
4
Ston
AZP 11 I97(ASM)
1000
1150
0
Ston
AZP 11 I98(ASM)
1000
1150
0
Ston
AZP 11 I99(ASM)
1000
1150
0
Ston
AZP 11 203(ASM)
1000
1150
0
Ston
AZP 11 204(ASM)
1000
1150
0
Ston
AZP 11 205(ASM)
1000
1150
0
Ston
AZP 11 207(ASM)
1000
1150
0
Ston
AZP 11 208(ASM)
1000
1150
0
Ston
AZP 11 209(ASM)
1000
1150
1
Ston
AZP 11 210(ASM)
1000
1150
0
Ston
AZP II 21I(ASM)
1000
1150
0
Ston
AZP 11 2I3(ASM)
1000
1150
0
Ston
AZP 11 214(ASM)
1000
1150
0
Ston
AZP II 2IS(ASM)
1000
1150
0
Ston
AZP II 216(ASM)
1000
1150
0
Ston
AZP 11 2I7(ASM)
1000
1150
2.5
Ston
AZP II 219(ASM)
1000
1150
0
Ston
AZP II 220(ASM)
1000
1150
0
Ston
AZP II 221(ASM)
1000
1150
0
Ston
AZP II 222(ASM)
1000
1150
2.5
Ston
AZP 11 223(ASM)
1000
1150
0
Wolf II
AZQ 14 009(ASM)
900
1050
0
Wolf II
AZQ 14 OIl(ASM)
900
1050
1
APPENDIX 2: SITE COUNTS BY STRATA
Period
Rooms Elcv
Soil Sites Extrapolated Total
Extrapolated
Rooms
Rooms
Sites
(sites X ratio) (uses actual # (uses actual #
for 20+)
for 20+)
1000-1049 1^
1800
1
22
318.22
16.06
232.30
1000-1049 1-4
1800
3
7
32.29
5.11
23.57
1000-1049 1-4
1800
5
2
13.22
1.46
9.65
1000-1049 1^
2040
1
5
82.31
3.65
60.08
1000-1049 1-4
2040
2
28
346.68
20.44
253.08
1000-1049 1-4
2040
3
139
1049.71
101.47
766.29
1000-1049 1-4
2040
12
IS
272.18
10.95
198.69
1000-1049 1^
2280
3
25
74.70
18.25
54.53
1000-1049 10-20
2040
1
1
16.46
13.33
219.43
1000-1049 4-10
1800
1
1
14.46
5.00
72.32
1000-1049 4-10
2040
0
1
0.00
0.00
1000-1049 4-10
2040
2
1
12.38
5.00
61.91
1000-1049 4-10
2040
3
11
83.07
55.00
415.35
1000-I049 4-10
2040
12
1
18.15
5.00
90.73
1050-1099 1^
1800
1
25
361.61
18.75
271.21
1050-1099
1800
3
10
46.13
7.50
34.60
1050-1099 1^
1800
5
1
6.61
0.75
4.96
1050-1099 1-4
1800
7
4
25.51
3.00
19.13
1050-1099 1-4
2040
1
5
82.31
3.75
61.73
1050-1099 1^
2040
2
31
383.83
23.25
287.87
1050-1099 1^
2040
3
142
1072.37
106.50
804.28
1050-1099
2040
12
15
272.18
11.25
204.14
0.75
0.00
1050-1099 1^
2280
0
1
1050-1099 1^
1050-1099 10-20
2280
3
26
77.69
19.50
58.27
2040
3
1
7.55
13.33
100.67
14.46
5.00
72.32
0.00
0.00
1050-1099 4-10
1800
1
1
1050-1099 4-10
2040
0
I
1050-1099 4-10
2040
2
2
24.76
10.00
123.82
1050-1099 4-10
2040
3
9
67.97
45-00
339.84
1050-1099 4-10
2040
12
1
18.15
5.00
90.73
1100-1149 1^
1800
1
26
376.08
17.42
251.97
1100-1149 1-4
1800
2
1
10.10
0.67
6.77
1100-1149 1^
1800
3
18
83.04
12.06
55.64
1100-1149 1^
1800
5
7
46.26
4.69
31.00
6.38
0.67
4.27
0.67
0.00
1100-1149 1-4
1800
7
1
1100-1149
2040
0
1
2040
llOa-1149 1^
2040
2
6
74.29
4.02
49.77
1100-1149 1-4
2040
3
5!
385.15
34.17
258.05
1
3
49.38
2.01
33.09
1100-1149 1-4
1100-1149 1-4
2040
12
10
181.46
6.70
121.58
1100-1149 1^
2280
3
14
41.83
9J8
28.03
1100-1149 10-20
1800
1
1
14.46
11.67
168.80
1100-1149 10-20
1800
3
3
13.84
35.01
161.51
1100-1149 10-20
2040
3
2
15.10
23.34
176.26
1100-1149 4-10
1800
1
1
14.46
5.00
72.32
1100-1149 4-10
1800
3
3
13.84
15.00
69.20
1100-1149 4-10
2040
0
1
5.00
0.00
1100-1149 4-10
2040
2
2
24.76
10.00
123.82
1100-1149 4-10
2040
3
4
30.21
20.00
151.04
1100-1149 4-10
2280
3
1
2.99
5.00
14.94
21.67
21.67
1100-1149 2(H
1150-1199 1-4
1800
1
19
274.83
13.11
189.63
1150-1199 1^
1800
2
1
10.10
0.69
6.97
1150-1199 1-4
1800
3
13
59.97
8.97
41.38
1150-1199 1-4
1800
5
6
39.65
4.14
27.36
1150-1199 1^
2040
2
4
49.53
2.76
34.17
1150-1199 1-4
2040
3
47
354.94
32.43
244.91
1150-1199 1-4
2040
12
7
127.02
4.83
87.64
1150-1199 1^
2280
0
1
0.00
0.00
1150-1199 1^
2280
3
3
8.96
2.07
6.19
1150-1199 10-20
1800
1
1
14.46
11.67
168.80
1150-1199 10-20
1800
3
3
13.84
35.01
161.51
1150-1199 10-20
2040
3
2
15.10
23.34
176.26
1150-1199 4-10
1800
1
1
14.46
5.00
72.32
13.84
15.00
69.20
5.00
0.00
1150-1199 4-10
1800
3
3
1150-1199 4-10
2040
0
1
1150-1199 4-10
2040
2
2
24.76
10.00
123.82
1150-1199 4-10
2040
3
3
22.66
15.00
113.28
5.00
14.94
2280
3
1
2.99
21.67
21.67
1200-1249 1^
1800
1
13
188.04
8.71
125.99
1200-1249 1-4
1800
2
1
10.10
0.67
6.77
1200-1249 1-4
1800
3
13
59.97
8.71
40.18
1200-1249 1-4
1800
5
6
39.65
4.02
26.57
1200-1249 1-4
2040
3
13
98.17
8.71
65.78
1200-1249
2040
12
1
18.15
0.67
12.16
1200-1249 1^
2280
3
3
8.96
2.01
6.01
1200-1249 LO-20
1800
1
1
14.46
11.67
168.80
1200-1249 10-20
1800
3
3
13.84
35.01
161.51
1200-1249 10-20
2040
3
1
7.55
11.67
88.13
1150-1199 4-10
1150-1199 20t-
1200-1249 4-10
1800
1
1
14.46
5.00
72.32
1200-1249 4-10
1800
3
3
13.84
15.00
69.20
1200-1249 4-10
2040
3
1
7.55
5.00
37.76
1200-1249 4-10
2280
3
1
2.99
1200-1249 204-
5.00
14.94
44.17
44.17
1250-1299 1-4
1800
1
4
57.86
2.00
28.93
1250-1299 1-4
1800
3
1
4.61
0.50
2.31
1250-1299 1-4
2040
3
13
98.17
6.50
49.09
1250-1299 1-4
2040
12
1
18.15
0.50
9.07
1250-1299 1^
2280
3
1
2.99
0.50
1.49
1250-1299 10-20
2040
3
1
7.55
13.33
100.67
1250-1299 4-10
2040
3
1
7.55
5.00
37.76
1250-1299 4-10
2280
3
1
2.99
1250-1299 204-
5.00
14.94
429.17
429.17
1800
3
1
4.61
1.29
5.95
1300-1349 1-4
2040
3
2
15.10
2.58
19.48
1300-1349 1-4
2280
3
1
2.99
1.29
3.85
1300-1349 4-10
2040
3
1
7.55
1300-1349 1^
1300-1349 2041350-1399 1^
1800
3
1
4.61
1350-1399 204-
5.00
37.76
493.67
493.67
0.33
1.52
343.67
343.67
3.62
1800
1
1
14.46
0.25
1-4
1800
3
1
4.61
0.25
1.15
1-4
2040
3
1
7.55
0.25
1.89
450-^99
1^
1800
1
1
14.46
0.25
3.62
450^99
1^
1800
3
1
4.61
0.25
1.15
450-499
1^
2040
3
1
7.55
0.25
1.89
500-549
1^
1800
1
1
14.46
0.25
3.62
500-549
1-4
1800
3
1
4.61
0.25
1.15
0.75
5.66
400-449
1^
400^9
400-^9
2040
3
3
22.66
550-599
1^
1800
1
1
14.46
0.25
3.62
550-599
1-4
1800
3
1
4.61
0.25
1.15
550-599
1-4
2040
3
3
22.66
0.75
5.66
600-649
1^
1800
1
2
28.93
0.34
4.92
500-549
600-649
1^
1800
3
1
4.61
0.17
0.78
600-649
1-4
2040
3
5
37.76
0.85
6.42
600-649
1^
2040
12
1
)S.1S
0.17
3.0S
650-699
1-^
1800
I
3
43.39
0.63
9.11
650-699
1-^
1800
3
1
4.61
0.21
0.97
650-699
1-4
2040
3
5
37.76
1.05
7.93
650-699
1-4
2040
12
1
18.15
0.21
3.81
700-749
1-4
1800
1
5
72.32
1.50
21.70
700-749
1-4
1800
3
3
13.84
0.90
4.15
700-749
1-4
2040
2
1
12.38
0.30
3.71
700-749
1-4
700-749
2040
3
4
30.21
1.20
9.06
2040
12
I
18.15
0.30
5.44
28.5
28.S
750-799
2<h1-*
1800
1
5
72.32
1.50
21.70
750-799
1-4
1800
3
3
13.84
0.90
4.15
750-799
1-4
2040
2
1
12.38
0.30
3.71
750-799
1-4
2040
3
4
30.21
1.20
9.06
750-799
1^
2040
12
1
18.15
0.30
5.44
750-799
2(H-
700-749
28.5
28.5
1.16
16.78
800-849
1-4
1800
1
4
800-849
1-4
1800
3
6
27.68
1.74
8.03
800-849
1^
2040
2
1
12.38
0.29
3.59
800-849
1-4
2040
3
5
37.76
1.45
10.95
800-849
1-4
2040
12
1
18.15
0.29
5.26
5.00
37.76
800-849
4-10
800-849
20+-
850-899
1^
850-899
850-899
57.86
2040
3
1
7.55
1800
1
4
57.86
1^
1800
3
6
1-4
2040
2
1
28.5
28.5
2.56
37.03
27.68
3.84
17.72
12.38
0.64
7.92
850-899
1-4
2040
3
8
60.42
5.12
38.67
850-899
1-4
2040
12
5
90.73
3.20
58.07
850-899
1^
2280
3
4
11.95
2.56
7.65
850-899
4-10
2040
3
1
7.55
5.00
37.76
850-899
20+-
28.5
28.5
900-949
1-4
1800
1
18
260.36
9.00
130.18
900-949
1-4
1800
3
7
32.29
3.50
16.15
900-949
1^
1800
5
3
19.83
1.50
9.91
900-949
1-4
2040
1
2
32.92
1.00
16.46
900-949
1-4
2040
2
8
99.05
4.00
49.53
900-949
1-4
2040
3
73
551.29
36.50
275.64
900-949
1-4
2040
12
8
145.16
4.00
72.58
900-949
1-4
2280
3
11
32.87
5.50
16.43
900-949
4-10
1800
1
1
14.46
7.00
101.25
900-949
4-10
2040
3
2
15.10
14.00
105.73
10.50
151.88
950-999
1-4
1800
1
21
303.76
950-999
1-4
1800
3
7
32.29
3.50
16.15
950-999
1-4
1800
5
3
19.83
1.50
9.91
950-999
1-4
2040
1
2
32.92
1.00
16.46
950-999
1^
2040
2
8
99.05
4.00
49.53
950-999
1-4
2040
3
79
596.60
39.50
298.30
950-999
1-4
2040
12
10
181.46
5.00
90.73
950-999
1-4
2280
3
11
32.87
5.50
16.43
950-999
4-10
1800
1
1
14.46
7.00
101.25
950-999
4-10
2040
3
4
30.21
28.00
211.45
APPENDIX 3: SITE LOCATIONS BY TIME PERIOD
AD 400-499
114
A.D. 5 0 0 - 5 9 9
i
A.D. 6 0 0 - 6 9 9
0 mil««
116
A.D. 700-799
i
f
117
A.D. 8 0 0 - 8 9 9
i
i
118
A.D. 9 0 0 - 9 9 9
I
f
6601-0001 a v
611
120
A.D. 1100-1199
121
A. D. 1200-1299
C
tk
E
A.D. 1300-1399
123
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