SETTLEMENT SYSTEMS, LANDSCAPES AND THE RISE OF THE TARASCAN EMPIRE:
A SETTLEMENT ANALYSIS IN THE LAKE PÁTZCUARO BASIN, MICHOACÁN,
MEXICO
By
Christopher J Stawski

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Anthropology
2012

ABSTRACT
SETTLEMENT SYSTEMS, LANDSCAPES AND THE RISE OF THE TARASCAN EMPIRE:
A SETTLEMENT ANALYSIS IN THE LAKE PÁTZCUARO BASIN, MICHOACÁN,
MEXICO
By
Christopher J Stawski

This dissertation investigates the settlement, landscape, and adaptation of the Prehispanic
populations in the Lake Pátzcuaro Basin, Michoacán, Mexico. Located in the highlands of
Mexico, the Lake Pátzcuaro Basin (LPB) was the center of the Tarascan Empire, a Late
Postclassic state that would become a major geopolitical core within Mesoamerica civilization.
This dissertation proposes a spatially and temporally dynamic study of the Tarascan (or
Purépecha) settlement system that ranges from the Late Preclassic, circa 100 B.C., to the Spanish
conquest in A.D. 1525. The data derives from full-coverage, intensive surveys that cover the
southwest portion of the lake basin, the southeastern portion of the lake basin, and the immediate
area around the capital of Tzintzuntzan. Through a landscape reconstruction of the Prehispanic
lake basin, a reconstruction and demographic analysis of the past communities, and intensive
spatial modeling and analyses in a Geographic Information System, this research provides the
overall trajectory of human settlement within the basin, ending at the Spanish Conquest. This
includes identifying the major variables that influenced settlement location in the lake basin,
including both economic, political and social variables. This dissertation provides new
commentary on human-environment interaction in the LPB, community formation and
settlement, and the emergence of the state. Ultimately, a testable model of settlement is
introduced, a model which can be applied to future research in the highlands of Mexico, thus
advancing research in this core area of Mesoamerican Prehistory.

Copyright by
CHRISTOPHER J STAWSKI
2012

ACKNOWLEDGMENTS

I would like to thank my committee chair, Dr. Helen Pollard, for her support, her
mentorship, and her dedication to my research and this dissertation. The data used in this
dissertation derives from a lifetime of work from Dr. Pollard in the Lake Pátzcuaro Basin, and I
am continually thankful for her permission to use this data in this dissertation. Her devotion to
her students and her dedication to the region, culture, and history of Mexico is an example of
scholarly excellence that I am grateful to have experienced.
Support for this project came from the National Science Foundation, award number
1151985, for the purchase of high resolution satellite imagery and digital elevation models used
in the GIS analysis and modeling. Thank you to the grant reviewers and the National Science
Foundation.
I would also like to thank my graduate committee, Dr. Bill Lovis, Dr. Ken Lewis and Dr.
Antoinette WinklerPrins, for their continual support and assistance during the preparation writing
of this dissertation, and for their guidance during my graduate career.
I cannot even begin to thank my friends and family enough for their support, love, and
encouragement during my graduate career. It would have been an impossible task without their
help. Special thanks go out to my parents, Jim and Denise, my brother Matt and my sisters Sarah
and Jessica for their unwavering faith, love, and support.
Thank you to my wife, Becca, who everyday believed in me and helped me to reach my
goals. I truly couldn’t have done this without you.
Finally, I dedicate this dissertation to my Grandfather, Leon.

iv

TABLE OF CONTENTS
LIST OF TABLES…..….…..…………..………………………………………………………viii
LIST OF FIGURES……….…..……………………………………………………………...…..xi
CHAPTER 1
INTRODUCING THE SETTLEMENT SYSTEMS
OF THE LAKE PÁTZCUARO BASIN……………………………………………......................1
Previous Research and Data Sources……………………………………………………...3
Geographic Information Systems…………………………………………………………8
Research Questions and Hypotheses………………………...……………………………9
Defining the Region……………………………………………………………………...11
Theoretical Discussion…………………………………………………………………..13
Settlement Model………………………………………………………………………...14
Method…………………………………………………………………………………...17
Quantitative Analyses……………………………………………………………………18
Analytic Expectations……………………………………………………………………20
Chapter Synthesis………………………………………………………………………...23
CHAPTER 2
THE TARASCAN EMPIRE AND THE LAKE PÁTZCUARO BASIN……………………….26
Research in West Mexico………………………………………………………………..26
The Tarascans……………………………………………………………………………29
The Prehispanic Lake Pátzcuaro Basin………………………………………………….30
The Ethnohistory of Communities: The Early Historic Periods…………………..…....35
The Protohistoric (A.D. 1450-1540)………………………………………………….....36
The Lake Pátzcuaro Environment……………………………………………………….39
Summary…………………………………………………………………………………45
CHAPTER 3
COMMUNITIES OF THE LAKE PÁTZCUARO BASIN………………………….………….47
Theory………………………………………………………………………….………..47
Settlement Systems…………………………………………………….……….48
Communities…………………………………………………………….………50
The Region……………………………………………………………….………56
The Macroregion……………………………………………………….………..57
Discussion……………………………………………………………….………60
Method………………………………………………………………………….………..61
Reconstruction Prehispanic Communities……………………………………..………..63
Identifying Communities……………………………………………….…….….63
Delineating Communities……………………………………………….…….…69
Analysis and Demographic Reconstruction of Communities………………….………...71
Loma Alta/Jaracuaro Phase Population Reconstruction………………..……..…80
Lupe/La Joya Phase Population Reconstruction…………………………..……..83
v

Early Urichu Phase Population Reconstruction………………………….….…...84
Late Urichu Phase Population Reconstruction……………………………..……86
Tariacuri Phase Reconstruction…………………………………………….……88
Functional Analysis of Communities……………………………………………….…..91
Summary………………………………………………………………………………….95
CHAPTER 4
LANDSCAPES OF THE LAKE PÁTZCUARO BASIN……………………………………….96
Theory……………………………………………………………………………………96
Landscape Approaches in Settlement Studies…………………………………..96
Political Economy and Settlement……………………………………………….98
Method for Landscape Reconstruction…………………………………………101
Reconstructing Lake Pátzcuaro…………………………………………………103
Reconstructing the Prehispanic Landscape…………………………………….113
Summary………………………………………………………………………………..120
CHAPTER 5
A SETTLEMENT SYSTEMS ANALYSIS……………………………………………………122
Geographical Theory…………………………………………………………………...122
Trade, Travel and Interaction…………………………………………………………..124
Cost-Surface and Cost-Distance Models……………………………………………….128
Community Interactions Analysis………………………………………………………133
Statistical Analysis of Interaction Values………………………………………141
Community-Landscape Interaction Analysis…………………………………………..142
Summary………………………………………………………………………………..154
CHAPTER 6
MODELING THE LAKE PÁTZCUARO SETTLEMENT SYSTEM…………………...........172
Discussion: The Community-to-Community Interaction Analysis…………………….173
Discussion: The Community-to-Landscape Interaction Analysis……………………...183
The Testable Settlement Model Revisited………………………………………...........192
The Microregional Settlement System Model………………………………………….195
Developing a Regional Model: Comparisons to the Southeast Communities…………203
Southeast Communities and Landscape……………………………………….204
The Case for a Regional Settlement System Model……………………………………214
CHAPTER 7
THE MACROREGIONAL SETTLEMENT SYSTEMS
AND CONCLUSIONS…………………………………………………………………………217
The Zacapu Basin………………………………………………………………………217
Making the Case for a Macro-Regional Settlement System…………………………..223
Problems and Hypothesis Revisited…………………………………………………..225
Future Research and Directions……………………………………………………….228
Conclusion…………………………………………………………………….……….229

vi

APPENDIX….……………………………………………………………………………….....231
BIBLIOGRAPHY…………………………………………………………………………...….313

vii

LIST OF TABLES
Table 1 – Mesoamerican Temporal Phases and Pátzcuaro Lake Basin Phases…………………..3
Table 2 – Survey Areas of the Lake Pátzcuaro Basin……………………………………………..4
Table 3 – Proposed Settlement Variables in the Lake Pátzcuaro Basin: 100 B.C.-A.D. 1525…..11
Table 4 – Protohistoric Settlement Classes………………………………………………………38
Table 5 – The Mapping of Communities in the LPB…………………………………………….69
Table 6 – Parson’s, Santley, Sanders (1979) Artifact to Population Density Estimates……...…76
Table 7 – Past Estimates concerning prehispanic Populations…………………………………..77
Table 8 – Population Estimates by Phase for the Prehispanic Southwest LPB………………….79
Table 9 – Functional Attributes for Communities by Phase…………………………………….93
Table 10 – The Historic/Modern Lake Pátzcuaro Reconstructions (Pollard 2008;O’Hare 1993;
Alocer, Bernal-Brooks, Rojas 2002;Stahle et al 2011)…………………………………………105
Table 11 – Historic/Modern Lake Level Correlates to Prehispanic Lake Levels………………113
Table 12 – Exploratory Interaction Statistics by Phase………………………………………...142
Table 13a - Loma Alta Landscape Analysis…………………………..………………………..148
Table 13b- Loma Alta Landscape Analysis…………………………..………………………..148
Table 14 – Loma Alta Slope Analysis………………………………………………………….150
Table 15 – Transportation and Travel Analysis by Community: Tariacuri Phase……………..153
Table 16 – Summary Statistics for Travel and Transport Analysis by Phase…………………..154
Table 17 – The Late Postclassic & Early Hispanic Community Correlations………………….183
Table 18 – The Archaeological Sites of the Southeast Survey Zone, and Occupation………...206
Table 19 – The Southeast Survey Community Reconstructions……………………………….208
Table 20 – Hypothesized Settlement Variables, Chapter One…………………………….……226
Table 21 – Settlement Variables Derived from the SW Settlement Systems Analysis……..….227
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Table 22 – Loma Alta Community Population Reconstruction……………………….………..237
Table 23 – Lupe/La Joya Community Population Reconstruction…………………….……….237
Table 24 – Early Urichu Community Population Reconstruction…………………..………….238
Table 25 – Late Urichu Community Population Reconstruction…………...…………………..239
Table 26 – Tariacuri Community Population Reconstructions…………………………………241
Table 27 – Loma Alta (1&2) Community Interaction Values…………………………………242
Table 28 – Lupe/La Joya Community Interaction Values……………………………………..243
Table 29 – Early Urichu Community Interaction Values………………………………………245
Table 30 – Late Urichu Community Interaction Values……………………………………….251
Table 31 – Tariacuri Community Interaction Values…………………………………………..288
Table 32a – Loma Alta Allocation Catchment Resource Zone Analysis………………..…….294
Table 32b – Loma Alta Allocation Catchment Resource Zone Analysis………………..…….294
Table 33 – Loma Alta Slope Analysis……………………………………………………….…294
Table 34a – Lupe/La Joya Allocation Catchment Resource Zone Analysis…..………………295
Table 34b – Lupe/La Joya Allocation Catchment Resource Zone Analysis…..………………295
Table 35 – Lupe/La Joya Slope Analysis………………………………………………………295
Table 36 – Early Urichu Allocation Catchment Resource Zone Analysis…………………….296
Table 37– Early Urichu Slope Analysis…………………………………………………….….298
Table 38a– Late Urichu Allocation Catchment Resource Zone Analysis…………..…………299
Table 38b– Late Urichu Allocation Catchment Resource Zone Analysis…………..…………300
Table 39 – Late Urichu Slope Analysis……………………………………………….………..302
Table 40 – Tariacuri Allocation Catchment Resource Zone Analysis…………………………303
Table 41 – Tariacuri Slope Analysis……………………………………………………………305

ix

Table 42– Loma Alta Travel/Transportation Network Analysis……………………………….305
Table 43– Lupe/La Joya Travel/Transportation Network Analysis………………...……….…306
Table 44 – Early Urichu Travel/Transportation Network Analysis……………………….……306
Table 45 - Late Urichu Travel/Transportation Network Analysis……………………………...307
Table 46 - Tariacuri Travel/Transportation Network Analysis………………………………...308

x

LIST OF FIGURES
Figure 1 – The Mexican State of Michoacán and the Pátzcuaro Lake Basin……………………..2
Figure 2 – The Pátzcuaro Lake Basin and Previous Archaeological Surveys……………………5
Figure 3 – The Southwest Survey with the Geoarchaeological Sites……………………………..7
Figure 4 – The Zacapu Basin in Relation to the Pátzcuaro Lake Basin,
and the Research Area of Tzintzuntzan………………………………………….………………..8
Figure 5 – The Protohistoric Settlements of the Lake Pátzcuaro Basin…………………………38
Figure 6 – The Resource Zones of the Lake Pátzcuaro Basin…………………………………..41
Figure 7 – Tariacuri Phase Artifact Densities and Clusters……………………………………..65
Figure 8 – Community Locations for the Tariacuri Phase, LPB………………………………..68
Figure 9 – Tariacuri Phase Delineated Communities, LPB……………………………………..70
Figure 10 – The Loma Alta 2 Rank-Size Graph…………………………………………………82
Figure 11 - Loma Alta 3 Rank-Size Graphs……………………………………………………..83
Figure 12 - Lupe/La Joya Rank-Size Graph……………………………………………………..84
Figure 13 - Early Urichu Rank-Size Graph……………………………………………………...86
Figure 14 - Late Urichu Rank-Size Graph………………………………………………….……88
Figure 15 - Tariacuri Phase Rank-Size Graph………………………………………….………..90
Figure 16 – The Comparative Rank-Size Graph: All Phases…………………………..………..91
Figure 17 – The 2033 m.a.s.l. Reconstructed Lake Level – Loma Alta Phase ….……….……..106
Figure 18 – The 2035 m.a.s.l. Reconstructed Lake Level- Lupe/La Joya Phase..…………....…107
Figure 19 – The 2028 m.a.s.l. Reconstructed Lake Level- Early Urichu Phase………………..108
Figure 20 - The 2030 m.a.s.l. Reconstructed Lake Level – Late Urichu Phase ……….………110
Figure 21 – The 2040 m.a.s.l. Reconstructed Lake Level- Tariacuri Phase, AD 1520…..……..111

xi

Figure 22 - The 2043 m.a.s.l. Reconstructed Lake Level- Tariacuri Phase, AD 1525….……..112
Figure 23 – The Environmental Zones of the LPB…………………………..…………………115
Figure 24 – The Lake Pátzcuaro Basin Slope Map……………………………………………..117
Figure 25 – The Early Hispanic Transport Network for the Lake Pátzcuaro Basin
(Gorenstein and Pollard 1983)…………………………………………………….……………119
Figure 26 – The Early Hispanic Transportation Network and Cost Surface Map……………..120
Figure 27 – The Source and Destinations in a Cost Surface Model……………………………135
Figure 28 – The Cost Distance Map…………………………………………………………....136
Figure 29 - The Back Link Map………………………………………………………………..137
Figure 30 – The Final Least Cost Path………………………………………………………....139
Figure 31 – Loma Alta Community Interactions………………………………………………141
Figure 32 – The Cost Allocation Catchment: No Boundaries …………………………………145
Figure 33 – The Cost Allocation Catchment: Two Kilometer Buffer………………………….146
Figure 34 – The Landscape Resource Zone Analysis…………………………….……………149
Figure 35 – A Comparison of Least-Cost Travel routes versus Pollard’s Transportation
Network………………………………………………………………………………………....152
Figure 36 – The Loma Alta Phase Community Interaction Analysis………………………..…156
Figure 37 – The Loma Alta Phase Allocation Catchment Analysis……………………………157
Figure 38 – The Loma Alta Phase Travel/Transport Analysis…………………………………158
Figure 39 – The Lupe/La Joya Phase Community Interaction Analysis………………………159
Figure 40 – The Lupe/La Joya Phase Allocation Catchment Analysis…………………...……160
Figure 41 – The Lupe/La Joya Phase Travel/Transport Analysis……………………………....161
Figure 42 – The Early Urichu Phase Community Interaction Analysis……………………..…162
Figure 43 – The Early Urichu Phase Allocation Catchment Analysis…………………………163

xii

Figure 44 – The Early Urichu Phase Travel/Transport Analysis………………………………164
Figure 45 – The Late Urichu Phase Community Interaction Analysis
(Primary Interaction)…………………………………………………………………………..165
Figure 46 – The Late Urichu Phase Community Interaction Analysis (Secondary
Interaction)………………………………………………………………………………….…..166
Figure 47 – The Late Urichu Phase Allocation Catchment Analysis………………………..…167
Figure 48 – The Late Urichu Phase Travel/Transport Analysis……………………………..…168
Figure 49 – The Tariacuri Phase Community Interaction Analysis……………………………169
Figure 50 – The Tariacuri Phase Allocation Catchment Analysis……………………………..170
Figure 51 – The Tariacuri Phase Travel/Transport Analysis…………………………………..171
Figure 52 – The Loma Alta Phase Community Interactions…………...………………………174
Figure 53 – Lupe/La Joya Interaction Zones/Community Boundaries…………………………176
Figure 54 – Early Urichu Interaction Zones/Community Boundaries………………………….177
Figure 55 – Late Urichu Primary Interaction Zones/Community Boundaries…………………179
Figure 56 - Tariacuri Interaction Zones/Community Boundaries………………………………182
Figure 57 – The Southeastern Survey Area, Summer 2009…………………………………….204
Figure 58 – Close-up of the Southeastern Survey Zone, with the Malpaís Highlighted……….205
Figure 59 – The Architectural Features of the Malpaís Survey………………………………..207
Figure 60 - Early Urichu Rank Size – Southeast Malpaís……………………………………...209
Figure 61 – Late Urichu Rank Size – Southeast Malpaís………………………………………210
Figure 62 – The Zacapu Basin Detailing the French Survey Zone…………………………….218
Figure 63 – The Site of Milpillas, Mich. 95 in the Zacapu Malpaís……………………………220
Figure 64 – Loma Alta Phase Communities……………………………………………………232
Figure 65 - Lupe/La Joya Communities……………………………….……………………….233

xiii

Figure 66– Early Urichu Communities …………………………………………………...……234
Figure 67- Late Urichu Communities……...……………………….……………….…………235
Figure 68 – Tariacuri Communities…………….……………………………………….….…..236
Figure 69 – The Lupe/La Joya Communities for the Southeast Malpaís Survey…………..….309
Figure 70- The Early Urichu Communities for the Southeast Malpaís Survey……………..…310
Figure 71 - The Late Urichu Communities for the Southeast Malpaís Survey…………...……311
Figure 72 - The Tariacuri Communities for the Southeast Malpaís Survey…………………...312

xiv

CHAPTER 1: INTRODUCING THE SETTLEMENT SYSTEMS OF THE LAKE PÁTZCUARO
BASIN
This chapter outlines an archaeological project that will provide a settlement system
analysis of the Lake Pátzcuaro Basin, Michoacán Mexico (Figure 1). The scope of the settlement
1

system analysis will cover the core area of the Tarascan Empire, an area of 803 square kilometers
and will range in time from the Late Preclassic (100 B.C.) until Spanish conquest (circa A.D.
1525) (see table 1for a list of periods/phases). The dissertation will systematically analyze the
communities in the lake basin through time, the shifting economic and ecological resources, and
will analyze the prehistoric landscape in terms of resource management and settlement. This will
be done through the testing of a model, derived from Pollard’s (2008) comprehensive
archaeological work on the rise of the Tarascan state.
After many decades of neglect, West Mexico has become the subject of significant

archaeological research over the past 40 years. According to Balkansky, one outcome of this work
is the understanding that this area constituted another Mesoamerican core and was not simply the
product of central Mexican influences (2006:72). With this realization comes a need for a
continuation of research that moves beyond the “patch work” and site focused research that has
been accomplished to date and toward research on a regional and macro-regional scale. This
proposed research will in fact, help to bridge this regional gap through the advancement of a
testable framework of settlement that can be applied elsewhere in West Mexico. This will
ultimately allow researchers from different “regions” within West Mexico to collaborate and
provide multi-disciplinary based projects that will aid in our understanding of the archaeology,
anthropology and ecology of Mesoamerica. The regional and macroregional research on
1

This figure was created by the author by utilizing ArcGIS to create a georeferenced image of the
extent of the lake basin, overlaying it on a current, rectified satellite image, and calculating the area
of the basin’s extent. This number is lower than those previously published by Pollard (1983), and
Toledo (1991, 1993), which were in the 920-930 km square range.
1

settlement and state emergence allow for this region to then be compared to other regions of early
state development, such as the Andes, thus providing a larger comparative framework for this
research to be analyzed and peer reviewed.
Figure 1 – The Mexican State of Michoacán and the Pátzcuaro Lake Basin

Furthermore, in keeping with the multi-disciplinary and collaborative nature of
Mesoamerican archaeology, the archival data that will be used for this research will be placed into
a database and digitally made available to other colleagues that work in the region. This region is
a core area of research for American, French, British and Mexican scholars. By providing this
data, we may bridge gaps and begin the collaboration process across many research projects.

2

Previous Research and Data Sources
The primary data that will be used to develop the settlement system model comes from
archaeological surveys conducted from 1970-2005 within the Pátzcuaro Lake Basin (see Figures 2
and 3, and Table 2). The first zone surveyed included the Imperial Tarascan capital of
Tzintzuntzan by Pollard in 1970. Pollard collected artifacts from areas of surface scatter across the
survey area, an area that was defined by both artifact density as well as local geomorphology of
soils. These dense concentrations of artifacts were noted and located on the survey maps by
Pollard, and those that were “spatially isolable were given numbers and considered sites” (Pollard
1972:28). These n=120 sites comprise the primary units of analysis for both Pollard’s survey
(1972).
Table 1 – Mesoamerican Temporal Phases and Pátzcuaro Lake Basin Phases
Period

Local Phases

Late Postclassic

Tariacuri

Time Range
A.D. 1350- 1525

Middle Postclassic

Late Urichu

A.D. 1000/1100 - 1350

Early Postclassic

Early Urichu

A.D. 900-1000/11000

Epiclassic

Lupe-La Joya

A.D. 600/700 - 900

Middle Classic

Jaracuaro

A.D. 500 - 600/700

Early Classic

Loma Alta 3

A.D. 350 - 500

Late/Terminal Preclassic Loma Alta 2

100 B.C. - A.D. 350

Following this survey, Pollard also surveyed what the ethnohistoric documents refer to as
the land of the pre-state polities of Urichu, Jaracuaro, and Pareo (1990-1996) (Pollard 2000). The
2

final portion, surveyed in 2001, was the town of Erongarícuaro and its surrounding areas (see
Table 1 for a review of the survey areas). All surveys were intensive and were supplemented by

th

2

The modern town names will be used in this dissertation, in the place of the 16 century names
derived from the ethnohistoric documents. For example, Jaracuaro instead of Xaracuaro.
3

archaeological excavations. The goal of these surveys in the southwest of the basin was to provide
a settlement pattern through time periods by locating major settlements and elite administration
centers, which had been described in the ethnohistoric document the Relación de Michoacán
(Pollard and Cahue 1999). The surveys included geoarchaeological research to test for lake and
climate fluctuations, and test for evidence of intensive agriculture and land degradation through
careful off-site placement of trenches and augers (Figure 4) (Fisher et al. 2003). The combined
data sets confirmed sequences of both Prehispanic and post-contact settlement and land
degradation, and extend our knowledge about Tarascan views of landscape and the primary role of
an adaptation to a highland lacustrine ecosystem (Pollard 2008).
Table 2 – Survey Areas of the Lake Pátzcuaro Basin

Archaeological Surveys

Lithic
#
Area
(Obsidian,
Survey
Ceramic
(hectares)
Basalt)
Sites

Tzintzuntzan

120

901

2172

Southwest
(Urichu/Pareo/Jaracuaro)

248

5401

173229

Erongarícuaro

41

228

4393

4

1041

27511
5447

Other
(Figurines,
Recortados,
Pipes)
327

472

33

Figure 2 – The Pátzcuaro Lake Basin and Previous Archaeological Surveys

The ethnohistoric documents from the region are a great asset when trying to reconstruct
the final stages of Tarascan civilization in the lake basin. One of the most important of these
primary sources is the Relación de Michoacán (RM). The RM is a historic document, recorded in
1538-1539 in the Tarascan capital of Tzintzuntzan, and was given to the Spanish Viceroy in 1540
(Pollard 1993:17). It documents many aspects of Tarascan life before and during the Spanish
conquest, and provides valuable data about to the locations of Prehispanic administration centers
and settlements in the lake basin at the time of Spanish conquest. A second and equally important
document is the Carvajal Visitas (Warren 1985), which is an account of the first inspections of

5

Antonio de Carvajal, which listed and cataloged the major settlements and their subject
settlements within the Tarascan Empire prior to the Spanish encomienda system (Gorenstein and
Pollard 1983:30). Other primary sources that will be used to reconstruct settlement and
demography at the time of contact will be the Suma de Visitas de Pueblos of 1547-1550, the series
of Relaciones Geográficas from 1579-1581, the Infante documents of 1528, and the Beaumont
(1932) and Seler (1908) maps which are reproductions of the cartographic pinturas drawn in the
decade following 1538 (Gorenstein and Pollard 1983:13). These ethnohistoric documents have
been reproduced and interpreted by Gorenstein and Pollard (1983), Gerhard (1972), Warren
(1985), and Espejel (2007).
A synthesis of the geoarchaeological work, and geographic work from a variety of sources,
will aid in the reconstruction of the history of climate, lake and landscape of the Prehispanic lake
basin. The geoarchaeological work from the southwest basin projects will be utilized in the
modeling of the lacustrine ecosystem, intensive agriculture, and resource management on a basinwide scale (Fisher 2003, 2005). Data from lake sediment cores (Watts and Bradbury 1982;
O’Hara 1993b), from ethnoecology (Toledo 1991, 1993), and from historical records (O’Hara
1993a; Metcalfe and Davies 2007; Metcalfe et. al. 2007) will supplement the geoarchaeological
research.

6

Figure 3 – The Southwest Survey with the Geoarchaeological Sites

The final source of archaeological data for this research comes from the nearby Zacapu
Basin, located to the immediate northwest of the Pátzcuaro Lake Basin (Figure 5). The work from
Zacapu represents a region incorporated into the Tarascan State early in its history and with a
similar cultural tradition. It is the only region in the heartland of the Tarascan Empire where

7

comparable full-coverage archaeological survey has taken place (Michelet 2008). The research
derives from the CEMCA Zacapu project, which has been a French-led archaeological project
begun in the early 1980s, focused on creating a full record of the history of settlement from initial
occupation up to the Spanish conquest. This data derives from Michelet (1988, 1998, 2008),
Migeon (2003), Arnauld (1998), Carot (2005), Faugere-Kalfon (1998), and Puaux (1989).
Figure 4 – The Zacapu Basin in Relation to the Pátzcuaro Lake Basin, and the Research Area of
Tzintzuntzan

Geographic Information Systems
The last aspect of the analysis that will aid in the modeling and mapping of the variables in

8

the settlement system model is the use of a Geographic Information System, or GIS. A GIS will
be used to create and manage the database that will contain all the necessary data (i.e. artifact,
geoarchaeological, demographic). A GIS is most useful because of its dual functionality as an
analytical tool as well as a display tool. All maps will be made in a GIS program (ESRI ArcMap),
and all data will be stored in the database and analyzed through the use of ArcToolbox, which
contains a suite of spatial statistic tools.
For this specific settlement model, a 3D DEM will be used to create a digital terrain model,
which will be used for a cost-distance analysis. This 3D modeling will take into account the
terrain when assessing travel and interaction ease, efficiency, and rates within the lake basin.
Ultimately, the most important types of distance that will be measured are economic distance and
time, measured in energy expenditure in kilocalories and walking velocity, as well as
canoe/boating velocity. Both variables will be factored into the gravity model to better estimate
interaction, both political and economic, when discussing settlement location.

Research Questions and Hypotheses
The primary goal of this research is to determine the structure of the settlement system
over a period of approximately 1,625 years leading up to the Spanish conquest. Ancillary to the
primary goal is the goal of explaining the role of state formation and the state’s political economy
in the latter years of settlement in the Basin. A tertiary goal is the identification of a
macroregional settlement of Tarascan society when analyzing the Zacapu and Pátzcuaro Basins.
In order to further develop theory about the settlement, formation and development of the
state in West Mexico, a settlement system analysis must be completed. This differs from a
settlement pattern analysis, in that settlement patterns are the arrays produced by sets of
interacting, interdependent local groups of people, whereas settlement systems are the processes

9

behind the patterns (Kowalewski 2008: 226). This research will focus on the patterns behind the
settlement of the lake basin, and will do so by analyzing shifts in settlements both spatially and
temporally, thus mapping the trajectory of development of civilization in the lake basin, from
small and intermediate scale societies to the emergence of the state, from the Late Preclassic (100
B.C.) to the Spanish Conquest (circa A.D. 1525).
The main research questions that will be addressed are as follows:
1) Using the data from the Pátzcuaro Lake Basin surveys, what is the overall trajectory (the
endpoint being that of the state at the time of Spanish contact) behind the regional settlement of
the lake basin?
2) Which variables, whether internal or external, best explain the system and patterning behind
the settlement of the lake basin?
3) What effect does the emergence of the state have on settlement within the lake basin? What
does this reveal about the resource management and the conception of landscape?
4) When compared to the model developed for the Zacapu Basin (Michelet 1998, 2008; Migeon
2003), what are the characteristics of a Tarascan macroregional settlement system?
The central hypothesis for this research is based on an emergence of the state and
settlement model for the Pátzcuaro Lake Basin (Pollard 2008). The theoretical frameworks for the
model combine a political economy and settlement ecology approach. Therefore, a central
hypothesis is that the primary variable that determined settlement within the lake basin was the
proximity to the lakeshore of Lake Pátzcuaro and its lacustrine zones of resources. This derives
from the lacustrine-based system model that was the dominating system of subsistence, both
prehispanically, historically and into the modern era. This variable remained the primary
settlement determinant until the emergence of the state in A.D. 1350, when the dominance of the
capital, Tzintzuntzan, altered the foundations of the political economy of the lake basin. During
10

this period, the primary factor changed, and settlement was now predicated upon proximity to the
capital and other major state-run centers of administration, religion, and economy. The lake
remained a secondary factor in settlement, primarily affecting peripheral settlement in the basin.
Tertiary to all periods of settlement is the variable of proximity to arable land both inland and
upland, followed by a fourth variable, proximity to travel/trade routes in and out of the basin.
An alternative hypothesis is that the lake is only a primary variable until the Middle
Postclassic (A.D. 1000 – 1350), when political instability becomes the primary motivator for
settlement in upland, defensible positions. Following this period, the emergence of the state and
the proximity to the capital of Tzintzuntzan assumes the primary motivator for settlement location
until Spanish conquest.
Table 3 – Proposed Settlement Variables in the Lake Pátzcuaro Basin: 100 B.C.-A.D. 1525
Period

Phase

Primary Variable

Secondary Variable Tertiary Variable

Late Preclassic

Loma Alta 2

lake/lacustrine

other communities

travel/trade routes

Early Classic

Loma Alta 3

lake/lacustrine

other communities

travel/trade routes

Middle Classic

Jaracuaro

lake/lacustrine

other communities

travel/trade routes

Epiclassic

Lupe-La Joya lake/lacustrine

arable land

travel/trade routes

Early Postclassic

Early Urichu lake/lacustrine

arable land

travel/trade routes

Middle Postclassic Late Urichu

lake/lacustrine

Late Postclassic

capital/admin. centers lake/lacustrine

Tariacuri

defensible positions arable land
arable land

Defining the Region
This research will operate on a regional scale of analysis. This research analyzes the Lake
Pátzcuaro Basin as a region in the physiographic sense, defined by its hydrographic limits. The
research by Pollard in the Pátzcuaro Lake Basin (see Figure 2), has been explicitly full- coverage
(on the individual, not regional scale), intensive, and has treated the survey site (i.e. collection
11

unit) as the smallest unit while keeping a larger regional framework in mind. Due to funding and
permit limitations over the last decades, the survey of the lake basin has had to be completed in a
piecemeal fashion, with areas still not surveyed today (Pollard 2010: in press). Therefore, the
structure of the survey data to be used is one that lacks continuity both spatially and temporally.
This challenges the rationale behind the regional scale analysis that will be attempted in this
research, and therefore needs to be examined. Examining in more detail the lake basin and the
actual area covered by the surveys, although non-contiguous the surveys show a level of coverage
that, I argue, allows for a regional scale analysis to be done. Gorenstein and Pollard (1983)
estimated that of the n=92 Early Hispanic settlements (A.D. 1520-1550), n=84 settlements
occupied the lakeshore and lower sierra slope ecological zones, or lower than 2300 meters in
elevation (1983: 149). Ethnography has shown that historically human populations occupied the
environmental zones occurring below 2,300 meters in elevation, with these settlements continuing
into the 20th century, thus creating a 500 year period trend (Gorenstein and Pollard 1983) (Pollard
1983) (Mexican National Census 1943, 1973). Therefore, I will assume that Prehispanically the
same approximate land classes were occupied, and thus the probable settlement area of the lake
basin (and area that could be surveyed) is reduced by 48%. My estimates of the area of the lake
basin, as taken from Toledo et. al. (1993), place the area at 80301 hectares, or 803 square
3

kilometers . By applying Gorenstein and Pollard’s estimates of percentage of land under 2300
meters in altitude, this leaves 41757 hectares of possible survey area. From this, if we subtract the
area of the lake, open water and marsh zones (7777 hectares), the unsurveyable urban zones of
Pátzcuaro (1109 hectares) and Quiroga (309 hectares), that leaves 32562 hectares of possible
survey area. Of this 32562 hectares, the surveys of Tzintzuntzan, Erongarícuaro, the southwest
3

Gorenstein and Pollard estimated the lake basin at 98,890 hectares, but with the use of rectified
aerial coverage, the same lake basin area was calculated as smaller (Gorenstein and Pollard 1983:
149).
12

survey (including Urichu, Pareo and Xaracuaro), the malpaís of Urichu, the archaeological zone of
4

Ihuatzio, and the southeast survey area currently being surveyed add up to 13820 hectares, or
approximately 42% of the possible survey area in the lake basin below 2300 meters in elevation
(see figure 2). With this in mind, I believe this to be an acceptable percentage of the lake basin that
has or will be surveyed to perform a systematic regional settlement analysis.

Theoretical Discussion
In order to test the settlement system model in the Lake Pátzcuaro Basin, a methodology
must be laid out that is explicit and allows for analysis of multiple lines of evidence. The
methodology requires a very clear-cut theoretical stance, one that fuses a political economy model
and the resource and subsistence models of a landscape approach. The basic tenets of each
theoretical approach will be discussed in terms of their use in this research, and will lead to a
discussion of the testable model of settlement of this research
An approach employing political economy will be most useful when explaining the
settlement systems in terms of access to resources, and the emergence of a state-level social and
political structure. A political economy model assumes an economic structure, or base, for human
interaction and decision-making and the critical role of social and political elites in allocation of
those resources. The variables that will be assessed in the settlement model, and discussed further
in the methodology section directly correlate with the economic and resource utilization of the
lake basin. These are variables that are thought to have played a major role in the structuring of
regional settlement and individual site/settlement function in the basin.
A landscape approach will also be utilized in the creation, analysis and interpretation of the

4

The LORE project (PI-C. Fisher and Senior Investigator H. Pollard) was initiated in 2009 to
survey and conduct geoarchaeological research in the SE zone of the Pátzcuaro Lake Basin.
13

settlement system model. Described by both Kantner (2008) and Kowalewski (2008), landscape
archaeology and historical ecology were “inspired by the traditional processual approach to
regional data, either tracing their epistemological and interpretive history directly to processual
archaeology, or reflecting the more humanistic postmodern approach”; the latter as a critique of
the former (Kantner 2008:56). Within the realm of landscape approaches, the approach most
aligned with the goals and variables set forth in this research is described by Anschuetz (2001) as
settlement ecology. This approach is closely aligned with settlement pattern and system
approaches, and addresses issues of archaeologically observed patterns of land use, occupation,
and transformation over time (2001:177). It acknowledges the human- environment interactions
that create landscapes, and emphasizes natural environmental variables, “including essential
subsistence resources, other raw materials needed for physical comfort and health, and items for
trade or exchange” (Anschuetz 2001: 177).

Settlement Model
The model for the settlement system is derived from the research by Helen Pollard in her
work on the emergence of the Tarascan state (2008). This study will employ Pollard’s model of
the emergence of the Tarascan state (2008), and will emphasize the settlement patterns derived
from her archaeological, ethnohistoric and ecological research (see Table 2 for a summary of the
model). Keep in mind that this model is limited geographically to the Pátzcuaro Lake Basin, yet
the sequence of events is in fact a product of a vast “open system” of interaction and
communication within Mesoamerica; Tarascans didn’t live in isolation.
The initial or starting point for the temporal sequence of settlement is in the Late Preclassic
(100 B.C.). Population within the lake basin was relatively low (5,000 – 8,000), and all cases of
settlement displayed the existence of small-scale, socially ranked agrarian societies (Pollard

14

2008:220). Furthermore, these ranked societies consisted of a hierarchy that would remain
relatively unchanged until the emergence the state. Settlement was located on or very near the
shorelines of Lake Pátzcuaro with the primary means of subsistence being lacustrine and wetland
based. The shorelines of the lake fluctuated minimally, marking the only movement of
settlements. Furthermore, settlements were not yet centralized spatially, but were situated
primarily on or near the key resources of the lake.
The Early Classic (A.D. 600) to Middle Classic (A.D. 700) was marked by a stable
population, between 6,000 and 7,000 (Pollard 2008:221). The settlements remained on or very
near the lakeshore, continuing lacustrine and wetland agricultural practices. Ceramic production
remained localized, yet preciosities found their way into the basin and were part of the way elite
status was derived and marked. The goods, however, were finished goods, meaning that local
level economic specialization had yet to permeate the economic structure in the basin. Each
settlement had variation in its social hierarchy as well as its spatial composition, with varying
types of architecture and no singular style dominating. This suggests the continuation of a local
elite-dominated settlement with a highly agrarian component.
During the Epiclassic (A.D. 700-900) the region of West Mexico began to see political
restructuring and climatic changes (Beekman 2009). The end of this period (A.D. 900) marked a
climatic shift towards more arid conditions, with a synchronous drop in the lake level. The
number of sites increased and the population rose to 12,000 at this time (Pollard 2008:224). These
climate shifts and slight rise in population coincided with the beginning of the Early Postclassic
(A.D. 900-1100). Lacustrine settlements, still the primary type of settlement in the basin, moved
to these new lake margins as the lake dropped to its lowest elevation in the past two millennia
(Pollard 2008:223).
With new arable land and a continued reliance on the lacustrine resources, the small-scale
15

socially ranked societies shifted to larger-scale chiefdom-like societies, a shift that began in the
Middle Classic periods. It is believed that with the rise in population, settlements began to
centralize at various distances inland from the lake while continuing to utilize its resources. During
the Middle Postclassic (A.D. 1000-1350), a large population increase occurred as it rose to 48,000,
along with the doubling of the area of occupation due to the low lake levels. Near the end of this
phase (~A.D. 1300), lake levels rose again, thus forcing settlements away from the low-lying areas
around the lake to concentrate around the marsh production zones (Pollard 2008:224). Pollard
asserts that due to these expanding, diminishing and shifting resources, competition must have
been fierce, leading large-scale chiefdoms into inter-basin warfare. Settlement then shifted
primarily due to the larger populations within the basin. Settlements continued to be located near
the lake and slightly inland from it, but also moved upland into defensible locations, such as in the
malpaís at Urichu (Pollard 2008:224).
The Late Postclassic period marked the emergence of the Tarascan state in A.D. 1350. The
continued rise in lake levels forced settlements out of low-lying to new lakeshore and inland areas
of high agricultural fertility. The added pressure from climate change and population size drove
settlements to develop new economic mechanisms, thus diversifying communities with a heavier
reliance on markets and state-run institutions. With the emergence of the state, settlement is now
dictated by the royal dynasty at the capital of Tzintzuntzan. Pollard proposes a power shift
towards the northern end of the basin, thus altering the spatial orientation of settlement. Resources
were managed by the state, and the social model that dominated the basin since 100 B.C. was
replaced by the state’s rigid social hierarchy system, where a three class system was put into place;
an upper elite class (containing the royal family), lower elite class, and a commoner class.
This model, as proposed by Pollard (2008), presents a testable framework upon which this
research may now build a method with which to analyze which of several variables are primary to
16

the structure of the settlement systems within the Pátzcuaro Lake Basin. The next step is to
explain how the data are to be used, and to provide archaeological correlates to the behavior that
has been discussed in the models.

Method
In order to test the hypothesis and settlement system model, a multi-step research strategy
will be implemented. Each of the survey areas will be analyzed on an individual basis, separate
from each other. From there, the survey areas can be analyzed on a multi-scalar basis dependent
upon the research question being addressed. In their research on the archaeology of regions,
Drennan and Peterson argue that in order to understand the settlement system of a region, one
must first understand what constitutes a site, how these units form a community, and how the
communities interact to form a regional pattern of social interaction and behavior (2005:6). In this
research and analysis, a site is defined as a non-random, dense clustering of architecture, ecofacts,
and/or artifacts that represents a form of human activity (Parsons 1972; Kowalewski 2008). The
southwest surveys in the Lake Pátzcuaro Basin were collected by agricultural fields, whereas
portions of the southeastern survey collected by clusters of structures (i.e. plaza groups), and
agricultural fields. When mapped, these collection units can be combined to display these areas of
human activity. From there, with help from the ethnographic data and archaeological excavations,
major urban or administrative centers can be mapped, with the surrounding areas forming a
community, which will be the main unit of analysis for the model in this research. Later in the
process of analyses they will be combined to provide a basin-wide settlement system analysis
using quantitative methods. So, for each survey area, the following will be done:
-1.) The survey units (i.e. survey sites or collection units) will be clustered, hopefully creating
meaningful units of analysis for a demographic reconstruction. This will be done phase by phase

17

so as to create a chronological series of occupations.
-2.) Based on these new clustered units (i.e. communities) the population will be reconstructed for
each phase by using the artifact densities from the surveys, architectural remains, and the
ethnohistoric documents.
-3.) Following the demographic reconstruction, the artifacts and ethnohistoric data will again be
utilized to designate any functional categorization of the communities (i.e. manufacturing,
administrative, agricultural, and ritual).
-4.) A reconstruction of the Prehispanic landscape will be completed by phase using the
geoarchaeological evidence, survey data, and the ethnohistoric documents. This will include the
lakeshore estimates by phase, estimating arable land used for agriculture, the basic topography,
and major travel and trade routes.
-5.) Finally, the communities will be mapped by phase, as identified in the ethnohistoric
documents. This will be attempted on a phase by phase basis, but may be limited due to the
temporal constraints of the documents.

Quantitative Analyses
Once the individual survey areas are modeled in terms of demography, landscape and
settlement, a basin-wide analysis will be undertaken in order to complete a regional model of
settlement. The major measurement becomes the distance of known settlements through time
(derived from the mapping of artifacts from the surveys listed above) to each of these variables;
lake and lake resources, arable land, major centers and/or capital, trade/travel routes, and other
communities).
When considering statistical tests to employ in a settlement systems model, the focus must
fall on the data and scale of the research. As discussed previously, the scale is at the regional

18

level, with the smallest unit being the collection unit and the main unit of analysis being the
settlement. In his research on regional analysis, Johnson (1977) analyzed interaction models such
as gravity models and modeling based on central place theory, and explored the quantification and
testing of behavior and human-decision making in settlement studies (1977:479). This research
will employ a gravity model in order to analyze economic interaction in the Pátzcuaro Lake Basin
in order to weigh the variables and assess their role in settlement. A gravity model is based on
Newton’s law of gravity, its basic assumption being that interaction between two locations is
directly proportional to their respective size and inversely related to the distance between them
(Kantner 2005:1203). There are assumptions that must be made explicit for a gravity model to
operate. For example, the simple assumption of minimization of energy expended in movement
explains the theoretical impetus behind the gravity model’s premise that interaction decreases with
the increase in the distance over which the interaction takes place (Johnson 1972:481). Therefore,
for the purpose of this settlement system model, interaction is defined by the political economy
framework, where it is assumed that settlement is predicated upon distance to resources, whether it
is the market, travel/trade routes, lacustrine resources, or other communities, and the size of these
variables, size being one of the multiple problem dependent variables including population,
artifact, resource area, or architectural density. The gravity model will analyze and rank the
variables based on the size and distance factors, and thus inferences can be made as to the primary
factors for settlement in the lake basin. Research by Hare (2004) and Ohnersorgen and Varien
(2008) are examples of analyses that have employed the gravity model to analyze interaction on
the regional level.
Once the individual survey areas are reconstructed for each phase in terms of communities,
each community will be analyzed with respect to other communities on a regional basis, with a
gravity model measuring the variables associated with settlement for each. In this statistical
19

analysis, each community will be measured on the basis of its population density to other variables
within the basin, i.e. the lakeshore, arable land, other centers or communities, and travel and trade
routes. The distance between the variables will be also measured using a cost- distance model.
This model doesn’t assume a linear distance, but instead factors in the topography and gives a realtime distance for energy expenditure and walking velocity (Hare 2004: 803). This will be
essential when factoring in the lake as a major travel variable within the basin. The variables,
based on size and distance between them, are then weighted and ranked, thus interpreted based on
their level of impact on the settlement. This will, in essence, allow for interpretations to be made
about which variables most influence settlement in the lake basin through time, both as a regional
measure of the settlement system through time and as a means to analyze each area separately.
Through the measures between the communities, a secondary outcome will be to ascribe
boundaries for the various polities in the basin through time. This will aid in our reconstruction of
the political economy of the settlements, and allow for interpretations on how the pre-state polities
operated and the effect the emergence of the state had on their infrastructure.
Analytic Expectations
The goal of the research presented here is to create a testable model of the Prehispanic
settlement systems within the lake basin. The crucial aspect of the model is to find variables that
will test the patterns behind the settlement: effectively asking why and how. As discussed
previously, the settlement system model to be tested relies on variables derived from a political
economic framework. Concurrently, these variables will also be used in conjunction with a
landscape approach in order to evaluate research questions concerning the Tarascan’s perception
of the landscape and its maintenance and/or alteration before, during and after the emergence of
the state. Therefore, the data must be discussed in terms of how it explains behavior in the
settlement system model, thus explicitly correlating archaeological, geological and ecological
20

evidence to settlement behavior. The following are the expectations for how the settlement model
may translate in the material record, and how the data will answer the research questions posed by
the author.
With the primary data being archaeologically surveyed artifacts, and having a sound
ceramic typology and chronology based on these ceramics, we are able to date clusters of artifacts,
and can give reliable estimates of periods of occupations for sites. As explained in the
methodology, the sites will be spatially clustered, with the larger units now being called
communities. We know from the ethnohistoric records (i.e. RM) and from elsewhere in
Mesoamerica that communities existed at the time of Spanish conquest, and therefore this analysis
must assume that the social unit “community” can be retrodicted back through the settlement
sequence. Here marks one of the more difficult aspects of this analysis, as the analysis will have
to utilize spatial statistics to be able to isolate these “communities” from archaeological data.
Researchers such as Drennan (2006), Peterson (2005), and Kowalewski (2003b) have grappled
with the concept of “community” as it is known and described in archaeology. Kowalewski
(2003b) has traced the variation and evolution of communities in Oaxaca, Mexico. His research
explains how communities change through time in their social, political, and economic
composition, but seem to remain spatially distinct units where dispersed, smaller groups located
themselves on the landscape and shared experiences through face-to-face interaction (2003b:16).
However, certain things such as political, ritual, and economic resources were not always shared,
and were depended highly on the larger regional social and political framework where these
communities were located. Thus, this research will not make the claim that the community is and
always has been an autonomous social unit, but instead treats the unit as a spatially isolable unit of
social organization whose composition and structure evolves over time.
Differential artifact frequencies can be used to attribute function to a site or a community
21

(Stawski 2008). By building bridging arguments through previous excavations and the
ethnohistoric record, one can infer certain economic, social, political or ritual behavior to certain
types or classes of artifacts. The artifacts can then be correlated with certain subsistence
strategies, rituals, economic specialization, household production, or political functions that were
carried out within the communities. This will be a valuable analytical tool as we may witness the
economic transitions through time as populations grow and the political economy evolves, thus
allowing interpretations of the role of communities and their variation in the lake basin.
If the demographic model proposed by Pollard (2008) is correct we would expect a
relatively low frequency of artifacts from the Late Preclassic (100 B.C.) to the Epiclassic (~ A.D.
700). With most artifact clusters found near the lakeshore, portions of the surveys will fluctuate
spatially in elevation with changes in the lakeshore. Likewise, the artifact assemblage, will
primarily be associated with a lacustrine based subsistence, and will include items such as
recortados, which are worked sherds used as net weights (Phillips 2002). As the Epiclassic
arrives, population increases and therefore a higher frequency of artifacts will be present from this
period. The dense concentrations from this time until the Late Postclassic should resemble more
centralized and more populated centers, leading up to the emergence of the state. It is during the
Late Postclassic that we expect to see more diverse artifact assemblages that will be more
functionally isolable, meaning that material goods represent the hierarchical organization of social
classes, such as the upper elite, the lower elite, and the commoner classes. As the state emerges,
we will also see a change in the mode of production of such items. For example, certain types of
obsidian was produced by specialists, and thus became standardized and in certain forms, such as
the prismatic blade. Ceramics also display more variation, and are produced on the household
level as well as through specialized production for the elite class.
It is expected that the emergence of the state created a pull within the basin both
22

economically and politically, meaning the settlements located based on proximity to the capital at
Tzintzuntzan and its major trade/travel routes. The major administrative centers in the basin
legitimized their relationship with the capital, and thus the centers exhibited a material record that
is indicative of religious and/or administrative centers such as the capital, only not on the same
scale. Due to the higher populations in the lake basin, we expect to see more land that was
intensively farmed, manifested as terraces in the archaeological record, around these centers, while
the more peripheral settlements were still in close proximity to the lake as well as to arable land.
With the emergence of the state came more foreign material goods in the archaeological record, as
the markets and trade networks began to exhibit a larger regional and macroregional scale of
operation.
Chapter Synthesis
CHAPTER 1- INTRODUCING THE SETTLEMENT SYSTEMS OF THE LAKE PÁTZCUARO
BASIN
This chapter introduces the proposed dissertation. Included is a brief background of the
research, including the archaeological, anthropological, geological and ecological that has focused
on the Prehispanic and historic settlement of the Lake Pátzcuaro Basin (LPB). Also introduced are
the major research questions, the working hypothesis, the proposed model for analysis and the
theoretical framework of the research.

CHAPTER 2- THE TARASCAN EMPIRE AND THE LAKE PÁTZCUARO BASIN
This chapter provides background information on first the archaeological research that has
shaped the academic and scholarly environment of the LPB. This is done to properly couch this
dissertation into the long line of research focusing on the LPB and the Tarascan Empire. Second,
this chapter provides a background of the culture history of the LPB and Prehispanic Tarascan
state, primarily informed by the archaeological research, and secondarily informed by the
23

ecological, anthropological and ethnohistoric research.

CHAPTER 3 - COMMUNITIES OF THE LAKE PÁTZCUARO BASIN
The primary spatial variable used in the dissertation is the community unit. In archaeology,
the community may be defined several ways, and the first half of the chapter aids in providing
theoretical and conceptual frameworks for communities in the LPB. The second half provides the
method and analysis of communities, and reconstructs them for the 1,600-year period leading up to
Spanish Conquest in the LPB. This reconstruction includes the demographic reconstruction, the
functional categorization of communities, and the rank-size analysis of the communities through
time.

CHAPTER 4 - LANDSCAPES OF THE LAKE PÁTZCUARO BASIN
The second major component of the dissertation is the reconstruction of the LPB landscape.
This chapter first discusses the many theoretical models, derived from archaeology, geography and
ecology, for landscape approaches in the social sciences, while providing a framework that this
dissertation utilizes. The second half of the chapter provides the method and reconstructs the
landscape over a 1,600-year period for the LPB. This reconstruction includes the physical and
social landscape, including the travel and trade networks, the lake and its resource zones, and the
surrounding environmental zones.

CHAPTER 5 - A SETTLEMENT SYSTEMS ANALYSIS
With the communities and landscape now reconstructed, this chapter will provide the major
analyses for the dissertation. The primary mechanism for these analyses is a spatial statistical
approach that combines innovative spatial approaches with a Geographic Information Systems
24

platform. The result is an analysis that effectively models the dynamic settlement of
Prehispanic peoples through time and space, and quantitatively measures the impetus behind
locational settlement and subsistence practices over a 1,600 year time period. Included are spatial
analyses such as gravity models, cost-surface modeling, and catchment modeling.

CHAPTER 6 - MODELING THE LAKE PÁTZCUARO SETTLEMENT SYSTEM
This chapter will provide a discussion of the settlement of the LPB. The section will first
look at the micro-regional settlement system of the southwest data, creating a testable model that
can be applied elsewhere in the basin. This section looks to expand to a regional scale and utilizes
recent archaeological findings in the southeast area of the basin for a comparative analysis.

CHAPTER 7 - THE MACROREGIONAL SETTLEMENT SYSTEMS AND CONCLUSIONS
The first section will then provide a regional analysis, which will compare the settlement of
the LPB to the nearby Zacapu Basin, two distinct geographical entities yet still part of the same
Prehispanic Tarascan state.
This chapter will conclude the dissertation, and provide the final interpretations of the
research. The research questions posed in the Introduction Chapter will be revisited, as will the
hypothesis. Finally, this chapter will summarize the dissertation, its intent and the finished results,
and the future direction that the author’s research will take based on this study and the manner in which
this dissertation will aid in future research and publications.

25

CHAPTER 2: THE TARASCAN EMPIRE AND THE LAKE PÁTZCUARO BASIN

This chapter will provide a brief background of two important facets of this research; the
Tarascan Empire, as currently understood from the archaeological and ethnohistoric data, and the
Lake Pátzcuaro Basin, which includes the basins historic and current geographic, geologic, and
5

ecological characteristics . The goal is to contextualize the analysis found in future chapters, which
are centered on communities within the 1,600 year period leading up to Spanish conquest, and the
dynamic landscape also associated with this time period.
Research in West Mexico
In order to understand the current research paradigms that revolve around the Tarascan state
and its core area in the Lake Pátzcuaro Basin, a brief history of the research must be presented. The
earliest research in West Mexico, including the Lake Pátzcuaro Basin, comes in the early 20th
century from seminal anthropologists such as Seler, Lumholtz, Hrldlicka, and Gamio. The leading
theories on the Prehispanic West Mexican cultures were plagued by the insistence on comparing
them within the context of other Prehispanic cultures, such as the Teotihuacán and Olmec
civilizations. The result were theories of origin and complexity based on diffusionist notions of
cultural contact. It is within this context that the Mesa Redonda (1946) work is referred to in order
to provide a context for the beginnings of archaeology in the Occidente. This collection of articles
provides a variety of research in the region of West Mexico that helps to better understand the
origin of the research questions that have fueled archaeology in the region over the past halfcentury.

5

There are much more extensive resources on the Tarascan State, its prehistory and the ecological,
geographical and environmental details. Please refer to Pollard 1993, Pollard 2008, Gorenstein and
Pollard 1983, Toldeo 1991, and Beekman 2009 for a more comprehensive background.
26

Included is a discussion from Noguera reporting on the initial findings from the northwest
portion of Michoacán, including shaft tombs and a culture that shares Tarascan traits as well as
traits from Teotihuacán. The vast majority of this work derives from an attempt to provide ceramic
analysis in hopes of creating a chronology for West Mexico. By analyzing the ceramics, the
authors propose theories that include interaction with the Mixtec, Puebla, and Teotihuacán cultures,
and pinpointing the role of the Chupicuaro ceramic culture leading up to the Tarascan Empire.
Ultimately, the articles rely heavily upon diffusion, thus trying to explain the complex civilizations
of West Mexico through directional diffusion with the state-level society cores of the Aztec, Olmec,
Toltec, and Teotihuacán. It is also in the 1940s that within the study of state emergence comes the neoevolutionary theory, where states are viewed as evolving from chiefdoms due to a variety of
environmentally deterministic variables (White 1943, Steward 1949).
It is not until 1960s and the onset of Processualism in Mesoamerican archaeology that researchers
begin to abandon the diffusionist models and focus on the broader issues of settlement,
subsistence and the emergence of states. This shift in research paradigms led to a shift in
research scale, where now regions and macro-regions play an important role in developing
settlement theory, and households and communities aid in reconstructing commoner life and
economic practices. The state emergence research is still littered with theories based on
deterministic models, such as Carniero (1970) and Fried (1960). Inherent in the research
couched in neo-evolutionary theory is the stage model, where societies move “up the ladder” so to
speak, from tribe, to chiefdom, to state. The unilinear view of social change was under fire
from processual archaeology in the late 1970s and 1980s. It is from this processual theory that this
dissertation finds its ancestral roots.
The primary research that is used in this dissertation comes from Helen Perlstein Pollard
(1972, 1980, 1983, 1993, 2008), whose work in the Lake Pátzcuaro Basin was the first to present a

27

testable ceramic chronology, and to apply multi-linear theory to the question of the emergence of
the Tarascan state. Pollard was the first to do extensive archaeological survey in the lake basin,
supplemented by excavation. Probably the most complete and extensive resource on the Tarascan
Empire is Pollard’s 1993 monograph, entitled Tariacuri’s Legacy. Pollard details all aspects of
Tarascan society in the lake basin, focusing on the emergence of the Tarascans and the eventual
state-level society they developed. Pollard looks to demystify the Tarascan state by discussing
aspects of the society ranging from the political and religious elite to the rural commoner class, all
the while bridging the research to include the ethnohistoric and ethnographic data.
Pollard’s research is in fact the most comprehensive in the sense that she has completed fieldwork
in many sites throughout the Pátzcuaro Lake Basin, and has been the predominant figure in the
archaeology of the basin and has published the most extensively on the subject. Included are her
studies on the urban characteristics of the Tarascan Empire, which focus on the Tarascan capital of
Tzintzuntzan (1972, 1980). Pollard also provides a detailed analysis of the economic and
agricultural potential of the Pátzcuaro Lake Basin, and provides a detailed ecological reconstruction
based on multiple lines of evidence (Gorenstein and Pollard 1983). Pollard has also research the
mortuary patterns of the regional elite in the basin, and discusses the archaeological evidence in
terms of the concept and visibility of social stratification and ritual that can be seen in the material
culture (1999).
Perhaps the most extensively researched issue by Pollard is the discussion of the emergence
of the Tarascan state and its conceptualization within the larger theoretical frameworks of statelevel societies in Mesoamerica (1980, 1993, 2008), (Gorenstein and Pollard, 1983). Pollard
analyzes the Tarascan state on several scales, within the Pátzcuaro Lake Basin (regional), within the
larger framework of West Mexico (macroregional), and finally within the Mesoamerican system
(Smith and Berdan 2003a, Smith and Berdan 2003b, Pollard 2005). Topics that Pollard includes in
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this type of research include the economic systems of the Tarascans, including the long-distance
and elite exchange networks, the long-term human- environment interactions in the Pátzcuaro Lake
Basin, and how power and political control is seen in the material culture and its pervasion in the
social stratification of Tarascan society. Pollard frames her research questions within the concept of
what Beekman describes as the dynamic network of linked economic, social, and political practices
of West Mexico, and then discusses these trends in terms of their place in the Mesoamerican world
system (Beekman 2008).
The Tarascans
The majority of archaeological research on the Tarascans has occurred over the last 40
years, flourishing in the florescence of the New Archaeology. However, research in the Lake
Pátzcuaro Basin, the core of the Tarascan Empire, has been under-researched and underfunded,
resulting in “patch-work” survey, or entirely site-focused research (Pollard 2011). Only until recently
has the cultural chronological sequence been confirmed, and a sufficient amount of data been
collected to create a model of the emergence of the Tarascan State (Pollard 2008) and attempt the
type of research this dissertation hopes to complete. The result of these 40 years of archaeological
research is a foundational understanding of the Tarascans, primarily during the Late Postclassic
period (A.D. 1350-1625) and the time during and after the Spanish conquest. This research also
focused on the elite of the Tarascan society who, during this period of florescence of the state,
were the most visible, both in ethnohistoric documents and the archaeological record. The
exception to this has been Pollard’s surveys in the southwest of the basin (Pollard 2000, 2001), the
goal of these having been focused on the settlement around an important ecological niche in the
basin, and providing evidence of the power relations held by both elites and non-elites in the
basin, including land tenure, resource management and allocation, and autonomy among the
lower classes within the state. Pollard’s work also ranges through the full occupation of these
29

areas, providing a longitudinal view of settlement in this part of the basin. Combined with
Pollard’s research at the Tarascan capital of Tzintzuntzan, there is ample information to provide a
holistic look at the Tarascan Empire during the last 500 years of its rule before conquest, thus
providing a definite “endpoint to the modeled transformation of the basin” (Pollard 2008:217).

The Prehispanic Lake Pátzcuaro Basin
The area that would one day become the extent of the Tarascan Empire in West Central
Mexico, was first settled by hunter-gatherers during the Paleo-Indian period, before 2500 B.C.
(Pollard 1993:6). This period, as well as the archaic period sheds little light on the populations that
settled in this area, as archaeological evidence is very limited, and what is known about this time
period is scant. We do know from recent findings, however, that maize had begun to be
domesticated during the Archaic, with the earliest evidence of maize pollen being found in the
Pátzcuaro basin at 1500 B.C. (Beekman 2009). However, sites associated with agriculture didn’t occur
until the Early Preclassic period, when after 2000 B.C. we find evidence from the Balsas Basin and
Lake Chapala of sedentary, agriculturally based villages, who were also the first ceramic producers
of the region (Pollard 1993:6). The early and middle Preclassic (also known as the Early and
Middle Formative) in Western Mexico was defined by the presence of shaft tombs, a type of burial
practice that also included burial offerings, sometimes of imported goods (Beekman 2009). The
middle and late Preclassic in the Bajio and Michoacan are marked by the florescence of the
Chupicuaro culture, with its indicative circular-style, earthen mound architecture found at the typesites for the period (Beekman 2009).
The Late Formative and early Classic period (300 B.C. – A.D 500), sees rapid population
growth, and evidence for social inequalities among the societies throughout West Mexico
(Beekman 2009). Beekman cites two new forms of architecture emerged during this period; the

30

ballcourt and the guachimonton, or ceremonial centers (Beekman 2009). Pollard sees the
ceremonial center as a major transformation for the West Mexican cultures, as it altered the layout
of settlement. Ceremonial centers, primarily within the Bajio region and during the Epiclassic,
showed evidence of Teotihuacan-style, talud-tablero architecture, and sites from this time show an
increase in long-distance trade items such as obsidian (1993:9-11). Rapid population growth
continued into the Epiclassic period (A.D. 500- 900), which had a dramatic effect on settlement
patterns in West Mexico. In many places, settlements become nucleated and defensive, and some
remain this way until the emergence of the Tarascan State (Pollard 1993:12).
Populations in the Lake Pátzcuaro basin began growing during the Epiclassic, and into the
Early Postclassic (A.D. 900) the basin is controlled by multiple, competing small-scale chiefdoms.
Population remains steadily rising, and specific regional cultures and traditions begin to emerge by
the Middle and Late Postclassic (A.D. 1200). According to Pollard, “The current archaeological
evidence suggests that during the Middle/Late/Postclassic, local elites competed for communities,
marking their relative success with polychrome pottery, metal goods, and patron deities. The
absence of regional authority and decision- making in the face of what appear to have been
increasing populations led to the formation of highly nucleated populations in some areas” (Pollard
1993:13).
This was the state of the Lake Pátzcuaro Basin just before the emergence of the state in
A.D. 1350. According to ethnohistoric documents, Tarascan history told of the warrior-leader
Tariacuri who united several independent polities to form the unified Tarascan state in the
Pátzcuaro basin (Pollard 1993:15). This state would flourish, even with the continued threat of
warfare from the Aztec Empire to the east, until Spanish conquest in A.D. 1525. The Tarascan
Empire, as it was leading up to the time of the Spanish conquest in the 16th century, was one of the
largest empires in Mesoamerica. Second in size only to their rivals, the Aztecs, the Tarascans ruled
31

an area that covers almost the entire current Mexican state of Michoacán, and extended to the north
beyond the Lerma River, to the south beyond the Balsas River, and to the north west into the current
Mexican state of Jalisco (Pollard 1993).
The core of this vast empire was located in the Lake Pátzcuaro Basin, with the urban center
of Tzintzuntzan as its capital. Tzintzuntzan was established as the capital in A.D. 1350, thus
becoming the religious, political, and economic center for the empire. Much of what researchers
know about the capital and those who resided there has been inferred from the ethnohistoric
documents, such as the Relación de Michoacán (RM). Data from these documents has caused
variation in population estimates, however, but Pollard has concluded that the population of
Tzintzuntzan had reached between 25,000 and 35,000 shortly before Spanish contact (1993:32).
Documented as covering an area of approximately 901 hectares (Stawski 2008), at the capital of
Tzintzuntzan is classified as having been a major urban center. Studies have combined the
ethnohistoric data and archaeological data to investigate the social complexity and the urban
characteristics of the capital (Pollard 1972, 1980, 1993; Stawski 2008, 2010). These studies have
shown that the capital showed high levels of residential zoning, where zones were divided by social
class such as lower elite, upper elite and commoner (Stawski 2008). Furthermore, we see a high
level of overlap when discussing functions of the state, such as political and administrative, and
economic, suggesting that there were no separate zones for these functions, but were all centralized
to the king’s residence (Stawski 2008). Only the main platform at Tzintzuntzan, which includes the
yacatas (pyramids), was shown to have had a centralized function and control over the state’s
religious functions (Pollard 1993). The data from these studies displays the capital of the Tarascan
state in an early stage of urban and social development, when compared to other Mesoamerican
urban centers, such as Tenochtitlan, or Teotihuacan. Suffice to say, researchers have estimated that
at the time of Spanish contact, the Tarascan state was just entering its florescence.
32

Research on Tzintzuntzan has been the most studied aspect of the Tarascans, primarily due
to the monumental architecture, the ethnohistoric documents recorded there during the Early
Historic period (RM, dictionaries) and the draw of studying the elite and royal lineage that once
existed there. However, given the scope of this research, attention must be paid to the larger
Tarascan population in the basin. To start, physical anthropological research of the current West
Mexican populations defined a demotype B, which includes the central and western portion of the
central plateau in West Mexico. This distinguishable population, called Purepecha, also speaks the
language of the same name, which is “recognized as a distinguishable linguistic isolate within
Mesoamerica…and for many basic features, it stands out from the patterns of other
Mesoamerican languages” (Pollard 1993: 15).
As was stated previously, these populations, according to archaeological evidence, seemed
to have established sedentary, lacustrine-based settlements during the Early Preclassic period (2500
B.C.). But what is primarily known of these populations comes from the Late Postclassic period
(A.D. 1350 – 1525), with help from the ethnohistoric records. The population in the lake basin was
located in various settlement forms, such as villages, hamlets and towns, with the capital being the
only “city”, although the term urban center is preferred. The lake basin, at the time of Spanish
contact, was densely populated, and estimated to hold a total population of between 60,750 and
105,000 (Pollard 1993:79). It is estimated, from documentary sources from the protohistoric, that
during that time there were n=91 settlements in the Lake Pátzcuaro Basin (Pollard 1993:77). Of
these n=91, the areas and estimated population size of the largest four are known, which are
Tzintzuntzan (see above), Ihuatzio, Pátzcuaro, and Erongarícuaro (Pollard 1993:77). When
discussing the administrative characteristics of the Tarascan state, there was a hierarchy which had
Tzintzuntzan as both the imperial capital as well as the regional administrative center. From there,
n=8 centers held the role of the third hierarchical center, which meant that they “were governed by
33

achaecha, or senores, who reported directly to the royal dynasty in Tzintzuntzan” (Pollard 1993:82).
Each of these would have had an ocambecha to organize tribute, labor for public works, and collect
census data (Pollard 1993:82). The remaining settlements fell under the 4th and 5th tier of
hierarchical administration, and are thought to have been run by local elites, or caciques, which
carried out the state’s political functions and answered to the senores of the closest level 3
administrative center.
Regarding the economic network in place at the time of Spanish contact, the primary
mechanism for exchange was through a system of three main markets in the basin; Tzintzuntzan,
Pareo, and Asajo (Pollard 1993:80). This information comes from the ethnohistoric record, and as
it stands, there is little to no archaeological evidence corroborating the placement or specific
attributes of these markets. Pollard uses ethnographic data to infer the nature of the markets in
connection to the local settlements in the basin. Based on the behavior of current populations, it is
thought that of these three markets, the Tzintzuntzan market probably occurred daily. Furthermore,
local markets would have serviced populations who couldn’t have easily accessed these markets
daily. Behavioral analyses during the 20th century “suggests that populations will generally
utilize marketplaces closet to them and that those populations midway between markets will attend
both” (Pollard 1993:80). This suggests a variety of economic networks in place to deal with any
possible demands that the settlements may have had. The transportation networks in the basin were
thought to have been closely associated with the economic network, for it was on the transportation
routes that “individuals traveled, information was transmitted, and with tumpline carriers, goods were
moved” (Pollard 1993:84). Transportation within the basin during the Protohistoric was limited to
canoe and foot travel. The transportation network during this period is vital in understanding the
connectedness of the settlements, and the characteristics of movement in the basin concerning
settlement location.
34

The Ethnohistory of Communities: The Early Historic Periods
The protohistoric and early Hispanic periods, approximately A.D. 1450 to 1520 in
Michoacán, offers a singularly unique opportunity for researchers to attempt to decipher the
environment and life ways of the Tarascan people. Several ethnohistoric texts survive that provides
excellent sources of data that aid in reconstructing the environmental, geographic, cultural, political
and economic features of the Lake Pátzcuaro Basin. As noted in the introductory chapter, they
include the Relacion de Michoacán (RM), the Carvajal Visitas (1524), the Suma de Visitas de
Pueblos of 1547-1550, the Relaciones Geograficas from 1579- 1581, the Infante documents of
1528, the Beamont (1932) and Seler (1908) maps, and Purepecha-language documents recently
translated (Roskamp 2010; Monzon, Roskamp, Warren 2009). Fortunately, all of these have been
heavily researched, translated and analyzed to aid in our understanding of the historic case of the
Tarascans. This section will present the data from these sources, as well as their subsequent
analysis by scholars and researchers, in order to understand the communities of the lake basin.
The primary means of ethnohistorical data analysis comes from the research by Gorenstein
and Pollard (1983). Their reconstruction of the population and environment of the Lake Pátzcuaro
Basin and Tarascan Empire combined both ethnohistoric data and archaeological data in an attempt
to redrodict what life was like in the basin pre-contact. For the purposes of this dissertation and
concerning communities, what is of most importance is Gorenstein and Pollard’s analysis of
settlement location, demography, and political and economic networks. The following is a summary
of their work, and is divided into two periods, the Protohistoric (A.D. 1450-1520) and the Early
Hispanic (A.D. 1520-1540) periods.

35

The Protohistoric (A.D. 1450-1540)
The only ethnohistoric document that covers the protohistoric period in the Lake Pátzcuaro
period is the Relacion de Michoacán (RM) (Gorenstein and Pollard 1983:55). According to
Gorenstein and Pollard, the RM “provided information on settlement names, settlement locations,
environmental features, and certain political, social, and economic data such as political
alliances, social classes, and market and tribute connection” (1983:55). And although an excellent
guide, the data from the RM in not entirely comprehensive, or reliable, and therefore,
Gorenstein and Pollard’s settlement data from this time period is a combination of the RM data,
archaeological field identification of protohistoric sites, and on extrapolation from 20th century
data and Early Hispanic data (Gorenstein and Pollard 1983:55). According to Gorenstein and
Pollard, the RM describes two definite time periods; “after the Spaniards came” and the “time before the
Conquest” (1983:55). It is assumed that “time before the Conquest” refers to the protohistoric, “unless
there was a clear distinction in the Relación of earlier time” (Gorenstein and Pollard 1983:55).
Derived from the 1976 field season, which was a non-collecting, observational project made
as part of the geographical and ethnographic surveys undertaken in 1976, Gorenstein and Pollard
“noted the locations of settlements, including structures, and routes that were identified as Protohistoric
by the presence of the surface artifacts that were…known to exist in the last period at Tzinztuntzan”
(1983:55). From these field observations, they located n=47 settlements as protohistoric. Other
settlements that were located and mapped were done so by matching Tarascan settlement names
and locations on the Beaumont and Seler maps with descriptions in the RM and other 16th century
documents. Therefore, according to Gorenstein and Pollard; “In summary, sixty-six Protohistoric
settlements were located on a map. The ethnohistoric sources provided eighty-seven names of
Protohistoric settlements. Forty-nine of the sixty-six located settlements could be assigned names
from the eighty-seven present. Sixteen located settlements had no names and thirty-eight of the
36

names could not be attached to located settlements.”
The early Hispanic settlement data was then used to “extrapolate the number of Protohistoric
settlements in the Lake Pátzcuaro Basin as well as the location of those Protohistoric settlements
not able to be located by either archaeological or ethnohistorical methods” (Gorenstein and Pollard
1983:59). Gorenstein and Pollard assumed that given the thirty year time span between the
Protohistoric and Early Hispanic, no major changes had taken place in the settlement pattern in the
lake basin (1983:59). So, in summary with the aid of the Early Hispanic data, n=91 settlements
were chosen for the Protohistoric period; with n=66 settlements known and n=25 settlements
inferred (Figure 6).
Gorenstein and Pollard then estimated settlement classes, which presented a range of the
population for each class. This was accomplished through the extrapolation of population size from
the 1940-1945 census data, the 1970-1977 census data, and the Early Hispanic data (Gorenstein and
Pollard 1983:62-63). From this, Gorenstein and Pollard reconstructed the protohistoric population
as seen in Table 4.

37

Figure 5 – The Protohistoric Settlements of the Lake Pátzcuaro Basin

Table 4 – Protohistoric Settlement Classes
Settlement Class
1
2
3
4
5

No. of Settlements
1
3
22
40
25

Mean Population
30,000
4,000
1,250
300
55

Range of Population
25,000 to 35,000
3,000 to 5,000
1,000 to 1,500
100 to 500
30 to 80

The protohistoric settlement data serves as an excellent starting point in an attempt to
reconstruct the 1,600 years leading up to Spanish Conquest. The retrodiction of communities back
through the temporal phases will rely heavily on archaeological data, though the ethnohistoric data
will aid in providing possible place names and locations. The ethnohistoric records reveals very
much about the main four communities, Tzintzuntzan in particular, and very little regarding the
38

smaller villages and hamlets. This is where one must rely heavily on archaeological data. What is
known, however, from the ethnohistory is that more than ninety communities existed within two
environmental zones within the basin; the lakeshore and the lower sierra slopes (Pollard 1993:84).
Furthermore, according to Pollard, “69 percent of the settlements and 74 percent of the population
was found in the lakeshore zone alone, including the capital city, Tzintzuntzan” (1993:84). This
reliance on lakeshore resources was a key component to the communities, and thus they are regarded
as having been a lacustrine-based society. This, though, is but a general fact in what was a very
complex settlement system. The functional, demographic and subsistence data for each individual
community must be analyzed in order to provide a more detailed look at Tarascan life in the basin,
as well as to provide a holistic view of the emergence and statehood of the Tarascan Empire.

The Lake Pátzcuaro Environment
The physical environment is a vital aspect to this analysis, where the climate, geology,
geography, geomorphology, and ecology all combine to describe the modern, historic and
prehistoric landscape in order to bridge the gap between ecology and anthropology/archaeology.
As discussed in the theoretical chapter of this dissertation, this historical ecology, or landscape
approach, is what will aid in the selection of variables for analysis. This section will introduce the
basic environmental, geographical and geological characteristics of the lake basin and the region,
and then explore the region’s ecology. Being one of the most heavily studied and researched lake
basins in Mexico, data concerning the geoarchaeology, geology, and paleoecology of the Lake
Pátzcuaro will aid in providing a comprehensive view of the current state of the lake basin
environment and ecology, as well as attempts at reconstructing past environments. This data
coupled with a landscape approach and the archaeological data will be used to reconstruct the
Prehispanic landscape for the modeling and analysis of the Prehispanic settlement system.

39

The Lake Pátzcuaro Basin is a relatively small lake basin, and is defined by the extent of its
hydrology. Past calculations have placed the area of the Lake Pátzcuaro basin at 929 km² (Pollard
1993, 2008), with Lake Pátzcuaro covering approximately 116 km², depending on the lake level at
a given time (Metcalfe et. al. 2007:273). Recently, through the use of Geographic Information
System (GIS) software, the extent of the basin was calculated through the georeferencing of a map
of the basin’s hydrology onto a rectified satellite image. The difference was somewhat drastic, and
at 803 km², quite smaller an area when compared to earlier estimates.
The lake basin is located in the landscape of the Central Mexican Altiplano, and lies within
the Michoacán-Guanajuato Volcanic field (Metcalfe et al. 2007: 273). Because of this volcanic
landscape, the lake basin terrain is steeply sloped and ranges in elevation from 2,030 meters at lake
level and to 3,200 meters in the alpine slopes. This elevation range covers six environmental zones
(Figure 1), which range in elevation from the 1) open water (lowest elevation), 2) tule-reed marsh,
3) lakeshore, 4) lower sierra slopes, 5) upper sierra slopes, and the 6) alpine (Pollard 1993:66-67).
It is because of this range in zones that, historically, the lake basin has had “considerable internal
variation in altitude, topography, rainfall, frost, soils and vegetation” (Gorenstein and Pollard
1983:4). Also, due to the volcanic landscape, the soils are volcanic andosols, the primary one
being charánda (which is the ethnosoil classification), or red earth (Gorenstein and Pollard
1983:136) (Toledo 1991). The charánda is red-brown clay, which occurs primarily below 2300
meters in elevation on the lower mountain slopes and the basin floor (Gorenstein and Pollard
1983:136).

40

Figure 6 – The Resource Zones of the Lake Pátzcuaro Basin

Climactically, the Lake Pátzcuaro Basin is located in a humid temperate area, as it is
located in the central chain of highland Mexico. It is affected by seasonal precipitation, with a
rainy season in the summer months and a dry season during the winter months. During the rainy
season, the moisture generally comes from the Gulf of Mexico and Caribbean, but moisture from
the Pacific also plays an important role given the western location of the lake (Metclafe et. al.
2007:273). The mean annual precipitation for the lake basin (taken between 1970 and 1986) is 901
mm, but has shown some decline since the 1921, when this recording started (Metcalfe et. al
2007:273). The average temperature varies between 12° and 16° Celsius, with the summer average
being 17° C, and the winter temperatures in the range of 3° and 10° C, with frost common 35 to 50
days in the year (Gorenstein and Pollard 1983:133). It must be stated that this dissertation assumes
41

similar climatic conditions, for both the modern period and prehispanically. New evidence from
Stahl et al. (2011), shows climatic and drought changes for the region through dendrochnological
dating, and these data have been taken into account when discussing the landscape and changes
through time. It is felt that given the similarities in flora and fauna and lake fluctuations between
the modern and prehistoric, it is appropriate to assume that, to an extent, conditions were similar.
At present, the environmental zones contain vegetation reminiscent of historic and
prehistoric periods, although they have been greatly reduced as human populations fluctuated. The
lowest vegetative zone in elevation, the tule-reed marsh, is “characterized by hydrophilous
vegetation dominated by tule and reeds” (Gorenstein and Pollard 1983:138). Agriculture and
secondary herbaceous plants and scrub dominate the lakeshore zone, and include grass shrubs,
shrub oak, and cacti (Gorenstein and Pollard 1983: 138) (Metclafe et al. 2007:273). The higher
elevations include deciduous stands of trees, and although mostly remnants of what existed,
contain primarily pine and oak. The next zone up in elevation, described as the upper slopes in the
basin between 2300 and 2800 meters, is dominated by pine and oak stands (Gorenstein and Pollard
1983:140) (Metcalfe et al. 2007:273). The highest zone, the alpine zone, occurs above 2800 meters
in elevation and is dominated by fir forests (Gorenstein and Pollard 1983:140-141.)
As stated before, many regard Lake Pátzcuaro as the most comprehensively studied lake in
Mexico, and perhaps even in Middle America (Bernal-Brooks, Rojas, Alcocer 2002:187). Yet,
even with decades of research, there are still many unknowns surrounding Lake Pátzcuaro. Lake
Pátzcuaro is a distinctive C-shape, currently containing eight islands and is a lake of interior
drainage, having no outlets and no important water inlets; it is fed by springs and temporary
streams during the rainy season (Torres et al. 1989:126). This fact has led to intense research
concerning the geomorphological and limnological characteristics of the lake and the surrounding
basin, most of which concern the relationship between the lake and climactic fluctuations as well as
42

human-induced change during both prehistoric and historic time periods. Both issues are
highlighted here, and play an exceedingly important role in how the environment and ecology of
the lake basin is modeled in an attempt to discuss past human-environmental relations. For
decades, limnological studies in Mexico have focused on Lake Pátzcuaro, in particular the cause
6

and effect of the lake level fluctuations it has had throughout its history . These fluctuations in the
lake level has created a very interesting case study for researchers, and has led to much debate as to
what is the root cause for the changes. The cause is typically discussed in reference to climactic
fluctuation, human-induced factors, or geological and tectonic events, and a combination of these is
cited as the causal variables for lake level change and/or changes in the lacustrine environment.
Recent paleolimnological research has attempted to uncover the chronological sequence of lake
level change for Lake Pátzcuaro, which includes providing exact levels through time in an attempt
to model these fluctuations. As one would imagine, the chronology of these lake level changes is
problematic, as gaps in the historical record, and larger gaps in the prehistoric record, leave large
periods of time overlooked. As O’Hara explain “Even in those instances where there is good
dating control, the time span between dates and the sampling strategy are such that small-scale and
abrupt events are often overlooked” (1993:51).
Although this may be the case, several researchers have proposed their own lake level
estimates through time, with some variation between. These studies can be broken into two
general groups; those whose lake level estimates and environmental reconstruction of the past cite
climactic and human induced variables as reason for fluctuations (O’Hara 1993, Metcalfe and
Davies 1997, Endfield and O’Hara 1999, O’Hara et. al 1993, and Metclafe et. al. 2007), and those
whose environmental and lake level reconstruction cites geologic processes, human induced
6

For a comprehensive synopsis of limnological studies in Mexico, please refer to Alcocer and
Bernal-Brooks 2010.
43

change and climactic fluctuation as the major variables (Fisher et. al 2003, Israde- Alcantara.et. al.
2005, Fisher 2005, 2007). The major theories proposed by these researchers use lake sediment
cores and terrestrial sediments that have been dated and calibrated through the use of C14 dating as
their primary source of data, as well as archaeological work, data from the ethnohistoric records,
and current limnological and ecological studies.
Both sides of the debate utilize the core taken by Watts and Bradbury (1982), or the
“Master Core”, as a baseline for their own research. The Watts and Bradbury core was taken in
1973, and was 1520 cm in length and dates back to the Pleistocene, or approximately 44,000 years
old at the base (1982:56) (Bradbury 2000). Since that initial core, others have been taken and
reported on in various parts of the lake and adjacent area. Fisher (2000), Israde-Alcantara et.al.
(2005) and Metcalfe and Davies (2007a, 2007b), and O’Hara (2007) all report on cores, trenches,
agricultural wells, and exposed cross sections in an attempt to report on the sedimentation record,
its chronology, and explanations for the deposition of certain sediments during certain time
periods. O’Hara, Davies, and Metcalfe argue that during the late Holocene that increased
sediments are a result of climactic change coupled with human impact in the basin, primarily in
terms of catchment erosion both during Prehispanic and Hispanic time periods (2007:293). The
work from Fisher, Pollard, Israde-Alcantra and Garduno-Monroy (2005) posit that this same
sediment layer claimed to be erosional in nature by Metcalfe, Davies and O’Hara, is in fact a
sediment layer that was caused by two specific geological events, the first being the collapse and
associated landslide of the El Estribo Volcano, the second being a series of tectonic uplifting,
which has distorted the sedimentation record (Israde-Alcantara et.al 2005:35). These arguments
paint very different pictures of a Prehispanic landscape, making its reconstruction and the
Prehispanic environment problematic.
Regardless of these debates, the two sides seem to be in agreement as to the approximate
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lake levels through time, although they don’t necessarily always overlap in their periods of
reconstruction. Chapter 4 which focuses on the Landscape Reconstruction, will synthesize the
work from these researchers, and provide a more comprehensive view of their research, as well as
provide a table that shows the lake levels through time. As one would imagine, the fluctuations of
the lake levels are vital in reconstructing the environment and landscape of the past. Directly
related to the lake level are also the resource zones that support local flora and fauna, and are the
same resource zones that are accessed, utilized and controlled by human populations as the lake
basin was settled and inhabited. As most causal relationships in nature, one can posit that as the
lake level changes, so do the extent and existence of these resource zones, including the tule-reed
marsh, the lakeshore, and land on the lower sierra slopes (Gorenstein and Pollard 1983). This
shifting limnetic (free of vegetation) and shallow litoral (submersed vegetation) zones (Chavez et.
al. 2002:172), affected, and continues to affect, the access to economic resources and subsistence
catchment areas for the populations in the lake basin. From a more historical ecology stance, these
shifts affected settlement location, trade and travel routes, property boundaries, agricultural land,
and technological systems such as irrigation and terraces. Suffice to say, these lake level estimates
through time will drastically affect the modeling of the past settlement systems.
Summary
This chapter introduced the Lake Pátzcuaro Basin, its culture history, its history of research,
and its environmental and climatic characteristics. Informed mostly by archaeological research, the
historical and Prehispanic contexts for the LPB provide the necessary backdrop for the future
chapters, which work on a temporal scale of 1,600 years until the time of Spanish conquest. And
although the immediate chapters following this look at a specific area of the lake basin, the
southwest portion, future chapters will provide a regional and macroregional scale of

45

analysis, where the information found in this chapter will be useful for reference.
Also provided were the basic information regarding the environments, climates, and
physical characteristics of the lake basin and its landscape. Chapter 4 will look to expand on this,
as it reconstructs the Prehispanic landscape and environment in order to systematically analyze the
settlement in the basin. Of significance is the information surrounding the lake itself, such as being
a lake of interior drainage, its dynamic nature, and how it affects the basin as a whole.

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CHAPTER 3: COMMUNITIES OF THE LAKE PÁTZCUARO BASIN
In order to perform a settlement systems analysis, two main variables must be discussed and
analyzed that will be essential in spatial analysis that gives insight into the Prehispanic settlement.
The first, which concerns this chapter, is the reconstruction of the Prehispanic communities of the
southwest portion of the LPB, ranging in time from 100 B.C. to the Spanish Conquest, ~ A.D. 1525.
This reconstruction is informed by archaeological, ecological, ethnohistoric and ethnographic data,
and utilizes demographic analysis to provide population estimates for the communities. Therefore,
this chapter covers both the theory and methodology concerning the communities of the Prehispanic
Lake Pátzcuaro Basin. First, a theoretical discussion is undertaken that allows for a better
understanding of variable selection while also being explicit and discussing scalar limitations, both
spatial and temporal. Second, the methodology for the analysis of communities is undertaken,
which involves the mapping, reconstruction, and functional and demographic analysis of these
communities through the basin’s 1,600 year time sequence.

Theory
To understand a research methodology, the analysis, and the interpretations of a study, one
must first understand its theoretical underpinnings. The theoretical framework from which research
must manifest is crucial in shaping the methodology, the selection of the variables to be assessed,
and dictates the manner in which the results of the analysis are interpreted.
Since the inception of processual research in anthropological archaeology, Mesoamerica has
been a testing ground for social theory, primarily concerning the paradigms of state-emergence,
both primary and secondary, and human adaptation. Processualism has allowed for a more
scientific approach to archaeology, where hypotheses are tested and rigorous statistical tests are
employed. Along with these additions to the archaeologist’s toolkit came the full- coverage survey, a

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technique made famous in Mesoamerica by its use in the Basin of Mexico survey (Sanders,
Parsons, Santley 1979) and the Oaxaca Valley surveys (Marcus and Flannery 1996). As discussed
in the Introduction, the data used in this dissertation derives from full- coverage, intensive survey of
the Lake Pátzcuaro Basin, which like the Basin of Mexico and Oaxaca Valley, is a highland area in
Mexico that is ideally situated for testing social theory of state emergence and human-environment
interaction.
Therefore, this section will outline and discuss the theoretical frameworks that this
dissertation will employ in order to analyze the nearly 2,000 year history of human settlement and
adaptation in the Lake Pátzcuaro Basin. First, the settlement system theory that this dissertation
uses as the primary means for assessing human settlement and human adaptation in a highland,
lacustrine ecosystem is discussed. Included is a discussion of the primary unit of analysis for the
settlement system model, the community. From there, the discussion will shift to focusing on the
larger scalar units of the region and the macroregion.

Settlement Systems
Typically, settlement studies can be discussed in the same vein as full coverage survey,
but the two aren’t mutually exclusive. Usually, full-coverage survey on the regional level leads to
an analysis of the settlement of that region. Sometimes this may be a settlement pattern study;
other times the more complex settlement systems study. The difference, as noted by
Kowalewski, is that “settlement patterns are the arrays formed by sets of interacting, interdependent
local groups of people,” whereas settlement systems are the “processes behind the patterns”
(Kowalewski 2008: 226). This is an important distinction to make, as this dissertation is an example
of a settlement systems analysis. Furthermore, full-coverage surveys are also usually carried out and
analyzed on a regional scale, although once again this isn’t always the case. What is the case, though,

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is that scale is always an important factor when discussing full-coverage survey as well as
settlement studies. The scale of the study defines the scope of the research questions, dictates the
appropriate variables to be assessed, and heavily influences the methodology in which one analyzes
the variables and performs quantitative tests. As was stated in the Introduction, this dissertation will
be analyzing the Tarascan settlement system on a regional scale. Because the smallest unit of
analysis is the survey site, one may analyze the data at different scales, and yet the main research
questions and the analysis will exist at the regional scale. For example, within the survey site, one
may be able to determine function of that site based on the artifact assemblage for a certain time
period. Then, several survey sites that cluster together may constitute a larger unit, and the
individual functions of each site can be projected onto the larger unit, which may be a community.
From there, the community is analyzed in comparison and in accordance with other
communities, thus constituting a region. From the regional scale, one region may be compared and
combined with a neighboring region in order to perform an analysis on the macroregional scale.
This process is further explained by Drennan and Peterson (2005), who argue that in order to
understand the settlement system of a region, one must first understand what constitutes a site, and
how these units form a community, and finally how the communities interact to form a regional
pattern of social interaction and behavior (2005:6). In order to legitimize the use of this scalar
sequence, the following sections will discuss and define these units in order to better explain their
role on the settlement system analysis. By making the underlying theoretical assumptions explicit,
we will be able to move forward in bridging the archaeological record to these spatial units to
define past human behavior. First, the unit “community” is visited, with a discussion of its use as a
unit of analysis and how it is defined and used in this dissertation. The second is the region, which
is the primary focus of this analysis and the research questions concerning the overall settlement
within the basin. The third is the macroregion, another scalar paradigm that will affect the larger

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comparative framework of the analysis.
Communities
In the introductory chapter, the term “community” was defined and partially explained as to its
use in the analysis. However, the term “community” can and is interchanged with the word
“settlement”, which is cause for confusion. This discussion looks to quell any assumptions in the
term “community”, and will define it as a meaningful unit of analysis. The following sections will
do the following; differentiate between settlement and community; define the term
“community”; examine the previous uses of community in anthropological and archaeological
research; discuss the theoretical impetus behind this usage; and finally link the theory with the
methodology and discuss how community will be used in this dissertation.
Gorenstein and Pollard (1983) were the first to provide a detailed study of the Tarascan
Cultural system through a settlement analysis. As Pollard and Gorenstein point out, “The institutions
of complex societies are expressed materially through the settlement system” (1983:3) and
therefore a logical place to start was the location of settlements and estimation of population.
Pollard and Gorenstein (1983) relied on the ethnohistoric data, field identification, and
archaeological data to locate and describe the protohistoric settlements from which they derived
settlement classes, which are “a classification of non-overlapping categories of population range”
(1983:3). The settlement classes, ranging from smallest estimated population to largest, are as
follows: class 5 – hamlet, class 4 – village, class 3 – town, class 2 – center, class 1 – capital (Pollard
1993: 78). It is expressed by Pollard and Gorenstein that these settlements were the larger units
identified by the Spanish in the protohistoric and documented in the ethnohistoric data, and while
these larger settlements could be placed into a specific class (i.e. hamlet, village, town), most of the
smaller communities could not (1983:78). Pollard and Gorenstein seem to make a subtle
distinction, but yet are not explicit about the terms “community” and “settlement”. The difference is
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that a settlement is a spatial unit expresses through the materiality of the archaeological record,
whereas a community is a behavioral unit of analysis. The following sources will aid in clarifying the
term “community” and how it may be used in archaeological research.
Early studies and publications of “community” derived in sociology and anthropology from
such seminal researchers as Hollingshead (1948) and Ahrensberg (1961). The definitions of
“community” from these works are the basis for how the term was used in the New Archaeology in the
1960’s, 1970’s and 1980’s (i.e. Flannery 1976). There are two fundamental definitions;
“communities” as an ideal definition, where they exist as “a form of group solidarity, cohesion, and
action around common and diverse interests”, and also communities in the real, as “a geographic area
with spatial limits” (Hollingshead 1948:145). Hollingshead also refers to a third definition, with
community as “a socio-geographic structure” which combines the ideas in the first and second
definitions (1948:145). It is within this third definition that archaeology and ethnography find
relevance within anthropological research. Added to this definition was the concept of a
community being composed of those who are in face-to-face association (Kolb and Snead 1997:
611). Drennan and Peterson (2005) follow up on this definition by using face-to-face
interaction to define a community. However, these are very broad definitions, and it is no wonder
why early uses of the term “community” in archaeological work lacked substantive middle-range
theory. Kolb and Snead, who describe these weaknesses, expand on these definitions by identifying
three “irreducible elements of human communities” (1997:611). They are, 1.) social reproduction,
where the community is a node of social interaction, 2.) subsistence production is a central element
to community life, and 3.) communities have the aspect of self- identification and social recognition
by its members. These three elements, as well as the earlier definitions, give a better understanding
of what a community is, but in order to understand its role in archaeological research, we must look
closer at the underlying concepts behind the term “community” and its use.
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The work from Kowalewski (2003), and Peterson and Drennan (2005) are good starting
points for such a discussion. Kowalewski (2003) in particular provides a sound explanation
with how anthropologists should use the term community. His work in Mesoamerica “traces change
in local formations in Oaxaca, Mexico, over 3,500 years, from early sedentary villages through
urbanism, centralized and decentralized states, Colonialism, and capitalist expansion” (2003:4).
This “long view” of these social groupings, which he refers to as communities, is essential in order to
create an explicit link between the model of a community from the ethnohistoric and
ethnographic data and their material correlates in the archaeological record. In order to do this,
Kowalewski explicitly states the basic characteristics of a community. First, local groupings,
such as communities, were “always members of large-scale formations and changed in their
composition and functions in response both to higher levels of integration (regional systems,
states) and to pressure from households and other constituent units (Kowalewski 2003:4).
Thus, communities are not a fixed, basal unit of society, were never autonomous, and were not
an early evolutionary stage (Kowalewski 2003:4). This definition finds itself in opposition to
the traditional way that community has been defined in settlement pattern studies. Yaeger and
Canuto (2000) point out that in these studies, communities are “often conceived as settlement types that
fulfill specific functions within a larger social system” (2000:4). In fact, this static view is one of
the main hindrances in the analysis of community. Instead, we should view them as operating
as dynamic, open systems, even when autonomous, and are a hub for interaction on a regional
scale.
The question now becomes, how does one isolate communities archaeologically, and
furthermore, how is variation and change in communal composition through time and space
deciphered in the archaeological record? The literature emphasizes several key theoretical issues in
order to deal with such an issue. First, as stated previously, we must view community as a
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behavioral unit that cannot be defined or arrived at through archaeological definitions. The
problem with these archaeological-driven definitions is, according to Yaeger and Canuto, “because
they are born of a keen awareness of the limits of the material record, they often represent methods for
operational recognition rather than analytical theories” (2000:5). This means that the “community is
not a spatial cluster of material remains to be observed, but rather a social process to be inferred”
(Yaeger and Canuto 2000:9). In this case, it is the theoretical paradigm that best informs
archaeologist how to proceed in the analyses of communities. Yaeger and Canuto have defined
what they feel is the most useful definition of community, deriving from a modified
interactionalist paradigm informed by practice theory; “it is an ever- emergent social institution that
generates and is generated by supra-household interactions that are structured and synchronized
by a set of places within a particular span of time” (2000:5).
The key term here, I believe, that makes the community an important, and accessible, unit
for analysis is interaction. The work from Kowalewski (2003) points this out, as communities are
described as open systems and hubs of interaction. Within the term interaction lays the central
operating theory that makes community an applicable unit for archaeological research.
Communities, as described by Peterson and Drennan are “constituted in the patterned interactions
between households, which are central to everyday life”, interactions which form a matrices that
produce the forces for social change (2005:5). Yaeger and Canuto also see interactions as
the vital component to identifying communities, but express it in terms of practice theory, where
individual practice is the locus “for the production of the patterned processes that create and recreate
society” (2000:3). Thus, interactions within and between communities are expected to reflect the
broader patterns in spatial distributions of residences (Peterson and Drennan 2005:6). By defining
community and the subsequent interactions that create, maintain and change them, one may now be
able to see this unit expressed materially in the archaeological record. Furthermore, because of the
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behavioral definition of the term “community” (rather than the ideational definition), one can
delineate and analyze the material traces of a community in terms of the open-system of interaction
that reflects human behavior, and is contingent upon human agency for its creation and continued
existence (Yaeger and Canuto 2000:5). Thus, by analyzing the communities in the Lake Pátzcuaro
Basin through time and space, an assessment can be made as to the influencing factors for
settlement within a larger system of regional interaction.
Having defined what a community is, its explicit assumptions, and the tenets for its use in
archaeological research, we must now look to explain this unit of analysis in terms of scale.
Peterson and Drennan (2005) offer an insight into what they believe is the appropriate scalar
component to applying the unit of community. By defining community as a social unit where
face-to-face interaction takes place, they are “entities within which variations in the nature of
households and in household activities and interactions can be investigated” (2005:6). Therefore,
household archaeology falls into this type of unit, where interactions between these residences
can create a community. Yet Peterson and Drennan do not stop at this the smallest scale:
“At the same time, small-scale communities become the units of analysis at a larger scale, where
study can focus on variations in the nature of communities and the patterns of interactions between
them. These patterns may permit the identification of yet larger social communities- entities to
which we are accustomed to applying terms such as “district”, “polity”, and others, but which exist,
in fact, like smaller communities, in the patterns of interaction between smaller units” (2005:6).
This patterned interaction can be witnessed up to the regional scale of settlement. What
Peterson and Drennan look to accomplish by providing us with this lengthy scalar definition is to
explain that there is no binding spatial scale that restricts the community, but instead communities
are defined in their patterns of intensity across space (2005:6).

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This patterned interaction, though, although seemingly well-conceived and essential in
defining community, needs to be discussed further in order to be able to identify the material traces
of community and attempt to define them spatially. We now move from the theoretical to the
methodological. Peterson and Drennan once again (2005, 2010) define the way in which they
proceed to treat communities in archaeological survey. By defining a “community” in the way
they have, Peterson and Drennan (2005) make a logical assessment of how interactions define the
spatial configuration of a community. Based on theory from household archaeology,
the authors argue that based on economic practicality there is a push and pull of forces that aid in
the settlement of households. These may be the locating near agricultural fields or other
landscapes of subsistence, or near other households to ensure trade, economic cooperation and
labor demands. The settlement, predicated upon economic needs, form a broad range of activities
that represent social interaction (Peterson and Drennan 2005:7). Furthermore, a “local community
is formed when this range of social interactions is intensely concentrated within single, welldefined groups of households that interact only much less intensely with households outside the
group” (Peterson and Drennan 2005:7). Thus, interactions such as these encourage households to
locate within close proximity to each other, thereby creating a recognizable spatial cluster of the
material remains of the community. Therefore, the methodology that Peterson and Drennan (2005,
2010) use to locate and isolate communities is to treat clusters of artifacts from archaeological
survey, not as sites, but as the unit themselves.
There are studies that go beyond this artifact delineated approach and attempt to define a
broader scale for “communities”. Kolb and Snead express their views on this, especially
concerning sedentary agricultural communities. They state that these groups “create…physical
“maps” reflecting social and economic relationships through direct modification of the physical
landscape and the construction of architectural sites (houses, agricultural fields, burial mounds,
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etc.)” (1997:611). The result is a spatial expression of the community that not only includes
artifacts, but also ecofacts (terraces, irrigation canals) expressed in the landscape. This creates a
“sociogeographic” unit that reverts to Hollingshead’s original definition of “community” (1948).
This ability to define the community as part of the landscape is essential to this analysis, and fits
well with the landscape approach that will be discussed later. It is this methodology that, when
coupled with the evidence from the ethnohistoric data will be used to map and analyze
communities in the Lake Pátzcuaro Basin.
The Region
The Introductory chapter discusses what this analysis defines as a region, and how a
regional analysis is legitimized for the Pátzcuaro Lake Basin by using percentage of area covered
by archaeological survey. However, now that this spatial area can be defined as a region, we must
explore the underlying theoretical assumptions of performing a settlement system analysis at this
level.
Like a community, a region can be conceptualized at many different scales. The broader
definition of a region is an area or space where “meaningful relationships can be defined between
past human behavior, the material signatures people left behind, and/or the varied and dynamic
physical and social contexts in which human activity occurred” (Kantner 2009:41). This definition
of a region is a behavioral one, and is defined, like a community, by the patterns of interactions and
behavior that exist within it. Although I have defined the Lake Pátzcuaro Basin as a physiographic
region, which is defined by the extent of the hydrography of the lake, the region may also be
analyzed as a behavioral unit, which “contains multiple communities and one or more politically
autonomous societies” (Kowalewski 2008:226). And although both physiographic and behavioral
regions are assumed to be open systems with fuzzy boundaries, it is easier to use a physiographic
definition when discussing the Lake Pátzcuaro Basin due the ease of setting the boundary.
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However, this research does not assume that this geographic boundary is also the same type of
criterion that people in the past would have used to define their landscape. This analysis
acknowledges fluid and changing boundaries and relationships between humans as well as between
humans and their environment in past societies (Kantner 2008:42).
With the region defined, the concept of regional analysis, or regional archaeology, must be
addressed which will help frame the research questions and the methodology for this analysis.
Regional archaeology, which encompasses “diverse spatial analytical methods available from a
variety of disciplines as well as developed by archaeologists themselves”, is “concerned with
spatial relationships among human entities and between them and the nonhuman physical world”
(Kantner 2008: 43). Once again, this definition finds itself conflated with settlement pattern
analysis, a distinction worth stating again. Kantner states that “Regional archaeology tends to be
more interested in spatial relationships among a diversity of human and environmental phenomena,
whereas settlement pattern analysis tends to concentrate more narrowly on quantifiable spatial
relationships among material remains” (2008:43). This definition of regional analysis is important,
because it correlates with the goals of a settlement systems analysis, and is well-situated to utilize
landscape archaeology as a basis for analysis on such a scale.
The Macroregion
The scalar issues of this analysis, having dealt with the communities and the region, must
now focus on the macroregion in order to apply the research from this regional analysis to a larger
scale. The macroregional paradigm is one that pervades the settlement study literature and
questions the empirical realities that have been constructed from years of regional survey data and
analysis. A macroregion is defined as two or more contiguous regions, each of which is definable
using conventional methods of regional analysis (Balkansky 2006:54). Further, it is argued that a
macroregion is the minimal unit needed to study early civilizations and are
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measured in the thousands of square kilometers (Balkansky 2006: 54). There have been many
models and theoretical frameworks that claimed to have as their goal that of explaining
macroregional variation between civilizations, and unfortunately many have fallen short of this
goal. Much of the research on this macroregional scale has derived from research in Mesoamerica,
where the literature is thick in macroregional studies. This section will briefly trace the history of
macroregional studies, in hopes that these studies will better define a macroregional analysis in
archaeology and provide a framework for the analysis in this dissertation.
Balkansky (2006) provides a very comprehensive discussion of the intellectual lineage of
macroregional studies in Mesoamerican archaeology. Therefore, only a few of these past studies
will be referenced in order to better understand the applicability of a macroregional analysis on
archaeology. Sanders’ work on the “central Mexican symbiotic regions” is perhaps the first to look at
a macroregional analysis in order to better understand change and variation cross- culturally
(Sanders 1956). Symbiosis arose from diversity in the Mesoamerican physical environment, where
this diversity created economic specialization at smaller spatial scales. Concerning cultural change,
Sanders and Price argue, “the implication of the concept of economic symbiosis is that when areas
were in constant historic contact, such contacts were a primary force in the enrichment of local
cultural traditions” (Sanders and Price 1968:190). However, as Balkansky states, “the essential – and
fatal- limitation of this analytical approach is demarcating the bounds of Mesoamerica’s several
symbiotic regions as closed systems” (2006:56). A second theory, regularly used on a
macroregional scale is that of world systems theory. This theory has also fallen under harsh
criticism in its use to explain macroregional variation cross-culturally. According to critics, the
theory fails to explain all aspects of culture change, instead relying primarily on top-down
processes of change as the core affects the periphery. Critics instead claim that, “the periphery has
a much larger effect on structuring their exchange relations, and their economic organizations are
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more variable, than the world system allows” (Balkansky 2006:57). For the purposes of this
dissertation, as well as other prehistoric analyses, it is not apparent whether the capitalist-based
core-periphery exploitative relationships that structure the theory are applicable to prehistoric
cases (Balkansky 2006).
This leaves the application of macroregional theory in archaeology at an impasse.
Kowalewski (1990) describes the problem; “none of our current explanations of how complex
societies evolved yet comprehends the prehistoric cases described by the regional archaeological
surveys. Most such explanations are not equipped to reflect the behaviorally significant variation
within a region at one time, between regions, or is one of the several regions over the long run” (1990:52).
With this in mind, Balkansky discusses the theory of concordant change when explaining cultural
change at the macroregional scale. Concordant change “is meant to describe an empirical reality in
which settlement shifts over multiple regions occur simultaneously (and are linked historically) but
with differing local outcomes” (2006:76). Therefore, studies looking to incorporate a macroregional
scale of analysis to explain cultural change must understand the operating forces of change at all
scalar levels of analysis (Balkansky 2006). This requires analysis and the understanding of the
interactions that occur within and between regions, namely by analyzing interaction at the
community scale. However, in order for this to happen, in order for the settlement system analysis
to be carried out on multiple scales leading to the macroregional scale, the one theoretical factor
that must be in place throughout the analysis is the analysis of these units as open systems. Once
the site, community, and regions are viewed as open systems with fuzzy boundaries, the analysis
can allow for interaction to be accessible to a settlement systems analysis on multiple scales.
Therefore, I believe that through the scalar unit and theoretical underpinnings of community
change and interactions, this dissertation will be able to extrapolate patterned interaction and change
of communities on the regional scale to those interactions and changes occurring in adjacent
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regions, thus allowing for a macroregional model to be developed and analyzed. It is the goal of
this dissertation to use this framework to compare the Zacapu and Lake Pátzcuaro Basins, thus
providing a macroregional analysis in the vein of concordant change. To be more explicit, the
comparison between these two basins does not claim to be a complete macroregional analysis, but
merely a settlement analysis between two regions that will hopefully aid in the construction of a
larger, testable model for macroregional, concordant change.
Discussion
This dissertation looks to several excellent settlement studies done in order to construct the
theoretical framework that will guide the methodology, analysis and interpretations of this
dissertation. The work from Balkansky et al. (2000) in the Mixteca Alta region near the Oaxaca
Valley, Mexico provides a regional analysis of a settlement system, spanning approximately 3,000
years (2000: 365). This survey, which was full-coverage and intensive in nature, sought to generate
specific regional data sets, such as site size over time, surface architecture and site plans, artifact
distributions, modern place names, trials and boundaries, ancient and modern agricultural features
and physical environment data (Balkansky et al. 2000:368). From these data sets, the project
attempted to establish a settlement pattern across phases, thus explaining phase transitions and
creating a connection to the nearby Oaxaca Valley surveys (2000: 368). The survey and the
subsequent data sets did pose limitations, as the survey was relatively small- scale and often noncontiguous, which according to Balkansky placed interpretive limitations on the results (2000:366).
Furthermore, the survey battled soil erosion, which limited site preservation and site visibility, thus
affecting artifact collection and site recording (2000:369). But because the study benefited from a
carefully planned research strategy, the data set accommodated research questions and variables
ranging from archaeological to ecological and from a regional to macroregional scale.

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Like the research from Balkansky et al. (2000), this dissertation is dynamic in its research
scope, and accommodates scales from the region to the macroregion. It is also similar in its goal of
analyzing spatial units over several periods of time, thus creating a longitudinal view of settlement.
And, like the Mixtec Alta survey, the surveys in the Lake Pátzcuaro Basin also had limitations in
the field that will hinder the analysis and interpretations. In chapter one of this dissertation, the
Lake Pátzcuaro Basin is defined as a region, and a case is made for why it can be analyzed as one.
The surveys in the basin have been non-contiguous, and the erosion and site visibility a factor in
every survey. Yet, like the Mixtec Alta surveys, because they were full- coverage and intensive in
nature, the data from the Lake Pátzcuaro basin surveys have the ability to answer research questions
on a regional and macroregional scale. Like the Mixtec Alta surveys abutting and being coupled
with the Oaxaca Valley surveys, it is also the case that, with the right research design, the Lake
Pátzcuaro surveys may be coupled with the Zacapu Research (Michelet 1988, Migeon 2003) to
form a macroregional unit of analysis for a settlement system study. It is this factor, coupled with
the ability to go beyond the collection of only archaeological data and to include ecological and
anthropological data, which makes Balkansky et al.’s (2000) study a good template for this
dissertation. The ability to look at the social and natural environment through the artifacts and
ecofacts across time allows the researcher to accommodate variables that directly assess the history
of interactions between the human component and the environment. By accommodating several
types of data from multiple lines of evidence (i.e. ethnohistoric, ecological, and archaeological) the
analysis and subsequent interpretation is able to comment on the nature of human adaptation in a
region, in this case exemplified by the settlement system of a society.

Method
This section will deal with the identification and mapping of communities in the Lake
Pátzcuaro Basin, which is an essential stage in the methods of this dissertation. The goal is to be
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able to isolate the communities in space and time, thus mapping them through the 1,600 year
temporal scope of this analysis. Once the communities are spatially isolable for each phase of
occupation, we can begin to analyze the material remains present within the community, which
provides two significant pieces of data that are necessary for the settlement system analysis and
testing of the settlement model presented in Chapter 1. The first is to be able to provide accurate
population estimates for each phase based on community area and artifact density, and also
informed by ethnohistoric sources and ethnographic research. Crucial aspects of demographic
reconstruction are the rank-size graphs which were completed for each phase to better analyze the
distribution of population among the communities for the survey area. Adopted from geography,
the rank-size graph is used to characterize the “evenness or unevenness of population distribution across
the settlements in a region” (Drennan and Peterson, 2004:533). The rank-size rule suggests “that we
might expect the rank 2 settlement to be half as large as the rank 1 settlement; the rank 3 settlement to be
one-third as large as the rank 1 settlement and so on”, producing a straight-line pattern (Drennan and
Peterson, 2004: 533). However, this comparison to what the previous sentence describes as a lognormal distribution isn’t entirely useful for Prehispanic populations. Drennan and Petterson make
the case that “direct comparison on observed rank-size curves to each other is of greater utility in
identifying chronological change and inter-regional variation in settlement dynamics as reflected in
rank-size patterns” (2004:533). Therefore, the rank-size graphs produced for each phase in this
analysis are compared to one another to better understand the dynamic changes and characteristics
of the population through time for the southwest survey area in the LPB.
A secondary goal to be used from this data is to attempt to provide a longitudinal view of
the use of space through artifact function. Through the employment of these techniques, we may
better understand the material correlates between a population, or community, and their economies.
Furthermore, by understanding the use of space of a community and the economic functions of the
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different populations in the society, one can not only analyze change over time regarding the
economy, but also social complexity and social class.

Reconstructing Prehispanic Communities
The essential variable of this analysis is the community, as it has been defined previously.
This essential unit is the basis for the demographic and spatial analysis to follow. Therefore, all
attempts are made to be explicit about the methods for identifying and delineating the community.
The following section moves away from theory and into practice, utilizing new technologies and
systems in order to best identify the communities of the southwest LPB. Once again, to reiterate,
the data that is the source for this analysis derives from two combined field seasons, the Urichu,
Pareo and Jaracuaro Projects from 1990 and 1996, and the Erongarícuaro Project from 2001. First,
the identification of communities is discussed, followed by the delineation of communities, the
analysis of the communities, and ultimately the demographic reconstruction of the communities.

Identifying Communities
The first step in reconstructing the communities of the southwest LPB was the creation of a
geographic as well as relational database for the archaeology. Geographically, the survey data from
Pollard’s surveys (Tzintzuntzan, Urichu, Pareo, Jaracuaro, and Erongarícuaro) were all converted into
digital form using the database program Filemaker. The product is a relational database that allows
for dynamic searches and analysis to be performed on a large host of archaeological data. The
second step was to digitize the survey maps and enter the survey data into a Geographic Information
System. The program used to accomplish this task was ESRI’s ArcInfo, with ArcMap being the mostused tool for analysis and database creation. The result was a dynamic mapping database, with each
survey site located using real world geographic coordinates (UTM), and containing an attribute
table that lists all artifacts and ecofacts found at that site. This geodatabase effectively allows for
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analysis by site, by phase, by artifact category (i.e. ceramic, lithic, obsidian, pipe, etc.), thus
allowing for basic spatial patterning of archaeological data. Furthermore, the use of high resolution
satellite imagery and digital elevation models allows for further analysis of the lake basin in
conjunction with the archaeological data. It is through these combined sources, as well as the use of
ethnohistoric, geographical and ethnographic data, that the communities were then analyzed.
The first step of the identification process was to view the survey site data by representing
the artifacts in a dot density map. Each artifact type, as recorded in the Project Catalog, was used
(ceramic, obsidian, basalt, figurine, pipe, recortado), and each single artifact was represented by
a dot on the landscape. This effectively produced a dot density map of every artifact found in
the survey area. Then, the map was further narrowed by phase, eliminating all artifacts except
for those in the specific phase defined by the user. This was done through an exclusion tool in
the symbology section for the survey sites in GIS. In order to be able to display the data by
phase, each survey site was given qualitative data for the phases and periods they were
occupied (i.e. Tariacuri, Loma Alta, Early Postclassic). Several sites, though, contained
artifacts from multiple phases, and therefore querying these results for a single phase soon
became difficult. To combat this, phases for the survey sites were then entered into the GIS
using a binary system, where the presence/absence of a phase was entered in 1’s and 0’s, 1’s being
presence, 0’s being absence. Thus, to view only sites that contained Late Urichu phase material, the
exclusion would be in the form of the following if/then statement “exclude all sites where Late
Urichu=0.” Once this was done, the GIS then displays a dot density map of artifacts that represent
only one phase. An example of this is Figure 8, which shows the Tariacuri Phase artifact
densities.

64

Figure 7 – Tariacuri Phase Artifact Densities and Clusters

65

This method was carried out for each of the phases beginning with the earliest; Loma Alta,
Jaracuaro, Lupe/La Joya, Early Urichu, Late Urichu, Tariacuri. The result was a map of artifact
clusters on the landscape. The next step was to then identify each community by phase. In the
literature, much of which has been discussed earlier, much is said about the subjective nature of
identifying and delineating communities. And not to be overly repetitive, but some of this will be
revisited in order to attempt to remain as explicit and objective in this process as possible. Some of
the best and most scholarly work on the subject comes from Drennan and Petersen (2005, 2006,
2010) and their work in China, Mesoamerica and northern Peru. It is their position that
identification of archaeological units varies at differential scales of analysis. For example, they
posit that at a smaller scale, certain clusters of artifacts or collection units may best represent a
“site”, as we know it in archaeological terms. Yet this is a misleading unit, and when viewed at a larger
scale, these “sites” may be clustered to then for a “community”. And further, at an even larger scale,
these communities may then be clustered to form larger, regional communities or community
groups (2005:8). The analysis discussed here looks as far as the “microregion” as the scale of
analysis. The identification of communities primarily took place at a scale of 1:20,000 in the GIS.
This was to ensure that smaller units would be able to be identified without further grouping into
larger units, so as to be able to locate possible hamlets or small villages. This scale also was useful
for identifying the larger communities, those which may be administrative centers or possible
towns.
Once the appropriate scale was chosen, a central point was created to locate the community
in space on the landscape. This was not done lightly however. First, the clusters were analyzed by
site to determine artifact densities, where heavy denotes areas with greater than 25 artifacts per
square meter, medium heavy are areas between 15 and 25 artifacts per square meter, medium being
between 10 and 15 artifacts per square meter, medium light being between 5 and 10 artifacts per
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square meter, and light being 1 to 5 artifacts per square meter. In some cases, a cluster comprised
only one site, and the center point was put in the middle of that site. However, sometimes clusters
contained several sites. In this case the central point was put in the area of highest artifact density,
with the surrounding sites having lower densities. If all sites contained equal artifact densities, the
central area was located and determined to be the center point.
The field notes for the survey seasons were often visited to note the field conditions of the
survey site, and the notes were also used, as well as 10-meter contour lines in the GIS to note the
topography of the area. In very few cases, sites were classified as erosional depositions, and I made
sure not to include these as possible communities. Furthermore, the topography aided in locating
the possible center of the community, or where community boundaries were hard to distinguish
based on artifact clusters alone, the topography was used to further aid in the identification and
delineation of communities. A map of the located communities is shown in Figure 9, for the
Tariacuri Phase.

67

Figure 8 – Community Locations for the Tariacuri Phase, LPB

68

Delineating Communities
Several factors were taken into consideration for the delineation of communities. Once
again, the issue of scale plays an important role, and once again the analysis was undertaken at the
micro-regional level, at a scale of approximately 1:20,000. The vital variables taken into
consideration were artifact density of the focal site, the artifact densities of surrounding sites (if
applicable), the area of these sites, and the topography of the landscape. Once again, the field notes
were visited to ensure that specifics on artifact concentrations and site topography were taken into
consideration. This was done for each the communities by phase. The difficult part, as one would
expect, was defining the outer edges of the communities. The delineation was done in such a
manner to include the major areas of artifact concentration, with the border coming at the areas of
drop-off of artifact densities. This isn’t to say that outer areas weren’t part of the communities, or even
that there were no habitations in these areas. Simply put, it is impossible to fully reconstruct the
exact boundaries of the social landscape of the past, and for this analysis the major determining
factor was the artifact concentrations.
The result was a map of communities, with attributes of artifact densities, specific artifact
counts, the survey sites included, and the area of the community in hectares. The results of both the
identification and delineation of communities can be seen below in Table 5. Figure 10 shows the
communities for the Tariacuri Phase. Final community maps for all phases are located in the
appendices, including initial locations and the community areas arrived at from the delineation.
Table 5 – The Mapping of Communities in the LPB
Period
Late Preclassic to Early Classic
Middle Classic - Epiclassic
Early Postclassic
Middle Postclassic
Late Postclassic

Phase
Loma Alta
Jaracuaro, Lupe/La Joya
Early Urichu
Late Urichu
Tariacuri

69

# Comm
6
11
17
35
18

Total Size of Comm
46.61 hectares
82.98 hectares
111.30 hectares
276.39 hectares
318.61 hectares

Figure 9 – Tariacuri Phase Delineated Communities, LPB

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Analysis and Demographic Reconstruction of Communities
The method and process for demographic reconstruction of prehistoric populations has
been a much debated and tenuous aspect of archaeological research. The roots of present-day
demographic reconstruction in archaeology can be traced to the New Archaeology, and several
seminal projects in Mesoamerica in the 1960s and 1970s, including those in the Basin of Mexico by
Sanders and Parsons. Along with William Sanders, Parsons completed an archaeological survey
in the Texcoco Region, which lies to the south of the Teotihuacan Valley. The survey built upon
the earlier theory and method of archaeological settlement pattern analysis, and consisted of four
major objectives: (1) a classification of the sites, which inferred site function, that was
methodologically sound, (2) an estimation of relative populations for different time periods, (3) a
chronological framework, (4) and some understanding of productive potential of the survey area
for each period in question (Parsons 1972:142). By defining and meeting these objectives, the
survey was considered methodologically sound and therefore the data derived from said survey
could be safely used to determine settlement patterns and to estimate population size across time
and space. Furthermore, larger research questions could be asked in relation to the surrounding
archaeological landscapes, such as the dominant state-level society of Teotihuacan to the north.
The methods introduced field-by-field surface survey, where large aerial photographs
were used to directly field plot archaeological features and detect ancient occupations. By doing
this Sanders and Parsons were able to plot the distribution of sites for eight or nine temporal
phases over large continuous tracts, with the confidence that all or most sites were accounted for
(Parsons 1972:141). According to Parsons, “these distributional patterns served as the basis for
inferences regarding changing patterns of land use, population expansion, sociopolitical
evolution, and economic integration” (1972:141). Unfortunately, problems arose with the

71

control of site function, and artifact density was estimated based solely on the visual appraisal in
the field, and no objective, quantitatively derived index was made. This, according to Parsons,
made it difficult for others to adequately analyze the survey data and arrive at an occupational
density (1972:141). This last aspect is intrinsically tied to issues of artifact sampling in
archaeological surveys.
This method developed by Sanders and applied in the Basin of Mexico was later applied
to the Oaxaca Valley, a region to the south of the Basin of Mexico that was once home to the
Zapotec Empire. Detailed by Feinman (1985), the basic strategy for the complete archaeological
survey as well as estimating population density was taken from Sanders and Parsons, with minor
changes made for their specific conditions. Both studies proceeded on the assumption of a
general relationship between the areal extent of a settlement and the population of that locality
(Feinman 1985: 336). Therefore it was possible to make a direct correlation with the settlement
size and a population size. On a smaller scale, the extent of a site was defined as a cluster of
cultural material separated by at least 100 meters from other such clusters (Feinman 1985). The
basic methodology that linked the site with a population estimate was by defining the artifact
density found for that site. Thus, the clustering of sites created the larger settlement, and by
calculating population density for the site, Feinman could then extrapolate the estimate for the
settlement.
The artifact density categories that were set up by Parsons and Sanders and utilized by
Feinman created a methodology to qualify the quantity of artifacts found for each site. The
categories, set up by Sanders, are explained as such: (1) trace to very light artifact densities
correlate with what Sanders described as a compact low density village or scattered village. They
are evidenced by a scatter of sherds separated by intervals of up to a meter, but most often

72

several centimeters, in wide scatter distributions. (2) Light to moderate density areas correspond
to Sanders’ compact low-density village, and are evidenced by sites with intact architecture and
denser concentrations of sherds distributed in an almost continuous layer with some areas of
appreciable build-up. (3) Moderate heavy and heavy densities correspond to Sanders’ highdensity compact village, and are evidenced by the densest forms of artifact distributions
(Sanders, Parsons, Santley 1979: 34-40, 52-60). After categorizing the sites based on the artifact
densities, one may associate a population density estimate for each of the artifact density
categories (Fisher 2000:95-96). For example, one may quantify a site with a moderate artifact
density as having a population density of 25-35 persons per hectare. Therefore, if the site was
five hectare in size, the population range for that site would be in the range of 125-175. Once the
site densities have been calculated, the settlement size may also be calculated in terms of a range
of population estimates. Feinman found that the sites in the Oaxaca Valley varied less in terms
of artifact density than in the Basin of Mexico project. He concluded that the majority of the
sites found in the Oaxaca Valley fell within the population density range of 10-25 people per
hectare (Feinman 1985:336).
Feinman also analyzed occupied, residential terraces in terms of population size. These
residential terraces were treated as “houselots”, and could therefore be analyzed in terms of
occupied households that inhabited each terraced area. This is another form of population
estimates from archaeological data that is more specific than dealing with survey data. As
Chamberlin (2006) explains, methods for estimating population size from the floor area of
dwelling space is a more precise method than dealing with issues of surface scatter of artifacts
for settlement sizes. Ethnoarchaeological studies have been done by Gorenstein and Pollard
(1983), De Roche (1983) and Hassan (1981) to determine the estimates of space occupancy per

73

person in instances where households and floor areas are present. By studying present-day
Mesoamerican peasants, Kolb and Snead’s study found that the space occupancy in
Mesoamerican agricultural households is 6.1 m² per person (1997). Chamberlin explains that
when applying these formulas it is important to distinguish between habitation space and storage
and livestock space, and to “take into account the proportion of buildings or rooms that were
occupied at any given time” (2006:126). Also, Chamberlin stresses the use of stratigraphic
evidence to determine the temporality of occupation (2006:127).
Specifically for the Lake Pátzcuaro Basin, Pollard’s ethnoarchaeological study in the late
1970s led to two methods for estimating Prehispanic populations. The first method looked at
census data from the 1940s and 1970s taken by the Mexican government. By mapping and
analyzing the settlements in the 40s and 70s, Pollard referred to the 16th and 17th century
ehnohistoric documents to try to correlate the modern settlements to those listed in the
ethnohistoric records. Doing such allowed for Pollard to create categories of settlement class
based upon the current settlements and the ethnohistoric data. She concluded that there were five
distinct classes that existed Prehispanically: (1) the capital, which we know was Tzintzuntzan,
whose population range is from 25,000 to 35,000, (2) the center, which ranges from 3,000 to
5,000, (3) the town, which ranges from 1,000 to 1,500, (4) the village, which ranges from 100 to
500, and (5) the hamlet, which ranges from 30 to 80 in population range (Pollard 1983:61).
The second ethnoarchaeological method tested the agrarian potential in the Pátzcuaro
Lake Basin, thus allowing for an estimate to be made on the possible carrying capacity of the
basin and the maximum population size it could hold. This type of paleoecological
reconstruction allowed Pollard to determine the extent and degree of land use and subsistence
agriculture (1980:274). These figures are based on potential maize crops and maize consumption

74

patterns of modern populations. Ultimately, Pollard found that the Tarascan state at the time of
Spanish contact was well above the local carrying capacity of the Lake Pátzcuaro Basin
(1980:274, 1983).
A study based on Pollard’s and that utilized her ethnoarchaeological census data was
developed by De Roche. The goal was to use modern data on settlement area and population to
retrodict population size of Prehispanic settlements in the lake basin for which “archaeological
investigations have provided or might provide estimates of settlement area” (De Roche
1980:187). Pollard’s original ethnohistoric research identified n=91 settlements that have been
evidenced in the ethnohistoric data from the 16th century. From the recent data (1940’s) and
then modern data (1970s), n=97 settlements have been identified and their sizes calculated in
order to predict populations. De Roche analyzed the populations at different scales, such as the
residence, the settlement and the regional populations. She found that by basing her predictions
on the residential units, where the average number of people per household was 5.972, a more
accurate regional population estimate was arrived at for the 1970s census data.
The goal of the prediction of the 1970s population data was to correctly arrive at an
estimate of persons per hectare, which would be a figure that could be applied to the Prehispanic
Tarascans. De Roche argues that because a complex agrarian society has persisted in the lake
basin through half a millennium with relatively little change, one may justify using the parallels
of settlement and population data to retrodict population and settlement size. This led to the
conclusion that an accurate range for persons per household falls between 5.71 and 6.11, which
also matches Kolb’s assessment (1983:191). De Roche also came to an average figure of thirty
persons per hectare for estimating the basin’s total population (1983:191).

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A final attempt at population and settlement estimation for the Tarascans comes from
Fisher. In his dissertation (2000), he utilizes the methods laid out by Parsons, Sanders and
Santley (1979) to calculate population density based on artifact density (See Table 6). His focus
is on the southwestern-most survey area which includes the lake basin centers of Jaracuaro,
Pareo, and Urichu.
Table 6 – Parson’s, Santley, Sanders (1979) Artifact to Population Density Estimates
Artifact Density

Sanders' Descriptor

Population Density

trace to very light

Low Density Village / Scattered
Village

5 to 10 persons per
hectare

light

Compact Low-Density Village

10 to 25 persons per
hectare

moderate

Compact Low-Density Village

25 to 35 persons per
hectare

moderate heavy /
heavy

High-Density Compact Village

35 to 50 persons per
hectare

Fisher calculated two measures of density based upon these ranges, occupation
concentration and settlement density. Concentration is a “measure of the number of persons
present at an occupied site for a given phase and is calculated by dividing the area of the site in
hectares by the estimated archaeological population” (Fisher 2000:96). Settlement density is a
“measure of the number of persons occupying the study area during a given phase and is
calculated by dividing the estimated archaeological population by the size of the survey area in
hectares” (Fisher 2000:96).
This analysis will utilize a combination of the three methods discussed above: 1.)
Pollards’ ecological and ethnohistoric study which relies heavily on the Spanish ethnohistoric
documents from the 16th and 17th centuries, 2.) DeRoche’s ethnographic study which retrodicts

76

population per household and per hectare, and 3.) Fisher’s approach to population using artifact
density to achieve an estimated range of population for a given area.
Table 7 – Past Estimates concerning prehispanic Populations

Author

Pollard (1980,
1983)

De Roche (1983)

Fisher (2000)

Method

Population
Estimates

paleoecological reconstruction
and carrying capacity analysis, 60,750 - 105,000 (total lake
ethnohistoric analysis
basin, protohistoric)

population
prediction/retrodiction,
settlement analysis

5.972 persons per residence;
30 persons per hectare

artifact density analysis,
paleoecological reconstruction

see table 6

For each approach, the communities were categorized based on artifact density and size
(hectares). The easiest populations to calculate were those using Fisher’s and DeRoche’s
methods, where simple calculations based on the community size were performed and a final
number, or range of numbers, were given for that communities population. Gorenstein and
Pollard’s method though, was more detailed. Pollard’s method involved placing each community
into settlement classes, as was done in the 1983 study. These settlement classes are a derivative
of the ethnohistoric documents and the populations recorded by the Spanish at the time of contact
and into the 17th century (described above). For this method, each community was given a rank,
and therefore an associated population range. However, this method was derived primarily for
the Protohistoric period in the lake basin. These population estimates, as well as the settlement
classes, can be retrodicted into the Late Postclassic period, during the Tariacuri phase (A.D. 1350
– 1525). However to apply these population estimates to any phases earlier assumes a total lake

77

basin population equal to a population during the height of the Tarascan state, which simply wasn’t
the case. To estimate population for earlier phases, Pollard has derived estimated populations for
both the survey areas as well as the basin, based on her earlier work as well as Fisher’s population
estimates (Pollard 2008:223). In order to follow through with a consistent analysis, Gorenstein and
Pollard’s population ranges from the 1983 research will be used through all phases, and although
primarily intended for use during the Protohistoric, they will allow comparative estimates for the
other demographic calculations. In any case, the 2008 estimates will be used as checks on the
method carried out for this analysis.
Each of the three methods was carried out through all phases of occupation for the survey
area of this analysis. The first method, established by Pollard, produced a range of population
based on the settlement rank given to the individual community. The second method also
produced a range of population, and was based on artifact density, giving a population estimate
per hectare. The third method, based on DeRoche’s ethnographic, demographic retrodiction,
produced a single population number based on her 30 people per hectare estimate. The
population results for the reconstruction for all three methods are given below in Table 8. More
detailed tables can be seen in the appendices, and display the population reconstruction data for
each time phase, and include the individual communities for that phase, the community area,
population for each method, and the survey site(s) that comprise it.

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Table 8 – Population Estimates by Phase for the Prehispanic Southwest LPB

Period

Phase

# Comm

Community
Size

Artifact
Ethnohistoric
DeRoche
Density
(Pollard)

(hectares)
Late Preclassic
to Early Classic

Loma Alta

Middle Classic - Jaracuaro,
Epiclassic
Lupe/La Joya

(1979)

6

46.61

422-1030
(726
mean)

1398

390-1740
(1065 mean)

11

82.98

702-1683
(1193
mean)

2500

610-2560
(1585 mean)

Early
Postclassic

Early Urichu

17

111.30

863-1792
(1328
mean)

3339

720-2620
(1670 mean)

Middle
Postclassic

Late Urichu

35

276.39

4175-8443
(6309)

8292

3860-9980
(6920 mean)

Late Postclassic

Tariacuri

18

338.41

7962-12708
(10335
mean)

10151

6980-13530
(10255 mean)

Several things are of note for the demographic reconstructions. The most accurate
reconstruction of population was for the Late Postclassic period, Tariacuri phase. The research
from Pollard (1980, 1983) and her work with the ethnohistoric documents provides this method
with sound correlations and bridging arguments with the archaeological data, as well as the
ethnographic data. The same can be said for DeRoche’s method (1983), which relies heavily on
ethnographic data to make correlations for the pre-Conquest period (DeRoche 1983:191). This
does, however, provide a useful and sound starting point from which to retrodict the populations
in earlier Prehispanic phases, while also relying heavily on the archaeological data to guide the
demographic analysis. The following sections will describe the method and results for each
phase, as well as provide a rank-size analysis for each phase to test against the population
numbers arrived at above. The method for the rank-size analysis utilizes the population numbers
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arrived at by Fisher’s method, and supported by Pollard’s method. In all cases, both Fisher’s method
and Pollard’s method produced similar population estimates. However, due to Fisher’s method also
taking into account community area, the estimates from this method will be the primary data used
for rank-size graphs.

Loma Alta/Jaracuaro Population Reconstruction
The Loma Alta phase, by its nature, is the most difficult phase to reconstruct population
for. It remains the phase from which there has been much less archaeological research done,
given the scarcity of sites in the basin, and its depth of stratigraphy. The archaeological research
from Pollard in the southwest portion of the basin yielded n=6 Loma Alta phase communities.
Due to the difficulty of detecting material from this phase in walk-over survey, all of these
communities, along with evidence in surveys, have been confirmed using excavation as well. The
artifact density counts were derived by using a combination of both surveyed material as well as
excavated material. The population reconstruction for this phase used categories, according to
Fisher (2000), of only trace to light and light artifact densities, equating to compact low density
villages or low density, scattered villages. According to Pollard’s method (1983) these also equate
to settlement ranks 4 and 5, which she describes as villages or hamlets. DeRoche’s estimates are
too high, at a 30 persons per hectare average, to accurately retrodict population at this time. Of the
six communities, three were given higher ranks or categories of higher density due to the
archaeological correlates. In the case of Erongarícuaro, architecture and burials, as well as longdistance exchange noted in the burials, mark this as a central community with higher ranked
individuals existing there. Excavations at Urichu also displayed similar densities of artifacts,
although no architecture was present. Still, these communities accurately represent what Pollard
describes is also present elsewhere in the basin for the Loma Alta phase, as “they document the

80

existence of small-scale, socially ranked agrarian societies.” (2008:220).
In each method, the estimated population for the communities in the survey area is higher
than Pollard’s projection, which is between 400-600 people for the survey area, and 4,500 for the
basin. However, Pollard’s assessment is only for the last Loma Alta phase (Loma Alta 3) and the
Jaracuaro phase. Pollard gives no estimate for the Loma Alta 2 phase. Communities 3, 4, 5,
and 6 from Erongarícuaro are all Loma Alta 2 phase communities. The population
reconstructions from these communities through Fisher’s method give a population range of 167393, with a mean of n=280. DeRoche’s method produces a population of n=1266. Pollard’s method
produces a population range of 190-740, with a mean of n=465. If we throw DeRoche’s
estimates out, and combine the low and high ranges of Fisher and Pollard’s estimates to produce an
average range, the population range becomes 176-567, with a mean of n=372. When we take
the Loma Alta 3 phase sites, and combine them with the Jaracuaro phase sites, we get much
higher population estimates than Pollard’s estimate of 400-600. Fisher’s method produces a range of
662-1623, with a mean of n=1143, DeRoche’s method a population of n=2157, and Pollard’s
method a range of 520-2320, with a mean of n=1420. Once again, relying on Fisher and
Pollard’s methods, we still have a much higher population for the Loma Alta 3/Jaracuaro period.
However, the lower range does fall within Pollard’s estimates, and I believe that an accurate
assessment of the population lies somewhere in the lower range of the estimates arrived at by
this analysis.
Rank size graphs were created for both Loma Alta 2 and Loma Alta 3 phases. In each
case, they represent a primate curve. The primate curve for the Loma Alta 2 graph is due to the
large population at one primate center, located at Community 3 at Erongarícuaro. The remaining
communities represent small villages or hamlets. Another possible reason for the primate curve

81

for Loma Alta 2 could be the lack of data and the small number of observations (n=4). For the
Loma Alta 3 phase, the number of observation increase, n=6, which is still a small number. Once
again, the rank size graph displays a primate curve, determined primarily by two larger
communities with higher populations and several smaller communities.
Figure 10 – The Loma Alta 2 Rank-Size Graph

Loma Alta 2 Phase
250
200
S 150
i
z
e 100

Loma Alta 2

50
0
1

2

3
Rank

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4

Figure 11 - Loma Alta 3 Rank-Size Graphs

Loma Alta 3 Phase

450
400
350
300
S
i 250
z 200
e
150

Rank-Size

100
50
0
1

2

3

Rank

4

5

6

Lupe/La Joya Phase Population Reconstruction
The Lupe/ La Joya phases coincide with the Epiclassic period, ranging from A.D. 600 to
A.D. 900. There were n=9 communities located for this phase, which is a slight increase from the
earlier Loma Alta/Jaracuaro phases. The demographic reconstruction for this phase yielded
population estimates as follows: The Fisher method yielded a population range of between 462
and 1090, with a mean of n=776; the DeRoche method produced a population of n=1741; the
Pollard method yielded a range of between 480 and 1980, with a mean of n=1230. According to
Pollard (2008), the population for the southwest survey area during this phase is estimated
between 600 and 1,000, with a lake basin estimate of 6,000 and 7,000. The population number
arrived at from Fisher seem to be the more conservative end of Pollard’s 2008 estimate, while the
Pollard method (1983) estimate seems to be on the higher end of that estimate. Once again, given
the nature of the DeRoche method, this population estimate once again seems too high, given the

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archaeological evidence.
The rank-size graph of this period follows similarly to that of the earlier phases,
displaying a primate curve. The larger communities are those at Urichu, set back from the
lakeshore. There is then a drop off in community size, as smaller hamlets and villages dot the
landscape closer to the estimated lakeshore and marsh zones. This disparity between community
sizes results once again in a primate curve. However, we also must take note of the smaller
sample size, which may conceivably lead to such a rank-size curve.
Figure 12 - Lupe/La Joya Rank-Size Graph

Lupe/La Joya Phase
450
400
350
300
S
i 250
z 200
e
150

Lupe/La Joya

100
50
0
1

2

3

4

5
6
Rank

7

8

9

Early Urichu Population Reconstruction
The Early Urichu phase marks the beginning of the Postclassic, ranging in time from
A.D. 900 to A.D. 1000/1100. The location of communities during this phases shows a marked
increase from the last phases, going from n=9 communities in the Lupe/La Joya phase to n=17

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communities in the Early Urichu phase. However, the communities remain relatively smaller in
size, mostly equating to hamlets and villages. The larger communities, at Urichu and
Erongarícuaro, represent what are most likely the central villages, or even towns, where the
population is the highest during this period. Pollard, in her 2008 synthetic article, estimates a
population for this southwest survey area of between 1,000 and 2,000, and approximately 12,000
for the LPB. This represents a doubling in population from the last phase, both for the SW
survey area and the basin. The population estimates arrived at for these analyses are as follows:
the Fisher method produced a population range of between 863 and 1792; the DeRoche method a
population of n=3339; and the Pollard method a population range of between 720 and 2620.
Once again, the estimate based on Fisher’s method fits the estimates from Pollards’ 2008
article the best, while still being on the conservative side. Pollard’s method also fits her 2008
estimation, and is once again on the more liberal side. Finally, DeRoche’s method is once again
an outlier, and falls much higher than either method as well as Pollard’s 2008 estimate.
The rank size graph for the Early Urichu phase once again shows a primate curve,
although not as drastic and shows a slight move towards log-normal. This phase also had an
increase in observations, thus making the confidence a little higher than the last two phases.
According to Drennan and Peterson, primateness in the rank-size curve “is produced by a very few
settlements in the topmost ranks, often depending almost entirely on the difference between the
first and second ranked settlements” (2004:546). The curve is again due to the few more densely
populated communities at Urichu and Erongarícuaro, and the higher frequency of low- population
hamlets and villages. However, the move towards log-normal from the last few phases shows this
dramatic increase in population for the basin, and the increase in communities and community
size. And, according to the community ranks and sizes, the communities are growing slightly in

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area (hectares) which shows a light growth in community make-up and demography.
Figure 13 - Early Urichu Rank-Size Graph

Early Urichu Phase
450
400
350
300
S
i 250
z 200
e
150

Early Urichu

100
50
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Rank
Late Urichu Population Reconstruction
The demographic reconstruction for the Late Urichu phase coincides with the Middle
Postclassic, between A.D. 1100 and A.D. 1350. According to Pollard (2008), we see “the number of
sites in the Pátzcuaro Basin increased, and the area of occupation again doubled” (2008:224).
According to Pollard (2008) the population at this time for the survey area ranged between 4,000
and 7,800, with a basin population estimated at 48,000. The demographic reconstruction for this
phase located n=43 settlements, a vast increase from the Early Urichu phase. However, the
communities were smaller in area, with the majority representing small villages and hamlets, and
only a few representing major central communities with denser populations. For the demographic
reconstruction method, only a few communities were characterized as compact, high-density
sites, or, according to Pollard, as rank 3 settlements.
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Most fell within the low-density compact site category (Fisher 2000), or the rank 4
settlement (Pollard 1983). The reconstructed populations for each method are as follows: the
Fisher method produced a population range of between 4175 and 8443, with a mean of n=6309;
the DeRoche method a population of n=8292; and the Pollard method a population range of
between 3860 and 9980, with a mean of n=6920. Once again, the Fisher and Pollard methods
produced population ranges in accord with Pollard’s 2008 estimates. The DeRoche estimate is once
again high, although not as drastically in the earlier phases.
The rank-size graph for the Late Urichu phase shows a slight primate curve, nearing
towards log-normal in its distribution. This means that with the increase in communities for the
phase, we are also seeing more communities change in their nature, with a more diverse range of
community types. The previous trends seemed to be that there were a few higher ranking
communities with larger populations, and a high frequency of dispersed, lower density
communities. However, the increase in basin population during the Late Urichu phase, for at
least this portion of the basin, seems to have reversed that trend, with a growth in the middle
range of community population.

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Figure 14 - Late Urichu Rank-Size Graph

Late Urichu Phase
400
350
300
250
S
i
200
z
e 150

Late Urichu

100
50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Rank
Tariacuri Phase Population Reconstruction
The final phase of the temporal sequence marks the emergence of the Tarascan Empire,
and the unification of the LPB under this socio-political system of the state. According to
Pollard’s estimate for this phase, the population reached 80,000 for the basin, between 7,000 and
13,000, with a mean of n=10,000, for the survey area; population density is estimated to have
been at 182-334 persons per hectare (Pollard 2008: 224). Due to the ethnohistoric data at the
time of the Spanish Conquest, which is at the end of this phase, strong correlations can be made
about communities, settlement, and the population, all of which were documented by the Spanish.
This is also the phase that most closely aligns with the ethnographic research done by DeRoche in
her demographic retrodiction analysis (1983). The number of communities dropped for this
phase, to n=17, which is dramatic when compared to the Late Urichu phase. However,

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community area size increased dramatically for this phase. The following results were produced
by the three population reconstruction methods: The Fisher method produced a population range
of between 7962 and 12708, with a mean of n=10335; the Deroche method produced a
population of n=10151; and the Pollard method produced a population range of between 6980
and 13530, with a mean of n=10255. Given, as stated previously, the level of confidence in the
ethnographic, ethnohistoric, and archaeological data, all these estimates are very strong, and
coincide closely with what Pollard estimates in her 2008 article. Of the communities, only one
was given the settlement rank of two, which according to Pollard, was described in the
ethnohistoric documents as a major administrative center during the Protohistoric period. This
community also received the classification, according to Fisher’s method, of a high-density,
compact town. Rank 3 settlements, the next largest community type, can then be found on the
outer area of Erongarícuaro, Urichu, Jaracuaro and Pareo. The remainder of communities
received the rank 4 settlement classifications, equivalent to the low density, compact village.
The rank-size graph for this phase reverts back to the dramatic primate curve trend that
was witnessed in the earlier phases of the sequence. Once again, the curve seems to be
determined by the few communities with high population densities, and the higher frequency of
communities with lower population densities. Also, there are fewer observations for
communities in this phase. However, due to the nature of the archaeological evidence, and the
strong confidence in the survey results, it is unlikely that the primate curve is due to sampling
error.

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Figure 15 - Tariacuri Phase Rank-Size Graph

Tariacuri Phase
3500
3000
2500
S 2000
i
z
1500
e

Tariacuri

1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Rank

The final rank size table displays all the rank-size curves for all phases. This allows for a
comparative view of the population through time, where it is easier to view the population shifts
and trends. The graph, Table 16, shows clearly the major demographic hifts that occurred within
the southwest region over the 1,600 year period, and the dynamic of settlement and community
size through this time period.

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Figure 16 – The Comparative Rank-Size Graph: All Phases

Total Rank Size: All Phases
3500
3000
2500
Tariacuri

S 2000
i
z
e 1500

Late Urichu
Early Urichu
Lupe/la Joya
Loma Alta

1000
500
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Rank

Functional Analysis of Communities
The final aspect of this analysis is an attempt to determine the function of the
communities through the material record and known correlates to human behavior, social classes
and class hierarchy, and human activities and economic practices. There are several types of
archaeological material, and archaeological assemblages, that one may infer social and economic
function from. Stawski, in his 2008 thesis, describes these material correlates through the use of
historic, ethnographic, ethnohistoric and archaeological evidence, primarily for the Late
Postclassic occupation in the LPB. His research looks primarily at ceramic form, frequency,
function, decoration, ware and slip, and determined that certain ceramic variables for each

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represented areas of occupation for distinct social classes, such as commoner, lower elite and
upper elite.
In the same manner, other material remains are also markers for economic and social
functional zones. For example, sites where there are a high number of recortados, or reworked
ceramic sherds used as fishing net weights, can be associated with fishing activities. Pipes have
also been documented as a marker for both function and class. Stawski (2008) used the
ethnohistoric documents, and excavations from Urichu and Tzintzuntzan, to determine that pipes,
especially decorated pipes, are strong markers for areas related to ritual and ceremonial use, as
well as those linked to the middle and upper elite classes (2008:44). Obsidian has also been used
as a marker for both social and economic function in the archaeological record. In her research
on obsidian production and consumption at the site of Erongarícuaro, Rebnegger identified areas
of high obsidian consumption, with the obsidian accessed by local elites through a controlled
market exchange system (2010:86). Furthermore she contends that although this area had a high
frequency of obsidian material, it was not enough, comparatively, to be considered a workshop
area. However, she does state that this was most likely an area where part-time craft
specialization took place (2010:86).
In an attempt to establish functional areas for communities, the method will be fairly
straightforward. Each survey site artifacts will be analyzed to determine what, if any, functional
category may be attributed to said site. The categories, such as a specific activity (economic,
subsistence) or social context (a commoner area, administrative zone), will be then attributed to
the communities that have located and delineated. This method isn’t intended to be a precise analysis
for site functionality. It is understood that the primary data source is survey data, which poses
taphonomic problems and is a difficult manner in which to interpolate an intensive categorical

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site analysis. Rather, this method and the results will be used post-settlement analysis in order to
provide further insight into community organization across the landscape.
The following functional attributes are listed by phase. The survey sites were analyzed
first, followed by an overlay of the communities to see which community could be attributed a
functional category based on the site assemblages. Of note is that not every community will have
a functional category given it. Only those sites that show a very high frequency of artifacts that
represent possible functional markers will be used.
Table 9 – Functional Attributes for Communities by Phase
Phase
Loma Alta
(2&3)
Loma Alta
(2&3)
Loma Alta
(2&3)
Jaracuaro

Comm
Comm 3

Artifacts/Assemblage
High frequency ceramics,
obsidian
Comm 2 High frequency ceramics,
obsidian
Comm 1 High frequency ceramics,
obsidian
Comm 1 high frequency circles
(recortados)
Lupe/La Joya Comm 2 high frequency circles
(recortados)
Lupe/La Joya Comm 5 high frequency artifacts
Lupe/La Joya Comm 7 high frequency artifacts
Early Urichu Comm 1 medium frequency of circles
(recortados)
Early Urichu Comm 3 medium, medium light
frequency of circles
Early Urichu Comm 4 medium, medium light
frequency of circles
Early Urichu Comm 11 high frequency artifacts, high
frequency circles
Early Urichu Comm 6 high frequency artifacts
Early Urichu
Late Urichu

Comm 11 high frequency artifacts
Comm 12 high frequency of circles

Late Urichu

Comm 10 high frequency of circles

Late Urichu

Comm 9

high frequency of circles

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Functional Interpretation
administrative center, possible
elite residence
administrative center, possible
elite residence
administrative center, possible
elite residence
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
elite residence (Pollard 2005)
administrative center
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
possible administrative center,
fishing/lakeshore resource zone
possible elite residence (Pollard
2005)
administrative center
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence

Table 9 (cont’d)
Late Urichu

Comm 25 high frequency of circles

Late Urichu

Comm 24 high frequency of cirlces

Late Urichu

Comm 21 high frequency of cirlces

Late Urichu

Comm 20 high frequency of circles

Late Urichu

Comm 2

Late Urichu

Comm 30 high frequency of artifacts,
high frequency of obsidian
Comm 28 high frequency of artifacts
Comm 16 high frequency of obsidian
Comm 11 high frequency of obsidian
Comm 13 high frequency of obsidian
Comm 35 high frequency of obsidian
Comm 33 high frequency of obsidian
Comm 1 High frequency of pipes,
circles, all artifact categories

Late Urichu
Late Urichu
Late Urichu
Late Urichu
Late Urichu
Late Urichu
Tariacuri

high frequency of circles

Tariacuri

Comm 2

high frequency of cirlces

Tariacuri

Comm 3

high frequency of cirlces

Tariacuri

Comm 16 medium frequency of pipes,
high freqeuncy of circles, high
frequency of obsidian &
ceramics
Comm 15 High frequency all artifact
categories, especially pipes,
obsidian
Comm 11 high frequency circles
(recortados)

Tariacuri

Tariacuri

zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
administrative center, possible
craft specialization area
administrative center
possible craft specialization area
possible craft specialization area
possible craft specialization area
possible craft specialization area
possible craft specialization area
administrative center, possible
craft specialization area,
fishing/lakeshore resource zone,
ritual zone
fishing/lakeshore subsistence
zone
fishing/lakeshore subsistence
zone
regional administrative center,
fishing/lakeshore subsistence
zone, possible ritual area
Administrative center, ritual
zone, elite zone
fishing/lakeshore subsistence
zone

The functional categories remain general, with certain assemblages and artifacts being
used to direct the analysis. The main artifacts used were obsidian, pipes, and recortados, which
have specific economic or social use correlated to them. Other categories, such as high
frequencies of artifacts, aided in determining areas of intense use, which possibly represent

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administrative centers or residences. In the cases of the elite residences, Pollard’s assessments,
derived from the informe and field reports, aided in the categorization. Although not extremely
detailed, those functional categories that have been assigned to communities will aid in further
narrowing down causation for settlement through time. A comment must be made in concern to
architecture at the survey sites. This analysis will forgo the use of architectural remains for the
southwest survey area, in part due to an incomplete dataset. Future research, however, will most
definitely take architectural data into account.

Summary
The focus of this chapter falls on the spatial and organizational unit of the community.
The first section details the theory surrounding the community, its use in archaeological research,
and its translation into fieldwork and research, especially concerning the archaeological survey.
A significant theoretical point of this chapter is that community may be applied across scalar
units, such as microregional, regional and macroregional. Ultimately, community is defined by
the interaction that these spatial units have with other communities, the landscape, and the
environment. Ultimately, these interactions that define the communities and create their
boundaries also leave material traces, which can be seen in the archaeological record. Therefore,
these clusters of artifacts that are witnessed and recorded as survey data can be interpreted as
communities.
With this assumption in mind, the rest of the chapter turns to method, and located,
delineated and defined communities for the southwest LPB surveys. This included a
demographic reconstruction using three separate methods to reconstruct the population for the
communities through time. The final aspect of this chapter used material-behavioral correlates to
aid in defining the functional categories of the communities.

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CHAPTER 4: LANDSCAPES OF THE LAKE PÁTZCUARO BASIN
Theory
Landscape Approaches in Settlement Studies
In an analysis where the variables being assessed infer interactions between humans and
their environments, a landscape approach allows for interpretation on the level of the human
adaptation paradigm. Although the first chapter discusses the basic tenets behind a landscape
approach, the discussion must go further in order to explain the role of this approach in this
dissertation. The paradigmatic nature of a landscape approach is different from that of a
metaphysical paradigm, or even an intermediate –level sociological paradigm. Landscape
approaches are considered a construct paradigm, and “are methodological in that they are
systems of tools for approaching particular kinds of scientific inquiry as well as interpreting
what they do” (Anshuetz, Wilhusen, and Scheick 2001:160). Thus, a landscape approach is defined
more by what is does than what it is. In order to explain what this approach will do for this
dissertation, the term “a landscape” must be defined and given parameters for its usefulness. Deriving
from the historical ecology literature, which is the approach most closely aligned with the aims
of this dissertation, a landscape is a “multidimensional physical entity that has both spatial and
temporal characteristics and has been modified by human activity such that human intentions
and actions can be inferred, if not read as material culture, from it” (Balee and Erickson 2006:1).
Furthermore, Crumley (1994) describes historical ecology as tracing the dialectical relationships
between human acts and acts of nature, made manifest in the landscape, where the “landscape
retains the physical evidence of these mental activities” (1994:9). With landscape defined, focus can
now turn to what this approach will contribute to the analysis of a settlement study.

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As explain, this dissertation will utilize the approach that is referred to as settlement
ecology (similar to historical ecology) by Anschuetz (2001), where geological, geographic,
ecological and archaeological data are combined to explain human relationships to their natural or
social environments. This approach is closely aligned with historical ecology, and in fact
sometimes are one in the same. The key point here is that “historical ecology and allied approaches
generally trace their intellectual heritage to processual archaeology, and they typically concentrate
on functional–economic relationships between humans and the regional landscapes in which they
live” (Kantner 2008:57). This last point is critical for this dissertation, as the main tenets of this
approach, as highlighted in the last sentence of the quote, fit well within the scale and scope of a
settlement systems analysis.
The research from Fisher (2005) is an excellent example of the kinds of studies a
historical ecology approach provides for a settlement analysis. Fisher’s (2005) work in the Pátzcuaro
Basin in Michoacán, Mexico is a long-term oriented project that utilizes regional settlement
pattern research and geoarchaeology to identify the impact of thousands of years of intensive
agriculture on the lake basin. Fisher’s work is instrumental in this dissertation, as the
geoarchaeological data aids in the reconstruction of the lake levels, the lacustrine resources, and
the arable land in the basin. Therefore, several key variables may be used in the analysis of the
settlement system that directly relate to settlement location with regard to economic resources. As
mentioned previously, this dissertation defines human adaptation as the means with which
humans locate themselves within an environment, their means for subsistence and economy, and
the relationships that alter their social and natural landscapes. Therefore, by analyzing the
settlement system that operated for 2,000 years in the Lake Pátzcuaro Basin, we can interpret the
results through a settlement ecology framework in order to elicit information about the

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relationship the Tarascans had with their landscape. This includes the means by which
populations utilized the resources in the lake basin, the resource management that occurred, the
perception of landscape through cultural markers (i.e petroglyphs), ritual and place names, and
the dynamic relationship between humans and the landscape as the climate and environment
changed.
These interpretations become even more crucial to our understanding of human adaptation
in the basin when placed within a study that is spatially and temporally dynamic. The issues of
landscape that this approach raises can now be viewed across a 2,000 year period on a regional
scale, thus making it possible to chart the relationship between humans and the environment in
the basin. Also, because of the tight chronological control of the artifacts and ecofacts in the
basin, it is now possible to distinguish these landscape issues before, during, and after the
emergence of the Tarascan state. Therefore, interpretations may be made that include the state’s
impact on the landscape, and the effect of state policy that altered the communities’ relationship
with the landscape. The usefulness of such an approach gives us valuable information about the
landscape, the communities and the state within a regional analytical scope.
Political Economy and Settlement
The basic operating mechanism that this analysis works within is that of a political
economy theoretical framework. It is within this framework that the variables for the settlement
analysis have been chosen, and within which lie the causal properties of social change. The
settlement model to be tested in this dissertation is derived from the emergence of the state model
proposed by Helen Pollard for the Tarascan Empire (2008). It is within this model that she states
how important the economic base of resources is to social and political change: “Thus during the
Middle Postclassic period a new political economy emerged dominated by a now socially

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stratified society.” (Pollard 2008:227). In other words, the profound changes within the economic
and political substructure, including the altering and shifting resources within and without the
lake basin, created the ideal environment for the rise of a state-level society (Pollard 2008:227).
It is the underlying mechanisms of economic change that fuel this analysis as well. I argue that in
order to perform a settlement systems analysis of the lake basin, the theoretical impetus of the
research must accommodate the forces of change that is argued by Pollard in a political economy
framework.
To gain a better understanding of how the political economy theoretical framework is
structured for this dissertation, several archaeological case studies will be analyzed to provide a
means of structure and comparison. An important fact must be addressed, that brings to light the
context in which the majority of these studies have been proposed. They all deal with the
emergence of primary or secondary states, as well as confront the issues of “chiefdoms” and the
dynamics that play into their structuring. This is an important fact because this dissertation
provides an analysis that leads up to and includes the emergence of the Tarascan state, considered
one of the great Late Postclassic secondary states to emerge in the highlands of Mexico. The
following sections highlight several important issues that shed light on how a political economy
framework aids the analysis for this dissertation.
The 1,600 year scope of this dissertation requires a theoretical paradigm that aids in
facilitating the processes of settlement and societal change, with a secondary goal of analyzing
the emergence of the state within the context of settlement. Through Marxist theory, several
archaeologists look to explain these types of changes through structural relations. Brumfiel
(1983) uses a structuralist approach in explaining the emergence of the Aztec state. Brumfiel
explains change and development of states as “structural transformation over time in which the

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trajectory of change is determined by the structural properties of the initial system” (1983:263).
However, the leading theories from a structuralist approach explain state formation in terms
of structurally induced social conflict (Brumfiel 1983:263). It is from this point that I refer to
Zagarell’s (1986) comments on structural relations in explaining the emergence of states and
the processes behind these phenomenon. Zagarell, by attempting to look at structural
relations rather than structural elements, states that “process and event (in context of social
structure) are two separate, although related phenomena” (1986:155). In other words, Zagarell is
hypothesizing
that one must understand the trajectory of pre-state societies prior to the emergence of the state in
order to understand the climate and circumstance of the rise of such a “revolutionary” social
phenomenon.
This dissertation looks to explain, not merely describe, the structural relations of the
settlement system of the pre-state societies of the Lake Pátzcuaro Basin. That is what is so
critical about a settlement systems analysis, it that it too looks to explain rather than describe. In
order to hold true to Zagarell’s theory behind structuralist change, we must forgo the final
comparison between states that ultimately leads to a mere trait list comparison of elements.
Instead, we must critically analyze the time leading up to state formation, and construct an
analytical framework in which the underlying processes may be evoked. It is within these
processes that variation may be found, thus allowing for a more holistic explanation for cultural
change. With this in mind, researchers may move outside the realm of neo-evolutionary theory,
which has marred the study of complex societies for too long.
It is through the analysis of this process that hopes to better explain the conditions and
processes that resulted in the rise of the Tarascan state. The analysis for this dissertation is

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organized to try to explain these phenomena through economic variables; variables that I
hypothesize structured the settlement, subsistence, and political and social organization, which
ultimately led to the climate in which the Tarascan state emerged. The following chapters will
take these theoretical stances and present a more tangible framework in terms of methodology
and variables for the settlement systems analysis.
This chapter will provide a method for reconstructing the Prehispanic landscape of the
Lake Pátzcuaro Basin in an attempt to qualify and locate the varying land classes during the
temporal sequence, as well as quantify their size. This chapter will first discuss the method that
will be used to reconstruct the landscape, explain the results in terms of environmental and lake
fluctuations and what that means in terms of quantity of each environmental zones, and finally,
apply the results to a model of the landscape for the basin that covers the temporal phases that
spans the 1,600 year focus of this research.

Method for Landscape Reconstruction
The method that will be used to reconstruct the Prehispanic landscape of the Lake
Pátzcuaro basin is comprised of two parts. The first part utilizes modern data, such as satellite
imagery, modern climatic and geological data, to first pinpoint the elevation of Lake Pátzcuaro
for a specific date and time, and then to locate the environmental zones (i.e. resource zones) that
Gorenstein and Pollard (1983), Toledo (1993) and Barrera-Bassols et al. (2006) refer to, and
estimate the size of these zones. The second part will look to the paleoecological,
geoarchaeological and geological data to estimate the prehistoric lake levels, and thus aid in the
reconstruction of the Prehispanic environmental zones. To reiterate, this dissertation works from
the explicit understanding that there exist today six distinct environmental zones; 1.) open water
2.) tule-reed marsh 3.) lakeshore 4.) lower sierra slopes 5.) upper sierra slopes, and 6.) alpine
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(Gorenstein and Pollard 1983: p.144). An assumption of this model, and an accepted fact among
researchers and scholars, is that the same environmental zones that are present in the modern era,
also existed during the Prehispanic time periods. However, it must be said that not all aspects of
these zones exist in the same capacity. For example, it is understood that certain fish varieties
that were abundant in the lake during Prehispanic and early historic periods have by now been all
but extinguished from Lake Pátzcuaro, either through commercial fishing practices, introduction
of alien species, pollution, or other human-induced factors (Alcocer and Bernal Brooks 2010).
Therefore, the environmental zones may not have the same compositional quantity as they did
during the prehistoric, historic or modern eras, but they are accepted as being the same
qualitatively.
The analysis of the modern data will provide a direct correlation between the observed
environmental zones (1940’s to present) and the expected environmental zones (100 B.C – A.D.
1525). A series of remotely sensed aerial imagery, including imagery from IKONOS, Landsat,
and SPOT satellites, will comprise the modern collection of data sets that the environmental
zones will be estimated from. Given the large amount of limnological, ecological, biological and
geological research that focuses on the Lake Pátzcuaro Basin, we have abundant and accurate
data concerning the lake level and environmental zones during the modern period.
Through the use of the ArcGis suite of tools, we are able to accurately locate and measure
the environmental zones in the lake basin from the aerial images. The specific method used for
this derives from Hritz (2010) as well as Gomez-Tagle Chavez, Bernal-Brooks and Alcocer
(2002), but locating both man made and environmental features on the earth’s surface from remotely
sensed images has for quite some time been a routine way to ground truth sites and landscape
features, both in archaeology and the physical sciences. Because of the advanced technology

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available, we are able to distinguish these landscape features, such as environmental zones, much
more accurately.
First, each aerial image has been georeferenced and rectified so that they are in UTM
coordinates, and features within the image can be accurately measured with reference to size and
location. Once the environmental zones have been located on the aerial images, a layer is created
in ArcGis that accurately “traces” the features into a vector shapefile. For example, the lake levels are
traced for each image, thus providing us with lake shapefiles that show the lake at varying levels
through time. Once the shapefiles are drawn, each can be measured in size, thus creating a layer
of data that can be analyzed in terms of what percentage of the total basin each environmental
zone comprises. This will be repeated for each aerial image in the collection from 1940 to
present. It should be noted that this method not only uses visual inspection to locate
environmental zones, but also relies on certain wavelength signatures found in the color satellite
images to locate certain environmental zones based on vegetation reflection, and also relies on
the data that is produced in a digital elevation model (DEM) to locate zones based on elevation
signatures. For each zone, the elevation is directly associated with the vegetation type that
comprises each zone, and the climatic variables that define the zones.
With the satellite images now analyzed in terms of locating, mapping and quantifying the
environmental zones, we can combine this data with the known lake level data at the time the
aerial images were taken. What we now have is a set of data that can associate lake levels at
specific dates with the size and locations of certain environmental zones.

Reconstructing Lake Pátzcuaro
This portion of the analysis first analyzes recent remotely sensed imagery, beginning with
the 1940’s and ending in 2010. Due to dramatic shifts in global climate and land use over the past
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century, Lake Pátzcuaro has undergone dramatic changes in its elevation, thus affecting it
surrounding resources zones. This accelerated shift in lake levels and resource zones,
specifically over the past 70 years as documented in the aerial imagery, is similar to lake
fluctuations and climatic shifts witnessed in the archaeological and geological record for the
Prehispanic scope of this analysis (100 B.C. – A.D. 1525). Therefore, the modern series of aerial
imagery will be used as a direct link to reconstruct the Prehistoric landscape.
As described previously, the aerial imagery was closely examined and analyzed, and
then edited to provide vector shapefiles that represent the lake, marsh zones, and islands. In
each case, these edits were done using visual inspection to determine the lake boundaries, the
lakeshore and the marsh zones. Due to the creation and editing in GIS, the areas of the
shapefiles (i.e. marshes, lake, and islands) were calculated, first in square kilometers, and
then converted to hectares. This was done for the sequence of aerial images including the
years 1940, 1970, 1973, 1989, 2000, and 2010. The 1940 images were derived from U.S.
Army Air Corps aerial reconnaissance, the 1970’s, 1980’s and 1990’s derived from the Landsat 7
satellite imagery, and the 2000 and 2010 derived from the SPOT satellite. Each of the
imagery differed in scale and resolution, but each was analyzed in the same manner in order
to provide sound shapefiles for analysis.
The process for determining the modern lake levels derives from a variety of sources.
Pollard and Gorenstein (1983) estimated lake levels for the years 1939, 1943, 1945-1948,
1961, 1963, and 1974 through the use of “some measurements, aerial photography, and
ethnographic accounts” (143). Their measurements, however, were prior to the advent of GIS and
georeferenced and rectified aerial imagery, the application of which have shown these initial
measurements to be incorrect in the LPB. The key reference for estimating the historic lake

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levels for the LPB come from Bernal-Brooks, Rojas, and Alcocer (2002) who provide research
that rearranges the historic data as they inspect the “historic records on water levels and climatic
variables; check out the altitude of ground references, and analyze traces of runoff watercourses
over the terrestrial basin by means of GIS” (187). Their method included re-measuring the water
levels from the geodesic point established in downtown Pátzcuaro by CETENAL in 1974, with
equipment that had a plus/minus 1mm accuracy. From there, the researchers calculated the
watershed precipitation and recalibrated the lake levels for the unstable water-level conditions
during the 1940’s, and then again during the 1970’s. The result was a chart that documents the
fluctuating lake levels after the rearrangement and corrections to the historic data. This data,
therefore, is what this analysis will use as the means for assessing lake levels for the historic
aerial imagery. The resulting lake reconstructions are summarized in Table 10.
Table 10 – The Historic/Modern Lake Pátzcuaro Reconstructions (Pollard 2008;O’Hare 1993;
Alocer, Bernal-Brooks, Rojas 2002;Stahle et al 2011)
Imagery
Year
1940
1970
1973
1989
1999
2000
2010

Lake Level
(masl)
2041
2038.5
2039
2037
2036
2033
2028

Open Lake Area
(hectares)
11439.02
10318.07
10512.05
8388.93
7517.75
7377.81
7374.69

Marsh Area
(hectares)
400.1
1509.3
643.51
1224.09
1306.33
1309.33
1611.05

Island Area
(hectares)
264.28
281.43
335.25
631.25
117.13
104.51
149.88

#
Islands
7
6
10
7
7
7
19

With the modern lake levels reconstructed and calculated, the analysis turns to
reconstructing the Prehispanic lake levels. The primary data derives from a variety of sources,
including paleoecological, geological, archaeological, and limnological. A summary of the
current debates and themes of the reconstruction of the LPB prehispanically was discussed in

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Chapter 2. Of the two perspectives on the matter of Prehispanic lake level fluctuations, this
dissertation will utilize the work from Pollard (1983, 1999, 2008) and Fisher (2005). Their work
incorporates geomorphological work as well as archaeological work, and they acknowledge
geological processes in their view of lake and landscape change. It must be noted though, that
between the two views (i.e. O’Hara, Davies, Metcalfe versus Pollard, Fisher) there exists only
minor differences in lake level estimates, especially when considering the large spatial and
temporal scale of this analysis.
The synthetic research from Pollard (2008) gives a summary of each Prehispanic
period and the corresponding lake levels associated with the phases. Pollard’s work, derived from her
archaeological research and from Fisher’s geomorphological research, begins in the Late
Preclassic Period (100B.C.) and ends at Spanish Conquest (A.D. 1525). The following lake
level descriptions for the LPB are by period and phase. The Loma Alta phase (the Late
Preclassic to Early Classic period, 100 B.C. to A.D. 600) had lake levels fluctuate between
2033 and 2035 m.a.s.l. (meters above sea level). The Jaracuaro and Lupe/La Joya phases
(spanning the Middle Classic to Epiclassic, A.D. 600 to A.D. 900) had lake levels remain steady
at 2035 m.a.s.l. The Early Urichu Phase (Early Postclassic, A.D. 900 to A.D. 1100) had lake
levels drop initially at A.D. 900 to 2033 m.a.s.l., and then go as low as 2028 m.a.s.l. at A.D.
1100. The Late Urichu Phase (Middle Postclassic) had levels that had risen to 2030 m.a.s.l. after
A.D. 1100, and risen again to 2039 m.a.s.l. by the end of the phase at A.D. 1350. The Tariacuri
Phase, (Late Post Classic, A.D. 1350 to A.D. 1525) had initial lake levels at 2039 m.a.s.l., which
rose again to 2043 m.a.s.l. at the time of Spanish Contact at A.D. 1525. The maps of these
reconstructed lake levels are depicted below, and can be correlated to each prehispanic phase in
Table 11.

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Figure 17 – The 2033 m.a.s.l. Reconstructed Lake Level – Loma Alta Phase

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Figure 18 – The 2035 m.a.s.l. Reconstructed Lake Level- Lupe/La Joya Phase

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Figure 19 – The 2028 m.a.s.l. Reconstructed Lake Level- Early Urichu Phase

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Figure 20 – The 2030 m.a.s.l. Reconstructed Lake Level – Late Urichu Phase

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Figure 21 – The 2040 m.a.s.l. Reconstructed Lake Level- Tariacuri Phase, AD 1520

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Figure 22 - The 2043 m.a.s.l. Reconstructed Lake Level- Tariacuri Phase, AD 1525

The process of lake reconstruction now turns to correlating the modern, GIS created lake
levels to the Prehispanic levels noted above. As noted, the modern aerial imagery covers lake
levels between 2028 m.a.s.l. and 2041 m.a.s.l. However, the extreme lake levels noted in the
geomorphology and ethnohistoric records for the fluctuating Prehispanic levels have no modern
counterpart. This includes the higher lake level of 2043 m.a.s.l. The method used to deal with this
extreme level utilizes the digital elevation model (DEM) acquired by SPOT satellite to interpolate
the highest lake level (2043 m.a.s.l.) and its resource zones through the use of contour and slope
maps. For this upper limit of the lake level (2043 m.a.s.l.) a one meter contour map was created for
the basin, and the 2043 meter contours were selected and then used to create the 2043 lake level. In
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the same manner, the islands were also created using the 2043 meter contours from the DEM.
However, the issue of creating the marsh resources zones proves more difficult. The reconstruction
of the marsh zone starts with the analysis of other constructed marsh zones. When viewing the
relationship between open water area and marsh zone, in general the larger the lake elevation, the
smaller the area of the marsh zone. The only anomaly for this rule is the 1970 areas, where the
marsh zone is much larger in size than predicted. However, with such a high lake level, the 2043
lake reconstruction would most likely have a marsh zone area of approximately 200 – 400 hectares.
Historically, the marsh zones occur around the island of Jaracuaro, along the southern shore near
Pátzcuaro, and along the southwestern shore near Erongarícuaro. The marsh zones for the 2043
m.a.s.l. lake will be reconstructed in this manner.
Table 11 – Historic/Modern Lake Level Correlates to Prehispanic Lake Levels
Period

Phase

Year Range

Late Preclassic to Early
100 B.C. - A.D.
Loma Alta
Classic
600
Middle Classic to
Jaracuaro, A.D. 600 - A.D.
Epiclassic
Lupe/La Joya
900
A.D. 900 - A.D.
Early Postclassic
Early Urichu
1100
A.D. 1100 - A.D.
Middle Postclassic
Late Urichu
1350
A.D. 1350 - A.D.
Late Postclassic
Tariacuri
1525

Year (Modern/Historic
Correlate)

Lake Level
(masl)

A.D. 2000, 1999

2033, 2035

A.D. 2000

2035

A.D. 2010

2028-2030

A.D. 2010, 1973

2030, 2039

A.D. 1940, estimated
2043 level

2041, 2043

Reconstructing the Prehispanic Landscape
This section utilizes a variety of resources, such as paleoecological, geomorphological,
geological and archaeological data, to decipher what constituted the Prehispanic landscape of the
LPB. The goal of this section is to provide as detailed information as possible so as to reconstruct
the landscape of the lake basin in a GIS. This includes the use of data to construct a digital dataset
in GIS that may be used to quantitatively measure the interaction between communities and
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landscape variables (i.e. lakeshore resource zones, open lake, agricultural land). Vital to this
reconstruction will be the SPOT satellite imagery of the lake basin. The high resolution of this
dataset will be a major factor when trying to detail the smaller scale at which the above variables
may exist at. This method is similar to the one used for the lake and the lake resources zones.
However, the difference is that although the lake has undergone drastic fluctuations during the
temporal sequence of this analysis, certain aspects of the landscape have not. As a whole, the
landscape is just as dynamic as the lake landscape, and yet certain aspects remain relatively stable.
It is the view of Watts and Bradbury, whose main cores aided in reconstructing flora, climate, lake,
and sediment changes for the basin since the Pleistocene, that “the character of the vegetation
surrounding Lake Pátzcuaro has not changed drastically in the last 40,000 years” (1982:59). With this
assumption leading the analysis, the reconstruction begins at the regional scale of analysis, and will
move to as fine of a scale as possible in locating and delineating resources zones and landscape
variables.
At the regional level, Pollard’s assessment of the land classes and the environmental zones are
the guiding data, which she collected from ethnographic, ethnohistoric, aerial and field
reconnaissance (1983:133-151). The environmental zones of the basin, which are the broadest
categories for the physical landscape, were introduced in Chapter 2. Again, there are six major
environmental zones; 1.) the open water zone, which occurs at the lake level to the lake bottom; 2.)
the tule-reed marsh, which occurs between the lake level and 3 meters below the lake; 3.) the
lakeshore, which generally occurs between 2034 m.a.s.l. and 2100 m.a.s.l., although is dependent
on the current lake level; 4.) the lower slopes of the sierra, which occur between 2100 m.a.s.l. and
2300 m.a.s.l.; 5.) the upper slopes of the sierra, occurring between 2300 m.a.s.l. and 2800 m.a.s.l.;
and the 6.) alpine, which occurs between 2800 and 3200 m.a.s.l.. A map of these zones, which
represent the modern era, can be seen below, in Figure 23.
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Figure 23 – The Environmental Zones of the LPB

With the general environmental zones now established as the larger scale landscape
variables with which one may measure basic resource zones, a smaller scale of analysis is now
required. Gorenstein and Pollard also cites three different types of land classes, based primarily on
their research on agricultural and resource productivity for estimates of carry capacity (1983:146147). These land classes have been designated as such by Pollard from the extensive ethnographic
data used to determine agricultural practices in the basin during the first half of the 20th century.
The Class I land consists of that land which is permanently watered, by “either canal or pot/ditch
techniques”, and seasonally watered, which the “land is under seasonal irrigation by flood water
techniques” (Gorenstein and Pollard 1983:146). Class II land consists of “land in the flattish floor
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of the basin (Lakeshore environmental zone) and the alluvial basins of the Upper Slopes environmental
zone”, which is farmed by rainfall agriculture. Finally Class III land includes all the remaining
agricultural land in the basin, including areas of the lower and upper slope environmental zones,
forest, pasture, the tule-reed marsh and open water (Gorenstein and Pollard 1983:147). The issue
with these land class assignments is that this is information based on data from farming and
agriculture in the 20th century. And although some of the methods and techniques for farming
were the same as those used prehispanically, we cannot assume the Prehispanic population put the
same emphasis on these lands, which is also biased toward agricultural production. However, we
can use these classes to help distinguish several important factors that will aid in reconstructing the
landscape.
The first is that areas of lower slope, or flatter land, were desired for agricultural purposes.
The lands were easier to maintain and irrigate. With the use of the SPOT digital elevation model,
this variable is easily reconstructed using the DEM to create the contour lines and slope map for the
basin. The contour lines created are at intervals of 5 meters, a resolution previously unattainable by
other data sources for the basin. The use of these digital datasets will allow for analysis of
differential types of landscapes based on slope and elevation classes. Ultimately, the slope and
elevations will also be used to produce the cost-surface map, which will detail movement across a
dynamic landscape and include energy expenditure as well. Figure 24 displays the slope map of the
basin.
Of note is the assumption of this research that similar topographic conditions existed
prehistorically as they do in the modern period. Although there have been some drastic changes to
landscape due to seismic or volcanic activity, the majority of change and/or degradation is assumed
to derive from human influence. However, research from Fisher shows that when erosion does
occur, which is the main source of topography change, the erosion is limited to local areas, and
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isn’t as widespread as to allow for major landscape change (Fisher 2000). It is with this
understanding that a cost-surface model was created that was felt to have similar characteristics as
the prehistoric landscape.
Figure 24 – The Lake Pátzcuaro Basin Slope Map

The second factor is the soil classes, which play a major role in agricultural productivity.
Pollard cites West (1948), for the data for the soil categories, and others (Barrera-Bassols, Zinck,
Ranst 2006; Toledo 1991) have made the correlations between indigenous Purepecha soil
classifications and the technical soil terms that are used here. The lakeshore provides the most
fertile soil, named lacustrine soil, a product of deposition when the lake was higher in elevation, and
containing high amounts of organic material allowing for annual cropping (Pollard 1983:136). This
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is the most sought after agricultural land. The next soil, charanda, also called red earth, is the
primary soil in the basin, located on the lower mountain slopes and the floor of the basin (Pollard
1983:136). This soil is a volcanic andisol, and is adequate for crops, although due to the high clay
content, is more susceptible to erosion. The next most commonly occurring soil is t’upuri, or
yellow earth, which occurs on the slopes of the volcanic hills surrounding the lake. This soil is “the
most productive of the mountain soils with an extremely fine texture and moisture retentive quality”
(Pollard 1983:135). Finally, the yellow-brown soil occurs in the highest margins of the basin, in the
area of the fir-pine forests in the upper elevations (1983:135). These soil classes are those used
presently, and are those that have been used historically and prehistorically. The fact that the words
charanda and t’upuri are Purepecha words attest to this fact. For reconstruction purposes, the soils
for the basin seem to correlate strongly with the land classes and environmental zones that Pollard
has laid out. These, then, will be used in tandem with the environmental zone reconstructions.
Ultimately, these reconstructions in GIS will be used as the areas of agricultural land variable that
will aid in the analysis of the settlement system.
Travel and trade routes are a final variable that defines the landscape. Defined by human
travel and interaction across the terrain, these routes are vital in reconstructing the social aspect of
the landscape. Pollard’s work on the Early Hispanic time period and analysis of ethnohistoric
documents gives us a better understanding of travel and transport in the basin. In her analysis,
Pollard ranks three types of travel routes based on “the fundamental transport property of
magnitude of traffic flow” (Gorenstein and Pollard 1983:48), with rank 1 being external routes,
rank 2 being water routes, and rank 3 being internal routes (see Figure 25). These routes, however,
are based on ethnohistoric, archaeological, and ethnographic data, and are known to have existed in
the Early Hispanic period. It is very plausible that these routes also existed in the Late Postclassic
period, and we can retrodict them as such. However, given that these routes were created, altered
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and adjusted within the landscape in conjunction with changes in the socio- political and physical
environment of the time, it is unlikely that these same routes existed in earlier phases. Therefore, it
would be too presumptive to retrodict these routes any further back than the late Postclassic period
(Espejel 1992).
Figure 25 – The Early Hispanic Transport Network for the Lake Pátzcuaro Basin (Gorenstein and
Pollard 1983)

However, through the use of GIS modeling, we can create a map that, based on slope,
predicts and models the cost of travel across the basin landscape. This is a cost-surface model, and
is discussed in much more detail in Chapter 5. However, we can use it to make simple correlations
and bridging arguments for its use to aid in reconstructing possible travel and trade networks
throughout all phase of the Prehispanic sequence. As you can see in Figure 26, the cost-surface
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map is detailed, with Pollard’s transportation network overlaid on top. Basic investigation shows
that there exists a strong correlation between the areas of least-cost travel and the Early Hispanic
travel network. Therefore, for earlier phases, the paths of least-cost will be used as potential travel
and trade routes for those time periods, and can be used in the spatial analysis when determining
variables that may have affected settlement.
Figure 26 – The Early Hispanic Transportation Network and Cost Surface Map

Summary
This chapter introduces the landscape approach as a major theoretical paradigm of this
dissertation. This theory is crucial in understanding the human-environment relationships of the
lake basin that, through a landscape approach, are defined as interactions that leave material traces
in the archaeological and geological record. This interaction between the landscape and the human
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components translate well into a political economy approach, which is also discussed in this
chapter.
With the theoretical underpinnings expressed, the chapter then moves to method, as the
Prehispanic landscape is reconstructed. The major portion of this section included the
reconstruction of the lake levels, the lakeshore and the lake resource zones. This involved using
the geological, geomorphological and archaeological data to reconstruct the lake levels for the
Prehispanic time periods. Once this was done, modern correlates of these lake levels were found,
and the satellite imagery analyzed to produce GIS shapefiles of the lake levels. Following the lake
reconstructions, the landscape and the environmental zones were reconstructed in a GIS, which
allows for the quantification of the variables and statistical analyses.

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CHAPTER 5: A SETTLEMENT SYSTEMS ANALYSIS
The goal of this chapter is to use the combined data from the previous two chapters in
order to analyze the communities of the Southwest portion of the lake basin, as well as the
landscape of the basin. The statistical analyses presented in this chapter are divided into two
separate methods, which are later integrated to provide a holistic assessment of the settlement
systems throughout the sequence. The first method will assess the relationship among the
individual communities within the lake basin through time. The second method will assess the
relationship between the communities and the economic-resource variables (i.e. lakeshore
resources, agricultural land, travel/ trade routes, forest resources). The primary means that will
be used in these analyses are measures of distance and size (density). With each of these
analyses, there is an explicit theory that will guide the method, and will be described in the
next section. This will include a background and history of the concepts that shape the
statistical analyses found in this chapter, including cost-surfaces, cost-distance, least-cost paths,
and gravity modeling. A brief explanation of the algorithms used will also be presented, in
order to understand the geographical theories that define the spatial statistics used. Finally, the
methods will be introduced and performed, and the result will be an analysis that effectively
tests the settlement systems of the southwest portion of the lake basin through time.

Geographical Theory
The one common variable that underlies both the landscape and community analyses is
interaction. The theoretical discussion in both chapters three and four, explain that this
analysis uses a behavioral definition of community, where one can delineate and analyze the
material traces of communities in terms of the open-system of interaction that reflects this
human behavior. Furthermore, it is because of the landscape theory used in this analysis and
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described in chapter four, that we can identify a unique relationship between humans and the
environment, and seen in this analysis as a relationship between communities and the
surrounding landscape of the lake basin. This theory also emphasizes the essential role of
interaction in this relationship; a relationship that is dialectical and where the landscape
contains “spatial and temporal characteristics and has been modified by human activity such
that human intentions and actions can be inferred, if not read as material culture from it,”
(Balee and Erickson 2006:1). It acknowledges the human-environment interactions that create
landscapes, and emphasizes natural environmental variables, “including essential subsistence
resources, other raw materials needed for physical comfort and health, and items for trade or
exchange” (Anschuetz 2001:177). With interaction playing such an important role in both the
relationship between communities as well as the relationship between communities and the
landscape, a method and set of statistical analyses must be introduced that can help to quantify
these relationships in order to better understand the role each variable (i.e. communities,
lakeshore resources, agricultural land, travel/ trade routes, forest resources, lake/lacustrine) in
determining settlement location and the larger systems behind settlement.
There are several factors that affect interaction. The theory behind communities and
human-environment relationship in the lake basin, interaction is defined as face-to-face (Kolb
and Snead 1997: Drennan and Petersen 2005). Prehispanically in the lake basin, the two
primary methods of travel by which this interaction occurred were by foot or by canoe, each
having their own limitations and advantages. With this in mind, factors such as distance and
time, topography, and access are vital when quantifying these interactions. And for quite some
time, geographical methods and theories have been grappling with these factors in an attempt
to reconstruct human behavior. The following sections will first discuss the evidence there is
for travel and interaction within the basin, both Prehispanically and historically, and then
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discuss the geographical approaches and spatial statistics that are used to reconstruct this
interaction on a landscape.

Travel, Trade and Interaction
There is good evidence concerning travel within the lake basin, both historically and
prehispanically, from ethnohistoric and ethnographic sources. A large amount of information
comes from multiple sources that document the extent of trade within Mesoamerica at the time
of Spanish conquest. Research from Hirshman and Stawski (2011), Drennan (1984a, 1984b),
Hassig (1986), Gorenflo and Gale (1990), and Pollard (1987), discusses the likely range of
travel for a porter carrying goods across the landscape. Upon review of a range of sources,
Hirshman and Stawski (2011) argued for 36 km as the maximal distance for a round trip to
market; that is, two four-hour 18 km trips at an average walking speed of 4.5 km per hour,
leaving a brief period of time within the destination market for transactions. Although this data
is intended to describe the market exchange, ceramic production, and ceramic porting in the
basin during the Late Postclassic, it does show that a vital aspect to interaction deals with the
energetic cost of travel within the basin. In fact Hirshman and Stawski go on to argue for a
maximum carrying load of 23 kg, based on principles of energy cost, time and distance to the
market.
Canoe travel is also discussed by several sources, including Goreflo and Gale (1990) in
their research in the Basin of Mexico, Gorenstein and Pollard (1983) and Pollard (1990, 2008)
in research of the Lake Pátzcuaro Basin. Gorenflo and Gale, in their research focusing on the
Late Formative to Late Toltec phases in the Basin of Mexico, looked to Spanish accounts and
estimated canoe travel to be 1/3 slower than foot travel, approximately 3.33 km/h (Gorenflo
and Gale 1990: 244). However, energy expended in canoe travel was less than porting items
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on ones back, and also more items could be carried by canoe than by porter. In terms of Lake
Pátzcuaro, this is truer of the larger canoes that traversed the open water of the lake, and not the
smaller fishing canoes that were more common in the Tule-reed marsh zones. Gorenstein and
Pollard’s work on the Early Hispanic time period and analysis of ethnohistoric documents
gives us a better understanding of water transport in the basin. In the analysis, Gorenstein and
Pollard rank three types of travel routes based on “the fundamental transport property of
magnitude of traffic flow” (1983:48), with rank 1 being external routes, rank two being water
routes, and rank 3 being internal routes. If this is the case, then according to Pollard and the
ethnohistoric documents, water transport was a more desirable mode of transportation than the
internal routes
These types of research tell us that interaction was costly, and that there was a very
complex decision-making framework in place for both individuals and communities that
structured their role in the political economy and their location on the landscape. To reiterate,
this analysis looks at three distinct variables, distance, topography and access, and their roles in
the decision-making process for travel, trade and interaction within the lake basin.
Distance is the most obvious of the three factors that can inhibit interactions within a
landscape. Using Hirshman and Stawski’s ceramic and market research, the estimate for travel
during a four hour time period is 18 kilometers. That is an average walking speed of 4.5
kilometers per hour. This walking velocity fits into the expected range of travel speed that has
been tested and observed in a variety of research scenarios (Tobler 1993; Gorenflo and Gale
1990; Aldenderfer 1998; Hare 2004). Therefore, using this estimate of walking velocity, the
communities in the LPB were mapped, and distance buffers were then calculated in concentric
rings of one-hour travel time from each other. The outcome is a basic map, based on Euclidean
distance, which shows which communities fall into certain distance classes. No matter the
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case, though, it is shown that there is no community outside of this travel range from any other
community. If this is the case, then one can assume that distance may be less of a factor in the
LPB than previously thought. One may argue that the lake plays a pivotal role in inhibiting
travel throughout the basin, and yet previous data shows that in most cases, canoe travel was
the favored type of travel within the basin (Pollard 1983). Therefore, we must explore other
options that affect interaction within the basin, and play a role in the settlement decisionmaking process.
The second variable that affects travel is the topography of the landscape. Although the
cost of human travel is not thought to have a simple linear relationship with slope, the latter
does have the most effect on the former when assessing variables. The issue of topography
compounds the already complex relationship that communities have with markets in the case of
distance and time. Once again, through the use of the ethnohistoric documents, Pollard has
reconstructed the transport network for the lake basin or the Early Hispanic period. For the
purpose of reanalyzing these networks, the original maps have been taken, scanned and
projected in ArcGis (see Figure 18). A visual inspection of these maps as well as others from
the Lake Pátzcuaro Basin shows topography indicative of the environments common in the
Mexican Highlands. This includes drastic elevation changes due to the volcanic activity of the
region, detailed by lower fields made fertile by volcanic ash and higher slopes made steep by
tectonic activity. Pollard’s retrodiction of the travel network shows that the majority of these
transportation paths coincide with areas of relatively lower degrees of slope. And a great many
of them focus around the lake, being the area of lowest elevation in the region. Likewise, we
may infer that travel throughout the basin was (and still is) greatly affected by the topography,
more specifically the slope of the landscape.

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The third variable, access, might be the most important of the three. We cannot assume
that if a community was in close proximity to the lake, that the residents had access to either a
canoe or a landing. Likewise, if a community was in close proximity to a “least cost” path,
meaning one which allowed for increased walking velocity and optimized energy expenditure,
we cannot assume they had access to this path and the lands it would cross. In this case, as
stated by Kantner, the researcher must consider the numerous cultural and practical
considerations that may cause people to alter their route, “making it unlikely that anyone in any
landscape will follow an optimal route (2004:328). In order to further investigate the role
access played in the basin, we once again turn to Pollard’s ethnohistoric analysis of the
Tarascans.
An important factor when considering access is the role of the Tarascan state in the lake
basin. We can get an understanding of the role of the Tarascan state in terms of control of the
markets, for example. In terms of the markets, Pollard states, “There is no indication that the
markets were state controlled or regulated, despite an extensive description of the judicial
systems in the Relacion de Michoacán” (1982:256-257). Furthermore, Pollard says that on
only two occasions did the state forbid market activity; on the death of the king and the arrival
of the Spanish. Of the three market locations, only Tzintzuntzan, the capital, was also an
administrative center. This non-congruence of the markets and administrative centers further
suggests a minimal politicization of the markets in the lake basin (Pollard 1982:257). Based on
this data, it seems that there was little in the way of limited access to participation in the
markets. However, we cannot assume the same level of control was also the case for broader
travel and access within the basin. According to the ethnohistoric documents, there did exist a
network that was state controlled and solely for state consumption. This included longdistance merchants, such as Nahua merchants (Monzon, Roskemp, Warren 2009) state
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agricultural lands, fisheries, state forest lands, and state mines (Pollard 1982:256, 2008:225). It
is unclear if the state limited access across these lands, however, we do know that there was
severe punishment for “neglecting the king’s fields” as well as “damaging the maguey”
(Pollard 1982:258). This steep cost alone may have been enough reason to avoid state
controlled land, even if it was the optimal path to traverse. Along with state controlled land, it
is known that the royal dynasty also officially allocated access to land, water, forests and
mineral resources (Pollard 2008: 225). According to Pollard, “access to land was distributed
within communities by traditional kinship ties, and land was acquired by kings for support of
state administrators and state temples” (2008:225). Access to water resources is less clearly
defined in the ethnohistoric documents, and whether access was traditionally held by
communities or allowed access by the king. In any case though, it seemed that some regulatory
entity had control of access to certain areas of lakeshore and lake resources, thus making access
to lake travel much more limited.

Cost-Surface and Cost-Distance Models
Through a combination of spatial statistics and the use of Geographic Information
Systems (GIS), this analysis uses a series of methods that take into account the aforementioned
issues of interaction on a landscape. This section will explain the spatial analyses used, as well
as the GIS technology that accommodates such spatial modeling. To begin, one must realize
the implicit issues regarding modeling human behavior in space and time. In such a case, the
only behavior that is assumed is that humans will take the least-cost path when traveling
through a given landscape. That means that in the decision-making process, humans will
attempt to travel a route that conserves time, money, or energy. And yet within each of these
three options, there are assumptions again about human behavior. Compounding these issues,
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there is the matter of the level of detail the GIS software, the satellite imagery, and the
algorithm employed to determine travel. In an attempt to limit error and assumption in the
method, each of these factors will be explicitly discussed.
The first issue arises when deciding how to statistically approach modeling human
behavior. The advent of GIS, and the increased processing speed and storage capacity of
computers has allowed for vast amounts of data to now be stored, managed, analyzed and
processed. In the case of human behavior, archaeologists have used such spatial techniques as
viewshed and cost-surface analyses in order to investigate the past. Cost-surface, specifically,
“has been used to enhance catchment analyses and the model prehistoric road networks”
(Kantner 2004:323). A cost-surface in GIS is a “grid map where each cell contains the
energetic cost of traveling” (Hare 2004:803). With each cell having a numeric output for the
cost of travel, one can then analyze the landscape and determine the cost distance between
features of a landscape, and the least-cost path that one could take to travel between features.
The research from Hare also uses a cost-surface to partition territory into proximity zones
around features in the landscape, thus creating social, political or economic boundaries (2004).
However, the production of the cost-surface depends on the algorithm used, which can
“represent the relative or absolute cost of travel over each unit of space, with cost measured
either by units of time or energy” (Kantner 2004:325). The majority of these algorithms
depend on slope to calculate the cost of movement through the “digital” landscape, with some
algorithms being simple, such as “simulating a cost-path between two points by moving from
one cell to another according to which neighboring cell represents the least amount of slope
(Kantner 2004:325, e.g., Anderson and Gillam 2000:47). These “drainage” algorithms, as they
are used commonly in hydrology analysis, contain merely a simple linear relationship with
slope. However, the cost of human travel is known to be more complex than this simple linear
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relationship. In that case, several algorithms have been created to aid in better depicting human
movements through a digital landscape.
Although many algorithms have been used to create cost-surfaces, not all algorithms
articulate human movement realistically across the landscape. The cost of travel for a costsurface can either be assumed to be “isotropic- the same no matter in which direction the space
is crossed, or anisotropic” (Kantner 2004:326). As explained by Kantner in his summary on
cost- surface algorithms: “The majority of analyses have implicitly or explicitly assumed that
travel cost is isotropic, usually because most software packages do not readily accommodate
anisotropic modeling. However, intuition suggests that the cost of traveling down a slope is
less than trudging uphill, and a few attempts to develop anisotropic algorithms have been
attempted.” (2004:326).
And yet, the algorithms that were first created to model anisotropic movement only
arbitrarily assigned different uphill and downhill costs to movement, which weren’t based on
empirical observations. The first successful use of empirical evidence derives from a report
published in 1950 by Imhof that studied the marching of the Swiss military. This data was
used to create Tobler’s “hiking function”, an anisotropic algorithm that suggests a more
symmetrical relationship between movement uphill and downhill (Tobler 1993). The equation
is as follows: v = 6 e

–3.5 * abs(s + .05)

where v is walking speed, s is the slope of the terrain,

calculated as vertical change divided by horizontal change, and e is the base of natural
logarithms. This means that on certain terrain, a traveler could spend as much energy braking
against a downhill slope as they would spend walking up that same slope. For the Tobler
equation, the function predicts a maximum velocity of six km/hour when going down a slope
of five to seven degrees, with any steeper slopes forcing the traveler to slow down (Kantner
2004:327).
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Tobler’s “hiking function” is a realistic algorithm that is based on empirically observed
data. But before a cost-surface can be created, there are still assumptions that must be
discussed. The one assumption is travel through a digital landscape. In a GIS created layer, a
surface is either represented through vector data or raster data, with vectors comprising lines,
polygons and nodes, and raster consisting of square pixels that produce a grid. Satellite
imagery, for example, is raster data. For the purpose of this analysis, the cost-surface map will
also be a raster, and its smallest component will be a pixel, or cell, the size of which is
determined by the resolution that the image was taken at. Thus, the larger the cell size, the
more likely topographic details will be obscured. But no matter what the resolution, we must
come to terms with how one theoretically “travels” across a square grid. Inherently, the costpath algorithms are artificial, and calculate movement in an unrealistic, non-human manner.
This movement, also called the “Queen’s case”, is frequently used to determine movement
from cell to cell. The Queen’s case provides only eight possible directions across cells, either
diagonally or horizontally. However, this can be slightly remedied if the cell sizes are small,
thus slightly masking the “jerky” movement across a grid.
Another question in creating a cost-surface is what exactly the measure of cost is. As
explained by Kantner, “If the researcher wants to determine a path that someone might choose
to walk from one point to another, the important question is whether humans choose the path
that takes the least energy or the path that takes the least time” (2004:328). For instance, when
looking at shorter distances, energetic cost may be the best measure, where time may be a
better criterion for longer distances. Also the context for movement must be considered. For
instance, if someone is transporting food, they must be aware of the energetic cost of travel
relative to the amount of food they are gathering/transporting.

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The final factor that affects the selection and use of an algorithm for a cost-surface are
the cultural and practical considerations of human movement across a landscape. These
factors, which in essence constitutes the landscape, may drastically alter human movement, and
cause paths to be traveled that aren’t optimal or the least-costly. Unfortunately, when dealing
with prehistory and a temporal range of 1,600 years, it is impossible to recreate the landscape
and all of its cultural, social and political attributes. However, for the Lake Pátzcuaro Basin,
some of these factors have already been discussed in terms of access and travel throughout the
basin. It is these variables that will be taken into account when determining the cost-surface
for this analysis.
Therefore, with the assumptions of the creation of a cost-surface taken into account, this
analysis will employ the use of Tobler’s hiking function in the creation of a cost-surface. This
anisotropic algorithm will be used to determine the energetic cost of traveling throughout the
LPB. I have chosen this for several reasons. Given the smaller size of the LPB and the study
area, and the exploratory analysis of Euclidean distance in the basin, it is seen that relatively
shorter distances were traveled within the basin. Also, the context of travel is broader for this
analysis, where “interaction” may be porters taking goods to market, farmers going to and from
fields, people gathering firewood, or messengers traveling between communities. With such
varying categories of interaction, it is felt that the majority would fall under the travel category
of taking a least energetic cost path.
Once created, the cost-surface grid will represent differential velocities across the
landscape that represents the real-time travel velocity. This will allow a least cost path analysis
that allows for realistic modeling of distances between communities to other communities, and
between communities and the landscape. The following sections will detail the analysis
utilizing this cost surface; first through the analysis of community interaction, and second
132

through the analysis of community-landscape interaction. The quantification of interaction and
the spatial analysis used will directly aid in determining the major variables that affect the
settlement system through the temporal phases.

Community Interaction Analysis
The community interaction analysis began with the application of the cost surface that
was discussed previously. The cost surface realistically portrays the walking velocity across
the landscape, a landscape that is by no means isotropic in its nature. The primary means that
will measure the impact of the communities on each other will be a gravity model (discussed in
Chapter 1). In short, the gravity model is a means in which to measure interaction, politically
and economically, and states that “the level of interactions between two elements is
proportional to the product of a measure of mass at each location and inversely proportional to
a measure of distance between locations” (Hare 2004:802). Chapter 3 detailed the method in
which communities were located, delineated and the population calculated for each. The
population, in this case, will be the mass that is calculated. The distance variable is calculated
using the cost surface, with the least cost path (LCP) measured between each pair of
communities.
The GIS method used to obtain the distance variable that will be used in the gravity
model equation is as follows. In order to realistically recreate the landscape for each phase,
thereconstructed lake level and marsh zones were applied for each phase to the cost surface, the
cost surface layer being a raster file in GIS. The cost surface was applied to the extent of the
lake and marsh zones for each phase, therefore representing the navigable terrain that was
present for each phase. This created a separate raster file that showed the velocity of traveling
across the landscape. However, what this doesn’t take into account is the lake and marsh
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zones, which, as we know from ethnohistoric and ethnographic data was and remains a major
travel corridor. To accommodate this, the lake and resource zone was transformed into a cost
surface whose velocity value was derived from research from the Basin of Mexico, at 3.33
km/h (Gorenflo and Gale 1990:244). Now we can model movement across terrain as well as
the lake, with foot and canoe travel represented. That way the least-cost paths may best
represent real world travel access, limitations, options and or constraints.
With an accurate cost surface now created for each phase, the communities were then
added to the GIS workspace, and altered to produce the necessary raster files. In order for a
least cost path to be created, each community was analyzed separately in relation to all other
communities. The single community being analyzed was considered the source, with the other
communities being the destination (Figure 27).
With the sources and destinations defined, a cost distance map and a backlink map were
created for the source. Both of these maps were created using the cost surface, as seen in
Figure 20. The cost distance file calculates the least accumulative cost distance for each cell to
the nearest source over a cost surface (Figure 28). The back link file defines the neighbor that
is the next cell on the least cost path to the nearest source (Figure 29). With these two defined,
the next step is to create a cost path, using the remaining communities as the destination input.
The result can be seen in Figure 30.

134

Figure 27 – The Source and Destinations in a Cost Surface Model

135

Figure 28– The Cost Distance Map

136

Figure 29 - The Back Link Map

137

This was done for each community, to create an array of cost path maps that represent the
least cost path between every combination of communities. The paths were then measured
individually between each community, giving an accurate geodesic distance measurement that is
used in the gravity model to determine interaction values. This was done for each community, for
each phase. Loma Alta had 6 communities, resulting in n=30 distance measurements; Lupe/La
Joya had n=9 communities, resulting in n=72 distance measurements; Early Urichu had n=17
communities, resulting in n=272 distance measurements; Late Urichu had n=42 communities,
resulting in 1806 distance measurements; and finally Tariacuri had n=17 communities, resulting in
n=272 distance measurements.

138

Figure 30 – The Final Least Cost Path

139

With the least cost path distances measurements complete, an excel database was then
created to calculate the interaction between communities through the application of the gravity
model equation. The equation is as follows: I = Pi * Pj / dij

1.9

, where Pi is the population of

community 1, Pj is the population of community 2, dij is the distance between the two
communities, which is raised to 1.9, the constant that alters the distance of extension of influence,
derived from Hare (2004: 802). This equation was calculated for each combination of
communities for each of the phases, thus creating an array of interaction values between
communities for each phase. The ultimate result of these interaction values is to determine which
communities had the most interaction for each phase, what the overall characteristics of interaction
of communities were, and to provide insight into which were the possible major communities, or
centers, through each phase. In order to do this, each community’s interaction values were
analyzed, and the highest interaction numbers were used to create a map of primary interactions
between communities. The interaction is shown using desire lines between the communities. An
example of these primary, and in this case, secondary interactions can be seen in Figure 31 for the
Loma Alta period. The tables for the community interactions can be found in the appendices.

140

Figure 31 – Loma Alta Community Interactions

Statistical Analysis of Interaction Values
In order to better understand the community interactions, an exploratory analysis of the
interaction values is done by phase. Given that the gravity equation divides the population by the
141

distance, one can assume that lower interaction values means that there exist a greater distance than
population value. Likewise, higher interaction values must assume that larger population values.
The “gravity aspect” of the equation involves the mass of the two communities, and the leading
goal is to see whether it was distance or population that inhibited or prohibited interaction amongst
the other communities. At the very basic level, these exploratory statistics will show the nature of
the interaction values for each phase, which will be interpreted along with the interaction maps in
the next chapter.
Table 12 – Exploratory Interaction Statistics by Phase
Phase
Tariacuri
Late Urichu
Early Urichu
Lupe/La Joya
Loma Alta (1 &2)

Year
A.D. 1350-1525
A.D. 1100-1350
A.D. 900-1100
A.D. 600-900
150 B.C. - A.D 600

Count Max
Min
272 1610.85 0.13
1722
0.33
0
272
0.07
0
72
0.06
0
30
0.07 0.014

Mean Median St. Dev.
58.14
7.6
195.6
0.008 0.0012
0.03
0.003 0.0002
0.009
0.0024 0.0001
0.009
0.014
0.002
0.02

Community- Landscape Interaction Analysis
In the same manner in which the community to community interactions were analyzed and
quantified, the community-landscape interactions will also utilize the cost surface model to
interpolate interaction. However, given the difficulty in quantifying mass in terms of the landscape
variables, such as lakeshore resources, with concern to the application of the gravity model, a
different method will be used to determine interaction levels between the communities and the
landscape variables. Instead of using the least cost path analysis, the cost surface will be used to
create a cost allocation surface, which assigns territory to each polity center with the smaller cost
distance (Hare 2004:805). In effect, this creates a site catchment for each community based on the
cost surface. Johnson outlines two important factors that make this method relevant. First,
“settlement location as well as sedentarization and settlement formation appears to be related to
142

movement-minimizing behavior”, with transport costs playing a central role in settlement location
(1977:489). Second, based on these assumptions “catchment analysis normally defines the radius
of that resource area as the distance beyond which energy expended in movement equals or
exceeds the energy return of exploitation” (Johnson 1977:489). However, these Euclidean radii
don’t take into effect the terrain, which the cost allocation does.
Therefore, the cost allocation territory represents the area surrounding the community most
easily and quickly accessed. For the sake of consistency, these output allocation polygons, which
represent catchments, will be referred to as allocation catchments. The premise of the allocation
catchments, also referred to as cost catchments, has its roots in site catchment analysis, where a
specified area around a site is analyzed, and the resources within that boundary, or catchment,
measures resources based on distance to the site. The catchment represents resource accessibility
for the site. However, the major difference between cost and site catchments is that cost
catchments “take into account the cost of moving through the landscape whereas the simple site
catchments do not” (Surface-Evans 2012:128). The research from Surface-Evans uses a least costpath analysis, and cost corridors between two points to ultimately create catchment areas for the
sites, rather than rely on a simple 5km catchment buffer based on Euclidean distance (2012:142143). Her research shows that although the cost catchments are smaller and model reduced
accessibility than the site catchment counterpart, they appropriately fit the constraints that affect
foot travel across the landscape (2012:146). In the same manner, the cost allocation function in
GIS, when coupled with a cost surface, creates boundaries of allocation based on least cost from
the point of origin, which in this case is the community.
The method that was followed to create these allocation catchments, as stated above, began
with the cost surface grid, based on Tobler’s hiking function, which includes the lake and marsh
zone, with canoe travel velocity of 3.33km/h. Modeling water travel was important to the analysis
143

because instead of the catchment stopping at the beginning of water features (as most land-based
analysis do), it will compute the area of the lake that is most easily accessed by certain
communities, which assumes canoe access and canoe travel. That way, we can compute the area of
specific marsh or lake zones within the allocation catchments. However, one factor that
complicates the resource and landscape catchment modeling is that when running the spatial
analysis in a GIS, it bases the boundaries both on the cost surface model as well as the other
communities around it. This is similar to how Theissen polygons are created. Whereas this creates
useful boundaries for the interior communities, the outer communities are defined by the extent of
the layer, which is an arbitrary outer edge. There really is no means by which an objective and
meaningful outer boundary can be created, so in order to limit the extent of the allocation
catchment, a two kilometer buffer was created around each community, which defines the overall
extent of the allocation catchment. Figure 32 below shows the allocation catchment before the
buffer was created, and also shows the allocation catchment after the buffer rings were applied and
used to clip the extent of the layer.

144

Figure 32 – The Cost Allocation Catchment: No Boundaries

145

Figure 33 - The Cost Allocation Catchment: Two Kilometer Buffer

146

With the allocation catchments defined for each community, the method next involves
quantifying the different resource and/or landscape variables within said catchments. In this case,
four separate analyses will take place: 1.) the analysis of percentage of resource zones in each
catchment (i.e. lakeshore, alpine, lower slopes, etc.); 2.) the analysis of the catchment’s slope; 3.)
the analysis of the satellite imagery to identify any major landscape features within the catchment;
and 4.) the creation of hypothetical travel/trade networks for the Southwest portion of the basin.
This was done for each phase and for each community in each phase.
The first method involved combining the resource zone layers in GIS (alpine, upper slopes,
lower slopes, lakeshore, marsh, open water), and applying the above allocation catchment
boundaries to each. For each cost catchment boundary, whatever resource zone was present in the
catchment was clipped, and the area calculated. Therefore, each cost allocation for each
community has the area of the resource zones located in it, and the percentage of the total area.
This will ultimately aid in our understanding of the resources that the community was most likely
accessing. An example of the results of this analysis can be found in Table 13, and a map
depicting the analysis can be seen in figure 33.

147

Table 13a - Loma Alta Landscape Analysis

Community Community Zone

Catchment
Area (m2)

Open
Water (m2)

Tule-Reed
% Marsh (m2)

%

Lower
1 Slopes/Lakeshore
2 Lakeshore

5695833
10477548

NA
NA

0
0

NA
NA

0
0

Lower
Slopes/Lakeshore
Lower Slopes
Lower Slopes
Lower Slopes

4367042
4939059
3996119
1638473

NA
NA
NA
NA

0
0
0
0

NA
NA
NA
NA

0
0
0
0

3
4
5
6

Table 13b- Loma Alta Landscape Analysis

Lakeshore (m2) %

Lower Slopes (m2) %

Upper Slopes (m2) %

695480.9
7007452

12.2
66.9

5000352
3470095

87.8
33.1

NA
NA

0
0

1798290
NA
2784231
NA

41.2
0.0
69.7
0.0

2568752
4939059
1211889
1638473

58.8
100.0
30.3
100.0

NA
NA
NA
NA

0
0
0
0

148

Figure 34 – The Landscape Resource Zone Analysis

149

The slope analysis was done in a similar fashion, where the slope map for the basin was
clipped to each specific allocation catchment, and the statistics for that catchment’s slope was
calculated in GIS. The slope at the location of the community was recorded, as was the elevation.
The remainder of the analysis looks at the specific catchment as a whole for each community,
giving the maximum (max) value, minimum (min) value, mean and standard deviation of the slope
frequency for that catchment. Examples of the slope analysis can be seen in Table 14. The
remaining maps and tables for all phases, for both the resource and slope analysis, can be found in
the appendices.
Table 14 – Loma Alta Slope Analysis
Community
1
2
3
4
5
6

Comm Slope
10.8
4.1
6.7
5
1.8
2.6

Comm Elevation
2098 masl
2084 masl
2108 masl
2130 masl
2103 masl
2125 masl

Max
25.2
19.5
24.1
33.6
21.3
24.1

Min
0
0
0
0
0
0

Mean
4.9
4.4
4.9
5.8
4.6
5.9

St. Dev
4.2
3.7
3.3
3.4
3.4
3.4

The final aspect of the analysis, the travel/transportation network analysis, included the same
method for creating a least-cost path based on Tobler’s hiking function as was done for the
community interaction. This method worked from one major assumption. First, although it was
stated earlier that it would be too presumptive to retrodict the Protohistoric transportation network
that Pollard reconstructed back any further than the Late Postclassic, it was noted that there was a
strong correlation between the cost-surface model, low slope, and the transport network, as
analyzed in a GIS. Therefore, we can safely assume that major internal and major external roads
would have these same characteristics of being in areas of flat terrain higher travel velocity.
Therefore, to recreate the possible transportation network for the southwest portion of the basin, a
cost path analysis was completed for each phase, taking into account the lake levels, foot and
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canoe travel, and resource zones. One variable that won’t be taken into consideration are the
communities themselves. Instead, the sources and destinations are locations outside of the
communities, to represent potential origins/destinations of travel from outside this southwest zone.
These sources, n=7, were selected from an array around the southwest area, and at least 2
kilometers away from the closest southwest zone community. The result was a “spider web” of
least-cost paths that surrounded the communities, many of which intersected or overlapped. The
result of the least cost path transportation network for the different phases for the southwest
communities.
One note must be made about the creation of these routes in GIS. A brief analysis was done
in order to test the feasibility of using travel routes created from the cost surface. In order to test
its application, the cost surface travel routes created for the Tariacuri phase were compared to the
transportation network presented by Pollard and Gorenstein (1983). In a GIS, the two layers were
overlaid, and compared, noting any deviations in routes and proximity of routes to each other (see
Figure 35). In the majority of cases, the major routes were very close in their location, with two
exceptions. Given the higher amount of detail, on a sub-regional level, the least cost method
predicted a route through to malpaís the link the southeastern portion with the northwestern
portion. Pollard’s network lacked these routes. Also, the routes running from the lake directly east
out of the basin differed from each other. However, I believe that the similarity outweighs the
differences, especially when considering scale of analysis, and therefore feel comfortable utilizing
the least cost routes as a variable in modeling community-to-landscape interaction.

151

Figure 35 – A Comparison of Least-Cost Travel routes versus Pollard’s Transportation Network

152

The method for quantifying the impact of the travel and transport network on the
communities is simple enough. The same allocation catchments that were used to quantify
accessible community resource zones were also used to estimate how many travel and trade routes
were accessible to the communities. First, any routes existing inside a community’s catchment
were counted, and an average geodesic distance was calculated. Second, the access to water travel
was assessed, and if access existed within the catchment, the water routes were counted. Once the
data was retrieved, exploratory statistics were done to examine the community’s relationship with
the routes, as a whole, by looking at the basic frequency of routes and the average distances to the
routes. Other notes were also made, such as if the communities were located on routes, near or on
major intersections, or if water or other major topographic features blocked access. The results of
the transport analysis can be seen in Table 15, with basic exploratory statistics for each phase
found in Table 16. The data from all other phases can be found in the appendices.
Table 15 – Transportation and Travel Analysis by Community: Tariacuri Phase

Community
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Travel Routes
(Land)
0
3
0
1
2
1
1
3
3
1
0
2
2
2
3
2
1
2

Average Distance to
Routes (meters)
NA
2394
NA
755
50
346
359
797
133
132
NA
1108
970
279
730
219
388
50
153

Water
Access
yes
yes
yes
no
yes
no
yes
no
no
no
yes
yes
no
yes
no
no
no
yes

Water
Routes
0
4
6
NA
0
NA
0
NA
NA
NA
2
2
NA
NA
NA
NA
NA
1

Table 16 – Summary Statistics for Travel and Transport Analysis by Phase

Phase

Average # of
Routes per
catchment

Communities
Average distance with water
to routes (meters)
access

Loma Alta (1 &
2)

2.2

663

(n=0)

Lupe/ La Joya

1.9

682

(n=7) 77%

Early Urichu

1.5

373

(n=7) 41%

Late Urichu

0.97

442

(n= 22) 51%

Tariacuri

1.6

586

(n=9) 50%

Summary
This chapter focused on the methods for analysis of the southwest portion of the Lake
Pátzcuaro Basin, including all phases from the Loma Alta (150 B.C. to the Tariacuri Phase
(A.D. 1350). This included first the community analysis, which utilized a cost surface and
least cost path (LCP) analysis to determine the interaction values between communities for
each phase. The equation used to measure this “interaction” was the gravity model, which uses
population and distance, in this case geodesic, real word distance between communities.
The second aspect of the analysis utilized the reconstructed landscape of the Lake
Pátzcuaro Basin to aid in quantifying the human-environment relationship that existed between
communities and the resource zones and landscape of the basin. First, allocation catchments
were created, using the cost surface for walking velocity that represented realistic resource
catchments for each community by phase. The resource zones were measured within each
community’s catchment, representing the potential resource allocation and accessibility for
each individual community. Second, the slope was analyzed for each community’s allocation
catchment, and exploratory statistics were run to measure the relationship between the
community and the terrain on which they settled and that they potentially interacted with and
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accessed most regularly. Finally, potential travel and transportation routes were created once
again using the cost surface model. These routes had origins and destinations outside of the
southwest portion of the basin, representing a network of possible routes, based on least cost
paths, coming in and out of the area. The community’s allocation catchment was once again
utilized to aid in defining which routes were most easily accessed by communities, with the
distances measured between each community and route. Also calculated was the number of
communities with water access and in relation to potential water travel routes. The following
maps display the results from each of these analyses, by phase; the community interaction
analysis, the allocation catchment resource analysis, and the travel-transport analysis.

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Figure 36 – The Loma Alta Phase Community Interaction Analysis

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Figure 37 – The Loma Alta Phase Allocation Catchment Analysis

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Figure 38 – The Loma Alta Phase Travel/Transport Analysis

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Figure 39 – The Lupe/La Joya Phase Community Interaction Analysis

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Figure 40 – The Lupe/La Joya Phase Allocation Catchment Analysis

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Figure 41 – The Lupe/La Joya Phase Travel/Transport Analysis

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Figure 42 – The Early Urichu Phase Community Interaction Analysis

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Figure 43 – The Early Urichu Phase Allocation Catchment Analysis

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Figure 44 – The Early Urichu Phase Travel/Transport Analysis

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Figure 45 – The Late Urichu Phase Community Interaction Analysis (Primary Interaction)

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Figure 46 – The Late Urichu Phase Community Interaction Analysis (Secondary Interaction)

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Figure 47 – The Late Urichu Phase Allocation Catchment Analysis

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Figure 48 – The Late Urichu Phase Travel/Transport Analysis

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Figure 49 – The Tariacuri Phase Community Interaction Analysis

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Figure 50 – The Tariacuri Phase Allocation Catchment Analysis

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Figure 51 – The Tariacuri Phase Travel/Transport Analysis

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CHAPTER 6: MODELING THE LAKE PÁTZCUARO SETTLEMENT SYSTEM

The southwest survey area of the Lake Pátzcuaro basin provides a unique view of the
settlement of the basin. Although many different factors were at play through the ~1,600 year
scope of this analysis, the primary variables used to analyze the settlement system included the
resource zones of the basin, the fluctuating lake levels, the surrounding communities, the terrain of
the landscape, and the travel and transport network of the basin. Chapter Five described the
analysis of these variables, attempting to quantify their impact on the communities and overall
settlement for each phase, with the hopes that a longitudinal analysis of the settlement systems may
be performed. This chapter summarizes and discusses the results of the aforementioned analysis,
and provides a micro-regional settlement system model based on the analysis. The original
settlement model that was tested, derived from Pollard (2008) is revisited, and a new model based
on the findings from this research will be proposed for the lake basin as a whole. In order to then
test this model, recent survey results from the southeast portion of the lake basin are used to
compare and contrast to the southwest survey zone, thus providing us with a more complete
regional settlement system model for the LPB.
This analysis very deliberately divided the analysis between the two main variables classes;
the communities and the landscape. And as was discussed in the previous chapter, the analysis for
each was done in a different manner; the community analysis utilized the gravity model between
communities to quantify interaction, while the landscape variables utilized the cost allocation
model and allocation catchment zones to quantify the community-landscape interaction.
Therefore, it must be said that it will be almost impossible to rank all the variables in terms of
impact on settlement, when they have had different analyses performed on them. However, this
discussion will assess each variable’s impact on the communities and the overall settlement by
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phase, with the understanding that in reality it is understood that there is not one variable alone that
totally influences settlement, but more likely a combination of all variables, with some being of
more import given the context of the analysis. It is in this manner that this section attempts to
summarize the analysis and present a settlement systems model, first for each phase, and second
for the micro-region of the southwest portion of the basin.
Discussion: The Community-to-Community Interaction Analysis
To quickly summarize the community-community analysis, each community was located
and delineated based on the artifact clusters from the walkover surveys. Then, through a
combination of ethnohistoric sources, ethnographic data, and archaeological correlates, the
population was calculated for each community, providing the “mass” component of the gravity
equation. The distance variable was calculated using a cost-surface, where the least cost path
between each community was defined and measured, thus providing a real-world measure of
distance. The interaction values were then calculated for each community to community
combination, providing an array of interaction values. The primary and secondary interaction
values were taken and mapped, thus creating a network of interaction between communities for
each phase. The results are discussed below, by phase.
The Loma Alta phase, having the fewest number of communities, had the fewest amount of
interaction calculations. Overall, the primary interactions between communities formed two
distinct areas of interaction, the first around communities 3 through 6, the second between
communities 1 and 2. Only the secondary interactions between communities 3 and 1, and 3 and 2
created a link between the two areas. The interaction numbers overall are very low (mean- 0.014),
which shows that distances are still very far, and population still very low. Only the slightly larger
population at community 1, 2 and 3 are large enough to derive a meaningful interaction, and yet
these values are still very low.
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Figure 52 – The Loma Alta Phase Community Interactions

These two distinct community interaction zones display the beginning of a unique dynamic
that is discussed at length further. That is, the emergence of two major communities in the
southwest portion of the basin, with communities 1 and 2 representing Urichu, and communities 3
through 6 representing Erongarícuaro. Excavation evidence and burial remains show that elites
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were present at later phases at both these locations and that at the time of Spanish conquest, both
were regarded as major centers, with Erongarícuaro a rank 2 center (Gorenstein and Pollard 1983).
However, the Loma Alta phases show the emergence and territorial development of these centers,
while populations are still low. A note must be made that post-analysis, one final community was
added to the Loma Alta phase communities. This community was present during the Jaracuaro
phase, which was a similar phase of 50 years added onto the end of Loma Alta 3. It was felt that
this community and its artifacts most closely representing a Loma Alta occupation, and was then
added to the dataset. The community was located on the lakeshore of the island of Jaracuaro, and
expands our understanding of the community settlement of this time. It is clear that this
community was engaged in a lacustrine based subsistence system, and that communities were very
early on locating themselves near these resources.
The Lupe/La Joya phase shows a slight increase in communities, to n=9, yet still low
population numbers. These lower population numbers (~700-1600) once again result in lower
overall interaction values (mean=0.0024). In fact, these numbers are lower than the previous
phase. This is because we see the emergence of communities on the lakeshore, on the
island/peninsula of Jaracuaro as well as to the north east of Urichu, north of the Malpaís, thus
increasing the distance between communities. The interaction map, Figure 52, displays three
interaction zones, with a more developed secondary interaction zone. The primary zones are still
very much at the local level, while secondary zones show the slightly increased influence of the
larger communities at Urichu (communities 5,7) and Erongarícuaro (communities 8,9). This is
interpreted as a more independently structured microregional community network, where
communities are using more local spheres of interaction first and most, with distance being a major
influencing factor.

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Figure 53 – Lupe/La Joya Interaction Zones/Community Boundaries

The Early Urichu phase shows a significant increase of communities (n=17), nearly
doubling the number from the previous phase. However, most of these communities emerge
around the modern town of Erongarícuaro, displaying a growing population at that center. Even
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with the growth in population for this part of the basin (~900-1800), it is still slightly, and the
distances great enough to produce another lower overall interaction value (n=0.003), only slight
higher than the previous phase. As you can see from Figure 53, the interaction zones remain the
same as the previous phase, with a slightly more developed secondary interaction relationship
between communities. This is once again interpreted as an independently structured microregional community network, with slightly growing centers are Urichu and Erongarícuaro.
Figure 54 – Early Urichu Interaction Zones/Community Boundaries

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The Late Urichu phase sees the most drastic shift in population to this point. The
southwest area population more than doubles and now ranges between 4000 and 8500. We also
see the most drastic increase in communities, from n=17 in the last phase, to n=43 in the late
Urichu phase. Several things are important to note. Although community number increases, their
size decreases, as smaller communities, such as hamlets or villages, are scattered more widely and
evenly across the landscape. And even though population drastically increases, it is more
uniform across the landscape instead of concentrated at central locations.

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Figure 55 – Late Urichu Primary Interaction Zones/Community Boundaries

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This represents a major shift in the interaction trends and settlement for the area. First,
the communities are moving further away from the earlier established centers at Urichu and
Erongarícuaro, and the community Urichu decreases in size. Furthermore, growth at the
Erongarícuaro communities remains constant in terms of population, yet the communities
remain centralized, and did not spread out like those in the southeast portion of the area. The
local interaction remains the prevalent form of community structure, as the individual
community populations are still relatively lower, and yet distance is less of a factor given the
more even distribution across the landscape. The interaction values overall for this phase are
still low (n=0.0012) due to this fact. Pockets of local interaction define the primary zones, with
the secondary zones display a more micro-regional trend, where interaction is being centralized
and distance is less of a factor. This is true for all communities except those at Erongarícuaro,
where interactions at all levels occur between local communities.
The final phase, the Tariacuri phase, represents the emergence of the state, and a major
shift in the southwest regional settlement system of communities. Overall for this phase, the
number of communities went down drastically to n=18 (from n=44 in the last phase), and yet
the population for the area increases to between ~7000 and 12500. This shift represents two
things; 1.) the move away from smaller communities to larger, more centralized communities
with larger populations, and 2.) the decrease in distance between these communities. This is
clearly displayed in the interaction values, with the average value being n=58.14, as population
is now the deciding factor for interaction, and distance a secondary factor. Three distinct
centers emerge, at Jaracuaro (community 1) Urichu (community 15), and Erongarícuaro
(community 16 & 17), with much larger interaction zones for each (Figure 55). Also, we can
now witness settlement at Pareo, community 18, which is also a regional market during at least
the Late Postclassic, and most likely as early as the Middle Postclassic. Furthermore, several
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other communities had centralized, and although not as large as the three listed above, were
mentioned and located in the ethnohistoric documents by Gorenstein and Pollard (1983:20-22).
These communities can be interpolated to those present during the Spanish arrival and into the
Early Hispanic period, suggesting that it was this drastic shift in settlement and community
formation during the Late Postclassic that formed the communities that are still present to this
day. Table 17 portrays these community to settlement patterns, as taken from Gorenstein and
Pollard (1983). Interaction is now defined on the location to these centers, with almost all
primary and secondary interactions occurring between these three. This shift represents the
move towards a micro-regional interaction scheme, and quite possibly displays the impact of
the emergence of the state on local settlement.

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Figure 56 - Tariacuri Interaction Zones/Community Boundaries

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Table 17 – The Late Postclassic & Early Hispanic Community Correlations
Tariacuri Phase
Early Hispanic
Community
Correlate
Survey Sites Comprised
X-10, X-6-1, X-6-2, X-6-4 to
Community 1, 2, 3
Jaracuaro
X-6-6
P-61, P-64, P-71, P-30, P-29,
Community 18
Pareo
P-32
Community 15
Urichu
U-1 to U-8, U-54 to U-57
Community 16, 17
Erongarícuaro
all Erongarícuaro Survey Sites
Community 7
Arocutin
U-60, P-98, P-96
Community 8
Nocutzepo
P-114, P-107
Community 5, 6
Toquaro
P-108 to P-113
Community 11, 12
Cuyameo
P-19, P-37 to P-39
Discussion: The Community-to-Landscape Interaction Analysis
The second analysis turns to landscape variables in an effort to quantify their impact on
settlement of communities. In brief, the method for analyzing the landscape variables can be
broken down into 3 categories: 1.) the analysis of resource zones, 2.) the analysis of slope and
terrain, and 3.) the analysis of transport and trade in the region. For each of these analyses, a
cost-surface model was used to create cost allocation zones, or as termed in the previous chapter,
allocation catchments. In each analysis, the catchments were used to create boundaries for each
community’s accessible area, with the variables measured accordingly. The area of each
resource zone found in each individual allocation catchment was calculated, the slope for each
catchment was analyzed, and the transport routes that fell inside these zones were counted and
measured. The following is a discussion of each analysis by phase.
The Loma Alta phase landscape analysis, with reference to the resource zones, saw all
community allocation catchments containing the lower slopes resource zone, with n=4 containing
the lakeshore zone. Only one community (community 2) was located entirely within the
lakeshore zone, and yet it occurred on the inner most area of the zone, away from the lake. No
community allocation catchments included the marsh area or the open lake, or the upper slope or
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alpine zones. Only two communities (community 2 and 5) had the lakeshore as the primary
resource zone in their catchment. When coupled with the slope analysis, this trend of community
location further inland and upland continues. With the lake at this phase occurring at 2035
m.a.s.l., the lake resource zones are at their most accessible, with abundant lakeshore and marsh
area. However, the communities are all located above elevations of above 2084 m.a.s.l., and on
somewhat uneven terrain, with average slopes all occurring above 4.4 degrees. Although this is
not entirely extreme, coupled with the higher elevations, and the fact that the communities exist
in transition areas, from the lower lying even terrain of the lake shore to the more uneven, areas,
it seems that communities are located in defensible positions, with access to agricultural and lake
resource zones.
The transport network proposed for the Loma Alta phase shows that the communities are
located near major routes, with an average of n= 2.2 routes per community, and an average
distance to routes being 663 meters. Furthermore, the routes that they are located by are more
inland occurring routes, specifically the southwest to northeast route inland, and the north to
south route out of the basin. Both of these routes have excellent access to the lakeshore and lake
resources. However, as discussed in the previous paragraph, no communities have direct lake
access for water travel.
The Loma Alta phase communities seem to have organized themselves on the landscape
in semi-defensible positions, allowing themselves access, although not direct, to transport routes
to the lake, and lower lying agricultural lands near the lakeshore. The fact that most of the
communities were inland and upland, in areas with more remote access lends credence to the
semi-defensible theory. This is especially true for communities 1 & 2 at Urichu, which were
situated on the outer edge of the Malpaís. Overall, it seems that terrain characteristics, followed

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closely by access to the lakeshore and more even terrain, influenced the community locations on
the landscape. Access to travel and trade routes also played an important role, considering that
the communities were located in remote areas and required access to the major resource zones.
The addition of the lakeshore community on Jaracuaro for this phase adds to our understanding
of the role of the lake and lacustrine subsistence for this phase. It is clear that even though it was
only one community, the lakeshore did have direct settlement for the lake resources, thus
pushing back our notion of when communities settled near the lake and began actively practicing
a lacustrine-based subsistence system.
The Lupe/ La Joya phase, with an increase in communities, saw a shift in settlement,
from upland to lowland areas. Every community, except community 8, had the lakeshore zone
located in their allocation catchment, 7 of 9 communities were located either entirely within the
lakeshore zone, or on the border between the lakeshore and the lower slopes. Also, of these n=8
communities that contained the lakeshore zone in their allocation catchment, the lakeshore
comprised the majority resource zone. This is a shift from the last phase, where the lower slopes
were the majority resource zone represented in the allocation catchments. Furthermore, n=5 of
the communities contained the Tule-reed marsh zone in their allocation catchment, with another
n=3 containing open water.
The slope analysis is also representative of the shift towards the lower-lying lakeshore
zone. Whereas the last phase saw all communities in higher elevations and more uneven terrain,
the Lupe/La Joya phase has n=5 communities located on terrain having a slope less than 4
degrees, n=4 communities with allocation catchments averaging a slope less than 4 degrees, and
n=5 communities located at elevations below 2100 m.a.s.l., n=4 of those below 2060 m.a.s.l..
However, there still exist n=6 communities that are located in fairly defensible positions along

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the Malpaís, and further inland near Erongarícuaro. These communities still exist on the borderareas, on the fringes of the lakeshore resources zone and the upper slopes, close enough to flat
terrain and lake resources, yet also right on the edge of drastic elevation changes and slope
changes.
The travel/transport network analysis for the Lupe/La Joya Phase shows an average of 1.9
routes per community, with an average distance of 682 meters. In this case, the average went
down from the last phase, with the distance going up. However, 77% of communities had water
access, increasing drastically from the last phase. The same major inland routes still exist for
Urichu and Erongarícuaro communities, but with the shift towards the lakeshore, the
communities are now closer to more accessible and navigable terrain, meaning more access to
the lakeshore routes and water routes.
Overall, the Lupe/La Joya phase showed a slight shift of communities moving towards
the lake and lake resources. The lakeshore zone is now the primary resource zone accessed,
presumably for both lake and marsh access as well as for the fertile soil and flat terrain for
agriculture. However, this shift is only slight, where the majority of communities are still located
on the fringes, both for elevation, slope and zones. Although these community locations can still
be deemed defensible, it seemed that the lakeshore resources and terrain were a major draw for
communities, and seem to be the primary motivator for community settlement and location
during this phase. The travel and transport network are still accessible, but don’t play as major of
a role as the last phase, given the increased distance from routes and the lowering routes per
community statistics.
The Early Urichu phase sees the lowest lake levels in the sequence, at 2028 m.a.s.l. The
community allocation catchments are pretty evenly divided, with n=14 communities containing

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the lakeshore resource zone, and n=14 containing the lower slopes zone. However, of these
community catchments, n=10 have the primary zone as the lower slopes. Regarding the
community locations themselves, n=6 are located within the lakeshore zone, another n=6 on the
lakeshore/lower slope zone border, and n=5 within the lower slope zone. One would think that
with the lowest lake level seen in the LPB, communities would move to take advantage of the
lacustrine soil that has been exposed, or the marsh areas now exposed. However, only n=4
communities are located right on the lakeshore/Tule-reed marsh boundary, with n=6
communities containing the Tule-reed marsh zone in their allocation catchment. Conversely, we
see three communities, communities 7, 8, and 9, move the furthest inland of any community yet.
The slope analysis shows a trend towards the higher elevations and more upland terrain.
Of the n=17 communities for the Early Urichu phase, only n=5 are below 2060 m.a.s.l., even
when the lake is at its lowest at 2028 m.a.s.l.. The majority, n=9 communities, are located above
2100 meters in elevation. The slope, although never drastic in any one area, shows that the
lakeshore communities located closest to the lake have the most even terrain, while those located
inland, closer to the malpaís or near the fringe boundaries of elevation change have higher slope
terrain. Some of the more inland communities had a mean slope of around 6 to 8 degrees
(community 5, 7, 8, 9).
This trend inland, however, shouldn’t be interpreted entirely as defensible positions. In
the case of the Urichu communities, the location on the malpaís seems to be somewhat defensive,
although they are in close proximity to agricultural land, the lakeshore, inland springs, and lake
resources. In the case of communities 7, 8 and 9, the inland communities, they are located in or
around a valley formed by the malpaís to the north, and an elevated hill chain to the south.
Current aerial imagery shows the remains of terraces at communities 7 and 8, with community 9

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located in the lower valley floor. In fact, according to the elevations and Pollard’s resource
zones, the lakeshore and fertile soil extends as far back as these communities. Although the
slope and elevation analysis may show these to be inland, upland and possibly defensive, it is
entirely possible that the communities formed here to take advantage of the fertile soil and
terrace agriculture, while still providing a safe community location. In fact, because this valley is
between two areas of higher slope and elevation, the proposed travel and transport network runs
right through community 9, and very close to communities 7 and 8. And although the average
number of routes per community went down from the last phase to n=1.5, the average distance to
routes dropped dramatically overall to 373 meters. Communities with direct lake access went
down to 41% however, with these moves inland.
Overall, the Early Urichu phase proves somewhat difficult to decipher. Although the lake
drops to its lowest point for the sequence, the communities are located in the lower slopes, away
from the lakeshore itself. Most seem to have good access to the lakeshore resource zone,
however, only a minority of communities seems to be directly accessing the marsh and open
water zones. The interpreting of the inland and higher elevation communities is interpreted as
mixed. Once again, they are in semi-defensible positions, yet have easy access to good
agricultural land, excellent access to transport networks for this phase, and good access to lake
resources. In fact, there seems to be a three tier development of settlement for this phase, when
discussing landscape resources. One third are located with excellent access to the lake, it’s
resources and the lake travel routes, another third with medium lake access but excellent
lakeshore and agricultural access, as well as good access to the inland travel routes, and a final
third in what I consider a defensible position, but with overall fair access to all resource zones
and travel routes.

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The Late Urichu phase saw an expansion of both communities and the area they covered
in the southwest zone. The lake levels fluctuated during this phase, from between the lowest
point at the beginning of the phase, ~2030 m.a.s.l., to the end of the phase ~2039 m.a.s.l. With
regards to the resource zone analysis, the overwhelming majority of communities are located in
the lakeshore (n=30), at the border of the lakeshore, (n=4), or had the lake shore in their
allocation catchment (n=40). Furthermore, n=22 (51%) communities had the lakeshore resource
zone as the majority zone in their catchment. The second most prevalent resource zone in the
allocation catchments was the lower slopes, followed closely by the Tule-reed marsh zone. This
expansion of communities towards the lakeshore zone also saw communities right on the water,
close to both the Tule-marsh zone and open water. In fact n=22 communities were located
within 100 yards of the water. This expansion also meant more communities move inland,
although not nearly as many as the lakeshore. For the first time, we have multiple communities
that have in their allocation catchments the upper slopes zones. And although the upper slope
areas are relatively small and on the edges of their respective catchments, they still represent
potential access to a different set of resources than previously witnessed.
With this expansion towards the lakeshore, we also see a movement towards more level
terrain and lower elevations. Of the n=43 communities, n=22 occur at elevations lower than
2050 m.a.s.l. Furthermore, for n=22 communities, the average slope for their respective
allocation catchments is at or less than 5 degrees. Also, for the individual community locations,
n=27 communities occur on a slope of less than 3 degrees. While some communities had located
themselves on hillside, or edges of higher elevated land, the majority of communities have now
shifted to existing on the lower lying, even terrain of the lakeshore.
Concerning the travel and transport network, the community average for routes within the

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allocation catchment went down to n=0.97. And yet, the distance to routes remains low, at an
average of 442 meters. One of the reasons for this lower number is because the communities that
had moved to the peninsula of Jaracuaro all scored zeroes for the analysis, since no major land
routes coming in our out of the area would traverse through a peninsula. However, due to their
location near the water, they do have access to water travel, as the number of communities that
had this access rose to 51%. I believe the reason for the lower routes per community yet lower
average distance derives from the fact that these communities are located in areas of very even
terrain with potential for greater access and higher walking velocity. Therefore, although they
may not have had as many routes at their disposal, the communities had increased access to the
routes they are near due to favorable terrain, which included water routes.
Overall, we see a marked difference in the scheme for settlement with concern to
landscape variables. The lakeshore is now the prevalent and dominating landscape feature that
predicates settlement and community location, with the added benefit of also being located near
even and more traversable terrain. It seems that the draw of the lakeshore terrain, the lake
resource zones, and potential for agriculture and lacustrine resources was the motivating factor
for the Late Urichu phase, even with the unstable lake levels for that period of time. However,
the communities at Urichu and Erongarícuaro stay located where they had been in previous
phases, in areas of semi-defensible positions. Also for the first time, we see a significant portion
of the Jaracuaro peninsula inhabited, which seems to draw communities nearer to lakeshore
resources.
The final phase, the Tariacuri phase, marks the emergence of the Tarascan state, around
A.D. 1350. In terms of the resource zone analysis, the lake is recorded at its highest in the
sequence, at 2043 m.a.s.l., according to the ethnohistoric evidence (i.e. the Beaumont and Seler

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maps, from Gorenstein and Pollard 1983:14-16). This fluctuation seems to have altered the
community scheme for locating across the landscape, considering that now many communities
establish in earlier phases are now under water. The resources zones are also altered drastically.
Because of the rising water, the Tule-reed marsh area is now smaller is size, as is the area that
was previously lakeshore. The open water is easier to access though, which may have led to an
increase in water based subsistence practices, especially as key lakeshore agricultural areas are
now flooded. Given the pushback from the lake, every community contains the lakeshore in its
allocation catchment zone, and the vast majority of communities are located within or on the
border of this zone, at n=16. Only one community is located outside the lakeshore, in the lower
slopes. However, out of n=18 communities, the lakeshore is the majority resource zone in only
n=8 of them. Given the shrinking marsh zone, only n=6 communities have access to that zone in
their catchments, with only=5 having direct open water access. The second most frequent
resource zone in the allocation catchments is the lower slope zone, with n=14 communities
having access, and n=7 of those communities having the lower slopes be the major resource zone
in their catchment.
In terms of the slope and terrain analysis, the majority of communities, n=16, are located
on land with a slope less than 5 degrees. And yet, due to the push back from the rising lake, and
the loss of low lying and flat terrain to the lake, we see the allocation catchment zones consisting
of average slopes of above 4 degrees. This is due to the move away from the lake towards the
steeper lower slope zone. Overall, it seems that with limited land, the communities are actively
locating where land is most flat. When we couple this analysis with the transport network
analysis, we see that in fact the lake level has moved the travel routes more interior, yet they are
still located on the lower-sloped lakeshore. The average route per community goes up from the

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late Urichu phase, to 1.6. However, the distance to routes also goes up, to an average of 586
meters. This is primarily because Jaracuaro, once a peninsula, is now an island, and is cut off
from the land travel routes. When excluding the Jaracuaro settlements, the remaining
communities are in very close proximity to the travel routes, with many being located at major
inland/lakeshore route intersections. There were n=8 communities with direct access to water
travel, with most communities now in closer proximity to the lake and more indirect access.
The Tariacuri phase doesn’t necessarily show a new paradigm for settlement with
concern to resource zones. That major shift came in the Late Urichu phase. However, the major
event of this phase, the rising lake level and loss of significant resource zone area, does signify a
change in settlement with concern to the changing landscape. For the first time in the sequence,
the lake rises significantly enough to alter the trend of the last two phases, which was moving to
closer proximity to the lake. Now, the move is further back from the lake, as the lake impedes on
communal land and overtakes areas previously inhabited. And yet, communities are locating
themselves almost entirely within this diminishing lakeshore zone, presumably to take advantage
of the remaining resources available, such as prime lacustrine soil and flat agricultural land, the
remaining Tule-marsh areas, and access to the open water. Once again, the communities at
Urichu and Erongarícuaro now considered major centers according to the ethnohistoric data
(Pollard and Gorenstein 1983) have stayed in the same area that they originated at in earlier
phases.
The Testable Settlement Model Revisited
The previous summary discussed the analyses that were performed for each phase of the
sequence, first with the community-to-community analysis and second with the community-tolandscape analysis. This section provides a holistic settlement systems model, as derived from

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the analysis of this research, which applies to the southwest portion of the basin. This
microregional settlement systems model will be discussed by phase, and provides a complete
trajectory of settlement, beginning with the Loma Alta phase at 100 B.C. and ending with
Spanish Conquest at A.D. 1525. First though, the testable settlement model, presented in
Chapter 1, will be revisited. It was this model that the hypothesis for this research was
formulated from, and for which the results of the analysis is compared to. This section, then
presents first the testable model, derived from Pollard (2008), and will compare and contrast the
newly formulated microregional settlement system model, as derived from this analysis. The
final portion of this chapter will utilize the data from the southeast survey, completed in 2009,
and present a brief analysis on the settlement from that portion of the lake basin in comparison to
the microregional settlement systems model. This is done to test for differential variables from a
different basin area, and to see if the variables from the southwest analyses are applicable to
other portions of the lake basin.
The initial, testable model, presented in Chapter 1, is revisited and presented again here
so that a direct comparison may be made between this first model, and the model derived from
the southwest settlement systems analysis.
The starting point for the temporal sequence of settlement is in the Late Preclassic (100
B.C.). Population within the lake basin was relatively low (5,000 – 8,000), and all cases of
settlement displayed the existence of small-scale, socially ranked agrarian societies (Pollard
2008:220). Furthermore, these ranked societies consisted of a hierarchy that would remain
relatively unchanged until the emergence the state. Settlement was located on or very near the
shorelines of Lake Pátzcuaro with the primary means of subsistence being lacustrine and wetland
based. The shorelines of the lake fluctuated minimally, marking the only movement of

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settlements. Furthermore, settlements were not yet centralized spatially, but were situated
primarily on or near the key resources of the lake.
The Early Classic (A.D. 600) to Middle Classic (A.D. 700) was marked by a stable
population, between 6,000 and 7,000 (Pollard 2008:221). The settlements remained on or very
near the lakeshore, continuing lacustrine and wetland agricultural practices. Ceramic production
remained localized, yet preciosities found their way into the basin and were part of the way elite
status was derived and marked. The goods, however, were finished goods, meaning that local
level economic specialization had yet to permeate the economic structure in the basin. Each
settlement had variation in its social hierarchy as well as its spatial composition, with varying
types of architecture and no singular style dominating. This suggests the continuation of a local
elite-dominated settlement with a highly agrarian component.
During the Epiclassic (A.D. 700-900) the region of West Mexico began to see political
restructuring and climatic changes (Beekman 2009). The end of this period (A.D. 900) marked a
climatic shift towards more arid conditions, with a synchronous drop in the lake level. The
number of sites increased and the population rose to 12,000 at this time (Pollard 2008:224).
These climate shifts and slight rise in population coincided with the beginning of the Early
Postclassic (A.D. 900-1100). Lacustrine settlements, still the primary type of settlement in the
basin, moved to these new lake margins as the lake dropped to its lowest elevation in the past
two millennia (Pollard 2008:223).
With new arable land and a continued reliance on the lacustrine resources, the small-scale
socially ranked societies shifted to larger-scale chiefdom-like societies, a shift that began in the
Middle Classic periods. It is believed that with the rise in population, settlements began to
centralize at various distances inland from the lake while continuing to utilize its resources.

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During the Middle Postclassic (A.D. 1000-1350), a large population increase occurred as it rose
to 48,000, along with the doubling of the area of occupation due to the low lake levels. Near the
end of this phase (~A.D. 1300), lake levels rose again, thus forcing settlements away from the
low-lying areas around the lake to concentrate around the marsh production zones (Pollard
2008:224). Pollard asserts that due to these expanding, diminishing and shifting resources,
competition must have been fierce, leading large-scale chiefdoms into inter-basin warfare.
Settlement then shifted primarily due to the larger populations within the basin. Settlements
continued to be located near the lake and slightly inland from it, but also moved upland into
defensible locations, such as in the malpaís at Urichu (Pollard 2008:224).
The Late Postclassic period marked the emergence of the Tarascan state in A.D. 1350.
The continued rise in lake levels forced settlements out of low-lying to new lakeshore and inland
areas of high agricultural fertility. The added pressure from climate change and population size
drove settlements to develop new economic mechanisms, thus diversifying communities with a
heavier reliance on markets and state-run institutions. With the emergence of the state,
settlement is now dictated by the royal dynasty at the capital of Tzintzuntzan. Pollard proposes a
power shift towards the northern end of the basin, thus altering the spatial orientation of
settlement. Resources were managed by the state, and the social model that dominated the basin
since 100 B.C. was replaced by the state’s rigid social hierarchy system, where a three class
system was put into place; an upper elite class (containing the royal family), lower elite class,
and a commoner class.
The Microregional Settlement System Model
The following settlement system model derives directly from the analysis of this
dissertation, and will be discussed phase by phase, providing a longitudinal view of settlement

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for the ~1,600 year sequence for which the study encapsulates.
The settlement system, for the purposes of this analysis, begins in the Loma Alta phase,
during the Late Preclassic and into the Early Classic. At this time, six communities are present
in the southwest portion of the lake basin, with a cluster just north of the modern town of
Erongarícuaro, and another south of the modern town of Urichu, along the western side of the
malpaís. The overall population of these communities is relatively low, and between 200-700
people occupied these communities during both the Loma Alta 2 and Loma Alta 3 phases. The
lake level was fairly stable, remaining around 2035 m.a.s.l. Even with a lower lying lake level,
and abundant lakeshore resources, most communities at this time were located in upland, semidefensible positions. However, one community was located directly on the lake shore, thus
displaying evidence of an early lacustrine based system of subsistence, though not a regional
system. The communities at Erongarícuaro had the best access to fertile agricultural land and
lakeshore resources, while the Urichu communities located them on the fringes of the dense and
steep malpaís. All communities are located to the north or west of the malpaís, and with the two
distinct, very separate clusters, represent two separate, local community zones. The primary
interactions for these communities are local in nature, with secondary interactions reaching from
Urichu to Erongarícuaro and vice versa. However, because interaction values are so low for this
phase, it is thought that distance and terrain are limiting factors in community interaction on a
microregional or regional scale, and locating near adjacent communities is not a significant
variable for settlement. There is evidence for elites and a ranked social organization, and yet it is
believed to be only a local system, given low population numbers. Communities 1 and 2, at
Urichu, and community 3 at Erongarícuaro, are believed to have been local administrative center
with ranked elites during this early phase (Pollard 2008:220).

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The primary variable that determined settlement for this phase was steeper terrain, higher
slope and defensible positions. Second most important was access to the flat lower slope
resource zone, and the lakeshore zone, presumably for agricultural practices. This access to
lower lying terrain also meant access to travel and transport routes, which is the third most
important variable. At this time, there is no direct access for communities to the lakeshore, and
to the Tule-reed marsh zone or open water. Because of this, it is thought that a lacustrine-based
subsistence system didn’t play as large of a role as perhaps agriculture did during this phase.
The Lupe-La Joya phase saw a slight increase in both community frequency and
community population. The number of communities increased slightly to n=9, and the
population for this area of the basin is estimated to be between 450 and 1200. Along with the
communities present at Urichu and Erongarícuaro, we see new communities spread to the
lakeshore, to the south and east of the malpaís, towards the peninsula of Jaracuaro. However,
this movement is only for a few communities, whereas the communities at Erongarícuaro and
Urichu remain in upland, semi-defensible positions. Community interaction values are still very
low, and now there are three primary interaction zones, located at Erongarícuaro, around the
malpaís of Urichu, and on the eastern edge of the Jaracuaro lakeshore. Given the large distance
between communities, distance is the primary reason for little interaction, whereas populations,
especially for the lakeshore hamlets, are still very small. There occurred a shift in the settlement
system for this phase, as several communities moved towards into the lakeshore resource zone,
and towards the lake resources, such as the marsh and open water. Evidence of lacustrine-based
subsistence practices are witnessed from the artifact assemblage from Community 2, where
recortados, or circle net weights for fishing were collected. However, not all communities made
this move, and several still remain in areas of semi-defensible, upland positions with access to

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key resource zones.
The primary variable for settlement system for this phase is the lakeshore, as n=6
communities move to either within its zone, or with access to a greater area of this zone.
Furthermore, the subsistence strategy seems to have changes to include lacustrine zones to a
greater extent. A secondary variable for settlement location is terrain and slope, as several
communities are located on or near semi-defensible position, especially around the malpaís of
Urichu. However, with the move of several communities towards lower lying areas and more
flat terrain, travel routes are also more accessible. And with communities moving away from the
established communities at Urichu and Erongarícuaro, community-to-community interaction is a
minor variable for settlement location, especially given the lower populations and increased
distance between settlements. Community interaction is limited primarily to the local scale.
Artifact analysis though, shows these areas, especially community 5 and 7 near Urichu, remain to
be centers where local elites are present, possibly both in an administrative and residential
capacity.
The Early Urichu phase marks a near doubling of communities from the Lupe/La Joya
phase, from n=9 to n=17 communities. The population for these communities is estimated to be
between 700 and 2,000, with the population centered around Erongarícuaro, where the number
of communities increased, and at Urichu. Artifact evidence once again shows communities 10,
and 11, located at Urichu and Erongarícuaro respectively, to be administrative centers due to
their higher frequencies of artifacts. Also, community 6 is believed to be an elite residential area,
given the artifact assemblage. However, only Erongarícuaro sees community numbers increase
around the major center in that portion of the lake basin. Other communities continue to access
and utilize the lakeshore and the lacustrine resources, as they continue to locate on or near the

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lakeshore. Communities 1, 3, 4 and 11, shows evidence of this lacustrine subsistence system in
recortados found at the sites, used as net weights for vertical nets in the marsh shallows (Phillips
2002). However, given the fact that evidence shows that the lake is at its lowest during this
period, at 2028 m.a.s.l., and that there is abundant access to both the Tule-reed marsh and fertile
lacustrine soil of the lakeshore, we don’t see a majority of communities locate to access these
resources. In fact, the lower slope is the primary resource zone accessed by the communities,
given their locations. We also see three communities, 7, 8, and 9, locate the furthest inland,
away from the lake but still near the lakeshore zone. It is thought that this area, located in the
valley between the malpaís and a southern hill chain, was located both near major travel routes in
and out of the basin, as well as fertile agricultural land, with access to terraced agriculture on the
lower slopes.
Interaction values again rise slightly from the last phase, although they are still low
comparatively. This is probably due to an offset occurrence of community location. The large
distance between communities 7, 8 and 9 and other communities drove interaction values down,
while the growth of communities around Erongarícuaro and the growing population drove the
values up, the creating a stable interaction value overall. This brings up the fascinating aspect of
this phase; that we see three separate, equally important variables for determining settlement
location. The larger population and growth at Erongarícuaro, and the fact it was a major local
center created community growth around it. In this case, community-to-community interaction is
the primary variable determining settlement. However, communities remain located on or near
the lake and in the lakeshore zone, and we see more artifact evidence suggesting more
communities are participating in the lacustrine subsistence strategy. In this case, the lake and
lakeshore resource zones and marsh zones are the primary variable for settlement structure. And

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finally, we see communities locating more inland, such as communities 7, 8, and 9, and the fact
that Urichu has remained on the fringe of the malpaís. It is probable that access to agricultural
land and terraced agriculture is the driving factor for communities 7, 8 and 9, as well as access to
the inland travel routes.
The Late Urichu phase marks a dramatic shift in the settlement system structure.
Population for the southwest area increases to a range between 4,000 and 8,500, a doubling of
population from the last phase. Furthermore, the number of communities present during this
phase was at n=43, a major increase from n=17 in the last phase. However, the increase in both
communities and population develops in the form of many smaller, hamlet and village size
communities, moving away from the upland centers of Urichu, and to a smaller extent
Erongarícuaro. This major increase is one aspect of this phase that marks a major paradigm shift.
The other is the major increase of communities to the lakeshore zone, on or near the lake. The
majority of communities are now located in the peninsula of Jaracuaro, or just south of the
peninsula. Furthermore, the lakeshore and the Tule-reed marsh are the most accessed resource
zone, and although the lake has fluctuated during this phase, beginning around 2030 m.a.s.l. and
ending at 2039 m.a.s.l. at the end of the phase, communities made a clear decision to locate in
very close proximity to the lake. The lacustrine zones were the primary means for subsistence
for these communities, evidenced from the archaeological remains that show n=8 communities
having assemblages including the recortados, or net weights.
The higher populations at both the Urichu and Erongarícuaro communities continued for
this phase, however, the area of the Urichu communities decreases slightly, whereas the
Erongarícuaro communities remain high in number and don’t seem to be as greatly affected by
the move of most communities to the water’s edge. Artifact types and high frequency of artifacts

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at community 30 of Erongarícuaro and community of 28 of Urichu show these to be the major
administrative centers in the area. A new development for this phase was the increase of
obsidian in the artifact assemblages. Rebnegger’s analysis (2010) shows that although many
sites contain high frequency of obsidian artifacts (communities 30, 33, 35, 11, 13, 16), they were
probably not manufacturing sites, but perhaps part-time craft specialization locations. This
increase of more exotic material suggests a development in the trade network for this phase, as
well as an established elite trade network. This suggests interaction, at least for the major
communities at Erongarícuaro and Urichu, of a more complex regional networks.
The primary mechanism for community settlement for this phase is clearly the lakeshore
and lake resource zones. Given the evidence of a more developed and complex trade network,
locations near flat terrain and travel routes also became desirable, as did the locations near water
routes. By locating near and around the lakeshore zones, these communities had access to both
types of travel, as well as the best agricultural land in the area and the abundant lacustrine
resources. With the significant increase in population, settlement is more aggressive to expand to
the outlying areas that offer these multiple subsistence strategies and pivotal resource allocations.
However, given that they settled in smaller communities, such as hamlets or villages, it is
believed that this was due to the fact that there was no strong, overarching political system in
place, and the “reach” of the elites at Erongarícuaro and Urichu are limited by distance. It is
clear, given the location along the lakeshore, that communities are making decisions based on
local interactions, both with communities and the landscape, and that there still doesn’t exist an
overarching community network for interaction.
The Tariacuri phase is the final phase in the sequence, and marks the emergence of the
Tarascan state. This also marks a dramatic climatic fluctuation, especially with regard to the

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lake and lake resources, as the lake rises to its highest point in the sequence, at 2043 m.a.s.l.
This phase also marks a second paradigm shift for settlement for the southwest region. Whereas
the primary means for settlement in earlier phases had been landscape related, the primary
mechanism then shifted for the Tariacuri phase to locating in areas near the major centers in the
southwest portion of the basin. Certainly the rising lake levels affected settlement for the
communities. However, what we see from the archaeological evidence is a large population
increase at three major centers, Urichu (community 15), Erongarícuaro (community 16 and 17)
and Jaracuaro (community 1). The evidence shows that when confronted with rising lake levels,
diminishing lakeshore resources and diminishing land, the smaller communities of the Late
Urichu phase actively moved inland and either formed larger towns or centers, such as at
Jaracuaro, or moved into the existing centers at Urichu and Erongarícuaro. However, not all
communities made this move to the major centers. And yet the interaction analysis suggests that
they settled in key strategic areas that allowed for primary and secondary interaction with these
major centers. This is what I am referring to as the creation of the inter-regional settlement
network, which was brought about by the emergence of the state. This newly formed, state-level
political structure of this phase has permeated through to the smaller sub-regions of the lake
basin, and created a system where, due to the high population numbers and loss of habitable land
and subsistence resources, centers such as Erongarícuaro, Urichu and Jaracuaro now manage the
population and communities through a complex social, political and economic network.
The archaeological evidence shows that at community 15 at Urichu has developed not
only into a regional administrative center, but given the high frequency of fine ware ceramics,
elite goods, obsidian, and pipes, is also an elite center and ritual center. Community 16 at
Erongarícuaro, with its high frequency of artifacts and elite goods, has developed into a regional

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administrative center, and according to the Relación de Michoacán, is a level two administrative
center, with a level one being the capital at Tzintzuntzan. Other artifact evidence shows the
continued reliance of lacustrine subsistence practices, as recortados continue to be found at
lakeshore communities.
Whereas an argument could be made that the rising lake was the motivator for settlement
for this phase, I argue that it was merely the catalyst for change in settlement, where the
emergence of a new socio-political system, on the regional level, was the primary mechanism for
settlement. The southwest areas large population for this phase, estimated at ~10,000, coupled
with the loss of significant habitable and subsistence space required an overarching system that
could manage the population, and provide a means for these communities to continue to thrive in
the lake basin. The Tarascan state was just that, and through a drastic increase in interaction
values between communities, it is believed that now communities rely more heavily on inter
community and market trade, and specialization has become the primary economic motivator.
However, it is clear that communities continued to practice self-subsistence agriculture, and yet
also had to participate in the state-run economic systems, meaning that households had to
specialize and rely on other communities and trade networks.
Developing a Regional Model: Comparisons to the Southeast Communities
In order to begin to discuss a regional settlement system for the Lake Pátzcuaro Basin,
other areas of the basin must be analyzed, and then compared to the settlement model from the
southwest analysis. In 2009, a survey of the southeast portion of the lake basin was undertaken
in order to examine the settlement and human/environment interactions occurring in that extent
of the lake basin (Figure 57, Figure 58). Like the southwest portion, the southeast area was also
affected greatly by the fluctuating lake levels, as the southeast arm of the lake was constantly

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moving east as the lake rose, or to the west, exposing great deals of arable land. Another
distinguishable trait that the landscapes of the southwest and southeast shared was the existence
of a malpaís, or lava flow, such as at Urichu. The 2009 southeast survey focused on such an
area, and the results of that malpaís survey are what is analyzed and discussed in this section.
Further survey in 2010 has not been included (Pollard and Stawski 2009 -Report to C Fisher, and
Fisher 2010 Informe a El Consejo de Arq. del INAH).
Figure 57 – The Southeastern Survey Area, Summer 2009

Southeast Communities and Landscape
The nature of the survey for the southeast portion of the basin was very different, both in
method and in results, from the southwest portion. Whereas the major survey collection units
were the agricultural fields for the southwest survey, the southeast survey used grids, which was
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the primary provenience for the sites. Also, for the southeast survey, collection sites and features
were recorded on-site with a hand help GPS unit, and quickly uploaded and processed through a
GIS while in the field and lab. The most distinguishable difference between the two, however,
was the very low frequency of artifacts found at the southeast survey site, and equally as
surprising was the very high frequency of architectural features found in the southeast survey,
both compared to the southwest survey. Therefore, the method for determining community
boundaries and size changed from the previous method used for the southwest survey area.
Because there were so few artifacts a combination of artifacts, architecture, and topography is
used to determine the community boundaries and the population density by phase.
Figure 58 – Close-up of the Southeastern Survey Zone

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Several key factors played into the creation of communities for the southeast survey zone.
First, only one site in the southeast survey zone could be reconstructed in terms of community
and demography. Four sites were located, as depicted in Figure 58, including Apupato, Cerro
Buena Vista, Chapultepec, and the malpaís sites. However, lack of GIS data meant that no
specific provenience could be attributed to the artifact data. However, GIS data was available
for the malpaís, and therefore will be the focus of this section. What can be deciphered for all
sites however is the sequence of occupation, as determined from artifact data and ceramic
sequencing. This data can be seen in Table 18.
Table 18 – The Archaeological Sites of the Southeast Survey Zone, and Occupation
Site

Occupation

Apupato

Lupe/La Joya, Early Urichu, Late Urichu, Tariacuri

Chapultepec

Urichu, Tariacuri

Cerro Buena Vista

Loma Alta, Lupe/La Joya, Jaracuaro, Early Urichu, Late Urichu,
Tariacuri

the malpaís

Jaracuaro, Lupe/La Joya, Early Urichu, Late Urichu, Tariacuri

Concerning the malpaís, the majority of sites were located in the southeast malpaís, and
not just on the fringe or lower slopes as at Urichu, but deep in the middle of the malpaís. It was
quickly noticed in the field, that the terrain and topography, which was very rocky and steep and
formed hills and valleys, was the main delineator for the sites. The sites were located in the
lower valleys of the malpaís, between rocky outcrops that were near impassable. These natural
features formed what was determined to be plaza groups, which were surrounded by house
platforms, walls and rooms. This theory was confirmed through GIS, and the use of 5 meter
contours, which showed groups of sites between contour lines and in the lower, flatter terrain of
the malpaís. However, the architecture alone couldn’t be dated without artifacts, and because of
this certain liberties were taken in creating like associations between architectural features that
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were linked and/or located in very close proximity in the same topographical unit (Figure 58
shows the GIS recorded architecture). If a few sites were able to be phased by the few ceramics,
then through law of association, a larger area was delineated and labeled a community.
Concerning the objective and intellectual merits of this method, the author can further support
this claim as he was one of the lead archaeologists on the survey team, the GIS specialist, and
aided in creating the survey method for which these sites were collected and recorded.
Figure 59 – The Architectural Features of the Malpaís Survey

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Regarding the specifics of the data, communities for four phases of the sequence were
located and delineated, with areas and population densities calculated for each. For the
population reconstruction, the same methods outlined and performed in Chapter 3 were followed
here. The only exception is that, given the abundance of architectural data, individual residences,
defined by such features as rooms, platforms, walls and plazas were used to calculate persons per
residence (n=5.97 persons per residence), as defined in DeRoche’s research (1983). Table 17
summarizes the community analysis for the southeast survey area of the malpaís. It must be
noted that due to a lack of artifacts at many of the survey areas, especially in relation to the
architecture, it was very hard to delineate boundaries for communities by phase. Therefore,
several areas that would have been grouped into communities were not, due to the insufficient
artifact data to date them. Therefore, the overall population estimates are predicted to be
approximately a third higher than those reported, but for this dissertation, the data reported in
Table 19 is used. The community maps for each phase can be found in the appendix.
Table 19 – The Malpaís: Southeast Survey Community Reconstructions
Period

Phase

Late Preclassic
to Early Classic

Loma Alta

Middle Classic - Jaracuaro,
Epiclassic
Lupe/La Joya

Early Postclassic Early Urichu

Total Size of
# Comm Communities
0
1

NA

0.29 hectares

7

6.53 hectares

Middle
Postclassic

Late Urichu

8

5.13 hectares

Late Postclassic

Tariacuri

2

8.47 hectares

208

Artifact
Density

DeRoche

Ethnohistoric
(Pollard)

NA

NA

NA

7-10
(8.5 mean)

24

NA

151-262
(207
mean)
145 - 215
(180
mean)
248-514
(381
mean)

291

282

292

210-560
(385
mean)
240-640
(440
mean)
200-1000
(600
mean)

There are several key issues to discuss for the community reconstruction for the southeast
survey area. First, is the very stable population that occurs from the Early Urichu phase through
the Tariacuri phase. The only real population change we see is from the Lupe/La Joya to the
Early Urichu, signaling the start of major occupation of the malpaís. When analyzing the ranksize graphs for the Early and Late Urichu phases, Figures 59 and 60 respectively, we see a near
log-normal curve with very slight areas of primate-ness for both. Both display an even
distribution of community sizes, and a stable distribution, as community size, in area, as well as
population density remains stable. The overall trend sows a slight population boom in the early
Urichu phase, with a stable population that slightly increases into the Tariacuri phase.
Figure 60 - Early Urichu Rank Size – Southeast Malpaís

Early Urichu Phase
90
80
70
60
S
i 50
z 40
e
30

Rank-Size

20
10
0
1

2

3

4
Rank

5

209

6

7

Figure 61 – Late Urichu Rank Size – Southeast Malpaís

Late Urichu Phase
100
90
80
70
S
i
z
e

60
50
40

Rank-Size

30
20
10
0
1

2

3

4
Rank

5

6

7

The final analytical aspect that we must look at in order to compare these communities to
the southwest settlement model, is the landscape interaction. Very simply put, every community
for every phase that encapsulates the southeastern malpaís survey occurs in the lower slopes
resource zone. As you can see from the resource zone map for this area, the lakeshore, given the
lower elevations for this area, abuts the malpaís, and the communities are located right on this
fringe area. Even given the abundant agricultural area of the lakeshore, the flat terrains, and the
closer proximity to the lake and lacustrine resources, the communities, through all phases,
continue to occupy the lower slopes zone in the malpaís. When analyzing community location
through the phases, only one shift occurs for overall location of communities. From the Lupe/La
Joya phase, the community is located on the upper and inner portion of the malpaís. This
continues into the Early and Late Urichu phase, and communities spread to the interior of the
malpaís, as well as onto the lower slope, below the major incline of the malpaís. And although
several communities move or change, this settling on the upper areas of the western edge
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remained the norm until the Tariacuri phase, where there is a move into the lower slope of the
malpaís, into more even and open “lobes” of the lava flow. However, the communities still do
not move full into the lakeshore zone below the malpaís during the Late Postclassic, and remain
on the malpaís.
There are a few things that can be concluded from this analysis. The lack of influence of
the lakeshore resource zone is very apparent, as the malpaís communities are very intentionally
settling in the malpaís, where a lack of subsistence resources and water was common. During
survey, a major natural spring was recorded and mapped on the south western portion of the
malpaís, which may have supplied sufficient water for the communities. It is assumed that
perhaps seasonal agricultural camps were located on the lower slopes of the malpaís and perhaps
even the lakeshore zone below the malpaís, however no archaeological data supports this claim
from the survey. The remaining survey from the southeastern zone recorded a few minor
settlements elsewhere in this portion of the basin, however, they seem to have had no effect on
the malpaís communities.
The only variable that may provide some insight into the settlement of the malpaís
communities is the transport network and the terrain of the lakeshore zone. Ethnohistoric
evidence shows the major route that had been established from Morelia, to the northeast of the
LPB, into the Pátzcuaro basin had run from the east of the malpaís, and around it to the south
(Pollard and Gorenstein 1983). Major routes also existed running north and south, taking
advantage of the flat terrain of the lakeshore zone in this portion of the basin. Furthermore, when
the higher lake elevations (i.e. 2043 m.a.s.l. lake level) are modeled, it shows that the impeding
lake closes off direct travel through the south and eastern portion of the lake and pushes the
proposed travel routes closer towards the malpaís. This provides both a positive and negative

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aspect to the malpaís communities. The positive is access to this travel and trade, and more
access to resources via these routes that may not be available in the malpaís. However, I believe
the negative effects to be more telling of the overall community settlement in the malpaís, which
is the possibility for greater inter-basin warfare along these travel routes, especially those going
in and out of the basin to the north-east. Ethnohistoric evidence from 1543, La Memoria de
Melchor Caltzin (Monzon, Roskamp, Warren, 2009) gives evidence to warfare within the
southeastern portion of the basin and on these external travel routes, even after the emergence of
the state in 1350. This may explain the malpaís defensive settlement.
When comparing the settlement structure of the southeastern malpaís communities to the
settlement model from the southwest portion of the basin, several key aspects stand out. With
regard to the population of the southeastern communities, we never see a major increase in
population during the Middle Postclassic (Early Urichu to late Urichu) such as occurred in the
southwestern communities. This almost doubling of population for the southwestern
communities was represented in the population as the communities nearly tripled in number,
spreading through the lakeshore resource zone. However, for the southeastern communities,
both the community numbers and the population remain very stable, and no drastic moves
towards the lakeshore resource zone occur. Also of note is the apparent desire of the
southwestern communities to locate in areas of more flat and even terrain, during the early,
Middle and Late Postclassic. This was seen as attractive for three reasons; 1.) proximity to
lakeshore, marsh and lake resources, including fertile and flat agricultural land, 2.) proximity to
travel and transport routes in and out of the basin and long the lakeshore, and 3.) proximity to
other communities. This is in stark contrast to the southeastern communities, who settlement in
the harsh terrain and vegetation of the malpaís lacked easily accessible travel routes, lacked

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accessible or abundant subsistence resources, such a prime agricultural land or lake resources,
and really were cut off from any other communities outside of those also existing in the malpaís.
The final comparison includes the last phase, the Tariacuri phase during the emergence of
the state. During this phase we see the most drastic shift in settlement for the southeastern
communities, as they move into the lower reaches of the malpaís, onto more open and even
terrain. They also comprise two larger communities, whereas the previous phase contained n=8
communities. This change in settlement is on par with what we see in the southwestern
communities, only not as drastic. The move to larger more inclusive communities and away
from the smaller village and hamlet style communities occurred at both locations. The major
difference is the amount of increase of population between the two areas. Whereas the
southwestern communities had a dramatic increase in the population, the southeastern
communities only have a very slight increase. Furthermore, given the population numbers and
the landscape resources of the southeastern malpaís communities, it is probable that the malpaís
was most likely at its carrying capacity for the population there, resulting in stable populations
through time. Also, the nature of the community centers at Erongarícuaro and Urichu were much
more established and in their nature, and were major administrative and elite areas for that area
of the basin. The artifacts from the southeastern communities did not display the same level of
function or class hierarchy as Erongarícuaro or Urichu, and although a ranked social system was
most definitely in place, it wasn’t to the extent of the southwestern communities.
In summary, it is believed that the communities of the malpaís in the southeastern area
had located and settled based primarily on defensive terrain. While recording features during the
survey, several very large and very labor intensive walls were found on the outer edges of the
malpaís, surrounding the plaza groups. It was interpreted that these were defensive structures,

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protecting the communities within. Ethnohistoric evidence from Monzon, Roskamp, and Warren
(2009), describes warfare in this portion of the basin even after the emergence of the state, and
gives insight into the reason for the defensive community locations. It is with the solidifying of
the state, we see a change in structure for the settlement system, as the smaller communities of
previous phases formed larger, more dynamic communities, and on the lower expanses of the
malpaís. This move down into the lower lying slopes of the malpaís is thought to have given the
Tariacuri communities more access to travel, trade, resources and other communities. This is
similar to the change in structure in the southwestern communities, displaying the effect of the
emergence of the Tarascan state on the entire basin.

The Case for a Regional Settlement System Model
In order to propose a regional settlement model for the Lake Pátzcuaro Basin, we must
understand the sub-regional contexts of the communities. This is very clear as this analysis
explores two distinct areas of the LPB, and provides two very different views of a settlement
system for the same temporal sequence. In summary, the analysis shows three very distinct
trends in the overall regional settlement system for the LPB. The first is that defensive positions
on the landscape were a primary motivator for settlement in the lake basin. Communities seem
to have selected areas of steep terrain and higher elevations, with access, though at times limited,
to major resources and travel routes. In these contexts, the communities are smaller in
population and in size (area). However, the time length of this trend differs, and is dependent on
area and context in the lake basin. We see this trend dissipate in the southwestern communities
in the early Postclassic, and for the southeastern communities it continues into the Late
Postclassic, due most likely to their location in the basin at a major intersection for inward and
outward travel and interaction.
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A second theme of settlement for the region is the relationship between the landscape and
the communities. The relationship between humans and the environment is highly contextual,
dependent in this case on the area of the lake basin the communities are located. In the case of
the southwestern communities, and for those that existed along the southern edge of the lake, the
climate changes and lake levels fluctuations had the most affect. However, communities on the
outer edge of the lake, such as those in the southeastern survey, or in areas where the lake levels
are stable, such as on the northern or western areas of the lake seemed to have been less affected
by the lake changes. This analysis shows two different areas, one, the southwestern area, and the
southeastern area, that had very different relationships with the local resource zones. The
southwestern communities moved as the lake levels moved, especially in the Postclassic, as they
positioned themselves in the best locations to access the lake and marsh resources. Analysis
showed that the lakeshore zone played a significant role in settlement location for the
southwestern communities, whereas the southeastern communities, although they had excellent
and close access, never fully settled within the lakeshore zone. This suggests different
subsistence strategies, different motivations for settlement, and different levels and types of
interactions in each sub-region.
The final trend that can be included into a regional settlement system is the emergence of
the state, and its influence on regional settlement. With the emergence of the state, at
approximately A.D. 1350, we see a drastic transformation in the overall schema for settlement.
The data from the southwest portion of the basin suggest two things; 1.) that the catalyst for
settlement change in the Late Postclassic was the dramatic increase in the lake levels and shifting
resources in the basin, and 2.) that settlement strategy shifted from smaller, hamlet and village
communities to larger, more centralized communities. In essence, both the environmental and

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socio-economic climate produced strains on existing communities, and the state emerged as an
answer to the growing tensions put on communities by the shifting lake levels, high populations
and dwindling resources. The emergence of the state introduced a new political economic
structure to the basin, predicated on the establishment of administrative centers created for the
management of resources and to carry out state policies on a sub-regional and regional level.
Therefore, in order to survive in this climate, community settlement shifted towards these larger
centers, or created larger centers (such as Jaracuaro) in order to better align themselves with the
new regional economic system. This is even visible in the comparatively smaller settlement in
the southeastern malpaís, where communities consolidated and moved in closer to proximity to
other major communities, such as the capital Tzintzuntzan, which was a mere 10 kilometers
away. In her model for the emergence of the Tarascan state (2008), Pollard described the
“perfect storm”, with several variables that occurred at the same time in order to facilitate the rise
of a state level institution in the Lake Pátzcuaro Basin. This analysis details, through the study of
two different areas within the lake basin, that this in fact seems to be the case. Specifically with
regards to the fluctuating lake, diminishing and shifting resources and the dramatic rise in
population during this time, we see a dramatic change in the settlement system for the basin,
marked by the emergence of the state. The primary interactions changed, thus changing the
nature of “community” in the lake basin, which can be witnessed in several aspects of the
archaeological record.

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CHAPTER 7: THE MACROREGIONAL SETTLEMENT SYSTEMS AND CONCLUSIONS
This final chapter will conclude this dissertation with two sections. The first section takes
a broader perspective on the Tarascan settlement systems, and looks at a macro-regional model of
settlement through the comparative analysis between the Lake Pátzcuaro Basin and the Zacapu
Basin, which are adjacent to each other. Archaeological work at the Zacapu basin has been led by
primarily French teams of anthropologists, and provides the necessary data to begin to model a
macro-regional scheme for settlement. The final section of this chapter will revisit the problem,
testable model and hypothesis for this research, and provide overarching commentary on the
analysis, the resulting settlement systems model, and future research based on this analysis.

The Zacapu Basin
The Zacapu Lake Basin is located to the northwest of the Lake Pátzcuaro Basin (see Figure
61). Situated around the modern city of Zacapu, and what used to be the lake or marsh of Zacapu,
the basin has been a major area of archaeological research in the West Mexican highlands. The
Zacapu Basin, and the sites within, represents an area that was incorporated in the Tarascan state,
having similar social stratification and organization as the sites in the lake Pátzcuaro Basin during
the Late Postclassic. However, the overall settlement trajectory of the Zacapu area has not been
established, as at present there has been no synthetic research published about the settlement
system of that region. The following section will attempt to outline and synthesize what the
settlement systems could possibly have looked like for the temporal sequence of this research,
using several articles and publications from the French archaeological teams.

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Figure 62 – The Zacapu Basin Detailing the French Survey Zone

There are several key sites that have been benchmarks for archaeological research in the
Zacapu area. One, the site of Loma Alta, is the type site for the phase it’s named for, and marks a
shift in settlement and community formation. The site details the ceramic tradition that followed
the Chupicuaro culture, and the emergence of the sunken plaza architecture, evidence of the civicceremonial center. This shift was a marked change from the hamlet style settlements of earlier
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phases, and along with burials from elsewhere in the basin, we see the possible emergence of a
socially ranked system (Pollard 1997:360). The second site, Las Milpillas or Mich. 95 (see Figure
62), details the Postclassic phases, and is located in the malpaís of Zacapu. Nearby, also located in
the malpaís of Zacapu is the site of El Palacio, which was the administrative, political and religious
center for the immediate Zacapu region during the Late Postclassic.
The discussion begins in the Loma Alta phase, with the Chupicuaro culture, a Late
Preclassic culture that was prevalent along the Lerma drainage in southern Guanajuato, the Lake
Cuitzeo Basin, and in the Pátzcuaro Basin (Pollard 1997:359). These Chupicuaro communities
“appear to have been primarily adapted to lacustrine ecosystems, locating their villages either on
islands within marshes or along lakeshores or rivers” (Pollard 1997:360). Pollard also states that
this settlement on the floor of the basin, and the general absence of settlements in defensible
positions, indicates “minimal local aggression and/or movement of peoples” (1997:360).
With the beginning of the Classic period, around A.D. 400-700, we see a dramatic shift in
settlement in the Zacapu region. Evidence from this period details a shift towards the ceremonial
center for settlement, as well as both rapid population growth and a doubling of sites, both in the
Jaracuaro phase (A.D. 500-700), and again in the Lupe Phase (A.D. 700-850) (Pollard 1997:362).
Michelet states, with concern to settlement that “no earlier than 600-700 A.D. the population in the
region, initially concentrated in the hills of the ex-lake of Zacapu and over its southern shore,
began to spread out over all the region and, notably, north-northwest, to the Lerma slope zone.
Basically smaller and medium settlements, villages and hamlets, were founded and occupied”
(Michelet: 593). Pollard goes on to confirm this settlement change, as settlements were “located
away from the lakeshore, especially in the zone between Zacapu and the Lerma River,”
(1997:362). According to Michelet, “during this time period (600- 900 A.D., the Malpaís of
Zacapu was almost uninhabited” (2008:597).
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Figure 63 – The Site of Milpillas, Mich. 95 in the Zacapu Malpaís

220

The start of the Postclassic, A.D. 900, marked a dramatic shift for settlements in the Zacapu
Basin. This period marks a peak in settlement coverage for a large part of the basin, and a
multiplication of sites, but not necessarily an increase in their size (Michelet 2008:597). Settlement
continues to expand into the Lerma slopes, and a slight occupation is noted at El Palacio in the
malpaís of Zacapu. Later in this same phase, Pollard notes a dramatic shift in settlement structure,
as populations nucleated at defensible positions, a trend that continues until the emergence of the
state (1997:365). Beekman also mentions the larger change as well, as he notes “a major increase
in population during the Early Postclassic, as the balance of population shifted from the lake basin
into the surrounding sierra” with a growing settlement near and within the malpaís of Zacapu
(Beekman 2009:29).
The Middle Postclassic, around A.D. 1250, marks a major shift for the Zacapu settlements.
Michelet notes that various large sites appear and develop very rapidly at the malpaís of Zacapu,
and elsewhere, in the surrounding sierra zone (2008:597). One such site is at El Palacio, estimated
to have grown to approximately 20,000 people and 11square kilometers (Pollard 1997:366).
Inversely, “occupation more to the north was notable reduced over the Lerma Slope and, aside
from small towns, became almost residual”, and the lake marsh was abandoned (Michelet
2008:597) (Pollard 1997:366). This is thought to have occurred primarily due to the migrating and
shifting populations during this time period in and out of the basin. What Michelet refers to as
predatory population, such as nomad hunter-gatherers, and other groups wishing to take control of
key resources (2008:617). Even as populations form these larger defensive settlements, research
displays fluctuation in these malpaís settlements, as they are abandoned and re-settled throughout
the Middle Postclassic and into the Late Postclassic (Migeon 2003). The populations in the
defensive sierra and malpaís locations remain in these areas into the Late Postclassic, and as the
Tarascan state emerges at the capital of Tzintzuntzan, El Palacio becomes the major center in the
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Zacapu area.
The accumulated research from several sources and research projects in the Zacapu basin
helps to develop an idea of settlement for the area. The major benchmarks for the Zacapu Basin
include a lakeshore settlement occupation during the Loma Alta, composed primarily of smaller
village or hamlet communities. The Classic period marks drastic change for the communities of
the Zacapu, as they begin to move away from the lakeshore, and form communities in the slope
zone of Zacapu. Also during the Classic period, settlements expand, multiply, and population
increases. This trend continues into the Terminal Classic, with population continuing to increase,
and settlement spreading throughout the slope zones of the Zacapu Basin, away from marsh and
lake resources. The Postclassic continues the settlement of the slopes of Zacapu, and introduced a
new occupation in the malpaís of Zacapu, although still relatively small. It is during the Early
Postclassic, and into the Middle Postclassic that communities nucleated, locating in defensible
locations. Populations and settlement begin to grow at a rapid rate in the Malpaís during the
Middle Postclassic, as aggressive population continued to move in and out of the basin, competing
for resources. Sites are abandoned and re-settled, displaying a level of unrest and disruption for
settlement during this time. The emergence of the state and beginning of the Late Postclassic saw
the populations remain in the malpaís, and grow to very large numbers, in both community size
and population. This increase in population and continued nucleation of settlements suggests two
things: 1.) stability and lack of warfare entered with the emergence of the state, allowing for more
permanent and large-scale settlements, and 2.) the emergence of the state introduced a new
political economy, from which the large settlement of El Palacio benefitted, as the upper-elites
aligned themselves with the Tarascan elites of the Lake Pátzcuaro Basin, and participated in longdistance exchange, state ritual and developed even further the complex social hierarchy system that
was most likely in place since the Classic period.
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Making the Case for a Macro-Regional Settlement System
The macro regional analyses are, and must be more general and overarching in their nature
in order to derive statements that are true for such large scalar units. However, it was the
goal of this dissertation to work explicitly from a growing scalar model, from individual
communities, to community groups, to sub-regions, to regions. These intrinsic details, complex
multi-scale modeling and sub-regional contexts aid in deriving a more precise picture of the overarching settlement system, as it pertains to the macro region that encompasses the Lake Pátzcuaro
and Zacapu Basins. Not only does this section attempt to provide a macro regional settlement
system model, or make the case for one, but it also aids in providing essential information and
context regarding the emergence of the Tarascan state.
The comparison between the Zacapu and Lake Pátzcuaro Basins show shared similarities,
but also differences, each unique to their own setting. The Loma Alta phase had both regions
composed of mostly hamlet and village sized communities. With lower populations, communities
from both regions were taking advantage of the lakeshore, situating themselves in close proximity.
Evidence from the Zacapu basin shows this to be truer than the LPB, where communities, although
in close proximity to the lakeshore, had already begun to locate in defensible positions (i.e. the
malpaís at Urichu). Communities seem to be taking advantage of similar resource zones, except at
different time periods. The Classic period saw a shift in settlement for both regions into the Lower
Slopes zones. The difference being in the LPB, the southwest region also had communities move
into the lakeshore, whereas in Zacapu, settlement was moving entirely away from the lakeshore.
The Classic, Terminal Classic and Early Postclassic mark several macroregional trends.
P opulation increases rapidly for all areas, and settlements increase in number, but not necessarily
size. Settlement is predicated upon local conditions. In the southwest of the LPB, settlement is
mixed between defensible positions, lakeshore resources and the lower slopes, with an emphasis on
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the malpaís of Urichu. In the southeast of the LPB, location is predicated solely on defensible
positions in the malpaís. In Zacapu, increased populations and presumably increasing competition
drives populations away from the lower lying terrain into defensible areas in the lower slopes, and
yet no major settlement is noted in the malpaís of Zacapu. As the Postclassic period begins,
communities centralize and nucleate in Zacapu. This is true to an extent for Urichu and
Erongarícuaro in the LPB, but for the most part the models suggest a greater degree of interaction
with the lakeshore resources than with other communities or local centers.
Perhaps the second most influential trend affecting settlement occurs during the Early and
Middle Postclassic. Populations double, as do settlements, and warfare, whether inter-basin or
external aggression, seems to be the norm. This is true for the southeast communities in the LPB,
and definitely for the Zacapu area. However, this is not the case for the southwestern portion of
the LPB. Instead, interaction with the lakeshore and fluctuating lake is driving settlement structure
and interaction. This potentially shows a regional trend of warfare that is located to the northern
parts of the LPB and Zacapu, and the east of both regions. It is possible that pressure from
communities deriving from Morelia and the Lake Cuitzeo Basin may have had a larger role than
previously thought on these populations. It is during this time period that interaction with the
malpaís areas (at Urichu, southeastern LPB and Zacapu) structures settlement, as these areas
provide both defensive locations and also resources and area enough to expand into sizable
communities.
The Late Postclassic marks the emergence of the state, and the most influential trend of
settlement systems is apparent. Full centralization and nucleation of communities occurs for
several reasons, but primary is the change in the social, economic and political sub-structure of the
regions. Communities now align themselves with the state authority and emerging regional
economies, as is evident at Erongarícuaroricuaro, Urichu, and El Palacio. For the southwestern
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portion of the basin, the catalyst for change was the rising lake levels, a factor that I believe
influenced many communities for the southern half of the LPB to re-settle in accordance with the
new state system. Competition for resources became too great, and the state became the great
manager of the region, providing a more diverse and regional economic and subsistence system.
This trend continues until the arrival of the Spanish, and the conquest of the Tarascan state.
Problems and Hypothesis Revisited
The goals of this dissertation were described in Chapter 1, and are worth revisiting in order
to perform a “self-analysis”. The primary goal was to determine the structure of the settlement
system over a period of approximately 1,625 years leading up to the Spanish conquest. Ancillary
to the primary goal is the goal of explaining the role of state formation and the state’s political
economy in the latter years of settlement in the Basin. A tertiary goal is the identification of a
macroregional settlement of Tarascan society when analyzing the Zacapu and Pátzcuaro Basins.
This dissertation performed these tasks through the use of intensive spatial modeling, utilization of
a complex GIS database, interaction analysis, landscape analysis, and cost surface analysis, and did
so using several scalar levels in order to provide multiple viewpoints of change in settlement and
community organization. The hypothesis that was put forth in Chapter One is detailed here;
The central hypothesis is that the primary variable that determined settlement within the
lake basin was the proximity to the lakeshore of Lake Pátzcuaro and its zones of resources. This
variable remained the primary settlement determinant until the emergence of the state in A.D.
1350, when the dominance of the capital, Tzintzuntzan, altered the foundations of the political
economy of the lake basin. During this period, the primary factor changed, and settlement was
now predicated upon proximity to the capital and other major state-run centers of administration,
religion, and economy. The lake remained a secondary factor in settlement, primarily affecting
peripheral settlement in the basin. Tertiary to all periods of settlement is the variable of proximity
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to arable land both inland and upland, followed by a fourth variable, proximity to travel/trade
routes in and out of the basin.
An alternative hypothesis is that the lake is only a primary variable until the Middle
Postclassic (A.D. 1000 – 1350), when political instability becomes the primary motivator for
settlement in upland, defensible positions. Following this period, the emergence of the state and
the proximity to the capital of Tzintzuntzan assumes the primary motivator for settlement location
until Spanish conquest.
The analysis, and the resulting settlement systems model for the southwestern potion of the
lake basin, proved the hypothesis wrong in certain areas, and failed to prove it wrong in others.
The variables that structured the settlement for communities differ based on the analysis from what
was hypothesized. The primary, secondary and tertiary variables derived from the settlement
systems analysis of this dissertation from the southwest area of the LPB, are detailed in Table 20,
whereas the hypothesized can be seen in Table 18 Ultimately, the sub-regional scale and context
provided much closer detail into what had actually occurred for these communities, and in some
cases there was no single primary variable, but there were multiple variables that affected the
communities for the phase equally.
Table 20 – Hypothesized Settlement Variables, Chapter One
Period

Phase

Primary Variable

Secondary Variable Tertiary Variable

Late Preclassic

Loma Alta 2

lake/lacustrine

other communities travel/trade routes

Early Classic

Loma Alta 3

lake/lacustrine

other communities travel/trade routes

Middle Classic

Jaracuaro

lake/lacustrine

other communities travel/trade routes

Epiclassic

Lupe-La Joya

lake/lacustrine

arable land

travel/trade routes

Early Postclassic

Early Urichu

lake/lacustrine

arable land

travel/trade routes

Middle Postclassic Late Urichu

lake/lacustrine

defensible positions

arable land

capital/admin. centers

lake/lacustrine

arable land

Late Postclassic

Tariacuri

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Table 21 – Settlement Variables Derived from the SW Settlement Systems Analysis
Period
Late
Preclassic
Early
Classic
Epiclassic

Phase
Loma Alta
2
Loma Alta
3
Lupe-La
Joya

Early
Early
Postclassic Urichu
Middle
Late
Postclassic Urichu
Late
Tariacuri
Postclassic

Primary Variable

Secondary Variable

Tertiary Variable

lower slope resources

travel/trade routes

lower slope resources

travel/trade routes

Lakeshore/Lacustrine
Resources

defensible positions

lower slopes

Lakeshore/Communities/
Agricultural land

Lower Slopes

travel/trade routes

Lakeshore/Lacustrine
Resources

Level Terrain

travel/trade routes

Upland, Steep Terrain and
Defensible
Positions
Upland, Steep Terrain and
Defensible
Positions

Major Centers, other
Communities

Lakeshore/Lacustrine

Agricultural
land/flat
terrain

Ultimately, the analysis and the tables above illustrates that the settlement system for the
LPB is much more complex than initially hypothesized, and that active decision-making, based on
a variety of variables, on the part of communities structured settlement. The analysis shows a high
level of interaction between communities and the landscape, which displays the complex humanenvironment relationships that defined the southern extent of the lake basin. The lakeshore and
fluctuating lake levels provided a complex environment to attempt to model, especially when the
communities were mapped and added to the analysis. However, it is evident that the communities
and the landscape form a symbiotic system that can only be discussed in tandem. This dissertation
aided in further investigating these vital relationships, and hopefully provided a unique and useful
method, through the utilization of GIS and spatial analysis, to further explore the evolution of
coupled human-environment systems.

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Future Research and Directions
The analysis performed for this dissertation made evident several factors that require
further research and analysis. The most glaring of these is the lack of data for the Terminal
Preclassic and Early Classic periods in the Lake Pátzcuaro Basin. Evidence from the Zacapu basin
demonstrated how the Loma Alta communities relied heavily on lacustrine resources, and situated
themselves in close proximity to the lakeshore and lake resources. This may be the case for the
LPB as well, but given the apparent lack of Loma Alta sites, the depth of provenience, and the
fluctuating lake levels, the artifact assemblages and data just aren’t present to accurately model
what is really occurring during this time period. Although we do have evidence of defensible
positions, which represents the primary variable that structures settlement for this phase, I believe
that other Loma Alta communities existed in the lakeshore zones closer to the lake. I also believe
that even though the data suggest possible competition and/or conflict, and the need for defensible
locations, I feel that the lower populations actually merited a decrease in warfare, and communities
did exist in these lower, more exposed areas. However, this is all conjecture until further research
is done to test the extent of the settlement and subsistence models for these time periods.
A second trend in the analysis that would be worth doing further research on is the Classic
period leading into the Postclassic. This was clearly a time of great change, population growth and
a shift in community structure. And as lake levels dropped to their lowest in the sequence, we
expected a mass movement towards the lakeshore. However, there were three competing variables
during the Early Postclassic that equally structured settlement. It is not until the Late Urichu, when
lake levels are on the rise, that we see a mass movement of communities to the lake’s edge. Why
is this? Could this be the period that warfare plays a greater role in structuring settlement for the
communities for the southwest region? If so, what evidence proves this? Further testing of these
periods is critical to better understand the emergence of the Tarascan state a few hundred years
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later in the sequence. Pollard describes the “perfect storm” for the emergence of the state at the
end of the Middle Postclassic, but perhaps evidence suggests that the major changes began in the
Early Postclassic, providing an equally advantageous period for state emergence. If so, why didn’t
the state emerge earlier?
This analysis presents a settlement systems model, based primarily on the communities and
landscape of the southwestern portion of the basin. This model is one that can only strengthen
with future fieldwork in areas where the archaeology and culture history is sorely lacking. This
includes areas both within and outside of the Lake Pátzcuaro Basin, in areas where no
archaeological work has been done, and also in areas where older fieldwork must be redone and
updated, such as Ihuatzio. Given the multiple scales of this model, and its appropriateness for
application outside the basin as well, it stands to reason that this model may be used as a template
for future research to ultimately strengthen and test the settlement systems model for the region
and macro region, and further our understanding of Prehispanic communities, human- environment
relationships, and the emergence of the Tarascan state.
Conclusion
It is with these last thoughts on future research directions that this dissertation hopes to find
a place in the significant archaeological literature. What has been presented is both a model and
method that may be replicated and applied to other regions and time periods to help answer
questions of settlement, subsistence, state emergence and human-environment relationships. Much
of this research was made possible due to the advancing of remote sensing technologies, GIS
research strategies and methods, and spatial analysis techniques. These platforms, along with the
advances in geoarchaeology, geology and ecology help to provide a much more abundant and
useful toolkit for anthropological archaeology. This research, I feel, is applicable and useful in its
utilization of a multi-disciplinary approach, one that uses multiple lines of evidence to approach
229

the issues at hand. With this multi-disciplinary approach comes the need for archaeologists to
continue to reach out among the social and physical sciences to incorporate, not merely methods,
techniques or technologies, but other researchers in order to perform these types of necessary
research. This also means collaboration with American, French and Mexican teams, all of whom
do excellent work in the Mexican highlands, and whose shared and contributed research would aid
in providing a large scale, holistic and multi-disciplinary view of Prehispanic life ways. This
contributed knowledge leads to progress in our respective fields, and helps to create inclusive
relationships and dialogue that may further the field of anthropological archaeology. It is my hope
that this dissertation will be used to help build these relationships, bridge these gaps, and further
our shared knowledge of Mesoamerican civilizations.

230

APPENDIX

231

Figure 64 – Loma Alta Phase Communities

232

Figure 65 – Lupe/La Joya Communities

233

Figure 66 – Early Urichu Communities

234

Figure 67 – Late Urichu Communities

235

Figure 68 – Tariacuri Communities

236

Table 22 – Loma Alta Community Population Reconstruction

1

Survey
Sites
U-5

2

U-1

Community

3
4
5
6

ER-1, ER3, ER-5,
ER-11,
ER-13
ER-18
ER-21
ER-23

Artifact
Density
heavy
heavy,
medium
heavy

Area
Population Population Population
(hectares)
1
2
3
23.11
231-577
693
100-500
2.38

24-60

72

100-500

heavy

12.08

121-303

363

100-500

light
light
light

2.47
2.99
3.46

13-25
15-30
18-35

90
105
75

30-80
30-80
30-80

Table 23 – Lupe/La Joya Community Population Reconstruction
Community

Survey Sites

1

P-63

2
3

X-5-1, X-5-2,
X-5-4
X-3-8I, X-38II

4

P-99

5

U-9, U-65

6
7
8
9

U-60, U-66
U-5
ER-23
ER-21

Artifact
Density
medium
light

Area
Population Population Population
(hectares)
1
2
3
0.88

5-9

28

30 - 80

light

6.03

30 - 60

180

30 - 80

light

1.57

8 - 16

47

30 -80

4.32

22 - 43

129

100 - 500

10.92

110 - 275

330

100 - 500

3.31
22.91
3.5
4.43

17 - 33
230 - 575
18 - 35
22 - 44

99
330
105
133

30 - 80
100 - 500
30 - 80
30 - 80

medium
light
medium
heavy
light
heavy
light
light

237

Table 24 – Early Urichu Community Population Reconstruction
Community

Survey Sites

2
3
4

X-3-9 to X3-14
X-5-2, X-5-4
P-1
P-13

5

P-96, P-97

6

U-9, U-65

7
8
9
10
11

P-124
P-88
P-91
U-5
ER-3
ER-31, ER33
ER-18
ER-23

1

12
13
14
15
16
17

ER-11, ER13, ER-21
ER-23
ER-12

Artifact
Density

Area
Population Population Population
(hectares)
1
2
3

light

5.74

29 - 57

172

30 - 80

light
medium
medium
medium
light
medium
heavy
light
light
light
heavy
heavy

5.62
0.78
0.25

28 - 56
20 - 27
7-9

169
23
8

30 - 80
30 - 80
30 - 80

6.41

32 - 64

192

30 - 80

11.58

116 - 290

347

100 - 500

12.13
7.28
10.58
22.86
5.46

61 - 121
37 - 73
53 - 106
228 - 570
138 - 193

364
218
317
686
164

30 - 80
30 - 80
30 - 80
100 - 500
100 - 500

light
light
light

8.75
2.32
2.85

44 - 87
12 - 23
14 - 29

light
light
light

5.54
1.51
1.62

28 - 56
8 - 15
8 - 16

238

263
70
86
166
45
49

30 - 80
30 - 80
30 - 80
30 - 80
30 - 80
30 - 80

Table 25 – Late Urichu Community Population Reconstruction
Community
1
2
3
4
5
6
7
9
10
11
12
13

Survey Sites
X-OM-1, XOM-2
X-7-1

Artifact
Density

Area
Population Population Population
(hectares)
1
2
3

medium

8.31

208 - 291

249

100-500

heavy

7.89

277 - 395

100-500
30-80
30-80
30-80
30-80
30-80
30-80

U-55b

light

8.6

86-215

X-8-2, X-8-4
P-115

light
light

3.54
5.54

35 - 88
55-138

P-117
P-126

light
light

8.94
1.54

89-222
15-38

X-4-12
X-3-17I, X-317II, X-3-18,
X-3-18II
X-3-9, X-3-10,
X-4-8, X-4-9
X-3-2, X-3-3,
X-3-4
X-3-8I, X-3-8II,
X-3-11, X-312I, X-3-12II

light

0.51

5-13

237
258
106
166
268
46
15

light

2.72

27-68

82

30-80

light

0.91

9-23

27

30-80

medium
light

2.26

55 - 77

68

30-80

light

2.26

55-77

68

30-80

4.53

113 - 158

136

30-80

7.68

193-270

100-500

14

X-6-2

15
16
17
18
19
20

P-36, P-37

medium
light
medium

P-8, P-10, P-22
P-78e

medium
medium

6.01
2.05

150-210
50-74

P-78b, P-78c
P-48

medium
light

0.88
13.86

23-32
138-345

P-24
P-6, P-7, P-9, P11

heavy

0.23

100-500

230
180
62
26
416
7

medium

2.09

53-74

63

100-500

2.87

70-98

86

30-80

3.11

78-109

93

30-80

4.97

124 - 174

149

100-500

0.81

20-28

24

30-80

5.43
3.57

54 - 136
90 - 126

30-80
100-500

6.42

224-320

163
107
193

1000-5000

3.61

90-126

108

30-80

5.36

190-270

161

1000-5000

21
22

P-94

23

P-97

24
25
26
27
28

medium
light
medium
light

X-4-19 - X-422
X-4-13, X-413b, X-4-14
X-6-3
X-7-2

medium
light
medium
light
light
medium

U-2

heavy
medium
light
heavy

29

P-95

30

ER-3

239

100-500
100-500

30-80
30-80
100-500

Table 25 (cont’d)
31
32
33
34
35
36
37
38
39
40
41
42
43
44

ER-18
ER-23
ER-31, ER-33
ER-12
ER-2, ER-13
P-82
P-31, P-63, P-72
X-9
U-60
P-40, P-41, P-46,
P-47
P-78e, P-78b
P-62
P-76
P-75

light
light
light
light
light
light
light
light
light

2.09
3.32
8.08
1.49
4.57
18.86
10.25
36.17
6.93

21-53
32-81
80-203
15-38
46-115
189-472
103-256
181-362
69-173

63
100
242
45
137
566
308
1085
208

30-80
30-80
30-80
30-80
30-80
30-80
30-80
30-80
30-80

light

20.05

201-501

602

30-80

light
light
light
light

3.32
15.47
10.21
9.07

33-83
155-387
102-255
91-227

100
464
306
272

30-80
30-80
30-80
30-80

240

Table 26 – Tariacuri Community Population Reconstructions
Community

Survey Sites

Artifact
Density

1

X-10, X-6-1
to X-6-5

heavy

50.92

1782-2546

1528

1000-5000

2

X-6-6, X-7-1,
X-7-2, X-7-3

heavy

21.37

748-1068

641

1000-5000

3

X-8-1

medium
heavy

9.21

230-322

276

100-500

4

P-78e, P-78d

medium
light

5.62

56-140

168

30-80

5

P-101, P-102,
P-112

medium

14.02

350-491

421

100-500

6

P-109, P-110

medium
light

7.72

77-192

232

100-500

7

P-98

medium
light

3.77

38-94

113

30-80

8

P-114

medium
light

6.18

62-155

185

100-500

9

P-91, P-104,
P-105, P-106

medium
light

9.29

93-232

279

30-80

10

P-79c

1.27

13-32

38

30-80

11

P-4, P-5
P-19, P-37 to
P-39
P-212, P-124
P-78b, P-78c
U-1 to U-8
ER-1, ER-2,
ER-3, ER-5,
ER-6, ER-8,
ER-9, ER-10,
ER-11, ER-13
ER-14, 15, 17,
18, 22, 23, 24,
26, 27, 28, 29,
30, 31, 32, 33,
37
P-61, P-64, P71

12
13
14
15

16

17

18

Area
Population Population Population
(hectares)
1
2
3

medium
light
medium
medium
light
light
medium
heavy

1.42

36-50

43

30-80

13.01

130-325

390

100-500

18.67
3.39
70.31

94-187
85-119
2461-3515

560
102
2109

30-80
100-500
1000-5000

heavy

27.37

959-1370

821

3000-5000

light

55.06

550-1375

1652

100-500

light

19.80

198-495

594

100-500

241

Table 27 – Loma Alta (1&2) Community Interaction Values
A
B
Comm Comm A pop B pop
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
5
5
5
5
5
6
6
6
6
6

2
3
4
5
6
1
3
4
5
6
1
2
4
5
6
1
2
3
5
6
1
2
3
4
6
1
2
3
4
5

808
808
808
808
808
42
42
42
42
42
212
212
212
212
212
19
19
19
19
19
23
23
23
23
23
27
27
27
27
27

42
212
19
23
27
808
212
19
23
27
808
42
19
23
27
808
42
212
23
27
808
42
212
19
27
808
42
212
19
23

distance
970
5085
5286
4632
5111
954
4993
4330
3691
4085
5162
4111
642
423
548
5380
4329
646
801
260
4767
4329
646
801
260
5138
4087
582
280
502

AxB

d^1.9

33936
171296
15352
18584
21816
33936
8904
798
966
1134
171296
8904
4028
4876
5724
15352
798
4028
437
513
18584
966
4876
437
621
21816
1134
5724
513
621

473005.614
11014196.5
11856093.1
9224869.82
11121443.9
458291.616
10638661.1
8115719.03
5992025.95
7265490.11
11333243
7353603.44
215931.774
97734.2305
159839.113
12259882.9
8112158.23
218495.141
328777.267
38765.8545
9742396.23
8112158.23
218495.141
328777.267
38765.8545
11233337.1
7272250.19
179206.464
44627.2637
135312.233

Bold = Primary
Italic = secondary

242

value
interaction (x1000
)
0.0717
71.75
0.0156
15.55
0.0013
1.29
0.0020
2.01
0.0020
1.96
0.0740
74.05
0.0008
0.84
0.0001
0.10
0.0002
0.16
0.0002
0.16
0.0151
15.11
0.0012
1.21
0.0187
18.65
0.0499
49.89
0.0358
35.81
0.0013
1.25
0.0001
0.10
0.0184
18.44
0.0013
1.33
0.0132
13.23
0.0019
1.91
0.0001
0.12
0.0223
22.32
0.0013
1.33
0.0160
16.02
0.0019
1.94
0.0002
0.16
0.0319
31.94
0.0115
11.50
0.0046
4.59

Table 28 – Lupe/La Joya Community Interaction Values
A
B
A
Comm Comm pop
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5

2
3
4
5
6
7
8
9
1
3
4
5
6
7
8
9
1
2
4
5
6
7
8
9
1
2
3
5
6
7
8
9
1
2
3
4
6
7
8
9

7
7
7
7
7
7
7
7
45
45
45
45
45
45
45
45
12
12
12
12
12
12
12
12
33
33
33
33
33
33
33
33
193
193
193
193
193
193
193
193

B pop
45
12
33
193
25
403
27
33
7
12
33
193
25
403
27
33
7
45
33
193
25
403
27
33
7
45
12
193
25
403
27
33
7
45
12
33
25
403
27
33

distance A x B d^1.9
2351
315 2543196.96
3290
84 4815841.74
5158
231 11316562.9
6740 1351 18812815.8
6798
175 19121598.9
8305 2821 27973403.5
12249
189 58531709.1
11993
231 56229326.8
2312
315 2463637.9
1067
540 566907.754
3871 1485 6559398.64
5454 8685 12582262.9
5511 1125 12833283.3
7614 18135 23717256.9
10962 1215 47401339.6
10707 1485 45328236.7
3271
84 4763136.57
1084
540 584192.041
5337
396 12074375.7
6919 2316 19773444.4
6977
300 20089566.9
8885 4836 31801611
12428
324 60167555.1
12172
396 57834594.2
5191
231 11454521.6
3938 1485 6776787.26
5410
396 12390099.8
1531 6369 1125778.27
1135
825 637517.538
3421 13299 5186696.56
7008
891 20259502.5
6753 1089 18881818.8
6666 1351 18422310.1
5406 8685 12372699.9
6877 2316 19546011.3
1469 6369
1040738
1450 4825 1015311.26
1661 77779 1314323.59
5344 5211 12104483.3
4964 6369 10521565.6
243

value
Interaction(x1000)
0.0001
0.0000
0.0000
0.0001
0.0000
0.0001
0.0000
0.0000
0.0001
0.0010
0.0002
0.0007
0.0001
0.0008
0.0000
0.0000
0.0000
0.0009
0.0000
0.0001
0.0000
0.0002
0.0000
0.0000
0.0000
0.0002
0.0000
0.0057
0.0013
0.0026
0.0000
0.0001
0.0001
0.0007
0.0001
0.0061
0.0048
0.0592
0.0004
0.0006

0.12
0.02
0.02
0.07
0.01
0.10
0.00
0.00
0.13
0.95
0.23
0.69
0.09
0.76
0.03
0.03
0.02
0.92
0.03
0.12
0.01
0.15
0.01
0.01
0.02
0.22
0.03
5.66
1.29
2.56
0.04
0.06
0.07
0.70
0.12
6.12
4.75
59.18
0.43
0.61

Table 28 (cont’d)
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
9
9
9
9
9
9
9
9

1
2
3
4
5
7
8
9
1
2
3
4
5
6
8
9
1
2
3
4
5
6
7
9
1
2
3
4
5
6
7
8

25
25
25
25
25
25
25
25
403
403
403
403
403
403
403
403
27
27
27
27
27
27
27
27
33
33
33
33
33
33
33
33

7
45
12
33
193
403
27
33
7
45
12
33
193
25
27
33
7
45
12
33
193
25
403
33
7
45
12
33
193
25
403
27

6767
5514
6986
1129
1365
3574
6089
5834
8332
7679
8923
3475
1788
3579
5299
4919
12150
10898
12369
6986
5386
6171
5296
474
11943
10690
12161
6779
5064
5963
4910
469

175
1125
300
825
4825
10075
675
825
2821
18135
4836
13299
77779
10075
10881
13299
189
1215
324
891
5211
675
10881
891
231
1485
396
1089
6369
825
13299
891

Bold = primary
Italic = secondary

244

18956263.4
12846559.9
20138833.2
631129.514
905215.616
5636294.04
15510912.6
14300005.6
28146448
24103431.1
32060529.9
5343356.33
1511813.7
5651285.22
11911554.6
10341081.5
57636145.4
46876904.6
59626006.1
20138833.2
12285874.2
15910196.3
11898744.9
121332.976
55784753.9
45191591.9
57735329.4
19020183.6
10927933
14906755.8
10305162.3
118912.744

0.0000
0.0001
0.0000
0.0013
0.0053
0.0018
0.0000
0.0001
0.0001
0.0008
0.0002
0.0025
0.0514
0.0018
0.0009
0.0013
0.0000
0.0000
0.0000
0.0000
0.0004
0.0000
0.0009
0.0073
0.0000
0.0000
0.0000
0.0001
0.0006
0.0001
0.0013
0.0075

0.01
0.09
0.01
1.31
5.33
1.79
0.04
0.06
0.10
0.75
0.15
2.49
51.45
1.78
0.91
1.29
0.00
0.03
0.01
0.04
0.42
0.04
0.91
7.34
0.00
0.03
0.01
0.06
0.58
0.06
1.29
7.49

Table 29 – Early Urichu Community Interaction Values
A
B
B
Comm Comm A pop pop
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1
2
4
5
6
7
8
9
10
11
12

43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
24
24
24
24
24
24
24
24
24
24
24

42
24
8
48
203
91
55
80
399
166
66
18
22
42
12
12
43
24
8
48
203
91
55
80
399
166
66
18
22
42
12
12
43
42
8
48
203
91
55
80
399
166
66

distance A x B d^1.9
1099 1806
599646.93
2676 1032 3252547.20
2781
344 3499305.66
4918 2064 10337087.59
5678 8729 13582234.91
7896 3913 25414031.59
7368 2365 22282518.34
7141 3440 20996270.67
8292 17157 27890266.08
11801 7138 54531286.00
11158 2838 49024602.21
12341
774 59369811.41
11825
946 54742192.04
11268 1806 49946952.25
11961
516 55944607.56
11394
516 51013463.30
1097 1806
597575.24
1882 1008 1666392.40
2889
336 3762013.33
3946 2016 6802968.40
5557 8526 13037573.02
6812 3822 19196489.45
6284 2310 16468298.68
6057 3360 15356399.08
7325 16758 22036087.85
10834 6972 46355234.18
10192 2772 41275551.85
11330
756 50470409.53
10858
924 46550536.53
10677 1764 45087230.68
10950
504 47302797.57
10428
504 43110389.11
2682 1032 3266417.32
1911 1008 1715518.10
817
192
341367.29
1861 1152 1631240.93
3477 4872 5349200.94
6059 2184 15366034.71
5531 1320 12921917.12
5107 1920 11104912.22
5060 9576 10911538.32
8754 3984 30916650.06
8112 1584 26751189.00
245

value
Interaction(x1000)
0.0030
0.0003
0.0001
0.0002
0.0006
0.0002
0.0001
0.0002
0.0006
0.0001
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0030
0.0006
0.0001
0.0003
0.0007
0.0002
0.0001
0.0002
0.0008
0.0002
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0003
0.0006
0.0006
0.0007
0.0009
0.0001
0.0001
0.0002
0.0009
0.0001
0.0001

3.01
0.32
0.10
0.20
0.64
0.15
0.11
0.16
0.62
0.13
0.06
0.01
0.02
0.04
0.01
0.01
3.02
0.60
0.09
0.30
0.65
0.20
0.14
0.22
0.76
0.15
0.07
0.01
0.02
0.04
0.01
0.01
0.32
0.59
0.56
0.71
0.91
0.14
0.10
0.17
0.88
0.13
0.06

Table 29 (cont’d)
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6

13
14
15
16
17
1
2
3
5
6
7
8
9
10
11
12
13
14
15
16
17
1
2
3
4
6
7
8
9
10
11
12
13
14
15
16
17
1
2
3
4
5
7
8
9
10
11

24
24
24
24
24
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
203
203
203
203
203
203
203
203
203
203

18
22
42
12
12
43
42
24
48
203
91
55
80
399
166
66
18
22
42
12
12
43
42
24
8
203
91
55
80
399
166
66
18
22
42
12
12
43
42
24
8
48
91
55
80
399
166

9251
8779
8597
8870
8348
3870
2936
818
1134
2739
5483
4955
4531
4322
8017
7374
8513
8041
7839
8133
7610
5002
4069
1951
1082
2004
6664
6135
5711
3699
6996
6354
7492
7020
6839
7112
6590
6670
5736
3618
2839
2001
7778
7250
6825
1754
5787

432
528
1008
288
288
344
336
192
384
1624
728
440
640
3192
1328
528
144
176
336
96
96
2064
2016
1152
384
9744
4368
2640
3840
19152
7968
3168
864
1056
2016
576
576
8729
8526
4872
1624
9744
18473
11165
16240
80997
33698
246

34336695.24
31084622.19
29871646.47
31699679.97
28249231.44
6556179.47
3879149.32
342161.61
636450.75
3399576.84
12709681.49
10485350.48
8846443.70
8087253.36
26159086.79
22317007.18
29319529.95
26308077.83
25066590.28
26882921.72
23693588.84
10675125.91
7211516.65
1784386.02
582145.84
1877611.18
18411809.72
15734309.09
13732610.56
6016725.93
20193640.55
16818594.51
23000419.68
20325466.08
19341312.73
20834559.42
18025291.13
18443319.27
13847053.53
5768863.58
3639269.74
1872274.27
24697277.63
21609375.32
19266154.82
1457659.94
14081911.69

0.0000
0.0000
0.0000
0.0000
0.0000
0.0001
0.0001
0.0006
0.0006
0.0005
0.0001
0.0000
0.0001
0.0004
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0003
0.0006
0.0007
0.0052
0.0002
0.0002
0.0003
0.0032
0.0004
0.0002
0.0000
0.0001
0.0001
0.0000
0.0000
0.0005
0.0006
0.0008
0.0004
0.0052
0.0007
0.0005
0.0008
0.0556
0.0024

0.01
0.02
0.03
0.01
0.01
0.05
0.09
0.56
0.60
0.48
0.06
0.04
0.07
0.39
0.05
0.02
0.00
0.01
0.01
0.00
0.00
0.19
0.28
0.65
0.66
5.19
0.24
0.17
0.28
3.18
0.39
0.19
0.04
0.05
0.10
0.03
0.03
0.47
0.62
0.84
0.45
5.20
0.75
0.52
0.84
55.57
2.39

Table 29 (cont’d)
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
9
9
9
9
9
9
9
9
9

12
13
14
15
16
17
1
2
3
4
5
6
8
9
10
11
12
13
14
15
16
17
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
1
2
3
4
5
6
7
8
10

203
203
203
203
203
203
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
91
55
55
55
55
55
55
55
55
55
55
55
55
55
55
55
55
80
80
80
80
80
80
80
80
80

66
18
22
42
12
12
43
42
24
8
48
203
55
80
399
166
66
18
22
42
12
12
43
42
24
8
48
203
91
80
399
166
66
18
22
42
12
12
43
42
24
8
48
203
91
55
399

4565
5610
5247
5059
5365
5501
7817
6732
5987
5413
6551
7634
760
982
6015
12888
12246
13384
12912
12711
12984
12482
7293
6208
5463
4889
6026
7110
760
1070
7134
12364
11721
12860
12388
12186
12460
11958
7135
6050
5079
4505
5642
6726
1012
1067
6355

13398
3654
4466
8526
2436
2436
3913
3822
2184
728
4368
18473
5005
7280
36309
15106
6006
1638
2002
3822
1092
1092
2365
2310
1320
440
2640
11165
5005
4400
21945
9130
3630
990
1210
2310
660
660
3440
3360
1920
640
3840
16240
7280
4400
31920
247

8972996.24
13274844.31
11690444.43
10907441.46
12195018.96
12789074.74
24933096.20
18770411.94
15020956.57
12403157.30
17823149.05
23835765.04
297540.32
484185.55
15154712.44
64469232.81
58504474.76
69264908.22
64697527.37
62797371.45
65384704.14
60665242.60
21853540.43
16091934.11
12621741.49
10221580.91
15207413.05
20823428.76
297540.32
569940.05
20957182.70
59580218.71
53831051.77
64203372.33
59800150.05
57961048.11
60462246.62
55917950.24
20962764.58
15322696.94
10989516.98
8750242.94
13419083.51
18738638.79
512675.98
566907.75
16823624.03

0.0015
0.0003
0.0004
0.0008
0.0002
0.0002
0.0002
0.0002
0.0001
0.0001
0.0002
0.0008
0.0168
0.0150
0.0024
0.0002
0.0001
0.0000
0.0000
0.0001
0.0000
0.0000
0.0001
0.0001
0.0001
0.0000
0.0002
0.0005
0.0168
0.0077
0.0010
0.0002
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0002
0.0002
0.0001
0.0003
0.0009
0.0142
0.0078
0.0019

1.49
0.28
0.38
0.78
0.20
0.19
0.16
0.20
0.15
0.06
0.25
0.78
16.82
15.04
2.40
0.23
0.10
0.02
0.03
0.06
0.02
0.02
0.11
0.14
0.10
0.04
0.17
0.54
16.82
7.72
1.05
0.15
0.07
0.02
0.02
0.04
0.01
0.01
0.16
0.22
0.17
0.07
0.29
0.87
14.20
7.76
1.90

Table 29 (cont’d)
9
9
9
9
9
9
9
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
12
12
12
12
12
12
12
12

11
12
13
14
15
16
17
1
2
3
4
5
6
7
8
9
11
12
13
14
15
16
17
1
2
3
4
5
6
7
8
9
10
12
13
14
15
16
17
1
2
3
4
5
6
7
8

80
80
80
80
80
80
80
399
399
399
399
399
399
399
399
399
399
399
399
399
399
399
399
166
166
166
166
166
166
166
166
166
166
166
166
166
166
166
166
66
66
66
66
66
66
66
66

166
66
18
22
42
12
12
43
42
24
8
48
203
91
55
80
166
66
18
22
42
12
12
43
42
24
8
48
203
91
55
80
399
66
18
22
42
12
12
43
42
24
8
48
203
91
55

11980
11338
12476
12004
11803
12096
11574
8279
7346
5095
4315
3682
1754
6075
7103
6309
5662
4634
5679
5196
5268
5565
5072
11864
10931
8813
8033
6977
5829
13040
12512
12088
5495
1187
676
684
416
291
438
11136
10202
8084
7304
6248
4512
11347
11783

13280
5280
1440
1760
3360
960
960
17157
16758
9576
3192
19152
80997
36309
21945
31920
66234
26334
7182
8778
16758
4788
4788
7138
6972
3984
1328
7968
33698
15106
9130
13280
66234
10956
2988
3652
6972
1992
1992
2838
2772
1584
528
3168
13398
6006
3630
248

56113577.04
50538140.69
60609848.10
56327357.15
54548846.77
57150413.70
52555552.77
27807245.87
22156275.44
11055387.20
8062384.76
5964296.02
1457659.94
15443222.77
20784493.61
16593003.40
13509607.88
9232439.19
13586780.23
11475493.48
11779502.70
13073257.65
10960757.40
55085736.74
47146971.31
31313756.00
26258369.62
20089566.88
14276728.65
65921552.03
60942574.63
57078619.08
12762584.30
694154.86
238176.40
243560.35
94684.16
48017.18
104424.12
48841109.64
41352532.01
26576022.28
21916209.97
16289506.96
8776094.13
50614389.66
54373359.57

0.0002
0.0001
0.0000
0.0000
0.0001
0.0000
0.0000
0.0006
0.0008
0.0009
0.0004
0.0032
0.0556
0.0024
0.0011
0.0019
0.0049
0.0029
0.0005
0.0008
0.0014
0.0004
0.0004
0.0001
0.0001
0.0001
0.0001
0.0004
0.0024
0.0002
0.0001
0.0002
0.0052
0.0158
0.0125
0.0150
0.0736
0.0415
0.0191
0.0001
0.0001
0.0001
0.0000
0.0002
0.0015
0.0001
0.0001

0.24
0.10
0.02
0.03
0.06
0.02
0.02
0.62
0.76
0.87
0.40
3.21
55.57
2.35
1.06
1.92
4.90
2.85
0.53
0.76
1.42
0.37
0.44
0.13
0.15
0.13
0.05
0.40
2.36
0.23
0.15
0.23
5.19
15.78
12.55
14.99
73.63
41.49
19.08
0.06
0.07
0.06
0.02
0.19
1.53
0.12
0.07

Table 29 (cont’d)
12
9
12
10
12
11
12
13
12
14
12
15
12
16
12
17
13
1
13
2
13
3
13
4
13
5
13
6
13
7
13
8
13
9
13
10
13
11
13
12
13
14
13
15
13
16
13
17
14
1
14
2
14
3
14
4
14
5
14
6
14
7
14
8
14
9
14
10
14
11
14
12
14
13
14
15
14
16
14
17
15
1
15
2
15
3
15
4
15
5
15
6
15
7

66
66
66
66
66
66
66
66
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
42
42
42
42
42
42
42

80
399
166
18
22
42
12
12
43
42
24
8
48
203
91
55
80
399
166
66
22
42
12
12
43
42
24
8
48
203
91
55
80
399
166
66
18
42
12
12
43
42
24
8
48
203
91

11359 5280 50716139.66
4626 26334 9202179.36
1211 10956
721064.10
1035 1188
535040.51
672 1452
235505.81
817 2772
341367.29
1114
792
615292.84
721
792
269201.21
12392
774 59836842.59
11459
756 51567819.37
9340
432 34967055.66
8561
144 29634427.69
7505
864 23076307.82
7830 3654 25011938.26
13568 1638 71085346.77
13040
990 65921552.03
12616 1440 61908629.88
7880 7182 25316275.50
664 2988
230207.44
2341 1188 2522683.03
280
396
44627.26
688
756
246273.70
376
216
78137.03
921
216
428640.59
11823
946 54724601.81
10890
924 46811544.61
8771
528 31030824.20
7992
176 26004314.09
6936 1056 19865854.97
5200 4466 11492284.08
12999 2002 65528298.78
12471 1210 60563704.32
12047 1760 56711342.68
5314 8778 11975701.02
726 3652
272759.31
682 1452
242209.02
300
396
50878.04
513
924
141001.26
423
264
97734.23
709
264
260752.12
11902 1806 55421451.68
10969 1764 47458867.37
8851 1008 31570789.85
8071
336 26494880.18
7015 2016 20297968.92
5867 8526 14454083.78
13079 3822 66296656.37
249

0.0001
0.0029
0.0152
0.0022
0.0062
0.0081
0.0013
0.0029
0.0000
0.0000
0.0000
0.0000
0.0000
0.0001
0.0000
0.0000
0.0000
0.0003
0.0130
0.0005
0.0089
0.0031
0.0028
0.0005
0.0000
0.0000
0.0000
0.0000
0.0001
0.0004
0.0000
0.0000
0.0000
0.0007
0.0134
0.0060
0.0078
0.0066
0.0027
0.0010
0.0000
0.0000
0.0000
0.0000
0.0001
0.0006
0.0001

0.10
2.86
15.19
2.22
6.17
8.12
1.29
2.94
0.01
0.01
0.01
0.00
0.04
0.15
0.02
0.02
0.02
0.28
12.98
0.47
8.87
3.07
2.76
0.50
0.02
0.02
0.02
0.01
0.05
0.39
0.03
0.02
0.03
0.73
13.39
5.99
7.78
6.55
2.70
1.01
0.03
0.04
0.03
0.01
0.10
0.59
0.06

Table 29 (cont’d)
15
15
15
15
15
15
15
15
15
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17

8
9
10
11
12
13
14
16
17
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
17
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

42
42
42
42
42
42
42
42
42
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12

55
80
399
166
66
18
22
12
12
43
42
24
8
48
203
91
55
80
399
166
66
18
22
42
12
43
42
24
8
48
203
91
55
80
399
166
66
18
22
42
12

12550 2310 61294721.91
12126 3360 57420024.23
5135 16758 11220878.27
409 6972
91679.93
798 2772
326441.60
688
756
246273.70
500
924
134289.79
308
504
53486.77
264
504
39906.85
12033
516 56586188.47
11099
504 48533243.64
8981
288 32457638.81
8201
96 27311587.38
7145
576 21018622.15
5207 2436 11521695.61
13208 1092 67544565.84
12680
660 62506701.51
12256
960 58595279.39
5256 4788 11728573.06
286 1992
46461.74
1040
792
539962.18
378
216
78928.60
415
264
94252.18
310
504
54148.60
543
144
157079.56
11506
516 51970429.07
10573
504 44256456.81
8455
288 28941155.23
7675
96 24079581.23
6619
576 18176301.76
5470 2436 12652487.59
12682 1092 62525435.14
12154
660 57672202.95
11730
960 53909614.10
5061 4788 10915635.90
430 1992
100830.07
725
792
272045.92
927
216
433961.79
697
264
252430.76
246
504
34896.08
547
144
159285.38

Bold = primary
Italic = secondary

250

0.0000
0.0001
0.0015
0.0760
0.0085
0.0031
0.0069
0.0094
0.0126
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0000
0.0000
0.0000
0.0004
0.0429
0.0015
0.0027
0.0028
0.0093
0.0009
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0000
0.0000
0.0000
0.0004
0.0198
0.0029
0.0005
0.0010
0.0144
0.0009

0.04
0.06
1.49
76.05
8.49
3.07
6.88
9.42
12.63
0.01
0.01
0.01
0.00
0.03
0.21
0.02
0.01
0.02
0.41
42.87
1.47
2.74
2.80
9.31
0.92
0.01
0.01
0.01
0.00
0.03
0.19
0.02
0.01
0.02
0.44
19.76
2.91
0.50
1.05
14.44
0.90

Table 30 – Late Urichu Community Interaction Values
Bold = Primary Italic = Secondary
A
B
value
Comm Comm A pop B pop distance A x B d^1.9
interaction (x1000)
1
2
62
271
1020 16802 520403.6409
0.0323
32.29
1
3
62
43
6846
2666 19378943.66
0.0001
0.14
1
5
62
97
9065
6014 33036865.56
0.0002
0.18
1
6
62
156
9570
9672 36621209.33
0.0003
0.26
1
7
62
27
10117
1674 40700366.97
0.0000
0.04
1
9
62
9
2651
558 3195056.06
0.0002
0.17
1
10
62
48
2970
2976 3964945.788
0.0008
0.75
1
11
62
16
2332
992 2504287.799
0.0004
0.40
1
12
62
66
2376
4092 2594825.929
0.0016
1.58
1
13
62
66
2559
4092 2987676.308
0.0014
1.37
1
14
62
136
1847
8432 1608003.913
0.0052
5.24
1
15
62
232
3793 14384 6310552.747
0.0023
2.28
1
16
62
180
4294 11160 7987996.689
0.0014
1.40
1
17
62
62
7134
3844 20957182.7
0.0002
0.18
1
18
62
28
6856
1736 19432762.23
0.0001
0.09
1
19
62
242
6421 15004 17157147.02
0.0009
0.87
1
20
62
300
5245 18600 11681979.39
0.0016
1.59
1
21
62
64
4787
3968 9820204.057
0.0004
0.40
1
22
62
84
5923
5208 14717338.95
0.0004
0.35
1
23
62
94
5387
5828 12290208.58
0.0005
0.47
1
24
62
149
2335
9238 2510412.457
0.0037
3.68
1
25
62
24
2170
1488 2184104.906
0.0007
0.68
1
26
62
95
2349
5890 2539087.877
0.0023
2.32
1
27
62
108
834
6696 354989.4907
0.0189
18.86
1
28
62
272
9715 16864 37682640.7
0.0004
0.45
1
29
62
108
5167
6696 11354109.46
0.0006
0.59
1
30
62
230
12424 14260 60130766.67
0.0002
0.24
1
31
62
37
13103
2294 66527990.85
0.0000
0.03
1
32
62
57
13609
3534 71494034.7
0.0000
0.05
1
33
62
142
12892
8804 64507255.36
0.0001
0.14
1
34
62
27
12078
1674 56988935.92
0.0000
0.03
1
35
62
81
12639
5022 62123248.34
0.0001
0.08
1
36
62
331
9035 20522 32829441.77
0.0006
0.63
1
37
62
180
5592 11160 13194034.31
0.0008
0.85
1
2
62
336
1438 20832 999405.8414
0.0208
20.84
1
39
62
121
5806
7502 14169886.12
0.0005
0.53
1
40
62
351
4637 21762 9243798.76
0.0024
2.35
1
41
62
58
6797
3596 19116254.9
0.0002
0.19
1
42
62
271
4892 16802 10233501.37
0.0016
1.64
1
43
62
179
6597 11098 18061687.33
0.0006
0.61
1
44
62
159
7840
9858 25072666.22
0.0004
0.39
251

Table 30 (cont’d)
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3

3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
2
4
5
6
7
9

336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
336
43
43
43
43
43
43

43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
62
97
156
27
9

7558 14448 23386923.03
1438 20832 999405.8414
7436 32592 22674870.95
8599 52416 29884851.56
8995
9072 32553839.55
2870
3024 3715143.589
3396 16128 5114917.049
2749
5376 3423197.889
2843 22176 3649018.233
2979 22176 3987805.391
1667 45696 1323358.891
3658 77952 5890647.461
3674 60480 5939698.337
5681 20832 13595873.01
5417
9408 12420577.46
5173 81312 11379173.23
4116 100800 7370605.987
3697 21504 6010546.427
5676 28224 13573146.45
4486 31584 8680257.557
2366 50064 2574115.363
2530
8064
2923674.3
1592 31920 1212528.124
588 36288 182732.9728
8626 91392 30063390.79
4921 36288 10349071.65
12507 77280 60896311.02
13185 12432 67321262.96
12585 19152 61619918.25
11868 47712 55121029.65
12161
9072 57735329.42
12721 27216 62891272.2
7849 111216 25127381.02
5352 60480 12138935.37
1963 91056 1805296.621
5853 40656 14388621.75
4337 117936 8140665.353
6007 19488 15116439.17
4269 91056 7899865.444
5967 60144 14925760.61
6674 53424 18464339.82
7558 14448 23386923.03
6846
2666 19378943.66
5116
4171 11142124.79
5621
6708 13324343.25
6913
1161 19740877.71
6800
387 19132289.08

252

0.0006
0.0208
0.0014
0.0018
0.0003
0.0008
0.0032
0.0016
0.0061
0.0056
0.0345
0.0132
0.0102
0.0015
0.0008
0.0071
0.0137
0.0036
0.0021
0.0036
0.0194
0.0028
0.0263
0.1986
0.0030
0.0035
0.0013
0.0002
0.0003
0.0009
0.0002
0.0004
0.0044
0.0050
0.0504
0.0028
0.0145
0.0013
0.0115
0.0040
0.0029
0.0006
0.0001
0.0004
0.0005
0.0001
0.0000

0.62
20.84
1.44
1.75
0.28
0.81
3.15
1.57
6.08
5.56
34.53
13.23
10.18
1.53
0.76
7.15
13.68
3.58
2.08
3.64
19.45
2.76
26.33
198.58
3.04
3.51
1.27
0.18
0.31
0.87
0.16
0.43
4.43
4.98
50.44
2.83
14.49
1.29
11.53
4.03
2.89
0.62
0.14
0.37
0.50
0.06
0.02

Table 30 (cont’d)
3
10
3
11
3
12
3
13
3
14
3
15
3
16
3
17
3
18
3
19
3
20
3
21
3
22
3
23
3
24
3
25
3
26
3
27
3
28
3
29
3
30
3
31
3
32
3
33
3
34
3
35
3
36
3
37
3
38
3
39
3
40
3
41
3
42
3
43
3
44
4
2
4
3
4
5
4
6
4
7
4
9
4
10
4
11
4
12
4
13
4
14
4
15
4
16

43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
62
62
62
62
62
62
62
62
62
62
62
62
62

48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
97
156
27
9
48
16
66
66
136
232
180

7226
7125
7527
7940
4995
4586
4112
6271
5998
4979
3465
3844
2649
2732
6077
6663
6062
7252
2344
2695
4460
4700
6381
5648
4088
4367
7797
7564
7674
1683
4694
6597
6448
7904
8367
1438
6846
9065
9570
10117
2651
2970
2332
2376
2559
1847
3793
4294

2064
688
2838
2838
5848
9976
7740
2666
1204
10406
12900
2752
3612
4042
6407
1032
4085
4644
11696
4644
9890
1591
2451
6106
1161
3483
14233
7740
11653
5203
15093
2494
11653
7697
6837
20832
2666
6014
9672
1674
558
2976
992
4092
4092
8432
14384
11160
253

21473662.29
20906977.43
23205003.62
25683780.79
10646759.3
9051586.286
7357002.462
16403628.36
15073436.53
10582055.62
5314178.696
6472743.84
3190477.754
3383088.201
15452884.16
18406560.61
15380493.51
21620703.02
2528828.946
3296565.142
8584919.587
9483877.377
16954641.64
13446210.54
7275631.349
8247988.84
24812031
23422210.96
24073620.52
1347596.332
9460887.145
18061687.33
17294481.84
25462976.55
28371517.22
999405.8414
19378943.66
33036865.56
36621209.33
40700366.97
3195056.06
3964945.788
2504287.799
2594825.929
2987676.308
1608003.913
6310552.747
7987996.689

0.0001
0.0000
0.0001
0.0001
0.0005
0.0011
0.0011
0.0002
0.0001
0.0010
0.0024
0.0004
0.0011
0.0012
0.0004
0.0001
0.0003
0.0002
0.0046
0.0014
0.0012
0.0002
0.0001
0.0005
0.0002
0.0004
0.0006
0.0003
0.0005
0.0039
0.0016
0.0001
0.0007
0.0003
0.0002
0.0208
0.0001
0.0002
0.0003
0.0000
0.0002
0.0008
0.0004
0.0016
0.0014
0.0052
0.0023
0.0014

0.10
0.03
0.12
0.11
0.55
1.10
1.05
0.16
0.08
0.98
2.43
0.43
1.13
1.19
0.41
0.06
0.27
0.21
4.63
1.41
1.15
0.17
0.14
0.45
0.16
0.42
0.57
0.33
0.48
3.86
1.60
0.14
0.67
0.30
0.24
20.84
0.14
0.18
0.26
0.04
0.17
0.75
0.40
1.58
1.37
5.24
2.28
1.40

Table 30 (cont’d)
4
17
4
18
4
19
4
20
4
21
4
22
4
23
4
24
4
25
4
26
4
27
4
28
4
29
4
30
4
31
4
32
4
33
4
34
4
35
4
36
4
37
4
38
4
39
4
40
4
41
4
42
4
43
4
44
5
2
5
3
5
4
5
6
5
7
5
9
5
10
5
11
5
12
5
13
5
14
5
15
5
16
5
17
5
18
5
19
5
20
5
21
5
22
5
23

62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97

62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94

7134
6856
6421
5245
4787
5923
5387
2335
2170
2349
834
9715
5167
12424
13103
13609
12892
12078
12639
9035
5592
1020
5806
4637
6797
4892
6597
7840
7436
5116
9065
845
2512
6716
6276
6797
7095
6759
6028
4224
3897
3296
3083
2600
2772
3291
3262
2517

3844
1736
15004
18600
3968
5208
5828
9238
1488
5890
6696
16864
6696
14260
2294
3534
8804
1674
5022
20522
11160
16802
7502
21762
3596
16802
11098
9858
32592
4171
6014
15132
2619
873
4656
1552
6402
6402
13192
22504
17460
6014
2716
23474
29100
6208
8148
9118
254

20957182.7
19432762.23
17157147.02
11681979.39
9820204.057
14717338.95
12290208.58
2510412.457
2184104.906
2539087.877
354989.4907
37682640.7
11354109.46
60130766.67
66527990.85
71494034.7
64507255.36
56988935.92
62123248.34
32829441.77
13194034.31
520403.6409
14169886.12
9243798.76
19116254.9
10233501.37
18061687.33
25072666.22
22674870.95
11142124.79
33036865.56
363938.2873
2884279.257
18685740.2
16428487.3
19116254.9
20740038.57
18913706.62
15217004.29
7742396.85
6643359.751
4832542.54
4256471.178
3079281.276
3477820.229
4818623.301
4738266.872
2895196.93

0.0002
0.0001
0.0009
0.0016
0.0004
0.0004
0.0005
0.0037
0.0007
0.0023
0.0189
0.0004
0.0006
0.0002
0.0000
0.0000
0.0001
0.0000
0.0001
0.0006
0.0008
0.0323
0.0005
0.0024
0.0002
0.0016
0.0006
0.0004
0.0014
0.0004
0.0002
0.0416
0.0009
0.0000
0.0003
0.0001
0.0003
0.0003
0.0009
0.0029
0.0026
0.0012
0.0006
0.0076
0.0084
0.0013
0.0017
0.0031

0.18
0.09
0.87
1.59
0.40
0.35
0.47
3.68
0.68
2.32
18.86
0.45
0.59
0.24
0.03
0.05
0.14
0.03
0.08
0.63
0.85
32.29
0.53
2.35
0.19
1.64
0.61
0.39
1.44
0.37
0.18
41.58
0.91
0.05
0.28
0.08
0.31
0.34
0.87
2.91
2.63
1.24
0.64
7.62
8.37
1.29
1.72
3.15

Table 30 (cont’d)
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6

24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
2
3
4
5
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
97
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156
156

149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230

5492
5905
5912
7122
4167
2486
9188
9117
8693
8020
8816
8782
3185
6151
7975
3560
3343
3607
5035
5679
5077
8599
5621
9570
845
1260
7258
6818
7447
7745
7450
7106
4763
4391
3366
3190
3094
4606
4620
6364
5471
6570
6894
6989
8200
4651
5588
9589

14453
2328
9215
10476
26384
10476
22310
3589
5529
13774
2619
7857
32107
17460
26287
11737
34047
5626
26287
17363
15423
52416
6708
9672
15132
4212
1404
7488
2496
10296
10296
21216
36192
28080
9672
4368
37752
46800
9984
13104
14664
23244
3744
14820
16848
42432
16848
35880
255

12749348.84
14632475.77
14665450.5
20890255.02
7545093.932
2827822.438
33893769.71
33397865.71
30508607.9
26177688.75
31334011.96
31104807.81
4528015.759
15812366.84
25899317.18
5594419.077
4964312.538
5735584.341
10809335.64
13586780.23
10981296.31
29884851.56
13324343.25
36621209.33
363938.2873
777506.513
21654702.99
19228627.85
22738644.52
24498568.14
22756052.04
20801175.87
9726869.85
8334326.892
5029407.35
4541531.155
4285372.593
9126735.697
9179515.374
16868921.8
12656882.78
17921493.63
19637917.46
20155267.98
27305260.21
9296897.548
13176108.28
36759475.44

0.0011
0.0002
0.0006
0.0005
0.0035
0.0037
0.0007
0.0001
0.0002
0.0005
0.0001
0.0003
0.0071
0.0011
0.0010
0.0021
0.0069
0.0010
0.0024
0.0013
0.0014
0.0018
0.0005
0.0003
0.0416
0.0054
0.0001
0.0004
0.0001
0.0004
0.0005
0.0010
0.0037
0.0034
0.0019
0.0010
0.0088
0.0051
0.0011
0.0008
0.0012
0.0013
0.0002
0.0007
0.0006
0.0046
0.0013
0.0010

1.13
0.16
0.63
0.50
3.50
3.70
0.66
0.11
0.18
0.53
0.08
0.25
7.09
1.10
1.01
2.10
6.86
0.98
2.43
1.28
1.40
1.75
0.50
0.26
41.58
5.42
0.06
0.39
0.11
0.42
0.45
1.02
3.72
3.37
1.92
0.96
8.81
5.13
1.09
0.78
1.16
1.30
0.19
0.74
0.62
4.56
1.28
0.98

Table 30 (cont’d)
6
31
6
32
6
33
6
34
6
35
6
36
6
37
6
38
6
39
6
40
6
41
6
42
6
43
6
44
7
2
7
3
7
4
7
5
7
6
7
9
7
10
7
11
7
12
7
13
7
14
7
15
7
16
7
17
7
18
7
19
7
20
7
21
7
22
7
23
7
24
7
25
7
26
7
27
7
28
7
29
7
30
7
31
7
32
7
33
7
34
7
35
7
36
7
37

156
156
156
156
156
156
156
156
156
156
156
156
156
156
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27

37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180

9605
9211
8509
9218
9270
2895
6683
8725
5012
3837
3692
5567
5528
5223
8995
6913
10117
2512
1260
7695
7254
7882
8181
7885
7542
5477
5105
3965
3710
3668
5320
5251
7077
6184
7006
7331
7426
8635
5856
6302
12595
10807
10394
9711
12224
10654
2712
7005

5772
8892
22152
4212
12636
51636
28080
42276
18876
54756
9048
42276
27924
24804
9072
1161
1674
2619
4212
243
1296
432
1782
1782
3672
6264
4860
1674
756
6534
8100
1728
2268
2538
4023
648
2565
2916
7344
2916
6210
999
1539
3834
729
2187
8937
4860
256

36876101.48
34055156.97
29293360.41
34104346.9
34470810.49
3776872.11
18511677.52
30722342.59
10715711.64
6450366.904
5995110.82
13082186.03
12908603.62
11589055.67
32553839.55
19740877.71
40700366.97
2884279.257
777506.513
24198942.41
21632033.53
25328485.25
27185175.81
25346805.11
23292945.11
12683269.12
11096650.77
6865340.221
6050766.981
5921281.68
12001405.27
11707383.23
20640179.58
15973938.71
20248518.48
22070395.57
22616968.61
30123015.8
14402637.47
16558041.16
61712981.18
46135984
42843718.16
37653167.2
58304939.41
44902871.6
3336187.124
20243027.51

0.0002
0.0003
0.0008
0.0001
0.0004
0.0137
0.0015
0.0014
0.0018
0.0085
0.0015
0.0032
0.0022
0.0021
0.0003
0.0001
0.0000
0.0009
0.0054
0.0000
0.0001
0.0000
0.0001
0.0001
0.0002
0.0005
0.0004
0.0002
0.0001
0.0011
0.0007
0.0001
0.0001
0.0002
0.0002
0.0000
0.0001
0.0001
0.0005
0.0002
0.0001
0.0000
0.0000
0.0001
0.0000
0.0000
0.0027
0.0002

0.16
0.26
0.76
0.12
0.37
13.67
1.52
1.38
1.76
8.49
1.51
3.23
2.16
2.14
0.28
0.06
0.04
0.91
5.42
0.01
0.06
0.02
0.07
0.07
0.16
0.49
0.44
0.24
0.12
1.10
0.67
0.15
0.11
0.16
0.20
0.03
0.11
0.10
0.51
0.18
0.10
0.02
0.04
0.10
0.01
0.05
2.68
0.24

Table 30 (cont’d)
7
38
7
39
7
40
7
41
7
42
7
43
7
44
9
2
9
3
9
4
9
5
9
6
9
7
9
10
9
11
9
12
9
13
9
14
9
15
9
16
9
17
9
18
9
19
9
20
9
21
9
22
9
23
9
24
9
25
9
26
9
27
9
28
9
29
9
30
9
31
9
32
9
33
9
34
9
35
9
36
9
37
9
38
9
39
9
40
9
41
9
42
9
43
9
44

27
27
27
27
27
27
27
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9

271
121
351
58
271
179
159
336
43
62
97
156
27
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159

9139
6622
4551
4285
5803
5144
4838
2870
6800
2651
6716
7258
7695
522
256
664
580
799
2088
2646
4687
4409
3974
3562
2874
4905
4121
540
247
1200
2356
8154
4129
11157
11836
11236
10519
10812
11352
6587
3144
1529
5194
2733
4342
2445
4150
5392

7317
3267
9477
1566
7317
4833
4293
3024
387
558
873
1404
243
432
144
594
594
1224
2088
1620
558
252
2178
2700
576
756
846
1341
216
855
972
2448
972
2070
333
513
1278
243
729
2979
1620
2439
1089
3159
522
2439
1611
1431
257

33551155.89
18191957.6
8920783.252
7956216.088
14155978.16
11258274.25
10019940.48
3715143.589
19132289.08
3195056.06
18685740.2
21654702.99
24198942.41
145738.386
37640.5477
230207.4376
178038.1971
327219.277
2029962.145
3183616.125
9434098.616
8399359.847
6894978.865
5600392.157
3724987.77
10285232.71
7387627.131
155434.7588
35166.10036
708670.5209
2553483.428
27014960.91
7414899.643
49016254.57
54838986.18
49677792.37
43827981
46176548.74
50656773.73
18009703.4
4417909.559
1122985.69
11467102.54
3385441.395
8158506.358
2739869.265
7486716.427
12311891.48

0.0002
0.0002
0.0011
0.0002
0.0005
0.0004
0.0004
0.0008
0.0000
0.0002
0.0000
0.0001
0.0000
0.0030
0.0038
0.0026
0.0033
0.0037
0.0010
0.0005
0.0001
0.0000
0.0003
0.0005
0.0002
0.0001
0.0001
0.0086
0.0061
0.0012
0.0004
0.0001
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0004
0.0022
0.0001
0.0009
0.0001
0.0009
0.0002
0.0001

0.22
0.18
1.06
0.20
0.52
0.43
0.43
0.81
0.02
0.17
0.05
0.06
0.01
2.96
3.83
2.58
3.34
3.74
1.03
0.51
0.06
0.03
0.32
0.48
0.15
0.07
0.11
8.63
6.14
1.21
0.38
0.09
0.13
0.04
0.01
0.01
0.03
0.01
0.01
0.17
0.37
2.17
0.09
0.93
0.06
0.89
0.22
0.12

Table 30 (cont’d)
10
2
10
3
10
4
10
5
10
6
10
7
10
9
10
11
10
12
10
13
10
14
10
15
10
16
10
17
10
18
10
19
10
20
10
21
10
22
10
23
10
24
10
25
10
26
10
27
10
28
10
29
10
30
10
31
10
32
10
33
10
34
10
35
10
36
10
37
10
38
10
39
10
40
10
41
10
42
10
43
10
44
11
2
11
3
11
4
11
5
11
6
11
7
11
9

48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
16
16
16
16
16
16
16

336
43
62
97
156
27
9
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9

3396
7226
2970
6276
6818
7254
522
668
967
687
1318
2518
3076
4155
3877
3480
4016
3352
5382
4542
850
717
1663
2850
8608
5607
11579
12258
11657
10940
11233
11774
6093
2612
1946
5616
3227
3818
1913
3618
4861
2749
7125
2332
6797
7447
7882
256

16128
2064
2976
4656
7488
1296
432
768
3168
3168
6528
11136
8640
2976
1344
11616
14400
3072
4032
4512
7152
1152
4560
5184
13056
5184
11040
1776
2736
6816
1296
3888
15888
8640
13008
5808
16848
2784
13008
8592
7632
5376
688
992
1552
2496
432
144
258

5114917.049
21473662.29
3964945.788
16428487.3
19228627.85
21632033.53
145738.386
232849.4834
470229.965
245594.0271
846913.9365
2897382.809
4238127.61
7503863.985
6578729.382
5357973.527
7034091.897
4989736.581
12268543.79
8887293.99
368040.7939
266370.6655
1317332.097
3666107.825
29944308.66
13261359.75
52598699.03
58613448.32
53273951.78
47220753.39
49652593.96
54294477.72
15530278.31
3106340.278
1775707.333
13301832.91
4642137.865
6389814.576
1718930.997
5768863.577
10110640.77
3423197.889
20906977.43
2504287.799
19116254.9
22738644.52
25328485.25
37640.5477

0.0032
0.0001
0.0008
0.0003
0.0004
0.0001
0.0030
0.0033
0.0067
0.0129
0.0077
0.0038
0.0020
0.0004
0.0002
0.0022
0.0020
0.0006
0.0003
0.0005
0.0194
0.0043
0.0035
0.0014
0.0004
0.0004
0.0002
0.0000
0.0001
0.0001
0.0000
0.0001
0.0010
0.0028
0.0073
0.0004
0.0036
0.0004
0.0076
0.0015
0.0008
0.0016
0.0000
0.0004
0.0001
0.0001
0.0000
0.0038

3.15
0.10
0.75
0.28
0.39
0.06
2.96
3.30
6.74
12.90
7.71
3.84
2.04
0.40
0.20
2.17
2.05
0.62
0.33
0.51
19.43
4.32
3.46
1.41
0.44
0.39
0.21
0.03
0.05
0.14
0.03
0.07
1.02
2.78
7.33
0.44
3.63
0.44
7.57
1.49
0.75
1.57
0.03
0.40
0.08
0.11
0.02
3.83

Table 30 (cont’d)
11
10
11
12
11
13
11
14
11
15
11
16
11
17
11
18
11
19
11
20
11
21
11
22
11
23
11
24
11
25
11
26
11
27
11
28
11
29
11
30
11
31
11
32
11
33
11
34
11
35
11
36
11
37
11
38
11
39
11
40
11
41
11
42
11
43
11
44
12
2
12
3
12
4
12
5
12
6
12
7
12
9
12
10
12
11
12
13
12
14
12
15
12
16
12
17

16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
66
66
66
66
66
66
66
66
66
66
66
66
66
66

48
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
136
232
180
62

668
389
321
957
2370
2630
4203
3925
4171
3844
3265
5295
4403
862
504
1515
2246
8436
4519
11439
12118
11517
10800
11093
11664
6784
3341
1237
5476
2947
4539
2642
4347
5589
2843
7527
2376
7095
7745
8181
664
967
389
290
577
2691
3578
4462

768
1056
1056
2176
3712
2880
992
448
3872
4800
1024
1344
1504
2384
384
1520
1728
4352
1728
3680
592
912
2272
432
1296
5296
2880
4336
1936
5616
928
4336
2864
2544
22176
2838
4092
6402
10296
1782
594
3168
1056
4356
8976
15312
11880
4092
259

232849.4834
83349.73867
57857.48635
461033.7104
2582390.155
3147138.98
7669425.639
6734344.916
7558861.027
6472743.84
4746549.919
11894476.5
8377655.591
377975.6348
136338.3454
1103529.64
2331731.44
28817711.22
8801981.441
51396945.99
57348069.32
52064871.04
46079221.74
48483406.32
53334750.87
19046847.06
4958671.105
750762.2536
12678869.59
3906809.707
8876144.162
3174478.172
8176365.863
13180588.71
3649018.233
23205003.62
2594825.929
20740038.57
24498568.14
27185175.81
230207.4376
470229.965
83349.73867
47704.15374
176292.5846
3287274.916
5648285.477
8592235.568

0.0033
0.0127
0.0183
0.0047
0.0014
0.0009
0.0001
0.0001
0.0005
0.0007
0.0002
0.0001
0.0002
0.0063
0.0028
0.0014
0.0007
0.0002
0.0002
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0003
0.0006
0.0058
0.0002
0.0014
0.0001
0.0014
0.0004
0.0002
0.0061
0.0001
0.0016
0.0003
0.0004
0.0001
0.0026
0.0067
0.0127
0.0913
0.0509
0.0047
0.0021
0.0005

3.30
12.67
18.25
4.72
1.44
0.92
0.13
0.07
0.51
0.74
0.22
0.11
0.18
6.31
2.82
1.38
0.74
0.15
0.20
0.07
0.01
0.02
0.05
0.01
0.02
0.28
0.58
5.78
0.15
1.44
0.10
1.37
0.35
0.19
6.08
0.12
1.58
0.31
0.42
0.07
2.58
6.74
12.67
91.31
50.92
4.66
2.10
0.48

Table 30 (cont’d)
12
18
12
19
12
20
12
21
12
22
12
23
12
24
12
25
12
26
12
27
12
28
12
29
12
30
12
31
12
32
12
33
12
34
12
35
12
36
12
37
12
38
12
39
12
40
12
41
12
42
12
43
12
44
13
2
13
3
13
4
13
5
13
6
13
7
13
9
13
10
13
11
13
12
13
14
13
15
13
16
13
17
13
18
13
19
13
20
13
21
13
22
13
23
13
24

66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66

28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
136
232
180
62
28
242
300
64
84
94
149

4184
3749
4078
3585
5615
4724
1143
825
1072
2327
8670
4840
11760
12439
11838
11121
11414
11955
7044
3601
1302
5797
3189
4806
2902
4607
5849
2979
7940
2559
6759
7450
7885
580
687
321
290
1256
3167
3724
4864
4586
4129
4765
4061
6092
5199
1108

1848
15972
19800
4224
5544
6204
9834
1584
6270
7128
17952
7128
15180
2442
3762
9372
1782
5346
21846
11880
17886
7986
23166
3828
17886
11814
10494
22176
2838
4092
6402
10296
1782
594
3168
1056
4356
8976
15312
11880
4092
1848
15972
19800
4224
5544
6204
9834
260

7603686.177
6172190.714
7241853.263
5669299.543
13297333.01
9576102.566
646082.2917
347746.2875
571965.8405
2494095.784
30355422.73
10027812.09
54171880.44
60268778.41
54856593.82
48716187.96
51183732.1
55891298.93
20457697.84
5717470.475
827486.4101
14128181.67
4538826.549
9894392.999
3794242.422
9130500.893
14369944.16
3987805.391
25683780.79
2987676.308
18913706.62
22756052.04
25346805.11
178038.1971
245594.0271
57857.48635
47704.15374
772823.4915
4479518.354
6094223.498
10122499.78
9051586.286
7414899.643
9734631.574
7184601.424
15525435.81
11488085.34
609011.5718

0.0002
0.0026
0.0027
0.0007
0.0004
0.0006
0.0152
0.0046
0.0110
0.0029
0.0006
0.0007
0.0003
0.0000
0.0001
0.0002
0.0000
0.0001
0.0011
0.0021
0.0216
0.0006
0.0051
0.0004
0.0047
0.0013
0.0007
0.0056
0.0001
0.0014
0.0003
0.0005
0.0001
0.0033
0.0129
0.0183
0.0913
0.0116
0.0034
0.0019
0.0004
0.0002
0.0022
0.0020
0.0006
0.0004
0.0005
0.0161

0.24
2.59
2.73
0.75
0.42
0.65
15.22
4.56
10.96
2.86
0.59
0.71
0.28
0.04
0.07
0.19
0.03
0.10
1.07
2.08
21.61
0.57
5.10
0.39
4.71
1.29
0.73
5.56
0.11
1.37
0.34
0.45
0.07
3.34
12.90
18.25
91.31
11.61
3.42
1.95
0.40
0.20
2.15
2.03
0.59
0.36
0.54
16.15

Table 30 (cont’d)
13
25
13
26
13
27
13
28
13
29
13
30
13
31
13
32
13
33
13
34
13
35
13
36
13
37
13
38
13
39
13
40
13
41
13
42
13
43
13
44
14
2
14
3
14
4
14
5
14
6
14
7
14
9
14
10
14
11
14
12
14
13
14
15
14
16
14
17
14
18
14
19
14
20
14
21
14
22
14
23
14
24
14
25
14
26
14
27
14
28
14
29
14
30
14
31

66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136
136

24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37

790
1751
2484
9318
5316
12235
12914
12313
11596
11889
12430
6743
3322
1511
6272
3876
4519
2622
4327
5570
1667
4995
1847
6028
7106
7542
799
1318
957
577
1256
2139
2748
4291
4026
3660
2972
2493
4473
3581
629
607
586
1832
7462
3697
10617
11296

1584
6270
7128
17952
7128
15180
2442
3762
9372
1782
5346
21846
11880
17886
7986
23166
3828
17886
11814
10494
45696
5848
8432
13192
21216
3672
1224
6528
2176
8976
8976
31552
24480
8432
3808
32912
40800
8704
11424
12784
20264
3264
12920
14688
36992
14688
31280
5032
261

320251.7219
1452926.609
2823501.507
34810730.87
11984266.2
58404666.72
64716569.18
59114139.35
52745521.76
55306493.1
60185953.38
18828728.94
4905229.071
1098000.359
16408598.72
6575505.72
8801981.441
3128975.092
8105038.85
13095584.01
1323358.891
10646759.3
1608003.913
15217004.29
20801175.87
23292945.11
327219.277
846913.9365
461033.7104
176292.5846
772823.4915
2125203.344
3420832.298
7977396.487
7067408.005
5896768.283
3970020.32
2842970.342
8632526.228
5657286.979
207699.8708
194114.7393
181553.8531
1583282.428
22825745.2
6010546.427
44607044.86
50183032.2

0.0049
0.0043
0.0025
0.0005
0.0006
0.0003
0.0000
0.0001
0.0002
0.0000
0.0001
0.0012
0.0024
0.0163
0.0005
0.0035
0.0004
0.0057
0.0015
0.0008
0.0345
0.0005
0.0052
0.0009
0.0010
0.0002
0.0037
0.0077
0.0047
0.0509
0.0116
0.0148
0.0072
0.0011
0.0005
0.0056
0.0103
0.0031
0.0013
0.0023
0.0976
0.0168
0.0712
0.0093
0.0016
0.0024
0.0007
0.0001

4.95
4.32
2.52
0.52
0.59
0.26
0.04
0.06
0.18
0.03
0.09
1.16
2.42
16.29
0.49
3.52
0.43
5.72
1.46
0.80
34.53
0.55
5.24
0.87
1.02
0.16
3.74
7.71
4.72
50.92
11.61
14.85
7.16
1.06
0.54
5.58
10.28
3.06
1.32
2.26
97.56
16.81
71.16
9.28
1.62
2.44
0.70
0.10

Table 30 (cont’d)
14
32
14
33
14
34
14
35
14
36
14
37
14
38
14
39
14
40
14
41
14
42
14
43
14
44
15
2
15
3
15
4
15
5
15
6
15
7
15
9
15
10
15
11
15
12
15
13
15
14
15
16
15
17
15
18
15
19
15
20
15
21
15
22
15
23
15
24
15
25
15
26
15
27
15
28
15
29
15
30
15
31
15
32
15
33
15
34
15
35
15
36
15
37
15
38

136
136
136
136
136
136
136
136
136
136
136
136
136
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232
232

57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271

10696
9979
10272
10812
6463
3693
1395
4654
2824
4616
2744
4442
5283
3658
4586
3793
4224
4763
5477
2088
2518
2370
2691
3167
2139
602
2565
2301
1773
1296
797
2777
1792
1592
1966
2011
3222
5796
2001
8829
9508
8907
8190
8483
9024
4756
3159
3634

7752
19312
3672
11016
45016
24480
36856
16456
47736
7888
36856
24344
21624
77952
9976
14384
22504
36192
6264
2088
11136
3712
15312
15312
31552
41760
14384
6496
56144
69600
14848
19488
21808
34568
5568
22040
25056
63104
25056
53360
8584
13224
32944
6264
18792
76792
41760
62872
262

45239797.17
39652022.41
41893295.04
46176548.74
17371003.04
5998196.437
943389.405
9308294.602
3602822.908
9164420.756
3411377.678
8519208.611
11843311.72
5890647.461
9051586.286
6310552.747
7742396.85
9726869.85
12683269.12
2029962.145
2897382.809
2582390.155
3287274.916
4479518.354
2125203.344
191087.9641
3001000.045
2441414.814
1487807
820256.1645
325664.7932
3489748.84
1518246.226
1212528.124
1810542.299
1890091.957
4628481.355
14123551.44
1872274.268
31421859.47
36171742.36
31951390.23
27242026.64
29123528.23
32753541.45
9699726.914
4458043.37
5817432.511

0.0002
0.0005
0.0001
0.0002
0.0026
0.0041
0.0391
0.0018
0.0132
0.0009
0.0108
0.0029
0.0018
0.0132
0.0011
0.0023
0.0029
0.0037
0.0005
0.0010
0.0038
0.0014
0.0047
0.0034
0.0148
0.2185
0.0048
0.0027
0.0377
0.0849
0.0456
0.0056
0.0144
0.0285
0.0031
0.0117
0.0054
0.0045
0.0134
0.0017
0.0002
0.0004
0.0012
0.0002
0.0006
0.0079
0.0094
0.0108

0.17
0.49
0.09
0.24
2.59
4.08
39.07
1.77
13.25
0.86
10.80
2.86
1.83
13.23
1.10
2.28
2.91
3.72
0.49
1.03
3.84
1.44
4.66
3.42
14.85
218.54
4.79
2.66
37.74
84.85
45.59
5.58
14.36
28.51
3.08
11.66
5.41
4.47
13.38
1.70
0.24
0.41
1.21
0.22
0.57
7.92
9.37
10.81

Table 30 (cont’d)
15
39
15
40
15
41
15
42
15
43
15
44
16
2
16
3
16
4
16
5
16
6
16
7
16
9
16
10
16
11
16
12
16
13
16
14
16
15
16
17
16
18
16
19
16
20
16
21
16
22
16
23
16
24
16
25
16
26
16
27
16
28
16
29
16
30
16
31
16
32
16
33
16
34
16
35
16
36
16
37
16
38
16
39
16
40
16
41
16
42
16
43
16
44
17
2

232
232
232
232
232
232
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
62

121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336

2865
909
2891
2043
3415
3978
3674
4112
4294
3897
4391
5105
2646
3076
2630
3578
3724
2748
602
2643
2370
1453
745
246
2225
1333
2106
2480
2144
3355
5255
1450
8368
9048
8447
7730
8023
8564
4476
3616
4034
2406
850
2969
2500
3247
3809
5681

28072
81432
13456
62872
41528
36888
60480
7740
11160
17460
28080
4860
1620
8640
2880
11880
11880
24480
41760
11160
5040
43560
54000
11520
15120
16920
26820
4320
17100
19440
48960
19440
41400
6660
10260
25560
4860
14580
59580
32400
48780
21780
63180
10440
48780
32220
28620
20832
263

3702855.717
418091.5384
3766963.171
1947645.432
5169426.267
6908171.007
5939698.337
7357002.462
7987996.689
6643359.751
8334326.892
11096650.77
3183616.125
4238127.61
3147138.98
5648285.477
6094223.498
3420832.298
191087.9641
3176761.494
2582390.155
1019306.203
286481.7218
34896.08479
2290482.77
865321.0805
2063340.473
2814869.035
2134651.997
4998224.933
11724333.64
1015311.264
28377960.24
32919249.43
28889148.34
24408497.04
26196296.98
29654161.7
8643530.084
5762806.023
7094114.575
2657429.083
368040.7939
3962409.675
2858156.575
4696954.449
6361226.333
13595873.01

0.0076
0.1948
0.0036
0.0323
0.0080
0.0053
0.0102
0.0011
0.0014
0.0026
0.0034
0.0004
0.0005
0.0020
0.0009
0.0021
0.0019
0.0072
0.2185
0.0035
0.0020
0.0427
0.1885
0.3301
0.0066
0.0196
0.0130
0.0015
0.0080
0.0039
0.0042
0.0191
0.0015
0.0002
0.0004
0.0010
0.0002
0.0005
0.0069
0.0056
0.0069
0.0082
0.1717
0.0026
0.0171
0.0069
0.0045
0.0015

7.58
194.77
3.57
32.28
8.03
5.34
10.18
1.05
1.40
2.63
3.37
0.44
0.51
2.04
0.92
2.10
1.95
7.16
218.54
3.51
1.95
42.73
188.49
330.12
6.60
19.55
13.00
1.53
8.01
3.89
4.18
19.15
1.46
0.20
0.36
1.05
0.19
0.49
6.89
5.62
6.88
8.20
171.67
2.63
17.07
6.86
4.50
1.53

Table 30 (cont’d)
17
3
17
4
17
5
17
6
17
7
17
9
17
10
17
11
17
12
17
13
17
14
17
15
17
16
17
18
17
19
17
20
17
21
17
22
17
23
17
24
17
25
17
26
17
27
17
28
17
29
17
30
17
31
17
32
17
33
17
34
17
35
17
36
17
37
17
38
17
39
17
40
17
41
17
42
17
43
17
44
18
2
18
3
18
4
18
5
18
6
18
7
18
9
18
10

62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
62
28
28
28
28
28
28
28
28

43
62
97
156
27
9
48
16
66
66
136
232
180
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48

6271
7134
3296
3366
3965
4687
4155
4203
4462
4864
4291
2565
2643
273
1223
2980
2994
4669
3845
3745
3946
4185
5396
6756
3893
10662
11341
10741
10023
10316
10857
2270
3717
6208
4922
2018
323
2427
1843
1792
5417
5998
6856
3083
3190
3710
4409
3877

2666
3844
6014
9672
1674
558
2976
992
4092
4092
8432
14384
11160
1736
15004
18600
3968
5208
5828
9238
1488
5890
6696
16864
6696
14260
2294
3534
8804
1674
5022
20522
11160
16802
7502
21762
3596
16802
11098
9858
9408
1204
1736
2716
4368
756
252
1344
264

16403628.36
20957182.7
4832542.54
5029407.35
6865340.221
9434098.616
7503863.985
7669425.639
8592235.568
10122499.78
7977396.487
3001000.045
3176761.494
42531.33642
734700.3866
3990349.189
4026043.031
9365379.071
6475943.542
6159684.412
6802968.401
7607139.465
12329250.82
18897759.5
6630409.735
44966955.92
50563550.96
45602112.48
39984869.94
42234906.16
46542391.17
2379299.584
6072476.826
16091934.11
10353067.79
1902611.895
58544.32357
2701671.709
1601393.778
1518246.226
12420577.46
15073436.53
19432762.23
4256471.178
4541531.155
6050766.981
8399359.847
6578729.382

0.0002
0.0002
0.0012
0.0019
0.0002
0.0001
0.0004
0.0001
0.0005
0.0004
0.0011
0.0048
0.0035
0.0408
0.0204
0.0047
0.0010
0.0006
0.0009
0.0015
0.0002
0.0008
0.0005
0.0009
0.0010
0.0003
0.0000
0.0001
0.0002
0.0000
0.0001
0.0086
0.0018
0.0010
0.0007
0.0114
0.0614
0.0062
0.0069
0.0065
0.0008
0.0001
0.0001
0.0006
0.0010
0.0001
0.0000
0.0002

0.16
0.18
1.24
1.92
0.24
0.06
0.40
0.13
0.48
0.40
1.06
4.79
3.51
40.82
20.42
4.66
0.99
0.56
0.90
1.50
0.22
0.77
0.54
0.89
1.01
0.32
0.05
0.08
0.22
0.04
0.11
8.63
1.84
1.04
0.72
11.44
61.42
6.22
6.93
6.49
0.76
0.08
0.09
0.64
0.96
0.12
0.03
0.20

Table 30 (cont’d)
18
11
18
12
18
13
18
14
18
15
18
16
18
17
18
19
18
20
18
21
18
22
18
23
18
24
18
25
18
26
18
27
18
28
18
29
18
30
18
31
18
32
18
33
18
34
18
35
18
36
18
37
18
38
18
39
18
40
18
41
18
42
18
43
18
44
19
2
19
3
19
4
19
5
19
6
19
7
19
9
19
10
19
11
19
12
19
13
19
14
19
15
19
16
19
17

28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242

16
66
66
136
232
180
62
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62

3925
4184
4586
4026
2301
2370
273
925
2653
2646
4391
3498
3492
3816
3911
5122
6668
3615
10246
10925
10326
9608
9901
10441
2395
3418
5851
4575
1720
535
2302
2063
2011
5173
4979
6421
2600
3094
3668
3974
3480
4171
3749
4129
3660
1773
1453
1223

448
1848
1848
3808
6496
5040
1736
6776
8400
1792
2352
2632
4172
672
2660
3024
7616
3024
6440
1036
1596
3976
756
2268
9268
5040
7588
3388
9828
1624
7588
5012
4452
81312
10406
15004
23474
37752
6534
2178
11616
3872
15972
15972
32912
56144
43560
15004
265

6734344.916
7603686.177
9051586.286
7067408.005
2441414.814
2582390.155
42531.33642
432184.6045
3199637.477
3183616.125
8334326.892
5410752.014
5393131.957
6383456.386
6688779.052
11166965.92
18432813.25
5759778.377
41692051.73
47097813.62
42312728.31
36897988.35
39065214.65
43212558.65
2634392.515
5178058.003
14379281.52
9010379.582
1404442.778
152711.6557
2443431.153
1984031.363
1890091.957
11379173.23
10582055.62
17157147.02
3079281.276
4285372.593
5921281.68
6894978.865
5357973.527
7558861.027
6172190.714
7414899.643
5896768.283
1487807
1019306.203
734700.3866

0.0001
0.0002
0.0002
0.0005
0.0027
0.0020
0.0408
0.0157
0.0026
0.0006
0.0003
0.0005
0.0008
0.0001
0.0004
0.0003
0.0004
0.0005
0.0002
0.0000
0.0000
0.0001
0.0000
0.0001
0.0035
0.0010
0.0005
0.0004
0.0070
0.0106
0.0031
0.0025
0.0024
0.0071
0.0010
0.0009
0.0076
0.0088
0.0011
0.0003
0.0022
0.0005
0.0026
0.0022
0.0056
0.0377
0.0427
0.0204

0.07
0.24
0.20
0.54
2.66
1.95
40.82
15.68
2.63
0.56
0.28
0.49
0.77
0.11
0.40
0.27
0.41
0.53
0.15
0.02
0.04
0.11
0.02
0.05
3.52
0.97
0.53
0.38
7.00
10.63
3.11
2.53
2.36
7.15
0.98
0.87
7.62
8.81
1.10
0.32
2.17
0.51
2.59
2.15
5.58
37.74
42.73
20.42

Table 30 (cont’d)
19
18
19
20
19
21
19
22
19
23
19
24
19
25
19
26
19
27
19
28
19
29
19
30
19
31
19
32
19
33
19
34
19
35
19
36
19
37
19
38
19
39
19
40
19
41
19
42
19
43
19
44
20
2
20
3
20
4
20
5
20
6
20
7
20
9
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
21
20
22
20
23
20
24
20
25

242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
242
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300

28
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
64
84
94
149
24

925
6776 432184.6045
1683 72600 1347596.332
2451 15488 2752658.221
3441 20328 5244461.215
2548 22748 2963322.407
3063 36058 4204160.562
3476
5808 5346278.258
3482 22990 5363825.701
4693 26136 9457058.01
5594 65824 13203001.66
2665 26136 3227191.265
9172 55660 33781714.52
9851
8954 38691236.24
9252 13794 34343747.76
8534 34364 29457101.64
8827
6534 31408336.89
9367 19602 35159362.21
3074 80102 4232893.483
3692 43560 5995110.82
5476 65582 12678869.59
3625 29282 5790088.752
879 84942 392264.3703
1500 14036 1082862.695
2539 65582 2943466.732
3349 43318 4981255.065
3157 38478 4452682.263
4116 100800 7370605.987
3465 12900 5314178.696
5245 18600 11681979.39
2772 29100 3477820.229
4606 46800 9126735.697
5320
8100 12001405.27
3562
2700 5600392.157
4016 14400 7034091.897
3844
4800 6472743.84
4078 19800 7241853.263
4765 19800 9734631.574
2972 40800 3970020.32
1296 69600 820256.1645
745 54000 286481.7218
2980 18600 3990349.189
2653
8400 3199637.477
1683 72600 1347596.332
471 19200 119878.0648
1204 25200 713165.4992
713 28200 263554.3002
2660 44700 3215696.919
3575
7200 5639290.765
266

0.0157
0.0539
0.0056
0.0039
0.0077
0.0086
0.0011
0.0043
0.0028
0.0050
0.0081
0.0016
0.0002
0.0004
0.0012
0.0002
0.0006
0.0189
0.0073
0.0052
0.0051
0.2165
0.0130
0.0223
0.0087
0.0086
0.0137
0.0024
0.0016
0.0084
0.0051
0.0007
0.0005
0.0020
0.0007
0.0027
0.0020
0.0103
0.0849
0.1885
0.0047
0.0026
0.0539
0.1602
0.0353
0.1070
0.0139
0.0013

15.68
53.87
5.63
3.88
7.68
8.58
1.09
4.29
2.76
4.99
8.10
1.65
0.23
0.40
1.17
0.21
0.56
18.92
7.27
5.17
5.06
216.54
12.96
22.28
8.70
8.64
13.68
2.43
1.59
8.37
5.13
0.67
0.48
2.05
0.74
2.73
2.03
10.28
84.85
188.49
4.66
2.63
53.87
160.16
35.34
107.00
13.90
1.28

Table 30 (cont’d)
20
26
20
27
20
28
20
29
20
30
20
31
20
32
20
33
20
34
20
35
20
36
20
37
20
38
20
39
20
40
20
41
20
42
20
43
20
44
21
2
21
3
21
4
21
5
21
6
21
7
21
9
21
10
21
11
21
12
21
13
21
14
21
15
21
16
21
17
21
18
21
19
21
20
21
22
21
23
21
24
21
25
21
26
21
27
21
28
21
29
21
30
21
31
21
32

300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
300
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64

95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
84
94
149
24
95
108
272
108
230
37
57

2545 28500 2956696.823
3755 32400 6190972.695
4534 81600 8857575.85
428 32400 99940.87815
7735 69000 24438503.28
8414 11100 28675088.35
7813 17100 24908860.83
7096 42600 20745592.98
7389
8100 22403339.82
7930 24300 25622355.7
4790 99300 9831900.516
4398 54000 8359589.033
4177 81300 7579533.957
1772 36300 1486213.026
1171 105300
676484.89
3282 17400 4793616.602
3245 81300 4691459.075
4381 53700 8298300.949
4843 47700
10039625
3697 21504 6010546.427
3844
2752 6472743.84
4787
3968 9820204.057
3291
6208 4818623.301
4620
9984 9179515.374
5251
1728 11707383.23
2874
576 3724987.77
3352
3072 4989736.581
3265
1024 4746549.919
3585
4224 5669299.543
4061
4224 7184601.424
2493
8704 2842970.342
797 14848 325664.7932
246 11520 34896.08479
2994
3968 4026043.031
2646
1792 3183616.125
2451 15488 2752658.221
471 19200 119878.0648
1990
5376 1852767.164
668
6016 232849.4834
2293
9536 2425312.494
2903
1536 3796726.977
2178
6080 2199429.082
3388
6912 5092047.699
4519 17408 8801981.441
1214
6912 724461.8237
8135 14720 26895483.67
8841
2368 31503052.87
8213
3648 27387567.65
267

0.0096
0.0052
0.0092
0.3242
0.0028
0.0004
0.0007
0.0021
0.0004
0.0009
0.0101
0.0065
0.0107
0.0244
0.1557
0.0036
0.0173
0.0065
0.0048
0.0036
0.0004
0.0004
0.0013
0.0011
0.0001
0.0002
0.0006
0.0002
0.0007
0.0006
0.0031
0.0456
0.3301
0.0010
0.0006
0.0056
0.1602
0.0029
0.0258
0.0039
0.0004
0.0028
0.0014
0.0020
0.0095
0.0005
0.0001
0.0001

9.64
5.23
9.21
324.19
2.82
0.39
0.69
2.05
0.36
0.95
10.10
6.46
10.73
24.42
155.66
3.63
17.33
6.47
4.75
3.58
0.43
0.40
1.29
1.09
0.15
0.15
0.62
0.22
0.75
0.59
3.06
45.59
330.12
0.99
0.56
5.63
160.16
2.90
25.84
3.93
0.40
2.76
1.36
1.98
9.54
0.55
0.08
0.13

Table 30 (cont’d)
21
33
21
34
21
35
21
36
21
37
21
38
21
39
21
40
21
41
21
42
21
43
21
44
22
2
22
3
22
4
22
5
22
6
22
7
22
9
22
10
22
11
22
12
22
13
22
14
22
15
22
16
22
17
22
18
22
19
22
20
22
21
22
23
22
24
22
25
22
26
22
27
22
28
22
29
22
30
22
31
22
32
22
33
22
34
22
35
22
36
22
37
22
38
22
39

64
64
64
64
64
64
64
64
64
64
64
64
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84
84

142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121

7496
7789
8330
4634
3974
4644
1741
1007
3126
2822
4186
4749
5676
2649
5923
3262
6364
7077
4905
5382
5295
5615
6092
4473
2777
2225
4669
4391
3441
1204
1990
1033
4344
4878
3241
4451
5135
735
6958
7636
7046
6328
6612
7152
6503
6115
5862
1101

9088
1728
5184
21184
11520
17344
7744
22464
3712
17344
11456
10176
28224
3612
5208
8148
13104
2268
756
4032
1344
5544
5544
11424
19488
15120
5208
2352
20328
25200
5376
7896
12516
2016
7980
9072
22848
9072
19320
3108
4788
11928
2268
6804
27804
15120
22764
10164
268

23023757.27
24763683.08
28133612.58
9232439.193
6894978.865
9270330.146
1437201.511
507874.0162
4369975.982
3597976.462
7610593.496
9672619.908
13573146.45
3190477.754
14717338.95
4738266.872
16868921.8
20640179.58
10285232.71
12268543.79
11894476.5
13297333.01
15525435.81
8632526.228
3489748.84
2290482.77
9365379.071
8334326.892
5244461.215
713165.4992
1852767.164
533077.8174
8165647.94
10177928.89
4680477.469
8552034.203
11220878.27
279219.6281
19985747.97
23847631.24
20468735.49
16688076.96
18139796.28
21057764.33
17575841.71
15636994.26
14430688.32
601722.0218

0.0004
0.0001
0.0002
0.0023
0.0017
0.0019
0.0054
0.0442
0.0008
0.0048
0.0015
0.0011
0.0021
0.0011
0.0004
0.0017
0.0008
0.0001
0.0001
0.0003
0.0001
0.0004
0.0004
0.0013
0.0056
0.0066
0.0006
0.0003
0.0039
0.0353
0.0029
0.0148
0.0015
0.0002
0.0017
0.0011
0.0020
0.0325
0.0010
0.0001
0.0002
0.0007
0.0001
0.0003
0.0016
0.0010
0.0016
0.0169

0.39
0.07
0.18
2.29
1.67
1.87
5.39
44.23
0.85
4.82
1.51
1.05
2.08
1.13
0.35
1.72
0.78
0.11
0.07
0.33
0.11
0.42
0.36
1.32
5.58
6.60
0.56
0.28
3.88
35.34
2.90
14.81
1.53
0.20
1.70
1.06
2.04
32.49
0.97
0.13
0.23
0.71
0.13
0.32
1.58
0.97
1.58
16.89

Table 30 (cont’d)
22
40
22
41
22
42
22
43
22
44
23
2
23
3
23
4
23
5
23
6
23
7
23
9
23
10
23
11
23
12
23
13
23
14
23
15
23
16
23
17
23
18
23
19
23
20
23
21
23
22
23
24
23
25
23
26
23
27
23
28
23
29
23
30
23
31
23
32
23
33
23
34
23
35
23
36
23
37
23
38
23
39
23
40
23
41
23
42
23
43
23
44
24
2
24
3

84
84
84
84
84
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
94
149
149

351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43

2887
4998
4999
6096
6559
4486
2732
5387
2517
5471
6184
4121
4542
4403
4724
5199
3581
1792
1333
3845
3498
2548
713
668
1033
3338
3952
3303
4513
3737
322
6992
7671
7070
6352
6646
7187
5579
5189
4934
1027
1960
4072
4073
5170
5633
2366
6077

29484
4872
22764
15036
13356
31584
4042
5828
9118
14664
2538
846
4512
1504
6204
6204
12784
21808
16920
5828
2632
22748
28200
6016
7896
14006
2256
8930
10152
25568
10152
21620
3478
5358
13348
2538
7614
31114
16920
25474
11374
32994
5452
25474
16826
14946
50064
6407
269

3757066.563
10658912.04
10662964.41
15544810.1
17864526.05
8680257.557
3383088.201
12290208.58
2895196.93
12656882.78
15973938.71
7387627.131
8887293.99
8377655.591
9576102.566
11488085.34
5657286.979
1518246.226
865321.0805
6475943.542
5410752.014
2963322.407
263554.3002
232849.4834
533077.8174
4950214.654
6822635.635
4852061.423
8779790.108
6134707.858
58200.42503
20171709.14
24055742.55
20601407.2
16808537.6
18317434.2
21253992.61
13135816.91
11446137.91
10401078.54
527210.2471
1800058.152
7221622.154
7224992.143
11366638.08
13378441.6
2574115.363
15452884.16

0.0078
0.0005
0.0021
0.0010
0.0007
0.0036
0.0012
0.0005
0.0031
0.0012
0.0002
0.0001
0.0005
0.0002
0.0006
0.0005
0.0023
0.0144
0.0196
0.0009
0.0005
0.0077
0.1070
0.0258
0.0148
0.0028
0.0003
0.0018
0.0012
0.0042
0.1744
0.0011
0.0001
0.0003
0.0008
0.0001
0.0004
0.0024
0.0015
0.0024
0.0216
0.0183
0.0008
0.0035
0.0015
0.0011
0.0194
0.0004

7.85
0.46
2.13
0.97
0.75
3.64
1.19
0.47
3.15
1.16
0.16
0.11
0.51
0.18
0.65
0.54
2.26
14.36
19.55
0.90
0.49
7.68
107.00
25.84
14.81
2.83
0.33
1.84
1.16
4.17
174.43
1.07
0.14
0.26
0.79
0.14
0.36
2.37
1.48
2.45
21.57
18.33
0.75
3.53
1.48
1.12
19.45
0.41

Table 30 (cont’d)
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
25
25
25
25
25
25
25
25
25

4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
2
3
4
5
6
7
9
10
11

149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
149
24
24
24
24
24
24
24
24
24

62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16

2335
5492
6570
7007
540
850
862
1143
1108
629
1592
2106
3745
3492
3063
2660
2293
4344
3338
389
717
2283
7684
3513
10434
11113
10512
9795
10088
10629
5922
2986
2071
4470
2175
4075
2053
3751
4743
2530
6663
2170
5905
6894
7331
247
717
504

9238
14453
23244
4023
1341
7152
2384
9834
9834
20264
34568
26820
9238
4172
36058
44700
9536
12516
14006
3576
14155
16092
40528
16092
34270
5513
8493
21158
4023
12069
49319
26820
40379
18029
52299
8642
40379
26671
23691
8064
1032
1488
2328
3744
648
216
1152
384
270

2510412.457
12749348.84
17921493.63
20254010.15
155434.7588
368040.7939
377975.6348
646082.2917
609011.5718
207699.8708
1212528.124
2063340.473
6159684.412
5393131.957
4204160.562
3215696.919
2425312.494
8165647.94
4950214.654
83349.73867
266370.6655
2405255.585
24133259.16
5454921.242
43157530.04
48649625.03
43772582.43
38274403.99
40478987.39
44702887.18
14712618.23
4005628.113
1998675.035
8621529.012
2193676.569
7231734.357
1965798.52
6178448.37
9649413.933
2923674.3
18406560.61
2184104.906
14632475.77
19637917.46
22070395.57
35166.10036
266370.6655
136338.3454

0.0037
0.0011
0.0013
0.0002
0.0086
0.0194
0.0063
0.0152
0.0161
0.0976
0.0285
0.0130
0.0015
0.0008
0.0086
0.0139
0.0039
0.0015
0.0028
0.0429
0.0531
0.0067
0.0017
0.0029
0.0008
0.0001
0.0002
0.0006
0.0001
0.0003
0.0034
0.0067
0.0202
0.0021
0.0238
0.0012
0.0205
0.0043
0.0025
0.0028
0.0001
0.0007
0.0002
0.0002
0.0000
0.0061
0.0043
0.0028

3.68
1.13
1.30
0.20
8.63
19.43
6.31
15.22
16.15
97.56
28.51
13.00
1.50
0.77
8.58
13.90
3.93
1.53
2.83
42.90
53.14
6.69
1.68
2.95
0.79
0.11
0.19
0.55
0.10
0.27
3.35
6.70
20.20
2.09
23.84
1.20
20.54
4.32
2.46
2.76
0.06
0.68
0.16
0.19
0.03
6.14
4.32
2.82

Table 30 (cont’d)
25
12
25
13
25
14
25
15
25
16
25
17
25
18
25
19
25
20
25
21
25
22
25
23
25
24
25
26
25
27
25
28
25
29
25
30
25
31
25
32
25
33
25
34
25
35
25
36
25
37
25
38
25
39
25
40
25
41
25
42
25
43
25
44
26
2
26
3
26
4
26
5
26
6
26
7
26
9
26
10
26
11
26
12
26
13
26
14
26
15
26
16
26
17
26
18

24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
24
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95

66
66
136
232
180
62
28
242
300
64
84
94
149
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28

825
790
607
1966
2480
3946
3816
3476
3575
2903
4878
3952
398
899
2127
8062
4145
11001
11680
11079
10362
10655
11196
6216
3363
1733
5038
2681
4226
2332
4038
5277
1592
6062
2349
5912
6989
7426
1200
1663
1515
1072
1751
586
2011
2144
4185
3911

1584
1584
3264
5568
4320
1488
672
5808
7200
1536
2016
2256
3576
2280
2592
6528
2592
5520
888
1368
3408
648
1944
7944
4320
6504
2904
8424
1392
6504
4296
3816
31920
4085
5890
9215
14820
2565
855
4560
1520
6270
6270
12920
22040
17100
5890
2660
271

347746.2875
320251.7219
194114.7393
1810542.299
2814869.035
6802968.401
6383456.386
5346278.258
5639290.765
3796726.977
10177928.89
6822635.635
87051.8163
409395.8296
2102607.605
26438773.74
7469587.453
47722272.13
53473843.57
48367213.58
42593449.85
44910879.78
49342311.49
16131357.31
5020893.944
1424679.808
10821575.9
3264103.691
7749363.559
2504287.799
7107485.752
11817768.52
1212528.124
15380493.51
2539087.877
14665450.5
20155267.98
22616968.61
708670.5209
1317332.097
1103529.64
571965.8405
1452926.609
181553.8531
1890091.957
2134651.997
7607139.465
6688779.052

0.0046
0.0049
0.0168
0.0031
0.0015
0.0002
0.0001
0.0011
0.0013
0.0004
0.0002
0.0003
0.0411
0.0056
0.0012
0.0002
0.0003
0.0001
0.0000
0.0000
0.0001
0.0000
0.0000
0.0005
0.0009
0.0046
0.0003
0.0026
0.0002
0.0026
0.0006
0.0003
0.0263
0.0003
0.0023
0.0006
0.0007
0.0001
0.0012
0.0035
0.0014
0.0110
0.0043
0.0712
0.0117
0.0080
0.0008
0.0004

4.56
4.95
16.81
3.08
1.53
0.22
0.11
1.09
1.28
0.40
0.20
0.33
41.08
5.57
1.23
0.25
0.35
0.12
0.02
0.03
0.08
0.01
0.04
0.49
0.86
4.57
0.27
2.58
0.18
2.60
0.60
0.32
26.33
0.27
2.32
0.63
0.74
0.11
1.21
3.46
1.38
10.96
4.32
71.16
11.66
8.01
0.77
0.40

Table 30 (cont’d)
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27

19
20
21
22
23
24
25
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
95
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108

242
300
64
84
94
149
24
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24

3482
2545
2178
3241
3303
717
899
1197
7157
3432
10352
11031
10430
9713
10006
10547
6627
3819
1829
4389
2808
4603
2735
4433
5271
589
7252
834
7122
8200
8635
2356
2850
2246
2327
2484
1832
3222
3355
5396
5122
4693
3755
3388
4451
4513
2283
2127

22990
28500
6080
7980
8930
14155
2280
10260
25840
10260
21850
3515
5415
13490
2565
7695
31445
17100
25745
11495
33345
5510
25745
17005
15105
36288
4644
6696
10476
16848
2916
972
5184
1728
7128
7128
14688
25056
19440
6696
3024
26136
32400
6912
9072
10152
16092
2592
272

5363825.701
2956696.823
2199429.082
4680477.469
4852061.423
266370.6655
409395.8296
705308.1232
21085744.16
5218429.618
42515383.44
47969841.19
43126100.04
37667902.59
39856113.53
44049907.16
18218064.87
6392994.796
1578359.906
8327115.789
3564137.88
9115444.523
3390150.109
8486442.822
11792251.44
183323.8883
21620703.02
354989.4907
20890255.02
27305260.21
30123015.8
2553483.428
3666107.825
2331731.44
2494095.784
2823501.507
1583282.428
4628481.355
4998224.933
12329250.82
11166965.92
9457058.01
6190972.695
5092047.699
8552034.203
8779790.108
2405255.585
2102607.605

0.0043
0.0096
0.0028
0.0017
0.0018
0.0531
0.0056
0.0145
0.0012
0.0020
0.0005
0.0001
0.0001
0.0004
0.0001
0.0002
0.0017
0.0027
0.0163
0.0014
0.0094
0.0006
0.0076
0.0020
0.0013
0.1979
0.0002
0.0189
0.0005
0.0006
0.0001
0.0004
0.0014
0.0007
0.0029
0.0025
0.0093
0.0054
0.0039
0.0005
0.0003
0.0028
0.0052
0.0014
0.0011
0.0012
0.0067
0.0012

4.29
9.64
2.76
1.70
1.84
53.14
5.57
14.55
1.23
1.97
0.51
0.07
0.13
0.36
0.06
0.17
1.73
2.67
16.31
1.38
9.36
0.60
7.59
2.00
1.28
197.94
0.21
18.86
0.50
0.62
0.10
0.38
1.41
0.74
2.86
2.52
9.28
5.41
3.89
0.54
0.27
2.76
5.23
1.36
1.06
1.16
6.69
1.23

Table 30 (cont’d)
27
26
27
28
27
29
27
30
27
31
27
32
27
33
27
34
27
35
27
36
27
37
27
38
27
39
27
40
27
41
27
42
27
43
27
44
28
2
28
3
28
4
28
5
28
6
28
7
28
9
28
10
28
11
28
12
28
13
28
14
28
15
28
16
28
17
28
18
28
19
28
20
28
21
28
22
28
23
28
24
28
25
28
26
28
27
28
29
28
30
28
31
28
32
28
33

108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272
272

95
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
108
230
37
57
142

1197
8256
4530
11451
12130
11529
10812
11105
11646
7540
5564
1465
5487
3900
5716
3786
5485
6360
8626
2344
9715
4167
4651
5856
8154
8608
8436
8670
9318
7462
5796
5255
6756
6668
5594
4534
4519
5135
3737
7684
8062
7157
8256
4069
4820
4998
4635
3902

10260
29376
11664
24840
3996
6156
15336
2916
8748
35748
19440
29268
13068
37908
6264
29268
19332
17172
91392
11696
16864
26384
42432
7344
2448
13056
4352
17952
17952
36992
63104
48960
16864
7616
65824
81600
17408
22848
25568
40528
6528
25840
29376
29376
62560
10064
15504
38624
273

705308.1232
27660651.19
8842734.462
51499437.78
57456017.71
52167991.26
46176548.74
48583105.21
53178476.72
23281210.48
13068794.55
1035360.249
12727304.2
6653080.118
13755463.16
6288443.428
12718491.4
16848782.33
30063390.79
2528828.946
37682640.7
7545093.932
9296897.548
14402637.47
27014960.91
29944308.66
28817711.22
30355422.73
34810730.87
22825745.2
14123551.44
11724333.64
18897759.5
18432813.25
13203001.66
8857575.85
8801981.441
11220878.27
6134707.858
24133259.16
26438773.74
21085744.16
27660651.19
7211516.653
9949227.753
10658912.04
9236224.98
6659564.102

0.0145
0.0011
0.0013
0.0005
0.0001
0.0001
0.0003
0.0001
0.0002
0.0015
0.0015
0.0283
0.0010
0.0057
0.0005
0.0047
0.0015
0.0010
0.0030
0.0046
0.0004
0.0035
0.0046
0.0005
0.0001
0.0004
0.0002
0.0006
0.0005
0.0016
0.0045
0.0042
0.0009
0.0004
0.0050
0.0092
0.0020
0.0020
0.0042
0.0017
0.0002
0.0012
0.0011
0.0041
0.0063
0.0009
0.0017
0.0058

14.55
1.06
1.32
0.48
0.07
0.12
0.33
0.06
0.16
1.54
1.49
28.27
1.03
5.70
0.46
4.65
1.52
1.02
3.04
4.63
0.45
3.50
4.56
0.51
0.09
0.44
0.15
0.59
0.52
1.62
4.47
4.18
0.89
0.41
4.99
9.21
1.98
2.04
4.17
1.68
0.25
1.23
1.06
4.07
6.29
0.94
1.68
5.80

Table 30 (cont’d)
28
34
28
35
28
36
28
37
28
38
28
39
28
40
28
41
28
42
28
43
28
44
29
2
29
3
29
4
29
5
29
6
29
7
29
9
29
10
29
11
29
12
29
13
29
14
29
15
29
16
29
17
29
18
29
19
29
20
29
21
29
22
29
23
29
24
29
25
29
26
29
27
29
28
29
30
29
31
29
32
29
33
29
34
29
35
29
36
29
37
29
38
29
39
29
40

272
272
272
272
272
272
272
272
272
272
272
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108
108

27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
230
37
57
142
27
81
331
180
271
121
351

4353
4672
7543
8881
8793
4152
5654
7246
4689
8867
8913
4921
2695
5167
2486
5580
6302
4129
4607
4519
4840
5316
3697
2001
1450
3893
3615
2665
428
1214
735
322
3513
4145
3432
4530
4069
6963
7642
7041
6323
6617
7158
5762
5372
5009
932
2143

7344
22032
90032
48960
73712
32912
95472
15776
73712
48688
43248
36288
4644
6696
10476
16848
2916
972
5184
1728
7128
7128
14688
25056
19440
6696
3024
26136
32400
6912
9072
10152
16092
2592
10260
11664
29376
24840
3996
6156
15336
2916
8748
35748
19440
29268
13068
37908
274

8197821.686
9376815.802
23298813.48
31774414.24
31178874.87
7493573.22
13473363.53
21586728.35
9441748.81
31679312.36
31992296.96
10349071.65
3296565.142
11354109.46
2827822.438
13140290.85
16558041.16
7414899.643
9130500.893
8801981.441
10027812.09
11984266.2
6010546.427
1872274.268
1015311.264
6630409.735
5759778.377
3227191.265
99940.87815
724461.8237
279219.6281
58200.42503
5454921.242
7469587.453
5218429.618
8842734.462
7211516.653
20013044.03
23883246.63
20441146.66
16663032.66
18165868.07
21091342.23
13966551.35
12225268.54
10703528.26
438419.8746
2132760.678

0.0009
0.0023
0.0039
0.0015
0.0024
0.0044
0.0071
0.0007
0.0078
0.0015
0.0014
0.0035
0.0014
0.0006
0.0037
0.0013
0.0002
0.0001
0.0006
0.0002
0.0007
0.0006
0.0024
0.0134
0.0191
0.0010
0.0005
0.0081
0.3242
0.0095
0.0325
0.1744
0.0029
0.0003
0.0020
0.0013
0.0041
0.0012
0.0002
0.0003
0.0009
0.0002
0.0004
0.0026
0.0016
0.0027
0.0298
0.0178

0.90
2.35
3.86
1.54
2.36
4.39
7.09
0.73
7.81
1.54
1.35
3.51
1.41
0.59
3.70
1.28
0.18
0.13
0.57
0.20
0.71
0.59
2.44
13.38
19.15
1.01
0.53
8.10
324.19
9.54
32.49
174.43
2.95
0.35
1.97
1.32
4.07
1.24
0.17
0.30
0.92
0.16
0.41
2.56
1.59
2.73
29.81
17.77

Table 30 (cont’d)
29
41
29
42
29
43
29
44
30
2
30
3
30
4
30
5
30
6
30
7
30
9
30
10
30
11
30
12
30
13
30
14
30
15
30
16
30
17
30
18
30
19
30
20
30
21
30
22
30
23
30
24
30
25
30
26
30
27
30
28
30
29
30
31
30
32
30
33
30
34
30
35
30
36
30
37
30
38
30
39
30
40
30
41
30
42
30
43
30
44
31
2
31
3
31
4

108
108
108
108
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
230
37
37
37

58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
37
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62

4254
4239
5353
5816
12507
4460
12424
9188
9589
12595
11157
11579
11439
11760
12235
10617
8829
8368
10662
10246
9172
7735
8135
6958
6992
10434
11001
10352
11451
4820
6963
689
689
1224
467
353
11926
12263
12015
6023
9034
10942
11131
12244
12707
13185
4700
13103

6264
29268
19332
17172
77280
9890
14260
22310
35880
6210
2070
11040
3680
15180
15180
31280
53360
41400
14260
6440
55660
69000
14720
19320
21620
34270
5520
21850
24840
62560
24840
8510
13110
32660
6210
18630
76130
41400
62330
27830
80730
13340
62330
41170
36570
12432
1591
2294
275

7847209.053
7794719.503
12143245.14
14216292.68
60896311.02
8584919.587
60130766.67
33893769.71
36759475.44
61712981.18
49016254.57
52598699.03
51396945.99
54171880.44
58404666.72
44607044.86
31421859.47
28377960.24
44966955.92
41692051.73
33781714.52
24438503.28
26895483.67
19985747.97
20171709.14
43157530.04
47722272.13
42515383.44
51499437.78
9949227.753
20013044.03
246954.2597
246954.2597
735842.2054
117951.1211
69306.1765
55633979.93
58658882.32
56425468.38
15193031.56
32822538.3
47237156.83
48799452.24
58486321.84
62759829.76
67321262.96
9483877.377
66527990.85

0.0008
0.0038
0.0016
0.0012
0.0013
0.0012
0.0002
0.0007
0.0010
0.0001
0.0000
0.0002
0.0001
0.0003
0.0003
0.0007
0.0017
0.0015
0.0003
0.0002
0.0016
0.0028
0.0005
0.0010
0.0011
0.0008
0.0001
0.0005
0.0005
0.0063
0.0012
0.0345
0.0531
0.0444
0.0526
0.2688
0.0014
0.0007
0.0011
0.0018
0.0025
0.0003
0.0013
0.0007
0.0006
0.0002
0.0002
0.0000

0.80
3.75
1.59
1.21
1.27
1.15
0.24
0.66
0.98
0.10
0.04
0.21
0.07
0.28
0.26
0.70
1.70
1.46
0.32
0.15
1.65
2.82
0.55
0.97
1.07
0.79
0.12
0.51
0.48
6.29
1.24
34.46
53.09
44.38
52.65
268.81
1.37
0.71
1.10
1.83
2.46
0.28
1.28
0.70
0.58
0.18
0.17
0.03

Table 30 (cont’d)
31
5
31
6
31
7
31
9
31
10
31
11
31
12
31
13
31
14
31
15
31
16
31
17
31
18
31
19
31
20
31
21
31
22
31
23
31
24
31
25
31
26
31
27
31
28
31
29
31
30
31
32
31
33
31
34
31
35
31
36
31
37
31
38
31
39
31
40
31
41
31
42
31
43
31
44
32
2
32
3
32
4
32
5
32
6
32
7
32
9
32
10
32
11
32
12

37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
57
57
57
57
57
57
57
57
57
57

97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
57
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66

9177
9605
10807
11836
12258
12118
12439
12914
11296
9508
9048
11341
10925
9851
8414
8814
7636
7671
11113
11680
11031
12130
4998
7642
689
280
1018
720
445
12738
12986
12430
6780
9637
11538
11734
12847
13310
12585
6381
13609
8693
9211
10394
11236
11657
11517
11838

3589
5772
999
333
1776
592
2442
2442
5032
8584
6660
2294
1036
8954
11100
2368
3108
3478
5513
888
3515
3996
10064
3996
8510
2109
5254
999
2997
12247
6660
10027
4477
12987
2146
10027
6623
5883
19152
2451
3534
5529
8892
1539
513
2736
912
3762
276

33816712.89
36876101.48
46135984
54838986.18
58613448.32
57348069.32
60268778.41
64716569.18
50183032.2
36171742.36
32919249.43
50563550.96
47097813.62
38691236.24
28675088.35
31320507.3
23847631.24
24055742.55
48649625.03
53473843.57
47969841.19
57456017.71
10658912.04
23883246.63
246954.2597
44627.26375
518466.593
268492.2431
107617.778
63051056
65403841.47
60185953.38
19025514.86
37109878.47
52245394.85
53944548.11
64080114.02
68539085.49
61619918.25
16954641.64
71494034.7
30508607.9
34055156.97
42843718.16
49677792.37
53273951.78
52064871.04
54856593.82

0.0001
0.0002
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0001
0.0002
0.0002
0.0000
0.0000
0.0002
0.0004
0.0001
0.0001
0.0001
0.0001
0.0000
0.0001
0.0001
0.0009
0.0002
0.0345
0.0473
0.0101
0.0037
0.0278
0.0002
0.0001
0.0002
0.0002
0.0003
0.0000
0.0002
0.0001
0.0001
0.0003
0.0001
0.0000
0.0002
0.0003
0.0000
0.0000
0.0001
0.0000
0.0001

0.11
0.16
0.02
0.01
0.03
0.01
0.04
0.04
0.10
0.24
0.20
0.05
0.02
0.23
0.39
0.08
0.13
0.14
0.11
0.02
0.07
0.07
0.94
0.17
34.46
47.26
10.13
3.72
27.85
0.19
0.10
0.17
0.24
0.35
0.04
0.19
0.10
0.09
0.31
0.14
0.05
0.18
0.26
0.04
0.01
0.05
0.02
0.07

Table 30 (cont’d)
32
13
32
14
32
15
32
16
32
17
32
18
32
19
32
20
32
21
32
22
32
23
32
24
32
25
32
26
32
27
32
28
32
29
32
30
32
31
32
33
32
34
32
35
32
36
32
37
32
38
32
39
32
40
32
41
32
42
32
43
32
44
33
2
33
3
33
4
33
5
33
6
33
7
33
9
33
10
33
11
33
12
33
13
33
14
33
15
33
16
33
17
33
18
33
19

57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
57
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142

66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
142
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242

12312
10696
8907
8447
10741
10326
9252
7813
8213
7046
7070
10512
11079
10430
11529
4635
7041
689
280
677
739
351
12021
11488
11616
5910
8920
10821
11017
12130
12593
11868
5648
12892
8020
8509
9711
10519
10940
10800
11121
11596
9979
8190
7730
10023
9608
8534

3762
7752
13224
10260
3534
1596
13794
17100
3648
4788
5358
8493
1368
5415
6156
15504
6156
13110
2109
8094
1539
4617
18867
10260
15447
6897
20007
3306
15447
10203
9063
47712
6106
8804
13774
22152
3834
1278
6816
2272
9372
9372
19312
32944
25560
8804
3976
34364
277

59105017.87
45239797.17
31951390.23
28889148.34
45602112.48
42312728.31
34343747.76
24908860.83
27387567.65
20468735.49
20601407.2
43772582.43
48367213.58
43126100.04
52167991.26
9236224.98
20441146.66
246954.2597
44627.26375
238846.2739
282113.8663
68562.00687
56479017.68
51816062.88
52918502.56
14656025.56
32040052.82
46249607.81
47854233.45
57456017.71
61694363.27
55121029.65
13446210.54
64507255.36
26177688.75
29293360.41
37653167.2
43827981
47220753.39
46079221.74
48716187.96
52745521.76
39652022.41
27242026.64
24408497.04
39984869.94
36897988.35
29457101.64

0.0001
0.0002
0.0004
0.0004
0.0001
0.0000
0.0004
0.0007
0.0001
0.0002
0.0003
0.0002
0.0000
0.0001
0.0001
0.0017
0.0003
0.0531
0.0473
0.0339
0.0055
0.0673
0.0003
0.0002
0.0003
0.0005
0.0006
0.0001
0.0003
0.0002
0.0001
0.0009
0.0005
0.0001
0.0005
0.0008
0.0001
0.0000
0.0001
0.0000
0.0002
0.0002
0.0005
0.0012
0.0010
0.0002
0.0001
0.0012

0.06
0.17
0.41
0.36
0.08
0.04
0.40
0.69
0.13
0.23
0.26
0.19
0.03
0.13
0.12
1.68
0.30
53.09
47.26
33.89
5.46
67.34
0.33
0.20
0.29
0.47
0.62
0.07
0.32
0.18
0.15
0.87
0.45
0.14
0.53
0.76
0.10
0.03
0.14
0.05
0.19
0.18
0.49
1.21
1.05
0.22
0.11
1.17

Table 30 (cont’d)
33
20
33
21
33
22
33
23
33
24
33
25
33
26
33
27
33
28
33
29
33
30
33
31
33
32
33
34
33
35
33
36
33
37
33
38
33
39
33
40
33
41
33
42
33
43
33
44
34
2
34
3
34
4
34
5
34
6
34
7
34
9
34
10
34
11
34
12
34
13
34
14
34
15
34
16
34
17
34
18
34
19
34
20
34
21
34
22
34
23
34
24
34
25
34
26

142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
142
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27

300
64
84
94
149
24
95
108
272
108
230
37
57
27
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95

7096
7496
6328
6352
9795
10362
9713
10812
3902
6323
1224
1018
677
724
806
11344
11364
11224
5233
8135
10144
10232
11345
11808
12161
4088
12078
8816
9218
12224
10812
11233
11093
11414
11889
10272
8483
8023
10316
9901
8827
7389
7789
6612
6646
10088
10655
1006

42600
9088
11928
13348
21158
3408
13490
15336
38624
15336
32660
5254
8094
3834
11502
47002
25560
38482
17182
49842
8236
38482
25418
22578
9072
1161
1674
2619
4212
729
243
1296
432
1782
1782
3672
6264
4860
1674
756
6534
8100
1728
2268
2538
4023
648
2565
278

20745592.98
23023757.27
16688076.96
16808537.6
38274403.99
42593449.85
37667902.59
46176548.74
6659564.102
16663032.66
735842.2054
518466.593
238846.2739
271333.4118
332687.5735
50588967.29
50758564.06
49577035.05
11631250.15
26895483.67
40906993.05
41583880.09
50597440.74
54592760.43
57735329.42
7275631.349
56988935.92
31334011.96
34104346.9
58304939.41
46176548.74
49652593.96
48483406.32
51183732.1
55306493.1
41893295.04
29123528.23
26196296.98
42234906.16
39065214.65
31408336.89
22403339.82
24763683.08
18139796.28
18317434.2
40478987.39
44910879.78
506916.1916

0.0021
0.0004
0.0007
0.0008
0.0006
0.0001
0.0004
0.0003
0.0058
0.0009
0.0444
0.0101
0.0339
0.0141
0.0346
0.0009
0.0005
0.0008
0.0015
0.0019
0.0002
0.0009
0.0005
0.0004
0.0002
0.0002
0.0000
0.0001
0.0001
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0001
0.0002
0.0002
0.0000
0.0000
0.0002
0.0004
0.0001
0.0001
0.0001
0.0001
0.0000
0.0051

2.05
0.39
0.71
0.79
0.55
0.08
0.36
0.33
5.80
0.92
44.38
10.13
33.89
14.13
34.57
0.93
0.50
0.78
1.48
1.85
0.20
0.93
0.50
0.41
0.16
0.16
0.03
0.08
0.12
0.01
0.01
0.03
0.01
0.03
0.03
0.09
0.22
0.19
0.04
0.02
0.21
0.36
0.07
0.13
0.14
0.10
0.01
5.06

Table 30 (cont’d)
34
27
34
28
34
29
34
30
34
31
34
32
34
33
34
35
34
36
34
37
34
38
34
39
34
40
34
41
34
42
34
43
34
44
35
2
35
3
35
4
35
5
35
6
35
7
35
9
35
10
35
11
35
12
35
13
35
14
35
15
35
16
35
17
35
18
35
19
35
20
35
21
35
22
35
23
35
24
35
25
35
26
35
27
35
28
35
29
35
30
35
31
35
32
35
33

27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81
81

108
272
108
230
37
57
142
81
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142

11105
4353
6617
467
720
739
724
505
11761
11882
11551
5642
8653
10560
10750
11863
12326
12721
4367
12639
8782
9270
10654
11352
11774
11664
11955
12430
10812
9024
8564
10857
10441
9367
7930
8330
7152
7187
10629
11196
10547
11646
4672
7158
353
445
351
806

2916
7344
2916
6210
999
1539
3834
2187
8937
4860
7317
3267
9477
1566
7317
4833
4293
27216
3483
5022
7857
12636
2187
729
3888
1296
5346
5346
11016
18792
14580
5022
2268
19602
24300
5184
6804
7614
12069
1944
7695
8748
22032
8748
18630
2997
4617
11502
279

48583105.21
8197821.686
18165868.07
117951.1211
268492.2431
282113.8663
271333.4118
136852.7782
54180633.04
55244639.16
52357296.01
13419083.51
30242433.69
44153124.77
45674739.81
55076915.18
59232779.23
62891272.2
8247988.84
62123248.34
31104807.81
34470810.49
44902871.6
50656773.73
54294477.72
53334750.87
55891298.93
60185953.38
46176548.74
32753541.45
29654161.7
46542391.17
43212558.65
35159362.21
25622355.7
28133612.58
21057764.33
21253992.61
44702887.18
49342311.49
44049907.16
53178476.72
9376815.802
21091342.23
69306.1765
107617.778
68562.00687
332687.5735

0.0001
0.0009
0.0002
0.0526
0.0037
0.0055
0.0141
0.0160
0.0002
0.0001
0.0001
0.0002
0.0003
0.0000
0.0002
0.0001
0.0001
0.0004
0.0004
0.0001
0.0003
0.0004
0.0000
0.0000
0.0001
0.0000
0.0001
0.0001
0.0002
0.0006
0.0005
0.0001
0.0001
0.0006
0.0009
0.0002
0.0003
0.0004
0.0003
0.0000
0.0002
0.0002
0.0023
0.0004
0.2688
0.0278
0.0673
0.0346

0.06
0.90
0.16
52.65
3.72
5.46
14.13
15.98
0.16
0.09
0.14
0.24
0.31
0.04
0.16
0.09
0.07
0.43
0.42
0.08
0.25
0.37
0.05
0.01
0.07
0.02
0.10
0.09
0.24
0.57
0.49
0.11
0.05
0.56
0.95
0.18
0.32
0.36
0.27
0.04
0.17
0.16
2.35
0.41
268.81
27.85
67.34
34.57

Table 30 (cont’d)
35
34
35
36
35
37
35
38
35
39
35
40
35
41
35
42
35
43
35
44
36
2
36
3
36
4
36
5
36
6
36
7
36
9
36
10
36
11
36
12
36
13
36
14
36
15
36
16
36
17
36
18
36
19
36
20
36
21
36
22
36
23
36
24
36
25
36
26
36
27
36
28
36
29
36
30
36
31
36
32
36
33
36
34
36
35
36
37
36
38
36
39
36
40
36
41

81
81
81
81
81
81
81
81
81
81
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331
331

27
331
180
271
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
180
271
121
351
58

505
2187 136852.7782
12432 26811 60204354.28
12560 14580 61387551.98
12312 21951 59105017.87
6320
9801 16648014.63
9331 28431 34903064.51
11231
4698 49635798.38
11428 21951 51303080.16
12541 14499 61211231.76
13004 12879 65576196.82
7849 111216 25127381.02
7797 14233
24812031
9035 20522 32829441.77
3185 32107 4528015.759
2895 51636 3776872.11
2712
8937 3336187.124
6587
2979 18009703.4
6093 15888 15530278.31
6784
5296 19046847.06
7044 21846 20457697.84
6743 21846 18828728.94
6463 45016 17371003.04
4756 76792 9699726.914
4476 59580 8643530.084
2270 20522 2379299.584
2395
9268 2634392.515
3074 80102 4232893.483
4790 99300 9831900.516
4634 21184 9232439.193
6503 27804 17575841.71
5579 31114 13135816.91
5922 49319 14712618.23
6216
7944 16131357.31
6627 31445 18218064.87
7540 35748 23281210.48
7543 90032 23298813.48
5762 35748 13966551.35
11926 76130 55633979.93
12738 12247
63051056
12021 18867 56479017.68
11344 47002 50588967.29
11761
8937 54180633.04
12432 26811 60204354.28
5963 59580 14906755.83
8012 89701 26128097.45
5963 40051 14906755.83
3891 116181 6623939.216
2655 19198 3204222.003
280

0.0160
0.0004
0.0002
0.0004
0.0006
0.0008
0.0001
0.0004
0.0002
0.0002
0.0044
0.0006
0.0006
0.0071
0.0137
0.0027
0.0002
0.0010
0.0003
0.0011
0.0012
0.0026
0.0079
0.0069
0.0086
0.0035
0.0189
0.0101
0.0023
0.0016
0.0024
0.0034
0.0005
0.0017
0.0015
0.0039
0.0026
0.0014
0.0002
0.0003
0.0009
0.0002
0.0004
0.0040
0.0034
0.0027
0.0175
0.0060

15.98
0.45
0.24
0.37
0.59
0.81
0.09
0.43
0.24
0.20
4.43
0.57
0.63
7.09
13.67
2.68
0.17
1.02
0.28
1.07
1.16
2.59
7.92
6.89
8.63
3.52
18.92
10.10
2.29
1.58
2.37
3.35
0.49
1.73
1.54
3.86
2.56
1.37
0.19
0.33
0.93
0.16
0.45
4.00
3.43
2.69
17.54
5.99

Table 30 (cont’d)
36
42
36
43
36
44
37
2
37
3
37
4
37
5
37
6
37
7
37
9
37
10
37
11
37
12
37
13
37
14
37
15
37
16
37
17
37
18
37
19
37
20
37
21
37
22
37
23
37
24
37
25
37
26
37
27
37
28
37
29
37
30
37
31
37
32
37
33
37
34
37
35
37
36
37
38
37
39
37
40
37
41
37
42
37
43
37
44
38
2
38
3
38
4
38
5

331
331
331
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
180
271
271
271
271

271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
271
121
351
58
271
179
159
336
43
62
97

4831
3842
3637
5352
7564
5592
6151
6683
7005
3144
2612
3341
3601
3322
3693
3159
3616
3717
3418
3692
4398
3974
6115
5189
2986
3363
3819
5564
8881
5372
12263
12986
11488
11364
11882
12560
5963
4530
5863
3374
2671
1234
1349
2310
1963
7674
1020
7975

89701
59249
52629
60480
7740
11160
17460
28080
4860
1620
8640
2880
11880
11880
24480
41760
32400
11160
5040
43560
54000
11520
15120
16920
26820
4320
17100
19440
48960
19440
41400
6660
10260
25560
4860
14580
59580
48780
21780
63180
10440
48780
32220
28620
91056
11653
16802
26287
281

9992412.897
6466346.684
5826560.657
12138935.37
23422210.96
13194034.31
15812366.84
18511677.52
20243027.51
4417909.559
3106340.278
4958671.105
5717470.475
4905229.071
5998196.437
4458043.37
5762806.023
6072476.826
5178058.003
5995110.82
8359589.033
6894978.865
15636994.26
11446137.91
4005628.113
5020893.944
6392994.796
13068794.55
31774414.24
12225268.54
58658882.32
65403841.47
51816062.88
50758564.06
55244639.16
61387551.98
14906755.83
8842734.462
14435365.98
5052143.163
3241010.121
747306.5752
885161.8838
2459590.244
1805296.621
24073620.52
520403.6409
25899317.18

0.0090
0.0092
0.0090
0.0050
0.0003
0.0008
0.0011
0.0015
0.0002
0.0004
0.0028
0.0006
0.0021
0.0024
0.0041
0.0094
0.0056
0.0018
0.0010
0.0073
0.0065
0.0017
0.0010
0.0015
0.0067
0.0009
0.0027
0.0015
0.0015
0.0016
0.0007
0.0001
0.0002
0.0005
0.0001
0.0002
0.0040
0.0055
0.0015
0.0125
0.0032
0.0653
0.0364
0.0116
0.0504
0.0005
0.0323
0.0010

8.98
9.16
9.03
4.98
0.33
0.85
1.10
1.52
0.24
0.37
2.78
0.58
2.08
2.42
4.08
9.37
5.62
1.84
0.97
7.27
6.46
1.67
0.97
1.48
6.70
0.86
2.67
1.49
1.54
1.59
0.71
0.10
0.20
0.50
0.09
0.24
4.00
5.52
1.51
12.51
3.22
65.27
36.40
11.64
50.44
0.48
32.29
1.01

Table 30 (cont’d)
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
38
39
39
39
39
39
39
39
39
39
39
39

6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
39
40
41
42
43
44
2
3
4
5
6
7
9
10
11
12
13

271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
121
121
121
121
121
121
121
121
121
121
121

156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
121
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66

8725
9139
1529
1946
1237
1302
1511
1394
3634
4034
6208
5815
5476
4177
4644
5862
4934
2071
1733
1829
1465
8793
5009
12015
12430
11616
11224
11551
12312
8012
4530
5675
3932
5797
3894
5599
6840
5853
1683
5806
3560
5012
6622
5194
5616
5476
5797
6272

42276
7317
2439
13008
4336
17886
17886
36856
62872
48780
16802
7588
65582
81300
17344
22764
25474
40379
6504
25745
29268
73712
29268
62330
10027
15447
38482
7317
21951
89701
48780
32791
95121
15718
73441
48509
43089
40656
5203
7502
11737
18876
3267
1089
5808
1936
7986
7986
282

30722342.59
33551155.89
1122985.69
1775707.333
750762.2536
827486.4101
1098000.359
942104.9163
5817432.511
7094114.575
16091934.11
14211648.79
12678869.59
7579533.957
9270330.146
14430688.32
10401078.54
1998675.035
1424679.808
1578359.906
1035360.249
31178874.87
10703528.26
56425468.38
60185953.38
52918502.56
49577035.05
52357296.01
59105017.87
26128097.45
8842734.462
13568603.29
6757182.792
14128181.67
6633646.117
13225432.65
19346686.46
14388621.75
1347596.332
14169886.12
5594419.077
10715711.64
18191957.6
11467102.54
13301832.91
12678869.59
14128181.67
16408598.72

0.0014
0.0002
0.0022
0.0073
0.0058
0.0216
0.0163
0.0391
0.0108
0.0069
0.0010
0.0005
0.0052
0.0107
0.0019
0.0016
0.0024
0.0202
0.0046
0.0163
0.0283
0.0024
0.0027
0.0011
0.0002
0.0003
0.0008
0.0001
0.0004
0.0034
0.0055
0.0024
0.0141
0.0011
0.0111
0.0037
0.0022
0.0028
0.0039
0.0005
0.0021
0.0018
0.0002
0.0001
0.0004
0.0002
0.0006
0.0005

1.38
0.22
2.17
7.33
5.78
21.61
16.29
39.12
10.81
6.88
1.04
0.53
5.17
10.73
1.87
1.58
2.45
20.20
4.57
16.31
28.27
2.36
2.73
1.10
0.17
0.29
0.78
0.14
0.37
3.43
5.52
2.42
14.08
1.11
11.07
3.67
2.23
2.83
3.86
0.53
2.10
1.76
0.18
0.09
0.44
0.15
0.57
0.49

Table 30 (cont’d)
39
14
39
15
39
16
39
17
39
18
39
19
39
20
39
21
39
22
39
23
39
24
39
25
39
26
39
27
39
28
39
29
39
30
39
31
39
32
39
33
39
34
39
35
39
36
39
37
39
38
39
40
39
41
39
42
39
43
39
44
40
2
40
3
40
4
40
5
40
6
40
7
40
9
40
10
40
11
40
12
40
13
40
14
40
15
40
16
40
17
40
18
40
19
40
20

121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
121
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351

136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
351
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300

4654 16456 9308294.602
2865 28072 3702855.717
2406 21780 2657429.083
4922
7502 10353067.79
4575
3388 9010379.582
3625 29282 5790088.752
1772 36300 1486213.026
1741
7744 1437201.511
1101 10164 601722.0218
1027 11374 527210.2471
4470 18029 8621529.012
5038
2904 10821575.9
4389 11495 8327115.789
4587 13068 9055336.766
4152 32912 7493573.22
932 13068 438419.8746
6023 27830 15193031.56
6780
4477 19025514.86
5910
6897 14656025.56
5233 17182 11631250.15
5642
3267 13419083.51
6320
9801 16648014.63
5963 40051 14906755.83
5863 21780 14435365.98
5675 32791 13568603.29
2996 42471 4031154.441
5107
7018 11104912.22
5109 32791 11113176.58
6205 21659 16077162.19
6668 19239 18432813.25
4337 117936 8140665.353
4694 15093 9460887.145
4637 21762 9243798.76
3343 34047 4964312.538
3837 54756 6450366.904
4551
9477 8920783.252
2733
3159 3385441.395
3227 16848 4642137.865
2947
5616 3906809.707
3189 23166 4538826.549
3876 23166 6575505.72
2824 47736 3602822.908
909 81432 418091.5384
850 63180 368040.7939
2018 21762 1902611.895
1720
9828 1404442.778
879 84942 392264.3703
1171 105300
676484.89
283

0.0018
0.0076
0.0082
0.0007
0.0004
0.0051
0.0244
0.0054
0.0169
0.0216
0.0021
0.0003
0.0014
0.0014
0.0044
0.0298
0.0018
0.0002
0.0005
0.0015
0.0002
0.0006
0.0027
0.0015
0.0024
0.0105
0.0006
0.0030
0.0013
0.0010
0.0145
0.0016
0.0024
0.0069
0.0085
0.0011
0.0009
0.0036
0.0014
0.0051
0.0035
0.0132
0.1948
0.1717
0.0114
0.0070
0.2165
0.1557

1.77
7.58
8.20
0.72
0.38
5.06
24.42
5.39
16.89
21.57
2.09
0.27
1.38
1.44
4.39
29.81
1.83
0.24
0.47
1.48
0.24
0.59
2.69
1.51
2.42
10.54
0.63
2.95
1.35
1.04
14.49
1.60
2.35
6.86
8.49
1.06
0.93
3.63
1.44
5.10
3.52
13.25
194.77
171.67
11.44
7.00
216.54
155.66

Table 30 (cont’d)
40
21
40
22
40
23
40
24
40
25
40
26
40
27
40
28
40
29
40
30
40
31
40
32
40
33
40
34
40
35
40
36
40
37
40
38
40
39
40
41
40
42
40
43
40
44
41
2
41
3
41
4
41
5
41
6
41
7
41
9
41
10
41
11
41
12
41
13
41
14
41
15
41
16
41
17
41
18
41
19
41
20
41
21
41
22
41
23
41
24
41
25
41
26
41
27

351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
351
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58

64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
58
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108

1007 22464 507874.0162
2887 29484 3757066.563
1960 32994 1800058.152
2175 52299 2193676.569
2681
8424 3264103.691
2808 33345 3564137.88
3900 37908 6653080.118
5654 95472 13473363.53
2143 37908 2132760.678
9034 80730 32822538.3
9637 12987 37109878.47
8920 20007 32040052.82
8135 49842 26895483.67
8653
9477 30242433.69
9331 28431 34903064.51
3891 116181 6623939.216
3374 63180 5052143.163
3932 95121 6757182.792
2996 42471 4031154.441
2272 20358 2383284.132
2263 95121 2365378.541
3225 62829 4636672.974
3687 55809 5979694.016
6007 19488 15116439.17
6597
2494 18061687.33
6797
3596 19116254.9
3607
5626 5735584.341
3692
9048 5995110.82
4285
1566 7956216.088
4342
522 8158506.358
3818
2784 6389814.576
4539
928 8876144.162
4806
3828 9894392.999
4519
3828 8801981.441
4616
7888 9164420.756
2891 13456 3766963.171
2969 10440 3962409.675
323
3596 58544.32357
535
1624 152711.6557
1500 14036 1082862.695
3282 17400 4793616.602
3126
3712 4369975.982
4998
4872 10658912.04
4072
5452 7221622.154
4075
8642 7231734.357
4226
1392 7749363.559
4603
5510 9115444.523
5716
6264 13755463.16
284

0.0442
0.0078
0.0183
0.0238
0.0026
0.0094
0.0057
0.0071
0.0178
0.0025
0.0003
0.0006
0.0019
0.0003
0.0008
0.0175
0.0125
0.0141
0.0105
0.0085
0.0402
0.0136
0.0093
0.0013
0.0001
0.0002
0.0010
0.0015
0.0002
0.0001
0.0004
0.0001
0.0004
0.0004
0.0009
0.0036
0.0026
0.0614
0.0106
0.0130
0.0036
0.0008
0.0005
0.0008
0.0012
0.0002
0.0006
0.0005

44.23
7.85
18.33
23.84
2.58
9.36
5.70
7.09
17.77
2.46
0.35
0.62
1.85
0.31
0.81
17.54
12.51
14.08
10.54
8.54
40.21
13.55
9.33
1.29
0.14
0.19
0.98
1.51
0.20
0.06
0.44
0.10
0.39
0.43
0.86
3.57
2.63
61.42
10.63
12.96
3.63
0.85
0.46
0.75
1.20
0.18
0.60
0.46

Table 30 (cont’d)
41
41
41
41
41
41
41
41
41
41
41
41
41
41
41
41
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42

28
29
30
31
32
33
34
35
36
37
38
39
40
42
43
44
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
58
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271
271

272
108
230
37
57
142
27
81
331
180
271
121
351
271
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27

7246
4254
10942
11538
10821
10144
10560
11231
2655
2671
5797
5107
2272
2235
1528
1477
4269
6448
4892
5035
5567
5803
2445
1913
2642
2902
2622
2744
2043
2500
2427
2302
2539
3245
2822
4999
4073
2053
2332
2735
3786
4689
4239
11131
11734
11017
10232
10750

15776
6264
13340
2146
3306
8236
1566
4698
19198
10440
15718
7018
20358
15718
10382
9222
91056
11653
16802
26287
42276
7317
2439
13008
4336
17886
17886
36856
62872
48780
16802
7588
65582
81300
17344
22764
25474
40379
6504
25745
29268
73712
29268
62330
10027
15447
38482
7317
285

21586728.35
7847209.053
47237156.83
52245394.85
46249607.81
40906993.05
44153124.77
49635798.38
3204222.003
3241010.121
14128181.67
11104912.22
2383284.132
2310081.5
1121590.632
1051533.084
7899865.444
17294481.84
10233501.37
10809335.64
13082186.03
14155978.16
2739869.265
1718930.997
3174478.172
3794242.422
3128975.092
3411377.678
1947645.432
2858156.575
2701671.709
2443431.153
2943466.732
4691459.075
3597976.462
10662964.41
7224992.143
1965798.52
2504287.799
3390150.109
6288443.428
9441748.81
7794719.503
48799452.24
53944548.11
47854233.45
41583880.09
45674739.81

0.0007
0.0008
0.0003
0.0000
0.0001
0.0002
0.0000
0.0001
0.0060
0.0032
0.0011
0.0006
0.0085
0.0068
0.0093
0.0088
0.0115
0.0007
0.0016
0.0024
0.0032
0.0005
0.0009
0.0076
0.0014
0.0047
0.0057
0.0108
0.0323
0.0171
0.0062
0.0031
0.0223
0.0173
0.0048
0.0021
0.0035
0.0205
0.0026
0.0076
0.0047
0.0078
0.0038
0.0013
0.0002
0.0003
0.0009
0.0002

0.73
0.80
0.28
0.04
0.07
0.20
0.04
0.09
5.99
3.22
1.11
0.63
8.54
6.80
9.26
8.77
11.53
0.67
1.64
2.43
3.23
0.52
0.89
7.57
1.37
4.71
5.72
10.80
32.28
17.07
6.22
3.11
22.28
17.33
4.82
2.13
3.53
20.54
2.60
7.59
4.65
7.81
3.75
1.28
0.19
0.32
0.93
0.16

Table 30 (cont’d)
42
42
42
42
42
42
42
42
42
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43

35
36
37
38
39
40
41
43
44
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

271
271
271
271
271
271
271
271
271
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179
179

81
331
180
271
121
351
58
179
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58

11428
4831
1234
3894
5109
2263
2235
2022
3264
5967
7904
6597
5679
5528
5144
4150
3618
4347
4607
4327
4442
3415
3247
1843
2063
3349
4381
4186
6096
5170
3751
4038
4433
5485
8867
5353
12244
12847
12130
11345
11863
12451
3842
1349
5599
6205
3225
1528

21951
89701
48780
73441
32791
95121
15718
48509
43089
60144
7697
11098
17363
27924
4833
1611
8592
2864
11814
11814
24344
41528
32220
11098
5012
43318
53700
11456
15036
16826
26671
4296
17005
19332
48688
19332
41170
6623
10203
25418
4833
14499
59249
32220
48509
21659
62829
10382
286

51303080.16
9992412.897
747306.5752
6633646.117
11113176.58
2365378.541
2310081.5
1909783.722
4743788.142
14925760.61
25462976.55
18061687.33
13586780.23
12908603.62
11258274.25
7486716.427
5768863.577
8176365.863
9130500.893
8105038.85
8519208.611
5169426.267
4696954.449
1601393.778
1984031.363
4981255.065
8298300.949
7610593.496
15544810.1
11366638.08
6178448.37
7107485.752
8486442.822
12718491.4
31679312.36
12143245.14
58486321.84
64080114.02
57456017.71
50597440.74
55076915.18
60379295.71
6466346.684
885161.8838
13225432.65
16077162.19
4636672.974
1121590.632

0.0004
0.0090
0.0653
0.0111
0.0030
0.0402
0.0068
0.0254
0.0091
0.0040
0.0003
0.0006
0.0013
0.0022
0.0004
0.0002
0.0015
0.0004
0.0013
0.0015
0.0029
0.0080
0.0069
0.0069
0.0025
0.0087
0.0065
0.0015
0.0010
0.0015
0.0043
0.0006
0.0020
0.0015
0.0015
0.0016
0.0007
0.0001
0.0002
0.0005
0.0001
0.0002
0.0092
0.0364
0.0037
0.0013
0.0136
0.0093

0.43
8.98
65.27
11.07
2.95
40.21
6.80
25.40
9.08
4.03
0.30
0.61
1.28
2.16
0.43
0.22
1.49
0.35
1.29
1.46
2.86
8.03
6.86
6.93
2.53
8.70
6.47
1.51
0.97
1.48
4.32
0.60
2.00
1.52
1.54
1.59
0.70
0.10
0.18
0.50
0.09
0.24
9.16
36.40
3.67
1.35
13.55
9.26

Table 30 (cont’d)
43
42
43
44
44
2
44
3
44
4
44
5
44
6
44
7
44
9
44
10
44
11
44
12
44
13
44
14
44
15
44
16
44
17
44
18
44
19
44
20
44
21
44
22
44
23
44
24
44
25
44
26
44
27
44
28
44
29
44
30
44
31
44
32
44
33
44
34
44
35
44
36
44
37
44
38
44
39
44
40
44
41
44
42
44
43

179
179
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159
159

271
159
336
43
62
97
156
27
9
48
16
66
66
136
232
180
62
28
242
300
64
84
94
149
24
95
108
272
108
230
37
57
142
27
81
331
180
271
121
351
58
271
179

2022
1053
6674
8367
7840
5077
5223
4838
5392
4861
5589
5849
5570
5283
3978
3809
1792
2011
3157
4843
4749
6559
5633
4743
5277
5271
6360
8913
5816
12707
13310
12593
11808
12326
13004
3537
2310
6840
6668
3687
1477
3264
1053

48509
28461
53424
6837
9858
15423
24804
4293
1431
7632
2544
10494
10494
21624
36888
28620
9858
4452
38478
47700
10176
13356
14946
23691
3816
15105
17172
43248
17172
36570
5883
9063
22578
4293
12879
52629
28620
43089
19239
55809
9222
43089
28461

287

1909783.722
552858.3907
18464339.82
28371517.22
25072666.22
10981296.31
11589055.67
10019940.48
12311891.48
10110640.77
13180588.71
14369944.16
13095584.01
11843311.72
6908171.007
6361226.333
1518246.226
1890091.957
4452682.263
10039625
9672619.908
17864526.05
13378441.6
9649413.933
11817768.52
11792251.44
16848782.33
31992296.96
14216292.68
62759829.76
68539085.49
61694363.27
54592760.43
59232779.23
65576196.82
5525945.707
2459590.244
19346686.46
18432813.25
5979694.016
1051533.084
4743788.142
552858.3907

0.0254
0.0515
0.0029
0.0002
0.0004
0.0014
0.0021
0.0004
0.0001
0.0008
0.0002
0.0007
0.0008
0.0018
0.0053
0.0045
0.0065
0.0024
0.0086
0.0048
0.0011
0.0007
0.0011
0.0025
0.0003
0.0013
0.0010
0.0014
0.0012
0.0006
0.0001
0.0001
0.0004
0.0001
0.0002
0.0095
0.0116
0.0022
0.0010
0.0093
0.0088
0.0091
0.0515

25.40
51.48
2.89
0.24
0.39
1.40
2.14
0.43
0.12
0.75
0.19
0.73
0.80
1.83
5.34
4.50
6.49
2.36
8.64
4.75
1.05
0.75
1.12
2.46
0.32
1.28
1.02
1.35
1.21
0.58
0.09
0.15
0.41
0.07
0.20
9.52
11.64
2.23
1.04
9.33
8.77
9.08
51.48

Table 31 – Tariacuri Community Interaction Values
Bold = primary
Italic = secondary
A
B
Comm Comm A pop
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1
2
4
5
6
7
8
9

2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
2164
908
908
908
908
908
908
908
908
908
908
908
908
908
908
908
908
276
276
276
276
276
276
276
276

B pop
908
276
98
421
135
66
109
163
23
43
228
141
102
2988
2329
963
2164
276
98
421
135
66
109
163
23
43
228
141
102
2988
2329
963
2164
908
98
421
135
66
109
163

distance A x B
642
1181
4193
2930
3413
2446
4902
6633
4891
1838
2270
8278
4008
6389
7535
7753
642
1043
4899
2776
3176
1193
5315
6834
5287
2079
2662
8478
4624
5101
5892
6104
1181
1043
6076
3947
4468
3170
6492
8131

1964912
597264
212072
911044
292140
142824
235876
352732
49772
93052
493392
305124
220728
6466032
5039956
2083932
1964912
250608
88984
382268
122580
59928
98972
148004
20884
39044
207024
128028
92616
2713104
2114732
874404
597264
250608
27048
116196
37260
18216
30084
44988
288

d^1.9
1219.8
2243.9
7966.7
5567
6484.7
4647.4
9313.8
12602.7
9292.9
3492.2
4313
15728.2
7615.2
12139.1
14316.5
14730.7
1219.8
1981.7
9308.1
5274.4
6034.4
2266.7
10098.5
12984.6
10045.3
3950.1
5057.8
16108.2
8785.6
9691.9
11194.8
11597.6
2243.9
1981.7
11544.4
7499.3
8489.2
6023
12334.8
15448.9

value
Interaction(x1000)
1610.85
266.17
26.62
163.65
45.05
30.73
25.33
27.99
5.36
26.65
114.40
19.40
28.99
532.66
352.04
141.47
1610.85
126.46
9.56
72.48
20.31
26.44
9.80
11.40
2.08
9.88
40.93
7.95
10.54
279.94
188.90
75.40
266.17
126.46
2.34
15.49
4.39
3.02
2.44
2.91

1610847.68
266172.2893
26619.80494
163650.7994
45050.6577
30732.02221
25325.43108
27988.60562
5355.916883
26645.66749
114396.4758
19399.80417
28985.18752
532661.5647
352038.2775
141468.6335
1610847.68
126461.1192
9559.845726
72476.11103
20313.53573
26438.43473
9800.663465
11398.42583
2078.982211
9884.306726
40931.63035
7948.001639
10541.79567
279935.2036
188903.0621
75395.25419
266172.2893
126461.1192
2342.95416
15494.24613
4389.106158
3024.406442
2438.953206
2912.051991

Table 31 (cont’d)
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6

10
11
12
13
14
15
16
17
1
2
3
5
6
7
8
9
10
11
12
13
14
15
16
17
1
2
3
4
6
7
8
9
10
11
12
13
14
15
16
17
1
2
3
4
5
7
8
9

276
276
276
276
276
276
276
276
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
421
421
421
421
421
421
421
421
421
421
421
421
421
421
421
421
135
135
135
135
135
135
135
135

23
43
228
141
102
2988
2329
963
2164
908
276
421
135
66
109
163
23
43
228
141
102
2988
2329
963
2164
908
276
98
135
66
109
163
23
43
228
141
102
2988
2329
963
2164
908
276
98
421
66
109
163

6583
6348
3256
11868
3789
62928
9775
38916
5800
28152
5845 824688
6138 642804
6468 265788
4193 212072
4899
88984
6076
27048
2968
41258
4183
13230
3781
6468
2401
10682
3394
15974
616
2254
2945
4214
3190
22344
5022
13818
222
9996
6742 292824
10351 228242
9778
94374
2930 911044
2776 382268
3947 116196
3290
41258
1022
56835
761
27786
1750
45889
4780
68623
3768
9683
1796
18103
2175
95988
6424
59361
2988
42942
3746 1257948
7366 980509
6793 405423
3413 292140
3176 122580
4468
37260
2540
13230
897
56835
1656
8910
703
14715
2990
22005
289

12507.7
6186.4
7199.1
18572.5
11020
11105.5
11662.2
12289.2
7966.7
9308.1
11544.4
5639.2
7947.7
7183.9
4561.9
6448.6
1170.4
5595.5
6061
9541.8
421.8
12809.8
19666.9
18578.2
5567
5274.4
7499.3
6251
1941.8
1445.9
3325
9082
7159.2
3412.4
4132.5
12205.6
5677.2
7117.4
13995.4
12906.7
6484.7
6034.4
8489.2
4826
1704.3
3146.4
1335.7
5681

0.51
1.92
8.74
2.10
2.55
74.26
55.12
21.63
26.62
9.56
2.34
7.32
1.66
0.90
2.34
2.48
1.93
0.75
3.69
1.45
23.70
22.86
11.61
5.08
163.65
72.48
15.49
6.60
29.27
19.22
13.80
7.56
1.35
5.31
23.23
4.86
7.56
176.74
70.06
31.41
45.05
20.31
4.39
2.74
33.35
2.83
11.02
3.87

507.5273631
1918.401655
8741.092637
2095.356037
2554.627949
74259.42101
55118.58826
21627.77073
26619.80494
9559.845726
2342.95416
7316.285998
1664.632535
900.3466084
2341.568206
2477.126818
1925.837321
753.1051738
3686.520376
1448.154436
23698.43528
22859.37329
11605.38773
5079.824741
163650.7994
72476.11103
15494.24613
6600.223964
29269.23473
19217.09662
13801.20301
7555.934816
1352.525422
5305.063885
23227.58621
4863.423347
7563.93997
176742.6307
70059.37665
31411.82487
45050.6577
20313.53573
4389.106158
2741.400746
33348.00211
2831.80778
11016.69537
3873.437775

Table 31 (cont’d)
6
10
6
11
6
12
6
13
6
14
6
15
6
16
6
17
7
1
7
2
7
3
7
4
7
5
7
6
7
8
7
9
7
10
7
11
7
12
7
13
7
14
7
15
7
16
7
17
8
1
8
2
8
3
8
4
8
5
8
6
8
7
8
9
8
10
8
11
8
12
8
13
8
14
8
15
8
16
8
17
9
1
9
2
9
3
9
4
9
5
9
6
9
7
9
8

135
135
135
135
135
135
135
135
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
66
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
109
163
163
163
163
163
163
163
163

23
43
228
141
102
2988
2329
963
2164
908
276
98
421
135
109
163
23
43
228
141
102
2988
2329
963
2164
908
276
98
421
135
66
163
23
43
228
141
102
2988
2329
963
2164
908
276
98
421
135
66
109

3991
1607
1875
4634
2238
4392
8284
7711
2446
1193
3170
3961
782
1693
2421
5451
4438
2466
2846
7095
3658
3555
6948
6375
4902
5315
6492
2474
2419
702
2378
2277
2434
3182
3451
3922
2078
4716
8983
8410
6633
6834
8131
3337
4798
3140
5578
2380

3105
5805
30780
19035
13770
403380
314415
130005
142824
59928
18216
6468
27786
8910
7194
10758
1518
2838
15048
9306
6732
197208
153714
63558
235876
98972
30084
10682
45889
14715
7194
17767
2507
4687
24852
15369
11118
325692
253861
104967
352732
148004
44988
15974
68623
22005
10758
17767
290

7582.9
3053.3
3562.5
8804.6
4252.2
8344.8
15739.6
14650.9
4647.4
2266.7
6023
7525.9
1485.8
3216.7
4599.9
10356.9
8432.2
4685.4
5407.4
13480.5
6950.2
6754.5
13201.2
12112.5
9313.8
10098.5
12334.8
4700.6
4596.1
1333.8
4518.2
4326.3
4624.6
6045.8
6556.9
7451.8
3948.2
8960.4
17067.7
15979
12602.7
12984.6
15448.9
6340.3
9116.2
5966
10598.2
4522

0.41
1.90
8.64
2.16
3.24
48.34
19.98
8.87
30.73
26.44
3.02
0.86
18.70
2.77
1.56
1.04
0.18
0.61
2.78
0.69
0.97
29.20
11.64
5.25
25.33
9.80
2.44
2.27
9.98
11.03
1.59
4.11
0.54
0.78
3.79
2.06
2.82
36.35
14.87
6.57
27.99
11.40
2.91
2.52
7.53
3.69
1.02
3.93

409.473948
1901.221629
8640
2161.938078
3238.323691
48339.08542
19976.04768
8873.516303
30732.02221
26438.43473
3024.406442
859.4320945
18701.03648
2769.919483
1563.947042
1038.727805
180.024193
605.7113587
2782.853127
690.3304774
968.60522
29196.53564
11643.94146
5247.306502
25325.43108
9800.663465
2438.953206
2272.475854
9984.334545
11032.38866
1592.226993
4106.742482
542.1009385
775.2489331
3790.205737
2062.454709
2815.96677
36347.9309
14873.76741
6569.05939
27988.60562
11398.42583
2912.051991
2519.439143
7527.588249
3688.400939
1015.078032
3929.013711

Table 31 (cont’d)
9
10
9
11
9
12
9
13
9
14
9
15
9
16
9
17
10
1
10
2
10
3
10
4
10
5
10
6
10
7
10
8
10
9
10
11
10
12
10
13
10
14
10
15
10
16
10
17
11
1
11
2
11
3
11
4
11
5
11
6
11
7
11
8
11
9
11
10
11
12
11
13
11
14
11
15
11
16
11
17
12
1
12
2
12
3
12
4
12
5
12
6
12
7
12
8

163
163
163
163
163
163
163
163
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
23
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
43
228
228
228
228
228
228
228
228

23
43
228
141
102
2988
2329
963
2164
908
276
98
421
135
66
109
163
43
228
141
102
2988
2329
963
2164
908
276
98
421
135
66
109
163
23
228
141
102
2988
2329
963
2164
908
276
98
421
135
66
109

2937
4982
5251
1653
3420
5927
12206
11633
4891
5287
6583
624
3648
3084
4427
2427
3016
3831
4100
4186
779
7389
10977
10404
1838
2079
3256
3080
1660
1671
2439
3242
4881
3866
609
6525
2778
5421
9068
8495
2270
2662
3789
3212
1974
1814
2773
3385

3749
7009
37164
22983
16626
487044
379627
156969
49772
20884
6348
2254
9683
3105
1518
2507
3749
989
5244
3243
2346
68724
53567
22149
93052
39044
11868
4214
18103
5805
2838
4687
7009
989
9804
6063
4386
128484
100147
41409
493392
207024
62928
22344
95988
30780
15048
24852
291

5580.3
9465.8
9976.9
3140.7
6498
11261.3
23191.4
22102.7
9292.9
10045.3
12507.7
1185.6
6931.2
5859.6
8411.3
4611.3
5730.4
7278.9
7790
7953.4
1480.1
14039.1
20856.3
19767.6
3492.2
3950.1
6186.4
5852
3154
3174.9
4634.1
6159.8
9273.9
7345.4
1157.1
12397.5
5278.2
10299.9
17229.2
16140.5
4313
5057.8
7199.1
6102.8
3750.6
3446.6
5268.7
6431.5

0.67
0.74
3.73
7.32
2.56
43.25
16.37
7.10
5.36
2.08
0.51
1.90
1.40
0.53
0.18
0.54
0.65
0.14
0.67
0.41
1.59
4.90
2.57
1.12
26.65
9.88
1.92
0.72
5.74
1.83
0.61
0.76
0.76
0.13
8.47
0.49
0.83
12.47
5.81
2.57
114.40
40.93
8.74
3.66
25.59
8.93
2.86
3.86

671.8276795
740.4551121
3725.004761
7317.795396
2558.633426
43249.35842
16369.30069
7101.802042
5355.916883
2078.982211
507.5273631
1901.147099
1397.01639
529.8996519
180.4715086
543.6644764
654.2300712
135.8721785
673.1707317
407.7501446
1585.028039
4895.185589
2568.384613
1120.46986
26645.66749
9884.306726
1918.401655
720.0956938
5739.695625
1828.404044
612.4166505
760.901328
755.7769655
134.6420889
8472.906404
489.0502117
830.9651017
12474.29587
5812.632043
2565.533905
114396.4758
40931.63035
8741.092637
3661.270237
25592.70517
8930.540243
2856.112514
3864.106352

Table 31 (cont’d)
12
9
12
10
12
11
12
13
12
14
12
15
12
16
12
17
13
1
13
2
13
3
13
4
13
5
13
6
13
7
13
8
13
9
13
10
13
11
13
12
13
14
13
15
13
16
13
17
14
1
14
2
14
3
14
4
14
5
14
6
14
7
14
8
14
9
14
10
14
11
14
12
14
13
14
15
14
16
14
17
15
1
15
2
15
3
15
4
15
5
15
6
15
7
15
8

228
228
228
228
228
228
228
228
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
141
102
102
102
102
102
102
102
102
102
102
102
102
102
102
102
102
2988
2988
2988
2988
2988
2988
2988
2988

163
23
43
141
102
2988
2329
963
2164
908
276
98
421
135
66
109
163
23
43
228
102
2988
2329
963
2164
908
276
98
421
135
66
109
163
23
43
228
141
2988
2329
963
2164
908
276
98
421
135
66
109

5019
37164
4009
5244
569
9804
6663
32148
2911
23256
6772 681264
9381 531012
8808 219564
8278 305124
8478 128028
4977
38916
4977
13818
6499
59361
4752
19035
7189
9306
3991
15369
1596
22983
4578
3243
6594
6063
6862
32148
5060
14382
7124 421308
12079 328389
11506 135783
4088 220728
4624
92616
5800
28152
292
9996
2782
42942
2218
13770
3561
6732
2037
11118
3458
16626
774
2346
2723
4386
2967
23256
4629
14382
6543 304776
10189 237558
9616
98226
6389 6466032
5101 2713104
5845 824688
6860 292824
3636 1257948
4422 403380
3583 197208
4689 325692
292

9536.1
7617.1
1081.1
12659.7
5530.9
12866.8
17823.9
16735.2
15728.2
16108.2
9456.3
9456.3
12348.1
9028.8
13659.1
7582.9
3032.4
8698.2
12528.6
13037.8
9614
13535.6
22950.1
21861.4
7767.2
8785.6
11020
554.8
5285.8
4214.2
6765.9
3870.3
6570.2
1470.6
5173.7
5637.3
8795.1
12431.7
19359.1
18270.4
12139.1
9691.9
11105.5
13034
6908.4
8401.8
6807.7
8909.1

3.90
0.69
9.07
2.54
4.20
52.95
29.79
13.12
19.40
7.95
4.12
1.46
4.81
2.11
0.68
2.03
7.58
0.37
0.48
2.47
1.50
31.13
14.31
6.21
28.42
10.54
2.55
18.02
8.12
3.27
0.99
2.87
2.53
1.60
0.85
4.13
1.64
24.52
12.27
5.38
532.66
279.94
74.26
22.47
182.09
48.01
28.97
36.56

3897.190675
688.4509853
9068.541301
2539.396668
4204.740639
52947.4306
29792.13303
13119.89101
19399.80417
7948.001639
4115.35167
1461.248057
4807.298289
2108.253589
681.3040391
2026.797136
7579.145231
372.8357591
483.9327618
2465.753425
1495.943416
31125.9198
14308.82654
6211.084377
28417.96272
10541.79567
2554.627949
18017.30353
8124.030421
3267.524085
994.9895801
2872.645531
2530.516575
1595.267238
847.749193
4125.379171
1635.228707
24516.03562
12271.12831
5376.236973
532661.5647
279935.2036
74259.42101
22466.16541
182089.63
48011.14047
28968.37405
36557.228

Table 31 (cont’d)
15
9
15
10
15
11
15
12
15
13
15
14
15
16
15
17
16
1
16
2
16
3
16
4
16
5
16
6
16
7
16
8
16
9
16
10
16
11
16
12
16
13
16
14
16
15
16
17
17
1
17
2
17
3
17
4
17
5
17
6
17
7
17
8
17
9
17
10
17
11
17
12
17
13
17
14
17
15
17
16

2988
2988
2988
2988
2988
2988
2988
2988
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
2329
963
963
963
963
963
963
963
963
963
963
963
963
963
963
963
963

163
23
43
228
141
102
2329
963
2164
908
276
98
421
135
66
109
163
23
43
228
141
102
2988
963
2164
908
276
98
421
135
66
109
163
23
43
228
141
102
2988
2329

5858
7337
5379
5758
7124
6557
4973
4400
7535
5892
6138
10646
7358
8398
6966
9007
12315
11124
9152
9531
12087
10343
4963
933
7753
6104
6468
10062
6774
7814
6383
8423
11732
10540
8568
8947
11503
9760
4379
917

487044
68724
128484
681264
421308
304776
6959052
2877444
5039956
2114732
642804
228242
980509
314415
153714
253861
379627
53567
100147
531012
328389
237558
6959052
2242827
2083932
874404
265788
94374
405423
130005
63558
104967
156969
22149
41409
219564
135783
98226
2877444
2242827

293

11130.2
13940.3
10220.1
10940.2
13535.6
12458.3
9448.7
8360
14316.5
11194.8
11662.2
20227.4
13980.2
15956.2
13235.4
17113.3
23398.5
21135.6
17388.8
18108.9
22965.3
19651.7
9429.7
1772.7
14730.7
11597.6
12289.2
19117.8
12870.6
14846.6
12127.7
16003.7
22290.8
20026
16279.2
16999.3
21855.7
18544
8320.1
1742.3

43.76
4.93
12.57
62.27
31.13
24.46
736.51
344.19
352.04
188.90
55.12
11.28
70.14
19.70
11.61
14.83
16.22
2.53
5.76
29.32
14.30
12.09
737.99
1265.20
141.47
75.40
21.63
4.94
31.50
8.76
5.24
6.56
7.04
1.11
2.54
12.92
6.21
5.30
345.84
1287.28

43758.78241
4929.879558
12571.69695
62271.62209
31125.9198
24463.69087
736508.9377
344191.866
352038.2775
188903.0621
55118.58826
11283.80316
70135.54885
19704.87961
11613.85376
14834.13485
16224.4161
2534.444255
5759.281837
29323.26094
14299.35598
12088.41983
737992.9372
1265203.926
141468.6335
75395.25419
21627.77073
4936.446662
31499.93007
8756.550321
5240.729899
6558.92075
7041.873778
1106.012184
2543.675365
12916.06125
6212.704237
5296.915444
345842.4779
1287279.458

Table 32a – Loma Alta Allocation Catchment Resource Zone Analysis

Community Community Zone
Lower
1 Slopes/Lakeshore
2 Lakeshore
Lower
3 Slopes/Lakeshore
4 Lower Slopes
5 Lower Slopes
6 Lower Slopes

Catchment Area
(m2)

Open Water
(m2)

Tule-Reed
% Marsh (m2)

%

5695832.75
10477547.5

NA
NA

0
0

NA
NA

0
0

4367042.21
4939059.49
3996119.49
1638473.09

NA
NA
NA
NA

0
0
0
0

NA
NA
NA
NA

0
0
0
0

Table 32b– Loma Alta Allocation Catchment Resource Zone Analysis

Lakeshore (m2) %

Lower Slopes (m2) %

Upper Slopes (m2) %

695480.9
7007452.2

12.2
66.9

5000351.9
3470095.3

87.8
33.1

NA
NA

0
0

1798290.1
NA
2784230.7
NA

41.2
0.0
69.7
0.0

2568752.1
4939059.5
1211888.7
1638473.1

58.8
100.0
30.3
100.0

NA
NA
NA
NA

0
0
0
0

Table 33 – Loma Alta Slope Analysis
Community Comm Slope Comm Elevation Max Min Mean St. Dev
1
10.8
2098 masl
25.2 0
4.9
4.2
2
4.1
2084 masl
19.5 0
4.4
3.7
3
6.7
2108 masl
24.1 0
4.9
3.3
4
5
2130 masl
33.6 0
5.8
3.4
5
1.8
2103 masl
21.3 0
4.6
3.4
6
2.6
2125 masl
24.1 0
5.9
3.4

294

Table 34a – Lupe/La Joya Allocation Catchment Resource Zone Analysis

Community Community Zone
1
Lakeshore
2
Lakeshore
3
Lakeshore
4
Lower Slopes/Lakeshore
5
Lakeshore/Lower Slopes
6
Lakeshore
7
Lower Slopes/Lakeshore
8
Lower Slopes
9
Lower Slopes

Catchment
Area (m2)
8248283.76
7154027.26
8539433.72
4048871.38
5055961.95
9366567.43
8040922.12
5475789.52
7836987.67

Open
Water (m2)
47639.87
445865.07
3914455.2
NA
NA
NA
NA
NA
NA

Tule-Reed
%
Marsh (m2)
0.6
65812.98
6.2
832349.31
45.8
1221796
NA 184021.97
NA
NA
NA 3801385.2
NA
NA
NA
NA
NA
NA

Table 34b - Lupe/La Joya Allocation Catchment Resource Zone Analysis
Lakeshore (m2)
5296027.7
6172583.5
3614288.1
2864811.8
714461.68
5508264.6
3839851.4
NA
4280530.9

%
Lower Slopes (m2) %
Upper Slopes (m2)
64.2
2783691
33.7
NA
86.3
NA
NA
NA
42.3
NA
NA
NA
70.8
1000038
24.7
NA
14.1
4341500
85.9
NA
58.8
56917.68
0.6
NA
47.8
4201071
52.2
NA
NA
5475790
100.0
NA
54.6
3556457
45.4
NA

Table 35 – Lupe/La Joya Slope Analysis
Community Comm Slope Comm Elevation
1
4.2 2057 masl
2
2.5 2044 masl
3
0.5 2048 masl
4
5.6 2104 masl
5
3.4 2115 masl
6
1.1 2040 masl
7
9.4 2095 masl
8
4.9 2125 masl
9
3.2 2103 masl
295

Max Min Mean St. Dev
36.3
0 3.7
3.8
28.6
0 2.4
2.3
13
0 1.4
1.7
37.4
0 5.5
5.5
36.7
0 4.8
4.7
32.3
0 2.4
2.7
25.2
0 5.4
4.2
33.6
0 5.8
3.4
29
0 4.9
3.6

%
0.8
11.6
14.3
4.5
NA
40.6
NA
NA
NA

Table 36 – Early Urichu Allocation Catchment Resource Zone Analysis

Comm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Community Zone
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore/Lower
Slopes
Lower Slopes
Lower
Slopes/Lakeshore
Lakeshore
Lower
Slopes/Lakeshore
Lower
Slopes/Lakeshore
Lower
Slopes/lakeshore
Lower Slopes
Lower Slopes
Lower Slopes
Lower Slopes
Lakeshore/Lower
Slopes

Catchment
Area (m2)
8140401.77
5298156.07
5476922.62
5248878.32
1134432.37

Open
Water
(m2)
3821789
176047.9
NA
NA
NA

%
46.9
3.3
NA
NA
NA

Tule-Reed
Marsh (m2)
1109885.27
1358879.62
2108097.65
257056.3
NA

%
13.6
25.6
38.5
4.9
NA

7085739.41
4710081.21

NA
NA

NA
NA

384967.51
NA

5.4
NA

3578955.17
9719357.9

NA
NA

NA
NA

NA
NA

NA
NA

9351776.84

NA

NA

NA

NA

2707153.38

NA

NA

NA

NA

4926840.56
4539648.11
1043818.11
159590.74
1207657.57

NA
NA
NA
NA
NA

NA
NA
NA
NA
NA

NA
NA
NA
NA
NA

NA
NA
NA
NA
NA

3089309.48

NA

NA

785247.09

25.4

296

Table 36 (cont’d)
Lakeshore
(m2)
3220051
3767287
3368825
4546001
477895
3341802
448026
201073
6055986
4245259
885561
2184256
NA
NA
18234.8
NA
2246233

%
39.6
71.1
61.5
86.6
42.1
47.2
9.5
5.6
62.3
45.4
32.7
44.3
NA
NA
11.4
NA
72.7

Lower Slopes
(m2)
NA
NA
NA
445820.5
656537.4
3358970
4262055
2758732
3663372
5106518
1821593
2742584
4539648
1043818
141356
1207658
57829.8

297

%
NA
NA
NA
8.5
57.9
47.4
90.5
77.1
37.7
54.6
67.3
55.7
100.0
100.0
88.6
100.0
1.9

Upper Slopes
(m2)
NA
NA
NA
NA
NA
NA
NA
619150.8
NA
NA
NA
NA
NA
NA
NA
NA
NA

%
NA
NA
NA
NA
NA
NA
NA
17.3
NA
NA
NA
NA
NA
NA
NA
NA
NA

Table 37– Early Urichu Slope Analysis
Slope at
Community Comm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

3.9
4.3
1.6
1.1
7.9
6.4
5.1
9.9
2.7
11.6
2.5
5.3
7.6
4.9
1.4
3.1
2.9

Elevation at
Comm
2045 masl
2044 masl
2043 masl
2042 masl
2095 masl
2111 masl
2117 masl
2111 masl
2058 masl
2103 masl
2100 masl
2099 masl
2132 masl
2125 masl
2109 masl
2111 masl
2097 masl

298

Max
13
29.6
20.9
37.4
30.4
36.7
32.7
20.8
39.9
25.2
27.2
24.1
33.6
17.6
17.5
32.1
19.3

Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

St.
Mean Dev.
1.3
1.6
2.3
2.5
2.6
1.9
3.6
4.1
7.8
5.9
3.4
3.9
6.4
4.7
8.1
2.3
6.1
6
5.3
4.1
5.6
3.8
4.9
3.7
5.7
3.2
5.8
2.9
4.3
3.2
5.9
3.7
3
2.5

Table 38a– Late Urichu Allocation Catchment Resource Zone Analysis

Comm
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35

Community Zone
Tule-Reed Marsh
Lakeshore
Lakeshore
Lakeshore
Lower Slopes
Lower Slopes
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lower Slopes
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lower
Slopes/Lakeshore
Lower Slopes
Lower Slopes
Lakeshore/Lower
Slopes
Lakeshore/Lower
Slopes
Lower Slopes

Catchment
Area (m2)
8749553.09
822173.24
7197972.35
1885566.16
3694429.77
2518997.39
6998652.80
156144.32
2538212.12
251728.40
3925010.86
209433.74
447364.79
1195390.29
70640.74
774278.40
1202495.08
3072480.99
1741369.66
691273.99
666820.98
1213484.66
634770.87
114488.93
1071918.00
1037434.27
10443990.31
222248.62

Open Water
(m2)
2553533.81
NA
NA
442067.99
NA
NA
NA
NA
1632603.79
NA
2984507.84
19158.40
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

%
29.2
NA
NA
23.4
NA
NA
NA
NA
64.3
NA
76.0
9.1
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

Tule-Reed
Marsh (m2)
4624108.07
409900.89
1369947.39
682605.64
NA
NA
NA
NA
868698.84
NA
446423.36
76471.88
NA
36324.45
NA
NA
NA
NA
114434.50
390309.21
299440.71
NA
NA
NA
461152.86
196128.91
NA
44446.68

%
52.8
49.9
19.0
36.2
NA
NA
NA
NA
34.2
NA
11.4
36.5
NA
3.0
NA
NA
NA
NA
6.6
56.5
44.9
NA
NA
NA
43.0
18.9
NA
20.0

3175149.93
4778771.08
1004455.94

NA
NA
NA

NA
NA
NA

NA
NA
NA

NA
NA
NA

3241457.39

NA

NA

NA

NA

1564987.12
529548.60

NA
NA

NA
NA

NA
NA

NA
NA

299

Table 38a (cont’d)
Lower
36 Slopes/Lakeshore
37 Lakeshore
38 Lakeshore
39 Lakeshore
40 Lakeshore
41 Lakeshore
42 Lakeshore
43 Lower Slopes
44 Lower Slopes

6366919.77
1071063.76
2523826.94
1985014.68
756110.92
1291888.78
6436876.73
2721333.44
5954794.84

NA
NA
860341.25
NA
NA
NA
112698.37
NA
NA

NA
NA
34.1
NA
NA
NA
1.8
NA
NA

NA
NA
514453.91
575368.11
NA
NA
85873.45
NA
NA

NA
NA
20.4
29.0
NA
NA
1.3
NA
NA

Table 38b - Late Urichu Allocation Catchment Resource Zone Analysis

Lakeshore (m2)
1588002.02
412272.35
5477715.10
765865.26
127882.67
299343.90
2100948.65
156144.32
573213.35
251728.40
546282.11
120311.00
447364.79
1159065.84
70640.74
774278.40
1202495.08
3072480.99
1553996.93
300964.79
356856.61
233592.00
634770.87
114488.93
610765.14

%
18.1
50.1
76.1
40.6
3.5
11.9
30.0
100.0
22.6
100.0
13.9
57.4
100.0
97.0
100.0
100.0
100.0
100.0
89.2
43.5
53.5
19.2
100.0
100.0
57.0

Lower Slopes (m2)
NA
NA
350309.82
NA
3566547.06
2159283.88
4897704.14
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
72938.22
NA
10523.66
979892.66
NA
NA
NA
300

%
NA
NA
4.9
NA
96.5
85.7
70.0
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
4.2
NA
1.6
80.8
NA
NA
NA

Upper Slopes
(m2)
NA
NA
NA
NA
4071.53
60369.63
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

%
NA
NA
NA
NA
0.1
2.4
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA

Table 38b (cont’d)
841305.37
4213629.13
151898.25
810085.82
NA
NA
919335.93
1445911.98
NA
2104320.50
1027565.08
1153494.16
582497.53
756110.92
413725.31
6115295.49
677620.86
NA

81.1
40.3
68.3
25.5
NA
NA
28.4
92.4
NA
33.1
95.9
45.7
29.3
100.0
32.0
95.0
24.9
NA

NA
6230361.18
25903.68
2365064.11
4778771.08
1004455.94
2322121.46
119075.14
529548.60
3181762.24
43498.68
NA
827149.03
NA
878163.47
179120.67
2043712.58
5487385.50

301

NA
59.7
11.7
74.5
100.0
100.0
71.6
7.6
100.0
50.0
4.1
NA
41.7
NA
68.0
2.8
75.1
92.2

NA
NA
NA
NA
NA
NA
NA
NA
NA
1080837.01
NA
NA
NA
NA
NA
NA
NA
467409.34

NA
NA
NA
NA
NA
NA
NA
NA
NA
17.0
NA
NA
NA
NA
NA
NA
NA
7.8

Table 39 – Late Urichu Slope Analysis
Comm Comm Slope
1
1.6
2
2.5
3
1.6
4
2.9
5
14.2
6
9.1
7
5.6
9
1.4
10
1.5
11
1.6
12
0.5
13
2.1
14
3
15
1
16
2.3
17
1.5
18
3.9
19
1.1
20
1.1
21
2.1
22
8.9
23
4
24
1.6
25
2
26
2.7
27
1.8
28
3
29
9.5
30
0.7
31
6.2
32
7.1
33
6.1
34
2.9
35
5.2
36
10.7
37
7
38
2.7
39
11.8
40
1.4
41
7.9
42
1.5
43
2.1
44
4

Comm Elevation
2045 masl
2059 masl
2050 masl
2046 masl
2138 masl
2117 masl
2081 masl
2045 masl
2044 masl
2047 masl
2046 masl
2047 masl
2047 masl
2045 masl
2049 masl
2064 masl
2054 masl
2047 masl
2042 masl
2045 masl
2059 masl
2104 masl
2043 masl
2048 masl
2044 masl
2051 masl
2080 masl
2050 masl
2101 masl
2136 masl
2129 masl
2098 masl
2099 masl
2111 masl
2101 masl
2061 masl
2048 masl
2048 masl
2043 masl
2091 masl
2040 masl
2106 masl
2144 masl
302

Max
Min
Mean
St. Dev.
17.4
0
1.6
1.8
12.8
0
2.5
1.7
17.8
0
2.4
2.5
8.2
0
1.7
1.5
39.9
0
7.8
7.3
34.7
0
10.9
8.4
32.7
0
6
4.7
8.2
0
2.6
1.4
15.3
0
1.4
2.1
8.5
0
2.1
1.4
34.7
0
0.8
2.3
8.2
0
2.4
1.5
8.5
0
2.6
1.5
8.1
0
2.1
1.3
6.7
0
3.1
1.4
9.5
0
3.4
1.9
7.7
0
2.2
1.2
32.1
0
3.2
3.5
30.7
0
4.7
4.8
6.1
0
1.8
1
23.3
0
4.4
4
36.7
0
5.9
5.6
7.1
0
2.1
1.2
10.9
0
2.7
1.6
11.6
0
2.7
1.8
9
0
2.5
1.5
20.6
0
4.9
3.9
24.3
0
7.9
6.3
27.2
0
5.9
3.7
33.6
0
5.8
3.3
17.6
0
5.7
2.8
24.1
0
5.8
3.9
19.3
0
3.7
2.9
19.3
0
4.4
2.5
20.8
0
6.9
3.5
10.6
0
4.2
1.9
9.3
0
1.5
1.6
32.3
0
4.5
4.5
7.1
0
1.9
1.2
11.3
0
4.4
2.4
36.5
0
2.6
4
20.9
0
4.5
2.9
23.9
0
6.3
4.1

Table 40 – Tariacuri Allocation Catchment Resource Zone Analysis

Comm
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Comm Zone
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lower
Slopes/Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lakeshore
Lower Slopes
Lakeshore
Lakeshore/Lower
Slopes
Lower
Slopes/Lakeshore
Lower
Slopes/Lakeshore
Lakeshore

Catchment
Area (m2)
511177.801
6228053.73
9802666.39
1681287.61
1352378.66
1705523.96

Open
Water
(m2)
NA
2299445.37
7346665.13
NA
NA
NA

%
NA
36.9
74.9
NA
NA
NA

Tule-Reed
Marsh (m2) %
NA
NA
398218.68
6.4
256854.26
2.6
NA
NA
7542.262505 0.6
NA
NA

1534480.15
3407648.4
5908960.06
4816956.25
3940259.61
8261412.38
8266863.05
825696.164

NA
NA
NA
NA
2625894.05
4848299.92
NA
NA

NA
NA
NA
NA
66.6
58.7
NA
NA

NA
NA
NA
NA
243659.216
616199.203
NA
NA

NA
NA
NA
NA
6.2
7.5
NA
NA

10415633

NA

NA

NA

NA

5321004.65

NA

NA

NA

NA

6245673.68
1664448.4

NA
NA

NA
NA

303

NA
NA
124833.6302 7.5

Table 40 (cont’d)
Lakeshore (m2)
%
Lower Slopes (m2)
511177.8006
100.0
NA
3480845.45
55.9
NA
1763960.15
18.0
NA
377179
22.4
1304108.608
797961.58
59.0
546874.8
973005.34
57.1
732518.616
94950.34473
6.2
1439529.81
2452594.765
72.0
955053.63
2965799.42
50.2
2943160.644
1831541.1
38.0
2985415.145
1071394.68
27.2
19062.5
2449371.74
29.6
388265.45
113189.28
1.4
8009488.07
825696.1635
100.0
NA
4620027.67
44.4
5795605.29
825501.79
15.5
4495502.859
1310786.33
21.0
4934887.348
907124.3795
54.5
632490.393

304

% Upper Slopes (m2)
NA
NA
NA
NA
NA
NA
77.6
NA
40.4
NA
42.9
NA
93.8
NA
28.0
NA
49.8
NA
62.0
NA
0.5
NA
4.7
NA
96.9
144185.69
NA
NA
55.6
NA
84.5
NA
79.0
NA
38.0
NA

%
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1.7
NA
NA
NA
NA
NA

Table 41 – Tariacuri Slope Analysis
Community Comm Slope
1
2.5
2
2.1
3
3.1
4
1.6
5
2.5
6
1.6
7
4.8
8
3.7
9
6.1
10
5.7
11
2.5
12
1.1
13
0.5
14
4.3
15
3.2
16
2.9
17
1.1
18
2.5

Comm Elevation
2067 masl
2060 masl
2045 masl
2063 masl
2051 masl
2057 masl
2106 masl
2053 masl
2074 masl
2066 masl
2046 masl
2046 masl
2155 masl
2054 masl
2085 masl
2103 masl
2108 masl
2057 masl

Max
Min
Mean
St. Dev.
11.6
0
3.4
2.1
32.3
0
3.2
3.6
24.1
0
2.3
2.2
15.3
0
3.8
2.2
36.7
0
6.8
5.9
27.6
0
5.4
4.8
22.6
0
3.7
2.9
39.9
0
6.7
7.5
34.7
0
6.4
5.9
20.6
0
5.9
3.6
25.6
0
2.8
2.9
12.5
0
2.3
1.6
32.7
0
6.4
4.4
7.3
0
2.2
1.3
27.5
0
4.7
3.9
33.6
0
5.4
3.6
24.1
0
5.7
3.5
7.1
0
3.5
2.2

Table 42– Loma Alta Travel/Transportation Network Analysis
Travel Routes
Community (Land)
1
2
2
4
3
2
4
1
5
3
6
1

Average Distance to Routes
(meters)
629
650
614
362
1322
406

305

Water
Access
no
no
no
no
no
no

Water
Routes
NA
NA
NA
NA
NA
NA

Table 43– Lupe/La Joya Travel/Transportation Network Analysis
Travel Routes
Community
(Land)
1
2
2
2
3
0
4
2
5
2
6
2
7
3
8
1
9
3

Average Distance to Routes
(meters)
784
522
NA
305
730
650
1067
797
606

Water
Access
yes
yes
yes
yes
yes
no
yes
no
yes

Water
Routes
1
0
2
1
0
NA
4
NA
0

Table 44 – Early Urichu Travel/Transportation Network Analysis
Travel Routes
Community (Land)
1
0
2
2
3
1
4
2
5
1
6
3
7
1
8
0
9
2
10
2
11
2
12
1
13
1
14
1
15
2
16
1
17
3

Average Distance to Routes
(meters)
NA
515
786
116
89
881
503
NA
171
622
481
248
314
396
40
20
415

306

Water
Access
yes
yes
yes
yes
yes
yes
no
no
no
no
no
no
no
no
no
no
yes

Water
Routes
2
2
2
1
0
2
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1

Table 45 - Late Urichu Travel/Transportation Network Analysis
Travel Routes Average Distance to Routes
Community (Land)
(meters)
1
1
1492
2
0
NA
3
5
495
4
0
NA
5
2
1371
6
2
1185
7
2
158
9
0
NA
10
0
NA
11
0
NA
12
0
NA
13
0
NA
14
0
NA
15
2
309
16
2
210
17
1
482
18
1
221
19
1
193
20
2
53
21
2
86
22
0
NA
23
1
10
24
1
546
25
0
NA
26
0
NA
27
0
NA
28
2
674
29
0
NA
30
2
602
31
1
334
32
1
410
33
1
262
34
2
175
35
1
78
36
1
818
37
1
718
38
0
NA
39
1
101
40
2
174
41
0
NA
42
2
356
43
0
NA
44
0
NA

307

Water Access
yes
yes
yes
yes
no
no
no
yes
yes
yes
yes
yes
no
yes
yes
no
no
no
yes
yes
yes
yes
yes
no
yes
yes
no
yes
no
no
no
no
no
no
no
no
yes
yes
no
no
yes
no
no

Water
Routes
4
1
3
1
NA
NA
NA
0
0
0
2
0
NA
0
0
NA
NA
NA
0
1
2
0
0
0
1
0
NA
0
NA
NA
NA
NA
NA
NA
NA
NA
1
1
0
NA
0
NA
NA

Table 46 - Tariacuri Travel/Transportation Network Analysis

Travel
Routes
Community (Land)
1
0
2
3
3
0
4
1
5
2
6
1
7
1
8
3
9
3
10
1
11
0
12
2
13
2
14
2
15
3
16
2
17
1
18
2

Average
Distance to
Routes
(meters)
NA
2394
NA
755
50
346
359
797
133
132
NA
1108
970
279
730
219
388
50

308

Water
Access
yes
yes
yes
no
yes
no
yes
no
no
no
yes
yes
no
yes
no
no
no
yes

Water
Routes
0
4
6
NA
0
NA
0
NA
NA
NA
2
2
NA
NA
NA
NA
NA
1

Figure 69 – The Lupe/La Joya Communities for the Southeast Malpaís Survey

309

Figure 70 - The Early Urichu Communities for the Southeast Malpaís Survey

310

Figure 71 - The Late Urichu Communities for the Southeast Malpaís Survey

311

Figure 72 - The Tariacuri Communities for the Southeast Malpaís Survey

312

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313

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