CULTURAL TRANSMISSION, STYLE AND CONTINUOUS
VARIATION AMONG NORTH CENTRAL
SIERRA NEVADA PROJECTILE POINTS
_______
A Thesis
Presented
to the Faculty of
California State University, Chico
_______
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
in
Anthropology
_______
by
Jesse Krautkramer
Fall 2009
CULTURAL TRANSMISSION, STYLE AND CONTINUOUS
VARIATION AMONG NORTH CENTRAL
SIERRA NEVADA PROJECTILE POINTS
A Thesis
by
Jesse Krautkramer
Fall 2009
APPROVED BY THE INTERIM DEAN OF THE SCHOOL OF
GRADUATE, INTERNATIONAL, AND INTERDISCIPLINARY STUDIES:
_______________________________
Mark J. Morlock, Ph.D.
APPROVED BY THE GRADUATE ADVISORY COMMITTEE
________________________________
Georgia Fox, Ph.D.
Graduate Coordinator
_______________________________
Frank Bayham, Ph.D, Chair
_______________________________
Antoinette Martinez, Ph.D.
ACKNOWLEDGEMENTS
I would like to thank Frank Bayham and Antoinette Martinez for their insight
and flexibility while serving on my committee. I would also like to thank Georgia Fox
who served temporarily on my committee and later as Graduate Coordinator for
facilitating this project. I also extend my thanks to Chris Laverne for her advice during
the final hours of formatting. I also thank my colleagues at Tahoe National Forest,
especially Bill Slater and Donna Day for providing references and Nolan Smith for
introducing me to the Obsidian Hydration data that drew me into this project. Finally,
my deepest gratitude goes out to my family, Kristin, Levi, and Henry, for their sacrifice
and support, without which this project would have been impossible.
iii
TABLE OF CONTENTS
PAGE
Acknowledgements .....................................................................................................
iii
List of Tables ..............................................................................................................
vi
List of Figures .............................................................................................................
viii
Abstract .......................................................................................................................
xi
CHAPTER
I.
Introduction ...............................................................................................
1
II.
Theoretical Overview................................................................................
10
Introduction ......................................................................................
Typology ..........................................................................................
Style .................................................................................................
Cultural Transmission ......................................................................
Chronology and North Central Sierra Nevada Archaeology ...........
Darts Versus Arrows ........................................................................
Discussion ........................................................................................
10
11
14
17
29
40
45
Methodology .............................................................................................
47
Introduction ......................................................................................
Discussion of Variables ...................................................................
The Sample ......................................................................................
Analysis............................................................................................
47
47
57
58
Univariate Analysis ...................................................................................
62
Introduction ......................................................................................
Univariate Analysis ..........................................................................
Summary ..........................................................................................
62
67
95
III.
IV.
iv
CHAPTER
V.
PAGE
Multivariate Analysis ................................................................................
98
Introduction ......................................................................................
Principle Components Analysis .......................................................
Darts versus Arrows .........................................................................
Shoulder and Haft Element Shape ...................................................
Unshouldered Points ........................................................................
Summary ..........................................................................................
98
99
110
117
123
126
Chronology and Spatial Patterns ...............................................................
127
Geographic Samples ........................................................................
Chronological Analysis ....................................................................
Geographic and Chronological Summary ........................................
127
133
151
Conclusion ................................................................................................
154
Discussion of Results .......................................................................
Conclusion .......................................................................................
154
165
References Cited .........................................................................................................
172
VI.
VII.
Appendix
A.
Projectile Point Data ..............................................................................
v
180
LIST OF TABLES
TABLE
PAGE
1.
Definitions for 13 Standard Projectile Point Variables.............................
51
2.
Sites Used in This Study. ..........................................................................
59
3.
LT Subset Data. ........................................................................................
68
4.
Weight Subset Data...................................................................................
73
5.
PSA Subset Data. ......................................................................................
76
6.
DSA Subset Data. .....................................................................................
79
7.
NO Subset Data.........................................................................................
82
8.
NW Subset Data. .......................................................................................
85
9.
WB Subset Data. .......................................................................................
88
10.
WB/WM Subset Data. ..............................................................................
91
11.
Principle Components, Eigenvalues, and Percent Variance. ....................
103
12.
Haft Element Shape Category Definitions. ...............................................
136
13.
Dart, Arrow, and Unknown Category Definitions. ...................................
137
14.
TNF Sites with Obsidian Hydration Readings..........................................
138
15.
TNF OH Sample Counts. ..........................................................................
140
16.
Counts of Projectile Points with Direct OH Readings. .............................
143
17.
Counts of South Warners Obsidian Projectile Points with Direct OH
Readings. .............................................................................................
145
vi
TABLE
18.
PAGE
Counts of Bodie Hills Obsidian Projectile Points with Direct OH
Readings. .............................................................................................
146
19.
C14 Dates from CA-NEV-407. ..................................................................
147
20.
Projectile Point Counts and Associated C14 Dates from CA-NEV-407. ..
149
21.
Projectile Point Counts and Associated C14 Dates from CA-NEV-407. ..
151
vii
LIST OF FIGURES
FIGURE
PAGE
1.
Project Vicinity and Site Locations. .........................................................
5
2.
Project Area and Site Locations. ...............................................................
6
3.
Major Watersheds of the Project Area. ....................................................
7
4.
Graphical Description of Projectile Point Measurements. ........................
53
5.
Graphical Description of Projectile Point Measurements. ........................
54
6.
Total Length Histogram. ...........................................................................
67
7.
LT Subset Variance...................................................................................
69
8.
Maximum Width Histogram. ....................................................................
70
9.
Thickness Histogram. ...............................................................................
71
10.
Weight Historgram....................................................................................
72
11.
Weight Subset Variance. ...........................................................................
74
12.
Proximal Shoulder Angle Histogram. .......................................................
75
13.
PSA Subset Variance. ...............................................................................
77
14.
Distal Shoulder Angle Histogram. ............................................................
79
15.
DSA Subset Variance. ..............................................................................
80
16.
Notch Opening Index Histogram. .............................................................
82
17.
NO Subset Variance. .................................................................................
83
18.
Neck Width Histogram. ............................................................................
85
viii
FIGURE
PAGE
19.
NW Subset Variance. ................................................................................
86
20.
Basal Width Histogram. ............................................................................
87
21.
WB Subset Variance. ................................................................................
88
22.
Length/ Width Ratio Histogram................................................................
89
23.
Maximum Width Position Histogram. ......................................................
90
24.
Base Width/ Maximum Width Histogram. ...............................................
91
25.
WB/WM Subset Variance.........................................................................
92
26.
Basal Indentation Ratio Histogram. ..........................................................
93
27.
Crv Comparison. .......................................................................................
94
28.
Component Scree Plot...............................................................................
104
29.
Component 1 Coefficient Loadings. .........................................................
105
30.
Component 2 Coefficient Loadings. .........................................................
105
31.
Component 3 Coefficient Loadings. .........................................................
106
32.
Scatter Plot of Components 1 and 2. .........................................................
107
33.
Scatter Plot of Components 1 and 3. .........................................................
108
34.
Scatter Plot of Components 2 and 3. .........................................................
109
35.
Plot of NW and WM with Linear Fit. .......................................................
113
36.
Plot of WM and g with Logistic Fit. .........................................................
114
37.
Plot of NW and g with Linear Fit. ............................................................
116
38.
Plot of WM and T with Linear Fit. ...........................................................
117
39.
Plot of DSA and PSA................................................................................
119
ix
FIGURE
PAGE
40.
Plot of DSA and PSA, Divided by the 3g Threshold. ...............................
121
41.
Plot of MaxWPos and WB/WM, Divided by the 3g Threshold. ..............
124
42.
Plot of MaxWPos and L/W, Divided by the 3g Threshold. ......................
122
43.
PSA Histogram for the Northern Sample. ................................................
129
44.
PSA Histogram for the Southern Sample. ................................................
130
45.
PSA Histogram for the Eastern Sample. ...................................................
132
46.
PSA Histogram for the Western Sample. .................................................
133
47.
TNF OH Sample. ......................................................................................
139
48.
All Projectile Points with Direct OH Readings. .......................................
142
49.
All South Warners Obsidian Projectile Points with Direct OH Readings.
144
50.
All Bodie Hills Obsidian Projectile Points with Direct OH Readings......
147
51.
Projectile Point Forms and Associated C14 Dates from CA-NEV-407.....
150
52.
Projectile Point Forms and associated C14 Dates from CA-NEV-407 ......
150
x
ABSTRACT
CULTURAL TRANSMISSION, STYLE AND CONTINUOUS
VARIATION AMONG NORTH CENTRAL
SIERRA NEVADA PROJECTILE POINTS
by
Jesse Krautkramer
Master of Arts in Anthropology
California State University, Chico
Fall 2009
Archaeologists working in the Sierra Nevada have long held out hope that a
strong projectile point typology might be the Rosetta Stone for understanding Sierran
prehistory. This hope has often led to the use of typologies that have not been tested
against empirical evidence. Chronological studies in the north central Sierra Nevada
have been hampered by poor organic preservation and a scarcity of stratified sites.
Morphological analysis of projectile points and cultural transmission theory offer an
alternative method for understanding Sierran prehistory. Changes in the form of material
culture over time and space are directly linked to changes in the context of cultural
transmission. This implies change in the general social context. Although well defined,
dated contexts are rare in the north central Sierra Nevada, the body of morphological
xi
projectile point data is large. The analysis presented in this thesis uses continuous
morphological variation in a sample of 673 projectile points from 30 sites both east and
west of the Sierra crest to examine style in north central Sierra Nevada prehistory.
Distinct trends in univariate and multivariate variation are compared to archaeological
contexts associated with C14 dates and obsidian hydration readings. Theories of style and
cultural transmission facilitate interpretation of these patterns and provide insight into
social changes and longstanding traditions within Sierra Nevada prehistory.
xii
CHAPTER I
INTRODUCTION
The environment of the north central Sierra Nevada context is not conducive
to preservation of organics or buried stratigraphy. Steep, rugged terrain and acidic soil
limit the potential for buried contexts with associated radiocarbon dates. The majority of
prehistoric sites in the north central Sierra Nevada consist of surface lithic scatters and
bedrock milling stations. These components of the archaeological record offer significant
sources of data with regards to flaked stone technology and subsistence practices. The
overall picture is difficult to interpret without a clear chronology, however. Projectile
points offer a unique link between material culture which is preserved in Sierra Nevada
contexts and shared ideas among the producers of this material culture. The complexity
of projectile point forms and their associated projectile technologies implies that a certain
degree of intention and planning was involved in their production. Patterns in projectile
point form, therefore, can be safely assumed to represent shared ideas relating to the way
projectiles should be produced. Cultural transmission theory provides models of how
such ideas may have been shared (Boyd and Richerson 1985, Cavali-Sforza and Feldman
1981). Anthropological studies of style have identified ways in which forms in material
culture can function in social contexts, allowing for inferences into the meaning behind
variation in transmitted forms (Hegman 1992). Most studies of projectile point variation
in the Sierra Nevada have focused on chronological associations, however (Elston et al.
1
2
1977, Elston et al. 1994, White and Origer 1987, Jackson and Ballard 1999, Rosenthal
2002).
Archaeologists working in the Sierra Nevada have long held out hope that a
strong projectile point typology might be the Rosetta Stone for understanding Sierran
prehistory. This hope has often led to the use of typologies that have not been properly
tested against empirical evidence. I am not against the use of working assumptions of
chronology, but over the years these assumption have been stretched thin. Although well
defined, dated contexts are rare in the north central Sierra Nevada, the body of
morphological projectile point data is large. These data can be used to compare
morphological patterns among projectile points collected in the Sierra Nevada.
Thomas (1970) proposed a series of standard measurements in order to reduce
subjectivity in visually sorted projectile point type designations. Thomas (1981) used
these measurements to define the Monitor Valley typology for the central and western
Great Basin. This typology was added to by Elston and others (Elston et al. 1977) to
create a typology for the north central Sierra Nevada. These typologies are accompanied
by complex morphological keys that allow projectile points to be placed into series and
types on the basis of metric attributes (Elston et al. 1977, Thomas 1981). The method of
using comparable metric data reduces subjectivity in observations of variation in
projectile point form. It does not reduce subjectivity in the interpretation of this
variation, however. The typologies proposed by Thomas (1981) and Elston (Elston et al.
1977) essentially provided metric definitions of previously defined visually sorted types.
The use of normative types such as these can impose arbitrary structure on continuous
variation (Shott 1996). Analysis of continuous variation among metric attributes is a
3
more accurate way of investigating patterns in projectile point form (Shott 1996). I argue
that strong patterns observed within continuous variation among a large sample of
projectile points from the north central Sierra Nevada are directly correlated to intended
forms which were recognized by the producers themselves.
Typology can potentially reveal patterns in an archaeological assemblage, but
it does not address the relationships between artifacts. Typological method generally
neglects the question of why artifacts are similar or different. Cultural transmission
theory has been developed as a way to fill this gap (Bettinger and Eerkins 1999).
Cultural transmission models outline ways in which hypothetical cultural units, such as
intended projectile point forms, may have been shared and perpetuated across time and
space (Boyd and Richerson 1985, Cavali-Sforza and Feldman 1981). Cultural
transmission theory is rooted in evolutionary theory (Shott 1997a). Fitness and drift, as
well as social factors are seen as causes of variation (Shott 1997a, Bettinger and Eerkins
1999). Cultural transmission theory offers a middle range between observed patterns of
variation and the intended forms which they may represent. Cultural transmission
models outline the ways in which the form of one artifact may have influenced the form
of another.
Studies of style in archaeology and anthropology generally do not consider
evolution as a driving factor in variation (Hegman 1992). Instead, these studies present
theories of style regarding its function as a communicator (Hegman 1992, Weissner 1997,
Lesure 2005). These theories also address the way in which style can be part of social
and group identity (Weissner 1997). In contrast to cultural transmission theory, many
studies of style consider variation as driven by the need to communicate, rather than
4
reproductive fitness. These two viewpoints are not mutually exclusive, however.
Cultural transmission studies do have a tendency to perpetuate the troublesome
dichotomy of style versus function (Bentley and Shennan 2003, Lesure 2005). This
dichotomy obscures the importance of the communicative function of style. Shott
(1997b) uses the term performance to refer to what most cultural transmission studies
refer to as function. This term facilitates the comparison of performance and
communication related functions. If the style versus function dichotomy is avoided, style
and cultural transmission can be considered together for a broader understanding of
variation in form. It is expected that performance and communication functions may be
combined in the same object. This seems especially true of projectile points, which have
heavy performance constraints but still show a wide variety of forms.
Changes in morphological patterns over time and space are directly linked to
changes in the context of cultural transmission (Lyman et al. 2009). This implies change
in the general social context. It is expected that examination of the morphological
variation in a large sample of projectile points from the north central Sierra Nevada will
reveal changing patterns or persistent trends which reflect social changes or longmaintained traditions. Theories of style and cultural transmission facilitate interpretation
of these patterns. The analyses presented in this thesis examines continuous variation in
thirteen variables produced by Thomas‟s (1981) standard projectile point measurements.
These measurements are described in detail in chapter III. The sample used for this
analysis includes 673 projectile points from 30 sites both east and west of the Sierra crest.
The project vicinity, project area and major local watersheds are presented in Figures 1,
2, and 3, respectively. It is expected that patterning related to trends in intentional
5
Figure 1. Project Vicinity and Site Locations.
Adapted from C. W. Clewlow, Jr., Richard D. Ambro, Allen G. Pastron, Steven G. Botkin, and Michael R.
Walsh, 1984. Stage II Final Report for CA-NEV-407 Archaeological Data Recovery Program. Report
submitted to CALTRANS, Marysville, California.; Robert J Jackson and H. S. Ballard, 1999. Once Upon
a Micron: A Story of Archaeological Site CA-ELD-145 Near Camino, El Dorado County, California.
Report submitted to Caltrans District 03, Marysville, CA. Pacific Legacy Inc., Cameron Park, CA.; Henry
S. Keesling, Jerald J. Johnson, William Jerems, and Michael Rondeau, 1978. Preliminary Test Excavations
Conducted at Nev-199, Truckee, California. Report submitted to California State Department of
Transportation, District 03. Archaeological Study Center, Department of Anthropology, California State
University, Sacramento.; Tahoe National Forest, 2009. Tahoe National Forest GIS Library. Tahoe
National Forest, Nevada City.
6
Figure 2. Project Area and Site Locations.
Adapted from C. W. Clewlow, Jr., Richard D. Ambro, Allen G. Pastron, Steven G. Botkin, and Michael R.
Walsh, 1984. Stage II Final Report for CA-NEV-407 Archaeological Data Recovery Program. Report
submitted to CALTRANS, Marysville, California.; Robert J Jackson and H. S. Ballard, 1999. Once Upon
a Micron: A Story of Archaeological Site CA-ELD-145 Near Camino, El Dorado County, California.
Report submitted to Caltrans District 03, Marysville, CA. Pacific Legacy Inc., Cameron Park, CA.; Henry
S. Keesling, Jerald J. Johnson, William Jerems, and Michael Rondeau, 1978. Preliminary Test Excavations
Conducted at Nev-199, Truckee, California. Report submitted to California State Department of
Transportation, District 03. Archaeological Study Center, Department of Anthropology, California State
University, Sacramento.; Tahoe National Forest, 2009. Tahoe National Forest GIS Library. Tahoe
National Forest, Nevada City.
7
Figure 3. Major Watersheds of the Project Area.
Adapted from C. W. Clewlow, Jr., Richard D. Ambro, Allen G. Pastron, Steven G. Botkin, and Michael R.
Walsh, 1984. Stage II Final Report for CA-NEV-407 Archaeological Data Recovery Program. Report
submitted to CALTRANS, Marysville, California.; Robert J Jackson and H. S. Ballard, 1999. Once Upon
a Micron: A Story of Archaeological Site CA-ELD-145 Near Camino, El Dorado County, California.
Report submitted to Caltrans District 03, Marysville, CA. Pacific Legacy Inc., Cameron Park, CA.; Henry
S. Keesling, Jerald J. Johnson, William Jerems, and Michael Rondeau, 1978. Preliminary Test Excavations
Conducted at Nev-199, Truckee, California. Report submitted to California State Department of
Transportation, District 03. Archaeological Study Center, Department of Anthropology, California State
University, Sacramento.; Tahoe National Forest, 2009. Tahoe National Forest GIS Library. Tahoe
National Forest, Nevada City.
8
projectile point forms will be revealed through analysis of this large sample. Common
forms revealed through analysis of the entire sample will be compared between contexts
with different temporal associations. It is hoped that this will reveal patterns over time
which can be interpreted in terms of cultural transmission.
The goal of this thesis is not to identify new types or modify existing
typologies, although comparison with these typologies may be useful. The goal is rather
to characterize projectile point variation in the north central Sierra Nevada in terms of
continuous morphological data. Common forms are represented as trends rather than
defined types. It is recognized that a continuum of less frequent forms may be found
between the common forms identified by trends. The distinctness of these common
forms and the likelihood that they represent trends in emic intended forms will be
evaluated on the basis of the strength of their associated morphological trend. Common
forms which show trends in morphological variation are not intended to be tied to
assumptions of temporal or cultural associations. These associations must be established
through the pairing of strong morphological trends with well dated contexts. Comparing
projectile points from different contexts on the basis of continuous variation and
morphological trends may be a more productive method than the use of normative types.
Rather than focusing on the definition of types, questions related to the degree of
similarity or difference between forms might be more appropriate. Since this study uses
Thomas‟s (1981) widely accepted projectile point measurements, the results of these
analyses should be directly comparable with typology based studies. Although it is not
the primary goal of this thesis, the following analyses can also serve as a test of the
validity of defined types used in Sierra Nevada archaeology. It is expected that if these
9
types are valid and useful, the forms they describe will correlate with strong
morphological trends in the continuous variation. If certain defined types are not valid, it
is expected that continuous variation patterning will not reflect their forms.
A sample of the size presented here should be representative of general
morphological variation in the north central Sierra Nevada. Strong trends in this
variation should represent real emic trends in intended projectile point forms. Trends in
intended forms correlate with social contexts of cultural transmission in the north central
Sierra Neveda. The distribution of common intended forms among different temporally
associated contexts may be interpreted in terms of cultural transmission models. These
models may offer insight into the social dynamics of the prehistoric Sierra Nevada.
CHAPTER II
THEORETICAL OVERVIEW
Artifacts change through time: that is a fact.
D. H. Thomas
Introduction
The above quote by Thomas (1970) is absolutely true with regards to use-life
and taphonomy, but in terms of material culture variation I would wholeheartedly
disagree. Thomas (1970) was obviously referring to changing trends of artifact form over
time, but I feel his direct statement that “artifacts change” is not mere semantics. It
reflects long-held attitudes about observed material culture variation and typological
method. No typology can reside within a single artifact. It is the relationship between
artifacts which lead archaeologists to create and follow typologies. Observed trends in
artifact form across time and space are often interpreted in terms of chronology and
cultural boundaries, but the meaning of these trends has rarely been discussed in Sierra
Nevada archaeology. The nature of the relationship between artifacts is a question to be
tested, or at least an assumption to be stated, before form and meaning are linked into
something like a type. Specifically, the meaning of trends in artifact form can be
interpreted in terms of their function for communicating through style. Cultural
transmission theory can be used to investigate how ideas about artifact form were shared
and perpetuated across time and space, and how one artifact might influence the form of
10
11
another. This thesis seeks to interpret morphological variation in north central Sierra
Nevada projectile points through a theoretical framework which reflects an
anthropological understanding of style and an evolutionary perspective of cultural
transmission. I argue that strong quantitative patterns are the result of past practices
pertaining to the operation of material culture in social relations. Theories of style and
cultural transmission provide a link between observed material culture variation and
those social relations.
Typology
Projectile point variation is the most consistent link to style and cultural
transmission in Sierra Nevada archaeological contexts. Projectile points have complex
forms. It is safe to assume that patterns within these complex forms reflect emic
intentional forms that were shared across time and space. Variation in intentional
projectile point forms can be interpreted in terms of the social context in which they
were made. This concern with morphological patterns raises the issue of typology and
how it has been used in archaeological studies within the Sierra Nevada and Great Basin.
Typology is a logical method of ordering spatial and temporal patterns in material
culture. The ordering of these patterns is essential to an accurate interpretation of the past
(Thomas 1981). The lack of stratigraphy and organic preservation in Sierra Nevada
contexts places a heavy burden on the interpretation of morphological patterns.
Typology has been a major concern of archaeologists in California since the
early twentieth century. The typology project in western North America was begun early
in the Culture History era (Kroeber 1936, Heizer and Fenenga 1939). A. V. Kidder‟s
(1917) ceramic style chronology, based on stratigraphic excavations at Pecos Pueblo,
12
represented the first typological culture history chronology in North America (Lyman et
al. 1997, Trigger 1989). During the 1930‟s this method was used by archaeologists,
including A. L. Kroeber (1936) and Heizer (Heizer and Fenenga 1939), to piece together
a chronology in California. Heizer was involved in similar efforts in the Great Basin,
leading to the formation of the Berkeley typology (Bettinger and Eerkins 1999, Thomas
1970). Under the culture history paradigm, typologies were directly associated with
cultural entities (Kroeber 1936, Heizer and Fenenga 1939). Sites were ordered into
temporal horizons on the basis of typology and assemblage composition (Kroeber 1936,
Heizer and Fenenga 1939, Beardsley 1948). Differences in material culture were
interpreted as direct markers of culture change (Kroeber 1936). Beardsley (1948) moved
beyond the assemblage ordering method to offer interpretations of social context and
subsistence strategy. These interpretations were based on inference from relatively
casual observations of material culture variation (Beardsley 1948).
The descriptive nature of culture history method lead some to argue that North
American archaeology was devoid of theory (Ford 1954). Ford (1954) raised the
important issue of whether types defined by archaeologists actually correspond to
recognizable or intended forms in the past. He cautioned that a high level of abstraction
distances typology from emic categories of form (Ford 1954). Ford (1954) also
recognized that material culture variation is continuous. Ford (1954) argued that the
boundaries defined by archaeological observations of artifact types were arbitrary breaks
in continuous variation caused primarily by temporal and spatial distance.
Steward (1954) countered Ford‟s criticism, arguing that typology was useful
as long as the nature of the types are clearly defined. Steward (1954) suggested that
13
types could be associated with varying degrees of interpretation. Types could be strictly
descriptive, referring only to patterns of specific forms with unknown functions or
associations (Steward 1954). If cultural and chronological associations were established,
types could serve as historical index markers (Steward 1954). Finally, types could be
defined on the basis of function (Steward 1954). Typology, therefore, was a valuable
method as long as historical and functional definitions were supported with evidence and
assumptions were clearly stated (Steward 1954). This is also the viewpoint of Thomas
(1981) who stated unapologetically that typology was crucial to the understanding of
Great Basin prehistory. In his formulation of the Monitor Valley typology, Thomas
(1981) was careful to state that the typology was meant only as a temporal marker. He
also cautioned against the use of this typology outside of the central and western Great
Basin (Thomas 1981). The Monitor Valley typology was heavily borrowed from in later
typological formulations made by Elston and others in the north central Sierra Nevada
(Elston et al. 1977, Elston et al. 1994). Like the Monitor Valley typology, Elston‟s
typology consists solely of historical index types (Elston et al. 1977, Elston 1994).
After Binford‟s (1962) call to processualism, more rigorous scientific method
and a shift in focus towards questions of subsistence and the process behind culture
change were introduced to California archaeology (Meighan 1978). Questions of
chronology and cultural association still tended to rely on long-standing projectile point
typologies, however (Meighan 1978). Sierra and Great Basin projectile point typologies,
which were rooted in the culture history period and modified by Thomas (1981) and
Elston (Elston et al. 1977, Elston et al. 1994), still form the basis of most chronological
interpretations in the north central Sierra Nevada.
14
Although the method proposed by Steward (1954) and followed by Thomas
(1981) of strictly defining types and their associated assumptions is an improvement over
earlier typological efforts, the typological method itself introduces inherent problems into
the study of material culture variation. A high degree of subjectivity is involved in
visually sorting descriptive types. Thomas (1970) sought to reduce this subjectivity by
introducing standard measurements which describe projectile point forms. These
measurements allow more accurate and comparative observations of projectile point
variation. They do not reduce subjectivity in interpretations of this variation, however.
As Ford (1954) points out, normative types can place an arbitrary structure onto variation
which is actually continuous. Shott (1996) argues that a method of analyzing continuous
variation gives a more accurate picture of material culture variation. The analyses
presented in this thesis are designed to investigate morphological patterning in
continuous metric data generated with the standard measurements proposed by Thomas
(1981). It is argued that strong morphological patterns observed in a large sample of
continuous data are correlated with emic intended forms recognizable to the producers of
projectile points found in the north central Sierra Nevada. A large body of published
work on the topics of style and cultural transmission aids in the interpretation of why
these forms were maintained or changed over time and space during Sierra Nevada
prehistory.
Style
Discussion of the anthropological significance of style as it relates to typology
is largely absent from Sierra Nevada archaeology. Studies which use typology make the
tacit assumption, derived from a culture history paradigm, that style is spatially and
15
temporally associated with cultural dynamics. I do not argue against this assumption, but
I feel that consideration of anthropological work on the cultural function of style can
strengthen the theoretical framework behind morphological studies.
The dichotomy between style and function frames many archaeological
discussions of material culture variation (Lesure 2005). This is especially true of
projectile point studies, where the function of various attributes has been the focus of
much research (Hughes 1998). The distinction between function and style can be
difficult to delineate. This may reflect a lack of conscious distinction between function
and style by the prehistoric producers of material culture (Lesure 2005). Some would
argue that this dichotomy is imposed on the archaeological record by biased observers
(Lesure 2005). Still, the laws of physics are a definite constraint on the form of utilitarian
objects. Hughes (1998) explains how projectile point form changes aerodynamics and
affects the flight of arrows, darts, and spears. Shott (1997b) clarifies the situation by
using the more specific term “performance” in lieu of function to describe physically
constrained (or motivated) attributes of projectile points. This clarification is significant
in relation to active theories of style. Style has been of major concern to archaeologists
since the formation of the field (Hegman 1992). Culture history studies and some
processualist work treat style as a passive phenomenon (Hegman 1992). Whether
implicitly stated in these studies or not, style is treated as a passive reflection of culture
which can be used to infer social boundaries (Hegman 1992). Breaking out of this
tradition, archaeologists and anthropologists have developed active theories of style over
the last 35 years (Hegman 1992). Style functions in social contexts as a medium of
communication (Wobst 1977, Hegman 1992) or as a component of individual or group
16
identity (Weissner 1997, Lesure 2005). In this sense, Shott‟s (1997b) terminology is
quite significant in distinguishing functions of physical performance from functions of
communication or identity construction.
Various theories of style have been formulated to examine the function of
style in social contexts (Hegman 1992). Weisner (1997) makes the distinction between
emblemic stylistic communication which maintains group identities, and assertive style
which expresses individual identity and creativity. This viewpoint allows for the
exploration of individual agency in the production and perpetuation of style. Franklin
(1986) defines stochastic style as those forms stemming from a cultural perspective, not
necessarily tied to group boundaries. Hegman (1992) cites ethnographic examples where
social distinctions exist without related changes in material culture. Other styles may
only be recognized and produced by a subset of society (Hegman 1992). She also cites
contexts where some styles mark social boundaries while others cross-cut these
boundaries (Hegman 1992). Lesure (2005) argues that style can provide a middle range
link between the archaeological record and complex anthropological theories such as
embodiment. Style, in this sense, can be internalized as a component of identity. Lesure
(2005) mentions gender as an example of a social role that is experienced as a physical
state but which in reality is performed in a social context. Style in material culture can
contribute to this experience of performing a social role (Lesure 2005). Hodder (1990)
offers a related definition of style which involves the activities of thinking, feeling, and
being. The concept of embodiment of style could also be described as communication
with the self. In terms of the production of style it is expected that a reflexive
relationship exists between thoughts and feelings related to style and the material form of
17
style (Lesure 2005). Sackette (1990) acknowledges that some stylistic variation is
passive. Sackette (1990) argues that passive variation applies to choices which have the
same functional outcome, and that this variation is produced by processes of cultural
transmission.
Cultural Transmission
Several recent archaeological studies have focused on the subject of cultural
transmission (Bettinger and Eerkins 1999, Eerkins and Bettinger 2001, Eerkins and Lipo
2005, Lyman et al. 2009, Lyman et al. 2008, Mesoudi and O'Brien 2008, Bently and
Shennan 2003, Shott 1997b). Cultural transmission refers, quite literally, to the
transmitting of objects of culture between individuals. These objects could be ideas
about style, ways of doing such as projectile production methods, or even visual cues
relating to form. Eerkins and Lipo (2005) describe the cultural units being measured as
conceptual packages of information exchange. They offer a definition similar to that of
genes in biological transmission, in which cultural units are the largest group of attributes
reproduced with appreciable frequency (Eerkins and Lipo 2005). Eerkins and Lipo
(2005) state that cultural transmission is not directly concerned with patterns of variation
in material culture, but rather with the information exchange that accompanies the
variation. In this sense, cultural transmission theory occupies a very similar middle range
to information-exchange theories of style (Hegman 1992). Cultural transmission
diverges from stylistic theory in its orientation toward evolution (Cavalli-Sforza and
Feldman 1981). Within an evolutionary framework, culture is viewed as not just an
adaptation, but as part of the human phenotype (Cavalli-Sforza and Feldman 1981). As
such, adaptive changes take place within the context of culture, with or without
18
associated genetic changes (Cavalli-Sforza and Feldman 1981). It is argued that since
human adaptation takes place within the context of culture, cultural transmission will be
affected by selective forces (Cavali-Sforza and Feldman 1981, Bettinger and Eerkins
1999). As with natural selection‟s effect on reproductive fitness, cultural traits which
function well in a given adaptive context will be transmitted more frequently than those
which do not (Cavali-Sforza 1981). In the absence of strong selective pressure,
phenomena such as drift may take place (Shott 1997b).
Cultural transmission is a significant departure from typology in that it
addresses the relationship between artifacts. Within the social context of transmission an
artifact form is copied or modified during the production of another (Eerkins and Lipo
2005). This is the basis of observed material culture patterns in the archaeological
record. Lyman et al. (2008) refer to these patterns as lineages or clades, analogous to
biological lineages. Archaeological studies of cultural transmission make the
evolutionary argument that the processes through which cultural traits are exchanged are
shaped by selective pressures. The effects of selective pressures on artifact form should
create predictable patterns of variation in the archaeological record (Lyman et al 2008,
Eerkins and Bettinger 1999). Patterns of variation may allow for interpretation of the
social contexts of cultural transmission which may inform inference about social
dynamics among the producers of the material culture.
Models have been proposed to predict the pattern of variation that might occur
from different types of cultural transmission (Cavali-Sforza and Feldman 1981, Boyd and
Richerson 1985). Cavali-Sforza and Feldman (1981) use a mathematical approach
borrowed from population genetics. They define vertical transmission as the direct
19
passing of cultural traits from parents to children (Cavali-Sforza and Feldman 1981).
Oblique and horizontal transmission are defined as the passing of traits from a nonparental individual of the previous generation in the former case, and the passing of traits
between individuals of the same generation in the latter (Cavali-Sforza and Feldman
1981). Boyd and Richerson (1985) devoloped cultural transmission models which
include more social dynamics. In their guided variation model, the individual producer
of a given artifact starts with approximate average values of existing variants and
modifies them by trial and error (Boyd and Richerson 1985, Shott 1997b). This scenario
emplies a heavy influence from selective pressure for the performance of the artifact,
with optimal performance as the goal of the trial and error. Boyd and Richerson (1985)
also propose a model of frequency-dependant adoption, in which the most common type
of an artifact is reproduced. Under this model, variation could be driven by both physical
performance and social trends, as well as randomness and drift. A third model, termed
indirect bias, groups variants into sets which are transmitted as a package (Boyd and
Richerson 1985, Shott 1997b). These sets are selected upon by socially determined
preference (Boyd and Richerson 1985, Shott 1997b). In this case cultural transmission is
directed more towards social pressures than physical performance (Boyd and Richerson
1985, Shott 1997b).
Most studies of cultural transmission make the assumption that the patterns
outlined in these models will be visible in the variation within defined types of artifacts
(Bettinger and Eerkins 1999, Eerkins and Bettinger 2001, Eerkins and Lipo 2005, Lyman
et al 2008, Mesoudi and O'Brien 2008, Bently and Shennan 2003, Shott 1997b). Three
basic methods have been used to characterize this variation. Differences in the
20
correlation of attributes have been used to infer cultural transmission contexts (Bettinger
and Eerkins 1999, Mesoudi and O‟Brien 2008), variation within single attributes of
specific artifact types have been analyzied (Eerkins and Bettinger 2001, Eerkins and Lipo
2005, Lyman et al. 2008), and variation in terms of the numbers of types present or the
frequency of motifs has been studied (Lyman et al. 2009, Bently and Shennan 2003).
These studies tend to rely on mathematical models, often borrowed from biological
science. Their interpretation of variation is heavily focused on performance attributes,
usually referred to as functional. Cultural transmission theory can get wound up in the
unnecessary dichotomy between style and function, even to the point of blunt statements
that style has no function (Bentley and Shennan 2003). The lack of consideration for the
function of style in cultural transmission studies (and the lack of evolution in stylistic
studies) may be due in part to the complexity of stylistic communication. Stylistic
communication is not constrained in the same way as physical performance attributes.
Also, complexity, and therefore variation, may increase the effectiveness of
communication in certain contexts. The complex relationship between communication
and selective fitness does not lend itself to the mathematical approach employed by
cultural transmission theorists. In spite of this issue, cultural transmission has the
potential to facilitate the interpretation of cultural dynamics. A more careful
consideration of the function of stylistic communication would strengthen this effort.
Bettinger and Eerkins (1999) were among the first proponents of cultural
transmission theory. Following the first method mentioned above, they compared
attribute correlations in samples of Rosegate projectile points (small corner-notched)
from Gatecliff Rockshelter in central Nevada and Owens Valley in eastern California.
21
Previous typological studies had provided evidence that Rosegate points represented the
first arrow points in the region, appearing about 1350 B. P. (Bettinger and Eerkins 1999).
There results showed that weight and basal width, attributes commonly used to
distinguish darts from arrows, were highly correlated in central Nevada but less so in
eastern California (Bettinger and Eerkins 1999). They argued that this evidenced a
different pattern of cultural transmission in the two study areas. The high correlation in
central Nevada was argued to represent indirect bias where the point type was adopted as
a complete package (Bettinger and Eerkins 1999). The lack of correlation in eastern
California was interpreted as guided variation, where bow and arrow technology may
have been learned through breaf contact and perfected by trial and error (Bettinger and
Eerkins 1999). Mesoudi and O‟Brian (2008) tested these results with a computer
simulation. The simulation created scenarios of individual experimentation and copying
through cultural transmission (Mesoudi and O‟Brian 2008). They found that copying
efforts (indirect bias) produced less variation than simulated experimentation (guided
variation), which supports Bettinger and Eerkin‟s (1999) hypothesis (Mesoudi and
O‟Brien 2008). These results are intriguing, but Bettinger and Eerkin‟s (1999)
hypothesis is dependant upon the typological assumption that Rosegate points were a
distinct cultural entity and that they were the first arrow points in the region.
Eerkins and Bettinger (2001) developed their method of studying cultural
transmission further by studying variation within specific attributes over time. They
suggested the use of the coefficient of variation (CV) as a measurement of variation
(Eerkins and Bettinger 2001). CV is calculated simply by dividing the sample standard
deviation by the sample mean:
22
CV= s/ 
(1)
Eerkins and Bettinger (2001) use CV to scale variability between a random sample and
the limits of human perception. They cite psycological literature describing a 3% limit to
visual perception of variation in linear length (Eerkins and Bettinger 2001). Significantly
this limit is scaled to size rather than absolute. Eerkins and Bettinger (2001) use this
limit, termed the Webber Fraction, as an approximation for the minimum amount of
perceptable variation in a given attribute. Eerkins and Bettinger (2001) calculate that the
CV of a uniformly distributed sample should be 57.7%. The CV of a sample varying by
6% (3% on either side of the mean) should have a CV of 1.7%. They argue that the
placement of sample CV‟s between these extremes can be used to judge the degree of
standardization in artifact types (Eerkins and Bettinger 2001). A CV close to 1.7%
indicates a highly standardized artifact type, while a CV close to 57.7% may indicate that
the type or attribute is not cohesive at all (Eerkins and Bettinger 2001). A CV value
greater than 57.7% may result from intentional variation such as stylistic diversification
(Eerkins and Bettinger 2001). CV provides a simple way of comparing the relatedness of
artifacts. This method is not necessarily tied to the use of typologies, and could be
applied to any set of artifacts to assess the level of variation.
Eerkins and Lipo (2005) use CV to investigate how variation is generated in
artifact attributes. They argue that variation arises during cultural transmission due to
both conscious and unconscious factors (Eerkins and Lipo 2005). The unconscious
factors include error in transmitting instructions and error in execution of the instructions
(Eerkins and Lipo 2005). These factors, along with raw material quality should create
random variation subject to drift (Eerkins and Lipo 2005). These could be considered
23
analogous to mutation in biological transmission. Conscious factors include
experimentation, recombination of previous forms, innovation, interpretation (perhaps
through worldview), and shifts to different raw materials (Eerkins and Lipo 2005). This
would lead to higher levels of variation. Eerkins and Lipo (2005) use a mathematical
model of random variation subject to drift as a null hypothesis for studying artifact
attributes. Samples with variation less than that expected for random drift could be
considered conformist or standardized, while those with variation significantly higher
than drift could be considered to show a trend towards diversification (Eerkins and Lipo
2005). Eerkins and Lipo (2005) found that basal width of Rosegate projectile points from
Owens valley was less variable than their drift model, indicating a trend towards
standardization. Rosegate point thickness showed a pattern of variation close to random,
indicating that it was not consciously standardized (Eerkins and Lipo 2005). Woodland
ceramic pot diameters from Illinois showed a greater than random level of variation,
indicating an intentional trend towards diversification (Eerkins and Lipo 2005). The
method used by Eerkins and Lipo (2005) is again tethered to typological definitions. It is
significant, however, in that it has the potential for revealing intentionality in artifact
production, linking material culture variation to social dynamics.
Lyman et al. (2008) use within-type variation to argue for a pattern of
experimentation following the introduction of bow and arrow technology at three
dispersed locations. Dispersed locations were chosen to test the validity of evolution as a
explanatory theory for processes of cultural transmission (Lyman et al. 2008). Lyman et
al. (2008) have chosen a unique research design in that it tests the link between cultural
transmission and evolution. Most other studies include a discussion of evolution, but do
24
not test it with the data set. Lyman et al. (2008) hypothesize that following the
introduction of bow and arrow technology, existing dart forms were modified through
trial and error to produce arrow points. This pattern would fall under Boyd and
Richerson‟s (1985) model of guided variation. This scenario would be expected to
produce a higher degree of variation around the time of bow and arrow introduction
which would be reduced as arrow forms were perfected. They tested this hypothesis at
Gatecliff Rockshelter, Nevada, Mummy Cave, Wyoming, and VerKamp Rockshelter,
Missouri, finding that the expected increase in variation in both dart and arrow points did
occur around the introduction of bow an arrow technology (Lyman et al 2008). The
expected decrease in arrow variation was observed at Mummy Cave, but not at Gateckliff
and VerKamp Rockshelters (Lyman et al. 2008). Lyman et al. (2008) contend that the
absence of their expected pattern of arrow type stabilization may be due to a lack of later
components at these sites. They cite their findings of similar patterns of variation in
dispersed geographical contexts as evidence that evolutionary processes of selection were
indeed acting on the cultural transmission of these projectile point forms (Lyman et al.
2008). Again, the findings of Lyman et al. (2008) are contingent on the acceptance of
projectile point typologies, especially as they relate to the distinction between darts and
arrows.
A third approach to cultural transmission assesses atifact diversity rather than
direct morphological variation. Bently and Shennan (2003) use the frequency of stylistic
motifs to show a trend towards non-conformity in Neolithic pottery. They rely on
complex mathematical models to predict the distribution of motif frequencies that would
be produced by different forms of cultural transmission. Lyman et al. (2009) assess the
25
number of contemporaneous types compared to patterns used in biodiversity studies.
They argue that the number of types, or “artidiversity”, should follow a battleship curve
over time, analogous to speciation and extinction (Lyman et al. 2009). Lyman et al.
(2009) argue that a rapid increase in the number of projectile point types, for example,
would result from a change in the context of cultural transmission. Lyman et al (2009)
further contend that a gradual decline in diversity should follow, as less optimal types are
abandoned. Diversity studies hold a lot of potential for tracking social changes which
result in changes in cultural transmission. The use of types to judge diversity may be
warranted when studying something as complicated as pottery decoration motifs (Bentley
and Shennan 2003), but it is unnecessary for projectile point studies. The use of
projectile point types introduces all the biases accumulated during the history of the
chosen typology into the study of diversity. Sample-wide diversity can more accurately
be judged by analyzing total variation in direct morphological data among artifact
classes.
Cultural transmission studies have potential for revealing aspects of social
contexts from variation in material culture. The goal of linking material culture variation
to past social contexts is shared by the studies of style discussed above (Lesure 2005).
Although most theories of style do not incorporate evolution, they are not necessarily in
conflict with evolutionary theory. If style indeed functions in social contexts, primarily
as a communicator, then it follows that selective pressure within the adaptive context of
culture should bear on patterns of the transmission and production of style. Much of the
gulf between cultural transmission and stylistic studies can be attributed to the adherence
to the false dichotomy of style vs. function (Lesure 2005). Cultural transmission studies
26
do focus on identifying trends in stylistic production, but they tend to contrast this with
trends in variation suggesting conformity, and therefore performance constraints (Bently
and Shennan 2003, Eerkins and Lipo 2005, Lyman et al. 2009). I think a more useful
approach would be to view material culture variation as reflecting a combination of
stylistic, communicative function and physical performance function. The degree to
which either type of function contributes to the design of an object of material culture is
an important question to be asked and tested. Combining style and performance does
complicate the issue of interpretation, however. Cultural transmission may not be as
simple as the proponents of mathematical models have hoped.
Weissner (1997) argues that evolutionary models of style and cultural
transmission are incomplete without a consideration of cognition. Weissner (1997)
criticizes theoretical approaches which refer to selective fitness of artifacts, asserting that
people reproduce, not material culture. Weissner (1997) views cognition as the link
between human evolution and variation in material culture through style. Specifically,
Weissner (1997) points to the cognitive process of identity formation by social
comparison as resulting from evolution in a social context and directly related to stylistic
communication. Social or group identity can be differentiated by personal identity
(Weissner 1997). Weissner (1997) argues that stylistic meanings which convey social
identity will lead to standardization in artifact types or specific attributes, while stylistic
meanings pertaining to personal identities will be associated with greater variation. From
this perspective, change in variation over time can be interpreted as a change in the
balance between social and personal identity (Weissner 1997). Inter-group competition
and aggression or other pressures which create a need for cooperation could strengthen
27
social identity resulting in an increase in stylistic standardization (Weissner 1997).
Individual competition, personal agrandization, resource abundance or a breakdown of
the social order could lead to an increase in expression of personal identity reflected by
an increase in stylistic variation (Weissner 1997).
Weissner‟s (1997) approach brings up a contradiction in interpretation. While
cultural transmission proponents view high variation as evidence for production of style,
Weissner (1997) argues that stylistic communication can produce high or low variation
depending on the situation. It is not unlikely that physical performance could be
demonstrated to have greater importance than stylistic communication for certain classes
of artifacts. Physical performance goals certainly play a large role in the design of
projectile points (Hughes 1998). Their form is heavily guided and constrained by the
physics of projectile flight (Hughes 1998). The models established for cultural
transmission might be appropriate for studying artifact attributes which seem to pertain
more to performance than style. Some objects of material culture are created solely for
communication. Physical traits such as bright colors, visibility, or size may affect their
performance, but most of their variation can be assumed to reflect attempts at conveying
ideas. In these cases, increased diversity may be a more effective form of
communication. This scenario is implied by cultural transmission studies that identify
greater than random variation (Eerkins and Lipo 2005, Bentley and Shennan 2003). It is
clear from artifacts such as decorated ceramics that communicative and utilitarian
functions can reside in the same object. A robust analysis of stylistic variation requires a
consideration of cognition, communication, and performance. This is why the dichotomy
of style and function should be avoided (Lesure 2005). A productive method of cultural
28
transmission study might be to compare optimal performance to observed performance,
following the research design of human behavioral ecology (Kelly 1995). Attributes
which diverge from optimal forms might be interpreted as having social constraints. This
is beyond the scope of the current analysis, however.
The use of predefined typologies in cultural transmission studies is another
issue to contend with. When typologies are used, all the assumptions and biases involved
in their creation are tied to the current analysis. The use of projectile point types may be
warranted in the Great Basin, where numerous stratified and well dated sites exist
(Eerkins and Bettinger 1999, Thomas 1981), but the situation is much different in the
Sierras. Although similar projectile point forms have been observed, stratigraphy and
dated contexts are rare, and typologies are not well established. This thesis will examine
trends in artifact variation over time as it relates to general patterns in morphological
form, without resorting to predefined projectile point types. The scarcity of dated
contexts in the north central Sierra Nevada necessitates a broad-scale approach to
chronological patterning. It is not expected that the nuances of cultural transmission
patterns will be revealed in these large time blocks. For this reason, the present
chronological analysis will rely on the expectation stated by Lyman et al. (2009) that an
increase in diversity signals a change in the context of cultural transmission. It follows
that a steady level of diversity implies the persistence of certain cultural transmission
practices. The reasons leading to change or persistence, whether they relate to stylistic
communication, physical performance, or both, will remain matters of hypothetical
interpretation and further study. With this in mind, a review of past archaeological work
in the Sierras will inform the following analysis and interpretation.
29
Chronology and North Central
Sierra Nevada Archaeology
An understanding of chronology is essential to the study of cultural changes
over time. Chronology is also crucial to the identification of contemporaneous contexts,
which form the basis of spatial patterns in the archaeological record. Buried contexts
containing datable carbon in clear association with archaeological materials are extremely
rare in the Sierra Nevada. This is due to steep mountainous terrain and acidic soil which
breaks down organic material. The lack of dated contexts has motivated many
researchers to focus on chronology building (Heizer and Elsasser 1953, Elston et al.
1977, Elston et al. 1994, Jackson and Ballard 1999). The methodology of these studies
has been the creation of projectile point typologies with inferred dates from other regions.
Gatecliff Rockshelter in central Nevada has been the source of most of the dates which
were inferred for Sierra Nevada typology (Thomas 1981, Elston et al 1977, Elston et al
1994). The assumption that projectile points of similar form were produced at
approximately the same time on an inter-regional scale has allowed Sierra Nevada
archaeology to move forward. Interpretations based on this methodology, however,
could be quite off-base if this assumption is not true. Most of the gray literature cultural
resource management (CRM) reports use projectile points as time markers without
questioning the typology or its chronological associations. If the current typology is to be
tested, Sierran archaeologists will need not only dated contexts associated with diagnostic
projectile points, but also an explanatory theory as to why similar forms are seen across
such wide areas. Cultural transmission theory may help investigate the relationship
between artifacts and further illuminate their connectedness or separation. The
morphological analysis presented in the current study may address the question of
30
whether these commonly accepted diagnostic forms are inherent to the region or whether
they are normative categories from different contexts imposed on Sierran projectile
points. The following discussion will show that the common acceptance of diagnostic
projectile point types in the Sierra Nevada has very deep roots.
The first work of major impact in pursuit of the elusive Sierra Nevada
chronology was undertaken by Heizer and Elsasser in the 1950's (Heizer and Elsasser
1953). Through their analysis of thirteen sites around Lake Tahoe and Truckee,
California, Heizer and Elsasser (1953) observed two distinct patterns which appeared to
be separated temporally, geographically, and environmentally. The theoretically earlier
pattern, termed the Martis complex after the type site in Martis Valley, tended to be
located further west towards the Sierra crest in good hunting and seed collecting habitat
(Heizer and Elsasser 1953). The later pattern was named after the King's Beach type site
on the north shore of Lake Tahoe. King's Beach sites tended to be near fishing resources
(Heizer and Elsasser 1953). Heizer and Elsasser acknowledged that these observations
could change with further research and noted that some sites exhibited both complexes
(Heizer and Elsasser 1953).
Importantly, Heizer and Elsasser (1953) defined these complexes on the basis
of assemblages, rather than solely on projectile point types. Their Martis complex
exhibites preferential use of basalt, rare use of chert and obsidian, large, roughly made
projectile points of variable forms, mano and metate groundstone, cylindrical pestles and
bowl mortars, boatstones (possible atlatl weights), economic emphasis on hunting and
seed gathering, basalt flake scrapers, and expanded base flaked drills or punches (Heizer
and Elsasser 1953). Kings Beach Complex elements include a preference for obsidian
31
and chert, rare basalt use, bedrock mortars used for grinding, small, side-notched
projectile points (Desert Side Notched), a focus on fishing and seed gathering, bow and
arrow use, an absence of drills and sparse instance of scrapers (Heizer and Elsasser
1953). Heizer and Elsasser (1953) note that the Washoe were dependent on fishing and
argue that the Kings Beach complex was probably ancestral to the Washoe tribe.
Heizer and Elsasser‟s (1953) method stemmed directly from a tradition of
culture history which began in the early 20th century (Meighan 1978). The culture history
method employed the tools of typology and serriation to order observed patterns of
artifact traits in space and time. Chronological horizons were described using long lists
of artifact types from sites assumed to represent different time periods (Kroeber 1936,
Heizer and Fenenga 1939, Beardsley 1948). Observed changes and relationships
between different assemblages were explained in terms of diffusion and migration
(Trigger 1989). This methodology is behind Heizer and Elsasser‟s (1953) use of
assemblages to characterize cultural sequences and their research question asking
whether the Martis culture they proposed originated in the Sierras or derived from
California or the Great Basin.
Elsasser (1960) later investigated a prehistoric site in the western foothills of
the Sierra Nevada near North San Juan, California. Elsasser interpreted projectile points
from this site as Martis and Kings Beach types (Elsasser 1960, White and Origer 1987).
This interpretation supported the argument for continuity between the Martis and Kings
Beach complexes. It also extended the theoretical extent of these complexes to the
western foothills (Elsasser 1960, White and Origer 1987). This argument for cultural
continuity east and west of the central Sierra Nevada encouraged the application of the
32
Martis and Kings Beach complexes to interpretation of western foothills prehistory
(Markley 1985). Elsasser's (1960) research question of weather the Martis Complex had
an eastern (Great Basin), western (California), or local origin was a major focus of later
research in the Sierra Nevada (Markley 1985).
Ritter (1970) obtained a C14 date of 3350 BP associated with Martis-like
materials at CA-PLA-101 near Foresthill, California in the western Sierra foothills
(Markley 1985, White and Origer 1987). Ritter (1970) reported dated components from
this site as late as 500 to 660 B.P. with a concentration of Martis-like materials around
900 to 1000 B.P. (Markley 1985, White and Origer 1987). He also observed a shift from
mano and matate use to mortar and pestle between 650 and 1000 B.P. (Markley 1985,
Ritter 1970, White and Origer 1987). It is important to note that designation of
assemblages as "Martis" at this time was based solely on the presence of large basalt
projectile points and tools, fairly ubiquitous traits in a region where basalt is the most
accessible toolstone.
Elston et al. (1977) attempted to address this issue in their survey of the Tahoe
Reach segment of the Truckee River, between Truckee, California and Lake Tahoe.
Their interpretations would form the basis of the majority of chronological arguments
made by later archaeological works in the northern Sierra Nevada. Elston et al. (1977)
used the standard measurements proposed by Thomas (1970) to define their projectile
point types. Thomas argued for a method of operationalism, which quantified
observations of projectile point form (Thomas 1970). Thomas (1981) later published a
key which defined the morphology of projectile points from Monitor Valley Nevada in
the central Great Basin. This key was based on the previous Berkeley typology for the
33
Great Basin (Thomas 1981, Bettinger and Eerkins 1999), but modified in light of
projectile points found in well dated contexts from Gatecliff Rockshelter (Thomas 1981).
Thomas‟s key established a new Monitor Valley typology which replaced the Berkeley
typology in the central and western Great Basin. Citing personal communication from
Thomas, Elston (Elston et al. 1977) adapted this key to include Martis types. The key,
formulated by Leventhal (Elston et al. 1977), was included as an appendix to the 1977
report. Elston (Elston et al. 1977) argued that the Thomas key was useful as an objective
standard of morphological variability for comparing data (Elston et al. 1977). Elston
(Elston et al. 1977) was responsible for introducing Thomas‟s (1981) operational method
of projectile point measurements to Sierra Nevada archaeology.
Elston (Elston et al. 1977) obtained a number of C14 dates, five of which were
associated with projectile points which keyed out to diagnostic types. A date of 160  60
B.P. associated with one Rose Spring Corner-Notched (small corner-notched), two
Steamboat (narrow shouldered, straight-stemmed), and one Martis Side-Notched LeafShaped (large leaf-shaped with side notches) point at CA-PLA-23 (Elston et al. 1977).
Elston interpreted this date as too recent, and suggested that the context was disturbed
(Elston et al. 1977). A date of 329060 B.P. from this site was associated with one
Martis Contracting-Stem (large contracting-stem) point (Elston et al. 1977). Elston
(Elston et al. 1977) dismissed another recent date of 33090 BP from CA-PLA-164 as a
plant root because of its association with points which keyed out to one Elko and two
Martis types. They also reported dates from CA-PLA-164 of 121070 BP associated
with a Rose Springs Corner-Notched (small corner-notched) point and 217070 BP
associated with two Elko (large corner-notched) points (Elston et al. 1977). Elston
34
(Elston et al. 1977) cast doubt on these dates, however, because they didn't agree with the
chronology he proposed for the numerous Martis and Elko type points recovered from the
same strata. They did report confidence in dates from lower strata including a date of
8130P. Elston (Elston et al. 1977) use this date, along with the interpretation of a
point from CA-PLA-23 as a Parman type (found in contemporaneous contexts at Last
Supper Cave, Nevada) to argue for an early occupation of the Tahoe Reach of the
Truckee River.
Although these C14 dates are interesting, the chronology proposed by Elston
(Elston et al. 1977) is based almost entirely on Thomas's (1981) dates form Gatecliff
Rockshelter. The early phase discussed above was named Tahoe Reach and placed
between 9000 B.P. and 7000 B.P. (Elston et al. 1977). Elston (Elston et al. 1977)
suggested the Spooner Phase as a hypothetical construct to bridge the gap in data between
7000 B.P. and 4000 B.P. (Elston et al. 1994). The Martis Complex was divided into three
phases. Early Martis was placed between 4000 B.P. and 3500 B.P. It was argued to be
associated with Elko and Martis Contracting-Stem (large contrancting-stem) points
(Elston et al. 1977). Middle Martis was placed between 3500 B.P. and 2500 B.P., and
associated with Steamboat (narrow-shouldered, straight-stemmed) points (Elston et al.
1977). Late Martis was placed between 2500 B.P. and 1500 B.P. and associated with
Martis and Elko notched and eared points (Elston et al. 1977, Elston et al. 1994). King's
Beach was divided into Early (1500-800 B.P.) and Late (post 800 B.P.) phases. The
King's Beach complex was argued to be associated with Rose Springs (small cornernotched arrows), Desert Side-Notched (small side-notched arrows), and Cottonwood
35
(small triangular or leaf-shaped) projectile points (Elston et al. 1977). This chronological
framework continues to be largely accepted in Sierra Nevada archaeology.
Elston‟s chronology has been met with some criticism (Elston et al. 1977).
Elston (Elston et al. 1994) himself revised the chronology based on a review of C14 dates
and updated information from Thomas (1981). Elston concludes that there is not enough
evidence to divide Martis into three phases (Elston et al. 1994). Instead he proposes an
Early Martis phase dated from 5000 to 3000 BP and a Late Martis Phase dated from 3000
to 1300 BP (Elston et al. 1994). The Early Martis Phase is associated with Martis
Contracting-Stemmed (large contracting-stem), Steamboat (narrow shouldered, straightstemmed), and Martis Split-Stemmed (large, stemmed, concave base) projectile points,
while the Late Martis Phase is associated with Martis Corner-Notched (large cornernotched), Elko Corner-Notched (large corner-notched) and Elko Eared (large cornernotched) projectile points (Elston et al. 1994). Elston stresses that these phases are
simply blocks of time, and that almost nothing is known about culture change in the
northern Sierra Nevada region (Elston et al. 1994). Translating these point names into
more descriptive morphology, Elston‟s argument can be summarized as a predominance
of various stemmed points between 5000 B.P. and 3000 BP, large corner-notched and
side-notched projectile points from 3000 B.P. to 1300 B.P., and the use of distinctive
Desert Side-Notch (small side-notched) and Cottonwood (small leaf shaped or triangular)
type projectile points after 1300 B.P.
The Leventhal key (Elston et al. 1977) was designed to compare Sierran
projectile points to types observed by Thomas (1981) in Monitor Valley (Elston et al.
1977). Elston‟s (Elston et al. 1977) typology amounts to a modification of the Monitor
36
Valley typology which includes extra types. The Martis types proposed by Elston (Elston
et al. 1977) and others (Heizer and Elsasser 1953) have close matches among the Elko
types defined by the Berkeley and Monitor Valley typologies. Like Elston (Elston et al
1994), Thomas (1981) made it clear that he only sought to identify temporal types. He
stated that the question of why projectile points change was beyond the scope of his study
(Thomas 1981). By simply acknowledging that there are reasons behind projectile point
change, Thomas (1981) went further than most subsequent researchers in discussing the
social context of Great Basin or Sierra Nevada projectile point variation. Elston (Elston
et al. 1977) did seek to define cultural boundaries through the differentiation of projectile
point types in the Sierras from those in the Great Basin. This method, however, is
directed more at culture history than explaining social dynamics.
The Monitor Valley typology (Thomas 1981) and Elston‟s modification of it
(Elston et al 1977) have not always fit very well with observed projectile point variation
in the western foothills of the Sierra Nevada. In spite of this, a method of chronology
building through typology has been pursued (White and Origer 1987, White 1991,
Jackson and Ballard 1999). White and Origer argued that the degree of precision
presented by Elston (Elston et al. 1977) was not justified in the western Sierra Nevada
(White and Origer 1987). They advocated lumping point styles into a simplified
chronology (White and Origer 1987). White (White and Origer 1987) suggested a
western Sierra chronology based on stratigraphy and radiocarbon dates from excavations
done around Nevada City, California. White defined an Early Period which he estimated
to date from 4500 B.P. to 3000 B.P. (White and Origer 1987). It was argued to be
associated with large, notched slate and basalt projectile points (White and Origer 1987).
37
White's Middle Period was divided into two C14 dated phases. The Early Phase of the
Middle Period had C14 dates between 3125 B.P. and 2380 B.P. (White 1991). These were
associated with contracting-stem and leaf-shaped projectile points (White 1991). The
Late Phase of the Middle Period had C14 dates between 2570 B.P. and 1290 B.P. and was
associated with corner-notched, stemmed, and contracting-stemmed projectile points
(White 1991). White also divided his Late Period into early and late phases. The Early
Phase of the Late Period was estimated to date between 1300 B.P. and 800 B.P. White
argued that this phase was associated with Large and Small Gunther Barbed (straight or
contracting stemmed, barbed) projectile points (White 1991). White's Late Phase of the
Late Period was estimated to post date 800 B.P. with associated Cottonwood Triangular
(small triangular), Desert Side-Notched (small side-notched), Small Gunther (small
stemmed), and Eastgate (small corner-notched) projectile points (White 1991).
White‟s (1991) findings for the earlier time periods do not agree with Elston‟s
observations (Elston et al. 1977). White (1991) observed that large, notched points were
used between 4500 B.P. and 3000 B.P., whereas Elston (Elston et al. 1977) argued that
large stemmed points are indicative of this period. During the next period, roughly 3000
B.P. to 1300 B.P., these patterns are flipped. White (1991) reports large stemmed points
and corner-notched points, while Elston et al. (1977) argue that large side and cornernotched points were used. For the late period (after 1300 B.P.) similar projectile point
forms were observed by White (1991) and Elston (Elston et al. 1977). These include
Desert Side-Notched and Cottonwood. White (1991) proposes an additional temporal
phase between 1300 B.P. and 800 B.P. in which small contracting-stem points
(Gunthers) were used. It remains to be seen whether the differences in these two
38
chronological schemes is due to a lack of well dated stratigraphy or actual cultural
differences between the east and west side of the Sierras. The prevalence of small
contracting-stem points in White‟s (1991) sample and their absence from Elston et al.‟s
(1977) observations is probably an indication of real cultural differences between westside and east-side contexts.
Jackson and Ballard (1999) argue that Great Basin types (and their Martis
analogues) are inadequate for describing western Sierra projectile points. They propose a
Western Sierran Descriptive point typology based on work in the American River
drainage. The typology focuses on stem and shoulder morphology (Jackson and Ballard
1999). Based on stratigraphy from CA-ELD-145 and 113 direct obsidian hydration
readings on projectile points from the American River drainage, Jackson and Ballard
(1999) argue that large corner-notched, side-notched, and contracting-stemmed dart
points persisted or reoccurred at least 1000 years after the introduction of the bow and
arrow (Jackson and Ballard 1999). If this hypothesis is accepted, large projectile points
would not be useable as time markers in the northern Sierra Nevada.
Rosenthal (2002) rejects this hypothesis, citing documented disturbance of
upper strata at CA-ELD-145 and the lack of patterning in obsidian hydration readings
from debitage. Rosenthal (2002) advocates the use of metric data based on Thomas's
(1981) measurements. He examines two western Sierra sites, as well as published data
from CA-ELD-145 (Rosenthal 2002, Jackson and Ballard 1999). Rosenthal (2002) opted
against the use of projectile point keys, instead focusing on just two of Thomas‟s (1981)
measurements. These are proximal shoulder angle (PSA) which reflects the shape of the
hafting element, and neck width (NW), which Rosenthal (2002) uses to discriminate
39
between darts and arrows. Based on stratigraphy and C14 dates, Rosenthal observes that
corner-notched and leaf-shaped points are older than stemmed points in the American
River Drainage (Rosenthal 2002). This is the opposite of Elston's projectile point
associations for Early and Late Martis (Elston et al. 1994), but similar to White‟s (1991)
observations.
Previous efforts towards the development of a projectile point chronology for
the north-central Sierra Nevada are promising, but many questions are left unanswered.
It is unclear whether differences between east-side and west-side assemblages are caused
by real cultural distinctions or a lack of well defined stratigraphy and dated contexts
(Elston et al. 1977, Jackson and Ballard 1999, Rosenthal 2002, White 1991).
Interpretations based on clear stratigraphic associations, such as those of White (1991)
and Rosenthal (2002) are the best way to address this issue. A larger question
surrounding this issue is whether complex projectile point typologies are the best
interpretive framework for understanding the archaeology in the north central Sierra
Nevada. White‟s (1991) and Rosenthal‟s (2002) simplified, descriptive observations of
projectile points avoid the cumbersome assumptions associated with predefined types.
Descriptive observations leave questions such as the nature of the relationship between
artifacts and the cultural contexts in which they were produced open to interpretation.
The C14 dates associated with some of the published data used in this thesis,
along with direct obsidian hydration measurements of projectile points should shed light
on the question of how projectile point forms are patterned over time. The morphological
distinctness of the projectile point types used in the studies discussed above has yet to be
tested. The large sample employed by this thesis of metric data from projectile points
40
collected in the north-central Sierra Nevada is well suited to test for distinct projectile
point forms.
All of these efforts discussed above could fit within the culture history method
of archaeology. This is not entirely a bad thing. Thomas (1981) argues that rigorous
attention to typology and chronology building are necessary if meaningful inferences
about culture and social change are to be made. The subject of this thesis should make it
clear that I agree with him. Too rarely, however, has the effort been made to discuss
changes in the make up of archaeological assemblages in the context of their cultural
meaning or the social dynamics they imply. It has been so difficult to discover when
changes occur that the larger question of why they occur has been lost. Some work has
been done in the north central Sierra Nevada which departs from the culture history
method to explore issues of subsistence and behavioral ecology (Bloomer and Lindström
2006, White and Origer 1987). These efforts are admirable, but they also rely on the
same troubled chronology which has consumed the much of the energy put toward
contemporary archaeological efforts. It is hoped that the current study can contribute to
the pursuit of this chronology, so that efforts towards understanding behavior ecology, as
well as style and cultural transmission can be better grounded.
Darts Versus Arrows
The introduction of the bow and arrow is probably the most recognizable
event in North American prehistory. The transition from atlatl to bow and arrow
technology is often viewed as a chronological marker, although the likelihood of
technological overlap has been discussed (Lyman et al. 2008, Jackson and Ballard 1999).
In many cases there appears to be continuity between dart and arrow forms (Lyman et al.
41
2008). Lyman et al. (2008) argue that this phenomenon is expected if the first arrow
points an individual or group uses are produced through experimentation on preexisting
dart point forms. This scenario highlights the importance of separating dart points from
arrow points. Without a clear distinction between darts and arrows, it is difficult to show
whether small and large points with similar forms represent continuous size variation
within one intended form or a pair of arrow and dart forms which share morphological
characteristics. It might be expected that size attributes of darts and arrows should show
a clear break rather than a continuous curve. This may not be the case if experimentation
as discussed by Lyman et al. (2008) is taking place. Overlap between dart and arrow size
could also obscure this break. The large sample of projectile point metric data presented
in this thesis may help to elucidate methods of distinguishing dart points from arrow
points.
The distinction between dart and arrow points relates to physical performance,
as they are employed with different projectile technology. The characteristics which
distinguish darts from arrows are mostly related to size. An exception is neck width,
which is not necessarily correlated with overall size. Arrow shafts tended to be narrower
than dart and spear shafts, probably allowing for smaller neck width measurements
(Thomas 1981). Weight, shoulder width, and neck width have been used as
discriminators between arrow and dart points (Hughes 1998). A neck width threshold of
9.3 M between arrows and darts has been suggested by Rosenthal (2002). Three grams
is often used as a threshold for this determination (Hughes 1998, Lyman et al. 2009, Van
Pool 2003).
42
Hughes (1998) used a methodology based on engineering principles of
projectile flight to analyze a large sample of projectile points found within dated strata
from Mummy Cave, Wyoming. She argues that cross-sectional area and cross-sectional
perimeter are significant factors in both flight and penetration of projectiles. She found a
significant difference in both cross-sectional area and perimeter between projectile points
found in strata 1 through 3 (340-1230 B.P.) and 4 through 23 (2050-9230 B.P.) (Hughes
1998). Hughes (1998), citing experimental studies, argues that arrow points can range up
to 11g. Hughes (1998) cites other studies which suggest three grams as a minimum
performance weight for dart points. This is due to the resistance factor of a weighted
stone tip necessary for successful launch of atlatl darts (Hughes 1998). Analysis of
projectile point mass from Mummy Cave also shows a significant difference between
strata 1 through 3 and 4 through 23 (Hughes 1998). The maximum mass of projectile
points assumed to be arrows was close to the expected value of 11g (Hughes 1998). All
of the projectile points assumed to be darts weighed more than three grams (Hughes
1998).
Thomas (1978) argues that the discrimination between darts and arrows is an
empirical matter which must be tested through direct evidence rather than relying on
functional assumptions. Thomas (1978) did endeavor an empirical test using museum
specimens of projectile points from archaeological and ethnographic contexts which
remained hafted to their shafts. The attached shafts provide concrete evidence of the
points function as an arrow or a dart tip (Thomas 1978). Through discriminant analysis
of 132 known arrow points and ten known dart points, Thomas (1978) developed a pair
43
of functions which would correctly classify most of the specimens. Thomas's (1978)
functions included length, width, thickness, and neck width.
Shott (1997a) repeated Thomas's analysis with an expanded hafted dart
sample of 39 individuals, including the original ten, and the same 132 hafted arrows.
Shott's (1997a) discriminant analysis produced similar results with four variables, but he
obtained better results when one or more variables were removed. Shott's (1997a)
discriminant functions using four variables (shoulder width replacing maximum width)
correctly classified 118 out of 132 arrows and 30 out of 39 darts. Shoulder width was the
highest weighted variable, followed closely by length, with thickness and neck width
contributing less to the discriminant function (Shott 1997a). Although length is a
significant discriminator in this sample, Shott (1997a) noted the problematic nature of
this variable due to resharpening evident on archaeological samples. Shott (1997a),
therefore, performed a three variable discriminant analysis excluding length. The three
variable discriminant analysis correctly identified 118 out of 130 arrows (two outliers
excluded) and 33 of 39 darts (Shott 1997a). Shoulder width was weighted much higher
than the other variables, with neck width a distant second. Shott (1997a) performed the
analysis a third time without including neck width. He cited problems with triangular
and leaf shaped points for which neck width is not defined. (Shott 1997a). The two
variable discriminant analysis of shoulder width and thickness correctly classified 116 of
130 arrows and 33 of 39 darts (Shott 1997a). A one variable discriminant analysis of
shoulder width produced similar results, which is an indication that thickness is not
necessary for discriminating these two samples. Shott (1997a) validated his results on a
collection of 83 Great Basin Numa arrows held by the Smithsonian institution. The
44
Shott's shoulder width discriminant function correctly classified 81 of 83 specimens
(Shott 1997a). The function also correctly classified a single hafted dart found in
southern Nevada (Shott 1997a).
Examination of histograms of shoulder width from the hafted dart and arrow
samples suggests a threshold of 20 mm (Shott 1997a). Using the simple threshold of 20
mm shoulder width, 122 of 132 arrows and 30 of 39 darts are correctly classified (Shott
1997a). The threshold measure performed better for arrows but considerably worse for
darts. It is important to recognize that the differences in accuracy between the threshold
and discriminant function involve only four to six arrows and zero to three darts. The
sample size may be two small in this case to adequately evaluate the performance of
these two methods. In either case, Shott's (1997a) analysis is significant in that it
identifies shoulder width as the strongest discriminating factor between these two
samples. Shoulder width or maximum width were not directly tested by Hughes (1998),
but she did find significant stratigraphic separation for cross-sectional area and perimeter,
which are both directly correlated with width. This lends support to the argument that
darts are significantly wider than arrows on average.
The hafted projectiles studied by Thomas (1978) and Shott (1997a) are rare
examples of empirical evidence pertaining to the distinction of darts from arrow points.
The sample of 39 darts is quite small, however, and discriminant analysis exaggerates
differences by design (Baxter 1993). All of the hafted arrows in Thomas‟s (1978) sample
were side-notched (Shott 1997a), which could create bias. Engineering and physics seem
logical platforms from which to expand the study of the technological distinction of darts
and arrows (Hughes 1998). Various thresholds based on neck width (Rosenthal 2002)
45
and weight (Hughes 1998, Lyman et al. 2008) have been proposed. These thresholds are
logical from a physical performance perspective (Hughes 1998). Another possible line of
inquiry is the comparison of projectile point attributes associated with well defined
stratigraphy such as the data from Mummy Cave, Wyoming discussed by Hughes (1998).
Threshold attributes would be expected to correlate with strata above and below the
assumed introduction of bow and arrow technology. A comparison of threshold
measurements in the large projectile point sample presented in this thesis should help to
evaluate their effectiveness in distinguishing darts and arrows.
Discussion
It is clear from a review of Sierra Nevada archaeological literature that
although many attempts have been made to estimate a chronology based on projectile
point typology, the data is not convincing. Dated contexts are scarce, and those that are
reported tend to reference projectile point types that are not adequately tested.
Chronology forms the basis of any understanding of archaeological assemblages. A well
tested typology is crucial to the understanding of Sierra Nevada prehistory.
Consideration of the social dynamics of style and cultural transmission is important for
forming the theoretical foundation of typology and chronology studies. Theories of how
style functions within social contexts provide a link between the observed variation in
material culture upon which typologies are based and the human behavior that produced
the variation. Cultural transmission illuminates the process which leads to observed
chronological patterns and has the potential to reveal details about social exchanges (i.e.
between teachers and learners of cultural traits). This thesis presents metric data from a
sample of 673 projectile points collected in the north central Sierra Nevada. Quantitative
46
analysis of these data should provide evidence for or against the existence of distinct
morphological trends of intended projectile point forms in the archaeological record of
this region. These data also have the potential to shed light on the effectiveness of
methods which discriminate darts from arrows. Finally, a smaller sample of projectile
points associated with C14 dates and obsidian hydration measurements may illuminate
chronological patterns in the distribution of projectile point forms. It is hoped that these
patterns can lead to a better understanding of the effectiveness of projectile point types as
time markers in the north central Sierra Nevada.
CHAPTER III
METHODOLOGY
Introduction
The goal of the following analysis is to identify general morphological
patterns in north central Sierra Nevada projectile points which can be compared between
dated and undated contexts. I argue that strong morphological patterns present in the data
represent shared ideas or conventions that were culturally transmitted over time and
space. It is hoped that the character and context of morphological patterns in these data
will inform interpretations of past social relationships, technological changes, and the
regional chronology of projectile point styles. The larger issues of whether cultural
transmission is directed by evolutionary processes and the way in which style functions
in cultural contexts will not be tested by this analysis, but they will inform the
interpretation of the results.
Discussion of Variables
For this analysis I will use data derived from metric attributes of stone and
glass projectile points to investigate morphological patterning. The morphological data
used in this thesis will derive from Thomas's standard projectile point measurements
(Thomas 1970, Thomas 1981). In addition to maximum width (WM), total length (LT),
thickness (T), and weight (g), these include: distal shoulder angle (DSA), proximal
47
48
shoulder angle (PSA), notch opening index (NO), neck width (NW) and basal width
(WB) (Thomas 1970, 1981). Thomas (1970) also defined ratios of measurements which
were designed to reflect general shape independent of size. These include the
length/width ratio (L/W), the basal width/ maximum width ratio (WB/WM), maximum
width position (MaxWPos), and the basal indentation ratio (BIR) (Thomas 1970, 1981).
These thirteen variables are defined below. Thomas (1970, 1981) and Leventhal (1977)
used these variables as discriminators in their projectile point keys.
Thomas first introduced this suite of standard measurements in 1970 (Thomas
1970, 1981). Other methods of describing projectile shape metrically have been
proposed (Dibble and Chase 1981), but Thomas‟s scheme has seen widespread use in
California and the Great Basin. Thomas‟s (1970) measurements were designed to
address the problem of subjectivity in visual and descriptive typologies. Quantitative
morphological data allows artifacts to be described more objectively before a type, with
its entourage of assumptions, associations and bias, is assigned. Morphological data also
allow a more precise comparison of published findings than can be provided by
photographs and illustrations.
Measurements themselves have a certain degree of subjectivity and error to
them. Thomas‟s (1970) definitions of DSA and PSA rely on the orientation of a
longitudinal axis. The longitudinal axis is supposed to be a straight line through the distal
tip of the projectile point and the center of the base (Thomas 1970). On asymmetrical
and resharpened projectile points, the distal tip is not always centered with respect to the
orientation of the hafting element. This introduces subjectivity into the exact orientation
of the longitudinal axis, and hence the measurements of DSA and PSA. Asymmetry is
49
almost always reflected by pairs of unequal DSA and PSA measurements from a single
artifact, raising issues for the interpretation of this variation. Thomas‟s (1970)
convention of using the smaller measurement in cases of asymmetry creates a common
standard, at least, by which different artifacts can be compared. Width variables also rely
on the orientation and placement of a distance measurement. Thomas‟s (1970)
definitions specify that widths should be measured perpendicular to the longitudinal axis,
leading to the same degree of subjectivity discussed above. WB and NW add an extra
degree of subjectivity, even on symmetrical points. The correct placement of a WB
measurement can be unclear on projectile points with rounded corners about the base.
This is especially true of contracting stem points. Some researchers choose to record the
WB of contracting stem points as zero. Neck Width also varies with placement due to
the curved edges often present between projectile point stems and shoulders. The
tendencies of different researchers involved in taking measurements will add further error
to the data set. Still, Thomas (1970) tested the comparability of these variables when
produced by different researchers and found positive results. It is assumed that since the
present data sample was produced by a number of different researchers from a large
sample of highly morphologically variable projectile points, the error produced by the
effects discussed above will be random, and will not introduce patterned bias to the
analysis. It is hoped that patterns reflecting real trends in intended projectile point forms
will be strong enough to reveal themselves in the analysis without being obscured by the
statistical noise created by error.
Thomas‟s (1970) measurements have enjoyed widespread use by
archaeologists in California and the Great Basin, as well as other regions of North
50
America. They have the advantage of producing highly comparable numerical data that
summarizes projectile point form. Quantitative analysis of projectile point form avoids
some of the subjectivities of purely descriptive typologies. Metric attributes are, at least
in theory, our best attempt at objective observations. Rigorous observation methods do
not necessarily reduce the subjectivity of interpretation, however. Thomas‟s Monitor
Valley projectile point key (1981) was based on significant empirical evidence. Still,
Thomas‟s key relied on previously conceived types identified in the older Berkeley
typology (Thomas 1970, 1981, Bettinger and Eerkins 1999). The Leventhal key added
types associated with the Martis series without much additional evidence (Elston et al.
1977). Although they use standard metric data, the typological keys of Leventhal (1977)
and Thomas (1981) essentially provide metric definitions of visually sorted types. A
reliance on normative types such as these creates the risk of arbitrarily splitting
continuous variation (Shott 1996).
Shott (1996) argues that studying metric data as continuous variables provides
a more accurate picture of variation. Following Shott's (1996) methodology, this thesis
will examine continuous variables of metric data and inductively search for
morphological patterns. It is argued that strong morphological patterns within continuous
variation represent emic, intended forms that were recognized by the producers of these
projectile points. These intended forms are the basis of culturally transmitted ideas
relating to projectile points. The measurements proposed by Thomas (1981) reduce
subjectivity in observations of projectile point form. This following analysis is designed
to reduce subjectivity in the interpretation of patterns of form. This is achieved by
considering continuous variation rather than normative types.
51
The thirteen variables used in the present analysis are defined below and
summarized in Table 1. These variables include are total length (LT), maximum width
(WM), thickness (T), weight (g), basal width (WB), neck width (NW), distal shoulder
angle (DSA), proximal shoulder angle (PSA), notch opening index (NO), length/width
Table 1: Definitions for 13 Standard Projectile Point Variables.
Measurement
Definition
Total Length (LT)
Max. distance parallel to longitudinal axis
Maximum Width (WM)
Max. distance perpendicular to longitudinal
axis between margins
Thickness (T)
Max. distance perpendicular to longitudinal
axis between facets
Weight (g)
Weight measured in grams
Neck Width (NW)
Width between notches or across distal edge
of the haft element
Base Width (WB)
Width measured at the base
Distal Shoulder Angle
(DSA)
Angle between line perpendicular to
longitudinal axis and the shoulder margin
Proximal Shoulder Angle
(PSA)
Angle between line perpendicular to
longitudinal axis and the haft element margin
Notch Opening Index
(NO)
Length/Width Ratio
(L/W)
Maximum Width Position
(MaxWPos)
Angle formed by notch margins, or DSA
minus PSA
LT divided by WM
Length from base to widest portion of point
(LM) divided by LT
Base Width/ Maximum
Width Ratio (WB/WM)
WB divided by WM
Basal Indentation Ratio
(BIR)
Distance directly along longitudinal axis
divided by LT
Adapted from D.H. Thomas, 1981. How to Classify Projectile Points from Monitor
Valley, Nevada. Journal of California and Great Basin Anthropology 3(1):7-43.
52
ratio (L/W), maximum width position (MaxWPos), base width/ maximum width ratio
(WB/WM), and basal indentation ratio (BIR). Figures 4 and 5 present graphical displays
of these measurements.
Total length (LT) is the maximum linear distance measured parallel to the
longitudinal axis (Thomas 1970, 1981).
Maximum width (WM) is defined as the maximum dimension perpendicular to the
longitudinal axis, between the left and right projectile point margins (Thomas 1970,
1981).
Basal width (WB) is measured perpendicular to the longitudinal axis at the widest
portion of the base (Thomas 1970, 1981).
Thickness (T) is measured as the maximum dimension perpendicular to the
longitudinal axis between the two facets of a projectile point.
Mass (g) is measured in grams.
Neck width (NW) is measured as the maximum linear distance perpendicular to the
longitudinal axis between the notches or across the stem at the most distal point possible.
Distal shoulder angle (DSA) is defined as the angle formed by the line following
the edge between the shoulder and the neck of the hafting element and a line drawn
perpendicular to the longitudinal axis at the point where the first line intersects (Thomas
1970, 1981). The shoulder of a projectile point is conventionally defined as the place
where the proximal end of the blade edge is differentiated, from the hafting element,
often by a sharp angle. DSA can theoretically range between 90° and 270° (Thomas
1970). DSA is measured to the nearest 5°. In cases of asymmetry the smaller
measurement is used.
53
Figure 4. Graphical Description of Projectile Point Measurements.
Adapted from D. H. Thomas, 1981. How to Classify Projectile Points from Monitor
Valley, Nevada. Journal of California and Great Basin Anthropology 3(1):7-43.
54
Figure 5. Graphical Description of Projectile Point Measurements.
Adapted from D. H. Thomas, 1981. How to Classify Projectile Points from Monitor
Valley, Nevada. Journal of California and Great Basin Anthropology 3(1):7-43.
55
Proximal shoulder angle (PSA) is defined as the angle formed by the line following
the margin between the base and shoulder and a line drawn perpendicular to the
longitudinal axis (Thomas 1970, 1981). PSA can theoretically range between 0° and
270° (Thomas 1970). It is measured to the nearest 5° and the smaller measurement is
used in cases of asymmetry (Thomas 1970). In practice the placement of the line
perpendicular to the longitudinal axis can vary without changing the DSA or PSA
measurement.
Notch opening index (NO) is the angle formed by the line which follows the margin
between the shoulder and neck and the line following the margin between the base and
shoulder (Thomas 1970, 1981). In other words, NO is the angle of the notch opening or
the space between the stem and shoulder. NO may be measured directly or derived by
subtracting PSA from DSA. It should be noted that for asymmetrical points, NO may not
necessarily equal DSA minus PSA, since these measurements may have been taken from
opposite sides of the projectile point.
The length/width (L/W) ratio is derived by dividing LT by WM (Thomas 1970,
1981).
Maximum width position (MaxWPos) is derived by measuring the maximum linear
distance parallel to the longitudinal axis between the base and the widest portion of the
projectile point. This measurement (LM) is divided by LT to produce the MaxWPos
value (Thomas 1970, 1981). MaxWPos represents the percentage of the total length
which lies between the widest portion of the projectile point and the base. If the base is
the widest portion, MaxWPos will be zero. MaxWPos theoretically ranges between zero
and one.
56
The base width/ maximum width ratio (WB/WM) is derived by dividing WB by
WM (Thomas 1970, 1981). WB/WM compares the relative proportion of WB to WM
independent of size. If the base is the widest portion of the projectile point, WB/WM will
equal 1. WB/WM theoretically ranges between zero and one.
The basal indentation ratio (BIR) is derived by measuring the linear distance
directly on the longitudinal axis (LA) and dividing it by LT (Thomas 1970, 1981). BIR
tracks the degree of indentation for concave base points. Projectile points with straight or
convex bases will have BIR values of 1. Values less than 1 are measured on concave
base points.
Shouldered and unshouldered categories are used in the current analysis. Following
Thomas‟s definition (1981), shouldered points are those points with measurable PSA and
DSA values. Unsholdered projectile points include lanceolate, leaf shaped, or un-notched
triangular forms. DSA, PSA, NO, and NW cannot be measured on these points.
For the data I collected personally, dimensional measurements were made
with Mitumoyu digital calipers, angle measurements were made with a two armed
protractor and right angle graph paper, and mass measurements were made with a digital
scale. The exact method of measurement used to produce the published data is unknown,
but it is assumed that the data collection was competent enough to avoid large margins of
error in the sample. I am quite confident that the data gathered from these varied sources
is completely comparable. This, indeed, was Thomas‟s (1970) motivation in designing
these standard measurements.
The following analysis is designed to reveal multivariate patterns in the data.
Multivariate patterns in metric data can be viewed as general patterns of morphology. If
57
a meaningful interpretation of these patterns is to be reached, it is important to understand
the specific properties associated with each variable. The variables LM, WM, T and g
represent overall size. PSA, DSA, NO, and BIR reflect the shape of the hafting element.
NW and WB may be correlated with overall size, but they can also vary independently of
size depending on the shape of the hafting element. L/W, WB/WM and MaxWPos are
indexes correlated with overall shape, independent of size. Resharpening during the uselife of projectile points has been identified as a significant factor in morphology
(Rosenthal 2002, Thomas 1970, 1981). LM, WM, g, DSA, NO, L/W, WB/WM, and
MaxWPos may be changed by resharpening (Rosenthal 2002, Thomas 1970, 1981). NW,
WB, PSA and BIR should theoretically not be affected by resharpening (Rosenthal 2002,
Thomas 1970, 1981). These expected properties of the variables used in the current
analysis will inform the interpretation of univariate, bivariate, and multivariate
patterning. Thomas‟s (1970) measurements represent discrete and recognizable
morphological characteristics, so patterning of these variables is not abstract. It
represents real relationships of recognizable morphological attributes. Correlation
between DSA and PSA, for example, represents a direct relationship between shoulder
shape and stem shape which may or may not be correlated with dimensions of size such
as LM and WM.
The Sample
The sample used in this thesis consists of 673 projectile points collected in the
north central Sierra Nevada. These points were selected due to their association with
obsidian hydration rind measurements or C14 dates. 199 projectile points from the Tahoe
National Forest were included. Of these, data for 141 was gathered from Tahoe National
58
Forest reports, while I personally measured 58. The largest portion of the current sample
was gathered from published data from sites outside of the Tahoe National Forest. Three
points from CA-NEV-199 near Truckee, California (Keesling et al. 1978) are included.
A large western foothills sample composed of 305 points from CA-NEV-407 near Grass
Valley, California (Clewlow et al. 1984) and 166 points from CA-ELD-145 near Camino,
California (Jackson and Ballard 1999) is also included. The sites from which this sample
was composed are presented in Table 2.
Analyses
The following three chapters present analyses designed to inductively search
for morphological patterns in continuous variation of the thirteen variables defined above.
It is argued that strong patterning among this variation represents emic intended forms
recognized by the producers and users of the projectile points. Observed patterns will be
interpreted with respect to cultural transmission and style. It is hoped that these analyses
can shed light on projectile variation in the north central Sierra Nevada and reveal
changes in the context of cultural transmission over time. The results of these analyses
may also help to gauge the usefulness of common projectile point typologies and the
validity of their temporal associations.
Chapter IV presents a univariate analysis of all thirteen variables. Histograms
are used to show the general frequency distribution of each variable. Observed trends are
tested by comparing subset variances from overlapping ranges of measurement. This is a
way of comparing peaks in the distribution to the troughs in between. A coefficient is
defined which combines subset range and variance. This range-variance coefficient (Crv)
facilitates comparison between subsets of different variables. It is assumed that trends in
59
Table 2. Sites Included in this Study.
Site
CA-NEV-199
CA-NEV-407
CA-ELD-145
05175300475
05175300645
05175500072
05175500208
05175600002
05175600016
05175600126
05175600178
05175600180
05175600251
05175600252
05175600269
05175600293`
05175600294
05175600295
05175600296
05175600302
05175600303
05175600360
05175600380
05175600454
05175600462
05175600464
05175700039
05175700256
05175700276
Number of Points
3
305
166
22
1
15
1
7
16
20
13
4
6
2
2
1
2
5
4
9
2
18
22
7
2
1
1
3
10
Reference
Keesling et al. 1978
Clewlow et al. 1984
Jackson and Ballard
1999
Weachter
1990
Padgett et al. 1995
Jensen 1977
Smith et al. 1982
Baldrica 1993
Deis et al. 1998
Deis and Duke 1998a
Deis and Duke 1998b
Gunderson et al. 1990
Deis and Duke 1998c
Miller and Zerga 1982
Sprowl and Carlson
1984 and Duke 1998e
Deis
Weachter et al. 1995
Deis and Duke 1998f
Eldred and Rouse 1991
Deis and Duke 1998g
Shapiro et al. 1989
Deis and Duke 1998h
Deis and Duke 1998i
Deis and Duke 1998j
Deis and Duke 1998k
Brook 1995
Bloomer and Slater
2001 1998
Rush
Bloomer and Slater
2001
intended forms will be reflected in univariate patterning. Variables which are not
correlated with intended forms, or for which intended forms overlap significantly, are
expected to have approximately normal frequency distributions. Variables correlated to
strong trends in intended form are expected to have bimodal or multimodal frequency
distributions.
60
Chapter V presents a multivariate analysis of the thirteen variables defined
above. A principle components analysis (PCA) is used to identify which variables
contribute most to overall variation. Variables which contribute largely to overall
variation are more likely to be correlated with distinct intended forms. The PCA may
also reveal multivariate clustering within a scatter plot of two components. Following the
PCA, three analyses relating to aspects of projectile point form are presented. A section
is devoted to the discrimination between dart and arrow points. The variables WM, NW,
T and g, which are associated with suggested threshold measurements (Thomas 1978,
Shott 1997b, Hughes 1998, Lyman et al. 2008, Rosenthal 2002), are compared using
bivariate plots. This method identifies points which are classified differently by different
dart and arrow thresholds. It is expected that the number of such points will be low if
these thresholds are successful dart and arrow discriminators. High numbers of
misclassified points are expected if thresholds do not discriminate between darts and
arrows well. Another section examines haft element and shoulder shape through a
comparison of PSA and DSA. It is expected that correlations between haft element and
shoulder shape will be reflected by patterning in the bivariate plot of PSA and DSA. A
lack of patterning is expected if shoulder and haft element shape are not correlated. A
final section examines patterning among unshouldered projectile points using MaxWPos,
WB/WM, and L/W. It is expected that trends toward leaf-shaped, lanceolate, or
triangular forms will be reflected by patterning between these variables.
Chapter VI compares common forms identified during the previous chapters
across time and space. An analysis based on geographically defined samples is
presented. It is expected that differing social contexts will be reflected by different
61
patterning among geographic samples. For the smaller samples from dated contexts,
projectile points are classified according to weight and haft element shape as either
unshouldered, stemmed, corner-notched, or side-notched dart and arrow points.
Although this represents a break from the continuous data method, the categories are
based on observed patterns in continuous data. The chronological and geographical
analyses could potentially reveal changes in the context of cultural transmission and
allow inferences into the cultural meanings of these forms. The measurements suggested
by Thomas (1981) allow detailed examination of morphological trends. By investigating
continuous variation in these thirteen variables the subjectivity and bias associated with
normative types can be avoided.
CHAPTER IV
UNIVARIATE ANALYSIS
Introduction
The examination of single variables can be quite informative of the structure
of a data set (Baxter 1993). The data presented here includes thirteen variables, each
with distinct properties relating to form, physical performance, and style. Histograms of
each variable will be presented below. This will illustrate the distribution of each
variable and allow for discussion of its implications for identifying technology,
distinctive style, and chronology in north central Sierra Nevada projectile points. A
kernel density estimation is plotted over each histogram. The PAST statistical program
(Hammer et al. 2008) uses a Gaussean kernel. Normal curves are fitted to kernels of data
the same width as the histogram bins. The normal curves are added together to produce a
smooth curve which estimates the frequency distribution of the histogram.
The objective of this univariate analysis is to identify trends in projectile point
form. It is assumed that strong trends in form represent culturally transmitted ideas about
projectile point style and performance. In other words, strong morphological trends
should reflect intended forms which are perpetuated through cultural transmission. It is
expected that variables which are not dependent on culturally transmitted ideas should
approximate a normal distribution among the entire sample. Those variables which
differentiate distinct intended forms should approximate normal curves around a separate
62
63
mean for each form. A combined sample of several forms, such as is likely with the
sample used here, should combine these separate normal curves into a multimodal
distribution. This effect will create more variance about the total sample mean than
would be found in a distribution of a variable which was more independent of culturally
transmitted ideas. The multimodal distribution may be obscured, however if intended
forms overlap significantly in a given variable, or if the means for distinct intended forms
are close together. This might be expected for variables such as length, width, and
thickness, which are often similar among commonly used projectile point type
designations. With this in mind, it is recognized that the absence of univariate patterning
does not preclude the existence of separate culturally transmitted projectile point forms. It
does represent, at least, a significant amount of overlap for the variable in question. A
bimodal or multimodal distribution, on the other hand, is strong evidence for the presence
of intended forms within a sample which implies the cultural transmission of ideas
reflected in these forms.
As discussed above, the specific properties of each variable will inform
interpretation of its variation. LM, WM, T, and g are all correlated with overall size.
Overall size is constrained by technology, as it has a large effect on the performance of
projectile points (Hughes 1998). As such, LM, WM, T, and g are expected to reflect the
distinction between darts and arrows. Strongly differentiated populations of dart and
arrow points within the same sample would be expected to produce bimodal distributions
in one or more of the size related variables. Multimodal distributions of one or more of
these variables would result if additional technological or stylistic pressures were
strongly influencing the data. A normal distribution would be expected if the variable is
64
independent of technological and stylistic distinctions, or if a high degree of overlap
obscures these distinctions. NW and WB have been suggested as discriminators between
darts and arrows (Rosenthal 2002, Thomas 1981). They are somewhat correlated to
overall size, but they can vary independently of size as well. It is expected that a strong
bimodal distribution in neck width would reflect the performance related distinction
between darts and arrows. WB was used by Thomas (1981) to discriminate dart and
arrow points of similar form (large corner-notched from small corner-notched, for
example). WB varies widely between stemmed and notched points, however. Since the
current sample contains a wide range of haft element forms, WB is not expected to show
a bimodal distribution. If WB truly differentiates darts from arrows, a multimodal
distribution with paired groups is expected. Multimodality in WB could also reflect
general trends in form.
DSA, PSA, NO, L/W, MaxWpos, WB/WM, and BIR vary independently of
overall size. Although they may affect the performance of a projectile (Hughes 1998),
the complete ranges of variation of these variables are compatible with both dart and
arrow technology. Bimodal or multimodal distributions of these variables indicate trends
in overall form which may represent stylistic communication. Ideally, the performance
implications of observed forms would be better identified so that the potential for form
related to style could be better evaluated. The performance requirements of projectile
points are complex, however, and a detailed study of these is beyond the scope of this
thesis. A lack of bimodal or multimodal distributions among DSA, PSA, NO, L/W,
MaxWpos, WB/WM, BIR, NW or WB would be expected if the variable in question is
65
unrelated to intended projectile point forms, or if overlap within the variable between
projectile point forms is high.
Where multimodal trends are suggested in the thirteen histograms presented
below, the strength of formal trends will be investigated through variance, or the square
of the standard deviation (s2). A strong multimodal trend should produce peaks and
troughs within the overall frequency distribution curve. The variance around peak
frequencies should be lower than the variance between peaks, which encompasses a
trough. This can be tested by sub-dividing the data. The subsets of data produced by this
method have artificial rather than natural boundaries. For this reason, the ranges of the
subsets must be equal in order for the subset variances to be comparable. By overlapping
datasets, the degree to which values cluster around a peak can be assessed relative to the
distribution of values between peaks. The means and variances of subsets of variables
will be presented in table and graph form when bimodal or multimodal distributions are
apparent. Subset variance is, in effect, a rough measure of the strength of morphological
trends inferred from the overall distribution of a variable.
Comparing the strength of trends between variables requires that variance
values be transformed. Eerkins and Bettinger (2001) recommend the use of the
coefficient of variation (CV). The CV is calculated by dividing the standard deviation by
the mean (Eerkins and Bettinger 2001).
s/
(2)
By using the mean as a divisor, differences in magnitude and measurement type can be
made comparable (Eerkins and Bettinger 2001). Eerkins and Bettinger (2001) use CV to
calculate variance within previously defined types. The current analysis depends on
66
aggregate data in which separate types are not defined. CV is an inappropriate way to
compare trends within multimodal data, because peaks of higher values will create higher
subset means, automatically leading to lower CV values. A coefficient calculated by
dividing the square root of the subset variance (or subset standard deviation) by the range
of the subset should correct for differences in order of magnitude, measurement type, and
range. This value will be referred to as the range-variance coefficient (Crv) and
calculated as follows:
Crv = √ s’2/r’ = s’/r’
(3)
where s‟2 = subset variance, s’ = subset standard deviation, and r’ = the subset range.
Standard deviation is used because it is directly comparable to the units which define the
total sample mean. It is important to note that subsets of data have arbitrary rather than
natural boundaries. If peaks observed in the total sample distribution do indeed represent
distinct morphological trends with their own normal curves of variation, it is probable
that subset variance will underestimate the variance within the trend due to the tails of the
normal curve being excluded. CV values calculated from previously defined types may
also be subject to this effect, depending on the definition of the type. This dampening
effect due to artificial subset boundaries should be directly related to subset range.
Smaller ranges will have a greater dampening effect. Crv corrects for this by using range
as a divisor. This does create the potential for inflated Crv values for subsets with
smaller ranges. It is expected that Crv will be a robust enough representation of
clustering around multimodal peaks to overcome this potential bias. Metrically defined
types, such as those used by the Monitor Valley (Thomas 1981) and Leventhal (Elston et
al 1977) keys also have arbitrary boundaries. As such, CV or Crv values of subsets of
67
multimodal data should be comparable with these types. CV is not comparable with Crv,
however. While CV is distorted by the size of the mean, Crv is distorted by the size of
the range. The immediate goal of this univariate analysis is to recognize morphological
trends within variables. The analysis therefore will utilize frequency distribution
(histograms), variance, and Crv. A discussion of each of the thirteen variables used in
this study is presented below, followed by a comparison of Crv values.
Univariate Analysis
Length
Total length is measured as the greatest linear distance parallel to the
longitudinal axis. Total length (LT) is closely related to overall size and shape. Size and
shape both affect projectile point aerodynamics (Hughes 1998). These factors could bear
on style as well. Length is a highly visible characteristic. Resharpening can significantly
reduce length, which could obscure morphological trends related to original intended
projectile point forms (Thomas 1981). Data counting the frequency of resharpening was
not available for this analysis. It is hoped that trends in projectile point length will be
strong enough to be visible despite the effect of resharpening. It is important to recognize
the potential bias for shorter lengths, however.
Figure 6 shows a close to normal distribution, somewhat skewed to the right
for LT. A sharp peak in frequency is apparent between 23mm and 25mm. There is a
slight bend, or elbow in the curve around 38mm, followed by a slight increase in
frequency around 43mm. A second bend and rise are located around 49mm and 51mm
respectively. These features are relatively minor compared to the peak around 23mm to
25mm. The bend at 38mm appears to approximate a boundary between the large, nearly
68
Figure 6. Total Length Histogram, n=352.
normally distributed peak and the skewed values to the right. Subset data for 38mm LT
ranges are presented in Table 3. The subset variance is presented in Figure 7. Figure 7
shows that the variance for the central subset, LT2 is higher than the other two. This is
evidence for bimodality. The subset variance of LT1 is lower than LT3 and less than half
of LT2. This reflects the clustering of data around the peak at 23mm to25mm. Overall
this pattern suggests weak bimodality. Although the distribution about the peak is nearly
Table 3. LT Subset Data.
Subset, Range
Number (n)
Mean ()
Variance (s2)
Crv
LT1
0-38
LT2
19-57
LT3
38-76
ALL 10-65.2
279
300
76
352
25.3
30.8
46.8
29.9
38.4
82.1
49.7
118.7
16
24
19
20
69
Figure 7. LT Subset Variance.
normal, a significant number of points fall to the right of the histogram. One possible
interpretation is that many dart points tend to be greater than 40mm in length, while
arrow points cluster around 25mm. Resharpened darts may add to the peak around
25mm.
Maximum Width
Maximum width (WM) is measured as the maximum linear distance
perpendicular to the longitudinal axis (Thomas 1970). Projectile point width variation
should be constrained by performance requirements (Hughes 1998). The overlap
between different intended arrow forms or intended dart forms is probably too great to
show any patterning. The performance requirements of darts and arrows may be different
enough from each other, however, to be reflected in a bimodal distribution. 20mm has
been identified as a possible threshold between darts and arrows (Shott 1997a, Thomas
70
1978). If 20mm is a good discriminator, it is expected that the distribution of WM should
be bimodal with a trough close to this value.
Figure 8 shows that this is not the case. The histigram of WM closely
approximates a normal curve with a peak around 17mm. This is evidence for a high
degree of overlap between dart and arrow widths. There is a large drop in frequency
from 20mm to 21mm, but frequency rises again at 22mm in line with the nearly normal
curve. There is no sign of bimodality in the distribution of WM, and the threshold of
20mm is not supported by this data.
Figure 8. Maximum Width Histogram, n=571.
Thickness
Thickness (T) is measured as the maximum linear distance between projectile
point facets. The range of variation in thickness is small, not leaving much room for
71
patterning. The process of bifacial reduction favors a generally lenticular cross-section in
which thickness is reduced. Hughes (1998) argues that cross-sectional area is a strong
discriminator between darts and arrows. This claim is supported with data from Mummy
Cave, Wyoming (Hughes 1998). Although thickness is correlated with cross-sectional
area, it is likely that the narrow range will obscure patterning. It is not expected that the
distinction between darts and arrows will be shown in a bimodal distribution. Figure 9
shows a nearly normal distribution with a sharp peak around 5mm and a few outliers. No
bimodal distribution is evident in this distribution. It is apparent that there is high degree
of overlap between dart and arrow thickness in this sample.
Figure 9. Thickness Histogram, n=642.
Weight
All weight values presented in Figure 10 are from complete specimens. The
weight of the 58 Tahoe National Forest projectile points which I recorded personally
72
Figure 10. Weight Historgram, n=317.
were measured using a digital scale. Much of the older published weight data was
probably measured with a three-beam scale. Weight may help to discriminate between
dart and arrow technology. Due to performance requirements it is probable that projectile
point types associated with the same technology will have similar mass (Hughes 1998).
A sample including both darts and arrows would be expected to have a bimodal
distribution of mass. A certain amount of overlap between dart and arrow mass is
expected, due to the overlap in performance requirements for mass with these
technologies. Hughes (1998) found all of the Mummy Cave projectile points from lower
strata which were assumed to contain darts weighed at least 3g, while those from upper
strata assumed to contain arrows ranged higher than this (Hughes 1998). This overlap
may obscure the bimodal distribution.
73
The histogram of the weight of complete specimens (Figure 10) shows a sharp
peak around 1g and a distribution skewed strongly to the right. A strong bimodal
distribution differentiating darts from arrows is not evident in this distribution. The curve
does level off between 2.5 and 4.5g, which may correlate with the theoretical 3g
threshold between arrows and darts (Hughes 1998, Lyman et al. 2008). Subset variances
for the 0-3g (g1), 1.5-4.5g (g2), and 3-6g (g3) intervals are compared in order to test the
bimodality of this distribution. Subset data are presented in Table 4. Figure 11 graphs
subset variances. The subset variances of the 0-3g and 3-6g intervals are 21% lower than
the subset variance of the 1.5-4.5g interval. This shows that projectile point masses are
more tightly clustered around the g1 mean of 1.4g and the g3 mean of 4.1g than the
central g2 mean of 2.9g. Although a trough is not visible in the g3 interval, it does have a
higher variance, as is apparent in Figure 11. This pattern of subset variance is indicative
of weak bimodality correlated with the 3g threshold.
Table 4. Weight (g) Subset Data.
Subset, Range
g1
g2
g3
All
0-3
1.5-4.5
3-6
0.2-17.1
Number (n)
Mean ()
Variance (s2)
Crv
213
141
90
317
1.4
2.9
4.1
2.6
0.65
0.82
0.65
5.8
27
30
27
14
If 3g is accepted as the minimum weight of dart points, it would follow that
arrows dominate this sample. It is not unlikely that later contexts associated with arrow
tips are better represented in this aggregate sample. Hughes' (1998) argument that arrow
points can range up to 11g would place almost the entire sample in the possible arrow
category. Factors such as fletching or arrow shaft material (wood vs. reed) could affect
74
Figure 11. Weight (g) Subset Variance.
the optimal mass for arrow points (Hughes 1998). Although it is possible that these
factors could lead to different mass distributions for different types of arrows, it is more
likely that darts point mass measurements cause the leveling off of the curve observed
between 2.5 and 4.5g. The sample is large enough to assume that darts are present in
significant enough numbers to affect this curve. The sharp peak in mass around 1g is
strong evidence that the number of arrows is greater than that of darts. This may account
for the lack of a strong bimodal distribution. The presence of larger arrows and
transitional forms may further obscure the distribution of dart point mass (Lyman et al.
2008). The long tail to the right of the mass histogram shows that small numbers of dart
points have significantly higher masses. These larger specimens could be misclassified
knives or spear points, however (Hughes 1998). Overall, the distribution of projectile
point weight in this sample supports a 3g threshold between darts and arrows.
75
Proximal Shoulder Angle
Proximal shoulder angle (PSA) is the angle measured between a line
perpendicular to the longitudinal axis and the opposite margin of the hafting element (see
Figure 5). The performance constraints on PSA are unknown, but it can vary
independently of size. As such, it has a high potential for showing stylistic patterns.
Trends in intentional forms related to style are expected to produce a multimodal
distribution in PSA.
Figure 12 is a histogram of all measurable PSA values (n=526). This
excludes 104 unshouldered points. Figure 12 shows a clear peak in frequency around 75
with a secondary peak at 125. This clearly shows a separation between stemmed and
notched projectile points. A smaller peak at 95 may reflect a differentiation between
Figure 12. Proximal Shoulder Angle Histogram, n=526.
76
contracting-stem and straight-stem points. Another small peak at 145 shows some
differentiation of side-notched points from corner-notched points.
Eleven subsets with ranges of 30 were compared to asses the multimodality of
this sample. The subsets were aligned to encompass the peaks between 75 and 105, 105
and 135, and 135 and 165. Table 5 presents subset data. Figure 13 presents a graph of
subset variance. Figure 13 shows low variance for PSA3 (45-75), PSA5 (75-105), PSA7
(105-135), and PSA9 (135-165). PSA11 (165-195) variance is also low, but the sample
size is too small to compare to the other subsets. PSA3 shows the lowest subset variance.
This reflects a strong trend for contracting stem forms. Straight-stemmed (PSA5),
corner-notched (PSA7) and side-notched (PSA9) forms also show significant trends. The
high variances of the overlapping subsets between these forms lends support to the
trends. The highest variance is in PSA6 (90-120), which covers the transition between
stemmed and notched points.
Table 5. PSA Subset Data.
Subset, Range
Number (n)
Mean ()
Variance (s2)
Crv
PSA1 15-45
PSA2 30-60
PSA3 45-75
PSA4 60-90
PSA5 75-105
PSA6 90-120
PSA7 105-135
PSA8 120-150
PSA9 135-165
PSA10 150-180
PSA11 165-195
ALL 20-190
5
33
173
298
195
177
114
100
48
27
14
526
32.8
53.2
66.5
76.3
87.3
102.5
118.4
131.2
146.1
160.3
177.9
94.2
89.2
62.1
39.3
94.7
62.0
138.4
66.2
105.8
63.1
115.2
65.7
897.6
10.0
8.4
6.7
10.3
8.4
12.5
8.6
10.9
8.4
8.6
8.6
31.8
77
Figure 13. PSA Subset Variance.
These numbers fall within the range of commonly used categories and
typologies (Rosenthal 2002, Thomas 1981, Elston et al. 1977, Elston et al. 1994).
Rosenthal (2002) suggests a PSA of 110 as the cutoff between stemmed and cornernotched points, and a PSA of 140 as the boundary between corner-notched and sidenotched points. The distribution of PSA in the current sample supports the slightly
different values of 105 and 135 for these distinctions. Although some overlap is evident,
strong multimodality in a sample of this size is clear evidence for the validity of distinct
morphological types differentiated by PSA. It is significant that this multimodality
reflects the categories of stemmed, corner-notched, and side-notched projectile points
commonly used by archaeologists. This correlation is evidence that the observed pattern
of stylistic variation approximates categories that were recognizable to people during
prehistoric times. The emergence of this patterning in an aggregate sample which
78
probably spans thousands of years is evidence that ideas about hafting element style
persisted for very long periods of time.
Distal Shoulder Angle
Distal shoulder angle (DSA) is the angle measured between a line
perpendicular to the longitudinal axis and the edge of the opposite shoulder (see Figure 4)
(Thomas 1970). DSA plays a large role in shoulder morphology. Low DSA values are
measured on barbed points and some corner notched points, while high DSA values can
be found on side-notched points and stemmed points with up-turned shoulders. Like
PSA, DSA can vary independent of overall size. Barbs have been argued to be designed
for holding projectile points in the wound of a targeted animal, increasing the chance of a
kill (Hughes 1998). Also, shoulder configuration may affect aerodynamics due to drag
(Hughes 1998). These performance factors may influence DSA, but they do not
constrain it. The full range of DSA variation is possible on both dart and arrow points. It
is expected that formal trends in DSA will produce a multimodal pattern. The intended
forms reflected in a multimodal DSA could be associated with style, performance, or a
combination of both. DSA may be affected by resharpening, which could destroy the
originally intended shape. It is hoped that morphological trends in DSA are strong
enough to show through any dampening or bias caused by resharpening.
A histogram of DSA presented in Figure 14 shows a multimodal pattern. The
histogram shows a frequency peak at 155, a larger peak at 185, and a leveling off of the
curve between 190 and 230. The multimodality is fairly distinct in this histogram, which
suggests the presence of morphological trends in DSA. DSA multimodality is evaluated
by comparing the variance of nine subsets, each with a range of 30. Table 6 presents
79
Figure 14. Distal Shoulder Angle Histogram, n=539.
subset data. The subset variance graph presented in Figure 15 shows that DSA3 (135165) and DSA5 (165-195) have low variances distinguished by high variances on either
Table 6. DSA Subset Data.
Subset, Range
DSA1
DSA2
DSA3
DSA4
DSA5
DSA6
DSA7
DSA8
DSA9
ALL
105-135
120-150
135-165
150-180
165-195
180-210
195-225
210-240
225-255
110-250
Number (n)
Mean ()
Variance (s2)
Crv
32
109
144
182
178
208
173
126
42
539
126.2
139.6
149.5
166.5
180.2
193.5
210.0
220.3
232.3
182.1
52.5
76.2
73.9
122.5
63.0
113.5
82.6
63.7
50.0
935.0
24
29
29
37
26
35
30
27
24
22
80
Figure 15. DSA Subset Variance.
side. This trend is strongest in DSA5 (165-195). DSA7, DSA8, and DSA9, representing
larger DSA values, have low variance as well, but they are not differentiated by peaks of
high variance. The strongest trends cluster around the peaks at 150 and 185. These
values represent forms usually designated as barbs and straight shoulders, respectively.
Upward sloping shoulders with associated DSA values of 190 to 230 are also well
represented. Upward sloping shoulders show a broad trend, however, as is evidenced by
the lack of histogram troughs or areas of high variance around these values. This
distribution supports the argument that barbs and straight shoulders were intended forms
in prehistory. Upturned shoulders may also be associated with an intended form, but the
boundaries are less clear. Resharpening may have augmented the population of projectile
points with upturned shoulders, as barbs and straight shoulders were removed. Overall,
81
the DSA distribution supports the hypothesis of intended projectile point forms which
agree with archaeological definitions of barbed and straight shouldered point types.
Notch Opening Index
Notch opening index (NO) can be measured directly as the angle between the
lateral margin of the hafting element and the adjacent shoulder margin or it can be
estimated by subtracting PSA from DSA. NO was measured directly in the Tahoe
National Forest sample. The published data used in the current study includes both types
of measurement. NO is directly correlated to both DSA and PSA. It is expected that
morphological trends affecting both of these variables will be visible in the univariate
distribution of NO. Trends in NO may combine different forms, however. High values
of PSA associated with high values of DSA (notched forms with upturned shoulders)
may have the same NO value as low PSA associated with low DSA (stemmed barbed
forms). Multimodal trends in NO may be interpreted as clues towards more complex
bivariate patterning in PSA and DSA. Trends in NO may also result from notching
methods used in projectile point production, or possibly intended notch forms.
Figure 16 shows the frequency distribution of NO (n=519). The distribution
suggests weak multimodality. Minor peaks, or bumps in the curve, are apparent at 65, 85
to 105 and 140. Eleven subset variances are compared to asses these trends. Subset
ranges are set at 30 so that subset data will be comparable with PSA and DSA subsets.
Table 7 presents subset data. Figure 17 graphs subset variance. A relatively strong trend
towards clustering is visible around NO4 (45-75). This is associated with the peak at 65.
A weaker clustering trend is evident for NO6 (75-105). This area of low variance
incorporates the wide peak from 85 to 105. NO10 (135-165), the subset which includes
82
Figure 16. Notch Opening Index Histogram, n=519.
the peak at 140, shows very low variance. The clustering trend around this peak is less
evident, because it lacks a high variance boundary around larger NO values. This is due
to its position near the top of the distribution range. NO10 also has a smaller sample size.
Table 7. NO Subset Data.
Subset, Range
NO1
NO2
NO3
NO4
N05
NO6
NO7
NO8
NO9
NO10
NO11
ALL
0-30
15-45
30-60
45-75
60-90
75-105
90-120
105-135
120-150
135-165
150-180
9-200
Number (n)
Mean ()
Variance (s2)
Crv
24
49
109
139
187
171
171
129
102
56
16
519
22.9
33.3
49.7
61.4
74.6
90.6
104.1
118.0
133.3
143.8
155.9
89.5
54.6
76.3
95.4
68.3
111.1
84.8
92.2
90.2
83.2
49.7
43.0
1151.9
25
29
33
28
35
31
32
32
30
23
22
17
83
Figure 17. NO Subset Variance.
NO9 (120-150) also includes the peak at 140. Its variance is much higher than NO10, but
lower than NO6. The placement of the peak at 140 near the edge of the distribution and
the alignment of the subsets used to test variance may be obscuring trends toward
clustering. The trends evident in NO distribution support a hypothesis for a set of
intended forms associated with relatively narrow notches (NO near 65), notches of
approximately 90° (NO near 85-105), and possibly wide notches as well (NO near 140).
The multimodality of the histogram (Figure 16) is weak, however, and a high degree of
overlap is apparent. As such, the argument for intended forms associated with NO is not
very strong.
Neck Width
Neck width is measured as the maximum linear distance perpendicular to the
longitudinal axis between the distal edges of the hafting element. Neck width should
84
logically be a good discriminator between darts and arrows, especially in light of the
difference between dart and arrow shaft diameters from Thomas's hafted projectile
sample (Thomas 1978, Shott 1997a). This logic depends on the assumption that neck
width will not deviate substantially from shaft diameter. If this assumption is met, neck
width should strongly reflect the difference in shaft diameters associated with dart and
arrow technologies. Neck width also has the advantage of being mostly immune to
resharpening changes. Neck width thresholds have been used ranging from 8.5mm to
10mm (Rosenthal 2002, Hughes 1998, Shott 1997a). These neck width thresholds
misclassified more than half of the arrows in Shott's (1997a) hafted projectile point
sample. All of the 39 hafted darts in Shott's sample were correctly classified by the neck
width thresholds (Shott 1997a). All of the arrows in this hafted sample were side
notched, which may account for some of the error (Thomas 1978, Shott 1997a). Sidenotched neck widths are produced by different notching techniques than corner-notched
or stemmed points. This may produce a somewhat different pattern of neck widths in
side-notch points with respect to darts and arrows. Tested against Thomas (1978) and
Shott's (1997) sample of hafted points, a neck width of between 9mm and 10mm appears
to be a good minimum threshold for dart points, but not a solid maximum limit for
arrows. This is similar to Hughes‟ (1998) findings for the 3g weight threshold. A
bimodal distribution is expected if NW trends for darts and arrows are distinct enough to
show through this overlap. A normal distribution is expected if NW does not correlate
with the distinction between dart and arrow technology or other trends in intended form.
Figure 18 is a histogram of all available neck widths from the current sample.
This sample excludes triangular, leaf shape and lanceolate forms for which NW cannot be
85
Figure 18. Neck Width Histogram, n=256.
measured. NW was not published for projectile points from CA-NEV-407 (Clewlow et
al. 1984). The NW histogram shows a bimodal distribution with peaks around 10mm and
14mm. The strength of this bimodality is assessed by comparing the variance of six
subsets, each with a range of six. Subset data is presented in Table 8. Figure 19 presents
a graph of NW subset variance. NW3 (6mm-12mm) has a very low variance. The trend
Table 8. NW Subset Data.
Subset, Range
Number (n)
Mean ()
Variance (s2)
Crv
NW1
0-6
NW2
3-9
NW3
6-12
NW4
9-15
NW5
12-18
NW6
15-21
ALL 1.7-22.4
40
82
64
112
88
57
256
4.3
6.5
10.3
11.7
14.8
17.0
11.0
1.8
2.8
.79
3.4
2.28
3.14
20.7
22
28
15
30
25
30
22
86
Figure 19. NW Subset Variance.
towards clustering is supported by high subset variances for NW2 and NW4. A second
clustering trend is evident around NW5 (12mm-18mm), which is bounded by higher
variances for NW4 and NW6. NW5 variance is higher than NW3, which suggests a less
tight clustering of data around the subset mean of 14.8mm. The distribution of NW
supports an argument for a bimodal trend. The peaks of this distribution are around
10mm and between 14mm and 15mm. This distribution supports a hypothetical
threshold of 11mm or 12mm between darts and arrows. This is a higher than the
thresholds suggested by other researchers (Rosenthal 2002, Hughs 1998, Shott 1997a).
Basal Width
Basal width is measured as the maximum linear distance perpendicular to the
longitudinal axis placed at the proximal end of the hafting element. WB is used by
Thomas (1981) to distinguish certain dart and arrow types with similar forms. WB
87
cannot be used as a general dart and arrow threshold due to its high variability between
stemmed and notched points. It is expected that strong morphological trends will be
reflected in a multimodal distribution of WB. A bimodal distribution of WB is more
likely to reflect the difference between stemmed and notched points rather than darts and
arrows. A lack of morphological trends or significant overlap is expected to produce a
normal distribution.
Figure 20 shows the distribution of WB measurements. The histogram could
be viewed as either a flat normal curve skewed to the right or a weak bimodal
distribution. Four subsets with ranges of 9mm are used to investigate possible trends in
WB distribution. Figure 21 is a graph of WB subset variance. Subset data is presented in
Table 9. The graph shows a low variance for WB1 (0mm-9mm) and weak clustering
trend around WB3 (9mm-18mm). This is suggestive of bimodality, but the evidence is
Figure 20, Basal Width Histogram, n=552.
88
Figure 21. WB Subset Variance.
not very strong. Perhaps the correlation of WB with both size and form leads to a high
degree of overall variance, obscuring morphological trends.
Table 9. WB Subset Data.
Subset, Range
Number (n)
Mean ()
Variance (s2)
Crv
WB1
0-9
WB2 4.5-13.5
WB3
9-18
WB4 13.5-22.5
ALL
0-25.7
264
296
235
151
552
5.3
8.7
13.0
16.9
10.2
5.3
6.9
6.4
6.6
32.4
26
29
28
29
22
Length/Width Ratio
The Length Width Ratio (L/W) is calculated by dividing LT by WM. This
gives an impression of the overall shape or squatness of a projectile point independent of
size(Thomas 1981). Morphological trends would be expected to produce a multimodal
89
distribution in L/W. Figure 22 is a histogram of L/W values. The distribution
approximates a steep normal curve around 1.5. It appears that L/W does not correlate
with any strong morphological trends. Perhaps this is due to performance requirements
that are common to all projectile points (Hughes 1998).
Figure 22. Length/ Width Ratio Histogram, n=337.
Maximum Width Position
The maximum width position (MaxWPos) is calculated by dividing the length
from the proximal end to the position of maximum width (LM) by total length (LT)
(Thomas 1981). MaxWPos is an index correlated to the overall form of a projectile
point. As a ratio, it varies independently of size. MaxWPos is calculated as zero when
the widest part of a projectile point is at the base. Strong morphological trends are
expected to produce a multimodal distribution in MaxWPos. Figure 23 is a histogram of
90
Figure 23. Maximum Width Position Histogram, n=356.
MaxWPos frequency distribution. There is a distinct peak associated with values of 0.
Apart from this, the distribution approximates a normal curve around 25, skewed slightly
to the right. This distribution could be interpreted as bimodal, with peaks at 0 and 25. A
MaxWPos value of 0 may be found on notched, triangular, or heavily barbed points. The
higher values could represent a variety of forms. This complexity makes the bimodal
distribution difficult to interpret.
Base Width/ Maximum Width
Base width/maximum width ratio (WB/WM) is calculated by dividing WB by
WM (Thomas 1981). This ratio captures some of the morphological correlations of WB
by using WM to correct for size variation. WB/WM values of 1 result from projectile
points where the widest portion is at the proximal end. It is expected that strong
morphological trends will produce a multimodal distribution in WB/WM. Figure 24
91
presents a histogram of WB/WM values. A multimodal distribution is evident, with
peaks around 0.3, 0.6 and 1. The peak at 1 reflects the same trend as the MaxWPos 0
Figure 24. Base Width/ Maximum Width Histogram, n=531.
values. Three sample subsets are compared to asses the distinction between the 0.3 and
0.6 peaks in frequency. Subset data is presented in Table 10. Subset variance is graphed
in Figure 25. Figure 25 shows that these two peaks are separated by an area of higher
variance. The distinction is not very strong, however. Overall, WB/WM distribution
shows a multimodal distribution with a strong peak at 1 and weaker peaks at 0.3 and 0.6.
Table 10. WB/WM Subset Data.
Subset, Range
WB/WM1 0.15-0.45
WB/WM2
0.3-0.6
WB/WM3 0.45-0.75
ALL
0-1
Number (n)
Mean ()
Variance (s2)
Crv
214
203
165
531
0.31
0.44
0.60
0.56
0.007
0.008
0.007
0.076
27
30
27
28
92
Figure 25. WB/WM Subset Variance.
As with MaxWPos, a variety of forms are possible for each of these values. The situation
is too complex to address these trends with univariate data.
Basal Indentation Ratio
Basal indentation ratio (BIR) is calculated by dividing the linear distance
along the longitudinal axis (LA) by the total length (LT) (Thomas 1981). This ratio
provides an index of proximal margin morphology that is independent of size. BIR
tracks the difference between convex and concave bases. Convex and straight bases will
have a BIR of 1. Concave bases will have BIR values less than 1. It is expected that
trends toward intended basal forms will produce a bimodal distribution in BIR. It is
unlikely that multiple degrees of intended concave base forms would be present in
numbers large enough to produce a multimodal distribution.
93
The distribution of BIR values is skewed very heavily towards 1. Of 515
projectile points measured, 463, or 90% have a BIR of 1. The remaining 52 values are
presented in Figure 26. This distribution is heavily skewed towards 1. This amounts to a
sample dominated by strait or convex basal forms. It is probable that the majority of the
52 BIR values below one reflect normal variation within straight base forms.
Figure 26. Basal Indentation Ratio Histogram, n=52.
Crv Comparison
Figure 27 compares Crv values for all of the subsets defined above. Total
sample values are included for comparison with these subsets. Crv is a somewhat rough
comparison between samples and measurement types. Most of the values fell between
the narrow range of 15 to 40, suggesting a decent amount of comparability. PSA, DSA,
and NO subsets all have a range of 30, and should be directly comparable. It is expected
94
Figure 27. Crv Comparison.
that strong morphological trends should stand out as low Crv values. Crv values below
20 are relatively rare among the sample subsets. NW3, LT1 and LT3 all fall below this
95
mark. This lends support to the hypothesis that NW and LT show bimodal
morphological trends. Contrast between adjacent subsets is another indication of
morphological trends. Subsets of low variance bounded by high variance subsets should
reflect clustering of values. LT subsets show some contrast. This may reflect
bimodality. Weight (g) shows a lesser degree of contrast, indicating weak bimodality.
NW shows a great deal of contrast around NW3. This is evidence for strong bimodality.
WB and WB/WM are both rather flat in terms of Crv contrast. PSA, DSA,
and NO show a general scalloped pattern reflecting lower Crv values near the sample
extremities. This effect is probably due to subset sample size. PSA shows a repeating
pattern of contrasting highs and lows, reflecting a multimodal distribution. PSA3 is close
to 20, representing the lowest Crv value among the degree measured subsets. PSA3
represents the 45 to 75 range, which is measured on contracting stem points. DSA shows
the most contrast around DSA5. DSA5 represents the 165 to 195 range, centered on 180.
This reflects a strong trend for straight shouldered forms. DSA subset Crv values are
suggestive of a multimodal pattern, but the pattern is not as strong as the one seen in
PSA. NO does not deviate as much from the scalloped pattern. There is some contrast
around NO4, however. NO4 covers the 45 to 75 range, which encompasses the peak at
65. This is evidence for a moderately strong trend for narrow notch openings. A final
observation is the vary large Crv values for the complete samples of PSA and DSA. This
may be another indication of the trend for multimodality in these variables.
Summary
As the preceding discussion shows, univariate analysis can provide much
information about the structure of a data set (Baxter 1993). Several of the variables show
96
signs of morphological trends. It is argued that strong morphological trends represent
shared ideas about intended forms. The relative strength of trends and their relationship
to actual intended forms is a matter for interpretation, however. A brief summary of
observations made on these thirteen variables sheds some light on the nature of the
projectile point forms present in this sample.
LT shows a weak trend for bimodality around peaks at 25mm and 40mm.
This may reflect the separation of darts and arrows, although this interpretation is
speculative. WM shows a nearly normal distribution. The use of WM at 20mm as a
threshold between arrows and darts (Thomas 1978, Shott 1997b) is not supported by this
data. Thickness shows a steep, nearly normal distribution over a small range. No
patterning in thickness is evident. Weight (g) shows a sharp peak around 1g and a
leveling of the curve between 2.5g and 4.5g. Subset variance indicates a weak trend
towards bimodality. Although the trend is weak, it does correlate with the 3g threshold
between darts and arrows (Hughes 1998, Lyman et al. 2008). The 3g threshold is
supported by this data. PSA shows a strong multimodal distribution. The peaks correlate
with PSA values for contracting stem, straight stem, corner-notched and side-notched
projectile point forms. This is strong evidence that forms intended by the producers of
Sierran projectile points correlate with morphological types frequently recognized by
archaeologists. DSA shows a moderately strong multimodal pattern correlated with
barbed and straight shouldered projectile point forms. A trend towards forms with
upturned shoulders is also suggested, although not as strongly as barbs and straight
shoulders. This is good evidence for intended forms of barbs and straight shoulders. It
is unclear whether the trend towards upturned shoulders was intentional or created by the
97
destruction of other forms through resharpening. NO shows weak multimodality, but not
enough to argue for trends in intended notch form. NW shows a fairly strong bimodal
distribution. NW distribution does not support the use of dart and arrow thresholds
between 8.5mm and 10mm. If the bimodal pattern is assumed to represent darts and
arrows, this distribution suggests a dart and arrow threshold of 11mm or 12mm. WB
shows a weak bimodal distribution and L/W is nearly normal. No trends in intended
form can be inferred from these distributions. MaxWPos shows a strong peak at 0 and a
nearly normal distribution around 25. This reflects a tendency for maximum widths to be
at the base of points and an average maximum width placement about a quarter of the
way from base to tip for other points. WB/WM shows a sharp peak at 1 and a weak
bimodal distribution for other points. The peak of WB/WM values around 1 represents
the same trend for maximum width to be at the projectile point base. BIR distribution
shows that concave base points are nearly absent in the sample.
The graph of Crv values generally reflects the patterns seen in the individual
variable discussions. The multimodality of PSA and DSA, as well as the bimodality of
NW stand out among Crv distribution. Univariate morphological trends are interesting,
but it is likely that intended forms would be reflected in more than one variable.
Bivariate and multivariate analysis should shed light on this issue.
CHAPTER V
MULTIVARIATE ANALYSIS
Introduction
The univariate analysis presented in Chapter IV revealed significant trends
which may relate to emic, intended forms within specific attributes. Univariate analysis
cannot reveal the relationship between these attributes, however. Typological studies
usually make the assumption that total projectile point form was analogous to cultural
units of style. Multivariate analysis of continuous variables can reveal weather attributes
were combined as cultural units or whether they varied independently of one another.
The following section of this chapter uses a principle components analysis to
identify the variables which account for the most variance in the sample and reveal
potential multivariate patterning. The subsequent section investigates the distinction
between dart and arrow points. This is done by comparing commonly used threshold
variables through bivariate plots. Following the dart and arrow analysis, shoulder and
haft element shape will be investigated. This section will focus on the relationship
between the strong multimodal trends in PSA and DSA revealed during the univariate
analysis. Finally, the general morphological pattern of unshouldered points will be
investigated. An attempt will be made to differentiate between triangular, leaf-shaped,
and lanceolate projectile points.
98
99
Principle Components Analysis
Principle components analysis is a statistical method for investigating patterns
in multivariate data (Baxter 1993). Principle components analysis works by maximizing
variance in the total data set (Baxter 1993). Specific coefficients are assigned to each
variable to maximize the variance or spread of the whole sample (Baxter 1993). The
coefficient of a variable is applied to the value of that variable for each row of data. The
coefficients are assigned in such a way that the total of these values produces the most
possible variation in the sample (Baxter 1993). Principle components analysis produces
what is in effect a weighted multivariate average (Baxter 1993). The coefficients
assigned to each variable give an idea of the amount of variation accounted for by that
variable. The coefficients can be either positive or negative, with higher absolute values
assigned to variables with more variance (Baxter 1993). The first component represents
the maximum variance derived from the principle components method. The second
component represents the second-most possible variance which is uncorrelated with the
first component variance (Baxter 1993). The third and subsequent components are
defined in a similar fashion, maximizing the variance which is not correlated with
previous components (Baxter 1993).
Principle components analysis can be a powerful tool for interpreted
multivariate data (Baxter 1993). Often the values of one component are plotted against
another. This gives a two dimensional representation of what would be a
multidimensional shape if all variables were plotted independently (Baxter 1993).
Various methods have been attempted to display multivariate data visually, but these are
generally difficult to interpret for more than three variables (Baxter 1993). Two
100
component scatter plots can reveal multivariate patterns through two dimensional
clustering (Baxter 1993). The individual components must be examined to accurately
interpret the plot. The coefficient weightings reveal which variables are most prominent
in each component. Eigenvalues can be used to calculate the percentage of total variation
accounted for by each component (Baxter 1993). In this way, the variables most
accounted for by each axis of the scatter plot and the percentage of variation accounted
for by each component can be determined. These factors help to translate the patterning
seen in the scatter plot to real attributes and multivariate trends (Baxter 1993). It is a
matter of opinion as to how much variation needs to be included to give an accurate
picture of the data structure (Baxter 1993). Several standards have been proposed such as
requiring 70% to 80% of the variation be included, or including all components with
eigenvalues greater than 1 or 0.7 (Baxter 1993). A scree plot, which is a graph of
eigenvalue against component number, is another method of determining the number of
components necessary to provide an accurate representation (Baxter 1993). The scree
plot will form a curve, the apex, or elbow of which can be used to identify the number of
components which represent a significant portion of the data (Baxter 1993). If more than
two components are needed to accurately represent the sample, multiple scatter plots can
be used to examine the relationship between the components.
The variables used in the current analysis include angles, linear dimensions,
and weight. When different types of measurements or measurements with different
orders of magnitude are used in a principle components analysis, it is necessary to first
standardize the data (Baxter 1993). This is accomplished by subtracting the mean of a
101
variable from each value of that variable and dividing each value by the standard
deviation (Baxter 1993). The equation for this transformation is as follows:
(xi –) / s
(4)
where xi represents the individual values of a variable,  equals the sample mean, and s
equals sample standard deviation. Standardizing the data in this way creates values with
means equal to zero and unit standard deviation (Baxter 1993). Standardization makes
different types of data comparable and reduces size differences which can dominate a
principle components analysis (Baxter 1993). The actual mathematics involved in
determining the coefficient values of a principle components analysis are complex and
will not be presented here (Baxter 1993). Statistical software is generally used to
perform the analysis (Baxter 1993).
A principle components analysis of the standardized values of twelve of the
thirteen metric variables used in this study will be undertaken to asses the variation in this
sample. NW data was not presented in the published data from CA-NEV-407 (Clewlow
et al 1984). NW is excluded from the following PCA so that the sample size can be kept
relatively high. The 104 unshouldered specimens in the total sample will also be
excluded. Only complete projectile point specimens with values for all twelve variables
will be included in this PCA. 217 specimens meet these requirements. A scree plot will
be employed to determine how many components should be considered. The coefficient
weightings for each component will be examined to determine which variables are
driving the variation within the component. One or more scatter plots of the components
will be examined for multivariate patterning and discussed in light of the variables which
are most prominent in each component.
102
The component scatter plots can also serve as a test of the method of using
multivariate keys to type projectile points in the north central Sierra Nevada. If the
Thomas (1981) or Leventhal (Elston et al. 1977) keys represent distinct types, then
multivariate patterning in agreement with these types would be expected in the current
sample. Multivariate patterning in agreement with these projectile point keys (Thomas
1981, Elston et al. 1977) would provide strong support for the validity of the point types
they define. A lack of multivariate patterning would call into question the validity of
these commonly used projectile point types in the north central Sierra Nevada. A lack of
patterning would not preclude the existence of projectile point styles roughly analogous
to these point types, but it would indicate at least a high degree of overlap. This overlap
would produce a high level of error when the multivariate key method was used.
The question of the validity of multivariate keys is related to the larger issue
of whether projectile point attributes are linked through style. It is often assumed that
intended projectile point forms included the complete package of attributes recognized by
archaeologists. It is possible that the producers of intended forms were more concerned
with certain aspects of form than others. It is also possible that projectile point attributes
were shared as independent ideas. For example, a certain barb shape might be combined
with a variety of hafting methods. It is expected that attributes which were linked within
intended forms will produce multivariate patterning. A lack of multivariate patterning
could result from weak trends in intended forms, overlapping forms, or independent
variable trends.
The PCA presented here involves twelve variables. These include length
(LT), thickness (T), maximum width (WM), weight (g), proximal shoulder angle (PSA),
103
distal shoulder angle (DSA), notch opening index (NO), base width (WB), length/ width
ratio (L/W), base width/ maximum width ratio (WB/WM), maximum width position
(MaxWPos), and the basal indentation ratio (BIR). Table 11 presents eigenvalues and
percentage of variance by component. Figure 28 is a scree plot of eigenvalues by
Table 11. Principle Components, Eigenvalues, and % Variance.
PC
1
2
3
4
5
6
7
8
9
10
11
12
Eigenvalue
4.74974
2.55825
1.41778
1.30293
0.722636
0.49126
0.411979
0.190534
0.0962516
0.0265469
0.0222253
0.00986097
% Variance
39.581
21.319
11.815
10.858
6.022
4.0938
3.4332
1.5878
0.8021
0.22122
0.18521
0.082175
component. The scree plot shows a bend, or elbow between components 3 and 4. The
first three components represent 72.6% of the total variation. It is assumed that this
proportion of the variation will reveal a significant amount of the multivariate structure of
this data set.
Coefficient loadings give an indication of the amount of variance within the
component accounted for by each variable. Figure 29 shows the coefficient loadings for
component 1. This component represents 39.6% of the variance. The loadings are fairly
level, although seven variables stand out with larger loadings. WB has the largest
loading, followed by g, T, WB/WM, LT, DSA and WM. DSA, WB and WB/WM are.
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Figure 28. Component Scree Plot.
related to shoulder and hafting element shape. LT, WM, T, and g are all related to size.
The variation in component 1 can be interpreted as representing a combination of size,
shoulder shape and haft element shape. The coefficient loadings of these seven variables
are all negative. High values in component one, therefore, can be interpreted as a trend
towards small size, narrow bases and down turned barbs. These characteristics are
combined on small, barbed, contracting-stem points. Low component 1 values represent
trends towards large size, wide bases and upturned shoulders. These attributes could be
found on large stemmed or notched points.
Figure 30 presents coefficient loadings for the second component. This
component represents 21.3% of the variance. PSA receives the largest loading, followed
by NO, BIR, and WB/WM. These four variables are all related to haft element shape.
PSA and WB/WM have negative loadings, while NO and BIR loadings are positive.
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Figure 29. Component 1 Coefficient Loadings.
High values in component 2 can be interpreted as representing trends towards contracting
stems (low PSA), wide notches and narrow base widths relative to maximum width. The
Figure 30. Component 2 Coefficient Loadings.
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univariate analysis of BIR has shown that the variation in this variable is not significant.
The attributes mentioned above are all present on small, contracting-stem points. Low
values in component 2 represent trends towards notched points (high PSA), narrow notch
openings, and relatively wide bases. Component 2 should discriminate between notched
and stemmed points rather well.
Coefficient loadings for component 3 are presented in Figure 31. This
component represents 11.8% of the total variance. Three values have significantly larger
loadings than the others. L/W receives the largest loading, followed by MaxWPos and
LT. These three variables are all associated with overall shape. L/W and LT receive
negative loadings. The loading of MaxWPos is positive. As such, high values in
component three can be interpreted to represent trends towards short, squat points with
the widest portion away from the base. These attributes are compatible with squat,
Figure 31. Component 3 Coefficient Loadings.
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stemmed points, but they can be found on small, notched points as well. Low values in
component 3 represent trends toward long, slender points with the widest portion at the
base. Low MaxWPos can occur on notched points or stemmed points where barbs reach
close to the base. Component 3 should be a strong discriminator of overall shape.
Size, overall shape, and hafting element shape are fairly well segregated by
components 1 through 3. This is evidence for multivariate correlation for these aspects of
projectile point form. Component 1 is more difficult to interpret as it combines aspects of
size, shoulder shape and haft element shape. Scatter plots of components 1 and 2 (Figure
32), 1 and 3 (Figure 33), and 2 and 3 (Figure 34) are presented below. The distribution of
data points across two components can potentially reveal multivariate patterning (Baxter
1993). In Figure 32, component 1 is represented by the horizontal axis while component
Figure 32. Scatter Plot of Components 1 and 2.
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Figure 33. Scatter Plot of Components 1 and 3.
2 is shown on the vertical axis. The scatter is rather amorphous, although clustering is
apparent to the right of the component 1 axis. This clustering occurs close to the
component 2 axis. This represents a trend towards smaller than average points with
narrow bases correlated with average haft element shapes. The univariate patterning of
PSA shows that stemmed points outnumber notched points. Average haft element
shapecan be interpreted as representing stemmed haft elements. The high component 1
values of this cluster also indicate a trend toward down turned barbs. This cluster can be
interpreted as a relatively strong trend towards small, barbed, stemmed points. The
distribution left of the vertical axis represents points which show a combined trend
towards large size, wide bases, and upturned shoulders. This group is much more
variable in terms of haft element shape. A small group of four data points with unusually
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Figure 34. Scatter Plot of Components 2 and 3.
low component 2 values may represent side-notched points. In general, clustering is not
apparent in the distribution of large points.
Figure 33 presents a scatter plot of components 1 and 3. Component 1, as
discussed above, represents size, shoulder shape, and haft element shape. Component 3
represents overall shape. No clustering is apparent, although positive values of
component 1 are more concentrated than negative values. This may reflect the clustering
which is more evident in the previous scatter plot. No patterning is recognized in
component 3. Figure 34 presents a scatter plot of components 2 and 3. Apart from
several outliers, the data points of this scatter plot are clustered in a circular pattern
around the origin of the graph. This pattern reflects a distribution close to multivariate
normality (Baxter 1993). Positive component 2 values are more densely distributed than
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negative values. This reflects the more distinct cluster on the right side of the component
1-component 2 scatter plot. Negative component 2 values are associated with several
outliers on the component 2-component 3 scatter plot. The small cluster which may
represent side-notched points is visible near the left edge of the graph.
In general, these scatter plots show a strong trend towards small, barbed,
stemmed points and a dispersed scatter of other forms. Stemmed forms in general are
more densely distributed than notched forms. Small forms are more densely distributed
than large forms. This apparent pattern may be exaggerated by trends toward narrow
bases and down turned barbs included in component 1. Overall shape appears to fall into
a nearly multivariate normal distribution. The effect of resharpening may obscure any
trends in intended forms relating to overall shape. Another possibility is that variation
related to L/W, LT, and MaxWPos was not part of ideas related to intended projectile
point forms in the past. The following sections of this chapter will present a more
focused examination of the trends observed in the univariate analysis and PCA. These
analyses are designed to address the issues of dart and arrow point discrimination, trends
in haft element shape, and variation among unshouldered projectile points.
Darts vs. Arrows
The following analysis is directed towards distinguishing dart points from
arrow points. Attempts to discriminate between these two categories generally rely on
either typology and stratigraphic relationships or assumed and experimentally tested
performance constraints (Hughes 1998). Theoretically, performance constraints for
attributes associated with size should be different for dart and arrow technologies
(Hughes 1998). These two sets of constraints would be expected to produce bimodal
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distributions in size related attributes. The univariate analysis has shown that bimodal
distributions for LT, WM, T, and g are not strong. Only NW showed a significant
bimodal trend. LT and g did show weak trends toward bimodal distributions, although
their general distributions approximated skewed normal curves. The weak bimodal trend
in g is notable because it corresponds with a suggested threshold between darts and
arrows (Hughes 1998, Lyman et al. 2008).
Several metric thresholds have been suggested by archaeologists concerned
with discriminating between dart and arrow points. These include a mass of 3g ( Hughes
1998, Lyman et al. 2008), maximum width or shoulder width of 20mm (Shott 1997,
Thomas 1978), and neck width of 9.3mm (Rosenthal 2002). Thomas (1978) used
shoulder width in his discriminate analysis of hafted dart and arrow specimens. Shoulder
width is not included in the data used in the present analysis, but maximum width is used
as a close approximation. Maximum width (WM) and shoulder width are often the same.
In cases where they are not, maximum width (WM) usually represents a basal width
slightly wider than the shoulder. Hughes (1998) argued that cross-sectional area and
cross-sectional perimeter discriminate strongly between darts and arrows. Crosssectional area and perimeter are complex measurements which are rare in published data.
They are absent from the published data used in this study. Cross-sectional trends may
be evident in a comparison of maximum width and thickness, however.
Thomas (1978), Shott (1997), and Hughes (1998) present rigorous analysis of
the problem of dart and arrow point differentiation tested against solid archaeological and
ethnographic data. Their findings do not contradict one another. An analysis of the
sample presented in this study with respect to these findings should be useful for
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distinguishing between darts and arrows. Bivariate plots will be used to compare these
metric thresholds and evaluate their effectiveness as dart and arrow discriminators. If the
thresholds discussed above are valid, then the majority of individual points should be
correctly classified by more than one type of measurement. If a large proportion of the
projectile points are classified as darts by one threshold and arrows by another, the
validity of these methods of discriminating projectile technology is called into question.
Of course, a situation of conflicting designations by these thresholds does not preclude
the validity of any single method. Bivariate plots of g against WM, g against NW, and
NW against WM will be used to test the validity of these thresholds. WM will also be
plotted against T in order to evaluate cross-sectional trends. Depending on the
distribution of these plots, a least squares line or logistic curve is fitted to the data. This
procedure will give an indication of the spread of the data around an approximately
average line or curve. The least squares line, takes into consideration the squared
distance of each point from the line and places the line where this distance is minimized.
It is defined by the following formula (Hammer et al. 2008):
y = ax + b
(5)
The logistic curve minimizes squared distance in a similar way, but a curve is used
instead of a line. The logistic curve is defined by the formula (Hammer et al. 2008):
y = a/(1 + b * e-cx)
(6)
Shott (1997a) reported a success rate of about 85% for discriminating the hafted dart and
arrow specimens with quantitative methods, and considered this a reasonably good result.
It is expected that some overlap between dart and arrow point metric attributes is present
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in the sample, but it is hoped that a similar degree of success can be demonstrated
through analysis of the threshold variables.
Figure 35 presents a plot of NW against WM. Lines are drawn through the
theoretical threshold values of 9.3mm NW and 20mm WM. NW and WM are linearly
correlated, with a moderate degree of clustering around the least squares line. This aspect
of the distribution supports the correlation of NW and WM thresholds between darts and
arrows. Few projectile points have a NW below 9.3mm and WM above 20mm. A large
proportion of the sample, however, has NW values above 9.3mm and WM values below
20mm. This is evidence against the validity of these thresholds. Even the NW threshold
of 12mm identified by the bimodal univariate distribution classifies a large number of
points differently than the WM threshold of 20mm. The general distribution of WM and
Figure 35. Plot of NW and WM with Linear Fit, n=208.
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NW does not support the 9.3mm NW and 20mm WM thresholds. The univariate analysis
of NW and WM revealed a high degree of overlap between small and large forms. A
good threshold discriminator should be located along the least squares line, representing
an average value between groups of darts and arrows, surrounded by forms which overlap
the two categories. The index line at 20mm WM intersects the least squares line near
12mm NW. There is no patterning in the distribution that would identify this a good
point to locate a dart and arrow threshold, however. Patterning in WM is not expected,
considering its nearly normal univariate distribution. NW is bimodally distributed, but
this pattern was not strong enough to show in the plot of NW against WM.
Figure 36 plots WM against g. A logistic curve is fitted to the data. The data
are clustered tightly around this curve. Index lines are drawn at the theoretical thresholds
Figure 36. Plot of WM and g with Logistic Fit, n=315
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of 20mm WM and 3g. The clustering of weight values below 3g is evident. Data points
above 3g are much less clustered around the curve. This indicates that heavier points are
less correlated with WM than light ones. Few points are valued below 3g with WM
values above 20mm. Many points have WM values below 20mm and weights above 3g.
The intersection of the index lines is close to the fitted curve. This pattern mimics the
distribution seen for NW and WM. The distribution does show a differentiation above
and below 3g. This supports the use of 3g as a dart and arrow threshold. The intersection
of 20mm WM with 3g near the fitted curve, and 12mm NW near the fitted line, lends
support to the use of 20mm WM as a dart and arrow threshold. It is clear from these
distributions that a large amount of error is to be expected with these or any thresholds
for NW, WM, or g.
Figure 37 is a plot of NW against g with a least squares line. These variables
are linearly correlated, but they are not tightly clustered around the line. A cluster of data
points is identifiable below 12mm NW and 3g weight. If 12mm NW is used as a
threshold rather than 9.3mm, few points would be classified differently by the 3g
threshold. 9.3mm NW intersects 3g closer to the fitted line than 12mm NW does. The
lack of clustering in this distribution makes the least squares line a weak estimator of
average form, however. The visible cluster below 3g and 12mm NW lends support to the
use of these values as dart and arrow thresholds.
Figure 38 is a plot of WM against T with a least squares line. The variables
are positively correlated and clustered, but the distribution varies widely from the least
squares line. This plot was included to investigate possible patterning associated with
cross-sectional area or perimeter. Other than the positive correlation, no patterning is
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Figure 37. Plot of NW and g with Linear Fit, n=89.
evident in this distribution. This probably reflects the nearly normal univariate
distributions of both T and WM. Better data may be needed to reveal patterning in crosssectional attributes.
The bivariate distribution of threshold variables against each other generally
supports their validity, but reveals a high degree of expected error. Twelve millimeter
NW appears to be a better threshold than 9.3mm. This is supported by the bimodal
univariate distribution of NW. The 3g threshold is supported both by clustering in
bivariate plots and the univariate distribution of weight. Threshold values tend to
intersect near the fitted curve and lines. This supports the hypothesis that thresholds
represent average values along continuums of variation. As such, a high degree of error
is expected. Perhaps further study could reduce this error to the 85% success rate
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Figure 38. Plot of WM and T with Linear Fit, n=545.
reported by Shott (1997a). The 20mm threshold of WM is supported only by its
relationship to the other thresholds and the fitted curve and lines. Its univariate
distribution is nearly normal, which indicates a high degree of overlap between darts and
arrows.
Shoulder and Haft Element Shape
Haft element shape is the attribute which has received the most attention by
archaeologists trying to categorize projectile points. It can vary independent of size, with
a wide variety of haft element forms compatible with both dart and arrow technology.
These forms are directly related to different methods of hafting. It can be reasonably
assumed that trends in haft element shape and hafting method relate more to style and
tradition than performance requirements. The lack of physical constraints for haft
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element shape and its direct link to hafting methods make it particularly sensitive for
tracking trends in culturally transmitted ideas.
It is clear from the previous analyses that the main sample of the current study
shows a strong trend towards small, stemmed points. The univariate distribution of PSA
shows trends toward corner-notched and side-notched points as well. The histogram of
DSA also shows a trend towards multimodality. Principle components analysis shows
that both of these variables account for a large proportion of the variance in the total
sample. DSA contributed significantly to component 1, while PSA was the strongest
variable in component 2. The trend towards small stemmed points was evident in the
scatter plot of these two components. It is hoped that examination of the bivariate
distribution of DSA and PSA will be informative of the relationship of stemmed and
notched points with shoulder shape.
The relationship of DSA to PSA is complex. A DSA of 155 or less was
probably measured on a barbed point, but the distinction between a corner-notched or a
stemmed haft element depends on PSA. Similarly, a DSA of about 180 reflects a straight
shoulder, but the point could be stemmed, corner-notched or side-notched depending on
PSA. It is expected that strong trends in intended haft element and shoulder forms will
produce clustering on a PSA-DSA bivariate plot.
A plot of DSA values against PSA (Figure 39) shows that their bivariate
patterning is not very clear. Univariate analysis revealed a strong differentiation between
stemmed and notched haft element forms at 105 PSA. Distinctions between shoulder
forms are less clear, but moderate trends were apparent for barbed forms (clustered
around 150 DSA), straight shoulders (clustered around 180 DSA), and upturned
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Figure 39. Plot of DSA and PSA, n=517.
shoulders (a range of DSA values above 200). Index lines at 105 PSA and 180 DSA are
used to compare these trends. They divide the scatter into arbitrary form categories. The
lower left quadrant (<105 PSA, <180 DSA) represents stemmed, barbed points. The
lower right quadrant (>105PSA, <180 DSA) represents notched points with barbs. The
upper left quadrant (<105 PSA, >180 DSA) includes stemmed points with upturned
shoulders. The upper right quadrant (>105 PSA, >180 DSA) includes notched points
with upturned shoulders. The trend towards straight shoulders should be represented by
data points near the 180 DSA index line.
The trend towards stemmed haft element forms is evident in the dense
distribution of points below 105 PSA relative to those with greater PSA values. A very
similar distribution is seen for stemmed points above and below the 180 DSA index.
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DSA variation appears to be quite continuous on stemmed forms. The distribution of
notched forms is more dispersed. It is clear that notched forms with straight or upturned
shoulders are much more variable in PSA than notched, barbed forms. This is probably
due to the physical relationship between PSA and DSA. DSA must be larger than PSA.
High PSA values paired with low DSA values necessitate a narrow notch opening which
can be difficult to achieve on hard materials such as basalt. The small amount of barbed
points above 145 PSA does differentiate side-notched forms from corner-notched forms,
which vary continuously between barbed, straight shouldered, and upturned shoulder
forms. This differentiation is not accompanied by distribution clustering, as would be
expected with distinct haft element styles. The univariate trend in DSA towards straight
shouldered forms is also not apparent in this plot. Apart from the trend towards stemmed
haft element forms, no clustering is evident in the PSA-DSA bivariate distribution.
It is possible that an attempt to differentiate dart points from arrow points will
reveal more distinct bivariate patterning within DSA and PSA, as darts and arrows may
show separate trends towards shoulder and haft element shape. The darts vs. arrows
analysis as well as univariate patterning support the use of a weight of 3g and 12mm NW
as threshold discriminators between these two technologies. NW has a reduced sample
size due to its absence in published data from CA-NEV-407 (Clewlow et al. 1984). For
this reason, 3g will be used as a dart and arrow threshold. The 3g threshold has been
corroborated by other studies (Hughes 1998, Lyman et al. 2008) and it is comparible with
much published data. Figure 40 presents a plot of DSA against PSA which separates
points assumed to be arrows (=3g) from those assumed to be darts (>3g). When divided
by a threshold of 3g, a theoretical boundary between arrows and darts, some patterning is
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Figure 40. Plot of DSA and PSA Divided by 3g Threshold. ● = ≤ 3g, ∆ > 3g, n=246.
evident. In general, most points weighing more than 3g are distributed on or above the
180 DSA index line. This is an indication that barbed dart points are rare. If points
weighing less than or equal to 3g are considered alone, the cluster of stemmed points
appears to be more concentrated on or below the 180 DSA index line. This is suggestive
of a trend towards small, stemmed, barbed projectile points. In keeping with the general
trend for straight or upturned shoulders, corner-notched darts (PSA 105-135) are
distributed near or above the 180 DSA index line. This shows a lack of large, notched
forms with barbs. The lighter corner-notched points (≤ 3g) show some separation
between straight shouldered to barbed forms (DSA =180) and upturned shoulder forms
(DSA >200). The small number of corner-notched points in this sample makes it difficult
to evaluate the significance of this patterning. Finally, it can be seen in Figure 40 that all
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but two of the points above 145 PSA weigh less than or equal to 3g. This is an indication
that side-notched dart points are very rare in this sample.
Although a high degree of overlap is evident, certain patterns can be seen in
the bivariate distribution of DSA and PSA. In general, the trend towards stemmed points
is supported. Stemmed and corner-notched forms show continuous undifferentiated
variation between barbed, straight and upturned shoulder forms. Side-notched forms lack
barbs, but this is to be expected considering the physical correlation between high PSA
and DSA. Discrimination between darts and arrows reveals further patterning. While
evaluating these trends, it is important to consider the high degree of error that likely is
associated with the threshold method of distinguishing darts and arrows. When divided
between darts and arrows, the distribution reveals a trend towards small, stemmed,
barbed points. The trend towards this form is also suggested by the PCA. Cornernotched arrows show a weak trend towards a separation between barbed to straight
shouldered forms and upturned shoulder forms. Dart points tend to be stemmed or
corner-notched with straight to upturned shoulders. Barbed darts and side-notched darts
are rare.
The trend towards small stemmed points is quite strong. I argue that this is
evidence for a standardized, intended form. If it is assumed that haft element form and
hafting method are not heavily constrained by performance requirements, then it is likely
that this trend relates directly to culturally transmitted ideas associated with style.
Judging by bivariate distribution alone, the other trends discussed above are not strong
enough to be confidently associated with intentional forms. Combined with univariate
distribution, however, there is significant support for a distinction between stemmed,
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corner-notched, and side-notched points that was recognized by the producers of these
artifacts. The prominence of these trends in a large sample from a wide geographical
area is strong evidence for the cultural transmission of these ideas across time and space.
Multimodal patterning is also evident in the univariate distribution of DSA. The lack of
correlation between shoulder forms and haft element forms raises questions about the
actual ideas being transmitted. It is possible that resharpening and overlap obscure these
trends in a bivariate distribution. Another possibility, though, is that ideas about shoulder
form were transmitted independently from ideas of haft element form. This is an entirely
different cultural transmission context then the one assumed by studies which rely on
projectile point typology.
Unshouldered Points
The following section addresses morphological variation in unshouldered
points. Thomas (1981) defines unshouldered points as those points for which DSA, PSA
and NO cannot be measured. One hundred-eleven such points are present in the total
sample used in this study. In general, unshouldered points vary between triangular,
lanceolate and leaf-shaped forms. L/W, MaxWpos, and WB/WM are the variables which
best describe this type of variation. L/W ratio can distinguish between squat, triangular
forms which would tend to have low values and lanceolate forms which would have high
values. MaxWpos and WB/WM should discriminate leaf-shaped forms, which have
narrow bases and hence low MaxWPos and WB/WM values from triangular points,
which should have MaxWPos and WB/WM values close to zero. Lanceolate points
could have a large range of MaxWPos values, but should have higher WB/WM
measurements than leaf-shaped points.
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Figure 41 shows a bivariate plot of MaxWPos and WB/WM. Points are
categorized as darts or arrows according to the 3g threshold. A moderate cluster of dart
points is present between the MaxWPos values of 30 and 50 and WB/WM values of 0.5
to 0.7. These ranges are not strongly indicative of either leaf-shaped or lanceolate forms.
Points where the base width is less than 50% of the maximum width (<0.5 WB/WM) are
confined between MaxWPos values of 25 and 45. This may reflect a weak trend toward
leaf-shaped points. Twenty points in the unshoulderd sample have MaxWPos values of
zero and WB/WM values of 1. This is strong evidence of a distinct group among
unshouldered points. This group is not apparent on the plot because it occupies a single
point. MaxWPos values of zero are directly correlated with WB/WM values of 1. Both
values can represent either triangular or lanceolate points. Apart from this trend,
distribution of MaxWPos against WB/WM is generally dispersed.
Figure 41. Plot of MaxWPos and WB/WM Divided by the 3g Threshold. ● = ≤ 3g, ∆
= > 3g, n=65.
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Figure 42 is a bivariate plot of MaxWPos and L/W. L/W discriminates
between relatively squat triangular forms (low L/W) and long, narrow, lanceolate forms
(high L/W). The group of zero MaxWPos values is clustered between 1 and 2 L/W. This
reflects a strong trend towards un-notched, triangular points. The majority of these
triangular points weigh less than 3g. Apart from this group, MaxWPos values above 30
are more common. Most of these points have L/W values between 1.5 and 3 and weights
above 3g. This may represent a variable group of leaf shape points. In general, the
unshouldered sample reflects a split between small triangular arrow points and larger leaf
shaped dart points. The bivariate plot of MaxWPos and L/W presents strong evidence for
two separate intended forms of unshouldered projectile points.
Figure 42. Plot of MaxWPos and L/W Divided by the 3g Threshold. ● = ≤ 3g, ∆ = >
3g, n=65.
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Summary
Strong multivariate patterning is not present in the current sample. Still,
certain morphological trends are apparent. The PCA separated size related variables, haft
element shape related variables and overall shape related variables between components
1, 2, and 3. Component scatter plots revealed a moderately strong trend towards small,
barbed, stemmed points. Variables related to overall shape showed a distribution close to
multivariate normality, indicating a lack of morphological trends. The dart and arrow
analysis did not have conclusive results. Univariate analysis supports a 3g threshold
between darts and arrows, however. Shoulder and haft element shape were both shown
to be strongly patterned in the univariate analysis. They are not correlated with each
other, however. Splitting the sample along the 3g threshold shows that barbed dart points
are rare. The trend toward small, barbed, stemmed points is also evident. Finally,
unshouldered points show a significant split between small triangular and large leafshaped forms.
CHAPTER VI
CHRONOLOGY AND SPATIAL PATTERNS
Chronology and spatial patterning in the archaeological record are crucial to
an understanding of social relationships in the past. Chronology building in the Sierras
has been hampered by a lack of buried contexts and poor preservation of organics. Most
chronological assumptions have been tied to a problematic projectile point typology
based largely on inference from the Great Basin (Elston et al. 1977, Elston et al 1994).
The following analysis is an attempt to address this problem by comparing morphological
data from geographically and chronologically associated samples. This analysis focuses
on proximal shoulder angle (PSA) and weight (g). The geographic samples are defined
by watersheds and the Sierra Crest. Chronologically controlled samples are derived from
C14 dates and obsidian hydration. Geographic samples are compared using continuous
data. Comparisons of the much smaller chronologically controlled samples are facilitated
by the use of discrete categories based on the overall pattern of continuous data.
Geographic Samples
An examination of projectile point forms within discrete geographical
contexts is presented in this chapter with the goal of identifying spatial patterning of
projectile point morphology in the North Central Sierra Nevada. The total projectile
point sample is divided into eastern and western segments, partly by the Sierra Crest.
127
128
Northern and southern segments are divided by watershed. PSA histograms are used to
compare trends in haft element shape within these samples. The geographic samples do
not have chronological associations. The mixture of projectile points from different time
periods is likely to obscure some time specific patterns, but it is hoped that certain trends
are strong enough to overcome this error. Strong contrasting patterns from separate
geographic areas are evidence of differences in the context of cultural transmission. This
also provides insight into social relations across geographic regions. Future research
directed at identifying these trends in contexts with chronological associations may reveal
the temporal dynamics of these relationships.
In the following section, continuous data will be used to compare geographic
samples. This provides a complete representation of shape variation within a given
variable without imposing an arbitrary framework of point type thresholds. Proximal
shoulder angle (PSA) values display the most distinct univariate patterning within the
total assemblage (Figure 12). PSA has a strong multimodal distribution which correlates
well with commonly used archaeological designations of stemmed, corner-notched, and
side-notched points. Principle components analysis of the 13 variables used in this study
shows PSA as a strong contributor to variation in the second component. PSA, therefore,
accounts for a significant amount of overall sample variation. For these reasons, PSA
histograms are used to investigate trends in projectile point style from discrete
geographical regions.
North-South Comparison
The northern and southern geographic sample areas are defined by watershed
(see Figure 3). The northern sample area includes sites located in the Yuba, Bear,
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Truckee, and Middle Feather watersheds. This group includes all of the Tahoe National
Forest sites, CA-NEV-407 and CA-NEV-199. The southern sample area includes the
American River watershed, represented here by CA-ELD-145. PSA histograms for the
Northern and Southern samples are presented in Figures 43 and 44, respectively.
Unshouldered points are included as PSA values of zero.
Both groups show distinct groups of unshouldered points on the left side of
the histograms. The pattern for points with measurable PSA is different for the Northern
and Southern samples. The Northern sample contains a significantly higher frequency of
PSA values below 105, representing stemmed points. Corner notched points are
represented by a distinct, but smaller group of PSA values between 105 and 135. PSA
Values above 135, representing side-notched points, are present in still smaller
Figure 43. PSA Histogram for the Northern Sample, n=482.
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Figure 44. PSA Histogram for the Southern Sample, n=166.
frequencies. The Southern sample shows higher frequencies of corner-notched and side
notched points relative to stemmed points. PSA values below 105 are the most frequent
in the Southern sample, but the frequencies of values between 105 and 135, as well as
those above 145 are proportionately higher. The different patterns in the relative
frequency of stemmed and corner-notched points within the Northern and Southern
samples is evidence for differences in the context of cultural transmission of projectile
point forms between these two geographic areas. The strong trend in stemmed points
evident in the northern pattern could be interpreted as a relatively homogenous context of
projectile point form transmission dominated by a single style. Other styles are present,
but in smaller numbers. The parallel trends in stemmed and corner-notched points
evident in the southern pattern may result from a more complex transmission context
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with separate or competing styles produced in close proximity. A complex transmission
context may be associated with identity-forming or communication functions of projectile
point style.
East-West Comparison
The Eastern and Western geographic sample areas are separated partly by the
Sierra Crest. Sites located within the Yuba, Bear and American River watersheds are
included in the Western sample. Sites located within the Truckee River and Middle
Feather River watersheds are included in the Eastern sample. The Middle Feather River
watershed is not technically east of the Sierra Crest as it flows west and is considered part
of the transition zone between the Sierras and Cascades. The sites within this watershed
which contribute to the current analysis are located in the Sierraville vicinity, well east of
the western Sierra foothills, and so are included in the Eastern sample. These sample
areas overlap the sample areas of the Northern and Southern geographic samples.
PSA histograms for the Eastern and Western geographic samples are
presented in Figures 45 and 46, respectively. Neither histogram reveals the high relative
frequencies between 105 and 135 evident in the Southern sample histogram (Figure 44).
This indicates a less prominent trend in corner notched points. Within the western
sample, the group of points with PSA values between 105 and 135 is more distinct from
the group of stemmed points (PSA values less than 105) than is evident in the Eastern
sample. This is due in part to the inclusion of CA-ELD-145 (the sole site in the Southern
sample) within the Western geographic sample. This pattern also reflects a separate trend
evident in the frequencies of PSA values below 105. Both samples show significantly
higher frequencies of stemmed points with PSA values below 105. The highest
132
Figure 45. PSA Histogram for the Eastern Sample, n=161.
frequency PSA value for the western sample is between 70 and 80, while the Eastern
sample's most frequent PSA values are between 90 and 100. This is evidence for
separate trends of contracting stemmed points in the west and straight or expanding
stemmed points in the East. The prominence of expanding stemmed points in the east
obscures the distinction between stemmed and corner notched points. The trend in
contracting stems in the Western sample fits well with the typological definition of
Gunther points, commonly viewed as a California phenomenon. Thomas's (1981)
definition of Rose Spring points includes PSA values as low as 105. This is somewhat
congruous with the trend in expanding stems shown in the Eastern sample. It is tempting
to view this pattern as evidence of Great Basin cultural influence on the Eastern Sierra
and influence from west along the Western Foothills. Comparative data from the regions
133
Figure 46. PSA Histogram for the Western Sample, n=510.
east and west of the north central Sierra Nevada is necessary to adequately address this
issue. This preliminary analysis, however, shows how the continuous data method can be
used to address questions typologists have been working on for years, with comparable
results. In terms of cultural transmission, the Eastern and Western samples may reflect a
similar context to the Northern sample discussed above, in which a single projectile point
form is dominant. The difference between contracting stems in the west and straight or
expanding stems in the east may reflect a difference in cultural transmission context,
perhaps related to identity-forming functions of style.
Chronological Analysis
Good descriptions of discrete components with chronological associations are
exceedingly rare in Sierra Nevada archaeology. The lack of organic preservation due to
134
acidic soils and the dearth of buried contexts in steep mountainous terrain are mostly to
blame for this. A small amount of chronological data from this region was available for
the current analysis. This includes published data as well as unpublished obsidian
hydration readings from Tahoe National Forest. Clewlow et al. (1984) recorded a series
of C14 dates in a western foothill context at CA-NEV-407. Shape variation in projectile
points from CA-NEV-407 will be discussed in relation to these C14 dates. A sample of
obsidian hydration readings from the Tahoe National Forest and a group of 36 obsidian
projectile points with direct obsidian hydration readings will also be analyzed.
Obsidian Hydration offers an alternative method of dating archaeological
contexts, at least in relative terms. This method is limited in the Sierras by the scarcity of
obsidian artifacts, a general lack of stratigraphy, and variable hydration rates (Jackson
and Ballard 1999). Still, obsidian hydration is a valuable source of chronological data in
a region where very few discrete contexts have been associated with C14 dates. Obsidian
hydration dating is based on the process through which water penetrates an exposed
obsidian surface and is absorbed. This process creates an area of discoloration, or a
hydration rind which can be measured with a microscope. It is assumed that hydration
occurs at a relatively stable rate. Therefore, the width of the hydration rind should be
directly correlated to the hydration rate and the elapsed time since the surface was
exposed (ie flake removal). Hydration rind widths, measured in microns (), can be used
as a relative index of artifact age. Comparisons of hydration rind widths are complicated
by many factors. Hydration rates vary by obsidian source (Jackson and Ballard 1999).
Elevation and effective temperature have also been argued to affect hydration rates
(Rosenthal 2002). The most meaningful comparisons are between obsidians of the same
135
source from similar archaeological contexts. Bloomer (1993) has suggested general
calendar year associations for hydration rind measurements. These do not take into
account hydration rate variation, however. Jackson and Ballard (1999) suggested a
hydration rate curve for Bodie Hills Obsidian which could be used to calculate calendar
dates. This curve, however, is anchored on the same Sierran projectile point typology
proposed by Elston (Elston et al. 1977, Elston et al 1994, Jackson and Ballard 1999).
Due to the uncertainty of calendar date calculations from hydration data, the current
analysis will consider obsidian hydration readings as relative dating indexes only.
Chronologically controlled contexts are associated with much smaller sample
segments. Interpretation of small samples such as these is facilitated by combining the
data into categories. This may seem contradictory to the continuous data method used
elsewhere in this thesis. Importantly, boundaries between the categories used are defined
from patterning in the continuous data. It is recognized that some bias is introduced by
defining boundaries between overlapping trends in projectile point form, but this bias
would be much greater if arbitrary boundaries were used without considering the overall
continuous pattern. It is hoped that patterning between distinct temporal contexts will be
strong enough to overcome any error introduced by assuming these formal categories.
For the chronological analysis, points will be categorized by haft element
shape on the basis of PSA values. Points with PSA values below 105 will be classified as
stemmed, points with PSA values of 105-134 will be classified as corner-notched, and
those with PSA values equal to or above 135 will be classified as side-notched. Due to
small sample size, points without measurable PSA values are added as a single
unshouldered category. Definitions of these categories are presented in Table 12.
136
Table 12. Haft Element Shape Category Definitions.
Category
Definition
Unshouldered (US)
PSA cannot be measured
Stemmed (ST)
PSA <105
Corner-Notched (CN)
PSA 105-134
Side-Notched (SN)
PSA 135
The introduction of the bow and arrow is widely viewed by archaeologists as
a broad-scale technological change with chronological implications (Eerkins and
Bettinger 1999, Shott 1997a, Hughes 1998). For the chronological analysis, projectile
points will be divided into hypothetical dart and arrow categories to facilitate
comparison. Width, neck width, and weight thresholds have been suggested as
discriminators between darts and arrows (Hughes 1998, Lyman et al. 2008, Rosenthal
2002, Shott 1997a). For the total sample, weight (g) shows a weak bimodal distribution
(Figure 10). However, the divide apparent in this distribution correlates with the
commonly used threshold of 3g (Hughes 1998, Lyman et al. 2008). Neck width (NW)
has the strongest bimodal distribution (Figure 18), but the apparent divide of 12mm is
significantly higher than NW thresholds used by other researchers (Rosenthal 2002, Shott
1997a). NW measurements are also absent from the published data from CA-NEV-407
used in the current study (Clewlow et al. 1984). Maximum width (WM) has a nearly
normal distribution in this sample (Figure 8), lacking any trend toward bimodality. For
these reasons, weight was chosen over NW and WM as dart and arrow discriminator.
Complete projectile points weighing 3g or less will be categorized as arrows.
Complete projectile points weighing more than 3g and projectile point fragments
137
weighing at least 3g will be categorized as darts. Projectile point fragments weighing
less than 3g will be categorized as unknown. Definitions of these categories are
presented in Table 13. It is expected that projectile points categorized as arrows will be
associated more frequently with later C14 dates and small hydration rind values, and that
those categorized as darts will be associated more frequently with earlier dates and larger
values.
Table 13. Dart, Arrow and Unknown Category Definitions.
Category
Arrow
Dart
Unknown
Definition
Complete  3g
Complete >3g; Fragment 3g
Fragment <3g
Tahoe National Forest ObsidianHydration
Sample
The following section will investigate broad scale chronological patterns in
projectile point form using a sample of 64 obsidian hydration readings from the Tahoe
National Forest. This sample will be referred to as the Tahoe National Forest Obsidian
Hydration sample (TNF OH). The sites and OH rind measurements are listed in Table
14. The sample includes readings from 15 sites. Twenty-five direct readings from
obsidian projectile points from the Tahoe National Forest, along with 11 direct projectile
point obsidian hydration readings from CA-ELD-145 (Jackson and Ballard 1999) will be
combined with the 126 projectile points associated with these readings for the following
analysis.
Nearly all of the associated projectile points were collected from surface or
shallow contexts without well defined stratigraphy. For the purpose of this analysis, sites
138
associated with OH readings will be treated as single, mixed components. The hydration
readings are averaged for a rough estimate of the relative ages of these sites. Associated
points will be categorized on the basis of site averages of OH measurements. Average
OH micron values are presented in Table 14, along with counts of associated points from
each site. The 36 projectile points with direct dates will be categorized according to their
individual OH reading. It is recognized that combining OH data from different sources
Table 14. TNF Sites With Obsidian Hydration Readings.
 Range
 Ave
 List
n=
53-475
1.3-3.4
2.2
20
56-002
NA
3.4
1.3, 1.4, 1.4, 1.4, 1.4, 1.7,
1.7, 1.7, 1.8, 2.0, 2.4, 2.7,
2.8, 2.8, 2.8, 2.8, 2.8, 3.1,
3.4
3.4
56-016
NA
1.3
1.3
14
56-126
1.2-4.3
2.8
1.2, 1.4, 1.5, 2.7, 2.7, 3.4,
3.5, 3.7, 3.7, 4.3
13
56-178
1.4-2.0
1.7
1.4, 1.8, 2.0
11
56-251
NA
1.6
1.6
5
56-292
2.1-2.6
2.3
2.1, 2.6
1
56-295
1.0-4.5
2.8
1.0, 4.5
3
56-296
2.3-4.9
3.1
2.3, 2.4, 2.6, 4.9
3
56-302
56-360
1.1-3.5
2.6-3.0
2.2
2.9
1.1, 1.4, 2.0, 2.8, 3.5
2.6, 3.0, 3.0
5
14
56-380
1.2-3.8
2.9
1.2, 2.5, 3.3, 3.8, 3.8
19
56-454
1.0-6.1
3.6
1.0, 3.8, 6.1
3
56-462
1.1-5.4
3.7
1.1, 3.3, 4.8, 5.4
1
57-276
NA
2.9
2.9
8
Site
Total
6
126
139
and averaging hydration rind measurements introduces a large amount of error into the
analysis. These samples were combined in order to increase sample size. It is hoped that
the relatively large sample size will contain patterns strong enough to show through this
error. Due to the high degree of expected error, a lack of patterning in the analysis is not
strong evidence against actual chronological patterning in the archaeological record. A
lack of patterning in this analysis must be considered as either a sign of chronological
stasis in artifact form or a level of error which obscures patterning.
Figure 47 presents the frequency of each of the four shape categories
associated with specific micron ranges. Projectile points are assigned to micron ranges
on the basis of direct readings or associated site-wide averages. Counts of projectile
point forms associated with each micron range are presented in Table 15. It can be seen
from Figure 47 that dart and arrow categories do not follow the expected pattern of
chronological association. Darts are more common than arrows in groups associated with
small as well as large value micron ranges. This may be due to a bias for increased
Figure 47. TNF OH Sample. US = Un-shouldered, ST = Stemmed, CN = Cornernotched, SN = Side-notched.
140
Table 15. TNF OH Sample Counts. US = Un-shouldered, ST = Stemmed,
CN = Corner-notched, SN = Side-notched
Micron Ave.
1.0-1.99
Form
US
ST
CN
SN
Arrows
3
4
3
Darts
5
6
8
3
Unknown
1
3
2
5
Total
9
13
13
8
2.0-2.99
US
ST
CN
SN
2
14
1
3
7
28
15
1
3
12
4
2
12
54
20
6
3.0-3.99
US
ST
CN
SN
1
5
1
1
5
2
1
4
1
1
3
14
4
1
4.0+
US
ST
CN
SN
1
1
1
1
2
2
39
82
1
41
1
162
Total
obsidian use in later periods at mixed context sites. It could also reflect a lack of success
in categorizing darts and arrows with a 3g threshold. Hughes (1998) argued that arrows
ranged well above this mark. It is possible that this predominance of points weighing
more than 3g is due to heavier arrows. It could also reflect bias due to the inclusion of
complete darts and dart fragments, but only complete arrows. As stated above a likely
explanation for unexpected chronological patterning is error due to the many uncertain
factors of separate hydration rates and mixed context sites. Jackson and Ballard (1999)
did argue for the persistence of darts alongside bow and arrow technology for thousands
of years. I would not make a corroborating argument for dart persistence based on this
error prone sample with hypothetical categories.
141
If the total frequencies of shape categories are considered without the dart and
arrow distinctions, some patterning is evident. The frequencies of unshouldered,
stemmed, corner-notched, and side-notched points associated with the 0.87-1.99 range
are all close to 10 individuals. This relatively flat distribution contrasts with the
distributions for the 2-2.99 and 3-3.99 ranges. Both of these ranges are associated
with distributions dominated by stemmed points. The frequencies in the 4+ range are
two small for meaningful comparison. One possible interpretation of the distribution in
this sample is that stemmed points were favored during earlier periods, with a later
diversification to where all four forms were produced in relatively similar frequencies.
Diversification such as this could have implications for cultural transmission
interpretations (Lyman et al. 2009). A switch from a persistent single form to a
diversified collection of forms may reflect changes in the context of cultural transmission
(Lyman et al. 2009). This pattern is interesting, although the high degree of expected
error diminishes the strength of this interpretation. The following section attempts to
reduce this error by using only direct OH readings from obsidian projectile points. The
sample size is much reduced in order to facilitate this, however.
Direct Obsidian Hydration Sample
The direct obsidian hydration sample of projectile points includes 25 from
various sites on the Tahoe National Forest and 11 from CA-ELD-145. Limiting the
sample to obsidian projectile points with direct hydration readings removes the error
produced by mixed context sites and averaged micron values. The error associated with
hydration rate variation due to source specific rates is still present, however. In addition
to the total 36 projectile point sample of direct OH readings, samples from specific
142
obsidian sources will be analyzed in order to reduce this error. Bodie Hills (n=13) and
South Warners (n=9) are the most common obsidian sources among the direct OH
sample. Bodie Hils and South Warners obsidian points are reanalyzed independent of the
total sample. Although these measures reduce error, they also decrease sample size.
Patterns strong enough to be apparent in these small samples may be interpreted as
reflecting real changes over time in prehistoric projectile point production. A lack of
patterning may be interpreted as either stasis in projectile point form frequencies, or a
result of small sample sizes.
Figure 48 charts the distribution of the total sample (n=36) of projectile points
with direct OH readings across specific micron ranges. Counts of these projectile points
with respect to OH readings and form are provided in Table 16. The distribution of
arrows is closer to the expected pattern in this sample. Points classified as arrows are
most frequent in the 0.87-1.99 range and least frequent in the greater than 4 range.
Figure 48. All Projectile Points with Direct OH Readings, n=36. US = Unshouldered, ST = Stemmed, CN = Corner-notched, SN = Side-notched.
143
Table 16. Counts of Projectile Points with Direct OH Readings. US = Unshouldered, ST = Stemmed, CN = Corner-notched, SN = Side-notched.
Microns
1.0-1.99
2.0-2.99
3.0-3.99
4.0+
Total
Form
US
ST
CN
SN
US
ST
CN
SN
US
ST
CN
SN
US
ST
CN
SN
Arrows
1
4
1
Darts
1
2
3
1
1
2
1
1
1
1
1
1
14
8
Unknown
2
2
Total
2
6
3
2
2
1
1
5
2
2
1
3
1
6
2
1
2
2
1
14
1
36
This correlates with the hypothesis that arrows were used during later periods only. The
observed pattern is weak, however, due to the small sample size. Points classified as
darts do not follow the expected pattern. They are also most common in the 0.87-1.99
range and least common in the 4+ range. This could not be argued to have been caused
by a mixed context. Hydration rate variation due to different contexts and obsidian
sources may have biased the data to produce this pattern. Small sample size could also
be a factor. Again, the sample sizes within categories are two small to be used to
corroborate the Jackson„s argument (Jackson and Ballard 1999) that dart technology
persisted alongside the bow and arrow for thousands of years. The general distribution of
144
haft element forms does show a disproportionately high number of stemmed points.
Stemmed points are the most frequent form in the 0.87-1.99, 2-2.99, and 3-3.99
ranges. Taken as a whole, the distribution of this sample of 36 points does support an
interpretation that stemmed points persisted as the most common form over a long period
of time. In terms of cultural transmission, this can be interpreted as a relatively stable
social context over time in which ideas related to the stemmed projectile point form were
shared.
Figure 49 shows the distribution of South Warners obsidian point forms for
different OH micron ranges. Counts of these points are provided in Table 17. Figure 49
shows that this distribution is completely flat. The sole point categorized as an arrow is
associated with the 1-1.99 range. The three points classified as darts are associated with
larger micron values. The sample is obviously two small for meaningful interpretation,
however. The Bodie Hills sample (n=13) is slightly more patterned. Table 18 presents
Figure 49. All South Warners Obsidian Projectile Points With Direct OH Readings.
US = Un-shouldered, ST = Stemmed, CN = Corner-notched, SN = Side-notched.
145
Table 17. Counts of South Warners Obsidian Projectile Points with Direct OH
Readings. US = Un-shouldered, ST = Stemmed, CN = Corner-notched,
SN = Side-notched.
Microns
Form
Arrows
Darts
Unknown
Total
1.0-1.99
US
1
1
ST
1
1
CN
SN
1
1
2.0-2.99
US
ST
CN
SN
3.0-3.99
US
ST
CN
SN
4.0+
US
ST
CN
SN
Total
1
1
1
1
1
1
1
1
1
1
1
1
3
1
5
9
point counts for this sample and Figure 50 shows the Bodie Hills direct OH projectile
point distribution. Stemmed points are the most frequent form represented in the 11.99, 2-2.99, and 3-3.99 ranges. This correlates with the pattern observed in the total
direct OH sample. Although the number of Bodie Hills projectile points is small, the
presence of this pattern in a single obsidian source sample lends support to the hypothesis
that stemmed points persisted as a culturally transmitted idea for a long period of time.
The persistence of stemmed points is the strongest pattern apparent in these data. The 3g
threshold between darts and arrows was not supported by the distribution of the direct
OH projectile point sample. It is unclear whether the deviation from the expected pattern
146
Table 18. Counts of Bodie Hills Obsidian Projectile Points with Direct OH
Readings. US = Un-shouldered, ST = Stemmed, CN = Corner-notched,
SN = Side-notched.
Microns
1.0-1.99
2.0-2.99
3.0-3.99
4.0+
Form
US
ST
CN
SN
US
ST
CN
SN
US
ST
CN
SN
Arrows
Darts
1
1
Total
1
2
1
1
2
1
1
2
4
1
1
1
7
1
13
2
1
2
US
ST
CN
SN
Total
Unknown
5
1
is due to small sample size, mixed contexts, hydration rate variation, or the failure of the
3g threshold to accurately classify darts and arrows.
CA-NEV-407
CA-NEV-407 is a prehistoric site located in the western foothills of the north
central Sierra Nevada, near Grass Valley, CA (Clewlow et al. 1984). Some basic
stratigraphy was recognized during excavations, but discrete dated components were not
identified (Clewlow et al. 1984). A series of C14 dates were procured from a pair of deep
units. Three additional C14 dates were procured from separate units. These dates are
listed in Table 19. The dates correlate well with depth, which lends support to the
147
Figure 50. All Bodie Hills Obsidian Projectile Points with Direct OH Readings. US =
unshouldered, ST = stemmed, CN = corner-notched, SN = side-notched.
argument that the site has vertical integrity. The published data from CA-NEV-407 is
provenienced to excavation block groups (Clewlow et al. 1984). For the following
analysis it is assumed that C14 dates are associated with the entire excavation block at the
depth they were collected. This assumption allows up to 194 projectile points to be
Table 19. C14 Dates from CA-NEV-407 (Clewlow et al. 1984).
CA-NEV-407
Depth (cm)
Excevation
C14 dates (B.P.)
Unit
Group
Unit 25
30cm
3
<300 B.P.
Unit 29
20cm
1
<300 B.P.
Unit 30
42cm
5
1290  250 B.P.
Unit 30
85cm
5
2730  250 B.P.
Unit 33
10cm
2
2255  250 B.P.
Unit 35
30cm
4
<300 B.P.
Unit 35
60cm
4
2570  280 B.P.
Unit 35
90cm
4
3125  270 B.P.
Adapted from Clewlow, C. W. Jr., Richard D. Ambro, Allen G. Pastron, Steven G.
Botkin, and Michael R. Walsh, 1984. Stage II Final Report for CA-NEV-407
Archaeological Data Recovery Program. Report submitted to CALTRANS, Marysville,
California.
148
associated with C14 based date ranges. It is recognized that this method introduces error
into date range associations, but a larger sample is better suited to reveal temporal
patterns. It is expected that strong temporal patterns will be reflected by the frequencies
of projectile point forms associated with different temporal ranges. It is argued that
strong patterns reflect culturally transmitted ideas relating to projectile point forms.
Group 1 (including 6 units) and group 3 (including 6 units) are both associated
with dates of 300 B.P. or less at shallow depths. Group 2 (with 14 units) is associated
with a shallow date of 2255  250 B.P. Group 2 was not included in this analysis as this
date does not fit well with the other ranges. Group 4 (including 9 units) is associated
with a shallow date of <300 B.P., a mid-depth date of 2570  280 B.P., and a deep date of
3125  270 B.P. (Clewlow et al. 1984). Group 5 (including 10 units) is associated with a
mid-depth date of 1290  250 B.P and a deep date of 2730  250 B.P. (Clewlow et al.
1984). Depth and excavation group provenience were used to assign projectile points to
temporal ranges. Two sets of sequential ranges are presented in order to maximize
sample size while preserving narrow time ranges when possible. Large ranges are
inclusive of smaller ranges. For example, the sample of points associated with the <1200
B.P. time range includes all points associated with the < 300 B. P. time range.
Counts of projectile point forms associated with the first series of temporal
ranges are presented in Table 20. Figure 51 presents the frequencies of projectile point
forms. Stemmed points are the most common form for all time ranges. This trend is most
prominent in the <300 B.P. range. Unshouldered points are the second most common
form for this time range, showing significantly higher numbers than corner or sidenotched points. Corner-notched points are the second most common form for the 300-
149
Table 20. Projectile Point Counts and Associated C14 Dates from CA-NEV-407.
C14 Dates
<300 B.P.
300-2500 B.P.
2500-3100 B.P.
>3100 B.P.
Form
US
ST
CN
SN
US
ST
CN
SN
Arrows
5
32
2
1
Darts
8
9
7
2
1
2
1
1
US
ST
CN
SN
2
8
1
6
2
1
US
ST
CN
SN
1
2
Total
63
Unknown
7
10
2
4
Total
20
51
4
5
8
2
1
1
17
5
2
3
1
8
13
3
1
1
1
1
4
1
33
39
135
2500 B.P. range. For the 2500-3100 B.P. range unsoldered points are nearly as
common as stemmed points. The trend towards frequent stemmed points over a long
period of time is prominent in this chart. Observations of the second most common
forms are based on numbers too small for meaningful interpretation. The dart and arrow
classification is based on the 3g threshold. Dart and arrow designations do not correlate
with temporal ranges. Arrows are most frequent in the <300 B.P. range, but this is
probably due to sample size.
Figure 52 presents the frequencies of projectile point forms for the temporal
ranges of series 2. Table 21 presents counts of these projectile point forms. The <1200
B.P. and > 2500 B.P. ranges represent larger blocks of time than those presented in series
150
Figure 51. Projectile Point Forms and Associated C14 Dates from CA-NEV-407.
1. The 1200-2500 B.P. range represents a more specific time range than 300-2500 B.P.,
however. Not surprisingly, stemmed points are the most common form for all time
ranges. This trend is most prominent in the <1200 B.P. range. Unshouldered points are.
the second most common form for all temporal ranges. Once again the dart and arrow
associations do not pattern with temporal range associations
Figure 52. Projectile Point Forms and Associated C14 Dates from CA-NEV-407.
151
Table 21. Projectile Point Counts and Associated C14 Dates from CA-NEV-407.
C14 Dates
<1200 B.P.
1200-2500 B.P.
>2500 B.P.
Total
Form
US
ST
CN
SN
Arrows
7
50
3
2
Darts
10
10
1
US
ST
CN
SN
3
4
2
8
US
ST
CN
SN
4
18
1
10
5
4
1
51
92
Unknown
8
18
3
4
Total
25
78
7
6
8
1
5
20
1
7
2
51
14
30
7
1
194
Geographic and Chronological Summary
Taken together these analyses show a strong trend towards the dominance of
stemmed projectile point forms for all time ranges and on both sides of the Sierra crest.
The Southern geographic sample, drawn entirely from CA-ELD-145, was an exception to
this. The trend in stemmed points may include a focus on expanding stems in the east
and contracting stems in the west. A weak trend for relatively frequent unshouldered
forms is apparent at CA-NEV-407. Unshouldered points are present in relatively
frequent numbers in all geographic samples. Dart and arrow classifications do not pattern
well with C14 date ranges. This may be due to error introduced by mixed contexts, but
these results do call to question the value of 3g as a dart and arrow threshold. Overall,
the distribution of point forms over associated temporal ranges supports the hypothesis
that stemmed points persisted as the most common point form for a very long time,
perhaps for 3000 years. The TNF OH sample deviates from this general pattern
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somewhat. The smallest value micron range of this sample shows a relatively flat
distribution of unshouldered, stemmed, corner-notched, and side-notched projectile
points. This may be evidence for diversification in terms of the most common projectile
point forms, which would suggest a change in the context of social transmission. This
pattern is not seen in the direct OH samples or the sample from CA-NEV-407. The high
degree of expected error for the TNF OH sample reduces confidence in this observed
pattern.
I argue that the chronological analyses presented here reveal strong evidence
that stemmed projectile points were the most common form in the north central Sierra
Nevada from at least 2500 B. P. to historic times. This persistence can be interpreted as a
very stable context of cultural transmission. Although darts and arrows were probably
not successfully categorized by this analysis, it can be assumed that bow and arrow
technology was introduced to the Sierra Nevada during this time period. The 2500 to
3000 year persistence of stemmed points as the most common form implies that this form
was translated from darts to arrows. Of the cultural transmission models proposed by
Boyd and Richardson (1985), frequency based adoption seems to fit best with the
observed pattern. This model predicts that variation will be reduced as producers of an
item of material culture copy the most common form. This pattern of transmission
implies that stemmed points were accepted as a highly recognizable cultural norm. The
long persistence of this form was probably accompanied by strong feelings of tradition.
The context of cultural transmission may have been somewhat different in the
American River watershed. While stemmed points were the most common form, the
relative proportion of notched points was larger than that observed to the north.
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Frequency based adoption may have been less significant in the southern portion of the
study area. Perhaps the communication of social or individual identity played a greater
role in projectile point production. The temporal association of this pattern is unknown,
so detailed interpretation is speculative. Still, there is moderately strong evidence for
different social contexts of projectile point production between the American River
watershed and the watersheds to the north. East and west of the Sierra Crest, the
respective trends in expanding and contracting stemmed points may have implications for
social relations between people in the north central Sierra Nevada and adjacent regions.
This pattern may correlate with typological distinctions between Gunther and Rosegate
point designations. Analysis of continuous data from Great Basin and California
assemblages would shed light on this issue. The analyses presented in this chapter
demonstrate that the pattern of projectile point form is not uniform over time or between
geographic regions. Further research should clarify these temporal and spatial
distinctions.
CHAPTER VII
CONCLUSION
Discussion of Results
This thesis presents a morphological analysis of 673 projectile points from
the north central Sierra Nevada. The analysis was designed to identify strong
morphological patterns within continuous variation of thirteen standard variables
originally proposed by Thomas (1970). I argue that a sample of this size is representative
of the general history of projectile point form in the north central Sierra Nevada region. I
further argue that strong morphological trends observed within this sample are directly
correlated with ideas about intended projectile point forms that were culturally
transmitted across time and space in the north central Sierra Nevada. Cultural
transmission across time and space is implied by the mix of contexts included in this
study. The patterning of morphological trends over time was tested using obsidian
hydration rind measurements and a series of radiocarbon dates from CA-NEV-407
spanning over 3000 years (Clewlow et al. 1984). Change in the pattern of morphological
trends over time implies a change in the context of cultural transmission (Lyman et al.
2009). Changes in the context of cultural transmission are likely linked to changes in the
broader social context. The results of the present analysis, therefore, bear directly on
154
155
issues of culture change in the north central Sierra Nevada and, in terms of the observed
forms themselves, on the validity and usefulness of projectile point typologies.
Univariate Analysis
The measurements used in this analysis include total length (LT), maximum
width (WM), thickness (T), weight (g), neck width (NW), base width (WB), proximal
shoulder angle (PSA), distal shoulder angle (DSA), notch opening index (NO),
length/width ratio (L/W), maximum width position (MaxWPos), base width/ maximum
width ratio (WB/WM), and the basal indentation ratio (BIR). Thomas (1970, 1981)
advocated the use of standard metric variables in order to reduce the subjectivity of
visually sorted types. Standard metric variables produce continuous data which is
sensitive to projectile point form and comparable between different contexts. Chapter IV
presented a univariate analysis of these thirteen variables. Histograms were used to
present the general distributions of values for each variable. Clustering around apparent
peak frequencies in these histograms was tested by comparing the variances of
overlapping data subsets.
LT, WM, T, and g are correlated with the overall size of a projectile point.
Due to performance constraints associated with dart and arrow technologies, projectile
point size should be a good discriminator between dart and arrow points (Hughes 1998).
A weight of 3g or a width of 20mm have been proposed as threshold measurements
between darts and arrows (Hughes 1998, Lyman et al 2008, Shott 1997a, Thomas 1978).
NW can vary independently of size, but its possible correlation with projectile shaft
diameter has led some to suggest its use as a dart and arrow discriminator (Rosenthal
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2002). Rosenthal (2002) uses 9.3mm neck width as a dart and arrow threshold. LT
shows a weak bimodal pattern, with the majority of values approximating a normal
distribution around 24mm. This pattern does not provide strong evidence for a separation
of darts and arrows within this variable. Overlapping values or resharpenning may have
obscured a bimodal distribution associated with darts and arrows. The WM histogram
showed a nearly normal distribution around 17mm. No bimodal patterning was present
which would support the 20mm threshold suggested for discriminating darts and arrows
(Thomas 1978, Shott 1997a). Thickness showed a steep nearly normal distribution. The
narrow range of thickness measurements probably obscures any possible patterning. The
weight histogram shows a strong peak at 1g and a leveling off of the curve between 2.5g
and 4.5g. The bimodality of this distribution is weak, but it does correspond with the 3g
threshold suggested for darts and arrows (Hughes 1998, Lyman et al 2008). NW shows a
strong bimodal pattern with peaks at 10mm and 14mm, and a trough near 12mm. This
distribution is expected if NW differentiates darts and arrows, but the 12mm value
separating this bimodal distribution is significantly higher than the 9.3mm threshold
suggested by Rosenthal (2002).
PSA, DSA, and NO all vary independently of size. They are sensitive to
haft element and shoulder shape. WB can be correlated with size, but it is also sensitive
to haft element shape. BIR is measures the indentation of the haft element base. The
distribution of PSA is strongly multimodal with strong peaks at 75 and 125, and a smaller
peak around 145. This clearly correlates with commonly used archaeological
classifications of stemmed, corner-notched and side-notched haft elements. The low
frequencies of values between these peaks suggest boundaries of 105 between stemmed
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and corner-notched points and 135 between corner-notched and side-notched points. A
smaller trough suggests a possible separation between straight and contracting stemmed
points at 85. These values are close to the boundaries suggested by Rosenthal (2002).
Rosenthal used 110 as a cutoff between stemmed and corner-notched points and 140 as a
cutoff between darts and arrows. Thomas (1981) classified large (1.5g) points
differently than small (1.5g) points. Large points were split at 110 and 150, while small
points were split at 90 and 130 (Thomas 1981). The values suggested by the histogram
are closer to Thomas‟s (1981) boundary between large stemmed and corner-notched
points and his boundary between small corner-notched and side-notched points.
Although the boundaries suggested by this histogram are slightly different from
previously suggested values, the patterning in the distribution of PSA is strong evidence
that these commonly used categories of haft element shape approximate emic intended
forms from the prehistoric north central Sierra Nevada.
DSA also shows a multimodal pattern, although not as strong. Peaks are
evident in the DSA histogram around 155 and 185 with a leveling off of the curve
between 190 and 230. This pattern corresponds with barbed, straight, and upturned
shoulders. Although the multimodal patterning in DSA is not as strong as in PSA, this
distribution provides moderately strong evidence for emic intended forms related to these
shoulder shapes. NO is also multimodal, but the pattern is week. Slight peaks are
present at 65, about 95, and 140. These values represent narrow, right angle, and wide
notches, respectively. The patterning in NO is not strong enough to argue for correlated
trends of intended form. WB shows a broad and somewhat irregular distribution. It is
weekly bimodal around a 9mm break. The patterning in WB is not strong enough to
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argue for any emic forms. Perhaps the correlation of WB with both size and haft element
shape causes a high degree of overlap, obscuring morphological trends. 90% of the BIR
values were 1 with the remaining 10% heavily skewed towards 1. This indicates that
projectile points with concave bases are very rare in this sample.
L/W, MaxWPos, and WB/WM are ratios which describe overall projectile
point shape independent of size. L/W differentiates squat point forms from relatively
long and narrow forms. The L/W histogram shows a steep, nearly normal distribution.
No arguments for intended forms can be made from this pattern. MaxWPos shows a
strong bimodal distribution with a sharp peak around 0 and the remaining values in a
nearly normal distribution around 25. This reflects a segregated group of points where
the widest portion is at the base. The remaining points show a broad distribution
averaging with the widest portion about a quarter of the way up the point. This
distribution is strong evidence for emic intended projectile point forms with the widest
portion at the base. The nearly normal distribution of the other forms makes trends
harder to define. WB/WM shows a multimodal distribution with a sharp peak at 1 and
weakly differentiated peaks at 0.3 and 0.6. The peak at 1 represents the same group of
points identified above with the widest portion at the base. The remaining values are
weakly split at about 0.45. Similar WB/WM values can be measured on a wide variety of
point forms. In general, the implications of WB/WM distribution are too complex to be
addressed with univariate analysis.
The strongest morphological trends revealed in the univariate analysis
include neck widths differentiated by 12mm, stemmed, corner-notched, and side-notched
haft element shapes, barbed, straight and upturned shoulder shapes, and points with the
159
widest portion at the base. The strength of these trends implies that they are associated
with emic intended forms from the prehistoric north central Sierra Nevada. Multivariate
analysis is necessary to investigate the relationship of these attributes.
Multivariate Analysis
A multivariate analysis of the thirteen variables discussed above is
presented in Chapter V. A principle components analysis (PCA) is conducted, along with
detailed examinations of dart and arrow discrimination, shoulder and haft element shape,
and unshouldered points. These analyses were aimed at identifying the relationships
between the emic intended forms above. Typologies tend to view projectile point shape
as a complete package. It is quite possible, however, that culturally transmitted intentions
about projectile point form were focused on certain attributes and not others. In other
words, the cultural units being transmitted may not have been contiguous with complete
point forms. This multivariate analysis has the potential to test the validity of the
typology formulated by Thomas (1981) and modified by Elston (Elston et al. 1977,
Elston 1994) for use in the north central Sierra Nevada. This typology relies on
multivariate definitions of morphological point types. If these types are useful categories
for the north central Sierra Nevada, then they should be reflected in multivariate
patterning within this data.
Principle Components Analysis. Principle components analysis (PCA)
compares the variance and covariance among multivariate data (Baxter 1993). Weighted
coefficients are assigned to all variables in order to produce the maximum possible
variance (Baxter 1993). The first component includes the most possible variance, while
the second component includes the maximum variance not correlated with the first
160
component (Baxter 1993). This process is repeated for the third and subsequent
components. Examination of the coefficients assigned to variables gives an idea of the
amount of variance in that component accounted for by each variable (Baxter 1993).
Variables which account for a high degree of variance are more likely to contribute to
multivariate trends. A scatter plot of one component against another can give a two
dimensional summary of multivariate trends (Baxter 1993).
The PCA conducted on these thirteen variables did not reveal many strong
multivariate trends, but it did allow inference into the general structure of the data.
Related attributes of projectile point shape were segregated well between components 1,
2, and 3. Component 1 accounted for about 40% of the variance in the sample (see Table
11). The strongest contributors to component 1 variance include the size-related
variables of LT, WM, T, and g along with the shape related variables DSA, WB, and
WB/WM (see Figure 29). Component 2 accounted for about 21% of the variance (see
Table 11). The strongest contributors to Component 2 were PSA, NO, WB/WM and
BIR. These variables are all associated with haft element shape (see Figure 30).
Component 3, accounting for about 12% of sample variance, was contributed to most
strongly by L/W, MaxWPos, and LT (see Figure 31). These variables are all related to
overall projectile point shape. A cluster of values is apparent on the scatter plot of
components 1 and 2 (see Figure 32). This cluster includes high component 1 values,
representing small size, narrow bases and barbed shoulders. The values are grouped
around and slightly above the component 2 axis, representing average haft element shape.
The univariate analysis of PSA reveals that stemmed forms are the average haft element
shape. The values above the component 2 axis represent contracting stem forms. This
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cluster, therefore, represents a strong multivariate trend towards small, stemmed, barbed
points. Strong multivariate trends in other forms are not apparent from this PCA.
Darts verses Arrows. The analysis of dart and arrow discrimination
compared commonly used discriminating variables in order to test the validity of dart and
arrow thresholds. A weight threshold of 3g has been proposed by Hughes (1998, Lyman
et al. 2008). Discriminant analysis of hafted dart and arrow specimens by Thomas (1978)
and Shott (1997b) suggest a 20mm WM threshold between darts and arrows. Rosenthal
(2002) uses a NW threshold of 9.3mm. If these thresholds are valid discriminators
between darts and arrows it is expected that two clusters will be apparent on bivariate
plots of these discriminating variables. Plots of WM and g, NW and g, and WM and NW
were presented in the analysis. This plots did not reveal any clustering of values. A large
number of points were classified as darts by one threshold and arrows by another. The
threshold values did tend to intersect near the line or curve fitted to each distribution.
The two fitted lines and one fitted curve can be interpreted as average values along a
continuum of variation. The intersection of threshold values along the fitted line or curve
indicates that, at least, the thresholds mark a midpoint for variation along the continuum.
A weight of 3g is close to the average value among points with 20mm WM values, for
example. This relationship might occur for valid thresholds if a high degree of overlap
occurred between darts and arrows. Hughes (1998) argued that cross-sectional area was
also a good discriminator between darts and arrows. WM was plotted against T to search
for any patterning related to cross-sectional area, but none was found. In general, the
results of this analysis do not outright reject the validity of these dart and arrow
thresholds, but they also do not support them.
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Shoulder and Haft Element Shape. Multimodal trends were apparent in the
univariate analysis of PSA and DSA. These variables were plotted against one another in
order to compare the relationship of shoulder and haft element shapes. It is expected that
strong correlations between shoulder and haft element shape will be reflected by clusters
on a bivariate plot of PSA and DSA. This was not the case, however (see Figures 39 and
40). Points were concentrated around low PSA values, but they varied widely in DSA.
Dividing the sample by the 3g dart and arrow threshold did reveal some patterning.
Among the concentration of low PSA values, points below 3g (presumably arrows) were
more strongly associated with low DSA values. This corresponds with the trend towards
small barbed points observed in the PCA. Nearly all of the points weighing more than 3g
(presumable darts) have straight or upturned shoulders.
Unshouldered Points. Thomas (1981) defines unshouldered points as those
for which PSA and DSA cannot be measured. Unshouldered points can range between
triangular, lanceolate and leaf-shaped forms. The variables which best discriminate
between these forms are L/W, MaxWPos, and WB/WM. Triangular forms should have
MaxWPos values near 0, WB/WM values close to 1 and a tendency towards low L/W
values. Leaf-shape points should have MaxWPos values higher than 0 and low WB/WM
values. Lanceolate points should have WB/WM values near 1 and high L/W values, but
they can vary widely in MaxWPos values. It is expected that strong trends towards these
shapes will be reflected by patterning on bivariate plots. A bivariate plot of L/W and
MaxWPos with the sample divided by the 3g dart and arrow threshold shows strong
trends towards triangular arrow points and leaf shaped darts (see Figure 42). These
163
trends are distinct enough to support the argument that these forms represent emic
culturally transmitted units.
The multivariate analysis reveals strong multivariate trends in small, barbed,
stemmed points, small triangular points, and large, leaf-shaped points. This may be due
to the low number of notched points relative to stemmed points. Notched points were not
represented by strong multivariate patterns, although it is clear from univariate analysis
that they are present in some numbers. The univariate and multivariate analyses
produced strong evidence that the forms described above represent cultural units
transmitted across space and time in the north central Sierra Nevada. Comparison
between dated contexts and geographical units is necessary to characterize the pattern of
cultural transmission, and hopefully shed light on the social context in which these ideas
were shared.
Chronology and Spatial Patterns
Dated contexts are necessary for changing trends in cultural transmission to
be understood. Comparison between geographical areas can also reveal differences in the
context of cultural transmission. Chapter VI presents a comparison of the frequencies of
specific projectile point forms across dated contexts and discrete geographic samples.
Due to the scarcity of well dated contexts in the Sierras, relative age estimates of mixed
contexts were made by averaging obsidian hydration readings from 15 sites on the Tahoe
National Forest (see Table 14). 123 projectile points are associated with these averaged
values. A sample of 36 obsidian projectile points with direct obsidian hydration readings
is also used (see table 16). Finally, a series of C14 dates from CA-NEV-407 (Clewlow et
164
al. 1984) are used to compare temporal patterning at that site. The geographic samples
are large enough to be analyzed with continuous data. Points from the smaller
chronologically controlled samples were classified by haft element shape based on trends
apparent in the univariate analysis of PSA. The classes include stemmed, cornernotched, side-notched, and unshouldered. The 3g threshold was chosen to differentiate
darts and arrows in the chronologically controlled samples. A high degree of error is
expected in the association of these points with dated contexts, but it is hoped that strong
trends would overcome this error.
Stemmed points are the most common form in each geographic sample.
The Southern sample, however, shows nearly comparable frequencies of corner-notched
points. This is strong evidence for a different context of cultural transmission in which
multiple styles were common. Possible contrasting trends toward expanding stemmed
points in the east and contracting stemmed points in the west are supported by data from
these geographic samples. A strong trend towards the persistence of stemmed points as
the most common form is apparent in the examination of the chronologically controlled
samples. The sample including site-wide averages shows a differentiation from this trend
in the 0.87 to 1.99 micron range, possibly indicating an increased use of notched and
unshouldered points during late prehistoric contexts. This would imply a change in the
context of cultural transmission. The sample limited to direct OH readings and the CANEV-407 sample do not show this trend, however. The CA-NEV-407 sample shows the
strong trend over time towards stemmed points as the most common form. This trend is
strongest in the <300 B.P. and <1200 B.P. time ranges. The dart and arrow
classifications generally do not pattern well with the temporal associations. This may be
165
due either to error in temporal association or the failure of the 3g threshold to accurately
discriminate between dart and arrow points.
Conclusion
The goal of this thesis is to characterize projectile point variation in the
north central Sierra Nevada and identify morphological trends which can be compared
between different contexts across space and time. The pattern of morphological variation
across space and time can be interpreted in terms of cultural transmission. Differences in
the context of cultural transmission may be linked to changes in the general social
context. Theories of style can be used to interpret how projectile point forms could have
communicated ideas or held meaning for the people who used them.
This analysis shows a strong multivariate trend toward small, barbed,
stemmed points. Small triangular points and large leaf shape points are also quite
evident. Trends toward notched forms are not as apparent in the multivariate analysis,
although they are distinctly shown in univariate analysis. Probably the most significant
pattern seen in this analysis is the multimodal distribution of PSA. This is strong
evidence that emic categories of haft element shape match general classifications used by
archaeologists. It seems very likely that prehistoric people in the north central Sierra
Nevada recognized the difference between stemmed, corner-notched, and side-notched
forms. The multimodal patterning of DSA also supports the hypothesis for emic
categories of shoulder shape. It is likely that the producers of these projectile points also
recognized the difference between barbed, straight, and upturned shoulder shapes.
166
It is interesting that these two sets of emic categories to not show a bivariate
pattern. It is possible that this is due to resharpening changes to DSA. Still, for the
multimodal pattern to be preserved, points would have to have been resharpened into
another recognized form rather than simply adding a sharp edge and point. This lack of
patterning between haft element shape and shoulder shape may reflect a context of
transmission where these two attributes are not necessarily linked. A stone knapper may
have learned to make corner-notched points at a different time or place from when he or
she learned to make barbed shoulders, for example. Shoulder shape may have been
copied in certain contexts, while haft element shapes were retained. Situating the context
of cultural transmission around specific attributes rather than complete forms has
implications for typological method.
Typologies generally rely on complete forms. The typologies developed by
Thomas (1981) and Elston (Elston et al 1977, Elston et al. 1994) are no exception to this
rule. The keys accompanying these typologies define types on the basis of multiple
variables (Thomas 1981, Elston et al. 1977). The expected multivariate patterning
correlating with these type definitions was not seen in the present sample. No patterning
was seen which would correlate with Jackson and Ballard‟s (1999) complex West Side
Descriptive (WSD) typology. This analysis does not support the use of these typologies.
The use of simple descriptive categories such as stemmed, corner-notched, side-notched,
triangular, or leaf-shaped would give a much more accurate depiction of the actual
variation present in projectile points from the north central Sierra Nevada region. A
method of comparing trends in continuous variation between contexts would be more
167
productive than placing projectile points into arbitrary types with problematic temporal
and cultural associations.
The dated contexts compared in this sample show a persistent trend of
stemmed points as the most frequent form over time. The C14 dates from CA-NEV-407
suggest that this trend may have existed from 3000 B.P. to later than 300 B.P. Elston
argues that large stemmed points are indicative of the 5000 B.P. to 3000 B.P. time period,
while corner and side-notched points were more prevalent from 3000 B.P. to 1300
B.P.(Elston et al. 1994). Elston (Elston et al. 1994) did not identify any stemmed points
with the period after 1300 B.P., although he does associate it with Rose Spring arrow
points which have a defined PSA range as low as 90. Rosenthal (2002) observed a
different pattern in the American River drainage in which corner-notched and leaf-shaped
points were older than stemmed points. The pattern observed in the present analysis does
not match well with either of these chronologies. It is consistent with White and Origer‟s
Nevada County work, however (White and Origer 1987, White 1991). White (1991)
found stemmed points associated with C14 dates of 3125 B.P., 2570 B.P., 2380 B.P., and
1290 B.P. near Nevada City in the western foothills. White (1991) observed small,
stemmed points (Small Gunther and Gunther Barbed) in later contexts as well.
The pattern of stemmed points in later contexts is confused by overlapping
typological definitions. Thomas (1981) and Elston (Elston et al. 1977) define the
Rosegate type as small corner-notched points having PSA values between 90 and 130.
The Gunther series of types refer to various small, stemmed forms (White 1991, Jackson
et al. 1999). The patterning of PSA revealed in this study supports a break between
stemmed and corner notched forms at 105. This would reclassify many Rosegate points
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as stemmed rather than corner-notched. The geographic sample analysis conducted in
chapter VI reveals seperate trends towards contracting-stemmed points in the west and
expanding-stemmed points in the east. This could reflect a geographic separation
between projectile point styles which are often classified as Gunther and Rosegate.
Direct comparison of PSA values would eliminate this confusion in future studies.
A 3000 year persistence of stemmed points as the most frequent form in the
north central Sierra Nevada indicates a very stable context of cultural transmission. The
cultural transmission model which most closely resembles this pattern is frequency based
adoption (Boyd and Richerson 1985). Under this model, the most common form is
copied, reducing variation over time (Boyd and Richerson 1985). A form perpetuated
over 3000 years was probably associated with strong feelings of tradition. The ubiquity
of this form makes it an unlikely candidate for communicating group or personal identity
(Weissner 1997). Haft element shape is partially hidden when a point is hafted. It is
most visible in intimate settings. Ideas about haft element shape were probably
transmitted in intimate settings during the production stages (before the points were
hafted). Ishi recalled a similar context from his younger days in which men sat in a circle
in a sunny place making arrowheads (Shackley 2001). In a context such as this,
traditions could be perpetuated through intimate relationships such as father to son. Still,
the prevalence of this form across the north central Sierra Nevada shows that this
tradition was shared widely.
Some deviations from this pattern are seen within spatial and temporal
contexts. In the smallest micron range of the TNF OH sample unshouldered, cornernotched, side-notched, and stemmed points occur at about the same frequencies. This
169
implies a different context of cultural transmission from the one described above. The
lack of a single prevalent form indicates a more diverse pattern of projectile point
production. This may have been associated with an increased communicative function of
projectile points. The diverse mix of prevalent forms may represent expressions of group
identity in a more complex social context. Analysis of the Southern geographic sample
produced similar results. Corner-notched points have a higher relative frequency in this
sample than in the stemmed point dominated Northern sample. Again, this is evidence
for a more complex context of cultural transmission in the southern sample area, possibly
related to communicative functions of style. The highest frequency PSA values among
the Eastern sample represent straight and expanding-stemmed points, while the Western
sample includes more PSA values in the range of contracting-stemmed points. This
pattern is somewhat weak, but the differentiation between the east-side and west-side
samples may indicate different contexts of cultural transmission on either side of the
Sierra crest. This would imply differences in the social contexts on the east and west
sides of the Sierras. Perhaps these differences reflect the contrast between widespread
ancient traditions and the need to communicate group identity discussed above. This is
not surprising considering the historic territories of the Washoe and Nisenan. The
temporal aspects of this geographic pattern remain unknown, however. Further
examination of this topic requires additional research.
Continuous data analysis can be a productive method for investigating
Sierra Nevada prehistory. Comparison of patterns of projectile point morphology
provides a direct way of studying stylistic change over time. Unlike projectile point
typologies, continuous data analysis does not assume discrete morphological boundaries
170
between projectile point styles. Profiles of morphological patterns may be a more useful
way of characterizing temporal periods than defined projectile point types. Continuous
data allows an entire projectile point assemblage to be considered without imposing a
framework of discrete types which may or may not fit. Typology assumes chronological
association between contexts in which only a portion of the assemblage consists of
diagnostic forms. Continuous data analysis allows a more meaningful comparison
between assemblages by considering the entire range of morphological variation.
The geographical analysis in the previous chapter shows how morphological
profiles can be used to compare spatially discrete assemblages. This comparison reveals
different patterns of stemmed and corner-notched points among the Northern and
Southern samples and suggests distinct patterns between contracting and expanding stem
points between the Western and Eastern samples. The samples with some degree of
chronological control were too small for meaningful continuous analysis. These smaller
samples can be compared through the use of morphological categories. Although the use
of categories represents a break from the continuous data method, the continuous pattern
among the entire assemblage is used to define these categories. Categories based on
strong morphological patterns from a larger, associated sample, can be reasonably
assumed to represent emic morphological styles. Of course, a large, chronologically
controlled sample would be ideal for comparing continuous morphological patterns over
time, but this sample is not yet available for the north central Sierra Nevada.
The data needed for further geographic comparison is already available in
museum collections and published data. Expanding this line of inquiry is simply a matter
of gathering more projectile point measurements. Our understanding of north central
171
Sierra Nevada prehistory would benefit both from larger samples of data from the Sierra
region and comparative analyses from the Sacramento and San Joaquin Valleys and the
Great Basin. The excavation of chronologically controlled contexts must be the most
crucial pursuit for future research in the Sierra Nevada. Obsidian hydration is an
important line of inquiry when C14 data is unavailable. As Rosenthal (2002) shows, basic
stratigraphy can be a powerful tool for understanding the change in projectile point
morphological patterns over time. A major stumbling block to this pursuit is the
disturbing lack of curation agreements for increasing numbers of archaeological projects.
Without curation of projectile points, this line of inquiry must rely on less accurate data
from drawings or photographs, or often a simple statement of the author's opinion of
which defined type a point belongs to. Still, large numbers of projectile points continue
to be found, and stratified sites are out there. There is great potential for answering
questions about north central Sierra Nevada prehistory through future research.
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APPENDIX A
Projectile Point Data
181
Cat. #
89-17-1
89-17-10
89-17-11
89-17-12
89-17-13
89-17-14
89-17-15
89-17-16
89-17-17
89-17-18
89-17-19
89-17-2
89-17-20
89-17-21
89-17-22
89-17-3
89-17-4
89-17-5
89-17-6
89-17-7
89-17-8
89-17-9
5249
7162
13029
12900
7818
166-121
166-181
166-195
Site
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-475
53-645
54-176
54-347
54-404
55-208
55-72
55-72
55-72
Complete
N
Y*
Y
Y
Y
Y
Y
Y*
N
N
N
Y
Y
Y
Y
N
N
N
N
N
Y
N
N
N
Y
Y
N
LT
27
38
19
30
21
58
49
34
28
35
30
22
51
29
WM
23
12
18
11
21
10
31
18
18
19
19
27
19
24
19
17
33
21
T
9
12
21
20.9
10.7
26
18
18
6.2
8
6
6
g
5.6
1
3.9
0.6
4.5
0.6
6.1
4.5
3
8.6
1.9
4.4
2.5
5.1
2.3
4
23.8
6.2
3.5
0.5
0.7
5.3
6.9
NW
2.5
9.4
2.2
13.6
WB
PSA
140
180
110
180
100
75
90
85
110
130
70
70
90
110
90
70
NUL
75
75
DSA
170
180
225
180
240
195
115
220
215
185
125
140
210
210
210
225
NUL
215
110
NO
30
NUL
115
35
140
75
25
135
105
55
50
65
120
100
120
200
125
2.04
13.02
12.3
65
70
NUL
175
205
NUL
110
105
NUL
16.2
19
13
12
105
210
105
12
14.04
11
14.7
4
10.85
12.06
14.04
6.08
25.65
10.07
16.08
12.92
6.97
0
14.07
20.9
90
90
220
230
MWP
0
39
0
57
14
17
20
25
29
23
40
20
130
140
23
BIR
<1
0.93
0.89
0.95
1
1
1
1
1
1
1
1
0.97
1
1
1
1
1
1
1
1
21.6
34.5
1
Projectile Point Data
Cat. #
166-197
166-239
166-251
166-265
166-276
166-277
166-294
166-321
166-348
166-398
166-404
166-415
27
44
54
117
118
119
122
2505
2506
2507
2510
6215
6216
6217
6219
6220
6221
6989
6990
Site
55-72
55-72
55-72
55-72
55-72
55-72
55-72
55-72
55-72
55-72
55-72
55-72
56-002
56-002
56-002
56-002
56-002
56-002
56-002
56-016
56-016
56-016
56-016
56-016
56-016
56-016
56-016
56-016
56-016
56-016
56-016
Complete
Y
N
Y
Y
N
Y
Y
Y
N
Y
N
Y
N
N
N
Y
Y
N
N
N
N
N
N
N
N
N
N
N
N
N
N
LT
44
25.5
55
24
25
42
38
29
28
WM
18.5
20.5
13
29
21
14
17
27
21
18
19
17
45.6
24.7
41.2
19
18.3
16.4
19.9
19.7
23.3
24.4
23.7
17.9
14.6
19.9
24.3
17.2
12.3
22.1
40.8
T
3.5
4.5
5.5
9
5
4.5
5
7
6.5
5
3
5
4.7
5.7
6.2
6.9
5.2
6.3
8.7
6.1
6.8
5.6
5.8
5.5
6.3
4.4
7
3.6
7.8
4.5
7.6
g
4.1
2.5
1.75
15.2
1.3
0.85
1.2
4.8
7.4
3
1.5
1.5
0.9
5.1
2.2
2.3
3
4.2
5.3
7.4
5
3.5
2.4
1.8
3.9
3.4
5.3
0.8
7.7
1.4
11.2
NW
WB
10.92
0
0
PSA
DSA
NO
75
80
180
150
105
70
0
0
110
70
70
135
90
160
180
180
180
180
100
110
110
45
90
70
180
110
105
85
90
90
75
85
90
125
115
140
110
190
150
180
185
185
205
205
220
180
180
195
235
230
220
180
210
210
175
200
165
40
100
40
75
85
95
120
125
135
95
60
95
95
22
11.97
5
0
6
15.1
11.1
8.7
11.7
15.2
13.3
11.2
15.5
12.3
14.7
17
13.7
11
6.9
9
14
13.4
14.6
17.9
12.8
16
13.7
15.8
12.3
13.4
19.8
11.7
22.8
140
130
120
175
80
90
120
80
50
90
35
95
110
45
MWP
45.4
19.4
0
47.3
14.3
12.5
24
16.7
39.2
34.2
10.3
25
BIR
27
0.98
<1
1
1
1
1
41
41
1
1
1
1
1
1
0.93
1
1
1
1
1
1
1
1
1
1
<1
1
1
182
Projectile Point Data
Cat. #
7025
7039
7056
7075
1256
1258
1259
2618
2619
2620
2621
6476
6478
6479
6480
6481
6482
6486
6766
6771
6777
6780
6818
6837
6240
6856
6881
6885
6901
6911
6933
Site
56-016
56-016
56-016
56-016
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-126
56-178
56-178
56-178
56-178
56-178
56-178
56-178
Complete
N
Y
N
N
N
N
N
N
Y
N
N
Y
Y
N
N
N
N
N
N
Y
Y
N
Y
Y
N
Y
N
N
N
N
N
LT
WM
22.4
14.6
11.8
36.5
23.2
33.6
37.3
31.8
32.4
23.8
32.5
25.4
16.9
25.7
25
18.8
16.8
22.5
17.2
12.8
20.7
23.5
22.2
24.5
12
17.2
15
24.2
14.4
21.1
15.2
20.8
19.6
21.1
21.5
22.6
T
2.8
4.8
3.2
4.2
9.1
5.4
6
8.1
7.3
5.6
5.2
5.8
4.1
3.5
6.9
5.9
4.7
6.1
5.4
5
5.1
4.3
3
9.4
3.1
5.5
6.2
8.2
7.1
27
7.9
g
0.2
1.2
0.7
1.9
7.8
3.9
4.3
7.4
2.5
1.9
1.6
3.9
2.7
0.8
5.1
4.6
2.7
7.5
1.6
2.4
2.3
2.4
1
5
1.1
2.4
3.7
7.8
8.1
3.7
6.9
NW
7.9
NUL
8.7
14.1
18.1
13.7
NUL
9
4.6
19.5
5.8
5.8
14.2
10.9
NUL
10.6
5.5
8.1
4.2
9.3
10.5
14.2
8.5
17.3
15.9
NUL
15
NUL
19.4
WB
22.8
7.6
11.8
NO
50
80
NUL
105
65
100
80
85
NUL
4.5
14.1
80
70
90
100
100
90
NUL
120
140
70
90
100
90
90
DSA
200
185
NUL
180
170
200
170
190
NUL
175
230
180
150
160
210
210
NUL
210
190
210
180
140
140
220
20.8
17.2
11
16.2
13.5
22.6
120
100
NUL
90
NUL
140
250
240
NUL
200
NUL
220
130
140
NUL
110
NUL
80
14.4
12.4
14.4
19.3
18.8
13.5
11.9
8.3
5.6
15.4
14.3
22.2
24.5
7.4
10
5.3
PSA
150
110
NUL
95
105
100
90
110
NUL
150
110
60
60
110
120
NUL
90
50
140
90
40
50
120
MWP
67
BIR
<1
1
<1
73
0
1
1
1
1
0.85
24
11
1
1
1
24
35
13
1
1
1
1
1
1
1
10
23
1
0.93
0
0.94
1
1
1
1
1
183
Projectile Point Data
Cat. #
6963
6965
6968
6969
6975
6981
3002
3003
3006
3026
2002
6241
6536
6547
6551
9097
0970
975
2055
2535
6340
6665
2600
2601
2602
2603
2604
6349
6357
2608
2609
Site
56-178
56-178
56-178
56-178
56-178
56-178
56-180
56-180
56-180
56-180
56-251
56-251
56-251
56-251
56-251
56-251
56-252
56-252
56-269
56-269
56-292
56-293
56-294
56-294
56-295
56-295
56-295
56-295
56-295
56-296
56-296
Complete
Y
N
N
N
N
N
N
N
N
N
Y
Y
Y
N
Y
N
N
Y
N
N
Y*
Y
N
Y
Y
Y
N
Y*
LT
42.5
WM
18.3
24.6
23
22.4
22.7
22.1
T
5.9
8.7
5.5
6.3
7.2
7
17.3
19.4
13.8
15.4
17
4.8
5.7
4.5
3.9
5.4
5.6
1.8
2.6
3.3
1.3
1.2
1.2
2.5
6.9
8.3
8
7.5
3.6
4.1
9.9
6.3
1.6
1
26
50.8
42.1
25.8
21.1
28.5
15.9
16.1
28.9
18.3
13.1
16
16.3
22.5
17.8
15.9
5.9
3.7
4.7
5.9
4.7
5.5
3.3
4.4
0.9
1.2
4.2
3.5
2.1
0.7
34.4
34
32.7
18.2
57.5
23.1
24
50.9
g
5.1
9.2
3.9
4.6
5.1
3.8
0.6
3
NW
NUL
19.1
17.8
14
18.4
14.4
7.2
17.2
NA
NUL
6.7
NUL
14.5
NA
12.2
10.7
5.8
15.7
10
5.4
6.6
7.6
9.5
18.5
6.9
WB
9.8
19.5
17.5
15
8.6
18.4
13
0
19.4
7.3
6.2
17
9.3
12.5
9.5
21.9
10
6.8
8.1
8.1
22.3
8
PSA
NUL
130
150
120
110
100
105
95
DSA
NUL
190
230
190
170
180
175
NO
NUL
60
80
60
60
75
60
MWP
36
BIR
1
1
1
1
1
1
1
1
NUL
NUL
105
95
NUL
95
NUL
NUL
165
170
NUL
130
NUL
NUL
55
75
NUL
40
39
0
16
17
0
18
1
1
1
1
1
1
140
NUL
90
115
120
110
70
135
95
55
70
140
95
180
NUL
180
210
135
195
190
170
155
201
180
165
160
40
NUL
90
95
15
60
120
45
60
140
50
25
30
36
16
30
25
1
1
31
20
14
19
28
1
1
1
1
1
1
1
1
1
184
Projectile Point Data
Cat. #
2610
8042
2622
2623
2624
6350
6367
6374
6388
6415
6430
8467
8468
4520
4521
6600
6605
6612
6626
6639
6640
6641
6655
6660
6687
6715
6718
6725
6730
6740
6741
Site
56-296
56-296
56-302
56-302
56-302
56-302
56-302
56-302
56-302
56-302
56-302
56-303
56-303
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
56-360
Complete
Y
Y
N
Y
N
N
Y
N
N
N
Y
N
N
N
N
Y
N
N
Y
Y
Y
N
N
N
N
N
N
N
N
N
N
LT
54
20.9
21.6
18.4
29.2
22.1
29.2
WM
17.9
11
21.4
14.2
11.6
16.9
22.6
13.8
15.5
16.8
19.9
23.5
26.1
25.9
38.6
20.3
16.6
19.5
19.7
18.5
11.5
13.2
22.7
21.5
23.3
19
27
19.2
14.1
20.5
17.2
T
6.4
4.1
6.4
2.9
2.6
7.4
3.7
5.4
3.9
3.5
3.6
5
6.2
6.7
5.4
5.5
4
5
2.1
3.8
7
4
4.2
6.4
5.5
9.2
3.9
4.2
5.2
g
6.2
0.7
5.7
0.7
0.6
3.6
1.6
2.7
0.9
1.7
1.7
2.5
1.8
5.5
9.1
2.1
4.9
7.9
1.6
0.3
1.3
5.1
2.5
2
1.1
2.3
6.9
1.5
0.9
2.8
3.4
NW
NUL
5.7
13.4
6.2
7.4
12.5
6.9
10.1
8.3
8.9
5.8
8.6
10.6
14.2
20.6
9.3
NUL
NUL
16.9
5.6
8.5
11.3
9.2
9.3
WB
8.3
5.2
13.8
7.7
7.3
7.3
PSA
NUL
85
90
120
170
80
95
9
5.7
10.6
110
90
90
120
11.8
11.2
9.2
NUL
NUL
9.5
21.12
8.4
8.5
18.5
11.5
10
11.3
120
65
NUL
NUL
NUL
185
130
90
8.6
90
12.2
11.7
9
14
15
9.8
80
80
100
NUL
NUL
80
DSA
NUL
205
220
155
165
190
150
140
200
220
150
185
135
185
190
130
NUL
NUL
NUL
195
160
140
150
160
140
230
200
155
NUL
NUL
220
NO
NUL
115
130
35
15
110
85
MWP
28
28
90
130
60
55
44
60
65
NUL
NUL
NUL
10
30
50
26
0
29
24
13
8
26
1
0
17
BIR
0.99
0.99
1
0.97
0.94
1
1
1
<1
1
1
1
1
1
1
0.8
1
1
70
1
150
120
55
NUL
NUL
140
1
1
1
<1
1
1
15
185
Projectile Point Data
Cat. #
6163
6201
6202
6203
6204
6205
6206
6207
6208
7867
8094
8095
8136
8138
8139
8214
8239
8243
8244
8269
8279
8288
8570
6211
6213
8341
8378
8380
8391
8532
6189
Site
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-380
56-454
56-454
56-454
56-454
56-454
56-454
56-454
56-462
Complete
Y
N
Y
N
N
N
N
N
N
N
Y
N
N
N
N
N
N
Y
N
Y
N
N
N
N
N
N
N
N
Y
Y
LT
65.2
31.6
19.1
24.8
39.5
26.8
23
55.6
WM
26.3
25.3
15.5
23.6
24.3
26.5
25.1
14.9
18.4
25.5
T
8.1
8.4
5.7
7.7
6.9
8.4
9.5
5.4
8.7
4.8
3.7
6.4
9.3
7
9.5
4.7
16.8
5.8
5.4
4.3
5.2
3.4
g
17.1
8.8
2.7
6.3
6.8
7.5
7.4
2.8
9.4
2.7
0.7
4.2
7.3
4.8
10.7
2.4
2.9
3.2
2.9
1.8
4.2
2.5
22.5
30
40
13.7
19.9
27.8
13.5
24.8
6.5
6.9
5.8
7.6
6.5
6.6
4.6
6.2
3.3
6.3
6.2
1.8
2.4
5.8
1.2
9
12
20.8
23.2
20.8
21.6
21.8
17
17.5
24.3
13.5
NW
NUL
17.5
9.3
16.7
15
11.6
13.7
8.8
14.1
7.2
5
10.5
12.2
NUL
11.8
12.9
11.9
15.7
NUL
17.1
21
NUL
NUL
NUL
21
5.7
13.7
WB
22.1
19.3
5.2
17.1
17.8
12.6
14.9
8.8
14
7.2
6
10.2
16.2
16.5
13.1
13.9
13.6
17.8
9.5
23.8
18.4
20.9
0
21
6.1
14.2
PSA
NUL
110
50
90
110
100
130
80
NUL
90
105
90
120
100
NUL
100
90
125
100
NUL
120
DSA
NUL
190
230
200
220
200
160
220
NUL
160
135
180
190
210
NUL
200
190
220
200
NUL
190
160
NO
NUL
80
150
110
110
110
30
140
NUL
70
30
100
70
110
NUL
100
100
95
100
NUL
70
110
85
NUL
NUL
NUL
NUL
80
95
190
175
NUL
NUL
NUL
NUL
185
195
90
90
NUL
NUL
NUL
120
105
100
MWP
19
14
29
40
BIR
1
1
1
<1
<1
1
1
1
<1
1
1
1
1
<1
1
1
1
1
<1
1
<1
1
1
1
1
1
1
1
186
Projectile Point Data
Cat. #
6190
8301
8306
8308
6460
13537
6198
6233
2628
2635
12719
12723
12741
389
390
391
392
393
2212
2213
2214
2216
3531
30
0012-003
0019-001
0054-001
0054-002
0089-001
0094-001
0106-001
Site
56-462
56-462
56-462
56-462
56-464
56-508
56-IF-101
56-IF-105
56-VI-18
56-VI-58
57-256
57-256
57-256
57-276
57-276
57-276
57-276
57-276
57-276
57-276
57-276
57-276
57-276
57-39
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
Complete
N
LT
Y
30.4
Y
N
N
Y
Y
N
N
N
N
Y
N
Y
Y*
Y
N
Y
N
Y
Y
Y
N
N
N
Y
Y
50.5
30.4
39.4
25.4
44
49.9
49.6
63.2
45.6
31.9
28.7
28.9
31.9
WM
23.9
T
4.5
g
8.5
NW
15.4
WB
PSA
DSA
185
NO
MWP
BIR
5.5
1.9
7.7
5.4
80
160
90
1
0.97
21.8
22.8
7.8
8.8
15.4
13.7
13.7
8.3
85
80
23
1
1
4.3
4.5
5.9
5.2
6.5
5.4
5
5.1
10.4
7.1
7.3
6.5
11.7
7.1
4
3
6.1
95
14
13.4
13.9
11.3
15.1
22.4
9
NA
13
17.2
12.3
15
10.8
65
80
75
75
85
95
85
225
185
140
170
140
105
18.5
24.6
25.7
7.6
8.8
1.9
1.8
4.3
5.3
1.1
4.2
2.4
2.4
3.3
10.3
5.7
6.3
5
13.7
2.8
8.2
2.8
4.2
6.8
2.1
3.5
4.2
3.1
180
120
160
155
225
180
NUL
200
230
85
25
70
38
1
1
1
1
1
1
1
15.4
6.6
20.5
19.1
14.4
NUL
15.2
16.7
5.5
20.5
21.1
17.8
19.5
26.9
24.5
33.1
26.9
17.9
18.3
19.1
21.6
23.6
17.2
22.9
21.5
15.8
22.6
15.2
7.2
5.9
7.4
8.9
6.4
6.4
6.6
5.9
23.2
5.8
9.8
18.5
9.6
18.4
14
100
70
NUL
125
100
60
110
70
90
120
130
NUL
120
115
200
210
220
220
200
NUL
170
235
125
135
NUL
75
140
60
90
140
130
100
90
NUL
50
120
40
18
49
30
1
1
0.99
<1
1
27
31
0.95
1
0.97
35
37
1
0.97
187
Projectile Point Data
Cat. #
0113-008
0113-009
0113-012
0120-001
0133-002
0133-003
0133-015
0142-010
0142-011
0149-003
0155-001
0155-002
0160-001
0160-002
0160-003
0160-004
0172-010
0177-005
0219-001
0223-001
0223-002
0229-001
0229-002
0237-001
0237-002
0242-002
0252-002
0252-002
0266-004
0274-002
0274-003
Site
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
Complete
N
Y
N
N
Y
N
N
N
Y
Y
Y
Y
N
N
N
N
N
Y
N
N
Y
Y
Y
N
Y
N
N
N
Y
N
N
LT
20.1
30.7
18
18.7
52
38.7
17.3
29.9
WM
21.8
19
11.5
18.3
17.2
21.1
17
12.5
16.3
17.3
22.8
41.6
18.3
19.2
23.2
22
44.3
42
16
22.3
19.9
16.8
11.5
26.1
19.2
15
12.8
16
16
10.9
20.8
19.4
T
4.2
7.3
3.6
2.7
3.1
3.1
5.5
4
3.2
5.7
3.4
5.8
4.3
4.6
4.6
2.6
6.8
3.6
4.8
6.2
3.6
5.6
3.4
3.9
3.4
3.3
3.2
3.2
3
6.2
5.6
g
3.1
4.6
2.3
0.4
0.5
1.9
2
1.3
4
2.8
0.7
2.2
2.4
1.3
1.7
1.8
3.7
2.7
1.3
4.9
3.2
4.2
0.6
1.1
0.8
0.6
1.1
1.1
0.3
4.9
1.4
NW
5.9
17.4
11
5
2.29
WB
5.8
21.8
10.5
5.6
1.8
PSA
80
120
85
120
50
65
DSA
160
230
200
160
160
185
NO
80
110
115
40
110
120
7.6
6.1
16.8
10.4
10.5
8
6.1
6.2
17.3
11.4
12.5
8
8.4
6.5
12.1
9.3
11.5
6.4
11.3
NUL
NUL
NUL
6.4
11.8
9.2
11.6
5.8
9.8
13.7
10.6
11.5
90
90
130
140
120
70
130
90
110
70
80
170
235
170
200
150
210
220
140
200
160
140
80
145
40
60
30
140
90
50
90
90
60
70
NUL
NUL
NUL
130
NUL
NUL
NUL
2.5
3.8
9.9
9.9
3.8
7.7
2.5
4.2
10.9
10.9
4.1
6.6
200
NUL
NUL
NUL
135
130
150
190
190
150
200
120
50
70
90
90
70
70
80
80
100
100
80
130
MWP
18
34
BIR
1
0.99
16
4
1
1
27
15
35
15
1
0.96
0.83
1
34
0.99
18
1
16
42
17
1
1
1
0
16
1
1
12
1
188
Projectile Point Data
Cat. #
0281-001
0284-001
0295-001
0295-002
0295-002
0313-003
0320-001
0323-001
0327-001
0337-001
0355-001
0355-002
0360-001
0377-001
0377-002
0377-002
0377-006
0388-001
0388-002
0404-001
0409-003
0409-004
0426-001
0436-001
0436-001
0441-001
0441-002
0441-003
0441-003
0446-001
0451-001
Site
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
Complete
Y
Y
Y
N
N
N
N
N
Y
N
N
N
N
Y
Y
Y
N
Y
N
N
N
N
N
N
N
N
N
Y
Y
N
N
LT
21.7
32.2
18.2
WM
12.6
19.5
13.4
44.8
16.5
19.6
13.1
23.1
23.6
33.7
29.1
29.1
43.1
19.4
19.8
19.8
22
21.5
20.1
27.3
20.6
31
31
18.4
20.2
20.2
17.6
23.5
T
4.1
4.8
2.6
4
4
10.2
5.5
3.9
6.3
9
5.4
5.3
10.52
7
6.9
6.9
5.5
5.6
8.33
5.1
4.8
5.8
6.3
6.7
6.7
3
5.6
5.6
5.6
2.5
3.5
g
0.7
2.7
0.6
0.8
0.8
5.4
2.5
0.6
5.8
5.2
7.2
1
6.9
4.1
3.6
3.6
2.1
5.3
3.2
3.1
2.5
2.4
2.2
2.3
2.3
0.4
4.7
3
3
1.7
4.1
NW
1.9
10
2.7
8.5
8.5
10.6
2.9
7.5
13.3
14.6
9.3
7.8
WB
2.4
10.8
3.2
16.4
14.4
14.4
15.9
13.9
14.3
9.6
15.2
16.1
15.6
17.4
19.8
19.8
22
13.7
14.8
10
18
17.4
3.9
9.4
16.9
16.9
9.3
8.6
12
6.4
7.8
7.8
17.1
9.2
9.2
20.2
20.2
8.2
PSA
60
110
30
150
150
120
65
100
75
125
70
130
DSA
120
170
140
210
210
220
185
160
205
175
150
170
NO
60
60
110
60
60
100
120
60
130
50
80
40
MWP
3
28
0
BIR
1
1
1
28
1
90
140
140
150
110
130
110
120
120
125
150
150
80
80
140
140
70
30
220
200
200
245
220
220
160
200
200
200
220
220
160
220
220
220
215
130
130
60
60
95
110
90
50
80
100
75
70
70
80
140
80
80
145
100
38
32
32
1
0.98
0.98
34
1
32
1
0
0
0.97
0.97
189
Projectile Point Data
Cat. #
0460-001
0464-001
0464-002
0474-001
0483-001
0487-004
0493-002
0504-001
0510-001
0514-001
0514-002
0518-001
0518-002
0518-002
0518-003
0523-001
0523-002
0534-001
0534-002
0534-003
0534-004
0540-001
0545-001
0549-001
0565-002
0569-001
0574-003
0608-009
0608-010
0613-008
0613-009
Site
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
Complete
N
Y
Y
N
N
N
N
N
N
N
N
N
N
N
N
Y
Y
Y
N
N
N
N
Y
N
N
N
N
N
N
N
N
LT
22.8
24.7
WM
16.5
21.7
13.1
13.8
27.2
14.8
17.2
21.7
16.1
21.9
34.3
39.5
45
33.9
32
24.1
20.2
18.5
20.3
17.1
27.1
19.4
22.6
18.6
22
14.3
27.5
16.3
T
4.7
2.4
4.8
5.2
4.4
5.6
8.9
4
7.2
5.4
4.2
3.5
7
7
4.5
6.8
9.5
4.7
3.5
6.3
3.6
2.8
7.4
2.9
5.3
4.2
3.3
4.7
4
5.7
4.4
g
2.1
1.3
1
0.7
5
1.4
6.9
0.9
4.5
0.8
4.3
0.8
3.1
3.1
2.5
3.6
6
4.4
2.5
3.9
2.8
1.8
3.9
1.5
1.3
4.3
1.4
0.5
1
2
0.4
NW
NUL
13.4
3.8
6
10.2
13.2
10.4
PSA
NUL
125
80
90
70
130
80
20
150
DSA
NUL
180
140
175
190
220
220
170
200
NO
NUL
55
60
85
120
90
140
150
50
10.3
19.1
11.3
6.6
12
13.5
22
14.9
90
120
120
150
160
70
110
80
95
80
110
70
80
140
130
70
60
60
60
40
20
110
65
95
105
80
90
100
110
60
90
140
8
6.9
11
7.3
90
140
90
150
180
180
190
180
180
175
175
200
160
200
170
190
195
220
210
150
150
170
150
15.4
13.3
12.8
7.5
15.2
15.2
16.5
11.7
11.5
9.9
9.8
15.3
9.93
6.7
WB
16.5
15.3
4
6.2
12.9
14.5
7.1
17.9
16.1
9.5
18.6
18.6
20.8
16.9
4.2
10.3
11.5
18
60
30
60
MWP
BIR
26
17
1
1
31
40
27
34
1
1
1
1
21
1
34
0.98
190
Projectile Point Data
Cat. #
0622-005
0625-001
0629-003
0667-001
0671-001
0684-001
0703-003
0703-004
0712-002
0712-005
0712-006
0712-007
0712-008
0718-002
0725-009
0731-002
0740-001
0745-001
0752-001
0762-004
0773-002
0778-001
0778-002
0783-001
0783-002
0796-001
0800-004
0803-001
0803-002
0803-003
0808-001
Site
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
Complete
N
N
N
N
N
Y
Y
N
Y
N
Y
N
N
N
N
N
N
Y
Y
N
N
Y
N
Y
Y
N
N
Y
N
Y
Y
LT
WM
17.2
26.7
57.8
23.5
28.4
34.7
26.8
19.4
27.9
23.2
20.2
18.7
19.7
18.8
15.3
58.2
13.2
22.3
17.1
16.7
18.1
15.1
15.6
15.5
23.2
15.7
16.2
23.3
20.1
10.2
27.4
20.9
17.2
10.8
35.7
33.7
20.8
43.6
26.8
27.8
T
3
7.7
4.61
2.7
6.1
9.6
6.5
4.3
8
4.8
4.7
2.8
5.2
5.8
4.4
3.4
4.4
7
7.2
3.3
4.7
4.6
6.6
5.2
8.1
5
6.4
4.4
5.8
4.9
4
g
5.2
2.1
5.7
8.9
2.3
2.1
3.5
3.2
2.9
0.3
2.3
9.6
0.6
0.5
1.5
3.7
3.8
0.6
2.6
1.3
6.7
2.3
2.4
5.8
2.3
0.6
1
2.1
0.8
NW
9.3
17.1
14.27
10.2
18.5
NUL
9.6
WB
9.3
NUL
9.5
16
9.8
6.9
16.3
3
3.3
7
11.3
13.8
3.3
8
4.9
NUL
11.5
NUL
9.6
3.1
19.7
13.4
16.7
15.3
6.5
17.1
4.6
3.8
7.2
12.3
18.1
3.3
9.4
7
18.3
11.3
6.9
9.1
8.3
3.8
7.3
1.7
6.1
2.4
14.5
20.6
22
9.5
PSA
80
130
80
70
120
NUL
85
NUL
60
120
160
70
100
70
80
110
120
140
70
110
90
NUL
70
NUL
60
50
130
70
50
DSA
210
220
225
210
180
NUL
210
130
NUL
180
220
240
170
230
190
160
130
240
230
120
150
185
NUL
220
NUL
190
150
140
180
200
160
NO
130
90
145
140
60
NUL
125
MWP
BIR
46
0.53
36
44
1
0.99
NUL
120
100
80
100
130
120
80
70
120
90
50
40
95
NUL
150
NUL
130
0
22
37
1
0.99
1
30
1
38
39
1
1
26
33
35
41
1
1
0.99
1
90
50
130
110
9
1
38
13
0.99
1
191
Projectile Point Data
Cat. #
0826-002
0856-001
0856-001
0856-002
0859-001
0870-003
0890-001
0890-002
0890-003
0890-006
0890-007
0894-001
0894-002
0898-001
0908-001
0922-002
0926-007
0930-001
0962-001
0964-001
0967-001
0967-002
0971-001
0971-002
0977-001
0979-001
0996-001
1000-001
1009-002
1015-001
1017-001
Site
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
Complete
N
N
Y
N
N
Y
Y
N
Y
Y
Y
N
N
N
N
N
Y
Y
Y
N
N
N
Y
Y
Y
N
Y
Y
Y
Y
N
LT
18
26
41.1
51.4
21.6
25.4
WM
24
22.3
18.4
19.1
16.4
14.2
14.7
19.9
16.4
14.2
28.1
22.3
35.2
24.9
27.9
23.7
20.5
17.6
12.5
45
19.3
52.2
15.9
17.6
24
13.5
17.9
30.6
49.2
35.6
60
21.2
17
19.9
24.5
18.5
T
6.3
5.5
6.1
5
5.5
5.7
5.9
6.2
5.4
4.2
5.4
4.3
6.4
5.5
4.1
4.7
4.1
4
5.3
3.7
4.6
6
4.4
3.4
3.8
3.1
4.4
7.6
4.1
3.5
3.4
g
2.6
3
1.5
1.5
2
1.7
2.8
3.3
7.2
0.9
1.8
2.2
6.2
3
0.8
2.6
2.9
1.4
1.2
1
0.8
2
5
0.6
3.7
0.4
3
6.4
3.1
5.9
2.1
NW
16.1
13.7
13.3
4.7
6.3
5.1
9.1
NUL
NUL
2.4
8.3
6
2.4
13.7
8.1
15.5
12.1
10
3.4
WB
18.2
13.6
18.4
6.6
5.9
5.7
5
6.4
3.3
5
6.2
5.7
11.5
13.6
8.5
16.3
13.4
12.1
3.7
PSA
140
110
140
65
80
70
90
NUL
NUL
50
80
90
40
110
110
120
70
120
80
7.3
NUL
NUL
4.4
NUL
3
18
11.8
8
17.1
8.3
7.7
15.9
9.1
4.5
7.2
5.4
17.6
13.1
7.3
17.2
110
NUL
NUL
100
NUL
70
90
120
80
90
95
DSA
190
190
210
150
160
140
210
NUL
NUL
180
220
140
200
190
150
190
200
160
150
120
210
NUL
NUL
150
NUL
150
220
230
220
220
130
NO
50
80
70
85
80
70
120
NUL
NUL
130
140
50
160
80
40
70
130
40
70
100
NUL
NUL
40
NUL
80
130
110
140
130
40
MWP
BIR
29
1
18
33
1
1
36
32
49
1
1
1
40
32
16
1
1
1
32
18
35
1
1
1
43
44
40
28
1
1
1
1
192
Projectile Point Data
Cat. #
1028-001
1029-001
1029-002
1056-003
A11-19-A
A14-7
A16-51
A17-28
A19-1
A19-2
A20-1
A21-29
A22-10
A22-11
A22-49
A22-52
A22-9
A23-28
A23-29
A24-27
A25-18
A25-27
A25-32
A26-14
A26-32
A27-10
A2-73
A27-32
A28-50
A29-16
A2-99
Site
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-ELD-145
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
Y
N
N
N
Y
Y
N
N
Y
N
Y
N
N
N
Y
Y
N
Y
Y
N
Y
N
Y
Y
Y
Y
N
N
N
N
N
LT
36.1
24.1
20
25
21
16
17
61
24
33.5
12.5
24.5
17
24
53.5
53
29
WM
16.3
12
15
31
16
14
11
11
11
11
11
28
17
20.5
9
11
11
20
10.5
13
17
16
25
20.5
9
17
25
T
3.8
4.7
6.4
6.1
4
5
2.5
5
4.5
6.5
4
2
2
2.5
3
8
5
6
3
0.3
3
7
2
6
7
6.5
8
3
3
3
5
g
1.9
4.7
2
2
0.7
1.5
4.1
2.8
0.2
3
0.6
0.3
0.6
0.2
0.5
12.6
2.1
4.4
0.3
1
0.7
3
0.4
1.4
4.9
5.3
9.9
0.6
1.6
3.5
NW
5.1
13
5.5
11.4
WB
6.2
5.8
4.44
3.75
20.77
16
14
1.87
11
4.51
3.96
2.42
14
20.5
1.8
2.97
3.63
4.2
4.29
6.8
13.12
20.5
20.5
6.2
PSA
80
80
90
69
64
118
NUL
NUL
51
65
NUL
98
71
90
NUL
NUL
58
90
71
NUL
62
90
67
NUL
123
NUL
117
DSA
150
220
120
185
207
185
180
NUL
NUL
139
132
NUL
175
NO
70
140
30
90
138
121
62
NUL
NUL
88
67
NUL
77
MWP
12
BIR
1
38
34
22
1
1
1
1
1
0.9
1
1
140
NUL
50
NUL
11
41
NUL
130
178
151
NUL
185
132
222
NUL
204
NUL
179
139
154
NUL
72
38
80
NUL
123
42
155
NUL
81
NUL
0
14
0
26
25
14
26
20
25
0
0
37
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
193
Projectile Point Data
Cat. #
A30-1
A30-132
A30-133
A30-18
A30-28
A30-48
A30-50
A30-67
A30-68
A30-81
A30-82
A32-12
A33-80
A34-149
A34-185
A34-186
A34-2
A34-59
A34-79
A34-80
A34-82
A34-90
A34-91
A35-112
A35-113
A35-114
A35-16
A35-50
A35-70
A36-224
A37-12
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
N
N
N
N
N
N
N
N
N
N
N
N
Y
Y
Y
Y
N
Y
Y
N
Y
Y
Y
Y
N
N
Y
Y
Y
N
Y
LT
18
18
34.5
46
32
34
21
45
28
22
42
32
46
21
19
34
31
WM
18
28
18
19
13.5
22
9
21
16
20
18
19
20
20
21.5
17
16
23
17
13
16
20
19
22
10
11
19
16
14.5
T
7
10
4
5
3
4
3
9
2
6
5
4
5.5
7
6
4
6.5
3
9
4
4
7
6
5
5
3.5
3
2
5
2.5
5
g
5.4
6.2
1.6
2.9
0.8
2.1
0.4
7.9
0.4
3.1
2.65
1.3
3.3
6.2
4.3
2.3
1.7
0.5
8.5
1.5
0.9
4.3
3.1
3
5.2
2.85
0.7
0.5
3.5
0.6
1.8
NW
WB
7.02
9.8
9.54
15.2
4.05
7.04
1.8
6.72
3.36
7
13.86
7.98
8.6
11.4
5.59
10.37
4
14.95
3.51
11.2
4.4
4.75
19.36
1.4
2.31
19
9.28
PSA
96
85
83
NUL
76
71
69
73
88
69
131
72
56
68
NUL
64
72
90
NUL
90
90
NUL
55
74
119
114
69
60
155
71
90
DSA
206
167
192
NUL
182
181
126
207
147
142
217
156
186
201
NUL
138
186
154
NUL
184
178
NUL
173
178
220
215
139
159
198
185
245
NO
110
82
109
NUL
106
110
57
134
59
73
86
84
130
121
NUL
74
114
64
NUL
94
88
NUL
118
104
101
101
70
99
43
114
155
MWP
28
38
3
10
29
30
11
20
41
11
23
33
48
16
24
15
20
8
28
35
22.5
BIR
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
194
Projectile Point Data
Cat. #
A37-13
A37-14
A37-28
A37-36
A37-42
A37-43
A37-59
A37-79
A38-1
A38-109
A38-27
A38-33
A38-41
A38-56
A39-11
A39-25
A39-59-A
A39-59-B
A40-119
A40-172
A40-188
A40-19
A40-291
A40-292
A40-37
A40-38
A40-81
A40-83
A41-1
A41-117
A41-158
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
Y
Y
N
Y
Y
N
Y
Y
Y
Y
Y
Y
N
N
Y
N
Y
Y
N
Y
Y
Y
Y
N
Y
N
Y
N
Y
Y
Y
LT
22
22
38
20
23.8
WM
16
15
15
14
19.4
21.6
42
20
29
26
17
17.3
19.8
12
23
15.5
10
22
17
8
15
19
21
21
15
23
16
18
19
19
21
22
9
11
20
17
28
31
25
26
28
31
39
40
17.5
29.5
36
T
4
2
4
5
4.1
7.8
4.7
7
3
6
4
3
6
3.5
3
6
5
4
4
3.5
5
3.5
4
8
8
6.5
5
2.5
3
3.5
5.5
g
0.9
0.8
1.8
0.8
1.5
2.3
1.1
5.4
0.5
3.6
1
0.4
3.5
3.4
0.4
2.9
1.9
2.5
2.1
0.95
3
1.25
1.8
4.6
4.8
3.9
3.5
0.4
0.7
1.5
2.6
NW
WB
5.92
3.45
5.25
5.04
5.04
4
9.31
3.48
23
1.86
2.1
22
2
7.95
6.08
6.93
8.82
4.95
23
4.64
5.58
6.65
14
15.96
8.8
4.5
2.09
7.2
6.97
PSA
65
68
69
68
72
NUL
72
52
65
NUL
80
80
NUL
70
67
71
76
69
74
70
136
67
78
78
NUL
130
90
71
60
61
72
DSA
144
167
180
172
155
NUL
153
222
140
NUL
153
215
NUL
151
175
219
137
168
200
179
227
121
192
209
NUL
235
145
175
170
146
217
NO
79
89
111
104
83
NUL
81
170
84
NUL
73
135
NUL
80
108
141
61
99
127
109
91
54
114
131
NUL
105
54
104
110
85
144
MWP
31
28
13
30
20
BIR
1
1
1
1
1
28
19
1
0
7
23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
11
26
17
22
8
0
3
19
41
22
29
17
13
30
195
Projectile Point Data
Cat. #
A41-24
A41-98
A42-1
A42-100
A43-10
A43-123
A43-151
A43-18
A43-19
A43-200
A43-219
A43-38
A43-42
A43-48
A43-54
A43-57
A43-91
A44-136
A44-137
A44-156
A44-172
A44-23
A44-24
A44-39
A44-40
A44-63
A44-83
A44-84
A44-85
A45-1
A45-119
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
Y
N
Y
Y
N
Y
N
Y
Y
N
N
Y
Y
Y
Y
N
Y
Y
N
Y
Y
Y
N
Y
Y
N
Y
N
N
Y
Y
LT
43
17
39
39
35
20
30
19
17
32
22
26
51
18
56
25
24
32
27.5
35
21
24
WM
18.5
16
10
24
19
13.5
12
15
17
19
21
9
18
T
6
10
2
6
6
4
4
4
4
2
5
4
6
4
2
3
7
3
2.5
4
5
3
6
3
6
15
21.5
9
8
17
6
3
2.5
4
3
14.5
14
13
17
15
18
8
17
18
14
g
3.2
7.5
0.5
5.2
2.4
2.4
2.2
0.8
1.9
0.6
4.5
0.7
2.3
1.3
0.7
0.6
4.8
0.7
0.9
3.9
1.9
1
2.7
0.8
2.9
1.3
3.2
1.3
0.3
0.6
1.1
NW
WB
2.1
13.92
12.04
5.74
3.25
6.97
3.6
11.88
2.08
6.29
3.96
4.9
8.55
3.105
9.45
10
2.85
18.06
2.25
3.96
7.95
5.81
6.72
3.91
PSA
62
NUL
70
100
63
NUL
90
80
86
70
NUL
79
74
73
64
90
90
82
76
NUL
80
65
NUL
60
44
107
72
68
90
NUL
68
DSA
172
NUL
131
152
180
NUL
NO
110
NUL
61
52
117
NUL
130
150
153
NUL
143
230
142
200
156
212
158
142
NUL
225
181
NUL
120
184
186
238
142
137
NUL
169
50
64
83
NUL
64
156
69
136
66
123
76
66
NUL
135
116
NUL
60
140
79
167
74
47
NUL
101
MWP
57
5
23
20
11
20
19
25
8
34
18
18
22
43
16
13
14
34
24
22
BIR
1
1
1
0.97
1
0.97
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.94
1
196
Projectile Point Data
Cat. #
A45-154
A45-155
A45-181
A45-182
A45-19
A45-2
A45-239
A45-283
A45-284
A45-285
A45-286
A45-287
A45-299
A45-3
A45-315
A45-3310
A45-344
A45-40
A45-72
A45-73
A45-74
A45-97
A46-1
A46-32
A46-56
A47-116
A47-128
A47-129
A47-24
A47-25
A47-29
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
N
N
Y
Y
Y
Y
Y
N
N
N
N
Y
N
N
Y
N
Y
N
LT
25.5
16.5
29
32
36
35
28
21
30
20
27
24
27
25
45
22
27.5
WM
24
12
10
15
20
25
23
16
11
17
17
16
15.5
17
19
21
12
23
25
12
24
20
15.5
13
8.5
16
23.5
25
T
5
3
5
4
2
5
5
4
5
3
2
5
3
4
6
3
3
5
5
4
2
6.5
2.5
3.5
6
4
3.5
4
4
7
7
g
2.8
0.9
4
0.9
0.4
1.25
2.5
3.9
3.8
1.5
0.65
1.6
1.5
0.9
3.1
0.7
1.1
1.3
2.3
1
2.3
4.9
1.15
4.95
0.85
0.7
0.75
1.2
4.5
5.8
NW
WB
15.84
3.96
2
7.95
9
16.5
18.86
4.96
1.43
5.1
5.1
6.88
3.875
4.93
4.37
10.92
4.92
18.63
8
24
8.4
6.2
2.125
16
23.5
10.25
PSA
129
51
105
75
76
89
96
120
138
60
72
75
79
73
75
66
65
88
62
84
113
54
90
142
63
66
84
64
NUL
NUL
70
DSA
212
155
185
180
147
220
198
203
210
175
150
180
141
173
228
181
180
155
174
224
172
155
185
NO
83
104
80
105
71
131
100
83
72
115
78
105
62
100
153
115
115
67
112
140
59
101
95
MWP
206
189
180
180
NUL
NUL
202
142
123
96
116
NUL
NUL
133
27
23
44
18
25
20
21
11
19
23
30
25
23
22
24
9
0
BIR
0.99
1
1
1
1
1
1
1
0.94
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
197
Projectile Point Data
Cat. #
A47-30
A47-60
A47-99
A48-34
A48-36
A49-10
A49-125
A49-134
A49-134
A49-16
A49-168
A49-179
A49-38
A49-40
A49-79
A49-80
A49-88
A50-1
A50-101
A50-139
A50-172
A50-30
A50-31
A50-31
A50-32
A50-56
A50-81
A50-82
A51-114
A51-114
A51-155
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
N
Y
N
Y
Y
Y
Y
Y
N
Y
Y
N
LT
44
19
27
18
35.5
41
58
26
26
26.5
21
26
16
39
39
24
20
WM
25
15
15
9
17
23
23
17
17
16.5
12
14
10
21
18
20
13
28
19
30
20
21
9
9
25
18
16
15
14
14
18
32
22
32
24
27
23
23
T
6
1.5
3.5
4
4
4
4
4
4
4.5
4
3
3.5
8
6
3
3
6
3
5
8
9
4
4
5
3
7
6
4
4
6
g
4.9
0.55
1.45
0.5
1.85
3.3
7.8
1.1
1.1
1.6
0.5
0.9
0.5
6.8
4
1.3
0.7
5.2
1.3
5.1
4.5
6
0.6
0.6
3.5
1.2
2.4
2.6
1
1
3
NW
WB
9.5
2.85
4.5
PSA
64
60
61
4.93
8.97
15.41
4.42
4.42
4.46
1.92
3.92
2
10.92
15.12
5
4.94
7.84
71
74
NUL
60
60
75
72
90
68
NUL
110
83
82
88
79
54
NUL
89
65
65
64
67
131
76
84
84
81
13
11.97
1.62
1.62
16
2.88
16
6.9
4.06
4.06
5.94
DSA
181
142
203
136
141
172
NUL
165
165
139
145
145
167
NUL
215
140
162
173
143
152
NUL
120
182
182
198
140
225
219
174
174
213
NO
120
82
142
MWP
20
10
22
70
98
NUL
105
105
64
73
55
99
NUL
105
57
70
85
64
96
NUL
128
117
117
133
69
94
142
90
90
132
19
19
46
17
17
7
14
15
12.5
43
28
25
30
56
11
11
18
8
0
27
27
BIR
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.99
1
1
1
1
198
Projectile Point Data
Cat. #
A51-19
A51-20
A51-20
A51-58
A51-59
A51-59
A52-1
A52-127
A52-129
A52-13
A52-145
A52-160
A53-1
A53-1
A53-104
A53-117
A53-137
A53-157
A53-215
A53-216
A53-29
A53-29
A53-30
A53-31
A53-34
A53-58
A53-59
A53-82
A53-83
A54-24
A54-25
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
Complete
N
Y
Y
Y
Y
Y
N
Y
N
N
N
Y
Y
Y
N
N
Y
Y
Y
N
Y
Y
N
Y
N
Y
N
N
N
Y
Y
LT
23
23
19
15
15
36
25
19
10
21.5
35.5
35.5
19
19
36
34
26
35
28
WM
18
15
15
16
10
10
22
20
10
18
16
11
16
16
18
17
21.5
20
18
18
19.5
12
22
14
13
19
20
13
T
4
4
4
3
2.5
2.5
4
4.5
3
4
6
8
2.5
2.5
7
3
4
5
7
5
4
4
3
5
5
7
3
5.2
4
5
g
1.6
1.2
1.2
0.7
0.3
0.3
1.5
3.4
0.5
1.8
4.1
2.6
0.6
0.6
2.8
0.95
0.75
2
4.3
1.9
0.8
0.8
0.8
3.65
2.7
5.1
1.3
2.7
2.55
3
1.5
NW
WB
3.15
3.15
4
2.2
2.2
8.4
5.94
10
8.47
2.72
2.72
1.44
4.25
17
7.2
4.68
4.68
13.07
6
13.2
3.64
4.94
8.36
11
3.9
PSA
90
63
63
68
84
84
71
78
62
62
NUL
NUL
63
63
118
73
63
65
108
90
65
65
73
62
69
NUL
81
81
90
90
68
DSA
150
181
181
143
165
165
132
166
143
197
NUL
NUL
190
180
201
147
142
173
201
199
173
173
157
202
225
NUL
180
230
248
226
210
NO
60
118
118
75
81
81
61
88
81
135
NUL
NUL
117
117
83
74
79
108
93
109
108
108
84
130
156
NUL
99
149
158
139
142
MWP
19
19
13
6
6
17
43
23
23
28
25
4
28
19
44
44
18
12
50
22
22
33
BIR
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
199
Projectile Point Data
Cat. #
A54-48
A54-49
A54-50
A57-1
A57-2
A57-25
A57-32
A6-1
A6-2
A6-35
A6-51
AX5
AX-7
B-U53
26
359
360
A1-1
A11-7
A14-29
A14-6
A14-8
A15-5
A16-21
A16-28
A16-67
A17-2
A17-27
A17-3
A18-5
A19-11
Site
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV -407
CA-NEV-199
CA-NEV-199
CA-NEV-199
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
Complete
Y
N
N
Y
Y
N
Y
N
Y
N
Y
Y
Y
N
Y
N
Y
Y
N
Y
Y
Y
Y
N
N
N
N
Y
N
Y
Y
LT
37
21
43
27
23
29
15
25
23
34
39.1
32.7
22
27
31
34
23
21
WM
25
21
18
24.5
18.5
16
15
16
12
11
12
17
24
28
24.1
31.2
28.5
14
13
17
15
15
19
15
41
15.5
12
13
15
21
21
11
14
T
7
4.5
4
6
5
4
3
6
1
2
3
3
6
4
6
6
7.5
3
4
5
5
6
5
2
3.5
1
3
5.5
2
3
3
g
6.7
2.7
1.2
4.8
2
1.2
0.85
2.1
0.2
0.5
0.7
1.3
4.7
5.2
4.6
3.8
6
0.9
1.5
2.3
2.2
3.2
2
0.2
1.4
0.5
0.7
2.5
0.35
0.6
0.7
NW
WB
14
5.88
6.48
9.8
4.81
16
6.15
5.76
2.4
8.8
2.16
4.42
13.44
14
13.13
15.12
14
9.88
17
7.95
7.95
8.17
15.5
0.48
13
3.45
5.5
14
PSA
NUL
69
67
90
NUL
78
67
61
NUL
63
72
76
66
90
120
120
NUL
NUL
NUL
NUL
NUL
55
NUL
131
51
NUL
NUL
190
112
183
DSA
NUL
142
143
144
136
NUL
140
232
204
NUL
212
145
206
208
180
180
180
NUL
NUL
NUL
NUL
NUL
153
NUL
199
137
NUL
NUL
206
141
195
NO
NUL
73
MWP
48
54
46
NUL
62
165
143
NUL
149
73
130
142
90
60
60
NUL
NUL
NUL
NUL
NUL
98
NUL
68
86
NUL
NUL
16
29
12
22
8
BIR
1
1
1
1
1
32
36
40
1
1
1
32
4
25
1
1
0.98
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
<1
1
0.92
0
0
45
38.8
0
39
16
0
200
Projectile Point Data
Cat. #
A19-40
A19-52
A21-16
A21-17
A21-2
A21-30
A22-50
A22-51
A23-2
A2-47
A25-7
A26-1
A2-64
A27-28
A27-29
A27-38
A27-9
A30-49
A31-25
A31-35
A31-4
A33-39
A33-44
A35-1
A37-35
A38-19
A39-12
A40-18A
A41-157
A41-99
A43-137
Site
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
Complete
N
Y
N
N
Y
Y
Y
Y
N
N
N
Y
N
N
Y
N
N
Y
Y
N
N
N
Y
N
N
Y
Y
Y
Y
Y
Y
LT
WM
45.5
12
22
20
17
23
15
11
10
16
14
21
11
14
14
9
3
34
14.5
13.5
9
23
16
16
30
18.5
28
39
16
42
16
16
15
16
7.5
14
T
3.5
3
3
3
3
2
2
3
4
3
3
2.5
3
2
2.5
2
3
4
3
5
3
3.5
4
5
4
7
4
5
4.5
2.5
6
g
0.5
1.7
0.9
0.4
1
0.4
0.5
1.2
0.6
0.5
0.8
0.7
0.7
0.3
0.2
0.5
0.8
1.8
0.2
1.4
0.8
0.7
1.4
1.7
0.7
4
1.3
1.8
2.5
0.3
4.1
NW
WB
12
10.2
11
6.6
16
6.38
3
10
9
16
16
8.96
12
6
8.48
7.5
5.04
PSA
141
176
147
184
119
158
129
NUL
NUL
84
NUL
126
142
DSA
NO
MWP
215
201
156
218
207
183
NUL
NUL
155
NUL
142
215
39
54
28
99
49
54
NUL
NUL
71
NUL
16
73
0
NUL
165
158
NUL
NUL
NUL
146
160
NUL
NUL
148
NUL
120
NUL
NUL
NUL
NUL
NUL
174
186
NUL
NUL
NUL
193
246
NUL
NUL
198
NUL
216
NUL
NUL
NUL
NUL
NUL
9
28
NUL
NUL
NUL
47
86
NUL
NUL
50
NUL
96
NUL
NUL
NUL
NUL
0
35
0
19
0
16
26
0
0
33
44
36
34
0
29
BIR
<1
0.89
0.9
1
0.87
0.99
0.97
1
1
1
1
<1
1
<1
1
1
1
1
<1
<1
1
0.97
1
1
0.97
1
1
1
1
201
Projectile Point Data
Cat. #
A43-218
A44-1
A44-2
A44-3
A44-4
A44-77
A45-134
A45-216
A46-80
A47-23
A49-9
A49-99
A50-102
A50-140
A52-161
A53-32
A5-61
A5-62
A56-31
A56-52
A59-12
A6-10
A6-23
A6-26
A6-49
A6-50
A6-70
A6-77
A7-32
A7-41
A9-20
Site
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
CA-NEV-407
Complete
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
N
Y
Y
N
N
N
Y
N
Y
N
Y
Y
Y
N
N
Y
Y
Y
Y
LT
52
13
34
58
31
25
26
16
22
WM
21
8
13.5
18
14
14
16
10
15
50
21
20
34
30
25.5
19
19
19
17
19
12
19
18
22
15
21
13
12
13.5
12
13
14
12
12
9
14
9
11
14
14
T
5
3
11
5
6
4
4.5
2
5
2
7
4
2
5
5
3
3
3
5
4
3.5
0.2
3
2
3
5
3
1
3
2
3
g
7.3
0.3
3.4
6.7
3
1.2
2.2
0.5
1.9
0.35
5.45
4.4
0.5
3.1
2.5
0.7
0.7
2
2
0.7
0.4
0.6
0.5
0.7
1.9
0.8
0.2
0.5
0.5
0.7
NW
WB
12.6
8
8.24
13
11.48
10.5
10.88
10
9
11.97
11
11.7
13.65
10.1
13.5
12
13
14
12
9.96
1.53
8.68
9
11
3.5
2.94
PSA
NUL
NUL
NUL
NUL
114
121
NUL
NUL
NUL
151
NUL
NUL
188
NUL
123
110
DSA
NUL
NUL
NUL
NUL
222
224
NUL
NUL
NUL
193
NUL
NUL
205
NUL
210
215
NO
NUL
NUL
NUL
NUL
108
103
NUL
NUL
NUL
42
NUL
NUL
17
NUL
87
105
148
NUL
NUL
164
NUL
167
NUL
62
116
58
NUL
152
70
78
209
NUL
NUL
204
NUL
230
NUL
152
217
157
NUL
214
135
156
61
NUL
NUL
40
NUL
63
NUL
90
101
99
NUL
62
65
78
MWP
35
0
38
37
42
28
46
0
41
30
37
24
0
0
0
0
32
40
41
0
0
20
BIR
1
1
1
1
1
1
1
1
1
<1
1
1
<1
1
1
1
<1
1
1
0.91
0.95
1
1
1
1
1
1
0.95
1
1
202
Projectile Point Data
Cat. #
AX-3
AX-6
13539
89-17-1
Site
CA-NEV-407
CA-NEV-407
D'ville Iso 1
53-475
Complete
N
Y
Y
N
LT
WM
23
29.1
15
19.6
23
T
3
5
9
g
0.85
1.9
4.5
5.6
NW
WB
14.9
12.1
PSA
167
130
75
140
DSA
199
226
225
170
NO
32
96
150
30
MWP
45
28
BIR
<1
1
1
<1
Adapted from Clewlow, C. W. Jr., Richard D. Ambro, Allen G. Pastron, Steven G. Botkin, and Michael R. Walsh, 1984. Stage II Final Report
for CA-NEV-407 Archaeological Data Recovery Program. Report submitted to CALTRANS, Marysville, California; Deis, Richard W. and
Daron Duke,1998a. National Register Evaluation of FS 05-17-56-126, CA-SIE-629/H. Report submitted to Tahoe National Forest, Nevada
City, California; Deis, Richard W. and Daron Duke 1998b National Register Evaluation of FS 05-17-56-178. Report submitted to Tahoe
National Forest, Nevada City, California; Deis, Richard W. and Daron Duke, 1998c. National Register Evaluation of FS 05-17-56-251. Report
submitted to Tahoe National Forest, Nevada City, California; Deis, Richard W. and Daron Duke, 1998d. National Register Evaluation of FS
05-17-56-292. Report submitted to Tahoe National Forest, Nevada City, California; Deis, Richard W. and Daron Duke, 1998e. National
Register Evaluation of FS 05-17-56-293. Report submitted to Tahoe National Forest, Nevada City, California; Deis, Richard W. and Daron
Duke, 1998f. National Register Evaluation of FS 05-17-56-295, CA-SIE-628. Report submitted to Tahoe National Forest, Nevada City,
California. Deis, Richard W. and Daron Duke, 1998g. National Register Evaluation of FS 05-17-56-302, CA-SIE-627. Report submitted to
Tahoe National Forest, Nevada City, California. Deis, Richard W. and Daron Duke, 1998h. National Register Evaluation of FS 05-17-56-360.
Report submitted to Tahoe National Forest, Nevada City, California; Deis, Richard W. and Daron Duke 1998i. National Register Evaluation of
FS 05-17-56-380, CA-SIE-744/H. Report submitted to Tahoe National Forest, Nevada City, California. Deis, Richard W. and Daron Duke
1998j. National Register Evaluation of FS 05-17-56-454. Report submitted to Tahoe National Forest, Nevada City, California. Deis, Richard
W. and Daron Duke 1998k. National Register Evaluation of FS 05-17-56-462. Report submitted to Tahoe National Forest, Nevada City,
California; Deis, Richard W., Daron Duke, Kelly J. Dixon, and Robert W. McQueen, 1998. National Register Evaluation of the Two in One
Site (FS 05-17-56-016), Sierraville Ranger District, Tahoe National Forest, Sierra County, California. Report submitted to Tahoe National
Forest, Nevada City, California; Jackson, Robert J., and H. S. Ballard, 1999. Once Upon a Micron: A Story of Archaeological Site CA-ELD145 Near Camino, El Dorado County, California. Report submitted to Caltrans District 03, Marysville, CA. Pacific Legacy Inc., Cameron
Park, CA; Weachter, Sharon A., 1990. Archaeological Test Excavations at Site #05-17-53-475 (Oak Flat) on Lafayette Ridge, Downieville,
Ranger District. Report submitted to Tahoe National Forest, Nevada City, California; Weachter, Sharon A., Julia G. Costello, Susan Lindstrom,
and William W. Bloomer, 1995. Final Report on the Assessment of Damages from the Cottonwood, Crystal, and Hirschdale Fires at Ten Sites
on the Tahoe and Toiyabe National Forest. CRR#05-17- 1129. Volume I: Report. Report submitted to Tahoe National Forest, Nevada City,
California.
203