Human Behavioral Response to the Younger Dryas in North Alabama: an Analysis of the Richard L

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Human behavioral response to the Younger Dryas in North Alabama: An analysis of the Richard L. Kilborn collection

Robert A. Barlow

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Human behavioral response to the Younger Dryas in North Alabama:

An analysis of the Richard L. Kilborn collection

By TITLE PAGE Robert A. Barlow

A Thesis Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Applied Anthropology in the Department of Anthropology and Middle Eastern Cultures

Mississippi State, Mississippi

August 2019

2019

Human behavioral response to the Younger Dryas in North Alabama:

An analysis of the Richard L. Kilborn collection

By APPROVAL PAGE Robert A. Barlow

Approved:

______Darcy Shane Miller (Major Professor)

______Evan Peacock (Committee Member)

______James W. Hardin (Committee Member)

______Ryan Parish (Committee Member)

______David M. Hoffman (Graduate Coordinator)

______Rick Travis Dean College of Arts & Sciences

Name: Robert A. Barlow ABSTRACT Date of Degree: August 9, 2019

Institution: Mississippi State University

Major Field: Applied Anthropology

Major Professor: Darcy Shane Miller

Title of Study: Human behavioral response to the Younger Dryas in North Alabama: An analysis of the Richard L. Kilborn collection

Pages in Study 105

Candidate for Degree of Master of Arts

This study is a collections-based project that employs approximately 1,300 projectile points to investigate behavioral response to the Younger Dryas in north

Alabama (12,900 to 11,700 BP). I apply a version of the marginal value theorem to determine how changing resource structures caused changes in projectile point technology. I argue that changes in technology during the Younger Dryas were not conditioned by access or availability of lithic raw material. Instead, variation in technology is likely a response to changes in return rates from hunting and foraging.

Further, the changes in hunting return rates correlate with changes in north Alabama forest structure, which were conditioned by the Younger Dryas. To this end, I argue that the sustained impact of the Younger Dryas, and subsequent Holocene warming, had an effect on the subsistence economies of hunter-gatherers living in northern Alabama during this time, which is exhibited by changes in projectile point technology.

DEDICATION

I dedicate this to Tatum and Emerson, my two shining stars. Daddy Loves you.

I also dedicate this to my mom for taking care of me at home and to Stone Cold Steve

Austin for raising me via satellite every Monday during my youth.

ACKNOWLEDGEMENTS

This work would not be possible without the support of many people over the past two (long) years. First and foremost, I want to acknowledge my major advisor, Shane

Miller. This would not be possible without his guidance and I could not be more grateful for him taking a chance on me. Shane’s investment and passion towards the success of his students is unparalleled. Likewise, I would like to thank my committee members

Evan Peacock, Jimmy Hardin, and Ryan Parish. These gentlemen provide a guideline for what it means to be a true academic and were available to help me at the drop of a hat.

Further thanks are warranted to Ryan for his guidance in the reflectance spectroscopy portion of this study. Next, I would like to thank Richard L. Kilborn. Without Richard’s willingness to loan the Kilborn Collection, this gap in Alabama archaeology would not be filled. Also, the Alabama Archaeological Society (AAS) has provided a generous amount of financial support for this project which funded the reflectance spectroscopy research.

Last, I would like to thank a great friend and mentor, Steven Meredith. Steven has provided countless conversations to aid in this research and all other things, both archaeology and not archaeology.

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TABLE OF CONTENTS

DEDICATION ...... ii

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

CHAPTER

I. INTRODUCTION AND STATEMENT OF RESEARCH PROBLEM ...... 1

Introduction ...... 1 Statement of Research Problem ...... 4

II. ENVIRONMENTAL AND GEOLOGIC SETTING ...... 6

Geology ...... 8 Paleo-environmental Record ...... 8 Late Paleoindian to Early Archaic Archaeology in Alabama ...... 11 Russell Cave ...... 13 La Grange Rock Shelter ...... 13 The Quad Locality ...... 14 Heaven’s Half Acre ...... 15 Stanfield-Worley ...... 16 Dust Cave ...... 16 Biface Chronology ...... 18

III. HUNTER-GATHERER STONE TOOL ECONOMIES ...... 24

The Organization of Technology ...... 24 The Marginal Value Theorem Applied to Stone Tools ...... 30 Hypotheses ...... 35

IV. MATERIALS AND METHODS ...... 39

Materials ...... 39 The Kilborn Collection ...... 39 Alabama Fluted Point Survey ...... 40 iv

The Cambron-Hulse Collection ...... 40 Raw Material ...... 41 Methods ...... 42 Raw Material Selection ...... 46 Artifact Discard ...... 47 Resharpening ...... 47

V. RESULTS...... 49

Raw Material Procurement ...... 49 Discard Rates ...... 52 Resharpening ...... 53 Biface Size and Dimensions Through Time ...... 56

VI. DISCUSSION ...... 58

Human Response to the YD in North Alabama ...... 60

VII. CONCLUSIONS ...... 63

Future Research ...... 64

REFERENCES...... 66

APPENDIX

A. PROJECTILE POINT DATA ...... 74

LIST OF TABLES

Table 2.1 Point type chronology ...... 18

Table 4.1 Numbers of type by collection ...... 41

Table 5.1 Raw material frequency by biface type ...... 51

Table 5.2 Chi-Square test...... 52

Table 5.3 Spearman’s rho results by type ...... 54

Table 5.4 Volume (mm3) by type...... 56

LIST OF FIGURES

Figure 1.1 Temperature changes (determined as proxy temperatures) taken from the central areas of Greenland's Ice Sheet during the Late Pleistocene and the beginning of Holocene (USGS 2016)...... 2

Figure 2.1 Map of the study area ...... 7

Figure 2.2 1Li573 Map of chert-bearing formations with a 20 km one-day foray buffer ...... 9

Figure 2.3 Alabama Paleoindian sites (modified from Futato 1996) ...... 12

Figure 2.4 Clovis point type (PIDBA 2019) ...... 20

Figure 2.5 Cumberland (left) and Redstone (right) point types (PIDBA 2019)...... 20

Figure 2.6 Quad (left) and Beaver Lake (right) point types (PIDBA 2019) ...... 21

Figure 2.7 Dalton point types from the Kilborn Collection (Photo Credit: Robert A. Barlow)...... 22

Figure 2.8 Big-Sandy point types from the Kilborn Collection (Photo Credit: Robert A. Barlow) ...... 23

Figure 3.1 The organization of technology hierarchy modified from Nelson 1991...... 25

Figure 3.2 Reformulated MVT modified from Kuhn and Miller 2015...... 31

Figure 3.3 Miller (2014) The relationship between E (the average expected instantaneous returns minus the cost of producing or procuring a new artifact) and time and/or the number of uses (modified from Kuhn and Miller 2015). (Right) The relationship between length and re-sharpening episodes for Folsom type bifaces fired and re-sharpened in an experimental context (modified from Hunzicker 2008)...... 32

Figure 3.4 Hypothetical relationship between length and width with resharpening (Kuhn and Miller 2015) Bottom portion depicts a width that has preserved by the hafting element...... 35

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Figure 4.1 PIDBA biface measurement guide...... 43

Figure 4.2 Example of prehistorically broken points ...... 45

Figure 4.3 Example of points with modern breaks ...... 46

Figure 5.1 Chi-Square goodness of fit results with all Dalton sample ...... 53

Figure 5.2 Resharpening through the course of the YD ...... 55

Figure 5.3 Boxplot of volume by type ...... 57

Figure 6.1 Trends in variables through time. Patterns in faunal diet breadth are from Styles and Klippell (1996) and Walker (1998). Patterns in floral diet breadth are from Hollenbach (2005), Hollenbach et al (2010) and Carmody (2009)...... 60

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CHAPTER I

INTRODUCTION AND STATEMENT OF RESEARCH PROBLEM

Introduction

The Younger Dryas (YD) is characterized as a global cooling event with a rapid onset around 12,900 cal BP followed by an equally abrupt termination around 11,700 cal

BP (Alley 2000; Alley et al. 1993; Broecker et al. 2010; Meeks and Anderson 2012).

Holliday and Meltzer (2010) argue that the climatic changes brought on during the 1,300- year span of the YD were not universal and its effects on regional environments varied in strength. This claim is supported by paleo-environmental data which reveals evidence of considerable variation in local paleoecological conditions (Ellis et al. 1998; Eren 2012;

Goebel et al. 2011; Meltzer and Holliday 2010). In the American Southeast, fossil-pollen records demonstrate changes in biotic communities, and previous archaeological research has shown a correlation between biotic shifts and behavioral changes in Paleoindian populations throughout the YD (Anderson et al. 2011; Meeks and Anderson 2012;

Smallwood et al. 2015; Thulman 2006; Tune 2016).

The effects of the YD on human populations have been the topic of recent scholarly debate (Tune 2016). Currently, there is no consensus as to whether humans noticed and responded to the YD event, or not. Meltzer and Holiday (2010; Holliday and

Meltzer 2010) argue that during the onset of the YD, North American populations were still highly mobile and familiar with dynamic climatic conditions. As a result the Early 1

YD went largely unnoticed by those groups. Anderson et al. (2011) and Meeks and

Anderson (2012) adopt a regionally specific approach to the YD in the American

Southeast and suggest a population decline which is attributed to declining frequencies of points between the Clovis and Cumberland point types. This pattern correlates with adverse ecological conditions at the onset of the YD.

Figure 1.1 Temperature changes (determined as proxy temperatures) taken from the central areas of Greenland's Ice Sheet during the Late Pleistocene and the beginning of Holocene (USGS 2016).

Anderson et al. (2011) and Meeks and Anderson (2012) further argue that a population rebound occurred which is based on an increase in frequency of Dalton type projectile points. This pattern coincides with improved ecological conditions. Smallwood et al.

(2015) point out a similar population trend as Anderson et al. (2011) and Meeks and

Anderson (2012) during the YD in Georgia. In addition to population models, Smallwood et al. (2015) address land-use patterns and determine that Clovis populations were concentrated in areas that were rich in high quality chert. They also suggest that subsequent Cumberland populations declined from Clovis populations, but Cumberland people may have become increasingly more tethered to chert sources compared to Clovis groups. Moreover, towards the end of the YD, Quad and Dalton-type numbers rebounded, and populations seem to have relocated inland to more elevated areas.

Smallwood et al. (2015) attribute this change to unstable and unpredictable coastal habitats resulting from fluctuating sea levels at the end of the YD.

Kuhn and Miller (2015) likened artifacts to resource patches and applied a modified version of the Marginal Value Theorem (MVT) to Paleoindian projectile points from Tennessee. Kuhn and Miller (2015) argue that shifts in curation strategies and discard rates of projectile points from the Late Pleistocene to the Early Holocene are related to changes in resource structure (i.e., availability of raw material and decreasing prey size). Likewise, Tune (2016a) also found significant changes in technological organization, land-use, and toolstone selection throughout the YD in the Midsouth. He demonstrated that Clovis and Cumberland bifaces exhibit a positive correlation between length and width, and were usually discarded when broken at a one-to-one (approx.) ratio. In other words, Clovis and Cumberland points were discarded more often as large 3

broken points or large whole points with little to no resharpening. In contrast, Tune

(2016a) found that Dalton bifaces exhibited no correlation between length and width and were discarded at an eight-to-one ratio. In other words, Dalton points were discarded more often as whole points with significant resharpening. This pattern suggests a stark change in artifact life histories from the Clovis to Dalton types in the Midsouth (Tune

2016a). Additionally, he found that overall numbers of bifaces declined throughout the

Pleistocene to Holocene transition (Tune 2016a). This finding contradicts both Anderson et al. (2011; Meeks and Anderson 2012) and Smallwood et al. (2015)’s findings concerning population trends that are represented by biface frequencies.

As shown, Previous research demonstrates complexity and regional variability in perceived behavioral adaptations and population models concerning the YD. However, all of the works I mentioned suggest or incorporate a local or regional specific approach and argue this type of approach is needed to understand the variable effects of the YD over space and time. My thesis research focuses on north Alabama. An area that is understudied in regard to the YD.

Statement of Research Problem

While north Alabama is well situated to fill in the regional gaps and conflicting findings regarding the YD, several biases in existing research impeded the use of this region, which needed to be addressed. First, most attention on human behavioral response to the YD in the American Southeast has been focused on the onset of the event, not the terminus or sustained effects (Eren 2012; Ellis 2011; Ellis et al. 2011; Meeks and

Anderson 2012; Meltzer and Holliday 2010; Smallwood et al. 2015; Tune 2015, 2016).

Second, in Alabama, the lack of a documented representative sample limits the research questions that can be investigated. For example, the Alabama Fluted Point Survey

(AFPS)’s database only contains data for projectile points that predate the Dalton phase

(Futato 1996). Finally, most attention is on caves sites and bluff shelters, not open-air sites, which highlights only one aspect of a north Alabama hunter-gatherer’s lifeway.

(Cobb 1987; Cobb et al. 1995; DeJarnette et al. 1962; Driskell 1996; Randall 2002;

Sherwood et al 2004; Walker 1997, 1998; Walker et al 2001, 2002; Walthall 1998).

Combined, these biases create a “black hole” in research for the extent of the YD in

North Alabama.

YD research in north Alabama requires attention to address the biases that are due to the lack of research on open-air sites, the lack of research on the full extent of YD, the need for a regional approach that considers the regional variation in the YD, and lack of usable data for the region . This study serves as a solution to biases in YD research for north Alabama. In this study, I use an organization of technology (e.g. Nelson 1991) approach and approximately 1,300 projectile points that repesent the full extent of the

YD, most of which are from open-air sites, to examine human behavioral response to the

YD. Here, I test three different hypotheses that can offern insights as to why there is variability in my study sample concerning point size, rates of resharpening, raw material procurement, and condition at time of discard. I found changes in artifact life histories that correlate with changes in environment and dietary resource structure brought on by the YD. These results are significant because they outline regional behavioral adaptations in stone tool economics as a response to changing resource structure, which in turn is caused by dramtic and dynamic fluctiations in temperature. 5

CHAPTER II

ENVIRONMENTAL AND GEOLOGIC SETTING

Limestone, Madison and Morgan Counties are located along the southern edge of the Highland Rim (Fenneman 1938) in the Lower Tennessee River Valley of north

Alabama (Figure 2.1). The Tennessee River meanders along the southern edge of

Limestone and Madison counties, and the northern edge of Morgan county, acting as a divide. The River continues flowing to the west, through the low uplands of the Highland

Rim and into the Cumberland Plateau as it loops back north into east Tennessee. More recently, the river has experienced considerable change compared to prehistoric times due to intense damming by the Tennessee Valley Authority (TVA) as part of President

Roosevelt’s New Deal. Since the TVA Act was signed by Roosevelt on May 18, 1933, the TVA has constructed nine dams along the Tennessee River, all of which include associated lakes that act as reservoirs. Additionally, multiple locks and dams were constructed for many of the river’s tributaries. TVA’s main objective was to improve the quality of life for people in the Tennessee Valley by providing inexpensive electricity, jobs, flood control and navigation. However, the damming inundated many towns along the Tennessee River, which required the relocation of many families and transformed the

Tennessee River Valley into the valley we recognize today (TVA 2017)

Figure 2.1 Map of the study area

Geology

The Tennessee River Valley of north Alabama has a dynamic geologic history. A portion of this geologic history, now known as the Mississippian Epoch, is thought to be the period for which the Fort Payne, Warsaw, Tuscumbia/St. Louis, and Monteagle/Ste.

Genevieve formations were formed. Further, these formations yield many outcrops of high-quality chert (Miller 1974). Prior to the damming of the Tennessee River, the Fort

Payne Formation was exposed in the Tennessee River Channel, but it is now mapped as water because of the construction of Wheeler Lake. This phenomenon is apparent in the tributaries to the north of the river (Figure 2.2) where the river cuts through the

Tuscumbia formation and down into the Fort Payne formation. Limestone, Madison, and

Morgan counties have an abundance of chert from the Fort Payne and Tuscumbia formations; however, a considerable amount of variability exists within the chert types, even in the same formation, across the study area (Barry 2004; Parish 2009, 2015). The variability in chert types makes visually sourcing specific outcrops in the region difficult.

This study uses provenance methods based on Visible Near-Infrared Spectrometry

(VNIR) and Fourier-Transform Infrared Spectroscopy (FTIR) (Parish 2009, 2015) to source raw materials and stone tool artifacts to their geologic and geographic parent formations.

Paleo-environmental Record

Beginning around 12,900 BP, the YD significantly influenced the climatic and biotic landscape in the North Atlantic for a period of 1,300 years before abruptly coming to an end around 11,700 BP (Alley et al. 1993; Alley 2000). As discussed below, fossil- pollen data from Cahaba and Anderson Ponds in the region indicate vegetation shifts 8

were occurring that correlate with the YD event (Delcourt 1979; Delcourt and Delcourt

1983; Delcourt et al 1983). However, like other areas throughout North America, the degree of strength of the YD was not uniform across the region (Ellis et al. 1998; Eren

2012; Goebel et al. 2011; Meltzer and Holliday 2010).

Figure 2.2 1Li573 Map of chert-bearing formations with a 20 km one-day foray buffer

Many local environments throughout the region experienced varying magnitudes of the YD, as reflected in a diverse set of factors, including amplified cold weather temperatures and dynamic oceanic/atmospheric circulation patterns causing temperature and moisture-balance gradients that influenced responses of biotic communities in the

region (Anderson et al. 2011; Ellis et al. 1998; Eren 2012; Goebel et al. 2011; Meltzer and Holliday 2010). Further, Russell et al. (2009) suggest that the American Southeast was a thermal enclave and protected from long term cold extremes during the late

Pleistocene. The enclave was bound by the Southern Appalachians to the west and the

Atlantic coast on the east. To the north, in present day Maryland, ground water palaeotemperatures, which are based on measurements of atmospheric noble gases dissolved in ground water, demonstrate cooler temperatures than found immediately south in the interior of the enclave. Moreover, the temperature gradient that was characteristic of the enclave and the areas bordering it allowed for a diversity of faunal and floral species (Russell et al. 2009). These data offer further support for a localized approach to examining the effects of the YD in the American Southeast.

Generally, in north Alabama, hardwood species became dominant over conifers as the late Pleistocene transitioned to the Early Holocene. This trend can be demonstrated with data from Anderson Pond in Tennessee and Cahaba Pond in Alabama (Delcourt

1979; Delcourt and Delcourt 1983; 1985; 2004). Anderson Pond is a 35-ha sinkhole pond located in the middle Tennessee region. The pond is situated at approximately 300 m elevation on the eastern edge of the Highland Rim and is in a depression formed in dolomitic limestone from the Tuscumbia formation. Fossil-pollen data show that conifer forests dominated the regional vegetation during much of the Pleistocene. Though later, through the late Pleistocene and Early Holocene, hardwoods such a hornbeam replaced pines and other conifers as the YD ended and the general warming trend characteristic of the Holocene was underway (Ballard et al. 2017; Delcourt et al. 1983; Delcourt and

Delcourt 1985; Liu et al. 2013). Farther south, in north Alabama, data from Cahaba Pond

exhibit a similar trend to those from Anderson Pond. Here, hardwoods such as beech, oak, hickory, elm, and ash dominated the forests of the surrounding watershed into the

Holocene (Delcourt 1979; Delcourt and Delcourt 1985).

The depositional history of Dust Cave also has offered valuable insight into the paleo-environment of north Alabama through the course of the YD. Dust Cave was entirely filled with alluvial sediment around 17,000 to 15,000 years ago (Collins et al.

1994; Sherwood 2001). Through time, as the Tennessee River down cut, Dust Cave developed into a conduit for spring water, which flushed the sediment out (Collins et al.

1994:50). After the removal of sediment, the Tennessee River periodically flooded the cave with alluvium, which is evidenced by the presence of sterile clays at the rear and base of the cave (Goldberg and Sherwood 1994). Around 11,000 years ago, the continuing reduction in the volume of the Tennessee River caused the cave spring to dry up, providing a suitable, habitable environment for Late Paleoindian groups (Sherwood

2001; Sherwood et al. 2004). However, Sherwood (2001:368) states that post- depositional, cold-climate features such as localized ice lensing and aggregated cryoturbation microfabrics in Zone U at Dust Cave may be the result of brief extreme cold phases or harsh winters during the YD.

Late Paleoindian to Early Archaic Archaeology in Alabama

North Alabama suffers from a lack of documented, stratified sites. As a result, chronological inferences suffer. Much of what is known about Paleoindians in Alabama comes from the Stanfield-Worley bluff shelter and Dust Cave (Futato 1996; Johnson

2019) Here, I discuss Russell Cave, La Grange Rock Shelter, the Quad Locality,

Heaven’s Half-Acre, Stanfield-Worley, and Dust Cave in terms of their significance to 11

north Alabama Paleoindian and Early Archaic archaeology. These sites are significant due to their contributions to the current dataset via the AFPS, and the knowledge they provide of north Alabama Hunter-Gatherer lifeways.

Figure 2.3 Alabama Paleoindian sites (modified from Futato 1996)

Russell Cave

Russell Cave (1JA940) is located in Jackson County, northeast Alabama. The site is in a mountainous area within the Cumberland Plateau, which, like most of north

Alabama, is an area rich in stone tool raw material (Lacefield 2013). In the 1950’s to the mid-1970’s, a number of sponsored investigations took place at Russell Cave (Broyes

1958; Miller 1956,1958; Griffin 1974). This research suggests that lower level G dates to the Early Archaic Period whereas the upper level G is dates to the Middle Archaic Period

(Futato 1996). Limited numbers of unifacial tools and simple bifacial tools were found in layer G, along with ground stone processing tools (Broyes 1958; Miller 1956,1958;

Griffin 1974). Bone tools are also present at Russel Cave, with a large portion of them being awls, but heavier bone tools used in the manufacture of chipped stone tools are also included (Futato 1996). The faunal remains at Russell Cave indicate that deer, squirrels and turkey were heavily favored in the diet, with squirrels comprising over 75% of the identified faunal remains (Weigel et al. 1974). Futato (1996) the site is considered to be a prehistoric cold weather camp.

La Grange Rock Shelter

La Grange (1CT90) is a rock shelter site in Colbert County, northwest Alabama.

The site overlooks many other Paleoindian sites in the adjacent valley (Gramley 2017).

There were two seasons of excavations through the University of Alabama at La Grange during the 1970’s. The rock shelter is presumed to date to the Early Archaic period due to a 11,280 BC 14C date gathered from charcoal found immediately below a Dalton occupation. However, Knight suggests that a collapse of the original shelter floor may have caused the charcoal to migrate to the lower level associated with the Dalton 13

component (Knight 1975). Later, Hollenbach (2010) employed faunal and botanical data from La Grange, and other rock shelters, to explore hunter-gatherer subsistence and mobility practices in north Alabama. She found that Early Holocene hunter-gatherers used a combination of local floral and faunal resources and practiced seasonal camp movements between uplands in the autumn and the bottomlands during spring and summer. The lack of evidence for storage in the region during the Paleoindian and Early

Archaic periods makes it difficult to say for certain what winter subsistence and mobility patterns were (Hollenbach 2010). However, Hollenbach (2010) postulates that rock shelter sites in the region may have been used due to the flexibility they provide to move between upland and bottomland settings during winter months, when resources are lean.

The Quad Locality

The Quad locality is an approximately three-mile-long site complex within the

Tennessee River Floodplain in Decatur, Alabama. The locality also includes the Stone

Pipe and Pine Tree sites (Hubert 1989). The Quad locality has been extensively collected since its discovery in 1951 (Futato 1996). Frank Soday, Jack Cambron and David Hulse were among three of the primary collectors when the site was exposed in the early 1950’s due to lowering of Wheeler Lake to repair Wheeler Dam. The Quad locality had numerous, multicomponent, surface scatters of artifacts which from the Paleoindian to

Early Archaic periods (Wilmsen 1968,1970). Gustasfon and Pigott (1981) note that over

200 fluted bifaces were recovered and documented from the locality. However, the number of Early Archaic projectile points at the locality far outnumbers the Paleoindian points (Futato 1996). Further, because the Quad locality has been continually, extensively collected, even to the present day, much of the information we could gain on the locality 14

will remain unknown due to many of the artifacts being in the hands of collectors who are unwilling to grant access to these collections.

Heaven’s Half Acre

Heaven’s Half Acre is a complex of Paleoindian sites situated on terraces overlooking a sinkhole in Colbert County, Alabama. To date, approximately 150 fluted projectile points have been recovered from the complex (Anderson 1990; Anderson and

Faught 1998; Horrace 1967; Wright et al 1982). The main site, 1CT161, is approximately located at the center of the complex, on a knoll surrounded by a pond, with around twenty other Paleoindian sites surrounding it (Gramley and Walley 2017). Additionally, nearly every elevated location within this complex has its share of fluted points (Futato 1996).

The complex went through two seasons of excavation in 1982, with focus on 1CT161.

The first season was a controlled surface collection in four-by-four-meter squares. This investigation was conducted by OAR with the help of the Alabama Archaeological

Society (AAS) (Futato 1982; Wright et al 1982). The second round of investigation consisted of backhoe trenches, which were placed in areas with the highest debitage concentrations as determined by the previously mentioned surface survey (Futato 1996;

Lee 1982). The trenches exhibit evidence that the high-elevations sites had been significantly disturbed due to modern agricultural practices. However, the soils at the slopes of the terraces was preserved, and material remains along, with charcoal, suggest an Early Archaic date, (Futato 1996).

Stanfield-Worley

Stanfield-Worley (1CT125) is a 60-meter wide bluff shelter located approximately seven miles south of the Tennessee River in northwest Alabama. The bluff in figure 2.3 with Dust Cave) was the principle site excavated in a joint effort by the

Alabama Archaeological Society, the Archaeological Research Association of Alabama, and the University of Alabama to explore Alabama’s Paleoindian prehistory. Zone D, the lowest stratum at Stanfield-Worley, contained both Dalton and Big Sandy material

(DeJarnette et al 1962). The Dalton and Big Sandy occupations at Stanfield-Worley show similarities in their toolkits, and the faunal material recovered from the site indicates that most of the diet was focused primarily on small animals and deer (Futato 1996, citing unpublished 1962 excavations). Other small animals, such as gray squirrels, turkeys, and turtles, are common in the assemblage (Parmalee 1962). Additionally, nutting stones were recovered during the unpublished 1962 excavations by David J. Dejarnette indicating a plant-based component to the diet. Stanfield-Worley was also one of the rock shelters investigated in Hollenbach (2010)’s study which expands on the importance of plant-based subsistence on north Alabama forager lifeways.

Dust Cave

Dust Cave (1LU496), a stratified cave located on the northern side of the

Tennessee River valley floodplain, is arguably one of the most important sites in southeastern archaeology because it produced a reliable 14C sequence (Driskill

1994,1996; Sherwood 2004) The site was first brought to the attention of archaeologists through an unpublished report by Dr. Richard Cobb, an educator who explored and documented many caves in northwest Alabama (Cobb 1987; Cobb et al. 1995; Driskell 16

1996). Years of research at Dust Cave have shown that the site was in use from 10,500

B.P. to 5,200 B.P. Throughout the Late Pleistocene and Early Holocene occupations, waterfowl and fish are heavily represented in the faunal assemblage, while white-tail deer and small animals occur less frequently at Dust Cave compared to other sites in the region (Walker 1998). In addition, the remains of plant foods at Dust Cave indicate that groups were utilizing a range of resources that included hickory, black walnut, hackberry, and probably grape (Walker 2010a). Further, research by Styles and Klippel (1996) suggests that during the Late Paleoindian/Early Holocene transition, diet breadths may have been wider compared to the following Early Holocene. The floral and faunal data also demonstrate that the site was occupied around the same time of year by the Late

Paleoindian and Early Archaic groups, with both groups favoring fall, early winter, and spring seasons (Hollenbach 2005; Hollenbach and Walker 2010).

The lithic assemblage from Dust Cave shows that the toolkits are similar between the Dalton and Big Sandy occupations, with side and end scrapers present alongside drills and hafted bifaces (Hollenbach 2005; Hollenbach and Walker 2010). Randall (2002) points out a dissimilarity between the two occupations in that prepared-core, unifacial blades are lacking in the Big Sandy occupation, while they are widespread throughout

Dalton occupations throughout the Tennessee River Valley. Randall (2002) suggests a major shift in the organization of technology, with increased reliance on bifacial tools over blade forms, something that could be due to Early Archaic populations more routinely repurposing hafted projectile points into other tools.

Recently, researchers have begun to apply site-to-regional-scale analysis and to use larger datasets to investigate settlement patterns and chronologies in the American

Southeast. However, the region presents many problems when attempting to build a chronological framework for Paleoindian points. The lack of large-scale, multidisciplinary excavations at stratified sites (Anderson et al. 2015:29–30) contributes to this problem. Further, the lack of excavated, stratified deposits at 1Li573 warrants a heavy reliance on what can be inferred from temporally diagnostic bifaces that were surface collected.

Biface Chronology

For my study, I use traditional biface types as representations of chronological time-slices that loosely correspond to changes in North Atlantic climate as recorded by the Greenland Ice Core Project (Alley 2000; Anderson 2001). Unfortunately, chronological dates for Clovis and Cumberland/Redstone point types are not consistent in north Alabama. Therefore, I use Waters and Stafford’s (2007:1123) standard age range for Clovis in North America and Miller (2018) for a Cumberland/Redstone range in the broader Southeast. Additionally, Thulman (2017)’s Bayesian approach to re-analysis of points from Dust Cave provides a chronology for Paleoindian and Early Archaic points that postdate Cumberland/Redstone in north Alabama.

Table 2.1 Point type chronology

Point Type Climatic Period Date Reference Clovis Bolling-Allerod 13,250-12,800 cal BP Waters and Stafford 2007 Cumberland/Redstone Early Younger Dryas 12,677-12,483 cal BP Miller 2018 Quad/Beaver Lake Middle Younger Dryas 12,540-11,870 cal BP Thulman 2017 Dalton Late Younger Dryas 12,040-11,260 cal BP Thulman 2017 Big-Sandy Early Holocene 11,450-10,950 cal BP Thulman 2017

Clovis points are generally larger lanceolate points with slightly concave bases and one or more flutes that never extend more than a third of the length of the body 18

(Howard 1990; Justice 1995; Morrow 1995; Sellards 1952; Smallwood 2012). Clovis

Points coincide with the end of Bølling-Allerød (BA) interstadial period, which is characterized by an abrupt increase in temperature and moisture (Anderson et al. 2015;

Thomas 1999). The termination of the BA is marked by the rapid onset of the Younger

Dryas (approx.12,800 c. B.P.) which corresponds with the appearance of

Cumberland/Redstone points in the Southeast. Cumberland bifaces are narrow lanceolate points with slightly concave bases, a waisted appearance, and faint ears (Lewis 1954a,

1954b; Justice 1995). Tune (2016b) places the appearance of the Cumberland point type as mostly likely overlapping or immediately post-dating the onset of the Younger Drays.

Goodyear (2006) defines Redstone point type as lanceolate points that are similar to

Clovis points, but which have a general triangular shape which is widest at the base.

Further, Redstone points usually have indented bases and more pronounced fluting than

Clovis points. Goodyear (2006) argues that they are contemporaneous with the

Cumberland point type. However, the placement of these points in this chronological position relies heavily on comparisons to similar, but well-dated projectile points in other regions, like Folsom on the Plains and deeply-indented fluted points in the American

Northeast (Anderson et al. 2015; Miller and Gingerich 2013; Tune 2016). Based on previous research (Smallwood et al. 2015; Tune 2015,2016a,2016b)

Cumberland/Redstone is considered to represent the Early Younger Dryas (EYD).

Figure 2.4 Clovis point type (PIDBA 2019)

Figure 2.5 Cumberland (left) and Redstone (right) point types (PIDBA 2019)

New research shed light on the Paleoindian biface chronology at Dust Cave

(Thulman 2017). The earliest 14C date associated with human occupation comes from

Zone U at the site, which produced Quad and Beaver Lake point types. Beaver Lake

Points are lanceolate points with a slightly waisted appearance, faint ears, and slight basal concavity and thinning (Cambron and Hulse 1975; Justice 1995). Quad points have pronounced ears, as well as concave, thinned bases (Cambron and Hulse 1975; Justice

1995). These two types represent the Middle Younger Dryas (MYD) and have a span of approximately c. 12,540-11,870 B.P. (Thulman 2017). This time-frame represents a period of continuing cold, of which the lowest temperatures of the YD are thought to have occurred. Further, A heavily reworked Cumberland was recovered in Zone U at

Dust Cave, and Sherwood et al (2004) states that it was below the Quad/Beaver Lake occupation. Consequently, this is evidence to further argue that any Cumberland components in the study area likely predate Zone U. Further, Daltons are considered to postdate Zone U, as they are associated with the overlying zone T at the site (Thulman

2017).

Figure 2.6 Quad (left) and Beaver Lake (right) point types (PIDBA 2019) 21

The Late Younger Dryas (LYD) is represented by the Dalton point type, which begin as lanceolate points with concave bases and serrated, excurvate blade margins

(Justice 1995). However, the blade margins transition from excurvate to incurvate through repeated resharpening episodes (Goodyear 1974; Shott and Ballenger 2007).

Dalton points date to ~11,700 (12,040-11,260 cal BP) (Thulman 2017). This was a period of frequent, and often extreme cold trends. Additionally, overlapping with this point type at Dust Cave and extending into the Early Holocene (EH) are Early Side-Notched points, which are triangular points with side notching and a concave base (Cambron and Hulse

1975). Early Side-Notched point types are represented by the Big-Sandy variant which occurs between 11,450 and10,950 cal B.P. at Dust Cave (Thulman 2017). The EH marks the termination of the YD and is a period of rapid warming in which shifts in biotic communities occurred.

Figure 2.7 Dalton point types from the Kilborn Collection (Photo Credit: Robert A. Barlow)

Figure 2.8 Big-Sandy point types from the Kilborn Collection (Photo Credit: Robert A. Barlow)

CHAPTER III

HUNTER-GATHERER STONE TOOL ECONOMIES

The Organization of Technology

In order to assess how north Alabama hunter-gatherers structured their stone tool economies to changes in biotic resource structure brought on by the YD, I take an organization of technology approach. Technological organization is the study of the selection and integration of strategies for making, using, transporting, and discarding tools and the materials needed for their manufacture and maintenance (Nelson 1991).

Technological organization seeks to understand human behavior in terms of economic constraints and payoffs, costs and benefits. Researchers working with technological organization concepts are mostly concerned with decisions accounting for time, energy, and risk, with particular focus on raw material economies and artifact life histories (Kelly

2013; Nelson 1991). Considering figure 3.1, if a researcher is trying to determine how an environmental condition, like the YD, is affecting the way someone designs and uses their tools, it needs to be understood it in terms of the economic strategy being used to cope with the environment. This is facilitated by technological strategies, which are implemented through design and artifact distribution. Which archaeologist should see as artifact form and artifact distribution (Kelly 2013).

Research on the organization of biface technology has a rich history in North

America (Andrefsky 2009; Nelson 1991). Lewis Binford considered the investigation of technology to be a strategy for archaeologists to interpret variation across artifact

assemblages (Binford 1977, 1979, 1980). Furthermore, researchers who employ this concept attribute variation to the different uses of place and economic decisions related to subsistence (Binford 1979, 1980). Later, Koldehoff (1987:573) and Kelly (1988:717) presented definitions for the organization of technology that recognize behavioral conditions for aspects of tool manufacture, use, and life history (maintenance, use, and discard).

Figure 3.1 The organization of technology hierarchy modified from Nelson 1991.

These definitions go beyond the requirements for the tool’s specific task and embrace context and planning as having a role in tool and toolkit design (Nelson 1991). Simply put, Nelson (1991) argues that the study of technological organization seeks to identify the economic decisions that arise when humans interact with the environment.

Nelson (1991) provides a synthesis of the “technological organization” theoretical perspective that serves as the impetus to analyze the various factors that could influence behavior regarding the manufacture, design, and life history, including maintenance, of stone tools. Her view includes behavioral aspects of stone tool manufacture that are concerned with access to raw material, time, and risk factors (Bleed 1986; Torrence

1983). Further, Nelson (1991) discusses stone tool economies couched in three technological strategies, which are curation, expediency, and opportunistic behavior.

These technological strategies explain the energetic trade-offs of a tool, throughout its use-life, or in other words, when is a piece of technology (tool) worth the trouble of acquiring, manufacturing, maintaining, and transporting in a given environmental or social context (Kelly 1988,1995)? She argues that it is important to consider that these definitions do not identify an artifact class or kind of assemblage. Instead, they represent different kinds of plans that can be executed in a variety of ways that respond to a diversity of environmental conditions. Further, the form an artifact assumes, and the overall structure of an individual’s toolkit, reflect a complex array of economic decisions

(Nelson 1991:62).

Curation is a technological strategy that includes advanced manufacture, transport, reshaping, and caching or storing of objects or materials (Shott 1986).

Although, Nelson (1991) argues that curation as a strategy does not need to meet all these

aspects, a primary factor in delineating it from expediency is the preparation of the raw materials in anticipation of conditions that are not favorable for production at the place of material procurement. Such behavior as this may include preparing cores and carrying them to a place to be further worked or carrying finished tools to a place of use. Plans for changes made to the forms of cores and tools depend on whether they persist in a curation strategy or if they are used expediently (Nelson 1991:62-63).

Kuhn (2004) presents a view of stone tool economies through the concept of technological provisioning. He presents the notion of provisioning strategies (Kuhn 1994,

1995, 2004), which are optimal strategies for securing finished tools and/or the raw materials required to manufacture them when needed. Kuhn (2004), concerning the ability to manufacture and use stone tool artifacts, offers three provisioning strategies and details their material implications.

The first strategy is to cache tools or tool-making materials at places where their use is anticipated. This strategy requires taking artifacts or materials from a procurement location to wherever the artifact is provisioned (Kuhn 2004). These caches would include artifacts at multiple levels of manufacture and mitigate the need for extensive resharpening or reworking the tool into another form. Therefore, in contrast with the other strategies, provisioning of place requires less investment in manufacture and maintenance. The next strategy involves maintaining individuals’ supplies of artifacts or toolstone that they anticipate using. This strategy is heavily influenced by transport cost and is rooted in maximizing potential utility of an artifact relative to its weight (Kuhn

2004). This strategy also assumes that individuals will more frequently provide themselves with finished tools instead of raw material. Additionally, to prolong artifact

use, maximize utility, and minimize the amount of material being transported, artifacts that fall under this strategy should be subjected to frequent resharpening and reshaping as they accrue use wear or break (Kuhn 2004). Further, Kuhn (2004) states that because all strategies involve some planning, by nature they are represent curative behaviors. The third and final provisioning strategy Kuhn (2004) discusses is provisioning activities, which are like Nelson’s (1991) concept of opportunistic behavior, in that opportunistic behaviors are produced as a response to abrupt and unforeseen conditions. This strategy involves minimal investment in manufacturing tools and, because focus is primarily on local materials, transport of raw material in anticipation of its use does not occur (Kuhn

2004). Last, there are two implications of this strategy. First, this on-the-fly production is only reliable where raw materials are plentiful and near an area of planned tool-use, such as a prey ambush location; and second, it could be an efficient strategy for someone who was set to go without tools.

Shott (1986) argues that the relationship between curation and expedient tool use is continuous, rather than categorical (Shott 1986). As a technological strategy, curation works to lessen the dichotomy between the availability of raw materials or tools and the location where the tools are utilized (Bamforth 1986; Binford 1979; Keeley 1982; Parry and Kelly 1986) and resolves the issues with obtaining mobile resources or other time- dependent stressors affecting resources (Ebert 1986; Gamble 1986; Torrence 1983).

Further, as Nelson (1991) states, the various characteristics of curation, including resharpening, have implications for the design and distribution of stone tools.

Nelson considers design to be “conceptual variables of utility that condition the forms of tools and the composition of toolkits” (Nelson 1991:64). She considers five

variables of design that suit certain technological strategies. The identified variables are reliability, maintainability, transportability, flexibility, and versatility. Nelson (1991) considers there are advantages and disadvantages to these variables that prioritize one design element over another in the production and utilization of a stone tool. Moreover, the prevalence of these variables is dependent on the conditions and adaptive strategies that are suitable to a context. In other words, they were selected for. Of the variables that

Nelson (1991) proposes, those that relate to resharpening of bifaces are maintainability and flexibility.

Bleed (1986) defines a maintainable design as one that is constructed to be efficient to use in a variety of conditions. Nelson builds upon this argument by adding flexible and versatile design alternatives associated with a maintainable design. A versatile design is kept in a generalized form to accommodate a diversity of tasks.

However, a tool with a flexible design changes form to achieve a variety of future tasks.

Nelson (1991) states that shape sequences identified by Bradley (1977:152; Granger

1980; Montet-White 1968) are possibly attributed to flexibly designed toolkits and not a product of a reduction strategy aimed at a final tool form (Nelson 1991:72). This observation also has implications for materials and rates of discard through time.

McAnany (1982) contends that the sequential reduction of Mayan bifaces in Belize is representative of conservation of stone tool raw material when access was circumscribed due to rigid social boundaries. Additionally, Tune (2016) also demonstrates a correlation between bifaces that were possibly reworked back into a functional tool form and restricted territorial boundaries as evidenced by raw material frequencies. Further, Nelson

(1991:77) argues that because lithic raw material is an immobile source that is constantly

available where it naturally occurs, it is easily manipulated by humans. However, she posits that if any raw material is unavailable, it is because social, economic, or technical decisions made by humans have worked to facilitate such a condition. Therefore, reworked artifacts in the archaeological record may be a product of raw material conservation (Nelson 1991).

The Marginal Value Theorem Applied to Stone Tools

Transitions in biface life histories could be further explained through examining the relationships between land-use and paleoecology (Tune 2016:10). In Kuhn and Miller

(2015), a variant of the Marginal Value Theorem (MVT) is presented that treats lithic artifacts as resource patches that represent utility. Stone tools do not directly supply users with nutrients, but they do make work more efficient and afford the user the possibility of a net gain in nutrients (Kuhn and Miller 2015:176). Further, treating stone tools as patches of utility agrees with many of the assumptions of the MVT. Artifacts have a limited utility, which declines as the artifact is used or modified (e.g., resharpened or reshaped) over time and the point of a catastrophic failure is approached. The user’s knowledge of the tool’s condition is central to this model. It assumes that people know if a new tool would be more efficient, as well as how much time and effort is involved in manufacturing a new one. Thus, the modified MVT model presented by Kuhn and Miller

(2015) asks how long people should use a tool, whose utility is perpetually declining, before it is replaced by a new tool.

The model considers the cost of switching between artifacts and energetic cost.

This cost is derived from tool manufacture and the energy spent in raw material 30

procurement. More importantly, regarding this study, raw material procurement is particularly significant where the manufacture of large bifacial points is concerned due to the sought-after functional properties of some raw materials (Beck and Jones 1990;

Goodyear 1989; Kelly and Todd 1988; Surovell 2009). Further, the reworking of tools such as arrowheads, spearheads, or inset barbs should be factored into replacement costs

(Keeley 1982). The assumptions of the model presented by Kuhn and Miller (2015) are similar to the original energy-based variant of the MVT and are as follows:

• Hunters use one point at a time, OR the size of the toolkit does not change. The ability to exploit several patches simultaneously affects predictions about residence time (McNair 1979), but if the number of artifacts (patches) is held constant, it should not matter. • The utility of artifacts (effectiveness, or the probability that they can be employed successfully) declines with successive uses. • People monitor tool effectiveness or condition. • The cost of replacing artifacts is a combination of raw material costs and production effort, and tool users are aware of those costs.

Figure 3.2 Reformulated MVT modified from Kuhn and Miller 2015.

Kuhn and Miller (2015) expect that artifact discard rates are influenced by replacement costs, as well as potential gain. In addition, as returns decline, tool users should keep artifacts longer and abandon them in later stages of reduction. To test the model, Kuhn and Miller (2015) use the Paleoindian Database of the Americas (PIDBA) to examine Paleoindian spearpoints from Tennessee. In their study, Kuhn and Miller

(2015) hypothesize changes in replacement costs and return rates throughout the

Paleoindian period as represented in the condition of discarded points. Their results show that Early and Middle Paleoindian points had different life histories than later point types, with the later point types being exposed to higher rates of modification via resharpening and reworking. Still, a question persists as to why there would have been changes in the life histories of Paleoindian spear points through time. To this end, Kuhn and Miller’s

(2015) model suggests two alternative working hypotheses. 32

First, point types being discarded later in their use life are indicative of an increased cost of replacement. Kuhn and Miller (2015) suggest that as populations increased after initial colonization, strict territorial boundaries could have restricted access to sparsely distributed raw materials. Simply put, some later Paleoindian populations could have been confined to areas that were farther from high quality lithic sources. This implies that if access to raw materials is the cause of differential conditions of bifaces at the time of discard, then the trends would vary by location according to access to lithic raw materials. Furthermore, where raw material is less abundant or access to it is restricted, there should be more evidence of varying levels of resharpening of bifaces (Kuhn and Miller 2015).

The other explanation for a change in artifact use-life stated in Kuhn and Miller’s

(2015) model is a decline in average hunting return rates from use of points. During the

Late Pleistocene and Early Holocene, two factors could have contributed to lower return rates. One is the extinction of large megafauna at the end of the Pleistocene. The other is a shift to smaller fauna with higher search and handling costs. This could be due to either a loss of large animals or to the smaller animals being more readily accessible. Kuhn and

Miller (2015) state that if the rate of return from using an artifact is high, then an artifact should be replaced more frequently. Although replacing an artifact with a high caloric return rate may seem counterintuitive, Kuhn and Miller (2015) state, “The cost of an artifact’s failure is proportionate to the benefits gained by its use. For activities with very high returns, the potential cost of failure is high compared to the cost of replacing artifacts as they wear out and become less effective or reliable”. Conversely, Kuhn and

Miller’s (2015) model also predicts that if expected hunting return rates from use of spear

points were to markedly decline, there would be longer periods of time between tool replacement. Thus, the increase in biface size and/or resharpening is seen as a proxy for extending the use life of an artifact and a decline in the overall return rates for using the artifact, and, in the case of spearpoints, this likely reflects either the pursuit of smaller prey and/or an increase in search and handling costs.

For example, Miller (2014) established a relationship between resharpening of points and the size of prey by comparing faunal remains and projectile points in areas where raw material can be held constant to show that variation in the resharpening of projectile points is likely due to demand, or fluctuations in hunting returns. Consistent with Kuhn and Miller’s (2015) model, Miller (2014) demonstrates that when a greater diversity of fauna is present, which indicates lower return rates, there should be more maintenance of tools. In sum, in areas like North Alabama, where raw material access can be held constant, examining the re-sharpening of bifaces through time could provide insight into hunting returns and economic decisions through the course of the YD.

Figure 3.4 Hypothetical relationship between length and width with resharpening (Kuhn and Miller 2015) Bottom portion depicts a width that has preserved by the hafting element.

Hypotheses

Meltzer and Holliday (2010) argue that during the onset of the YD, North

American Paleoindian populations were highly mobile and familiar with dynamic climatic conditions. Therefore, groups likely did not notice the early impacts of the event.

One way to gauge if individuals would have been impacted by the Younger Dryas would

to be monitor changes in the organization of their stone tool technology (e.g. Smallwood et al. 2015; Tune 2016). If individuals in North Alabama were not impacted by the YD, this would be exhibited by no discernable changes the design, use, maintenance, and discard of the projectile points. I assess this by testing for statistically significant changes in volume (L*W*T) as a proxy for absolute size, length-to width ratios as a proxy for resharpening and hunting returns, complete-to-broken ratios tool as a proxy for condition at time of discard, and selection of raw material to determine mobility. If there is a lack of change in north Alabama it is likely due to the lack of a substantial change in hunting returns over time. I would expect to see projectile points with similar sizes, length-to- width ratios, and discard rates.

Alternatively, several studies have argued that the onset and sustained effects of the Younger Dryas had an observable effect on technological organization and landscape use (Anderson et al. 2011; Kuhn and Miller 2015; Meeks and Anderson 2012; Miller

2018; Sherwood et al. 2015; Tune 2016a). Moreover, the termination of the YD was likely just as rapid as the onset (Alley et al. 1993; Alley 2000). If individuals in North

Alabama were impacted by the YD, this would be exhibited by discernable changes the design, use, maintenance, and discard of the projectile points. Moreover, these patterns would not be random, but correspond with changes in foraging return rates brought on by the YD, and would suggest that the event had a significant impact on dietary resource structure and mobility in north Alabama.

The archaeological signature of a response to changing resource structure is that the sizes of projectile points would have fluctuated through time as a result of variation in

returns from hunting (e.g. Kuhn and Miller 2015). For example, as diet-breadth is widened to include smaller animals with a higher search and handling cost, individuals should be expected to discard smaller points with higher rates of resharpening if access to raw material is held constant. As Kuhn and Miller (2015) suggest, if differential levels of resharpening are present, one possible explanation is varying degrees of access to high- quality toolstone. However, in North Alabama, high quality chert was no farther than a one-day logistic foray (e.g. Surovell 2009). Consequently, any variability in biface resharpening is likely due to differential hunting returns (e.g. Miller 2014). I expect

Points representing this foraging pattern to exhibit smaller overall size (volume) and /or a declining relationship between length and width (lower length-to-width ratios).

Moreover, I expect these points would likely be discarded more often as smaller, whole points, and when discarded they would be heavily re-sharpened, and therefore closer to the point of catastrophic failure and further along the gradient of expended utility.

Conversely, points would be larger and exhibit less resharpening if diet breadth is narrowly focused on fewer species of larger, easier to catch fauna with higher return rates. I expect Points representing this foraging pattern would have a larger absolute size

(volume) and a positive relationship between length and width (length-to-width ratios).

Further, these points would be discarded more as broken points, or larger complete points with unexpended utility.

Additionally, random variation in size, length-to-width ratios, and discard may also be present without any correlation to climate change brought on by the YD. This pattern could be the result of many casual factors. For instance, a currently unknown variable affecting access, size, and quality of raw material, such a change in the size of 37

knappable cobbles in a region could lead to changes in point size and resharpening.

Further, reduced accessibility of raw material in an area could lead to variation which is not influenced by climate change, but centered around a less risky design (e.g. Hofman

1992; Jennings 2016). Alternatively, changes in the size and shape of bifaces could be due to the effects of neutral cultural transmission. For example, Eerkins and Lipo (2005) posit that copying errors affect the fidelity the transmission of tool design from person to person. Eerkins and Lipo (2005) further argue that copying error should be considered when investigating chronological change because a copy error during a single transmission event can go without notice, but the effects are collective and noticeable over time, and cause variation that is neutral, or not the result of changes in the functional use of the tool.

CHAPTER IV

MATERIALS AND METHODS

Materials

I limited the sample in this study to Limestone, Madison, and Morgan counties in northeast Alabama. These counties are located on the south side of the Highland Rim, an area rich in stone tool raw material (Pashin 1993). I used information from the Kilborn

Collection, the Alabama Fluted Point Survey (AFPS), and the Dalton portion of the

Cambron Hulse Collection (Craib 2016) from north Alabama in order to get a sample of bifaces from Clovis to Big-Sandy types.

The Kilborn Collection

Richard L. Kilborn is an avocational archaeologist from north Alabama who surface collected artifacts from 1Li573, an open air, plowed-field site in Limestone

County, Alabama. The most common artifacts in this collection are hafted bifaces

(n=687). The bifaces in the Kilborn collection consist of Clovis (n=10), Cumberland

(n=17), Quad/Beaver Lake (n=7), Dalton (n=56), Big-Sandy (n=542), and undetermined bifaces or bifacial fragments (n=55). The collection also includes numerous uniface tools, hammerstones, and debitage from various stages of lithic reduction.

Alabama Fluted Point Survey

The AFPS is a part of the Paleoindian Database of the Americas (PIDBA) and is a combined effort of numerous scholars, and both advocational and professional archaeologists, to record fluted point types that are in the possession of collectors or housed in curation facilities. I used both broken and complete hafted bifaces downloaded from the AFPS for Limestone, Madison, and Morgan Counties for this study. I downloaded the Excel file containing all bifaces from the counties in the study area from

PIDBA (2018) to prepare the AFPS data for statistical analysis. I excluded point types that could not be identified through accompanying pictures. I also excluded artifacts I designated as preforms and unfinished, unidentified, and unknown point types. After these exclusions, a total of n=563 remained for analysis, including Clovis (n=218),

Cumberland/Redstone (n=184), and Quad/Beaver Lake (n=161). I then cross-referenced the data reported in the excel file to pictures of the bifaces, also available through the

AFPS, to verify if the points were whole or broken, using only complete measurements.

Unfortunately, the AFPS does not contain data for the Dalton and Big-Sandy point types.

The Cambron-Hulse Collection

The Cambron-Hulse collection is a combined collection of over 38,000 points currently housed at the University of Tennessee’s McClung Museum. James W.

Cambron and David C. Hulse were well-known avocational archaeologists who meticulously recorded data on the artifacts in their collections (Pike et al. 2006). Further, their recording methods are considered a standard for record keeping among avocationalists in north Alabama and the broader Mid-South. The collection includes several thousand Paleoindian and Archaic bifaces, many of which come from counties in 40

north Alabama. Data for Dalton points from the Cambron and Hulse collection (n=85) were provided by Alex Craib (2016), but include Limestone County points only.

Together, these collections provide a substantial sample of basic measurements and represent a considerable temporal range in which one can potentially discern shifts in the organization of hunter-gatherer stone tool technology throughout the YD in north

Alabama.

Table 4.1 Numbers of type by collection

Alabama Fluted Point Survey Count Clovis 218 Cumberland 184 Quad/Beaver Lake 161 Total: 563 The Kilborn Collection Count Clovis 10 Cumberland 17

Quad/Beaver Lake 7 Dalton 56 Big-Sandy 542 Unidentified 56 Total: 688 Craib (2016) Count Dalton 85 Total: 85

Raw Material

For the provenience portion of this study, I compared all points from the Kilborn collection to an abridged version of the Chert Type Database of the Southeast (Parish

2019). Samples in this library include Fort Payne chert from Illinois, Kentucky,

Tennessee, Alabama, and Georgia; Bangor from Alabama, Tuscumbia from Illinois,

Kentucky, and Tennessee; St. Genevieve/Monteagle from Indiana, Kentucky, and

Tennessee; Brassfield from Tennessee; Burlington from Missouri, Kaolin from Illinois; and Dover from Tennessee.

Methods

I measured maximum length, width, and thickness for all points from the Kilborn

Collection with digital calipers. The measurements generally follow Anderson et al.

(1990), (figure 4.1) with the exception of maximum width. The widest point of the blade was measured, which would have been protected by the haft while being reworked

(Hoffman 1985; Kuhn and Miller 2015; Randall 2002; Shott 1997; Shott and Ballenger

2007). I did not alter the metrics from the AFPS and the data from Craib (2016), since I could not obtain a more accurate measurement from the available photographs.

Additionally, I calculated all values for volume (L*W*T) and length-to-width ratios with formulas in the Excel database.

Figure 4.1 PIDBA biface measurement guide

Specifically, for the Kilborn Collection, I classified bifaces as broken if the point of the break was covered in patina. If the point of the break was not covered with patina, I classified it as a modern break, likely the result of modern agricultural practices (Figures

4.2 and 4.3). In the case of a refit where all parts of the biface are present, but not intact, I combined the pieces and took measurements for maximum length, width, and thickness. I assessed the completeness of the points from Craib (2016) and the AFPS based on the data provided (metric and photos). However, numerous points in the AFPS were recorded as proximal or distal ends only and classified as broken. The artifact pictures provided by

AFPS shows that both pieces were actually present and refit. These samples had measurements for only one half of the biface, but the actual full measurements entered in

the “estimated” columns. I classified these cases as complete for this study and the estimated values were used.

Next, I collected reflectance spectrometer data from the Kilborn Collection with

Dr. Ryan Parish at the University of Memphis. I gathered all VNIR spectral data using a

PSR+ by Spectral Evolution and all FTIR data using a 4300 FTIR by Agilent. I then processed the resulting data with DARWin SP v 1.4.6660 (Spectral Evolution 2018). I entered and organized the resulting measurements, broken-to-complete observations, and spectral data into Microsoft Excel (2016) in preparation for statistical analysis in SPSS statistical software (IBM 2016). Last, I prepared artifact pictures using an Epson

Perfection V600 Photo scanner and further processed the images using Gimp image manipulation software 2.10.8 (GIMP 2018). Use of the Epson Perfection V600 allowed multiple bifaces to be scanned at once at a 1:1 scale. This method provides flexibility when working with loaned collections, or collections where access is temporarily granted, by providing a way to gather metric data well after the artifacts are no longer accessible.

Figure 4.2 Example of prehistorically broken points

Figure 4.3 Example of points with modern breaks

Raw Material Selection

Due to the expansive range of geologic formations in north Alabama and inconsistent results when visually identifying chert (Parish 2011,2013; Parish and

Durham 2015), reflectance spectroscopy analysis was used to source bifaces from the

Kilborn collection to nearby formations. Only the Kilborn Collection was available for this analysis. I employed a Discriminant Function Analysis (Fisher 1936) to compare the

spectral data of the bifaces to samples representative of known formations near the study area.

Artifact Discard

The rate at which artifacts were discarded can inform on artifact maintenance and curation behaviors (Amick 1996; Kuhn and Miller 2015). Likewise, it can also be a proxy to hunting returns (Miller 2018). These decisions include condition of the biface that was discarded as either broken or complete. To investigate rates of artifact discard, I classified the points as either complete or broken and used a Chi-Square Goodness of Fit (Pearson

1900) to determine if the global percentage of broken-to-complete bifaces is the same across all artifact types, or if there is a change through the course of the YD.

Resharpening

I divided the maximum length by the maximum width to quantify relative biface reduction and resharpening for complete bifaces only (Kuhn and Miller 2015; Shott and

Ballenger 2007; Tune 2016:). Throughout the life history of an artifact, the ratio between length-to-width is expected to change as the object is reworked (Shott and Ballenger

2007). This is due to most of the damage and wear occurring at the distal end of the biface and the assumption that the tool was reworked while still in the hafting element

(Hoffman 1985; Kuhn and Miller 2015; Randall 2002; Shott 1997; Shott and Ballenger

2007). Further, as Tune (2016:3) states, no matter the orientation in which the biface is reduced, whether distally, laterally, or proximally, a positive correlation between both variables would indicate no reworking (see also Kuhn and Miller 2015).

To assess bifacial resharpening as an index of curation and hunting returns, Spearman’s

Rank Correlation Coefficient (rho) (Spearman 1904) will be used to assess length to width ratios. To investigate whether biface size changed through time, volume (L*W*T) was used as a proxy for absolute size (Miller 2014). Next, I compared the means of biface volume for each biface type through time to determine whether size changes exist and if those changes coincide with the YD.

CHAPTER V

RESULTS

In this chapter I present the results of the Spearman’s rho, size analysis (volume),

Chi-Square goodness of fit, and the Discriminant Function Analysis. All omitted samples and potential biases are detailed. Explanations of the results are provided at the end of each section within this chapter.

Raw Material Procurement

I subjected n=687 projectile points from the Kilborn Collection to reflectance spectrometry analysis for this portion of my experiment. I excluded n=58 points from the analysis, n=3 due to analyzer error (i.e., only having VNIR and not FTIR) and n=55 consisted of unidentifiable point types. The final count was n= 629 projectile points that I grouped and assessed according to their geological parent formation. I generated a base discriminant function model using the Chert Type Database of the Southeast as a background to characterize chert by formation. The internal accuracy assessment of the model by Parent Geological Formation (type) returned 2,363 correct source assignments out of 2,548 (93% correct classification). A total of 185 source samples were misclassified to other geological formations. I present the raw material source of each biface type in the table below (Table 5.2). My results show that Fort Payne chert is preferred over other chert types, but is more pronounced with the Dalton and Big Sandy.

This is likely a result of the Dalton and Big-Sandy artifacts in my sample being from the 49

Kilborn Collection sample, which is in close proximity to the Fort Payne Formation.

Otherwise, it appears as groups from all time periods were preferentially using cherts from formations that are locally available.

Table 5.1 Raw material frequency by biface type

Raw Material by Biface Type Raw Material Frequency

CLOVIS Brassfield 3 Dover 1 Fort Payne 5 St. Louis/Tuscumbia 2 Total 11 CUMBERLAND Brassfield 1 Dover 2 Fort Payne 9 St. Genevieve/Monteagle 1 St. Louis/Tuscumbia 1 Total 14 QUAD/BEAVER LAKE Dover 1 Fort Payne 6 Total 7 DALTON Brassfield 6 Dover 7 Fort Payne 33 St. Genevieve/Monteagle 4 St. Louis/Tuscumbia 5 Total 55 BIG-SANDY Bangor 4 Brassfield 45 Dover 20 Fort Payne 309 St. Genevieve/Monteagle 52 St. Louis/Tuscumbia 112 Total 542 GRAND TOTAL 629

Discard Rates

I perform a Chi-Square goodness-of-fit test (Pearson 1900) to determine if the global percentage of complete-to-broken bifaces is the same across all artifact types or if there is a change over the course of the YD1. The results indicate that the Dalton point type is the statistical outlier, which could suggest that Daltons were discarded more often as whole points. However, a further analysis indicates that the Dalton sample obtained from Craib (2016) is the driving factor for the type being an outlier. There is no significant difference between point types when all Daltons are removed, or when only

Kilborn’s sample is included. This may be due to Craib inadvertently oversampling complete points over broken ones. Further, intense agricultural practices in the study area may have introduced an additional bias to this study, which manifest itself in an increased frequency of broken points, which may have obscured the usefulness of this metric for assessing discard rates.

Table 5.2 Chi-Square test

TYPE Observed Expected Complete X2 Complete Clovis 77 83.20 0.46 Cumberland/Redstone 53 72.08 5.05 Quad/Beaver Lake 61 64.41 0.18 Dalton 76 52.53 10.49 Big-Sandy 200 194.77 0.14 Total 467 467

2 X 16.32 df 4 p 0.00

1 All samples I determined to be modern breaks from the Kilborn collection were omitted. 52

ALL SAMPLES

Expected Complete Observed Complete

TOTAL BIG-SANDY DALTON QUAD/BEAVER LAKE CUMBERLAND/REDSTONE CLOVIS 0 100 200 300 400 500

Figure 5.1 Chi-Square goodness of fit results with all Dalton sample

Resharpening

Here I analyze n= 467 Paleoindian and Early Archaic bifaces to assess the hypothetical relationship between length-to-width ratios and resharpening2. In addition, I perform a Spearman's rank-order correlation to determine significance of this relationship for Clovis, Cumberland/Redstone, Quad/Beaver Lake, Dalton, and Big-Sandy projectile points.

My results show that the Clovis point type exhibits a strong, positive correlation between maximum length and maximum width, which was statistically significant (r =

.625, p = .001). Additionally, the Cumberland/Redstone point type has a relatively strong, positive correlation between maximum length and maximum width, which was statistically significant (r = .497, p = .001). However, I found that the Quad/Beaver Lake

2 I eliminated two outliers from Spearman’s rho and size analysis in this study. AFPS # 264, which was classified as a Clovis awl, but could not be verified through photographs on the AFPS and AFPS # 214 due lacking a thickness measurement. 53

type experiences a significant decline in this relationship as evidenced by the apparent weak, positive correlation between maximum length and maximum width, which was not statistically significant (r = .152, p = .242). The Dalton type shows a weaker, positive correlation between maximum length and maximum width, which was statistically significant (r = .388, p = .001). Finally, I found that the Big-Sandy points, in comparison to the preceding Dalton points, exhibited a weaker relationship, which was statistically significant (r = .312, p = .001).

Table 5.3 Spearman’s rho results by type TYPE N r Sig. (2-tailed) df CLOVIS 77 0.633 0.000 1 CUMBERLAND/REDSTONE 53 0.497 0.000 1 QUAD/BEAVER LAKE 61 0.152 0.242 1 DALTON 76 0.388 0.001 1 BIG-SANDY 200 0.312 0.000 1

My results indicate that the relationship between length and width are strong and positive during the BA. However, I found the relationship for length and width for

Cumberland/Redstone through Quad/Beaver Lake types decline through the YD. This indicates that resharpening increases after the BA and during the YD. The Dalton and

Big-Sandy types show a rebound in this relationship at the end of the YD. This correlates with the general warming trend associated with the EH.

Figure 5.2 Resharpening through the course of the YD

Top Left: Clovis; Top Center: Cumberland/Redstone; Top Right: Quad/Beaver Lake; Lower Left: Dalton; Lower Right: Big-Sandy

Biface Size and Dimensions Through Time

Here, I use the volume of Paleoindian bifaces as a proxy to determine the absolute size of projectile points for this portion of my analysis, I then compare the means for volume of each point type. My results show that means for volume decrease through time. The total mean for all samples was 7994.73 mm3 (SD=5193.54 mm3). Clovis exhibits the highest average volume (M=13440.37 mm3, SD=7566.67 mm3) followed by

Cumberland/Redstone which exhibits a slightly lower volume (M=12340.11 mm3,

SD=4766.32 mm3). The Quad/Beaver Lake sample exhibits a reduced volume

(M=9260.19 mm3, SD=3428.62 mm3) followed by Dalton, which has the starkest change in volume (M=5931.08 mm3, SD=2056.83 mm3). Finally, Big-Sandy comes in with the least volume (M=5123.96 mm3, SD=1717.04 mm3). My results show a continual decrease in both the size, and variability in size, of projectile points through time. This correlates with trends in the size of common prey during the Pleistocene to Holocene transition.

Table 5.4 Volume (mm3) by type.

Type Mean N Std. Variance CV Deviation Clovis 13440.3674 78 7566.67 57928327.48 .56 Cumberland/Redstone 12340.1132 53 4766.32 22717818.49 .39 Quad/Beaver Lake 9260.1905 60 3428.62 11755437.27 .37 Dalton 5931.0813 76 2056.83 4230559.02 .35 Big-Sandy 5123.9633 200 1717.04 2948212.91 .34 Total 7977.8046 466 5193.54 26896697.61 -

Figure 5.3 Boxplot of volume by type

DISCUSSION

My results demonstrate that there are marked changes in point size and rates of resharpening, which correlate with climatic changes brought on by the YD. However, there seems to be no change in complete to broken discard rates, and selection of raw material appears to be consistent through time. The Clovis points, representing the BA in my sample, were discarded with the lowest rate of resharpening of all of the types included in my study. In addition, I found that the complete Clovis points that were discarded were comparatively larger than the later point types, but show the highest variance. In other words, Clovis points had the largest overall size, most variable size, and the least amount of resharpening episodes. Further, I found that more points were being discarded as broken, indicating that once a point was discarded, that’s where it remained. With access to raw material not being a factor, Clovis people likely discarded a point altogether once it was broken, such as in the case of a misfire, due to the focus of the hunting encounter being on butchering a larger animal.

I found that the Cumberland/Redstone points, representing the Early YD in my sample, exhibit an increased rate of resharpening and a slight decrease in size when compared to Clovis points. Further, I found that the variance in size was significantly lower than Clovis, but higher than the other types. In addition, the complete to broken discard rates are roughly the same as exhibited in the Clovis point type.

The Quad/Beaver Lake points, representing the Middle YD, exhibit the highest rate of resharpening for all of the point types. Additionally, I found this point type displays a sharp decrease in size from Cumberland/Redstone type, but continues the trend of declining variance. Further, the rate of complete to broken discard did not drastically change from the Clovis or Cumberland/Redstone.

Dalton and Big-Sandy point types represent the Late YD to EH, with Dalton types occupying a Late YD to EH transition period and Big-Sandy types slightly overlapping with the end of Dalton, but extending further into the EH. Here, I found a break in the resharpening trend from the Quad/Beaver Lake type that is associated with the Dalton and continues with the Big-Sandy type. Additionally, the most drastic decrease in size occurs from the Quad/Beaver Lake to the Dalton type, with the Big-Sandy type exhibiting a slight decrease in size from Dalton. Moreover, variance shows a continual decline with both types. The complete to broken discard of Dalton points may be biased towards broken points due to the inadvertent sampling of complete points by Craib

(2016). However, Big Sandy points show no marked deviation from any of the previous point types regarding complete to broken discard rates.

Human Response to the YD in North Alabama

In the previous section, I presented data for point types that occur over the course of the YD (Figure 6.1). There is a gradual decline in absolute size throughout the course of the YD. Employing the modified MVT model presented by Kuhn and Miller (2015), I interpret the pattern of minimal maintenance and discard with substantial unexpended utility of Clovis bifaces as consistent with arguments that Clovis hunters in the BA were focused on large mammals (e.g. Kelly and Todd 1988; Surovell 2000). However, as elsewhere North America, many species of megafauna in the American Southeast were extinct at the onset of the YD (Martin 1973). The pattern of decreasing size and increased resharpening for Cumberland/Redstone and Quad/Beaver Lake bifaces are consistent

with a decline in hunting returns after the Rancholabrean extinction. However, what is surprising is that, rather than seeing a substantial change in biface technological organization between Clovis and Cumberland in conjunction with Rancholabrean extinction, the most significant break is the shift both to, and away, from the use of the

Quad/Beaver Lake type. This finding is consistent with Tune (2016a), who argued that it was not necessarily the rapid onset of Younger Dryas that affected hunter-gatherer groups in the Mid-South, but the prolonged effects of the YD coupled with increasing population that affected biface technological organization. The shift to, and from, Quad/Beaver Lake types correlates with subsistence patterns at Dust Cave, where a high frequency of avian and waterfowl species dominate the faunal assemblage (Walker 1998).

With the appearance of Dalton and Big-Sandy bifaces (after the YD), I found that point size continues to decrease. However, I also found that resharpening rebounds from the Quad/Beaver Lake type. One possible explanation of this pattern could be a technological change or innovation, such as a different hafting strategy or the use of bifaces as both points and knives, which allowed Dalton and Big-Sandy populations to repeatedly re-sharpen and re-use smaller points. Again, employing Kuhn and Miller’s

(2015) model and holding raw material constant, this pattern suggests that while Dalton and Big-Sandy populations were using smaller points, their rates of resharpening reflect a higher return rate than Quad/Beaver Lake, but less than Clovis. This is to be expected given the warming trend that is synonymous with the end of the YD and emergence of the Holocene that coincides with the expansion of Oak and Hickory forests and the fauna that feed on those resources. Therefore, the smaller biface size is easily explained by the overall shift from megafauna to deer and other small animals throughout the YD, and the

lower rate of resharpening is indicative of increased return rates from animals who prefer to feed in hardwood forests.

As for noticing the end of the YD, there is no stark break in the pattern between the Dalton and Big Sandy point types. Perhaps populations might not have recognized the end of the YD in a generational sense, but the prolonged effect likely changed biotic structure to such a degree that the succession of groups using Quad/Beaver Lake, Dalton, and then Big Sandy projectile points began to organize their lithic technology differently.

This change ultimately relates back to hunting and, specifically, changes in hunting returns from using points. Alternatively, with our current chronological resolution, it might be impossible to discern the effect of rapid change such as the onset and the terminus of the YD, but here my results are consistent with previous studies (e.g.

Anderson et al. 2011; Kuhn and Miller 2015; Meeks and Anderson 2012; Miller 2018;

Smallwood et al. 2015; Tune 2016) that find a response to the YD at a coarser temporal resolution.

CHAPTER VII

CONCLUSIONS

This study was a collections-based project that employed an organization of technology (Nelson 1991) approach to investigate behavioral response to the Younger

Dryas climatic event (12,900 to 11,700 BP) in north Alabama. I applied a modified version of the marginal value theorem to investigate changes in the life histories of projectile points. I argue that changes during the Younger Dryas were not conditioned by access or availability of lithic raw material. Instead, variation in biface size, resharpening, and discard are likely a response to changes in return rates from the hunting and foraging of biotic communities. Further, hunting return rates changed due to the structure of biotic resources evolved. To that end, I argue that the sustained impact of the Younger Dryas, and subsequent Holocene warming, had an effect on the subsistence economies of hunter- gatherers living in northern Alabama.

This study offers insight on how prehistoric populations reacted to rapid and intense climate change. Although climate change itself may not have been noticed generationally speaking, or our analysis is not fine-grained enough to infer that generational change was noticed, it appears that the sustained effects of events such as the YD cause broad trends in biotic communities in such a way that humans changed the way they organized their technology as a means to adapt. Finally, while this study has its limitations (i.e., preliminary nature of the raw material analysis and the potential

oversampling of complete points), it is a useful approach that makes use of extant collections, both from museum and private collections.

Future Research

There are several avenues for future research that could help refine this analysis.

First, incorporating more projectile points currently in the possession of collectors in north Alabama or that are housed in curation facilities in the state would increase the sample size, particularly for the earlier time periods. Second, more professionally excavated or surface collected samples from sites that have not been subjected to modern agricultural practices would remove any biases towards modernly broken points or collectors preferentially selecting for complete points in good condition. Third, an excavation of a stratified site could potentially yield radiocarbon dates that would further hone the chronology of north Alabama hunter-gatherer archaeology beyond those currently available from Dust Cave.

Extending the study to places where larger game persisted through the course of the Younger Dryas, such as bison in the Great Plains would offer a complementing, but alternative approach to understanding technological organization around the YD.

Specifically, testing Kuhn and Miller (2015)’s model could inform how technology was situated to encounters with less predictable, but larger game and where diets began to broaden earlier, relative to the broader American southeast (Cannon and Meltzer 2008;

Elston and Zeanah 2002; Kuhn and Miller 2015).

Currently, further analysis is required to determine if biface materials were sourced from local outcrops. However, the raw material types represented in the results of this analysis could all potentially occur within the formations within the study area. At 64

this time, nothing can be definitively said about the raw material procurement results of this study. Future studies on these samples could drastically change the results. For example, once samples near 1Li573 are obtained, added to the Chert Type Database of the Southeast, and reanalyzed, the Fort Payne samples in this study could have a signature associated more with Tuscumbia chert.

Finally, my study offers prehistoric account of rapid climate change during a time when humans lived at relatively low-density populations with free range on the landscape and climate change hits them out of nowhere and they have to find ways to re-adjust.

Although my analysis is only able to discern broad changes over hundreds of years, there is no reason to think that responses did not begin to happen sooner. Further, for ones who argue that climate change is not real one can point to research like this to argue that rapid climate change has happened before. Climate change could possibly be more detrimental now. Currently, with even higher populations in areas of the world that would feel adverse effects of a rapidly changing environment at a strong level, like coastal environments, careful attention must be paid to educating the public on climate change, mitigating adverse effects of climate change, and climate change prevention,

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PROJECTILE POINT DATA

Kilborn AFPS no. TYPE COUNTY B/C L W T no. or Cambron Hulse (CH) 423 BIG-SANDY LI 0m 0 17.9 9.1

2849 BIG-SANDY LI 0m 0 0 8.7

3261 BIG-SANDY LI 0m 0 24.1 8.4

3548 BIG-SANDY LI 0m 0 19.6 8

3591 BIG-SANDY LI 0m 33 0 8

3549 BIG-SANDY LI 0m 0 19.9 7.8

478 BIG-SANDY LI 0m 0 19.4 7.7

472 BIG-SANDY LI 0m 0 18.6 7.7

2827 BIG-SANDY LI 0m 0 0 7.2

110 BIG-SANDY LI 0m 0 0 7.2

149 BIG-SANDY LI 0m 0 22.8 6.8

473 BIG-SANDY LI 0m 0 17.8 6.7

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520 BIG-SANDY LI 0 0 0 0

519 BIG-SANDY LI 0 0 0 0

518 BIG-SANDY LI 0 0 0 0

517 BIG-SANDY LI 0 0 0 0

516 BIG-SANDY LI 0 0 0 0

515 BIG-SANDY LI 0 0 0 0

514 BIG-SANDY LI 0 0 0 0

513 BIG-SANDY LI 0 0 0 0

512 BIG-SANDY LI 0 0 0 0

511 BIG-SANDY LI 0 0 0 0

509 BIG-SANDY LI 0 0 0 0

508 BIG-SANDY LI 0 0 0 0

507 BIG-SANDY LI 0 0 0 0

506 BIG-SANDY LI 0 0 0 0

505 BIG-SANDY LI 0 0 0 0

503 BIG-SANDY LI 0 0 0 0

501 BIG-SANDY LI 0 0 0 0

499 BIG-SANDY LI 0 0 0 0

498 BIG-SANDY LI 0 0 0 0

497 BIG-SANDY LI 0 0 0 0

494 BIG-SANDY LI 0 0 0 0

490 BIG-SANDY LI 0 0 0 0

488 BIG-SANDY LI 0 0 0 0

487 BIG-SANDY LI 0 0 0 0

486 BIG-SANDY LI 0 0 0 0

480 BIG-SANDY LI 0 0 0 0

466 BIG-SANDY LI 0 0 0 0

175 BIG-SANDY LI 0 0 0 0

174 BIG-SANDY LI 0 0 0 0

173 BIG-SANDY LI 0 0 0 0

172 BIG-SANDY LI 0 0 0 0

171 BIG-SANDY LI 0 0 0 0

170 BIG-SANDY LI 0 0 0 0

169 BIG-SANDY LI 0 0 0 0

168 BIG-SANDY LI 0 0 0 0

167 BIG-SANDY LI 0 0 0 0

166 BIG-SANDY LI 0 0 0 0

164 BIG-SANDY LI 0 0 0 0

162 BIG-SANDY LI 0 0 0 0

160 BIG-SANDY LI 0 0 0 0

155 BIG-SANDY LI 0 0 0 0

154 BIG-SANDY LI 0 0 0 0

152 BIG-SANDY LI 0 0 0 0

147 BIG-SANDY LI 0 0 0 0

146 BIG-SANDY LI 0 0 0 0

140 BIG-SANDY LI 0 0 0 0

138 BIG-SANDY LI 0 0 0 0

133 BIG-SANDY LI 0 0 0 0 549 387 CLOVIS LI 0m 0 0 8

3602 CLOVIS LI 0m 0 21 6

682 CLOVIS LI 1 95 40 11

264 CLOVIS* MG 1 38 38 11

7 CLOVIS MA 1 106 42 10

31 CLOVIS LI 1 113 34 9

1353 CLOVIS MG 1 84 31 9

866 CLOVIS MG 1 91 30 9

865 CLOVIS LI 1 90 30 9

1067 CLOVIS MA 1 73 30 9

242 CLOVIS MG 1 45 28 9

721 CLOVIS MA 1 66 25 9

995 CLOVIS LI 1 57 25 9

2 CLOVIS LI 1 94 40 8

357 CLOVIS LI 1 72 32 8

278 CLOVIS LI 1 84 30 8

1010 CLOVIS MA 1 76 30 8

363 CLOVIS LI 1 54 30 8

864 CLOVIS LI 1 83 29 8

620 CLOVIS LI 1 78 29 8

1113 CLOVIS MA 1 77 29 8

1009 CLOVIS LI 1 58 28 8

1012 CLOVIS LI 1 84 27 8

1321 CLOVIS LI 1 78 27 8

653 CLOVIS MA 1 59 27 8

400 CLOVIS LI 1 77 26 8

842 CLOVIS LI 1 67 26 8

1225 CLOVIS MA 1 63 26 8

1323 CLOVIS LI 1 79 25 8

1227 CLOVIS MA 1 47 24 8 548 386 CLOVIS LI 1 42 24 8

210 CLOVIS LI 1 45.8 25.9 7.2

361 CLOVIS LI 1 89 33 7

1105 CLOVIS LI 1 88 31 7

860 CLOVIS MA 1 85 29 7

1005 CLOVIS MA 1 69 29 7

356 CLOVIS LI 1 62 29 7

863 CLOVIS MA 1 82 28 7

626 CLOVIS LI 1 66 28 7

403 CLOVIS LI 1 61 28 7

373 CLOVIS LI 1 57 28 7

1230 CLOVIS LI 1 64 27 7

364 CLOVIS LI 1 61 27 7

404 CLOVIS LI 1 52 27 7

495 CLOVIS LI 1 73 26 7

711 CLOVIS LI 1 65 26 7

1231 CLOVIS LI 1 62 26 7

862 CLOVIS MA 1 53 26 7

420 CLOVIS LI 1 53 26 7

1358 CLOVIS LI 1 71 25 7

469 CLOVIS MA 1 55 25 7

418 CLOVIS LI 1 55 25 7

338 CLOVIS LI 1 51 25 7

402 CLOVIS LI 1 62 24 7

248 CLOVIS LI 1 62 23 7

494 CLOVIS LI 1 54 23 7

405 CLOVIS LI 1 51 21 7

941 CLOVIS LI 1 30 30 6

409 CLOVIS LI 1 79 29 6

867 CLOVIS LI 1 91 28 6

1324 CLOVIS LI 1 59 27 6

367 CLOVIS LI 1 44 27 6

467 CLOVIS LI 1 63 26 6

698 CLOVIS MA 1 50 26 6

230 CLOVIS LI 1 50 26 6

846 CLOVIS MA 1 32 26 6

1 CLOVIS LI 1 74 25 6

1104 CLOVIS LI 1 68 25 6

22 CLOVIS LI 1 52 25 6

1085 CLOVIS MA 1 67 24 6

1235 CLOVIS MG 1 42 24 6

983 CLOVIS LI 1 62 23 6

1095 CLOVIS LI 1 45 22 6

449 CLOVIS LI 1 50 21 6

601 CLOVIS MG 1 41 20 6

204 CLOVIS LI 1 41 19 6

1325 CLOVIS LI 1 59 24 5

1001 CLOVIS LI 1 60 20 5

1233 CLOVIS MG 1 44 18 4

943 CLOVIS LI 0 0 0 30

365 CLOVIS LI 0 0 0 14

329 CLOVIS LI 0 0 32 9

1089 CLOVIS MA 0 47 30 9

346 CLOVIS LI 0 0 25 9

185 CLOVIS LI 0 0 39 8

201 CLOVIS LI 0 0 34 8

41 CLOVIS MA 0 0 32 8

223 CLOVIS MA 0 0 31 8

408 CLOVIS LI 0 0 30 8

625 CLOVIS LI 0 0 29 8

343 CLOVIS LI 0 0 28 8

888 CLOVIS MA 0 0 27 8

882 CLOVIS MA 0 0 27 8

388 CLOVIS LI 0 0 25 8

416 CLOVIS LI 0 0 21 8 526 384 CLOVIS LI 0 0 0 8

957 CLOVIS LI 0 0 0 8

893 CLOVIS MA 0 0 0 8

622 CLOVIS LI 0 0 0 8

412 CLOVIS LI 0 0 0 8

407 CLOVIS LI 0 0 0 8

316 CLOVIS LI 0 0 0 8

40 CLOVIS LI 0 0 0 8

1082 CLOVIS MA 0 38 35 7

1084 CLOVIS MA 0 76 31 7

14 CLOVIS MG 0 0 28 7

633 CLOVIS MG 0 1 27 7

885 CLOVIS MA 0 0 25 7

347 CLOVIS LI 0 0 24 7

911 CLOVIS MA 0 0 22 7

884 CLOVIS MA 0 0 0 7

1405 CLOVIS MG 0 0 0 7

944 CLOVIS LI 0 0 0 7

896 CLOVIS MA 0 0 0 7

830 CLOVIS MA 0 0 0 7

647 CLOVIS MA 0 0 0 7

413 CLOVIS LI 0 0 0 7

208 CLOVIS LI 0 0 0 7

202 CLOVIS LI 0 0 0 7

189 CLOVIS MG 0 0 0 7

907 CLOVIS MA 0 0 28 6

206 CLOVIS LI 0 0 26 6

919 CLOVIS LI 0 0 25 6 179 376 CLOVIS LI 0 0 23 6

203 CLOVIS LI 0 0 22 6

1049 CLOVIS LI 0 55 0 6

350 CLOVIS LI 0 0 0 6

349 CLOVIS LI 0 0 0 6

332 CLOVIS LI 0 0 0 6

328 CLOVIS LI 0 0 0 6

210 CLOVIS LI 0 0 0 6

209 CLOVIS LI 0 0 0 6

69 CLOVIS MA 0 0 0 6

450 CLOVIS MA 0 0 24 5

1040 CLOVIS MG 0 17 23 5

857 CLOVIS MA 0 0 20 5

362 CLOVIS LI 0 0 20 5

207 CLOVIS LI 0 0 0 5

183 CLOVIS LI 0 0 0 5 3294 395 CLOVIS LI 0 1 0 0 2922 393 CLOVIS LI 0 0 0 0 525 383 CLOVIS LI 0 0 0 0

478 CLOVIS MA 0 0 28 0

1216 CLOVIS MA 0 45 22 0

1280 CLOVIS MA 0 51 0 0

1050 CLOVIS MA 0 48 0 0

1070 CLOVIS MA 0 44 0 0

1281 CLOVIS MA 0 37 0 0

1075 CLOVIS LI 0 30 0 0

1279 CLOVIS MA 0 29 0 0

1076 CLOVIS LI 0 27 0 0

1080 CLOVIS MA 0 23 0 0

1074 CLOVIS LI 0 22 0 0

1406 CLOVIS MG 0 0 0 0

1218 CLOVIS LI 0 0 0 0

1136 CLOVIS LI 0 0 0 0

1129 CLOVIS LI 0 0 0 0

1112 CLOVIS MA 0 0 0 0

1107 CLOVIS LI 0 0 0 0

1102 CLOVIS LI 0 0 0 0

1096 CLOVIS LI 0 0 0 0

1061 CLOVIS MA 0 0 0 0

990 CLOVIS LI 0 0 0 0

989 CLOVIS LI 0 0 0 0

952 CLOVIS LI 0 0 0 0

927 CLOVIS LI 0 0 0 0

926 CLOVIS LI 0 0 0 0

925 CLOVIS LI 0 0 0 0

924 CLOVIS LI 0 0 0 0

922 CLOVIS LI 0 0 0 0

921 CLOVIS LI 0 0 0 0

891 CLOVIS MA 0 0 0 0

854 CLOVIS MA 0 0 0 0

843 CLOVIS LI 0 0 0 0

837 CLOVIS MA 0 0 0 0

635 CLOVIS LI 0 0 0 0

632 CLOVIS MG 0 0 0 0

627 CLOVIS LI 0 0 0 0

597 CLOVIS MG 0 0 0 0

596 CLOVIS LI 0 0 0 0

487 CLOVIS MA 0 0 0 0

452 CLOVIS MA 0 0 0 0

448 CLOVIS MG 0 0 0 0

444 CLOVIS MA 0 0 0 0

410 CLOVIS LI 0 0 0 0

406 CLOVIS LI 0 0 0 0

398 CLOVIS LI 0 0 0 0

371 CLOVIS LI 0 0 0 0

370 CLOVIS LI 0 0 0 0

369 CLOVIS LI 0 0 0 0

368 CLOVIS LI 0 0 0 0

351 CLOVIS LI 0 0 0 0

348 CLOVIS LI 0 0 0 0

345 CLOVIS LI 0 0 0 0

344 CLOVIS LI 0 0 0 0

342 CLOVIS LI 0 0 0 0

341 CLOVIS LI 0 0 0 0

340 CLOVIS LI 0 0 0 0

339 CLOVIS LI 0 0 0 0

336 CLOVIS LI 0 0 0 0

334 CLOVIS LI 0 0 0 0

333 CLOVIS LI 0 0 0 0

331 CLOVIS LI 0 0 0 0

322 CLOVIS LI 0 0 0 0

290 CLOVIS MA 0 0 0 0

287 CLOVIS MA 0 0 0 0

286 CLOVIS MA 0 0 0 0

275 CLOVIS LI 0 0 0 0

270 CLOVIS LI 0 0 0 0

268 CLOVIS LI 0 0 0 0

213 CLOVIS MA 0 0 0 0

191 CLOVIS MG 0 0 0 0

184 CLOVIS LI 0 0 0 0

181 CLOVIS LI 0 0 0 0

75 CLOVIS LI 0 0 0 0

44 CLOVIS LI 0 0 0 0

43 CLOVIS LI 0 0 0 0

42 CLOVIS LI 0 0 0 0

38 CLOVIS LI 0 0 0 0 2910 391 CLOVIS LI 1 37.5 23.9 5.9

3295 CUMBERLAND/REDSTONE LI 0m 0 0 0

254 CUMBERLAND/REDSTONE LI 1 36 21 19

1408 CUMBERLAND/REDSTONE MA 1 71 23 11

872 CUMBERLAND/REDSTONE MA 1 71 30 10

1014 CUMBERLAND/REDSTONE LI 1 73 21 10

714 CUMBERLAND/REDSTONE LI 1 89 35 9

231 CUMBERLAND/REDSTONE MA 1 79 25 9

1232 CUMBERLAND/REDSTONE LI 1 61 23 9

717 CUMBERLAND/REDSTONE MA 1 60 23 9 524 382 CUMBERLAND/REDSTONE LI 1 78 21 9

903 CUMBERLAND/REDSTONE MA 1 79 20 9

1015 CUMBERLAND/REDSTONE LI 1 74 21 8

15 CUMBERLAND/REDSTONE MA 1 85 29 8

483 CUMBERLAND/REDSTONE MA 1 75 28 8

276 CUMBERLAND/REDSTONE LI 1 68 28 8

869 CUMBERLAND/REDSTONE MA 1 92 25 8

1333 CUMBERLAND/REDSTONE MG 1 66 25 8

873 CUMBERLAND/REDSTONE MA 1 64 25 8

996 CUMBERLAND/REDSTONE LI 1 56 25 8

998 CUMBERLAND/REDSTONE LI 1 68 24 8

1006 CUMBERLAND/REDSTONE LI 1 60 24 8

851 CUMBERLAND/REDSTONE MA 1 75 23 8

699 CUMBERLAND/REDSTONE MA 1 67 23 8

870 CUMBERLAND/REDSTONE LI 1 88 22 8

235 CUMBERLAND/REDSTONE MA 1 60 19 8

225 CUMBERLAND/REDSTONE MA 1 74 33 7

251 CUMBERLAND/REDSTONE LI 1 45 31 7

887 CUMBERLAND/REDSTONE MA 1 88 30 7

1228 CUMBERLAND/REDSTONE MG 1 109 25 7

1326 CUMBERLAND/REDSTONE LI 1 82 25 7

252 CUMBERLAND/REDSTONE LI 1 79 25 7

904 CUMBERLAND/REDSTONE MA 1 72 25 7

234 CUMBERLAND/REDSTONE MA 1 78 24 7

838 CUMBERLAND/REDSTONE MA 1 67 24 7

277 CUMBERLAND/REDSTONE LI 1 49 24 7

1322 CUMBERLAND/REDSTONE LI 1 67 23 7

871 CUMBERLAND/REDSTONE MA 1 80 22 7

415 CUMBERLAND/REDSTONE LI 1 44 22 7

861 CUMBERLAND/REDSTONE MA 1 61 21 7

1215 CUMBERLAND/REDSTONE MA 1 61 20 7

708 CUMBERLAND/REDSTONE MG 1 51 20 7

700 CUMBERLAND/REDSTONE MA 1 43 19 7

226 CUMBERLAND/REDSTONE MA 1 63 27 6

715 CUMBERLAND/REDSTONE LI 1 84 25 6

1359 CUMBERLAND/REDSTONE LI 1 71 25 6

1066 CUMBERLAND/REDSTONE MA 1 68 25 6

710 CUMBERLAND/REDSTONE LI 1 52 23 6

716 CUMBERLAND/REDSTONE MA 1 62 22 6

693 CUMBERLAND/REDSTONE MA 1 60 22 6

188 CUMBERLAND/REDSTONE MG 1 60 22 6

874 CUMBERLAND/REDSTONE MA 1 50 20 6

705 CUMBERLAND/REDSTONE MA 1 46 18 6

324 CUMBERLAND/REDSTONE MA 1 29 18 6

190 CUMBERLAND/REDSTONE MG 1 44 18 4

325 CUMBERLAND/REDSTONE LI 0 0 23 9

1211 CUMBERLAND/REDSTONE LI 0 82 27 10

465 CUMBERLAND/REDSTONE LI 0 0 24 10

883 CUMBERLAND/REDSTONE MA 0 0 23 10 177 374 CUMBERLAND/REDSTONE LI 0 0 0 9.2

360 CUMBERLAND/REDSTONE LI 0 0 25 9

439 CUMBERLAND/REDSTONE LI 0 -2 25 9

26 CUMBERLAND/REDSTONE MA 0 0 23 9 181 378 CUMBERLAND/REDSTONE LI 0 93 21 9

1083 CUMBERLAND/REDSTONE LI 0 70 0 9

1338 CUMBERLAND/REDSTONE MA 0 0 24 8

606 CUMBERLAND/REDSTONE MG 0 0 23 8

358 CUMBERLAND/REDSTONE LI 0 0 21 8

327 CUMBERLAND/REDSTONE MG 0 0 21 8

186 CUMBERLAND/REDSTONE LI 0 0 20 8

1261 CUMBERLAND/REDSTONE LI 0 90 0 8

628 CUMBERLAND/REDSTONE LI 0 0 0 8

624 CUMBERLAND/REDSTONE LI 0 0 0 8

372 CUMBERLAND/REDSTONE LI 0 0 0 8

330 CUMBERLAND/REDSTONE LI 0 0 0 8

690 CUMBERLAND/REDSTONE MA 0 0 33 7

1278 CUMBERLAND/REDSTONE MA 0 57 27 7

355 CUMBERLAND/REDSTONE LI 0 0 26 7

335 CUMBERLAND/REDSTONE LI 0 0 24 7

354 CUMBERLAND/REDSTONE LI 0 0 21 7

182 CUMBERLAND/REDSTONE LI 0 0 21 7

1137 CUMBERLAND/REDSTONE LI 0 0 20 7

1094 CUMBERLAND/REDSTONE LI 0 0 20 7

636 CUMBERLAND/REDSTONE LI 0 0 19 7

856 CUMBERLAND/REDSTONE MA 0 0 0 7

637 CUMBERLAND/REDSTONE LI 0 0 0 7

631 CUMBERLAND/REDSTONE LI 0 0 0 7

224 CUMBERLAND/REDSTONE MA 0 0 0 7

222 CUMBERLAND/REDSTONE MA 0 0 31 6

1086 CUMBERLAND/REDSTONE MA 0 32 25 6

1402 CUMBERLAND/REDSTONE LI 0 0 25 6

1099 CUMBERLAND/REDSTONE LI 0 0 24 6

600 CUMBERLAND/REDSTONE LI 0 0 21 6

855 CUMBERLAND/REDSTONE LI 0 0 0 6

16 CUMBERLAND/REDSTONE MA 0 0 0 6

1044 CUMBERLAND/REDSTONE LI 0 21 0 5 3298 401 CUMBERLAND/REDSTONE LI 0 0 0 0 3296 396 CUMBERLAND/REDSTONE LI 0 0 0 0 3171 394 CUMBERLAND/REDSTONE LI 0 0 0 0 2919 392 CUMBERLAND/REDSTONE LI 0 0 0 0 184 381 CUMBERLAND/REDSTONE LI 0 0 0 0 183 380 CUMBERLAND/REDSTONE LI 0 0 0 0 182 379 CUMBERLAND/REDSTONE LI 0 0 0 0 180 377 CUMBERLAND/REDSTONE LI 0 0 0 0 178 375 CUMBERLAND/REDSTONE LI 0 0 0 0

3605 CUMBERLAND/REDSTONE LI 0 0 0 0

3438 CUMBERLAND/REDSTONE LI 0 0 0 0

1723 CUMBERLAND/REDSTONE LI 0 0 0 0

1151 CUMBERLAND/REDSTONE LI 0 0 0 0

32 CUMBERLAND/REDSTONE LI 0 0 27 0

352 CUMBERLAND/REDSTONE LI 0 0 26 0

337 CUMBERLAND/REDSTONE LI 0 0 24 0

1032 CUMBERLAND/REDSTONE LI 0 40 22 0

1217 CUMBERLAND/REDSTONE LI 0 18 21 0

417 CUMBERLAND/REDSTONE LI 0 0 21 0

1069 CUMBERLAND/REDSTONE MA 0 42 0 0

1210 CUMBERLAND/REDSTONE LI 0 39 0 0

1041 CUMBERLAND/REDSTONE MA 0 34 0 0

1073 CUMBERLAND/REDSTONE LI 0 30 0 0

1038 CUMBERLAND/REDSTONE LI 0 27 0 0

1072 CUMBERLAND/REDSTONE LI 0 17 0 0

849 CUMBERLAND/REDSTONE MA 0 0 0 0

1401 CUMBERLAND/REDSTONE LI 0 0 0 0

1385 CUMBERLAND/REDSTONE MG 0 0 0 0

1355 CUMBERLAND/REDSTONE LI 0 0 0 0

1336 CUMBERLAND/REDSTONE MA 0 0 0 0

1138 CUMBERLAND/REDSTONE LI 0 0 0 0

1134 CUMBERLAND/REDSTONE LI 0 0 0 0

1131 CUMBERLAND/REDSTONE LI 0 0 0 0

1128 CUMBERLAND/REDSTONE LI 0 0 0 0

1118 CUMBERLAND/REDSTONE LI 0 0 0 0

1108 CUMBERLAND/REDSTONE LI 0 0 0 0

1060 CUMBERLAND/REDSTONE MA 0 0 0 0

992 CUMBERLAND/REDSTONE LI 0 0 0 0

987 CUMBERLAND/REDSTONE LI 0 0 0 0

984 CUMBERLAND/REDSTONE LI 0 0 0 0

978 CUMBERLAND/REDSTONE LI 0 0 0 0

960 CUMBERLAND/REDSTONE LI 0 0 0 0

958 CUMBERLAND/REDSTONE LI 0 0 0 0

953 CUMBERLAND/REDSTONE LI 0 0 0 0

914 CUMBERLAND/REDSTONE MA 0 0 0 0

912 CUMBERLAND/REDSTONE MA 0 0 0 0

908 CUMBERLAND/REDSTONE MA 0 0 0 0

901 CUMBERLAND/REDSTONE MA 0 0 0 0

900 CUMBERLAND/REDSTONE MA 0 0 0 0

899 CUMBERLAND/REDSTONE MA 0 0 0 0

898 CUMBERLAND/REDSTONE MA 0 0 0 0

833 CUMBERLAND/REDSTONE MA 0 0 0 0

829 CUMBERLAND/REDSTONE MA 0 0 0 0

743 CUMBERLAND/REDSTONE MA 0 0 0 0

686 CUMBERLAND/REDSTONE MG 0 0 0 0

684 CUMBERLAND/REDSTONE MA 0 0 0 0

674 CUMBERLAND/REDSTONE LI 0 0 0 0

648 CUMBERLAND/REDSTONE MA 0 0 0 0

629 CUMBERLAND/REDSTONE LI 0 0 0 0

621 CUMBERLAND/REDSTONE LI 0 0 0 0

599 CUMBERLAND/REDSTONE MG 0 0 0 0

497 CUMBERLAND/REDSTONE MA 0 0 0 0

496 CUMBERLAND/REDSTONE LI 0 0 0 0

490 CUMBERLAND/REDSTONE MA 0 0 0 0

485 CUMBERLAND/REDSTONE MA 0 0 0 0

474 CUMBERLAND/REDSTONE MA 0 0 0 0

471 CUMBERLAND/REDSTONE MA 0 0 0 0

414 CUMBERLAND/REDSTONE LI 0 0 0 0

399 CUMBERLAND/REDSTONE LI 0 0 0 0

390 CUMBERLAND/REDSTONE LI 0 0 0 0

366 CUMBERLAND/REDSTONE LI 0 0 0 0

359 CUMBERLAND/REDSTONE LI 0 0 0 0

353 CUMBERLAND/REDSTONE LI 0 0 0 0

326 CUMBERLAND/REDSTONE MG 0 0 0 0

323 CUMBERLAND/REDSTONE LI 0 0 0 0

321 CUMBERLAND/REDSTONE MG 0 0 0 0

307 CUMBERLAND/REDSTONE MA 0 0 0 0

295 CUMBERLAND/REDSTONE MA 0 0 0 0

294 CUMBERLAND/REDSTONE MA 0 0 0 0

293 CUMBERLAND/REDSTONE MA 0 0 0 0

292 CUMBERLAND/REDSTONE MA 0 0 0 0

291 CUMBERLAND/REDSTONE MA 0 0 0 0

289 CUMBERLAND/REDSTONE MA 0 0 0 0

288 CUMBERLAND/REDSTONE MA 0 0 0 0

285 CUMBERLAND/REDSTONE MA 0 0 0 0

284 CUMBERLAND/REDSTONE MA 0 0 0 0

269 CUMBERLAND/REDSTONE LI 0 0 0 0

267 CUMBERLAND/REDSTONE LI 0 0 0 0

266 CUMBERLAND/REDSTONE LI 0 0 0 0

265 CUMBERLAND/REDSTONE LI 0 0 0 0

187 CUMBERLAND/REDSTONE LI 0 0 0 0

37 CUMBERLAND/REDSTONE LI 0 0 0 0

34 CUMBERLAND/REDSTONE LI 0 0 0 0

447 CUMBERLAND/REDSTONE MG 0 0 0 0

3310 DALTON LI 0m 0 20.6 7.3

529 DALTON LI 0m 0 23.8 6.6

186 DALTON LI 0m 0 21.9 6.5

3309 DALTON LI 0m 0 0 0

3597 DALTON LI 1 36.8 18.8 8.7

37 DALTON LI 1 36 21.8 7.5

197 DALTON LI 1 43.3 21.3 7.5

3596 DALTON LI 1 36.1 16.6 7.3

2842 DALTON LI 1 34.4 17.4 7.2

192 DALTON LI 1 47.5 20.1 7.1

534 DALTON LI 1 35.3 20.8 7

194 DALTON LI 1 46.8 15.9 7

190 DALTON LI 1 55.6 21.3 6.8

66 DALTON LI 1 40.5 20.5 6.7

1140 DALTON LI 1 37 16.4 6.7

543 DALTON LI 1 43.1 24.7 6.6

38 DALTON LI 1 36 21 6.6

2907 DALTON LI 1 47.3 19.2 6.6

2905 DALTON LI 1 29.5 14.5 6.6

2903 DALTON LI 1 38 17.4 6.5

1112 DALTON LI 1 30.8 15 6.5

195 DALTON LI 1 37.3 23.3 6.4

2902 DALTON LI 1 41.6 19.5 6.3

1145 DALTON LI 1 50.1 22.6 6.2

3613 DALTON LI 1 60 19.5 6.1

226 DALTON LI 1 40.6 18.8 6

532 DALTON LI 1 31.5 16.8 6

3614 DALTON LI 1 29.3 14.1 5.8

530 DALTON LI 1 38 17.8 5.6

196 DALTON LI 1 23.2 16.1 5.4

1139 DALTON LI 1 33 18.7 5.1

531 DALTON LI 1 35.6 14.6 4.8

CH-154-1 DALTON No Data 1 53.85 20.52 5.95

CH-162-1 DALTON No Data 1 56.2 24.8 6.21

CH-162-4 DALTON No Data 1 41.43 22.44 6.88

CH-195-1 DALTON No Data 1 59.74 20.47 5.76

CH-195-2 DALTON No Data 1 49.23 17.75 5.63

CH-29-1 DALTON No Data 1 61.64 21.87 6.22

CH-32-1 DALTON No Data 1 51.59 19.8 5.6

CH-32-2 DALTON No Data 1 72.18 19.02 6.22

CH-32B-1 DALTON No Data 1 42.97 21.49 6.1

CH-32B-2 DALTON No Data 1 54.54 16.11 6.55

CH-32B-3 DALTON No Data 1 69.6 17.79 5.33

CH-32S-1 DALTON No Data 1 40.62 24.36 5.19

CH-37-1 DALTON No Data 1 44.66 18.14 5.38

CH-37-2 DALTON No Data 1 46.39 18.93 6.45

CH-37-3 DALTON No Data 1 48.82 16.38 6.75

CH-37E-2 DALTON No Data 1 39.93 19.69 6.6

CH-37M-1 DALTON No Data 1 42.71 18.96 4.77

CH-38-2 DALTON No Data 1 51.49 17.43 6.15

CH-39-1 DALTON No Data 1 66.42 18.9 6.75

CH-39-4 DALTON No Data 1 48.69 20.97 4.73

CH-39-5 DALTON No Data 1 62.22 21.51 5.6

CH-39-7 DALTON No Data 1 49.27 20.58 5.71

CH-54-1 DALTON No Data 1 40.43 19.25 7.29

CH-55-1 DALTON No Data 1 42.12 20.03 5.91

CH-76-10 DALTON No Data 1 47.01 18.47 6.59

CH-76-11 DALTON No Data 1 58.66 22.46 6.32

CH-76-12 DALTON No Data 1 61.53 26.05 5.6

CH-76-13 DALTON No Data 1 52.02 19.86 5.47

CH-76-14 DALTON No Data 1 46.01 25.74 6.11

CH-76-15 DALTON No Data 1 76.67 28.16 5.74

CH-76-4 DALTON No Data 1 62.55 23.43 9.1

CH-76-5 DALTON No Data 1 38.05 20.4 4.8

CH-76-6 DALTON No Data 1 55.69 20.39 5.91

CH-76-7 DALTON No Data 1 57.03 25.35 4.94

CH-76-8 DALTON No Data 1 55.63 17.16 7.32

CH-76-9 DALTON No Data 1 61.01 23.4 6.86

CH-84-10 DALTON No Data 1 63.26 18.8 6.97

CH-84-12 DALTON No Data 1 59.26 21.29 6.1

CH-84-13 DALTON No Data 1 38.82 19.21 5.73

CH-84-15 DALTON No Data 1 48.4 19.27 6.09

CH-84-3 DALTON No Data 1 46.21 18.22 6.32

CH-84-4 DALTON No Data 1 52.52 17.84 6.02

CH-84-5 DALTON No Data 1 44.05 18.59 5.18

CH-84-6 DALTON No Data 1 66.19 20.07 5.93

CH-84-7 DALTON No Data 1 65.15 22.47 5.89

CH-84-8 DALTON No Data 1 48.56 16.71 5.59

CH-84-9 DALTON No Data 1 43.85 15.32 5.23

NoID-1 DALTON No Data 1 47 18.58 6.41

528 DALTON LI 0 0 18 7.3

533 DALTON LI 0 0 19.9 7.2

537 DALTON LI 0 0 16.8 6.9

2909 DALTON LI 0 0 20 6.7

1103 DALTON LI 0 0 17.7 6.5

2904 DALTON LI 0 0 16.7 6.5

191 DALTON LI 0 0 22.3 6.3

3303 DALTON LI 0 39.5 0 6.3

1138 DALTON LI 0 0 19.9 6.1

225 DALTON LI 0 0 16.4 6

540 DALTON LI 0 0 25.8 5.9

3615 DALTON LI 0 0 22 5.9

2906 DALTON LI 0 0 15.4 5.9

3304 DALTON LI 0 0 20.7 5.7

189 DALTON LI 0 0 21.1 5.6

193 DALTON LI 0 0 21.2 5.5

536 DALTON LI 0 0 14.6 5.5

500 DALTON LI 0 0 15.6 5.4

535 DALTON LI 0 0 12.4 4.8

198 DALTON LI 0 0 17.4 0

3616 DALTON LI 0 0 0 0

3610 DALTON LI 0 0 0 0

3370 DALTON LI 0 0 0 0

188 DALTON LI 0 0 0 0

CH062-2 DALTON No Data 0 0 0 5.35

CH062-3 DALTON No Data 0 0 0 5.51

CH095-3 DALTON No Data 0 0 22.68 6.87

CH095-4 DALTON No Data 0 28.18 24.65 4.92

CH-3010 DALTON No Data 0 0 17.35 7.39

CH-32-3 DALTON No Data 0 0 21.95 5.77

CH-32-4 DALTON No Data 0 0 0 5.53

CH-32-5 DALTON No Data 0 0 0 4.79

CH-33W0 DALTON No Data 0 0 0 5.27

CH-37-4 DALTON No Data 0 0 21.08 4.98

CH-37-5 DALTON No Data 0 0 19.72 7.48

CH-37E0 DALTON No Data 0 0 21.57 6.81

CH-380 DALTON No Data 0 0 0 6.53

CH-3880 DALTON No Data 0 0 21.51 9.21

CH-38E0 DALTON No Data 0 0 0 6.41

CH-38N0 DALTON No Data 0 0 0 4.84

CH-38NE0 DALTON No Data 0 0 18.31 4.48

CH-39-2 DALTON No Data 0 0 21.63 6.81

CH-39-3 DALTON No Data 0 0 0 4.53

CH-3940 DALTON No Data 0 0 0 5.14

CH-39-6 DALTON No Data 0 0 16.8 6.7

CH-500 DALTON No Data 0 0 21.73 6.83

CH-530 DALTON No Data 0 43.9 25.61 5.29

CH-54-2 DALTON No Data 0 0 0 5.74

CH-54-3 DALTON No Data 0 0 19.32 7.82

CH-630 DALTON No Data 0 67.49 0 6.1

CH-760 DALTON No Data 0 0 0 5.28

CH-76-2 DALTON No Data 0 0 0 5.02

CH-76-3 DALTON No Data 0 0 20.92 5.47

CH-790 DALTON No Data 0 0 18.75 4.6

CH-79-2 DALTON No Data 0 0 20.1 5.19

CH-830 DALTON No Data 0 0 22.11 6.81

CH-840 DALTON No Data 0 0 20.11 5.17

CH-8401 DALTON No Data 0 40.26 20.65 5.75

CH-8404 DALTON No Data 0 0 25.46 5.83

100

CH-84-2 DALTON No Data 0 0 18.27 5.17

NoID-2 DALTON No Data 0 43.27 18.74 5.92

1079 QUAD/BEAVER LAKE MA 1 29 22 8

1236 QUAD/BEAVER LAKE MG 1 47 22 17

1020 QUAD/BEAVER LAKE LI 1 62 29 10

878 QUAD/BEAVER LAKE MA 1 67 22 10

1352 QUAD/BEAVER LAKE MG 1 68 22 9

76 QUAD/BEAVER LAKE MA 1 45 22 9

1214 QUAD/BEAVER LAKE MG 1 59 19 9

831 QUAD/BEAVER LAKE MA 1 60 32 8

1256 QUAD/BEAVER LAKE MA 1 70 25 8

934 QUAD/BEAVER LAKE LI 1 45 25 8

1018 QUAD/BEAVER LAKE MA 1 88 24 8

39 QUAD/BEAVER LAKE LI 1 60 23 8

1008 QUAD/BEAVER LAKE MA 1 73 22 8

491 QUAD/BEAVER LAKE MA 1 46 22 8

852 QUAD/BEAVER LAKE MG 1 43 21 8

1213 QUAD/BEAVER LAKE MG 1 42 20 8

605 QUAD/BEAVER LAKE LI 1 53 17 8

997 QUAD/BEAVER LAKE LI 1 80 30 7

880 QUAD/BEAVER LAKE LI 1 66 30 7

650 QUAD/BEAVER LAKE MA 1 51 29 7

229 QUAD/BEAVER LAKE MA 1 40 29 7

1078 QUAD/BEAVER LAKE LI 1 72 28 7

1356 QUAD/BEAVER LAKE MG 1 60 28 7

228 QUAD/BEAVER LAKE MA 1 42 28 7

598 QUAD/BEAVER LAKE MG 1 72 24 7

1064 QUAD/BEAVER LAKE MA 1 60 24 7

1114 QUAD/BEAVER LAKE MA 1 49 24 7

704 QUAD/BEAVER LAKE MA 1 49 24 7

1257 QUAD/BEAVER LAKE MA 1 66 23 7

741 QUAD/BEAVER LAKE MA 1 64 23 7

1212 QUAD/BEAVER LAKE LI 1 42 18 7

3300 QUAD/BEAVER LAKE LI 1 40.7 26.3 6.7

1091 QUAD/BEAVER LAKE MA 1 49 25 6

1103 QUAD/BEAVER LAKE LI 1 43 24 6

492 QUAD/BEAVER LAKE MA 1 47 30 6

999 QUAD/BEAVER LAKE LI 1 62 29 6

1220 QUAD/BEAVER LAKE MA 1 57 29 6

232 QUAD/BEAVER LAKE LI 1 51 29 6

227 QUAD/BEAVER LAKE MA 1 51 29 6

603 QUAD/BEAVER LAKE MG 1 52 28 6

101

719 QUAD/BEAVER LAKE LI 1 59 27 6

1090 QUAD/BEAVER LAKE MA 1 58 26 6

1260 QUAD/BEAVER LAKE MA 1 47 26 6

1004 QUAD/BEAVER LAKE LI 1 56 25 6

709 QUAD/BEAVER LAKE MA 1 53 25 6

859 QUAD/BEAVER LAKE MA 1 46 24 6

1087 QUAD/BEAVER LAKE MA 1 54 23 6

1258 QUAD/BEAVER LAKE MA 1 63 22 6

1000 QUAD/BEAVER LAKE LI 1 57 22 6

1002 QUAD/BEAVER LAKE LI 1 55 22 6

1360 QUAD/BEAVER LAKE LI 1 50 19 6

1135 QUAD/BEAVER LAKE LI 1 44 17 6

1047 QUAD/BEAVER LAKE MA 1 37 17 6

3301 QUAD/BEAVER LAKE LI 1 49.3 23.2 5.5

1088 QUAD/BEAVER LAKE MA 1 48 24 5

834 QUAD/BEAVER LAKE MA 1 39 24 5

1100 QUAD/BEAVER LAKE LI 1 59 22 5

703 QUAD/BEAVER LAKE MG 1 55 22 5

1255 QUAD/BEAVER LAKE MA 1 59 21 5

1403 QUAD/BEAVER LAKE MG 1 60 20 5

214 QUAD/BEAVER LAKE MA 1 70 23 0

1237 QUAD/BEAVER LAKE MA 0 37 28 9

909 QUAD/BEAVER LAKE MA 0 0 26 9

1110 QUAD/BEAVER LAKE LI 0 0 32 8

1092 QUAD/BEAVER LAKE MA 0 52 24 8

1133 QUAD/BEAVER LAKE LI 0 68 21 8

1127 QUAD/BEAVER LAKE MG 0 0 21 8

649 QUAD/BEAVER LAKE MA 0 0 0 8

614 QUAD/BEAVER LAKE MG 0 0 28 7

604 QUAD/BEAVER LAKE LI 0 0 28 7

858 QUAD/BEAVER LAKE MA 0 0 25 7

1123 QUAD/BEAVER LAKE MG 0 0 22 7

1109 QUAD/BEAVER LAKE LI 0 0 22 7

905 QUAD/BEAVER LAKE MA 0 0 0 7

239 QUAD/BEAVER LAKE MA 0 0 0 7

50 QUAD/BEAVER LAKE LI 0 0 0 7

541 QUAD/BEAVER LAKE LI 0 0 31.6 6.4

2908 QUAD/BEAVER LAKE LI 0 0 21.9 6.4

1240 QUAD/BEAVER LAKE MA 0 25 26 6

1239 QUAD/BEAVER LAKE MA 0 22 24 6

1146 QUAD/BEAVER LAKE LI 0 0 23.4 6

1051 QUAD/BEAVER LAKE LI 0 30 22 6

102

1241 QUAD/BEAVER LAKE MA 0 23 22 6

641 QUAD/BEAVER LAKE LI 0 0 21 6

939 QUAD/BEAVER LAKE LI 0 0 20 6

1003 QUAD/BEAVER LAKE LI 0 48 18 6

640 QUAD/BEAVER LAKE LI 0 1 18 6

1223 QUAD/BEAVER LAKE MA 0 24 0 6

853 QUAD/BEAVER LAKE MA 0 0 28 5

1238 QUAD/BEAVER LAKE MA 0 25 23 5

652 QUAD/BEAVER LAKE MA 0 0 22 5

315 QUAD/BEAVER LAKE MA 0 0 19 5

1071 QUAD/BEAVER LAKE LI 0 30 17 5

1046 QUAD/BEAVER LAKE MA 0 21 0 5

933 QUAD/BEAVER LAKE LI 0 47 23 4

542 QUAD/BEAVER LAKE LI 0 0 0 0

200 QUAD/BEAVER LAKE LI 0 0 0 0

946 QUAD/BEAVER LAKE LI 0 0 28 0

1242 QUAD/BEAVER LAKE MA 0 25 26 0

981 QUAD/BEAVER LAKE LI 0 0 25 0

947 QUAD/BEAVER LAKE LI 0 0 25 0

1287 QUAD/BEAVER LAKE MA 0 29 24 0

1224 QUAD/BEAVER LAKE MA 0 25 22 0

638 QUAD/BEAVER LAKE LI 0 1 1 0

1077 QUAD/BEAVER LAKE LI 0 36 0 0

1045 QUAD/BEAVER LAKE MG 0 34 0 0

1222 QUAD/BEAVER LAKE MA 0 33 0 0

1234 QUAD/BEAVER LAKE LI 0 27 0 0

1243 QUAD/BEAVER LAKE MA 0 24 0 0

1081 QUAD/BEAVER LAKE MA 0 18 0 0

1404 QUAD/BEAVER LAKE MG 0 0 0 0

1337 QUAD/BEAVER LAKE MA 0 0 0 0

1132 QUAD/BEAVER LAKE LI 0 0 0 0

1126 QUAD/BEAVER LAKE LI 0 0 0 0

1106 QUAD/BEAVER LAKE LI 0 0 0 0

1101 QUAD/BEAVER LAKE LI 0 0 0 0

1098 QUAD/BEAVER LAKE LI 0 0 0 0

1093 QUAD/BEAVER LAKE MA 0 0 0 0

1058 QUAD/BEAVER LAKE LI 0 0 0 0

1052 QUAD/BEAVER LAKE MA 0 0 0 0

994 QUAD/BEAVER LAKE LI 0 0 0 0

993 QUAD/BEAVER LAKE LI 0 0 0 0

991 QUAD/BEAVER LAKE LI 0 0 0 0

988 QUAD/BEAVER LAKE LI 0 0 0 0

103

986 QUAD/BEAVER LAKE LI 0 0 0 0

974 QUAD/BEAVER LAKE LI 0 0 0 0

973 QUAD/BEAVER LAKE LI 0 0 0 0

972 QUAD/BEAVER LAKE LI 0 0 0 0

964 QUAD/BEAVER LAKE LI 0 0 0 0

963 QUAD/BEAVER LAKE LI 0 0 0 0

961 QUAD/BEAVER LAKE LI 0 0 0 0

955 QUAD/BEAVER LAKE LI 0 0 0 0

954 QUAD/BEAVER LAKE LI 0 0 0 0

949 QUAD/BEAVER LAKE LI 0 0 0 0

937 QUAD/BEAVER LAKE LI 0 0 0 0

936 QUAD/BEAVER LAKE LI 0 0 0 0

835 QUAD/BEAVER LAKE MA 0 0 0 0

688 QUAD/BEAVER LAKE MG 0 0 0 0

687 QUAD/BEAVER LAKE MG 0 0 0 0

683 QUAD/BEAVER LAKE LI 0 0 0 0

651 QUAD/BEAVER LAKE MA 0 0 0 0

639 QUAD/BEAVER LAKE LI 0 0 0 0

445 QUAD/BEAVER LAKE MA 0 0 0 0

419 QUAD/BEAVER LAKE LI 0 0 0 0

305 QUAD/BEAVER LAKE MA 0 0 0 0

304 QUAD/BEAVER LAKE MA 0 0 0 0

303 QUAD/BEAVER LAKE MA 0 0 0 0

302 QUAD/BEAVER LAKE MA 0 0 0 0

301 QUAD/BEAVER LAKE MA 0 0 0 0

300 QUAD/BEAVER LAKE MA 0 0 0 0

299 QUAD/BEAVER LAKE MA 0 0 0 0

298 QUAD/BEAVER LAKE MA 0 0 0 0

297 QUAD/BEAVER LAKE MA 0 0 0 0

296 QUAD/BEAVER LAKE MA 0 0 0 0

282 QUAD/BEAVER LAKE MA 0 0 0 0

281 QUAD/BEAVER LAKE MA 0 0 0 0

280 QUAD/BEAVER LAKE MA 0 0 0 0

272 QUAD/BEAVER LAKE MG 0 0 0 0

250 QUAD/BEAVER LAKE LI 0 0 0 0

236 QUAD/BEAVER LAKE MA 0 0 0 0

233 QUAD/BEAVER LAKE MA 0 0 0 0

83 QUAD/BEAVER LAKE LI 0 0 0 0

74 QUAD/BEAVER LAKE MA 0 0 0 0

73 QUAD/BEAVER LAKE MA 0 0 0 0

35 QUAD/BEAVER LAKE LI 0 0 0 0

33 QUAD/BEAVER LAKE LI 0 0 0 0

104

454 QUAD/BEAVER LAKE MA 0 0 0 0

739 QUAD/BEAVER LAKE MA 0 0 0 0

*AFPS #264 artifact omitted due to it being an ambiguous case. It was called an awl, but appears to be a broken point.

0m designation is for points deemed modern breaks likely due to agriculture.

105