University of Nevada, Reno

Environmental Variables Associated with the Location of Arborglyphs in the Eastern Sierra Nevada, Alpine County, California

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geography

By

Nicholas J. Connolly

Dr. Kate Berry/Thesis Advisor

May, 2012

Copyright by Nicholas J. Connolly 2012 All Rights Reserved

THE GRADUATE SCHOOL

We recommend that the thesis prepared under our supervision by

NICHOLAS J. CONNOLLY

entitled

Environmental Variables Associated with the Location of Arborglyphs in the Eastern Sierra Nevada, Alpine County, California

be accepted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

Kate Berry, Ph. D., Advisor

Scott Mensing, Ph. D., Committee Member

Louis Forline, Ph. D., Graduate School Representative

Marsha H. Read, Ph. D., Dean, Graduate School

May, 2012

i

Abstract

Arborglyphs are a unique form of expression created by Basque sheepherders.

The sheepherder’s arborglyphs are in part a type of history, art, and literature that is inherently linked to location. Arborglyphs disintegrate as the aspen trees they are on die so it is important to locate and document them before they are lost. This research was guided by three questions: a) which environmental variables are associated with the location of arborglyphs, b) are arborglyphs carved more frequently on the uphill side of the tree, and c) does the Taylor Grazing Act influence quantity of arborglyphs? Nine environmental variables were investigated as they related to the location of arborglyphs. The environmental features studied were: elevation, aspect, slope, distance to surface water, depth to water table, soil drainage, soil depth, precipitation, and temperature. These variables were analyzed in a geographic information system implementing a weights of evidence analysis and each of the environmental variables proved to be correlated (at the 90 percent level) to the location of the arborglyphs. Six of these variables – elevation, slope, soil depth, aspect, precipitation, and distance to surface water – are recommended to land managers as being particularly useful in locating arborglyphs. The second analysis in this study involved the placement of arborglyph on an individual tree with respect to slope and was done with arborglyphs on all slopes and then again with arborglyphs on slopes greater than ten degrees. No correlation was found. The third analysis considered whether the Taylor Grazing Act of

1934, which has been identified as contributing to the decline of Basque sheepherding ii in the American West, was associated with a decrease in arborglyphs. In examining dated arborglyphs before and after the Taylor Grazing Act, a noticeable decline was found in the quantity of arborglyphs in the post-1934 period. iii

Acknowledgements

This thesis would not have been possible without the help of many people. First,

I would like to thank my advisor Dr. Kate Berry for her advice and help during the course of my thesis and geography education. I would also like to thank Dr. Scott Mensing and

Dr. Louis Forline for their ideas and guidance throughout the thesis process. A big thanks to the Humboldt-Toiyabe National Forest for their support; archaeologists, Alyce

Branigan and Fred Frampton for their counsel on all things cultural; David McMorran

(retired) and Cheryl Johnson for their assistance with GIS and my supervisor Kathy

Lucich for allowing me the flexibility in my schedule to complete this thesis and my

Master’s degree.

The field work for this thesis was a wonderful time and with the effort of great volunteers we were able to at least in part preserve hundreds of previously undocumented arborglyphs. Therefore, I would like to thank my Passport in Time volunteers: Jim Blaes, Jim Goertzen, Ruby Lowery, Nancy Nagel, Sue Shuman, Lauren

Smyth, Jana Williams, and Diane Wilson. Dr. Joxe Mallea-Olaetxe, thank you for your research on arborglyphs because without it this thesis would not have been possible.

Also, thank you for your support of this thesis and your help with the PIT field work. I would also like to thank the Fall 2011, UNR Geography, Field Methods students:

Michelle Lam, Catherine Magee, James Rosenburg, and Timothy Tabbada.

Last but not least, I would like to thank my friends and family, especially

Catherine Connolly, my wife for her unconditional support. iv

Contents

Abstract ...... i

Acknowledgements ...... iii

1 Introduction ...... 1

1.1 Research Questions ...... 2

1.2 Background and Justification of Study...... 3

1.3 Definitions ...... 4

1.4 Overview ...... 5

2 Literature Review ...... 6

2.1 Populus tremuloides ...... 6

2.1.1 Distribution and Ecological Requirements ...... 8

2.1.2 The Lifespan of Aspen ...... 12

2.1.3 Cultural Connections to Aspen ...... 17

2.2 Basque Sheepherding and Arborglyphs ...... 18

2.2.1 Historical Overview ...... 20

2.2.2 Arborglyphs ...... 30

3 Study Area ...... 37

3.1.1 Topography...... 39

3.1.2 Climate ...... 44

3.1.3 Geology and Soil ...... 46

3.1.4 Vegetation ...... 49

4 Methods ...... 51 v

4.1 Data Collection ...... 51

4.2 Data Analysis ...... 53

4.2.1 Weights of Evidence ...... 56

4.2.2 Analysis of Arborglyph Placement on Tree ...... 60

4.2.3 Effect of Taylor Grazing Act on Arborglyph Quantity ...... 62

5 Results ...... 63

5.1 Weights of Evidence ...... 63

5.1.1 Elevation ...... 63

5.1.2 Aspect ...... 65

5.1.3 Slope ...... 66

5.1.4 Distance to Surface Water ...... 67

5.1.5 Depth to Water Table ...... 69

5.1.6 Soil Drainage ...... 70

5.1.7 Soil Depth ...... 72

5.1.8 Precipitation ...... 74

5.1.9 Temperature ...... 76

5.2 Weights of Evidence Sensitivity Testing ...... 77

5.2.1 Recategorized Elevation ...... 78

5.2.2 Recategorized Aspect ...... 80

5.2.3 Recategorized Slope ...... 81

5.2.4 Recategorized Soil Drainage ...... 83

5.3 Analysis of Arborglyph Placement on Tree ...... 84

5.4 Effect of Taylor Grazing Act on Arborglyph Quantity ...... 85 vi

6 Discussion and Conclusion...... 87

6.1 Weights of Evidence ...... 87

6.1.1 Elevation ...... 93

6.1.2 Aspect ...... 95

6.1.3 Slope ...... 97

6.1.4 Distance to Surface Water ...... 98

6.1.5 Depth to Water Table ...... 99

6.1.6 Soil Drainage ...... 99

6.1.7 Soil Depth ...... 100

6.1.8 Precipitation ...... 101

6.1.9 Temperature ...... 101

6.2 Analysis of Arborglyph Placement on Tree ...... 102

6.3 Effect of Taylor Grazing Act on Arborglyph Quantity ...... 103

6.4 Conclusions ...... 105

7 Bibliography ...... 110

Appendix A: Study Area Access ...... 118

Appendix B: Steps to Record and Arborglyph ...... 120

Appendix C: Overview of Passport in Time ...... 125

List of Figures

Figure 2-1 Quaking aspen: leaf, staminate flower, pistillate flower, and fruiting

branchlet. (Sargent 1905, 154) ...... 7 vii

Figure 2-2 Quaking aspen distribution for North and Central America. (USDA) .... 8

Figure 2-3 “Relationship of aspen site index to water table depth.” (Fralish 1972,

53) ...... 9

Figure 2-4 Aspect of 72 aspen communities with respect to elevation. (Reed

1971, 331) ...... 11

Figure 2-5 Vegetative reproduction hormone affects to apsen. (Bartos 2001, 9) 13

Figure 2-6 Goe Gabiola 1954, Arborglyph Horsethief Canyon Photo credit: Nancy

Nagel...... 18

Figure 2-7 Typical scene of a sheepherder in the American West ...... 23

Figure 2-8 Herder watching from the wagon “Season Graze in Nevada” (Sawyer

1971, Frontispiece) ...... 24

Figure 2-9 Carving of a hand, Hope Valley, California ...... 32

Figure 3-1 Study Area Vicinity Map ...... 38

Figure 3-2 Horsethief Canyon topography and arborglyph survey boundary...... 41

Figure 3-3 Hope Valley topography and arborglyph survey boundary...... 42

Figure 3-4 Scott’s Lake topography and arborglyph survey boundary...... 43

Figure 4-1 Arborglyph Training Point Location Map...... 58

Figure 5-1 Elevation Variable Map ...... 64

Figure 5-2 Aspect Variable Map ...... 65

Figure 5-3 Slope Variable Map ...... 66

Figure 5-4 Distance to Surface ...... 68

Figure 5-5 Depth to Water ...... 70 viii

Figure 5-6 Soil Drainage Variable Map ...... 71

Figure 5-7 Soil Depth Variable Map ...... 73

Figure 5-8 Mean Annual...... 75

Figure 5-9 Mean Annual Temperature Variable Map ...... 76

Figure 5-10 Recategorized Elevation ...... 78

Figure 5-11 Recategorized Aspect ...... 80

Figure 5-12 Recategorized Slope ...... 82

Figure 5-13 Recategorized Soil Drainage Variable Map ...... 83

Figure 5-14 Effect of Taylor Grazing Act on Arborglyph Quantity ...... 86

Figure 6-1 Modeled Arborglyph Locations, Original Variables ...... 89

Figure 6-2 Modeled Arborglyph Locations, Recategorized Variables ...... 93

Figure Appendix C- 1 PIT volunteers on the first day in the field. Photo credit:

Nancy Nagel...... 126

List of Tables

Table 3-1 Climate Data Summary ...... 46

Table 5-1 Elevation Variable ...... 64

Table 5-2 Elevation Results ...... 64

Table 5-3 Aspect Variable ...... 65

Table 5-4 Aspect Results ...... 66

Table 5-5 Slope Variable ...... 66

Table 5-6 Slope Results ...... 67 ix

Table 5-7 Distance to Surface Water Variable ...... 68

Table 5-8 Distance to Surface Water Results ...... 69

Table 5-9 Depth to Water Table Variable ...... 70

Table 5-10 Depth to Water Table Results ...... 70

Table 5-11 Soil Drainage Variable ...... 71

Table 5-12 Soil Drainage Results ...... 72

Table 5-13 Soil Depth Variable...... 73

Table 5-14 Soil Depth Variable Results ...... 74

Table 5-15 Mean Annual Precipitation Variable ...... 75

Table 5-16 Mean Annual Precipitation Results ...... 76

Table 5-17 Mean Annual Temperature Variable ...... 76

Table 5-18 Mean Annual Temperature Results ...... 77

Table 5-19 Recategorized Elevation Variable ...... 78

Table 5-20 Recategorized Elevation ...... 79

Table 5-21 Recategorized Aspect Variable ...... 80

Table 5-22 Recategorized Aspect ...... 81

Table 5-23 Recategorized Slope Variable ...... 82

Table 5-24 Recategorized Slope ...... 83

Table 5-25 Recategorized Soil Drainage Variable ...... 83

Table 5-26 Recategorized Drainage ...... 84

Table 5-27 Arborglyph Location Results ...... 85 x

Table 6-1 Environmental Variables Significant to the Location of Arborglyphs,

Original ...... 87

Table 6-2 Original Variables Ranked by Studentized Contrast ...... 88

Table 6-3 Environmental Variables Significant to the Location of Arborglyphs,

Recategorized ...... 91

Table 6-4 Recategorized Variables Ranked by Studentized Contrast ...... 92 1

1 Introduction

Immigrant Basque sheepherders carved arborglyphs on aspen while herding

sheep in the remote high country of the American West. Arborglyphs are simply tree

carvings. Herding sheep required little attention from sheepherders throughout the day

especially if they had a well-trained sheep dog, leaving much time to do other things.

According to Mallea-Olaetxe, “the soft, smooth bark of the aspen is compliant, and the

trees became the keepers of the herders’ most intimate secrets” (2008, 43). Not only were the arborglyphs a way for the sheepherders to occupy their time but it also created an, “illusion of not being alone” (Lane and Douglass 1985, 63).

One of the major problems with the arborglyph medium is that when the tree dies the carvings are lost or become indecipherable. This problem is exacerbated by aspens relatively short life expectancy. Aspen generally live 60 to 80 years and can live up to 120 years but deteriorate quickly after that (Mueggler 1989). Nonetheless, many carvings date from the 1910s and even earlier. The oldest arborglyph ever recorded had a date of 1870 that is still clearly readable (Mallea-Olaetxe 2008). As a result, arborglyphs may be considered a cultural and historical resource on the verge of extinction.

The only way to preserve arborglyphs is to document them. Documentation of arborglyphs is particularly important because Basque sheepherders are underrepresented in the history of the American West. Arborglyphs have been documented many ways over the years, Mallea-Olaetxe (2001) captures images using a 2

video camera while dictating translations and history. Dekorne (1970) takes photographs, while others do rubbings or sketches. But arborglyphs must be located before documentation can happen. Location is a facet of arborglyph research that has yet to be explored.

Methods for locating arborglyphs then become the central theme of this research. For example, what environmental variables indicate arborglyphs? How does slope affect the location of arborglyphs on the tree? Exploring these questions will facilitate arborglyph preservation by documentation because the first step for documentation is locating an arborglyph. Preservation through documentation is important because, “[arborglyphs] provide the closest thing to a compressed autobiography of sheepherders, who are one of the most forgotten social groups in

American history” (Mallea-Olaetxe 2001, 44).

1.1 Research Questions

This thesis attempts to answer three research questions.

1) Which environmental variables are associated with the location of

arborglyphs?

2) Are arborglyphs carved more frequently on the uphill side of the tree?

3) Does the Taylor Grazing Act influence quantity of arborglyphs in a

location?

These research questions were addressed using arborglyph location information collected during a field survey along with publicly available environmental data. During 3

the field survey arborglyphs were documented to record their location, photo documentation, transcription, and environmental surroundings, thus, preserving the information they contain for future researchers.

1.2 Background and Justification of Study

The number of aspen with Basque arborglyphs is declining in the American West due to death of aspen trees. As the aged aspen die, the arborglyph is lost as well. If the

arborglyphs are not recorded a large piece of history, art, and literature will be lost.

Arborglyphs were the preferred method for Basque sheepherders to document their

daily life. Arborglyphs are a primary source; they are unique in that they contain the

sheepherder’s message, the location of the sheepherder and in many cases other useful

information. This other useful information could include but is not limited to, name,

date, or origin. Arborglyphs provide information on location because the tree has not

moved since it was carved by the sheepherder. This is a benefit of the arborglyph

medium.

The intent behind this research was to gain a better understanding of how the

location of arborglyphs is linked to environmental variables. The environmental variables identified in this study provide information that could be used to identify areas where arborglyphs have not been previously documented. This allows public land managers to more easily recognize areas that are in need of arborglyph survey, documentation, and preservation. It is important for public land managers to document arborglyphs because of their temporary nature. 4

Lastly, the arborglyph field survey for this study recorded much more information than was used in the study. All the information was documented using

California cultural site record forms1. The forms were filed to archive and preserve the arborglyph data as well as provide future researchers with more detailed information than can be found in this study.

1.3 Definitions

Several terms are integral to the understanding of this thesis. Terms not listed here will be defined within the text.

• Arborglyph – are a type of culturally modified tree where a carving was

done on the bark of a quaking aspen tree. The arborglyphs were created

by sheepherders and historically have been done by people of Basque

cultural heritage.

• Basque – are a people and cultural group from the Pyrenees Mountains

of southern France and northern Spain. Basque is also the name of their

language, natively it is known as Euskara. Many Basque immigrated to

the American West during the early part of the 20th century. The Basque

are considered the oldest cultural group in Europe.

1 The site records are located at the California Historical Resources Information System at the Central California Information Center at California State University, Stanislaus. Reference site forms: 04ALP350H, 04ALP362H, and 04ALP698H through 04ALP707H. 5

• Environmental variable – is a physical variable connected with a

particular location. For this study, soil, climate, hydrologic, terrain, and

biotic features were included.

1.4 Overview

This thesis is divided into six chapters. The first chapter introduces the research and provides background on Basque sheepherders, arborglyphs, and the analysis used

to explore their location. This chapter also discusses what is known about arborglyphs

and provides the background necessary for the analysis. The second chapter is a review

of the literature associated with aspen and Basque sheepherding in the American West.

Chapter three is an overview of the study area investigated in the analysis. The

topography, climate, geology, soil, and vegetation are discussed. Chapter four discusses

methods used in the field survey and analysis. Chapter five presents the results of the

analysis. Chapter six is a discussion of the thesis. It includes limitations of this research

and a collection of future research directions. 6

2 Literature Review

The recipe for arborglyphs is fairly simple: it is one part aspen and one part

Basque sheepherders. Because it was common for sheepherders to be without human

contact for weeks on end, they had an abundance of time, so they carved arborglyphs

into aspen as well as tended their flocks, fished in nearby streams, baked bread, and

carried out their daily living routines. Sheepherders did not usually need to go far to find

aspen. Generally aspen communities promote the vegetation that provided good fodder

for their sheep and aspen water requirements also meant there was ample water for

the sheep. Aspens provided shade from the afternoon sun, which allowed for the sheep

to graze for longer periods of time making the sheep larger for sale.

2.1 Populus tremuloides

Pando is the name of the world’s heaviest living organism (Grant 1993). The

name is a Latin word meaning I spread (Mitton and Grant 1996). So, what is the world’s heaviest living organism? Covering 106 acres and weighing in at more than 13 million pounds Pando is undoubtedly the Earth’s heavy-weight champion and takes the form of a Populus tremuloides (quaking aspen2) community (Grant 1993). Pando is

located in the state of Utah and is immediately south of the Wasatch Mountains (Grant

1993). The reason that Pando was able to grow so heavy is because aspen reproduce primarily by vegetative reproduction also known as cloning or suckering (Peterson and

2 Quaking aspen (Populus tremuloides Michx.) will be here on referred to as aspen. 7

Peterson 1993). Within each aspen

community, ramets are thrust upward

from the root system and each ramet

appears as a tree, but they are all part of the same root system, and create more Figure 2-1 Quaking aspen: leaf, staminate flower, pistillate flower, and fruiting roots themselves which in turn produce branchlet. (Sargent 1905, 154) more ramets (Mitton and Grant 1996). Technically since they all have the same exact genetic material and they are all interconnected, they can all be considered the same organism, hence the meaning of the name, I spread (Mitton and Grant 1996). This reproductive process can continue for quite a long time making the root system extremely large and thus creating one extremely heavy individual.

The aspen was named and described by French botanist and explorer André

Michaux in 1803 (Harper, Shane, and Jones 1985). The tree is in the willow (Salicaceae) family and is closely related to trees such as the cottonwood (Mitton and Grant 1996).

Paleobotany has provided evidence that the quaking aspen has evolved from the tree

Populus voyana which existed during the Miocene (Harper, Shane, and Jones 1985). It is thought that the aspen first emerged during the late Cenozoic era (Harper, Shane, and

Jones 1985).

8

2.1.1 Distribution and Ecological Requirements

Quaking aspen are the most widespread tree found in North America (Romme et

al. 2001; Mitton and Grant 1980; Fralish and Franklin 2002). They can be found in

Alaska, Canada, the Continental United States (coast to coast), and even parts of Mexico

(Figure 2-2) (Mitton and Grant 1996; USDA). Populus tremuloides is related to other aspen species found in other parts of the world, including Asia and Europe. In the

American West aspen tend to grow at high elevation and along reliable sources of water, but this is not the case everywhere in North America, where they are found from sea level all the way up to 3,700 meters (Mitton and Grant 1996; Haeussler, Coates, and

Mather 1990; Jones 1985).

Aspen is a tolerant species as shown by its distribution, but they do have some requirements and preferences. Aspen require moist soil that has decent drainage because they cannot grow in permanently saturated soils (MacKinnon et al. 1992). Aspen also prefer steady sources of moisture. However, quaking aspen can also grow in droughty soils with sufficient moisture content (Fralish and

Franklin 2002). Aspen prefer soils composed of

50-70 percent silt or clay loams (Peterson and

Peterson 1993). Soils that contain many rocks inhibit the growth and spread of the aspen Figure 2-2 Quaking aspen distribution for North and Central America. (USDA) 9

because their roots have difficultly penetrating such hard soils (Haeussler, Coates, and

Mather 1990).

There is some contention about how close to the surface aspen prefer the water

table. Haeussler et al. (1990) explain that quaking aspen require a water table that is

between 1 – 2.5 meters from the surface, while Fralish (1972) believe it is closer to the surface at 0.7 – 2 meters. In fact, aspen root systems are generally between 0.9 meters to 1.5 meters in depth, thus making 2 – 2.5 meters an extreme depth (Fowells 1965).

While there is a range of preferred water table depths, the function of site index to water table is not linear and site index quickly declines at the extremes as can be seen in

Figure 2-3 (Fralish 1972). Site index is a forestry and natural resource term that can be viewed here as a proxy for aspen health. Site index is derived from the height of a tree at a particular base age (Bettinger et al. 2008). The reason for the non-linear function is that the roots are not able to reach depths much past 2.5 meters, thus they are unable to access water below this depth (Fralish 1972). When the water table is less than 0.5 meters from the surface the soil becomes too saturated and, as stated previously, aspen are unable to grow in continuously saturated soils (MacKinnon et al. 1992). This has been tested in the laboratory by Maini and Horton (1966), who Figure 2-3 “Relationship of aspen site index to water table depth.” (Fralish 1972, 53) 10

discovered that saturated aspen roots do not produce suckers, eventually decaying

without undergoing vegetative reproduction. It has also been observed in the field by

Bates et al. (1990) and Crouch (1986) who theorize that aspen cannot perform vegetative reproduction when completely saturated because of a lack of oxygen in the soil. Mature aspen are not as affected by complete saturation of the soil, but because saturation restricts vegetative reproduction it effectively means that the current generation is the last if the soil stays permanently saturated.

While aspen are generally tolerant of many soil types they perform best in soils that have high magnesium, lime, calcium, potassium, and nitrogen (Fowells 1965;

Peterson and Peterson 1993). Aspen can also improve soil nutrients after a forest fire.

When aspen reestablish after a fire, they redistribute nitrogen to the soil due to surface deposition of leaf litter (Stoeckeler 1961). Finally, aspen do best in soils that are basic,

with intermediate to high levels of pH (Bell 1991).

Solar radiation is the most important requirement for aspen as they have a very low shade tolerance (Bartos 2008). Solar radiation is required for both successful vegetative reproduction and general survival (Peterson and Peterson 1993). The shade produced by mature aspen stands effectively makes it impossible for reproduction to occur (Ohmann 1982). This would be one of the reasons that the quaking aspen spread out and become Pando-like. Aspen need sun so much that their white bark has chlorophyll and is able to perform photosynthesis (Knight 2001, 441).

In contrast, the shade from aspen provide habitat for other plant and animal species, especially in the mountainous regions of the American West and in the Great 11

Basin (Bartos 2008). The herbaceous vegetation under an aspen community provides forage for animals as well as providing the vegetative biodiversity needed for the habitat of insect pollinators and birds. In fact, if an aspen community were to be replaced by Figure 2-4 Aspect of 72 aspen conifers, the understory forage is estimated to communities with respect to elevation. (Reed 1971, 331) decrease by up to 70 percent (Strong et al. 2010). Thus, quaking aspen in the American

West is considered a keystone3 species that is worth protecting (E. A. Smith et al. 2011).

Aspen prefer different slope aspects in relation to elevation (Jones 1985). This effect has been observed by Reed (1971) and Beetle (1974) who both came to similar conclusions that aspen prefer northern slopes at lower elevation (2,300m to 2,500m) and southern slopes at higher elevation (2,500m to 3,100m) (Reed 1971; Beetle 1974).

Reed (1971, 331) explains further that, “[a]t middle elevations aspen stands occur on all slopes...” (Figure 2-4). The reason for this phenomena is that aspen prefer cooler temperatures when at lower elevations and this occurs primarily on slopes with a northern aspect (Jones, Kaufmann, and Richardson 1985). The opposite is true at higher elevations where temperatures are generally cooler to begin with, and the aspen prefer the warmer south facing slopes (Jones, Kaufmann, and Richardson 1985).

3 A keystone species is, “crucial in maintaining the organization and diversity of [its] ecological communit[y]” (Mills, Soulé, and Doak 1993, 219). 12

Temperature also affects the growth of aspen. Hanna and Kulakowski (2012) found that the growth of aspen is inhibited by higher temperatures during the current and previous year with the exception of aspen at the highest elevation. Aspen establishment is linked to temperature and precipitation. Greater aspen establishment

generally coincides with high summer temperatures followed by low spring precipitation

(Elliott and Baker 2004).

2.1.2 The Lifespan of Aspen

Aspen have two forms of reproduction, they can perform vegetative

reproduction or cloning, as mentioned previously, and they can reproduce sexually.

However, the majority of the aspen reproduction is vegetative.

Vegetative reproduction is a type of asexual reproduction in which the process does not require sex cells and thus, only contains genetic material from one individual

(Rudolf 2011). Specifically, aspen perform what is called root suckering. This process

involves sending up suckers also called ramets from the root of an adult tree, when a

sucker has matured it is essentially an aspen tree (Mitton and Grant 1996). The suckers

are extruded from buds on the roots that are between 7.5 to 10 centimeters below the

surface (Fowells 1965). The trees produce these buds every growing season and the

majority of the successful suckers come from the buds produced that season (Fowells

1965). 13

Aspen use a check and balance chemical process to regulate the production of

suckers. The root system is always producing a hormonal chemical called cytokinin,

which increases suckering, while the tree is producing a chemical called auxin, that

inhibits suckering as shown in Figure 2-5 (Bartos 2001). So, when a tree is disturbed (i.e.

removed from the system) the chemical process becomes off balance and relative

cytokinin levels increase and the aspen community begins vegetative reproduction

(Bartos 2001). The crux to vegetative reproduction for aspen is the disturbance, without

disturbance the chemicals stay in balance and there is no need to produce offspring.

Sexual reproduction among aspen is extremely rare in the wild, primarily

because seeds have a very short life span and need a specific environment to germinate

(Bartos 2001). These germination requirements are so specific that, in the western

United States, aspen sexual reproduction has not occurred on a large scale since the last glaciations near the beginning of the Holocene (Mitton and Grant 1996). Aspen produce lots of seeds, approximately 2.5 to 3 million seeds per pound of tree and they usually are viable when they are dispersed. The seeds are covered in a long silky material that

Figure 2-5 Vegetative reproduction hormone affects to apsen. (Bartos 2001, 9) 14

allows them to be easily carried by wind or water (Fowells 1965). The challenge is that

they are short lived, two to three weeks, and need a moist mineral soil to become

established, requirements that are rarely met (Strong et al. 2010; Fowells 1965).

As already discussed, aspen can become extremely large when viewed as an individual made up of a community of trees. Through vegetative reproduction these individuals essentially live forever (Mitton and Grant 1996). Pando the giant aspen community is estimated to be approximately one-million years old and shows no signs of stopping (Mitton and Grant 1996). With most trees, age is determined by counting the number of annual growth rings by extracting a core sample. Aspen produce growth rings but they only tell the age of that particular ramet and not the age of the entire community. Direct methods of measurement of age for the communities are not possible at this time (Mitton and Grant 1996).

There appears to be some incongruities as to the life expectancy of ramets.

Mitton and Grant (1980) believe that the maximum life expectancy of an individual tree is 50 to 70 years, while Strand et al. (2009) state that quaking aspen can live to 120 years old. Mueggler compromises and takes his analysis one step further by concluding that, “aspen mature at between 60 and 80 years, deteriorate rather rapidly after about

120 years, and only in rare cases attains ages over 200 years” (1989, 43). Discrepancies may be based on environmental differences between the researchers’ study areas.

The growth rate of aspen is tied to the environmental characteristics of the sites they are in (Mitton and Grant 1996). Jelinski and Cheliak (1992) found that a combination of environmental factors influence aspen growth patterns: elevation, 15

slope, age, and exposure to wind. Grant and Mitton (1979) found that elevation was the environmental variable most directly tied to growth rate (Grant and Mitton 1979).

The gender of aspen also seems to affect growth rate. While both genders seem to be influenced by elevation, female communities tend to have larger diameter ramets than males at a given elevation (Grant and Mitton 1979). The male communities have approximately 12 percent smaller diameter ramets at all elevations than females

(Mitton and Grant 1996). With an exception, males maintain smaller communities and therefore are able to produce the largest ramets within both genders (Sakai and Burris

1985). The hypothesis is that since the males have smaller communities more resources can be diverted to certain ramets causing them to grow much larger than the others

(Sakai and Burris 1985).

Gender also affects the amount of area a given community occupies; Sakai and

Burris (1985) found that females tend to occupy larger areas than that of males at two sites in lower northern Michigan (1985). Over the 25 years their research was conducted the male communities increased in area by 219 percent, while the female communities increased by 292 percent (Sakai and Burris 1985).

A current area of research on aspen addresses threats to communities. The data suggest that quaking aspen communities in the American West have declined between

51 to 96 percent over a time period from the mid-19th century to end of the 20th century

(Mitchell 2000). There are two major threats; global changing climate and decreased

amounts of disturbance to which aspen are well adapted. There is limited research on

how the changing climate will affect aspen but there has been considerable research 16

done on how decreased disturbance has threatened aspen communities. Aspen are

considered a disturbance dependent species. The main disturbance is fire. Due to fire

suppression over the last century, aspen communities have declined (E. A. Smith et al.

2011). Without regular fire aspen are eventually replaced by shade tolerant conifers

(Strand et al. 2009).

After fire, an aspen stand quickly regenerates through vegetative reproduction

(Strand et al. 2009). It has been estimated that in North America fire burned stands of

aspen at intervals of approximately 70 to 80 years (Strand, Vierling, and Bunting 2009).

These fires allowed for the aspen to reproduce and maintain dominance in many areas.

This might be viewed as beneficial for many reasons, but biodiversity is perhaps the

most significant. The biodiversity found under aspen cover in the American West is

second only to riparian areas (Kay 1997). In fact, since aspen need reliable water sources

often riparian areas and aspen coincide. Some Native American groups may have

contributed to the success of the aspen in the Americas by routinely starting fires as well

as killing many deer and elk that fed on new suckers (Kay 1997). The combination of

these two factors, Kay argues, allowed the aspen to flourish more than would be possible without human intervention.

There is also much less grazing in National Forest lands when compared to

historic levels with animals such as sheep, which actually help the quaking aspen but

contribute to loss in understory. However, more research is needed to better

understand resource management that could allow for grazing on specific cycles that

could promote quaking aspen health and understory biodiversity. 17

Aspen have a number of adaptations that will aid in their persistence, but

resource managers can and will be pivotal in this. Managing the environment is a tricky

thing, as the first law of ecology says, “we can never do merely one thing” (Knight 2001,

445). Protecting humans from fire has caused the aspen to decline but has saved people

their property. Unintended consequences of management decisions and the need to make compromises are now becoming apparent.

2.1.3 Cultural Connections to Aspen

Aspen have some unique cultural aspects surrounding them. In Judeo-Christian folklore, for example, the quaking aspen, “...[quake] in fear because the cross on which

Jesus was crucified was made of aspen” (Grant 1993, 4).The Blackfoot Native American group has a similar folklore. Napi, a Blackfoot god who the aspen decided was not important, struck them with lightning bolts when they did not bow to him while walking through the area. The aspen now tremble whenever they hear somebody walking amongst the grove (Willard 1992). Others related to the noise of quaking aspen. Several groups of Native Americans called the tree, “noisy leaf” and “noisy tree,” while at least several European languages referred to it as “woman’s tongue.” the implication being that women make lots of noise (Bennet and Tiner 2003).

Aspen is also believed to be medicinal by both native and immigrant cultures.

The leaves and bark are steeped to make a herbal tea that is thought to help with colds, flu, arthritis, and allergies (Angier 1978). 18

As alluded to in the introduction, aspen

were used as a type of artist’s canvas for

arborglyphs. The trees’ soft white bark took the

carver’s point with an ease that could not be

passed by. Aspen were used for decades by Basque

sheepherders to carve arborglyphs. The

arborglyphs were a way for sheepherders to

connect with others. Much of the time the

connection was as simple as, “I was here” as

depicted in Figure 2-6. The connection was distant Figure 2-6 Goe Gabiola 1954, Arborglyph Horsethief Canyon both spatially and temporally. It was spatially Photo credit: Nancy Nagel.

distant because of the remote locations the sheepherders worked and temporally

because the carvings were not legible until years after they were carved. Thus, the

communication the arborglyph was conveying happened later. What follows is a brief

history of the Basque in the new world and sheepherding in the American West, which

all led up to the creation of arborglyphs.

2.2 Basque Sheepherding and Arborglyphs

The Basque are synonymous with a single occupation in the American West,

sheepherding (Paris and Douglass 1979). Sheepherding for the Basque in the United

States became a way for young Basque men to become financially independent which in the Basque country meant owning a baserria. A baserria is a farmstead and being able 19

to own one represented work and sustenance, but more importantly family. These

young men wanted their own baserria and family. However, from the 1850s well

through to the 1950s finding work in the Basque country was difficult, which is part of

the reason why young men from the Basque country started thinking about heading to

America to earn money. In the United States sheepherding was not desirable work and

“the salary was modest by American standards [but] it still represented several times

the going rate in Spain” (McGregor 1980, 12; Lane and Douglass 1985, 22). Many left the

Basque country to the American West frontier in hopes of making and saving money to

take back to their home country.

The Basque men knew that coming to the American West and working was going

to be hard labor but what they did not know was how mentally taxing sheepherding can

be. They would be entrusted to watch up to two-thousand sheep, worth approximately forty-thousand dollars, all while in remote ranges with little human contact (DeKorne

1970; Lane and Douglass 1985). The sheep also required that the sheepherder always be on the move to find new and better grazing land (Lane and Douglass 1985). The sheepherders were never able to call one place home at least for very long which was tough mentally. As a result the sheepherders devised ways of passing the time. First many sheepherders had said that while sheepherding they, “slow down their mental process” (Lane and Douglass 1985, 59). The second is that they created arborglyphs.

Douglas and Lane (1985) explain, arborglyphs were a way to humanize their environment, making them feel as though they are not alone. The arborglyphs provide 20

communication with past sheepherders and their arborglyphs continue the conversation

into the future.

2.2.1 Historical Overview

Sheepherding was not the original reason that the Basque came to the New

World; they came to strike it rich in the California gold rush of 1849 (Mallea-Olaetxe

2008). This is a little misleading, however, because the Basque traveled to the New

World even before that. On the famous “discovery” of the Americas in 1492 by

Christopher Columbus, one of ships he commanded, the Santa María was Basque-built

and manned. The Niña also had a sizable Basque crew (Douglass and Bilbao 1975).

Soon after the initial trips Basque began immigrating to South America. The

Basque that came from baserriak4 on these and subsequent voyages were well

accustomed to rural farming and livestock lifestyles and almost immediately took to

raising sheep under the frontier conditions in South America. There was plentiful land to

be grazed and Basque sheepherders could settle on unclaimed land, rent it from an

estanciero5, or the government (Douglass and Bilbao 1975). The Basque sheepherders

were generally not interested in buying land because they had always intended to

return to the Basque country. Buying land also required capital and the sheepherders

did not have much of that, so renting land was a less expensive alternative (Douglass

4 Baserriak is the plural form of baserria, meaning farmstead. 5 Estanciero is owner of a large ranching operation Río de la Plata region of Argentina and Uruguay. 21

and Bilbao 1975). The sheepherders that were able to buy land did extremely well

because the land prices in the region only grew (Douglass and Bilbao 1975).

In 1598 European sheep were introduced to what we now call the state of New

Mexico. Juan de Oñate on an expedition, introduced 3,000 sheep, as well as founded

the oldest European city in the American West, Santa Fe (Mallea-Olaetxe 2008).

Sheepherding in South America and southwestern North America continued for many

years, but with the news of significant finds of gold in California scores of people,

including the Basque, headed north to strike it rich. Most Basque miners quickly lost

interest in mining for more lucrative careers in the livestock industry that provided meat

and wool fiber to the growing population of the American West. Douglass and Bilbao

(1975) performed research on fourteen Basque men that came north for the gold rush

and of the fourteen, “twelve subsequently entered the livestock industry. Two became

cattlemen, and ten were sheepmen in southern and central California” (Douglass and

Bilbao 1975, 212). During the decade of 1850s the sheep industry took off. Douglass and

Bilbao (1975) searched census records and at the beginning of the decade their analysis

surmised there were less than 18,000 sheep in California, and by the end of the decade

there were well over a million sheep initiating sheepherding by Basque in the American

West.

Finding enough grazing land in California became an issue in the 1860s due to crowded rangeland, so sheepherding spread eastward into Nevada, Utah, Idaho,

Wyoming, Montana, Colorado, New Mexico, and parts of Oregon (Douglass 1980). What facilitated this largely eastward movement was the mining industry and specifically the 22

Comstock Lode at Virginia City, Nevada 1859. The need for laborers in the mines and supporting industries such as sheepherding spurred more Basque immigration (Mallea-

Olaetxe 2009). By the turn of the 20th century Basque came to the American West in

ever-increasing numbers. The rate of immigration peaked in the decade of 1900

(Mallea-Olaetxe 2008). Most of the recently immigrated Basque men initially had jobs in the sheep industry (Mallea-Olaetxe 2008).

Coming to the American West from the Basque Country presented many challenges but offered financial potential. Sheepherding was the job for the newly immigrated Basque because it offered advantages over other work. Sheepherding offered a low capital solution to run their own sheep business and offered inroads to the larger livestock industry.

Little English language knowledge was required for sheepherding; this was largely because the sheepherder worked alone most of the time. A sheepherder would often go weeks without human contact and his only daily company was his dog (Paris and Douglass 1979). This may have been seen as a positive attribute for sheepherding; however, it quickly became a detriment to the Basque sheepherders. Often the camp- tenders were English speakers, and so even when they came to drop off provisions to the sheep camp, the Basque herders were not able to communicate well with them and the herders became more lonely (Paris and Douglass 1979). 23

It was the camp-tenders job to bring supplies to the sheepherders while they

were out in the remote ranges. Common supplies were canned foods, meat, flour,

tobacco, and wine. The camp-tenders were the eyes and ears of the sheep company.

Paris and Douglass describe the situation like this, “[t]he boss believed the foreman and the foreman believed the [camp-tender]. No matter how good you were, whatever the

[camp-tender] said that was it” (1979, 40). These potential conflicts alluded to by Paris

between the sheepherders and the camp-tenders often manifested in the creation of

arborglyphs on the subject.

It is said that sheepherding is one of the loneliest professions in the world, and

the Basque sheepherders are characterized by Robert Laxalt as, “lonely sentinels of the

American West” in his National Geographic article (1966, 870). The idea of loneliness is

the most pervasive theme in literature about the Basque sheepherder of the American

West and the loneliness manifests itself in unique ways, often reflected in the form of

arborglyphs. Loneliness and boredom can be viewed as being similar, and author Gretel

Figure 2-7 Typical scene of a sheepherder in the American West “A sheepherder watching his flocks. Madison County, Montana” (Rothstein 1939) 24

Ehrlich who herded sheep for some time said in

her essay, “From A Sheepherder’s Notebook”,

“[t]o herd sheep is to discover a new human gear

somewhere between second and reverse—a

slow, steady trot of keenness with no speed”

(1986, 59).

Another advantage for Basque

immigrants was that little to no education was

required to tend sheep. This was a positive Figure 2-8 Herder watching from the aspect of sheepherding because the men coming wagon “Season Graze in Nevada” (Sawyer 1971, Frontispiece) over from the Basque country often did not receive much education. This was fairly common at the turn of the 20th century. The baserria was the family’s priority and a

child’s education came later, as children were to help with the farm work where they

could.

The final advantage was that sheepherding offered the Basque herder a chance

to make it on his own. The sheepherders soon realized that they would have two

economic options of making it on their own, the first was to go back to Europe with the

money they earned and the second was to start a new life in the West and establish

their own sheepherding outfit and maybe later a cattle ranch. Sheepherding seemed to

many to offer a quick way to make enough money to make either scenario a reality.

Paris and Douglass describe that many of the Basque sheepherders, “viewed

their stay here as a sojourn, a kind of purgatory, in which to acquire one’s nest egg and 25

return to Europe” (1979, xiii). From an anthropological perspective, sheepherding in the

West for Basque young men was a rite of passage and when completed they truly

became men. However, many of the men soon discovered that sheepherding soon

became almost a nightmarish “vision quest” that lasted many months to several years, and was brought on by being alone for too long with one’s own thoughts.

The terms that Basque sheepherders used for the men that did not recover from

this rite of passage were, “sheeped” or “sagebrushed” (Lane and Douglass 1985). The

terms come from people who believed that the sheepherders became crazy because

they spent too much time isolated with the sheep or in the sagebrush. Sheepherding took its toll on the herder mentally and often physically due to the isolation of the profession. Beltran describes situations where he got in to fits of loneliness where he,

“would lie there looking up at the sky, the hours went slowly and then I was lonesome.

[s]ome guys cried”, but not Beltran (Paris and Douglass 1979, 32). There were some

Basque sheepherders who became all too comfortable with the concept of being alone with the sheep and people had a derogatory term for those herders and that was “crazy

Basco” (Paris and Douglass 1979). Ehrlich in her essay “Obituary” describes a sheepherder who has become crazy due to a couple of things. She says, “[he was] ‘not quite right in the head.’ [h]e was the one we found scolding a dead cow and saying, ‘I never want you to do this again’” (1986, 18). This herder went crazy because of isolation, but what compounded it was that he had contracted syphilis from a prostitute that went untreated (Ehrlich 1986). 26

Visiting prostitutes was a common practice of Basque sheepherders, in part

because of their need for human contact. Douglass explains, “[f]or such men infrequent visits to town became the sole release—an occasion to dissipate a year's wages on liquor, gambling, and prostitutes” (1985, 30). While the sheepherder Ehrlich discussed may not have been Basque his situation was by no means unique. The theme of women and prostitutes is sometimes expressed in the sheepherders’ arborglyphs.

The years between 1900 and 1934 were a time of expansion for the sheep industry. The ebb and flow of the sheep market provoked by events such as the Great

Depression made some years better than others. Until 1934 and the implementation of the Taylor Grazing Act shepherding was on the rise and range for grazing livestock was hard to find at least for immigrant sheepherders. The Taylor Grazing Act implemented a system for managing livestock on public lands (Douglass 1980).

The Taylor Grazing Act was the culmination of what had been coming for some time. Created in 1891 the National Forest Reserves, the predecessor to the United

States Forest Service, started managing grazing in the forests and implemented some rudimentary grazing rules (Starrs 2000). In 1894 the Secretary of the Interior, then the head manager of the Forest Reserves, banned grazing in the Forest Reserves due to concerns with erosion and overgrazing (Williams 2007). The ban on grazing in the Forest

Reserves was lifted in 1896 and went to a free permit system to limit the amount of domestic animals grazing on the Forests and remained unchanged until 1906 (Williams

2007). The rules were changed when the Forest Reserves were transformed into the

Forest Service in 1905 and then came under direction of the Department of Agriculture 27

(Williams 2007). The Forest Service introduced a grazing allotment and fee permit

system which charged five to eight cents per sheep for the summer season and eight to ten cents for the year. These fees fluctuated occasionally due to the quality of the range and economic outlook of the country (Dutton 1953).

The fee permits and allotment systems did not work for long. Due to the increasing pressure from cattle ranchers constituents, Senator Edward Taylor from

Colorado was persuaded that itinerant sheepherders needed to be banned from the public land (Lane and Douglass 1985). The senator drafted a bill and it was passed through government and became the most famous grazing law in the American West, the Taylor Grazing Act of 1934. This act essentially, “abolished itinerant sheepmen,

Basque and otherwise, from the western ranges. In the first few years of the act, there was a shaking out period in which trespass cases and other violations were frequent”

(Douglass and Bilbao 1975). While the Taylor Grazing Act did not stop sheep grazing altogether in the West, it did slow it down by creating roadblocks in the form of grazing permits. It was then extremely difficult for the generally smaller Basque sheepherding outfits to compete with the larger established ranching outfits.

Basque sheepherders utilized the public land for grazing to the fullest extent possible. Basque sheepherders that grazed sheep on public land generally could not afford to purchase land to graze on, and even when they could afford it, they were unlikely to be able to acquire property because they were not usually United States citizens (Douglass and Bilbao 1975). Moreover, grazing on public land was sought out because sheep preferred cooler temperatures in the summer and warmer temperatures 28

in the winter, thus this offered the itinerant sheepherder an advantage by being able to

move the sheep.

The Basque sheepherders that utilized public lands lived nomadically following

the sheep and the rangeland were referred to as itinerant and historically as, “tramp sheepmen” (Lane and Douglass 1985, 7). These so called tramps, “were small-scale, landless, mobile outfits operating off the back of the burro that carried the sheepman’s tent and supplies” (Douglass and Bilbao 1975, 231). Their lifestyle was that of continuous transhumance6. Mallea-Olaetxe explains, “[t]he sheepherders life was a

never-ending journey. [e]ven in the summertime, when he camped in the same spot for

a few weeks, he had to follow the sheep every day” (2008, 97). Transhumance was

beneficial in the sense that it worked well for raising fat sheep. It also kept operating

costs low, meaning a sheepherder could save more money for themself, but it was

tough mentally.

To worsen their already tough situation, more established American owned

sheep and cattle operations often worked against them. The dislike of the itinerant

sheepherder was based in xenophobia as they were considered to “ likely [be] a

‘foreigner’ and scarcely conversant with English made it all the easier to dismiss and

even despise him as an interloper” (Lane and Douglass 1985, 7). As a result, Basque sheepherders often quarreled with other operators and most famously with cowboys.

6 Transhumance is a seasonal movement of people and livestock. For sheepherders in the American West, this meant moving to range in the high Sierras in the summer and then moving to the desert valleys in the winter (Douglass 1980). 29

The itinerant sheepherders and the cowboys were often in “fights,” which were

not usually violent but sometimes they escalated to that point. One case where the

cowboy versus sheepherder battle became violent was in the, “range wars in central

Oregon when many thousands of sheep were slaughtered by masked night riders”

(Hanley and Lucia 2003, 177). Some cowboys that quarreled with sheepherders often

kept a small number of sheep amongst their cattle and horses. Their tactic involved

releasing their sheep into the sheepherders herd with the expressed intent to mix them

up (Douglass and Bilbao 1975). It could take several days for the sheepherder to

separate their sheep from the cowboys sheep and that usually meant the sheep were

not eating and the herder would lose money, sometimes this sort of “accident” was the difference in having a profitable season or not (Douglass and Bilbao 1975).

Mallea-Olaetxa gives a different perspective of the sheepherder and cowboy rivalry in Speaking through the Aspens. He gives the lesser-known perspective of the

sheepherder. It is explained that the sheepherders and the cowboys were on good

terms for the most part and that, “'cowboys don't often pack lunch and often they go

hungry and then they know where to go, to the sheep camp'” (Mallea-Olaetxe 2008,

105). It would seem as if the sheepherders got the raw end of that deal, but one would

venture to guess that the effort to make a meal was offset by the companionship of

another person. Mallea-Olaetxe based his conclusion on personal correspondence with

the sheepherders themselves as well as the fact that the arborglyphs make no

suggestion of any discrimination, injustice, or fear of violence of the cowboys (Mallea-

Olaetxe 2008). Mallea-Olaetxe believes that the literature refers to these cowboy versus 30 sheepherder confrontations because it makes for “good reading”, but he also recognizes that lack of arborglyph evidence could be a purposeful omission by the sheepherders

(Mallea-Olaetxe 2008). These omissions were the sheepherders way of discriminating against the cowboys (Mallea-Olaetxe 2008). Given Mallea-Olaetxe’s unique view and popular literature, the grazing range likely had combination of both perspectives. While arborglyphs may not tell us about the relationships between sheepherders and cowboys, they can tell us about numerous other things.

2.2.2 Arborglyphs

Sheepherders are “shadowy personages” explain Douglass and Lane (1985) and this is also true for the associated literature concerning the arborglyphs sheepherders create. The literature on arborglyphs appears to start sometime during the mid-1960s and continues intermittently to the present. There has only been one major academic work done specifically on arborglyphs.

This research was done by Joxe Mallea-Olaetxe and is published in his book

Speaking Through the Aspens: Basque Tree Carvings in California and Nevada (2008).

Mallea-Olaexte is a historian and was a scholar at the Center for Basque Studies,

University of Nevada, Reno. His research into arborglyphs started in the mid-1960s and continues today, although he has retired from the University. Over his career of studying arborglyphs he estimates that he has seen no less than 27,000 arborglyphs which have aided him in developing his conclusions (Mallea-Olaetxe 2009, 43). The author thoroughly explains the various perspectives in which to view arborglyphs, whether they 31

are art, literature, history, or something else and tries not to impose any particular view

on the reader or arborglyph but are generally viewed with a historical slant. The work is

lacking in geographic analysis that could have included location, space, and time. This

has also been noticed by others as well in critical reviews of Mallea-Olaexte’s book

(Sayre 2001). Speaking Through the Aspens also mentions the arborglyphs within the

study area of Hope Valley and explains that future work is needed to record arborglyphs in Horsethief Canyon. This study uses Mallea-Olaetxe (2008) as the foundational reference, as it is the only major analysis done on arborglyphs in the American West.

Basque arborglyphs are primarily carved on aspen trees. However, in times

where carvers found themselves in a position to carve but there were no aspen, they

carved on cottonwood, conifers, and alder (Mallea-Olaetxe 2008). Trees such as conifers were rarely chosen due to time and effort; the bark would have to be removed before an arborglyph could be created (Mallea-Olaetxe 2008). Basque carvers sought two criteria when choosing an aspen to carve on. First they looked for large trees, allowing more room for detail (Mallea-Olaetxe 2008). Attempts were made at carving on smaller trees but they expanded radially so quickly that the arborglyphs were soon not legible

(Mallea-Olaetxe 2008). The second criteria carvers looked for was smoothness of the bark. The bark needs to be free of limbs and cankers; these features generally ruin the arborglyphs (Mallea-Olaetxe 2008). However, there are a few documented instances of the carver using a canker or limb as part of the arborglyph and has been done to enhance certain aspects of human anatomy. 32

The carvers used several types of tools to create an arborglyph. These consisted of knives, bullet shells, sharp rocks, and nails (Mallea-Olaetxe 2008). One Basque sheepherder, Jean Lekumberry believes that 6-penny nail is the best tool for the job and that scratching the bark produces the best results (Mallea-Olaetxe 2008). A few researchers explain that a single incision was the best method and that it was difficult to master the technique because they would not see their work for several years and sometimes never (Mallea-Olaetxe 2008; Lane 1971). While the best way to produce an arborglyph may be up for debate, there is consensus that carving too deep into the tree ruins the effect of the arborglyph and damages the tree (Mallea-Olaetxe 2008).

When the tree is scarred by the carver the aspen heals and the scar turns gray or black in color, thus leaving a dark on white contrast that makes the art readable after a few years. While the carver initiates the arborglyph the aspen tree takes over and finishes the work years later. Both the aspen and the carver only have one try to get it right. This type of art, Mallea-Olaetxe (2008) explains, cannot be corrected or erased.

“The human hand: is one of the most universal symbols used by man… [Their use] predat[es] the use of written language, hand prints are found all over the world, and say most simply: ‘I was here’” (DeKorne 1970, 10).

Figure 2-9 Carving of a hand, Hope Valley, California 33

Once it is carved there is no going back.

Arborglyphs can be viewed either as art, literature, or history. Traditionally, the

Basque in the Old World transmitted history orally but when they came to the New

World they switched to written histories (Mallea-Olaetxe 2009). What makes the

arborglyph histories unique from other forms is that they are tied directly to a specific

place. Mallea-Olaetxe explains, “each [sheep]herder became a part-time recorder of

history, and each tree trunk a living document” (2008, 13). He continues, “tree carvings are superior to paper as information bearers…. [p]aper can be moved from its original historical environment, can be easily taken out of context, but the aspen stands witness to that one day when the [sheep]herder walked up to it and carved his message” (2008,

161). Other written mediums are more portable. This, in a sense, makes arborglyphs not

unlike a form of art which cannot be as easily removed from its specific place. The

reality is that arborglyphs are unique combination of both.

Arborglyphs contain many themes but can generally be lumped into two

overarching categories, textual and graphic. From there, historic and cultural themes can be identified. Mallea-Olaetxe (2008) has identified many themes and has developed

an archaeological documentation supplement to aid in identification. Common themes

along with possible examples include: language (Basque, Spanish, English), country

(Spain, France, Basque), patriotism (flags, symbols), news on sheep industry (camp-

tenders, bosses, wages), personal statements (loneliness, wants), female/erotic/sexual

(figures, messages), self-portraits (figures), animal (figures), humor (swear words, art), 34

symbols (crosses, stars, clovers), name and dates, and old/home country (houses,

family, town) (Mallea-Olaetxe 2008).

As stated, place is a unique aspect of arborglyphs but it can be more generically termed as location. When location and theme are combined this leads to the notion of why arborglyphs are in a specific location. The location of the aspen is essential in determining what is going to be carved and the opposite is true as well (Mallea-Olaetxe

2008, 23). Some aspen were in a strategic location and became “billboards” for other people but mostly for herders to see (Mallea-Olaetxe 2008). These strategic locations

included but were not limited to: the roadside, fishing holes, camps, and sheep driveways7 . These locations would assure the carver that somebody was going to see their work. For example, a name on a billboard tree is less significant than a humorous remark or arborglyph that would elicit a response (Mallea-Olaetxe 2008).

Names and location of arborglyphs are important though. A name on a tree could indicate that the carver was claiming a particular piece of grazing land or camping spot (Mallea-Olaetxe 2008). The sheepherder with the earliest dates on the trees would generally be entitled to the area in which it’s carved. Mallea-Olaetxe (2008) explains that the “here first” idea is not new and that ranchers often used the argument to keep sheepherders away from their ranches. This type of arborglyph land claiming done with names and dates was typically done before the Taylor Grazing Act, which cemented

7 A sheep driveway is a designated route for sheepherders to drive their sheep across the landscape. 35

many rules of the range that would eliminate the need to make arborglyph claiming

necessary (Mallea-Olaetxe 2008).

One of the major problems with the arborglyph medium is that when the tree

dies the carvings are lost. Aspen trees, as noted previously, are not as long lived as many

other trees in the American West. However, this fact may make it seem that the

situation is worse than it really is. There are many carvings of dates from the 1910s and

even earlier, such as the, “oldest date on an aspen ever videotaped, 1870, which is still

clearly readable” (Mallea-Olaetxe 2008, 33). While not discussed directly in the

literature the aspen that have arborglyphs today appear to not fit within the average

lifespan. My study documented 95 arborglyphs 80 years old and older. Some logical

conclusions were that Basque sheepherders carved on long lived aspen, the trees live

longer than expected, or that many aspen with arborglyphs have died leaving only older

trees.

Regardless of the specific dates, arborglyphs eventually are lost when the aspen

on which it is carved dies. The primary method currently being used to preserve

arborglyphs is documentation. Documentation captures the information the arborglyph

maintains. As previously stated documentation takes on many forms, but the most

popular methods for arborglyphs is video and photographic documentation. Mallea-

Olaetxe (2008) advocates video methods because it allows for more information to be captured at the arborglyphs location. This is because the surveyor can narrate the arborglyph as it is being documented, including translations and descriptions(Mallea-

Olaetxe 2008). But documentation is not a complete form of preservation, even though 36

the information is safe the cultural landscape is affected by the arborglyphs passing.

Mallea-Olaetxe (2008) has suggested that the best of the remaining arborglyphs be cloned on younger aspen nearby in an effort to preserve the landscape, however, it is

not clear how practical cloning would be. 37

3 Study Area

The study area is along the northeastern flank of the Sierra Nevada range in the general vicinity of Carson Pass and Lake Tahoe (Figure 3-1). The study area is 28,809 acres in size. Three locations within the study area were surveyed for arborglyphs,

Horsethief Canyon (Figure 3-2), Hope Valley (Figure 3-3), and Scott’s Lake (Figure 3-4).

See Appendix A: Study Area Access for additional information on access to the study area.

Survey was done in Horsethief Canyon and Hope Valley as part of this study.

Approximately 387.1 acres were surveyed in Horsethief Canyon and approximately 7.1 acres were surveyed in Hope Valley. The Scott’s Lake survey took place in 2008 for the

Forest Service, “Scott’s Lake Fuelwood Sale” (B. P. Smith 2008). Approximately 168.0 acres were surveyed for the Scott’s Lake survey site.

The study area was chosen because of its proximity to Reno and Mallea-Olaetxe

(2008) suggested that the area had not been surveyed for arborglyphs. Additional research was conducted at the Forest Service confirming that the area had not been surveyed for arborglyphs. The study area was also chosen because it could be representative of arborglyphs located on the eastern slope of the Sierra Nevada. 38

Figure 3-1 Study Area Vicinity Map 39

3.1.1 Topography

Within the study area, mountain peaks range from 2,438 to over 3,048 meters.

The topography of Horsethief Canyon is characterized as a canyon trending in a north to

south direction originating at approximately 2,627 meters elevation. The canyon is 4.5

kilometers north to south and 2.5 kilometers west to east. Horsethief canyon has a

steep slope to the west starting at approximately 2,377 meters and climbing to 2,652 meters in less than 0.4 kilometers. The east side of the canyon has a more gradual slope and again begins at 2,377 meters and climbs to 2,807 meters in approximately 1.6 kilometers. The bottom of Horsethief Canyon contains Horsethief Creek and towards the north where the canyon is wide, the creek includes a marshy area. The elevation ranges in Horsethief Canyon from 2,125 meters to 9,382 feet. For more detailed information on the topography of Horsethief Canyon see Figure 3-2.

Hope Valley is approximately eight kilometers north to south and approximately four kilometers west to east. Hope Valley is situated between several mountain peaks.

These peaks are: Waterhouse Peak (2,874 m) to the north, Stevens Peak (3,060 m) to the west-northwest, Red Lake Peak (3,059 m) to the west-southwest, Picket Peak to the northeast, and Hawkins Peak (3,051 m) to the east. The slope to the east of the valley is generally steeper than the slope to the west. Hope Valley contains several major water features, Red Lake Creek and the west fork of the Carson River both originate from the south, while Hawkins Creek originates from the east. A small shallow pond can be found just east of the intersection of highway CA-88 and Blue Lakes road. The area surveyed 40 for arborglyphs was on the southwestern edge of Hope Valley. For more detailed information on the topography of Hope Valley see Figure 3-3.

Scott’s Lake is approximately two kilometers directly west from highway CA-88.

Scott’s Lake in actuality is a small reservoir with an earthen dam. The outlet creates a creek that flows into Hope Valley and feeds the west fork of the Carson River. Scott’s

Lake is the northwestern edge of Hope Valley and has the same associated mountain peaks. The closest mountain peak is Waterhouse peak. The terrain around Scott’s Lake generally has gentle slopes with eastern aspects. For more detailed information on the topography of Scott’s Lake see Figure 3-4. 41

Figure 3-2 Horsethief Canyon topography and arborglyph survey boundary. 42

Figure 3-3 Hope Valley topography and arborglyph survey boundary. 43

Figure 3-4 Scott’s Lake topography and arborglyph survey boundary. 44

3.1.2 Climate

The climate of the Sierra is largely due to its geographic position in relation to the Pacific Ocean (Storer and Usinger 1963). The Pacific Ocean provides much of the

water that provides the mountain range with its namesake, which in Spanish means,

“snowy mountain range”. The Sierra, however, are not consistently snow packed, due to the climate variability within the range (Tomback et al. 2001).

The Eastern Sierra have four distinct seasons. Winter generally starts with strong winds that bring colder temperatures to the region (Tomback et al. 2001). The first snow in the high elevations usually fall sometime in October, but the snow does not typically accumulate until November (Tomback et al. 2001). The majority of precipitation for the year comes during the months between November and April, with January being the month with the most precipitation (Tomback et al. 2001). Spring is when the snow at the lower elevations begins to melt, but winter-like storms are likely to occur anytime throughout the spring (Tomback et al. 2001). The temperature rises in steps, but it is not uncommon for the warm weather to be interrupted by cold air (Tomback et al. 2001).

Summer usually arrives in the study area in late June and comes all at once, this is due to warm air coming from a warm and dry air mass from the Pacific Ocean (Tomback et al. 2001). The summer’s most striking climate phenomena are the afternoon thunderstorms. The storms are caused by airflow from the south either from Baja

California, Gulf of California, or Gulf of Mexico (Tomback et al. 2001). These types of storms may bring rain which can cause flash floods, and lighting strikes which can initiate wildfires (Tomback et al. 2001). 45

Fall is the time of the year that the field surveys were completed. The fall

generally starts in September and lasts until November. The weather tends to be sunny,

with little wind (Tomback et al. 2001). The late afternoon summer-like thunderstorms

may still occur during this time of year (Tomback et al. 2001). The temperature in fall is

frequently mild during the daytime with cool nights. During October it is common for

there to be freezing temperatures at night throughout the Sierra (Tomback et al. 2001).

The winds in the study area are generally out of the west, this is due to the Sierra

as a whole lying in the mid-latitudes which places it in the westerly winds (Tomback et

al. 2001). This is not the only wind in the area, however, it is not uncommon to have

winds from the southwest which bring the most precipitation (Tomback et al. 2001).

Sometimes there are winds from the north, which are generally very cold (Tomback et

al. 2001). The region has a wind occurrence known locally as a Washoe Zephyr, which

comes from the west. The Washoe Zephyr occurs due to the heating of the Great Basin

to the east, which causes low pressure and air rushes over the eastern Sierra Nevada to

fill the void (O’Hara et al. 2007).

Climate data from the Western Regional Climate Center, the Desert Research

Institute in Reno, Nevada and was updated with California Department of Parks and

Recreation data. The Western Regional Climate Center publishes historic climate data for weather stations across California and other western states. The closest long-term record of climate in the study area is from Woodfords, CA. The Woodfords station collected data from 1909 through 1990. The California Department of Parks and recreation data is recorded at Grover Hot Springs and contains data for precipitation. 46

The station has been collecting precipitation data from 1990 to present, however, only

data through 2011 is used to update the climate data.

The annual mean maximum temperature is 62.6°F and the annual mean

minimum temperature is 35.8°F. The coldest monthly mean temperature occurs in

January at 22.2° F, while the hottest monthly temperature average occurs in July at

84.9° F. The average annual precipitation is 55 cm, with an average annual snowfall depth of 239 cm. See Table 3-1 for more detailed climate information.

Table 3-1 Climate Data Summary Temperature 1909–1990, Precipiation 1909-2011 Annual Winter Sping Summer Fall Monthly Mean Max. 62.6 45.3 58.9 81.6 64.4 (°F) Monthly Mean Min. 35.8 23.6 32.6 49.9 37.3 (°F) Monthly 49.2 34.4 48.8 65.7 50.9 Mean (°F) Daily Extreme 98 73 90 98 94 Max. (°F) (8/11/1940) (12/3/1958) (5/13/1987) (8/11/1940) (9/1/1955) Mean Precipitation 55 18 7 3 8 (cm) (Western Regional Climate Center; California Department of Parks and Recreation 2012)

3.1.3 Geology and Soil

During the Triassic and Jurassic periods the geology of the Sierra Nevada started to take shape when volcanic and sedimentary materials were deposited and then transformed into rock, this rock was then folded and pushed up to form the mountain ridges (Storer and Usinger 1963). In the middle Cretaceous vast amounts of granite was introduced to the mountain range and as the granite cooled it formed a very large mass 47

of rock (Storer and Usinger 1963). This granite mass is of unknown size, as it is not

known how deep it protrudes into Earth, this means the amount of granite exposed is only a small portion of the total amount (Tomback et al. 2001). In actuality the Sierra

Nevada granite mass, named Sierra Nevada batholith, is comprised of smaller granite forms called plutons (Tomback et al. 2001). Each pluton in the Sierra Nevada batholith

has a slightly different mixture of minerals that make up the granite (Tomback et al.

2001).

The geology in the study area, like the rest of the Sierra Nevada mountain range is dominated by granite. It is the only rock that can be seen when exposed and the soil is also dominated by decomposed granite. During the late Cretaceous erosion began to expose the granite, but at this point the Sierra Nevada’s were still mostly a rounded top hill (Storer and Usinger 1963).During the Eocene more folding took place and massive erosion removed the clay type material that was covering the mountain range (Storer and Usinger 1963). More uprising occurred in the Oligocene and Miocene epochs, by the end of the Miocene the mountain range was thousands of feet about the valleys (Storer and Usinger 1963). Finally, during the later portion of the Pliocene the Sierra Nevada were lifted up to their present height (Storer and Usinger 1963).

Geology in a sense made the Sierra Nevada mountain range the most famous mountain range in the Americas in the mid-19th century and this is due to one mineral,

gold (Tomback et al. 2001, 37). Once the word got out in 1848, the gold rush was on and by 1849 the Sierra Nevada were full of miners looking to get rich, but the “easy” to get gold was quickly gone and by 1850 mining technology needed to be advanced in the 48

area to extract the gold rich ore (Tomback et al. 2001). The gold was deposited in the

Sierras during the Mesozoic era when, “[h]ot liquids and gases carr[ied] gold and other

metals upward into cracks within the ancient rocks and cooled granite” (Tomback et al.

2001, 40). These gold deposits are what made the region famous, and are what brought

miners and sheepherders to the Sierra Nevada to claim their riches. There were at least two mines within the study area and many prospecting pits as indicated on the topographic maps. The Alpine and Alhambra mines were both targeting primarily gold and silver, however, they did collect lead, copper, zinc, and tungsten as well (Clark and

Evans 1977).

In some places the soil is characterized by organics making it dark and rich

(Tomback et al. 2001). The ground is often very saturated with water right after the snow melt and stays wet much throughout the growing season (Tomback et al. 2001).

The study area is broken into twenty-four soil complexes by the Natural Resources and

Conservation Service (NRCS 2012b). The complexes each consist of two to three major soil associations. The relevant soil attributes for this study were: depth to restrictive feature, drainage class, and depth to water table.

The Natural Resources and Conservation Service indicate that generally the soil throughout the study area had the following properties. The surface of the soil had a range of one to twenty percent coverage of rocks. The drainage of the soil within the study area ranged from very poorly drained to excessively drained. The majority of the soil had drainage that would be classified as well drained to excessively drained (NRCS

2012b). The soil depth in the study area ranged from 0.23 meter to 2.03 meters. A 49 significant portion of the study area had a soil depth that was greater than 0.75 meter in depth (NRCS 2012b). Finally, the depth to the water table fluctuated ranging from 0.13 meter to 2.03 meters. The majority of the soil had a water table depth of greater than

2.03 meters (NRCS 2012b).

3.1.4 Vegetation

The dominant overstory in the study area is a pine and aspen habitat.

Understory species include Artemisia tridentata (sagebrush), Ceanothous cordulatus

(snowbrush), Arctostaphylos patula (greenleaf manzanita), Balsamorhiza sagittata

(arrowleaf balsamroot), Wyethia mollis (wooly mules ear), Sarcodes sanguinea

(snowplant), Pterospora andromedea (pinedrops), and various annual grasses. Along riparian corridors, thickets of aspen and Salix (willow) prevail. These riparian areas were classified as a montane meadow, which are meadows that are typically protected by groves of quaking aspen (Tomback et al. 2001). The study area could also be more broadly classified as lower Boreal region or Canadian life zone (Storer and Usinger

1963). The growing season in the area is three to four and half months with between forty to seventy frost free days (Storer and Usinger 1963).

The trees within the study area is varied and is as follows: Pinus contorta

(lodgepole pine), Pinus jeffreyi (jeffrey pine), Abies magnifica (red fir), Juniperus occidentalis (Sierra juniper), and aspen. Occasionally, there are small Juniperus osteosperma (Utah juniper), Pinus ponderosa (ponderosa pine), and Tsuga mertensiana

(mountain hemlock) present (Tomback et al. 2001). 50

Wildfires are an important occurrence for the vegetation in the region. The

wildfire season in the vicinity of the study area is between the months of May through

October (O’Hara et al. 2007). The fire occurs because of small amounts of rainfall and during the early spring and summer, the vegetation dries out and fire becomes more likely. The wildfires in the region are commonly started by lightning strikes from thunderstorms which usually happen in summer afternoons, there is generally very little rainfall associated with these storms and therefore this weather is called dry thunderstorms (O’Hara et al. 2007). Wind will drive the wildfires. 51

4 Methods

4.1 Data Collection

The arborglyph field surveys were done in a manner and can be generalized as an archaeological style pedestrian survey (Banning 2002). Much of the study area did not contain aspen. Given limited time and resources, two areas with many aspen stands were surveyed and another third area that had been recently surveyed was included as well. The two areas surveyed for this study were Hope Valley and Horsethief Canyon.

The third was surveyed for the “Scott's Lake Fuelwood Sale” (B. P. Smith 2008). The survey was conducted similarly to that of the survey for this study. The Scott’s Lake survey was conducted by the author. The study areas are shown in Figure 3-2 through

Figure 3-4. The objective of the survey was to document each arborglyph within the areas surveyed.

Data were collected using: a personal data recorder, digital camera, tape measure, and compass with integrated clinometer. These instruments were accurate, portable, and relatively inexpensive. The expense was an important consideration because each survey team would need a complete set with some redundancy in the event that items were lost or broken.

Surveys began at the lowest elevation aspen and then continued up the slope until there were no more aspen or the aspen became so small that they could not support arborglyphs. Every arborglyph that was spotted was recorded. 52

The survey teams consisted of three to four people and one of them was the

team leader. The survey had multiple teams surveying at one time, this was done for

safety. The team leader was well versed in survey methods and could answer questions

less experienced team members might have.

A survey team would begin a survey by aligning themselves perpendicular to the slope and then hiking their way up slope through the aspen grove. Each team member was spaced approximately three to ten meters apart as recommended by Banning in

Archaeological Survey (2002). The spacing varied depending on how thick the vegetation was. When in thick vegetation the transects became closer, as the vegetation became more sparse the transects became wider. This was partly due to safety concerns; team members should be able to see each other as to not get separated. It was also done because the when vegetation became dense it became more difficult to spot arborglyphs.

When a team member located an arborglyph, that person called to the other team members and they recorded the arborglyph together. Each team member was assigned specific tasks as to distribute the work. Each task is fairly simple and thus tasks could be exchanged with other team members. This survey had approximately fourteen steps to record a single arborglyph. Each team member was given a rubric. For a more detailed description of how arborglyphs were recorded see Appendix B: Steps to Record and Arborglyph.

While great lengths were taken to ensure that the arborglyphs were documented in a way that completely captured their information, only a small portion 53

that data was utilized in this study. Complete documentation was done in an attempt to

preserve the information the arborglyphs contain. This type of complete documentation of arborglyphs was time consuming and was made possible by work done by volunteers.

Using volunteers located through the Passport in Time (PIT) program, the project was able to collect detailed field data. While, overall using volunteers was successful it was not without complications. Volunteers can be helpful in the gathering of field data but they, “...take time to train, and researchers must design their projects so that the data gathered by volunteers cannot be called into question” (Basinger 1998, 1). This project took half a day to train the volunteers and they still required professional team leaders, but with this arrangement the teams collected more accurate field data. See

Appendix C: Overview of Passport in Time for more information in the use of volunteers and the PIT program.

4.2 Data Analysis

The questions as outlined in this study revolve around location and temporal

aspects. All the environmental variables explored for this study had more than two dimensions, including the spatial component. Elevation serves as an example. Elevation as it is represented for this analysis had three dimensions: X, Y, and Z. X and Y correspond to the location of a point on Earth and Z is the distance that point is from sea level. Because of the multi-dimensional data required to do the analysis for this study a geographic information system, was chosen for the analysis. A geographic information system or as it is commonly referred GIS, is a computer system that can 54

collect, store and analyze spatial data (Chang 2004). GIS are commonly used and were

built for spatial analysis.

The analysis used in this study also used two types of data, vector and raster.

Vector data uses points, magnitudes, and direction to construct spatial features and is

best used to represent discrete data (Chang 2004). The locations of the arborglyphs

surveyed for this study is an example of vector data. Raster data uses a grid of cells to

construct spatial features and it is best used for data that is continuous. The digital

elevation model used as a variable in this analysis is an example of raster data.

Spatial data were analyzed in ESRI’s, ArcGIS Desktop, ArcMap 10 and used two

additional pieces of software (2010). The Spatial Analyst extension which provided

additional raster functionality and Spatial Data Modeller (Raines 2010). Spatial Data

Modeller is a geoprocessing toolset that uses point data (such as arborglyph locations) along with environmental data (such as soil type) to calculate the probability of association (Raines 2010). For example, if aspen with arborglyphs are randomly distributed, no soil type would have a probability of co-occurrence higher than any

other. But if aspen with arborglyphs occur more often on specific soil types, the weights

of evidence tool in Spatial Data Modeller will help identify this relationship –which may

then have predictive value. The Spatial Data Modeller weights of evidence tool requires three basic inputs, training points, a spatially defined study area, and one or more variables also known as evidence themes (Mensing et al. 2000).

Identifying variables was the first step in the analysis. Nine variables were recognized that could be tested to determine which of the variables could be predictive 55 of arborglyphs. These nine variables were: elevation, aspect, slope, distance to surface water, soil drainage, soil depth, depth to water table, temperature, and precipitation. All the variables have root in the literature as being important to the success of aspen and thus arborglyphs and are described in detail in the chapters to follow. Data sources for these variables involved searching through the Keck Library at the University of Nevada,

Reno, Cal-Atlas the California State GIS data clearinghouse, GIS at Humboldt-Toiyabe

National Forest, Web Soil Survey and Geospatial Data Gateway at the Natural Resources and Conservation Service (NRCS). Ultimately, only two sources of data were used, the

Humboldt-Toiyabe National Forest and the NRCS. All of the data sources had the same datum and projection, thus eliminating conversions that could introduce error into the

GIS analysis.

The first three variables were all derived from a digital elevation model (DEM) provided by the Forest Service. The 10m-cell size DEM provides elevation information in meters and covers an area much greater than that of the study area. This DEM was chosen because it had the finest available resolution covering the study area. The DEM for the study area became the basis for the elevation, aspect, and slope variables.

The drainage, soil depth, and depth to water table variables were collected from the NRCS “Web Soil Survey” (NRCS 2012b). To acquire the data shapefiles of the study area were uploaded to the Web Soil Survey. The Web Soil Survey had a 10,000 acre area of interest limit. The study area is much larger than this limit. To circumvent this area restriction, the study area was enclosed by a minimum bounding envelope and then that envelope was split into four equal parts. These rectangles were then used to clip 56

the study area. All parts of the study area were then less than the 10,000 acre area limit.

The spatial data had sparse attributes but did include a feature identification number.

The pieces were put back together based on the feature identification number. This feature identification number could also then be used to relate the spatial data to the attributes in the datasheets. The datasheet contained soil complexes for all the types present within the study area. Each soil complex was made from a percentage of two to three major soil types. The remainder was then lumped into a minor complex, which was not used.

4.2.1 Weights of Evidence

Weights of evidence assumes the variables are conditionally independent with respect to the training points when applying weights of evidence to two or more variables (Bonham-Carter 1998; Mensing et al. 2000). However, since this study is analyzing the variables separately this assumption does not have to be met within the

Spatial Data Modeller weights of evidence tool. Any potentially dependent variables will be addressed in the discussion chapter. Finally, while conditional independence is a requirement of Spatial Data Modeller, it is practically always violated (Bonham-Carter

1998).

Weights of evidence uses a set of known training points and “estimate[s] the relative importance of evidence [variables] by statistical means” thus, making it a data- 57

driven8 model (Bonham-Carter 1998, 13:303). For this study, each arborglyph was

considered a training point. All training points were used when running the weights of

evidence calculations. The training points were placed in to a grid which represents the

minimum spatial resolution (Mensing et al. 2000). For this study the grid was set to 0.01

kilometers squared, this is the resolution of the digital elevation model. Kilometers

squared were used because Spatial Data Modeller required it in these units.

The training point feature class included 349 arborglyph locations all within the

boundaries of the study area. The points represent GPS collected data at the trees

location. Some of the trees surveyed had multiple arborglyph panels on a single aspen

tree. A GPS point was only created for the first panel and then the point was duplicated

to maintain the locations of all the arborglyphs. Other than the duplication, the points

were not modified.

The training points for the weights of evidence calculation were combined from

two sources. The first set was the locations of arborglyphs surveyed for this study, which

totaled 294. The second set was the location of arborglyphs surveyed for the Forest

Service “Scott’s Lake Fuelwood Sale” cultural report (B. P. Smith 2008). This surveyed

documented the location of 55 arborglyphs. All the training point locations can be seen in Figure 4-1.

Sensitivity testing was done to augment the weights of evidence analysis. The sensitivity testing was performed by recategorizing four variables from the original

8 Data-driven is an inductive method of analysis. This type of analysis discovers patterns and relationships in variables without any prior assumptions (Anselin and Getis 1992). 58 weights of evidence analysis and running the weights of evidence again. This allowed for comparisons to be made between the original analysis and the recategorized analysis.

Figure 4-1 Arborglyph Training Point Location Map

An important concept for weights of evidence is both prior probability and posterior probability. Prior probability is an initial calculation for weights of evidence 59

that determines if the weight is positive or negative (Raines, Bonham-Carter, and Kemp

2000). The prior probability is calculated by dividing the number of training points by the number of cells in the study area (Raines, Bonham-Carter, and Kemp 2000). This

result is a training point density that is then distributed over the training area. The

posterior probability is the “probability that a [study area] cell contains a training point

after consideration of the [variables]” (Raines, Bonham-Carter, and Kemp 2000, 48).

The Spatial Data Modeller weights of evidence tool calculates several useful

values all based on weight. Weight is a value that expresses how correlated the training points are to a given variable category (Raines, Bonham-Carter, and Kemp 2000). Weight is often expressed as W+ or W- indicating that the value is either positive or negative and are inside or outside of the pattern (Raines 1999). When a positive weight is calculated it, “indicates that more training points occur on that [variable category] than would occur due to chance” and the opposite is true for negative weights (Raines,

Bonham-Carter, and Kemp 2000, 45). When the training points are spatial uncorrelated the weight is zero (Raines, Bonham-Carter, and Kemp 2000).

Weights of evidence also calculates a contrast value. Contrast is, “the strength of the correlation between the training points and [variable categories]” (Mensing et al.

2000, 112). Contrast is W+ minus W-. Contrast is useful in determining predictive

strength of a variable. As a rule of thumb contrast values of 0–0.5 are considered slightly

predictive, 0.5–1.0 are moderately predictive, 1.0–2.0 strongly predictive, and >2.0

extremely predictive (Mensing et al. 2000). 60

Studentized contrast also known as normalized contrast is the final calculation

provided. Studentized contrast is calculated by dividing contrast by the standard deviation of contrast (Bonham-Carter 1998). The studentized contrast is a, “a useful measure of the significance of the contrast due to the uncertainties of the weights and missing data” (Raines 1999, 259). The studentized contrast is similar to a Student-t test of the contrast except all the values are used instead of a random sample as in the

Student-t test (Raines 2010).

During the setup of the weights of evidence tool, a value is assigned for the

Generalization of Evidence Criteria which equates to the studentized contrast where a table can be consulted to relate this number to a percent of confidence (Raines 2010).

This study used the value 1.282 which equates to 90 percent confidence. Variables with values below 1.282 were considered not predictive.

4.2.2 Analysis of Arborglyph Placement on Tree

The analysis to determine whether arborglyphs were predominantly carved on the upslope side of the tree was done in Microsoft Excel (2010). The analysis was run on a subset of all the surveyed arborglyphs for this study. The subset consisted of the arborglyphs that had a documented location on the tree (arborglyph aspect) and a field documented value for slope aspect. The total number of arborglyphs meeting these criteria was 205.

A second subset was created from all the surveyed arborglyphs. This subset consisted of arborglyphs with a documented arborglyph aspect, a field documented 61

value for slope aspect, and a field documented slope of greater than or equal to 10

degrees. The total number of arborglyphs meeting these criteria was 97. The second

subset was tested to determine if the effect was intensified as slope increased.

Columns were created for arborglyph aspect and recorded slope aspect. A

calculated field was created by taking the absolute value of the difference between

arborglyph aspect and 180 degrees creating a value that represents the opposite side of

the tree. Then another field was calculated by taking the absolute value of the

difference between the aspect of the slope and opposite side of the tree. The resulting value was the difference in degrees an arborglyph was from being up slope. Then mean and standard deviation were calculated.

A standard score was then calculated based on the difference in degrees from upslope. The type of standard score used was z-score, which was calculated by subtracting the upslope difference of an arborglyph from the mean and then dividing by the standard deviation (Sprinthall 2011). Then a logical statement was used to determine how many of the arborglyphs were within ten percent equating to plus or minus 36 degrees of having an upslope aspect. The value representing ten percent in z-score is -1.28 (Sprinthall 2011). Thus, the logical statement said, the z-score was less than or equal to -1.28. If the logic statement was true then a value of 1 was assigned, if the logic was false then a value of 0 was assigned. Lastly, a percentage was calculated based on the number of arborglyphs within 10% of being situated upslope location. 62

4.2.3 Effect of Taylor Grazing Act on Arborglyph Quantity

This analysis is principally concerned with how the arborglyphs within the study

area are arranged temporally in respect to the Taylor Grazing Act. Up to 90 to 95 percent of the arborglyphs carved in California and Nevada are simply a name (Mallea-

Olaetxe 2008). Many of the names had dates to accompany them. This study also documented many dated arborglyphs during the field survey. Of the 294 arborglyphs documented, 140 had dates. The exact number of dated arborglyphs is not known because many of the dates had a component of the date missing. An example is: 19?4, where the question mark represents an indecipherable number. These arborglyphs were not included in analysis.

The analysis was done in Excel and was done so because the system allowed for better organization of the data, which helped eliminate errors in handling and manipulation of the data. The analysis used basic counting and percentages to achieve the desired results. To perform the analysis the amount of arborglyphs with dates were counted before 1935 and then 1935 and after. The two amounts of arborglyphs were then compared and a percentage difference was calculated. 63

5 Results

5.1 Weights of Evidence

The results of the weights of evidence analysis are organized by environmental

variable. Each section starts with a brief discussion of how the variable was created and

categorized, followed by a figure and table that aids in the visualization of the environmental data. The results of the weights of evidence analysis are then discussed followed by a table representing the output of the analysis.

5.1.1 Elevation

The elevation variable was created by converting the digital elevation model to integers, and then classifying elevation ranges (Table 5-1). The categories were determined based on the half standard deviation of the data and then ranked from 1 to

13, starting at the lowest elevation. The half standard deviation was used because it divided the elevation into approximately 100 meter categories. It also created enough categories to make the weights of evidence analysis useful in identifying elevation ranges that contain arborglyphs (Figure 5-1).

64

Table 5-1 Elevation Variable Category Elevation Range (meter) 1 1791 1852 2 1852 1953 3 1953 2054 4 2054 2155 5 2155 2256 6 2256 2357 7 2357 2458 8 2458 2558 9 2558 2659 10 2659 2760 11 2760 2861 12 2861 2962 13 2962 3061 Figure 5-1 Elevation Variable Map

All arborglyphs sampled fell into only three categories starting at 2,256 meters and go up to 2,558 meters. Most arborglyphs were in category 8, 2,458 to 2,558 meters elevation (Table 5-2). This relates to the upper third of elevation in which arborglyphs were found. Both categories 8 and 6 had contrast greater than two making them extremely predictive within the elevation variable. The studentized contrast values for categories 8 and 6 are greater than 1.282 (greater than 90 percent confidence).

Table 5-2 Elevation Results Studentized Category Training Points W+ W- Contrast Contrast Elevation (meter) 8 204 1.4646 -0.7332 2.1978 19.6141 2,458 – 2,558 6 82 0.3138 -0.0795 0.3932 3.0557 2,256– 2,357 7* 63 0.1407 -0.0285 0.1692 1.1955 2,357 – 2,458 *These categories are part of the SDM Weights of evidence tool category 99. This is because the studentized contrast is below the Generalization of Evidence Criteria, which was set at 1.282. 65

5.1.2 Aspect

The aspect variable was derived from the DEM using the aspect tool within

ArcMap. The result was then reclassified into five categories, no aspect, north, east, south, and west (Table 5-3). Features that have no aspect such as bodies of water and flat ground have been assigned the value of -1.

Table 5-3 Aspect Variable Aspect Range Cardinal Category (degree) Direction 0 -1 No Aspect 1 315 45 North 2 45 135 East 3 135 225 South 4 225 315 West

Figure 5-2 Aspect Variable Map

Arborglyphs were only found on aspects of east, south, and west (Table 5-4). The majority of arborglyphs fall within the western aspect. The largest contrast within the categories is for the west aspect and its values are between one and two making it strongly predictive. Category 3 corresponding to a southern aspect had a negative contrast. This indicates that fewer arborglyphs were in southern aspects than would be expected by chance alone (Raines, Bonham-Carter, and Kemp 2000).

66

Table 5-4 Aspect Results Studentized Category Training Points W+ W- Contrast Contrast Aspect (degree) 4 212 0.8234 -0.625 1.4483 12.9707 225 – 325 (West) 2* 105 0.0115 -0.0049 0.0163 0.1379 45 – 135 (East) 3 32 -0.9557 0.1762 -1.1319 -6.0595 135 – 225 (South) *These categories are part of the SDM Weights of evidence tool category 99. This is because the studentized contrast is below the Generalization of Evidence Criteria, which was set at 1.282.

5.1.3 Slope

Degree slope was calculated for the DEM within ArcMap. Slope data were collected in degrees. The slope recorded in the field was not used in the analysis due to it did not extend over the entire study area. The result was reclassified into ten categories based on the half standard deviation (Table 5-5, Figure 5-3).

Table 5-5 Slope Variable Category Slope Range (degree) 1 0 2.7 2 2.7 7.8 3 7.8 12.8 4 12.8 17.8 5 17.8 22.9 6 22.9 27.9 7 27.9 33 8 33 38 9 38 43 10 43 72.6

Figure 5-3 Slope Variable Map

Of the ten categories only three contained all arborglyph training points, categories 2, 3, and 4 corresponding to slopes ranging from 2.7 degrees to 17.8 degrees.

Category 4, slopes ranging from 12.8 to 17.8 degrees had a negative contrast indicating that fewer training point arborglyphs could be found than due to chance. Thus, the 67

arborglyphs in the study area are most likely to be found on slopes of 7.8 degrees to

12.8 degrees as indicated by category 3. Based on this analysis there is a 90 percent confidence that arborglyphs can be found on slopes ranging from 2.7 to 12.8 degrees.

This analysis indicates that slopes of 7.8 to 12.8 degrees are strongly predictive of

arborglyphs with the study area.

Table 5-6 Slope Results Studentized Category Training Points W+ W- Contrast Contrast Slope (degree) 3 217 1.1984 -0.7645 1.9629 17.3738 7.8 – 12.8 2 117 0.6429 -0.2144 0.8573 7.3878 2.7 – 7.8 4 15 -1.4801 0.1653 -1.6455 -6.2088 12.8 – 17.8

5.1.4 Distance to Surface Water

The distance to surface water variable was created from a Forest Service dataset.

The data is derived from water features shown on United States Geological Survey

1:24,000 scale topographic maps. The dataset had been split into three feature classes,

based on points, lines, and polygons. These feature classes where clipped to the study

area. In the case of the study area the points were springs locations, the lines where

streams and rivers, and the polygons were lakes. The majority of the features were

streams and rivers. The three feature classes were then converted into raster files that

corresponded to both the extent of the study area and the type of the feature class.

Each water feature cell was assigned a value of one and everything else was given a

value of zero. Next, the three rasters were added together and the result was

reclassified to make all water feature cells one and everything else null. The result of the

previous step was then processed by the Euclidean distance tool. This tool measures the 68

straight distance from a feature cell center to other cell centers. This is done repeatedly

until all the cells in the study area had a minimum distance value from the feature cells

(ESRI 2011). Lastly, the output from the Euclidian distance was reclassified base on the quarter standard deviation (Table 5-7, Figure 5-4).

Table 5-7 Distance to Surface Water Variable Category DTSW (meter) 1 0 15 2 15 88 3 88 161 4 161 234 5 234 306 6 306 379 7 379 452 8 452 525 9 525 598 10 598 671 11 671 744 12 744 817 Figure 5-4 Distance to Surface 13 817 889 Water Variable Map 14 889 962 15 962 1035 16 1035 1108 17 1108 1811

For distance to surface water of the seventeen categories only three have a

studentized contrast that corresponds to greater than 90 percent confidence. These

categories range in distance from 306 meters to 525 meters from a surface water

source. This is the only variable where the majority of the categories have arborglyphs

evenly spread throughout, causing the contrasts to be low overall. Distances from water

of 379 meters to 452 meters is strongly predictive for arborglyphs, but not as strong a

predictor as other variables tested (Table 5-8). 69

Table 5-8 Distance to Surface Water Results Studentized Category Training Points W+ W- Contrast Contrast Distance (meter) 7 75 1.0256 -0.1618 1.1874 8.7952 379 – 452 6 64 0.7339 -0.1104 0.8443 5.9354 306 – 379 8 32 0.3538 -0.0296 0.3835 2.0246 452 – 525 5* 43 0.1688 -0.0216 0.1904 1.1489 234 – 306 11* 9 -0.14 0.004 -0.144 -0.4208 671 – 744 4* 39 -0.0802 0.0106 -0.0907 -0.5266 161 – 234 12* 6 -0.3386 0.0071 -0.3457 -0.8302 744 – 817 14* 3 -0.6242 0.0075 -0.6317 -1.0805 889 - 962 3* 39 -0.1849 0.0259 -0.2108 -1.2248 88 – 161 9 11 -0.4172 0.017 -0.4341 -1.4026 525 – 598 13 2 -1.2302 0.0141 -1.2442 -1.7466 817 – 889 1 6 -0.8861 0.0253 -0.9113 -2.1987 0 – 15 10 4 -1.1907 0.0269 -1.2176 -2.4097 598 – 671 2 16 -1.1552 0.1104 -1.2655 -4.9181 15 – 88 *These categories are part of the SDM Weights of evidence tool category 99. This is because the studentized contrast is below the Generalization of Evidence Criteria which was set at 1.282.

5.1.5 Depth to Water Table

The variable for depth to water table was derived from the NRCS Web Soil

Survey dataset. The data was combined into soil complexes that were made up of soil components, each soil component had a percentage of the complex as well as a range for water table depth. The first step was to calculate the mean of the depth to the water table. The mean was used because water table depth fluctuates over time. Then a

weighted average was calculated based on the mean and the percentage of the complex

and the result was assigned to the variable categories. There are several lakes in the

study area, these areas have a depth of zero and have not been categorized. There were

a total of four categories (Table 5-9, Figure 5-5). 70

Table 5-9 Depth to Water Table Variable Category DTWT (meter) 1 0.13 2 0.27 3 0.47 4 >2.03

Figure 5-5 Depth to Water Table Variable Map

Of the four categories of the depth to water table variable only two categories

contained all the arborglyph training points; categories 1 and 4 (Table 5-10). Both

categories met the 90 percent confidence value. The majority of the points fell into the

category 4 corresponding to a water table depth of greater than 2.03 meters. The

remaining arborglyphs fell into category 1 corresponding to water table depth of 0.13 meter. The contrasts for both depths are predictive, category 1 being mildly so and category 4 is strongly predictive.

Table 5-10 Depth to Water Table Results Studentized Category Training Points W+ W- Contrast Contrast Depth (meter) 4 332 0.1124 -1.1237 1.2361 4.9431 >2.03 1 17 0.6221 -0.0234 0.6455 2.5259 0.13

5.1.6 Soil Drainage

The drainage variable was created from a NRCS dataset obtained from the Web

Soil Survey. The first step was to compile the major soil components, the percentage of 71

the component, and the drainage category. The datasheet broke drainage into six

categories: very poorly drained, poorly drained, moderately well drained, well drained,

somewhat excessively drained, and excessively drained. These categories were then

assigned numbers of one through six, starting with very poorly drained. This was done because many of the soil complexes contained different drainage categories with different percentages. A weighted average was calculated to determine the drainage value of entire spatial feature. The result of the weighted average was often a fraction, which indicates that the drainage for the spatial feature is between drainage categories.

The drainage values were then assigned to the spatial features and then a raster was created. There were a total of nine drainage values and therefore the raster was broken into nine categories (Table 5-11, Figure 5-6).

Table 5-11 Soil Drainage Variable Category Drainage 1 1.0 very poorly drained 2 1.6 2.0 poorly drained 3 2.2 4 3.0 moderately well drained 5 4.0 well drained 6 4.3 7 5.0 somewhat excessively drained 8 5.2 9 5.6 6.0 excessively drained

Figure 5-6 Soil Drainage Variable Map

For soil drainage the weights of evidence analysis found that all the arborglyphs

were located in four categories (Table 5-12). The four categories represent a soil 72

drainage that range from very poorly drained to somewhat excessively drained and of

these four categories, well drained and very poorly drained have a confidence of greater

than 90 percent. Well drained soil had a contrast between one and two indicating that it is a strongly predictive category. Very poorly drained soil had a contrast of greater than two indicating that it is an extremely predictive category. Of note is that very poorly drained soil has a larger contrast than well drained soil, despite well drained soil having

a much larger studentized contrast. This result is due to the areas corresponding to both

categories within the study area. The study area contains 6,963 acres of well drained

soil, while very poorly drained soil is only 134 acres. Therefore, the density of

arborglyphs in the very poorly drained soil is greater than well drained soil in the study

area causing the contrasts to be the way they are. The studentized contrast then

normalizes the data and accounts for this situation and ranks the soils properly.

Table 5-12 Soil Drainage Results Category Training Points W+ W- Contrast Studentized Contrast 5 226 1.0369 -0.782 1.8189 15.9188 1 17 2.688 -0.0466 2.7346 9.1839 6* 88 0.0055 -0.0018 0.0073 0.0587 8 18 -1.232 0.1416 -1.3736 -5.6461 Legend Category 1 1.0 – Very Poorly Drained Category 6 4.3 – Well Drained Category 5 4.0 – Well Drained Category 8 5.2 – Somewhat Excessively Drained *These categories are part of the SDM Weights of evidence tool category 99. This is because the studentized contrast is below the Generalization of Evidence Criteria, which was set at 1.282.

5.1.7 Soil Depth

The soil depth variable was created from the depth to restrictive feature

attribute from the Web Soil Survey dataset. The data for this variable like the 73

previous was arranged into soil complexes. The soil depth variable was given in a

range per soil component. First the percentages for the major soil components

were gathered along with the minimum values of the range. The minimum value

was chosen because it is the most restrictive value and could potentially affect

aspen the greatest. Next a weighted average was calculated to incorporate the

different soil component percentages into the soil complex. This produced

twelve categories of soil depth (Table 5-13, Figure 5-7).

Table 5-13 Soil Depth Variable Category DTRF (meter) 1 0.23 2 0.25 3 0.35 4 0.37 5 0.42 6 0.49 7 0.53 8 0.73 9 0.75 10 1.29 11 1.52 12 >2.03

Figure 5-7 Soil Depth Variable Map

For the soil depth the weights of evidence analysis found that of the twelve categories four contained all the arborglyph training points. The categories corresponded to depths of 0.73 meter to 1.29 meters and then greater than 2.03 meters. Only category 8 corresponding to 0.73 meter depth had a confidence of greater than 90 percent. Category 8 also has the highest level of studentized contrast throughout all the variables. This is due to density, category 8 has more arborglyphs per 74 unit area than all the other categories and variables. The contrast for category 8 makes it extremely predictive of arborglyphs.

Table 5-14 Soil Depth Variable Results Studentized Category Training Points W+ W- Contrast Contrast Depth (meter) 8 221 2.8523 -0.9658 3.8182 31.3285 0.73 12* 105 -0.0892 0.041 -0.1302 -1.0998 >2.03 10 18 -0.9403 0.0887 -1.029 -4.224 1.29 9 5 -2.2449 0.1309 -2.3758 -5.2644 0.75 *These categories are part of the SDM Weights of evidence tool category 99. This is because the studentized contrast is below the Generalization of Evidence Criteria, which was set at 1.282.

5.1.8 Precipitation

The precipitation data originated from the Geospatial Data Gateway at the NRCS as a state wide feature class that was derived from Parameter-elevation Regressions on

Independent Slopes Model (Prism Climate Group). The model used point precipitation and elevation data for a 30 year period from 1971 to 2000 (NRCS 2012a). Modeled precipitation data was used because of a lack of climate information in proximity to the study area. The variable was created by clipping the feature class to the study area.

Then the precipitation attribute was dissolved and then it was converted into a raster

(Table 5-15, Figure 5-8).

75

Table 5-15 Mean Annual Precipitation Variable Category MAP (cm) 1 79 2 84 3 89 4 94 5 99 6 104 7 109 8 114 9 119 10 124 11 130 12 135 Figure 5-8 Mean Annual 13 140 Precipitation Variable Map

For the precipitation variable of the thirteen categories, six contained arborglyph

training point locations as shown in Table 5-16. These six categories correspond to 94

cm to 119 cm of precipitation. Of these six only the categories corresponding to 99 cm

and 119 cm of precipitation have studentized contrast values that correspond to greater

than 90 percent confidence. The weights of evidence analysis contrast for 99 cm of precipitation is 1.9334 which indicates it is strongly predictive with in the study area.

The contrast for category 9 corresponding to 119 cm of precipitation indicates it is mildly predictive of arborglyphs. The contrasts for both depths are predictive category 1 being mildly so and category 4 is strongly predictive.

76

Table 5-16 Mean Annual Precipitation Results Studentized Mean Annual Category Training Points W+ W- Contrast Contrast Precipitation (cm) 5 238 1.058 -0.8754 1.9334 16.518 99 9 33 0.3322 -0.0291 0.3612 1.9347 119 8 48 -0.3772 0.0759 -0.4531 -2.8827 114 4 10 -1.2029 0.0712 -1.2741 -3.9513 94 6 13 -1.1306 0.0846 -1.2152 -4.2761 104 7 7 -2.1111 0.1608 -2.2719 -5.9373 109

5.1.9 Temperature

The temperature variable also originated from the Geospatial Data Gateway at

the NRCS and is derived from the Parameter-elevation Regressions on Independent

Slopes Model (Prism Climate Group). The temperature attribute was then dissolved to combine like values. Lastly, it was converted into a raster and classified into seven categories (Table 5-17).

Table 5-17 Mean Annual Temperature Variable Category Mean Temp. (°F) 1 35 2 37 3 39 4 41 5 43 6 45 7 47

Figure 5-9 Mean Annual Temperature Variable Map

77

Of the seven categories the weights of evidence analysis found that only three categories contained all arborglyph training points (Table 5-18). Only category 4

corresponding to 39 °F meets the 90 percent confidence requirement. The other

categories had negative contrasts which indicate that fewer arborglyph training points

were found than due to chance alone. Category 4 is also considered an extremely

predictive category for arborglyphs within the study area. The contrast for category 4

had the highest contrast of any other category including those in other variables.

Table 5-18 Mean Annual Temperature Results Studentized Mean Annual Category Training Points W+ W- Contrast Contrast Temperature (°F) 4 341 0.823 -3.2152 4.0382 11.2743 41 5 1 -4.5742 0.3226 -4.8967 -4.8886 43 3 7 -2.4754 0.2521 -2.7275 -7.1316 39

5.2 Weights of Evidence Sensitivity Testing

The weights of evidence sensitivity testing was done to determine how

recategorizing a specific set variables would change the output of the weights of

evidence analysis when compared to the original analysis. The variables that were

recategorized were elevation, aspect, slope, and soil drainage. The other variables were

not tested because of limitations in spatial data and because this analysis was a pilot to

determine if the further recategorization was necessary. These limitations were that the data was already categorized at the lowest level and that categories could not be expanded upon. 78

5.2.1 Recategorized Elevation

The recategorized elevation variable was created by converting the DEM to integers, and then classifying elevation ranges into categories (Table 5-19). The

categories were created based on the elevation range of the arborglyph training points.

All the elevations less than 2,558 meters and greater than 2,515 meters were lumped

into category 0. The values between represent the elevations of the surveyed

arborglyphs. The elevation range of the arborglyphs was then split based on the half

standard deviation. This categorized the variable into eight categories that represent the

elevation range of the arborglyph training points (Figure 5-10.

Table 5-19 Recategorized Elevation Variable Category Elevation Range (meter) 0 1790 2258 1 2258 2288 2 2288 2326 3 2326 2364 4 2364 2402 5 2402 2440 6 2440 2478 7 2478 2515 0 2515 3061

Figure 5-10 Recategorized Elevation Variable Map

The weights of evidence analysis on the recategorized elevation variable

determined that the arborglyphs fell into all eight categories. Due to the categorization

of the variable it was expected that category 0 would have no arborglyphs but because 79

of the 10m cell size a single arborglyph fell into category 0. Categories 1, 2, 6, and 7 had studentized contrasts that indicate confidence above the 90 percent level. These categories represent the two lowest (2258 – 2326) and the two highest (2440 – 2515 meters) elevation ranges. Categories 6 and 7 have contrasts larger than two indicating that they are extremely predictive. Categories 1 and 2 have contrasts that indicate they are moderately predictive. The remainder of the elevation categories had negative contrasts, which indicate that fewer arborglyphs are found than would be expected by

chance (Table 5-20).

Table 5-20 Recategorized Elevation Studentized Category Training Points W+ W- Contrast Contrast Elevation (meter) 7 120 1.9402 -0.3707 2.3108 19.1357 2478 – 2515 6 116 1.8217 -0.3488 2.1705 17.9316 2440 – 2478 2 45 0.7975 -0.0782 0.8758 5.3171 2288 – 2326 1 36 0.575 -0.0491 0.6241 3.457 2258 – 2288 4 12 -0.5025 0.0235 -0.526 -1.7737 2364 – 2402 5 12 -0.5557 0.0268 -0.5825 -1.9651 2402 – 2440 3 7 -1.1228 0.0434 -1.1662 -3.0384 2326 – 2364 1790 – 2258 0 1 -5.3481 0.9192 -6.2673 -6.2571 2515 - 3061

80

5.2.2 Recategorized Aspect

The recategorized aspect variable was derived from the DEM by using the aspect

tool within ArcMap. The result was then reclassified into nine categories corresponding

to no aspect and then by dividing the four cardinal compass directions in half (Figure

5-11).

Table 5-21 Recategorized Aspect Variable Aspect Range Cardinal Category (degree) Direction 0 -1 No Aspect 1 0 45 N-NE 2 45 90 E-NE 3 90 135 E-SE 4 135 180 S-SE 5 180 225 S-SW 6 225 270 W-SW 7 270 315 W-NW 8 315 360 N-NW

Figure 5-11 Recategorized Aspect Variable Map

Arborglyphs were found in all categories except, 0, 1, and 8. Categories 1 and 8 correspond to northern aspects. The majority of arborglyphs fall within west-southwest aspect which has a contrast of 1.877 indicating the category is strongly predictive.

Category 3 corresponding to an east-southeast aspect had the second most arborglyphs, however, the contrast value indicates that it is only mildly predictive. The remainder of the aspect categories had negative contrasts, which indicate that fewer arborglyphs are found than would be expected by chance (Table 5-22). 81

Table 5-22 Recategorized Aspect Training Studentized Category Points W+ W- Contrast Contrast Aspect (degrees) 6 182 1.2948 -0.5829 1.8777 16.9966 225 – 270, W-SW 3 88 0.3806 -0.1014 0.482 3.8324 90 – 135, E-SE 5* 31 -0.0534 0.0054 -0.0588 -0.308 180 – 225, S-SW 7 30 -0.3646 0.0423 -0.4068 -2.107 270 – 315, W-NW 4 1 -3.9222 0.1535 -4.0757 -4.0684 135 – 180, S-SE 2 17 -0.9432 0.0837 -1.0269 -4.1028 45 – 90, E-NE *These categories are part of the SDM Weights of evidence tool category 99. This is because the studentized contrast is below the Generalization of Evidence Criteria, which was set at 1.282.

5.2.3 Recategorized Slope

The recategorized slope variable was created from the DEM by running the slope tool in ArcMap with the degree slope option. The categories were created based on the range of slope for the arborglyph training points. All the slopes less than 3 degrees and greater than 17 degrees were lumped into category 0. The values between the minimum and maximum above represent the range of slopes for the surveyed arborglyphs. The remaining categories were then split based on the half standard deviation of the slope associated with the arborglyph. The result categorized the variable into eleven categories that represent the range of slope for the arborglyph training points (Figure 5-12).

82

Table 5-23 Recategorized Slope Variable Category Slope Range (degree) 0 0 3 1 3 3.2 2 3.2 5.1 3 5.1 7.1 4 7.1 9.1 5 9.1 11 6 11 13 7 13 14.9 8 14.9 16.9 9 16.9 17 10 17 72.6

Figure 5-12 Recategorized Slope Variable Map

Of the eleven categories the weights of evidence analysis found that only

categories 2 through 8 corresponding to slopes of 3.2 through 16.9 degrees have

arborglyphs. Due to the categorization of the variable it was expected that categories 1

and 9 would also have arborglyphs but because of the 10 meter cell size these

arborglyphs fell into the adjacent category. Slopes ranging from 5.1 through 13 degrees

have a studentized contrast that provides greater than 90 percent confidence. The

slopes ranging from 5.1 to 11 degrees have a contrast that indicates the categories are

strongly predictive with in the study area. Slopes of 11 through 13 are considered

moderately predictive. The remainder of the slope categories had negative contrasts, which indicate that fewer arborglyphs are found than would be expected by chance

(Table 5-24).

83

Table 5-24 Recategorized Slope Studentized Category Training Points W+ W- Contrast Contrast Slope (degree) 4 107 1.4747 -0.2934 1.7681 14.5172 7.1 – 9.1 5 107 1.4582 -0.2921 1.7503 14.3813 9.1 – 11 3 63 0.9141 -0.124 1.0381 7.2174 5.1 – 7.1 6 50 0.6347 -0.0756 0.7103 4.5285 11 – 13 7 10 -0.9883 0.051 -1.0393 -3.2199 13 – 14.9 2 7 -1.1954 0.0483 -1.2437 -3.2415 3.2 – 5.1 8 5 -1.6241 0.061 -1.6851 -3.729 14.9 – 16.9

5.2.4 Recategorized Soil Drainage

The recategorized soil drainage variable was created from a NRCS dataset obtained from the Web Soil Survey. The variable was created in the same manner as the soil drainage variable in section 5.1.6. The variable’s categories were then lumped into

three categories representing: poorly drained, moderately drained, and well drained as

shown in (Table 5-25, Figure 5-13).

Table 5-25 Recategorized Soil Drainage Variable Category Drainage 1 Poorly Drained 2 Moderately Drained 3 Well Drained

Figure 5-13 Recategorized Soil Drainage Variable Map

84

For the recategorized soil drainage variable the weights of evidence analysis

found that the surveyed arborglyphs are found in every category of the variable. Only

category two corresponding to moderately drained met the exceeded the 90 percent

confidence threshold. The moderately drained category also was the only category to be

predictive of surveyed arborglyphs. The category had a contrast of 1.9851 which is

considered strongly predictive (Table 5-26).

Table 5-26 Recategorized Drainage Training Studentized Category Points W+ W- Contrast Contrast Drainage 2 314 0.4885 -1.4967 1.9851 11.0775 Moderately Drained 1 17 -0.4731 0.0315 -0.5045 -2.009 Poorly Drained 3 18 -1.9699 0.4087 -2.3786 -9.7962 Well Drained

5.3 Analysis of Arborglyph Placement on Tree

The following results address the question whether arborglyphs are located on the upslope aspect of the tree. The analysis was done two times, first on all arborglyphs

and then on a subset of arborglyphs on slopes greater than or equal to ten degrees,

because the effect may be greater as slope increases.

The results indicate that on all slopes 10.2 percent of arborglyphs within the dataset are located on the upslope side of the tree. The results for the dataset of arborglyphs on a slope greater than or equal to 10 degrees are as follows: 7.22 percent of arborglyphs were located on the upslope side of the tree.

The results of the analysis suggest that within the study area there were not many arborglyphs located on the upslope side of the tree. The results also indicate that 85 there were less arborglyphs associated with an upslope location on slopes greater than or equal to 10 degrees.

Table 5-27 Arborglyph Location Results Mean Difference Standard Percent of arborglyphs (degrees) Deviation within ±36° of upslope Arborglyphs at all slopes 123 65.0 10.2% Arborglyphs on slope ≥10° 134 63.9 7.22%

5.4 Effect of Taylor Grazing Act on Arborglyph Quantity

The literature suggests that the Taylor Grazing Act of 1934 was a pivotal point for

Basque sheepherders and may mark the beginning of the decline of Basque sheepherding in the American West. The goal of this analysis was to determine if this decline in Basque sheepherding is reflected in the arborglyphs with dates.

The results of the analysis suggest that Basque sheepherding declined in the study area after the implementation of the Taylor Grazing Act of 1934. There were 85 arborglyphs identified that had dates younger than 1935 and there were 55 arborglyphs with dates between the years of 1935 and the present. This indicates a decline of 35.3 percent in the number of arborglyphs carved after the Taylor Grazing Act went into effect. The amount of arborglyphs were indicated by the bars and the percentage decline is represented as a trendline in Figure 5-14. 86

Figure 5-14 Effect of Taylor Grazing Act on Arborglyph Quantity 87

6 Discussion and Conclusion

6.1 Weights of Evidence

The weights of evidence analysis provided many insights into the location of arborglyphs in relationship to the environmental variables used for this study. The analysis suggests that based on the study area and arborglyph training points that all the variables explored provide a means to locate arborglyphs, however, not all of them are as useful as others and not all of them could be used in a weights of evidence predictive model which is a direction for future research.

It is useful to visualize the original environmental variable categories associated with the locations of arborglyphs, so they have been ranked by studentized contrast in

Table 6-1 Environmental Variables Significant to the Location of Arborglyphs, Original Variable Category Value Studentized Contrast Soil Depth 8 0.73 (m) 31.3285 Elevation 8 2,457.5 – 2,558.4 (m) 19.6141 Slope 3 7.8 – 12.8 (degree) 17.3738 Mean Annual Precipitation 5 99 (cm) 16.518 Soil Drainage 5 Well Drained 15.9188 Aspect 4 225 – 325 (West) 12.9707 Mean Annual Temperature 4 41 (°F) 11.2743 Soil Drainage 1 Very Poorly Drained 9.1839 Distance to Surface Water 7 379 – 452 (m) 8.7952 Slope 2 2.7 – 7.8 (degree) 7.3878 Distance to Surface Water 6 306 – 379 (m) 5.9354 Depth to Water Table* 4 >2.03 (m) 4.9431 Elevation 6 2,255.6 – 2,356.5 (m) 3.0557 Depth to Water Table 1 0.13 (m) 2.5259 Distance to Surface Water 8 452 – 525 (m) 2.0246 Mean Annual Precipitation 9 119 (cm) 1.9347 Bold outline indicates the nine environmental variable positions. * Indicates a variable not found in the nine positions. 88

Table 6-1. Only categories with studentized contrasts greater than 90 percent

confidence were included as these are the categories that correlate with the location of arborglyphs within the study area. This was done to give some insight as to which variables and categories are important in the location of arborglyphs. The highest category is associated with the soil depth variable and corresponds to 0.73 meter of soil.

The lowest category was associated with the precipitation variable and corresponds to

119 cm of precipitation. When looking at the ranks all but one variable shows up in the top nine positions. The variable missing was depth to water table and the variable duplicated was soil drainage.

To account for duplication in the table the studentized contrasts were added together and then ranked. The result was the nine original environmental variables ranked in a manner that indicated that soil depth, soil drainage, slope, elevation, precipitation, and distance to surface water variables were well correlated to the arborglyph locations within the study area (Table 6-2).

Table 6-2 Original Variables Ranked by Studentized Contrast Environmental Variable Sum of Studentized Contrast Soil Depth 31.329 Soil Drainage 25.096 Slope 24.762 Elevation 22.670 Mean Annual Precipitation 18.453 Distance to Surface Water 16.755 Aspect 12.971 Mean Annual Temperature 11.274 Depth to Water Table 7.469 Bold outline indicates the variables used in recategorized variable model.

89

To continue the weights of evidence analysis the top two-thirds of the

environmental variables were combined and the known arborglyph locations were

overlaid (Figure 6-1). The map shows low, moderate, and high probability areas for arborglyphs within the study area, based on three natural breaks in the data. This map is

not predicting the location of arborglyphs and is only being used to illustrate how well

the variables align with known arborglyph locations.

Figure 6-1 Modeled Arborglyph Locations, Original Variables 90

The majority of arborglyphs in Horsethief Canyon were modeled in areas of high

probability with a few outliers in the low probability (Figure 6-1). The model is

over-predicting the location of arborglyphs on the eastern edge of Horsethief Canyon.

Complications arise in the areas of Hope Valley and Scott’s Lake because the model is showing low probability even though there are arborglyphs located in these areas. A

possible reason that Hope Valley and Scott’s Lake arborglyphs were not well modeled is

that the amount of arborglyphs used in the analysis is less in these areas, 88 arborglyphs

as opposed to 261 in Horsethief. This would change how the variable categories manifest themselves in the model. Another possible reason for the low probability for

Hope Valley and Scott’s Lake is that the data the variables were created from could be at a scale not conducive to locating the small number of arborglyphs located at these locations, however, since the whole study area was not surveyed for arborglyphs it is not known how many arborglyphs occur at Hope Valley and Scott’s Lake.

The recategorized variables have also been ranked by studentized contrast Table

6-3. The original variables associated with the recategorized variables have been excluded as well as the categories within the variables not meeting the 90 percent confidence value. This was done as part of the sensitivity testing to compare how the rank of original variables differs from that of the recategorized variables. As with the original variables, the top spot is associated with soil depth variable and the category corresponding to 0.73 meters deep. The bottom spot went to the precipitation corresponding to 119 cm of precipitation, same as the original variables. 91

Table 6-3 Environmental Variables Significant to the Location of Arborglyphs, Recategorized Variable Category Value Studentized Contrast Soil Depth 8 0.73 (m) 31.3285 Recategorized Elevation 7 2477.7 – 2515 19.1357 Recategorized Elevation 6 2439.9 – 2477.9 17.9316 Recategorized Aspect 6 225 – 270, W-SW 16.9966 Mean Annual Precipitation 5 99 (cm) 16.518 Recategorized Slope 4 7.1 – 9.1 14.5172 Recategorized Slope 5 9.1 – 11 14.3813 Mean Annual Temperature 4 41 (°F) 11.2743 Recategorized Drainage 2 Moderately Drained 11.0775 Distance to Surface Water* 7 379 – 452 (m) 8.7952 Recategorized Slope 3 5.1 – 7.1 7.2174 Distance to Surface Water* 6 306 – 379 (m) 5.9354 Recategorized Elevation 2 2288.3 – 2326.3 5.3171 Depth to Water Table* 4 >2.03 (m) 4.9431 Recategorized Slope 6 11 – 13 4.5285 Recategorized Aspect 3 90 – 135, E-SE 3.8324 Recategorized Elevation 1 2258 – 2288.3 3.457 Depth to Water Table* 1 0.13 (m) 2.5259 Distance to Surface Water 8 452 – 525 (m) 2.0246 Mean Annual Precipitation 9 119 (cm) 1.9347 Bold outline indicates the nine environmental variable positions. * Indicates a variable not found in the nine positions.

Then by looking at the ranks all but two variables show up in the top nine positions. The variables missing were depth to water table and distance to surface water. The variables duplicated were recategorized elevation and recategorized slope.

Like before, to account for duplication in the table the studentized contrasts for like variables were added together and then ranked (Table 6-4).

The top two-thirds of the environmental variables above were combined and the known arborglyph locations were overlaid (Figure 6-2). The map shows low, moderate, 92

and high probability areas for arborglyphs within the study area, based on three natural breaks in the data.

Table 6-4 Recategorized Variables Ranked by Studentized Contrast Environmental Variable Sum of Studentized Contrast Recategorized Elevation 45.841 Recategorized Slope 40.644 Soil Depth 31.329 Recategorized Aspect 20.829 Mean Annual Precipitation 18.453 Distance to Surface Water 16.755 Mean Annual Temperature 11.274 Recategorized Soil Drainage 11.078 Depth to Water Table 7.469 Bold outline indicates the variables used in recategorized variable model.

The majority of arborglyphs in Horsethief Canyon were modeled in areas of high

probability with a few in the moderate and again outliers in low probability (Figure 6.2).

In this re-analysis, the area at the eastern edge of Horsethief Canyon has low probability, which better represents the fact that no arborglyphs are located there. In this analysis, arborglyphs in the area around the Hope Valley are more accurately modeled. The area of Scott’s Lake, however, still has low probability despite the fact that there were arborglyphs located in this area. This could be attributable to fewer recorded arborglyphs Scott’s Lake, 55 as opposed to 294 in Horsethief and Hope Valley combined. The six environmental variables that include the recategorized variables appear to be locating the arborglyphs better than was achieved with the original variables. 93

Figure 6-2 Modeled Arborglyph Locations, Recategorized Variables

6.1.1 Elevation

Elevation was explored because the literature indicates that it is an important factor in aspen development. The analysis suggests that the elevations between 2,256 and 2,558 meters are good places to find arborglyphs within the study area. Two possible reasons for this are that aspen in the study area prefer this elevation range or that the Basque sheepherders were carving arborglyphs at these elevations. Elevation 94

also has a role in the location of aspen in relationship to aspect (Reed 1971; Beetle

1974). Aspect does not directly relate to arborglyphs but does influence the growth of aspen. This is discussed in more detail below. Elevation is tied to the diameter of aspen

and as previously noted arborglyphs are more likely to be carved on large trees (Grant

and Mitton 1979; Mallea-Olaetxe 2008). Most importantly for arborglyphs, elevation is

linked to the growth rate of aspen (Grant and Mitton 1979). Elevation physically affects

the arborglyphs because the trees at higher elevation grow more slowly, thus keeping

the arborglyph clearer for a longer period of time (Mallea-Olaetxe 2008).

The recategorized elevation variable was chosen for sensitivity testing because

the arborglyphs were not distributed well through the categories of original variable.

The test was also done to determine if different elevations categories would cause

different results from the weights of evidence analysis. There were five less categories

in the recategorized elevation variable, however, the categories provided more

granularity. The granularity was achieved by lumping elevations outside of those found

in the arborglyphs into one category. The remaining range of elevation was then split to

providing 39.9 meters of separation between categories as opposed to 100.9 meters in

the original categorization.

The original elevation variable contained only two categories that meet 90

percent confidence, corresponding to the elevation ranges of 2,256 through 2,357 meters and 2,458 through 2,558 meters. The recategorized elevation variable in contrast had four categories meeting the confidence requirement. Like the original variable the elevation ranges were divided. The first range was 2,258 through 2,326 95

meters and the second was 2,440 through 2,515 meters. When comparing ranges of elevation they are no farther apart than 44 meters. Therefore, the sensitivity testing suggests that either way the variable is categorized it does not greatly affect the result.

6.1.2 Aspect

Aspect of the slope was examined because aspen prefer to grow in certain locations based on aspect relative elevation, and temperature (Reed 1971; Beetle 1974).

The aspect of the slope was not addressed in the literature as being a factor that affects arborglyphs, thus, aspect is only useful in determining the location of aspen but not necessarily arborglyphs. Aspect provides insight into the location of aspen and therefore it also provides insight on the location of arborglyphs because arborglyphs are carved on aspen. The weights of evidence analysis suggests that within the study area arborglyphs are likely to be found on western aspects.

As shown in the elevation variable (section 5.1.1), all the arborglyphs fall between 2,256 to 2,558 meters elevation. Since aspect and elevation are linked as shown by Reed (1971) and Beetle’s (1974) studies these elevations would indicate that aspen would occur on all aspects evenly. The analysis for this study shows that arborglyphs within the study area occur mostly on western and eastern aspects instead of spread across each aspect as would be expected from the literature. To explain this,

Forest Service remotely sensed aspen data was used to determine if the aspen are evenly distributed on all slopes throughout the study area. The analysis determined that

92.7 percent of remotely sensed aspen have eastern (44.8%), southern (26.1%), and 96

western (21.8%) aspects. This suggests that there should be relatively few arborglyphs

with northern aspects within the study because there are fewer aspen trees. This could at least partly explain why there were no surveyed arborglyphs with northern aspects.

The aspect variable was recategorized and chosen for sensitivity testing because

the arborglyph training points were not well distributed through the original variable

and because the original categorization may have been oversimplified into north, south,

east, and west aspects. The recategorized aspect variable doubled the amount of

categories and increased the granularity of the analysis.

The original analysis found that only one category corresponding to an aspect of west met the 90 percent confidence requirement, although there were a large amount of arborglyphs found in the eastern aspect category. The east aspect had a low contrast and a low studentized contrast. For the recategorized aspect variable two categories met the 90 percent confidence requirement, west-southwest and east-southeast. This

was as expected when considering the original weights of evidence analysis both

generally trending west and east. The difference is that the eastern direction became

significant and it further refined the direction of the aspects to west-southwest and

east-southeast. In addition the recategorized analysis shows that both aspects fall to the

southern side. Therefore, the sensitivity testing suggests a better categorization of the

variable than the original. It also suggests that a more refined the aspect variable is

better at locating arborglyphs within the study area. 97

6.1.3 Slope

Slope was chosen as a variable because there is indication in the literature that slope was a factor when choosing a sheep camp and it was common for sheep camps to be set near or within aspen groves (DeKorne 1970; Mallea-Olaetxe 2008). While not discussed in the literature it seems reasonable to believe that steep slopes were avoided by the carver when choosing a location of an arborglyph, even though aspen are likely to grow on them, because of the effort it would take to access these locations. Also, the

Basque sheepherders were likely carving arborglyphs close to where they were tending the sheep or by camp. Locations with less slope were more ideal locations for these activities because camping on slopes is not practical and generally the grazing was better on more gentle slopes.

The recategorized slope variable was chosen for sensitivity testing because the arborglyphs were not well distributed through the categories of original variable. The test was also done to determine if different slope categories would cause different results from the weights of evidence analysis. Each variable had ten categories but recategorized slope variable provided more granularity. The granularity was achieved by lumping slopes outside of those found in the arborglyphs into one category. The remaining range of elevation was then split to providing 1.9 degrees of separation between categories as opposed to 5 degrees in the original categorization.

The original slope variable after running the weights of evidence analysis had only two categories that met the 90 percent confidence requirement. These categories corresponded to slopes of 2.7 through 12.8 degrees. The result of the weights of 98 evidence analysis on the recategorized slope variable was that four categories met the

90 percent confidence requirement. These slopes ranged from 5.1 through 13 degrees.

This represents a maximum discrepancy of 2.4 degrees between the two analyses and further refines the minimum slope of arborglyphs within the study area to 5.1 degrees as opposed to 2.7 degrees. Therefore, the recategorized variable provides minimal refinement of the variable but in general produced the same result as the original slope variable.

6.1.4 Distance to Surface Water

Distance to surface water affects aspen and arborglyphs. The literature indicates that aspen grow near reliable sources of water (Mitton and Grant 1996; Haeussler,

Coates, and Mather 1990; Jones, Kaufmann, and Richardson 1985). Mallea-Olaetxe

(2008) has observed that aspen located along creek banks grow quicker and therefore distorts the arborglyph in contrast, arborglyphs on aspen located further from the water source better preserve the carving (Mallea-Olaetxe 2008).

This could indicate an ideal position for arborglyph, creation, longevity, and preservation. The arborglyphs that may have been carved before the range have been lost or have become so distorted they are unrecognizable. Aspen after the range may have been too far from a reliable water source to produce the smooth bark the sheepherder was looking for. 99

6.1.5 Depth to Water Table

The variable depth to water table was explored because the literature indicates

that this is an important environmental characteristic for aspen. For aspen to survive the

water table must be between 0.7 to 2.5 meters from the surface (Haeussler, Coates, and

Mather 1990; Fralish 1972). The amount of water received by the aspen also affects the arborglyphs. The more water an aspen gets the more quickly it grows and this distorts the arborglyphs, making them illegible in some instances (Mallea-Olaetxe 2008).

This indicates that the depth to water table may be an indicator of arborglyphs.

The depth of greater than 2.03 meters suggests that of all the arborglyphs carved in the study area the remaining were found with a depth to water table that is at the aspens limit. This supports the idea that aspen with less water availability preserve the arborglyphs legibility.

6.1.6 Soil Drainage

Soil drainage was examined as a variable because aspen are thought to require well drained soils (MacKinnon et al. 1992). This is because aspen are unable to reproduce and grow in continually saturated soils (MacKinnon et al. 1992; Maini and

Horton 1966; Bates et al. 1990). Also, if soil becomes too excessively drained due to rocky soil conditions, aspen are unlikely to do well because their roots are unable to penetrate and spread in rocky soils (Haeussler, Coates, and Mather 1990).

The soil drainage variable was recategorized and chosen for sensitivity testing because the result of the original weights of evidence analysis was not expected. The 100

unexpected result was that both very poorly drained soil and well drained soils are

indicators of arborglyphs within the study area. Therefore, rather than refine the

categories as was done with the other variables, the soil drainage variable was recategorized into fewer categories. These categories became poorly drained, moderately drained, and well drained.

The results from the weights of evidence analysis for the recategorized soil

drainage variable were that only one category met the 90 percent confidence

requirement. This category was for moderately drained soil. This is a completely

different result from that of the original variable analysis and is as expected based on

the preferences aspen in respect to soil drainage. The likely reason for the differences

between the original and recategorized variable is that the evidence data is not of

enough detail to provide an accurate enough result to answer questions relating to the

location of arborglyphs. Therefore, the sensitivity testing provided additional evidence

that soil drainage variable as represented for this study is not a good indicator of the

location of aspen and would not be included in further analyses.

6.1.7 Soil Depth

Soil depth was chosen as a variable because aspen need and prefer certain

depths for their root system to reach. Aspen root systems are generally between 0.9

meters to 1.5 meters in depth (Fowells 1965). Therefore, if the soil only extends less than 0.9 meters from the surface, aspen is unlikely to be successful. 101

For soil depth, the categories containing arborglyph training points aligns well

with the literature and as shown by the analysis is highly correlated with the location of

arborglyphs. A possible future research opportunity based on the soil depth variable

could be integrated with wind variable. This would be valuable research because wind is

the environmental variable that could affect arborglyphs and has not been explored

here. The wind often blows down aspen at the periphery of aspen groves and the soil

depth could indicate how stable the aspen are. However, there is little high speed wind data available in or around aspen stands.

6.1.8 Precipitation

The precipitation variable was chosen because it has been shown to affect aspen establishment and growth. Increases in aspen establishment have been observed to happen when there are several years of high annual mean temperature and low precipitation (Elliott and Baker 2004). Aspen growth is inhibited by low precipitation

(Hanna and Kulakowski 2012). As mentioned before, quick growth of aspen makes the arborglyphs distorted (Mallea-Olaetxe 2008).

The greater availability of water to aspen the more likely an arborglyph is to become distorted and illegible (Mallea-Olaetxe 2008). The analysis suggests arborglyphs within the study area are preserved in a zone of precipitation that is 99 cm.

6.1.9 Temperature

The variable temperature was explored because temperature is linked to location, establishment, and growth of aspen. For the location of aspen temperature is 102

related to aspect and elevation (Jones, Kaufmann, and Richardson 1985). As stated in

the previous variable several years of high temperature increase the establishment of

aspen (Elliott and Baker 2004). Finally, aspen growth is inhibited by high temperature as observed by Hanna and Kulakowski (2012). And quick growth of the aspen distorts arborglyphs (Mallea-Olaetxe 2008).

The literature suggests that areas of high temperature should preserve arborglyphs but produce smaller aspen, however, this was most likely balanced by the sheepherders selection of large smooth tree (Mallea-Olaetxe 2008). The analysis suggests arborglyphs within the study area are created and preserved in a zone of temperature that is 39 °F.

6.2 Analysis of Arborglyph Placement on Tree

The environmental variable of aspect was explored further because there

appeared to be a pattern associated with the location of arborglyphs on the trees as

they relate to the slope aspect. It was thought that the arborglyphs would have been

carved on the upslope side of the tree because the carver would not have wanted to fall

downhill while carving. Positioning themselves on the upslope side of the tree would

have allowed them to brace themselves against the tree for support. Finally, initial

observations indicated that arborglyphs were carved on the upslope sides of the trees. It also appeared as if the effect was intensified as slope increased. However, the results of

the analysis fail to support this hypothesis on both accounts. 103

A possible reason these results do not support the hypothesis are due to

creation of arborglyphs. When a Basque sheepherder decided to carve a tree, slope was

not high on the list of criteria for choosing its location. As previously indicated the carver

would have been looking for a large tree with smooth bark free of any cankers or limbs

(Mallea-Olaetxe 2008). Also, few trees would meet these requirements completely and

would generally have one or both of these features present. Then it would have been

the carver’s task to find the best location on a tree given the features. Again, the

location then is completely independent of slope.

A potential problem with this analysis is that the amount of arborglyphs included

in this analysis is relatively small when compared to the amount of arborglyphs in the

American West. While, this analysis fails to find a correlation between the slopes aspect

and location of the arborglyph on the tree, this may not be the case when looking at a

larger sample. Also, only one subset of steepness was analyzed, so including steeper

slopes could manifest different results. Thus, future research should investigate the link

between slope and location of arborglyphs further.

6.3 Effect of Taylor Grazing Act on Arborglyph Quantity

The literature suggests that the Taylor Grazing Act is the start of the decline of the Basque Sheepherder in the American West. With the decline of the Basque sheepherder it reasons that the amount of arborglyphs would decrease too. This analysis provides evidence for a 35.3 percent decline in the amount of arborglyphs with dates after the Taylor Grazing Act went into effect in 1934 within the study area. The 104

result of the analysis corresponds with the literature in the sense that the time period associated with Taylor Grazing Act had a decline of arborglyphs, which may serve as a proxy for Basque sheepherding. This result, however, is not without complication.

The first complication is that there is no definitive way to link the decline in arborglyphs specifically to the Taylor Grazing Act. Also, as mentioned previously the

Forest Service and its predecessors had been implementing rules similar to the Taylor

Grazing Act as early as 1896 (Williams 2007). Since the Basque sheepherders had been dealing with those earlier rules the Taylor Grazing Act might not have affected them as much as is suggested in the literature. Second, the study area is a relatively small when compared to the area of which arborglyphs cover. The results could be different when studying a larger dataset of arborglyphs. In addition, since only arborglyphs with dates were used in this analysis it could be skewing the results because some Basque sheepherders did not date their work. If all the arborglyphs could be dated within the study area it would improve the analysis. Finally, this analysis assumes that all the sheepherders in the study area carved arborglyphs and in relatively equal numbers, but this may not be the case.

Also of note is that given the generally 60 to 80 year lifespan of aspen it could be expected that the amount of arborglyphs prior to 1935 would be less than the amount of arborglyphs between the years of 1935 and present due to aspen dying off. This however, does not appear to be the case as suggested by the 35.3 percent decline in amount of arborglyphs after the Taylor Grazing Act which happened to be within the later range of the average life expectancy of aspen. For the analysis to be valid as it 105

stands the aspen would have to be dying equally across all the date ranges but this is

not the case. It reasons that the aspen having the oldest arborglyphs dates die more

frequently. Thus, this analysis is likely an underestimation of the decline in arborglyphs after the implementation of the Taylor Grazing Act. The spatial component is missing from this analysis and could be a possible direction for future research. As previously mentioned the arborglyph dates are many times also accompanied by a name. If a larger

arborglyph dataset was acquired and covered much more area, such as Nevada and

California then researchers could explore how the Taylor Grazing Act might have

redistributed the sheepherders over the landscape. The sheepherders might move to

avoid Forest Rangers upholding the Taylor Grazing Act or they might be forced to be a

sheepherding contractor for an established American ranch causing them to have to

move. This type of research could answer questions on the life of Basque sheepherders

not well addressed by the literature.

6.4 Conclusions

Each of the environmental variables examined in this study was analyzed in a GIS weights of evidence analysis in various combinations to model the location of arborglyphs. The variables from this study that are best suited for use in future analyses were six from the recategorized model: elevation, slope, soil depth, aspect, precipitation, and distance to surface water (Table 6.4).

Based on the results, the variables associated with terrain – elevation, aspect, and slope – correlate well with the location arborglyphs. One potential problem is that 106

all of these variables are derived from the same data and therefore could introduce

conditional dependence which could overestimate the location of arborglyphs.

However, they are all measuring different topographical features. In other words,

elevation has no direct bearing over aspect; a slope can be facing any direction at a

given elevation. A similar argument can be made for slope, a particular elevation could

have any number of associated slopes. Consequently, the variables for elevation, aspect,

and slope can be used together as indicators of arborglyphs. A benefit of elevation,

slope, and aspect is that the data to create them is available at the resolution used for this study or finer across the western United States. Elevation, slope, and aspect

variables are recommended for this type of analysis.

The variables for precipitation and temperature are also a likely pair that could

be used to locate arborglyphs. The literature suggests that aspen growth is linked to

both, but water is a requirement of aspen so it reasons that precipitation is more highly

correlated then temperature. There is some potential that temperature and

precipitation could be dependent; however, from the perspective of the arborglyphs

there is no link. Precipitation would work well with elevation, slope, and aspect.

Temperature, while not shown by either analysis to be highly correlated with the location of arborglyphs, would not likely interfere with other variables. Also both temperature and precipitation variables were derived from modeled Prism data the data which is readily available throughout the western United States.

Two variables that should not be combined because they may measure similar features are depth to water table and soil depth. These variables may be 107

interdependent because the depth to the water table is frequently dependent on the

same impermeable feature that determines the soil depth. The depth to water table variable was the variable with the lowest correlation with the location of arborglyphs.

The soil depth variable on the other hand was well correlated with the location of arborglyphs.

The variable, distance to surface water, moderately correlates with the location of arborglyphs based on the analysis. Distance to surface water was used in both models of arborglyphs within the study area. It has no direct dependence on the other variables explored in this study so it may be a good candidate in future modeling. The water sources used to create the variable are from topographic maps that have coverage for the entire western United States so these may be applied in analyzing different areas.

The soil drainage variable was used as part of the first model using the original variables but was not used in the recategorized model. This was due to sensitivity testing that indicating changing the soil drainage variable categories also changes the output so this variable is not recommended for modeling the location of arborglyphs. A possible reason was poor data quality.

It is recommended that six variables, elevation, slope, soil depth, aspect, precipitation, and distance to surface water, be used in frameworks for future research on locating arborglyphs. The six variables potentially allow public land managers to locate concentrations of arborglyphs previously undocumented by using known arborglyph locations, concentrating their preservation and documentation efforts where it is most needed. With further refinement along the lines of this work, land managers 108

may be in a position to make decisions based on the probability of arborglyphs being

present without having to do field survey for arborglyphs.

The recommended variables could be used in future studies where a systematic

random survey could be implemented over a much broader area. The random survey

should use arborglyph presence and absence data which could strengthen the

arborglyph location model.

The analysis of the placement of arborglyphs on the upslope side of the tree was done to gain a better understanding of how a micro-environmental variable affects the placement of arborglyphs on a tree. The result of the analysis was that within the study area arborglyphs were not carved on the upslope side of the tree as had been hypothesized. This analysis could be developed further by incorporating other microsite variables such as, distance to water, vegetation type, or historic archaeological sites.

The analysis of the Taylor Grazing Act on arborglyph quantities found that the amount of arborglyphs carved before the act was greater than after. This analysis corresponds with the idea that the Taylor Grazing Act could have initiated the decline of

Basque sheepherding. This analysis could be improved with a similar analysis over a

larger dataset. Future research could incorporate spatial aspects that follow

sheepherders across the landscape over time. While this study looked at arborglyphs on

Forest Service managed land, future research could include investigating arborglyphs on

the Bureau of Land Management land as the Taylor Grazing Act may have manifested

differently under other agencies. 109

Lastly, the arborglyphs for this study were completely field documented,

although this study did not utilize most of this data due to the research questions posed

and the type of analysis used. The intent of the high level of documentation was to

collect the information they contain but also will provide future researchers with an arborglyph dataset that could be utilized to answer questions not well discussed in the literature about Basque sheepherders and arborglyphs.

110

7 Bibliography

Angier, Bradford. 1978. Field Guide to Medicinal Wild Plants. Stackpole Books.

Anselin, Luc, and Arthur Getis. 1992. “Spatial Statistical Analysis and Geographic Information Systems.” The Annals of Regional Science 26 (1): 19–33. doi:10.1007/BF01581478.

Banning, Edward Bruce. 2002. Archaeological Survey. Manuals in Archaeological Method, Theory, and Technique. New York, NY: Kluwer Academic / Plenum Publishers.

Bartos, Dale L. 2001. “Landscape Dynamics of Aspen and Conifer Forests.” In USDA Forest Service Preceedings RMRS-P-18. Logan, UT. http://www.fs.fed.us/rm/pubs/rmrs_p018/rmrs_p018_005_014.pdf.

———. 2008. Great Basin Aspen Ecosystems. Logan, UT: Rocky Mountain Reserach Station.

Basinger, Julianne. 1998. “To Scientists Who Use Paying Volunteers in Fieldwork, the Benefits Outweigh the Bother.” Chronicle of Higher Education 44 (41) (June 19): A14.

Bates, P. C, C. R Blinn, A. A Alm, and R. D Adams. 1990. “A survey of the harvesting histories of some poorly regenerated aspen stands in northern Minnesota.” In , 221–230. http://www.cabdirect.org/abstracts/19940600394.html?freeview=true.

Beetle, Alan. 1974. “Range Survey in Teton County, Wyoming : Part IV, Quaking Aspen.” Aspen Bibliography (SM-27) (January 1). http://digitalcommons.usu.edu/aspen_bib/5257.

Bell, F. Wayne. 1991. Canadian Forest Service Publications : Critical silvics of conifer crop species and selected competitive vegetation in northwestern Ontario. Thunder Bay. Ontario: Natural Resources Canada, Great Lakes Forestry Centre. http://cfs.nrcan.gc.ca/publications?id=29815.

Bennet, Doug, and Tim Tiner. 2003. The Wild Woods Guide: From Minnesota to Maine, the Nature and Lore of the Great North Woods. HarperCollins.

Bettinger, Pete, Jacek Siry, Kevin Boston, and Donald L. Grebner. 2008. Forest Management and Planning. Academic Press. 111

Bonham-Carter, Graeme F. 1998. Geographic Information Systems for Geoscientists: Modelling with GIS. Vol. 13. 13 vols. Computer Methods in the Geosciences. Tarrytown, NY: Pergamon.

California Department of Parks and Recreation. 2012. “CDEC Historical Data: GROVER HOT SPRINGS.” Department of Water Resources, California Data Exchange Center. http://cdec.water.ca.gov/cgi- progs/selectQuery?station_id=GHS&dur_code=M&sensor_num=2&start_date=0 1/01/1990+00:00&end_date=03/23/2012+12:33.

Chang, Kang-Tsung. 2004. Introduction to Geographic Information Systems. 2nd ed. Boston: McGraw-Hill Higher Education.

Clark, William B., and James R. Evans. 1977. Mines and Mineral Resources of Alpine County, California. California Division of Mines and Geology.

Crouch, Glenn LeRoy. 1986. Aspen Regeneration in 6- to 10-year-old Clearcuts in Southwestern Colorado. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station.

DeKorne, James B. 1970. Aspen Art in the New Mexico Highlands: A Photo Essay. Santa Fe, New Mexico: Mueseum of New Mexico Press.

Douglass, William A. 1980. “Basque Immigrants: Contrasting Patterns of Adaptation in Argentina and the American West.” In Currents in Anthropology: Essays in Honor of Sol Tax, ed. Robert Hinshaw, 3:287–303. Bristol, England: Walter de Gruyter. http://www.degruyter.com/dg/viewbookchapter.fullcontentlink:pdfeventlink/co ntentUri?t:ac=books$002f9783110809299$002f9783110809299.287$002f97831 10809299.287.xml.

Douglass, William A., and Jon Bilbao. 1975. Amerikanuak: Basques in the New World. 1st ed. Reno, NV: University of Nevada Press.

Dutton, W. L. 1953. “History of Forest Service Grazing Fees.” Journal of Range Management 6 (6) (November 1): 393–398. doi:10.2307/3893763.

Ehrlich, Gretel. 1986. The Solace of Open Spaces. New York: Penguin Books.

Elliott, Grant P, and William L Baker. 2004. “Quaking Aspen (Populus Tremuloides Michx.) at Treeline: a Century of Change in the San Juan Mountains, Colorado, USA.” Journal of Biogeography 31 (5) (May 1): 733–745. doi:10.1111/j.1365- 2699.2004.01064.x. 112

ESRI. 2010. ArcMap 10. Windows. ArcGIS Desktop. Redlands, CA: ESRI. http://www.esri.com/software/arcgis/arcgis-for-desktop/index.html.

———. 2011. “Desktop Help 10.0 - Understanding Euclidean Distance Analysis.” ArcGIS Resouce Center. http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Understanding_ Euclidean_distance_analysis/009z0000001t000000/.

———. “ArcPad User Guide.” http://webhelp.esri.com/arcpad/8.0/userguide/index.htm#editing_data/feature s_gps/concept_quality.htm.

Fowells, Harry Ardell. 1965. Silvics of forest trees of the United States. USDA Agriculture Handbooks. USDA Forest Service. http://handle.nal.usda.gov/10113/CAT87209027.

Fralish, James Steven. 1972. Youth, Maturity, and Old Age. General Technical Report. St. Paul, MN: USDA Forest Service, North Central Forest Experiment Station. http://www.ncrs.fs.fed.us/pubs/viewpub.asp?key=66.

Fralish, James Steven, and Scott B. Franklin. 2002. Taxonomy and Ecology of Woody Plants in North American Forests (excluding Mexico and Subtropical Florida). John Wiley and Sons.

Grant, Michael C. 1993. “The Trembling Giant.” Discover 14 (10) (October): 82.

Grant, Michael C., and Jeffry B. Mitton. 1979. “Elevational Gradients in Adult Sex Ratios and Sexual Differentiation in Vegetative Growth Rates of Populus Tremuloides Michx.” Evolution 33 (3): 914–918. doi:10.2307/2407654.

Haeussler, Sybille, K. Dave Coates, and W. Jean Mather. 1990. Autecology of Common Plants in British Columbia: A Literature Review. FRDA. B.C. Ministry of Forest: Forest Science Program. http://www.for.gov.bc.ca/hfd/pubs/docs/Frr/Frr158.htm.

Hanley, Mike, and Ellis Lucia. 2003. Owyhee Trails: The West’s Forgotten Corner. Caxton Press.

Hanna, Philip, and Dominik Kulakowski. 2012. “The Influences of Climate on Aspen Dieback.” Forest Ecology and Management 274 (June 15): 91–98. doi:10.1016/j.foreco.2012.02.009.

Harper, Kimball T., John D. Shane, and John R. Jones. 1985. “Taxonomy.” In Aspen: Ecology and Management in the Western United States, 7–8. Fort Collins, CO: 113

Rocky Mountain Forest and Range Experiment Station. http://www.treesearch.fs.fed.us/pubs/24942.

Jelinski, Dennis E., and W. M. Cheliak. 1992. “Genetic Diversity and Spatial Subdivision of Populus Tremuloides (Salicaceae) in a Heterogeneous Landscape.” American Journal of Botany 79 (7) (July 1): 728–736. doi:10.2307/2444937.

Jones, John R. 1985. “Distribution.” In Aspen: Ecology and management in the western United States, 9–10. Fort Collins, CO: Rocky Mountain Forest and Range Experiment Station. http://www.treesearch.fs.fed.us/pubs/24942.

Jones, John R., Merrill R. Kaufmann, and E. Arlo Richardson. 1985. “Effects of Water and Temperature.” In Aspen: Ecology and Management in the Western United States. Fort Collins, CO: Rocky Mountain Forest and Range Experiment Station. http://www.treesearch.fs.fed.us/pubs/24942.

Kay, Charles E. 1997. “Is Aspen Doomed?” Journal of Forestry 95 (5): 4–11.

Knight, Dennis H. 2001. “Summary: Aspen Decline in the West?” In Sustaining aspen in western landscapes: Symposium proceedings, 441–446. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research Station. http://www.treesearch.fs.fed.us/pubs/35850.

Lane, Richard. 1971. “Basque Tree Carvings.” Northeastern Nevada Historical Society Quarterly 1 (3): 1–7.

Lane, Richard, and William A. Douglass. 1985. Basque Sheep Herders of the American West: A Photographic Documentary. The Basque Series. Reno, Nevada: University of Nevada Press.

Laxalt, Robert. 1966. “Basque Sheepherders, Lonely Sentinels of the American West.” National Geographic, June.

MacKinnon, A, Jim Pojar, R Coupe, and George W Argus. 1992. Plants of northern British Columbia. Edmonton: Lone Pine Pub.

Maini, J. S., and K. W. Horton. 1966. “Vegetative Propagation of Populus Spp.: I. Influence of Temperature on Formation and Initial Growth of Aspen Suckers.” Can. J. Bot. 44 (9): 1183–1189. doi:10.1139/b66-130.

Mallea-Olaetxe, Joxe. 2001. “Carving Out History.” Forest History Today Spring/Fall: 44 – 50. 114

———. 2008. Speaking Through the Aspens: Basque Tree Carvings in Nevada and California. University of Nevada Paperback ed. Reno, Nevada: University of Nevada Press.

———. 2009. The Basques of Reno and the Northeastern Sierra. Images of America. San Francisco: Arcadia Publishing.

McGregor, Alex C. 1980. “From Sheep Range to Agribusiness: A Case History of Agricultural Transformation on the Columbia Plateau.” Agricultural History 54 (1) (January): 11–27.

Mensing, Scott A., Robert G. Elston, Jr, Gary L. Raines, Robin J. Tausch, and Cheryl L. Nowak. 2000. “A GIS Model to Predict the Location of Fossil Packrat ( Neotoma ) Middens in Central Nevada.” Western North American Naturalist 60 (2) (May 24): 111–120.

Microsoft Corporation. 2010. Excel 2010. 32bit. Redmond, WA: Microsoft Corporation. http://office.microsoft.com/en-us/excel/.

Mills, L. Scott, Michael E. Soulé, and Daniel F. Doak. 1993. “The Keystone-Species Concept in Ecology and Conservation.” BioScience 43 (4) (April 1): 219–224. doi:10.2307/1312122.

Minter, Chad, and Jeff LeProwse. 2010. “Add XYZ to Table.” ESRI AcrScripts. http://arcscripts.esri.com/details.asp?dbid=16785.

Mitchell, John E. 2000. Rangeland Resource Trends in the United States: A Technical Document Supporting the 2000 USDA Forest Service RPA Assessment. General Technical Report. Fort Collins, CO: Rocky Mountain Reserach Station. http://www.fs.fed.us/rm/pubs/rmrs_gtr068.html.

Mitton, Jeffry B., and Michael C. Grant. 1980. “Observations on the Ecology and Evolution of Quaking Aspen, Populus Tremuloides, in the Colorado Front Range.” American Journal of Botany 67 (2) (February 1): 202–209. doi:10.2307/2442643.

———. 1996. “Genetic Variation and the Natural History of Quaking Aspen.” BioScience 46 (1) (January 1): 25–31. doi:10.2307/1312652.

Mueggler, Walter F. 1989. “Age Distribution and Reproduction of Intermountain Aspen Stands.” Western Journal of Applied Forestry 4 (2): 41–45.

NRCS. 2012a. “USDA:NRCS:Geospatial Data Gateway:Home.” USDA, Natural Resources Conservation Service. http://datagateway.nrcs.usda.gov/. 115

———. 2012b. “Web Soil Survey - Home.” USDA, Natural Resources Conservation Service. http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm.

O’Hara, Brian, Gary Barbato, John James, Heather Angeloff, and Tom Cylke. 2007. Weather and Climate of the Reno-Carson City-Lake Tahoe Region: Nevada Bureau of Mines and Geology. Special Publication 34. Nevada Bureau of Mines & Geology.

Ohmann, L. 1982. Tall Shrub Layer Biomass in Conifer Plantations of Northeastern Minnesota. Saint Paul, MN: U.S. Dept. of Agriculture Forest Service North Central Forest Experiment Station.

Paris, Beltran, and William A. Douglass. 1979. Beltran: Basque Sheepman of the American West. The Basque Series 1. Reno, Nevada: University of Nevada Press.

Passport in Time. “Welcome to Passport in Time.” http://www.passportintime.com/.

Peterson, Everett B, and N Merle Peterson. 1993. Ecology and Silviculture of Trembling Aspen. Victoria: Ecology and Management of B.C. Hardwoods.

Prism Climate Group. “Prism Mean Annual Precipitation.” Prism Climate Group. http://prism.oregonstate.edu.

———. “Prism Mean Annual Temperature.” Prism Climate Group. http://prism.oregonstate.edu.

Raines, Gary L. 1999. “Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States.” Natural Resources Research 8 (4) (December 1): 257–276. doi:10.1023/A:1021602316101.

———. 2010. “Spatial Data Modeller (SDM).” ArcGIS Resource Center. http://resources.arcgis.com/gallery/file/geoprocessing/details?entryID=B43F13B 5-1422-2418-8867-84E8E8667754.

Raines, Gary L., Graeme F. Bonham-Carter, and Laura Kemp. 2000. “Predictive Probabilistic Modeling Using ArcView GIS.” ArcUser (April-June): 45–48.

Reed, Robert M. 1971. “Aspen Forests of the Wind River Mountains, Wyoming.” American Midland Naturalist 86 (2) (October 1): 327–343. doi:10.2307/2423627.

Romme, William H, Lisa Floyd-Hanna, David D Hanna, and Elisabeth Bartlett. 2001. “Aspen ’s Ecological Role in the West.” In Methods, ed. Wayne D Shepperd, Dan Binkley, Dale L Bartos, Thomas J Stohlgren, and Lane G Eskew, 243–260. U. S. Department of Agriculture. http://www.treesearch.fs.fed.us/pubs/35832. 116

Rothstein, Arthur. 1939. “A Sheepherder Watching His Flocks. Madison County, Montana”. Still image. Library of Congress. http://www.loc.gov/pictures/item/fsa2000008683/PP/.

Rudolf, Schmid. 2011. “Plant (biology) :: Asexual Reproduction.” Encyclopedia Britannica. http://0- www.britannica.com.innopac.library.unr.edu/EBchecked/topic/463192/plant/66 093/Asexual-reproduction?anchor=ref536898.

Sakai, Ann K., and Timothy A. Burris. 1985. “Growth in Male and Female Aspen Clones: A Twenty-Five-Year Longitudinal Study.” Ecology 66 (6) (December 1): 1921–1927. doi:10.2307/2937388.

Sargent, Charles Sprague. 1905. Manual of the Trees of North America (exclusive of Mexico). Boston: Houghton Mifflin Company.

Sawyer, Byrd Wall. 1971. Nevada Nomads: A Story of the Sheep Industry. Harlan-Young Press.

Sayre, Nathan F. 2001. “Review: Speaking Through the Aspens: Basque Tree Cravings in California and Nevada.” Geographical Review 91 (3) (July): 604–606.

Smith, Beth P. 2008. Scott’s Lake Fuelwood Sale. Cultural. Carson City, NV: Humboldt- Toiyabe National Forest.

Smith, Eric A., Daniel O’Loughlin, Joshua R. Buck, and Samuel B. St. Clair. 2011. “The Influences of Conifer Succession, Physiographic Conditions and Herbivory on Quaking Aspen Regeneration After Fire.” Forest Ecology and Management 262 (3) (August 1): 325–330. doi:10.1016/j.foreco.2011.03.038.

Sprinthall, Richard C. 2011. Basic Statistical Analysis. 9th ed. Boston, MA: Prentice Hall PTR.

Starrs, Paul F. 2000. Let the Cowboy Ride: Cattle Ranching in the American West. Paperback. Baltimore: Johns Hopkins University Press.

Stoeckeler, Joseph H. 1961. “Organic Layers in Minnesota Aspen Stands and Their Role in Soil Improvement.” Forest Science 7 (1): 66–71.

Storer, Tracy Irwin, and Robert Leslie Usinger. 1963. Sierra Nevada Natural History. University of California Press.

Strand, Eva K., Lee A. Vierling, and Stephen C. Bunting. 2009. “A Spatially Explicit Model to Predict Future Landscape Composition of Aspen Woodlands Under Various 117

Management Scenarios.” Ecological Modelling 220 (2) (January 24): 175–191. doi:10.1016/j.ecolmodel.2008.09.010.

Strand, Eva K., Lee A. Vierling, Stephen C. Bunting, and Paul E. Gessler. 2009. “Quantifying Successional Rates in Western Aspen Woodlands: Current Conditions, Future Predictions.” Forest Ecology and Management 257 (8) (March 31): 1705–1715. doi:10.1016/j.foreco.2009.01.026.

Strong, Nicole, Darin Stringer, Teresa Welch, and Betsy Littlefield. 2010. “Land Manager’s Guide to Aspen Management in Oregon”. Technical Report. Oregon State University. http://scholarsarchive.library.oregonstate.edu/xmlui/handle/1957/18399.

Tomback, Diana, Ann Howald, Mary Hill, and Harold Klieforth. 2001. Sierra East: Edge of the Great Basin. Ed. Genny Smith. 1st ed. University of California Press.

Trimble. “Trimble - Mapping & GIS - Nomad G Series Handhelds.” http://www.trimble.com/mappingGIS/nomadg.aspx?dtID=technical_specs.

USDA, Forest Service. “Celebrating Wildflowers - Fading Gold - How Aspens Grow.” US Forest Service: How Aspen Grow. http://www.fs.fed.us/wildflowers/communities/aspen/grow.shtml.

Western Regional Climate Center. “WOODFORDS, CALIFORNIA - Climate Summary.” Western Regional Climate Center. http://www.wrcc.dri.edu/cgi- bin/cliMAIN.pl?ca9775.

Willard, Terry. 1992. Edible and Medicinal Plants of the Rocky Mountains and Neighbouring Territories. First ed. Wild Rose College of Natural.

Williams, Gerald W. 2007. The Forest Service: Fighting for Public Lands. Greenwood Publishing Group.

118

Appendix A: Study Area Access

Horsethief Canyon is accessed from highway CA-89 via dirt access road FS-31025.

This access road can be found at 38.785237° latitude by -119.919588° longitude. The access road is located on the east side of CA-89 and is gated and locked. The dirt road is approximately six miles long and is in good shape. A high clearance vehicle is needed to traverse this route due to several undulations. The road is only passable during early summer through late fall because of snow. The road ends within Horsethief Canyon.

Many of the arborglyphs within Horsethief Canyon can be reached from location

38.808500° latitude by -119.876277° longitude.

Hope Valley is bisected west and east by California state highway CA-88. The arborglyphs surveyed are accessed from highway CA-88 and can be reached via two dirt access roads. The northern access road, FS-31091 is on the west side of highway CA-88 and is located at 38.726856° latitude by -119.954216° longitude. The northern access road is short but steep and dead ends directly in the surveyed arborglyphs. This road could be traversed by a low clearance vehicle. This road is the best access to the Hope

Valley South portion of the study area.

The southern access road to the arborglyphs surveyed in Hope Valley is south on

CA-88 and is on the northern side of the highway. The access road is unnamed and is located just passed a small area of private land at location 38.705175° latitude by -119.969224° longitude. The surveyed arborglyphs lie to the east and north, from location 38.706470° latitude by -119.964700° longitude. 119

Scott’s Lake is located to the west of highway CA-88 just above Hope Valley. The

Scott’s Lake arborglyphs can be accessed from road FS-079 at 38.76548° latitude and -119.94020° longitude. The road is frequently used by campers and can be traversed by a low clearance vehicle. The majority of the arborglyphs were found between highway CA-88 and Scott’s Lake. 120

Appendix B: Steps to Record and Arborglyph

The fourteen steps to record an arborglyph for this survey are outlined below.

When an arborglyph was located that team tied a length of white with red dots

surveyors flagging tape around the tree’s trunk and waited for the other team members

to arrive to help record the arborglyph. A permanent marker was then used to write the

arborglyph number on the flagging tape.

The arborglyph number is a unique number that was found on the top of the

Sketch Form. The arborglyph number consists of a prefix indicating the survey

“HVAS2011-” followed by a number that started at one and increases sequentially, an optional letter could be given if the tree contained separate arborglyph panels. The arborglyph numbers were distributed this way in order to reduce the risk of assigning the same number to different trees. The surveyor then wrote the arborglyph number on the notes form as well as their initials and the date.

The next step was to measure the circumference of the tree in centimeters with the tape measure with the intent that later it could be converted to diameter at breast height a standard measurement for forestry. The circumference was measured at 54 inches up the tree from the ground. From there the measurements continued and both the maximum height and width of the arborglyph panel was recorded. Then the arborglyph panel’s height from ground was measured and recorded. This measurement was taken from the lowest point on the arborglyph panel. 121

The tape measures used for the survey were flexible sewing type tape measures reinforced by fiberglass. Each tape measure had inches divided into eighths on one side and centimeters divided into millimeters on the other. The tape measure was numbered up to 96 inches (245 centimeters). Both inches and centimeters were used in the survey.

The compass was then used to record the location of the arborglyph on the tree, this could also be thought of as the face or aspect of the arborglyph panel. The rubric explains the process in a very different manner than the way actually was recorded, both ways would achieve the same results. The more practical way to record the location of the arborglyph panel that was developed in the field was for the surveyor to stand with their back to the arborglyph panel and then record the compass bearing. This method appeared to be more accurate and reliable. The compass’ used had a built in clinometer that was then used to document the slope within the vicinity of the arborglyph in degrees. The clinometer had good accuracy but the measurements themselves were not reliable because of the tendency by surveyors to not position the clinometer properly when taking measurements. The compass was also used to record the aspect of the slope measured in the previous step.

The compass used during the survey was the Brunton Type 15. The compass has an adjustable bezel with markings every two degrees, thus making it accurate to one degree. All the compass readings were given based on magnetic north. This was done to eliminate confusion and that feature works best for orienteering. All the collected data can be converted to true north if necessary. The clinometer is built into the bezel of the compass. It measures degrees of slope in increments of two making it accurate to one 122

degree of slope. The clinometer must be set so that the compass bezel is in an east to

west orientation to operate.

Next the Nomad GPS was used to record the location of the arborglyph in UTMs.

The Nomad also contained a database that use used as a backup to the paper notes. The

database contained fields for project area, last name of GPS user, arborglyph number,

circumference, panel height, panel width, height from ground, panel location (panel

aspect), and finally a photograph was taken with the Nomad as back up photos.

The personal data recorders used were Trimble Nomads running Windows

Mobile 6.1 operating system. The Nomads are equipped with integrated global

positioning system and wide area augmentation system receivers with an accuracy of

two to five meters when performing real-time differential correction (Trimble). The

software used to collect the location data was ESRI’s ArcPad 8.0 running the script, “Add

XYZ to Table” (Minter and LeProwse 2010).

The ArcPad 8.0 software was chosen because of its ability to display many pieces of spatial data simultaneously. This allowed the user to more easily orient themselves on the landscape, as well as increase efficiency by indicating where survey had already been completed. The software also collects into the shapefile file format, which is handled natively within ESRI’s ArcMap 10; this saves many post-processing steps required by other software.

The “Add XYZ to Table” script in general terms records information regarding the current estimated accuracy of the spatial data at the time it was collected. These data 123

are not otherwise collected by ArcPad 8.0 but was useful in identifying problems in the

locations of the points.

The spatial information collected by the Nomad was placed into a point feature

shapefile. The ArcPad software was setup to record thirty position intervals and then

average them for the documented location. Thirty positions was chosen because the

ArcPad 8.0 documentation indicated that at least 20 positions be recorded and averaged

for best results (ESRI).

The arborglyph was then photographed. There was not a set number of

photographs taken but rather the arborglyph was photographed enough to represent

the panel. The photograph numbers from the camera were then recorded on the Notes

Form.

Three models of digital cameras were used to document the arborglyphs. They

were all manufactured by Olympus and were chosen due to their quality photographs at

dots per inch greater than 300 and their rugged design. Each camera saved the images

in the jpeg file and compression format at the cameras maximum settings, thus

preserving the most image information.

The arborglyph was then transcribed and then a basic description was recorded

on the Notes Form. If the surveyors could easily identify the theme of the arborglyph it

was recorded as well. To facilitate this a selection of pages from appendix five were included in the rubric from Speaking Through the Aspens (Mallea-Olaetxe 2008).

The last step the surveyors did was to sketch the arborglyph panels. The sketch was done on the sketch form and was not necessarily to scale. The reason for sketching 124

the arborglyphs was to supplement the photographs. Because of poor lighting under the

aspens, the photographs do not pick up all the detail that the naked human eye can pick up. Also, many of the arborglyphs are faint or the colors are not dissimilar enough to show up in the well in the photos, the sketches are able to pick up on the details not shown in the photographs. 125

Appendix C: Overview of Passport in Time

Fieldwork for this project utilized a Forest Service volunteer program entitled

Passport in Time but it is commonly referred to as PIT. The objective of PIT is to educate

the public about our nation’s cultural resources while preserving it. The majority of PIT

projects across the country involve an archaeological excavation, thus, the acronym of

PIT is fitting. However, PIT describes itself as:

“a volunteer archaeology and historic preservation program of the

USDA Forest Service (FS). PIT volunteers work with professional FS

archaeologists [, geographers,] and historians on national forests

throughout the U.S. on such diverse activities as archaeological survey

and excavation, rock art restoration, survey, archival research, historic

structure restoration, oral history gathering, and analysis and curation of

artifact” (Passport in Time).

This description of PIT allows for all historic preservation work and even names

archaeological survey directly, which is the type of data collection implemented in this

study. Using the PIT program allowed for a larger, more intensive survey to locate and record arborglyphs, while providing the volunteers a fun and informative experience.

Volunteers were located by submitting a PIT proposal to the PIT clearinghouse, a contractor that runs the program for the Forest Service. Then the clearinghouse sends the PIT leader a packet with all the potential volunteers contact information and write- ups. A selection is then made from this pool of volunteers. 126

The start date for the PIT project was September 19th, 2011 and continued for five days. The project had a maximum capacity of twelve volunteers, however because, the advertisement drew the interest of sixteen, all sixteen were invited to participate in the PIT project with the idea that some would not show up. In the end a total of ten volunteers participated in the PIT project. Not all were able to attend for the duration and so the number participating per day fluctuated.

The volunteers were given the option to stay at a Forest Service campground near the project area for the duration of the project at no charge. This was a mutually beneficial arrangement because the volunteers were close and more time could be spent surveying the project areas due to less travel time. The volunteers benefited by

Figure Appendix C- 1 PIT volunteers on the first day in the field. Photo credit: Nancy Nagel. 127

having a low cost living option, as well as an exclusive place to stay; the campground

was closed for the year and was opened especially for the project. It is not very often or

ever that this campground is not full of people.

Collecting field data with volunteers has many potential benefits but can also

cause potential problems. There are three considerations when using volunteer field

workers, reduced accuracy, reduced field time, and low cost. Using volunteer field

workers has the potential to reduce time in the field and cost but could negatively affect

accuracy. Due to the relatively short time frame to comfortably do survey in the project

areas the potential to acquire more data in the same period of time was the factor that

held the most value. The budget to do the field survey was also very small. Using

volunteers allowed for more survey to be completed without any additional cost.

Finally, the accuracy and consistency of the data collected by volunteers could be a

potential problem, but this problem was largely mitigated by professionals being with the volunteers at all times so that questions and issues could be addressed on the spot.